ANYTHING ON THE TRACK MAY OCCUR变数——2009F1德国、匈牙利大奖赛
高考英语阅读七选五解题技巧课件
enjoy meals. C. My mother is hard—working and never
wastes money. D. As a mother,she takes good care of us and gives us every comfort.
技巧 成功没有快车道，幸福没有高速路，所有的成功都来
自不倦的努力和奔跑。所有的幸福都来自平凡的奋斗和 坚持。
一）题型解读：
• 试题模式：给出一篇缺少5个句子的文章，
对应有七个选项，要求同学们根据文章结构 、内容，选出正确的句子，填入相应的空白 处。
• 考查重点：主要考查考生对文章的整体内容
和结构以及上下文逻辑意义的理解和掌握。 （考试说明）
A. Rock and roll B. What today’s rock music is like C. What popular music is D. The changes of country music E. Traditional folk music F. What country music is
dancing... and many more.
A. Rock and roll B. What today’s rock music is like C. What popular music is D. The changes of country music E. Traditional folk music F. What country music is
Part II Passage 3
创新英语翻译赛2022年真题
2022年翻译赛英译汉真题All the planets of the solar system orbit around the Sun in the same direction.From our vantage point on the Earth,this makes them appear to march across the sky from season to season in the same direction While you can't discern this movement with your eyes in a single night,you can capture it if you pay close enough attention:one night the planet will be around a certain group of stars,and many months later it will be around a new clump of stellar friends.But sometimes the planets appear to retrace their steps and move backward over the course of a few days or months,a phenomenon known as retrograde motion.If all the planets are moving in the same direction,then what gives?The appearance of retrograde motion is due to our vantage point on the Earth.Just like all the other planets,we are also orbiting around the Sun at our own speed From our point of view,sometimes we"catch up"to another planet in its own orbit,making it appear as if it's moving backward relative to us.Think of runners on a racetrack.From the point of view of the stands,everybody's running in the same direction.But from the point of view of one of the runners,their fellow racers may be moving,away from them or coming closer to them,depending on their speeds and where on the track they happen to be.You may have recently seen some articles talking about how six planets are currently in simultaneous retrograde.What does this mean for you,personally?Nothing.It means nothing at all.One planet moving in retrograde doesn't mean anything either it's literally just a planet moving in its orbit,doing the same thing that it's been doing for the past four and a half billion years.Multiple planets moving in retrograde is just a random coincidence.If those runners on the racetrack were going at it for cons,circling around and around without and,those kinds of coincidences would be bound to happen.If anyone had asked an astronomer from centuries ago,they would have been able to predict with extreme precision when this simultaneous retrograde would occur.In fact, you can fire up any free astronomy software and do the same thing yourself,finding out exactly when the next one will happen,and the next,and the next,for millennia to come.2022年翻译赛汉译英真题2021年被普遍认为是“元宇宙”的元年。
3.2 IS Extension...................................... 8
Automatic Instruction Set Design Through Efficient Instruction Encoding for Application-Specific ProcessorsJong-eun Lee jelee@poppy.snu.ac.krKiyoung Choikchoi@azalea.snu.ac.krNikil D.Duttdutt@Architectures and Compilers for Embedded Systems(ACES)Center for Embedded Computer Systems,University of California,Irvine,CA92697Technical Report#02-23Center for Embedded Computer SystemsUniversity of California,Irvine,CA92697,USAMay2002—Updated April2003AbstractApplication-specific instructions can significantly improve the performance,energy-efficiency,and code size of configurable processors.While generating new instructions from application-specific operation patterns has been a common way to improve the instruction set(IS)of a configurable pro-cessor,automating the design of IS’s for given applications poses new challenges.This IS synthesis typically requires these questions to be answered:how to create as well as utilize new instructions in a systematic manner,and how to choose the best set of application-specific instructions taking into account the conflicting effects of adding new instructions?To address these problems,we present a novel IS synthesis framework that optimizes the performance for the given data path architecture through an efficient instruction encoding.We build a library of new instructions with various encoding alternatives and select the best set while satisfying the instruction bitwidth con-straint.We formulate the problem using integer linear programming and also present an effective heuristic algorithm.Experimental results using our technique generate instruction sets that show improvements of up to about40%over the native instruction set for several realistic benchmark applications running on typical embedded RISC processors.Contents1Introduction4 2Motivation6 3Related Work73.1Automatic IS Synthesis (7)3.2IS Extension (8)4Instruction Set Synthesis Framework94.1Instruction Sets (9)4.2Features (10)4.2.1Structure-Centric IS Synthesis (10)4.2.2Optimized Instruction Encoding (11)4.3Generation of Optimal C-Instructions (12)4.3.1Creating C-Instructions (12)4.3.2Selecting C-Instructions (13)4.4Utilization of C-Instructions in Code Generation (13)5Creating C-Instructions145.1Rescheduling the Operations (14)5.2Generalizing with Operand Classes (15)6Selecting C-Instructions166.1Instruction Set Selection (16)6.2ILP Problem Formulation (18)6.3Heuristic Algorithm (19)7Experiments207.1Experimental Setup (20)7.2Comparison of ILP and Heuristic Algorithm (21)7.3Comparison of Basic,Synthesized and Native IS’s for SH-3 (22)7.4Comparison of Basic,Synthesized and Native IS’s for MIPS (23)8Conclusion24 9Acknowledgements25 A Proof of NP-hardness28List of Figures1Customizing the instruction set of an application-specific instruction set processor (ASIP) (5)2Instruction sets (9)3Application-specific instruction set synthesis framework (10)4Flexible instruction format and the instruction code space (11)5Synthesisflow to generate optimal C-instructions (12)6Cycle reduction by rescheduling the operations of a basic instruction sequence.RR,EX,MEM,and WB are the four pipe stages,and the horizontal lines repre-sent the cycles or the control steps;the diagrams illustrate the execution of theinstructions as they go through the pipeline,performing their operations at differ-ent pipe stages in different cycles.The right-hand side diagram shows that the datadependency,represented by arrows,is still satisfied in the C-instruction CXX (14)7Creating C-instructions from a basic instruction sequence (16)8Heuristic algorithm for C-instruction selection (29)9Subroutine update-benefit (30)10Performance of the native and the synthesized IS’s,normalized to that of the basic IS(SH-3) (30)11Performance of the native and the synthesized IS’s,normalized to that of the basic IS(MIPS) (31)List of Tables1C-instructions and associated information (17)2Comparison of ILP and heuristic algorithm(SH-3) (22)3Comparison of synthesized IS’s with the basic&native IS’s(SH-3) (22)4Comparison of synthesized IS’s with the basic&native IS’s(MIPS) (24)AbstractApplication-specific instructions can significantly improve the performance,energy-efficiency, and code size of configurable processors.While generating new instructions from application-specific operation patterns has been a common way to improve the instruction set(IS)of a con-figurable processor,automating the design of IS’s for given applications poses new challenges. This IS synthesis typically requires these questions to be answered:how to create as well as utilize new instructions in a systematic manner,and how to choose the best set of application-specific in-structions taking into account the conflicting effects of adding new instructions?To address these problems,we present a novel IS synthesis framework that optimizes the performance for the given data path architecture through an efficient instruction encoding.We build a library of new instruc-tions with various encoding alternatives and select the best set while satisfying the instruction bitwidth constraint.We formulate the problem using integer linear programming and also present an effective heuristic algorithm.Experimental results using our technique generate instruction sets that show improvements of up to about40%over the native instruction set for several realistic benchmark applications running on typical embedded RISC processors.1IntroductionConfigurable processors are application-specific synthesizable processors where the instruction set(IS)and/or microarchitectural parameters such as registerfile size,functional unit bitwidth, etc.can be easily changed for different applications at the time of the processor design.Eas-ier integration and manufacturing,and architectural and implementationalflexibility make them better suited for embedded processors in system-on-a-chip(SOC)designs than off-the-shelf pro-cessors[1].With the commercial offerings of such configurable processors[2,3]and the recent development in the retargetable compiler technology[4–6]as well as the increased interest in the platform-based SOCs employing configurable processors,the problem of IS customization for application-specific IS processors(ASIPs)is drawing more and more attention from both industry and academia[7–12].Customizing the IS of an ASIP requires several phases of time-consuming processes(Fig.1). First,the application program can be compiled and simulated for the initial IS of the processor, generating some profiling results.By analyzing the results,one can identify potentially useful “new”instructions,which can be included in the new IS while there may be some other instruc-tions removed from the old IS.This new IS can then be used to retarget the toolchain(compiler, assembler,simulator,etc.),generating a new set of analysis results.These results can lead to an-other set of new instructions and the process of customizing the IS continues until the desired goal (performance,code size,etc.)is reached.Thus,even with a retargetable toolchain available,de-signing an IS optimized for a given application involves complex optimization tasks.Furthermore, when it comes to the automatic design of such IS’s,which is called IS synthesis,there are even greater challenges.For instance,how can we generate“new”instructions in a systematic manner, that might help achieve the optimization goal,and also how can we utilize them in later compila-tion stages?Further,considering the conflicting effects of adding application-specific instructionsCompiler 1 /build IS ver. 2IS ver. 1Application ProgramsPerform. (IS2)Assembly /Perform. (IS1)Assembly /Simulator 1Simulator 2Compiler 2 /build Figure 1.Customizing the instruction set of an application-specific instruction set processor (ASIP).