Computation of Joint Moment Functions on Convolutional Factor Graphs

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Cracking Load and Ultimate Moment 开裂荷载和极限弯矩.ppt

当剪切和粘结比弯曲强的预应力受弯构件受荷失效时，它们会以下列方式中的一种失效：
cracks in the tensile flange. (The linearity（直线性）is a function of the rate at which the load is applied.) For this reason, normal elastic-design relationships（关系式）can be used in computing the cracking load by
（压力线）for beams in the elastic range. This is a fundamental principle （规律）of prestressed construction. In a normal prestressed beam, this shift（移动）in the location of the pressure line continues at a relatively
uniform rate（速度）, as（随着）the external load is increased, to the
point where cracks develop in the tension fiber. After the cracking load has
been exceeded, the rate of movement in the pressure line decreases as
desirable（想要）to make a precise estimate of the cracking load, such as is required in some research work, this effect（影响）should be considered.

采用Belikov列推和跨阶次递推方法计算超高阶缔合勒让德函数

采用Belikov列推和跨阶次递推方法计算超高阶缔合勒让德函数欧阳明达;张敏利;于亮【摘要】超高阶球谐重力场模型的精确构制与快速计算取决于缔合勒让德函数的计算方法.在前人研究的基础上,文中对适合超高阶缔合勒让德函数计算的Belikov 列推和跨阶次递推方法进行介绍,为验证精度,通过两种途径对计算结果进行检验,并比较其计算速度.结果表明,采用两种算法得到的每个勒让德函数的绝对精度均优于10-12,在低阶,跨阶次递推方法的计算用时大约是Belikov列推法的2倍,随着阶数的升高,跨阶次递推算法表现出明显的速度优势.%Precision construction and rapid calculation of ultra-high-order spherical harmonic gravity field model,depend on the calculation method of the associated legendre functions.On the basis of previous studies,the suitable Belikov column method and recursion method between every other order and degree for ultra-high-order legendre function are introduced.The accuracy of calculations verified results in two ways after are their calculation speeds compared.The result shows that:every associated legendre function calculated by this two algorithms is obtained with absolute accuracy better than 10-12.In low-order,the recursion method between every other order and degree takes time as twice as Belikov column method extrapolation.As the order increasing,the recursion method between every other order and degree shows a significant speed advantage.【期刊名称】《测绘工程》【年(卷),期】2017(026)007【总页数】5页(P12-15,21)【关键词】勒让德函数;递推公式;球谐分析【作者】欧阳明达;张敏利;于亮【作者单位】地理信息工程国家重点实验室,陕西西安710054;西安测绘总站,陕西西安710054;西安测绘总站,陕西西安710054;西安测绘总站,陕西西安710054【正文语种】中文【中图分类】P223随着地球重力场模型的不断精化，超高阶次缔合勒让德函数的计算已经成为了地球重力场和相关领域中的重要研究课题[1-8]。

压力容器常用英语词汇

序号 No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
分类 classification defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect defect design design design design design design arc crater burrs chattered flash trim crack
3/22
序号 No. 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188
英文 English allowance for abrasion allowance for corrosion allowance for erosion analysis applied linings, tightness area replacement method basic seismic acceleration basic wind pressure basis for establishing bolt land bolt stress bolted design calculate calculated thickness capacity category chart check circumferential /hoop stress circumferential efficiency circumferential joint computation computation of opening conversion corrosion allowance corrosion rate curvature design Design by analysis design by rules design pressure designed life Dynamic load earthquake load effective gasket width efficiency elasticity modulu empty weight erosion allowance factor fixed flammable flange moment flange stresse fluid fluid property

中国科学英文版模板

中国科学英文版模板1.Identification of Wiener systems with nonlinearity being piece wise-linear function HUANG YiQing，CHEN HanFu，FANG HaiTao2.A novel algorithm for explicit optimal multi-degree reduction of triangular surfaces HU QianQian，WANG GuoJin3.New approach to the automatic segmentation of coronary arte ry in X-ray angiograms ZHOU ShouJun，YANG Jun，CHEN WuFan，WANG YongTian4.Novel Ω-protocols for NP DENG Yi，LIN DongDai5.Non-coherent space-time code based on full diversity space-ti me block coding GUO YongLiang，ZHU ShiHua6.Recursive algorithm and accurate computation of dyadic Green 's functions for stratified uniaxial anisotropic media WEI BaoJun，ZH ANG GengJi，LIU QingHuo7.A blind separation method of overlapped multi-components b ased on time varying AR model CAI QuanWei，WEI Ping，XIAO Xian Ci8.Joint multiple parameters estimation for coherent chirp signals using vector sensor array WEN Zhong，LI LiPing，CHEN TianQi，ZH ANG XiXiang9.Vision implants: An electrical device will bring light to the blind NIU JinHai，LIU YiFei，REN QiuShi，ZHOU Yang，ZHOU Ye，NIU S huaibining search space partition and search Space partition and ab straction for LTL model checking PU Fei，ZHANG WenHui2.Dynamic replication of Web contents Amjad Mahmood3.On global controllability of affine nonlinear systems with a tria ngular-like structure SUN YiMin，MEI ShengWei，LU Qiang4.A fuzzy model of predicting RNA secondary structure SONG D anDan，DENG ZhiDong5.Randomization of classical inference patterns and its applicatio n WANG GuoJun，HUI XiaoJing6.Pulse shaping method to compensate for antenna distortion in ultra-wideband communications WU XuanLi，SHA XueJun，ZHANG NaiTong7.Study on modulation techniques free of orthogonality restricti on CAO QiSheng，LIANG DeQun8.Joint-state differential detection algorithm and its application in UWB wireless communication systems ZHANG Peng，BI GuangGuo，CAO XiuYing9.Accurate and robust estimation of phase error and its uncertai nty of 50 GHz bandwidth sampling circuit ZHANG Zhe，LIN MaoLiu，XU QingHua，TAN JiuBin10.Solving SAT problem by heuristic polarity decision-making al gorithm JING MingE，ZHOU Dian，TANG PuShan，ZHOU XiaoFang，ZHANG Hua1.A novel formal approach to program slicing ZHANG YingZhou2.On Hamiltonian realization of time-varying nonlinear systems WANG YuZhen，Ge S. S.，CHENG DaiZhan3.Primary exploration of nonlinear information fusion control the ory WANG ZhiSheng，WANG DaoBo，ZHEN ZiYang4.Center-configur ation selection technique for the reconfigurable modular robot LIU J inGuo，WANG YueChao，LI Bin，MA ShuGen，TAN DaLong5.Stabilization of switched linear systems with bounded disturba nces and unobservable switchings LIU Feng6.Solution to the Generalized Champagne Problem on simultane ous stabilization of linear systems GUAN Qiang，WANG Long，XIA B iCan，YANG Lu，YU WenSheng，ZENG ZhenBing7.Supporting service differentiation with enhancements of the IE EE 802.11 MAC protocol: Models and analysis LI Bo，LI JianDong，R oberto Battiti8.Differential space-time block-diagonal codes LUO ZhenDong，L IU YuanAn，GAO JinChun9.Cross-layer optimization in ultra wideband networks WU Qi，BI JingPing，GUO ZiHua，XIONG YongQiang，ZHANG Qian，LI ZhongC heng10.Searching-and-averaging method of underdetermined blind s peech signal separation in time domain XIAO Ming，XIE ShengLi，F U YuLi11.New theoretical framework for OFDM/CDMA systems with pe ak-limited nonlinearities WANG Jian，ZHANG Lin，SHAN XiuMing，R EN Yong1.Fractional Fourier domain analysis of decimation and interpolat ion MENG XiangYi，TAO Ran，WANG Yue2.A reduced state SISO iterative decoding algorithm for serially concatenated continuous phase modulation SUN JinHua，LI JianDong，JIN LiJun3.On the linear span of the p-ary cascaded GMW sequences TA NG XiaoHu4.De-interlacing technique based on total variation with spatial-t emporal smoothness constraint YIN XueMin，YUAN JianHua，LU Xia oPeng，ZOU MouYan5.Constrained total least squares algorithm for passive location based on bearing-only measurements WANG Ding，ZHANG Li，WU Ying6.Phase noise analysis of oscillators with Sylvester representation for periodic time-varying modulus matrix by regular perturbations FAN JianXing，YANG HuaZhong，WANG Hui，YAN XiaoLang，HOU ChaoHuan7.New optimal algorithm of data association for multi-passive-se nsor location system ZHOU Li，HE You，ZHANG WeiHua8.Application research on the chaos synchronization self-mainten ance characteristic to secret communication WU DanHui，ZHAO Che nFei，ZHANG YuJie9.The changes on synchronizing ability of coupled networks fro m ring networks to chain networks HAN XiuPing，LU JunAn10.A new approach to consensus problems in discrete-time mult iagent systems with time-delays WANG Long，XIAO Feng11.Unified stabilizing controller synthesis approach for discrete-ti me intelligent systems with time delays by dynamic output feedbac k LIU MeiQin1.Survey of information security SHEN ChangXiang，ZHANG Hua ngGuo，FENG DengGuo，CAO ZhenFu，HUANG JiWu2.Analysis of affinely equivalent Boolean functions MENG QingSh u，ZHANG HuanGuo，YANG Min，WANG ZhangYi3.Boolean functions of an odd number of variables with maximu m algebraic immunity LI Na，QI WenFeng4.Pirate decoder for the broadcast encryption schemes from Cry pto 2005 WENG Jian，LIU ShengLi，CHEN KeFei5.Symmetric-key cryptosystem with DNA technology LU MingXin，LAI XueJia，XIAO GuoZhen，QIN Lei6.A chaos-based image encryption algorithm using alternate stru cture ZHANG YiWei，WANG YuMin，SHEN XuBang7.Impossible differential cryptanalysis of advanced encryption sta ndard CHEN Jie，HU YuPu，ZHANG YueYu8.Classification and counting on multi-continued fractions and its application to multi-sequences DAI ZongDuo，FENG XiuTao9.A trinomial type of σ-LFSR oriented toward software implemen tation ZENG Guang，HE KaiCheng，HAN WenBao10.Identity-based signature scheme based on quadratic residues CHAI ZhenChuan，CAO ZhenFu，DONG XiaoLei11.Modular approach to the design and analysis of password-ba sed security protocols FENG DengGuo，CHEN WeiDong12.Design of secure operating systems with high security levels QING SiHan，SHEN ChangXiang13.A formal model for access control with supporting spatial co ntext ZHANG Hong，HE YePing，SHI ZhiGuo14.Universally composable anonymous Hash certification model ZHANG Fan，MA JianFeng，SangJae MOON15.Trusted dynamic level scheduling based on Bayes trust model WANG Wei，ZENG GuoSun16.Log-scaling magnitude modulated watermarking scheme LING HeFei，YUAN WuGang，ZOU FuHao，LU ZhengDing17.A digital authentication watermarking scheme for JPEG image s with superior localization and security YU Miao，HE HongJie，ZHA NG JiaShu18.Blind reconnaissance of the pseudo-random sequence in DS/ SS signal with negative SNR HUANG XianGao，HUANG Wei，WANG Chao，L(U) ZeJun，HU YanHua1.Analysis of security protocols based on challenge-response LU O JunZhou，YANG Ming2.Notes on automata theory based on quantum logic QIU Dao Wen3.Optimality analysis of one-step OOSM filtering algorithms in t arget tracking ZHOU WenHui，LI Lin，CHEN GuoHai，YU AnXi4.A general approach to attribute reduction in rough set theory ZHANG WenXiuiu，QIU GuoFang，WU WeiZhi5.Multiscale stochastic hierarchical image segmentation by spectr al clustering LI XiaoBin，TIAN Zheng6.Energy-based adaptive orthogonal FRIT and its application in i mage denoising LIU YunXia，PENG YuHua，QU HuaiJing，YiN Yong7.Remote sensing image fusion based on Bayesian linear estimat ion GE ZhiRong，WANG Bin，ZHANG LiMing8.Fiber soliton-form 3R regenerator and its performance analysis ZHU Bo，YANG XiangLin9.Study on relationships of electromagnetic band structures and left/right handed structures GAO Chu，CHEN ZhiNing，WANG YunY i，YANG Ning10.Study on joint Bayesian model selection and parameter estim ation method of GTD model SHI ZhiGuang，ZHOU JianXiong，ZHAO HongZhong，FU Qiang。

A Comprehensive Survey of Multiagent Reinforcement Learning

156
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 38, NO. 2, MARCH 2008
A Comprehensive Survey of Multiagent ReinfoN
A
MULTIAGENT system [1] can be deﬁned as a group of autonomous, interacting entities sharing a common environment, which they perceive with sensors and upon which they act with actuators [2]. Multiagent systems are ﬁnding applications in a wide variety of domains including robotic teams, distributed control, resource management, collaborative decision support systems, data mining, etc. [3], [4]. They may arise as the most natural way of looking at the system, or may provide an alternative perspective on systems that are originally regarded as centralized. For instance, in robotic teams, the control authority is naturally distributed among the robots [4]. In resource management, while resources can be managed by a central authority, identifying each resource with an agent may provide a helpful, distributed perspective on the system [5].

介绍“计算物理”发展概况

引
言
计算物理学（Computational Physics）是伴随着电子计算机的出现和发展而逐步形成的一门新兴的边缘学科。它是以电子计算机为工具，应用数学的方法，解决物理问题的应用科学，它是物理、数学和计算机三者相结合的产物。
计算物理学起源于第二次世界大战期间美国国对核武器的研制，适应核科学技术的需要而产生。当时的物理学提出了大量的计算要求，同时又提供了计算机发展所需要的材料和元件，推动计算技术的迅猛发展。由于计算机的出现，彻底改变了物理实验室的面貌，扩大了“实验”的涵义；也改变了理论工作者的生活方式，同时利用计算机在计算过程中本身也产生新的物理概念，带来了新的物理学——计算物理学
国外“计算物理”的发展
美国从1942年8月13日开始曼哈顿计划，到1945年制造出三颗原子弹：代号为“三一”，用于试验（7月16日）， “瘦子”投于广岛（8月6日），“胖子”投于长崎（8月9 日）。历时三年，投入人力15万，耗资20亿美元。涉及到理论物理、爆轰物理、中子物理、金属物理、弹体弹道等计算。 1945年8月11日，日本宣布无条件投降后，作为原子弹之父的奥本海默（J．R.Oppenheimer）等人辞职，纷纷离开，洛斯阿拉莫斯呈现一片荒凉景象。直到1947年春，军工部门为实验室拨款，一些科学家处于自身难保的境地，又纷纷回到实验室，从事理论研究，并扩大在物理、化学、工艺和生物等方面的研究范围。
19847况蕙孙蒋伯诚张树发编著计算物理引论湖南科学技术出版村社19877张开明顾昌鑫编著计算物理学复旦大学出版社198710张纯祥编著计算物理基础广东高教出版社199112马文淦张子平编著计算物理学中国科学技术大学出版社19929赵金保主编计算物理导引兰州大学出版社1993910马文淦编著计算物理学中国科学技术大学出版社200111井孝功编著计算物理吉林大学出版社200112陈锺贤编著计算物理学哈尔滨工业大学出版社20013iccpiccpinternationalconferencecomputationalphysics系列国际会议是由中国北京的应用物理与计算数学研究所iapcm

空间万向传动当量夹角的计算和优化设计

空间万向传动当量夹角的计算和优化设计田中旭;祁平;高学峰【摘要】传动轴当量夹角是衡量含多万向节传动轴传动性能的重要指标,它的准确计算对传动轴的设计具有重要的意义.当传动轴空间布置时,当量夹角的准确计算变得困难.研究了空间当量夹角的精确计算方法,主要包括:给定了更加准确的万向节叉方位的描述模型、推导了精确的万向节叉初相位和传动轴夹角计算的数学模型;论文通过解析方法和算例,研究了当量夹角计算过程中产生误差的根本原因;还通过多体动力学软件ADAMS的运动学仿真对数学模型和算法进行了验证.基于当量夹角准确计算模型的基础上,对某商用车传动轴当量夹角进行了优化布置,使得当量夹角有了大幅度减小.【期刊名称】《机械设计与制造》【年(卷),期】2018(000)009【总页数】3页(P48-50)【关键词】传动轴当量夹角;万向节初相位;传动轴布置优化;传动轴运动学仿真【作者】田中旭;祁平;高学峰【作者单位】上海海洋大学工程学院,上海 201306;中国北方发动机研究所,山西大同 037036;蒂森克虏伯汽车系统计算有限公司,上海 201201【正文语种】中文【中图分类】TH161 引言十字轴万向节因其可靠性高，成本低廉，仍得到了广泛的应用。

当前，含万向节的传动轴在运动学[1]与强度[2]方面仍然存在很多值得研究的问题，这些问题的关键点在于万向节两侧的传动轴存在一定夹角。

在含多万向节的传动轴中，综合评价传动轴布置的参数就是当量夹角[3]。

在万向节传动当量夹角不为零时，传动轴的转速将产生波动[4-5]，同时还在传动轴上产生波动附加弯矩作用[6]。

转速的波动会引起传动系统的齿轮啮合冲击和噪声，影响其可靠性；附加弯矩则会引起传动轴中间支撑的振动，会进一步引起设备或车辆的振动和噪声[7]，同时也易引起传动轴本身的弯曲振动。

