【实时动态】卢松松博客发文全纪录(二)

博还是不博官员们真的很纠结作者：暂无来源：《华声·观察》 2011年第4期当奥巴马、梅德韦杰夫在Twitter上互相follow，以至于奥巴马认为白宫与克林姆林宫之间已经不再需要红色电话专线时，中国的官员也开始尝试注册一个微博帐户。

可由于真正的活跃用户少得可怜，一个官员微博被公开通常都会被当做新闻来报道，而由于众所周知的原因，他们也没有多少通过微博提升公众形象、加强与公众沟通的动机。

那么，官员微博，是否会和过去的电子邮箱和博客一样，承载着民意沟通渠道的期许出世，最终却陷入无所作为的尴尬？白宫与克林姆林富之间已经不再需要红色电话1月11日，“潜水”一年的浙江省委常委、组织部长蔡奇的腾讯微博“浮出水面”。

副省级干部、前杭州市长、负责浙江省委组织部这个要害部门，蔡奇身上的这些标签引来网民的关注。

以个人身份开通微博的官员并不少，人民微博中甚至有党和国家领导人的ID，但在主流微博中公开亮相并正常更新的，蔡奇算是级别最高的。

2009年9月，广东省卫生厅副厅长廖新波开通新浪微博，廖的博客一直有很高的知名度，其开通微博后，依然受到热捧，如今粉丝接近10万。

官员中经营微博最成功的当属前云南省委宣传部副部长、现任云南省红河州委常委兼宣传部长的伍皓。

2009年底，伍皓的新浪微博出现在网民面前，“粉丝短短几天就1万多了”，引发轰动。

伍皓的微博更新频率高，发言常引发争议，现在粉丝已经超过13万。

此后，陆续有地方官员加入到微博主行列，惹人注目的还有江苏省文化厅厅长章剑华。

章去年9月22日开通新浪微博，目前粉丝数超过6万……官员们对微博的热衷，时下算得上是“国际潮流”。

世界各国领导人中，开通Twitter（国际主流微博客服务网站）的至少有数十位，Twitter已经成为政客与选民沟通的重要渠道，这些政客纷纷施展“微外交”，以巩固选民基础，甚至一些领导人还聘请专业团队打理微博。

Twitter还成为一个外交关系渠道，俄罗斯总统梅德韦杰夫成为Twitter用户后，奥巴马于是有了那句“不再需要红色电话专线”的名言。

【宣传】元培年度人物各大奖项宣传——2010年最给力的元培人！来源：元培时讯的日志不要问我们从哪里来。

我的故事最有爱。

元培年度人物，八大奖项风起云涌，让我们来看看元培人2010年的别样风采！年度光光明明寝室（1）48楼10611061光明寝室申请材料师父（小宅），张亚婕，委屈：我不过就是宅了一点嘛~（众人：不要辩解！）小晴，丁雨晴，哭闹：我是女生！我是理科生！~（众人：不哭不哭，你说了算啊~）熊仔，熊婉茹，自白：我是温柔宜家的仔哥，用情专一的天秤座~（众人：你个矛盾统一体！）霄仔，谭逸霄，众人：老娘们！~旁白：太阳照常升起，光明照耀48楼1061。

Action1雌心壮志小晴：三个月以后要交政经论文，今天无论如何要把它写出来！！！师父：今天要把@$%&^#弄懂……什么？！明天要交形策论文？！仔哥：论文？你说哪一科的？政，经，哲？霄仔：论文是什么……我只知道pre……Action2锄禾日当午，不如学术苦。

小晴：问君能有几多愁，恰似政经论文开不了头……仔哥：天若有情天亦老，内容太多背不了……师父：三更燈火五更雞啊五更雞，為誰辛苦為誰忙啊為誰忙……霄仔：Ladies and gentlemen…….Thank you for l istening!Action3有食同当师父：（拆开一包豆干）嗟！来食！小晴：求送货上门！求空投救济！仔哥：其实我觉得我们应该设计一个吊篮……液体的话可以考虑连通器……霄仔：咕唧咕唧…咕咚…啊~真好吃！Action4 be to not or be to仔哥：是背单词还是去睡觉？我去睡了。

师父：是熬夜呢还是早睡呢还是早睡呢？我在思考。

小晴：是睡呢还是睡呢还是睡呢还是睡呢？我睡着了……霄仔：总算清醒了！旁白：十年以后，当我们再也担不起明明寝室的称号，我们会不会偶尔想起这些曾经一起做过的疯狂的小事，会不会有些怀念那些彼此依偎的遥远的梦想。

忙处抛人闲处住，忙里偷闲，且为吾寝赋：惟燕园之南隅，秉北大之纯菁。

中国黑客年代史黑客人物 2007-06-16 01:34 阅读28 评论0字号：大中小图片：黑客们的反应与大众正好相反。

听到=0>"红盟"解散的消息后，一个叫大鹰的黑客说，"那群小孩儿长大了。

"从1994年中国邮电部对普通客户开放网络服务算起，中国的网龄已过10岁，中国黑客的年龄还要更长久些。

1993年，中国科学院高能物理研究院建设了一个试验性质的网络，开通不久，一名欧洲黑客发现了这个陌生的地址并闯进来，成为中国第一个黑客案例。

1996年，为数不多的中国人开始尝试建立BBS。

这一年，英国17岁的女中学生莱安诺·拉斯特凭想象写出的《骇客帝国》，成为那一年的畅销书，"hacker"这个词传入中国，被译为"黑客"。

次年，中国最老牌的黑客组织"绿色兵团"成立，黑客从此有了自己的江湖。

真正的黑客追求的是技术，极少人懂得他们世界的语言。

1998年，一件意外的事件使中国黑客浮了上来。

当年5月，印度尼西亚发生排华事件，直到8月，一部分相关的图片和报道才通过互联网传到了中国，一群愤怒的黑客决定对向印尼网站发起攻击。

这次攻击引起了网民的关注和赞许，为次年的中美黑客大战打下良好基础。

fC5qR D`X fU7 Qt Q1999年5月8日美国"误炸"中国驻南使馆后，中国网民向白宫投掷了大量的垃圾邮件阻塞网路，并成功图改了部分美国军方网页。

在这次行动中，一位黑客赋予了这个群体新的颜色--象征着革命的红色，还发明一个对应的英文单词"honker"，意为"爱国的黑客"。

[ *\^D B5PKk w1999年正是网络泡沫年，黑客在这阵势不可挡的浪潮中不可避免地泛起了泡沫，一群技术刚刚起步的黑客开始建设自己的黑客网站。

从1999年到2000年，"中国黑客联盟"、"中国鹰派"、"中国红客联盟"等一大批黑客网站兴起，带来了黑客普及教育。

第46期基金动态基金动态Investment Fund:田磊E-mail:******************招商基金11月14日发布公告称，招商大盘蓝筹股票基金将增聘陈玉辉为该基金经理，任职日期为11月14日，与其共同管理该基金的基金经理为袁野先生。

