A Mixture Model for Learning Sparse Representations

2020 Reviews of the Final Exam of Intermediate EnglishVocabularyUnit 1V ocabulary (A)1. (d) boundless: without limits, unlimited2. (g) shriek: cry out with a high sound3. (a) sketch: a rough drawing4. (h) curiosity: the desire to know, find out or learn5. (b) doctor’s kit: a bag or box containing doctor’s instruments and medicine6. (c) pajamas: jacket and trousers for sleeping in7. (i) creativity: the ability to produce new and original ideas and / or things8. (j) garbage:waste material9. (e) cross-examine:question somebody very closely or severely10. (f) accomplish: finish successfully, succeed in doingV ocabulary (B)1. admiration: a feeling of respect and approval for a person2. tiptoe: walk on one's toes with the rest of one's feet off ground3. spontaneous: acting immediately from natural feeling4. compliment: an expression of praise, admiration or respect5. escapement:the part of a clock or a watch which controls the moving parts inside6. jovially: in a friendly way, good-humoredly7. carve:cut (wood or stone) into a special shape8. whittle:cut (wood) to a smaller size by taking off small thin pieces9. commotion: great and noisy confusion or excitement10. considerate: thoughtful as far as the feelings or needs of others are concerned11. snarl: speak in an angry, bad-tempered way12. sprawl: stretch out oneself or one’s limbs in a lying or sitting positionUnit 2V ocabulary (A)1. pray: speak (usually silently) to God, showing love, giving thanks for asking for something2. was escorted:was taken3. moan:low sound of pain or suffering4. dire: terrible5. knelt:go down and/or remain on the knees6. jet-black: very dark or shiny black7. rocked:shook or or moved gently8. serenely: calmly or peacefully9. grin:smile broadly10. deceive: make sb. believe sth. that is falseV ocabulary (B)1. preach: give a religious talk, usually as part of a service in church2. by leaps and bounds:very quickly3. rhythmical:marked by regular succession of weak and strong stresses, accents, movements4. sermon: a talk usually based on a sentence or “verse” from the Bible and preached as part of a church service5. braided:twisted together into one plait6. work-gnarled: twisted, with swollen joints and rough skin as from hard work or old age7. rounder: a person who lives a vicious life, a habitual drunkard8. take his (i.e., god's) name in vain: use god's name in cursing, speak of god without respect9. punctuate: interrupt from time to time with sth.10. ecstatic:causing great joy and happinessUnit 3V ocabulary (A)1. contend: argue, claim2. mutilation: destruction3. purchase: buying4. possession:ownership5. transfer: move from one place to another6. dog-eared:having the corners of the pages turned up or down with use so that they look like a dog's ears7. intact:whole because no part has been touched or spoilt8. indispensable: absolutely, essential9. scratch pad:loosely joined sheets of paper (a pad) for writing notes10. sacred:to be treated with great respectV ocabulary (B)1. bluntly: plainly, directly2. Restrain:hold back (from doing sth.)3. dilapidated: broken and old; falling to pieces4. scribble: write hastily or carelessly5. unblemished:not spoiled, as new6. crayon:pencil of soft colored chalk or wax, used for drawing7. symphony: a musical work for a large group of instruments8. typography: the arrangement, style and appearance of printed matter9. humility: humble state of mind10. receptacle: a containerUnit 4V ocabulary (A)1. (c) zip off: move away with speed2. (f) unencumbered: not obstructed3. (j) nifty: clever4. (a) loose:let out5. (d) noodle around: play about6. (b) span:extend across7. (h) debut: make first public appearance8. (e) the élite: a group of people with a high professional or social level9. (g) juncture: a particular point in time10. (i) sparse: inadequately furnishedV ocabulary (B)1. exotic:striking or unusual in appearance2. hack: a person paid to do hard and uninteresting work3. stint:fixed amount of work4. random: chance, unplanned, unlooked for5. reside: be present (in some place)6. access:the opportunity or right to use or see sth.7. cobble:put together quickly or roughly8. lingua franca:language or way of communicating which is used by people do not speak the same native language9. quintessential: the most typical10. unconventionally: doing things not in the accepted way11. Compromise:sth. That is midway between two different things12. cash in on: profit from; turn to one's advantageUnit 5V ocabulary (A)1. radiate: send out (lights) in all directions2. appreciate: understand fully3. outweigh:are greater than4. hemmed in:surrounded5. habitation: a place to live in6. obscure: make difficult to see7. shatter: break suddenly into small pieces8. haul up: pull up with some effort9. pore:very small opening in the skin through which sweat may pass10. unveiling:discovering, learning aboutV ocabulary (B)1. distinctive: clearly marking a person or thing different from others2. spectacular: striking, out of the ordinary, amazing to see3. phenomenon: thing in nature as it appears or is experienced by the senses4. tenure: right of holding (land)5. tempestuous: very rough, stormy6. inclined: likely, tending to, accustomed to7. precipitation: (the amount of) rainfall, snow etc. which has fallen onto the ground8. disintegrate:break up into small particles or pieces, come apart9. granules:small pieces like fine grains10. mercury: a heavy silver-white metal which is liquid at ordinary temperature and is used in scientific instruments such as thermometers11. disrupt:upset, disturb12. cushion: paddingUnit 6V ocabulary (A)1. (f) brush house: house made of small branches2. (i) pulsing and vibrating:beating steadily (as the heart does) and moving rapidly, here “active”, “aler t”3. (b) strangle out: get the words out with difficulty in their keenness to speak4. (j) sting: a wound in the skin caused by the insect5. (e) giggle:laugh, not heartily, but often in a rather embarrassed way6. (a) alms-giver: person who gives money, food and clothes to poor people (NB: now a rather old-fashioned concept)7. (c) residue:that which remains after a part disappears, or is taken or used (here, a metaphor using a chemical term)8. (d) lust: very strong, obsessive desire9. (h) withheld:deliberately refused10. (g) venom: (liquid) poisonV ocabulary (B)1. scramble: move, possible climb, quickly and often with some difficulty2. dart:move forward suddenly and quickly3. panting: breathing quickly4. foaming:forming white mass of small air bubbles5. baptize: perform the Christian religious ceremony of baptism, i.e., of acceptance into the Christian Church6. judicious: with good judgment7. fat hammocks: (here) the doctor’s thick eyelids8. cackle:laugh or talk loudly and unpleasantly9. semblance: appearance, seeming likeness10. squint: look with almost closed eyes11. speculation: thoughts of possible profits12. distillate:product of distillationParaphraseUnit 11、Pretty clearly, anyone who followed my collection of rules would be blessed with a richer life, boundless love from his family and the admiration of the community.Para：Quite obviously, anyone who was determined to be guided by the rules of self improvement I collected would be happy and have a richer life, infinite affection from his family and the love and respect of the community.十分明显，遵循我所收藏的规则的人将享有丰富多彩的生活，包括来自家庭无尽的爱和邻居们的羡慕、钦佩。

Quizzes for Chapter 11单选(1分)图灵测试旨在给予哪一种令人满意的操作定义得分/总分A.人类思考B.人工智能C.机器智能D.机器动作正确答案：C你选对了2多选(1分)选择以下关于人工智能概念的正确表述得分/总分A.人工智能旨在创造智能机器该题无法得分/B.人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/C.人工智能将其定义为人类智能体的研究该题无法得分/D.人工智能是为了开发一类计算机使之能够完成通常由人类所能做的事该题无法得分/正确答案：A、B、D你错选为A、B、C、D3多选(1分)如下学科哪些是人工智能的基础？得分/总分A.经济学B.哲学C.心理学D.数学正确答案：A、B、C、D你选对了4多选(1分)下列陈述中哪些是描述强AI(通用AI)的正确答案？得分/总分A.指的是一种机器，具有将智能应用于任何问题的能力B.是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑C.指的是一种机器，仅针对一个具体问题D.其定义为无知觉的计算机智能，或专注于一个狭窄任务的AI正确答案：A、B你选对了5多选(1分)选择下列计算机系统中属于人工智能的实例得分/总分搜索引擎B.超市条形码扫描器C.声控电话菜单该题无法得分/D.智能个人助理该题无法得分/正确答案：A、D你错选为C、D6多选(1分)选择下列哪些是人工智能的研究领域得分/总分A.人脸识别B.专家系统C.图像理解D.分布式计算正确答案：A、B、C你错选为A、B7多选(1分)考察人工智能(AI)的一些应用，去发现目前下列哪些任务可以通过AI来解决得分/总分A.以竞技水平玩德州扑克游戏B.打一场像样的乒乓球比赛C.在Web上购买一周的食品杂货D.在市场上购买一周的食品杂货正确答案：A、B、C你错选为A、C8填空(1分)理性指的是一个系统的属性，即在_________的环境下做正确的事。

得分/总分正确答案：已知1单选(1分)图灵测试旨在给予哪一种令人满意的操作定义得分/总分A.人类思考B.人工智能C.机器智能D.机器动作正确答案：C你选对了2多选(1分)选择以下关于人工智能概念的正确表述得分/总分A.人工智能旨在创造智能机器该题无法得分/B.人工智能是研究和构建在给定环境下表现良好的智能体程序该题无法得分/C.人工智能将其定义为人类智能体的研究该题无法得分/D.人工智能是为了开发一类计算机使之能够完成通常由人类所能做的事该题无法得分/正确答案：A、B、D你错选为A、B、C、D3多选(1分)如下学科哪些是人工智能的基础？得分/总分A.经济学B.哲学C.心理学D.数学正确答案：A、B、C、D你选对了4多选(1分)下列陈述中哪些是描述强AI(通用AI)的正确答案？得分/总分A.指的是一种机器，具有将智能应用于任何问题的能力B.是经过适当编程的具有正确输入和输出的计算机，因此有与人类同样判断力的头脑C.指的是一种机器，仅针对一个具体问题D.其定义为无知觉的计算机智能，或专注于一个狭窄任务的AI正确答案：A、B你选对了5多选(1分)选择下列计算机系统中属于人工智能的实例得分/总分搜索引擎B.超市条形码扫描器C.声控电话菜单该题无法得分/D.智能个人助理该题无法得分/正确答案：A、D你错选为C、D6多选(1分)选择下列哪些是人工智能的研究领域得分/总分A.人脸识别B.专家系统C.图像理解D.分布式计算正确答案：A、B、C你错选为A、B7多选(1分)考察人工智能(AI)的一些应用，去发现目前下列哪些任务可以通过AI来解决得分/总分A.以竞技水平玩德州扑克游戏B.打一场像样的乒乓球比赛C.在Web上购买一周的食品杂货D.在市场上购买一周的食品杂货正确答案：A、B、C你错选为A、C8填空(1分)理性指的是一个系统的属性，即在_________的环境下做正确的事。