因此，传动轴当量夹角的设计和控制，是传动轴布置中最为重要的指标之一[8]。

而当量夹角的准确计算则是传动轴设计和优化的前提和基础。

计算机学科国际会议分级 2012

计算机学科国际会议分级说明：本列表合并了UCLA、NUS、NTU、CCF、清华大学计算机系、上海交大计算机系认可的国际会议，分级时采用了“就高”的原则。

Rank #1(1) AAAI: AAAI Conference on Artificial IntelligenceRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(2) CCS: ACM Conf on Comp and Communications SecurityRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(3) CRYPTO: International Cryptology ConferenceRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(4) CVPR: IEEE Conf on Comp Vision and Pattern RecognitionRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(5) FOCS: IEEE Symp on Foundations of Computer ScienceRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(6) HPCA: IEEE Symp on High-Perf Comp ArchitectureRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(7) ICCV: International Conf on Computer VisionRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(8) ICDE: Intl Conf on Data EngineeringRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(9) ICML: Intl Conf on Machine LearningRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(10) ICSE: Intl Conf on Software EngineeringRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(11) IJCAI: International Joint Conference on Artificial IntelligenceRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(12) INFOCOM: Annual Joint Conf IEEE Comp & Comm SocRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(13) ISCA: ACM/IEEE Symp on Computer ArchitectureRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(14) MICRO: Intl Symp on MicroarchitectureRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(15) MOBICOM: ACM Intl Conf on Mobile Computing and NetworkingRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(16) OOPSLA: OO Programming Systems, Languages and ApplicationsRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(17) POPL: ACM-SIGACT Symp on Principles of Prog LangsRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(18) SIGCOMM: ACM Conf on Comm Architectures, Protocols & AppsRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(19) SIGGRAPH: ACM SIGGRAPH ConferenceRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(20) SIGMOD: ACM SIGMOD Conf on Management of DataRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(21) STOC: ACM Symp on Theory of ComputingRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(22) VLDB: Very Large Data BasesRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(23) ACL: Annual Meeting of the Association for Computational LinguisticsRefs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(24) ACM-MM: ACM Multimedia ConferenceRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(2) NUS(1)(25) NIPS: Annual Conference on Neural Information Processing SystemsRefs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(26) PLDI: ACM SIGPLAN Symposium on Programming Language Design & Implementation Refs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(27) S&P: IEEE Symposium on Security and PrivacyRefs: UCLA(1) CCF(1) NTU(1) SJTU(1) TSINGHUA(2) NUS(1)(28) SIGMETRICS: ACM Conf on Meas. & Modelling of Comp SysRefs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(29) WWW: International World Wide Web ConferenceRefs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(1) NUS(1)(30) FSE: ACM Conf on the Foundations of Software EngineeringRefs: UCLA(1) CCF(1) NTU(1) TSINGHUA(1) NUS(1)(31) ICDCS: International Conference on Distributed Computing SystemsRefs: UCLA(2) CCF(2) NTU(1) SJTU(1) TSINGHUA(2) NUS(1)(32) LICS: IEEE Symp on Logic in Computer ScienceRefs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(2) NUS(1)(33) ASPLOS: Architectural Support for Programming Languages and Operating Systems Refs: UCLA(1) CCF(2) NTU(1) TSINGHUA(1) NUS(1)(34) COLT: Annual Conference on Computational Learning TheoryRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(1) NUS(1)(35) PODS: ACM SIGMOD Conf on Principles of DB SystemsRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(1) NUS(1)(36) PPoPP: Principles and Practice of Parallel ProgrammingRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(1) NUS(1)(37) SIGIR: ACM SIGIR Conf on Information RetrievalRefs: UCLA(1) CCF(2) NTU(1) SJTU(1) TSINGHUA(1)(38) SODA: ACM/SIAM Symp on Discrete AlgorithmsRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(1) NUS(1)(39) UAI: Conference on Uncertainty in AIRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(1) NUS(1)(40) DAC: Design Automation ConfRefs: UCLA(1) NTU(1) TSINGHUA(1) NUS(1)(41) KDD: Knowledge Discovery and Data MiningRefs: UCLA(1) NTU(1) TSINGHUA(1) NUS(1)(42) SOSP: ACM SIGOPS Symp on OS PrinciplesRefs: UCLA(1) NTU(1) TSINGHUA(1) NUS(1)(43) ICALP: International Colloquium on Automata, Languages and ProgrammingRefs: UCLA(2) CCF(2) NTU(2) SJTU(1) TSINGHUA(2) NUS(2)(44) IPDPS: Intl Parallel and Dist Processing SympRefs: UCLA(2) CCF(2) NTU(2) SJTU(1) TSINGHUA(3) NUS(2)(45) AAMAS: Intl Conf on Autonomous Agents and Multi-Agent SystemsRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(3) NUS(1)(46) CAV: Computer Aided VerificationRefs: UCLA(1) CCF(3) NTU(1) TSINGHUA(2) NUS(1)(47) FM/FME: Formal Methods, World Congress/EuropeRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(3) NUS(1)(48) I3DG: ACM-SIGRAPH Interactive 3D GraphicsRefs: UCLA(1) CCF(3) NTU(1) TSINGHUA(2) NUS(1)(49) ICDT: Intl Conf on Database TheoryRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(50) ICFP: Intl Conf on Function ProgrammingRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(51) ICNP: Intl Conf on Network ProtocolsRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(52) JICSLP/ICLP/ILPS: Joint Intl Conf/Symp on Logic ProgRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(53) KR: International Conference on Principles of Knowledge Representation and Reasoning Refs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(54) PACT: International Conference on Parallel Architectures and Compilation Techniques Refs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(55) RTSS: IEEE Real-Time Systems SymposiumRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(56) SPAA: ACM Symp on Parallel Algorithms and ArchitecturesRefs: UCLA(1) CCF(2) NTU(1) TSINGHUA(2) NUS(1)(57) DCC: Data Compression ConfRefs: UCLA(1) NTU(1) TSINGHUA(3) NUS(1)(58) ICCAD: Intl Conf on Computer-Aided DesignRefs: UCLA(1) NTU(1) TSINGHUA(2) NUS(1)(59) ISSAC: Intl Symp on Symbolic and Algebraic ComputationRefs: UCLA(1) NTU(1) TSINGHUA(3) NUS(1)(60) PODC: ACM Symp on Principles of Distributed ComputingRefs: UCLA(1) NTU(1) TSINGHUA(2) NUS(1)(61) SCG: ACM Symp on Computational GeometryRefs: UCLA(1) CCF(2) NTU(1) NUS(1)(62) PECCS: IFIP Intl Conf on Perf Eval of Comp & Comm SysRefs: UCLA(1) NTU(1) NUS(1)(63) SOSDI: Usenix Symp on OS Design and ImplementationRefs: UCLA(1) NTU(1) NUS(1)(64) CIKM: Intl Conf on Information and Knowledge ManagementRefs: UCLA(2) CCF(2) NTU(1) TSINGHUA(2) NUS(2)(65) RECOMB: Annual Intl Conf on Comp Molecular BiologyRefs: UCLA(2) NTU(1) TSINGHUA(2) NUS(1)(66) RTAS: IEEE Real-Time and Embedded Technology and Applications SymposiumRefs: CCF(3) NTU(1) TSINGHUA(2) NUS(1)(67) ISMB: International Conference on Intelligent Systems for Molecular BiologyRefs: NTU(1) TSINGHUA(2) NUS(1)(68) OSDI: Symposium on Operation systems design and implementationRefs: SJTU(1) TSINGHUA(1)(69) SIGKDD: ACM Conf on Knowledge Discovery and Data MiningRefs: CCF(1) SJTU(1)(70) EUROCRYPT: European Conf on CryptographyRefs: UCLA(1) CCF(2) NTU(2) TSINGHUA(2) NUS(2)(71) MOBIHOC: ACM International Symposium on Mobile Ad Hoc Networking and Computing Refs: UCLA(1) CCF(2) TSINGHUA(2)(72) FAST: Conference on File and Storage TechnologiesRefs: CCF(2) TSINGHUA(1)(73) NSDI: Symposium on Network System Design and Implementation Refs: CCF(2) TSINGHUA(1)(74) SC: IEEE/ACM Conference on SupercomputingRefs: SJTU(1) TSINGHUA(2)(75) USENIX Symp on Internet Tech and SysRefs: UCLA(2) NTU(1)(76) MassPar: Symp on Frontiers of Massively Parallel ProcRefs: UCLA(1)(77) OPENARCH: IEEE Conf on Open Arch and Network ProgRefs: UCLA(1)(78) SIGCHI: ACM SIG CHIRefs: CCF(1)(79) Ubicomp: International Conference on Ubiquitous Computing Refs: TSINGHUA(1)Rank #2(80) COLING: International Conference on Computational LinguisticsRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(2) NUS(2)(81) CONCUR: International Conference on Concurrency TheoryRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(2) NUS(2)(82) ECCV: European Conference on Computer VisionRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(2) NUS(2)(83) USENIX Security: USENIX Security SymposiumRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(2) NUS(2)(84) ALT: International Conference on Algorithmic Learning TheoryRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(4) NUS(2)(85) ASE: International Conference on Automated Software EngineeringRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(86) ASIACRYPT: Annual International Conference on the Theory and Application of Cryptology and Information SecurityRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(87) CC: International Conference on Compiler ConstructionRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(88) DATE: IEEE/ACM Design, Automation & Test in Europe ConferenceRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(89) ECAI: European Conference on Artificial IntelligenceRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(4) NUS(2)(90) ECML: European Conference on Machine LearningRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(91) EDBT: International Conference on Extending DB TechnologyRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(92) EMNLP: Conference on Empirical Methods in Natural Language ProcessingRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(93) ER: Intl Conf on Conceptual ModelingRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(94) ESOP: European Symposium on ProgrammingRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(2) NUS(2)(95) Euro-Par: European Conference on Parallel ProcessingRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(96) FSTTCS: Conference on Foundations of Software Technology and Theoretical Computer ScienceRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(97) ICPP: Intl Conf on Parallel ProcessingRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(98) ICSR: IEEE Intl Conf on Software ReuseRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(4) NUS(2)(99) MFCS: Mathematical Foundations of Computer ScienceRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(100) PEPM: ACM SIGPLAN Symposium on Partial Evaluation and Semantics Based Programming ManipulationRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(4) NUS(2)(101) RTA: International Conference on Rewriting Techniques and ApplicationsRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(102) SAS: International Static Analysis SymposiumRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(103) STACS: Symp on Theoretical Aspects of Computer ScienceRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(104) TACAS: International Conference on Tools and Algorithms for the Construction and Analysis of SystemsRefs: UCLA(2) CCF(2) NTU(2) TSINGHUA(3) NUS(2)(105) CP: Intl Conf on Principles & Practice of Constraint ProgRefs: UCLA(2) CCF(2) NTU(2) NUS(2)(106) CSFW: IEEE Computer Security Foundations WorkshopRefs: CCF(2) NTU(2) TSINGHUA(2) NUS(2)(107) CSSAC: Cognitive Science Society Annual ConferenceRefs: UCLA(2) CCF(2) NTU(2) NUS(2)(108) ECOOP: European Conference on Object-Oriented ProgrammingRefs: CCF(2) NTU(2) TSINGHUA(2) NUS(2)(109) IEEEIT: IEEE Symposium on Information TheoryRefs: UCLA(2) CCF(2) NTU(2) NUS(2)(110) ISRE: Requirements EngineeringRefs: UCLA(2) CCF(2) NTU(2) NUS(2)(111) PG: Pacific GraphicsRefs: UCLA(2) CCF(2) NTU(2) NUS(2)(112) CGI: Computer Graphics InternationalRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(3) NUS(2)(113) FCCM: IEEE Symposium on Field Programmable Custom Computing MachinesRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(3) NUS(2)(114) FoSSaCS: International Conference on Foundations of Software Science and Computation StructuresRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(3) NUS(2)(115) ICC: IEEE International Conference on CommunicationsRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(4) NUS(2)(116) ICDM: IEEE International Conference on Data MiningRefs: UCLA(3) CCF(2) NTU(2) TSINGHUA(2) NUS(3)(117) ICME: IEEE International Conference on Multimedia & ExpoRefs: UCLA(3) CCF(2) NTU(2) TSINGHUA(4) NUS(2)(118) ICPR: Intl Conf on Pattern RecognitionRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(4) NUS(2)(119) ICS: Intl Conf on SupercomputingRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(3) NUS(2)(120) LCN: IEEE Annual Conference on Local Computer NetworksRefs: UCLA(2) CCF(3) NTU(2) TSINGHUA(4) NUS(2)(121) NDSS: Network and Distributed System Security SymposiumRefs: UCLA(2) CCF(2) NTU(4) TSINGHUA(2) NUS(4)(122) NOSSDAV: Network and Operating System Support for Digital Audio and Video Refs: UCLA(2) CCF(3) NTU(2) TSINGHUA(3) NUS(2)(123) CADE: Conference on Automated DeductionRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(124) CAiSE: Intl Conf on Advanced Info System EngineeringRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(125) CoNLL: Conference on Natural Language LearningRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(126) CoopIS: Conference on Cooperative Information SystemsRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(127) DAS: International Workshop on Document Analysis SystemsRefs: UCLA(2) NTU(2) TSINGHUA(4) NUS(2)(128) DASFAA: Database Systems for Advanced ApplicationsRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(129) DEXA: Database and Expert System ApplicationsRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(130) EACL: Annual Meeting of European Association Computational Linguistics Refs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(131) ESA: European Symp on AlgorithmsRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(132) ICCL: IEEE Intl Conf on Computer LanguagesRefs: UCLA(2) NTU(2) TSINGHUA(4) NUS(2)(133) ICDAR: International Conference on Document Analysis and Recognition Refs: UCLA(2) CCF(3) NTU(2) NUS(2)(134) ICECCS: IEEE Intl Conf on Eng. of Complex Computer SystemsRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(135) ICIP: Intl Conf on Image ProcessingRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(136) ICSM: Intl Conf on Software MaintenanceRefs: CCF(2) NTU(2) TSINGHUA(4) NUS(2)(137) ICTAI: IEEE International Conference on Tools with Artificial IntelligenceRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(138) IJCNN: Intl Joint Conference on Neural NetworksRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(139) IPCO: MPS Conf on integer programming & comb optimizationRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(140) ISAAC: Intl Symp on Algorithms and ComputationRefs: UCLA(2) NTU(2) TSINGHUA(4) NUS(2)(141) JCDL: ACM/IEEE Joint Conference on Digital LibrariesRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(142) MASCOTS: Modeling, Analysis, and Simulation On Computer and Telecommunication SystemsRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(143) NAACL: The Annual Conference of the North American Chapter of the Association for Computational LinguisticsRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(144) PADL: Practical Aspects of Declarative LanguagesRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(145) SEKE: International Conference on Software Engineering and Knowledge Engineering Refs: UCLA(2) CCF(3) NTU(2) NUS(2)(146) SRDS: Symp on Reliable Distributed SystemsRefs: UCLA(2) NTU(2) TSINGHUA(3) NUS(2)(147) SSDBM: Intl Conf on Scientific and Statistical DB MgmtRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(148) VLSI: IEEE Symp VLSI CircuitsRefs: UCLA(2) NTU(2) TSINGHUA(4) NUS(2)(149) WACV: IEEE Workshop on Apps of Computer VisionRefs: UCLA(2) NTU(2) TSINGHUA(4) NUS(2)(150) WCNC: IEEE Wireless Communications & Networking ConferenceRefs: UCLA(2) CCF(3) NTU(2) NUS(2)(151) AI-ED: World Conference on AI in EducationRefs: UCLA(2) NTU(2) NUS(2)(152) AID: Intl Conf on AI in DesignRefs: UCLA(2) NTU(2) NUS(2)(153) AMAI: Artificial Intelligence and MathsRefs: UCLA(2) NTU(2) NUS(2)(154) AMIA: American Medical Informatics Annual Fall Symposium Refs: UCLA(2) NTU(2) NUS(2)(155) ASAP: Intl Conf on Apps for Specific Array Processors Refs: UCLA(2) NTU(2) NUS(2)(156) ASS: IEEE Annual Simulation SymposiumRefs: UCLA(2) NTU(2) NUS(2)(157) CAIP: Inttl Conf on Comp. Analysis of Images and Patterns Refs: UCLA(2) NTU(2) NUS(2)(158) CANIM: Computer AnimationRefs: UCLA(2) NTU(2) NUS(2)(159) CC: IEEE Symp on Computational ComplexityRefs: UCLA(2) NTU(2) NUS(2)(160) CCC: Cluster Computing ConferenceRefs: UCLA(2) NTU(2) NUS(2)(161) DNA: Meeting on DNA Based ComputersRefs: UCLA(2) NTU(2) NUS(2)(162) DOOD: Deductive and Object-Oriented DatabasesRefs: UCLA(2) NTU(2) NUS(2)(163) EUROCOLT: European Conf on Learning TheoryRefs: UCLA(2) NTU(2) NUS(2)(164) EUROGRAPH: European Graphics ConferenceRefs: UCLA(2) NTU(2) NUS(2)(165) FODO: Intl Conf on Foundation on Data OrganizationRefs: UCLA(2) NTU(2) NUS(2)(166) HCS: Hot Chips SympRefs: UCLA(2) NTU(2) NUS(2)(167) IAAI: Innovative Applications in AIRefs: UCLA(2) NTU(2) NUS(2)(168) IEEE Intl Conf on Formal Engineering MethodsRefs: UCLA(2) NTU(2) NUS(2)(169) IPCCC: IEEE Intl Phoenix Conf on Comp & Communications Refs: UCLA(2) NTU(2) NUS(2)(170) IPTPS: Annual International Workshop on Peer-To-Peer Systems Refs: UCLA(2) CCF(2) TSINGHUA(2)(171) ISTCS: Israel Symp on Theory of Computing and Systems Refs: UCLA(2) NTU(2) NUS(2)(172) Intl Conf on Integrated Formal MethodsRefs: UCLA(2) NTU(2) NUS(2)(173) LATIN: Intl Symp on Latin American Theoretical Informatics Refs: UCLA(2) NTU(2) NUS(2)(174) LFCS: Logical Foundations of Computer ScienceRefs: UCLA(2) NTU(2) NUS(2)(175) MMCN: ACM/SPIE Multimedia Computing and Networking Refs: UCLA(2) NTU(2) NUS(2)(176) NetStore: Network Storage SymposiumRefs: UCLA(2) NTU(2) NUS(2)(177) PADS: ACM/IEEE/SCS Workshop on Parallel & Dist Simulation Refs: UCLA(2) NTU(2) NUS(2)(178) PT: Perf Tools - Intl Conf on Model Tech & Tools for CPE Refs: UCLA(2) NTU(2) NUS(2)(179) SSD: Intl Symp on Large Spatial DatabasesRefs: UCLA(2) NTU(2) NUS(2)(180) SUPER: ACM/IEEE Supercomputing ConferenceRefs: UCLA(2) NTU(2) NUS(2)(181) SWAT: Scandinavian Workshop on Algorithm TheoryRefs: UCLA(2) NTU(2) NUS(2)(182) SenSys: ACM Conference on Embedded Networked Sensor SystemsRefs: UCLA(2) CCF(2) TSINGHUA(2)(183) WADS: Workshop on Algorithms and Data StructuresRefs: UCLA(2) NTU(2) NUS(2)(184) WCW: Web Caching WorkshopRefs: UCLA(2) NTU(2) NUS(2)(185) WSC: Winter Simulation ConferenceRefs: UCLA(2) NTU(2) NUS(2)(186) DSN: The International Conference on Dependable Systems and NetworksRefs: UCLA(2) CCF(2) NTU(4) TSINGHUA(3) NUS(4)(187) CASES: International Conference on Compilers, Architecture, and Synthesis for Embedded SystemsRefs: CCF(3) NTU(2) TSINGHUA(3) NUS(2)(188) CODES+ISSS: Intl Conf on Hardware/Software Codesign & System SynthesisRefs: CCF(3) NTU(2) TSINGHUA(3) NUS(2)(189) ISSTA: International Symposium on Software Testing and AnalysisRefs: CCF(2) NTU(4) TSINGHUA(2) NUS(4)(190) WCRE: SIGSOFT Working Conf on Reverse EngineeringRefs: UCLA(3) NTU(3) TSINGHUA(4) NUS(2)(191) ACSAC: Annual Computer Security Applications ConferenceRefs: UCLA(2) CCF(2) TSINGHUA(3)(192) APLAS: Asian Symposium on Programming Languages and SystemsRefs: CCF(3) NTU(2) NUS(2)(193) CSCW: Conference on Computer Supported Cooperative WorkRefs: NTU(2) TSINGHUA(3) NUS(2)(194) ESEC: European Software Engineering ConfRefs: UCLA(2) CCF(2) TSINGHUA(3)(195) ESORICS: European Symposium on Research in Computer Security Refs: UCLA(2) CCF(2) TSINGHUA(3)(196) FPL: Field-Programmable Logic and ApplicationsRefs: CCF(3) NTU(2) NUS(2)(197) Fast Software EncryptionRefs: UCLA(3) NTU(2) NUS(2)(198) GECCO: Genetic and Evolutionary Computation ConferenceRefs: CCF(3) NTU(2) NUS(2)(199) HASKELL: Haskell WorkshopRefs: UCLA(4) NTU(2) NUS(2)(200) IC3N: Intl Conf on Comp Comm and NetworksRefs: UCLA(3) NTU(2) NUS(2)(201) IEEE VisualizationRefs: NTU(2) TSINGHUA(3) NUS(2)(202) IMC: Internet Measurement Conference/WorkshopRefs: UCLA(3) CCF(2) TSINGHUA(2)(203) IWSSD: International Workshop on Software Specifications & Design Refs: UCLA(2) CCF(3) NTU(2)(204) PPDP: Principles and Practice of Declarative ProgrammingRefs: NTU(4) TSINGHUA(4) NUS(2)(205) RAID: International Symposium on Recent Advances in Intrusion Detection Refs: UCLA(2) CCF(2) TSINGHUA(4)(206) WABI: Workshop on Algorithms in BioinformaticsRefs: NTU(2) TSINGHUA(3) NUS(2)(207) DSIC: Intl Symp om Distributed ComputingRefs: NTU(2) NUS(2)(208) DocEng: ACM Symposium on Document EngineeringRefs: NTU(2) NUS(2)(209) EUROSYS: EUROSYSRefs: CCF(2) TSINGHUA(2)(210) European Symposium on Research in Computer SecurityRefs: NTU(2) NUS(2)(211) HPDC: IEEE International Symposium on High Performance Distributed Computing Refs: CCF(2) TSINGHUA(2)(212) IEEE/WIC: International Joint Conf on Web Intelligence and Intelligent Agent Technology Refs: NTU(2) NUS(2)(213) ISSCC: IEEE Intl Solid-State Circuits ConfRefs: UCLA(2) NTU(2)(214) MOBISYS: International Conference on Mobile Systems, Applications, and Services Refs: UCLA(2) TSINGHUA(2)(215) MPC: Mathematics of Program ConstructionRefs: NTU(2) NUS(2)(216) MPPOI: Massively Par Proc Using Opt InterconnsRefs: UCLA(2) NTU(2)(217) SCA: ACM/Eurographics Symposium on Computer AnimationRefs: CCF(2) TSINGHUA(2)(218) SSR: ACM SIGSOFT Working Conf on Software ReusabilityRefs: UCLA(2) NTU(2)(219) UIST: ACM Symposium on User Interface Software and TechnologyRefs: CCF(2) TSINGHUA(2)(220) CSL: Annual Conf on Computer Science LogicRefs: UCLA(3) CCF(2) NTU(3) TSINGHUA(4) NUS(3)(221) IH: Workshop on Information HidingRefs: UCLA(3) CCF(2) NTU(3) TSINGHUA(3) NUS(3)(222) COMPSAC: International Computer Software and Applications ConferenceRefs: UCLA(3) CCF(2) NTU(3) NUS(3)(223) FCT: International Symposium Fundamentals of Computation TheoryRefs: UCLA(3) CCF(2) NTU(3) NUS(3)(224) ICCB: International Conference on Case-Based ReasoningRefs: UCLA(3) CCF(2) NTU(3) NUS(3)(225) ICRA: IEEE Intl Conf on Robotics and AutomationRefs: UCLA(4) CCF(2) NTU(3) NUS(4)(226) ILP: International Workshop on Inductive Logic ProgrammingRefs: CCF(2) NTU(4) TSINGHUA(4) NUS(4)(227) PKDD: European Conference on Principles and Practice of Knowledge Discovery in DatabasesRefs: CCF(2) NTU(3) TSINGHUA(3) NUS(4)(228) EC: ACM Conference on Electronic CommerceRefs: NTU(4) TSINGHUA(2) NUS(4)(229) IJCAR: International Joint Conference on Automated ReasoningRefs: CCF(2) NTU(4) NUS(4)(230) IPSN: International Conference on Information Processing in Sensor NetworksRefs: UCLA(2) CCF(3) TSINGHUA(3)(231) ITC: IEEE Intl Test ConfRefs: UCLA(2) NTU(4) NUS(4)(232) MiddlewareRefs: NTU(4) TSINGHUA(2) NUS(4)(233) PPSN: Parallel Problem Solving from NatureRefs: NTU(4) CORE(2) NUS(4)(234) TLCA: Typed Lambda Calculus and ApplicationsRefs: CCF(2) NTU(4) NUS(4)(235) CCC: IEEE Conference on Computational ComplexityRefs: CCF(2) TSINGHUA(4)(236) CEC: IEEE Congress on Evolutionary ComputationRefs: CCF(3) CORE(2)(237) CLUSTER: Cluster ComputingRefs: CCF(2) TSINGHUA(3)(238) CoNEXT: ACM International Conference on emerging Networking EXperiments and TechnologiesRefs: CCF(2) TSINGHUA(4)(239) ECDL: European Conference on Digital LibrariesRefs: NTU(4) NUS(2)(240) EGSR: Eurographics Symposium on RenderingRefs: CCF(2) TSINGHUA(4)(241) 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正向运动学英文

正向运动学英文Here is an essay on the topic of "Forward Kinematics" with a word count of over 1000 words, written in English without any additional title or unnecessary punctuation marks.Forward kinematics is a fundamental concept in the field of robotics and mechanical engineering, which deals with the relationship between the joint angles or joint positions of a robot and the position and orientation of the end-effector or tool. It is the process of determining the position and orientation of a robot's end-effector based on the known values of its joint angles or joint positions.In a robotic system, the end-effector is the part of the robot that interacts with the environment, such as a gripper, a welding torch, or a painting tool. The forward kinematics problem involves finding the position and orientation of the end-effector in the robot's reference frame, given the values of the joint angles or joint positions.To understand the forward kinematics problem, consider a simple two-link robot arm, as shown in Figure 1. The robot arm has two joints, each with a single degree of freedom, and two links of lengths L1 and L2. The position and orientation of the end-effector can bedescribed by the Cartesian coordinates (x, y) and the angle θ, which represents the rotation of the end-effector around the z-axis.Figure 1: A two-link robot armThe forward kinematics problem for this robot arm can be formulated as follows: Given the joint angles θ1 and θ2, find the position (x, y) and orientation θ of the end-effector.To solve this problem, we can use the following equations:x = L1 * cos(θ1) + L2 * cos(θ1 + θ2)y = L1 * sin(θ1) + L2 * sin(θ1 + θ2)θ = θ1 + θ2These equations represent the forward kinematics of the two-link robot arm, and they can be used to calculate the position and orientation of the end-effector for any given values of the joint angles θ1 and θ2.The forward kinematics problem becomes more complex as the number of joints and links in the robot increases. For a robot with n joints, the forward kinematics equations can be expressed in matrix form using the Denavit-Hartenberg (DH) convention, which is a systematic way of assigning coordinate frames to each joint in therobot.The DH convention defines four parameters for each joint: the linkl ength (a), the link twist (α), the joint offset (d), and the joint angle (θ). These parameters are used to construct a homogeneous transformation matrix, which represents the position and orientation of the end-effector with respect to the base frame of the robot.The forward kinematics equations for an n-joint robot can be written as:T_0^n = T_0^1 * T_1^2 * ... * T_(n-1)^nwhere T_i^(i+1) is the homogeneous transformation matrix that relates the (i+1)th coordinate frame to the ith coordinate frame, and T_0^n is the final homogeneous transformation matrix that represents the position and orientation of the end-effector with respect to the base frame.The computation of the forward kinematics for a complex robot can be a challenging task, especially when the robot has a large number of joints or when the robot's structure is complex. In such cases, numerical methods and computer software are often used to solve the forward kinematics problem.One common approach is to use the Denavit-Hartenberg (DH) parameters to construct the homogeneous transformation matrices and then multiply them together to obtain the final transformation matrix. This method is widely used in robotics and is implemented in many software libraries and frameworks, such as the Robot Operating System (ROS) and the Robotics Toolbox for MATLAB.Another approach is to use symbolic computation to derive the forward kinematics equations. This method involves expressing the forward kinematics equations in terms of the joint angles and link parameters, and then simplifying and solving the resulting equations using symbolic algebra software, such as Mathematica or Maple.In addition to the mathematical formulation of the forward kinematics problem, there are also practical considerations in the implementation of forward kinematics in real-world robotic systems. These include issues such as sensor calibration, joint encoder resolution, and the effects of mechanical compliance and backlash in the robot's joints and links.Overall, the forward kinematics problem is a fundamental concept in robotics and plays a crucial role in the design, control, and programming of robotic systems. Understanding and solving the forward kinematics problem is essential for many roboticapplications, such as pick-and-place operations, assembly tasks, and trajectory planning.。

Inverse kinematics positioning using nonlinear programming for highly articulated figures

