Rapid prototyping of a Self-Timed ALU with FPGAs

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信息素平滑机制

信息素平滑机制

信息素平滑机制
信息素平滑机制是一种常见的机器学习技术,用于解决分类问题中的过拟合问题。

在计算机视觉、自然语言处理等领域,信息素平滑机制被广泛应用。

信息素平滑机制的基本思想是通过在训练数据中加入噪音来减少模型的过拟合。

具体来说,信息素平滑机制会对训练数据中的特征进行随机扰动,使得模型在训练过程中不会过于依赖某些特征,从而提高模型的泛化能力。

信息素平滑机制的实现有多种方法,其中一种常见的方法是拉普拉斯平滑。

拉普拉斯平滑是通过在特征出现的次数中添加一个小的常数来实现的,这样可以避免某些特征在训练数据中没有出现而导致的概率为零的情况。

除了拉普拉斯平滑,信息素平滑机制还有其他实现方法,例如贝叶斯平滑、加和平滑等。

这些方法都是在原始模型的基础上添加一些噪音或平滑项,从而减少模型的过拟合。

信息素平滑机制的优点在于可以有效地减少模型的过拟合,提高模型的泛化能力。

另外,信息素平滑机制的实现比较简单,不需要对模型进行过多的修改。

然而,信息素平滑机制也存在一些缺点。

首先,信息素平滑机制可
能会降低模型的准确率,因为它会对训练数据进行扰动。

其次,信息素平滑机制需要进行大量的计算,可能会增加训练时间和计算资源的消耗。

总的来说,信息素平滑机制是一种常见的机器学习技术,可以有效地减少模型的过拟合。

但是,在使用信息素平滑机制时,需要权衡准确率和计算资源的消耗,选择合适的平滑方法和参数。

Self-timed CMOS static logic circuit

Self-timed CMOS static logic circuit

专利名称:Self-timed CMOS static logic circuit发明人:Christopher McCall Durham,Peter JuergenKlim申请号:US09067153申请日:19980427公开号:US06522170B1公开日:20030218专利内容由知识产权出版社提供专利附图:摘要:A Self-Timed CMOS Static Circuit Technique has been invented that provides full handshaking to the source circuits; prevention of input data loss by virtue off interlocking both internal and incoming signals; full handshaking between the circuit and sink self-timed circuitry; prevention of lost access operation information by virtue of an internal lock-out for the output data information; and plug-in compatibility for some classes of dynamic self-timed systems. The net result of the overall system is that static CMOS circuits can now be used to generate a self-timed system. This is in contrast to existing self-timed systems that rely on dynamic circuits. Thus, the qualities of the static circuitry can be preserved and utilized to their fullest advantage.申请人:INTERNATIONAL BUSINESS MACHINES CORPORATION代理机构:Winstead Sechrest & Minick P.C.代理人:Kelly K. Kordzik,Robert M. Carwell更多信息请下载全文后查看。

吴恩达提示词系列解读

吴恩达提示词系列解读

吴恩达提示词系列解读摘要:1.吴恩达简介2.提示词系列的背景和意义3.深度学习提示词解读4.强化学习提示词解读5.计算机视觉提示词解读6.自然语言处理提示词解读7.总结正文:吴恩达,全球知名的AI专家,拥有丰富的学术和产业经验,他的一系列提示词为广大AI学习者提供了宝贵的指导。

本文将针对吴恩达提示词系列进行解读,以期帮助大家更好地理解和学习AI技术。

1.吴恩达简介吴恩达,Andrew Ng,曾是斯坦福大学的人工智能教授,后来创立了Google Brain项目,并成为了百度首席科学家。

他一直致力于推动AI技术的发展和应用,尤其是在深度学习和强化学习领域。

2.提示词系列的背景和意义吴恩达提示词系列是他对AI领域的重要观点和思考的总结,涵盖了深度学习、强化学习、计算机视觉、自然语言处理等多个领域。

这些提示词对于AI学习者来说,具有很高的参考价值,可以帮助我们更好地理解AI技术的发展趋势和应用方向。

3.深度学习提示词解读吴恩达的深度学习提示词主要包括“神经网络”、“反向传播”、“卷积神经网络”、“循环神经网络”等。

这些提示词概括了深度学习的核心概念和技术,对于理解深度学习的基本原理和应用至关重要。

4.强化学习提示词解读吴恩达的强化学习提示词主要包括“智能体”、“环境”、“状态”、“动作”、“奖励”等。

这些提示词揭示了强化学习的本质,即智能体如何在环境中通过选择动作来获得奖励,从而实现学习。

5.计算机视觉提示词解读吴恩达的计算机视觉提示词主要包括“图像分类”、“目标检测”、“语义分割”等。

这些提示词代表了计算机视觉的主要任务,对于我们理解和应用计算机视觉技术具有重要意义。

6.自然语言处理提示词解读吴恩达的自然语言处理提示词主要包括“词向量”、“序列到序列模型”、“注意力机制”等。

这些提示词概括了自然语言处理的核心技术,对于我们理解和应用自然语言处理技术具有重要价值。

大语言模型增强因果推断

大语言模型增强因果推断

大语言模型(LLM)是一种强大的自然语言处理技术,它可以理解和生成自然语言文本,并具有广泛的应用场景。

然而,虽然LLM能够生成流畅、自然的文本,但在因果推断方面,它仍存在一些限制。

通过增强LLM的因果推断能力,我们可以更好地理解和解释人工智能系统的行为,从而提高其可信度和可靠性。

首先,我们可以通过将LLM与额外的上下文信息结合,来增强其因果推断能力。

上下文信息包括时间、地点、背景、情感等各个方面,它们可以为LLM提供更全面的信息,使其能够更好地理解事件之间的因果关系。

通过这种方式,LLM可以更好地预测未来的结果,并解释其预测的依据。

其次,我们可以通过引入可解释性建模技术,来增强LLM的因果推断能力。

这些技术包括决策树、规则归纳、贝叶斯网络等,它们可以帮助我们更好地理解LLM的决策过程,从而更准确地预测其结果。

此外,这些技术还可以帮助我们识别因果关系的路径,从而更深入地了解因果关系。

最后,我们可以通过将LLM与其他领域的知识结合,来增强其因果推断能力。

例如,我们可以将经济学、心理学、社会学等领域的知识融入LLM中,以帮助其更好地理解和解释因果关系。

通过这种方式,LLM可以更全面地考虑各种因素,从而更准确地预测和解释因果关系。

在应用方面,增强因果推断能力的LLM可以为许多领域提供更准确、更可靠的决策支持。

例如,在医疗领域,它可以辅助医生制定更有效的治疗方案;在金融领域,它可以辅助投资者做出更明智的投资决策;在政策制定领域,它可以为政策制定者提供更全面、更准确的政策建议。

总之,通过增强大语言模型(LLM)的因果推断能力,我们可以更好地理解和解释人工智能系统的行为,从而提高其可信度和可靠性。

这将有助于推动人工智能技术的广泛应用和发展,为社会带来更多的便利和价值。

同时,我们也需要关注和解决相关伦理和社会问题,以确保人工智能技术的发展符合人类的价值观和利益。

Ellis-Corrective-Feedback

Ellis-Corrective-Feedback

T tries to elicit correct pronunciation and the corrects
S: alib[ai]
S fails again
T: okay, listen, listen, alb[ay] T models correct pronunciation
SS: alib(ay)
Theoretical perspectives
1. The Interaction Hypothesis (Long 1996) 2. The Output Hypothesis (Swain 1985;
1995) 3. The Noticing Hypothesis (Schmidt
1994; 2001) 4. Focus on form (Long 1991)
2. In the course of this, they produce errors. 3. They receive feedback that they recognize as
corrective. 4. The feedback causes them to notice the errors they
first row. (uptake)
The complexity of corrective feedback
Corrective feedback (CF) occurs frequently in instructional settings (but much less frequently in naturalistic settings)
Commentary
Initial focus on meaning Student perceives the feedback as corrective

3D快速建模【英文】

3D快速建模【英文】
Rapid Prototyping Operations
CHAPTER 19
Rapid prototyping
• • • • • Introduction Subtractive processes Additive process Virtual Prototyping Applications
Additive Process
Require elaborate software
1 : Obtain cad file
2 : Computer then constructs slices of a 3-dimensional part 3 : slice analyzed and compiled to provide the rapid prototyping machine 4 : setup of the proper unattended and provide rough part after few hours 5 : Finishing operations and sanding and painting
Advantages
• CAD data files can be manufactured in hours. • Tool for visualization and concept verification. • Prototype used in subsequent manufacturing operations to obtain final part • Tooling for manufacturing operations can be produced
• Manufacturing Software (Planning Machining operations)

T.W. ANDERSON (1971). The Statistical Analysis of Time Series. Series in Probability and Ma

T.W. ANDERSON (1971). The Statistical Analysis of Time Series. Series in Probability and Ma

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Ornstein–Uhlenbeck process - Wikipedia, the f

Ornstein–Uhlenbeck process - Wikipedia, the f

Ornstein–Uhlenbeck process - Wikipedia,the f...Ornstein–Uhlenbeck process undefinedundefinedFrom Wikipedia, the free encyclopediaJump to: navigation, searchNot to be confused with Ornstein–Uhlenbeck operator.In mathematics, the Ornstein–Uhlenbeck process (named after LeonardOrnstein and George Eugene Uhlenbeck), is a stochastic process that, roughly speaking, describes the velocity of a massive Brownian particle under the influence of friction. The process is stationary, Gaussian, and Markov, and is the only nontrivial process that satisfies these three conditions, up to allowing linear transformations of the space and time variables.[1] Over time, the process tends to drift towards its long-term mean: such a process is called mean-reverting.The process x t satisfies the following stochastic differential equation:where θ> 0, μ and σ> 0 are parameters and W t denotes the Wiener process. Contents[hide]1 Application in physical sciences2 Application in financialmathematics3 Mathematical properties4 Solution5 Alternative representation6 Scaling limit interpretation7 Fokker–Planck equationrepresentation8 Generalizations9 See also10 References11 External links[edit] Application in physical sciencesThe Ornstein–Uhlenbeck process is a prototype of a noisy relaxation process. Consider for example a Hookean spring with spring constant k whose dynamics is highly overdamped with friction coefficient γ. In the presence of thermal fluctuations with temperature T, the length x(t) of the spring will fluctuate stochastically around the spring rest length x0; its stochastic dynamic is described by an Ornstein–Uhlenbeck process with:where σ is derived from the Stokes-Einstein equation D = σ2 / 2 = k B T / γ for theeffective diffusion constant.In physical sciences, the stochastic differential equation of an Ornstein–Uhlenbeck process is rewritten as a Langevin equationwhere ξ(t) is white Gaussian noise with .At equilibrium, the spring stores an averageenergy in accordance with the equipartition theorem.[edit] Application in financial mathematicsThe Ornstein–Uhlenbeck process is one of several approaches used to model (with modifications) interest rates, currency exchange rates, and commodity prices stochastically. The parameter μ represents the equilibrium or mean value supported by fundamentals; σ the degree of volatility around it caused by shocks, and θ the rate by which these shocks dissipate and the variable reverts towards the mean. One application of the process is a trading strategy pairs trade.[2][3][edit] Mathematical propertiesThe Ornstein–Uhlenbeck process is an example of a Gaussian process that has a bounded variance and admits a stationary probability distribution, in contrast tothe Wiener process; the difference between the two is in their "drift" term. For the Wiener process the drift term is constant, whereas for the Ornstein–Uhlenbeck process it is dependent on the current value of the process: if the current value of the process is less than the (long-term) mean, the drift will be positive; if the current valueof the process is greater than the (long-term) mean, the drift will be negative. In other words, the mean acts as an equilibrium level for the process. This gives the process its informative name, "mean-reverting." The stationary (long-term) variance is given byThe Ornstein–Uhlenbeck process is the continuous-time analogue ofthe discrete-time AR(1) process.three sample paths of different OU-processes with θ = 1, μ = 1.2, σ = 0.3:blue: initial value a = 0 (a.s.)green: initial value a = 2 (a.s.)red: initial value normally distributed so that the process has invariant measure [edit] SolutionThis equation is solved by variation of parameters. Apply Itō–Doeblin's formula to thefunctionto getIntegrating from 0 to t we getwhereupon we seeThus, the first moment is given by (assuming that x0 is a constant)We can use the Itōisometry to calculate the covariance function byThus if s < t (so that min(s, t) = s), then we have[edit] Alternative representationIt is also possible (and often convenient) to represent x t (unconditionally, i.e.as ) as a scaled time-transformed Wiener process:or conditionally (given x0) asThe time integral of this process can be used to generate noise with a 1/ƒpower spectrum.[edit] Scaling limit interpretationThe Ornstein–Uhlenbeck process can be interpreted as a scaling limit of a discrete process, in the same way that Brownian motion is a scaling limit of random walks. Consider an urn containing n blue and yellow balls. At each step a ball is chosen at random and replaced by a ball of the opposite colour. Let X n be the number of blueballs in the urn after n steps. Then converges to a Ornstein–Uhlenbeck process as n tends to infinity.[edit] Fokker–Planck equation representationThe probability density function ƒ(x, t) of the Ornstein–Uhlenbeck process satisfies the Fokker–Planck equationThe stationary solution of this equation is a Gaussian distribution with mean μ and variance σ2 / (2θ)[edit ] GeneralizationsIt is possible to extend the OU processes to processes where the background driving process is a L évy process . These processes are widely studied by OleBarndorff-Nielsen and Neil Shephard and others.In addition, processes are used in finance where the volatility increases for larger values of X . In particular, the CKLS (Chan-Karolyi-Longstaff-Sanders) process [4] with the volatility term replaced by can be solved in closed form for γ = 1 / 2 or 1, as well as for γ = 0, which corresponds to the conventional OU process.[edit ] See alsoThe Vasicek model of interest rates is an example of an Ornstein –Uhlenbeck process.Short rate model – contains more examples.This article includes a list of references , but its sources remain unclear because it has insufficient inline citations .Please help to improve this article by introducing more precise citations where appropriate . (January 2011)[edit ] References^ Doob 1942^ Advantages of Pair Trading: Market Neutrality^ An Ornstein-Uhlenbeck Framework for Pairs Trading ^ Chan et al. (1992)G.E.Uhlenbeck and L.S.Ornstein: "On the theory of Brownian Motion", Phys.Rev.36:823–41, 1930. doi:10.1103/PhysRev.36.823D.T.Gillespie: "Exact numerical simulation of the Ornstein–Uhlenbeck process and its integral", Phys.Rev.E 54:2084–91, 1996. PMID9965289doi:10.1103/PhysRevE.54.2084H. Risken: "The Fokker–Planck Equation: Method of Solution and Applications", Springer-Verlag, New York, 1989E. Bibbona, G. Panfilo and P. Tavella: "The Ornstein-Uhlenbeck process as a model of a low pass filtered white noise", Metrologia 45:S117-S126,2008 doi:10.1088/0026-1394/45/6/S17Chan. K. C., Karolyi, G. A., Longstaff, F. A. & Sanders, A. B.: "An empirical comparison of alternative models of the short-term interest rate", Journal of Finance 52:1209–27, 1992.Doob, J.L. (1942), "The Brownian movement and stochastic equations", Ann. of Math.43: 351–369.[edit] External linksA Stochastic Processes Toolkit for Risk Management, Damiano Brigo, Antonio Dalessandro, Matthias Neugebauer and Fares TrikiSimulating and Calibrating the Ornstein–Uhlenbeck process, M.A. van den Berg Calibrating the Ornstein-Uhlenbeck model, M.A. van den BergMaximum likelihood estimation of mean reverting processes, Jose Carlos Garcia FrancoRetrieved from ""。