光大保德信基金11月15日发布公告称，光大保德信行业轮动基金将增聘魏晓雪为该基金基金经理，魏晓雪具有6年证券从业经验，任职日期11月15日，与其共同管理该基金的基金经理为于进杰。

中海基金11月15日发布公告称，中海货币基金11月15日将进行收益集中支付并自动结转为基金份额，其中收益累计期间为10月15日至11月14日。

安信基金11月15日发布公告称，安信平稳增长混合型发起式基金自11月19日起至12月14日公开发售，该基金为契约型、混合型发起式基金，该基金拟任基金经理为汪建先生，副总经理，经济学硕士，以及李勇先生，经济学硕士。

鹏华基金11月15日发布公告称，鹏华货币(A 类、B 类)基金11月1日进行收益集中支付并自动结转为基金份额，收益累计期间为10月9日至11月1日。

长信基金11月15日发布公告称，长信金利趋势股票型基金原基金经理付勇因个人原因离职，离职日期11月13日，与其共同管理该基金的另一位基金经理为胡志宝。

博时基金11月15日发布公告称，博时标普500指数基金原基金经理王政因个人发展于11月13日离职，接任其职位的基金经理为胡俊敏，曾管理过上证自然资源交易型开放式指数基金、博时上证自然资源交易型开放式指数联接基金以及博时特许价值股票型基金。

景顺长城基金11月15日发布公告称，景顺长城货币基金11月15日进行收益集中支付并自动结转为基金份额，其中收益累计期间为10月15日至11月14日，基金基金份额持有人收益结转的基金份额于11月15日直接计入其基金账户，11月16日起可查询及赎回。

工银瑞信基金11月15日发布公告称，工银14天理财债券发起基金将增聘谷衡先生为该基金基金经理，11月13日任职，与其共同管理该基金的基金经理为魏欣。

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土方工程承包合同协议书土方工程承包合同协议书模板土石方承包合同协议书范文发包方（甲方）：____________________________________承包方（乙方）：____________________________________本着平等互利的原则，按照国家《中华人民共和国劳动法》等相关的法律法规。

经甲、乙双方共同协商，_____________土石方平场工程承包合同协议如下：一、工程承包内容土方开挖、运输、回填、弃土、场地平整和清淤除表等全部工作内容（土方开挖_____立方米、回填碾压_____立方米、弃土_____立方米、平整场地_____平方米）。

二、工程地点____________________。

三、安全在施工过程中，一切安全事故由__方自行负责。

四、工程技术要求乙方按设计图纸和现行施工规范规定及项目部（甲方）的技术要求施工，安全施工交底，进行组织施工。

土方运输过程中的道路维修、卫生由乙方自行负责搞好。

五、乙方应认真按照标准、规范和设计图纸要求以及项目部依据合同发出的指令施工，随时接受项目部的检查和检验，工程质量达不到约定标准的部分，项目部一经发现，应要求乙方重新施工，因乙方原因达不到约定标准，由乙方承担重新施工的费用，工期不予顺延。