Inverse Kinematics Positioning Using Nonlinear Programmingfor Highly Articulated FiguresJianmin Zhao and Norman I.BadlerDepartment of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphia,PA19104-6389AbstractAn articulatedﬁgure is often modeled as a set of rigid segments connected with joints.Its conﬁguration can be altered by varying the joint angles.Although it is straightforward to computeﬁgure conﬁgurations given joint angles(forward kinematics),it is not so toﬁnd the joint angles for a desired conﬁguration (inverse kinematics).Since the inverse kinematics problem is of special importance to an animator wishing to set aﬁgure to a posture satisfying a set of positioning constraints,researchers have proposed many approaches.But when we try to follow these approaches in an interactive animation system where the object to operate on is as highly articulated as a realistic humanﬁgure,they fail in either generality or performance,and so a new approach is fostered.Our approach is based on nonlinear programming techniques.It has been used for several years in the spatial constraint system in the Jack TM humanﬁgure simulation software developed at the Computer Graphics Research Lab of the University of Pennsylvania,and proves to be satisfactorily efﬁcient,controllable,and robust.A spatial constraint in our system involves two parts:one on theﬁgure,called the end-effector,and the other one on the spatial environment,called the goal.These two parts are dealt with separately,so that a neat modular implementation is achieved.Constraints can be added one at a time with appropriate weights designating the importance of this constraint relative to the others,and the system solves them and retains them whenever a constraint is violated because either theﬁgure or the goal is moved.In case it is impossible to satisfy all the constraints thanks to physical limits,the system stops with the optimal solution for the given weights.In addition,the rigidity of each joint angle can be controlled,which is useful when degrees of freedom are redundant.Categories and Subject Descriptors:I.3.7[Computer Graphics]:Three-Dimensional Graphics and Realism–animationGeneral Terms:Algorithms,PerformanceAdditional Key Words and Phrases:Inverse kinematics,highly articulatedﬁgures,nonlinear pro-gramming1IntroductionIn computer animation,an articulatedﬁgure is often modeled as a set of rigid segments connected by joints.A joint is,abstractly,a constraint on the geometric relationship between two adjacent segments.This “relationship”is expressed by a number of parameters called joint angles.With judicious selection of joints, so that,e.g.,segments are connected to form a tree structure,a collection of the joint angles of all the joints corresponds one-on-one to a conﬁguration of theﬁgure.While this correspondence provides an immediate computer representation of articulatedﬁgure conﬁgurations in the sense that given a set of joint angles it is straightforward to compute the corresponding conﬁguration,the problem ofﬁnding a set of joint angles that corresponds to a given conﬁguration,the inverse kinematics problem,persists in practice.The inverse kinematics problem,however,is extremely important in computer animation,since it is often the spatial appearance,rather than the joint angles,that an animator is interested in.Naturally,the problem has received attention of many researchers in computer animation,as well as in robotics(see the next section),but the various algorithms reﬂect particular aspects of the problem and fail to provide a general,efﬁcient,and robust solution for positioning highly articulatedﬁgures in an interactive animation system.In interactive manipulation of articulatedﬁgures,where an animator poses aﬁgure in the spatial context whereas joint angles are merely internal(and possibly hidden)representations of postures(conﬁgurations) [18],the joint angles that deﬁne the target conﬁguration is much more interesting than the process that the joint angles take in arriving at the target.It is the responsiveness that is essential.Quick response is also essential for practical control of articulatedﬁgures where the mapping from spatial conﬁgurations to joint angles has to be done repeatedly.For example,in path planning with strength constraints,the predictionof the next conﬁguration is transformed to joint angles iteratively[14].Workspace computation is another example[1].In the former example,the time sequence is handled by some other level of control;and in the latter example,the process that the joint angles take in arriving at target postures is not pertinent.It is in this context that we offer a new approach to the inverse kinematics problem.In the following section,we shall talk about our motivation in more detail.Our approach is based on nonlinear programming, a numerical method for solving the minimum of a nonlinear function.It searches for the solution in the high-dimensional joint angle space based on computational economy rather than physical meanings.It deals with joint limits intrinsically rather than as a special case.It is successfully implemented and has found wide uses,as noted above.Because of the complex nature of nonlinear functions,many efﬁcient nonlinear programming algorithms terminate when theyﬁnd local minima.The algorithm we picked has this limitation,too.In practice, however,this is not an unacceptably serious problem.Local minima are less likely when the target conﬁguration is not too distant from the starting one.If they do occur during interactive manipulation,users can easily perturb theﬁgure conﬁguration slightly to get around the local minima.2BackgroundInverse kinematics for determining mechanism motion is a common technique in mechanical engineering, particularly in robot research[16].In robotics,however,people are mostly concerned about the functionality of manipulators;overly redundant degrees of freedom are usually not desired except for special purposes. Moreover,the computation is usually carried out on particular linkage geometries.In contrast,many interesting objects in the computer animation domain,the humanﬁgure,for example,have many redundant degrees of freedom when viewed as a tree-structured kinematic mechanism.So it was necessary to look for effective means for solving this problem under various circumstances peculiar to computer animation.Korein and Badler began to study and implement methods for kinematic chain positioning,especially in the context of joint limits and redundant degrees of freedom[12,13].In[3],Badler et al used position constraints to specify spatial conﬁgurations of articulatedﬁgures.They recursively solved for joint angles to satisfy multiple position constraints.But,owing to their simple solver,the constraints handled were limited to the type of point-to-point position constraints only.Girard and Maciejewski adopted a method from robotics.In[11],they calculated the pseudo-inverse of the Jacobian matrix which relates the increment of the joint angles to the displacement of the end-effector in space.The main formula is∆∆where∆is the increment of the joint angle vector,∆is the displacement of the vector representing the position and/or orientation of the end-effector in space,and is the pseudo-inverse of the Jacobian. To understand this,we can think of as a3-D column vector denoting the position of the hand,and as a n-D column vector consisting of all joint angles which may contribute to the motion of the hand—e.g.,all the joint angles from the shoulder to the wrist.This is a differential equality;in other words,the equality holds only if we ignore the displacement of higher order∆2.It was developed to drive the robot, where the increment is small because actual motion has to be carried out physically in continuous way.To simply position a humanﬁgure in a computer simulated environment,however,it would not be economical to move the end-effector by“small”steps;in making a computer animation sequence,it would not be optimal either to take a step size smaller than necessary.Moreover,the pseudo-inverse calculation required for each step in this formula is normally quite expensive and they did not deal with joint limits.Witkin et al used energy constraints for positioning purposes[24].Constraints can be positional or orientational.They are satisﬁed if and only if the energy function is zero.The way they solved constraints is to integrate the differential equation:where is the parameter(e.g.,joint angle)vector which deﬁnes the conﬁguration of the system,is the energy function of,and is the gradient operator.Clearly,if is the integral with some initial condition,monotonically decreases with time,because2In the joint angle space,constantdeﬁnes a line,called the iso-energy line,on which the energy function takes an identical value.For any number(energy level),there is such a line.Under this physical meaning of the energy function,Witkin et al’s method searches the path from the initial conﬁguration to the target conﬁguration which is,at any point, perpendicular to the iso-energy lines.Instead of associating energy functions with constraints,Barzel and Barr introduced deviation functions which measure the deviation of two constrained parts[5].They discussed a variety of constraints in[5],such as point-to-point,point-to-nail,etc.,and their associated deviation functions.A segment in their system of rigid bodies is subjected to both external forces,such as the gravity,and constraint forces,which bring the deviations to zero whenever they are greater.Constraint forces are solved from a set of dynamic differential equations which requires that all deviations go to zero exponentially in a certain amount of time.It is worth noting that an approach based on physical modeling and interpretation is also used by Witkin and Welch on nonrigid bodies whose deformations are controlled by a number of parameters[25].To apply this kind of methods to articulatedﬁgures,a joint would be considered as a point-to-point constraint and added to the system as an algebraic equation.This poses some practical problems that render such solutions inappropriate to highly articulatedﬁgures.First,it is not unusual to have several dozen joints in a highly articulatedﬁgure,which would add to the number of constraint equations substantially.Second,a joint of an articulatedﬁgure is meant to be an absolute constraint.In other words,it should not compete with any constraint that relates a point on a segment of theﬁgure to a point in space.This competition often gives rise to numerical instability.We notice that all those methods have a property in common:the target conﬁguration is the result of a process from a starting one.This process bears some physical meaning.In Girard and Maciejewski’s method [11],the process is determined by the end-effector path;in Witkin et al’s method[24],it is determined by the energy function(the path in space is perpendicular to the family of iso-energy lines);in Barzel and Barr’s method[5]or other dynamic methods([25]),the process is determined by the physical interpretations of each segment,and external and constraint forces exerted on it.Not only can these methods solve the constraints,but also offer a smooth process in which the constraints are satisﬁed in certain contexts.The achieved target conﬁguration is,therefore,natural in the sense that it results from a process that the user is more or less able to comprehend and control.But this property is not free.If we are only concernedabout the target conﬁguration deﬁned by the spatial constraints,rather than the physical realization,which is true in many circumstances,physical methods could be computationally inefﬁcient,because they add extra burdens to the original geometric problem.For example,in searching for a(local)minimum along a line,one mayﬁrst choose a small step size and then compute the function value until it rises.Another way toﬁnd the solution could be like this.First locate an interval in which the minimum lies,and then use the golden ratio method,a method similar to binary search,toﬁnd the minimum.Theﬁrst method shows a vivid picture of how the function changes to the minimum gradually,whereas the second method is statistically much faster.Therefore,since a target conﬁguration can be deﬁned by the minimum of an energy function(see [24]),why don’t we look for the minimum directly?As for naturalness of the target conﬁguration,we may give the user more immediate control by allowing the user to specify more constraints,if it remains affordable.Nonlinear programming is a numerical technique to solve for(local)minima of nonlinear functions. The solution search maintains numerical efﬁciency and robustness;the intermediate values from the starting state to theﬁnal one could be in general fairly“irregular”.There are two classes of nonlinear programming problems.One is the unconstrained nonlinear programming,where the variables are free to take any values; the other one is the constrained nonlinear programming,where the variables can only take values in a certain range.The constraints on the variablesﬁt exactly to joint limits of articulatedﬁgures.Although the latter problem can be theoretically reduced to the former one,both unconstrained and constrained nonlinear programming problems have been studied extensively,because simple reduction may cause numerical instability.So we propose a new approach to the inverse kinematics problem based on nonlinear programming methods.Our target application is interactive manipulation of highly articulatedﬁgures,such as human ﬁgures,where joints and joint limits must not be violated.3Spatial ConstraintsThe basic geometric entity considered here is the articulatedﬁgure.The data structure of an articulated ﬁgure we used is deﬁned by the Peabody language developed at the Computer Graphics Research Lab atcnstr.........joint angle index table weight,joint chain,θGoalG, gAssemblerMG, mgNon-linearObjective FunctionGenerator Programminggoal type,parametersEnd-effector Figure 1:Multiple Spatial Constraint Systemthe University of Pennsylvania [17].A Peabody ﬁgure is composed of rigid segments connected together by joints.Each joint has several rotational and translational degrees of freedom subject to joint limits.The data structure can be viewed as a tree,where nodes represent segments and edges represent joints.Having decided on the data structure,we need to address the problem of setting a ﬁgure to a desired posture.As discussed in the introduction,we wish to be able to adjust the posture directly in the spatial domain.Our spatial constraints are designed for this purpose.A spatial constraint is simply a demand that the end-effector on a segment of a ﬁgure be placed at and/or aligned with the goal in space.To say that a constraint is satisﬁed is equivalent to saying that the goal is reached.The end-effector’s propensity to hold on to the goal persists until the constraint is disabled or deleted.Figure 1is a diagram of the multiple spatial constraint system in Jack .The system consists of threemajor components:Objective Function Generator,Assembler,and Nonlinear Programming solver.They are described in the following sections.4End-effectors4.1End-effector MappingsFormally,we can view an end-effector as a mapping::Θ1ΘwhereΘis the joint angle space,the set consisting of all joint angle vectors,and3222 where3denotes the set of3-D vectors,and2the set of3-D unit vectors.Accordingly,is a9-D vector,whoseﬁrst three components form a positional vector,designating the spatial position of a point on the end-effector segment,the second and the third three components form two unit vectors,designating directions of two independent unit vectors on the end-effector segment.Given an instance of the joint angles of all the joints,,the end-effector associates a9-D vector according to theﬁgure deﬁnition.Since segments of aﬁgure are rigid,the angle expanded by the last two unit vectors should remain unchanged.A convenient choice is to set it to90degrees.These nine numbers uniquely determine the position and orientation of the end-effector segment in space.Theﬁrst three numbers are independent, but the next six numbers are not.They must satisfy two unity equations and one expanded angle equation. These three equations take away three degrees of freedom from,so that has only six independent quantities,which are exactly needed to determine the position and orientation of a rigid body in space.Let’s take an example.Let the end-effector segment be the right hand,and the pelvis beﬁxed temporarily, serving as the root of theﬁgure tree deﬁnition.Given joint angles of all the joints from the waist to the right wrist present in vector,the location and orientation of the right hand can be computed and the result is put in,provided that a point and two orthonormal vectors attached on the hand have been selected for reference.21 1solve the constraint requires the derivative quantities.1The matrix is the Jacobian matrix.Its use will be explained later.Naturally,it is this module’s responsibility to compute it.The vector is composed of some combination of a point vector and two unit vectors on the end-effector segment.Referring to Figure2,let be a point vector and be a unit vector on the end-effector segment.It is clear that in order to compute and,it is sufﬁcient to know how to compute, ,,and.Because all the joints in our current humanﬁgure model are rotational joints,we discuss only rotational joints here.1Let the th joint angle along the chain be,and the rotation axis of this joint be unit vector. It turns out that and can be easily computed with cascaded multiplications of4by4homogeneous matrices.The derivatives can be easily computed,too(see[26]):.(4) 5Goals5.1Goal Potential FunctionsA goal can also be viewed as a mapping::5 where the domain is the same as the range of the end-effector mapping deﬁned in(2),and is the set of non-negative real numbers.Since the function assigns a scalar to a combination of position and directions in space,we call it a potential function.When the end-effector vector is plugged into the potential function as the argument,it produces a non-negative real number,,which is to be understood asthe distance from the current end-effector location(position and/or orientation)to the associated goal.For a pair of an end-effector and a goal,the range of the end-effector must be the same as the domain of the potential function.5.2Goal Computational ModuleThe Goal module is the other part of the Objective Function Generator(Figure1).It is to compute the potential and its gradient,the column vector formed by all its partial derivatives.Let2In practice,however,it may not be adequate,because this potential function,when combined witha position goal,would in effect make one unit difference in length as important as about one radiandifference in angle,which is not always intended.To make one length unit commensurate with degrees in angle,we need to multiply the above by a factor such that1360or,explicitly3602.8 To be moreﬂexible,the potential function is chosen to be2222.9 The gradient is then22(10)22.(11)A goal direction,such as,could be unconstrained by setting to0.This is useful,for example,to orientationally constrain the normal to the palm of a person holding a cup of water.Position/Orientation Goals.The position and orientation goal can be treated as two goals,but some-times it is more convenient to combine them together as one goal.The potential function for the position/orientation goal is chosen to be a weighted sum of the position and orientation components:2222212 where and are weights assigned to position and orientation,respectively,such that1.The domain322and the gradients,and can be calculated from(7),(10),and(11)above.Aiming-at Goals.The goal is deﬁned by a point in space;the end-effector is deﬁned by a position vector and a unit vector on the end-effector segment.The goal is reached if and only if the ray emanating from in the direction passes through.The domain of the potential function32This type of goal is useful,for example,in posing a humanﬁgure facing toward a certain point.The potential function2.(15)Line Goals.The goal is deﬁned by a line which passes through points and,where is a unit vector.This line is meant for a point on the end-effector segment to lie on.The potential function2;16its domain3and gradient2.17Plane Goals.The goal is deﬁned by a plane with the unit normal to and a point on it.Similar to the Line Goal,the plane is meant for a point on the end-effector segment to lie on.The potential function2;18its domain3and the gradient2.19 Half-space Goals.The goal is deﬁned by a plane speciﬁed the same way as in the Plane Goal.The plane is used to divide the space into two halves.A point on the end-effector segment“reaches”the goal if and only if it is in the same half-space as the point is.The potential function0if0;202otherwiseits domain3and the gradient0if0212otherwise.6Spatial Constraint as a Nonlinear Programming ProblemA spatial constraint constrains an end-effector to a goal.From Section4and5,with the current joint angles being,the“distance”from the end-effector to the goal is simply22 This quantity can be computed byﬁrst invoking the end-effector module to compute,and then invoking the goal module with as the input argument of the potential function.This process is illustrated in Figure1.Ideally,we want to solve the algebraic equation,In reality,however,this equation is not always satisﬁable,for the goal is not always reachable.Thus the problem would be naturally toﬁnd in a feasible region that minimizes the function.Most of the joint angles in ourﬁgure deﬁnition have lower limits and upper limits.The joint angles for the shoulderare conﬁned in a polygon.They can all be expressed in linear inequalities.Therefore,we formulate the problem as a problem of nonlinear programming subject to linear constraints on variables,that is,formally,minimize23subject to1212where12are column vectors whose dimensions are the same as that of’s.The equalities allow for linear relationships among the joint angles,and the inequalities admit of the lower limit and upper limit on,the th joint angle,as do the inequalities.The polygonal region for the shoulder joint angles(elevation,abduction,and twist)can be similarly expressed as a set of inequalities.7Solving the Nonlinear Programming ProblemThe problem posed in(23)toﬁnd the minimum of the objective function is intractable without knowledge of the regularity of the objective function.Properties such as linearity or convexity that regulate the global behavior of a function may help toﬁnd the global minimum.Otherwise,research in nonlinear programming area is mostly done to solve for local minima.It is worthwhile because,in practice,functions are moderate:the local minimum is often what one wants,or if it fails to be,some other local minimum found by another attempt with a new initial point would quite likely be.In order to have quick response,we chose to compromise for local minima.From years of observation, we have not seen many serious problems.The algorithm we used to solve the problem(23)is described in the Appendix.It iterates to approach the solution.At each iteration,it searches for a minimum along a certain direction.In order for the search direction to point to the solution more accurately so that fewer iterations will be needed,the direction is determined based on not only the gradient at the current point,but also the gradients at the previous steps of iteration.Our method is monotonic,namely that after any iterations the value that the objective function takes never increases,and globally convergent,namely that it converges to a(local)minimum regardless of the initial point.These two properties are very attractive to us because the conﬁguration could otherwise diverge arbitrarily,which could cause disaster had the previous posture resulted from substantial effort.To carry out the computation,we need to compute and its gradient.It becomes easy now after preparation in Sections4and5.The function value can be computed as in(22),and the gradient can be computed as follows:defTherefore,our system handles multiple constraints.Since the objective function deﬁned in(22)is non-negative,the multiple constraints are solved by minimizingthe sum of the objective functions associated with all the goalsall251where is the number of constraints,subscript denotes the association with the th constraint,is a non-negative weight assigned to the th constraint to reﬂect the relative importance of the constraint,and26 Thus,the multiple constraints can be solved as the problem(23)with replaced by all deﬁned in (25).Note that’s can be computed independently,and only a number of additions are needed to compute all.This is also true for the gradient,for the gradient operator is additive,too.Constraints may also be tied together disjunctively,that is,they are considered satisﬁed if any one of them is satisﬁed.To solve this problem,we deﬁne the objective function asall min271It is useful,for example,to constrain an end-effector outside a convex polyhedron,because the outside space can be viewed as the disjunction of the outward half-spaces deﬁned by the polygonal faces.9Assembler of Multiple ConstraintsAs stated in the previous sections,the overall objective function of multiple constraints can be found by computing separately and independently the objective functions of individual constraints and then adding them together.In this section,we shall explain how the Assembler works.The module Objective Function Generator takes a joint chain,an array of corresponding joint angles, goal type,and other parameters of a constraint as its input and computes the objective function value and its gradient.Since the partial derivatives with respect to the joint angles other than those on the joint chain are zero,the gradient determined by this module has to include only the derivatives with respect to the joint angles on the chain.This property lends itself to a clean modular implementation.However,two gradientvectors so structured for different constraints do not add directly—the th joint angle in one chain may not be the same as the th joint angle in another chain.The difference is resolved by the Assembler module.Suppose there are constraints.LetΘbe the ordered set of joint angles on the joint chain of the th constraint,and be the number of joint angles inΘ.LetΘ1Θ28 the union of allΘ’s with the order deﬁned in certain way,and be the number of joint angles inΘ.In general,1,because of possible overlap amongΘ’s.Let’s deﬁne the index table as a mapping:121229 such that the th joint angle inΘcorresponds to the th joint angle in the overall index systemΘ.This index table,along with the weight of the constraint,are passed to the Assembler so that the effect of the th constraint to the gradient of the overall objective function all can be correctly accounted.Once the’s, the derivative of the objective function of the th constraint with regard to the th joint angle inΘ,are available,the Assembler does:For1to do,for12,where stands for the partial derivative of all with regard to the th joint angle inΘ.They are initially set to zero.10Reconciliation of Joint ChainsIt was suggested in Expression(28)that only a union was needed to combine all the joint chains.In fact,it is slightly more complicated,because we allow the user to specify the set of joints in the joint chain as the resource for the constraint satisfaction.The joint chain does not have to go from the end-effector segment back to the root in theﬁgure deﬁnition,and is speciﬁed by the user when he or she deﬁnes the constraint. Since the constraints may be input one by one,a joint which may affect the end-effector of one constraint but is not picked for the joint chain could well be picked for the joint chain of another constraint.For。

NOgII4.I-K)ILAND Uncertainty Relations and Time-Frequency Distributions

1.
INTRODUCTION
G a b o r [1] published a pioneering p a p e r in 1946 on time-frequency representations of signals in the context of c o m m u n i c a t i o n theory. It was Wigner [2] who first investigated the possibility of constructing joint distribution functions in phase-space and their importance in the d o m a i n of q u a n t u m mechanics. Later on, Ville [3] studied the joint distribution functions in signal analysis in the spirit of Wigner. In 1965, C o h e n [4] suggested a generalized a p p r o a c h for constructing the joint distributions in INFORMATION SCIENCES 89, 193-209 (1996) © Elsevier Science Inc. 1996 655 Avenue of the Americas, New York, NY 10010 0020-0255/96/$15.00 SSDI 0020-0255(95)00232-4
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phase-space. Most of the joint distributions are shown to be special cases in Cohen's class of distributions. Since the publications by Gabor, Wigner, and Ville, a large number of interesting works [5] on joint distributions have been reported in the context of quantum mechanics, optics, and in signal processing. In the 1980s, the interpretation of the uncertainty relation in signal processing and its relation to the Heisenberg uncertainty relation renewed the interest of the signal processing community. It was started after the publication of papers by Wilson et al. [6, 7] and Daugman et al. [8] in the context of computer vision. Daugman tried to analyze the psychophysiological evidence of receptive fields in the mammalian visual cortex and discussed the existence of the uncertainty relation in case of a 2D-signal representation. Recently, Cohen made an excellent review [9] on time-frequency distributions and their relation to the Heisenberg uncertainty relation, especially with reference to signal processing. He tried to make a systematic study on time-frequency analysis of signals and investigated the correspondence between quantum mechanics and signal analysis. He claimed that the uncertainty principle is a relationship concerning the marginals only and has no bearing on the existence of joint distributions. He has also criticized the mathematical correspondence between quantum mechanics and signal analysis as often discussed by various authors [8]. The recent developments of the theory of unsharp measurements [10-12] in quantum mechanics opens up a new possibility of constructing joint distributions of unsharp observables and studying their relevance to the Heisenberg uncertainty relation. This theory is a natural extension of the usual Hilbert space formalism of quantum theory. This development is quite interesting not only in quantum mechanics but also in the context of signal processing, especially in image processing and pattern recognition. If one considers the case of object-background segmentation in a gray level image, a great deal of ambiguity arises in a decision process due to the continuous gray level distribution from object to background. This kind of situation could be handled more efficiently if fuzzy set theoretical formalism is used instead of ordinary set theory [13]. Similarly, in extending the usual Hilbert space formalism of quantum mechanics to describe unsharp observables, it is generally required to introduce the concept of fuzzy sets in place of ordinary sets [10]. Consequently, Cohen's analysis needs reinvestigation in the light of this extended framework of quantum mechanics. In this paper, we shall briefly discuss Cohen's main arguments regarding joint distributions and their relation to the Heisenberg uncertainty relation in Section 2. In Section 3, we shall try to reinvestigate the whole issue within the framework of unsharp measurements. It will be shown that Cohen's analysis is not true in the case of unsharp observables, which is much more relevant in signal analysis. In our opinion, a more detailed