Stability of Time-Delay Systems Equivalence between Lyapunov and Scaled Small-Gain Conditio

Stability of Time-Delay Systems Equivalence between Lyapunov and Scaled Small-Gain Conditio

Stability of Time-Delay Systems:Equivalence between Lyapunov and Scaled Small-Gain ConditionsJianrong Zhang,Carl R.Knopse,and Panagiotis Tsiotras Abstract—It is demonstrated that many previously reported Lyapunov-based stability conditions for time-delay systems are equivalent to the ro-bust stability analysis of an uncertain comparison system free of delays via the use of the scaled small-gain lemma with constant scales.The novelty of this note stems from the fact that it unifies several existing stability results under the same framework.In addition,it offers insights on how new,less conservative results can be developed.Index Terms—Stability,time-delay systems.II.I NTRODUCTIONThe analysis of linear time-delay systems(LTDS)has attracted much interest in the literature over the half century,especially in the last decade.Two types of stability conditions,namely delay-inde-pendent and delay-dependent,have been studied[17].As the name implies,delay-independent results guarantee stability for arbitrarily large delays.Delay-dependent results take into account the maximum delay that can be tolerated by the system and,thus,are more useful in applications.One of the first stability analysis results was the polyno-mial criteria[8]–[10].An important result was later provided by[3], which gives necessary and sufficient conditions for efficient compu-tation of the delay margin for the linear systems with commensurate delays.This result only requires the computation of the eigenvalues and generalized eigenvalues of constant matrices.Unfortunately,it is not straightforward to extend this to many problems of interest, such as the stability of general(noncommensurate)delays systems, H1performance of LTDS with exogenous disturbances,robust stability of LTDS with dynamical uncertainties,and robust controller synthesis,etc.Recently,much effort has been devoted to developing frequency-domain and time-domain based techniques which may be extendable to such problems.The frequency-domain approaches include integral quadratic constraints[6],singular value tests[25], framework-based criteria[4],and other similar techniques.In[20], the traditional -framework was extended for time-delay systems to obtain a necessary and sufficient stability condition,which was then relaxed to a convex sufficient condition.Other recent stability analysis results have been developed in the time-domain,based on Lyapunov’s Second Method using either Lyapunov–Krasovskii functionals or Lyapunov–Razumikhin functions [26],[12],[13],[16],[22],[14],[17],[19].These results are formulated in terms of linear matrix inequalities(LMIs),and,hence,can be solved efficiently[1].While these results are often extendable to the systems with general multiple delays and/or dynamical uncertainties,they can be rather conservative and the corresponding Lyapunov functionals are complex.A formal procedure for constructing Lyapunov functionals for LTDS was proposed in[11],but a Lyapunov functional,in general, Manuscript received June10,1999;revised August10,2000.Recommended by Associate Editor J.Chen.This work was supported by the National Science Foundation under Grant DMI-9713488.J.Zhang and C.R.Knospe are with the Department of Mechanical and Aerospace Engineering,University of Virginia,Charlottesville,V A22904-4746 USA(e-mail:jz9n@;crk4y@).P.Tsiotras is with the School of Aerospace Engineering,Georgia Institute of Technology,Atlanta,GA30332-0150USA(e-mail:p.tsiotras@). Publisher Item Identifier S0018-9286(01)01015-7.does not provide direct information on how conservative the resultant condition may be in practice.In this note,we show that several existing Lyapunov-based results, both delay-independent and delay-dependent,are equivalent to the scaled small-gain condition for robust stability of a comparison system that is free of delay.This result provides a new frequency-domain in-terpretation to some common Lyapunov-based results in the literature. Via a numerical example,we investigate the potential conservatism of the stability conditions,and demonstrate that a major source of conservatism is the embedding of the delay uncertainties in unit disks that the comparison system employs.This source of conservatism is hidden in the Lyapunov-based framework but is quite apparent in the comparison system interpretation.These results also provide insight into how to reduce the conservatism of the stability tests.After a conference version of this note appeared in[28],we be-came aware of the results of[15]and[7]which are related to our approach.Unlike the model transformation class in[15],which con-tains distributed delays,the comparison system employed herein is a delay-free uncertain system stated in frequency domain and permits the immediate application of the standard frequency-domain techniques, such as the framework.The results in[7]are based on a special case of our comparison system,namely M=I n.Neither[15]nor[7]exam-ined the equivalence of existing Lyapunov-based criteria and the scaled small-gain conditions,which is the contribution of this note.The notation is conventional.Let n2m)be the set of all real(complex)n2mmatrices,[f1g,I n be n2n identity matrix,W T be the transpose of real matrix W,and RH1:=f H(s): H(s)2H1,H(s)is a real rational transfer matrix g.P>0indicates that P is a symmetric and positive definite matrix,and k1k1indicates the H1norm defined by k G k1:=sup!2n2n with respect to a block structure 3is defined by 3(M)=0if there is no323such that I0M3 is singular,and3(M)=[min f (3):det(I0M3)=0;323g]01 otherwise.We also define the set1r:=f diag[ 1I n,111, r I n and the closed norm-bounded set B1r:=f12 H1:k1k1 1;1(s)21r g.Finally,for linear time-invariant system P(s)and its input x(t),we define a signal P(s)[x](t)asP(s)[x](t):=L01[P(s)X(s)]where X(s)is the Laplace transform of x(t),and L01[1]is the inverse Laplace operator.III.C OMPARISON S YSTEMFor ease of exposition,we will examine the single-delay case. However,the Lyapunov stability conditions examined here may all be straightforwardly extended to the case of systems with multiple (noncommensurate)delays.Consider the linear time-delay system_x(t)=Ax(t)+A d x(t0 )(1) where A2n2n are constant matrices,and the delay is constant,unknown,but bounded by a known bound as0 . The following assumption is a necessary condition when investigating asymptotic stability of the system(1).Assumption1:The system(1)free of delay is asymptotically stable, that is,the matrix A:=A+A d is Hurwitz.Taking Laplace transforms of both sides,the system(1)can be ex-pressed in the s domain assX(s)=AX(s)+A d e0 s X(s):(2)0018–9286/01$10.00©2001IEEEFig.1.A system with uncertainty.The results of this note depend on the notion of robust stability of afeedback interconnection of a finite-dimensional,linear,time-invariant (FDLTI)system and an uncertain system with known uncertainty struc-ture.The following definition clarifies the type of robust stability used herein.More on this definition can be found in [32].Definition 1:Consider a linear,time-invariant (finite-dimensional)system G (s )interconnected with an uncertain block 1,as shown in Fig.1.The uncertain block 1belongs to a known,uncertainty structure set 121.Then,the system is said to be robustly stable if G (s )is internally stable and the interconnection is well posed and remains internally stable for all 121:To proceed with our analysis,we need the following preliminary results.Lemma 1:Let M2e 0s 01 s e 0s 01 s=A +MA d )X (s )+(I 0M )A d e0 s X (s )+d AX(s )+e 0 s1d A d X (s ):In view of the fact that k e 0 s k 1=1and k (e 0 s 01)=( s )k 1== 1,it follows from the above equation that (2)is a special case of the uncertain system (3)with 11=e 0 s I n ,and 12=(e 0 s 01)=( s )I n .Therefore,the robust stability of (3)guarantees that (1)is asymptotically stable for all 2[0; ].As shown in the next section,the comparison system (3)can be rewritten as an interconnection of an FDLTI system G (s )with a block 1,where 1=diag[11;12]2B 12.Hence,the analysis of the ro-bust stability of the system (3)may be performed via -analysis,since the small- theorem applies even to the case where the uncertainty is nonrational [23].Because the calculation of is NP-hard in general[2],its upper bound with D scales is typically used instead.In partic-ular,the interconnection in Fig.1is robustly stable if G (s )2RH1is internally stable andsup !2(j!)D 01n 2n;D i =D 3i >0g :The test (4),although a convex optimization problem,requires a fre-quency sweep.Alternatively,the analysis of robust stability may be performed without the frequency sweep by solving an LMI.The fol-lowing lemma states this result.Additional conservatism is introduced in this formulation,however,since it implies satisfaction of (4)with the same constant real scaling matrix used for all frequencies.Lemma 2[21](Scaled Small-Gain LMI)1:Consider the system in-terconnection shown in Fig.1where the plant G (s )is FDLTI and the uncertainty block is such that 12B 1r .Let (A;B;C;D )be a min-imal realization of G (s )withG (s )=:Then,the closed-loop system is robustly stable if there exist matricesX >0and Q =diag[Q 1,Q 2,111,Qr ]>0,Qi 22nXA d AXA d A dA T A T d X011XA T d A T dX012X>0(7)where =0 01[(A +A d )T X +X (A +A d )]0( 011+ 012)X .b)[13]There exist matrices P >0;P 1>0and P 2>0satisfyingH P A T P A T dAP 0 P 10 A dP0 P 2<0(8)1Thesmall gain theorem applies to the case where the uncertainty blockscontain infinite dimensional dynamic systems [32].where H=P(A+A d)T+(A+A d)P+ A d(P1+ P2)A T d.c)[19]There exist matrices X>0;U>0;V>0and Wsatisfying10W A d A T A T d V (W+X)0A T d W T0U A T d A T d V0V A d A V A d A d0V0000V<0(9)where 1=(A+A d)T X+X(A+A d)+W A d+A T d W T+U.The following proposition shows that all of above conditions areequivalent to the SSGS conditions for the special case of the compar-ison system(3).Proposition1:For the comparison system(3),if M=0,the SSGScondition is equivalent to the condition(6),2and,if M=I n,the SSGScondition is equivalent to the condition(8)and can also be reducedto the condition(7).Moreover,the delay-dependent condition(9)isequivalent to the SSGS condition for(3)with M as a free-matrix vari-able.Proof:First,let M=0,then the comparison system(3)becomessX(s)=AX(s)+11A d X(s)112B11which can be described as the following closed-loop system:_x=Ax+A d uy=xu=11[y](t):WithG(s)=t h e S S G S c o n d i t i o n b e c o m e s(6).N e x t,w e l e tM=I n a n d13=1112.Equation(3)then becomessX(s)=(A+A d)X(s)+12 A d AX(s)+13 A d A d X(s)(10)with diag[12;13]2B12.The last equation can be rewritten as theclosed-loop system_x=(A+A d)x+ A d u1+ A d u2y1=Axy2=A d xu1=12[y1](t)u2=13[y2](t):Then,by applying Lemma2withG(s)=AA dw e s e e t h a t t h e s y s t e m(1)i s a s y m p t o t i c a l l y s t a b l e f o r a n y c o n s t a n t,0 ,i f t h e r e e x i s tX>0and Q=diag[Q1;Q2]>0suchthatR XA d XA d A T Q1A T d Q2A T d X0Q1000A T d X00Q200Q1A000Q10Q2A d0000Q2<02Similar observations can also be found,for example,in[26]and[4].where R=(A+A d)T X+X(A+A d).Multiplying bydiag[X01;I;I; Q011; Q012]on both sides and using Schurcomplements,the above inequality is equivalenttoH XA d A XA d A dA T A T d X0Q10A T d A T d X00Q2<0X>0;Q1>0;Q2>0(12)where H=(A+A d)T X+X(A+A d)+Q1+Q2.Now,lettingQ1= 011X and Q2= 012X,where constants 1>0and2>0,(12)is reduced to(7).Finally,consider the general case of(3)and rewrite it as the fol-lowing:_x=(A+MA d)x+(I0M)A d u2+ Mu1y1=A d Ax+A d A d u2y2=xu1=12[y1](t)u2=11[y2](t):(13)Therefore,applying Lemma2withG(s)=w h e r e^A=A+MA d,^B=[ M(I0M)AA I]Fig.2.Delay margin versus K.(1)Nyquist Criterion.(2) upper bound withfrequency-dependent D scaling.(3)Condition of[19].(4)Condition of[13].(5)Condition of[16].(6)Condition of[25],[26]for K<Kc o n t r o l o f u n c e r t a i n l i n e a r13t h I F A C W o r l d C o n g r.,1996,p p.113–118.d e l a y s y s t e m s,”i n[14]S.-I.N i c u l e s c u,“O n t h e s t a b i l i t y a n d s t a b i lw i t h d e l a y e d s t a t e,”P h.D.d i s s e r t a t i o n,L a b o r aG r e n o b l e,I N P G,1996.[15]S.-I.Niculescu and J.Chen,“Frequency sweeping tests for asymptoticstability:A model transformation for multiple delays,”in Proc.38th IEEE Conf.Decision Control,1999,pp.4678–4683.[16]S.-I.Niculescu,o,J.-M.Dion,and L.Dugard,“Delay-depen-dent stability of linear systems with delayed state:An LMI approach,”in Proc.34th IEEE Conf.Decision Control,1995,pp.1495–1497. [17]S.-I.Niculescu,E.I.Verriest,L.Dugard,and J.-M.Dion,“Stability androbust stability of time-delay systems:A guided tour,”in Stability and Robust Control of Time Delay Systems.New York:Springer-Verlag, 1997,pp.1–71.[18] A.Packard and J.C.Doyle,“The complex structured singular value,”Automatica,vol.29,no.1,pp.77–109,1993.[19]P.Park,“A delay-dependent stability criterion for systems with uncer-tain time-invariant delays,”IEEE Trans.Automat.Contr.,vol.44,pp.876–877,Apr.1999.[20]G.Scorletti,“Robustness analysis with time-delays,”in Proc.36th IEEEConf.Decision Control,1997,pp.3824–3829.[21]R.E.Skelton,T.Iwasaki,and K.Grigoriadis,A Unified Algebraic Ap-proach to Linear Control Design.New York:Taylor&Francis,1998.[22] E.Tissir and A.Hmamed,“Further results on stability of_x(t)=Ax(t)+Bx(t0 ),”Automatica,vol.32,no.12,pp.1723–1726,1996.[23] A.L.Tits and M.K.H.Fan,“On the small- theorem,”Automatica,vol.31,no.8,pp.1199–1201,1995.[24]J.Tlusty,“Machine dynamics,”in Handbook of High Speed MachiningTechnology,R.I.King,Ed.New York:Chapman&Hall,1985,pp.48–153.[25] E.I.Verriest,M.K.H.Fan,and J.Kullstam,“Frequency domain robuststability criteria for linear delay systems,”in Proc.32nd IEEE Conf.Decision Control,1993,pp.3473–3478.[26] E.I.Verriest and A.F.Ivanov,“Robust stability of systems with delayedfeedback,”Circuits,Syst.Signal Processing,vol.13,pp.213–222,1994.[27]M.Vidyasagar,Nonlinear Systems Analysis,2nd ed.EnglewoodCliffs,NJ:Prentice-Hall,1993.[28]J.Zhang,C.R.Knospe,and P.Tsiotras,“A unified approach to time-delay system stability via scaled small gain,”in Proc.Amer.Control Conf.,1999,pp.307–308.[29],“Toward less conservative stability analysis of time-delay sys-tems,”in Proc.38th IEEE Conf.Decision Control,1999,pp.2017–2022.[30],“Stability of linear time-delay systems:A delay-dependent cri-terion with a tight conservatism bound,”in Proc.2000Amer.Control Conf.,2000.[31],“Asymptotic stability of linear systems with multiple time-in-variant state-delays,”in Proc.2nd IFAC Workshop Linear Time Delay Systems,to be published.[32]K.Zhou,J. C.Doyle,and K.Glover,Robust and Optimal Con-trol.Englewood-Cliffs,NJ:Prentice-Hall,1996.Bounded Stochastic Distributions Control forPseudo-ARMAX Stochastic SystemsHong Wang and Jian Hua ZhangAbstract—Following the recently developed algorithms for the control of the shape of the output probability density functions for general dy-namic stochastic systems[6]–[8],this note presents the modeling and con-trol algorithms for pseudo-ARMAX systems,where,different from all the existing ARMAX systems,the considered system is subjected to any arbi-trary bounded random input and the purpose of the control input design is to make the output probability density function of the system output as close as possible to a given distribution function.At first,the relationship between the input noise distribution and the output distribution is estab-lished.This is then followed by the description on the control algorithm de-sign.A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained.IndexTerms—i=1v i(k)B i(y)y2[a;b](1)whereu k control input;(y;u)measured probability density function of the system output;V(k)=(v1;v2;...;v M)T,weight vector;B i(y)pre-specified basis functions for the approximation of(y;u)[2];A andB constant matrices.Although there are several advantages in using this type of model to de-sign the required control algorithm,it is difficult to link such a model structure to a physical system.In particular,the key assumption that the control input only affects the weights of the output probability density function is strict for some applications.As such,it would be ideal if aManuscript received March30,2000;revised July31,2000.Recommended by Associate Editor Q.Zhang.This work was supported in part by the U.K. EPSRC under Grant(GB/K97721),and in part by the Overseas Scholarship Committee of the P.R.China.H.Wang is with the Department of Paper Science,Affiliated Member of Con-trol Systems Centre,University of Manchester Institute of Science and Tech-nology,Manchester M601QD,U.K.H.Zhang is on leave from the Department of Power Engineering,North China University of Electrical Power,Beijing,P.R.China.Publisher Item Identifier S0018-9286(01)01014-5.0018–9286/01$10.00©2001IEEE。

