Robustness and informativeness of systemic risk measures

Chapter1Power System Control:An OverviewThis introductory chapter provides a general description of power system control. Fundamental concepts/definitions of power system stability and existing controls are emphasized.The role of power system controls(using automatic processing and human operating)is to preserve system integrity and restore the normal operation subjected to a physical(small or large)disturbance[1].In other words,power sys-tem control means maintaining the desired performance and stabilizing of the sys-tem following a disturbance,such as a short circuit and loss of generation or load.From the viewpoint of control engineering,a power system is a highly non-linear and large-scale multi-input multi-output(MIMO)dynamical system with numerous variables,protection devices and control loops,with different dynamic responses and characteristics.The term power system control is used to define the application of control theory and technology,optimization methodologies and expert and intel-ligent systems to improve the performance and functions of power systems during normal and abnormal operations.Power system controls keep the power system ina secure state and protect it from dangerous phenomena[1,2].1.1A Brief Historical ReviewPower system stability and control wasfirst recognized as an important problem in the1920s[3,4].Until recently,most engineering efforts and interests have been concentrated on rotor angle(transient and steady state)stability.For this purpose, many powerful modelling and simulation programs,and various control and protec-tion schemes have been developed.A survey on the basics of power system controls, literature and past achievements is given in[5,6].Frequency stability problems,related control solutions and long-term dynamic simulation programs have been emphasized in the1970s and1980s follow-ing some major system events[7–10].Useful guidelines were developed by an IEEE working group for enhancing power plant response during major frequency disturbances[11].H.Bevrani,Robust Power System Frequency Control,Power Electronics1 and Power Systems,DOI10.1007/978-0-387-84878-51,c Springer Science+Business Media LLC200921Power System Control:An Overview Since the1990s,supplementary control of generator excitation systems,static V AR compensator(SVC)and high voltage direct current(HVDC)converters are increasingly being used to solve power system oscillation problems[5].There has also been a general interest in the application of power-electronics-based controllers known asflexible AC transmission system(FACTS)controllers for the damping of system oscillations[12].Following several power system collapses worldwide [13–15],in the1990s,voltage stability attracted more research interests.Powerful analytical tools and synthesis methodologies have been developed.Since the1980s,several integrated control design approaches have been de-veloped for power system oscillation damping and voltage regulation[16–19]. Recently,following the development of synchronized phasor measurement units (PMUs),communication channels and digital processing,wide-area power system stabilization and control have become areas of interest[20,21].Attempts to improve data exchange and coordination between the different existing control systems[22], as a wide-area control solution,are considered as an important control trend.In a modern power system,the generation,transmission and distribution of electric energy can only be met by the use of robust/optimal control methodolo-gies,infrastructure communication and information technology(IT)services in the designing of control units and supervisory control and data acquisition system (SCADA)centres.Some important issues for power system control solutions in a new environment are appropriate lines of defence[21],uncertainties consideration and more effective dynamic modelling[23],assessments/predictions and optimal allocations and processing of synchronized devices[24],appropriate visualizations of disturbance evaluations,proper consideration of distributed generation units[25] and robust control design for stabilizing power systems against danger phenom-ena[26].Considerable developments have recently been made on renewable energy sources(RESs)technologies.The increasing penetration of RESs has many tech-nical implications and raises important questions,as to whether the conventional power system control approaches to operate in the new environment are still ade-quate.Recently,there has been a strong interest in the area of RESs and their impacts on power systems dynamics and stability,and possible control solutions[27–31].1.2Instability PhenomenaThe most recent proposed definition of power system stability is[32]“the ability of an electric power system,for a given initial operating condition,to regain a state of operating equilibrium after being subjected to a physical disturbance,with most system variables bounded so that practically the entire system remains intact”.As the electric power industry has evolved over the last century,different forms of instability have emerged as being important during different periods.Similarly, depending on the developments in control theory,power system control technology and computational tools,different control syntheses/analyses have been developed.1.2Instability Phenomena3Fig.1.1Different phenomena that lead to power system instabilityPower system control can take different forms and is influenced by the instabilizing phenomena.Conceptually,definitions and classifications are well founded in[32]. As shown in Fig.1.1,important phenomena that lead to power system instability are rotor angle instability,voltage instability and frequency instability.Rotor angle instability is the inability of the power system to maintain synchro-nization after being subjected to a disturbance.In case of transient(large distur-bance)angle instability,a severe disturbance does not allow a generator to deliver its output electricity power into the network.Small signal(steady state)angle insta-bility is the inability of the power system to maintain synchronization under small disturbances.The considered disturbances must be small enough that the assump-tion of system dynamics being linear remains valid for analysis purposes[1,32–34].The rotor angle instability problem has been fairly well solved by power system stabilizers(PSSs),thyristor exciters,fast fault clearing and other stability controllers and protection actions such as generator tripping.Voltage instability is the inability of a power system to maintain steady accep-tance voltages at all system’s buses after being subjected to a disturbance from an assumed initial equilibrium point.A system enters a state of voltage instability when a disturbance changes the system’s condition to make a progressive fall or rise of voltages of some buses.Loss of load in an area,tripping of transmission lines and other protected equipments are possible results of voltage instability.Frequency instability is the inability of a power system to maintain system fre-quency within the specified operating limits.Generally,frequency instability is a result of a significant imbalance between load and generation,and it is associated with poor coordination of control and protection equipment,insufficient generation reserves and inadequacies in equipment responses[35,36].The size of disturbance,physical nature of the resulting instability,the dynamic structure and the time span are important factors to determine the instability form [1].The above instability classification is mainly based on dominant initiating phe-nomena.Each instability form does not always occur in its pure form.One may lead to the other,and the distinction may not be clear.41Power System Control:An OverviewFig.1.2Progressive power system response to a serious disturbanceAs shown in Fig.1.2,a fault on a critical element(serious disturbance)may influence much of the control loops and the equipments through different channels, andfinally,may affect the power system performance and even stability[1].Therefore,during frequency excursions following a major disturbance,voltage magnitudes and powerflow may change significantly,especially for islanding condi-tions with under-frequency load shedding that unloads the system[3].In real power systems,there is clearly some overlap between the different forms of instability, since as systems fail,more than one form of instability may ultimately emerge[5]. However,distinguishing between different instability forms is important in under-standing the underlying causes of the problem in order to develop appropriate design and operating procedures.1.3Controls Configuration5Fig.1.3General structure for power system controls1.3Controls ConfigurationPower system controls are of many types including[1,21,37]generation excitation controls,prime mover controls,generator/load tripping,fast fault clearing,high-speed re-closing,dynamic braking,reactive power compensation,load–frequency control,current injection,fast phase angle control and HVDC special controls.From the point of view of operations,all controls can be classified into continuous and dis-continuous controls.A general structure for a power system with the main required control loops in a closed-loop scheme is shown in Fig.1.3.Most of continuous control loops such as prime mover and excitation controls operate directly on generator units and are located at power plants.The continuous controls include generator excitation controls(PSS and automatic voltage regulator (A VR)),prime mover controls,reactive power controls and HVDC controls.All these controls are usually linear,continuously active and use local measurements.In a power plant,the governor voltage and reactive power output are regulated by excitation control,while energy supply system parameters(temperatures,flows and pressures)and speed regulation are performed by prime mover controls.Au-tomatic generation control balances the total generation and load(plus losses)to reach the nominal system frequency(commonly50or60Hz)and scheduled power interchange with neighbouring systems.61Power System Control:An Overview The discontinuous controls generally stabilize the system after severe distur-bances and are usually applicable for highly stressed operating conditions.They perform actions such as generator/load tripping,capacitor/reactor switching and other protection plans.These power system controls may be local at power plants and substations,or over a wide area.These kinds of controls usually ensure a post-disturbance equilibrium with sufficient region of attraction[21].Discontinuous controls evolve discrete supplementary controls[38],special stability controls[39] and emergency control/protection schemes[40–42].Furthermore,there are many controls and protections systems on transmission and distribution sides,such as switching capacitor/reactors,tap-changing/phase shifting transformers,HVDC controls,synchronous condensers and static V AR compensators.Despite numerous existing nested control loops that control differ-ent quantities in the system,working in a secure attraction region with a desired performance is the objective of an overall power system control strategy.It means generating and delivering power in an interconnected system is as economical and reliable manner as possible while maintaining the frequency and the voltage within permissible limits.1.4Controls at Different Operating StatesPower system controls attempt to return the system in off-normal operating states to a normal state.Classifying the power system operating states to normal,alert, emergency,in extremis and restorative is conceptually useful to designing appropri-ate control systems[1,43].In the normal state,all system variables(such as voltage and frequency)are within the normal range.In the alert state,all system variables are still within the acceptable range.However,the system may be ready to move into the emergency state following disturbance.In the emergency state,some sys-tem variables are outside of the acceptable range and the system is ready to fall into the in extremis state.Partial or system wide blackout could occur in the in extremis state.Finally,energizing of the system or its parts and reconnecting/resynchronizing of system parts occurs during the restorative state.Based on the above classification,power system controls can be divided into the main two different categories(1)normal/preventive controls,which are applied in the normal and alert states to stay in or return into normal condition and(2) emergency controls,which are applied in emergency or in extremis state to stop the further progress of the failure and return the system to a normal or alert state.Automatic frequency and voltage controls are part of the normal and the preven-tive controls,while some of the other