吕叔湘1942中国文法要略北京商务印书馆198Universityof

CHN Ling Rdg List, 9/11/06CHINESE LINGUISTICSReading List * Required of Ph.D. students** Required of M.A. students1. C HINESE S YNCHRONIC L INGUISTICS[IN ENGLISH]Alleton, Viviane. 1981. Final particles and expression of modality in modern Chinese. Journal of ChineseLinguistics 9.1: 91-115.Bao, Zhiming. 1990. On the nature of tone. Cambridge, MA: MIT dissertation.Chang, C. 1991. Verb copying: Toward a balance between formalism and functionalism. Journal ofChinese Language Teachers Association 26.1: 1-32. Chao, Yuan-Ren. 1968. A Grammar of Spoken Chinese.Berkeley and Los Angeles: University of CaliforniaPress.Chen, Chung-yu. 1984. Neutral tone in Mandarin: Phonotactic description and the issue of the norm,Journal of Chinese Linguistics 12.2: 299-333. Chen, Matthew Y. 1987. The Syntax of Xiamen tone Sandhi. Phonology Yearbook 4: 109-149. Cheng, Chin-chuan. 1973. A quantitative study of Chinese tones, Journal of Chinese Linguistics 1.1. Cheng, Robert. 1966. 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Interaction of Semantics and Syntax in Chinese, JCLTA 6.2: 58-78.Li, Y. C. 1972. Sentences with be, exist, and have in Chinese, Language 48.3: 573-583.Li, Y. C. 1972. Problems of Subject, Object, Etc. in Chinese Grammar, Papers in Linguistics in Honor ofA. A. Hill, Eds. Charles Tang, et al. (Taipei: Rainbow-Bridge Book Co.), pp. 109-32. Li, Y. C. 1976. Order of semantic units in Chinese, JCLTA11.1: 26-38.Li, Y. C. 1973. A Review of Y. R. Chao's A Grammar ofSpoken Chinese, JCL 1.l: 126-51.Li, Y. C. 1974. What Does 'Disposal' Mean? - Features ofthe Verb and Noun in Chinese, Journal of ChineseLinguistics (JCL) 2.2: 200-18.Li, Y. C. 1976. Order of Semantic Units in Chinese, JCLTA11.1: 26-38.Li, Y. C. 1983. Aspects of Quantification and Negation inChinese, in Studies in East Asian Linguistics, Eds.Nam-kil Kim and Henry H. Tiee (University ofSouthern California, L.A.), pp. 24-37.Li, Ying-che. 1983. Aspects of quantification andnegation in Chinese. Studies in Chinese syntax andsemantics, ed. by Robert Cheng, Ting-chi Tang, 227-240. Taipei: Student Book Co., Ltd.Li, Y. C. 2005. Aspects of Chinese Grammar from theCognitive Perspective, in Reflecting on the Future ofChinese Language Pedagogy: Honoring the 40-yearDistinguished Career of Prof. George C. C. Chao,Eds. C. C. Li et al. (Taipei: Shida Shiyuan Publisher),pp. 105-117.Norman, Jerry. 1988. Chinese. Cambridge: CambridgeUniversity Press.Pan, Wuyun. 1982. Several problems in the developmentof Chinese tones. Journal of ChineseLinguistics 10.2: 359-386.Ross, Claudia. 1983. On the functions of Mandarin le,Journal of Chinese Linguistics 11.2: 214-246.Tai, James H.Y. 1985. Temporal sequence and Chineseword order, in Iconicity in Syntax, ed. JohnHaiman (Amsterdam: John BenjaminsPublishing Co.), pp. 49-72.Tai, James H.Y. 1989. Toward a cognition-based functionalgrammar of Chinese, in Functionalism andChinese grammar, eds. James H.Y. Tai andFrank F.S. Hsueh (Seton Hall University: TheChinese Language Teachers Association), pp.187-226.Teng, Shou-hsin. 1974. Double nominatives in Chinese,Language 50.3: 455-473.Teng, Shou-hsin. 1975. A Semantic Study of TransitivityRelations in Chinese. Berkeley and Los Angeles:University of California Press.Teng, Shou-hsin. 1979. Remarks on cleft sentences inChinese, Journal of Chinese Linguistics 7.1:101-114.Thompson, Sandra A. 1973. Resultative verb compounds inMandarin Chinese: A case for lexical rules,Language 49.2: 361-379.Tsao, Feng-fu. 1976. A Functional Study of Topic inChinese: A First Step towards DiscourseAnalysis. Los Angeles, CA: University ofSouthern California Dissertation.Xu, Jie. 2003. Sentence Head and Sentence Structure.Tokyo and Singapore: Longman.Zhang, Jialu, Lu Shinan and Qi Shiqian. 1982. A clusteranalysis of the perceptual features of Chinese speechsounds. Journal of Chinese Linguistics 10.2: 189-206.[IN CHINESE]曹逢甫（199５）《主题在汉语中的功能研究》，北京：语文出版社。

Distributionally robust joint chance constraints with second-order moment information

Math.Program.,Ser.ADOI10.1007/s10107-011-0494-7FULL LENGTH PAPERDistributionally robust joint chance constraintswith second-order moment informationSteve Zymler·Daniel Kuhn·BerçRustemReceived:20August2010/Accepted:26August2011©Springer and Mathematical Optimization Society2011Abstract We develop tractable semidefinite programming based approximations for distributionally robust individual and joint chance constraints,assuming that only theﬁrst-and second-order moments as well as the support of the uncertain parameters are given.It is known that robust chance constraints can be conservatively approxi-mated by Worst-Case Conditional Value-at-Risk(CVaR)constraints.Weﬁrst prove that this approximation is exact for robust individual chance constraints with concave or(not necessarily concave)quadratic constraint functions,and we demonstrate that the Worst-Case CVaR can be computed efﬁciently for these classes of constraint func-tions.Next,we study the Worst-Case CVaR approximation for joint chance constraints. This approximation affords intuitive dual interpretations and is provably tighter than two popular benchmark approximations.The tightness depends on a set of scaling parameters,which can be tuned via a sequential convex optimization algorithm.We show that the approximation becomes essentially exact when the scaling parameters are chosen optimally and that the Worst-Case CVaR can be evaluated efﬁciently if the scaling parameters are kept constant.We evaluate our joint chance constraint approx-imation in the context of a dynamic water reservoir control problem and numerically demonstrate its superiority over the two benchmark approximations. Mathematics Subject Classiﬁcation(2010)90C15·90C221IntroductionA large class of decision problems in engineering andﬁnance can be formulated as chance constrained programs of the formS.Zymler(B)·D.Kuhn·B.RustemDepartment of Computing,Imperial College London,180Queen’s Gate,London SW72AZ,UKe-mail:sz02@S.Zymler et al.minimize x ∈R n c T x subject to Q a i (˜ξ)T x ≤b i (˜ξ)∀i =1,...,m ≥1−(1)x ∈X ,where x ∈R n is the decision vector,X ⊆R n is a convex closed set that can be rep-resented by semidefinite constraints,and c ∈R n is a cost vector.Without much loss of generality,we assume that c is deterministic.The chance constraint in (1)requires a set of m uncertainty-affected inequalities to be jointly satisﬁed with a probability of at least 1− ,where ∈(0,1)is a desired safety factor speciﬁed by the modeler.The uncertain constraint coefﬁcients a i (˜ξ)∈R n and b i (˜ξ)∈R ,i =1,...,m ,depend afﬁnely on a random vector ˜ξ∈R k ,whose distribution Q is assumed to be known.We thus havea i (˜ξ)=a 0i +kj =1a j i ˜ξj and b i (˜ξ)=b 0i +k j =1b j i ˜ξj .For ease of notation we introduce auxiliary functions y j i :R n →R ,which are deﬁned throughy j i (x )=(a j i )T x −b j i ,i =1,...,n ,j =0,...,k .These functions enable us to rewrite the chance constraint in problem (1)asQ y 0i (x )+y i (x )T ˜ξ≤0∀i =1,...,m ≥1− ,(2)where y i (x )=[y 1i (x ),...,y k i (x )]T is afﬁne in x for i =1,...,m .By convention,(2)is referred to as an individual or joint chance constraint if m =1or m >1,respec-tively.Chance constrained programs were ﬁrst discussed by Charnes et al.[8],Miller and Wagner [18]and Prékopa [23].Although they have been studied for a long time,they have not found wide application in practice due to the following reasons.Firstly,computing the optimal solution of a chance constrained program is noto-riously difﬁcult.In fact,even checking the feasibility of a ﬁxed decision x requires the computation of a multi-dimensional integral,which becomes increasingly difﬁcult as the dimension k of the random vector ˜ξincreases.Furthermore,even though the inequalities in the chance constraint (2)are biafﬁne in x and ˜ξ,the feasible set of problem (1)is typically nonconvex and sometimes even disconnected.Secondly,in order to evaluate the chance constraint (2),full and accurate informa-tion about the probability distribution Q of the random vector ˜ξis required.However,in many practical situations Q must be estimated from historical data and is therefore itself uncertain.Typically,one has only partial information about Q ,e.g.about its moments or its support.Replacing the unknown distribution Q in (1)by an estimate ˆQ corrupted by measurement errors may lead to over-optimistic solutions which often fail to satisfy the chance constraint under the true distribution Q .Distributionally robust joint chance constraintsIn a few special cases chance constraints can be reformulated as tractable convex constraints.For example,it is known that if the random vector˜ξfollows a Gauss-ian distribution and ≤0.5,then an individual chance constraint can be equivalently expressed as a single second-order cone constraint.In this case,the chance constrained problem becomes a tractable second-order cone program(SOCP),which can be solved in polynomial time,see Alizadeh and Goldfarb[1].More generally,Calaﬁore and El Ghaoui[6]have shown that for ≤0.5individual chance constraints can be con-verted to second-order cone constraints whenever the random vector˜ξis governed by a radial distribution.Tractability results for joint chance constraints are even more scarce.In a seminal paper,Prékopa[23]has shown that joint chance constraints are convex when only the right-hand side coefﬁcients b i(˜ξ)are uncertain and follow a log-concave distribution.However,under generic distributions,chance constrained programs are computationally intractable.Indeed,Shapiro and Nemirovski[20]point out that computing the probability of a weighted sum of uniformly distributed variables being nonpositive is already N P-hard.Recently,Calaﬁore and Campi[5]as well as Luedtke and Ahmed[17]have pro-posed to replace the chance constraint(2)by a pointwise constraint that must hold at aﬁnite number of sample points drawn randomly from the distribution Q.A similar approach was suggested by Erdoˇg an and Iyengar[12].The advantage of this Monte Carlo approach is that no structural assumptions about Q are needed and that the resulting approximate problem is convex.Calaﬁore and Campi[5]showed that one requires O(n/ )samples to guarantee that a solution of the approximate problem is feasible in the original chance constrained program.However,this implies that it may be computationally prohibitive to solve large problems or to solve problems for which a small violation probability is required.A natural way to immunize the chance constraint(2)against uncertainty in the prob-ability distribution is to adopt a distributionally robust approach.To this end,let P denote the set of all probability distributions on R k that are consistent with the known properties of Q,such as itsﬁrst and second moments and/or its support.Consider now the following ambiguous or distributionally robust chance constraint.inf P∈P Py0i(x)+y i(x)T˜ξ≤0∀i=1,...,m≥1− (3)It is easily veriﬁed that whenever x satisﬁes(3)and Q∈P,then x also satisﬁes the chance constraint(2)under the true probability distribution Q.Replacing the chance constraint(2)with its distributionally robust counterpart(3)yields the following dis-tributionally robust chance constrained programminimizex∈R nc T xsubject to infP∈P Py0i(x)+y i(x)T˜ξ≤0∀i=1,...,m≥1−x∈X,(4)which constitutes a conservative approximation for problem(1)in the sense that it has the same objective function but a smaller feasible set.S.Zymler et al.A common method to simplify the distributionally robust joint chance constraint(3),which looks even less tractable than(2),is to decompose it into m individualchance constraints by using Bonferroni’s inequality.Indeed,by ensuring that the totalsum of violation probabilities of the individual chance constraints does not exceed ,the feasibility of the joint chance constraint is guaranteed.Nemirovski and Shapiro[20]propose to divide the overall violation probability equally among the m indi-vidual chance constraints.However,the Bonferroni inequality is not necessarily tight,and the corresponding decomposition could therefore be over-conservative.In fact,forpositively correlated constraint functions,the quality of the approximation is knownto decrease as m increases[9].Consequently,the Bonferroni method may result ina poor approximation for problems with joint chance constraints that involve manyinequalities.A recent attempt to improve on the Bonferroni approximation is due to Chen et al.[9].Theyﬁrst elaborate a convex conservative approximation for a joint chance con-straint in terms of a Worst-Case Conditional Value-at-Risk(CVaR)constraint.Then,they rely on a classical inequality in order statistics to determine a tractable conserva-tive approximation for the Worst-Case CVaR and show that the resulting approximationfor the joint chance constraint necessarily outperforms the Bonferroni approximation.An attractive feature of this method is that the arising approximate constraints aresecond-order conic representable.However,the employed probabilistic inequality isnot necessarily tight,which may again render the approximation over-conservative.The principal aim of this paper is to develop new tools and models for approximatingrobust individual and joint chance constraints under the assumption that only theﬁrst-and second-order moments as well as the support of the random vector˜ξare known.We embrace the modern approach to approximate robust chance constraints by Worst-CaseCVaR constraints,but in contrast to the state-of-the-art methods described above,weﬁnd exact semidefinite programming(SDP)reformulations of the Worst-Case CVaRwhich do not rely on potentially loose probabilistic inequalities.These reformulationsare facilitated by the theory of moment problems and by conic duality arguments.Weprove that the CVaR approximation is in fact exact for individual chance constraintswhose constraint functions are either concave or(possibly nonconcave)quadratic inξand for joint chance constraints whose constraint functions depend linearly onξ.Wealso demonstrate that robust individual chance constraints have manifestly tractableSDP representations in most cases in which the CVaR approximation is exact.The main contributions of this paper can be summarized as follows:(1)In Sect.2we review and extend existing approximations for distributionallyrobust individual chance constraints and prove that a robust individual chanceconstraint is equivalent to a tractable Worst-Case CVaR constraint if theunderlying constraint function is either concave or(possibly nonconcave)qua-dratic inξ.We also demonstrate that this equivalence can fail to hold even if theconstraint function is convex and piecewise linear inξ.(2)In Sect.3we develop a new tractable CVaR approximation for robust joint chanceconstraints and prove that this approximation consistently outperforms the state-of-the-art methods described above.We show that the approximation qualityis controlled by a set of scaling parameters and that the CVaR approximationDistributionally robust joint chance constraintsbecomes essentially exact if the scaling parameters are chosen optimally.We also present an intuitive dual interpretation for the CVaR approximation in this case.(3)In Sect.4we analyze the performance of the new joint chance constraint approx-imation when applied to a dynamic water reservoir control problem.Notation We use lower-case bold face letters to denote vectors and upper-case bold face letters to denote matrices.The space of symmetric matrices of dimension n is denoted by S n.For any two matrices X,Y∈S n,we let X,Y =Tr(XY)be the trace scalar product,while the relation X Y(X Y)implies that X−Y is positive semidefinite(positive definite).Random variables are always represented by symbols with tildes,while their realizations are denoted by the same symbols without tildes. For x∈R,we deﬁne x+=max{x,0}.2Distributionally robust individual chance constraintsIt is known that robust individual chance constraints can be conservatively approxi-mated by Worst-Case CVaR constraints.In this section,weﬁrst show how the theory of moment problems can be used to reformulate these Worst-Case CVaR constraints in terms of tractable semidefinite constraints.Subsequently,we prove that the Worst-Case CVaR constraints are in fact equivalent to the underlying robust chance constraints for a large class of constraint functions.Distributional assumptions In the remainder of this paper we letμ∈R k be the mean vector and ∈S k be the covariance matrix of the random vector˜ξunder the true distribution Q.Thus,we implicitly assume that Q hasﬁnite second-order moments. Without loss of generality we also assume that 0.Furthermore,we let P denote the set of all probability distributions on R k that have the sameﬁrst-and second-order moments as Q.For notational simplicity,we let=+μμTμμT1be the second-order moment matrix of˜ξ.2.1The Worst-Case CVaR approximationFor m=1,(3)reduces to a distributionally robust individual chance constraintinf P∈P Py0(x)+y(x)T˜ξ≤0≥1− ,(5)whose feasible set is denoted byS.Zymler et al.X ICC=x∈R n:infP∈PPy0(x)+y(x)T˜ξ≤0≥1−.In the remainder of this section we will demonstrate that X ICC has a manifestly trac-table representation in terms of Linear Matrix Inequalities(LMIs).To this end,we ﬁrst recall the definition of CVaR due to Rockafellar and Uryasev[24].For a given measurable loss function L:R k→R,probability distribution P on R k,and tolerance ∈(0,1),the CVaR at level with respect to P is deﬁned asP-CVaR (L(˜ξ))=infβ∈Rβ+1E P(L(˜ξ)−β)+,(6)where E P(·)denotes expectation with respect to P.CVaR essentially evaluates the conditional expectation of loss above the(1− )-quantile of the loss distribution.It can be shown that CVaR represents a convex functional of the random variable L(˜ξ).CVaR can be used to construct convex approximations for chance constraints. Indeed,it is well known thatPL(˜ξ)≤P-CVaR (L(˜ξ))≥1−for any measurable loss function L,see,e.g.,Ben-Tal et al.[3,Sect.4.3.3].Thus, P-CVaR (L(˜ξ))≤0is sufﬁcient to imply P(L(˜ξ)≤0)≥1− .As this implication holds for any probability distribution and loss function,we conclude thatsup P∈P P-CVaRy0(x)+y(x)T˜ξ≤0 ⇒infP∈PPy0(x)+y(x)T˜ξ≤0≥1− .(7)Thus,the Worst-Case CVaR constraint on the left hand side constitutes a conservative approximation for the distributionally robust chance constraint on the right hand side of(7).The above discussion motivates us to deﬁne the feasible setZ ICC=x∈R n:supP∈PP-CVaRy0(x)+y(x)T˜ξ≤0,(8)and the implication(7)gives rise to the following elementary result.Proposition2.1The feasible set Z ICC constitutes a conservative approximation for X ICC,that is,Z ICC⊆X ICC.We will now show that Z ICC has a tractable representation in terms of LMIs. Theorem21The feasible set Z ICC can be written asZ ICC=⎧⎪⎪⎨⎪⎪⎩x∈Rn:∃(β,M)∈R×S k+1,M 0,β+1 ,M ≤0,M−012y(x)12y(x)T y0(x)−β⎫⎪⎪⎬⎪⎪⎭.Distributionally robust joint chance constraintsProof By using(6),the Worst-Case CVaR in(8)can be expressed assup P∈P P-CVaRy0(x)+y(x)T˜ξ=supP∈P infβ∈Rβ+1E P(y0(x)+y(x)T˜ξ−β)+=infβ∈Rβ+1supP∈PE P(y0(x)+y(x)T˜ξ−β)+,(9)where the interchange of the maximization and minimization operations is justiﬁed by a stochastic saddle point theorem due to Shapiro and Kleywegt[26],see also Delage and Ye[11]or Natarajan et al.[19].We now show that the Worst-Case CVaR(9)of someﬁxed decision x∈R n can be computed by solving a tractable SDP.To this end, weﬁrst derive an SDP reformulation of the worst-case expectation problemsup P∈P E P(y0(x)+y(x)T˜ξ−β)+,which can be identiﬁed as the subordinate maximization problem in(9).Lemma A.1 in the Appendix enables us to reformulate this worst-case expectation problem as infM∈S k+1,M s.t.M 0,ξT1MξT1T≥y0(x)+y(x)Tξ−β∀ξ∈R k.(10)Note that the semi-inﬁnite constraint in(10)can be written as the following LMI.ξ1 TM−012y(x)12y(x)T y0(x)−βξ1≥0∀ξ∈R k⇐⇒M−012y(x)12y(x)T y0(x)−βThis in turn allows us to reformulate the worst-case expectation problem asinfM∈S k+1,Ms.t.M 0,M−012y(x)12y(x)T y0(x)−β0.(11)S.Zymler et al. By replacing the subordinate worst-case expectation problem in(9)by(11),we obtainsup P∈P P-CVaRy0(x)+y(x)T˜ξ=infβ+1 ,Ms.t.M∈S k+1,β∈RM 0,M−012y(x)12y(x)T y0(x)−β0,(12)and thus the claim follows.2.2Exactness of the Worst-Case CVaR approximationSo far we have shown that the feasible set Z ICC deﬁned in terms of a Worst-Case CVaRconstraint constitutes a tractable conservative approximation for X ICC.We now dem-onstrate that this approximation is in fact exact,that is,we show that the implication(7)is in fact an equivalence.Weﬁrst recall the nonlinear Farkas Lemma as well as the S-lemma,which are crucial ingredients for the proof of this result.We refer to Pólik and Terlaky[22]for a derivation and an in-depth survey of the S-lemma as well as areview of the Farkas Lemma.Lemma2.2(Farkas Lemma)Let f0,...,f p:R k→R be convex functions,and assume that there exists a strictly feasible point¯ξwith f i(¯ξ)<0,i=1,...,p.Then, f0(ξ)≥0for allξwith f i(ξ)≤0,i=1,...,p,if and only if there exist constants τi≥0such thatf0(ξ)+pi=1τi f i(ξ)≥0∀ξ∈R k.Lemma2.2(S-lemma)Let f i(ξ)=ξT A iξwith A i∈S n be quadratic functions of ξ∈R n for i=0,...,p.Then,f0(ξ)≥0for allξwith f i(ξ)≤0,i=1,...,p,if there exist constantsτi≥0such thatA0+pi=1τi A i 0.For p=1,the converse implication holds if there exists a strictly feasible point¯ξwith f1(¯ξ)<0.Theorem2.2Let L:R k→R be a continuous loss function that is either(i)concave inξ,or(ii)(possibly nonconcave)quadratic inξ.Then,the following equivalence holds.sup P∈P P-CVaRL(˜ξ)≤0⇐⇒infP∈PPL(˜ξ)≤0≥1− (13)Distributionally robust joint chance constraintsProof Consider the Worst-Case Value-at-Risk of the loss function L ,which is deﬁned asWC-VaR (L (˜ξ))=inf γ∈R γ:inf P ∈PP L (˜ξ)≤γ ≥1− .(14)By definition,the WC-VaR is indeed equal to the (1− )-quantile of L (˜ξ)evaluated under some worst-case distribution in P .We ﬁrst show that the following equivalenceholds.inf P ∈PP L (˜ξ)≤0 ≥1− ⇐⇒WC-VaR L (˜ξ) ≤0(15)Indeed,if the left hand side of (15)is satisﬁed,then γ=0is feasible in (14),which implies that WC-VaR (L (˜ξ))≤0.To see that the converse implication holds as well,we note that for any ﬁxed P ∈P ,the mapping γ→P (L (˜ξ)≤γ)is upper semi-continuous,see [21].Thus,the related mapping γ→inf P ∈P P (L (˜ξ)≤γ)is also upper semi-continuous.If WC-VaR (L (˜ξ))≤0,there exists a sequence {γn }n ∈N that converges to zero and is feasible in (14),which impliesinf P ∈P PL (˜ξ)≤0 ≥lim sup n →∞inf P ∈PP L (˜ξ)≤γn ≥1− .Thus,(15)follows.To prove the postulated equivalence (13),it is now sufﬁcient to show thatsup P ∈PP -CVaR L (˜ξ) =WC-VaR L (˜ξ) .Note that (14)can be rewritten asWC-VaR (L (˜ξ))=inf γ∈R γ:sup P ∈P P L (˜ξ)>γ ≤ .(16)We proceed by simplifying the subordinate worst-case probability problem sup P ∈PP (L (˜ξ)>γ),which,by Lemma A.2in the Appendix,can be expressed as inf M ∈S k +1 ,M :M 0, ξT 1 M ξT 1 T ≥1∀ξ:γ−L (ξ)<0 .(17)We will now argue that for all but one value of γproblem (17)is equivalent toinf ,Ms .t .M ∈S k +1,τ∈R ,M 0,τ≥0 ξT 1 M ξT 1 T −1+τ(γ−L (ξ))≥0∀ξ∈R k .(18)S.Zymler et al.For ease of exposition,we deﬁne h =inf ξ∈R k γ−L (ξ).The equivalence of (17)and (18)is proved case by case.Assume ﬁrst that h <0.Then,the strict inequal-ity in the parameter range of the semi-inﬁnite constraint in (17)can be replaced by a weak inequality without affecting its optimal value.The equivalence then follows from the Farkas Lemma (when L (ξ)is concave in ξ)or from the S -lemma (when L (ξ)is quadratic in ξ).Assume next that h >0.Then,the semi-inﬁnite constraint in(17)becomes redundant and,since 0,the optimal solution of (17)is given by M =0with a corresponding optimal value of 0.The optimal value of problem (18)is also equal to 0.Indeed,by choosing τ=1/h ,the semi-inﬁnite constraint in (18)is satisﬁed for any M 0.Finally,note that (17)and (18)may be different for h =0.Since (17)and (18)are equivalent for all but one value of γand since their optimal values are nonincreasing in γ,we can express WC-VaR (L (˜ξ))in (16)as WC-VaR (L (˜ξ))=inf γs .t .M ∈S k +1,τ∈R ,γ∈R ,M ≤ ,M 0,τ≥0 ξT 1 M ξT 1 T −1+τ(γ−L (ξ))≥0∀ξ∈R k .(19)It can easily be shown that ,M ≥1for any feasible solution of (19)with vanishing τ-component.However,since <1,this is in conﬂict with the constraint ,M ≤ .We thus conclude that no feasible point can have a vanishing τ-component.This allows us to divide the semi-inﬁnite constraint in problem (19)by τ.Subsequently we per-form variable substitutions in which we replace τby 1/τand M by M /τ.This yields the following reformulation of problem (19).WC-VaR (L (˜ξ))=inf γs .t .M ∈S k +1,τ∈R ,γ∈R 1 ,M ≤τ,M 0,τ≥0 ξT 1 M ξT 1 T −τ+γ−L (ξ)≥0∀ξ∈R kNote that,since 0and M 0,we have 1 ,M ≥0.This allows us to remove the redundant nonnegativity constraint on τ.We now introduce a new decision variable β=γ−τ,which allows us to eliminate γ.WC-VaR (L (˜ξ))=inf β+τs .t .M ∈S k +1,τ∈R ,β∈R 1 ,M ≤τ,M 0 ξT 1 M ξT 1 T +β−L (ξ)≥0∀ξ∈R kNote that at optimality τ=1 ,M ,which ﬁnally allows us to express WC-VaR (L (˜ξ))asWC-VaR (L (˜ξ))=inf β+1,M s .t .M ∈S k +1,β∈R ,M 0ξT 1 M ξT 1 T+β−L (ξ)≥0∀ξ∈R k .(20)Recall now that by Lemma A.1we have sup P ∈PP -CVaRL (˜ξ)=inf β∈Rβ+1 sup P ∈P E P (L (˜ξ)−β)+ =inf β+1,Ms .t .M ∈S k +1,β∈R ,M 0ξT 1 M ξT 1 T+β−L (ξ)≥0∀ξ∈R k ,which is clearly equivalent to (20).This observation completes the proof.Corollary 2.1The following equivalence holdssup P ∈PP -CVaRy 0(x )+y (x )T ˜ξ≤0⇐⇒inf P ∈PPy 0(x )+y (x )T ˜ξ≤0≥1− ,which implies that Z ICC =X ICC .Proof The claim follows immediately from Theorem 2.2by observing that L (ξ)=y 0(x )+y (x )T ξis linear (and therefore concave)in ξ.In the following example we demonstrate that the equivalence (13)can fail to holdeven if the loss function L is convex and piecewise linear in ξ.Example 2.1Let ˜ξbe a scalar random variable with mean μ=0and standard devi-ation σ=1.Moreover,let P be the set of all probability distributions on R con-sistent with the given mean and standard deviation.Consider now the loss function L (ξ)=max {ξ−1,4ξ−4},and note that L is strictly increasing and convex in ξ.In particular,L is neither concave nor quadratic and thus falls outside the scope of Theorem 2.2.We now show that for this particular L the Worst-Case CVaR constraintsup P ∈P P -CVaR 12(L (˜ξ))≤0is violated even though the distributionally robust indi-vidual chance constraint inf P ∈P P (L (˜ξ)≤0)≥1/2is satisﬁed.To this end,we note that the Chebychev inequality P (˜ξ−μ≥κσ)≤1/(1+κ2)for κ=1implies sup P ∈P P˜ξ≥1 ≤12⇐⇒sup P ∈PP L (˜ξ)≥L (1)=0 ≤12 ⇒sup P ∈PP L (˜ξ)>0 ≤12⇐⇒inf P ∈PP L (˜ξ)≤0 ≥12,where the ﬁrst equivalence follows from the monotonicity of L .Assume now thatthe true distribution Q of ˜ξis discrete and deﬁned through Q (˜ξ=−2)=1/8,Q (˜ξ=0)=3/4,and Q (˜ξ=2)=1/8.It is easy to verify that Q ∈P and that Q -CVaR 12(L (˜ξ))=0.25.Thus,sup P ∈P P -CVaR 12(L (˜ξ))≥0.25>0.We therefore conclude that the Worst-Case CVaR constraint is not equivalent to the robust chance constraint.2.3Tractability of the Worst-Case CVaR approximationWe have already seen that Worst-Case CVaR constraints are equivalent to distribution-ally robust chance constraints when the loss function is continuous and either concave or quadratic in ξ.We now prove that the Worst-Case CVaR can also be computed efﬁciently for these classes of loss functions.Theorem 2.3Assume that L :R k →R is either(i)concave piecewise afﬁne in ξwith a ﬁnite number of pieces or (ii)(possibly nonconcave )quadratic in ξ.Then,sup P ∈P P -CVaR (L (˜ξ))can be computed efﬁciently as the optimal value of a tractable SDP .Proof Assume that (i)holds and that L (˜ξ)=min i =1,...,l {a i +b T i ˜ξ}for some a i ∈Rand b i ∈R k ,i =1,...,l .Then,the Worst-Case CVaR is representable asinfβ∈Rβ+1sup P ∈PE Pmin i =1,...,l{a i +b T i ˜ξ}−β +.(21)By Lemma A.1,the subordinate worst-case expectation problem in (21)can be rewrit-ten asinfM ∈S k +1,M s .t .M 0,ξT 1 M ξT 1 T≥min i =1,...,l{a i +b T i ξ}−β∀ξ∈R k .(22)Noting thatmin i =1,...,l{a i +b T i ξ}=minλ∈l i =1λi (a i +b T i ξ),where ={λ∈R l : l i =1λi =1,λ≥0}denotes the probability simplex in R l ,we can use techniques developed in [4,Theorem 2.1]to reexpress the semi-inﬁnite constraint in (22)asξT 1 M ξT1 T −min λ∈li =1λi (a i +b T i ξ)+β≥0∀ξ∈R k⇐⇒min ξ∈R kmaxλ∈ξT 1 M ξT 1 T −li =1λi (a i +b T i ξ)+β≥0⇐⇒max λ∈ min ξ∈R kξT 1 M ξT1 T −l i =1λi (a i +b T i ξ)+β≥0⇐⇒minξ∈R kξT 1 M ξT1 T −li =1λi (a i +b T i ξ)+β≥0,λ∈⇐⇒M −l i =1λi2b il i =1λi2b Ti l i =1λi a i −β 0,λ∈ .The second equivalence in the above expression follows from the classical saddlepoint theorem.Thus,the Worst-Case CVaR (21)can be rewritten as the optimal value of the following tractable SDP.inf β+1 ,Ms .t .β∈R ,M ∈S k +1,λ∈R lM 0,M − 0l i =1λi 2b il i =1λi 2b T i l i =1λi a i −β0,λ∈ (23)Assume now that (ii)holds and that L (ξ)=ξT Q ξ+q T ξ+q 0for some Q ∈S k ,q ∈R k ,and q 0∈R .In this case we havesup P ∈P P -CVaR (L (˜ξ))=inf β∈R β+1 sup P ∈PE P ˜ξT Q ˜ξ+˜ξT q +q 0−β + .(24)As usual,we ﬁrst ﬁnd an SDP reformulation of the subordinate worst-case expectation problem in (24).By Lemma A.1,this problem can be rewritten asinfM ∈S k +1,Ms .t .M 0, ξT 1 M ξT 1 T≥ξT Q ξ+ξT q +q 0−β∀ξ∈R k .(25)Note that the semi-inﬁnite constraint in (25)is equivalent to ξ1 TM − Q 12q 12qTq 0−βξ1≥0∀ξ∈R k ⇐⇒M − Q 12q 12qT q 0−β0,which enables us to rewrite the Worst-Case CVaR (24)as the optimal value ofinf β+1 ,Ms .t .M ∈S k +1,β∈RM 0,M − Q12q 12qTq 0−β0,which is indeed a tractable SDP.Remark If the loss function is concave but not piecewise afﬁne,the Worst-Case CVaRcan sometimes still be evaluated efﬁciently,though not by solving an explicit SDP.Indeed,the Worst-Case CVaR can be computed in polynomial time with an ellipsoid method if L (ξ)is concave and if,for any ξ∈R k ,one can evaluate both L (ξ)as well as a super-gradient ∇ξL (ξ)in polynomial time.This is an immediate conse-quence of a result on the computation of worst-case expectations by Delage and Ye [11,Proposition 2].3Distributionally robust joint chance constraintsWe deﬁne the feasible set X JCC of the distributionally robust joint chance constraint (3)asXJCC = x ∈R n :inf P ∈PPy 0i (x )+y i (x )T ˜ξ≤0∀i =1,...,m ≥1− .The aim of this section is to investigate the structure of X JCC and to elaborate tractable conservative approximations.We ﬁrst review two existing approximations and discuss their beneﬁts and shortcomings.3.1The Bonferroni approximationA popular approximation for X JCC is based on Bonferroni’s inequality.Note that the robust joint chance constraint (3)is equivalent toinf P ∈PPm i =1y 0i (x )+y i (x )T ˜ξ≤0≥1−⇐⇒sup P ∈PPm i =1y 0i (x )+y i (x )T ˜ξ>0 ≤ .Furthermore,Bonferroni’s inequality implies thatPm i =1y 0i (x )+y i (x )T ˜ξ>0≤mi =1P y 0i (x )+y i (x )T ˜ξ>0 ∀P ∈P .。

