Non-Existence of Local Integrals of Motion in the Multi-Deformed Ising Model

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lovasz local lemma的证明 -回复

lovasz local lemma的证明 -回复

lovasz local lemma的证明-回复lovasz local lemma是一种概率方法,用于证明存在性问题。

它由鲁迅大学的匈牙利裔数学家Miklós Lovász于1975年发现并证明。

这个引人注目的定理最初是为组合问题提出的,但后来发展成为解决其他领域中的存在性问题的有力工具。

在本文中,我们将逐步探讨lovasz local lemma 的证明过程。

首先,让我们定义lovasz local lemma用到的一些基本概念。

假设有一个由n个事件组成的事件空间Ω={A_1,A_2,...,A_n},其中每个事件A_i都与其他k个事件有关。

这些事件出现或不出现被认为是随机的。

我们的目标是证明至少存在一个事件A_i,使其不受其他事件的影响,即事件A_i 的发生仅取决于自身。

根据lovasz local lemma的条件,如果我们可以证明每个事件A_i最多依赖于其他d个事件的发生,并且Pr(A_i) ≤p < 1/4d,那么至少存在一个事件A_i是独立的。

这将揭示出事件的某种自由性,即使在其他事件发生的情况下,它也可以独立地发生。

为了证明这个引人注目的定理,我们需要一个严格的算法。

接下来,我们将介绍一种被称为染色法的方法,这是证明lovasz local lemma的一种常见方法。

首先,我们为每个事件A_i分配一个颜色c_i,这些颜色取值范围为{1,2,...,n}。

然后,我们定义事件的邻接列表G={A_1,A_2,...,A_n},其中A_i与事件A_j相关联,如果A_i依赖于A_j的发生。

在染色时,我们假设颜色分配是随机的,即每个事件有相同的概率获得任何颜色。

现在,我们要构建一个函数Bad(A_i),用于判断事件A_i是否处于“坏”的状态。

我们定义坏事件为,事件A_i依赖于其他事件A_j的发生,并且A_j与A_i具有相同的颜色。

简而言之,如果存在一个相邻事件A_j具有相同的颜色,那么事件A_i被认为是坏的。

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册迟清华鄢明才编著地质出版社·北京·1内容提要本书汇编了国内外不同研究者提出的火成岩、沉积岩、变质岩、土壤、水系沉积物、泛滥平原沉积物、浅海沉积物和大陆地壳的化学组成与元素丰度,同时列出了勘查地球化学和环境地球化学研究中常用的中国主要地球化学标准物质的标准值,所提供内容均为地球化学工作者所必须了解的各种重要地质介质的地球化学基础数据。

本书供从事地球化学、岩石学、勘查地球化学、生态环境与农业地球化学、地质样品分析测试、矿产勘查、基础地质等领域的研究者阅读,也可供地球科学其它领域的研究者使用。

图书在版编目(CIP)数据应用地球化学元素丰度数据手册/迟清华,鄢明才编著. -北京:地质出版社,2007.12ISBN 978-7-116-05536-0Ⅰ. 应… Ⅱ. ①迟…②鄢…Ⅲ. 地球化学丰度-化学元素-数据-手册Ⅳ. P595-62中国版本图书馆CIP数据核字(2007)第185917号责任编辑:王永奉陈军中责任校对:李玫出版发行:地质出版社社址邮编:北京市海淀区学院路31号,100083电话:(010)82324508(邮购部)网址:电子邮箱:zbs@传真:(010)82310759印刷:北京地大彩印厂开本:889mm×1194mm 1/16印张:10.25字数:260千字印数:1-3000册版次:2007年12月北京第1版•第1次印刷定价:28.00元书号:ISBN 978-7-116-05536-0(如对本书有建议或意见,敬请致电本社;如本社有印装问题,本社负责调换)2关于应用地球化学元素丰度数据手册(代序)地球化学元素丰度数据,即地壳五个圈内多种元素在各种介质、各种尺度内含量的统计数据。