Int J Comput Vis(2009)82:205–229DOI10.1007/s11263-008-0197-6Fields of ExpertsStefan Roth·Michael J.BlackReceived:22January2008/Accepted:17November2008/Published online:24January2009©Springer Science+Business Media,LLC2009Abstract We develop a framework for learning generic, expressive image priors that capture the statistics of nat-ural scenes and can be used for a variety of machine vi-sion tasks.The approach provides a practical method for learning high-order Markov randomfield(MRF)models with potential functions that extend over large pixel neigh-borhoods.These clique potentials are modeled using the Product-of-Experts framework that uses non-linear func-tions of many linearfilter responses.In contrast to previous MRF approaches all parameters,including the linearfilters themselves,are learned from training data.We demonstrate the capabilities of this Field-of-Experts model with two ex-ample applications,image denoising and image inpainting, which are implemented using a simple,approximate infer-ence scheme.While the model is trained on a generic im-age database and is not tuned toward a specific applica-tion,we obtain results that compete with specialized tech-niques.Keywords Markov randomfields·Low-level vision·Image modeling·Learning·Image restorationThe work for this paper was performed while S.R.was at Brown University.S.Roth( )Department of Computer Science,TU Darmstadt,Darmstadt, Germanye-mail:sroth@cs.tu-darmstadt.deM.J.BlackDepartment of Computer Science,Brown University,Providence, RI,USAe-mail:black@ 1IntroductionThe need for prior models of image or scene structure oc-curs in many machine vision and graphics problems includ-ing stereo,opticalflow,denoising,super-resolution,image-based rendering,volumetric surface reconstruction,and tex-ture synthesis to name a few.Whenever one has“noise”or uncertainty,prior models of images(or depth maps,flowfields,three-dimensional volumes,etc.)come into play. Here we develop a method for learning priors for low-level vision problems that can be used in many standard vision, graphics,and image processing algorithms.The key idea is to formulate these priors as a high-order Markov random field(MRF)defined over large neighborhood systems.This is facilitated by exploiting ideas from sparse image patch representations.The resulting Field of Experts(FoE)mod-els the prior probability of an image,or other low-level rep-resentation,in terms of a randomfield with overlapping cliques,whose potentials are represented as a Product of Experts(Hinton1999).While this model applies to a wide range of low-level representations,this paper focuses on its applications to modeling images.In other work(Roth and Black2007b)we have already studied the application to modeling vector-valued opticalflowfields;other potential applications will be discussed in more detail below.To study the application of Fields of Experts to modeling natural images,we train the model on a standard database of natural images(Martin et al.2001)and develop a diffusion-like scheme that exploits the prior for approximate Bayesian inference.To demonstrate the power of the FoE model,we use it in two different applications:image denoising and im-age inpainting(Bertalmío et al.2000)(i.e.,filling in missing pixels in an image).Despite the generic nature of the prior and the simplicity of the approximate inference,we obtainFig.1Image restoration using a Field of Experts.(a )Image from the Corel database with additive Gaussian noise (σ=15,PSNR =24.63dB).(b )Image denoised using a Field of Experts(PSNR =30.72dB).(c )Original photograph with scratches.(d )Im-age inpainting using the FoE modelresults near the state of the art that,until now,were not pos-sible with MRF approaches.Figure 1illustrates the appli-cation of the FoE model to image denoising and image in-painting.We perform a detailed analysis of various aspects of the model and use image denoising as a running exam-ple for quantitative comparisons with the state of the art.We also provide quantitative results for the problem of image inpainting.Modeling image priors is challenging due to the high-dimensionality of images,their non-Gaussian statistics,and the need to model correlations in image structure over ex-tended image neighborhoods.It has been often observed that,for a wide variety of linear filters,the marginal filter responses are non-Gaussian,and that the responses of differ-ent filters are usually not independent (Huang and Mumford 1999;Srivastava et al.2002;Portilla et al.2003).As discussed in more detail below,there have been a number of attempts to overcome these difficulties and to model the statistics of small image patches as well as of entire images.Image patches have been modeled using a variety of sparse coding approaches or other sparse rep-resentations (Olshausen and Field 1997;Teh et al.2003).Many of these models,however,do not easily generalize to models for entire images,which has limited their impact for machine vision applications.Markov random fields on the other hand can be used to model the statistics of en-tire images (Geman and Geman 1984;Besag 1986).They have been widely used in machine vision,but often exhibit serious limitations.In particular,MRF priors typically ex-ploit hand-crafted clique potentials and small neighborhood systems,which limit the expressiveness of the models and only crudely capture the statistics of natural images.A no-table exception to this is the FRAME model by Zhu et al.(1998),which learns clique potentials for larger neighbor-hoods from training data by modeling the responses of a set of predefined linear filters.The goal of the current paper is to develop a frame-work for learning expressive yet generic prior models for low-level vision problems.In contrast to example-based ap-proaches,we develop a parametric representation that usesexamples for training,but does not rely on examples as part of the representation.Such a parametric model has advan-tages over example-based methods in that it generalizes bet-ter beyond the training data and allows for the use of more elegant optimization methods.The core contribution is to extend Markov random fields beyond FRAME by model-ing the local field potentials with learned filters.To do so,we exploit ideas from the Product-of-Experts (PoE)frame-work (Hinton 1999),which is a generic method for learn-ing high dimensional probability distributions.Previous ef-forts to model images using Products of Experts (Teh et al.2003)were patch-based and hence inappropriate for learn-ing generic priors for images or other low-level representa-tions of arbitrary size.We extend these methods,yielding a translation-invariant prior.The Field-of-Experts framework provides a principled way to learn MRFs from examples and the improved modeling power makes them practical for complex tasks.12Background and Previous WorkFormal models of image or scene structure play an impor-tant role in many vision problems where ambiguity,noise,or missing sensor data make the recovery of world or image structure difficult or impossible.Models of a priori structure are used to resolve,or regularize,such problems by provid-ing additional constraints that impose prior assumptions or knowledge.For low-level vision applications the need for modeling such prior knowledge has long been recognized (Geman and Geman 1984;Poggio et al.1985),for example due to their frequently ill-posed nature.Often these mod-els entail assuming spatial smoothness or piecewise-smooth-ness of various image properties.While there are many ways of imposing prior knowledge,we focus here on probabilistic prior models,which have a long history and provide a rig-orous framework within which to combine different sources1Thispaper is an extended version of Roth and Black (2005).of information.Other regularization methods,such as deter-ministic ones,including variational approaches(Poggio et al.1985)will only be discussed briefly.For problems in low-level vision,such probabilistic prior models of the spatial structure of images or scene proper-ties are often formulated as Markov randomfields(MRFs) (Wong1968;Kashyap and Chellappa1981;Geman and Ge-man1984;Besag1986;Marroquin et al.1987;Szeliski 1990)(see Li2001for a recent overview and introduction). Markov randomfields have found many areas of applica-tion including image denoising(Sebastiani and Godtlieb-sen1997),stereo(Sun et al.2003),opticalflow estimation (Heitz and Bouthemy1993),texture classification(Varma and Zisserman2005),to name a few.MRFs are undirected graphical models,where the nodes of the graph represent random variables which,in low-level vision applications, typically correspond to image measurements such as pixel intensities,range values,surface normals,or opticalflow vectors.Formally,we let the image measurements x be rep-resented by nodes V in a graph G=(V,E),where E are the edges connecting nodes.The edges between the nodes indicate the factorization structure of the probability den-sity p(x)described by the MRF.More precisely,the maxi-mal cliques x(k),k=1,...,K of the graph directly corre-spond to factors of the probability density.The Hammersley-Clifford theorem(Moussouris1974)establishes that we can write the probability density of this graphical model as a Gibbs distributionp(x)=1Zexp−kU k(x(k)),(1)where x is an image,U k(x(k))is the so-called potential func-tion for clique x(k),and Z is a normalizing term called the partition function.In many cases,it is reasonably assumed that the MRF is homogeneous;i.e.,the potential function is the same for all cliques(or in other terms U k(x(k))= U(x(k))).This property gives rise to the translation-invari-ance of an MRF model for low-level vision applications.2 Equivalently,we can also write the density under this model asp(x)=1Zkf k(x(k)),(2)which makes the factorization structure of the model even more explicit.Here,f k(x(k))are the factors defined on clique x(k),which,in an abuse of terminology,we also sometimes call potentials.2When we talk about translation-invariance,we disregard the fact that thefinite size of the image will make this property hold only approxi-mately.Because of the regular structure of images,the edges of the graph are usually chosen according to some regular neighborhood structure.In almost all cases,this neighbor-hood structure is chosen a priori by hand,although the type of edge structure and the choice of potentials varies sub-stantially.The vast majority of models use a pairwise graph structure;each node(i.e.,pixel)is connected to its4di-rect neighbors to the left,right,top,and bottom(Geman and Geman1984;Besag1986;Sebastiani and Godtlieb-sen1997;Tappen et al.2003;Neher and Srivastava2005). This induces a so-called pairwise MRF,because the maxi-mal cliques are simply pairs of neighboring nodes(pixels), and hence each potential is a function of two pixel values:p(x)=1Zexp⎛⎝−(i,j)∈EU(x i,x j)⎞⎠.(3)Moreover,the potential is typically defined in terms of some robust function of the difference between neighboring pixel valuesU(x i,x j)=ρ(x i−x j),(4)where a typicalρ-function is shown in Fig.2.The truncated quadraticρ-function in Fig.2allows spatial discontinuities by not heavily penalizing large neighbor differences.The difference between neighboring pixel values also has an intuitive interpretation,as it approximates a hori-zontal or vertical image derivative.The robust function can thus be understood as modeling the statistics of thefirst derivatives of the images.These statistics,as well as the study of the statistics of natural images in general have re-ceived a lot of attention in the literature(Ruderman1994; Olshausen and Field1996;Huang and Mumford1999; Srivastava et al.2003).A review of this literature is well be-yond the scope of this paper and the reader is thus referred to above papers for an overview.Despite their long history,MRF methods have often pro-duced disappointing results when applied to the recovery of complex scene structure.One of the reasons for this is that the typical pairwise model structure severely restricts the image structures that can be represented.In the major-ity of the cases,the potentials are furthermore hand-defined, and consequently are only ad hoc models of image or scene structure.The resulting probabilistic models typically do not well represent the statistical properties of natural images and scenes,which leads to poor application performance.For example,Fig.2shows the result of using a pairwise MRF model with a truncated quadratic potential function to re-move noise from an image.The estimated image is char-acteristic of many MRF results;the robust potential func-tion produces sharp boundaries but the result is piecewise smooth and does not capture the more complex textural pro-perties of natural scenes.Fig.2Typical pairwise MRF potential and results:(a)Example of a common robust potential function(negative log-probability).This truncated quadratic is often used to model piecewise smooth surfaces.(b)Image with Gaussian noise added.(c)Typical result of denoising using an ad-hoc pairwise MRF(obtained using the method of Felzen-szwalb and Huttenlocher2004).Note the piecewise smooth nature of the restoration and how it lacks the textural detail of natural scenesFor some years it was unclear whether the limited ap-plication performance of pairwise MRFs was due to limi-tations of the model,or due to limitations of the optimiza-tion approaches used with non-convex models.Yanover et al.(2006)have recently obtained global solutions to low-level vision problems even with non-convex pairwise MRFs. Their results indicate that pairwise models are incapable of producing very high-quality solutions for stereo prob-lems and suggest that richer models are needed for low-level modeling.Gimel’farb(1996)proposes a model with multiple and more distant neighbors,which are able to model more com-plex spatial properties(see also Zalesny and van Gool2001). Of particular note,this method learns the neighborhood structure that best represents a set of training data;in the case of texture modeling,different textures result in quite different neighborhood systems.This work however has been limited to modeling specific classes of image texture and our experiments with modeling more diverse classes of generic image structure suggest these methods do not scale well beyond narrow,class-specific,image priors.2.1High-Order Markov Random FieldsThere have been a number of attempts to go beyond these very simple pairwise models,which only model the statistics offirst derivatives in the image structure(Geman et al.1992; Zhu and Mumford1997;Zhu et al.1998;Tjelmeland and Besag1998;Paget and Longstaff1998).The basic insight behind such high-order models is that the generality of MRFs allows for richer models through the use of larger maximal cliques.One approach uses the second derivatives of image structure.Geman and Reynolds(1992),for ex-ample,formulate MRF potentials using polynomials deter-mined by the order of the(image)surface being modeled (k=1,2,3for constant,planar,or quadric).In the context of this work,we think of these polynomi-als as defining linearfilters,J i,over local neighborhoodsof Fig.3Filters representingfirst and second order neighborhood sys-tems(Geman and Reynolds1992).The left twofilters correspond to first derivatives,the right threefilters to second derivatives pixels.For the quadric case,the corresponding3×3filters are shown in Fig.3.In this example,the maximal cliques are square patches of3×3pixels and their corresponding potential for clique x(k)centered at pixel k is written asU(x(k))=5i=1ρ(J T i x(k)),(5)where the J i are the shown derivativefilters.Whenρis a robust potential,this corresponds to the weak plate model (Blake and Zisserman1987).The above models are capable of representing richer structural properties beyond the piecewise spatial smooth-ness of pairwise models,but have remained largely hand-defined.The designer decides what might be a good model for a particular problem and chooses a neighborhood sys-tem,the potential function,and its parameters.2.2Learning MRF ModelsHand selection of parameters is not only somewhat arbitrary and can cause models to only poorly capture the statistics of the data,but is also particularly cumbersome for models with many parameters.There exist a number of methods for learning the parameters of the potentials from training data (see Li2001for an overview).In the context of images,Be-sag(1986)for example uses the pseudo-likelihood criterion to learn the parameters of a parametric potential function for a pairwise MRF from training data.Applying pseudo-likelihood in the high-order case is,however,hindered bythe fact that computing the necessary conditionals is often difficult.For Markov randomfield modeling in general(i.e.,not specifically for vision applications),maximum likelihood (ML)(Geyer1991)is probably the most widely used learn-ing criterion.Nevertheless,due to its often extreme compu-tational demands,it has long been avoided.Hinton(2002) recently proposed a learning rule for energy-based models, called contrastive divergence(CD),which resembles max-imum likelihood,but allows for much more efficient com-putation.In this paper we apply contrastive divergence to the problem of learning Markov randomfield models of im-ages;details will be discussed below.Other learning meth-ods include iterative scaling(Darroch and Ratcliff1972; della Pietra et al.1997),score matching(Hyvaärinen2005), discriminative training of energy-based models(LeCun and Huang2005),as well as a large set of variational(and re-lated)approximations to maximum likelihood(Jordan et al.1999;Yedidia et al.2003;Welling and Sutton2005; Minka2005).In this work,Markov randomfields are used to model prior distributions of images and potentially other scene properties,but in the literature,MRF models have also been used to directly model the posterior distribution for partic-ular low-level vision applications.For these applications, it can be beneficial to train MRF models discriminatively (Ning et al.2005;Kumar and Hebert2006).This is not pur-sued here.In low-level vision applications,most of these learn-ing methods have not found widespread use.Nevertheless, maximum likelihood has been successfully applied to the problem of modeling images(Zhu and Mumford1997; Descombes et al.1999).One model that is of particular im-portance in the context of this paper is the FRAME model of Zhu et al.(1998).It took a step toward more practical MRF models,as it is of high-order and allows its parameters to be learned from training data,for example from a set of nat-ural images(Zhu and Mumford1997).This method uses a “filter pursuit”strategy to selectfilters from a pre-defined set of standard imagefilters;the potential functions model the responses of thesefilters using aflexible,discrete,non-parametric representation.The discrete nature of this repre-sentation complicates its use,and,while the method exhib-ited good results for texture synthesis,the reported image restoration results appear to fall below the current state of the art.To model more complex local statistics a number of au-thors have turned to empirical probabilistic models captured by a database of image patches.Freeman et al.(2000)pro-pose an MRF model that uses example image patches and a measure of consistency between them to model scene struc-ture.This idea has been exploited as a prior model for image based rendering(Fitzgibbon et al.2003)and super-resolu-tion(Pickup et al.2004).The roots of these models are in example-based texture synthesis(Efros and Leung1999).In contrast,our approach uses parametric(and differen-tiable)potential functions applied tofilter responses.Unlike the FRAME model,we learn thefilters themselves as well as the parameters of the potential functions.As we will show, the resultingfilters appear quite different from standardfil-ters and achieve better performance than do standardfilters in a variety of tasks.A computational advantage of our para-metric model is that it is differentiable,which facilitates var-ious learning and inference methods.2.3InferenceTo apply MRF models to actual problems in low-level vi-sion,we compute a solution using tools from probabilis-tic inference.Inference in this context typically means ei-ther performing maximum a posteriori(MAP)estimation, or computing expectations over the solution mon to all MRF models in low-level vision is the fact that infer-ence is challenging,both algorithmically and computation-ally.The loopy structure of the underlying graph makes ex-act inference NP-hard in the general case,although special cases exist where polynomial time algorithms are known. Because of that,inference is usually performed in an approx-imate fashion,for which there are a wealth of different tech-niques.Classical techniques include Gibbs sampling(Ge-man and Geman1984),deterministic annealing(Hofmann et al.1998),and iterated conditional modes(Besag1986). More recently,algorithms based on graph cuts(Kolmogorov and Zabih2004)have become very popular for MAP infer-ence.Variational techniques and related ones,such as belief propagation(Yedidia et al.2003),have also enjoyed enor-mous popularity,both for MAP inference and computing marginals.Nevertheless,even with such modern approxi-mate techniques,inference can be quite slow,which has prompted the development of models that simplify infer-ence(Felzenszwalb and Huttenlocher2004).While these may make inference easier,they typically give the answer to the wrong problem,as the model does not capture the rel-evant statistics well(cf.Fig.2).Inference in high-order MRF models is particularly de-manding,because the larger size of the cliques complicates the(approximate)inference process.Because of that,we rely on very simple approximate inference schemes using the conjugate gradient method.Nevertheless,the applica-bility of more sophisticated inference techniques to models such as the one proposed here,promises to be a fruitful area for future work(cf.Potetz2007;Kohli et al.2007).2.4Other Regularization MethodsIt is worth noting that prior models of spatial structure are also often formulated as energy terms(e.g.,log-probability)and used in non-probabilistic regularization methods(Pog-gio et al.1985).While we pursue a probabilistic framework here,the methods are applicable to contexts where determin-istic regularization methods are applied.This suggests that our FoE framework is applicable to a wide class of varia-tional frameworks(see Schnörr et al.1996for a review of such techniques).Interestingly,many of these deterministic regularization approaches,for example variational(Schnörr et al.1996)or nonlinear-diffusion related methods(Weickert1997),suffer from very similar limitations as typical MRF approaches. This is because they penalize large image derivatives sim-ilar to pairwise MRFs.Moreover,in order to show the ex-istence of a unique global optimum,many models are re-stricted to be convex,and are furthermore mostly hand-defined.Non-convex regularizers often show superior per-formance in practice(Black et al.1998),and the missing connection to the statistics of natural images or scenes can be viewed as problematic.There have been variational and diffusion-related approaches that try to overcome some of these limitations(Gilboa et al.2004;Trobin et al.2008).2.5Models of Image PatchesEven though typically motivated from an image-coding or neurophysiological point of view,there is a large amount of related work in the area of sparse coding and component analysis,which attempts to model complex image struc-ture.Such models typically encode structural properties of images through a set of linearfilter responses or compo-nents.For example,Principal Component Analysis(PCA) (Roweis and Ghahramani1999)of image patches yields vi-sually intuitive components,some of which resemble deriv-ativefilters of various orders and orientations.The marginal statistics of suchfilters are highly non-Gaussian(Ruder-man1994)and are furthermore not independent,making this model unsuitable for probabilistically modeling image patches.Independent Component Analysis(ICA)(Bell and Sej-nowski1995),for example,assumes non-Gaussian statis-tics andfinds the linear components such that the statisti-cal dependence between the components is minimized.As opposed to the principal components,ICA yields localized components,which resemble Gaborfilters of various orien-tations,scales,and locations.Since the components(i.e.,fil-ters)J i∈R n found by ICA are by assumption indepen-dent,one can define a probabilistic model of image patchesx∈R n by multiplying the marginal distributions,p i(J T i x), of thefilter responses:p(x)∝ni=1p i(J T i x).(6)Notice that projecting an image patch onto a linear compo-nent(J T i x)is equivalent tofiltering the patch with a linearfilter described by J i.However,in the case of image patchesof n pixels it is generally impossible tofind n fully inde-pendent linear components,which makes the ICA modelonly an approximation.Somewhat similar to ICA are sparse-coding approaches(e.g.,Olshausen and Field1996),whichalso represent image patches in terms of a linear combina-tion of learnedfilters,but in a synthesis-based manner(seealso Elad et al.2006).Most of these methods,however,focus on image patchesand provide no direct way of modeling the statistics of wholeimages.Several authors have explored extending sparse cod-ing models to full images.For example,Sallee and Ol-shausen(2003)propose a prior model for entire images,butinference with this model requires Gibbs sampling,whichmakes it somewhat problematic for many machine visionapplications.Other work has integrated translation invari-ance constraints into the basisfinding process(Hashimotoand Kurata2000;Wersing et al.2003).The focus in thatwork,however,remains on modeling the image in terms ofa sparse linear combination of basisfilters with an empha-sis on the implications for human vision.Modeling entireimages has also been considered in the context of image de-noising(Elad and Aharon2006).While these approaches aremotivated in a way that is quite different from Markov ran-domfield approaches as emphasized here,they are similarin that they model the response to linearfilters and even al-low thefilters themselves to be learned.Another differenceis that the model of Elad and Aharon(2006)is not trainedoffline on a general database of natural images,but the pa-rameters are instead inferred“online”in the context of theapplication at hand.While this may also have advantages,itfor example makes the application to problems with missingdata(e.g.,inpainting)more difficult.Popular approaches to modeling images also includewavelet-based methods(Portilla et al.2003).Since neigh-boring wavelet coefficients are not independent,it is ben-eficial to model their dependencies.This has for examplebeen done in patches using Products of Experts(Gehler andWelling2006)or over entire wavelet subbands using MRFs(Lyu and Simoncelli2007).While such a modeling of thedependencies between wavelet coefficients bears similaritiesto the FoE model,these wavelet approaches do not directlyyield generic image priors due to the fact that they modelthe coefficients of an overcomplete wavelet transform.Theirapplicability has thus mostly been restricted to specific ap-plications,such as denoising.2.6Products of ExpertsProducts of Experts(PoE)(Hinton1999)have also beenused to model image patches(Welling et al.2003;Teh et al.。