计算机学科学术会议等级排名 Computer Science Conference Rankings

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Conf. on Mobile Data Access/Management (MDA/MDM) ICDM - IEEE International Conference on Data MiningER - Intl Conf on Conceptual Modeling (ER)Rank 3:COMAD: Intl Conf on Management of DataBNCOD: British National Conference on DatabasesADC: Australasian Database ConferenceADBIS: Symposium on Advances in DB and Information SystemsDaWaK - Data Warehousing and Knowledge DiscoveryRIDE WorkshopIFIP-DS: IFIP-DS ConferenceIFIP-DBSEC - IFIP Workshop on Database SecurityNGDB: Intl Symp on Next Generation DB Systems and AppsADTI: Intl Symp on Advanced DB Technologies and IntegrationFEWFDB: Far East Workshop on Future DB SystemsVDB - Visual Database SystemsIDEAS - International Database Engineering and Application Symposium Others:ARTDB - Active and Real-Time Database SystemsCODAS: Intl Symp on Cooperative DB Systems for Adv AppsDBPL - Workshop on Database Programming LanguagesEFIS/EFDBS - Engineering Federated Information (Database) Systems KRDB - Knowledge Representation Meets DatabasesNDB - National Database Conference (China)NLDB - Applications of Natural Language to Data BasesKDDMBD - Knowledge Discovery and Data Mining in Biological Databases MeetingFQAS - Flexible Query-Answering SystemsIDC(W) - International Database Conference (HK CS)RTDB - Workshop on Real-Time DatabasesSBBD: Brazilian Symposium on DatabasesWebDB - International Workshop on the Web and DatabasesWAIM: Interational Conference on Web Age Information Management(1) DASWIS - Data Semantics in Web Information Systems(1) DMDW - Design and Management of Data Warehouses(1) DOLAP - International Workshop on Data Warehousing and OLAP(1) DMKD - Workshop on Research Issues in Data Mining and Knowledge Discovery(1) KDEX - Knowledge and Data Engineering Exchange Workshop(1) NRDM - Workshop on Network-Related Data Management(1) MobiDE - Workshop on Data Engineering for Wireless and Mobile Access (1) MDDS - Mobility in Databases and Distributed Systems(1) MEWS - Mining for Enhanced Web Search(1) TAKMA - Theory and Applications of Knowledge MAnagement(1) WIDM: International Workshop on Web Information and Data Management (1) W2GIS - International Workshop on Web and Wireless Geographical Information Systems* CDB - Constraint Databases and Applications* DTVE - Workshop on Database Technology for Virtual Enterprises* IWDOM - International Workshop on Distributed Object Management* IW-MMDBMS - Int. 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Analysis of Images and PatternsCSSAC: Cognitive Science Society Annual ConferenceECCV: European Conference on Computer VisionEAI: European Conf on AIEML: European Conf on Machine LearningGP: Genetic Programming ConferenceIAAI: Innovative Applications in AIICIP: Intl Conf on Image ProcessingICNN/IJCNN: Intl (Joint) Conference on Neural NetworksICPR: Intl Conf on Pattern RecognitionICDAR: International Conference on Document Analysis and Recognition ICTAI: IEEE conference on Tools with AIAMAI: Artificial Intelligence and MathsDAS: International Workshop on Document Analysis SystemsWACV: IEEE Workshop on Apps of Computer VisionCOLING: International Conference on Computational Liguistics EMNLP: Empirical Methods in Natural Language ProcessingRank 3:PRICAI: Pacific Rim Intl Conf on AIAAI: Australian National Conf on AIACCV: Asian Conference on Computer VisionAI*IA: Congress of the Italian Assoc for AIANNIE: Artificial Neural Networks in EngineeringANZIIS: Australian/NZ Conf on Intelligent Inf. SystemsCAIA: Conf on AI for ApplicationsCAAI: Canadian Artificial Intelligence ConferenceASADM: Chicago ASA Data Mining Conf: A Hard Look at DMEPIA: Portuguese Conference on Artificial IntelligenceFCKAML: French Conf on Know. Acquisition & Machine LearningICANN: International Conf on Artificial Neural NetworksICCB: International Conference on Case-Based ReasoningICGA: International Conference on Genetic AlgorithmsICONIP: Intl Conf on Neural Information ProcessingIEA/AIE: Intl Conf on Ind. & Eng. Apps of AI & Expert SysICMS: International Conference on Multiagent SystemsICPS: International conference on Planning SystemsIWANN: Intl Work-Conf on Art & Natural Neural NetworksPACES: Pacific Asian Conference on Expert SystemsSCAI: Scandinavian Conference on Artifical IntelligenceSPICIS: Singapore Intl Conf on Intelligent SystemPAKDD: Pacific-Asia Conf on Know. Discovery & Data MiningSMC: IEEE Intl Conf on Systems, Man and CyberneticsPAKDDM: Practical App of Knowledge Discovery & Data MiningWCNN: The World Congress on Neural NetworksWCES: World Congress on Expert SystemsINBS: IEEE Intl Symp on Intell. in Neural \& Bio SystemsASC: Intl Conf on AI and Soft ComputingPACLIC: Pacific Asia Conference on Language, Information and Computation ICCC: International Conference on Chinese ComputingOthers:ICRA: IEEE Intl Conf on Robotics and AutomationNNSP: Neural Networks for Signal ProcessingICASSP: IEEE Intl Conf on Acoustics, Speech and SPGCCCE: Global Chinese Conference on Computers in EducationICAI: Intl Conf on Artificial IntelligenceAEN: IASTED Intl Conf on AI, Exp Sys & Neural Networks WMSCI: World Multiconfs on Sys, Cybernetics & InformaticsRank 1:ASPLOS: Architectural Support for Prog Lang and OSISCA: ACM/IEEE Symp on Computer ArchitectureICCAD: Intl Conf on Computer-Aided DesignDAC: Design Automation ConfMICRO: Intl Symp on MicroarchitectureHPCA: IEEE Symp on High-Perf Comp ArchitectureRank 2:FCCM: IEEE Symposium on Field Programmable Custom Computing MachinesSUPER: ACM/IEEE Supercomputing ConferenceICS: Intl Conf on SupercomputingISSCC: IEEE Intl Solid-State Circuits ConfHCS: Hot Chips SympVLSI: IEEE Symp VLSI CircuitsISSS: International Symposium on System SynthesisDATE: IEEE/ACM Design, Automation & Test in Europe Conference Rank 3:ICA3PP: Algs and Archs for Parall ProcEuroMICRO: New Frontiers of Information TechnologyACS: Australian Supercomputing ConfUnranked:Advanced Research in VLSIInternational Symposium on System SynthesisInternational Symposium on Computer DesignInternational Symposium on Circuits and SystemsAsia Pacific Design Automation ConferenceInternational Symposium on Physical DesignInternational Conference on VLSI DesignRank 1:I3DG: ACM-SIGRAPH Interactive 3D GraphicsSIGGRAPH: ACM SIGGRAPH ConferenceACM-MM: ACM Multimedia ConferenceDCC: Data Compression ConfSIGMETRICS: ACM Conf on Meas. & Modelling of Comp Sys SIGIR: ACM SIGIR Conf on Information RetrievalPECCS: IFIP Intl Conf on Perf Eval of Comp \& Comm Sys WWW: World-Wide Web ConferenceRank 2:EUROGRAPH: European Graphics ConferenceCGI: Computer Graphics InternationalCANIM: Computer AnimationPG: Pacific GraphicsIEEE-MM: IEEE Intl Conf on Multimedia Computing and Sys NOSSDAV: Network and OS Support for Digital A/VPADS: ACM/IEEE/SCS Workshop on Parallel \& Dist Simulation WSC: Winter Simulation ConferenceASS: IEEE Annual Simulation SymposiumMASCOTS: Symp Model Analysis & Sim of Comp & Telecom Sys PT: Perf Tools - Intl Conf on Model Tech \& Tools for CPE NetStore: Network Storage SymposiumMMCN: ACM/SPIE Multimedia Computing and NetworkingRank 3:ACM-HPC: ACM Hypertext ConfMMM: Multimedia ModellingDSS: Distributed Simulation SymposiumSCSC: Summer Computer Simulation ConferenceWCSS: World Congress on Systems SimulationESS: European Simulation SymposiumESM: European Simulation MulticonferenceHPCN: High-Performance Computing and Networking Geometry Modeling and ProcessingWISEDS-RT: Distributed Simulation and Real-time ApplicationsIEEE Intl Wshop on Dist Int Simul and Real-Time ApplicationsUn-ranked:DVAT: IS&T/SPIE Conf on Dig Video Compression Alg & Tech MME: IEEE Intl Conf. on Multimedia in EducationICMSO: Intl Conf on Modelling, Simulation and Optimisation ICMS: IASTED Intl Conf on Modelling and SimulationRank 1:SIGCOMM: ACM Conf on Comm Architectures, Protocols & Apps INFOCOM: Annual Joint Conf IEEE Comp & Comm Soc SPAA: Symp on Parallel Algms and ArchitecturePODC: ACM Symp on Principles of Distributed Computing PPoPP: Principles and Practice of Parallel Programming MassPar: Symp on Frontiers of Massively Parallel ProcRTSS: Real Time Systems SympSOSP: ACM SIGOPS Symp on OS PrinciplesSOSDI: Usenix Symp on OS Design and ImplementationCCS: ACM Conf on Comp and Communications SecurityIEEE Symposium on Security and PrivacyMOBICOM: ACM Intl Conf on Mobile Computing and Networking USENIX Conf on Internet Tech and SysICNP: Intl Conf on Network ProtocolsOPENARCH: IEEE Conf on Open Arch and Network Prog PACT: Intl Conf on Parallel Arch and Compil TechRank 2:CC: Compiler ConstructionIPDPS: Intl Parallel and Dist Processing SympIC3N: Intl Conf on Comp Comm and NetworksICPP: Intl Conf on Parallel ProcessingICDCS: IEEE Intl Conf on Distributed Comp SystemsSRDS: Symp on Reliable Distributed SystemsMPPOI: Massively Par Proc Using Opt InterconnsASAP: Intl Conf on Apps for Specific Array ProcessorsEuro-Par: European Conf. on Parallel ComputingFast Software EncryptionUsenix Security SymposiumEuropean Symposium on Research in Computer SecurityWCW: Web Caching WorkshopLCN: IEEE Annual Conference on Local Computer Networks IPCCC: IEEE Intl Phoenix Conf on Comp & Communications CCC: Cluster Computing ConferenceICC: Intl Conf on CommWCNC: IEEE Wireless Communications and Networking Conference Rank 3:MPCS: Intl. Conf. on Massively Parallel Computing Systems GLOBECOM: Global CommICCC: Intl Conf on Comp CommunicationNOMS: IEEE Network Operations and Management Symp CONPAR: Intl Conf on Vector and Parallel ProcessingVAPP: Vector and Parallel ProcessingICPADS: Intl Conf. on Parallel and Distributed SystemsPublic Key CryptosystemsIEEE Computer Security Foundations WorkshopAnnual Workshop on Selected Areas in CryptographyAustralasia Conference on Information Security and PrivacyInt. Conf on Inofrm and Comm. SecurityFinancial CryptographyWorkshop on Information HidingSmart Card Research and Advanced Application Conference ICON: Intl Conf on NetworksIMSA: Intl Conf on Internet and MMedia SysNCC: Nat Conf CommIN: IEEE Intell Network WorkshopICME: Intl Conf on MMedia & ExpoSoftcomm: Conf on Software in Tcomms and Comp Networks INET: Internet Society ConfWorkshop on Security and Privacy in E-commerceUn-ranked:PARCO: Parallel ComputingSE: Intl Conf on Systems EngineeringRank 1:POPL: ACM-SIGACT Symp on Principles of Prog LangsPLDI: ACM-SIGPLAN Symp on Prog Lang Design & ImplOOPSLA: OO Prog Systems, Langs and ApplicationsICFP: Intl Conf on Function ProgrammingJICSLP/ICLP/ILPS: (Joint) Intl Conf/Symp on Logic ProgICSE: Intl Conf on Software EngineeringFSE: ACM Conference on the Foundations of Software Engineering (inc: ESEC-FSE when held jointly)FM/FME: Formal Methods, World Congress/EuropeCAV: Computer Aided VerificationRank 2:CP: Intl Conf on Principles & Practice of Constraint ProgTACAS: Tools and Algos for the Const and An of SystemsESOP: European Conf on ProgrammingICCL: IEEE Intl Conf on Computer LanguagesPEPM: Symp on Partial Evalutation and Prog ManipulationSAS: Static Analysis SymposiumRTA: Rewriting Techniques and ApplicationsESEC: European Software Engineering ConfIWSSD: Intl Workshop on S/W Spec & DesignCAiSE: Intl Conf on Advanced Info System EngineeringITC: IEEE Intl Test ConfIWCASE: Intl Workshop on Cumpter-Aided Software EngSSR: ACM SIGSOFT Working Conf on Software ReusabilitySEKE: Intl Conf on S/E and Knowledge EngineeringICSR: IEEE Intl Conf on Software ReuseASE: Automated Software Engineering ConferencePADL: Practical Aspects of Declarative LanguagesISRE: Requirements EngineeringICECCS: IEEE Intl Conf on Eng. of Complex Computer SystemsIEEE Intl Conf on Formal Engineering MethodsIntl Conf on Integrated Formal MethodsFOSSACS: Foundations of Software Science and Comp StructRank 3:FASE: Fund Appr to Soft EngAPSEC: Asia-Pacific S/E ConfPAP/PACT: Practical Aspects of PROLOG/Constraint TechALP: Intl Conf on Algebraic and Logic ProgrammingPLILP: Prog, Lang Implentation & Logic ProgrammingLOPSTR: Intl Workshop on Logic Prog Synthesis & TransfICCC: Intl Conf on Compiler ConstructionCOMPSAC: Intl. Computer S/W and Applications ConfCSM: Conf on Software MaintenanceTAPSOFT: Intl Joint Conf on Theory & Pract of S/W DevWCRE: SIGSOFT Working Conf on Reverse EngineeringAQSDT: Symp on Assessment of Quality S/W Dev ToolsIFIP Intl Conf on Open Distributed ProcessingIntl Conf of Z UsersIFIP Joint Int'l Conference on Formal Description Techniques and Protocol Specification, Testing, And VerificationPSI (Ershov conference)UML: International Conference on the Unified Modeling LanguageUn-ranked:Australian Software Engineering ConferenceIEEE Int. W'shop on Object-oriented Real-time Dependable Sys. (WORDS) IEEE International Symposium on High Assurance Systems EngineeringThe Northern Formal Methods WorkshopsFormal Methods PacificInt. Workshop on Formal Methods for Industrial Critical SystemsJFPLC - International French Speaking Conference on Logic and Constraint ProgrammingL&L - Workshop on Logic and LearningSFP - Scottish Functional Programming WorkshopHASKELL - Haskell WorkshopLCCS - International Workshop on Logic and Complexity in Computer Science VLFM - Visual Languages and Formal MethodsNASA LaRC Formal Methods Workshop(1) FATES - A Satellite workshop on Formal Approaches to Testing of Software(1) Workshop On Java For High-Performance Computing(1) DSLSE - Domain-Specific Languages for Software Engineering(1) FTJP - Workshop on Formal Techniques for Java Programs(*) WFLP - International Workshop on Functional and (Constraint) Logic Programming(*) FOOL - International Workshop on Foundations of Object-Oriented Languages(*) SREIS - Symposium on Requirements Engineering for Information Security (*) HLPP - International workshop on High-level parallel programming and applications(*) INAP - International Conference on Applications of Prolog(*) MPOOL - Workshop on Multiparadigm Programming with OO Languages (*) PADO - Symposium on Programs as Data Objects(*) TOOLS: Int'l Conf Technology of Object-Oriented Languages and Systems (*) Australasian Conference on Parallel And Real-Time SystemsRank 1:STOC: ACM Symp on Theory of ComputingFOCS: IEEE Symp on Foundations of Computer ScienceCOLT: Computational Learning TheoryLICS: IEEE Symp on Logic in Computer ScienceSCG: ACM Symp on Computational GeometrySODA: ACM/SIAM Symp on Discrete AlgorithmsSPAA: ACM Symp on Parallel Algorithms and ArchitecturesPODC: ACM Symp on Principles of Distributed ComputingISSAC: Intl. Symp on Symbolic and Algebraic ComputationCRYPTO: Advances in CryptologyEUROCRYPT: European Conf on CryptographyRank 2:CONCUR: International Conference on Concurrency TheoryICALP: Intl Colloquium on Automata, Languages and ProgSTACS: Symp on Theoretical Aspects of Computer ScienceCC: IEEE Symp on Computational ComplexityWADS: Workshop on Algorithms and Data Structures MFCS: Mathematical Foundations of Computer Science SWAT: Scandinavian Workshop on Algorithm TheoryESA: European Symp on AlgorithmsIPCO: MPS Conf on integer programming & comb optimization LFCS: Logical Foundations of Computer ScienceALT: Algorithmic Learning TheoryEUROCOLT: European Conf on Learning TheoryWDAG: Workshop on Distributed AlgorithmsISTCS: Israel Symp on Theory of Computing and Systems ISAAC: Intl Symp on Algorithms and ComputationFST&TCS: Foundations of S/W Tech & Theoretical CS LATIN: Intl Symp on Latin American Theoretical Informatics RECOMB: Annual Intl Conf on Comp Molecular Biology CADE: Conf on Automated DeductionIEEEIT: IEEE Symposium on Information TheoryAsiacryptRank 3:MEGA: Methods Effectives en Geometrie Algebrique ASIAN: Asian Computing Science ConfCCCG: Canadian Conf on Computational GeometryFCT: Fundamentals of Computation TheoryWG: Workshop on Graph TheoryCIAC: Italian Conf on Algorithms and ComplexityICCI: Advances in Computing and InformationAWTI: Argentine Workshop on Theoretical Informatics CATS: The Australian Theory SympCOCOON: Annual Intl Computing and Combinatorics Conf UMC: Unconventional Models of ComputationMCU: Universal Machines and ComputationsGD: Graph DrawingSIROCCO: Structural Info & Communication Complexity ALEX: Algorithms and ExperimentsALG: ENGG Workshop on Algorithm EngineeringLPMA: Intl Workshop on Logic Programming and Multi-Agents EWLR: European Workshop on Learning RobotsCITB: Complexity & info-theoretic approaches to biology FTP: Intl Workshop on First-Order Theorem Proving (FTP) CSL: Annual Conf on Computer Science Logic (CSL)AAAAECC: Conf On Applied Algebra, Algebraic Algms & ECCDMTCS: Intl Conf on Disc Math and TCSUn-ranked:Information Theory WorkshopRank 1:Rank 2:AMIA: American Medical Informatics Annual Fall SymposiumDNA: Meeting on DNA Based ComputersRank 3:MEDINFO: World Congress on Medical InformaticsInternational Conference on Sequences and their ApplicationsECAIM: European Conf on AI in MedicineAPAMI: Asia Pacific Assoc for Medical Informatics ConfSAC: ACM/SIGAPP Symposium on Applied ComputingICSC: Internal Computer Science ConferenceISCIS: Intl Symp on Computer and Information SciencesICSC2: International Computer Symposium ConferenceICCE: Intl Conf on Comps in EduEd-MediaWCC: World Computing CongressPATAT: Practice and Theory of Automated TimetablingNot Encouraged (due to dubious referee process):International Multiconferences in Computer Science -- 14 joint int'l confs. SCI: World Multi confs on systemics, sybernetics and informatics SSGRR: International conf on Advances in Infrastructure for e-B, e-Edu and e-Science and e-MedicineIASTED conferencesCCCT: International Conference on Computer, Communication and Control Technologies。