(potentially increasing the performance;however,at the cost of the increased resources such as the chip area,the cycle time,and the instruction bitwidth),how can we find the best set of such new instructions for the given application as well as the given resources?To address these prob-lems,we need an IS synthesis framework which can,preferably,be used for modern configurable processors.Since the early 1990’s,there have been different approaches to application-specific IS synthe-sis [13–18].However,they are often targeted for their own architectural styles (quite different from pipelined RISC architectures),and therefore difficult to apply in the context of RISC-based configurable processors.Also,none of them are designed as a methodology to improve an existing processor,which has become increasingly important in SOC platform-based designs.In this paper,we present an IS synthesis framework that is targeted for modern RISC-based configurable proces-sors,also with advanced features such as multi-cycle instruction support and complex instruction encoding.Our framework takes a structure-centric approach—it tries to improve the processor only by changing the IS for a given data path architecture.In this way we can eliminate the need for costly re-engineering of the entire processor design as well as easily accommodate the need for IS specialization arising in SOC designs.With a fixed data path architecture,customization can be made in such areas as the instruction encoding,the number of (application-specific)instructions,and their definitions,etc.Since such instruction encoding and application-specific instructions can significantly affect the code size,performance,and energy-efficiency,the IS design should be well crafted for any embedded processor that needs to be optimized under limited resources.Although the proposed IS synthesis framework can generally support different optimization goals,in this paper we focus on performance improvement through IS synthesis.To achieve maxi-mal performance improvement,our technique first builds a library of candidate application-specific instructions with various encoding alternatives satisfying the architectural constraints,and then se-lects the best set satisfying the instruction encoding constraint coming from the instruction bitwidth limitation.We formulate the problem using an integer linear programming (ILP)framework.Butsince solving an ILP problem takes a prohibitively long time even for a moderately sized prob-lem,we also present an effective heuristic algorithm.Experimental results show that our proposed technique can synthesize IS’s that generate up to about40%performance improvement over the processor’s native1IS for different application domains.The contributions of our work are three-fold.First,it is aimed at modern RISC pipelined archi-tectures with multi-cycle instruction support—representative of current configurable processors—while most existing methodologies[15,16,18]apply to only VLIW-like processors.Second,it tries to improve a given processor hardware through IS specialization,which makes our technique suitable for an emerging class of configurable processors that build on existing,popular ISA fami-lies.Third,our technique takes instruction encoding into account so that the obtained IS can be as compact and efficient as those manually designed by experts.The rest of this paper is organized as follows.In Section2we introduce application-specific instruction set synthesis using motivating examples and in Section3we summarize related work. In Section4we present the IS synthesis framework and outline the synthesisflow.The two major steps in theflow are discussed in more detail in Section5and Section6.In Section7we demon-strate the efficacy of our techniques through experiments on typical embedded RISC processors running realistic applications,and conclude the paper in Section8.2MotivationWe illustrate the potential for significant performance improvements using application-specific instructions on a typical embedded RISC processor(the Hitachi SH-3[19])and a realistic appli-cation(the H.263decoder algorithm).Initial profiling of the H.263decoder application shows that about50%of the actual execution time is spent in a simple function that does not contain any function calls and which consists of only two nested loops,either one of which,depending on a conditional,is actually executed.One of these inner-most loops,when processed by an SH-3 targeted GCC compiler,generates13native instructions that executes in14cycles(not includ-ing branch stall).By devising a custom instruction that takes6cycles,the loop is reduced to4 instructions that execute in9cycles.Alternatively,the entire loop can be encoded into a single custom instruction that executes in7cycles.In this example,it is obvious that greater performance enhancement and code size reduction can be obtained by introducing custom instructions than by relying on traditional compiler optimizations or assembly coding.However,those two custom instructions require4and5register arguments respectively:since one SH-3register argument requires4bits,each of these seemingly attractive custom instructions cannotfit into a16bit instruction.On the other hand,if wefix the positions of register arguments for these custom instructions,we do not need to specify arguments and only the opcodes(operation codes)need to be specified(similar to a function call withfixed register arguments).Furthermore, such instructions are easily handled by compilers in a manner similar to function calls.Although such custom instructions can be used very effectively and do not cause an argument encoding problem,they are only suitable for the hot spots of the application,where the introduction of custom instructions can be justified by their heavy dynamic usage counts.1By“native”we mean the existing IS for a processor family.Another way to improve performance through IS specialization is to combine frequently occur-ring operation patterns into complex instructions,where a complex instruction is an instruction with more than one micro-operation.Such a methodology is particularly useful since it automates the task offinding promising patterns over the entire application program.Even though there have been similar works for VLIW-like architectures[16,18],to our knowledge no prior work has ad-dressed pipelined RISC architectures.VLIW-like architectures have ample hardware resources supporting multiple parallel operations.Therefore performance improvements can easily be ob-tained by defining and using a complex instruction with multiple parallel operations rather than using a sequence of simple instructions.But a typical RISC architecture has little or no instruction level parallelism and thus cannot benefit from parallel operations combined into one complex in-struction.However,even in RISC architectures with no explicit instruction level parallelism,we can exploit the parallelism in between the pipeline stages:auto inc/decrement load/store are typical examples.Many other possibilities that combine multiple operations from different pipeline stages are also feasible,and can contribute to performance improvements over the processor’s native IS. Due to the instruction bitwidth constraint,however,instruction sets may not have the full power to express all the possible combinations of the operations in the hardware.Furthermore,each processor family has its own IS quirks.For instance the SH-3has a two-operand IS with an implicit destination:a typical SH-3ADD instruction assumes the destination is the same as one of the source operands.This instruction encoding restriction adversely affects the performance, which further motivates the need for an application-specific instruction encoding method.One practical consideration is that it is very difficult to add any new complex instructions into an existing IS,due to the limited size of the IS code space.One possibility is to use“undefined”instruction opcodes,which,however,often provide too small a code space for complex instruc-tions.Another way to solve this problem is to define a basic IS using only a part of the instruction code plex instructions can then be created in an