control schemes such as under-frequency load shedding,under-voltage load shedding and special system protection plans can be considered under emergency controls.Control command signals for normal/preventive controls usually include active power generation set points,flow controlling reference points(FACTS),voltage set point of generators,SVC,reactor/capacitor switching,etc.Emergency control mea-1.5Dynamics and Control Timescales7 sures are some control commands such as tripping of generators,shedding of load blocks,opening of interconnection to neighbouring systems and blocking of trans-formers’tap changer.1.5Dynamics and Control TimescalesFor the purpose of dynamic analysis,it is noteworthy that the timescale of interest for rotor angle stability in transient(large disturbance)stability studies is usually limited to3–10s,and in steady state(small signal)studies is of the order of10–20s. The rotor angle stability is known as a short-term stability problem,while a voltage stability problem can be either a short-term or a long-term stability problem.The time frame of interest for voltage stability problems may vary from a few seconds to several minutes.Although power system frequency stability is impacted by fast as well as slow dynamics,the time frame will range from a few seconds to several minutes[5].Therefore,it is known as a long-term stability problem.For the purpose of power system control designs,generally the control loops at lower system levels(locally in a generator)are characterized by smaller time constants than the control loops active at a higher system level.For example,the A VR,which regulates the voltage of the generator terminals to the reference value, responds typically in a timescale of a second or less.The secondary voltage control, which determines the reference values of the voltage controlling devices,among which the generators,operates in a timescale of several seconds or minutes.That means these two control loops are virtually de-coupled.On the other hand,since the excitation system time constant is much smaller than the prime mover time constant and its transient decay is much faster and does not affect the LFC system dynamic,the cross-coupling between the LFC loop and the A VR loop is negligible.This is also generally true for the other control loops.As a result,for the purpose of system protection,turbine control,frequency and volt-age control,a number of de-coupled control loops are operating in a power system operating in different timescales.The overall control system is complex.However,due to the de-coupling,in most cases it is possible to study each control loop individually.Depending on the loop nature,the required model,important variables,uncertainties and objectives,differ-ent control strategies may be applicable.A schematic diagram showing the impor-tant different timescales for the power system controls and the dynamics is shown in Fig.1.4.81Power System Control:AnOverviewTimeFig.1.4Schematic diagram of different timescales of power system dynamics and controls1.6Power System Frequency Control1.6.1Load–Frequency ControlA severe system stress resulting in an imbalance between generation and load se-riously degrades the power system performance (and even stability),which cannot be described in conventional transient stability and voltage stability studies.This type of usually slow phenomena must be considered in relation with power system frequency control issue.Power system frequency regulation entitled load–frequency control (LFC),as a major function of automatic generation control (AGC),has been one of the impor-tant control problems in electric power system design and operation.Off-normal fre-quency can directly impact on power system operation and system reliability [1].A large frequency deviation can damage equipment,degrade load performance,cause the transmission lines to be overloaded and can interfere with system protection schemes,ultimately leading to an unstable condition for the power system [44].Maintaining frequency and power interchanges with neighbouring control areas at the scheduled values are the two main primary objectives of a power system LFC.These objectives are met by measuring a control error signal,called the area control error (ACE),which represents the real power imbalance between generation and load,and is a linear combination of net interchange and frequency deviations.After filtering,the ACE is used to perform an input control signal for a usually proportional integral (PI)controller.Depending on the control area characteristics,the resulting output control signal is conditioned by limiters,delays and gain con-stants.This control signal is then distributed among the LFC participant generator units in accordance with their participation factors to provide appropriate control1.6Power System Frequency Control9 commands for set points of specified plants.The probable accumulated errors in frequency and net interchange due to used integral control have to be corrected by tuning the controller settings according to procedures agreed upon by the whole interconnection.Tuning of the dynamic controller is an important factor to obtain optimal LFC performance.Proper tuning of controller parameters is needed to ob-tain good control without excessive movement of units[45].The frequency control is becoming more significant today due to the increasing size,the changing structure and the complexity of interconnected power systems.In-creasing economic pressures for power system efficiency and reliability have led to a requirement for maintaining system frequency and tie-lineflows closer to scheduled values as much as possible.Therefore,in a modern power system,LFC plays a fun-damental role,as an ancillary service,in supporting power exchanges and providing better conditions for the electricity trading.1.6.2Why Robust Power System Frequency Control?As mentioned,the power systems are being operated under increasingly stressed conditions due to the prevailing trend to make the most of existing facilities.In-creased competition,open transmission access and construction and environmen-tal constraints are shaping the operation of electric power systems in new ways that present greater challenges for secure system operation[5].Frequently changing power transfer patterns causes new stability problems.Different ownership of gen-eration,transmission and distribution makes power system control more difficult.A main complication brought on by the separation of ownership of generation and transmission is lack of coordination in long-term system expansion planning.This results in the much-reduced predictability(increased uncertainty)of the utilization of transmission assets and correct allocation of controls.The increasing number of major power grid blackouts that have been experi-enced in recent years[46–49],for example,the Brazil blackout of March1999, Iran blackout of Spring2001and Spring2002,Northeast USA-Canada blackout of August2003,Southern Sweden and Eastern Denmark blackout of September2003, the Italian blackout of September2003and the Russia blackout of May2005shows that today’s power system operations require more careful consideration of all forms of system instability and control problems.The network blackouts show that to im-prove the overall power system control response,it is important to provide more effective and robust control strategies in order to achieve a new trade-off between system security,efficiency and dynamic robustness.Significant interconnection frequency deviations can cause under-/over-frequency relaying and disconnect some loads and generations.Under unfavourable conditions,this may result in a cascading failure and system collapse[48].In the last two decades,many studies have focused on damping control and voltage stability and related issues.However,there has been much less work on power sys-101Power System Control:An Overview tem frequency control analysis and synthesis,while violation of frequency control requirements was known as a main reason for numerous power grid blackouts[46].Most published research in this area neglects new uncertainties[23]and practical constraints[50],and furthermore,suggest complex control structures with impracti-cal frameworks,which may have some difficulties while implementing in real-time applications[51,52].Operating the power system in the new environment will certainly be more com-plex than in the past,due to the considerable degree of interconnection,and due to the presence of technical and economic constraints(deriving by the open market)to be considered,together with the traditional requirements of system reliability and security.In addition to various market policies,the sitting of numerous generators units and RESs in distribution areas and the growing number of independent players is likely to have an impact on the operation and control of the power system,which is already designed to operate with large,central generating facilities.At present,the power system utilities participate in the LFC task with simple and classical tuned controllers.Most of the parameters adjustments are usually made in thefield using heuristic procedures.Existing LFC systems’parameters are usually tuned based on experiences,classical methods and trial and error approaches,and they are incapable of providing good dynamical performance over a wide range of operating conditions and various load scenarios.Therefore,the novel modelling and control approaches are strongly required,to obtain a new trade-off between market outcome(efficiency)and market dynamics(robustness).In response to the above challenge,recent development in robust linear control theory has provided powerful tools such asμsynthesis/analysis,optimal H2,H∞and mixed H2/H∞techniques for power system load–frequency control design.The resulting robust controls will play an important role in system security and reliable operation.Robust power system frequency control means the control must provide adequate minimization on a system’s frequency and tie-line power deviation,and expend the security margin to cover all operating conditions and possible system configurations.The main goal of robust LFC designs in the present monograph is to develop new load–frequency control synthesis methodologies for multi-area power systems based on the fundamental LFC concepts,together with the powerful robust control theory and tools.The LFC objectives are satisfied,i.e.,frequency regulation and maintaining the tie-line power interchanges to specified values in the presence of physical constraints and model uncertainties.The proposed control techniques meet all or a combination of the following specifications:•Robustness.Guarantee robust stability and robust performance for a wide range of operating conditions.For this purpose,robust control techniques are to be used in synthesis and analysis procedures.•Decentralized property.In a new power system environment,centralized design is difficult to numerically/practically implement for a large-scale multi-area fre-quency control synthesis.Because of the practical advantages it provides,the decentralized LFC design is emphasized in the proposed design procedures for real-world power system applications.References11•Simplicity of structure.In order to meet the practical merits,the robust decentral-ized LFC design problem is reduced to a synthesis of low-order or a proportional integral control problem,which is used usually in a real LFC system.•Formulation of uncertainties and constraints.The LFC synthesis procedure must beflexible enough to include generation rate constraints,time delays and uncer-tainties in the power system model and control synthesis procedure.The pro-posed approaches advocate the use of a physical understanding of the system for robust LFC synthesis.The presented techniques and algorithms in this monograph address systematic,fast andflexible design methodologies for robust power system frequency regulation. The developed control strategies attempt to invoke the well-known strict conditions and bridge the gap between the power of robust/optimal control theory and practical power system frequency control synthesis.1.7SummaryThis chapter provides an introduction on the general aspects of power system con-trols with a brief historical review.Fundamental concepts and definitions of stability and existing controls are emphasized.The timescales and characteristics of various power system controls are described,and the importance of frequency stability and control and the main goal of robust frequency control designs in the next chapters are explained.References1.P.Kundur,Power System Stability and Control.New York,NY:McGraw-Hill,1994.2.P.W.Sauer and M.A.Pai,Power System Dynamics and Stability.Champaign,IL:Stipes,2007.3. 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术语对应的学科名词解释在我们的日常生活和学习中，我们经常会遇到各种各样的术语。