2025年全国大学英语CET四级考试模拟试卷及答案指导一、写作（15分）CET-4 Writing SectionDirections: For this part, you are allowed 30 minutes to write a short essay entitled “The Importance of Teamwork”. You should write at least 120 words but no more than 180 words.Sample Essay: The Importance of TeamworkIn today’s fast-paced and highly competitive world, the concept of teamwork has become more crucial than ever. It is often said that one can go fast alone, but to go far, one must go together. This saying underlines the importance of teamwork in achieving common goals effectively and efficiently.Teamwork allows for the pooling of diverse skills and talents, which leads to more innovative solutions and better decision-making. When individuals with different backgrounds and expertise collaborate, they bring unique perspectives to the table, fostering an environment where creativity thrives. Furthermore, working as a team builds a support system, enabling members to rely on each other during challenging times, thus reducing stress and increasing job satisfaction.Another significant benefit of teamwork is the ability to accomplish tasksthat would be impossible for an individual to handle. By dividing work among team members based on their strengths, teams can tackle complex projects, ensuring all aspects are thoroughly covered. This not only improves the quality of work but also accelerizes the completion time.In conclusion, the value of teamwork cannot be overstated. It is through collaboration and mutual support that we can achieve great things, overcome obstacles, and reach our full potential. Embracing the spirit of teamwork is essential for both personal and professional success in our interconnected world.Analysis:•Introduction: The essay begins with a clear statement about the increasing significance of teamwork in the modern era, setting up the main argument.•Body Paragraphs:•The first body paragraph discusses how teamwork enhances innovation and decision-making by combining varied skills and viewpoints.•The second body paragraph highlights the supportive nature of teamwork, emphasizing its role in managing stress and boosting morale.• A third point is made about the efficiency and effectiveness gained from dividing labor according to individual strengths, allowing for thesuccessful execution of complex tasks.•Conclusion: The concluding paragraph reinforces the thesis, summarizing the key benefits of teamwork and linking them to broader concepts ofachievement and personal growth.This sample response adheres to the word limit (156 words), maintains a coherent structure, and provides specific examples to support the main points, making it a strong example for the CET-4 writing section.二、听力理解-短篇新闻（选择题，共7分）第一题News Item 1:A new study has found that the popularity of online shopping has led to a significant increase in the use of plastic packaging. The researchers analyzed data from various e-commerce platforms and discovered that the amount of plastic packaging used in online orders has doubled over the past five years. This has raised concerns about the environmental impact of e-commerce and the need for more sustainable packaging solutions.Questions:1、What is the main issue addressed in the news?A) The decline of traditional shopping methods.B) The environmental impact of online shopping.C) The growth of e-commerce platforms.D) The advantages of plastic packaging.2、According to the news, what has happened to the use of plastic packaging in online orders over the past five years?A) It has decreased by 50%.B) It has remained stable.C) It has increased by 25%.D) It has doubled.3、What is the primary concern raised by the study regarding online shopping?A) The increase in the number of e-commerce platforms.B) The high cost of online shopping.C) The environmental impact of plastic packaging.D) The difficulty in returning products.Answers:1、B) The environmental impact of online shopping.2、D) It has doubled.3、C) The environmental impact of plastic packaging.第二题Section B: Short NewsIn this section, you will hear one short news report. At the end of the news report, you will hear three questions. After each question, there is a pause. During the pause, you must read the four choices marked A), B), C) and D), and decide which is the best answer. Then mark the corresponding letter on the Answer Sheet with a single line through the center.News Report:The World Health Organization announced today that it has added the ChineseSinovac COVID-19 vaccine to its list of vaccines approved for emergency use. This move will facilitate the distribution of the vaccine in lower-income countries participating in the COVAX initiative aimed at ensuring equitable access to vaccines globally. The WHO praised the Sinovac vaccine for its easy storage requirements, making it ideal for areas with less sophisticated medical infrastructure.Questions:1、According to the news report, what did the WHO announce?A)The end of the pandemicB)Approval of a new vaccineC)Launch of a global health campaignD)Increased funding for vaccine researchAnswer: B) Approval of a new vaccine2、What was highlighted about the Sinovac vaccine by the WHO?A)It is the most effective vaccine availableB)It requires simple storage conditionsC)It is cheaper than other vaccinesD)It has no side effectsAnswer: B) It requires simple storage conditions3、What is the purpose of the COVAX initiative mentioned in the report?A)To speed up vaccine developmentB)To provide financial support to vaccine manufacturersC)To ensure equal access to vaccines worldwideD)To promote travel between countriesAnswer: C) To ensure equal access to vaccines worldwide三、听力理解-长对话（选择题，共8分）第一题Part Three: Long ConversationsIn this section, you will hear 1 long conversation. The conversation will be played twice. After you hear a part of the conversation, there will be a pause. Both the questions and the conversation will be spoken only once. After you hear a question, you must choose the best answer from the four choices marked A), B), C), and D). Then mark the corresponding letter on Answer Sheet 2 with a single line through the center.Now, listen to the conversation.Conversational Excerpt:M: Hey, Jane, how was your day at the office today?W: Oh, it was quite a challenge. I had to deal with a lot of issues. But I think I handled them pretty well.M: That’s good to hear. What were the main issues you faced?W: Well, first, we had a problem with the new software we’re tryin g to implement. It seems to be causing some technical difficulties.M: Oh no, that sounds frustrating. Did you manage to fix it?W: Not yet. I’m still trying to figure out what’s wrong. But I’m workingon it.M: That’s important. The company can’t afford a ny downtime with this software.W: Exactly. And then, I had to deal with a customer complaint. The customer was really upset because of a delayed shipment.M: That’s never a good situation. How did you handle it?W: I tried to be understanding and offered a discount on their next order. It seemed to calm them down a bit.M: That was a good move. Did it resolve the issue?W: Yes, it did. They’re satisfied now, and I think we’ve avoided a bigger problem.M: It sounds like you had a busy day. But you did a good job handling everything.W: Thanks, I’m glad you think so.Questions:1、What was the main issue the woman faced with the new software?A) It was causing problems with the computer systems.B) It was taking longer to install than expected.C) It was causing technical difficulties.D) It was not compatible with their existing systems.2、How did the woman deal with the customer complaint?A) She escalated the issue to her supervisor.B) She offered a discount on the customer’s next order.C) She apologized directly to the customer.D) She sent the customer a refund check.3、What was the woman’s impression of her day at work?A) It was uneventful and unchallenging.B) It was quite stressful but rewarding.C) It was a day filled with unnecessary meetings.D) It was a day where she didn’t accomplish much.4、What did the man say about the woman’s day at work?A) He thought it was unproductive.B) He felt she had handled everything well.C) He thought she should have asked for help.D) He believed she should take a break.Answers:1、C2、B3、B4、B第二题对话内容:Man:Hey, Sarah. I heard you’re planning to go on a trip next month. Where are you heading?Sarah:Oh, hi, Mike! Yes, I’m really excited about it. I’m going to Japan. It’s my first time there.Man:That sounds amazing! How long will you be staying? And what places are you planning to visit?Sarah:I’ll be there for two weeks. My plan is to start in Tokyo and then travel to Kyoto, Osaka, and Hiroshima. I’ve always been fascinated by the mix of traditional and modern culture in Japan.Man: Two weeks should give you plenty of time to see a lot. Are you going alone or with someone?Sarah:Actually, I’m going with a group of friends from college. We all decided to take this trip together after graduation. It’ll be great to experience it with them.Man:That’s wonderful! Do you have everything planned out, like accommodations and transportation?Sarah:Mostly, yes. We’ve booked our flights and hotels, and we’re using the Japan Rail Pass for getting around. B ut we’re leaving some room for spontaneity too. Sometimes the best experiences come unexpectedly!Man:Absolutely, that’s the spirit of traveling. Well, I hope you have an incredible time. Don’t forget to try some local food and maybe bring back some souvenirs!Sarah:Thanks, Mike! I definitely won’t miss out on trying sushi and ramen, and I already have a list of gifts to buy for family and friends. I can’t waitto share my adventures with everyone when I get back.1、How long is Sarah planning to stay in Japan?•A) One week•B) Two weeks•C) Three weeks•D) One month答案: B) Two weeks2、Which of the following ci ties is NOT mentioned as part of Sarah’s itinerary?•A) Tokyo•B) Kyoto•C) Sapporo•D) Hiroshima答案: C) Sapporo3、Who is Sarah going to Japan with?•A) By herself•B) With her family•C) With a group of friends•D) With coworkers答案: C) With a group of friends4、What has Sarah and her friends prepared for their trip besides booking flights and hotels?•A) They have hired a personal guide.•B) They have reserved spots for cultural workshops.•C) They have purchased a Japan Rail Pass.•D) They have enrolled in a language course.答案: C) They have purchased a Japan Rail Pass.四、听力理解-听力篇章（选择题，共20分）第一题Section CDirections: In this section, you will hear a passage three times. When the passage is read for the first time, listen carefully for its general idea. When the passage is read for the second time, fill in the blanks with the exact words you have just heard. Finally, when the passage is read for the third time, check what you have written.Passage:In recent years, the concept of “soft skills” has become increasingly popular in the workplace. These are skills that are not traditionally taught in schools but are essential for success in the professional world. Soft skills include communication, teamwork, problem-solving, and time management.1、Many employers believe that soft skills are just as important as technical skills because they help employees adapt to changing work environments.2、One of the most important soft skills is communication. Effectivecommunication can prevent misunderstandings and improve relationships with colleagues.3、Teamwork is also crucial in today’s workplace. Being able to work well with others can lead to better productivity and innovation.4、Problem-solving skills are essential for overcoming obstacles and achieving goals. Employees who can think creatively and solve problems efficiently are highly valued.5、Time management is another key soft skill. Being able to prioritize tasks and manage time effectively can help employees meet deadlines and reduce stress.Questions:1、What is the main idea of the passage?A) The importance of technical skills in the workplace.B) The definition and examples of soft skills.C) The increasing popularity of soft skills in the workplace.D) The impact of soft skills on employee performance.2、Why do many employers believe soft skills are important?A) They are easier to teach than technical skills.B) They are not necessary for most jobs.C) They help employees adapt to changing work environments.D) They are more difficult to acquire than technical skills.3、Which of the following is NOT mentioned as a soft skill in the passage?A) Communication.B) Leadership.C) Problem-solving.D) Time management.Answers:1、C) The increasing popularity of soft skills in the workplace.2、C) They help employees adapt to changing work environments.3、B) Leadership.Second Part: Listening Comprehension - Passage QuestionsListen to the following passage carefully and then choose the best answer for each question.Passage:Every year, millions of people flock to beaches around the world for their vacations. While enjoying the sun and sand, few give much thought to the tiny organisms that make up the very sand they’re lying on. Sand is actually made from rock particles that have been broken down over time by natural processes. However, on some unique beaches, like those found in Hawaii, the sand has a significant component of coral and shell fragments, giving it a distinctive white color. Beaches not only provide relaxation but also play a crucial role in supporting marine life and protecting coastal areas from erosion.Questions:1、What do millions of people go to the beaches for annually?2、What makes the sand on Hawaiian beaches distinctive?3、Besides providing relaxation, what other important role do beaches serve?Answers:1、Vacations.2、The presence of coral and shell fragments.3、Supporting marine life and protecting coastal areas from erosion.第三题PassageThe rise of e-commerce has revolutionized the way we shop. With just a few clicks, customers can purchase products from all over the world and have them delivered to their doorstep. However, this convenience has also brought about some challenges, particularly in terms of logistics and environmental impact.One of the biggest concerns is the environmental impact of packaging. Traditional packaging materials, such as plastic bags and boxes, are not biodegradable and often end up in landfills, contributing to pollution.E-commerce companies have started to address this issue by offering packaging-free options and promoting the use of sustainable materials.Another challenge is the issue of returns. With the ease of online shopping, customers often order more items than they need, leading to a high rate of returns. This not only increases the carbon footprint of shipping but also creates additional waste. Some companies have introduced policies to encourage customers to return fewer items, such as offering incentives for reuse or donation.Despite these challenges, the e-commerce industry is not standing still. There are innovative solutions being developed to make the process more sustainable. For example, some companies are experimenting with drone delivery to reduce the number of vehicles on the road. Others are investing in energy-efficient data centers to power their operations.1、What is one of the main concerns related to e-commerce packaging?A)The high cost of shipping materials.B)The environmental impact of non-biodegradable materials.C)The difficulty in recycling packaging materials.2、How does the high rate of returns affect e-commerce?A)It increases the demand for new packaging materials.B)It leads to a decrease in the cost of shipping.C)It creates additional waste and increases the carbon footprint.3、What is an innovative solution being developed to make e-commerce more sustainable?A)The use of reusable packaging.B)The implementation of strict return policies.C)The introduction of drone delivery.Answers:1、B2、C3、A五、阅读理解-词汇理解（填空题，共5分）First QuestionPassage:In today’s fast-paced world, conservation has become a major concern for environmentalists and policymakers alike. Preserving natural resources is not just about protecting the environment; it also plays a critical role in ensuring sustainable development and improving the quality of life for future generations. Innovative methods are being explored to achieve this goal, including the use of renewable energy sources and promoting eco-friendly practices in industries.Questions:1、The word “conservation” in the passage most likely means:A) The act of using something economically or sparingly.B) The protection of natural resources from being wasted.C) The process of changing something fundamentally.D) The act of restoring something to its original state.Answer: B) The protection of natural resources from being wasted.2、The word “innovative” in the passage is closest in meaning to:A) Outdated.B) Traditional.C) Creative.D) Unchanged.Answer: C) Creative.3、Based on the context, t he term “eco-friendly” would be best described as:A) Practices that are harmful to the environment.B) Practices that are beneficial to the environment.C) Practices that have no impact on the environment.D) Practices that focus solely on economic growth.Answer: B) Practices that are beneficial to the environment.4、The phrase “sustainable development” in the text refers to:A) Development that uses up all available resources quickly.B) Development that meets present needs without compromising the ability of future generations to meet their own needs.C) Development that focuses only on immediate economic gains.D) Development that disregards environmental concerns.Answer: B) Development that meets present needs without compromising the ability of future generations to meet their own needs.5、When the passage mentions “quality of life,” it implies:A) A decrease in living standards over time.B) An improvement in the overall conditions under which people live and work.C) The absence of any efforts to improve living conditions.D) The focus on increasing industrial activities regardless of their impact.Answer: B) An improvement in the overall conditions under which people live and work.This format closely follows the structure you might find in an actual CET Band 4 exam, with a passage followed by vocabulary questions that test understanding of context and word meanings.第二题Reading PassagesIn today’s fast-paced world, staying informed about current events is more important than ever. One of the best ways to keep up with the news is to read newspapers. However, not all newspapers are created equal. Here is an overview of some of the most popular newspapers in the world.1.The New York Times (USA): Established in 1851, The New York Times is one of the most prestigious and influential newspapers in the world. It covers a wide range of topics, including national and international news, politics, business, science, technology, and culture.2.The Guardian (UK): The Guardian is a British newspaper that has been in circulation since 1821. It is known for its liberal bias and its commitment to investigative journalism. The Guardian covers a variety of issues, including politics, the environment, and social justice.3.Le Monde (France): Le Monde is a French newspaper that was founded in 1944. It is one of the most widely read newspapers in France and is known for its in-depth reporting and analysis of global events.4.The Times (UK): The Times is another British newspaper that has been in circulation since 1785. It is a conservative newspaper that focuses on politics, business, and finance.5.El País (Spain): El País is a Spanish newspaper that was founde d in 1976. It is one of the most popular newspapers in Spain and is known for its comprehensive coverage of national and international news.Vocabulary UnderstandingChoose the best word or phrase to complete each sentence. Write your answers in the spaces provided.1、The____________of The New York Times is that it is one of the most prestigious and influential newspapers in the world.a.reputationb.historyc.popularityd.bias2、The Guardian is known for its____________bias and its commitment to investigative journalism.a.liberalb.conservativec.moderated.biased3、Le Monde is one of the most widely read newspapers in France and is known forits____________reporting and analysis.a.shallowb.superficialc.in-depthd.brief4、The Times is a conservative newspaper that focuses on____________issues.a.socialb.economicc.politicald.cultural5、El País is one of the most popular newspapers in Spain and is known for its comprehensive____________of national and international news.a.reportingb.analysisc.coveraged.editorialAnswers:1、a. reputation2、a. liberal3、c. in-depth4、c. political5、c. coverage六、阅读理解-长篇阅读（选择题，共10分）第一题Reading Passage OneIn recent years, with the rapid development of the internet and mobile technology, online learning has become increasingly popular among students. Online courses, such as those offered by MOOCs (Massive Open Online Courses), provide students with convenient access to high-quality educational resources from around the world. However, despite the benefits of online learning, there are also some challenges and considerations that need to be addressed.1.The following passage is about:A. The advantages and disadvantages of online learningB. The impact of online learning on traditional educationC. The history of MOOCs and their role in educationD. The challenges faced by students in online learning2.According to the passage, what is one of the main benefits of online learning?A. It allows students to study at their own paceB. It provides access to a wider range of educational resourcesC. It increases the interaction between students and teachersD. It reduces the cost of education3.The passage mentions that online learning has become increasingly popular due to:A. The advancements in internet technologyB. The decline of traditional education systemsC. The desire for flexible learning schedulesD. All of the above4.What is one of the challenges mentioned in the passage that online learners may face?A. Limited access to technological devicesB. Difficulty in maintaining self-disciplineC. Lack of face-to-face interaction with teachersD. All of the above5.The passage suggests that in order to succeed in online learning, students should:A. Attend online classes regularlyB. Engage in active discussions with peersC. Set clear goals and deadlines for their studiesD. All of the above答案：1.A2.B3.D4.D5.D第二题Reading Passage OneThe rise of the Internet has revolutionized the way we communicate and accessinformation. One of the most significant impacts has been the transformation of education, with online learning becoming increasingly popular. This passage explores the benefits and challenges of online learning.The Benefits of Online Learning1.Flexibility: Online learning offers students the flexibility to study at their own pace and on their own schedule. This is particularly beneficial for working professionals and those with other commitments.2.Access to a Wide Range of Resources: Online courses often provide access to a wealth of resources, including textbooks, videos, and interactive materials that can enhance the learning experience.3.Diverse Learning Opportunities: Online learning platforms offer a wide variety of courses, ranging from traditional academic subjects to specialized and niche areas of study.4.Cost-Effective: Online courses can be more affordable than traditional classroom-based programs, especially for those who live far from educational institutions.The Challenges of Online Learning1.Self-Discipline: Online learning requires a high level of self-discipline and motivation, as students must manage their time and stay focused without the structure of a traditional classroom.2.Limited Interaction: Online courses often lack the face-to-face interaction that is common in traditional classrooms, which can impact the learning experience and social development of students.3.Technical Issues: Online learning relies heavily on technology, which can lead to technical issues that disrupt the learning process.4.Quality Assurance: With the proliferation of online courses, ensuring the quality and integrity of these courses can be a challenge.Questions:1、What is one of the main advantages of online learning mentioned in the passage?A. It is more expensive than traditional education.B. It requires students to be self-disciplined.C. It provides flexibility in studying.D. It lacks face-to-face interaction.2、According to the passage, what can online learning platforms offer that traditional classrooms might not?A. Limited access to textbooks.B. Fewer specialized courses.C. More interactive learning materials.D. No video resources.3、Which of the following is a challenge that online learning may present?A. Students can easily attend classes at a local university.B. There are no technical issues with online learning.C. It is difficult to ensure the quality of online courses.D. Online learning is always more affordable than traditional education.4、The passage suggests that online learning can be beneficial for:A. Students who prefer face-to-face interaction.B. Individuals with other commitments.C. Those who want to avoid textbooks.D. People who have no access to technology.5、What is one potential drawback of online learning that the passage discusses?A. The ability to study at any time.B. The use of a wide range of resources.C. The possibility of technical disruptions.D. The convenience of studying from home.Answers:1、C2、C3、C4、B5、C七、阅读理解-仔细阅读（选择题，共20分）第一题Reading PassagesIn the following passage, there are some blanks. For each blank there arefour choices marked A, B, C, and D. You should choose the one that best fits into the passage.The digital revolution is changing the way we live, work, and communicate. One of the most significant changes is the rise of artificial intelligence (AI). AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, and decision-making.The potential of AI is enormous. It has the potential to transform industries, improve efficiency, and make our lives more convenient. However, with great power comes great responsibility. The ethical implications of AI are complex and multifaceted.1、The passage is mainly aboutA. the benefits of the digital revolutionB. the rise of artificial intelligenceC. the challenges of the digital revolutionD. the ethical implications of AI2、What is the main concern regarding AI mentioned in the passage?A. Its potential to disrupt traditional industriesB. Its potential to replace human jobsC. Its potential to be used for unethical purposesD. Its potential to cause social inequalities3、The author suggests that AI has the potential to。