application-specific manner and added into the remaining code space.To gain performance benefit in this manner,the generated com-plex instructions should be optimized in terms of their encoding so that more of those instructions can be located within the limited instruction code space.These observations motivate our code space-economical instruction set synthesis technique.3Related WorkWe summarize the related work in two categories:instruction set synthesis and ISA(Instruction Set Architecture)enhancement.The former deals with the automatic generation of instruction sets for specific applications and shares a similar context with this paper.The latter includes manual customization of existing ISAs for specific applications and sometimes exploits similar opportunities.The synthesis of application-specific IS’s have been approached in different ways:application-centric and structure-centric,depending on which part of the ISA is decidedfirst.In application-centric approaches[16–18],the IS isfirst optimized from the application’s behavior using thetechniques similar to[20].The hardware is later designed to implement the instruction set,man-ually or automatically.Among the approaches in this direction,PEAS-I[17]is most similar to our approach in that both approaches assume a basic IS and target pipelined RISC architectures. However,PEAS-I has afixed set of instructions from which a subset is selected;thus instruction encoding is never an issue,unlike in our approach.Other approaches,however,tend to presume their own architectural styles(e.g.,‘transport triggered architecture’in[18])and thus cannot be applied to modern pipelined RISC processors.More importantly they do not give any hints on how to improve an existing processor architecture,which can be very helpful particularly in the context of configurable processor-based SOC design.Structure-centric approaches[13–15]have a structural model of the architecture either implicitly assumed or as an explicit input and try tofind the best instruction set matching the application.This approach has the advantage that it can leverage existing processor designs.However,previous work in this direction has not fully addressed the issue of instruction encoding:opcode and operand fields all havefixed widths.As a result,the instruction bitwidth cannot be fully utilized as in custom processors,leading to limited performance improvement for the same bitwidth or increased instruction bitwidth and code size.Our approach is significantly different,since multiplefield widths are allowed for the same operand type,with different benefits and cost profiles,so that the optimal set of complex instructions,depending on the application,can be selected satisfying the instruction bitwidth constraint.The application-specific ISA customization has received much attention recently as commercial configurable processors become available.For instance,Zhao et al.[7]demonstrated24times performance improvement in a case study with a commercial configurable processor core.They expanded the data path width through configuration and added several custom instructions such as bit operations,compound instructions,and parallel processing on the widened data path.Our approach is significantly different,since it automates the design of application-specific instructions, aims to improve the processor by changing only the IS(minimizing the change in the data path), and generates the instructions that can easily be supported by compilers without re-coding the program.The bitwidth optimization technique in this paper has similarity with other bitwidth reduction techniques.Typically in synthesizing application-specific hardware from behavioral description, the smallest bitwidth can be chosen for data path components to achieve smaller area and lesser energy consumption[21,22].Even instruction set processors sometimes have reduced-bitwidth instruction set architecture(rISA),of which the main benefit is smaller code size.Examples of the rISA instruction set include Thumb[23]of the ARM7TDMI processor and MIPS16[24]of the MIPS processor.Recently,Halambi et al.[25,26]have proposed compilation and design space exploration techniques to take advantage of the rISA more effectively.Further extending the rISA scheme,Kwon et al.[27]proposed a new ISA called PARE(Partitioned Registers Extension), which is an enhanced version of the Thumb ing the concept of the partitioned registerfile, they could further reduce the bitwidth of register operandfields and increase that of immediate or opcodefield,thereby reducing the entire code size.In this paper we deal with the problem ofSynthesized ISNative IS C−InstructionsBasic ISFigure2.Instruction sets.synthesizing instruction sets that are optimized in the bitwidth of each operand and opcodefield, and also competitive(in terms of the performance)to the native IS by supporting application-specific complex operation patterns with the synthesized instructions.4Instruction Set Synthesis FrameworkWe now present our IS synthesis framework that improves the processor architecture through IS specialization.We introduce various IS’s,present the salient features of our framework,and outline the IS synthesisflow.To be able to generate application-specific instructions automatically,we need to define“in-structions”and how a“new”instruction can be defined.Some of the previous ASIP synthesis approaches[17]have assumed a set of potential“special”instructions,out of which an application-specific set of special instructions can be defined for each application.In those approaches,a new instruction can be chosen only from the predefined pool of instructions;therefore,the generation of new,application-specific instructions is limited by,not only dependent on,the predefined set of special instructions.In our IS synthesis framework,instead of explicitly defining the set of possi-ble new instructions,we define a basic IS once for each ing the resource usage and timing information of basic instructions,combinations of basic instructions can be generated with possibly less number of execution cycles or control steps than their basic instructions versions. Instructions built in this way are called C-instructions,2which are the result of our IS synthesis flow.Fig.2illustrates the relationship between the three IS’s relevant to our IS synthesis approach. The native IS is the existing IS of the processor,which is presumably optimized for general appli-cations and may include its own complicated instructions as well as simple ones.From the native IS,the basic IS is defined,which serves three purposes.First,a basic instruction is defined to have only one micro-operation,to facilitate the construction of C-instructions.Second,the basic IS, together with the resource usage and timing information of each basic instruction,represent the processor data path in a ready-to-use form for the IS synthesis.This information includes the num-ber of each resource type(functional units,ports,buses)that may be used at each pipeline stage. Third,the basic IS is defined to have the simplest instruction format,so that the saved code space may be used for new instructions generated for each application.The union of the basic IS and the C-instructions generated for each application is called the synthesized IS for the application.2C-instruction means compound-operation instruction,that is,an instruction with more than one micro-operation.resource usage info Basic IS w/ hardwareApplication code in Basic IS assembly Execution profilingBasic IS C−Instructions D−InstructionsData path structureFigure 3.Application-specific instruction set synthesis framework.In addition to the C-instructions,users may want to have custom instructions that are designed by hand,often with the designers’intuition.In our IS synthesis framework,these instructions are called D-instructions ,which may be difficult to discover with an automatic method unlike with C-instructions.These needs arise especially when the application-specific instruction is very complex as in FFT (Fast Fourier Transform)operations,or special functional units can be afforded.Although these instructions are very effective in boosting the application performance,they pose very difficult problems for automation,in both the instruction design phase and the code generation phase of compilers.Our IS synthesis methodology deals only with the automated generation and application of C-instructions for application-specific IS processors.In our IS synthesis scheme,an application-specific IS consists of the basic instructions,C-instructions,and D-instructions as illustrated in Fig.3.While the basic instructions are provided by the user as a part of the architecture description,the C-instructions are synthesized using the information from both the data path architecture and the application program.The D-instructions,which may be generated manually by an expert,take effect on the code space allocated for the C-instructions.In this subsection,we highlight the two main features of our proposed IS synthesis framework.4.2.1Structure-Centric IS SynthesisCurrently,a typical approach to the application-specific IS synthesis is first identify the most promising operation patterns from the application and later implement the application-specific instructions using additional hardware such as special functional units.However,in that approach it is difficult to find an optimal IS (e.g.,of maximal performance for the given power,area budget)due to the difficulty of