这些术语代表着特定学科中的概念或理论，是学术交流和专业研究的重要工具。

本文将通过一些常见的术语，为读者解释这些术语所对应的学科名词。

1. 熵（Entropy）熵是热力学中的一个重要概念，它代表了一个系统的无序程度。

在物理学中，熵被用来描述能量分布的混乱程度。

而在信息论中，熵则表示信息的不确定性或信息的平均信息量。

因此，熵这个术语既可在物理学中使用，也可在信息科学中使用。

2. 主观性（Subjectivity）主观性是哲学和心理学中常见的概念。

它指的是个体的主观经验和观点，与客观事实相对立。

主观性认为，每个人的认知和感受都是独特而个体化的，因此无法被客观的标准或规则所完全捕捉和测量。

主观性在哲学和心理学领域内有广泛的应用和研究。

3. 多样性（Diversity）多样性是生物学和社会科学领域中的一个重要概念。

在生物学中，多样性描述了一个生态系统或物种群体中的种类和数量多样性。

而在社会科学中，多样性则指代不同文化、民族、性别、背景等方面的差异和多元化。

多样性这个术语在不同学科中的具体定义和应用有所不同，但都强调了不同元素的多样性和丰富性。

4. 可持续发展（Sustainable Development）可持续发展是环境学和经济学中的一个重要概念。

它强调了人类社会的发展需求与生态环境的保护之间的平衡。

可持续发展要求我们在满足当前需求的同时，不损害未来世代的需求和资源。

该概念涉及到经济、社会和环境等多个领域，因此它的学科背景非常广泛。

5. 数据挖掘（Data Mining）数据挖掘是计算机科学和统计学中的一个术语。

它指的是通过从大量数据中提取模式、关联和知识等信息来发现隐藏在数据中的有价值的信息。

数据挖掘可以用于商业、科学和决策等领域，帮助我们了解数据背后的规律和趋势，从而做出更准确的预测和决策。

6. 经典条件作用（Classical Conditioning）经典条件作用是心理学中的一个重要概念，也被称为条件反射。

系统性硬化症遗传学研究进展*濮伟霖**郭士成 王久存***（复旦大学生命科学学院 上海 200438）摘 要系统性硬化症又称为硬皮病，是一种复杂的自身免疫性疾病，目前对于该疾病的病因了解尚不清晰。

多项研究显示，遗传因素在硬皮病的发病过程中起重要作用。

因此对于硬皮病遗传因素的探索有助于我们发现硬皮病发病的相关通路和分子机制。

本文介绍并综述了硬皮病截止目前的遗传学相关研究的前沿进展。

关键词 硬皮病 遗传 全基因组关联研究（GWAS）中图分类号：R593.25文献标识码：A 文章编号：1006-1533(2017)S1-0030-07The review of the advancement of SSc genetic studies*PU Weilin**, GUO Shicheng, WANG Jiucun***(Department of Life Sciences, Fudan University, Shanghai 200438, China)ABSTRACT Systemic sclerosis is a kind of complex autoimmune diseases with different manifestations. The pathogenesis of SSc is still unknown. Multiple studies have revealed the important role of genetic factors in the pathogenesis of SSc. As a result, the exploration of the genetic factors of SSc will be of great benefit to the discovery of the associated pathways and mechanisms of SSc pathogenesis. This paper summarizes the advances and leading edge of the SSc genetic studies till now.KEY WORDS Scleroderma; Genetics; genome-wide association study (GWAS)系统性硬化症（systemic sclerosis，SSc，又称硬皮病）是不明病因的复杂性自身免疫性疾病。