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

Sparse RepresentationsTara N. SainathSLTC Newsletter, November 2010Sparse representations (SRs), including compressive sensing (CS), have gained popularity in the last few years as a technique used to reconstruct a signal from few training examples, a problem which arises in many machine learning applications. This reconstruction can be defined as adaptively finding a dictionary which best represents the signal on a per sample basis. This dictionary could include random projections, as is typically done for signal reconstruction, or actual training samples from the data, which is explored in many machine learning applications. SRs is a rapidly growing field with contributions in a variety of signal processing and machine learning conferences such as ICASSP, ICML and NIPS, and more recently in speech recognition. Recently, a special session on Sparse Representations took place at Interspeech 2010 in Makuhari, Japan from March 26-30, 2010. Below work from this special session is summarized in more detail.FACE RECOGNITION VIA COMPRESSIVE SENSINGYang et al. present a method for image-based robust face recognition using sparse representations [1]. Most state-of-the art face recognition systems suffer from limited abilities to handle image nuisances such as illumination, facial disguise, and pose misalignment. Motivated by work in compressive sensing, the described method finds the sparsest linear combination of a query image using all prior training images, where the dominant sparse coefficients reveal the identity of the query image. In addition, extensions of applying sparse representations for face recognition also address a wide range of problems in the field of face recognition, such as dimensionality reduction, image corruption, and face alignment. The paper also provides useful guidelines to practitioners working in similar fields, such as speech recognition.EXEMPLAR-BASED SPARSE REPRESENTATION FEATURESIn Sainath et al. [2], the authors explore the use of exemplar-based sparse representations (SRs) to map test features into the linear span of training examples. Specifically, given a test vector y and a set of exemplars from the training set, which are put into a dictionary H, y is represented as a linear combination of training examples by solving y = H&beta subject to a spareness constraint on &beta . The feature H&beta can be thought of as mapping test sample y back into the linear span of training examples in H.The authors show that the frame classification accuracy using SRs is higher than using a Gaussian Mixture Model (GMM), showing that not only do SRs move test features closer totraining, but also move the features closer to the correct class. A Hidden Markov Model (HMM) is trained on these new SR features and evaluated in a speech recognition task. On the TIMIT corpus, applying the SR features on top of our best discriminatively trained system allows for a 0.7% absolute reduction in phonetic error rate (PER). Furthermore, on a large vocabulary 50 hour broadcast news task, a reduction in word error rate (WER) of 0.3% absolute, demonstrating the benefit of these SR features for large vocabulary.OBSERVATION UNCERTAINTY MEASURES FOR SPARSE IMPUTATIONMissing data techniques are used to estimate clean speech features from noisy environments by finding reliable information in the noisy speech signal. Decoding is then performed based on either the reliable information alone or using both reliable and unreliable information where unreliable parts of the signal are reconstructed using missing data imputation prior to decoding. Sparse imputation (SI) is an exemplar-based reconstruction method which is based on representing segments of the noisy speech signal as linear combinations of as few as possible clean speech example segments.Decoding accuracy depends on several factors including the uncertainty in the speech segment. Gemmeke et al. propose various uncertainty measures to characterize the expected accuracy of a sparse imputation based missing data method [3]. In experiments on noisy large vocabulary speech data, using observation uncertainties derived from the proposed measures improved the speech recognition performance on features estimated with SI. Relative error reductions up to 15% compared to the baseline system using SI without uncertainties were achieved with the best measures.SPARSE AUTO-ASSOCIATIVE NEURAL NETWORKS: THEORY AND APPLICATION TO SPEECH RECOGNITIONGarimella et al. introduce a sparse auto-associative neural network (SAANN) in which the internal hidden layer output is forced to be sparse [4]. This is done by adding a sparse regularization term to the original reconstruction error cost function, and updating the parameters of the network to minimize the overall cost. The authors show the benefit of SAANN on the TIMIT phonetic recognition task. Specifically, a set of perceptual linear prediction (PLP) features are provided as input into the SAANN structure, and a set of sparse hidden layer outputs are produced and used as features. Experiments with the SAANN features on the TIMIT phoneme recognition system show a relative improvement in phoneme error rate of 5.1% over the baseline PLP features.DATA SELECTION FOR LANGUAGE MODELING USING SPARSE REPRESENTATIONSThe ability to adapt language models to specific domains from large generic text corpora is of considerable interest to the language modeling community. One of the key challenges is toidentify the text material relevant to a domain in the generic text collection. The text selection problem can be cast in a semi-supervised learning framework where the initial hypothesis from a speech recognition system is used to identify relevant training material. Sethy et al [5] present a novel sparse representation formulation which selects a sparse set of relevant sentences from the training data which match the test set distribution. In this formulation, the training sentences are treated as the columns of the sparse representation matrix and then-gram counts as the rows. The target vector is the n-gram probability distribution for the test data. A sparse solution to this problem formulation identifies a few columns which can best represent the target test vector, thus identifying the relevant set of sentences from the training data. Rescoring results with the language model built from the data selected using the proposed method yields modest gains on the English broadcast news RT-04 task, reducing the word error rate from 14.6% to 14.4%.SPARSE REPRESENTATIONS FOR TEXT CATEGORIZATIONGiven the superior performance of SRs compared to other classifiers for both image classification and phonetic classification, Sainath et al. extends the use of SRs for text classification [6], a method which has thus far not been explored for this domain. Specifically, Sainath et al. show how SRs can be used for text classification and how their performance varies with the vocabulary size of the documents. The research finds that the SR method offers promising results over the Naive Bayes (NB) classifier, a standard baseline classifier used for text categorization, thus introducing an alternative class of methods for text categorization.CONCLUSIONSThis article presented an overview about sparse representation research in the areas of face recognition, speech recognition, language modeling and text classification. For more information, please see:[1] A. Yang, Z. Zhou, Y. Ma and S. Shankar Sastry, "Towards a robust face recognition system using compressive sensing", in Proc. Interspeech, September 2010.[2] T. N. Sainath, B. Ramabhadran, D. Nahamoo, D. Kanevsky and A. Sethy,“ Exemplar-Based Sparse Representation Features for Speech Recognition ," in Proc. Interspeech, September 2010.[3] J. F. Gemmeke, U. Remes and K. J. Palomäki, "Observation Uncertainty Measures for Sparse Imputation", in Proc. Interspeech, September 2010.[4] G.S.V.S. Sivaram, S. Ganapathy and H. Hermansky, "Sparse Auto-associative Neural Networks: Theory and Application to Speech Recognition", in Proc. Interspeech, September 2010.[5] A. Sethy, T. N. Sainath, B. Ramabhadran and D. Kanevsky, “ Data Selection for Language Modeling Using Sparse Representations," in Proc. Interspeech, September 2010.[6] T. N. Sainath, S. Maskey, D. Kanevsky, B. Ramabhadran, D. Nahamoo and J. Hirschberg, “ Sparse Representations for Text Categorization," in Proc. Inte rspeech, September 2010.。

隐语义模型常用的训练方法隐语义模型（Latent Semantic Model）是一种常用的文本表示方法，它可以将文本表示为一个低维的向量空间中的点，从而方便进行文本分类、聚类等任务。

在实际应用中，如何训练一个高效的隐语义模型是非常重要的。

本文将介绍隐语义模型常用的训练方法。

一、基于矩阵分解的训练方法1.1 SVD分解SVD（Singular Value Decomposition）分解是一种基于矩阵分解的方法，它可以将一个矩阵分解为三个矩阵相乘的形式，即A=UΣV^T。

其中U和V都是正交矩阵，Σ是对角线上元素为奇异值的对角矩阵。

在隐语义模型中，我们可以将用户-物品评分矩阵R分解为两个低维矩阵P和Q相乘的形式，即R≈PQ^T。

其中P表示用户向量矩阵，Q表示物品向量矩阵。

具体地，在SVD分解中，我们首先需要将评分矩阵R进行预处理。

一般来说，我们需要减去每个用户或每个物品评分的平均值，并对剩余部分进行归一化处理。

然后，我们可以使用SVD分解将处理后的评分矩阵R分解为P、Q和Σ三个矩阵。

其中，P和Q都是低维矩阵，Σ是对角线上元素为奇异值的对角矩阵。

通过调整P和Q的维度，我们可以控制模型的复杂度。

在训练过程中，我们需要使用梯度下降等方法来最小化预测评分与实际评分之间的误差。

具体地，在每次迭代中，我们可以随机选择一个用户-物品对（ui），计算预测评分pui，并根据实际评分rui更新P 和Q中相应向量的值。

具体地，更新公式如下：pu=pu+η(euiq-uλpu)qi=qi+η(euip-uλqi)其中η是学习率，λ是正则化参数，eui=rui-pui表示预测评分与实际评分之间的误差。