六、工程量的计算1、以施工图纸挖方工程量计算（挖方综合单价已包括土方开挖、运输、回填、弃土、场地平整和清淤除表等全部内容）。

2、施工中，发生工程量增减和新增工程时，经项目部确认后调整合同价款后签定补充协议。

弃土地点施工现场南边场外就近弃土、红番弃土场弃土，土方堆积高度按规划设计规定的高度以下。

七、价格本工程采用以挖方工程量为计量基础的综合单价包干（挖方综合单价已包括土方开挖、运输、回填碾压、弃土、场地平整和清淤除表等全部内容）。

综合单价人民币____元/m3由乙方自负盈亏包干施工。

八、施工现场内的障碍物（含房屋搬迁、水电拆迁）由甲方负责清除，决不影响乙方施工。

The Decision Reliability of MAP, Log-MAP, Max-Log-MAP and SOV A Algorithmsfor Turbo CodesAbstract —In this paper, we study the reliability of decisions ofe Codes, Channel Reliability,e N comm llular, satellite and we also consider two improved versions, named Log-MAP two different or identicalRecursi s, connectedin pFig. 1. The turbo encoder with rate 1/3.The first encoder operat ed b e u , i ond encoderp Lucian Andrei Peri şoar ă, and Rodica Stoianth MAP, Log-MAP, Max-Log-MAP and SOVA decoding algorithms for turbo codes, in terms of the a priori information, a posteriori information, extrinsic information and channel reliability. We also analyze how important an accurate estimate of channel reliability factor is to the good performances of the iterative turbo decoder. The simulations are made for parallel concatenation of two recursive systematic convolutional codes with a block interleaver at the transmitter, AWGN channel and iterative decoding with mentioned algorithms at the receiver.Keywords —Convolutional Turbo D cision Reliability, Extrinsic Information, Iterative Decoding.I. I NTRODUCTIONunication systems, like ce computer fields, the information is represented as a sequence of binary digits. The binary message is modulated to an analog signal and transmitted over a communication channel affected by noise that corrupt the transmitted signal.The channel coding is used to protect the information fromnoise and to reduce the number of error bits.One of the most used channel codes are convolutional codes, with the decoding strategy based on the Viterbialgorithm. The advantages of convolutional codes are used inTurbo Codes (TC), which can achieve performances within a2 dB of channel capacity [1]. These codes are parallelconcatenation of two Recursive Systematic Convolutional (RSC) codes separated by an interleaver. The performances of the turbo codes are due to parallel concatenation ofcomponent codes, the interleaver schemes and the iterative decoding using the Soft Input Soft Output (SISO) algorithms [2], [3].In this paper we study the decision reliability problem for turbo coding schemes in the case of two different decodingstrategies: Maximum A Posteriori (MAP) algorithm and Soft Output Viterbi Algorithm (SOVA). For the MAP algorithmand Max-Log-MAP algorithms. The first one is a simplified algorithm which offers the same optimal performance with a reasonable complexity. The second one and the SOVA are less complex again, but give a slightly degraded performance. The paper is organized as follows. In Section II, the turbo encoder is presented. In Section III, the turbo decoder is ex Manuscript received December 10, 2008. This work was supported in part by the Romanian National University Research Council (CNCSIS) under theGrant type TD (young doctoral students), no. 24.L. A. Peri şoar ă is with the Applied Electronics and InformationEngineering Department, Politehnica University of Bucharest, Romania (e-mail: lucian@orfeu.pub.ro, lperisoara@, www.orfeu.pub.ro).R. Stoian is with the Applied Electronics and Information Engineering Department, Politehnica University of Bucharest, Romania (e-mail: rodica@orfeu.pub.ro, rodicastoian2004@, www.orfeu.pub.ro).plained in detail, presenting firstly the iterative decoding principle (turbo principle), specifying the concepts of a priori information, a posteriori information, extrinsic information, channel reliability and source reliability. Then, we review the MAP, Log-MAP, Max-Log-MAP and SOVA decoding algorithms for which we discuss the decision reliability. In Section IV is analyzed the influence of channel reliability factor on decoding performances for the mentioned decoding algorithms. Section V presents some simulation results, which we obtained.II. T HE T URBO C ODING S CHEME The turbo encoder can use ve Systematic Convolutional (RSC) code arallel, see Fig. 1.es on the input bits represent n their original order, while the sec y the fram o erates on the input bits which are permuted by the interleaver, frame u ’, [4]. The output of the turbo encoder is represented by the frame: I2)()()1211,12,121,22,21,2,,,,,,,,,...,,,k k k u c c u c c u c c ==v u c c /R k n = to b , (1)is less likely where frame c1 is the output of the first RSC and frame c2 is the output of the second RSC. If the input frame u is of length k and the output frame x is of length n , then the encoder rate is .For block encoding data is segmented into non-overlapping blocks of length k with each block encoded (and decoded)independently. This scheme imposes the use of a blockinterleaver with the constraint that the RSC’s must begin in the same state for each new block. This requires either trellis termination or trellis truncation. Trellis termination need appending extra symbols (usually named tail bits) to the inputframe to ensure that the shift registers of the constituent RSC encoders starts and ends at the same zero state. If the encoder has code rate 1/3, then it maps k data bits into 3k coded bits plus 3m tail bits. Trellis truncation simply involves resettingthe state of the RSC’s for each new block.The interleaver used for parallel concatenation is a device that permutes coordinates either on a block basis (a generalized “block” interleaver) or on a sliding window basis(a generalized “convolutional” interleaver). The interleaver ensures that the set of code sequences generated by the turbo code has nice weight properties, which reduces the probabilitythat the decoder will mistake one codeword for another.The output codeword is then modulated, for example with Binary Phase Shift Keying (BPSK), resulting the sequence , which is transmitted over an Additive White Gaussian Noise (AWGN) channel.(12,,=v u c c 12,)p x x )(,s p =x x e a low weight codeword due to the interleaver in front of it. The interleaver shuffles the inputsequence It is known that turbo codes are the best practical codes due to their performance at low SNR. One reason for their better performance is that turbo codes produce high weight code words [4]. For example, if the input sequence u is originally low weight, the systematic u and parity c 1 outputs mayproduce a low weight codeword. However, the parity output c 2 is less likely to be a low weight codeword due to the u , in such a way that when introduced to the second encoder, it is more likely probable to produce a high weight codeword. This is ideal for the code because high weight code words result in better decoder performance. III. T HE T URBO D ECODING S CHEME Let be the received sequence of length n , 12(,,)s p p =y y y y where the vector y s is formed only by the received informationsymbols s y 222222(,,...,)p p p p n y y y =y p 1 and y p 2and . These three streams are applied to the input of the turbo decoder presented in Fig. 2. 11112(,,...,)p p p p n y y y 1=y y At time j , decoder 1 using partial received information 1,s p j j y y (), makes its decision and outputs the a posterioriinformation s j L x +()()()e s s s s . Then, the extrinsic information is computed j j j c jL x L x L x L y +−=−−. Decoder 2 makes itsdecision based on the extrinsic information ()e sj L x 2 from decoder 1 and the received information ',s p j jy y . The term(')s j L x + is the a posteriori information derived from decoder 2 and used by decoder 1 as a priori information about thereceived sequence, noted with (')sj L x −(). Now, the second iteration can begin, and the first decoder decodes the same channel symbols, but now with additional information about the value of the input symbols provided by the second decoder in the first iteration. After some iterations, the algorithm converges and the extrinsic information values remains the same. Now the decision about the message bits u j is made based on the a posteriori values s j L x +.e s y p 2y p 1y sFig. 2. The turbo decoder.Each constituent decoder operates based on the Logarithm Likelihood Ratio (LLR).A. The Decision Reliability of MAP DecoderBahl, Cocke, Jelinek and Raviv proposed the Maximum APosteriori (MAP) decoding algorithm for convolutional codesin 1974 [1]. The iterative decoder developed by Berrou et al.