The behaviourist approach to psychology心理学的行为主义研究方法J.B.Watson：‘Give me a dozen healthy infants……and my own specified world to bring them up in and I’ll guarantee to take any one at random and train him to become any type of specialist I might select-doctor, lawyer…and yes, even beggarman and thief. ’华生：“给我一打健全的婴儿和我可用以培育他们的特殊世界，我就可以保证随机选出任何一个，我都可以把他训练成为我所选定的任何类型的特殊人物如医生、律师……或甚至乞丐、小偷。

”Origins and history1.起源与历史The behaviourist approach was influenced by the philosophy of empiricism （which argues that knowledge comes from the environment via the senses,since humans are like a ‘tabula rasa’,or blank slate,at birth）and the physical sciences（which emphasise scientific and objective methods of investigation）.行为主义的研究方法受到经验主义哲学和物理科学的影响。

前者主张知识来自于感官接受的环境信息，认为人类在出生时像一块白板或空白黑板，后者强调研究方法的客观性和科学性。

Watson started the behaviourist movement in 1913 when he wrote an article entitled ‘Psychology as the behaviourist views it’,which set out its main principles and assumptions.Drawing on earlier work by Pavlov,behaviourists such as Watson,Thorndike and Skinner proceeded to develop theories of learning（such as classical and operant conditioning）that they attempted to use to explain virtually all behaviour.华生在1913年发表的论文《一个行为主义研究者眼中的心理学》标志着行为主义运动的开始，文中阐述了行为主义的主要原理和假设。