foreknowing the hardware implementation characteristics during the synthe-sis step.As a result,an iterative optimization strategy is unavoidable,which may involve hardware synthesis for the IS design.Also,since the data path is heavily changed for the new IS,the huge cost of re-engineering the entire processor design is another disadvantage of this approach.2w1w22w2Code spacew1imm2imm1R3R2R1opcode immediate R2R1opcode Figure 4.Flexible instruction format and the instruction code space.Our IS synthesis framework takes a structure-centric approach.In the structure-centric IS syn-thesis,an optimal IS can be more directly found within the limited hardware change allowed,as the data path structure is given and frozen from the beginning of the IS synthesis process.The IS synthesis system tries to find a better IS exercising the data path more efficiently for the given application.Hence,our IS synthesis framework explicitly takes inputs of the data path architecture as well as of the application program.Another benefit of this approach is that it readily enables IS specialization—improving the processor architecture through only the IS modification—which has become important in the platform-based SOCs employing configurable processors.4.2.2Optimized Instruction EncodingPrevious work on IS synthesis has taken a very simplistic view on the instruction format;typically,all synthesized instructions have the same format,with the number of (maximum)operands and their bitwidths determined before the IS synthesis.However,custom-designed IS’s of embedded processors have varying formats for different instructions in order to maximize the utilization of the limited instruction bitwidth,and even encoded operands are often used.For example,an immediate field may take 8bits in the ADDI instruction while it may take only 4bits in the ADD +LOAD type instructions.Register fields may also have a reduced width to allow more operands to be encoded,at the cost of accessing only a subset of the registers in that instruction.For example,the SH-3microprocessor has complex load instructions,which have the (implicit)R0register as their destination so that the destination register need not be specified at all.Our IS synthesis framework addresses this problem of diverse instruction formats through the instruction code space and the operand class .First,to allow different instruction formats in a single IS,our framework does not constrain the opcode/operand field widths;rather,the operand fields may have different bitwidths (determined by the synthesis process itself)and only the opcode field width is later set to use the remaining bits of the instruction bitwidth.Thus,the only condition to be satisfied in this scheme is that the sum of the code space (defined as 2ˆthe number of bits needed for operands )of every instruction should not exceed the allowed total code space (defined as 2ˆthe instruction bitwidth )(see Fig.4).Obviously,this condition guarantees that every in-struction will be given an opcode,possibly with a different field width.Second,to support the operand encoding as well as the multiple choices for operand field widths,our technique employs the concept of operand class,which encapsulates the field width,the set of compatible operand instances,and an (optional)encoding scheme.The operand classes provide a convenient means to generalize the operands encountered in the assembly code of the application into various versions。
tracking
Chapter19TRACKING Tracking is the problem of generating an inference about the motion of an ob-ject given a sequence of images.Good solutions to this problem have a variety ofapplications:•Motion Capture:if we can track a moving person accurately,then we can make an accurate record of their motions.Once we have this record,we can use it to drive a rendering process;for example,we might control a cartoon character,thousands of virtual extras in a crowd scene,or a virtual stunt avatar.Furthermore,we could modify the motion record to obtain slightly different motions.This means that a single performer can produce sequences they wouldn’t want to do in person.•Recognition From Motion:the motion of objects is quite characteristic.We may be able to determine the identity of the object from its motion;we should be able to tell what it’s doing.•Surveillance:knowing what objects are doing can be very useful.For ex-ample,different kinds of trucks should move in different,fixed patterns in an airport;if they do not,then something is going very wrong.Similarly,there are combinations of places and patterns of motions that should never occur (no truck should ever stop on an active runway,say).It could be helpful to have a computer system that can monitor activities and give a warning if it detects a problem case.•Targeting:a significant fraction of the tracking literature is oriented towards(a)deciding what to shoot and(b)hitting it.Typically,this literature de-scribes tracking using radar or infra-red signals(rather than vision),but the basic issues are the same—what do we infer about an object’s future position from a sequence of measurements?(i.e.where should we aim?)In typical tracking problems,we have a model for the object’s motion,and some set of measurements from a sequence of images.These measurements could be the position of some image points,the position and moments of some image regions, or pretty much anything else.They are not guaranteed to be relevant,in the sense 520Section19.1.Tracking as an Abstract Inference Problem521 that some could come from the object of interest and some might come from other objects,or from noise.19.1Tracking as an Abstract Inference ProblemMuch of this chapter will deal with the algorithmics of tracking.In particular,we will see tracking as a probabilistic inference problem.The key technical difficultyis maintaining an accurate representation of the posterior on object position given measurements,and doing so efficiently.We model the object as having some internal state;the state of the object at the i’th frame is typically written as X i.The capital letters indicate that this is a random variable—when we want to talk about a particular value that this variable takes,we will use small letters.The measurements obtained in the i’th frame are values of a random variable Y i;we shall write y i for the value of a measurement, and,on occasion,we shall write Y i=y i for emphasis.There are three main problems:•Prediction:we have seen y0,...,y i−1—what state does this set of mea-surements predict for the i’th frame?to solve this problem,we need to obtaina representation of P(X i|Y0=y0,...,Y i−1=y i−1).•Data association:Some of the measurements obtained from the i-th framemay tell us about the object’s state.Typically,we use P(X i|Y0=y0,...,Y i−1= y i−1)to identify these measurements.•Correction:now that we have y i—the relevant measurements—we needto compute a representation of P(X i|Y0=y0,...,Y i=y i).19.1.1Independence AssumptionsTracking is very difficult without the following assumptions:•Only the immediate past matters:formally,we requireP(X i|X1,...,X i−1)=P(X i|X i−1)This assumption hugely simplifies the design of algorithms,as we shall see;furthermore,it isn’t terribly restrictive if we’re clever about interpreting X ias we shall show in the next section.•Measurements depend only on the current state:we assume that Y iis conditionally independent of all other measurements given X i.This meansthatP(Y i,Y j,...Y k|X i)=P(Y i|X i)P(Y j,...,Y k|X i) Again,this isn’t a particularly restrictive or controversial assumption,but ityields important simplifications.522Tracking Chapter19 These assumptions mean that a tracking problem has the structure of inference on a hidden Markov model(where both state and measurements may be on a continuous domain).You should compare this chapter with section??,which the use of hidden Markov models in recognition.19.1.2Tracking as InferenceWe shall proceed inductively.Firstly,we assume that we have P(X0),which is our “prediction”in the absence of any evidence.Now correcting this is easy:when we obtain the value of Y0—which is y0—we have thatP(X0|Y0=y0)=P(y0|X0)P(X0)P(y0)=P(y0|X0)P(X0)P(y0|X0)P(X0)d X0∝P(y0|X0)P(X0)All this is just Bayes rule,and we either compute or ignore the constant of pro-portionality depending on what we need.Now assume we have a representation of P(X i−1|y0,...,y i−1).PredictionPrediction involves representingP(X i|y0,...,y i−1)Our independence assumptions make it possible to writeP(X i|y0,...,y i−1)=P(X i,X i−1|y0,...,y i−1)d X i−1=P(X i|X i−1,y0,...,y i−1)P(X i−1|y0,...,y i−1)d X i−1=P(X i|X i−1)P(X i−1|y0,...,y i−1)d X i−1CorrectionCorrection involves obtaining a representation ofP(X i|y0,...,y i)Our independence assumptions make it possible to writeP(X i|y0,...,y i)=P(X i,y0,...,y i) P(y0,...,y i)Section 19.2.Linear Dynamic Models and the Kalman Filter 523=P (y i |X i ,y 0,...,y i −1)P (X i |y 0,...,y i −1)P (y 0,...,y i −1)P (y 0,...,y i )=P (y i |X i )P (X i |y 0,...,y i −1)P (y 0,...,y i −1)P (y 0,...,y i )=P (y i |X i )P (X i |y 0,...,y i −1) P (y i |X i )P (X i |y 0,...,y i −1)d X i19.1.3OverviewThe key algorithmic issue involves finding a representation of the relevant prob-ability densities that (a)is sufficiently accurate for our purposes and (b)allows these two crucial sums to be done quickly and easily.The simplest case occurs when the dynamics are linear,the measurement model is linear,and the noise models are Gaussian (section 19.2).Non-linearities introduce a host