[5] in 1993 has a greatly increased attention. In their paper,they considered the iterative decoding of two RSC codesconcatenated in parallel through a non-uniform interleaver and the MAP algorithm was modified to minimize the sequence error probability instead of bit error probability.Because of its increased complexity, the MAP algorithm was simplified in [6] and the optimal MAP algorithm calledthe Log-MAP algorithm was developed. The LLR of a transmitted bit is defined as [2]:(1)()log ()(1)s Wenoted def j s sj j s j P x L x L x P x −⎛⎞=+==⎜⎟⎜⎟=−⎝⎠where the sign of the LLR ()s j L x indicate whether the bit s j xis more likely to be +1 or -1 and the magnitude of the LLRgives an indication of the correct value of s j x . The term()sj L x − is defined as the a priori information about s j x .In channel coding theory we are interested in theprobability that , based or conditioned on some received sequence 1s j x =±s j y . Hence, we use the conditional LLR: ()()()1||log (1|s s We noted def j j s s s j j j s s j j P x y L x y L x P x y +⎛⎞=+⎜⎟=⎜⎟=−⎝⎠=) The conditional probabilities (1|s sj j P x y =± are the a posteriori probabilities of the decoded bit s j x and ()s j L x + is thea posteriori information about sj x , which is the information that the decoder gives us, including the received frame, the a priori information for the systematic symbols y s j and the apriori information for symbol x s j . It is the output of the MAPalgorithm. In addition, we will use the conditional LLR ()|s s j j L y x based on the probability that the receiver’s output would be s j y when the transmitted bit s j x was either +1 or -1:()()()|1|log |1s s defj j s s jjs s j j P y x L y x P y x ⎛⎞=+⎜=⎜=−⎝⎠⎟⎟. (3)For AWGN channel using BPSK modulation, we can write the conditional probability density functions, [7]:()()20|12s s b j j j EP y x y a N ⎡⎤=±=−⎢⎣⎦m ⎥, (4)where is the transmitted energy per bit, a is the fadingamplitude and is the noise variance.b E 0/2N We can rewrite the (3) as follows: ()()()2200|4,s s s s b j j j j Noteds s b j c j E L y x y a y a N E a y L y N ⎡⎤=−−−+⎢⎥⎣⎦== (5) the fading amplitude and is the noise power. For nonfading AWGN channels a = 1 and 0N /204c b L E N =. Theratio is defined as the Signal to Noise Ration (SNR) of thechannel.0/b E N The extrinsic information can be computed as [1], [2], [9]: ()()()()()()1|()log 1|1|log log 1|()().s s j j e sj s s jj s s j j s sj j s s s j j c j P x y L x P x y P x P y x P x P y x L x L x L y +−⎛⎞=+⎜⎟=⎜⎟=−⎝⎠⎛⎞⎛=+=+⎜⎟⎜−−⎜⎟⎜=−=−⎝⎠⎝=−−11s j s j ⎞⎟⎟⎠ (6)The a posteriori information defined in (2), can be written asthe following [1], [10]:11(')()(',)()log (')()(',)e j j j s j e j j j s s s s L x s s s s −++−−α⋅β⋅γ=α⋅β⋅γ∑∑, (7)where +∑is the summation over all possible transition branch pairs (s ’,s ) in the trellis, at time j , given the transmittedsymbol x s j = +1. Analog, −∑is for transmitted symbol x s j =-1.The forward and backward terms, represented in Fig. 3 as transitions between two consecutive states from the trellis, can be computed recursively as following [7], [10], [11]:1'()(')(',)j j j s s s s s −α=αγ∑, (8)1(')()(',)j j j ss s s s −β=βγ∑. (9)For systematic codes, which is our case, the branch transition probabilities (',)js s γ are given by the relation:11(',)exp ()(',)22s s s s e j j j c jj j s s L x x L x y s −⎡γ=+⋅γ⎢⎣⎦s ⎤⎥, (10) where:112211(',)exp 22e p p j c j j c p p j j s s L x y L x ⎡⎤γ=+⎢⎥⎣⎦y .(11)At each iteration and for each frame y, ()s j L x + is computedat the output of the second decoder and the decision is done,symbol by symbol j = 1…k , based on the sign of ()sj L x +, original information bit u j being estimated as [2], [3]: {ˆ()sj usign L x +=}j . (12) In the iterative decoding procedure, the extrinsicinformation ()e s j L x is permuted by the interleaver andbecomes the a priori information ()sj L x − for the next decoder. influence on ()s j L x + is insignificant.B. The Decision Reliability of Max-Log-MAP DecoderThe MAP algorithm as described in previous section is much more complex than the Viterbi algorithm and with hard decision outputs performs almost identically to it. Therefore for almost 20 years it was largely ignored. However, its application in turbo codes renewed interest in this algorithm. Its complexity can be dramatically reduced without affecting its performance by using the sub-optimal Max-Log-MAP algorithm, proposed in [12]. This technique simplifies the MAP algorithm by transferring the recursions into the log domain and invoking the approximation: ln max()i x i ii e x ⎛⎞≈⎜⎟⎝⎠∑. (13)where max()i i x means the maximum value of x i . If we note:()()ln ()j j A s =αs , (14)()()ln ()j j B s s =β, (15)and:()(',)ln (',)j j G s s s s =γ, (16)then the equations (8), (9) and (10) can be written as: ()(()1'1'1'()ln ()ln (')(',)ln exp (')(',)max (')(',),j j j j s j j s j j s )A s s s s A s G s s A s G s s −−−⎛⎞=α=αγ⎜⎟⎝⎠⎛=+⎜⎝⎠≈+∑∑s ⎞⎟⎞⎟(17) ()()()11(')ln (')ln ()(',)ln exp ()(',)max ()(',),j j j j s j j s j j s B s s s s s B s G s s B s G s s −−⎛⎞=β=βγ⎜⎟⎝⎠⎛=+⎜⎝⎠≈+∑∑ (18) 11(',)()22s s s s jj j c j G s s C x L x L x y −=++j , (19) term ()s s j j x L x −.Finally, the a posteriori LLR ()s j L x + which the Max-Log-MAP algorithm calculates is:Fig. 3. Trellis states transitions.for ()j s αfor 1(')j s −β((1(',)11(',)1()max(')(',)()max (')(',)().j j s j j j j s s for u j j j s s for u L x As G s s B s ))A s G s s B s +−=+−=−≈++−++ (20)In [12] and [13] the authors shows that the complexity of Max-Log-MAP algorithm is bigger than two times that of a classical Viterbi algorithm Unfortunately, the storage requirements are much greater for Max-Log-MAP algorithm, due to the need to store both the forward and backward recursively calculated metrics and before the ()j A s ()j B s ()s j L x + values can be calculated.C. The Decision Reliability of Log-MAP DecoderThe Max-Log-MAP algorithm gives a slight degradation in performance compared to the MAP algorithm due to the approximation of (13). When used for the iterative decodingof turbo codes, Robertson found this degradation to result in a drop in performance of about 0.35 dB, [12]. However, the approximation of (13) can be made exact by using the Jacobian logarithm:()(()121212121212ln()max(,)ln 1exp ||max(,)||(,),x x e e x x x x )x x f x x g x x +=++−−=+−= (21)where ()f δ can be thought of as a correction term. However,the maximization in (17) and (18) is completed by the correction term ()f δ in (21). This means that the exact ratherthan approximate values of and are calculated. For binary trellises, the maximization will be done only for two terms. Therefore we can correct the approximations in (17) and (18) by adding the term ()j A s ()j B s ()f δ, where δ is the magnitude of the difference between the metrics of the twomerging paths. This is the basis of the Log-MAP algorithmproposed by Robertson, Villebrun and Hoeher in [12]. Thus we must generalize the previous equation for more than two 1x terms, by nesting the 12(,)g x x operations as follows: (((13211ln ,,,(,)i n x n n i e g x g x g x g x x −=⎛⎞=⎜⎟⎝⎠∑K ))), (22)The correction term ()f δδ need not to be computed for every value of , but instead can be stored in a look-up table. In [12], Robertson found that such a look-up table need containonly eight values for , ranging between 0 and 5. This meansthat the Log-MAP algorithm is only slightly more complexthan the Max-Log-MAP algorithm, but it gives exactly the same performance as the MAP algorithm. Therefore, it is a very attractive algorithm to use in the component decoders of an iterative turbo decoder. δD. The Decision Reliability of SOVA DecoderThe MAP algorithm has a high computational complexityfor providing the Soft Input Soft Output (SISO) decoding. However, we obtain easily the optimal a posteriori probabilities for each decoded symbol. The Viterbi algorithm provides the Maximum Likelihood (ML) decoding for convolutional codes, with optimalsequence estimation. The conventional Viterbi decoder has two main drawbacks for a serial decoding scheme: the inner Viterbi decoder produces bursts of error bits and hard decision output, which degrades the performance of the outer Viterbi decoder [3]. Hagenauer and Hoeher modified the classical Viterbi algorithms and they provided a substantially less complex and suboptimal alternative in their Soft OutputViterbi Algorithm (SOVA). The performance improvement is obtained if the Viterbi decoders are able to produce reliability values or soft outputs by using a modified metric [14]. These reliability values are passed on to the subsequent Viterbi decoders as a priori information .In soft decision decoding, the receiver doesn’t assign a zero or a one to each received symbol from the AWGN channel, but uses multi-bit quantized values for the received sequence y , because the channel alphabet is greater than the sourcealphabet [3]. In this case, the metric derived from Maximum Likelihood principle, is used instead of Hamming distance. For an AWGN channel, the soft decision decoding produces again of 2÷3 dB over hard decision decoding, and an eight-level quantization offers enough performance in comparison with an infinite bit quantization [7].The original Viterbi algorithm searches for an informationsequence u that maximizes the a posteriori probability, s being the states sequence generated by the message u . Using the Bayes theorem and taking into account that thereceived sequence y is fixed for the metric computation and it can be discarded, the maximization of is: (|)P s y (|)P s y {}{max (|)max (|)()P P =u us }P y y s s . (23)For a systematic code, this relation can be expanded to:(1211max (,,)|,()k s p p j j j j j j j P y y y s s P s −=)⎧⎫⎪⎪⎨⎬⎪⎪⎩⎭∏u. (24) Taking into account that:()()()(1211122(,,)|,|||s p p j j j j j s s p p p p j j j j j j P y y y s s P y x P y x P y x −==⋅⋅), (25)where 1(,)j j s s − denotes the transitions between the states attime j -1 and the states at time j , the SOVA metric is obtained from (24) as [15]:()()***1***|1(1)log log ,(0)|1j j j j j j j j j jP y x P u M M x u P u P y x −⎛⎞=+⎛⎞=⎜⎟=++⎜⎟⎜⎟⎜⎟==−⎝⎠⎝⎠∑ (26)where *1,2,(,,)j j j j x u c c = is the RSC output code word at timej , at channel input and *1(,,)s p p j j j j 2y y y y = is the channeloutput. The summation is made for each pair of information symbols (,s j j u y ) and for each pair of parity symbols (11,,p j j c y )and (2,2,p j j y 1*c ).According [14] and [7], the relation (26) can be reduced as: **c j j ()j j j j M M L −=+∑x y u L u +(), (27)where the source reliability j L u , defined in (26), is the log-likelihood ratio of the binary symbol u j . The sign of ()j L u ) is the hard decision of u j and the magnitude of (j L u is the decision reliability .According [10], the SOVA metric includes values from the past metric M j -1, the channel reliability L c and the source reliability ()j L u (, as an a priori value. If the channel is very good, the second term in (27) is greater than the third term andthe decoding relies on the received channel values. If thechannel is very bad, the decoding relies on the a priori information )j L u . If M 1j , M 2j are two metrics of the survivor path and concurrent path in the trellis, at time j , then the metric difference is defined as [7]:01212j j j M M −)(s m Δ=. (28)The probability of path m , at time j , is related as:()/2mjM (path )exp m j P m P ==. (29) where j s is a states vector and mj M is the metric. The probability of choosing the survivor path is: 001)(path (correc ath 1)(path 2)1jjP e P P P e ΔΔ==++t)(p . (30)The reliability of this path decision is calculated as:(correct)orrect)log 1-(c j P P =Δ. (31) The reliability values along the survivor paths, for aparticular node and time j , are denoted as d j Δ, where d is the distance from the current node at time j . If the survivor path bit for is the same with the associated bit on the competing path, then there would be no error if the competing path is chosen. The reliability value remains unchanged.d j =To improve the reliability values an updating process must be used, so the “soft” values of a decision symbol are:(')'di j d j di L u u −−=j=Δ∑, (32)which can be approximated as:{}0...