BLACK-BOX TESTING IN THE INTRODUCTORYPROGRAMMING CLASSTamara BabaianComputer Information Systems DepartmentBentley Collegetbabaian@Wendy LucasComputer Information Systems DepartmentBentley CollegeABSTRACTIntroductory programming courses are often a challenge to both the students taking them and the instructors teaching them. The scope and complexity of topics required for learning how to program can distract from the importance of learning how to test. Even the textbooks on introductory programming rarely address the topic of testing. Yet, anyone who will be involved in the system development process should understand the critical need for testing and know how to design test cases that identify bugs and verify the correct functionality of applications. This paper describes a testing exercise that has been integrated into an introductory programming course as part of an overall effort to focus attention on effective software testing techniques.1 A comparison of the performance on a common programming assignment of students who had participated in the testing exercise to that of students who had not demonstrates the value of following such an approach.Keywords: testing, debugging, black-box method, introductory programming1 A shorter version of this paper, entitled Developing Testing Skills in an Introductory Programming Class, was presented at the 2005 International Conference on Informatics Education Research.I. INTRODUCTIONFor several years now, object-oriented languages have predominated within introductory programming courses in the Computer Science and Information Systems curricula. Programming in general does not come naturally to all students, and object-oriented concepts can be especially daunting. Students struggling to write their first programs quickly succumb to the mantra that it compiles and runs - therefore it is correct. The importance of testing is lost on these novices in their rush to submit functioning code. Integrated Development Environments (IDEs), which are invaluable in many ways, may have the unintended consequence of supporting this attitude; a simple click of a button compiles and runs code with astonishing speed (particularly to those of us who remember punch cards). It is so easy to recompile that one can fall into the trap of making changes and rerunning the program without analyzing errors and thinking through the code to address them. While syntactical errors are caught and promptly drawn to the programmer’s attention by the IDE, trapping logical errors requires careful design of test cases and thorough analysis of outputs. The necessity for these skills is often lost on the novice. A far greater risk is that the novice will become a developer who never learned the value of thorough testing. Attesting to the validity of this concern is the estimated $59.5 billion that software bugs are costing the U.S. each year [Tassey, 2002]; early detection of these errors could greatly reduce these costs [Baziuk, 1995]. As noted by Shepard et al. [2001], although testing typically takes at least 50% of the resources for software development projects, the level of resources devoted to testing in the software curriculum is very low. This is largely due to a perceived lack of available time within a semester for covering all of the required topics, let alone making room for one that may not be viewed as core to the curriculum. The motivation for the work presented here arises from the need for teaching solid testing skills right from the start. Students must learn that testing should be givenat least as much priority as providing the required functionality if they are to become developers of high-quality software.This paper describes a testing exercise that has been used successfully within an introductory programming course taught using the Java language at Bentley College. This course is part of the curriculum within the Computer Information Systems (CIS) Department, and is required for CIS majors but open to all interested students. The contents of this course are in keeping with the IS2002 Model Curriculum [Gorgone et al., 2002], which recommends the teaching of object-oriented programming and recognizes the need for testing as a required part of the coursework. While faculty readily acknowledge this need, developing a similar appreciation for testing in our students has proven far more difficult. The testing exercise described here has been found to be an effective step in this process.The next section of this paper reviews research that is relevant to the work presented here. We then provide an overview of the course and a detailed description of the testing exercise. In order to assess the impact of this exercise, we present an analysis of student performance on a related coding assignment. This paper concludes with a discussion of directions for future work.II. LITERATURE REVIEWThe low priority given to testing within the software curriculum and the need for that to change has been acknowledged in the literature. Shepard, Lamb, and Kelly [2001], who strongly argue for more focus on testing, note that Verification and Validation (V&V) techniques are hardly taught, even within software engineering curriculum. They propose having several courses on testing, software quality, and other issues associated with V&V available for undergraduates. Christensen [2003] agrees that testing should not be treated as an isolated topic, but rather should be integrated throughout the curriculum as“core knowledge.” The goal must be on producing reliable software, and he proposes that systematic testing is a good way to achieve this.Much of the relevant literature describes the use of Extreme Programming (XP) [Beck, 2000] techniques in programming courses for teaching testing. XP advocates a test-first approach in which unit tests are created prior to writing the code. For students, benefits of this approach include developing a better understanding of the project’s requirements and learning how to test one module or component at a time.XP plays a key role in the teaching guidelines proposed by Christensen [2003], which include: (1) fixing the requirements of software engineering exercises on high quality, (2) making quality measurable by teaching systematic testing and having students follow the test-driven approach of XP, and (3) formulating exercises as a progression, so that each builds on the solution to the prior exercise. These guidelines have been applied by Christensen in an advanced programming class.Allen, Cartwright, and Reis [2003] describe an approach for teaching production programming based on the XP methodology. The authors note that, “It is impossible to overstate the importance of comprehensive, rigorous unit testing since it provides the safeguard that allows students to modify the code without breaking it” [Allen et al., 2003, p. 91]. To familiarize students with the test-first programming approach, they are given a simple, standalone practice assignment at the beginning of the course for which most of their grade is based on the quality of the unit tests they write. Another warm-up assignment involves writing units tests for a program written by the course’s instructors. These exercises were found to be effective in teaching students how to write suitable tests for subsequent assignments.The approaches to teaching testing described above are very similar to the approach described in this paper. What differentiates our testing exercise andfollow-up coding assignment is that they are intended for beginning programmers, not the more experienced ones who would be found in advanced or production-level programming courses. This presents the challenge of teaching students who are only beginning to grasp the concept of programming about the importance of testing and the complexities associated with developing effective test cases.Edwards [2004] does address the issues of teaching testing in an introductory CS course and recommends a shift from trial-and-error testing techniques to reflection in action [Schön, 1983], which is based on hypothesis-forming and experimental validation. He advocates the Test Driven Development (TDD) method [Edwards, 2003], which requires, from the very first assignment, that students also submit test cases they have composed for verifying the correctness of their code. Their performance is assessed on the basis of “how well they have demonstrated the correctness of their program through testing” [Edwards, 2004, p. 27]. Edwards [2004] focuses on tools that support students in writing and testing code, including JUnit (/), DrJava [Allen et al., 2002], and BlueJ [Kölling, 2005], and on an automated testing prototype tool called Web-CAT (Web-based Center for Automated Testing) for providing feedback to students. Patterson, Kölling, and Rosenberg [2003] also describe an approach to teaching unit testing to beginning students that relies on the integration of JUnit into BlueJ. While Snyder [2004] describes an example that introduces testing to beginning programmers, his work is built around the use of an automated system for conditional compilation.What differentiates these works from our own is our explicit focus on the testing exercise itself, rather than on the different types of tools that provide assistance with testing, as a means for supporting the teaching of testing to novices. Our testing assignment requires a thorough analysis by students of the inner workings of a program for which they do not have access to the code. Theassignment’s components must therefore be carefully designed for use by beginning programmers.III. COURSE BACKGROUNDIn this section we present an overview of the Programming Fundamentals course and describe how instruction in software testing is positioned within its curriculum. This is the first programming course within the CIS Major at Bentley College, and it is taught using the Java programming language. While it is required for majors, it also attracts non-majors, with students also differing in terms of backgrounds in programming and class levels. To accommodate the majority of students enrolled in this course and prepare them for subsequent classes in software development, it is targeted towards those students who do not have any prior programming experience. The goal of this course is for students to develop basic programming and problem-solving skills. This is accomplished through lectures, in-class laboratory sessions for writing and testing code, and assignments that are completed outside of the classroom.Approximately two-thirds of the material covered in this course focuses on basic data types, control structures, and arrays. The remainder of the semester is spent introducing object-oriented programming concepts, including classes and objects, and instance versus static variables and methods. All of these concepts are reinforced through frequent programming assignments, with an assignment due every one to two weeks. Students are expected to complete all assignments on their own, without collaborating with others in the class, in accordance with our academic honesty policy. There are no group assignments in this course, as we feel that, at the introductory level, individual effort is required to absorb abstract programming concepts. Laboratory assistants and instructors are always on-hand to answer any questions with assignments and help direct student efforts without revealing solutions.Concepts related to the system lifecycle are sprinkled throughout the course to keep the students aware of the big picture and to help explain and motivate effective development practices associated with object-oriented languages. Strongly emphasized are testing and debugging techniques, the development of sound programming logic, and the writing of well-structured code. The decision to devote class time specifically to teaching program verification as part of this course arose from a curriculum revision process. Several of the faculty who teach development courses acknowledged that insufficient training in testing methodologies during the introductory programming classes was adversely impacting the students’ attitudes toward program verification in later courses. By addressing testing early and often in the sequence of courses within our major, we could help students develop proper testing techniques while stressing the important role of program validation within the system development process.As part of this effort, during the introductory lectures we stress the fact that the longest and most expensive part of the software lifecycle is spent in maintenance. We point out that maintenance expenses depend on the clarity of the code and its documentation, as well as on the robustness of the testing performed during the software development process. The formal introduction to testing and verification of software is given in the third week of the course, after most of the basic programming concepts have been covered and students are capable of composing a program with more than one possible outcome. Such an early introduction is necessary to facilitate the early application of testing techniques by students. This also serves to reinforce the importance of testing and good testing practices, which students will apply throughout the rest of the semester in their programming assignments. In addition, opportunities to develop test cases arise during completion of in-class programming exercises. These present students with the opportunity to learn from both the instructor and each other about the process of developing and implementing test cases.IV. TESTING EXERCISEIn this section, we provide a detailed description of the testing exercise that has been included in the Programming Fundamentals