of unpleasant problems (section 19.3)and we discuss some current methods for handling them (section 19.4;the appendix gives another method that is unreliable but occasion-ally useful).We discuss data association in section 19.5,and show some examples of tracking systems in action in section 21.3.19.2Linear Dynamic Models and the Kalman FilterThere are good relations between linear transformations and Gaussian probability densities.The practical consequence is that,if we restrict attention to linear dy-namic models and linear measurement models,both with additive Gaussian noise,all the densities we are interested in will be Gaussians.Furthermore,the question of solving the various integrals we encounter can usually be avoided by tricks that allow us to determine directly which Gaussian we are dealing with.19.2.1Linear Dynamic ModelsIn the simplest possible dynamic model,the state is advanced by multiplying it by some known matrix (which may depend on the frame),and then adding a normal random variable of zero mean,and known covariance.Similarly,the measurement is obtained by multiplying the state by some matrix (which may depend on the frame),and then adding a normal random variable of zero mean and known covariance.We use the notationx ∼N (µ,Σ)to mean that x is the value of a random variable with a normal probability dis-tribution with mean µand covariance Σ;notice that this means that,if x is one-dimensional —we’d write x ∼N (µ,v )—that its standard deviation is √v .We can write our dynamic model asx i ∼N (D i x i −1;Σd i )524Tracking Chapter19y i∼N(M i x i;Σm i)Notice that the covariances could be different from frame to frame,as could the matrices.While this model appears very limited,it is in fact extremely powerful; we show how to model some common situations below.Drifting PointsLet us assume that x encodes the position of a point.If D i=Id,then the point is moving under random walk—its new position is its old position,plus some Gaussian noise term.This form of dynamics isn’t obviously useful,because it appears that we are tracking stationary objects.It is quite commonly used for objects for which no better dynamic model is known—we assume that the random component is quite large,and hope we can get away with it.This model also illustrates aspects of the measurement matrix M.The most important thing to keep in mind is that we don’t need to measure every aspect of the state of the point at every step.For example,assume that the point is in3D: now if M3k=(0,0,1),M3k+1=(0,1,0)and M3k+2=(1,0,0),then at every third frame we measure,respectively,the z,y,or x position of the point.Notice that we can still expect to be able to track the point,even though we measure only one component of its position at a given frame.If we have sufficient measurements,we can reconstruct the state—the state is observable.We explore observability in the exercises.Constant VelocityAssume that the vector p gives the position and v the velocity of a point moving with constant velocity.In this case,p i=p i−1+(∆t)v i−1and v i=v i−1.This means that we can stack the position and velocity into a single state vector,and our model applies.In particular,x=pvandD i=Id(∆t)Id0IdNotice that,again,we don’t have to observe the whole state vector to make a useful measurement.For example,in many cases we would expect thatM i=Id0i.e.that we see only the position of the point.Because we know that it’s moving with constant velocity—that’s the model—we expect that we could use these measurements to estimate the whole state vector rather well.Section19.2.Linear Dynamic Models and the Kalman Filter525the state space is two dimensional—one coordinate for position,one for velocity.The figure on the top left shows a plot of the state;each asterisk is a different state.Notice that the vertical axis(velocity)shows some small change,compared with the horizontal axis.This small change is generated only by the random component of the model,so that the velocity is constant up to a random change.Thefigure on the top right shows thefirst component of state(which is position)plotted against the time axis.Notice we have something that is moving with roughly constant velocity.Thefigure on the bottom overlays the measurements(the circles)on this plot.We are assuming that the measurements are of position only,and are quite poor;as we shall see,this doesn’t significantly affect our ability to track.Constant AccelerationAssume that the vector p gives the position,vector v the velocity and vector a the acceleration of a point moving with constant acceleration.In this case,p i= p i−1+(∆t)v i−1,v i=v i−1+(∆t)a i−1and a i=a i−1.Again,we can stack the position,velocity and acceleration into a single state vector,and our model applies.526Tracking Chapter19In particular,x=pvaandD i=Id(∆t)Id00Id(∆t)Id00IdNotice that,again,we don’t have to observe the whole state vector to make a useful measurement.For example,in many cases we would expect thatM i=Id00i.e.that we see only the position of the point.Because we know that it’s moving with constant acceleration—that’s the model—we expect that we could use thesethe line.On the left,we show a plot of thefirst two components of state—the position on the x-axis and the velocity on the y-axis.In this case,we expect the plot to look like (t2,t),which it does.On the right,we show a plot of the position against time—note that the point is moving away from its start position increasingly quickly.Periodic MotionAssume we have a point,moving on a line with a periodic movement.Typically,its position p satisfies a differential equation liked2pdt2=−pSection 19.2.Linear Dynamic Models and the Kalman Filter 527This can be turned into a first order linear differential equation by writing the velocity as v ,and stacking position and velocity into a vector u =(p,v );we then have d u dt= 01−10 u =S u Now assume we are integrating this equation with a forward Euler method,where the steplength is ∆t ;we haveu i =u i −1+∆t d udt=u i −1+∆t S u i −1= 1∆t −∆t 1u i −1We can either use this as a state equation,or we can use a different integrator.If we used a different integrator,we might have some expression in u i −1,...,u i −n —we would need to stack u i −1,...,u i −n into a state vector and arrange the matrix appropriately (see the exercises).This method works for points on the plane,in 3D,etc.as well (again,see the exercises).Highe r Orde r Mode lsAnother way to look at a constant velocity model is that we have augmented the state vector to get around the requirement that P (x i |x 1,...,x i −1)=P (x i |x i −1).We could write a constant velocity model in terms of point position alone,as long as we were willing to use the position of the i −2’th point as well as that of the i −1’th point.In particular,writing position as p ,we would haveP (p i |p 1,...,p i −1)=N (p i −1+(p i −1−p i −2),Σd i )This model assumes that the difference between p i and p i −1is the same as the difference between p i −1and p i −2—i.e.that the velocity is constant,up to the random element.A similar remark applies to the constant acceleration model,which is now in terms of p i −1,p i −2and p i −3.We augmented the position vector with the velocity vector (which represents p i −1−p i −2)to get the state vector for a constant velocity model;similarly,we augmented the position vector with the velocity vector and the acceleration vector (which represents (p i −1−p i −2)−(p i −2−p i −3))to get a constant acceleration model.We might reasonably want the new position of the point to depend on p i −4or other points even further back in the history of the point’s track;to represent dynamics like this,all we need to do is augment the state vector to a suitable size.Notice that it can be somewhat difficult to visualize how the model will behave.There are two approaches to determining what D i needs to be;in the first,we know something about the dynamics and can write it down,as we have done here;in the second,we need to learn it from data —we put discussion of this topic off.528Tracking Chapter1919.2.2Kalman FilteringAn important feature of the class of models we have described is that all the conditional probability models we need to deal with are normal.In particular,P(X i|y1,...,y i−1)is normal;as is P(X i|y1,...,y i).This means that they are relatively easy to represent—all we need to do is maintain representations of the mean and the covariance for the prediction and correction phase.In particular,our model will admit a relatively simple process where the representation of the mean and covariance for the prediction and estimation phase are updated.19.2.3The Kalman Filter for a1D State VectorThe dynamic model is nowx i∼N(d i x i−1,σ2di)y i∼N(m i x i,σ2m i)We need to maintain a representation of P(X i|y0,...,y i−1)and of P(X i|y0,...,y i).In each case,we need only represent the mean and the standard deviation,becausethe distributions are normal.NotationWe will represent the mean of P(X i|y0,...,y i−1)as X−i and the mean of P(X i|y0,...,y i) as X+i—the superscripts suggest that they represent our belief about X i immedi-ately before and immediately after the i’th measurement arrives.Similarly,we will represent the standard deviation of P(X i|y0,...,y i−1)asσ−i and of P(X i|y0,...,y i)asσ+i.In each case,we will assume that we know P(X i−1|y0,...,y i−1),meaningthat we know X+i−1andσ+i−1.Tricks with IntegralsThe main reason that we work with normal distributions is that their integrals are quite well behaved.We are going to obtain values for various