(')'min i j d j d i d L u u −−=j =⋅Δ. (33)The SOVA algorithm described in this section is the least complex of all the SISO decoders discussed in this section. In [12], Robertson shows that the SOVA algorithm is about halfas complex as the Max-Log-MAP algorithm. However, theSOVA algorithm is also the least accurate of the algorithmsdescribed in this section and, when used in an iterative turbo decoder, performs about 0.6 dB worse than a decoder using the MAP algorithm. If we represent the outputs of the SOVA algorithm they will be significantly more noisy than thosefrom the MAP algorithm, so an increased number of decodingiterations must be used for SOVA to obtain the sameperformances as for MAP algorithm.The same results are reported also for the iterative decoding (turbo decoding) of the turbo product codes, which are basedon two concatenated Hamming block codes not on convolutional codes [19]. IV. T HE INFLUENCE OF L C ON DECODING PERFORMANCE In this section we analyze the importance of an accurate estimate of the channel reliability factor L c is to the good performance of an iterative turbo decoder which uses the MAP, SOVA, Max-Log-MAP and Log-MAP algorithms. In the MAP algorithm the channel inputs and the a priori information are used to calculate the transition probabilities from one state to another, that are then used to calculate theforward and backward recursion terms [2], [8]. Finally, the aposteriori information ()s j L x + is computed and the decision about the original message is made based on it. In the iterative decoding with MAP algorithm, the channelreliability is calculated from the received channel values. At first iteration, the decoder 1 has no a priori information available (the ()s j L x − is zero) and the output from thealgorithm is calculated based on channel values. If an incorrect value of L c is used the decoder will make more decision errors and the extrinsic information from the output of the first decoder will have incorrect values, for the softchannel inputs [16].In the SOVA algorithm the channel values are used torecursively calculate the metric *c j L y j M for the current state s along a path from the metric 1j M − for the previous state along that path added to an a priori information term and to a cross-correlation term between the transmitted and the receivedchannel values, *j x and *j y , using (27). The channel reliability factor is used to scale this cross-correlation. When we usec Lan incorrect value of , e.g. , we are scaling the channel values applied to the inputs of component decoders by a factor of one instead of the correct value of . This has the effect of scaling all the metrics by the same factor, see (8), and the metric differences are also scaled by the same factor, see (9). This scaling of the metrics do not affect the path chosen by the algorithm as a survivor path or as a Maximum Likelihood (ML) path, so the hard decisions given by the algorithm are not affected by using an incorrect value of L c [16]-[18].c L ()j B s 1c L =c L c In the iterative decoding with SOVA algorithm, in the first iteration we assume that no a-priori information about the transmitted bits is available to the decoder (the a-priori information is zero), the first component decoder takes only the channel values. If channel reliability factor is incorrect, the channel values are scaled, the extrinsic information will be also scaled by the same factor and the a-priori information for the second decoder will also be scaled. Because of the linearity of the SOVA, the effect of using an incorrect value of the channel reliability factor is that the output LLR from the decoder is scaled by a constant factor. The relative importance of the two inputs to the decoder, the a priori information and the channel information, will not change, since the LLRs for both these sources of information will be scaled by the same factor. In the final iteration, the soft outputs from the final component decoder will have the same sign as those that would have been calculated using the correct value of . So, the hard outputs from the turbo decoder using the SOVA algorithm are not affected by the channel reliability factor [16].L c L The Max-Log-MAP algorithm has the same linearity that is found in the SOVA algorithm. Instead of one metric, now two metrics and are calculated, for forward andbackward recursions, see (17), (18) and (19), were are used only simple additions of the cross-correlation of the transmitted and received symbols. But, if an incorrect value of the channel reliability value is used, all the metrics are simply scaled by a factor as in the SOVA algorithm. The soft outputs given by the differences in metrics between different paths will also be scaled by the same factor, with the sign unchanged and the final hard decisions given by the turbo decoder will not be affected.()j A s The Log-MAP algorithm is identical to the Max-Log-MAP algorithm, except for a correction term ()()ln exp()f δ=−δ1+, used in the calculation of the forward and backward metrics and ()j A s ()j B s , and the soft output LLRs. The function()f δ is not a linear function, it decreases asymptoticallytowards zero as δ increases. Hence the linearity that is present in the Max-Log-MAP and SOVA algorithms is not present in the Log-MAP algorithm. This effect of non-linearity determines more hard decision errors of thecomponent decoders if the channel reliability factor is incorrect, and the extrinsic information derived from the first component decoder have incorrect amplitudes, which become the a-priori information for the second decoder in the first iteration. Both decoders in subsequent iterations will have incorrect amplitudes relative to the soft channel inputs.c L In the iterative decoding with Log-MAP algorithm, the extrinsic information exchange from one component decoder to another, determines a rapid decrease in the BER as the number of iterations increases. When the incorrect value of is used, no such rapid fall in the BER occurs due to the incorrect scaling of the a priori information relative to the channel inputs. In fact, the performance of the decoder is largely unaffected by the number of iterations used.c L For wireless communications, some of them modeled as Multiple Input Multiple Output (MIMO) systems [23], the channel is considered to be Rayleigh or Rician fading channel. If the Channel State Information (CSI) is not known at the receiver, a natural approach is to estimate the channel impulse response and to use the estimated values to compute the channel reliability factor required by the MAP algorithm to calculate the correct decoding metric.c L In [20], the degradation in the performance of a turbo decoder using the MAP algorithm is studied when the channel SNR is not correctly estimated. The authors propose a method for blind estimation of the channel SNR, using the ratio of the average squared received channel value to the square of the average of the magnitudes of the received channel values. In addition, they showed that using these estimates for SNR gives almost identical performances for the turbo decoder to that given using the true SNR.In [8], the authors proposes a simple estimation scheme for from the statistical computation on the block observation of matched filter outputs. The channel estimator includes the error variance of the channel estimates. In [24], is used the Minimum Mean Squared Error (MMSE) estimation criterion and is studied an iterative joint channel MMSE estimation and MAP decoding.c L None of above works requires a training sequence with pilot symbols to estimate the channel reliability factor. Other studies used pilot symbols to estimate the channel parameters, like [22] and [25].In [22] it is shown that it is not necessary to estimate the channel SNR for a turbo decoder with Max-Log-MAP or SOVA algorithms. If the MAP or the Log-MAP algorithm is used then the value of does not have to be very close to the true value for a good BER performance to be obtained. c LV. S IMULATION RESULTSThis section presents some simulation results for the turbo codes ensembles, with MAP, Max-Log-MAP, Log-MAP and SOVA decoding algorithms. The turbo encoder is the same for the four decoding algorithms and is described by two identical RSC codes with constraint length 3 and the generator polynomials and . No tail bitsand no puncturing are performed. The two constituent encoders are parallel concatenated by a classical block interleaver, with dimensions variable according to the frame21f G =+D D 21b G D =++。