course. To set the stage for the testing exercise, the black-box (specification-based) method of testing was introduced in a lecture given during the third week of the course. This lecture was then followed by the testing assignment, in which the students were asked to perform black-box testing of a completed program. They were provided with a requirements specification for the program and with a compiled Java application, created by the instructor, which implemented those requirements with varying degrees of completeness and correctness. As part of their task, students would need to identify the ways in which the program failed to meet the specification. In the following sections, we describe the set of requirements for the program, the compiled code to be tested, the student deliverables and evaluation guidelines, and the instructor’s evaluation process.PROBLEM REQUIREMENTS SPECIFICATIONThe application described in the requirements specification for the testing assignment is for automating the billing process for an Internet café (see Figure 1). The specified billing rules resemble those that are typically found in contemporary commerce applications and are based on multiple dimensions, including: the time when the service was provided, the length of that service, the charges associated with the service, and whether or not the customer holds a membership in the Café-Club.In selecting the application domain for this assignment, we wanted one that would reinforce the importance of testing. An Internet café is something with which students are familiar, most likely in the capacity of a customer who would want to be sure that the café was correctly billing for its services. Students could also conceivably be owners of such an enterprise, who would be equally if notmore concerned with the correctness of the billing process. This domain should therefore contribute to the students’ motivation to verify the billing functionality.A new Internet café operates between the hours of 8 a.m. and 11 p.m. The regular billing rate for Internet usage from its computers is 25 cents per minute. In addition, the following rules apply:1. Regular (non-member) customers are always billed at the regular rate.2. Café-Club members only receive a special discount during the discount period between 8a.m. and 2 p.m.: the first two hours within that period are billed at the rate of 10 cents perminute; all time past the first two hours (but within the discount period) is billed at the rate of 20 cents per minute. Any time outside of the discount period is billed at the regular rate. 3. If the total cost exceeds $50, it is discounted by 10%.Note that rule 2 above applies to Café-Club members only and rule 3 applies to all customers. The program should help automate customer billing for the Internet café. The program should work exactly as follows.The user should be prompted to enter:1. The letter-code designating the type of the customer: 'r' or 'R' for a regular customer, 'm'or 'M' for a club member.2. The starting hour.3. The starting minute.4. The ending hour.5. The ending minute of the customer's Internet session.The starting and ending hours are to be entered based on a 24 hour clock. Your program must then output the cost in dollars of the service accordin g to the above billing rules.Figure 1. Billing Program RequirementsIt was also important to provide an application that was understandable without being trivial. The logic of the billing rules is straightforward; at the same time, there is a rich variety of situations requiring different computational processes. Several categories of test cases as well as a number of different boundary conditions are necessitated and require thorough testing to verify the correctness of the application.PROGRAM TO BE TESTEDEvery student was e-mailed a compiled Java application implementing the billing program requirements presented in Figure 1. In order to maximize independent discovery and minimize the potential for students to discuss andcopy each other’s solutions, two different billing programs were implemented. Students were informed that more than one program was being distributed, but since the names of both programs and their compiled file sizes were identical and they did not have access to the source code, they could not readily tell who else had been sent the same version.Both versions contained four logical errors that were deliberately and carefully entered into the code by the instructor. While the errors in each version were different, the scope of the input data with which students would need to test in order to identify the incorrect operations was consistent. Hence, the likelihood of finding the problems with the implementations was comparable for the two versions.ASSIGNMENT DELIVERABLES AND EVALUATION GUIDELINES There are two parts to the deliverable that students were required to submit for this assignment (see Appendix I for the complete description). The purpose of the first part is to document the set of test cases they designed and the outcomes of each of the individual tests they ran using those test cases. Test case descriptions must include a complete specification of program inputs, the correct output value (i.e., given those inputs, the cost in dollars of the service based on the business rules shown in Figure 1), and the actual output value produced by the program. The objective of the test must also be described. For example, an objective might be to: “Test regular customer outside of the discount period.” The aim of this requirement is to help the students organize their testing process and learn to identify and experiment with distinct categories of input data.Students were encouraged to design test cases for different computational scenarios and boundary conditions. While there were no explicit requirements on the number of test cases, students were told that they should only include cases with valid application data (e.g. hour values between 0 and 23, inclusive). Thiswas done to limit the scope of the problem to a manageable size for beginning programmers.The second part of the assignment is to summarize the errors identified during testing in the form of hypotheses regarding the unmet requirements of the program. An example of a hypothesis might be: “The 10% discount is not applied within the discount period.” In order for a student to form such a hypothesis, which precisely identifies the error and the circumstances in which it occurs, observations from multiple test cases must be combined. For this particular example, one must combine the results of testing for the correct application of the 10% discount rule during different periods of service for each of the customer types. Thus, students must use their analytical skills to generalize the results of individual tests to a higher level of abstraction. In order to direct the students in this analytical process, the assignment explicitly suggests that they form additional test cases to verify or refine their initial hypotheses.INSTRUCTOR’S EVALUATION OF THE ASSIGNMENTIn evaluating the first part of this assignment, student submissions were checked against a list of twenty-five categories of test cases derived by the instructor. For the assignment’s second part, the summary of findings was checked for consistency with each student’s test case results. Appendix II shows the point value assigned to each graded component of the assignment, with a maximum possible score of 10 points. The first 5.5 points were awarded based on the degree of coverage of the students’ test sets with respect to the instructor’s categorizations. The next 2 points were for the number of actual problems with the code that were correctly identified (Diagnosed problems/summary of findings). The final 2.5 points were for the completeness of the descriptions provided for each test case (Presentation). This last component refers to the format rather than the content of the tests. For example, using the interaction shown in Appendix I, the student should show the starting hour of 12and the starting minute of 0 as two separate values rather than as one value of 12:00.The majority of students precisely identified two of the four program errors. Approximately 68% of submissions received scores of 8 and above out of a possible 10, 20% scored between 6 and 8, and 12% scored below 6. The value of this assignment cannot, however, be discerned solely on the basis of the students’ performance on it; rather, it is how it influences performance on future programming assignments that is most important, as discussed next.V. ASSESSING THE IMPACT OF TRAINING IN TESTINGIn this section, we present an assessment of the results of using the previously described approach to teaching students how to test by evaluating the performance of two groups of students on a common programming assignment. Students in Group 1 were enrolled in this course in a prior semester and did not receive any class time or homework training in testing methodology. They also did not complete the testing exercise. Group 2 students were enrolled in this course in the following semester; they were given a lecture on the black-box method and completed the testing exercise (but had not yet received the instructor’s evaluations of that exercise) prior to being given the programming assignment described below. These differences in testing preparation were the only distinguishing variation between the two groups; there were no significant differences between the number of students in each group or their composition in terms of their majors and prior exposure to programming. All the students were beginning programmers enrolled for the first time in a programming course at Bentley College, and most were in either their sophomore or junior year. Attendance by students in both groups was typically 85% or more for all class sessions.The assignment given to the students was to create a program for the billing requirements specification presented in Figure 1. Both groups were given the programming assignment at approximately the same point in the course. The students’ submissions were tested against the same suite of sixteen test cases. Table 1 summarizes the results of the comparison between the two groups of students on the common programming assignment.Table 1. Students’ Performance on the Billing Requirements ProgramGroup 1: Without testing assignmentGroup 2: With testing assignmentTotal number of students enrolled 39 40Total number of submissions 25 35Percentage of students who submitted 64% 87%Median number of failed tests 5 5Percentage of submitted programs with0 errors detected8% 20% The above comparison yields interesting results. The submission rate, i.e.,the percentage of enrolled students who submitted a program that compiled and ran, is far higher for the Group 2 students, who had received instruction in testing and completed the testing exercise. Based on a two-tailed t-test comparison, the means of the number of submissions are significantly different, with p = 0.015 using the standard 0.05 significance level. This suggests that problem analysis inthe form of creating test cases brings students closer to an understanding of the algorithm being tested. Based on this increased level of understanding, studentsin Group 2 had the confidence to complete an assignment that was perceived by many in Group 1 as being too difficult.The median number of failed test cases is the same for both groups and, while the percentage of “error-free” submissions (those that passed all 16 tests)is 2.5 times higher for Group 2, the means are not significantly different (usingthe two-tailed t-test, p = 0.206). A likely explanation is that only the “best” students in Group 1 were able to complete the programming assignment, so theirperformance was similar to that of those in Group 2, in which a far greater percentage of students were able to complete the assignment.Throughout the semester, it was also observed by the instructor that the explicit lecture on testing coupled with the testing exercise had served to increase the Group 2 students’ awareness of the variety of usage scenarios that could be derived from a program specification. Students were more likely to consider different input categories and suggest test cases capturing important boundary conditions based on the specification. The instructor felt that the introduction of black-box testing to the curriculum had an overall positive impact on the students’ ability to produce robust applications.VI. DISCUSSIONWhile introducing the concept of testing and having students create test cases are not uncommon activities throughout Computer Science and Information Systems curricula, the approach described here has several unique characteristics and advantages. First of all, the testing exercise requires that students develop a set of test cases for an instructor-created, compiled program, rather than for code they wrote themselves. This approach clearly separates the testing of the code from its development and is, therefore, a purer way for students to experience black-box testing than the TDD methodology [Edwards 2003] described earlier. Since creating the set of test cases prior to working on the implementation is not enforced by the TDD method, the testing performed by students may be biased by their knowledge of the code’s structure and how they chose to implement the program. Students participating in our testing exercise did not have access to the source code and were, thus, solely dependent on the requirements specification for developing their test cases.Providing students with a program that has been carefully crafted to include observable errors enables a second unique aspect to the assignment:。