国际贸易法律术语

International Business Law TermsA Note on the Incoterms（国际贸易术语通则解释）Absolute Advantage(亚当.斯密的绝对优势理论)Acceptance with Modifications（对邀约做出修改、变更的承诺）Acceptance（承诺/受盘）Act of State Doctrine（国家行为主义）Act of the Parties （当事人的行为）Administrative Management （经营管理）Advising and Confirming Letters of Credit（信用证的通知和确认）Agent for International Settlements（国际结算代理人）Agreement of the Parties（协议选择原则）Agriculture（农业协定）Alternative Dispute Resolution （ADR解决方式）Anticipatory Breach in Common Law （普通法上预期违约）Antidumping Authority（反倾销机构）Applicability of the CISG (CISG的适用范围)Application of Home State Labor Laws Extraterritorially（内国劳工法律域外适用）Applying for a Letter of Credit（信用证的申请）Approval of Foreign Investment Applications（外国投资申请的批准）Arbitrage（套汇）Arbitration Agreement and Arbitration Clauses （仲裁协议和合同中的仲裁条款）Arbitration Tribunals（仲裁机构）Artistic Property Agreements（保护文学艺术作品的协定）Artistic Property Agreements（文学艺术品产权协定）Assignment（合同权利转让）Attorney-General（法律总顾问）Automatic Dissolution （自动散伙）Average Clauses（海损条款）Avoidance（解除）Bank Deposits（银行储蓄）Bases of Income Taxation（所得税的征税依据/基础）Battle of the Forms（形式上的分歧／冲突）Bills of Lading （提单）Branch Banking（银行的分支机构）Business Form and Registered Capital （企业形式和注册资本）Business Forms（商业组织形式）Buyer's Remedies（买方可以采取的救济措施）Carriage of Goods by Air（航空货物运输）Carriage of Goods by Sea and Marine Cargo Insurance（海上货物运输及其保险）Carrier's Duties under a Bill of Lading（在提单运输方式下承运人的责任/义务）Carrier's Immunities（承运人责任/义务的豁免）Cartels (企业联合/卡特尔)Categories of Investment Projects （外国投资的项目类别）Charterparties （租船合同）Charterparties by Demise （光船出租合同）China's Fundamental Policies for Encouraging Foreign Investments （中国大陆鼓励外国投资的基本政策）Choosing the Governing Law（准据法的选择）CIF (cost, insurance and freight) (port of destination) （ＣＩＦ成本＼保险费加运费付至指定的目的港）Civil Law (民法法系)Clearance and Settlement Procedures（交换和转让程序）Collection of Documentary Bills Through Banks（银行跟单托收）Commercial Arbitration （国际商事仲裁）Commodity Arrangements（初级产品/农产品安排）Common Enterprise Liability（企业的一般责任）Common Law (普通法系)Common Procedures in Handling Bills of Exchange （汇票处理的一般程序）Common Stock （股票）Company Taxpayers（公司/法人企业纳税人）Comparative Advantage(大卫.李嘉图的比较优势理论)Comparison of Municipal Legal Systems(内国法系的比较研究)Compensation for Winding up （清算补偿）Comprehensive Agreements （综合性的协定）Compulsory Licenses（强制许可）Computation of Income（收入计算）Conformity of Goods（与合同约定相符合的货物）Consent to the Jurisdiction of the Host State（给予东道国管辖权的许可/同意）Consideration in Common Law（英美法上的对价）Contemporary International Trade Law（当代国际贸易法）Contract Law for the International Sale of Goods（国际货物销售合同法）Contract Liability of the Agent （代理人的合同义务）Contract Liability of the Principal （委托人的合同义务）Contractual Issues Excluded from the Coverage of CISG（排除在CISG适用范围之外的合同问题）Copyrights （著作权/版权）Council for Trade-Related Aspects of Intellectual Property Rights（与知识产权有关的理事会）Coverage of Tax Treaties（税收条约的覆盖范围）Creation of Agency （代理创立）Creditors of Partners（合伙人的债权人）Currency Crises: The Role of Monetary Policy（金融危机：货币政策的作用与地位）Currency Exchange Obligations of IMF Member States（国际货币基金组织成员国在外汇交易中的义务）Currency Exchange（外汇交易）Currency Support（资金/财政援助）Custom（习惯）Customs Valuation（海关估价协定）Debt Securities （债券）Decision Making within the WTO（WTO内部决定作出机制）Deficiencies in the GATT 1947 Dispute Process （关税及贸易总协定1947争端解决程序的不足）Definite Sum of Money or Monetary Unit of Account（确定货币的总额或者计价的货币单位）Definition and Special Features（定义和特征）Delayed Bills of Lading（提单迟延）Denial of Justice（司法不公）Development Banks （发展银行）Direct Effect（直接效力）Direct Exporting(直接出口)Directors' and Officer's Duties to the Corporation（董事和经理/首席执行官对公司的义务）Dispute Settlement（争端的解决）Dissolution by Agreement （协议解散）Dissolution by Court Order （依法院令状散伙）Dissolution of the Partnership （散伙）Distribution of Earnings and Recovery of Investments （收入分配和投资回收）Distribution to Shareholders （红利分配权）Doctrine of Imputability (归责原则)Documentary Formalities（文本格式要求）Double Taxation Provision（双重征税的规定）Double Taxation（双重征税）Duress （胁迫行为）Duties of Agent and Principal （代理人和委托人的义务）Duties of Agent to Principal （委托人的义务）Duties of Principal to Agent （代理人、的义务）Duty of Care in Partnership Business（对合伙事务尽心看护义务）Duty of Loyalty and Good Faith （忠诚和诚信义务）Effectiveness of an Offer（邀约/发盘的效力）Employment Laws in the European Union（欧洲联盟雇佣/劳工法）Employment Standards of the Organization for Economic Cooperation and Development（经济合作与发展组织雇佣/劳工标准）Enforcement of Exchange Control Regulations of IMF Member States（国际货币基金组织成员国对外汇交易管理规则的履行）Enforcement of Foreign Arbitral Awards in the People's Republic of China （在中华人民共和国境内外国仲裁裁决的执行）Enforcement of Foreign Judgment （外国法院判决的执行）Enforcement of Partnership Rights and Liabilities（执行合伙事务的权利和责任）Enforcement of Securities Regulations Internationally（国际证券规则的执行）Environmental Regulation（环境规则）Escape Clause（免责条款）Euro-currency Deposits（欧洲货币储蓄）European Communities - Regime for the Importation, Sale, and Distribution of Bananas（欧洲共同体对于香蕉的进口、销售和分销的管理）European Union Law on Trade in Services（欧洲联盟关于服务贸易的法律）Exceptio non Adimpleti Contractus in Civil Law （大陆法上履行契约之抗辩权）Exceptions（例外）Exclusive Licenses（独占许可）Excuses for Non-performance （不履行的免责）Excuses for Nonperformance（不履行合同的抗辩/借口）Exemptions for New Members from IMF Member State Currency Exchange Obligations（国际货币基金组织新成员国在外汇交易中义务的免除）Export Restrictions (出口限制)Exporting(出口)Expropriation（征收）Extraterritorial Application of U. S. Securities Laws（美国证券法域外的适用问题）Failure to Exhaust remedies（没有用尽法律救济）Fault and Causation（过错和因果关系）Finance Ministry（财政部）Finance of International Trade(国际贸易的结算/支付)Financing Foreign Trade（对外贸易的价金支付）FOB (free on hoard) (port of shipment)（ＦＯＢ装运港船上交货）Force Majeure Clauses （不可抗力条款）Foreign Investment Guarantees（外国投资的担保）Foreign Investment Laws and Codes（外国投资法）Formal and Informal Application Process（正式和非正式申请程序）Formation of the Contract（合同的成立）Forsed Endorsements（虚假背书）Fraud Exception in Letters of Credit Transaction (信用证交易的欺诈例外)Frauds on Bills of Lading（提单欺诈）Fraudulent Misrepresentation（受欺诈的误解）Free Zones（保税区/自由贸易区）Fundamental Breach（根本违约）GATS Schedules of Specific Commitments（服务贸易总协定减让表中的特别承诺）General Agreement on Trade in Services (服务贸易总协定)General Requirements and Rights of the Holder in Due Course（票据持有人的一般要求和权利）General Standards of Performance（履行的一般标准）Geographic Limitations（地区限制）Government Controls over Trade (政府对贸易的管制)Government Guarantees（政府担保）Governmental Interest（政府利益原则）Governmental Sources of Capital（官方资金）Grant Back Provisions（回授的规定）Home state Regulation of Multinational Enterprises（本国对跨国企业的管理）Host State Regulation of Multinational Enterprises（东道国对跨国企业的管理）Illegality and Incompetency（行为不合法性与主体不适当资格的认定）IMF "Conditionality"(国际货币基金组织的制约性)IMF Facilities（国际货币基金组织的机制）IMF Operations（国际货币基金组织的运作）IMF Quotas（国际货币基金的份额）Immunities of States from the Jurisdiction of Municipal Courts（国家豁免于内国法院的管辖权）Import-Licensing Procedures（进口许可证程序协定）Income Categories（收入分类）Income Tax Rates（所得税税率）Income Taxes（所得税）Independence Principles and Rule of Strict Compliance （信用证独立原则和单证严格相符规则）Indirect Exporting(间接出口)Industrial Property Agreements （保护工业产权的协定）Innocent Misrepresentation（因无知的误解）Inquiry（调查）Insider Trading Regulations（内幕交易规则）Insurance Cover （保险范围）Integration of Company and Personal Income Taxes（公司和个人所得税的征收）Intellectual Property Right Law （知识产权法）International Center for the Settlement of Investment Disputes （解决投资争端国际中心）International Commercial Dispute Settlement （国际商事争端的解决）International Court of Justice （海牙联合国国际法院）International Factoring （国际保理）International Franchising(国际特许经营权)International Labor Standards（国际劳工标准）International Licensing Agreement(国际许可证协议)International Licensing Agreements （国际许可证协定）International Model Law(国际示范法)International Organizations(国际组织)International Persons（国际法主体）International Rules for the Interpretation of Trade Terms(国际贸易术语解释通则) International Trade Customs and Usages(国际贸易惯例和习惯)International Treaties and Conventions(国际条约和公约)International Tribunals （国际法庭）Interpreting of the CISG (CISG的解释)Invitation Offer （要约邀请／要约引诱／询盘）Involuntary Dissolution （非自愿解散）Issuance of Securities（证券发行）Jurisdiction and Venue （管辖权和法院地）Jurisdiction in Civil Cases（民事案件的管辖权）Jurisdiction in Criminal Cases（刑事案件的管辖权）Know-how （技术秘密/专有技术）Lack of Genuine Link（缺乏真实的联系）Lack of Nationality（无国籍）Lack of Standing（身份不明）Law Applicable to Letters of Credit （调整信用证的法律）Law of Foreign Investment Enterprises of China （中国的外商投资企业法）Law of the People's Republic of China on Chinese Foreign Contractual Joint Ventures（中华人民共和国中外合作企业法）Law of the People's Republic of China on Chinese Foreign Equity Joint Ventures （中华人民共和国中外合资企业法）Law of the People's Republic of China on Foreign Capital Enterprises（中华人民共和国外资企业法）Legal Characteristics （定义和法律特征）Legal Structure of the WTO （世界贸易组织的法律框架）Legal System of International Business(国际商事的法律体系)Letters of Credit (L/C)（信用证）Liabilities of Makers, Drawers, Drawees, Endorsers and Accommodation Parties（票据制作人、出票人、付款人、背书人、代发人/担保人的责任）Liability for Environmental Damage（环境损害责任）Liability Limits（承运人责任/义务的限制）Licensing Regulations（许可证制度）Limitations on Foreign Equity（外国投资的资金比例限制）Limitations on the Excuses That Drawers and Makers Can Use to Avoid Paying Off a Bill or Note 661 （票据制作人、出票人拒绝付款借口的限制）Liquidated Damages （约定的损害赔偿金）Liquidation （清算）Maintaining Monetary Value（维护币值稳定）Major Principles of GATT 1994（关税及贸易总协定1947的主要原则）Marine Insurance Policies and Certificates （海运保险单和证书）Maritime Insurance（海运保险）Maritime Liens （留置权）Means of Delivery（根据交付方式）Mediation（调停/调解）Membership（成员）Memorandums of Understanding（谅解备忘录）Methods of Investment Contribution（出资方式）Mini-trial （模拟审判方式）Miscellaneous Taxes（混杂的，各种各样的税）Misrepresentation（误解）Mixed Sales（混合销售）Modification of Foreign In vestment Agreements（外国投资协议的修改）Money and Banking(货币与金融)Monopoly Control Authority (反垄断机构)Most Significant Relationship（最密切联系原则）Most-favored-nation Treatment （最惠国待遇原则）Movement of Workers（劳工流动）Multilateral Investment Guaranty Programs（多边投资担保计划／安排）Multilateral Trade Agreements（多边贸易协定）Multilateral Trade Negotiations （多边贸易谈判）Multinational Enterprise（跨国企业）Municipal Courts（国内法院的实践）Municipal Legal Systems（内国法系）National Foreign Investment Policies（内国的外国投资政策）National Investment Guarantee Programs（内国／国家投资担保计划／安排）National Law(国内法)National Monetary Systems（国内金融/货币体系）National Treatment （国民待遇原则）Nationality Principle（国籍原则）Negligent (innocent) Misrepresentation（因疏忽的误解）Negotiability of Bills and Negotiability of Notes（可流通的汇票和可流通的本票）Negotiation (谈判，议付)Noncompetition Clauses（限制竞争条款）Nondiscrimination（非歧视原则）Nonimputable Acts（免责行为）Nontariff Barriers to Trade(非关税贸易壁垒)Nonwrongful Dissolution （非不法原因散伙）Objections（异议）Obligations of the Parties （当事人各方的义务）Obligations of the Seller and the Buyer （买卖双方的合同义务）Offer （要约／发盘）Operation of Law （法律的原因而终止）Operational Reviews（营业审查）Opting In and Out（加入和退出）Organization of the IMF（国际货币基金组织的机构）Organizations Affiliated with the United Nations(联合国的相关组织)Overseas Private Investment Corporation（海外私人投资公司的案件）Parent Company（母公司）Passing of Property （产权的转移）Passing of Risk （风险的转移）Patents （专利权）Payable on Demand or at a Definite Time（付款要求或者在指定的付款时间）Payment of the Price（支付价款）Penalties for Noncompliance（对于不遵守法规的处罚）Perils and Losses（保险危险和损失）Persons Immune from Taxation（个人所得税的免除）Piercing the Corporate Veil（普通法上揭开公司的面纱/大陆法上公司人格否认原则）Place for Delivery（交付的地点）Post -Termination Relationship（代理终止后的有关问题）Powers during Winding up （合伙人在清算过程中的权力/权利）Practices and Usages（交易习惯和商业惯例）Preemption（先买权/优先权）Preshipment Inspection（装运前检验协定）Price-Fixing（定价）Private Insurers（私人／商业保险）Private Sources of Capital（私人资金）Products Liability Laws（产品质量法）Promissory Notes（本票）Promoter of International Monetary Cooperation（国际金融合作的促进者）Promoters（公司的发起人）Protection of Natural Resources（自然资源的保护）Protection of Subsidiaries（分支机构的保护制度）Protection of Workers' Rights by the Council of Europe（欧洲理事会关于劳工权利的保护）Protection through Tariffs（关税保护）Proving Foreign Law（外国法的查明）Provisions Governing Trade in Services in the North American Free Trade Agreement （北美自由贸易区协定中关于服务贸易的规定）Quality Controls（质量控制）Quantity and Field-of-Use Restrictions（对数量和使用领域的限制）Recognition and Enforcement of Awards （仲裁裁决的承认和执行）Recognition of Foreign Judgments（外国裁决的承认）Refusal to Exercise Jurisdiction（拒绝执行管辖权）Regional and International Development Agencies（区域性和国际性发展机构）Regional Integration（区域联合）Regional Intergovernmental Regulations on Labor（区域性政府间关于劳工的规定）Regional Intergovernmental Regulations on Trade in Services（关于服务贸易的区域性政府间管理规则）Regional Monetary Systems（区域性金融体系）Regulation of Foreign Workers（外籍员工的的管理规定）Regulation of Pollution（防止污染规则）Relief（救济、赔偿）Remedies Available to Both Buyers and Sellers（买卖双方都可以采取的救济措施）Remedies for Breach of Contract（违反合同的救济）Requests for Specific Performance（要求继续/特定履行）Residency Principle（居住地原则）Restrictions on Research and Development（对技术研究和发展的限制）Restrictions That Apply after the Expiration of Intellectual Property Rights（知识产权保护期满后应用的限制）Restrictions That Apply after the Expiration of the Licensing Agreement（知识产权使用许可合同期满后应用的限制）Right to Compensation （主张赔偿的权利）Rights and Duties （权利与义务）Rights and Responsibilities of Beneficiaries（收款人/收益人的权利与义务）Rights and Responsibilities of the Account Party（付款人/信用证帐户申请人的权利与义务）Rules of Origin（原产地规则）Rules of Private International Law（国际私法规则）Safeguards（保障措施协定）Sanitary and PhytosanitaryMeasures（卫生与植物卫生措施协定）Scope and Coverage of GATT 1947 and GATT 1994 （关税及贸易总协定1947和1994文本的调整范围）Screening Foreign Investment Applications（对外国投资申请的筛选/审查）Sectoral Limitations（行业/部门限制）Securities and Exchange Commission（证券交易委员会）Securities Exchanges （证券交易所）Securities Regulations（证券规章）Seller's Obligations（卖方的义务）Seller's Remedies（卖方可以采取的救济措施）Settlement of Disputes between ILO Member States（国际劳工组织成员国之间争端的解决）Settlement of Disputes between Intergovernmental Organizations and Their Employees （政府间国际组织与它的雇员之间争端的解决）Settlement of Disputes in International Tribunals（在国际法庭解决争端）Settlement of Disputes in Municipal Courts（内国法院的争端解决途径）Settlement of Disputes through Diplomacy（通过外交途径解决争端）Settlement of Disputes through Municipal Courts （通过内国法院解决国际商事争端）Shareholders' Inspection and Information Rights（股东的监督和知情权）Shareholders' Lawsuits （股东的诉权）Shareholders' Meetings（股东会议/大会）Shareholders' Rights and Liabilities （股东的权利和责任）Sharp Practices （欺诈行为）Signed by the Maker or Drawer（票据制作人或者出票人签名）Source Principle（税收发生来源原则）Sources of Corporate Financing （公司资本的来源）Sources of Foreign Investment Law of China （中国外国投资法的渊源）Sources of International Business Law(国际商法的渊源)Sources of International Law（国际法的渊源）Sources of Investment （投资范围）Sovereign or State Immunity（国家主权豁免）Specialization(国际分工专门化)Standard of Care(给予外国人的待遇/关照标准)Start-Up Standards（设立标准）State Responsibility（国家责任）Statements and Conduct of the Parties（当事人的陈述和行为）Statutory Choice-of-Law Provisions （强制选择条款）Structure of the WTO（WTO的组织结构）Subordinate Business Structures（商业分支机构）Subsidies and Countervailing Measures（补贴与反补贴措施协定）Supervision of Foreign Investment（外国投资的监管）Supreme Court Decision（最高法院的裁决）Systems for Relief from Double Taxation（避免双重征税的救济体制）Takeover Regulations（接管/收购规则）Taking Delivery（接受交付）Tariff-based Import Restriction （约束进口关税）Tariffs(关税)Tax Avoidance（避税）Tax Evasion（逃税）Tax Incentives (税收激励)Tax Sparing（节税）Tax Treaties（税收条约）Taxation（税收）Taxpayers（纳税人）Technical Barriers to Trade（贸易的技术壁垒）Technology Transfer(技术转让)Termination of an Agency （代理的终止）Termination of Corporations （公司的终止）Territorial Restrictions（地区限制）Textiles and Clothing（纺织品和服装协定）The Acceptance（承诺／受盘）The Administrative Discretion of Screening Authorities（筛选/监管机构的管理权）The Anglo-American Common Law System(普通法系或者英美法系)The Applicable Procedure Law （应用的程序法）The Applicable Substantive Law（应用的实体法）The Bank for International Settlements（巴塞尔国际清算银行）The Bill of Exchange（汇票）The Board of Directors （董事会）The Bretton Woods System（布雷敦森林体系）The Business Form（商业组织形式）The Buyer's Right to Avoid the Contract（买方解除合同的权利）The Central Bank（中央银行）The Choice of Money（货币的选择）The Convention on Insider Trading（内幕交易的公约）The Drafting of the CISG(CISG的起草)the Economic Globalization （经济全球化）The Final Act Embodying the Results of the Uruguay Round of Multilateral Trade Negotiations（乌拉圭回合多边贸易谈判结果的最后文本）The Foreign Exchange Market（外汇交易市场）The Founding of GATT（关税及贸易总协定的成立）The Framework Agreement（协定的框架）The General Agreement on Tariffs and Trade（关税及贸易总协定）The Importance of the Separate Legal Identity of Juridical Entities（跨国企业作为拥有独立法律地位的实体之重要性）The Interbank Deposit Market（银行间的储蓄市场）The International Labor Organization（国际劳工组织）The International Monetary Fund（国际货币基金组织）The International Standard(国际待遇/标准)The International Transfer of Intellectual Property（工业产权的国际转让）The Islamic Law System （伊斯兰法系）The Law Governing Bills of Exchange （调整汇票的法律制度）The Law of Agency （国际商事代理法）The Making of International Law （国际法的构成）The National Standard(国民待遇/标准)The Negotiation and Transfer of Bills and Notes（票据权利的转让和背书转让）The Obligations of Banks（银行的义务/责任）The Principal Characteristics（基本特征）The Role of Banks in Collecting and Paying Negotiable Instruments（银行在可流通票据的托收和付款中的角色）The Roman-Germanic Civil Law System(大陆法系或者罗马日耳曼法系)The Scope of International Law in Actual Practice （实践中国际法的范围）The Subordinate Structure（分支结构）The Transfer of Money（货币转移）The Turning Over of Documents（交付与货物有关的单证）The Uruguay Round（乌拉圭回合）The Value of Money（币值）The World Trade Organization (WTO) （世界贸易组织）The WTO Agreement （WTO协定）Third Party Relations of the Principal and the Agent （与委托人和代理人有关的第三人）Third-Party Claims and Personal Injuries（第三方的权利和人身伤害）Third-Party Rights (Himalaya Clause)（第三方的权利----喜玛拉亚条款）Time Charterparties （定期租船合同）Time for Delivery （交付的时间）Time Limitations（时效）Trade Barriers （贸易壁垒）Trade in Goods(货物贸易)Trade Liberalization Through Cooperation （通过合作实现贸易自由化）Trade Policy Review（贸易政策评审机制）Trade Terms（贸易术语）Trademarks（商标权）Trade-Related Investment Measures（与贸易有关的投资措施协定）Trading in Securities （证券交易）Transactions Covered in CISG（CISG适用的交易范围）Transnational Organized Labor（有组织的跨境服务的劳工）Transparency(透明度)Treaties and Conventions（条约和公约）Trial Court Decision（初等法院的裁决）Turnover Taxes（流转/交易税）Tying Clauses（搭售条款）Unconditional Promise or Order to Pay（无条件的付款承诺和要求）Unfair Competition Laws（不正当竞争法）UNIDROIT Principles of International Commercial Contracts (PICC)(国际统一私法协会国际商事合同通则,简称PICC)精品文档. United Nations Convention on Contracts for the International Sale of Goods (CISG)(联合国国际货物销售合同公约,简称CISG)Validity and Formation of International Sale of Contracts（国际货物销售合同的成立和效力）Visas（签证）Voyage Charterparties （航次租船合同）Waivers（让渡、放弃）Winding Up （合伙清算）World Intellectual Property Organization（世界知识产权组织）World Trade Organization Dispute Settlement Procedures（世界贸易组织争端解决机制）Wrongful Dissolution （不法原因散伙）WTO Antidumping Agreement （世界贸易组织反倾销协议）WTO Dispute Settlement Procedures（世界贸易组织的争端解决程序）。