_S NPJH-50311_G 英雄传说：零之轨迹_C0 钱MAX_L 0x2045C058 0x05F5E0FF _C0 Sepith All MAX_L 0x4045C070 0x00070001 _L 0x0098967F 0x00000000 _C0 移动速度2倍_L 0x2027217C 0x3E2E147B _C0 鹰目の効果_L 0x2012E7FC 0x00000000 _C0 探知の効果_L 0x2012E830 0x00000000 _C0 一斉攻撃_L 0x2006DC0C 0x50430005 _L 0xD027E4C4 0x00002000 _L 0x2006DC0C 0x50000005 _C0 无咏唱时间_L 0x20080B48 0x24120000 _C0 钓り自动化゛エも减りません_L 0x2017C708 0x10000004 _L 0x2017C63C 0x140003CF _L 0x2017D680 0x24060000 _C0 戦闘中ゕ゗テム入手率100％_L 0x2006BB74 0x24020000 _C0 获得経験値4倍_L 0x20035688 0x00068080 _C0 获得経験値8倍_L 0x20035688 0x000680C0 _C0 全员Lv MAX_L 0x80457E74 0x000A0034 _L 0x00000032 0x00000000 _C0 全员CP MAX_L 0x80457E7A 0x000A0034 _L 0x000000C8 0x00000000 _C0 全员MP满_L 0x80457E76 0x000A001A _L 0x100003E7 0x00000000 _L 0x80457E78 0x000A001A _L 0x100003E7 0x00000000 _C0 全员HP满_L 0x80457E6C 0x000A001A _L 0x1000270F 0x00000000 _L 0x80457E70 0x000A001A_C0 全员移动力_L 0x80457E8C 0x000A0034 _L 0x00000063 0x00000000 _C0 料理手册全开_L 0x2045CCF8 0x01FFFFFE _L 0x2045CCFC 0x01FFFFFE _L 0x2045CD00 0x01FFFFFE _C0 ロ゗ド全能力最大（当前）_L 0x80457E80 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457E8E 0x0000270F _C0 ゛リゖ全能力最大（当前）_L 0x80457EB4 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457EC2 0x0000270F _C0 テゖゝ全能力最大（当前）_L 0x80457EE8 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457EF6 0x0000270F _C0 ランデゖ全能力最大（当前）_L 0x80457F1C 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457F2A 0x0000270F _C0 全道具9个持有_L 0x80458B8E 0x00C80004 _L 0x00000019 0x00000000 _C0 银加入_L 0x10457E5E 0x00000005 _C0 小艾加入_L 0x10457E60 0x00000006 _C0 小约加入_L 0x10457E62 0x00000007 中文版可用（经验证）第二个是耀晶石满第八个是钓鱼自动，并鱼饵不减_S NPJH-50311_G 英雄传说：零之轨迹_C0 钱多多_L 0x2045C058 0x05F5E0FF _C0 Sepith All MAX_L 0x4045C070 0x00070001 _L 0x0098967F 0x00000000 _C0 全员Lv MAX_L 0x00000032 0x00000000 _C0 全员CP MAX_L 0x80457E7A 0x000A0034 _L 0x000000C8 0x00000000 _C0 全员移动力_L 0x80457E8C 0x000A0034 _L 0x00000063 0x00000000 _C0 ロ゗ドHP（当前）_L 0x20457E6C 0x0001869F _L 0x20457E70 0x0001869F _C0 ロ゗ドEP（当前）_L 0x10457E76 0x000003E7 _L 0x10457E78 0x000003E7 _C0 ロ゗ド全能力最大（当前）_L 0x80457E80 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457E8E 0x0000270F _C0 ゛リゖHP（当前）_L 0x20457EA0 0x0001869F _L 0x20457EA4 0x0001869F _C0 ゛リゖEP（当前）_L 0x10457EAA 0x000003E7 _L 0x10457EAC 0x000003E7 _C0 ゛リゖ全能力最大（当前）_L 0x80457EB4 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457EC2 0x0000270F _C0 テゖゝHP（当前）_L 0x20457ED4 0x0001869F _L 0x20457ED8 0x0001869F _C0 テゖゝEP（当前）_L 0x10457EDE 0x000003E7 _L 0x10457EE0 0x000003E7 _C0 テゖゝ全能力最大（当前）_L 0x80457EE8 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457EF6 0x0000270F _C0 ランデゖHP（当前）_L 0x20457F08 0x0001869F _L 0x20457F0C 0x0001869F _C0 ランデゖEP（当前）_L 0x10457F12 0x000003E7 _L 0x10457F14 0x000003E7_C0 ランデゖ全能力最大（当前）_L 0x80457F1C 0x00060001 _L 0x1000270F 0x00000000 _L 0x10457F2A 0x0000270F _C0 全道具9个持有_L 0x80458B8E 0x00C80004 _L 0x00000009 0x00000000 S NPJH-50311_G 零之軌迹_C0 米拉_L 0×0045C058 0×000F423F _C0 Sepith All MAX_L 0x4045C070 0x00070001 _L 0x0098967F 0x00000000 _C0 全員Lv MAX_L 0x80457E74 0x000A0034 _L 0x00000032 0x00000000 _C0 全員CP MAX_L 0x80457E7A 0x000A0034 _L 0x000000C8 0x00000000 _C0 全員移動力_L 0x80457E8C 0x000A0034 _L 0x00000063 0x00000000 _C0 全員經驗滿_L 0x80457E7C 0x000A001A _L 0x10008D5F 0x00000000 _C0 CP200 （動態）_L 0x80457E7A 0x000A001A _L 0x100000C8 0x00000000 _C0 鎖定CP_L 0x6045F06C 0x000000C8 _L 0x00000004 0x000050CA _L 0x90002438 0x00000000 _C0 全員HP滿_L 0x80457E6C 0x000A001A _L 0x1000270F 0x00000000 _L 0x80457E70 0x000A001A _L 0x1000270F 0x00000000 _C0 全員MP滿_L 0x80457E76 0x000A001A _L 0x100003E7 0x00000000 _L 0x80457E78 0x000A001A_L 0x100003E7 0x00000000_C0 羅伊德·班甯斯HP（當前）_L 0x20457E6C 0x0001869F_L 0x20457E70 0x0001869F_C0 羅伊德·班甯斯EP（當前）_L 0x10457E76 0x000003E7_L 0x10457E78 0x000003E7_C0 羅伊德·班甯斯全能力最大（當前）_L 0x80457E80 0x00060001_L 0x1000270F 0x00000000_L 0x10457E8E 0x0000270F_C0 艾莉·麥克道爾HP（當前）_L 0x20457EA0 0x0001869F_L 0x20457EA4 0x0001869F_C0 艾莉·麥克道爾EP（當前）_L 0x10457EAA 0x000003E7_L 0x10457EAC 0x000003E7_C0 艾莉·麥克道爾全能力最大（當前）_L 0x80457EB4 0x00060001_L 0x1000270F 0x00000000_L 0x10457EC2 0x0000270F_C0 缇歐·布拉特HP（當前）_L 0x20457ED4 0x0001869F_L 0x20457ED8 0x0001869F_C0 缇歐·布拉特EP（當前）_L 0x10457EDE 0x000003E7_L 0x10457EE0 0x000003E7_C0 缇歐·布拉特全能力最大（當前）_L 0x80457EE8 0x00060001_L 0x1000270F 0x00000000_L 0x10457EF6 0x0000270F_C0 蘭迪·歐蘭德HP（當前）_L 0x20457F08 0x0001869F_L 0x20457F0C 0x0001869F_C0 蘭迪·歐蘭德EP（當前）_L 0x10457F12 0x000003E7_L 0x10457F14 0x000003E7_C0 蘭迪·歐蘭德全能力最大（當前）_L 0x80457F1C 0x00060001_L 0x1000270F 0x00000000_L 0x10457F2A 0x0000270FS NPJH-50311_G 英雄传说：零之轨迹ミラ増えるとMAX_C0 Mira Up To Max_L 0x200FD72C 0x00000000メコルMAX_C0 Medal Max_L 0x2045C060 0x000F423FギピガMAXギーブに反映されます_C0 Sepith Max_L 0x1045C070 0x0000270F_L 0x1045C074 0x0000270F_L 0x1045C078 0x0000270F_L 0x1045C07C 0x0000270F_L 0x1045C080 0x0000270F_L 0x1045C084 0x0000270F_L 0x1045C088 0x0000270F难易度変更デフ゜ルトはNORMALですギーブに反映されます_C0 Difficulty_L 0x00483515 0x000000xxxx00NORMAL01HARD02NIGHTMARE03EASY料理手帐全部ギーブに反映されます_C0 Recipe All_L 0x2045CCF8 0x01FFFFFE_L 0x2045CCFC 0x01FFFFFE_L 0x2045CD00 0x01FFFFFERECORD全部ギーブに反映されます_C0 Record All_L 0x2045E1A4 0xFFFFFFFF_L 0x2045E1A8 0x0000FFFFEP减らない戦闘中/移动中両方効果があります_C0 Ep No Use_L 0x200353E4 0x00401823CP増えるとMAX_C0 Cp Up To Max_L 0x20035500 0x240300C8戦闘後LVMAX_C0 Battle A Lv Max_L 0x200356AC 0x10000022获得経験値x倍表示には反映されませんが、増える时に効果が出ますLV差によって経験値が贳えなくなるので倍率を多目にして下さい_C0 Get EXP Twice_L 0x20035688 0x00068xxxxxx040 2倍080 4倍0C0 8倍100 16倍140 32倍180 64倍1C0 128倍200 256倍240 512倍280 1024倍2C0 2048倍300 4096倍340 8192倍380 16384倍ゝーバルゕーツ全て使用可能アゝーツを付け外しすると効果が出ます_C0 Orbal Arts All_L 0x201011F0 0x2403000A_L 0x20101204 0x2403000C_L 0x20101218 0x2403000C_L 0x2010122C 0x2403000A_L 0x20101240 0x2403000A_L 0x20101254 0x2403000C钓り自动化゛エも减りません_C0 Auto Fishing_L 0x2017C708 0x10000004_L 0x2017C63C 0x140003CF_L 0x2017D680 0x24060000移动速度変更通常_C0 Move Speed Nomal_L 0x2027217C 0x3DAE147B2倍_C0 Move Speed 2 Twice_L 0x2027217C 0x3E2E147B4倍_C0 Move Speed 4 Twice_L 0x2027217C 0x3EAE147Bアゝーツ鹰目の効果ミニマップに敌の位置が表示されます_C0 Enemy position_L 0x2012E7FC 0x00000000アゝーツ探知の効果ミニマップに宝箱の位置が表示されます_C0 Detection_L 0x2012E830 0x00000000一斉攻撃味方ゲーン开始时に○ボゲンを押していると一斉攻撃になります_C0 Team Rush_L 0x2006DC0C 0x50430005_L 0xD027E4C4 0x00002000_L 0x2006DC0C 0x50000005一撃死攻撃时に○ボゲンを押していると一撃死になります敌の攻撃にも効果があります即死耐性がある场合は効きません_C0 Attack Of Death_L 0x2008B970 0x5443FF41_L 0xD027E4C4 0x00002000駆动を付けていると咏唱时间无し駆动/双児珠のアゝーツが付いている゠ャラのみ効果があります_C0 No Driving Time_L 0x20080B48 0x24120000戦闘开始时必ず先制攻撃_C0 Preemptive strike_L 0x20060380 0x240900xx_L 0x20075568 0x240900xxxx01背後をとられた02先制攻撃04奇袭攻撃戦闘中ゕ゗テム入手率100％_C0 Item Get 100_L 0x2006BB74 0x24020000米拉跟耀晶片我就不发了四处都是来点难找的……料理全开_C0 COOK ALL_L 0x2045CCF8 0x1FFFFFFE_L 0x2045CCFC 0x1FFFFFFE_L 0x2045CD00 0x1FFFFFFE回路装备限制解除_C0 Quratz Limit Release_L 0x200FF2C0 0x1400000E小说和杂志全收集_C0 ITEM闇医者ィレン①-?_L 0x4045922C 0x000E0001_L 0x000102C6 0x00000001_C0 ITEMアロガベルゲ゗ムキ①-③、号外、④-⑧_L 0x40459208 0x00090001_L 0x000102BC 0x00000001耀晶片入手ｎ倍_L 0x20001FF0 0x0A21ACB9 _L 0x20001FF4 0x00063XXX _L 0x2006B6F0 0x0E2007FC XXX：040：2倍080：4倍0C0：8倍100：16倍任务报酬ｎ倍_L 0x20001fe0 0x00651821 _L 0x20001fe4 0x0a241422 _L 0x20001fe8 0x00031XXX _L 0x20105080 0x0a2007f8 XXX：840：2倍880：4倍8C0：8倍900：16倍_C0 DP_L 0x0045C06C 0x000000XX 戦闘手帳の戦歴総戦闘回数_L 0x1045DE28 0x0000xxxx 戦闘勝利回数_L 0x1045DE2C 0x0000xxxx 戦闘不能回数_L 0x1045DE2A 0x0000xxxx 戦闘退却回数_L 0x1045DE30 0x0000xxxx 敵撃破数_L 0x1045DE3C 0x0000xxxx Sブレ゗ア発動回数_L 0x1045DE34 0x0000xxxx エポートアラフト発動回数_L 0x1045DE36 0x0000xxxx奇襲?先制攻撃発生回数_L 0x1045DE38 0x0000xxxx敵先制攻撃発生回数_L 0x1045DE3A 0x0000xxxxリトラ゗回数_L 0x1045DE32 0x0000xxxx物品第一格（XXXX为物品代码详细请查询物品代码YY为数量建议进入数据表找到第一格物品后移动到最后空白处修改而不是直接添加锁定码）_L 0x20458B8C 0x00YYXXXX以下为物品码（括号内为10进码无视掉吧）物品回路结晶0001(1) 捜査手帳警察官の身分を証明する、警察紋が入った黒革の手帳。