a r X i v :0805.2757v 1 [q u a n t -p h ] 18 M a y 2008Robustness of Adiabatic Quantum Computing Seth Lloyd,MIT Abstract:Adiabatic quantum computation for performing quantum computations such as Shor’s algorithm is protected against thermal errors by an energy gap of size O (1/n ),where n is the length of the computation to be performed.Adiabatic quantum computing is a novel form of quantum information processing that allows one to find solutions to NP-hard problems with at least a square root speed-up [1].In some cases,adiabatic quantum computing may afford an exponential speed-up over classical computation.It is known that adiabatic quantum computing is no stronger than conventional quantum computing,since a quantum computer can be used to simulate an adiabatic quantum computer.Aharonov et al.showed that adiabatic quantum computing is no weaker than conventional quantum computation [2].This paper presents novel modelsfor adiabatic quantum computation and shows that adiabatic quantum computation is intrinsically protected against thermal noise from the environment.Indeed,thermal noise can actually be used to ‘help’an adiabatic quantum computation along.A simple way to do adiabatic versions of ‘conventional’quantum computing is to use the Feynman pointer consisting of a line of qubits [3].The Feynman Hamiltonian isH =−n −1ℓ=0U ℓ⊗|ℓ+1 ℓ|+U †ℓ⊗|ℓ ℓ+1|,(1)where U ℓis the unitary operator for the ℓ’th gate and |ℓ is a state of the pointer where the ℓ’th qubit is 1and all the remaining qubits are 0.Clearly,H is local and each of itsterms acts on four qubits at once for two-qubit gates.If we consider the pointer to be a ‘unary’variable,then the each of the terms of H acts on two qubits and the unary pointer variable.Assume that the computation has been set up so that all qubits start in the state 0.The computation takes place and the answer is placed in an answer register.Now a long set of steps,say n/2takes place in which nothing happens.Then the computation is undone,returning the system to its initial state at the n−1’th step.The computational circuit then wraps around and begins again.The eigenstates of H then have the form|b,k =(1/√Now gradually turn on an H term while turning offthe H1term:the total Hamiltonian isηH0+(1−λ)H1+λH.Asλis turned on,the system goes to its new ground state |b=0,k=0 .It can be verified numerically and analytically[7]that the minimum energygap in this system occurs atλ=1:consequently,the minimum gap goes as1/n2.In fact, the energy gap due to the interaction between the H1and the H terms is just the energy gap of the simpler system consisting just of the chain qubits on their own,confined to the subspace in which exactly one qubit is1:that is,it is the energy gap of a qubit chain with Hamiltonian(1−λ)H1+λH′,where H′=− ℓ|ℓ+1 ℓ|+|ℓ ℓ+1|.This gap goes as1/n2.Accordingly,the amount of time required to perform the adiabatic passage is polynomial in n.When the adiabatic passage is complete,the energy gap of the H term on its own goes as1/n2from the cosine dependence of the eigenvalues of H:it is also just the energy gap of the simplified system in the previous paragraph.This implies that the adiabatic passage can accurately be performed in a time polynomial in n.Measuring the answer register now gives the answer to the computation with probability1/2.This is an alternative(and considerably simpler)demonstration to that of[2]that‘conventional’quantum computation can be performed efficiently in an adiabatic setting.An interesting feature of this procedure is that the adiabatic passage can be faulty and still work justfine:all of the energy eigenstates in the|b=0,k sector give the correct answer to the computation with probability1/2,for any k.The real issue is making sure we do not transition to the|b=0,k sector.But the Hamiltonians H1and H do not couple to this sector:so in fact,we can perform the passage non-adiabatically and still get the answer to the computation.For example,if we turn offthe H1Hamiltonian very rapidly and turn on the H Hamiltonian at the same time,the system is now in an equal superposition of all the|b=0,k eigenstates.If we wait for a time∝n2(corresponding to the inverse of the minimum separation between the eigenvalues of H),then the state of the system will be spread throughout the|b=0,k sector,and we can read out the answer with probability1/2.This method effectively relies on dispersion of the wavepacket tofind the answer.Since coherence of the pointer doesn’t matter,we can also apply a Hamiltonian to the pointer that tilts the energy landscape so that higher pointer values have lower energy.(E.g.,H pointer=− ℓℓE|ℓ ℓ|.)Starting the pointer offin the initial state above and letting it interact with a thermal environment will obtain the answer to the computationin time of O(n).Similarly,in the absence of an environment,starting the pointer offin a wavepacket with zero momentum at time0and letting it accelerate will get the answer to the computation in even shorter time.Clearly,this method is quite a robust way of performing quantum computation.Let us look more closely at the sources of this robustness.Ifηis big,then there is a separation of energy of O(η/n)between the|b=0,k sector—states which give the correct answer to the computation—and the|b=0,k sector—states which give the incorrect answer to the computation.This is because b=0,k|ηH0|b=0,k =η/n.This energy gap goesdown only linearly in the length of the computation and can be made much larger than the gap between the ground andfirst excited state by increasingη>>1.This second energy gap is very useful:it means that thermal excitations with an energy below the gap will not interfere with obtaining the proper answer.That is,this method is intrinsically error-tolerant in the face of thermal noise.Indeed,it is this O(η/n) gap that determines how rapidly the computation can take place rather than the O(1/n2) gap between the ground and excited states.Of course,the actual errors in a system that realizes the above scheme are likely to arise from variability in the applied Hamiltonians.The energy gap arguments for robustness only apply to the translational dynamics of the system(this is what makes the analysis of the system tractable in thefirst place).That is,errors that affect each Uℓon its own are not protected against:but these are the errors that cause the computation to come out wrong.Of course,one can always program the circuit to perform‘conventional’robust quantum computation to protect against such errors.One must be careful,however,that errors that entangle the pointer with the logical qubits do not contaminate other qubits: conventional robust quantum computation protocols will have to be modified to address this issue.Farhi et al.have recently exhibited error correcting codes for‘conventional’adiabatic quantum computation[1]that can protect against such computational errors[8].The use of error correcting codes to correct the variation in the Uℓmay well be overkill. In any system manufactured to implement adiabatic quantum computing,these errors in the Uℓare essentially deterministic:the Uℓcould in principle be measured and their varia-tion from their nominal values compensated for by tuning the local Hamiltonians.Because it involves no added redundancy,such an approach is potentially more efficient than the use of quantum error correcting codes.Exactly how to detect and correct variations in the Uℓwill depend on the techniques(e.g.,quantum dots or superconducting systems)used toconstruct adiabatic quantum circuits.It is also interesting to note that performing quantum computation adiabatically is intrinsically more energy efficient than performing a sequence of quantum logic gates via the application of a series of external pulses.The external pulses must be accurately clocked and shaped,which requires large amounts of energy.In the schemes investigated here,the internal dynamics of the computer insure that quantum logic operations are performed in the proper order,so no clocking or external pulses need be applied.The adiabatic technique also avoids the Anderson localization problem raised by Landauer.The above construction requires an external pointer and four qubit interactions.One can also set up a pointerless model that requires only pairwise interactions between spin1/2 particles(compare the following method with the method proposed in reference[9]).Let each qubit in the computational circuit correspond to a particle with two internal states. Let each wire in the circuit correspond to a mode that can be occupied by a particle.The ℓ’th quantum logic gate then corresponds to an operator˜Hℓ=Aℓ+A†,where Aℓis anℓoperator that takes two particles from the two input modes and moves them to the output modes while performing a quantum logic operation on their two qubits.That is,Aℓ=a†out1a in1a†out2a in2⊗Uℓ.(5) Note that Aℓacts only when both input modes are occupied by a qubit-carrying particle. If we use the Hamiltonian˜H= ℓ˜Hℓin place of H in the construction above,the ground state of this Hamiltonian is a superposition of states in which the computation is at various stages of completion.Just as above,measurement on the ground state will reveal the answer to the computation with probability1/2.Note that even though the Hamiltonian in equation(5)involves a product of opera-tors on four degrees of freedom(the internal degrees of freedom of the particles together with their positions),it is nonetheless a physically reasonable local Hamiltonian involv-ing pairwise interactions between spin-1/2particles.To simulate its operation using an array of qubits as in[2]would require four qubit interactions,as in the pointer model dis-cussed above.This point is raised here because of the emphasis in the quantum computing literature on reducing computation to pairwise interactions between qubits.Pairwise in-teractions between particles orfields–i.e.,the sort of interactions found in nature–may correspond to interactions between more than two qubits.Without further restrictions on the form of the quantum logic circuit,evaluating the energy gap in this particle model is difficult,even for thefinal Hamiltonian1−˜H.But wecan always set up the computational circuit in a way that allows the adiabatic passage to be mapped directly on the Feynman pointer model above.The method is straightforward: order the quantum logic gates as above.Now insert additional quantum logic gates between each consecutive pair of gates in the original circuit.The additional gate inserted between theℓ’th andℓ+1’th quantum logic gates couples one output qubit of theℓ’th quantum logic gate with one input qubit of theℓ+1’th gate,and performs a trivial operation U=1on the internal qubits of these gates.The purpose of these gates is ensure that the quantum logic operations are performed in the proper sequence.Effectively,one of the qubits from theℓ’th gate must‘tag’one of the qubits from theℓ+1’th gate before theℓ+1’th gate can be implemented.Accordingly,we call this trick,a‘tag-team’quantum circuit.Tag-team quantum circuits are unitarily equivalent to the Feynman pointer model with an extra,identity gate inserted between each of the original quantum logic gates. Accordingly,the spectral gap for tag-team quantum circuits goes as1/n2and the quantum computation can be performed in time O(poly(n)).Just as for the pointer version of adiabatic quantum computing,the important spectral gap for tag-team adiabatic quantum computation is not the minimum gap,but rather the gap of size O(η/n)between the ground-state manifold of‘correct’states and the next higher manifold of‘incorrect’states. Once again,the existence of this gap is a powerful protection against errors in adiabatic quantum computation.The methods described above represent an alternative derivation of the fact that adiabatic quantum information processing can efficiently perform conventional quantum computation.The relative simplicity of the derivation from the original Feymnan Hamil-tonian[3]allows an analysis of the robustness of the scheme against thermal excitations. Adiabatic implementations of quantum computation are robust against thermal noise at temperatures below the appropriate energy gap.The appropriate energy gap is not the minimum gap,which scales as n2,but the gap between the lowest sector of eigenstates, which give the correct answer,and the next sector.This gap scales asη/n,whereηis an energy parameter that is within the control of the experimentalist.Acknowledgements:This work was supported by ARDA,ARO,DARPA,CMI,and the W.M.Keck foundation.The author would like to thank D.Gottesman and D.Nagaj for helpful conversations.References:[1]E.Farhi,J.Goldstone,S.Gutmann,Science292,472(2001).[2]D.Aharonov,W.van Dam,J.Kempe,ndau,S.Lloyd,O.Regev,‘Adiabatic Quantum Computation is Equivalent to Standard Quantum Computation,’Proceedings of the45th Annual IEEE Symposium on Foundations of Computer Science(FOCS’04), 42-51(2004);quant-ph/0405098.[3]R.Feynman,Found Phys.16,507-531(1986).[4]E.Farhi,S.Gutmann,Phys.Rev.A58,915-928(1998).[5]D.Aharonov,A.Ambainis,J.Kempe,U.Vazirani,Proceeding of the thirty-third annual ACM symposium on Theory of Computing,pp.50-59(2001).[6]ndauer,Phil.Trans.:Phys.Sci.and Eng.,353,367-376(1995).[7]P.Deift,M.B.Ruskai,W.Spitzer,arXiv:quant-ph/0605156.[8]S.P.Jordan,E.Farhi,P.Shor,Phys Rev A74,052322(2006);quant-ph/0512170.[9]A.Mizel,D.A.Lidar,M.Mitchell,Phys.Rev.Lett.99,070502(2007)arXiv:quant-ph/0609067.。