1.2 NMF分解NMF（Nonnegative Matrix Factorization）分解是另一种基于矩阵分解的方法，在隐语义模型中也有广泛应用。

与SVD不同的是，在NMF中要求所有矩阵元素都为非负数。

具体地，在NMF中，我们需要将评分矩阵R进行预处理，并将其分解为P和Q两个非负矩阵相乘的形式，即R≈PQ。

Package‘hibayes’November28,2023Title Individual-Level,Summary-Level and Single-Step BayesianRegression ModelVersion3.0.1Date2023-11-27Description A user-friendly tool tofit Bayesian regression models.It canfit3types of Bayesian mod-els using individual-level,summary-level,and individual plus pedigree-level(single-step)data for both Genomic prediction/selection(GS)and Genome-Wide Associa-tion Study(GW AS),it was designed to estimate joint effects and genetic parameters for a com-plex trait,including:(1)fixed effects and coefficients of covariates,(2)environmental random effects,and its corresponding variance,(3)genetic variance,(4)residual variance,(5)heritability,(6)genomic estimated breeding values(GEBV)for both genotyped and non-genotyped individuals,(7)SNP effect size,(8)phenotype/genetic variance explained(PVE)for single or multiple SNPs,(9)posterior probability of association of the genomic window(WPPA),(10)posterior inclusive probability(PIP).The functions are not limited,we will keep on going in enriching it with more features.References:Meuwissen et al.(2001)<doi:10.1093/genetics/157.4.1819>;Gus-tavo et al.(2013)<doi:10.1534/genetics.112.143313>;Habier et al.(2011)<doi:10.1186/1471-2105-12-186>;Yi et al.(2008)<doi:10.1534/genetics.107.085589>;Zhou et al.(2013)<doi:10.1371/journal.pgen.1003264>;Moser Jones et al.(2019)<doi:10.1038/s41467-019-12653-0>;Hender-son(1976)<doi:10.2307/2529339>;Fernando et al.(2014)<doi:10.1186/1297-9686-46-50>.License GPL-3Maintainer Lilin Yin<**************>URL https:///YinLiLin/hibayesBugReports https:///YinLiLin/hibayes/issuesEncoding UTF-8Imports utils,stats,methods,stringr,CMplot12ibrmDepends R(>=3.3.0),bigmemory,MatrixLinkingTo Rcpp,RcppArmadillo(>=0.9.600.0.0),RcppProgress,BH,bigmemory,MatrixRoxygenNote7.2.3NeedsCompilation yesAuthor Lilin Yin[aut,cre,cph],Haohao Zhang[aut,cph],Xiaolei Liu[aut,cph]Repository CRANDate/Publication2023-11-2813:00:03UTCR topics documented:ibrm (2)ldmat (6)read_plink (8)sbrm (9)ssbrm (12)Index17 ibrm Bayes modelDescriptionBayes linear regression model using individual level datay=Xβ+Rr+Mα+ewhereβis a vector of estimated coefficient for covariates,and r is a vector of environmental random effects.M is a matrix of genotype covariate,αis a vector of estimated marker effect size.e is a vector of residuals.Usageibrm(formula,data=NULL,M=NULL,M.id=NULL,method=c("BayesCpi","BayesA","BayesL","BSLMM","BayesR","BayesB","BayesC", "BayesBpi","BayesRR"),map=NULL,Pi=NULL,fold=NULL,ibrm3 niter=NULL,nburn=NULL,thin=5,windsize=NULL,windnum=NULL,dfvr=NULL,s2vr=NULL,vg=NULL,dfvg=NULL,s2vg=NULL,ve=NULL,dfve=NULL,s2ve=NULL,lambda=0,printfreq=100,seed=666666,threads=4,verbose=TRUE)Argumentsformula a two-sided linear formula object describing both thefixed-effects and random-effects part of the model,with the response on the left of a‘~’operator andthe terms,separated by‘+’operators,on the right.Random-effects terms aredistinguished by vertical bars(1|’)separating expressions for design matricesfrom grouping factors.data the data frame containing the variables named in’formula’,NOTE that thefirst column in’data’should be the individual id.M numeric matrix of genotype with individuals in rows and markers in columns, NAs are not allowed.M.id vector of id for genotyped individuals,NOTE that no need to adjust the order of id to be the same between’data’and’M’,the package will do it automatically.method bayes methods including:"BayesB","BayesA","BayesL","BayesRR","Bayes-Bpi","BayesC","BayesCpi","BayesR","BSLMM".•"BayesRR":Bayes Ridge Regression,all SNPs have non-zero effects andshare the same variance,equals to RRBLUP or GBLUP.•"BayesA":all SNPs have non-zero effects,and take different variance whichfollows an inverse chi-square distribution.•"BayesB":only a small proportion of SNPs(1-Pi)have non-zero effects,and take different variance which follows an inverse chi-square distribution.•"BayesBpi":the same with"BayesB",but’Pi’is notfixed.•"BayesC":only a small proportion of SNPs(1-Pi)have non-zero effects,and share the same variance.•"BayesCpi":the same with"BayesC",but’Pi’is notfixed.•"BayesL":BayesLASSO,all SNPs have non-zero effects,and take differentvariance which follows an exponential distribution.4ibrm•"BSLMM":all SNPs have non-zero effects,and take the same variance,buta small proportion of SNPs have additional shared variance.•"BayesR":only a small proportion of SNPs have non-zero effects,and theSNPs are allocated into different groups,each group has the same variance.map(optional,only for GW AS)the map information of genotype,at least3columns are:SNPs,chromosome,physical position.Pi vector,the proportion of zero effect and non-zero effect SNPs,thefirst value must be the proportion of non-effect markers.fold proportion of variance explained for groups of SNPs,the default is c(0,0.0001,0.001,0.01).niter the number of MCMC iteration.nburn the number of iterations to be discarded.thin the number of thinning after burn-in.Note that smaller thinning frequency may have higher accuracy of estimated parameters,but would result in more memoryfor collecting process,on contrary,bigger frequency may have negative effecton accuracy of estimations.windsize window size in bp for GW AS,the default is NULL.windnumfixed number of SNPs in a window for GW AS,if it is specified,’windsize’will be invalid,the default is NULL.dfvr the number of degrees of freedom for the distribution of environmental variance.s2vr scale parameter for the distribution of environmental variance.vg prior value of genetic variance.dfvg the number of degrees of freedom for the distribution of genetic variance.s2vg scale parameter for the distribution of genetic variance.ve prior value of residual variance.dfve the number of degrees of freedom for the distribution of residual variance.s2ve scale parameter for the distribution of residual variance.lambda value of ridge regression for inverting a matrix.printfreq frequency of printing iterative details on console.seed seed for random sample.threads number of threads used for OpenMP.verbose whether to print the iteration information on console.Details•thefixed effects and covariates in’formula’must be in factors and numeric,respectively.if not,please remember to use’as.factor’and’as.numeric’to transform.•the package has the automatical function of taking the intersection and adjusting the order of id between’data’and the genotype’M’,thus thefirst column in’data’should be the individual id.ibrm5•if any one of the options’windsize’and’windnum’is specified,the GW AS results will be returned,and the’map’information must be provided,in which the physical positions should be all in digital values.•the’windsize’or’windnum’option only works for the methods of which the assumption has a proportion of zero effect markers,e.g.,BayesB,BayesBpi,BayesC,BayesCpi,BSLMM,and BayesR.Valuethe function returns a’blrMod’object containing$mu the regression intercept$pi estimated proportion of zero effect and non-zero effect SNPs$beta estimated coefficients for all covariates$r estimated environmental random effects$Vr estimated variance for all environmental random effect$Vg estimated genetic variance$Ve estimated residual variance$h2estimated heritability(h2=Vg/(Vr+Vg+Ve))$alpha estimated effect size of all markers$g genomic estimated breeding value$e residuals of the model$pip the frequency for markers to be included in the model during MCMC iteration,known as posterior inclusive probability(PIP)$gwas WPPA is defined to be the window posterior probability of association,it is estimated by counting the number of MCMC samples in whichαis nonzero for at least one SNP in the window$MCMCsamples the collected samples of posterior estimation for all the above parameters across MCMC iterationsReferencesMeuwissen,Theo HE,Ben J.Hayes,and Michael E.Goddard."Prediction of total genetic value using genome-wide dense marker maps."Genetics157.4(2001):1819-1829.de los Campos,G.,Hickey,J.M.,Pong-Wong,R.,Daetwyler,H.D.,and Calus,M.P.(2013).Whole-genome regression and prediction methods applied to plant and animal breeding.Genetics, 193(2),327-345.Habier,David,et al."Extension of the Bayesian alphabet for genomic selection."BMC bioinfor-matics12.1(2011):1-12.Yi,Nengjun,and Shizhong Xu."Bayesian LASSO for quantitative trait loci mapping."Genetics 179.2(2008):1045-1055.Zhou,Xiang,Peter Carbonetto,and Matthew Stephens."Polygenic modeling with Bayesian sparselinear mixed models."PLoS genetics9.2(2013):e1003264.Moser,Gerhard,et al."Simultaneous discovery,estimation and prediction analysis of complex traits using a Bayesian mixture model."PLoS genetics11.4(2015):e1004969.Examples#Load the example data attached in the packagepheno_file_path=system.file("extdata","demo.phe",package="hibayes")pheno=read.table(pheno_file_path,header=TRUE)bfile_path=system.file("extdata","demo",package="hibayes")bin=read_plink(bfile_path,threads=1)fam=bin$famgeno=bin$genomap=bin$map#For GS/GP##no environmental effects:fit=ibrm(T1~1,data=pheno,M=geno,M.id=fam[,2],method="BayesCpi",niter=2000,nburn=1200,thin=5,threads=1)##overview of the returned resultssummary(fit)##add fixed effects or covariates:fit=ibrm(T1~sex+season+day+bwt,data=pheno,M=geno,M.id=fam[,2],method="BayesCpi")##add environmental random effects:fit=ibrm(T1~sex+(1|loc)+(1|dam),data=pheno,M=geno,M.id=fam[,2],method="BayesCpi")#For GWASfit=ibrm(T1~sex+bwt+(1|dam),data=pheno,M=geno,M.id=fam[,2],method="BayesCpi",map=map,windsize=1e6)#get the SD of estimated SNP effects for markerssummary(fit)$alpha#get the SD of estimated breeding valuessummary(fit)$gldmat LD variance-covariance matrix calculationDescriptionTo calculate density or sparse LD variance-covariance matrix with genotype in bigmemory format. Usageldmat(geno,map=NULL,gwas.geno=NULL,gwas.map=NULL,chisq=NULL,ldchr=FALSE,threads=4,verbose=FALSE)Argumentsgeno the reference genotype panel in bigmemory format.map the map information of reference genotype panel,columns are:SNPs,chromo-some,physical position.gwas.geno(optional)the genotype of gwas samples which were used to generate the sum-mary data.gwas.map(optional)the map information of the genotype of gwas samples,columns are: SNPs,chromosome,physical position.chisq chi-squre value for generating sparse matrix,if n*r2<chisq,it would be set to zero.ldchr lpgical,whether to calulate the LD between chromosomes.threads the number of threads used in computation.verbose whether to print the information.ValueFor full ld matrix,it returns a standard R matrix,for sparse matrix,it returns a’dgCMatrix’. Examplesbfile_path=system.file("extdata","demo",package="hibayes")data=read_plink(bfile_path)geno=data$genomap=data$mapxx=ldmat(geno,threads=4,verbose=FALSE)#chromosome wide full ld matrix#xx=ldmat(geno,chisq=5,threads=4)#chromosome wide sparse ld matrix#xx=ldmat(geno,map,ldchr=FALSE,threads=4)#chromosome block ld matrix#xx=ldmat(geno,map,ldchr=FALSE,chisq=5,threads=4)#chromosome block+sparse ld matrix8read_plink read_plink data loadDescriptionTo load plink binary dataUsageread_plink(bfile="",maxLine=10000,impute=TRUE,mode=c("A","D"),out=NULL,threads=4)Argumentsbfile character,prefix of Plink binary format data.maxLine number,set the number of lines to read at a time.impute logical,whether to impute missing values in genotype by major alleles.mode"A"or"D",additive effect or dominant effect.out character,path and prefix of outputfilethreads number,the number of used threads for parallel processValuefourfiles will be generated in the directed folder:"xx.desc","xx.bin","xx.id,"xx.map",where’xx’is the prefix of the argument’out’,the memory-mappingfiles can be fast loaded into memory by ’geno=attach.big.matrix("xx.desc")’.Note that hibayes will code the genotype A1A1as2,A1A2 as1,and A2A2as0,where A1is thefirst allele of each marker in".bim"file,therefore the estimated effect size is on A1allele,users should pay attention to it when a process involves marker effect. Examplesbfile_path=system.file("extdata","demo",package="hibayes")data=read_plink(bfile_path,out=tempfile(),mode="A")fam=data$famgeno=data$genomap=data$mapsbrm9 sbrm SBayes modelDescriptionBayes linear regression model using summary level dataUsagesbrm(sumstat,ldm,method=c("BayesB","BayesA","BayesL","BayesRR","BayesBpi","BayesC","BayesCpi", "BayesR","CG"),map=NULL,Pi=NULL,lambda=NULL,fold=NULL,niter=NULL,nburn=NULL,thin=5,windsize=NULL,windnum=NULL,vg=NULL,dfvg=NULL,s2vg=NULL,ve=NULL,dfve=NULL,s2ve=NULL,printfreq=100,seed=666666,threads=4,verbose=TRUE)Argumentssumstat matrix of summary data,details refer to https:///software/gcta/#COJO.ldm dense or sparse matrix,ld for reference panel(m*m,m is the number of SNPs).NOTE that the order of SNPs should be consistent with summary data.method bayes methods including:"BayesB","BayesA","BayesL","BayesRR","Bayes-Bpi","BayesC","BayesCpi","BayesR","CG".•"BayesRR":Bayes Ridge Regression,all SNPs have non-zero effects andshare the same variance,equals to RRBLUP or GBLUP.•"BayesA":all SNPs have non-zero effects,and take different variance whichfollows an inverse chi-square distribution.10sbrm•"BayesB":only a small proportion of SNPs(1-Pi)have non-zero effects,and take different variance which follows an inverse chi-square distribution.•"BayesBpi":the same with"BayesB",but’Pi’is notfixed.•"BayesC":only a small proportion of SNPs(1-Pi)have non-zero effects,and share the same variance.•"BayesCpi":the same with"BayesC",but’Pi’is notfixed.•"BayesL":BayesLASSO,all SNPs have non-zero effects,and take differentvariance which follows an exponential distribution.•"BayesR":only a small proportion of SNPs have non-zero effects,and theSNPs are allocated into different groups,each group has the same variance.•"CG":conjugate gradient algorithm with assigned lambda.map(optional,only for GW AS)the map information of genotype,at least3columns are:SNPs,chromosome,physical position.Pi vector,the proportion of zero effect and non-zero effect SNPs,thefirst value must be the proportion of non-effect markers.lambda value or vector,the ridge regression value for each SNPs.fold percentage of variance explained for groups of SNPs,the default is c(0,0.0001,0.001,0.01).niter the number of MCMC iteration.nburn the number of iterations to be discarded.thin the number of thinning after burn-in.Note that smaller thinning frequency may have higher accuracy of estimated parameters,but would result in more memoryfor collecting process,on contrary,bigger frequency may have negative effecton accuracy of estimations.windsize window size in bp for GW AS,the default is1e6.windnumfixed number of SNPs in a window for GW AS,if it is specified,’windsize’will be invalid,the default is NULL.vg prior value of genetic variance.dfvg the number of degrees of freedom for the distribution of genetic variance.s2vg scale parameter for the distribution of genetic variance.ve prior value of residual variance.dfve the number of degrees of freedom for the distribution of residual variance.s2ve scale parameter for the distribution of residual variance.printfreq frequency of collecting the estimated parameters and printing on console.Note that smaller frequency may have higher accuracy of estimated parameters,butwould result in more time and memory for collecting process,on contrary,big-ger frequency may have an negative effect on accuracy of estimations.seed seed for random sample.threads number of threads used for OpenMP.verbose whether to print the iteration information on console.sbrm11Details•if any one of the options’windsize’and’windnum’is specified,the GW AS results will be returned,and the’map’information must be provided,in which the physical positions should be all in digital values.•the’windsize’or’windnum’option only works for the methods of which the assumption has a proportion of zero effect markers,e.g.,BayesB,BayesBpi,BayesC,BayesCpi,BSLMM,and BayesR.Valuethe function returns a’blrMod’object containing$pi estimated proportion of zero effect and non-zero effect SNPs$Vg estimated genetic variance$Ve estimated residual variance$h2estimated heritability(h2=Vg/(Vg+Ve))$alpha estimated effect size of all markers$pip the frequency for markers to be included in the model during MCMC iteration,also known as posterior inclusive probability(PIP)$gwas WPPA is defined to be the window posterior probability of association,it is estimated by counting the number of MCMC samples in whichαis nonzero for at least one SNP in the window$MCMCsamples the collected samples of posterior estimation for all the above parameters across MCMC iterationsReferencesLloyd-Jones,Luke R.,et al."Improved polygenic prediction by Bayesian multiple regression on summary statistics."Nature communications10.1(2019):1-11.Examplesbfile_path=system.file("extdata","demo",package="hibayes")bin=read_plink(bfile_path,threads=1)fam=bin$famgeno=bin$genomap=bin$mapsumstat_path=system.file("extdata","demo.ma",package="hibayes")sumstat=read.table(sumstat_path,header=TRUE)head(sumstat)#computate ld variance covariance matrix##construct genome wide full variance-covariance matrixldm1<-ldmat(geno,threads=4)##construct genome wide sparse variance-covariance matrix#ldm2<-ldmat(geno,chisq=5,threads=4)##construct chromosome wide full variance-covariance matrix#ldm3<-ldmat(geno,map,ldchr=FALSE,threads=4)##construct chromosome wide sparse variance-covariance matrix#ldm4<-ldmat(geno,map,ldchr=FALSE,chisq=5,threads=4)#if the order of SNPs in genotype is not consistent with the order in sumstat file,#prior adjusting is necessary.indx=match(map[,1],sumstat[,1])sumstat=sumstat[indx,]#fit modelfit=sbrm(sumstat=sumstat,ldm=ldm1,method="BayesCpi",Pi=c(0.95,0.05),niter=20000,nburn=12000,seed=666666,map=map,windsize=1e6,threads=1)#overview of the returned resultssummary(fit)#get the SD of estimated SNP effects for markerssummary(fit)$alphassbrm Single-step Bayes modelDescriptionSingle-step Bayes linear regression model using individual level data and pedigree informationy=Xβ+Rr+Mα+U +ewhere y is the vector of phenotypic values for both genotyped and non-genotyped individuals,βis a vector of estimated coefficient for covariates,M contains the genotype(M2)for genotyped individuals and the imputed genotype(M1=A12A−122M2)for non-genotyped individuals, is the vector of genotype imputation error,e is a vector of residuals.Usagessbrm(formula,data=NULL,M=NULL,M.id=NULL,pedigree=NULL,method=c("BayesCpi","BayesA","BayesL","BayesR","BayesB","BayesC","BayesBpi", "BayesRR"),map=NULL,Pi=NULL,fold=NULL,niter=NULL,nburn=NULL,thin=5,windsize=NULL,windnum=NULL,maf=0.01,dfvr=NULL,s2vr=NULL,vg=NULL,dfvg=NULL,s2vg=NULL,ve=NULL,dfve=NULL,s2ve=NULL,printfreq=100,seed=666666,threads=4,verbose=TRUE)Argumentsformula a two-sided linear formula object describing both thefixed-effects and random-effects part of the model,with the response on the left of a‘~’operator andthe terms,separated by‘+’operators,on the right.Random-effects terms aredistinguished by vertical bars(1|’)separating expressions for design matricesfrom grouping factors.data the data frame containing the variables named in’formula’,NOTE that thefirst column in’data’should be the individual id.M numeric matrix of genotype with individuals in rows and markers in columns, NAs are not allowed.M.id vector of id for genotype.pedigree matrix of pedigree,3columns limited,the order of columns shoud be"id","sir", "dam".method bayes methods including:"BayesB","BayesA","BayesL","BayesRR","Bayes-Bpi","BayesC","BayesCpi","BayesR".•"BayesRR":Bayes Ridge Regression,all SNPs have non-zero effects andshare the same variance,equals to RRBLUP or GBLUP.•"BayesA":all SNPs have non-zero effects,and take different variance whichfollows an inverse chi-square distribution.•"BayesB":only a small proportion of SNPs(1-Pi)have non-zero effects,and take different variance which follows an inverse chi-square distribution.•"BayesBpi":the same with"BayesB",but’Pi’is notfixed.•"BayesC":only a small proportion of SNPs(1-Pi)have non-zero effects,and share the same variance.•"BayesCpi":the same with"BayesC",but’Pi’is notfixed.•"BayesL":BayesLASSO,all SNPs have non-zero effects,and take differentvariance which follows an exponential distribution.•"BayesR":only a small proportion of SNPs have non-zero effects,and theSNPs are allocated into different groups,each group has the same variance.map(optional,only for GW AS)the map information of genotype,at least3columns are:SNPs,chromosome,physical position.Pi vector,the proportion of zero effect and non-zero effect SNPs,thefirst value must be the proportion of non-effect markers.fold proportion of variance explained for groups of SNPs,the default is c(0,0.0001,0.001,0.01).niter the number of MCMC iteration.nburn the number of iterations to be discarded.thin the number of thinning after burn-in.Note that smaller thinning frequency may have higher accuracy of estimated parameters,but would result in more memoryfor collecting process,on contrary,bigger frequency may have negative effecton accuracy of estimations.windsize window size in bp for GW AS,the default is NULL.windnumfixed number of SNPs in a window for GW AS,if it is specified,’windsize’will be invalid,the default is NULL.maf the effects of markers whose MAF is lower than the threshold will not be esti-mated.dfvr the number of degrees of freedom for the distribution of environmental variance.s2vr scale parameter for the distribution of environmental variance.vg prior value of genetic variance.dfvg the number of degrees of freedom for the distribution of genetic variance.s2vg scale parameter for the distribution of genetic variance.ve prior value of residual variance.dfve the number of degrees of freedom for the distribution of residual variance.s2ve scale parameter for the distribution of residual variance.printfreq frequency of printing iterative details on console.seed seed for random sample.threads number of threads used for OpenMP.verbose whether to print the iteration information on console.Valuethe function returns a a’blrMod’object containing$J coefficient for genotype imputation residuals$Veps estimated variance of genotype imputation residuals$epsilon genotype imputation residuals$mu the regression intercept$pi estimated proportion of zero effect and non-zero effect SNPs$beta estimated coefficients for all covariates$r estimated environmental random effects$Vr estimated variance for all environmental random effect$Vg estimated genetic variance$Ve estimated residual variance$h2estimated heritability(h2=Vg/(Vr+Vg+Ve))$g data.frame,thefirst column is the list of individual id,the second column is the genomic esti-mated breeding value for all individuals,including genotyped and non-genotyped.$alpha estimated effect size of all markers$e residuals of the model$pip the frequency for markers to be included in the model during MCMC iteration,also known as posterior inclusive probability(PIP)$gwas WPPA is defined to be the window posterior probability of association,it is estimated by counting the number of MCMC samples in whichαis nonzero for at least one SNP in the window$MCMCsamples the collected samples of posterior estimation for all the above parameters across MCMC iterationsReferencesFernando,Rohan L.,Jack CM Dekkers,and Dorian J.Garrick."A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses."Genetics Selection Evolution46.1(2014):1-13.Henderson,C.R.:A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values.Biometrics32(1),69-83(1976).Examples#Load the example data attached in the packagepheno_file_path=system.file("extdata","demo.phe",package="hibayes")pheno=read.table(pheno_file_path,header=TRUE)bfile_path=system.file("extdata","demo",package="hibayes")bin=read_plink(bfile_path,threads=1)fam=bin$famgeno=bin$genomap=bin$mappedigree_file_path=system.file("extdata","demo.ped",package="hibayes")ped=read.table(pedigree_file_path,header=TRUE)#For GS/GP##no environmental effects:fit=ssbrm(T1~1,data=pheno,M=geno,M.id=fam[,2],pedigree=ped,method="BayesCpi",niter=1000,nburn=600,thin=5,printfreq=100,threads=1) ##overview of the returned resultssummary(fit)##add fixed effects or covariates:fit=ssbrm(T1~sex+bwt,data=pheno,M=geno,M.id=fam[,2],pedigree=ped, method="BayesCpi")##add environmental random effects:fit=ssbrm(T1~(1|loc)+(1|dam),data=pheno,M=geno,M.id=fam[,2],pedigree=ped,method="BayesCpi")#For GWASfit=ssbrm(T1~sex+bwt+(1|dam),data=pheno,M=geno,M.id=fam[,2],pedigree=ped,method="BayesCpi",map=map,windsize=1e6)#get the SD of estimated SNP effects for markerssummary(fit)$alpha#get the SD of estimated breeding valuessummary(fit)$g。