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Berntson0249106Hollins University / Roanoke College Electronic Resources/hww/login.jhtmlusername: AWZ05password: SSNJ037636username: columbiahpassword: columbiahovidusername: hq9901paswword: mitty2美国Gale公司数据库/itweb/帐号：Leon85897密码：Leon85897OVID 帐号/alca456/alca456Ebsco login/Username: OYSTERH Password: 03824CNKI－医学库帐号帐号/密码: dx0210 /wzyy98(医学库)CNKI密码帐号：dx0043密码：jgy43医学类维普帐号/index.asp帐号/密码:jxlxl/abc123456中国学术会议论文全文万方数据库http://218.69.114.37/wf/hylw/pacccn.htmEzproxy of San Diego State Universityhttps:///cgi-bin/ezpiiib.cgi1、soudoc/BROWN/8087838922、KELLY JEAN /DUFFY/800273429/library/noproxy.htmMarit A. Berntson0249106Hollins University / Roanoke College Electronic Resources/hww/login.jhtmlusername: AWZ05password: SSNJ037636username: columbiahpassword: columbiah维普帐号/index.asp帐号/密码:jxlxl/abc123456台湾佛光大學.tw:3128hlchiang|hlchiang:2048/login906/smith453/Johnson456/Johnson231/Johnson:2048/loginpassword:13100944/login100505320001/SMITH中宏产业库帐号和密码/UserLogin.asp用户名：xzsfdx密码：xzsfbian中经网密码/index/index.asp用户名xnmd密码：xnmd维普全库http://210.32.205.31:8080/CNKI(期刊+博硕学位论文+会议论文+重要报纸全文)/用户名/密码：sybjlhdx/sybjlhdx复旦大学文献检索课程http://202.120.76.225/jiaoc/enter.htm用户guest；密码jiaoan2002《中文科技期刊数据库》（全文版）http://61.154.14.107:8080/index.asp帐号和密码：fytsg/3357199北京交通大学超星http://218.249.29.200/bookhtm/index.asp密码：jiazhuosheng用户：jiazhuoshengez/menu601951000011105California University of PA Louis L. 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2019年第33期曹中铭（知名独立财经撰稿人）：为了防范科创板的投资风险，监管部门允许科创板新股挂牌首日开展融券交易，将有利于抑制市场的非理性冲动，控制投资风险，并让科创板估值更加合理。