协作移动机器人-前因和方向外文文献翻译、中英文翻译、外文翻译

Cooperative Mobile Robotics: Antecedents and DirectionsY. UNY CAOComputer Science Department, University of California, Los Angeles, CA 90024-1596ALEX S. FUKUNAGAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109-8099ANDREW B. KAHNGComputer Science Department, University of California, Los Angeles, CA 90024-1596Editors: R.C. Arkin and G.A. BekeyAbstract. There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.Keywords: cooperative robotics, swarm intelligence, distributed robotics, artificial intelligence, mobile robots, multiagent systems1. PreliminariesThere has been much recent activity toward achieving systems of multiple mobile robots engaged in collective behavior. Such systems are of interest for several reasons:•tasks may be inherently too complex (or im-possible) for a single robot to accomplish, or performance benefits can be gained from using multiple robots;•building and using several simple robots can be easier, cheaper, more flexible and more fault-tolerant than having a single powerful robot foreach separate task; and•the constructive, synthetic approach inherent in cooperative mobile robotics can possibly∗This is an expanded version of a paper which originally appeared in the proceedings of the 1995 IEEE/RSJ IROS conference. yield insights into fundamental problems in the social sciences (organization theory, economics, cognitive psychology), and life sciences (theoretical biology, animal ethology).The study of multiple-robot systems naturally extends research on single-robot systems, butis also a discipline unto itself: multiple-robot systems can accomplish tasks that no single robot can accomplish, since ultimately a single robot, no matter how capable, is spatially limited. Multiple-robot systems are also different from other distributed systems because of their implicit “real-world” environment, which is presumably more difficult to model and reason about than traditional components of distributed system environments (i.e., computers, databases, networks).The term collective behavior generically denotes any behavior of agents in a system having more than one agent. the subject of the present survey, is a subclass of collective behavior that is characterized by cooperation. Webster’s dictionary [118] defines “cooperate” as “to associate with anoth er or others for mutual, often economic, benefit”. Explicit definitions of cooperation in the robotics literature, while surprisingly sparse, include:1. “joint collaborative behavior that is directed toward some goal in which there is a common interest or reward” [22];2. “a form of interaction, usually based on communication” [108]; and3. “[joining] together for doing something that creates a progressive result such as increasing performance or saving time” [137].These definitions show the wide range of possible motivating perspectives. For example, definitions such as (1) typically lead to the study of task decomposition, task allocation, and other dis-tributed artificial intelligence (DAI) issues (e.g., learning, rationality). Definitions along the lines of (2) reflect a concern with requirements for information or other resources, and may be accompanied by studies of related issues such as correctness and fault-tolerance. Finally, definition (3) reflects a concern with quantified measures of cooperation, such as speedup in time to complete a task. Thus, in these definitions we see three fundamental seeds: the task, the mechanism of cooperation, and system performance.We define cooperative behavior as follows: Given some task specified by a designer, a multiple-robot system displays cooperative behavior if, due to some underlying mechanism (i.e., the “mechanism of cooperation”), there is an increase in the total utility of the system. Intuitively, cooperative behavior entails some type of performance gain over naive collective behavior. The mechanism of cooperation may lie in the imposition by the designer of a control or communication structure, in aspects of the task specification, in the interaction dynamics of agent behaviors, etc.In this paper, we survey the intellectual heritage and major research directions of the field of cooperative robotics. For this survey of cooperative robotics to remain tractable, we restrict our discussion to works involving mobile robots or simulations of mobile robots, where a mobile robot is taken to be an autonomous, physically independent, mobile robot. In particular, we concentrated on fundamental theoretical issues that impinge on cooperative robotics. Thus, the following related subjects were outside the scope of this work:•coordination of multiple manipulators, articulated arms, or multi-fingered hands, etc.•human-robot cooperative systems, and user-interface issues that arise with multiple-robot systems [184] [8] [124] [1].•the competitive subclass of coll ective behavior, which includes pursuit-evasion [139], [120] and one-on-one competitive games [12]. Note that a cooperative team strategy for, e.g., work on the robot soccer league recently started in Japan[87] would lie within our present scope.•emerging technologies such as nanotechnology [48] and Micro Electro-Mechanical Systems[117] that are likely to be very important to co-operative robotics are beyond the scope of this paper.Even with these restrictions, we find that over the past 8 years (1987-1995) alone, well over 200papers have been published in this field of cooperative (mobile) robotics, encompassing theories from such diverse disciplines as artificial intelligence, game theory/economics, theoretical biology, distributed computing/control, animal ethology and artificial life.We are aware of two previous works that have surveyed or taxonomized the literature. [13] is abroad, relatively succinct survey whose scope encompasses distributed autonomous robotic systems(i.e., not restricted to mobile robots). [50] focuses on several well-known “swarm” architectures (e.g., SWARM and Mataric’s Behavior-based architecture –see Section 2.1) and proposes a taxonomy to characterize these architectures. The scope and intent of our work differs significantly from these, in that (1) we extensively survey the field of co-operative mobile robotics, and (2) we provide a taxonomical organization of the literature based on problems and solutions that have arisen in the field (as opposed to a selected group of architectures). In addition, we survey much new material that has appeared since these earlier works were published.Towards a Picture of Cooperative RoboticsIn the mid-1940’s Grey Walter, along with Wiener and Shannon, studied turtle-like robots equipped wit h light and touch sensors; these simple robots exhibited “complex social behavior” in responding to each other’s movements [46]. Coordination and interactions of multiple intelligent agents have been actively studied in the field of distributed artificial intelligence (DAI) since the early 1970’s[28], but the DAI field concerned itself mainly with problems involving software agents. In the late 1980’s, the robotics research community be-came very active in cooperative robotics, beginning with projects such as CEBOT [59], SWARM[25], ACTRESS [16], GOFER [35], and the work at Brussels [151]. These early projects were done primarily in simulation, and, while the early work on CEBOT, ACTRESS and GOFER have all had physical implementations (with≤3 robots), in some sense these implementations were presented by way of proving the simulation results. Thus, several more recent works (cf. [91], [111], [131])are significant for establishing an emphasis on the actual physical implementation of cooperative robotic systems. Many of the recent cooperative robotic systems, in contrast to the earlier works, are based on a behavior-based approach (cf. [30]).Various perspectives on autonomy and on the connection between intelligence and environment are strongly associated with the behavior-based approach [31], but are not intrinsic to multiple-robot systems and thus lie beyond our present scope. Also note that a recent incarnation of CEBOT, which has been implemented on physical robots, is based on a behavior-based control architecture[34].The rapid progress of cooperative robotics since the late 1980’s has been an interplay of systems, theories and problems: to solve a given problem, systems are envisioned, simulated and built; theories of cooperation are brought from other fields; and new problems are identified (prompting further systems and theories). Since so much of this progress is recent, it is not easy to discern deep intellectual heritages from within the field. More apparent are the intellectualheritages from other fields, as well as the canonical task domains which have driven research. Three examples of the latter are:•Traffic Control. When multiple agents move within a common environment, they typically attempt to avoid collisions. Fundamentally, this may be viewed as a problem of resource conflict, which may be resolved by introducing, e.g., traffic rules, priorities, or communication architectures. From another perspective, path planning must be performed taking into con-sideration other robots and the global environment; this multiple-robot path planning is an intrinsically geometric problem in configuration space-time. Note that prioritization and communication protocols – as well as the internal modeling of other robots – all reflect possible variants of the group architecture of the robots. For example, traffic rules are commonly used to reduce planning cost for avoiding collision and deadlock in a real-world environment, such as a network of roads. (Interestingly, behavior-based approaches identify collision avoidance as one of the most basic behaviors [30], and achieving a collision-avoidance behavior is the natural solution to collision avoidance among multiple robots. However, in reported experiments that use the behavior-based approach, robots are never restricted to road networks.) •Box-Pushing/Cooperative Manipulation. Many works have addressed the box-pushing (or couch-pushing) problem, for widely varying reasons. The focus in [134] is on task allocation, fault-tolerance and (reinforcement) learning. By contrast, [45] studies two boxpushing protocols in terms of their intrinsic communication and hardware requirements, via the concept of information invariants. Cooperative manipulation of large objects is particularly interesting in that cooperation can be achieved without the robots even knowing of each others’ existence [147], [159]. Other works in the class of box-pushing/object manipulation include [175] [153] [82] [33] [91] [94] [92][114] [145] [72] [146].•Foraging. In foraging, a group of robots must pick up objects scattered in the environment; this is evocative of toxic waste cleanup, harvesting, search and rescue, etc. The foraging task is one of the canonical testbeds for cooperative robotics [32] [151] [10] [67] [102] [49] [108] [9][24]. The task is interesting because (1) it can be performed by each robot independently (i.e., the issue is whether multiple robots achieve a performance gain), and (2) as discussed in Section 3.2, the task is also interesting due to motivations related to the biological inspirations behind cooperative robot systems. There are some conceptual overlaps with the related task of materials handling in a manufacturing work-cell [47]. A wide variety of techniques have been applied, ranging from simple stigmergy (essentially random movements that result in the fortuitous collection of objects [24] to more complex algorithms in which robots form chains along which objects are passed to the goal [49].[24] defines stigmergy as “the production of a certain behaviour in agents as a consequence of the effects produced in the local environment by previous behaviour”. This is actually a form of “cooperation without communication”, which has been the stated object of several for-aging solutions since the corresponding formulations become nearly trivial if communication is used. On the other hand, that stigmergy may not satisfy our definition of cooperation given above, since there is no performance improvement over the “naive algorithm” –in this particular case, the proposed stigmergic algorithm is the naive algorithm. Again, group architecture and learning are major research themes in addressing this problem.Other interesting task domains that have received attention in the literature includemulti-robot security systems [53], landmine detection and clearance [54], robotic structural support systems (i.e., keeping structures stable in case of, say ,an earthquake) [107], map making [149], and assembly of objects using multiple robots [175].Organization of PaperWith respect to our above definition of cooperative behavior, we find that the great majority of the cooperative robotics literature centers on the mechanism of cooperation (i.e., few works study a task without also claiming some novel approach to achieving cooperation). Thus, our study has led to the synthesis of five “Research Axes” which we believe comprise the major themes of investigation to date into the underlying mechanism of cooperation.Section 2 of this paper describes these axes, which are: 2.1 Group Architecture, 2.2 Resource Conflict, 2.3 Origin of Cooperation, 2.4 Learning, and 2.5 Geometric Problems. In Section 3,we present more synthetic reviews of cooperative robotics: Section 3.1 discusses constraints arising from technological limitations; and Section 3.2discusses possible lacunae in existing work (e.g., formalisms for measuring performance of a cooperative robot system), then reviews three fields which we believe must strongly influence future work. We conclude in Section 4 with a list of key research challenges facing the field.2. Research AxesSeeking a mechanism of cooperation may be rephrased as the “cooperative behavior design problem”: Given a group of robots, an environment, and a task, how should cooperative behavior arise? In some sense, every work in cooperative robotics has addressed facets of this problem, and the major research axes of the field follow from elements of this problem. (Note that certain basic robot interactions are not task-performing interactions per se, but are rather basic primitives upon which task-performing interactions can be built, e.g., following ([39], [45] and many others) or flocking [140], [108]. It might be argued that these interactions entail “control and coordination” tasks rather than “cooperation” tasks, but o ur treatment does not make such a distinction).First, the realization of cooperative behavior must rely on some infrastructure, the group architecture. This encompasses such concepts as robot heterogeneity/homogeneity, the ability of a given robot to recognize and model other robots, and communication structure. Second, for multiple robots to inhabit a shared environment, manipulate objects in the environment, and possibly communicate with each other, a mechanism is needed to resolve resource conflicts. The third research axis, origins of cooperation, refers to how cooperative behavior is actually motivated and achieved. Here, we do not discuss instances where cooperation has been “explicitly engineered” into the robots’ behavior since this is the default approach. Instead, we are more interested in biological parallels (e.g., to social insect behavior), game-theoretic justifications for cooperation, and concepts of emergence. Because adaptability and flexibility are essential traits in a task-solving group of robots, we view learning as a fourth key to achieving cooperative behavior. One important mechanism in generating cooperation, namely,task decomposition and allocation, is not considered a research axis since (i) very few works in cooperative robotics have centered on task decomposition and allocation (with the notable exceptions of [126], [106], [134]), (ii) cooperative robot tasks (foraging, box-pushing) in the literature are simple enough that decomposition and allocation are not required in the solution, and (iii) the use of decomposition and allocation depends almost entirely on the group architectures(e.g. whether it is centralized or decentralized).Note that there is also a related, geometric problem of optimizing the allocation of tasks spatially. This has been recently studied in the context of the division of the search of a work area by multiple robots [97]. Whereas the first four axes are related to the generation of cooperative behavior, our fifth and final axis –geometric problems–covers research issues that are tied to the embed-ding of robot tasks in a two- or three-dimensional world. These issues include multi-agent path planning, moving to formation, and pattern generation.2.1. Group ArchitectureThe architecture of a computing sys tem has been defined as “the part of the system that remains unchanged unless an external agent changes it”[165]. The group architecture of a cooperative robotic system provides the infrastructure upon which collective behaviors are implemented, and determines the capabilities and limitations of the system. We now briefly discuss some of the key architectural features of a group architecture for mobile robots: centralization/decentralization, differentiation, communications, and the ability to model other agents. We then describe several representative systems that have addressed these specific problems.Centralization/Decentralization The most fundamental decision that is made when defining a group architecture is whether the system is centralized or decentralized, and if it is decentralized, whether the system is hierarchical or distributed. Centralized architectures are characterized by a single control agent. Decentralized architectures lack such an agent. There are two types of decentralized architectures: distributed architectures in which all agents are equal with respect to control, and hierarchical architectures which are locally centralized. Currently, the dominant paradigm is the decentralized approach.The behavior of decentralized systems is of-ten described using such terms as “emergence” and “self-organization.” It is widely claimed that decentralized architectures (e.g., [24], [10], [152],[108]) have several inherent advantages over centralized architectures, including fault tolerance, natural exploitation of parallelism, reliability, and scalability. However, we are not aware of any published empirical or theoretical comparison that supports these claims directly. Such a comparison would be interesting, particularly in scenarios where the team of robots is relatively small(e.g., two robots pushing a box), and it is not clear whether the scaling properties of decentralization offset the coordinative advantage of centralized systems.In practice, many systems do not conform toa strict centralized/decentralized dichotomy, e.g., many largely decentralized architectures utilize “leader” agents. We are not aware of any in-stances of systems that are completely centralized, although there are some hybrid centralized/decentralized architectures wherein there is a central planner that exerts high-levelcontrol over mostly autonomous agents [126], [106], [3], [36].Differentiation We define a group of robots to be homogeneous if the capabilities of the individual robots are identical, and heterogeneous otherwise. In general, heterogeneity introduces complexity since task allocation becomes more difficult, and agents have a greater need to model other individuals in the group. [134] has introduced the concept of task coverage, which measures the ability of a given team member to achieve a given task. This parameter is an index of the demand for cooperation: when task coverage is high, tasks can be accomplished without much cooperation, but otherwise, cooperation is necessary. Task coverage is maximal in homogeneous groups, and decreases as groups become more heterogeneous (i.e., in the limit only one agent in the group can perform any given task).The literature is currently dominated by works that assume homogeneous groups of robots. How-ever, some notable architectures can handle het-erogeneity, e.g., ACTRESS and ALLIANCE (see Section 2.1 below). In heterogeneous groups, task allocation may be determined by individual capabilities, but in homogeneous systems, agents may need to differentiate into distinct roles that are either known at design-time, or arise dynamically at run-time.Communication Structures The communication structure of a group determines the possible modes of inter-agent interaction. We characterize three major types of interactions that can be sup-ported. ([50] proposes a more detailed taxonomy of communication structures). Interaction via environmentThe simplest, most limited type of interaction occurs when the environment itself is the communication medium (in effect, a shared memory),and there is no explicit communication or interaction between agents. This modality has also been called “cooperation without communication” by some researchers. Systems that depend on this form of interaction include [67], [24], [10], [151],[159], [160], [147].Interaction via sensing Corresponding to arms-length relationships inorganization theory [75], interaction via sensing refers to local interactions that occur between agents as a result of agents sensing one another, but without explicit communication. This type of interaction requires the ability of agents to distinguish between other agents in the group and other objects in the environment, which is called “kin recognition” in some literatures [108]. Interaction via sensing is indispensable for modeling of other agents (see Section 2.1.4 below). Because of hard-ware limitations, interaction via sensing has often been emulated using radio or infrared communications. However, several recent works attempt to implement true interaction via sensing, based on vision [95], [96], [154]. Collective behaviors that can use this kind of interaction include flocking and pattern formation (keeping in formation with nearest neighbors).Interaction via communicationsThe third form of interaction involves explicit communication with other agents, by either directed or broadcast intentional messages (i.e. the recipient(s) of the message may be either known or unknown). Because architectures that enable this form of communication are similar tocommunication networks, many standard issues from the field of networks arise, including the design of network topologies and communications protocols. For ex-ample, in [168] a media access protocol (similar to that of Ethernet) is used for inter-robot communication. In [78], robots with limited communication range communicate to each other using the “hello-call” protocol, by which they establish “chains” in order to extend their effective communication ranges. [61] describes methods for communicating to many (“zillions”) robots, including a variety of schemes ranging from broadcast channels (where a message is sent to all other robots in the system) to modulated retroreflection (where a master sends out a laser signal to slaves and interprets the response by the nature of the re-flection). [174] describes and simulates a wireless SMA/CD ( Carrier Sense Multiple Access with Collision Detection ) protocol for the distributed robotic systems.There are also communication mechanisms designed specially for multiple-robot systems. For example, [171] proposes the “sign-board” as a communication mechanism for distributed robotic systems. [7] gives a communication protocol modeled after diffusion, wherein local communication similar to chemical communication mechanisms in animals is used. The communication is engineered to decay away at a preset rate. Similar communications mechanisms are studied in [102], [49], [67].Additional work on communication can be found in [185], which analyzes optimal group sizes for local communications and communication delays. In a related vein, [186], [187] analyzes optimal local communication ranges in broadcast communication.Modeling of Other Agents Modeling the intentions, beliefs, actions, capabilities, and states of other agents can lead to more effective cooperation between robots. Communications requirements can also be lowered if each agent has the capability to model other agents. Note that the modeling of other agents entails more than implicit communication via the environment or perception: modeling requires that the modeler has some representation of another agent, and that this representation can be used to make inferences about the actions of the other agent.In cooperative robotics, agent modeling has been explored most extensively in the context of manipulating a large object. Many solutions have exploited the fact that the object can serve as a common medium by which the agents can model each other.The second of two box-pushing protocols in[45] can achieve “cooperation without commun ication” since the object being manipulated also functions as a “communication channel” that is shared by the robot agents; other works capitalize on the same concept to derive distributed control laws which rely only on local measures of force, torque, orientation, or distance, i.e., no explicit communication is necessary (cf. [153] [73]).In a two-robot bar carrying task, Fukuda and Sekiyama’s agents [60] each uses a probabilistic model of the other agent. When a risk threshold is exceeded, an agent communicates with its partner to maintain coordination. In [43], [44], the theory of information invariants is used to show that extra hardware capabilities can be added in order to infer the actions of the other agent, thus reducing communication requirements. This is in contrast to [147], where the robots achieve box pushing but are not aware of each other at all. For a more com-plex task involving the placement of five desks in[154], a homogeneous group of four robots share a ceiling camera to get positional information, but do not communicate with each other. Each robot relies on modeling of otheragents to detect conflicts of paths and placements of desks, and to change plans accordingly.Representative Architectures All systems implement some group architecture. We now de-scribe several particularly well-defined representative architectures, along with works done within each of their frameworks. It is interesting to note that these architectures encompass the entire spectrum from traditional AI to highly decentralized approaches.CEBOTCEBOT (Cellular roBOTics System) is a decentralized, hierarchical architecture inspired by the cellular organization of biological entities (cf.[59] [57], [162] [161] [56]). The system is dynamically reconfigurable in tha t basic autonomous “cells” (robots), which can be physically coupled to other cells, dynamically reconfigure their structure to an “optimal” configuration in response to changing environments. In the CEBOT hierarchy there are “master cells” that coordinate subtasks and communicate with other master cells. A solution to the problem of electing these master cells was discussed in [164]. Formation of structured cellular modules from a population of initially separated cells was studied in [162]. Communications requirements have been studied extensively with respect to the CEBOT architecture, and various methods have been proposed that seek to reduce communication requirements by making individual cells more intelligent (e.g., enabling them to model the behavior of other cells). [60] studies the problem of modeling the behavior of other cells, while [85], [86] present a control method that calculates the goal of a cell based on its previous goal and on its master’s goal. [58] gives a means of estimating the amount of information exchanged be-tween cells, and [163] gives a heuristic for finding master cells for a binary communication tree. Anew behavior selection mechanism is introduced in [34], based on two matrices, the priority matrix and the interest relation matrix, with a learning algorithm used to adjust the priority matrix. Recently, a Micro Autonomous Robotic System(MARS) has been built consisting of robots of 20cubic mm and equipped with infrared communications [121].ACTRESSThe ACTRESS (ACTor-based Robot and Equipments Synthetic System) project [16], [80],[15] is inspired by the Universal Modular AC-TOR Formalism [76]. In the ACTRESS system,“robotors”, including 3 robots and 3 workstations(one as interface to human operator, one as im-age processor and one as global environment man-ager), form a heterogeneous group trying to per-form tasks such as object pushing [14] that cannot be accomplished by any of the individual robotors alone [79], [156]. Communication protocols at different abstraction levels [115] provide a means upon which “group cast” and negotiation mechanisms based on Contract Net [150] and multistage negotiation protocols are built [18]. Various is-sues are studied, such as efficient communications between robots and environment managers [17],collision avoidance [19].SWARM。