acl 2023随笔 自然语言中的复杂推理

acl 2023随笔 自然语言中的复杂推理

acl 2023随笔自然语言中的复杂推理自然语言中的复杂推理自然语言是人类交流和表达思想的主要方式之一。

然而,尽管人类在日常对话中能够轻松地进行复杂的推理,但要让计算机系统能够理解和进行类似的推理却是一项巨大的挑战。

在人工智能领域,自然语言处理(NLP)的发展旨在使计算机系统能够理解和处理自然语言。

其中一个重要的研究方向是如何使计算机系统能够进行复杂推理。

复杂推理是指基于已知事实和逻辑规则,通过推导和演绎得出新的结论。

这种推理过程需要对语义、逻辑和常识等多个层面进行综合考虑。

例如,当我们听到一句话“如果今天下雨,那么我就会带伞”,我们可以根据这个条件来判断如果今天确实下雨了,那么我就会带伞。

这种推理过程涉及到条件语句、逻辑关系以及我们对天气和带伞习惯等方面的常识。

在自然语言处理中,实现复杂推理需要解决多个问题。

首先是语义表示问题。

自然语言中存在歧义和多义现象,同一句话可能有不同的解释。

因此,需要将自然语言转化为计算机能够理解的形式,如逻辑形式或图形表示。

其次是推理规则的建模问题。

推理规则是指根据已知事实和逻辑规则进行推导和演绎的方法。

这些规则需要能够覆盖自然语言中各种复杂的推理情况,并且能够灵活地应对不同的语境和背景知识。

最后是常识表示和推理问题。

常识是人类在日常生活中积累的一种普遍知识,它对于理解和进行复杂推理至关重要。

因此,需要将常识以某种方式表示,并将其融入到推理过程中。

近年来,随着深度学习技术的发展,自然语言处理取得了一些重要进展。

例如,基于神经网络的模型可以通过大量数据进行训练,并学习到自然语言中的一些模式和规律。

这些模型可以用于语义表示、句法分析、情感分析等任务,并在某种程度上实现了对复杂推理的支持。

然而,目前仍然存在许多挑战和限制。

首先,复杂推理需要对大量的背景知识和常识进行建模。

目前的自然语言处理模型往往只能处理局部的语义和逻辑关系,对于全局的推理和常识表示仍然存在困难。

其次,复杂推理需要对不同层面的信息进行综合考虑,包括语义、逻辑、常识等。

莱维飞行、混沌映射和自适应t分布 蜣螂算法

莱维飞行、混沌映射和自适应t分布 蜣螂算法

莱维飞行、混沌映射和自适应t分布是蜣螂算法中的三个重要概念。

莱维飞行是一种随机过程,描述了在随机游走过程中,每一步的长度和方向都遵循某种概率分布。

在蜣螂算法中,莱维飞行用于模拟蜣螂在搜寻食物时所走的路径,通过不断随机游走,可以寻找更好的食物来源。

混沌映射是一种非线性动力学系统中的映射关系,具有对初值敏感的特点。

在蜣螂算法中,混沌映射用于模拟蜣螂在移动过程中的行为,使得蜣螂能够根据当前环境进行灵活的移动和决策。

自适应t分布是一种概率分布函数,用于描述在复杂系统中数据的分布情况。

在蜣螂算法中,自适应t分布用于描述蜣螂在不同环境下的行为特征,帮助蜣螂更好地适应环境变化。

蜣螂算法是一种模拟自然界中蜣螂行为的优化算法,通过模拟蜣螂的移动、搜寻和合作等行为,可以解决许多优化问题。

莱维飞行、混沌映射和自适应t分布是蜣螂算法中的三个关键技术,它们共同作用,使得蜣螂算法具有很好的全局优化能力。

Peters (2010) Episodic Future Thinking Reduces Reward Delay Discounting

Peters (2010) Episodic Future Thinking Reduces Reward Delay Discounting

NeuronArticleEpisodic Future Thinking ReducesReward Delay Discounting through an Enhancement of Prefrontal-Mediotemporal InteractionsJan Peters1,*and Christian Bu¨chel11NeuroimageNord,Department of Systems Neuroscience,University Medical Center Hamburg-Eppendorf,Hamburg20246,Germany*Correspondence:j.peters@uke.uni-hamburg.deDOI10.1016/j.neuron.2010.03.026SUMMARYHumans discount the value of future rewards over time.Here we show using functional magnetic reso-nance imaging(fMRI)and neural coupling analyses that episodic future thinking reduces the rate of delay discounting through a modulation of neural decision-making and episodic future thinking networks.In addition to a standard control condition,real subject-specific episodic event cues were presented during a delay discounting task.Spontaneous episodic imagery during cue processing predicted how much subjects changed their preferences toward more future-minded choice behavior.Neural valuation signals in the anterior cingulate cortex and functional coupling of this region with hippo-campus and amygdala predicted the degree to which future thinking modulated individual preference functions.A second experiment replicated the behavioral effects and ruled out alternative explana-tions such as date-based processing and temporal focus.The present data reveal a mechanism through which neural decision-making and prospection networks can interact to generate future-minded choice behavior.INTRODUCTIONThe consequences of choices are often delayed in time,and in many cases it pays off to wait.While agents normally prefer larger over smaller rewards,this situation changes when rewards are associated with costs,such as delays,uncertainties,or effort requirements.Agents integrate such costs into a value function in an individual manner.In the hyperbolic model of delay dis-counting(also referred to as intertemporal choice),for example, a subject-specific discount parameter accurately describes how individuals discount delayed rewards in value(Green and Myer-son,2004;Mazur,1987).Although the degree of delay discount-ing varies considerably between individuals,humans in general have a particularly pronounced ability to delay gratification, and many of our choices only pay off after months or even years. It has been speculated that the capacity for episodic future thought(also referred to as mental time travel or prospective thinking)(Bar,2009;Schacter et al.,2007;Szpunar et al.,2007) may underlie the human ability to make choices with high long-term benefits(Boyer,2008),yielding higher evolutionaryfitness of our species.At the neural level,a number of models have been proposed for intertemporal decision-making in humans.In the so-called b-d model(McClure et al.,2004,2007),a limbic system(b)is thought to place special weight on immediate rewards,whereas a more cognitive,prefrontal-cortex-based system(d)is more involved in patient choices.In an alternative model,the values of both immediate and delayed rewards are thought to be repre-sented in a unitary system encompassing medial prefrontal cortex(mPFC),posterior cingulate cortex(PCC),and ventral striatum(VS)(Kable and Glimcher,2007;Kable and Glimcher, 2010;Peters and Bu¨chel,2009).Finally,in the self-control model, values are assumed to be represented in structures such as the ventromedial prefrontal cortex(vmPFC)but are subject to top-down modulation by prefrontal control regions such as the lateral PFC(Figner et al.,2010;Hare et al.,2009).Both the b-d model and the self-control model predict that reduced impulsivity in in-tertemporal choice,induced for example by episodic future thought,would involve prefrontal cortex regions implicated in cognitive control,such as the lateral PFC or the anterior cingulate cortex(ACC).Lesion studies,on the other hand,also implicated medial temporal lobe regions in decision-making and delay discounting. In rodents,damage to the basolateral amygdala(BLA)increases delay discounting(Winstanley et al.,2004),effort discounting (Floresco and Ghods-Sharifi,2007;Ghods-Sharifiet al.,2009), and probability discounting(Ghods-Sharifiet al.,2009).Interac-tions between the ACC and the BLA in particular have been proposed to regulate behavior in order to allow organisms to overcome a variety of different decision costs,including delays (Floresco and Ghods-Sharifi,2007).In line with thesefindings, impairments in decision-making are also observed in humans with damage to the ACC or amygdala(Bechara et al.,1994, 1999;Manes et al.,2002;Naccache et al.,2005).Along similar lines,hippocampal damage affects decision-making.Disadvantageous choice behavior has recently been documented in patients suffering from amnesia due to hippo-campal lesions(Gupta et al.,2009),and rats with hippocampal damage show increased delay discounting(Cheung and Cardinal,2005;Mariano et al.,2009;Rawlins et al.,1985).These observations are of particular interest given that hippocampal138Neuron66,138–148,April15,2010ª2010Elsevier Inc.damage impairs the ability to imagine novel experiences (Hassa-bis et al.,2007).Based on this and a range of other studies,it has recently been proposed that hippocampus and parahippocam-pal cortex play a crucial role in the formation of vivid event repre-sentations,regardless of whether they lie in the past,present,or future (Schacter and Addis,2009).The hippocampus may thus contribute to decision-making through its role in self-projection into the future (Bar,2009;Schacter et al.,2007),allowing an organism to evaluate future payoffs through mental simulation (Johnson and Redish,2007;Johnson et al.,2007).Future thinking may thus affect intertemporal choice through hippo-campal involvement.Here we used model-based fMRI,analyses of functional coupling,and extensive behavioral procedures to investigate how episodic future thinking affects delay discounting.In Exper-iment 1,subjects performed a classical delay discounting task(Kable and Glimcher,2007;Peters and Bu¨chel,2009)that involved a series of choices between smaller immediate and larger delayed rewards,while brain activity was measured using fMRI.Critically,we introduced a novel episodic condition that involved the presentation of episodic cue words (tags )obtained during an extensive prescan interview,referring to real,subject-specific future events planned for the respective day of reward delivery.This design allowed us to assess individual discount rates separately for the two experimental conditions,allowing us to investigate neural mechanisms mediating changes in delay discounting associated with episodic thinking.In a second behavioral study,we replicated the behavioral effects of Exper-iment 1and addressed a number of alternative explanations for the observed effects of episodic tags on discount rates.RESULTSExperiment 1:Prescan InterviewOn day 1,healthy young volunteers (n =30,mean age =25,15male)completed a computer-based delay discounting proce-dure to estimate their individual discount rate (Peters and Bu ¨-chel,2009).This discount rate was used solely for the purpose of constructing subject-specific trials for the fMRI session (see Experimental Procedures ).Furthermore,participants compiled a list of events that they had planned in the next 7months (e.g.,vacations,weddings,parties,courses,and so forth)andrated them on scales from 1to 6with respect to personal rele-vance,arousal,and valence.For each participant,seven subject-specific events were selected such that the spacing between events increased with increasing delay to the episode,and that events were roughly matched based on personal rele-vance,arousal,and valence.Multiple regression analysis of these ratings across the different delays showed no linear effects (relevance:p =0.867,arousal:p =0.120,valence:p =0.977,see Figure S1available online).For each subject,a separate set of seven delays was computed that was later used as delays in the control condition.Median and range for the delays used in each condition are listed in Table S1(available online).For each event,a label was selected that would serve as a verbal tag for the fMRI session.Experiment 1:fMRI Behavioral ResultsOn day 2,volunteers performed two sessions of a delay dis-counting procedure while fMRI was measured using a 3T Siemens Scanner with a 32-channel head-coil.In each session,subjects made a total of 118choices between 20V available immediately and larger but delayed amounts.Subjects were told that one of their choices would be randomly selected and paid out following scanning,with the respective delay.Critically,in half the trials,an additional subject-specific episodic tag (see above,e.g.,‘‘vacation paris’’or ‘‘birthday john’’)was displayed based on the prescan interview (see Figure 1)indicating which event they had planned on the particular day (episodic condi-tion),whereas in the remaining trials,no episodic tag was pre-sented (control condition).Amount and waiting time were thus displayed in both conditions,but only the episodic condition involved the presentation of an additional subject-specific event tag.Importantly,nonoverlapping sets of delays were used in the two conditions.Following scanning,subjects rated for each episodic tag how often it evoked episodic associations during scanning (frequency of associations:1,never;to 6,always)and how vivid these associations were (vividness of associa-tions:1,not vivid at all;to 6,highly vivid;see Figure S1).Addition-ally,written reports were obtained (see Supplemental Informa-tion ).Multiple regression revealed no significant linear effects of delay on postscan ratings (frequency:p =0.224,vividness:p =0.770).We averaged the postscan ratings acrosseventsFigure 1.Behavioral TaskDuring fMRI,subjects made repeated choices between a fixed immediate reward of 20V and larger but delayed amounts.In the control condi-tion,amounts were paired with a waiting time only,whereas in the episodic condition,amounts were paired with a waiting time and a subject-specific verbal episodic tag indicating to the subjects which event they had planned at the respective day of reward delivery.Events were real and collected in a separate testing session prior to the day of scanning.NeuronEpisodic Modulation of Delay DiscountingNeuron 66,138–148,April 15,2010ª2010Elsevier Inc.139and the frequency/vividness dimensions,yielding an‘‘imagery score’’for each subject.Individual participants’choice data from the fMRI session were then analyzed byfitting hyperbolic discount functions to subject-specific indifference points to obtain discount rates (k-parameters),separately for the episodic and control condi-tions(see Experimental Procedures).Subjective preferences were well-characterized by hyperbolic functions(median R2 episodic condition=0.81,control condition=0.85).Discount functions of four exemplary subjects are shown in Figure2A. For both conditions,considerable variability in the discount rate was observed(median[range]of discount rates:control condition=0.014[0.003–0.19],episodic condition=0.013 [0.002–0.18]).To account for the skewed distribution of discount rates,all further analyses were conducted on the log-trans-formed k-parameters.Across subjects,log-transformed discount rates were significantly lower in the episodic condition compared with the control condition(t(29)=2.27,p=0.016),indi-cating that participants’choice behavior was less impulsive in the episodic condition.The difference in log-discount rates between conditions is henceforth referred to as the episodic tag effect.Fitting hyperbolic functions to the median indifference points across subjects also showed reduced discounting in the episodic condition(discount rate control condition=0.0099, episodic condition=0.0077).The size of the tag effect was not related to the discount rate in the control condition(p=0.56). We next hypothesized that the tag effect would be positively correlated with postscan ratings of episodic thought(imagery scores,see above).Robust regression revealed an increase in the size of the tag effect with increasing imagery scores (t=2.08,p=0.023,see Figure2B),suggesting that the effect of the tags on preferences was stronger the more vividly subjects imagined the episodes.Examples of written postscan reports are provided in the Supplemental Results for participants from the entire range of imagination ratings.We also correlated the tag effect with standard neuropsychological measures,the Sensation Seeking Scale(SSS)V(Beauducel et al.,2003;Zuck-erman,1996)and the Behavioral Inhibition Scale/Behavioral Approach Scale(BIS/BAS)(Carver and White,1994).The tag effect was positively correlated with the experience-seeking subscale of the SSS(p=0.026)and inversely correlated with the reward-responsiveness subscale of the BIS/BAS scales (p<0.005).Repeated-measures ANOVA of reaction times(RTs)as a func-tion of option value(lower,similar,or higher relative to the refer-ence option;see Experimental Procedures and Figure2C)did not show a main effect of condition(p=0.712)or a condition 3value interaction(p=0.220),but revealed a main effect of value(F(1.8,53.9)=16.740,p<0.001).Post hoc comparisons revealed faster RTs for higher-valued options relative to similarly (p=0.002)or lower valued options(p<0.001)but no difference between lower and similarly valued options(p=0.081).FMRI DataFMRI data were modeled using the general linear model(GLM) as implemented in SPM5.Subjective value of each decision option was calculated by multiplying the objective amount of each delayed reward with the discount fraction estimated behaviorally based on the choices during scanning,and included as a parametric regressor in the GLM.Note that discount rates were estimated separately for the control and episodic conditions(see above and Figure2),and we thus used condition-specific k-parameters for calculation of the subjective value regressor.Additional parametric regressors for inverse delay-to-reward and absolute reward magnitude, orthogonalized with respect to subjective value,were included in theGLM.Figure2.Behavioral Data from Experiment1Shown are experimentally derived discount func-tions from the fMRI session for four exemplaryparticipants(A),correlation with imagery scores(B),and reaction times(RTs)(C).