姓名:唐一源性别：男出生年月：1966年2月专业职称：教授，博士生导师办公电话:84706046,84706039EMAIL :yy2100@,brain@主要社会职务：★ 大连理工大学神经信息学研究所所长★ 中美合作脑智实验室主任★ 中国科学院心理所心理健康重点实验室客座研究员★ 中国科学院生物物理所视觉信息加工重点实验室/脑智中心高级访问学者★ 中华人类脑计划/神经信息学”发起人之一★ 全球人类脑计划/神经信息学工作组中国代表研究领域（研究课题）:★ 脑功能成像（fMRI/PET/SPECT等）★ 语言认知加工及其脑内信息网络★ 神经/心理/精神问题（疾病）的脑机制及其防治策略（含中药防治）★ 生物医学功能诊断/治疗的新技术和方法★ 汉语认知脑功能成像数据库及其知识发现★ 神经心理语言学出版著作和论文:2004年：1.Mingjun Zhong, Huanwen Tang, Hongjun Chen, & Yiyuan Tang. An EM Algorithm forLearning Sparse and Overcomplete Representations. Neurocomputing,(2004), 57:469-476.2.Mingjun Zhong,Huanwen Tang, Huili Wang& Yiyuan Tang. An EM Algorithm forIndependent Component Analysis. Neural Information Processing - Letters and Reviews,(2004), 2(1):11-17.3.Liu H, Gao HM, Zhang WQ, Tang YY, Song HS. Effects of chronic administration of PL017and beta-funaltrexamine hydrochloride on susceptibility of kainic acid-induced seizures in rats. Acta Physiol Sin, 2004; 56(1): 101-106.4.Zhenwei Shi, Huanwen Tang,Yiyuan Tang. A new fixed-point algorithm for IndependentComponent Analysis. Neurocomputing,(2004), 56:467-4735.Mingjun Zhong, Huanwen Tang, Yiyuan Tang.Expectation–Maximization approaches toindependent component analysis.Neurocomputing,(2004).6.Zhenwei Shi, Huanwen Tang,Yiyuan Tang.Blind source separation of more sources thanmixtures using generalized exponential mixture models. Neurocomputing,(2004).2003年：1.Guojun He, Li-Hai Tan, Yiyuan Tang et al. Modulation of Neural Connectivity during TongueMovement and Chinese "Pin-Yin" Speech. Human Brain Mapping,18（3）（2003）222-232 （封面文章）2.Shun-ichi Amari, Francesco Beltrame, ……, Stephen Koslow, ……, Yiyuan Tang et al.Neuroscience Data and Tool Sharing: A Legal and Policy Framework for Neuroinformatics.Neuroinformatics Journal, （2003）, 1:149-165.3.Ma Lin, Tang Yiyuan, Wang Yan et al.Mapping cortical areas associated with Chinese wordprocessing using functional magnetic resonance imaging，Chin Med J, 2003, 116(2): 176-1804.钟明军，唐焕文，唐一源.空间独立成分分析实现fMRI信号的盲源分离.生物物理学报，V ol.19,No.1,(2003),79-83.5.单保慈，张武田，马林，李德军，曹丙利，唐一源等，汉语语义加工的相关脑区, 科学通报，（2003），48, 2257-22606.范丽伟，唐焕文，唐一源,独立成分分析应用于fMRI数据研究，大连理工大学学报（2003）,43（4）：23-282002年：1.Shun-ichi Amari, Francesco Beltrame, ……, Stephen Koslow, ……, Yiyuan Tang.Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience. Journal of Integrative Neuroscience, Vol. 1, No. 2 (2002) 117-1282.Luo Yuejia, Jiang Yang, Tang Yiyuan.R. Parasuraman. Neural mechanisms of unconsciousvisual motion priming. Chinese Science Bulletin, 47(3),(2002),193-1973.马林，唐一源, 王岩等。

DOI : 10.11992/tis.202007003深度自编码与自更新稀疏组合的异常事件检测算法王倩倩，苗夺谦，张远健（同济大学 嵌入式系统与服务计算教育部重点实验室，上海 201804）摘 要：基于深度学习的异常检测算法输入通常为视频帧或光流图像，检测精度和速度较低。

针对上述问题，提出了一种以运动前景块为中心的卷积自动编码器和自更新稀疏组合学习(convolutional auto-encoders and self-updating sparse combination learning, CASSC)算法。

首先，采用自适应混合高斯模型(gaussian mixture model,GMM)提取视频前景，并以滑动窗口的方式根据前景像素点占比过滤噪声；其次，构建3个卷积自动编码器提取运动前景块的时空特征；最后，使用自更新稀疏组合学习对特征进行重构，依据重构误差进行异常判断。

实验结果表明，与现有算法相比，该方法不仅有效地提高了异常事件检测的准确性，且可以满足实时检测需求。

关键词：深度学习；稀疏组合；自动编码器；自更新；异常事件检测；卷积神经网络；无监督学习；稀疏学习中图分类号：TP391 文献标志码：A 文章编号：1673−4785(2020)06−1197−07中文引用格式：王倩倩, 苗夺谦, 张远健. 深度自编码与自更新稀疏组合的异常事件检测算法[J]. 智能系统学报, 2020, 15(6):1197–1203.英文引用格式：WANG Qianqian, MIAO Duoqian, ZHANG Yuanjian. Abnormal event detection method based on deep auto-encoder and self-updating sparse combination[J]. CAAI transactions on intelligent systems, 2020, 15(6): 1197–1203.Abnormal event detection method based on deep auto-encoder andself-updating sparse combinationWANG Qianqian ，MIAO Duoqian ，ZHANG Yuanjian(Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai 201804, China)Abstract : In the construction of a deep learning model for abnormal event detection, frames or optical flow are con-sidered but the resulting accuracy and speed are not satisfactory. To address these problems, we present an algorithm based on convolutional auto-encoders and self-updating sparse combination learning, which is centered on the move-ment of foreground blocks. First, we use an adaptive Gaussian mixture model to extract the foreground. Using a sliding window, the foreground blocks that are moving, are filtered based on the number of foreground pixels. Three convolu-tional auto-encoders are then constructed to extract the temporal and spatial features of the moving foreground stly, self-updating sparse combination learning is applied to reconstruct the features and identify abnormal events based on the reconstruction error. The experimental results show that compared with existing algorithms, the proposed method improves the accuracy of abnormality detection and enables real-time detection.Keywords : deep learning; sparse combination; auto-encoder; self-updating; abnormal event detection; convolution neur-al network; unsupervised learning; sparse representation异常事件检测是指通过图像处理、模式识别和计算机视觉等技术，分析视频中的有效信息，判断异常事件。