parameters as integrals,usually by change of variable.Our current notation can make appropriate changes a bit difficult to spot,so we writeg(x;µ,v)=exp−(x−µ)22vWe have dropped the constant,and for convenience are representing the variance (as v),rather than the standard deviation.This expression allows some convenient transformations;in particular,we haveg(x;µ,v)=g(x−µ;0,v)Section19.2.Linear Dynamic Models and the Kalman Filter529g(m;n,v)=g(n;m,v)g(ax;µ,v)=g(x;µ/a,v/a2)We will also need the following fact:∞−∞g(x−u;µ,v a)g(u;0,v b)du∝g(x;µ,v2a+v2b)(there are several ways to confirm that this is true:the easiest is to look it up in tables;more subtle is to think about convolution directly;more subtle still is to think about the sum of two independent random variables).We need a further identity.We haveg(x;a,b)g(x;c,d)=g(x;ad+cbb+d,bdb+d)f(a,b,c,d)here the form of f is not significant,but the fact that it is not a function of x is. The exercises show you how to prove this identity.PredictionWe haveP(X i|y0,...,y i−1)=P(X i|X i−1)P(X i−1|y0,...,y i−1)dX i−1NowP(X i|y0,...,y i−1)=P(X i|X i−1)P(X i−1|y0,...,y i−1)dX i−1)∝ ∞−∞g(X i;d i X i−1,σ2di)g(X i−1;X+i−1,(σ+i−1)2)dX i−1∝ ∞−∞g((X i−d i X i−1);0,σ2d i)g((X i−1−X+i−1);0,(σ+i−1)2)dX i−1∝ ∞−∞g((X i−d i(u+X+i−1));0,(σd i)2)g(u;0,(σ+i−1)2)du∝ ∞−∞g((X i−d i u);d i X+i−1,σ2d i)g(u;0,(σ+i−1)2)du∝ ∞−∞g((X i−v);d i X+i−1,σ2d i)g(v;0,(d iσ+i−1)2)dv∝g(X i;d i X+0,σ2di +(d iσ+i−1)2)530Tracking Chapter19 where we have applied the transformations above,and changed variable twice.All this means thatX−i=d i X+i−1(σ−i)2=σ2di+(d iσ+i−1)2CorrectionWe haveP(X i|y0,...,y i)=P(y i|X i)P(X i|y0,...,y i−1)P(y i|X i)P(X i|y0,...,y i−1)dX i∝P(y i|X i)P(X i|y0,...,y i−1)We know X−i andσ−i ,which represent P(X i|y0,...,y i−1).Using the notation above,we haveP(X i|y0,...,y i)∝g(y i;m i X i,σ2m i)g(X i;X−i,(σ−i)2)=g(m i X i;y i,σ2mi )g(X i;X−i,(σ−i)2)=g(X i;y im i,σ2mim2i)g(X i;X−i,(σ−i)2)and by pattern matching to the identity above,we haveX+i=X−iσ2mi+m i y i(σ−i)2σ2mi+m2i(σ−i)2σ+i=σ2mi(σ−i)2(σ2mi+m2i(σ−i)2)19.2.4The Kalman Update Equations for a General State Vector We obtained a1D tracker without having to do any integration using special prop-erties of normal distributions.This approach works for a state vector of arbitrary dimension,but the process of guessing integrals,etc.,is a good deal more elabo-rate than that shown in section19.2.3.We omit the necessary orgy of notation —it’s a tough but straightforward exercise for those who really care(you should figure out the identitiesfirst and the rest follows)—and simply give the result in algorithm??.Section19.2.Linear Dynamic Models and the Kalman Filter531 Dynamic Model:x i∼N(d i x i−1,σd i)y i∼N(m i x i,σm i)Start Assumptions:x−0andσ−0are knownUpdate Equations:Predictionx−i =d i x+i−1σ−i=σ2d i+(d iσ+i−1)2Update Equations:Correctionx+ i =x−iσ2mi+m i y i(σ−i)2σ2mi+m2i(σ−i)2σ+i=σ2mi(σ−i)2(σ2mi+m2i(σ−i)2)Algorithm19.1:The1D Kalmanfilter updates estimates of the mean and co-variance of the various distributions encountered while tracking a one-dimensional state variable using the given dynamic model.19.2.5Forward-Backward SmoothingIt is important to notice that P(X i|y0,...,y i)is not the best available represen-tation of X i;this is because it doesn’t take into account the future behaviour of the point.In particular,all the measurements after y i could affect our represen-tation of X i.This is because these future measurements might contradict the estimates obtained to date—perhaps the future movements of the point are more in agreement with a slightly different estimate of the position of the point.However, P(X i|y0,...,y i)is the best estimate available at step i.What we do with this observation depends on the circumstances.If our appli-532Tracking Chapter19 Dynamic Model:x i∼N(D i x i−1,Σd i)y i∼N(M i x i,Σm i)Start Assumptions:x−0andΣ−0are knownUpdate Equations:Predictionx−i =D i x+i−1Σ−i =Σdi+D iσ+i−1D iUpdate Equations:CorrectionK i=Σ−i M T iM iΣ−i M T i+Σm i−1x+i=x−i+K iy i−M i x−iΣ+i=[Id−K i M i]Σ−iAlgorithm19.2:The Kalmanfilter updates estimates of the mean and covariance of the various distributions encountered while tracking a state variable of some fixed dimension using the given dynamic model.cation requires an immediate estimate of position—perhaps we are tracking a car in the opposite lane—there isn’t much we can do.If we are tracking off-line—perhaps,for forensic purposes,we need the best estimate of what an object was doing given a videotape—then we can use all data points,and so we want to rep-resent P(X i|y0,...,y N).A common alternative is that we need a rough estimate immediately,and can use an improved estimate that has been time-delayed by a number of steps.This means we want to represent P(X i|y0,...,y i+k)—we have to wait till time i+k for this representation,but it should be an improvement on P(X i|y0,...,y i).Section19.2.Linear Dynamic Models and the Kalman Filterconstant velocity(compare withfigure19.1).The state is plotted with open circles,asa function of the step i.The*-s give x−i ,which is plotted slightly to the left of thestate to indicate that the estimate is made before the measurement.The x-s give themeasurements,and the+-s give x+i ,which is plotted slightly to the right of the state.Thevertical bars around the*-s and the+-s are3standard deviation bars,using the estimate of variance obtained before and after the measurement,respectively.When the measurement is noisy,the bars don’t contract all that much when a measurement is obtained(compare withfigure19.4).Introducing a Backward FilterNow we haveP(X i|y0,...,y N)=P(X i,y i+1,...,y N|y0,...,y i)P(y0,...,y i)P(y0,...,y N)=P(y i+1,...,y N|X i,y0,...,y i)P(X i|y0,...,y i)P(y0,...,y i)P(y0,...,y N)=P(y i+1,...,y N|X i)P(X i|y0,...,y i)P(y0,...,y i)P(y0,...,y N)=P(X i|y i+1,...,y N)P(X i|y0,...,y i)P(y i+1,...,y N)P(y0,...,y i) P(X i)P(y0,...,y N)Tracking Chapter19 constant acceleration(compare withfigure19.2).The state is plotted with open circles,as a function of the step i.The*-s give x−i ,which is plotted slightly to the left of thestate to indicate that the estimate is made before the measurement.The x-s give themeasurements,and the+-s give x+i ,which is plotted slightly to the right of the state.Thevertical bars around the*-s and the+-s are3standard deviation bars,using the estimate of variance obtained before and after the measurement,respectively.When the measurement is noisy,the bars don’t contract all that much when a measurement is obtained.The fraction in brackets should look like a potential source of problems to you;in fact,we will be able to avoid tangling with it by a clever trick.What is impor-tant about this form is that we are combining P(X i|y0,...,y i)—which we know how to obtain—with P(X i|y i+1,...,y N).We actually know how to obtain arepresentation of P(X i|y i+1,...,y N),too.We could simply run the Kalmanfilter backwards in time,using backward dynamics,and take the predicted representation of X i(we leave the details of relabelling the sequence,etc.to the exercises).Combining RepresentationsNow we have two representations of X i:one obtained by running a forwardfilter, and incorporating all measurements up to y i;and one obtained by running a back-wardfilter,and incorporating all measurements after y i.We need to combine these representations.Instead of explicitly determining the missing terms in equation??, we can get the answer by noting that this is like having another measurement.InSection19.2.Linear Dynamic Models and the Kalman Filter535 particular,we have a new measurement generated by X i—that is,the result of the backwardfilter—to combine with our estimate from the forwardfilter.We know how to combine estimates with measurements,because that’s what the Kalman filter equations are for.All we need is a little notation.We will attach the superscript f to the estimate from the forwardfilter,and the superscript b to the estimate from the backward filter.We will write the mean of P(X i|y0,...,y N)as X∗i and the covariance of P(X i|y0,...,y N)asΣ∗i.We regard the representation of X b i as a measurementof X i with mean X b,−i and covarianceΣb,−i—the minus sign is because the i’thmeasurement cannot be used twice,meaning the backwardfilter predicts X i using y N...y i+1.This measurement needs to be combined with P(X i|y0,...,y i),whichhas mean X f,+i and covarianceΣf,+i(when we substitute into the Kalman equations,these will take the role of the representation before a measurement,because the valueof the measurement is now X b,−i ).Substituting into the Kalman equations,wefindK∗i=Σf,+iΣf,+i+Σb,−i−1Σ∗i=[I−K i]Σ+,fiX∗i=X f,+i +K∗iX b,−i−X f,+iIt turns out that a little manipulation(exercises!)yields a simpler form,which we give in algorithm3.Forward-backward estimates can make a substantial difference, asfigure19.5illustrates.PriorsIn typical vision applications,we are tracking forward in time.This leads to aninconvenient asymmetry:we may have a good idea of where the object started, but only a poor one of where it stopped,i.e.we are likely to have a fair prior for P(x0),but may have difficulty supplying a prior for P(x N)for the forward-backwardfilter.One option is to use P(x N|y0,...,y N)as a prior.This is a dubious act,as this probability distribution does not in fact reflect our prior beliefabout P(x N)—we’ve used all the measurements to obtain it.The consequences can be that this distribution understates our uncertainty in x N,and so leads to a forward-backward estimate that significantly underestimates the covariance for the later states.An alternative is to use a the mean supplied by the forwardfilter, but enlarge the covariance substantially;the consequences are a forward-backward estimate that overestimates the covariance for the later states(comparefigure19.5 withfigure19.6).Not all applications have this asymmetry;for example,if we are engaged in a forensic study of a videotape,we might be able to start both the forward tracker536Tracking Chapter19 Forwardfilter:Obtain the mean and variance of P(X i|y0,...,y i)using the Kalmanfilter.These are X f,+i andΣf,+i.Backwardfilter:Obtain the mean and variance of P(X i|y i+1,...,y N)usingthe Kalmanfilter running backwards in time.These are X b,−i andΣb,−i.Combining forward and backward estimates:Regard the backward esti-mate as a new measurement for X i,and insert into the Kalmanfilter equations to obtainΣ∗i=(Σf,+i)−1+(Σb,−i)−1−1X∗i=Σ∗i(Σf,+i)−1X f,+i+(Σb,−i)−1X b,−iAlgorithm19.3:The forward backward algorithm combines forward and backward estimates of state to come up with an improved estimate.and the backward tracker by hand,and provide a good estimate of the prior in each case.If this is possible,then we have a good deal more information which may be able to help choose correspondences,etc.