(A)Hyperbolicfunctions werefit to the indifference points sepa-rately for the control(dashed lines)and episodic(solid lines,filled circles)conditions,and thebest-fitting k-parameters(discount rates)and R2values are shown for each subject.The log-trans-formed difference between discount rates wastaken as a measure of the effect of the episodictags on choice preferences.(B)Robust regressionrevealed an association between log-differences indiscount rates and imagery scores obtained frompostscan ratings(see text).(C)RTs were signifi-cantly modulated by option value(main effectvalue p<0.001)with faster responses in trialswith a value of the delayed reward higher thanthe20V reference amount.Note that althoughseven delays were used for each condition,somedata points are missing,e.g.,onlyfive delay indif-ference points for the episodic condition areplotted for sub20.This indicates that,for the twolongest delays,this subject never chose the de-layed reward.***p<0.005.Error bars=SEM.Neuron Episodic Modulation of Delay Discounting140Neuron66,138–148,April15,2010ª2010Elsevier Inc.Episodic Tags Activate the Future Thinking NetworkWe first analyzed differences in the condition regressors without parametric pared to those of the control condi-tion,BOLD responses to the presentation of the delayed reward in the episodic condition yielded highly significant activations (corrected for whole-brain volume)in an extensive network of brain regions previously implicated in episodic future thinking (Addis et al.,2007;Schacter et al.,2007;Szpunar et al.,2007)(see Figure 3and Table S2),including retrosplenial cortex (RSC)/PCC (peak MNI coordinates:À6,À54,14,peak z value =6.26),left lateral parietal cortex (LPC,À44,À66,32,z value =5.35),and vmPFC (À8,34,À12,z value =5.50).Distributed Neural Coding of Subjective ValueWe then replicated previous findings (Kable and Glimcher,2007;Kable and Glimcher,2010;Peters and Bu¨chel,2009)using a conjunction analysis (Nichols et al.,2005)searching for regions showing a positive correlation between the height of the BOLD response and subjective value in the control and episodic condi-tions in a parametric analysis (Figure 4A and Table S3).Note that this is a conservative analysis that requires that a given voxel exceed the statistical threshold in both contrasts separately.This analysis revealed clusters in the lateral orbitofrontal cortex (OFC,À36,50,À10,z value =4.50)and central OFC (À18,12,À14,z value =4.05),bilateral VS (right:10,8,0,z value =4.22;left:À10,8,À6,z value =3.51),mPFC (6,26,16,z value =3.72),and PCC (À2,À28,24,z value =4.09),representing subjective (discounted)value in both conditions.We next analyzed the neural tag effect,i.e.,regions in which the subjective value correlation was greater for the episodic condi-tion as compared with the control condition (Figure 4B and Table S4).This analysis revealed clusters in the left LPC (À66,À42,32,z value =4.96,),ACC (À2,16,36,z value =4.76),left dorsolateral prefrontal cortex (DLPFC,À38,36,36,z value =4.81),and right amygdala (24,2,À24,z value =3.75).Finally,we performed a triple-conjunction analysis,testing for regions that were correlated with subjective value in both conditions,but in which the value correlation increased in the episodic condition.Only left LPC showed this pattern (À66,À42,30,z value =3.55,see Figure 4C and Table S5),the same region that we previously identified as delay-specific in valuation (Petersand Bu¨chel,2009).There were no regions in which the subjective value correlation was greater in the control condition when compared with the episodic condition at p <0.001uncorrected.ACC Valuation Signals and Functional Connectivity Predict Interindividual Differences in Discount Function ShiftsWe next correlated differences in the neural tag effect with inter-individual differences in the size of the behavioral tag effect.To this end,we performed a simple regression analysis in SPM5on the single-subject contrast images of the neural tag effect (i.e.,subjective value correlation episodic >control)using the behavioral tag effect [log(k control )–log(k episodic )]as an explana-tory variable.This analysis revealed clusters in the bilateral ACC (right:18,34,18,z value =3.95,p =0.021corrected,left:À20,34,20,z value =3.52,Figure 5,see Table S6for a complete list).Coronal sections (Figure 5C)clearly show that both ACC clusters are located in gray matter of the cingulate sulcus.Because ACC-limbic interactions have previously been impli-cated in the control of choice behavior (Floresco and Ghods-Sharifi,2007;Roiser et al.,2009),we next analyzed functional coupling with the right ACC from the above regression contrast (coordinates 18,34,18,see Figure 6A)using a psychophysiolog-ical interaction analysis (PPI)(Friston et al.,1997).Note that this analysis was conducted on a separate first-level GLM in which control and episodic trials were modeled as 10s miniblocks (see Experimental Procedures for details).We first identified regions in which coupling with the ACC changed in the episodic condition compared with the control condition (see Table S7)and then performed a simple regression analysis on these coupling parameters using the behavioral tag effect as an explanatory variable.The tag effect was associated with increased coupling between ACC and hippocampus (À32,À18,À16,z value =3.18,p =0.031corrected,Figure 6B)and ACC and left amygdala (À26,À4,À26,z value =2.95,p =0.051corrected,Figure 6B,see Table S8for a complete list of activa-tions).The same regression analysis in a second PPI with the seed voxel placed in the contralateral ACC region from the same regression contrast (À20,34,22,see above)yielded qual-itatively similar,though subthreshold,results in these same structures (hippocampus:À28,À32,À6,z value =1.96,amyg-dala:À28,À6,À16,z value =1.97).Experiment 2We conducted an additional behavioral experiment to address a number of alternative explanations for the observed effects of tags on choice behavior.First,it could be argued thatepisodicFigure 3.Categorical Effect of Episodic Tags on Brain ActivityGreater activity in lateral parietal cortex (left)and posterior cingulate/retrosplenial and ventro-medial prefrontal cortex (right)was observed in the episodic condition compared with the control condition.p <0.05,FWE-corrected for whole-brain volume.NeuronEpisodic Modulation of Delay DiscountingNeuron 66,138–148,April 15,2010ª2010Elsevier Inc.141tags increase subjective certainty that a reward would be forth-coming.In Experiment 2,we therefore collected postscan ratings of reward confidence.Second,it could be argued that events,always being associated with a particular date,may have shifted temporal focus from delay-based to more date-based processing.This would represent a potential confound,because date-associated rewards are discounted less than delay-associated rewards (Read et al.,2005).We therefore now collected postscan ratings of temporal focus (date-based versus delay-based).Finally,Experiment 1left open the question of whether the tag effect depends on the temporal specificity of the episodic cues.We therefore introduced an additional exper-imental condition that involved the presentation of subject-specific temporally unspecific future event cues.These tags (henceforth referred to as unspecific tags)were obtained by asking subjects to imagine events that could realistically happen to them in the next couple of months,but that were not directly tied to a particular point in time (see Experimental Procedures ).Episodic Imagery,Not Temporal Specificity,Reward Confidence,or Temporal Focus,Predicts the Size of the Tag EffectIn total,data from 16participants (9female)are included.Anal-ysis of pretest ratings confirmed that temporally unspecific and specific tags were matched in terms of personal relevance,arousal,valence,and preexisting associations (all p >0.15).Choice preferences were again well described by hyperbolic functions (median R 2control =0.84,unspecific =0.81,specific =0.80).We replicated the parametric tag effect (i.e.,increasing effect of tags on discount rates with increasing posttest imagery scores)in this independent sample for both temporally specific (p =0.047,Figure 7A)and temporally unspecific (p =0.022,Figure 7A)tags,showing that the effect depends on future thinking,rather than being specifically tied to the temporal spec-ificity of the event cues.Following testing,subjects rated how certain they were that a particular reward would actually be forth-coming.Overall,confidence in the payment procedure washighFigure 4.Neural Representation of Subjective Value (Parametric Analysis)(A)Regions in which the correlation with subjective value (parametric analysis)was significant in both the control and the episodic conditions (conjunction analysis)included central and lateral orbitofrontal cortex (OFC),bilateral ventral striatum (VS),medial prefrontal cortex (mPFC),and posterior cingulate cortex(PCC),replicating previous studies (Kable and Glimcher,2007;Peters and Bu¨chel,2009).(B)Regions in which the subjective value correlation was greater for the episodic compared with the control condition included lateral parietal cortex (LPC),ante-rior cingulate cortex (ACC),dorsolateral prefrontal cortex (DLPFC),and the right amygdala (Amy).(C)A conjunction analysis revealed that only LPC activity was positively correlated with subjective value in both conditions,but showed a greater regression slope in the episodic condition.No regions showed a better correlation with subjective value in the control condition.Error bars =SEM.All peaks are significant at p <0.001,uncorrected;(A)and (B)are thresholded at p <0.001uncorrected and (C)is thresholded at p <0.005,uncorrected for display purposes.NeuronEpisodic Modulation of Delay Discounting142Neuron 66,138–148,April 15,2010ª2010Elsevier Inc.(Figure 7B),and neither unspecific nor specific tags altered these subjective certainty estimates (one-way ANOVA:F (2,45)=0.113,p =0.894).Subjects also rated their temporal focus as either delay-based or date-based (see Experimental Procedures ),i.e.,whether they based their decisions on the delay-to-reward that was actually displayed,or whether they attempted to convert delays into the corresponding dates and then made their choices based on these dates.There was no overall significant effect of condition on temporal focus (one-way ANOVA:F (2,45)=1.485,p =0.237,Figure 7C),but a direct comparison between the control and the temporally specific condition showed a significant difference (t (15)=3.18,p =0.006).We there-fore correlated the differences in temporal focus ratings between conditions (control:unspecific and control:specific)with the respective tag effects (Figure 7D).There were no correlations (unspecific:p =0.71,specific:p =0.94),suggesting that the observed differences in discounting cannot be attributed to differences in temporal focus.High-Imagery,but Not Low-Imagery,Subjects Adjust Their Discount Function in an Episodic ContextFor a final analysis,we pooled the samples of Experiments 1and 2(n =46subjects in total),using only the temporally specific tag data from Experiment 2.We performed a median split into low-and high-imagery participants according to posttest imagery scores (low-imagery subjects:n =23[15/8Exp1/Exp2],imagery range =1.5–3.4,high-imagery subjects:n =23[15/8Exp1/Exp2],imagery range =3.5–5).The tag effect was significantly greater than 0in the high-imagery group (t (22)=2.6,p =0.0085,see Figure 7D),where subjects reduced their discount rate by onaverage 16%in the presence of episodic tags.In the low-imagery group,on the other hand,the tag effect was not different from zero (t (22)=0.573,p =0.286),yielding a significant group difference (t (44)=2.40,p =0.011).DISCUSSIONWe investigated the interactions between episodic future thought and intertemporal decision-making using behavioral testing and fMRI.Experiment 1shows that reward delay dis-counting is modulated by episodic future event cues,and the extent of this modulation is predicted by the degree of sponta-neous episodic imagery during decision-making,an effect that we replicated in Experiment 2(episodic tag effect).The neuroi-maging data (Experiment 1)highlight two mechanisms that support this effect:(1)valuation signals in the lateral ACC and (2)neural coupling between ACC and hippocampus/amygdala,both predicting the size of the tag effect.The size of the tag effect was directly related to posttest imagery scores,strongly suggesting that future thinking signifi-cantly contributed to this effect.Pooling subjects across both experiments revealed that high-imagery subjects reduced their discount rate by on average 16%in the episodic condition,whereas low-imagery subjects did not.Experiment 2addressed a number of alternative accounts for this effect.First,reward confidence was comparable for all conditions,arguing against the possibility that the tags may have somehow altered subjec-tive certainty that a reward would be forthcoming.Second,differences in temporal focus between conditions(date-basedFigure 5.Correlation between the Neural and Behavioral Tag Effect(A)Glass brain and (B and C)anatomical projection of the correlation between the neural tag effect (subjective value correlation episodic >control)and the behav-ioral tag effect (log difference between discount rates)in the bilateral ACC (p =0.021,FWE-corrected across an anatomical mask of bilateral ACC).(C)Coronal sections of the same contrast at a liberal threshold of p <0.01show that both left and right ACC clusters encompass gray matter of the cingulate gyrus.(D)Scatter-plot depicting the linear relationship between the neural and the behavioral tag effect in the right ACC.(A)and (B)are thresholded at p <0.001with 10contiguous voxels,whereas (C)is thresholded at p <0.01with 10contiguousvoxels.Figure 6.Results of the Psychophysiolog-ical Interaction Analysis(A)The seed for the psychophysiological interac-tion (PPI)analysis was placed in the right ACC (18,34,18).(B)The tag effect was associated with increased ACC-hippocampal coupling (p =0.031,corrected across bilateral hippocampus)and ACC-amyg-dala coupling (p =0.051,corrected across bilateral amygdala).Maps are thresholded at p <0.005,uncorrected for display purposes and projected onto the mean structural scan of all participants;HC,hippocampus;Amy,Amygdala;rACC,right anterior cingulate cortex.NeuronEpisodic Modulation of Delay DiscountingNeuron 66,138–148,April 15,2010ª2010Elsevier Inc.143。