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

2024年6月大学英语六级考试真题和答案（第3套）Part I Writing (30 minutes)Directions: For this part, you are allowed 30 minutes to write an essay that begins with the sentence “Nowadays, cultivating independent learning ability is becoming increasingly crucial for personal development.” You can make comments, cite examples or use your personal experiences to develop your essay. You should write at least 150 words but no more than 200 words.You should copy the sentence given in quotes at the beginning of your essay.Part Ⅱ Listening Comprehension (30 minutes)Section ADirections:In this section, you will hear two long conversations. At the end of each conversation, you will hear four questions. Both the conversation and the questions will be spoken only once. After you hear a question, you must choose the best answer from the four choices marked A), B), C) and D). Then mark the corresponding letter on Answer Sheet 1 with a single line through the centre.Questions 1 to 4 are based on the conversation you have just heard.1. A) Read numerous comments users put online.B) Blended all his food without using a machine.C) Searched for the state-of-the-art models of blenders.D) Did thorough research on the price of kitchen appliances.2. A) Eating any blended food.B) Buying a blender herself.C) Using machines to do her cooking.D) Making soups and juices for herself.3. A) Cooking every meal creatively in the kitchen.B) Paying due attention to his personal hygiene.C) Eating breakfast punctually every morning.D) Making his own fresh fruit juice regularly.4. A) One-tenth of it is sugar.B) It looks healthy and attractive.C) One’s fancy may be tickled by it.D) It contains an assortment of nutrients.Questions 5 to 8 are based on the conversation you have just heard.5. A) How he has made himself popular as the mayor of Berkton.B) How the residents will turn Berkton into a tourist attraction.C) How charming he himself considers the village of Berkton to be.D) How he has led people of Berkton to change the village radically.6. A) It was developed only to a limited extent.B) It was totally isolated as a sleepy village.C) It was relatively unknown to the outside.D) It was endowed with rare natural resources.7. A) The people in Berkton were in a harmonious atmosphere.B) The majority of residents lived in harmony with their neighbors.C) The majority of residents enjoyed cosy housing conditions.D) All the houses in Berkton looked aesthetically similar.8. A) They have helped boost the local economy.B) They have made the residents unusually proud.C) They have contributed considerably to its popularity.D) They have brought happiness to everyone in the village.Section BDirections: In this section, you will hear two passages. At the end of each passage, you will hear three or four questions. Both the passage and the questions will be spoken only once. After you hear a question, you must choose the best answer from the four choices marked A)，B)，C) and D). Then mark the corresponding letter on Answer Sheet 1 with a single line through the centre.Questions 9 to 11 are based on the passage you have just heard.9. A) They have created the smallest remote-controlled walking robot in the world.B) They are going to publish their research findings in the journal Science Robotics.C) They are the first to build a robot that can bend, crawl, walk, turn and even jump.D) They are engaged in research on a remote-controlled robot which uses special power.10. A) It changes its shape by complex hardware.B) It is operated by a special type of tiny motor.C) It moves from one place to another by memory.D) It is powered by the elastic property of its body.11. A) Replace humans in exploratory tasks.B) Perform tasks in tightly confined spaces.C) Explore the structure of clogged arteries.D) Assist surgeons in highly complex surgery.Questions 12 to 15 are based on the passage you have just heard.12. A) She threw up in the bathroom.B) She slept during the entire ride.C) She dozed off for a few minutes.D) She boasted of her marathon race.13. A) They are mostly immune to cognitive impairment.B) They can sleep soundly during a rough ride at sea.C) They are genetically determined to need less sleep.D) They constitute about 13 percent of the population.14. A) Whether there is a way to reach elite status.B) Whether it is possible to modify one’s genes.C) Whether having a baby impacts one’s passion.D) Whether one can train themselves to sleep less.15. A) It is in fact quite possible to nurture a passion for sleep.B) Babies can severely disrupt their parents’ sleep patterns.C) Being forced to rise early differs from being an early bird.D) New parents are forced to jump out of bed at the crack of dawn.Section CDirections: In this section, you will hear three recordings of lectures or talks followed by three or four questions. The recordings will be played only once. After you hear a question, you must choose the best answer from the four choices marked A), B), C) and D). Then mark the corresponding letter on Answer Sheet 1 with a single line through the centre.Questions 16 to 18 are based on the recording you have just heard. 16. A) We have poor awareness of how many controversial issues are being debated.B) No one knows better than yourself what you are thinking about at the moment.C) No one can change your opinions more than those who speak in a convincing tone.D) We are likely to underestimate how much we can be swayed by a convincing article.17. A) Their belief about physical punishment changed.B) Their memory pushed them toward a current belief.C) The memory of their initial belief came back to them.D) Their experiences of physical punishment haunted them.18. A) They apparently have little to do with moderate beliefs.B) They don’t reflect the changes of view on physical punishment.C) They may not apply to changes to extreme or deeply held beliefs.D) They are unlikely to alter people’s position without more evidence.Questions 19 to 21 are based on the recording you have just heard.19. A) American moms have been increasingly inclined to live alone.B) The American population has been on the rise in the past 25 years.C) American motherhood has actually been on the decline.D) The fertility rates in America have in fact been falling sharply.20. A) More new mothers tend to take greater care of their children.B) More new mothers are economically able to raise children.C) A larger proportion of women take pride in their children.D) A larger proportion of women really enjoy motherhood.21. A) The meaning of motherhood has changed considerably.B) More and more mothers go shopping to treat themselves.C) More mothers have adult children celebrating the holiday.D) The number of American mothers has been growing steadily.Questions 22 to 25 are based on the recording you have just heard.22. A) Add to indoor toxic pollutants.B) Absorb poisonous chemicals.C) Beautify the home environment.D) Soak up surrounding moisture.23. A) NASA did experiments in sealed containers resembling thesuper-insulated offices of 1970s.B) It was based on experiments under conditions unlike those in most homes or offices.C) NASA conducted tests in outer space whose environment is different from ours.D) It drew its conclusion without any contrastive data from other experiments.24. A) Natural ventilation proves much more efficient for cleaning the air than house plants.B) House plants disperse chemical compounds more quickly with people moving around.C) Natural ventilation turns out to be most effective with doors and windows wide open.D) House plants in a normal environment rarely have any adverse impact on the air.25. A) The root cause for misinterpretations of scientific findings.B) The difficulty in understanding what’s actually happening.C) The steps to be taken in arriving at any conclusion with certainty.D) The necessity of continually re-examining and challenging findings.Part III Reading Comprehension (40 minutes)Section ADirections: In this section, there is a passage with ten blanks. You are required to select one word for each blank from a list of choices given in a word bank following the passage. Read the passage through carefully before making your choices. Each choice in the bank is identified by a letter. Please mark the corresponding letter for each item on Answer Sheet 2with a single line through the centre. You may not use any of the words in the bank more than once.A rainbow is a multi-colored, arc-shaped phenomenon that can appearin the sky. The colors of a rainbow are produced by the reflectionand____26____of light through water droplets (小滴) present in the atmosphere. An observer may____27____a rainbow to be located either near or far away, but this phenomenon is not actually located at any specific spot. Instead, the appearance of a rainbow depends entirely upon the position of the observer in____28____to the direction of light. In essence, a rainbow is an____29____illusion.Rainbows present a____30____made up of seven colors in a specific order. In fact, school children in many English-speaking countries are taught to remember the name “Roy G. Biv” as an aid for remembering the colors of a rainbow and their order. “Roy G. Biv”____31____for: red, orange, yellow, green, blue, indigo, and violet. The outer edge of the rainbow arc is red, while the inner edge is violet.A rainbow is formed when light (generally sunlight) passes through water droplets____32____in the atmosphere. The light waves change direction as they pass through the water droplets, resulting in two processes: reflection and refraction (折射). When light reflects off a water droplet, it simply____33____back in the opposite direction from where it____34____. When light refracts, it takes a different direction. Some individuals refer to refracted light as “bent light waves.” A rainbow is formed because white light enters the water droplet, where it bends in several different directions. When these bent light waves reach the other side of the water droplet, they reflect back out of the droplet instead of____35____passing through the water. Since the white light is separated inside of the water, the refracted light appears as separate colors to the human eye.A) bouncesB) completelyC) dispersionD) eccentricE) hangingF) opticalG) originatesH) perceiveI) permeatesJ) ponderK) precedingL) recklesslyM) relationN) spectrumO) standsSection BDirections: In this section, you are going to read a passage with tenstatements attached to it. Each statement contains information given in one of the paragraphs. Identify the paragraph from which the information is derived. You may choose a paragraph more than once. Each paragraph is marked with a letter. Answer the questions by marking the corresponding letter on Answer Sheet 2.Blame your worthless workdays on meeting recovery syndromeA) Phyllis Hartman knows what it’s like to make one’s way through the depths of office meeting hell. Managers at one of her former human resources jobs arranged so many meetings that attendees would fall asleep at the table or intentionally arrive late. With hours of her day blocked up with unnecessary meetings, she was often forced to make up her work during overtime. “I was actually working more hours than I probably would have needed to get the work done,” says Hartman, who is founder and president of PGHR Consulting in Pittsburgh, Pennsylvania.B) She isn’t alone in her frustration. Between 11 million and 55 million meetings are held each day in the United States, costing most organisations between 7% and 15% of their personnel budgets. Every week, employees spend about six hours in meetings, while the average manager meets for a staggering 23 hours.C) And though experts agree that traditional meetings are essential for making certain decisions and developing strategy, some employees view them as one of the most unnecessary parts of the workday. The result is not only hundreds of billions of wasted dollars, but an annoyance of what organisational psychologists call “meeting recovery syndrome (MRS)”: time spent cooling off and regaining focus after a useless meeting. If you run to the office kitchen to get some relief with colleagues after a frustrating meeting,you’re likely experiencing meeting recovery syndrome.D) Meeting recovery syndrome is a concept that should be familiar to almost anyone who has held a formal job. It isn’t ground-breaking to say workers feel fatigued after a meeting, but only in recent decades have scientists deemed the condition worthy of further investigation. With its links to organisational efficiency and employee wellbeing, MRS has attracted the attention of psychologists aware of the need to understand its precise causes and cures.E) Today, in so far as researchers can hypothesise, MRS is most easily understood as a slow renewal of finite mental and physical resources. When an employee sits through an ineffective meeting their brain power is essentially being drained away. Meetings drain vitality if they last too long, fail to engage employees or turn into one-sided lectures. The conservation of resources theory, originally proposed in 1989 by Dr. Stevan Hobfoll, states that psychological stress occurs when a person’s resources are threatened or lost. When resources are low, a person will shift into defence to conserve their remaining supply. In the case ofoffice meetings, where some of employees’ most valuable resources are their focus, alertness and motivation, this can mean an abrupt halt in productivity as they take time to recover.F) As humans, when we transition from one task to another on the job —say from sitting in a meeting to doing normal work—it takes an effortful cognitive switch. We must detach ourselves from the previous task and expend significant mental energy to move on. If we are already drained to dangerous levels, then making the mental switch to the next thing is extra tough. It’s common to see people cyber-loafing after a frustrating meeting, going and getting coffee, interrupting a colleague and telling them about the meeting, and so on.G) Each person’s ability to recover from horrible meetings is different. Some can bounce back quickly, while others carry their fatigue until the end of the workday. Yet while no formal MRS studies are currently underway, one can loosely speculate on the length of an average employee’s lag time. Switching tasks in a non-MRS condition takes about 10 to 15 minutes. With MRS, it may take as long as 45 minutes on average. It’s even worse when a worker has several meetings that are separated by 30 minutes. “Not enough time to transition in a non-MRS situation to get anything done, and in an MRS situation, not quite enough time to recover for the next meeting,” says researcher Joseph Allen. “Then, add the compounding of back-to-back bad meetings and we may have an epidemic on our hands.”H) In an effort to combat the side effects of MRS, Allen, along with researcher Joseph Mroz and colleagues at the University of Nebraska-Omaha, published a study detailing the best ways to avoid common traps, including a concise checklist of do’s and don’ts applicable to any workplace. Drawing from around 200 papers to compile their comprehensive list, Mroz and his team may now hold a remedy to the largely undefined problem of MRS.I) Mroz says a good place to start is asking ourselves if our meetings are even necessary in the first place. If all that’s on the agenda is a quick catch-up, or some non-urgent information sharing, it may better suit the group to send around an email instead. “The second thing I would always recommend is keep the meeting as small as possible,” says Mroz. “If they don’t actually have some kind of immediate input, then they can follow up later. They don’t need to be sitting in this hour-long meeting.” Less time in meetings would ultimately lead to more employee engagement in the meetings they do attend, which experts agree is a proven remedy for MRS.J) Employees also feel taxed when they are invited together to meetings that don’t inspire participation, says Cliff Scott, professor of organisational science. It takes precious time for them to vent their emotions, complain and try to regain focus after a pointless meeting—one of the main traps of MRS. Over time as employees find themselves tied up in more and more unnecessary meetings—and thus dealing with increasing lag times from MRS—the waste of workday hours can feel insulting.K) Despite the relative scarcity of research behind the subject, Hartman has taught herself many of the same tricks suggested in Mroz’s study, and has come a long way since her days of being stuck with unnecessary meetings. The people she invites to meetings today include not just the essential employees, but also representatives from every department that might have a stake in the issue at hand. Managers like her, who seek input even from non-experts to shape their decisions, can find greater support and cooperation from their workforce, she says.L) If an organisation were to apply all 22 suggestions from Mroz and Allen’s findings, the most noticeable difference would be a stark decrease in the total number of meetings on the schedule, Mroz says. Lesstime in meetings would ultimately lead to increased productivity,which is the ultimate objective of convening a meeting. While none of the counter-MRS ideas have been tested empirically yet, Allen says one trick with promise is for employees to identify things that quickly change their mood from negative to positive. As simple as it sounds, finding a personal happy place, going there and then coming straight back to work might be key to facilitating recovery.M) Leaders should see also themselves as “stewards of everyone else’s valuable time”, adds Steven Rogelberg, author of The Surprising Science of Meetings. Having the skills to foresee potential traps and treat employees’ endurance with care allows leaders to provide effective short-term deterrents to MRS.N) Most important, however, is for organisations to awaken to the concept of meetings being flexible, says Allen. By reshaping the way they prioritise employees’ time, companies can eliminate the very sources of MRS in their tracks.36. Although employees are said to be fatigued by meetings, the condition has not been considered worthy of further research until recently. 37. Mroz and his team compiled a list of what to do and what not to do to remedy the problem of MRS.38. Companies can get rid of the root cause of MRS if they give priority to workers’ time.39. If workers are exhausted to a dangerous degree, it is extremely hard for them to transition to the next task.40. Employees in America spend a lot of time attending meetings while the number of hours managers meet is several times more.41. Phyllis Hartman has learned by herself many of the ways Mroz suggested in his study and made remarkable success in freeing herself fromunnecessary meetings.42. When meetings continue too long or don’t engage employees, they deplete vitality.43. When the time of meetings is reduced, employees will be more engaged in the meetings they do participate in.44. Some employees consider meetings one of the most dispensable parts of the workday.45. According to Mroz, if all his suggestions were applied, a very obvious change would be a steep decrease in the number of meetings scheduled.Section CDirections:There are 2 passages in this section. Each passage is followed by some questions or unfinished statements. For each of them there are four choices marked A), B), C) and D). You should decide on the best choice and mark the corresponding letter on Answer Sheet 2 with a single line through the centre.Passage OneQuestions 46 to 50 are based on the following passage.Sarcasm and jazz have something surprisingly in common: You know them when you hear them. Sarcasm is mostly understood through tone of voice, which is used to portray the opposite of the literal words. For example, when someone says, “Well, that’s exactly what I need right now,” their tone can tell you it’s not what they need at all.Most frequently, sarcasm highlights an irritation or is, quite simply, mean.If you want to be happier and improve your relationships, cut out sarcasm. Why? Because sarcasm is actually hostility disguised as humor.Despite smiling outwardly, many people who receive sarcastic comments feel put down and often think the sarcastic person is rude, or contemptible. Indeed, it’s not surprising that the origin of the word sarcasm derives from the Greek word “sarkazein” which literally means “to tear or strip the flesh off.” Hence, it’s no wonder that sarcasm is often preceded by the word “cutting” and that it hurts.What’s more, since actions strongly determine thoughts and feelings, when a person consistently acts sarcastically it may only serve to heighten their underlying hostility and insecurity. After all, when you come right down to it, sarcasm can be used as a subtle form of bullying —and most bullies are angry, insecure, or cowardly.Alternatively, when a person stops voicing negative comments, especially sarcastic ones, they may soon start to feel happier and more self-confident. Also, other people in their life benefit even more because they no longer have to hear the emotionally hurtful language of sarcasm.Now, I’m not saying all sarcasm is bad. It may just be better usedsparingly—like a potent spice in cooking. Too much of the spice, and the dish will be overwhelmed by it. Similarly, an occasional dash of sarcastic wit can spice up a chat and add an element of humor to it. But a big or steady serving of sarcasm will overwhelm the emotional flavor of any conversation and can taste very bitter to its recipient.So, tone down the sarcasm and work on clever wit instead, which is usually without any hostility and thus more appreciated by those you’re communicating with. In essence, sarcasm is easy while true, harmless wit takes talent.Thus, the main difference between wit and sarcasm is that, as already stated, sarcasm is often hostility disguised as humor. It can be intended to hurt and is often bitter and biting. Witty statements are usually in response to someone’s unhelpful remarks or behaviors, and the intent is to untangle and clarify the issue by emphasizing its absurdities. Sarcastic statements are expressed in a cutting manner; witty remarks are delivered with undisguised and harmless humor.46. Why does the author say sarcasm and jazz have something surprisingly in common?A) Both are recognized when heard.B) Both have exactly the same tone.C) Both mean the opposite of what they appear to.D) Both have hidden in them an evident irritation.47. How do many people feel when they hear sarcastic comments?A) They feel hostile towards the sarcastic person.B) They feel belittled and disrespected.C) They feel a strong urge to retaliate.D) They feel incapable of disguising their irritation.48. What happens when a person consistently acts sarcastically?A) They feel their dignity greatly heightened.B) They feel increasingly insecure and hostile.C) They endure hostility under the disguise of humor.D) They taste bitterness even in pleasant interactions.49. What does the author say about people quitting sarcastic comments?A) It makes others happier and more self-confident.B) It restrains them from being irritating and bullying.C) It benefits not only themselves but also those around them.D) It shields them from negative comments and outright hostility.50. What is the chief difference between a speaker’s wit and sarcasm?A) Their clarity.B) Their appreciation.C) Their emphasis.D) Their intention.Passage TwoQuestions 51 to 55 are based on the following passage.Variability is crucially important for learning new skills. Consider learning how to serve in tennis. Should you always practise serving from the exactly same location on the court, aiming at the same spot? Although practising in more variable conditions will be slower at first, it will likely make you a better tennis player in the end. This is because variability leads to better generalisation of what is learned.This principle is found in many domains, including speech perception and learning categories. For instance, infants will struggle to learn the category “dog” if they are only exposed to Chihuahuas, instead of many different kinds of dogs.“There are over ten different names for this basic principle,” says Limor Raviv, the senior investigator of a recent study. “Learning from less variable input is often fast, but may fail to generalise to new stimuli.”To identify key patterns and understand the underlying principles of variability effects, Raviv and her colleagues reviewed over 150 studies on variability and generalisation across fields, including computer science, linguistics, categorisation, visual perception and formal education.The researchers discovered that, across studies, the term variability can refer to at least four different kinds of variability, such as set size and scheduling. “These four kinds of variability have never been directly compared—which means that we currently don’t know which is most effective for learning,” says Raviv.The impact of variability depends on whether it is relevant to the task or not. But according to the ‘Mr. Miyagi principle’, practising seemingly unrelated skills may actually benefit learning of other skills.But why does variability impact learning and generalisation? One theory is that more variable input can highlight which aspects of a task are relevant and which are not.Another theory is that greater variability leads to broader generalisations. This is because variability will represent the real world better, including atypical (非典型的) examples.A third reason has to do with the way memory works: when training is variable, learners are forced to actively reconstruct their memories.“Understanding the impact of variability is important for literally every aspect of our daily life. Beyond affecting the way we learn language, motor skills, and categories, it even has an impact on our social lives,”explains Raviv. “For example, face recognition is affected by whether people grew up in a small community or in a larger community. Exposure to fewer faces during childhood is associated with diminished face memory.”“We hope this work will spark people’ s curiosity and generate morework on the topic,” concludes Raviv.“Our paper raises a lot of open questions. Can we find similar effects of variability beyond the brain, for instance, in the immune system?”51. What does the passage say about infants learning the category “dog”if they are exposed to Chihuahuas only?A) They will encounter some degree of difficulty.B) They will try to categorise other objects first.C) They will prefer Chihuahuas to other dog species.D) They will imagine Chihuahuas in various conditions.52. What does Raviv say about the four different kinds of variability?A) Which of them is most relevant to the task at hand is to be confirmed.B) Why they have an impact on learning is far from being understood.C) Why they have never been directly compared remains a mystery.D) Which of them is most conducive to learning is yet to be identified.53. How does one of the theories explain the importance of variability for learning new skills?A) Learners regard variable training as typical of what happens in the real world.B) Learners receiving variable training are compelled to reorganise their memories.C) Learners pay attention to the relevant aspects of a task and ignore those irrelevant.D) Learners focus on related skills instead of wasting time and effort on unrelated ones.54. What does the passage say about face recognition?A) People growing up in a small community may find it easy to remember familiar faces.B) Face recognition has a significant impact on literally every aspect of our social lives.C) People growing up in a large community can readily recognise any individual faces.D) The size of the community people grow up in impacts their face recognition ability.55. What does Raviv hope to do with their research work?A) Highlight which aspects of a task are relevant and which are not to learning a skill.B) Use the principle of variability in teaching seemingly unrelated skills in education.C) Arouse people’s interest in variability and stimulate more research on the topic.D) Apply the principle of variability to such fields of study as the immune system.。

专业英语词汇总结Section 1生药部分中药研究现状及中药现代化一、加强中国药用植物基础研究及其与中药现代化的联系/Strengthening basic researches on Chinese Medicinal Plants and its relations to realizing the modernization of CMM记载be recorded来源derived from中医药Traditional Chinese Medicine,short for TCM卫生事业health care,health undertakings中草药Chinese traditional medicinal herbs疗效reliable therapeutical effectstherapeutic[,θer?'pju:t?k]adj.治疗(学)的;疗法的；对身心健康有益的副作用side-effectsl中医药的健康理念和临床医疗模式体现了现代医学的发展趋势。

The health concept and clinical practice reflect the trend of modern science新的科学技术潮流（the new tide of science and technology）二、中药资源及其研究成果/Chinese Medicinal Plant resources and achievement of its scientific research中药资源(medicinal plant resources)普查(surveys)专项研究(special projects)药用植物资源(the Chinese medicinal resources)科学鉴定(scientific identification)化学成分(chemical constituents)药理实验(pharmacological experiments临床适应症(clinical applications)研究(projects)新著作(new works)各论(monographs)手册(manuals)《中国药典》The pharmacopoeia of the people’s Republic of China药典Pharmacopoeia药用植物学Pharmaceutical Botany本草学Herbology中药学The Chinese Materia Medica药用植物分类学Pharmaceutical Plant Taxonomy植物化学Phytochemistry植物化学分类学Plant Chemotaxonomy药用植物志Flora of Medicinal Plant中药药剂学traditional Chinese Pharmaceutics中药炮制学Science of processing Chinese Crude Drugs中药鉴定学Identification of Traditional Chinese Medicine中药药理学Pharmacology of Traditional Chinese Medicines青蒿素artemisin奎宁quinine、氯奎宁chloroquine衍生物derivatives氯奎宁耐受性疟疾chloroquine resistant malaria急性疟疾pernicious malaria脑部疟疾cerebral malaria显著疗效marked effect chloroquine resistant malaria/抗氯喹啉疟疾Pernicious（有害的）malaria/急性疟疾cerebral malaria/脑疟疾derivatives/衍生物quinine/喹啉含有氮原子的化合物，在英文命名中多以-ine结尾Mono-/一Di-/二Tri-/三Tetra-/四Petan-/五Hexa-/六Hepta-/七Octa-/八Nona-/九Deca-/十三尖杉酯碱harringtonine、高三尖杉酯碱homoharringtonine白血病leukemia和恶性淋巴瘤malignant lymphoma银杏黄酮ginkgetin丹参酮tanshinon IIA治疗冠心病coronary heart diseasesNew drug developments/新药开发Health products/保健品质量控制Quality control修订revise常用中药common-used Chinese materia medica国家标准the national standards三、中药所面临的挑战/Chinese Medicinal Herbs Facing a Challenge中成药及其制剂traditional Chinese patent medicines and preparations基础研究basic researches生产production、流通marketing研究researchIdentification of species/品种鉴定鉴定和鉴别identifying and clarifying变种varieties伪品false matters。

2023cupt题目English Answer:Task 1: Data Augmentation.One of the primary challenges of working with natural language data is the scarcity of labeled examples. This constraint can result in overfitting and poorgeneralization performance of models trained on relatively small datasets. Data augmentation techniques can be employed to mitigate this issue by artificially expanding the training set with diverse and realistic examples.Data augmentation can be categorized into two main types:1. Syntactic transformations: These techniques modify the syntactic structure of the input data without affecting its meaning. Examples include synonym replacement, word deletion, and sentence shuffling.2. Semantic transformations: These techniques preserve the syntactic structure but introduce subtle semantic changes. Methods such as paraphrasing, back-translation, and adversarial training fall under this category.Task 2: Text Classification Model Selection.Selecting the appropriate text classification model depends on several factors, including:1. Data characteristics: The size, complexity, and quality of the training data should inform the choice of model. For small datasets, simpler models like logistic regression or Naive Bayes may suffice, while complex datasets may require deep learning models or ensemble methods.2. Task requirements: The specific classification task, such as binary or multi-class classification, will influence the selection of model architecture.3. Computational resources: The availability oftraining time and computational power can limit the choice of models. Some models, such as large transformer networks, require extensive training and may not be feasible for resource-constrained settings.Common text classification models include:Logistic regression.Naive Bayes.Support Vector Machines.Random Forests.Gradient Boosting Machines.Transformers.Task 3: Model Evaluation and Comparison.Evaluating and comparing the performance of different text classification models is crucial for identifying the optimal model for a given task. Common evaluation metrics include:1. Accuracy: Proportion of correctly classified instances.2. Precision: Proportion of true positives among all predicted positives.3. Recall: Proportion of true positives among all actual positives.4. F1-score: Harmonic mean of precision and recall.To compare models effectively, consider the following:1. Use a held-out test set: Avoid using the training data for evaluation to prevent overfitting.2. Use multiple metrics: Relying on a single metric canbe misleading, as different metrics may provide complementary insights into model performance.3. Consider statistical significance: Performstatistical tests to determine if observed differences in model performance are statistically significant.中文回答：任务1，数据增强。