什么是“检定语言模式”检定语言模式是NLP最重要的技巧之一，是理查德－班德勒和约翰－格林德在１９７５年发展完成的一套语言技巧。

他们研究了形疗法大师弗里兹－佩尔斯和家庭治疗大师弗吉尼亚－萨特尔在治疗工作中运用的语言技巧。

他们发现这两位大师有一套极为有效的发问技巧，从受导者口中取得大量有用的资料，同时又有另一套回应技巧，使得受导者重组他的内心世界，因而在思想、心态及行为上有所改变。

检定语言模式便是由此发展而来。

"Meta"源出于希腊文，意为超越。

检定语言模式教我们如何用语言去澄清语言，使我们有驾御语言的能力：不被语言所困惑，不误以为语言就代表真实，而能够去挑战语言的不足，去探索一段话中的逻辑，因而掌握一套有效思考的技巧。

检定语言模式显露受导者说的话和他对世界的看法里被忽略的资料，这些资料往往是使受导者过去受困的原因。

所有说的话，都始终是内心深层的一些意念（深层结构），经过扭曲、归纳和删减３个程序的不断运用，终于形成一些文字语言而说出。

因为来自内心深层，所以一个人说的话，总是在显示他的身份、信念、价值观和规条。

１扭曲我们需要把储存在深层结构的资料简化才能有效表达，而在简化的过程中，很多资料被扭曲了。

换句话说，在我们对一件事情的认知过程中，必有扭曲的情况出现，例如一个人看到树影中的绳子而喊"有蛇！"。

这份扭曲的能力使我们能够享受音乐、美术、文艺等。

我们也能看着天上的一朵云而幻想出动物和人物。

（每当我们用某种动物或植物去形容一个人的时候，我们便是在做"扭曲"的工作）扭曲类包括以下语式：１猜臆式２因果式３相等式４假设式５虚泛词式包括"单一价值词""虚假词"２归纳当新的知识进入我们的大脑时，大脑会把它与我们原有的类似资料作比较和归类，这个程序是我们能够学得如此多和快的原因。

把人、事、物归类能使得我定出它们在我们人生里的意义与地位，以及让我们能够有效地运用它们。

信工所博士英语As a doctoral student in the field of Information and Communication Engineering, I have been exposed to a wide range of advanced research topics and cutting-edge technologies. My research focuses on the application of machine learning algorithms in wireless communication systems, specifically in the optimization of resource allocation and interference management.I am currently investigating the potential of deep learning techniques to enhance the performance of massive MIMO systems. By leveraging the power of artificial intelligence, I aim to develop novel algorithms that can adaptively allocate resources and mitigate interference in real-time, thereby improving the overall efficiency and reliability of wireless networks.Moreover, I have also been involved in projects related to the integration of IoT devices in 5G networks, where I have explored the challenges and opportunities associated with the massive deployment of IoT devices and the impact on network performance.In addition to my research work, I have actively participated in academic conferences and workshops, where I have presented my findings and engaged in discussions with experts in the field. These experiences have not only broadened my knowledge but also provided me with valuable insights and feedback that have helped shape my research direction.Overall, I am passionate about leveraging my expertisein information and communication engineering to addressreal-world challenges and contribute to the advancement of wireless communication technologies.作为信息与通信工程领域的博士生，我接触了广泛的先进研究课题和尖端技术。