gpt越狱语句-概述说明以及解释

gpt越狱语句-概述说明以及解释

gpt越狱语句-概述说明以及解释1.引言1.1 概述概述部分是文章引言的第一小节,用于对整篇文章进行简要介绍和概括。

下面是可以使用的一种可能的写法:概述随着人工智能技术的不断发展和应用,生成预训练模型(GPT)成为了当前研究和应用领域的热点之一。

GPT是一种基于深度学习模型的自然语言处理工具,通过大量文本的训练来生成高质量的语句和段落。

然而,随着GPT的应用范围不断扩大,人们也开始关注到其中存在的一些潜在风险和限制。

本文将围绕GPT的越狱语句展开讨论。

所谓GPT越狱语句,指的是GPT生成的含有煽动性、侮辱性、不实信息或其他有害内容的语句。

这些语句可能产生广泛的负面影响,如误导大众、引发社会争议、侵犯个人隐私等。

因此,了解和应对GPT越狱语句的潜在风险至关重要。

在本文的正文部分,我们将首先介绍GPT的定义、背景和基本原理,以帮助读者对GPT有一个全面的了解。

其次,我们将探讨GPT在实际应用中的一些限制和挑战,特别是在生成内容方面存在的一些问题。

这将为我们进一步讨论GPT越狱语句的潜在风险奠定基础。

最后,在结论部分,我们将提出一些应对GPT越狱语句的方法和措施,以期能在尽可能减少负面影响的同时,更好地应用和发展GPT技术。

通过本文的阅读,读者将能够深入了解GPT越狱语句及其潜在风险,并掌握应对这些问题的方法和策略。

同时,本文也将为相关研究者和从业人员提供一些有益的参考和指导,以推动人工智能技术的合理、安全和可持续发展。

1.2文章结构在文章结构的部分,我们将会提到本文的主要组成部分,以及每个部分的内容和目的。

这样可以帮助读者更好地理解文章的整体框架和各个章节的关系。

本文的结构如下:1. 引言1.1 概述1.2 文章结构1.3 目的2. 正文2.1 GPT的定义与背景2.2 GPT的应用与限制3. 结论3.1 GPT越狱语句的潜在风险3.2 应对GPT越狱语句的方法与措施在引言部分,我们将会概述本文的主题和目的。

SELF-TIMED PULSE CONTROL CIRCUIT

SELF-TIMED PULSE CONTROL CIRCUIT

专利名称:SELF-TIMED PULSE CONTROL CIRCUIT发明人:PARTOVI, Hamid,HOLST, John, Christian,BEN-MEIR, Amos申请号:EP97945362.0申请日:19970929公开号:EP0929939A1公开日:19990721专利内容由知识产权出版社提供摘要:A Self-Timed Pulse Control circuit and operating method is highly useful for adjusting delays of timing circuits to prevent logic races. In an illustrative example, the STPC circuit is used to adjust timing in self-timed sense amplifiers. The Self-Timed Pulse Control (STPC) circuit is integrated onto an integrated circuit chip along with the circuit structures that are timed using timing structures that are adjusted using STPC. The STPC is also advantageously used to modify the duty cycle of clocks, determine critical timing paths so that overall circuit speed is optimized, and adjusting dynamic circuit timing so that inoperable circuits become useful.申请人:ADVANCED MICRO DEVICES INC.地址:One AMD Place, P.O. Box 3453 Sunnyvale, California 94088-3453 US国籍:US代理机构:Sanders, Peter Colin Christopher更多信息请下载全文后查看。