但战略投资者获配股份可出借进行融券交易，意味着出借股份已经提前进入了流通环节。

虽然并非战略投资者直接卖出股票，但融券卖出，实际上也是增加了市场的供给，与战投的直接卖出，并没有本质上的区别，只不过卖出方与受益人不同罢了。

因此，科创板战投获配股份要求锁定的规定，实际上已成一纸空文，需要采取措施进行完善。

/caozhongming 易宪容（前社科院金融研究所研究员）：对于国内不少城市来说，如果住房投机炒作者无法进入房地产市场，或他们进入房地产市场受到银行信贷上的限制（比如个人住房按揭贷款工资收入、个人资金流水等严格审查），那么这些城市的房地产市场将面临着重大调整。

其调整不仅在于房地产市场价格可能下降，更重要的是会让房地产市场价格上涨的预期逆转。

如果这种情况出现，房地产投机炒作者所面临风险就来了。

这就是严格遏制消费贷进入房地产的重要意义。

对此大家要密切关注。

//2377371197郭施亮（财经名博，职业投资者）：可以预期，对于未来的城市家庭，在房产投资的比例空间已经比较有限，但在金融资产投资比重上，却存在着很大的提升空间。

退一步思考，假如股票资产投资比重提升5%，那么将会为股票市场带来庞大的新增流动性补充，而对于尚处历史估值底部区域的中国股市，一旦形成了持续赚钱效应，估计各路资金也会蜂拥而至。

/gslbo金晶科技：拟定增6亿元投资光伏玻璃金晶科技(600586)公告称拟定增募资不超过6亿元，募资净额将全部用于马来西亚两条日融化量500吨玻璃生产线项目、废液资源化再利用30万吨/年氯化钙环保项目。

金晶科技表示，本次定增是公司现有玻璃制造业务及纯碱业务的拓展与延伸。

本次募投项目的实施紧紧围绕公司的主营业务，顺应公司发展战略。