—the forward tracker shouldfinish rather close to where the backward tracker starts.Smoothing over an IntervalWhile our formulation of forward-backward smoothing assumed that the backward filter started at the last data point,it is easy to start thisfilter afixed number of steps ahead of the forwardfilter.If we do this,we obtain an estimate of state in real time(essentially immediately after the measurement),and an improved estimate somefixed numbers of measurements later.This is sometimes useful.Furthermore, it is an efficient way to obtain most of the improvement available from a backward filter,if we can assume that the effect of the distant future on our estimate is relatively small compared with the effect of the immediate future.Notice that we need to be careful about priors for the backwardfilter here;we might take the forward estimate and enlarge its covariance somewhat.19.3Non-Linear Dynamic ModelsIf we can assume that noise is normally distributed,linear dynamic models are rea-sonably easy to deal with,because a linear map takes a random variable with a normal distribution to another random variable with a(different,but easily deter-。
高考英语一轮复习 MODULE 1 Pride and Prejudice
入舵市安恙阳光实验学校（圆梦优化测试）高考英语一轮复习 MODULE 1 Pride and Prejudice 外研版选修10一、单项填空（本大题共10小题，共10分）1.－Did you have a good sleep last night?－Yes, never sleep ____．A．badly B．better C．worse D．best2.If you keep on, you’ll succeed _____. Wish you success in theexaminations.A. in timeB. at one timeC. for the same timeD. sometimes(·东北三省三校第二次联考，23)—Have you figured out how much the tuition is in American universities?—$19 000 or ________ like that.A．anything B．everythingC．nothing D．something3.Tony is coming with _____ boys.A. little two otherB. two other littleC. two little otherD. little other two4. I bought three DVD copies of the film Avatar and now _ is left.Somebody just borrows something and never returns.A. noneB. nothingC. eitherD. neither5.【2013湖南】34. —I don’t understand why you didn’t go to the lectureyesterday afternoon.—I’m so sorry. But I _________ my homework.A. had doneB. was doingC. would doD. am doing6.I was lucky enough to get on the train before it ________.A. pulled onB. pulled downC. pulled inD. pulled out7.(·毕业班质检，35)—I wonder ________ so many people are crazy aboutGangnam Style.—It's good for bodybuilding, and it brings people a lot of fun, you know.A．how B．whereC．why D．that8. (2014·江西上饶一模)—Would you like to see the movie？They say it’sreally nice.—I’m afraid I can’t make________.I’m not myself today.A．that B．itC．this D．one9. (2014·北京东城区高三教学统一检测)It was a small room，________itafforded a fine view of the old city.A．so B．forC．or D．but二、完形填空（本大题共1小题，共30分）10.【浙江温州十校第一次联考】完形填空(共20小题；每小题1分，满分20分)阅读下面短文，掌握其大意，然后从21-40各题所给的四个选项（A、B、C 和D）中，选出最佳选项。
卡丁车安全注意事项
卡丁车安全注意事项●Please do not wear any clothes with loose parts or bring anything thatcould flap during your drive (such as: scarf, bag, sack or loosely clothes).Injury may occur.上车前请勿穿着或携带任何会在驾驶过程中飘动的衣物（诸如：围巾、包、袋、宽大的衣服等）以免被车辆链条或后轴卡住。
●Please do not wear slippers or high-heel shoes while driving, for ladiesplease put your hair into the balaclava, in case it entangle with the tyres.请勿穿着拖鞋，高跟鞋驾驶卡丁车.女士请将头发整理塞入面罩内，严禁将头发散落在肩上，以防止头发卷入轮胎。
●Visitors who have a heart disease, hypertensive and myocardial infarctionor other diseases that could develop during the driving, will not be allowed to drive the karts.患有心脏病，高血压，心肌梗塞等疾病不能参与激烈运动的游客，请勿驾驶卡丁车。
●Must wear balaclava and helmet before you get on the vehicle.上车前请务必佩戴面罩及头盔。
●Please fix the helmet before entering the track.在进入赛道之前，请务必扣紧头盔。
●To remove the helmet, unfasten the red lock; if you can not do it, pleaseask our staff to assist you.下车后先解开红色锁扣，然后摘下头盔；如果您不能解开，可寻求工作人员帮助。
Journey.to.the.Edge.of.the.Universe.2008.Bluray.1080p.AC3.Audio.x264-CHD.eng [字幕转换助手]
![Journey.to.the.Edge.of.the.Universe.2008.Bluray.1080p.AC3.Audio.x264-CHD.eng [字幕转换助手]](https://img.taocdn.com/s3/m/7751e5687e21af45b307a812.png)
Luring us onward on, like a moth to a flame.
Wait ,there's something else, obscured by the sun
It must be Mercury.
She can welcome the new day in the east...
...say good night in the west
A sister to our planet...
...she's about the same size and gravity as Earth.
Where are the twinkling stars?
The beautiful spheres gliding through space?
Maybe we shouldn't be out here, maybe we should turn back
But there's something about the Sun, something hypnotic, like the Medusa
The atmosphere is choking with carbon dioxide
Never expected this Venus is one angry goddess.
The air is noxious, the pressure unbearable.
And it's hot, approaching 900 degrees
Down there, life continues.
2023-2024学年广东省惠州市高三上学期第三次调研考试试题英语试题
2023-2024学年广东省惠州市高三上学期第三次调研考试试题英语试题If you wish to feel happier, cook better or just learn random things, here are some of the most exciting podcast (播客) series that can help you through your learning journey.Ten Percent HappierTen Percent Happier was created by Dan Harris. He has some interesting guests, such as Pandora Skyes, to talk about personal development. Dan believes that you can train yourself to be happy, and you should expect progress to come slowly. The podcast currently has over 700 episodes, each with different time lengths, from 6 minutes to 30 minutes.Delicious Ways to Feel BetterElla Woodward approaches a sensitive subject about our relationship with food. More than 100 episodes are waiting for you, and the average length of an episode is about one hour. This podcast can help you fix your negative food experiences with positive ones, which can result in enjoying all types of food in moderation and respecting you r body’s natural hunger cues.Power HourAdrienne Herbert has made more than 250 episodes in which she talks about motivation, trends, career paths and anything about self-improvement. A new episode airs every week and usually lasts for an hour, so it’s a perfect podcast when you need a little bit of a boost to keep going. Adrienne also invites professionals to her podcasts to discuss important matters.Listening to a podcast can teach you many things but also help you relax after a long day at work. However, the soothing voices of the hosts and the engaging subjects will make you become interested in personal growth and development, which is essential for you to have a better life.1. Who created the podcast with the most episodes?A．Adrienne Herbert. B．Ella Woodward.C．Pandora Skyes. D．Dan Harris.2. What do Delicious Ways to Feel Better and Power Hour have in common?A．Sensitive subjects. B．Average length.C．Special guests. D．Target audiences.3. Why are the podcasts introduced?A．To emphasize self-growth. B．To suggest new lifestyles.C．To offer ways of making food. D．To arouse discussion on hot topics.My mom spent years as a stay-at-home mom so that my brothers and I could focus on our education. However, I could tell from her curiosity of and attitudes toward working women that she enviedtheir financial freedom and the self-esteem that must come with it. When I asked her about working again, she would tell me to focus on achieving my dream. I knew she had once dreamed for herself.For years, I watched het effortlessly light up conversations with both strangers and family. Her empathy and ability to reach the heart could make anyone laugh, even when the story itself did not apply to them at all. “Mom, have you ever thought about being a stan d-up comedian?” “It is too late for me, son,” she responded, laughing at the idea. I could not bear to watch her struggle between ambition and doubt.Her birthday was coming up. Although I had already bought her a present, I knew what I actually wanted to give her. I placed little notes of encouragement inside the present. I asked my family and her friends to do the same. Eventually I had collected 146 notes, and each with the same sentiment: “You are humorous, full of life, and ready to take on the stage.”On the day of her birthday, my mom unwrapped my present. She was not surprised as she had hinted at it for long. But then she saw those little notes. She started to weep with her hands full of notes. She could not believe the support was real.Within two months, my mom gave her first performance in a New York comedy club. I have read the notes countless times with my mom. They are framed and line the walls of her new office space that she rented with the profits she made from working as a professional comedian. For many parents, their children’s careers are their greatest accomplishment, but for me my mom’s is mine. 4. What was the attitude of the author’s mother to working women?A．