Abstract Robotic Path Planning using Rapidly exploring Random Trees

Abstract Robotic Path Planning using Rapidly exploring Random Trees

Robotic Path Planning using Rapidly exploring Random TreesZoltan Deak Jnr. zoltanj@ Professor Ray JarvisIntelligent Robotics Research Centre – Monash UniversityRay.Jarvis@.auAbstractRapidly exploring Random Tree (RRT) path planning methods provide feasible paths between a start and goal point in configuration spaces containing obstacles, sacrificing optimality (eg. Shortest path) for speed. The raw resultant paths are generally jagged and the cost of extending the tree can increase steeply as the number of existing branches grow. This paper provides details of a speed-up method using KD trees and a path smoothing procedure of practical interest.1 IntroductionFigure 1: Basic RRT simulation in a small environmentRobotic path planning is the process of discovering obstacle-free paths from points in free-space to nominated goal locations. Most past path planning has concentrated on finding such paths, which fulfil optimality criteria such as shortest path, least time path or least energy usage path. Both Euclidean (real number) space [Lozano-Perez and Wesley, 1979] and tessellated spaces [Jarvis, 1985] containing obstacles, goal, and start points have been used for such analysis. More recently, relaxation of strict optimality requirements in favour of safety (from collision) and timeliness (of planning) have been of practical interest and notions of feasibility have been of more concern than strict optimality. The Rapidly Exploring Random Tree (RRT) methodology dealt with in this paper is concerned only with finding feasible paths quickly in potentially high dimensional spaces. In what follows the RRT algorithm and some of its computational issues are dealt with along with a refinement which leads to simpler (less convoluted) paths.The basic algorithm relies on a tree structurebeing built (a forest) that contains the start point as its root node, and eventually, the goal point as one of its leaves. The simulation shown in figure 1 demonstrates the basic RRT algorithm working in a simple environment with no obstacles. The squares are the goal and start points, whilst the jagged line is the resultant RRT path, whilst the straight line is the algorithmically smoothed path. The construction of the tree revolves around picking random points in the environment, finding the nearest point in the tree to this random point, then moving towards that point an incremental distance. If the incremental movement does not encounter an obstacle, insert the point as a new node in the tree. Eventually, the newly inserted node will be close to the goal node. Once this occurs, a path from goal to start point can be made by traversing up the tree until the root node is reached. Figure 1 shows a typical result, including a path refinement to be described later. A more formal description is presented in Algorithm 1.2 RRTThe rapidly exploring random tree (RRT) approach to path planning [LaValle, 1998] introduces the notion of determining a planned path by selecting random points within an environment and moving towards that point an incremental distance from the nearest node of an expanding tree. The movement from existing traversed points to random points in the environment will lead to a path that branches out somewhat like a tree, and will cover most of the free space in the environment. A planned path will be found once a branch in the tree comes close the destination (goal). Build a tree, with the start point as the root of the treeLOOP ( until path found OR maximum nodes created) - Choose a random point in the environment.- Find the closest node in the tree to this random point, which would be to calculate the nearest neighbour to this random point. - Move from this node an incremental distance to the random point.- If an obstacle is not encountered whilst moving to the new point, add this new point to the tree stemming from the closest node found. END LOOPAlgorithm 1: Basic RRTOverall, the RRT algorithm excels at exploring free space in large environments, where conventional algorithms prove to be computationally expensive. Another benefit is that the algorithm is parallelisable, unlike many conventional path-finding algorithms.However, the algorithm does have a drawback. It uses random points in the environment to build the tree. The formation of the tree only ceases once the goal point is found by one of the newly created nodes, or the maximum number of nodes has been reached. This could result in the tree containing 100 nodes or 100,000 nodes.There are numerous strategies that can aid the basic RRT algorithm to lower the average number of nodes created, and hence help it to converge faster towards the goal position. When the tree contains many thousands of nodes, the nearest neighbour searches will become increasingly expensive. There are also numerous strategies to speed up the nearest neighbour calculations.3 Nearest Neighbour searchingSearching through every node in a tree is computationally expensive, particularly as the tree grows larger. An alternate approach to finding the nearest neighbour is by using a KD tree [Stern, 2002]. Due to the geometric partitioning a KD Tree creates, performing a nearest neighbour search does not require searching every node in the tree, and thus the O(n2) performance of searching every node in the tree is reduced to O(log n).Every time a node is inserted into the KD-tree, the geometric range that the node is contained in is stored for that point. Therefore, for any node in the tree, all nodes below a certain branch, can be described as being contained within the geometric range or bounding box of the branch in question. This is very useful when trying to deduce whether a node is nearer to narrow the search for a nearest neighbour.To start the search a search range or bounding box needs to be established, around the random point. This range should be as large as possible. A good start would be to make it surrounding the random point (Random), using the distance from the random point to the root node as a size measure. An important function “IntersectionArea” will return the intersection area between two bounding boxes. One bounding box will be the search range bounding box, and the other will be the bounding box of the current node under consideration. If the intersection area is greater than zero, it indicates that the bounding box of the current node is within range of the specified search area. If it equals zero, it indicates that the range of the node in question is not within the search scope, and therefore, its children do not need to be analysed.If the intersection area is greater than zero, the node that generated the positive intersection is inserted into the priority queue. The priority queue will effectively contain all nodes that have made a positive intersection within the given search area. If the node under consideration has a distance to the random point that is smaller than the currently known minimum distance, the new minimum distance becomes this distance. The search area is then narrowed down using this new minimum distance. All the elements stored in the priority queue are required to be re-evaluated with this new search area. Those elements that do not have a positive intersection within the new search area will drop off the priority queue, leaving only the ones that do. Eventually, the search will finish when there are no remaining elements in the priority queue. A formal description can be seen in algorithm 2.NearestNode = RootNodeMinDist = Distance(RootNode, RandomPoint)SearchBox .LeftTop = (Random.X – MinDist, Random.Y– MinDist)SearchBox.RightBottom = (Random.X + MinDist, Random.Y + MinDist)Priority = IntersectionArea (SearchBox, BoundingBox of the root’s left child)IF (Priority > 0) THEN Priority-Queue.Push(Root.LeftChild, Priority)Priority = IntersectionArea (SearchBox, BoundingBox of the root’s right child)IF (Priority > 0) THEN Priority-Queue.Push(Root.RightChild, Priority)LOOP (continuously)IF ( Priority-Queue is empty) THENbreak the loop and return NearestNodeNode = Priority-Queue.Pop()TempDist = Distance(Node, Random)IF (TempDist < MinDist)MinDist=TempDistNearestNode=NodeSearchBox .LeftTop = (Random.X – MinDist, Random.Y – MinDist)SearchBox.RightBottom=(Random.X+MinDist,Random.Y+ MinDist)Priority-Queue.Resort( SearchBox)Priority = IntersectionArea(SearchBox,Node.LeftChild.BoundingBox)IF (Priority > 0) THEN Priority-Queue.Push(Node.LeftChild, Priority)Priority = IntersectionArea (SearchBox, Node.RightChild.BoundingBox)IF (Priority > 0) THEN Priority-Queue.Push(Node.RightChild, Priority)END LOOPAlgorithm 2: Nearest neighbour search using a KD tree4 Smoothing RRT pathThe resultant path calculated using the RRT algorithm isat best very jagged. It would not be practical for a robot to follow the path, as there are potentially many scenarios where this would lead the robot in a backwards direction (momentarily) from a direct path to the goal.If a string was to be taken from the start point and laid out following the calculated path to the goal, and then the string was pulled taut such that the string bent around any obstacles (but otherwise remained straight),the resultant path could be a possible minimum path. Whilst the idea might seem rather simple visually, it is a very complicated task computationally, and would negate the computational benefits of using the RRT algorithm. Nonetheless, some sort of smoothing operation needs to be performed on the found path, to make it feasible for a physical robot to follow, but not complicated enough to impact the computational time complexity of the RRT algorithm itself.Instead of trying to find the best minimum pathfrom the calculated RRT path, it would be better to smooth the resultant path just enough so that it would be a practical route for a physical robot to travel. An algorithm was developed which smoothed the path enough to allow for practicality, without being too computationally expensive. Its objective is to make a straight line from one point along the resultant path to another, such that the straight line does not encounter any obstacles. The algorithm tests for straight lines from a node point in the resultant path to another point further down the path.Figure 2: Basic RRT simulation in a large environmentThe smoothing algorithm begins by pushing allnodes that make up the calculated RRT path onto a stack (PathList), starting from the found goal node. This is done because it places the start node at the head of the stack, so that every time a node is popped, the order of nodes analysed is sequentially going from start node to goal node. When the goal node is eventually popped off the stack, the algorithm will terminate, without testing for a straight line to goal. Therefore, in the initial construction of the PathList stack, the goal node needs to be added twice. A more formal description of the algorithm can be seen in algorithm 3. Figure 3: Basic RRT simulation with a simple obstacle5 RTT ModificationsA possible modification to the basic RRT algorithm, as proposed in [Kuffner and LaValle, 2000], would be to alter the incremental distance taken by the algorithm on each pass. Instead of moving an incremental distance to a random point just once, keep moving towards it until it is reached, or an obstacle is hit, as is more formally described in algorithm 4.LastNode = PathList.Pop() NextNode = PathList.Pop()LOOP ( until the PathList stack is empty)IF( It is possible to draw a straight line from LastNode to NextNode)NextNode = PathList.Pop()ELSEThe previous NextNode was successful in the above test, so push that node onto the MinimumPath stack.Build a tree, with the start point as the root of the tree LOOP (until path found OR maximum nodes created)¾ Choose a random point in the environment.Make the LastNode equal to this previous NextNode.¾ Find the closest node in the tree to this random point,which would be to calculate the nearest neighbour to this random point.END LOOPAlgorithm 3: Smoothing algorithm¾ Move from this node an incremental distance to therandom point.The algorithm works by condensing the resultantnumber of nodes to a few nodes joined by a straight line. As can be readily noted from figures 2 and 3, the smoothing algorithm takes the jagged RRT planned path (red line) and smoothes it out (blue line). This procedure requires negligible amounts of computational time since it is calculating a minimal path using a linear search, through a list, which is orders of magnitude smaller than the actual tree size. LOOP (while we do not encounter an obstacle whilst moving to this new point)o Add this new point to the tree, stemmingfrom the node we just moved from.o Move from this new point an incrementaldistance to the random point.END LOOPEND LOOPAlgorithm 4: RRT ConnectFigure 4: RRT-Connect simulation in a large environment From the simulation shown in figure 4 in comparison to figure 2 and 3, it is readily seen that the RRT-Connect algorithm can cover larger areas of free space with a smaller tree size than the basic algorithm.The average node count for solutions was literally halved when using the RRT-Connect algorithm as opposed to the basic algorithm, which can be seen from the graph in figure 5. The environment for this simulation can be seen in figure 6, which involves a complicated spiral obstacle. The RRT-Connect will be able to work its way out of more complicated environments faster because it will keep trying to reach the random point, as opposed to moving only one incremental distance. The basic RRT algorithm tends to clump nodes closer together as opposed to spreading out via straighter lines.Figure 5: Comparison of Basic RRT vs. RRT Connect whensimulated using a spiral obstacle mapAnother possible modification to the basic RRT algorithm would be to bias the random point generated [Bruce and Veloso, 2002] so that every so often, instead of choosing a random point to move to, move to the goal point instead. Simulations using algorithm 5 demonstrated that this helped lower the average number of node counts from solution to solution, especially when the tree was near the goal point. Nonetheless, too much bias (over 20% bias towards goal) would incur more computational time when the environment contained complex obstacles. This occurs because tree nodes are biased towards the goal point, which is hidden behind an obstacle, and will keep trying to reach the goal through the obstacle.Return value: Cartesian pointParameter: A bias value from 0.00 to 1.00FUNCTION{Choose a random number from 0.00 to 1.00IF (random number is greater than or equal to biasvalue)Return random point within environment ELSEReturn Goal point}Algorithm 5: Biasing the random point towards goal Since the RRT algorithm uses randomness to explore free space, the amount of nodes created for a particular solution could vary substantially from execution to execution. By using the above two modifications in conjunction, it was possible to minimize the large degree of variance between solutions.Figure 6: RRT Basic simulation using a spiral map6 DiscussionClearly the RRT can find feasible paths quickly. The optimality of the path in terms of minimal length, least time or minimal energy is not even considered. The smoothing algorithm does tend to shorten such paths by taking the ‘kinks’ out of them. Whilst optimality concerns for known environments can be the basis of criticism of the RRT method, such concerns can be reasonably quashed in situations where the environment is initially unknown and particularly when changing in time, since there is no guarantee that a path considered minimal in length to a goal within the context of partial knowledge will turn out to, indeed, be a minimal path when more is known about the environment. Generally, a robot would be launched along a devised path, its environmental map updated when possible and a new plan devised using the current location as a start point. The optimality of a path can, in such circumstances, be only judged in hindsight.The speed of the RRT algorithm and its property of rapid expansion recommending it for high dimensionalpath planning where many other methods are computationally too complex to cope with real time constraints regarding the control of robotic manipulators, mobile robots or humanoids.7 FutureWorkThe RRT algorithm, along with the various modifications is able to calculate solutions to large environmental maps very quickly, and most importantly, reliably. But most of the computational time is spent on finding the nearest neighbour. Simulations were carried out that used approximate nearest neighbours, instead of exact, which demonstrated similar results to that of exact nearest neighbours. Further simulations showed that 99% of the approximated nearest neighbours used were in fact half the environment away from the exact nearest neighbour! More needs to be done in these investigations. The application of RRT methodology to situations with initially unknown and possibly time varying environments is another area for future research.8 ConclusionThis paper describes the RRT algorithm for a feasible collision-free path discovery, shows how KD tree search methods reduce the complexity of finding the nearest neighbour of a newly introduced random point amongst the nodes of an expanding tree (critical to the method) and presents a path simplifying refinement. A number of examples demonstrate the power of the method.It is clear that the methodology can generate very fast solutions for most obstacle and start/goal configurations, but the number of branches of the generated tree required for any given example is hard to estimate. Also, further investigations are warranted regarding the use of inexact nearest neighbours to reduce the cost of computation.References[Lozano-Perez and Wesly, 1979] T. Lozano-Perez and M.A. Wesly, An Algorithm for Planning Collision-Free Paths among Polyhedral Obstacles, Commun, ACM, Vol 22 No 10, Oct 1979, pp560-570.[Jarvis, 1985] R.A. Jarvis Collision-Free Trajectory Planning Using Distance Transforms, Proc National Conference and Exhibition on Robotics-1984, Melbourne 20-24 August, 1984. Also in Mechanical Engineering Transactions, Journal of the Institution of Engineers, Australia, 1985.[LaValle, 1998] Steve M. LaValle. Rapidly-exploring Random Trees: A new tool for path planning. In Technical report No. 98-11, October 1998.[Kuffner and LaValle, 2000] James Kuffner Jnr. And Steve M. LaValle. RRT-Connect: an efficient approach to single-query path planning. In Proc. IEEE Int’l Conf. On Robotics and Automation, 2000.[Stern, 2002] Henry Stern. Nearest Neighbour matching using KD-Tree. Computer Science Dept., Dalhousie University, Halifax, Nova Scotia, August 2002. [Adali, 2001] Sibel Adali. Multi dimensional indexing Part 2, </~sibel/mmdb/lectures/multi-d-indexing2.pdf>, 2001.[Bruce and Veloso, 2002] James Bruce and Manuela Veloso Real Time randomised path planning for robot navigation. In Proceedings of IROS-2002, Switzerland, October 2002.。

自注意力机制特征融合的例子

自注意力机制特征融合的例子

自注意力机制特征融合的例子自注意力机制(Self-Attention Mechanism)是一种用于处理序列数据的机制,能够对序列中的每个元素进行自适应加权,以便更好地捕捉元素之间的关系。