常用术语中英对照

常用术语中英对照一、建筑结构永久荷载：permanent load可变荷载：variable load偶然荷载：accidental load荷载代表值：representative values of a load 设计基准期：design reference period标准值：characteristic value/nominal value组合值：combination value频遇值：frequent value准永久值：quasi-permanent value荷载设计值：design value of a load荷载效应：load effect荷载组合：load combination基本组合：fundamental combination偶然组合：accidental combination标准组合：characteristic/nominal combination 频遇组合：frequent combinations准永久组合：quasi-permanent combination等效均布荷载：equivalent uniform live load 从属面积：tributary area动力系数：dynamic coefficient基本雪压：reference snow pressure基本风压：reference wind pressure地面粗糙度：terrain roughness混凝土结构：concrete structure现浇结构：cast-in-situ concrete structure装配式结构：prefabricated concrete structure缺陷：defect严重缺陷：serious defect一般缺陷：common defect施工缝：construction joint结构性能检验：inspection of structural performance锚具：anchorage夹具：grip连接器：coupler预应力钢材：prestressing steel预应力筋：prestressing tendon预应力筋-锚具组装件：prestressing tendon-anchorage assembly预应力筋-夹具组装件：prestressing tendon-grip assembly预应力筋-连接器具组装件：prestressing tendon-coupler assembly内缩：draw-in预应力筋-锚具组装件的实测极限拉力：ultimate tensile force of tendon-anchorage assembly预应力筋-夹具组装件的实测极限拉力：ultimate tensile force of tendon-grip assembly受力长度：tension length预应力筋的效率系数：efficiency factor og prestressing tendon 二、钢结构零件：part部件：component构件：element小拼单元：the smallest assembled rigid unit中拼单元：intermediate assembled structure高强度螺栓连接副：set of high strength bolt抗滑移系数：slip coefficent of faying surface预拼装：test assembling空间刚度单元：space rigid unit焊钉（栓钉）焊接：stud welding环境温度：ambient temperature钢结构防火涂料：fire resistive coating for steel struture 三、抗震地震震级：earthquake magnitude地震面波：surface wave质点运动：particle motion地动位移：displacement of ground motion质点运动速度：velocity of particle motion震中距：epicentral distance量规函数：calibration function地震烈度：seismic intensity抗震设防烈度：seismic fortification intensity抗震设防标准：seismic fortification criterion地震作用：earthquake action设计地震动参数：design parameters of ground motion设计基本地震加速度：design basic acceleration of ground motion 设计特征周期：design characteristic perild of guound motion场地：site建筑抗震概念设计：seismic concept design of buildings抗震措施：seismic fortification measures抗震构造措施：details of seismic design工程抗震：earthquake engineering工程抗震决策：earthquake engineering decision抗震对策：earthquake protective counter-measure抗震措施：earthquake protective counter抗震设防：earthquake fortification搞震设防标准：earthquake fortification level抗震设防区: earthquake fortification zone抗震设防区划：earthquake fortification zoning基本烈度：basic intensity多遇地震烈度：intensity of frequently occurred earthquake 罕遇地震烈度：intensity of seldomly occurred设计地震震动：design ground motion人工地震震动：artificial ground motion极限安全地震震动：ultimate-safe guound motion运动安全地震震动：operation-safe ground环境振动：ambient vibration;microtremer卓越周期：predominant period结构抗震性能：earthquake resistant behavior of structure 结构延性：ductility of structure抗震鉴定：seismic evaluation for engineering抗震加固：seismic strengthening for engineer-ing结构体系加固：structural system strengthening构件加固：structural member strengthening生命线工程：lifeling engineering工程地震学：engineering seismology地震：earthquake板内地震：intraplate earthquake板间地震：interplate earthquake人工诱发地震：artificially induced earthquake爆破诱发地震：explosion induced earthquake水库诱发地震：reservoir induced earthquake矿山陷落地震：mine depression earthquake 地震波：seismic wave地震震级：earthquake magnitude里氏震级：Richter’s magnitude活断裂：active fracture断裂活动段：fracturing segment地表断裂：surface fuacture断裂距：fracture distance震源：earthquake focus;hypocenter震源深度：focal depth浅源地震：shallow-focus earthquake深源地震：deep-focus earthquake震中：earthquake epicenter仪器震中：instrumental epicenter现场震中：field epicenter震中距：epicentral distance地震烈度：earthquake intensity烈度分布：intensity distribution烈度异常：abnormal intensity烈度异常区：intensity abnormal rigion等震线：isoseismal;isoseism等震线图：isoseismal map极震区：meizoseismal srea有感面积：felt area;area of perceptivity地震烈度表：earthquake intensity scale地震预报：earthquake prediction地震危险性：seismic hazard潜在震源：potential source点源：point source线源：linear source面源：areal source本底地震：background earthquake地震发生概率：earthquake occurrence probability 地震活动性：seismicity地震重现期：earthquake return period年平均发生率：amerage annual occurrence rate超越概率：exceedance probability地震震动参数：ground motion parameter地震震动衰减规律：attenuation law of ground motion烈度衰减规律：intensity attenuation地震能量耗散：seismic energy dissipation地震能量吸收：seismic energy absorption地震区划：seismic zonation中国地震烈度区划图：Chinese seismic intensity zoning map 地震小区划:seismic microzoning结构动态特性：dynamic properties of structure自由振动：free vibration自振周期：matural perild of vibration基本周期：fundamental period振型：vibration mode基本振型：fundamental mode高阶振型：high order mode共振：resonance振幅：amplitude of vibration阻尼振动：damping vibration阻尼：damping临界阻尼：critical damping阻尼比：damping ratio耗能系数：energy dissipation coefficient自由度：degree of freedom单自由度体系：single-degree of freedom system多自由度体系：multi-degree of freedom system集中质量：lumped mass地震反应：earthquake response随机地震反应：random earthquake response结构—液体耦联振动：structure-liquid coupling vibration强震观测：strong motion observation强震观测台网：strong motion observation metwork强震观测台阵：strong motion observation array强震仪：strong motion instrument三分量地震计仪：three-component seismometer(seismoscope) 加速度仪：accelerograph 光学记录加速度仪：optically recording accelerograph磁带记录加速度仪：magnetic-tape recording accelerograph 数字加速度仪：digital accelerograph加速度仪启动器：starter of accelerograph启动时间：starting time触发阈值：triggering threshold value加速度仪放大倍数：magnification of accelerograph时标：time marking强震记录：strong motion record加速度图：accelerogram数据处理：data proccessing基线校正：base-line correction地震震动：ground motion强地震震动:strong ground motion自由场地震震动：free field ground motion地震震动持续时间：ground motion duration地震震动强度：ground motion intensity谱烈度：spectral intensity峰值加速度：peak acceleration峰值速度：peak velocity峰值位移：peak displacement抗震试验：earthquake resistant test现场试验：in-sitr test天然地震试验：natural earthquake test人工地震试验：artificial earthquake test模拟地震震动试验：simulated ground motion tes t伪动力试验：pseudo dynamic test振动台试验：shaking table test结构动态特性测量：dynamic properties measurement of structure 自由振动试验：free vibration test初位移试验：initial displacement test初速度试验：initial vibuation test强迫振动试验：forced vibration test偏心块起振试验：rotation eccentric mass excitation test液压激振试验：hydraulic excitation test人激振动试验：man-escitation test环境振动试验：ambient(environmental) excitation test动态参数识别：dynamic parameter identification伪静力试验：pseudo static test偱环加载试验：cyclic loading test滞回曲线：hysteretic curve骨架曲线：skeleton curve恢复力模型：restoring mod el土动态特性试验：dynamic property test for soil共振柱试验：resonant column test动力三轴试验：dynamic triaxial test剪切波速测试：shear wave velocity measurement单孔法：single hold method跨孔法：cross hole method场地：site危险条件site condition：有利地段：favoruable area不利地段：unfavourable area危险地段：dangerous area场地类别：site classification计算基岩面：nominal bedrock场地土：site soil场地土类型：type of site soil土层平均剪切波速：average velocity of shear wave of soil layer 土体抗震稳定性：seismic stability of soil地裂缝：ground crack构造性地裂缝：tectonic ground crack非构造性地裂缝：non-tectonic ground crack震陷：subsidence due to earthquake矿坑震陷：mining subsidence due to earthquake4.2、地基抗震术语地震地基失效：ground failure due to earthquake液化：liquefaction液化势：liquefaction potintial喷水冒砂：sandboil and waterspouts液化初步判别：preliminary discrimination of liquefaction标准贯入锤击数临界值：critical blow count in standard penetration test 液化指数：liquefaction index液化等级：class of soil liquefaction液化安全系数：liquefaction safety coefficient液化强度：liquefaction safety coefficient抗液化措施:liquefaction defence measures地基承载力抗震调整系数：modified coefficient of seismic bearing capacity of subgrade5、工程抗震设计术语5.1、抗震设计术语抗震设计：seismic design二阶段设计：two-stage design工程结构抗震类别：seismic categoryof engineering structures5.2、抗震概念设计术语抗震概念设计：conceptual design of earthquake设计近震和设计远震：design mear earthquake and design far earthquake 多道抗震设防：multi-defence system of seismic engineering抗震结构整体性：integral behaviour of seismic structure塑性变形集中：concentration of plastic deformation强柱弱梁：strong column and weak beam强剪弱弯：strong shear and weak bending capacity柔性底层：soft ground floor5.3、抗震构造设计术语抗震构造措施：earthquake resistant constructional measure 抗侧力体系：lateral resisting system抗震墙：seismic structural wall抗震支撑：seismic bracing约束砌体：confined masonry圈梁：ring beam;tie column构造柱：constructional column;tie column约束混凝土：confined concrete防震缝：seismic joint隔震：base isolation;seismic isolation滑动摩擦隔震：friction isolation滚球隔震：ball bearing isolation叠层橡胶隔震：steel-plate-laminated-rubber-bearing isolation 耗能：energy dissipation5.4抗震计算设计术语抗震计算方法：seismic checking computation method静力法：static method底部剪力法：equivalent base shear method振型分解法：modal analysis method振型参与系数：mode-participation coefficient平方和方根法：aquare root of sumsquare combination method 完全二次型方根法：complete quadric combination method时程分析法：time history method时域分析法：time history method频域分析：frequency domain analysis地震作用：earthquake action设计反应谱：response apectrum楼面反应谱：floor response spectrum反应谱特征周期：characteristic period of response spectrum 地震影响系数：seismic influence coefficient地震作用效应：seismic action effect地震作用效应系数：coefficient of seismic action地震作用效应调整系数：modified coefficient of seismic action effect 变形二次效应：secondary effect of deformation 鞭梢效应：whipping effect晃动效应：sloshing effect地震动水压力：earthquake hydraulic dynamic pressure地震动土压力：earthquake dynamic earth pressure结构抗震可靠性：reliability of earthquake resistance of structure材料抗震强度：earthquake resistant strength of materials结构抗震承载能力：seismic bearing capacity of structure杆件承载力抗震调整系数：modified coefficient of seismic bearing capacity of member结构抗震变形能力：earthquake resistant deformability of structure6、地震危害和减灾术语6.1地震危害术语危害：risk危险：hazard地震危害分析：seismic risk analysis可接受的地震危害：acceqtable seismic risk灾害:disaster地震灾害：earthquake disaster地震原生灾害：primary earthquake disaster地震次生灾害：secondary earthquake disaster海啸：tsunami震害调查：earthquake damage investigation工程结构地震破坏等级：grade of earthquake damaged engineering structure完好：intact轻微破坏：slight damage中等破坏：moderate damage严重耍破坏：severe damage倒塌：collapse震害指数：esrthquake damage结构性破坏：structural damage非结构性破坏：nonstructural damage撞击损坏：pounding damage工程震害分析：earthquake damage analysis of engineering6.2减轻地震灾害术语减轻地震灾害：earthquake disaster mitigation震害预测：earthquake disaster prediction易损性：vulnerability累积损坏：cumulative damage地震经济损失：economic loss due to earthquake地震直接经济损失：direct economic loss due to earthquake 地震间接经济损失：indirect economic loss due to earthquake 地震社会损失（影响）：social effect due to earthquake地震人员伤亡：earthquake casualty地震破坏率：earthquake casualty修复费用：rehabilitation cost抗震减灾规划：earthquake disaster reduction planing城市抗震减灾规划：urban earthquake disaster reduction planning工矿企业抗震减灾规划：earthquake disaster reduction planning for industrial enterpriss土地利用规划：land use planning灾害保险：disaster insurance地震灾害保险：earthquake disaster insurance震后救援：post-earthquake relief震后恢复：post-earthquake rehabilitation四、幕墙建筑幕墙：building curtain wall组合幕墙：composite curtain wall玻璃幕墙：glass curtain wall斜玻璃幕墙：inclinde building curtain wall框支承玻璃幕墙：frame supported glass curtain wall明框玻璃幕墙：exposed frame supported glass curtain wall 隐框玻璃幕墙：hidden frame supported glass curtain wall半隐框玻璃幕墙：semi-hidden frame supported glass curtain wall单元式玻璃幕墙：frame supported glass curtain wall assembled inprefabricated units构件式玻璃幕墙：frame supported glass curtain wallassembled inelements全玻璃幕墙：full glass curtain wall点支承玻璃幕墙：point-supported glass curtain wall支承装置：supporting device支承结构：suppouting structure钢绞线：strand硅酮结构密封胶：structural silicone sealant硅酮建筑密封胶：weather proofing silicone双面胶带：double-faced adhesive tape双金属腐蚀：bimetallic corrosion相容性：compatibility五、防火高层民用建筑设计防火规范裙房：skirt building建筑高度：building altitude耐火极限：duration of fire resistance不燃烧体：non-combustible component难燃烧体：hard-combustible component燃烧体：combustible component综合楼：multiple-use building商住楼：business-living building网局级电力调度楼：large-scale power dispatcher’building 高级旅馆：high-grade hotel高级住宅：high-grade hotel重要的办公楼、科研楼、档案楼：important office building、laboratory、archive半地下室：semi-basement地下室：basement安全出口：safety exit挡烟垂壁：hang wall六、防雷和采光建筑物防雷设计规范接闪器：air-termination system引下线：down-conductor system接地装置：earth-termination system接地体：earth-termination接地线：earth electrode防雷装置：lightning protection system,LPS 直击雷：direct lightning flash雷电感应：lightning induction。

安全多方计算英文

安全多方计算英文Secure Multi-Party ComputationThe concept of secure multi-party computation (SMC) has emerged as a powerful paradigm in the field of cryptography and data privacy. It is a cryptographic technique that allows multiple parties to jointly compute a function over their inputs, without revealing any of the individual inputs to each other. This innovative approach has far-reaching implications for various applications, ranging from financial transactions and healthcare data analysis to collaborative decision-making and secure auctions.At its core, SMC addresses the fundamental challenge of maintaining the confidentiality of sensitive data while still allowing for meaningful computations and collaborative efforts. In a typical SMC scenario, a group of parties, each with their own private data, wish to compute a joint function of their inputs without revealing any individual's data to the others. This is achieved through the use of advanced cryptographic protocols that ensure the privacy and integrity of the computation.One of the key advantages of SMC is its ability to enable securecollaboration and data sharing without compromising individual privacy. In many real-world scenarios, organizations or individuals may need to share sensitive information or perform joint computations, but are reluctant to do so due to the risks of data breaches or unauthorized access. SMC provides a solution by allowing the parties to engage in these computations while keeping their inputs confidential.For example, consider a scenario where several hospitals want to analyze patient data to identify trends and patterns in disease outbreaks, but they are hesitant to share the raw patient data due to privacy concerns. With SMC, the hospitals can jointly compute the desired statistical analysis without revealing any individual patient's data to the other hospitals. The result of the computation is then shared, providing valuable insights while preserving the privacy of the patients.Another application of SMC is in the realm of secure auctions and negotiations. Parties involved in these processes often need to keep their bids or offers confidential to maintain their competitive advantage. SMC enables the parties to securely submit their bids or offers and have the auction or negotiation process carried out without revealing any individual's information to the others.The implementation of SMC involves the use of advancedcryptographic techniques, such as garbled circuits, homomorphic encryption, and secure multiparty computation protocols. These techniques ensure that the computation is carried out in a secure and verifiable manner, with the participating parties unable to learn anything about each other's inputs beyond the final result of the computation.Despite the technical complexity of SMC, the practical applications of this technology are numerous and diverse. In the financial sector, SMC can be used to perform secure credit scoring, fraud detection, and portfolio optimization without exposing sensitive financial data. In the healthcare domain, SMC can enable collaborative research and disease surveillance while preserving patient privacy. In the field of smart grid and energy management, SMC can facilitate secure energy trading and load balancing among different energy providers.As the demand for secure data sharing and collaborative computing grows, the importance of SMC is expected to continue to rise. Ongoing research and development in this field are focused on improving the efficiency, scalability, and usability of SMC protocols, making them more accessible and practical for a wide range of real-world applications.In conclusion, secure multi-party computation represents a significant advancement in the field of cryptography and dataprivacy. By enabling secure collaboration and data sharing without compromising individual privacy, SMC has the potential to transform various industries and unlock new opportunities for innovation and cooperation. As the technology continues to evolve, the impact of SMC is likely to be felt across a wide range of domains, paving the way for a more secure and privacy-preserving digital landscape.。

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Theorem 1 For a cycle-free convolutional factor graph representing function f , the summary message pi(xi) at each variable node i computed using the algorithm above is precisely M ; (xi). Theorem 2 Suppose that f ( x ) is the joint pdf of random Then, for any subset variables X = { X 1 , X z , . . . X,}. C C I and non-negative integer n-vector s , the expectation E [ X ” I X c = xc]of the product X ” conditioned upon X c = xc
c
n,,,
nzEI
When the algorithm converges, we compute the sum) t each variable node i as pi(zi) = mary message p i ( % %a z:~ p j + i ( z z ) where stands for convolution.
ISlT 2002, Lausanne, Switzerland, June 30 -July 5,2002
Computation of Joint Moment Functions on Convolutional Factor Graphs
Yongyi Mao
Department of Electrical & Computer Engineering, University of Toronto yymaoQcomm.utoronto.ca
+
YБайду номын сангаас”B)h(xAnB
-
Y A n B , ZB\A),
where, for all i E I, yz takes on values in the same set as 2 % . Let s = ( S I , . . . , ) s , be a vector with non-negative integer components. We let x; denote denote x : ” Likewise, for 5:’. Given a function g A ( x A ) , a nonA I , z> denotes negative integer vector s and a subset C A, we define the joint sth moment functzon of g A at xc as
e,
0-7803-7501 -7/02/$17.00@2002 IEEE.
281
In the convolutional factor graph representing a global function f ,we let set I index the set of variable nodes and we let set J index the set of factor nodes. A node v has neighbors N ( v ) and degree d(v). For any non-negative integer m, let Z ( m ) denote the set {0,1,.. . ,m}. Given a positive integer let Ze((m) denote the l-fold Cartesian product of Z ( m ) with itself. Let P ( T ) denote the set {(a1,a2,. . . , a t ) E Z e ( m ) : E : = , ai = m}. For a non-negative integer vector ( m l , m2,. . . ,me), let W(m1, m2,. . . ,me) be the Cartesian product T(m1) x Z ( m 2 )x REFERENCES . . . x Z ( m e ) . Let Q be the power set of I U J , and F be the sort- [I] F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, “Factor ing map that takes any nonempty element A E Q t o the vector graphs and the sum-product algorithm,” IEEE Duns. Inform. Theory, vol. 47, no. 2, pp. 498-519, Feb 2001. a ( A ) = ( o l ( A )F , ~ ( A ). ,. . , U I A ( I A ) )having the property that m ( A ) 5 az(A) 5 . . . 5 o ~ A I ( A ) Given . an n-vector s and a [2] Y. Mao and F. R. Kschischang, “On factor graphs and the Fourier transform,” in Proc. ISIT 2001, Washington, D.C., subset A I , S A is the IAl-vector sol(^), s b z ( a ).,. . , s o l A l ( ~ ) ) . Jun. 2001, p. 224. Finally, for any A E Q and any vector a of IAl elements, define the map p that takes the pair (A,a) to the n-vector p(A,a) [3] Y. Mao and F. R. Kschischang, “On factor graphs and the Fourier transform,” in preparation. with the property that the ai(A)th component of p(A,a) is
I. INTRODUCTION
Just as a conventional (multiplicative) factor graph [l]represents the multiplicative factorization of a function as a product of local factors, a convolutional factor graph [2,3] represents the convolutional factorization of a function. A convolutional factor graph may be used to represent the characteristic function of a code, the joint probability density function (pdf) of multiple random variables, etc. Let function f have n arguments, 2 1 , x2,.. . , xn, indexed by the set I = { 1 , 2 , . . . , n } . If A I , let Z A denote the set of have ) variables {xz : z E A}. Two functions g ( Z A ) and ~ ( z B a convolution g * h defined (using an obvious notation) as
is
M ;( z ~ ) / M ~ ~ ~ ~ - ~ )
11. THEALGORITHM
111. CONCLUSION
Iterative algorithms on graphical models are of current research interest. In this paper, we show that for a function represented by a convolutional factor graph, its joint moment functions can be computed by a message-passing algorithm on the graph, without explicitly computing the function itself; when the function represented by the graph is a joint pdf, these joint moment functions are effectively conditional expectations. It is also worth noting that, as an application of factor graph duality, the algorithm translates t o a new message-passing algorithm on multiplicative factor graphs.
g(ZA\B,
YAnB
a,, for all z E { 1 , 2 , . . . , IAI}, while the other components of p(A, a ) are all zero. Assume variable node i and factor node j are neighbors. is a The message passed from j to i , denoted by p J + 2 ( x 2 ) function of the single variable x,, while the message passed P : ~ , p ! 2 J ,. . . ,p!:;), of from z t o j is a vector, denoted by ( sz 1 scalar values. The messages are computed as follows.