She was curious about their income. B．She admired what work brought them.C．She felt indifferent to working women. D．She appreciated their ambitions infinance.5. According to the author, what makes his mother a good comedian?A．Her effort in making friends. B．Her talent to bring people joy.C．Her curiosity about working women. D．Her desire for financial independence. 6. How did his mother feel when reading the notes on her birthday?A．Amazed and hesitant. B．Sad and disappointed.C．Moved and encouraged. D．Delighted and proud.7. What is the author’s greatest accomplishment?A．Supporting Mom’s dream.B．Achieving his own dream.C．Securing financial freedom. D．Becoming a successful comedian.Some words imitate the sounds made by the things they describe, like “buzz” or “hiss”, which is called onomatopoeia (拟声词). But what if the way a word sounds could arouse some other feature of an object, like its shape?Marcus Perlman, a lecturer at the University of Birmingham, says that a century ago, linguists (语言学家) insisted that the words of objects don’t necessarily sound like the very things. There’s nothing doggy-sounding about the word dog or catlike-sounding about the word cat. But there’s plenty of evidence now proving it false. To further explore this connection, Perlman and his colleagues turned to something called the bouba/kiki effect.What it is about is that when you see two shapes-one looks like a cloud, kind of roundish, and the other one is more spiky (尖形的), like a star-and when you’re asked to say which one is bouba, you will be more likely to point to a rounded one and, for kiki, to a spiky one. One explanation for the effect could be the appearance of the letters. The round shape of b-o-u-b-a might arouse the sense of roundness. But what happens when you don’t see the words but hear them?In a following test, participants were told to look at the two shapes and then listen to the sound: either bouba or kiki. Whatever their native language is, most participants said the rounder shape was bouba and the spiky one was kiki. This suggests that the effect seems to be driven by some correspondence (对应关系) between the spoken words and the shapes, which might bring us closer to how the first words came.8. What may Marcus Parlman believe about the words of objects?A．Words sounding like objects don’t exist.B．Words don’t have to so und like objects.C．Words of objects are difficult to understand. D．Words pronunciation is connected to objects.9. What is paragraph 3 mainly about?A．The distinction between various shapes. B．The explanation of the bouba/kiki effect.C．The comparison between bouba and kiki. D．The introduction to the bouba/kiki effect.10. Which shape may the participants choose after hearing the “bauba” sound?A．A star. B．A circle. C．A pyramid. D．A diamond. 11. What is suggested in the text?A．Different languages may have the same origins.B．The word bouba or kiki can be found in languages.C．The effect may help understand the origin of language.D．The secret of language formation has been discovered.We all sometimes behave in ways that seem to conflict with our goals and intentions. One person might wish to spend more time with family but instead find themselves mindlessly browsing social media. Another may repeatedly ignore their alarm and miss their morning workout. If we only had more willpower, the conventional wisdom goes, we could eat healthier, exercise regularly, and spend more time with loved ones. However, in Determined: A Science of Life without Free Will, scientist Robert Sapolsky argues that such choices are actually determined by factors beyond our control.Sapolsky demonstrates in his book how various factors influence our intentions and actions. He describes scientific evidence that those influences may occur minutes, hours, or days before our actions; some may even begin years earlier. For example, people raised in a collectivist culture tend to avoid obstacles (障碍) when walking whereas those raised in independent cultures will remove the obstacles.In particular, Sapolsky argues against the claim that “luck” evens (均等) out over time, with fortune and misfortune striking most people in equal measure, an idea favored by others. Instead, he provides convincing examples that many who are born “unlucky” have little chance of getting ahead.Although Sapolsky is careful not to confuse determinism with the inability to affect change in the world, his unclear attitude toward how determinism might coexist with free will is one of the book’s weak points. Nonetheless, this book is worth reading. Better yet, pair it with Kevin Mitchell’s book Free Agents: How Evolution Gave Us Free Will, which makes the opposite argument, and then, decide for yourself whether you had a choice to do so or it was all predetermined.12. How is the topic introduced in the first paragraph?A．By giving examples. B．By listing statistics.C．By raising arguments. D．By drawing conclusions.13. According to Sapolsky, what determine(s) our choices and actions?A．Level of willpower. B．Random luck.C．Uncontrollable elements. D．Conventional wisdom.14. What does the underlined word “collectivist” mean?A．Low-esteemed. B．Determined. C．Self-centered. D．Cooperative.15. Which of the following can be a suitable title for the passage?A．Exploration on Free Will B．Wisdom in Decision MakingC．Views on Determinism and Free Will D．Conflicts between Goals and Choices Shopping in the sales feels like a subtle way to spend. Lower prices naturally equal money saved. 16 Sometimes you think you’re getting a bargain, but actually you’re paying more than you need to. So, follow the tips below an d you’ll hopefully be on the right track.Only buy essentials. You might have been tempted to buy a much higher item than you actually need, simply because we’re dazzled by the size of the discount. So despite the savings made on the full price item, it’s actually a huge waste of cash. 17 You can even have a “need” note which you add to regularly, allowing you to focus your shopping during the sales.18 Just because there’s a sale running with a lower price, doesn’t mean you can scramble for the goods immediately. If you can wait, some price history websites are a great way to get a sense of any regularly occurring price variations. Certain items will have a life cycle too. Christmas cards anddecorations are bargains in January while TVs tend to be at their lowest prices in the early summer when the newer models are released. 19 So this is only a strategy for non-essentials.Check for alternatives. Finally, even if you think you’ve got the best price out there-or at least one you’re happy to pay-it’s worth checking you’re not overpaying still when compared to similar products. 20 . That could mean staying away from high-end brands, looking for secondhand items or trying a different but similar style.My grandmother suffers from Alzheimer’s disease. Seeing her condition worsen over time, and knowing I could do nothing, created a feeling of ________ in me.As the years passed and my understanding of the disease grew, my frustration turned first to anger, then resignation (顺从), and finally ________. Though the disease was bound to slowly eat away at her, there were ways to ________ its progress. Every night the two of us would sit together and________ while my grandmother would count beans. The exercise could help to keep her mentally ________.Gradually her counting became slower, and she would lose track of things more ________. While at night we would count beans, in the day I involved myself in studies of ________. My earlier feelings of despair gave way to ________; advances in genetic engineering offered the possibility that one day those like my grandmother could be cured, or ________ from developing Alzheimer’s in the first place.Seeing my grandmother slip away ________ me to take biology courses to learn more about cells, the nervous system, and genetic information, which helped me better understand how our ________ of this disease has grown in the past decade. ________ I began to see new possibilities for preventing diseases like Alzheimer’s.Nowadays, my grandmother cannot count beans anymore and doesn’t ________ me at all. Once she used to tell me stories and I whispered to her all my problems. Now I am a ________ to her. How I wish one day I could contribute to a future, where no child has to watch their grandmother count the ________ they have left together.21.A．regret B．dilemma C．annoyance D．hopelessness 22.A．pity B．sympathy C．tolerance D．acceptance 23.A．stop B．slow C．change D．accelerate 24.A．chat B．sigh C．think D．complain 25.A．strong B．sharp. C．busy D．fresh26.A．easily B．naturally C．slowly D．painfully 27.A．nursing B．biology C．companion D．psychology 28.A．anxiety B．calmness C．optimism D．indifference 29.A．excused B．defended C．prevented D．separated 30.A．urged B．forced C．required D．motivated 31.A．concern B．sympathy C．knowledge D．experience 32.A．Instantly B．Gradually C．Temporarily D．Accidentally 33.A．miss B．expect C．notice D．recognize 34.A．listener B．follower C．stranger D．watcher 35.A．time B．beans C．money D．stories阅读下面短文，在空白处填入1个适当的单词或括号内单词的正确形式。
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．
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的解 读 出现 了 偏 差