它在很多自然语言处理领域中都有广泛的应用,其中一个重要的应用就是特征融合。

下面将列举一些利用自注意力机制进行特征融合的例子。

1. 机器翻译:在机器翻译任务中,可以使用自注意力机制将源语言和目标语言的词向量序列进行特征融合,以便更好地理解源语言和生成目标语言。

2. 文本分类:在文本分类任务中,可以使用自注意力机制将文本中的每个词向量与其他词向量进行关联,以获取更准确的特征表示,从而提高分类性能。

3. 问答系统:在问答系统中,可以使用自注意力机制将问题和候选答案的表示进行特征融合,以便更好地匹配问题和答案,提高准确性。

4. 文档摘要:在文档摘要任务中,可以使用自注意力机制将文档中的句子进行特征融合,以获取重要句子的表示,从而生成更准确的摘要。

5. 语言建模:在语言建模任务中,可以使用自注意力机制将当前位置的词向量与前面的词向量进行特征融合,以获取更好的上下文表示,提高预测准确性。

6. 命名实体识别:在命名实体识别任务中,可以使用自注意力机制将输入句子中的每个词与其他词进行关联,以获取更准确的特征表示,从而提高识别性能。

7. 情感分析:在情感分析任务中,可以使用自注意力机制将文本中的每个词与其他词进行关联,以获取更准确的特征表示,从而提高情感分类的准确性。

8. 关系抽取:在关系抽取任务中,可以使用自注意力机制将输入句子中的每个词与其他词进行关联,以获取更准确的特征表示,从而提高关系抽取的准确性。

9. 文本生成:在文本生成任务中,可以使用自注意力机制将输入序列中的每个词与其他词进行关联,以获取更准确的特征表示,从而生成更流畅和连贯的文本。

10. 音乐生成:在音乐生成任务中,可以使用自注意力机制将音乐序列中的每个音符与其他音符进行关联,以获取更准确的特征表示,从而生成更优质的音乐。

FPGA技术介绍

FPGA技术介绍

外文翻译FPGA技术介绍系部:文翻译班级:姓名:学号:指导教师:年月日FPGA技术介绍概述:场域可程式化闸阵列FPGA技术正持续发展,而全世界FPGA市场的产值,则预估可从 2005 年的 19 亿美金提升到 2010 年的 27 亿 5 千万美金。

FPGA是在 1984 年由Xilinx 公司所发明,从简单的胶合逻辑Glue logic 晶片,演变为可取代客制的特定应用积体电路 ASIC 与处理器,适用于讯号处理与控制应用。

为何FPGA技术如此成功?此篇文章将介绍FPGA,并说明数项让FPGA如此独特的优点。

什么是FPGA?最笼统来说,FPGAs 即为可再程式化的晶片。

透过预先建立的逻辑区块与可程式化路由资源,不需更改面包板或焊锡部分,即可设定这些晶片以建置客制硬体功能。

使用者可于软体中开发数位运算系统 Computing task 并将之编译为组态档案或位元流Bitstream,可包含元件接线的相关资讯。

此外,FPGA完全为可重设性质,当使用者重新编译不同的电路设定时,可立刻拥有不同的特性。

在过去,工程师必须深入了解数位硬体设计,才能够使用FPGA技术。

然而,高阶设计工具的新技术可针对图形化程式区或 C 程式码,转换为数位硬体电路,即变更了FPGA程式设计的规则。

FPGA整合了 ASIC 与处理器架构系统的最佳部分,使FPGA晶片可应用于所有产业。

FPGA具有硬体时脉的速度与可靠性,且其仅需少量即可进行作业;可降低客制化 ASIC设计的费用。

可重新程式设计的晶片,具有与软体相同的弹性,却不受限于处理核心的数量。

与处理器不同的是,FPGA为实际的平行架构,因此不同的处理作业并不需要占用相同资源。

每个独立的处理作业均将指派至专属的晶片区块,不需影响其他逻辑区块即可自动产生功能。

因此,当新增其他处理作业时,应用某部分的效能亦不会受到影响。

FPGA技术的 5 大优点:效能–透过硬体的平行机制,FPGA可突破依序执行 Sequential execution 的固定运算,并于每时脉循环完成更多作业,以超越数位讯号处理器 DSP 的计算功能。

贝叶斯超参数优化 多层感知器

贝叶斯超参数优化 多层感知器

贝叶斯超参数优化是一种用于自动调整机器学习模型超参数的优化技术。

它使用贝叶斯概率理论来估计超参数的最佳值,以优化模型的性能。

多层感知器(MLP)是一种常用的神经网络模型,由多个隐藏层组成,每个层包含多个神经元。

MLP可以用于分类、回归等多种任务。

当使用贝叶斯超参数优化来调整MLP的超参数时,通常会选择一些常见的超参数,如学习率、批量大小、迭代次数等。

贝叶斯优化器会根据这些超参数的性能,选择下一个可能的最佳值。

它通过在每个步骤中随机选择少量的超参数组合,而不是搜索每个可能的组合,来提高效率。

在实践中,贝叶斯超参数优化通常使用一种称为高斯过程回归(Gaussian Process Regression)的方法,该方法可以估计每个超参数的可能值以及它们的概率分布。

然后,根据这些信息选择下一个超参数的值,以最大化模型性能的预期改善。

使用贝叶斯超参数优化可以自动调整超参数,避免了手动调整的困难和耗时。

此外,它还可以帮助找到更好的超参数组合,从而提高模型的性能和准确性。

这对于机器学习任务的实验和开发非常重要,因为它可以帮助快速找到最佳的模型配置。

ChatGPT 在多轮对话中的长期记忆能力

ChatGPT 在多轮对话中的长期记忆能力

ChatGPT 在多轮对话中的长期记忆能力人工智能的发展已经取得了巨大的进步,尤其是在自然语言处理领域。

ChatGPT作为一种新兴的语言模型,具有出色的对话生成能力,但其长期记忆能力也备受关注。

本文将探讨ChatGPT在多轮对话中的长期记忆能力,并讨论其潜在的应用和局限性。

首先,ChatGPT在多轮对话中的长期记忆能力是通过其深度学习模型实现的。

该模型采用了自注意力机制,使得ChatGPT能够对输入的上下文进行有效的编码和理解。

这种机制使得ChatGPT能够在对话的不同轮次中保持对先前对话内容的记忆,并根据上下文生成连贯的回复。

这种长期记忆能力使得ChatGPT在复杂的对话场景中表现出色,能够更好地理解用户意图并提供个性化的回应。

其次,ChatGPT的长期记忆能力对于实际应用具有重要意义。

在客服领域,ChatGPT可以作为一个虚拟助手,与用户进行多轮对话,帮助解决问题和提供服务。

通过具备长期记忆能力,ChatGPT可以更好地理解用户的需求,并在对话的不同阶段提供连贯的帮助。

此外,在教育领域,ChatGPT也可以作为一个个性化的学习伙伴,与学生进行互动,提供个性化的学习资源和答疑解惑。

长期记忆能力使得ChatGPT能够更好地适应学生的学习进程,并根据学生的需求进行针对性的辅导。

然而,ChatGPT的长期记忆能力也存在一定的局限性。

首先,ChatGPT的记忆是基于其对先前对话内容的编码和理解,但其记忆是有限的。

当对话过于复杂或涉及大量信息时,ChatGPT可能会出现记忆不足的情况,导致对话的连贯性下降。

其次,ChatGPT的长期记忆能力也受到语境的限制。

如果对话中存在歧义或上下文缺失,ChatGPT可能会出现误解或回答不准确的情况。

这些局限性需要在实际应用中加以注意和解决,以提高ChatGPT的长期记忆能力和对话质量。

为了进一步提升ChatGPT在多轮对话中的长期记忆能力,可以考虑以下方法。

首先,可以引入外部的知识库或语料库,以增加ChatGPT的知识储备和记忆容量。

布谷鸟算法里发现概率英文表达

布谷鸟算法里发现概率英文表达

布谷鸟算法里发现概率英文表达Discovery Probability in the Cuckoodle Algorithm.The Cuckoodle algorithm, named after its characteristic call that resembles the sound of the cuckoo bird, is an innovative approach in the field of optimization techniques. It finds its applications in various domains, ranging from engineering design to financial modeling, where the goal is to identify the best possible solution among a vast search space. A crucial aspect of this algorithm is the discovery probability, which refers to the likelihood of finding a superior solution during the search process.The discovery probability is not a static parameter; it evolves dynamically based on the algorithm's interactions with the search space. Initially, the algorithm has a low discovery probability because it is exploring a vast and diverse landscape of potential solutions. As the search progresses, the algorithm learns from its previousiterations and gradually improves its ability to identifypromising regions. This improvement is reflected in an increasing discovery probability.The Cuckoodle algorithm employs several strategies to enhance its discovery probability. One such strategy is the utilization of heuristic rules, which guide the search towards regions that are more likely to contain optimal solutions. These rules are derived from past experiences and domain-specific knowledge, enabling the algorithm to make informed decisions about where to explore next.Another key aspect is the balance between exploration and exploitation. Exploration involves searching for new and potentially better solutions, while exploitation focuses on refining the current best solution. The Cuckoodle algorithm strikes a careful balance between these two objectives, ensuring that it doesn't get stuck in local optima while also maintaining the ability to discover globally optimal solutions.The discovery probability is also influenced by the diversity of the search population. In the Cuckoodlealgorithm, a population of candidate solutions evolves over time, with each individual representing a potentialsolution to the problem. By maintaining a diverse population, the algorithm increases its chances of discovering novel and innovative solutions. Techniques such as crossover and mutation are employed to introduce genetic diversity among the population members, enabling the algorithm to explore a broader range of solutions.The evaluation function plays a crucial role in determining the discovery probability. This function assigns a fitness score to each candidate solution, indicating its proximity to the optimal solution. By continuously evaluating and comparing the fitness scores, the algorithm can identify regions that are rich in promising solutions, thus increasing the discovery probability.The Cuckoodle algorithm also incorporates learning mechanisms that enable it to adapt and improve its discovery probability over time. By analyzing thehistorical data and patterns, the algorithm can learn fromits past successes and failures, refining its search strategies and becoming more efficient at finding optimal solutions.In summary, the discovery probability is a fundamental aspect of the Cuckoodle algorithm that governs its ability to find the best possible solution in a given search space. Through dynamic adaptation, heuristic rules, exploration-exploitation balance, population diversity, evaluation functions, and learning mechanisms, the algorithm continuously enhances its discovery probability, ensuring efficient and effective optimization.。