Like a fish in the ocean, man is confined to a very shallow layer of atmosphere.The gaseous envelope of the Earth is physically inhomogeneous in both the vertical and horizontal directions, although the horizontal inhomogeneity is much less marked than the vertical inhomogeneity.Various criteria have been devised for dividing the atmosphere into layers. This division can be based on the nature of the vertical temperature profile, on the gaseous composition of the air at different altitudes, and the effect of the atmosphere on aircraft at different altitudes, etc. The division based on the variation of the air temperature with altitude is used most commonly in the meteorological literature.According to a publication of the agrological commission of the World Meteorological Organization (WMO) in 1961, the Earth’s atmosphere, is divided into five main layers: the troposphere, the stratosphere, the mesosphere, the thermosphere and the exosphere. These layers are bounded by four thin transition regions: the tropospause, the stratospause, the mesospause, the thermospause .The troposphere is the lower layer of the atmosphere between the Earth’s surface and the tropopause. The temperature drops with increasing height in the troposphere, at a mean rate of 6.5 ℃per kilometer (lapse rate). The upper boundary of the troposphere lies at a height of approximately 8 to 12 km in the polar and troposphere contains about 75% of the total 就像海洋中的鱼一样,人类被局限在大气中一个非常狭窄的层次之内。

Instructional designFrom Wikipedia, the free encyclopediaInstructional Design(also called Instructional Systems Design (ISD)) is the practice of maximizing the effectiveness, efficiency and appeal of instruction and other learning experiences. The process consists broadly of determining the current state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. Ideally the process is informed by pedagogically(process of teaching) and andragogically(adult learning) tested theories of learning and may take place in student-only, teacher-led or community-based settings. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. As a field, instructional design is historically and traditionally rooted in cognitive and behavioral psychology.HistoryMuch of the foundations of the field of instructional design was laid in World War II, when the U.S. military faced the need to rapidly train large numbers of people to perform complex technical tasks, fromfield-stripping a carbine to navigating across the ocean to building a bomber—see "Training Within Industry(TWI)". Drawing on the research and theories of B.F. Skinner on operant conditioning, training programs focused on observable behaviors. Tasks were broken down into subtasks, and each subtask treated as a separate learning goal. Training was designed to reward correct performance and remediate incorrect performance. Mastery was assumed to be possible for every learner, given enough repetition and feedback. After the war, the success of the wartime training model was replicated in business and industrial training, and to a lesser extent in the primary and secondary classroom. The approach is still common in the U.S. military.[1]In 1956, a committee led by Benjamin Bloom published an influential taxonomy of what he termed the three domains of learning: Cognitive(what one knows or thinks), Psychomotor (what one does, physically) and Affective (what one feels, or what attitudes one has). These taxonomies still influence the design of instruction.[2]During the latter half of the 20th century, learning theories began to be influenced by the growth of digital computers.In the 1970s, many instructional design theorists began to adopt an information-processing-based approach to the design of instruction. David Merrill for instance developed Component Display Theory (CDT), which concentrates on the means of presenting instructional materials (presentation techniques).[3]Later in the 1980s and throughout the 1990s cognitive load theory began to find empirical support for a variety of presentation techniques.[4]Cognitive load theory and the design of instructionCognitive load theory developed out of several empirical studies of learners, as they interacted with instructional materials.[5]Sweller and his associates began to measure the effects of working memory load, and found that the format of instructional materials has a direct effect on the performance of the learners using those materials.[6][7][8]While the media debates of the 1990s focused on the influences of media on learning, cognitive load effects were being documented in several journals. Rather than attempting to substantiate the use of media, these cognitive load learning effects provided an empirical basis for the use of instructional strategies. Mayer asked the instructional design community to reassess the media debate, to refocus their attention on what was most important: learning.[9]By the mid- to late-1990s, Sweller and his associates had discovered several learning effects related to cognitive load and the design of instruction (e.g. the split attention effect, redundancy effect, and the worked-example effect). Later, other researchers like Richard Mayer began to attribute learning effects to cognitive load.[9] Mayer and his associates soon developed a Cognitive Theory of MultimediaLearning.[10][11][12]In the past decade, cognitive load theory has begun to be internationally accepted[13]and begun to revolutionize how practitioners of instructional design view instruction. Recently, human performance experts have even taken notice of cognitive load theory, and have begun to promote this theory base as the science of instruction, with instructional designers as the practitioners of this field.[14]Finally Clark, Nguyen and Sweller[15]published a textbook describing how Instructional Designers can promote efficient learning using evidence-based guidelines of cognitive load theory.Instructional Designers use various instructional strategies to reduce cognitive load. For example, they think that the onscreen text should not be more than 150 words or the text should be presented in small meaningful chunks.[citation needed] The designers also use auditory and visual methods to communicate information to the learner.Learning designThe concept of learning design arrived in the literature of technology for education in the late nineties and early 2000s [16] with the idea that "designers and instructors need to choose for themselves the best mixture of behaviourist and constructivist learning experiences for their online courses" [17]. But the concept of learning design is probably as old as the concept of teaching. Learning design might be defined as "the description of the teaching-learning process that takes place in a unit of learning (eg, a course, a lesson or any other designed learning event)" [18].As summarized by Britain[19], learning design may be associated with:∙The concept of learning design∙The implementation of the concept made by learning design specifications like PALO, IMS Learning Design[20], LDL, SLD 2.0, etc... ∙The technical realisations around the implementation of the concept like TELOS, RELOAD LD-Author, etc...Instructional design modelsADDIE processPerhaps the most common model used for creating instructional materials is the ADDIE Process. This acronym stands for the 5 phases contained in the model:∙Analyze– analyze learner characteristics, task to be learned, etc.Identify Instructional Goals, Conduct Instructional Analysis, Analyze Learners and Contexts∙Design– develop learning objectives, choose an instructional approachWrite Performance Objectives, Develop Assessment Instruments, Develop Instructional Strategy∙Develop– create instructional or training materialsDesign and selection of materials appropriate for learning activity, Design and Conduct Formative Evaluation∙Implement– deliver or distribute the instructional materials ∙Evaluate– make sure the materials achieved the desired goals Design and Conduct Summative EvaluationMost of the current instructional design models are variations of the ADDIE process.[21] Dick,W.O,.Carey, L.,&Carey, J.O.(2004)Systematic Design of Instruction. Boston,MA:Allyn&Bacon.Rapid prototypingA sometimes utilized adaptation to the ADDIE model is in a practice known as rapid prototyping.Proponents suggest that through an iterative process the verification of the design documents saves time and money by catching problems while they are still easy to fix. This approach is not novel to the design of instruction, but appears in many design-related domains including software design, architecture, transportation planning, product development, message design, user experience design, etc.[21][22][23]In fact, some proponents of design prototyping assert that a sophisticated understanding of a problem is incomplete without creating and evaluating some type of prototype, regardless of the analysis rigor that may have been applied up front.[24] In other words, up-front analysis is rarely sufficient to allow one to confidently select an instructional model. For this reason many traditional methods of instructional design are beginning to be seen as incomplete, naive, and even counter-productive.[25]However, some consider rapid prototyping to be a somewhat simplistic type of model. As this argument goes, at the heart of Instructional Design is the analysis phase. After you thoroughly conduct the analysis—you can then choose a model based on your findings. That is the area where mostpeople get snagged—they simply do not do a thorough-enough analysis. (Part of Article By Chris Bressi on LinkedIn)Dick and CareyAnother well-known instructional design model is The Dick and Carey Systems Approach Model.[26] The model was originally published in 1978 by Walter Dick and Lou Carey in their book entitled The Systematic Design of Instruction.Dick and Carey made a significant contribution to the instructional design field by championing a systems view of instruction as opposed to viewing instruction as a sum of isolated parts. The model addresses instruction as an entire system, focusing on the interrelationship between context, content, learning and instruction. According to Dick and Carey, "Components such as the instructor, learners, materials, instructional activities, delivery system, and learning and performance environments interact with each other and work together to bring about the desired student learning outcomes".[26] The components of the Systems Approach Model, also known as the Dick and Carey Model, are as follows:∙Identify Instructional Goal(s): goal statement describes a skill, knowledge or attitude(SKA) that a learner will be expected to acquire ∙Conduct Instructional Analysis: Identify what a learner must recall and identify what learner must be able to do to perform particular task ∙Analyze Learners and Contexts: General characteristic of the target audience, Characteristic directly related to the skill to be taught, Analysis of Performance Setting, Analysis of Learning Setting∙Write Performance Objectives: Objectives consists of a description of the behavior, the condition and criteria. The component of anobjective that describes the criteria that will be used to judge the learner's performance.∙Develop Assessment Instruments: Purpose of entry behavior testing, purpose of pretesting, purpose of posttesting, purpose of practive items/practive problems∙Develop Instructional Strategy: Pre-instructional activities, content presentation, Learner participation, assessment∙Develop and Select Instructional Materials∙Design and Conduct Formative Evaluation of Instruction: Designer try to identify areas of the instructional materials that are in need to improvement.∙Revise Instruction: To identify poor test items and to identify poor instruction∙Design and Conduct Summative EvaluationWith this model, components are executed iteratively and in parallel rather than linearly.[26]/akteacher/dick-cary-instructional-design-mo delInstructional Development Learning System (IDLS)Another instructional design model is the Instructional Development Learning System (IDLS).[27] The model was originally published in 1970 by Peter J. Esseff, PhD and Mary Sullivan Esseff, PhD in their book entitled IDLS—Pro Trainer 1: How to Design, Develop, and Validate Instructional Materials.[28]Peter (1968) & Mary (1972) Esseff both received their doctorates in Educational Technology from the Catholic University of America under the mentorship of Dr. Gabriel Ofiesh, a Founding Father of the Military Model mentioned above. Esseff and Esseff contributed synthesized existing theories to develop their approach to systematic design, "Instructional Development Learning System" (IDLS).The components of the IDLS Model are:∙Design a Task Analysis∙Develop Criterion Tests and Performance Measures∙Develop Interactive Instructional Materials∙Validate the Interactive Instructional MaterialsOther modelsSome other useful models of instructional design include: the Smith/Ragan Model, the Morrison/Ross/Kemp Model and the OAR model , as well as, Wiggins theory of backward design .Learning theories also play an important role in the design ofinstructional materials. Theories such as behaviorism , constructivism , social learning and cognitivism help shape and define the outcome of instructional materials.Influential researchers and theoristsThe lists in this article may contain items that are not notable , not encyclopedic , or not helpful . Please help out by removing such elements and incorporating appropriate items into the main body of the article. (December 2010)Alphabetic by last name∙ Bloom, Benjamin – Taxonomies of the cognitive, affective, and psychomotor domains – 1955 ∙Bonk, Curtis – Blended learning – 2000s ∙ Bransford, John D. – How People Learn: Bridging Research and Practice – 1999 ∙ Bruner, Jerome – Constructivism ∙Carr-Chellman, Alison – Instructional Design for Teachers ID4T -2010 ∙Carey, L. – "The Systematic Design of Instruction" ∙Clark, Richard – Clark-Kosma "Media vs Methods debate", "Guidance" debate . ∙Clark, Ruth – Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load / Guided Instruction / Cognitive Load Theory ∙Dick, W. – "The Systematic Design of Instruction" ∙ Gagné, Robert M. – Nine Events of Instruction (Gagné and Merrill Video Seminar) ∙Heinich, Robert – Instructional Media and the new technologies of instruction 3rd ed. – Educational Technology – 1989 ∙Jonassen, David – problem-solving strategies – 1990s ∙Langdon, Danny G - The Instructional Designs Library: 40 Instructional Designs, Educational Tech. Publications ∙Mager, Robert F. – ABCD model for instructional objectives – 1962 ∙Merrill, M. David - Component Display Theory / Knowledge Objects ∙ Papert, Seymour – Constructionism, LOGO – 1970s ∙ Piaget, Jean – Cognitive development – 1960s∙Piskurich, George – Rapid Instructional Design – 2006∙Simonson, Michael –Instructional Systems and Design via Distance Education – 1980s∙Schank, Roger– Constructivist simulations – 1990s∙Sweller, John - Cognitive load, Worked-example effect, Split-attention effect∙Roberts, Clifton Lee - From Analysis to Design, Practical Applications of ADDIE within the Enterprise - 2011∙Reigeluth, Charles –Elaboration Theory, "Green Books" I, II, and III - 1999-2010∙Skinner, B.F.– Radical Behaviorism, Programed Instruction∙Vygotsky, Lev– Learning as a social activity – 1930s∙Wiley, David– Learning Objects, Open Learning – 2000sSee alsoSince instructional design deals with creating useful instruction and instructional materials, there are many other areas that are related to the field of instructional design.∙educational assessment∙confidence-based learning∙educational animation∙educational psychology∙educational technology∙e-learning∙electronic portfolio∙evaluation∙human–computer interaction∙instructional design context∙instructional technology∙instructional theory∙interaction design∙learning object∙learning science∙m-learning∙multimedia learning∙online education∙instructional design coordinator∙storyboarding∙training∙interdisciplinary teaching∙rapid prototyping∙lesson study∙Understanding by DesignReferences1.^MIL-HDBK-29612/2A Instructional Systems Development/SystemsApproach to Training and Education2.^Bloom's Taxonomy3.^TIP: Theories4.^Lawrence Erlbaum Associates, Inc. - Educational Psychologist -38(1):1 - Citation5.^ Sweller, J. (1988). "Cognitive load during problem solving:Effects on learning". Cognitive Science12 (1): 257–285.doi:10.1016/0364-0213(88)90023-7.6.^ Chandler, P. & Sweller, J. (1991). "Cognitive Load Theory andthe Format of Instruction". Cognition and Instruction8 (4): 293–332.doi:10.1207/s1532690xci0804_2.7.^ Sweller, J., & Cooper, G.A. (1985). "The use of worked examplesas a substitute for problem solving in learning algebra". Cognition and Instruction2 (1): 59–89. doi:10.1207/s1532690xci0201_3.8.^Cooper, G., & Sweller, J. (1987). "Effects of schema acquisitionand rule automation on mathematical problem-solving transfer". Journal of Educational Psychology79 (4): 347–362.doi:10.1037/0022-0663.79.4.347.9.^ a b Mayer, R.E. (1997). "Multimedia Learning: Are We Asking theRight Questions?". Educational Psychologist32 (41): 1–19.doi:10.1207/s1*******ep3201_1.10.^ Mayer, R.E. (2001). Multimedia Learning. Cambridge: CambridgeUniversity Press. ISBN0-521-78239-2.11.^Mayer, R.E., Bove, W. Bryman, A. Mars, R. & Tapangco, L. (1996)."When Less Is More: Meaningful Learning From Visual and Verbal Summaries of Science Textbook Lessons". Journal of Educational Psychology88 (1): 64–73. doi:10.1037/0022-0663.88.1.64.12.^ Mayer, R.E., Steinhoff, K., Bower, G. and Mars, R. (1995). "Agenerative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text". Educational TechnologyResearch and Development43 (1): 31–41. doi:10.1007/BF02300480.13.^Paas, F., Renkl, A. & Sweller, J. (2004). "Cognitive Load Theory:Instructional Implications of the Interaction between InformationStructures and Cognitive Architecture". Instructional Science32: 1–8.doi:10.1023/B:TRUC.0000021806.17516.d0.14.^ Clark, R.C., Mayer, R.E. (2002). e-Learning and the Science ofInstruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Pfeiffer. ISBN0-7879-6051-9.15.^ Clark, R.C., Nguyen, F., and Sweller, J. (2006). Efficiency inLearning: Evidence-Based Guidelines to Manage Cognitive Load. SanFrancisco: Pfeiffer. ISBN0-7879-7728-4.16.^Conole G., and Fill K., “A learning design toolkit to createpedagogically effective learning activities”. Journal of Interactive Media in Education, 2005 (08).17.^Carr-Chellman A. and Duchastel P., “The ideal online course,”British Journal of Educational Technology, 31(3), 229-241, July 2000.18.^Koper R., “Current Research in Learning Design,” EducationalTechnology & Society, 9 (1), 13-22, 2006.19.^Britain S., “A Review of Learning Design: Concept,Specifications and Tools” A report for the JISC E-learning Pedagogy Programme, May 2004.20.^IMS Learning Design webpage21.^ a b Piskurich, G.M. (2006). Rapid Instructional Design: LearningID fast and right.22.^ Saettler, P. (1990). The evolution of American educationaltechnology.23.^ Stolovitch, H.D., & Keeps, E. (1999). Handbook of humanperformance technology.24.^ Kelley, T., & Littman, J. (2005). The ten faces of innovation:IDEO's strategies for beating the devil's advocate & driving creativity throughout your organization. New York: Doubleday.25.^ Hokanson, B., & Miller, C. (2009). Role-based design: Acontemporary framework for innovation and creativity in instructional design. Educational Technology, 49(2), 21–28.26.^ a b c Dick, Walter, Lou Carey, and James O. Carey (2005) [1978].The Systematic Design of Instruction(6th ed.). Allyn & Bacon. pp. 1–12.ISBN020*******./?id=sYQCAAAACAAJ&dq=the+systematic+design+of+instruction.27.^ Esseff, Peter J. and Esseff, Mary Sullivan (1998) [1970].Instructional Development Learning System (IDLS) (8th ed.). ESF Press.pp. 1–12. ISBN1582830371. /Materials.html.28.^/Materials.htmlExternal links∙Instructional Design - An overview of Instructional Design∙ISD Handbook∙Edutech wiki: Instructional design model [1]∙Debby Kalk, Real World Instructional Design InterviewRetrieved from "/wiki/Instructional_design" Categories: Educational technology | Educational psychology | Learning | Pedagogy | Communication design | Curricula。