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Rapid prototyping of a Self-Timed ALU with FPGAs1Ortega-Cisneros S., 1Raygoza-Panduro J.J., 2Suardíaz Muro J., 1Boemo E. 1Escuela Politécnica Superior, Universidad Autónoma de Madrid, España 2Escuela Técnica Superior de Ingenieros Industriales, Universidad de Cartagenasusana.ortega@uam.es, jjraygoza@AbstractThis article presents the design and implementation of a Self-Timed Arithmetic Logic Unit (ALU) that has been developed as part o fan asynchronous microprocessor. This displays an inherent operational characteristic o flow consumption, owing to the synchronization signals that stop when the execution of an operation finishes (stoppable clock); that is to say, the dynamic consumption is zero, while it is not required again by an external request signal.It demonstrates the methodology of design of the Sel f-Timed controls which synchronize the data trans f er, as well as the characterization o f delay macros designed in FPGA editor for the adjustment of ALU processing times. It also summarizes the results of the implementation for a FPGA virtex II, as well as the parameters of area, distribution of tracks, delay, latency, consumption and fan-out.1. IntroductionThe design of non-synchronous digital systems constitutes an alternative for synchronizing large circuits. Therefore the methodology of self-timed (ST) design has advanced in the recent years. Among the advantages we can mention is its inherent operation in stoppable-clock mode, the absence of consumption peaks and its immunity to the skew of the clock. In a synchronous circuit the transmission or data processing is controlled globally by one or more phases of the clock. Whereas in a ST system, the data transfer is controlled by two signals: "request and acknowledge" as is normal in any asynchronous system [1]. The definition of the control signal format gives rise to two types of synchronization: protocol of 2 phases [2] and protocol of 4 phases [3,4]. This article presents the implementation of an ALU using the protocol of 4 phases. This has been selected in place of 2 phases, dueto the robustness of the technique, simplicity of implementation of the transmission blocks and the minimum use of the resources of the F PGA device. [5,6].At the present time the development of ST circuits has been centered on full-custom or cell-based prototypes, although the F PGAs are oriented towards the efficient implementation of synchronous circuits, at the present they constitute the only option available for fast prototypes and for the low cost of self-timed circuits.2. Description of a ST ALUThe ST Arithmetical Logical Unit has been developed as part of the asynchronous implementation of a microprocessor. The asynchronous circuit is composed of 4 main modules, as shown in the figure 1: 1. Arithmetical and Logical Unit 2. Instructions decoder 3. Asynchronous control 4.ST 4 phases pipelineThe Arithmetical Logical Unit is a combinatorial device composed of 3 main modules as can be seen in figure 1. The Module (a) or instruction block has a feeding signal of 16 bits, which enters directly from the outside to one of the two instruction entrances. The other entrance is feedback from the exit of the accumulator (module c).The ALU has 4 arithmetical instructions, 6 logics, 1 of comparison, 2 of register transference and 2 of input-output.The selection of the functions is made by means of the 15 signals “Io” up to “I14”, that activate the 15 channels of the multiplexor to illustrate the result of the operations (module b), which are related to the entrances and allow the passage of one of the logic, arithmetic or input-output functions.Figure 1. ST Arithmetical Logical UnitThe general module has 5 codifications for 15 instructions. Table 1 shows the selection code, the decoding of the operations (deco) and the occupation of these in the FPGA.3. Asynchronous ControlThe instructions are classified into 4 types ofdifferent operations, according to the number of activation pulses that are required for their execution. These are shown in the diagram of figure 2 and constitute the asynchronous control. The operations of type 1 require 4 pulses at the entrance that come from the ST 4 phase pipeline module to activate the ALU. The operations of type 2, require 2 pulses at the entrance, the operations of type 3 require 5 pulses and finally the operations type 4 require 9 pulses to codify 4 activation signals from the ALU. Some control lines are concentrated in an exit circuit allowing the accumulator to capture the data correctly. The line of "total test" of the circuit in figure 2, generates a pulse whenever an instruction is made, which is connected to an operations counter.InstructionMUX lineSelectionDeco OccupationSl LUT Reg. GatesLDA I2 000010001- - - -ADD I1 00010 0002 9 17 0 186 ROT_D I3 00011 0004 0 0 16131 ROT_I I4 00100 0008 0 0 16131COMPL I5 00101 0010 22 42 0 369 DES_D I6 00110 0020 0 0 16123 LDA, X I900111 0040 0 0 16131 INC, AI801000008013 21126 COMP I7 01001 0100 0 0 16131 LDA, Y I10 01010 0200 0 0 16131AND I11 01011 0400 16 16 0 96 OR I12 01100 0800 16 16 0 96 PTO_SAL- 01101 1000 0 0 16131RESTA I14 01110 2000 9 17 0 189 MUL I13 01111 4000 0 0 164,134Table 1. ALU InstructionsFigure 2. Asynchronous controlThe decoding of operation 1 is shown in figure 3, it requires several logic gates to make the transformation of the control signals “xi1” to “xi4” in order to activate the signals of the multiplexer and the accumulator; the elements comp_1 to comp_3 are controlled by the signal “deco1” that comes from the instructions decoder, permitting the transmission of the signals aslong as “deco” signal is actived.Figure 3. Logic operation 1The operation 2 requires 2 logical elements to transform the pulses from 2 signals from the ST control pipeline. One of these is required to activate the capture of the register and the other signal in order to count the instructions made. The decoding of operation 3 requires 5 signals from the ST control pipeline to activate the capture of data from the register, multiplexer and from the accumulator.F inally operation 4 is shown in figure 4, this operation requires 9 pulses for the decoding of theoperation. The control signal “xi1” and “deco 4” allow the capture of the operations in the register in order tomake the multiplication.Figure 4. Arithmetical operation 44. ST pipeline controlThe control units developed with ST circuits are frequently composed of micropipeline structures. These were proposals made by Ivan Sutherland at the end of the 1980s [1]. An ST pipeline structure consists of a successive series of control blocks that possess request and acknowledge signals, interconnected block by block, to facilitate the movement of information along all the circuit in a controlled and phased way. This type of structure is the fundamental base of the control circuits that are presented in this work.4.1. Four phase pipeline structuresThe majority of synchronous circuits have a data path by which the data is transferred during a process. A typical synchronous data path is formed by pipelines that have registers in their entrances and exits to store data that are processed by combinatorial circuits. These registers are controlled by clocks. On the other hand, asynchronous designs use two methods to control the transfer of data: bundled data [1] and dual rail [7].Pipeline architectures operating in bundled data consist of delegating the control over the validity of the data in the signal, in such a way that it operates together with the acknowledge signal of thecorresponding protocol.Figure 5. A bundled data pipelineIn figure 5 the illustration shows an architecture that follows the codification method in bundled data. One can see that it has a request signal “Req”, an acknowledge signal “Ack” and a data bus. A combinatorial logic block sends a request signal to the next block when the data is available, and this then sends a recognition signal back to the previous block, to indicate that the data has been received and is available for the next transfer. With this method we can completely separate the interface part from the combinatorial. In this way we can interact separately with both.F or the synchronization of the pipeline circuit to operate correctly the delay between the exit request signal of each block and the entrance request of the next stage should have an equal value at the time of the combinatorial circuit process [8].The advantage of this method is its simplicity. However, it has the inconvenience of operating over the maximum processing time for the combinatorial circuit. In this sense we fail to take advantage of quicker operating times.4.2. Delay MacrosThe implementation of delays in reconfigurable circuits is achieved through a macro in FPGA editor, as shown in figure 6.The slice of the F PGA virtex II is composed of 2 LUTs and 2 latches. F or the implementation of the delay the LUT <G> is used in the upper part of figure 6. It has a logic depth of 1, between the entrance “s_in_ibuf” and the output “s_sal_obuf’”.The delay total ΔTOT is composed of two classes of intrinsic delays of the F PGA that are the logic delay and that introduced by the interconnection path or route, these, in turn subdivide into different partial delays described in the equation 1.¨TOT = įPI + įLUT + įPO + įRUT(1)Where:įPI is the propagation delay between the entrance and exit pad of the tiopi module, with a value of 0.825 ns.įLUT is the combinational delay between the entrances of the LUTs F/G at the exits X/Y of the tilo module, with a value of 0.439 ns.įPO is the propagation delay between the entrance and the exit pad of the tioop module, with a value of 6.107 ns.įRUT is the propagation delay of the path or route of connection to the previous module.Figure 6 shows the architecture of a delay of 9.637 ns with three logia levels corresponding to tiopi, tilo and tioop modules. The values correspond to the FPGA Virtex II Xilinx XC2V1000-4FG256 [9].Figure 6. Implementation of delay macro in FPGATo increase the delay various macros are connectedin series, increasing the logic depth between theentrance and exit.The figure 7 describes the characterization of 75different delay modules, which shows that the totaldelay does not present a lineal behavior with respect tothe number of macros of which it is composed, owingto the variation of the delay values of the FPGA routes.The table 2 presents 6 examples of the circuitscharacterized. One observes that the number of macrosused in the implementation of the delay modulesmaintains a direct relationship with respect to the logiclevels; this provides an approximation of the resourcesthat will be used in the FPGA.Delay(ns)Figure 7. Total delays vs. macrosThis same table shows 6 results of themeasurements in the 75 delay modules onprogramming the FPGA. The value of T X1 indicates theentrance pulse (signal “s_in_ibuf”). T X2 is the exitpulse present in the circuit (signal “s_sal_obuf”). Thevalue T real represents the propagation delay present ineach module (T X2-T X1) and the T ps is the delay post-layout. The value of the delay in the FPGA tends to beless than the result measured during the simulation.Although one should consider that the increase inthe temperature in the FPGA alters the values of delayafter the device has operated for a long period.Table 2. Measurements of the delay circuit4.3 Implementation of the ST control pipelineA control element pipeline ST is used to regulatethe data flow through a segmented system. This canalso be used as an activation control for the differentstages of the system. The tasks carried out by each oneof the blocks are independent of one another and thetime of the stage can be different. [10]:Macros LogicallevelT X1(μs)T X2(μs)T real(ns)T ps(ns)1 343.6643.67109.635 722.8522.831217.5210 12 15.7215.711424.5130 32 25.3925.412236.9750 52 1.751.782650.5875 77 2.402.373465.49 Proceedings of the 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig 2005)In a pipeline structure (figure 5) of 25 asynchronous control blocks, the process of data transfer begins with the control pulse ‘Xi1’ and finalizes with ‘Xi25’,in such a way that the processing time X Lat is related to the difference between the last (Xi_u) and the first Xi (Xi_p) as is seen in the figure 5.or thecharacterization of these structures and to anticipate thenumber of macros (Nω) to use in a ST pipeline of aspecific size, we use the equation 2.NȦ = NIJ * (ret_2 + ret_1) (2)Where:Nt is the number of control blocks within thestructure minus 1.Ret_1 is equal to the delay in feedback, with a valueof 1.Ret_2 establishes the calculation time between theblocks, for this characterization, with a value of 3.Figure 8. Latency of different pipelinesFigure 8 shows a diagram with the characterizationof the latency for 25 pipeline control structures ofdifferent sizes. In that of the group of xi_p it is the timeform the start of the first control pulse generated by thefirst, and xi_u is the start of the final pulse produced bythe last control. D_n is the time that passes in order togenerate all the control pulses, or what it takes for thecomplete process to send data or latency.The value of the rising edge for the first pulse isfound to be around 409.71 ns and the rising edge of thefinal pulse is found to be in the range of 412.75 to509.57ns. The time cycle for each one of the structuresdemonstrates a gradual behavior on increasing thenumber of elements of which it is composed, for thismotive a pipeline control of 2 elements implemented inthe VIRTEX II is of 3.03ns and for 25 elements 99.30ns.5. Execution of ST ALUIn synchronous circuits the measurement ofoperations is traditionally based on operationfrequency of the clock. In the case of ST circuits thespeed depends on the delays incorporated into the STcontrol to modulate the transfer and activation of thecircuit operations.Each type of operation was characterized varyingthe delay and the number of millions of instructionsper second (MIPS) executed was observed. It displayeda diminution when increasing the delays. As shown infigure 9.MIPSDelay (ns)Figure 9. MIPS vs. delay (ns)The latency by type of operation which was displayedby the ALU when varying the number of delay macroswas more significant for the operations that require agreater number of pulses of control Xi during theprocess (type 3 and 4). The behavior of the latencyagainst the number of macros by type of operation isshown in figure 10.In synchronous circuits the fan-out of the globallines tends to be greater, especially the clock lines,enable, reset etc. In asynchronous circuits theinterconnection lines are local and therefore the level of fan-out is smaller. F or ST ALU 95% of the lines have a fan-out smaller than 20 and net delays of lessthan 4ns, as shown in figure 11.Figure 10. Latency of the ALU operations The line of reset that it is not drawn has a fan-out of 92 and a delay of 6 ns.D e l a y (n s )Fan OutFigure 11. Fan-out vs. net delayThe results of the implementation of the circuits in the F PGA virtexII are summarized in the figure 12. Where the ALU occupy more LUTs and registers.Figure 12. Occupation of the ST ALU6. Instantaneous CurrentIn order to measure the instantaneous current a current probe was used. This is an indirect method of measurement that detects the electromagnetic variation of the feed cable. The results of the measurement of the instantaneous current can be seen in figure 13.The measurements were made on an Avnet evaluation card. An ammeter was connected in series with the main feeder cable and the current probe monitored the same cable. The measurements of instantaneous current are registered on an oscilloscope. The results obtained of the measurement of current registered by the ammeter for ST ALU were 494 mA. With respect to its synchronous counterpart the current measured with the ammeter was 496 mA in execution. The measurements with the current probe, in the ST ALU detected a change of voltage peak to peak of 25 mV, as is observed in figure 13. In the case of its synchronous counterpart a voltage increase was observed, peak to peak, of 24 mV, throughout the time that the circuit stayed operating.As was observed in the synchronous ALU the changes of instantaneous current were more than those of the ST, from which we can deduce that the consumption of a synchronous circuit is greater.Y1 323.850 mV Y2 323.850 mV ΔY 25.2 mV CHI 10:115.0 mV/divDC FullFigure 13. Instantaneous current of the STALU7. ConclusionsThis article describes to the implementation of a Self-Timed ALU in reconfigurable circuits. In addition, it suggests some ideas for the design of these circuits in FPGAs, as well as the characterization of the measurements of the delays generated in real time and the occupation of the resources in Virtex II.The ALU has the characteristic to activate with an external pulse and eliminates the dependency on a global clock.It presented an analysis of the effect of the delay macros with different values on the behavior of the ST ALU with respect to the number of operations executed per second. A small reduction was observed in the consumption in the power supply line during the execution of an operation of the ST ALU compared to its synchronous counterpart. The feasibility was tested of making a fast prototype of ST circuits in a synchronous tool.8. AcknowledgmentThis work has been financed by the National Advice of Science and Technology of México (CONACYT).9. References[1] I. E. Sutherland, “Micropipelines”, Communications of the ACM , vol. 32, No. 6, June 1989, pp. 720-738.[2] P. Kudva and V. Akella, “Testing two-phase transition signalling based self-timed circuits in a synthesis environment,” in Proceedings o fthe 7th InternationalSymposium on High-Level Synthesis , IEEE Computer Society Press, May 1994, pp. 104–111.[3] S. B. Furber and P. Day, “Four-phase micropipeline latch control circuits,” IEEE Transactions on VLSI Systems , vol. 4 June 1996, pp. 247–253.[4] A. J. McAuley, “Four State Asynchronous Architectures”, IEEE transactions on computers , vol. 41, No. 2, Feb. 1992.[5] S. B. F urber and J. Liu, “Dynamic logic in four-phase micropipelines”, in Proc. Interna-tionalSymposium on Advanced Research in Asynchronous Circuits and Systems ,IEEE Computer Society Press, Mar. 1996.[6] Kees van Berkel and Arjan Bink. “Single-track handshaking signaling with application to micropipelines and handshake circuits”, In Proc. International Symposium on Advanced Research in Asynchronous Circuits and Systems,IEEE Computer Society Press, March 1996, pp. 122-133. [7] M. Dean, T. Williams, and D. Dill, “Efficient self-timing with level-encoded 2-phase dual-rail (LEDR),” in AdvancedResearch in VLSI (C. H. S´equin, ed.), MIT Press, 1991, pp. 55–70.[8] R. Kelly “Asynchronous Design Aspects of High-Performance Logic”, Thesis . University of Manchester. Department of Computer Science UK, 1995.[9] Co. Xilinx “Virtex-II Platform F PGA” User Guide , 1-800-255-7778 UG002 (v1.5). . Dec. 2002. [10] D. A. Gilbert and J. D. Garside, “A result forwarding mechanism for asynchronous pipelined systems,” in Proc. International Symposium on Advanced Research in Asynchronous Circuits and Systems, IEEE Computer Society Press, Apr. 1997, pp. 2–11.。

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