它是应用地球化学研究解决资源与环境问题上重要的资料。

将这些数据资料汇编在一起将使研究人员节省不少查找文献的劳动与时间。

这本小册子就是按照这样的想法编汇的。

A Discriminatively Trained, Multiscale, Deformable Part Model

A Discriminatively Trained, Multiscale, Deformable Part Model

A Discriminatively Trained,Multiscale,Deformable Part ModelPedro Felzenszwalb University of Chicago pff@David McAllesterToyota Technological Institute at Chicagomcallester@Deva RamananUC Irvinedramanan@AbstractThis paper describes a discriminatively trained,multi-scale,deformable part model for object detection.Our sys-tem achieves a two-fold improvement in average precision over the best performance in the2006PASCAL person de-tection challenge.It also outperforms the best results in the 2007challenge in ten out of twenty categories.The system relies heavily on deformable parts.While deformable part models have become quite popular,their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge.Our system also relies heavily on new methods for discriminative training.We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM.A latent SVM,like a hid-den CRF,leads to a non-convex training problem.How-ever,a latent SVM is semi-convex and the training prob-lem becomes convex once latent information is specified for the positive examples.We believe that our training meth-ods will eventually make possible the effective use of more latent information such as hierarchical(grammar)models and models involving latent three dimensional pose.1.IntroductionWe consider the problem of detecting and localizing ob-jects of a generic category,such as people or cars,in static images.We have developed a new multiscale deformable part model for solving this problem.The models are trained using a discriminative procedure that only requires bound-ing box labels for the positive ing these mod-els we implemented a detection system that is both highly efficient and accurate,processing an image in about2sec-onds and achieving recognition rates that are significantly better than previous systems.Our system achieves a two-fold improvement in average precision over the winning system[5]in the2006PASCAL person detection challenge.The system also outperforms the best results in the2007challenge in ten out of twenty This material is based upon work supported by the National Science Foundation under Grant No.0534820and0535174.Figure1.Example detection obtained with the person model.The model is defined by a coarse template,several higher resolution part templates and a spatial model for the location of each part. object categories.Figure1shows an example detection ob-tained with our person model.The notion that objects can be modeled by parts in a de-formable configuration provides an elegant framework for representing object categories[1–3,6,10,12,13,15,16,22]. While these models are appealing from a conceptual point of view,it has been difficult to establish their value in prac-tice.On difficult datasets,deformable models are often out-performed by“conceptually weaker”models such as rigid templates[5]or bag-of-features[23].One of our main goals is to address this performance gap.Our models include both a coarse global template cov-ering an entire object and higher resolution part templates. The templates represent histogram of gradient features[5]. As in[14,19,21],we train models discriminatively.How-ever,our system is semi-supervised,trained with a max-margin framework,and does not rely on feature detection. We also describe a simple and effective strategy for learn-ing parts from weakly-labeled data.In contrast to computa-tionally demanding approaches such as[4],we can learn a model in3hours on a single CPU.Another contribution of our work is a new methodology for discriminative training.We generalize SVMs for han-dling latent variables such as part positions,and introduce a new method for data mining“hard negative”examples dur-ing training.We believe that handling partially labeled data is a significant issue in machine learning for computer vi-sion.For example,the PASCAL dataset only specifies abounding box for each positive example of an object.We treat the position of each object part as a latent variable.We also treat the exact location of the object as a latent vari-able,requiring only that our classifier select a window that has large overlap with the labeled bounding box.A latent SVM,like a hidden CRF[19],leads to a non-convex training problem.However,unlike a hidden CRF, a latent SVM is semi-convex and the training problem be-comes convex once latent information is specified for thepositive training examples.This leads to a general coordi-nate descent algorithm for latent SVMs.System Overview Our system uses a scanning window approach.A model for an object consists of a global“root”filter and several part models.Each part model specifies a spatial model and a partfilter.The spatial model defines a set of allowed placements for a part relative to a detection window,and a deformation cost for each placement.The score of a detection window is the score of the root filter on the window plus the sum over parts,of the maxi-mum over placements of that part,of the partfilter score on the resulting subwindow minus the deformation cost.This is similar to classical part-based models[10,13].Both root and partfilters are scored by computing the dot product be-tween a set of weights and histogram of gradient(HOG) features within a window.The rootfilter is equivalent to a Dalal-Triggs model[5].The features for the partfilters are computed at twice the spatial resolution of the rootfilter. Our model is defined at afixed scale,and we detect objects by searching over an image pyramid.In training we are given a set of images annotated with bounding boxes around each instance of an object.We re-duce the detection problem to a binary classification prob-lem.Each example x is scored by a function of the form, fβ(x)=max zβ·Φ(x,z).Hereβis a vector of model pa-rameters and z are latent values(e.g.the part placements). To learn a model we define a generalization of SVMs that we call latent variable SVM(LSVM).An important prop-erty of LSVMs is that the training problem becomes convex if wefix the latent values for positive examples.This can be used in a coordinate descent algorithm.In practice we iteratively apply classical SVM training to triples( x1,z1,y1 ,..., x n,z n,y n )where z i is selected to be the best scoring latent label for x i under the model learned in the previous iteration.An initial rootfilter is generated from the bounding boxes in the PASCAL dataset. The parts are initialized from this rootfilter.2.ModelThe underlying building blocks for our models are the Histogram of Oriented Gradient(HOG)features from[5]. We represent HOG features at two different scales.Coarse features are captured by a rigid template covering anentireImage pyramidFigure2.The HOG feature pyramid and an object hypothesis de-fined in terms of a placement of the rootfilter(near the top of the pyramid)and the partfilters(near the bottom of the pyramid). detection window.Finer scale features are captured by part templates that can be moved with respect to the detection window.The spatial model for the part locations is equiv-alent to a star graph or1-fan[3]where the coarse template serves as a reference position.2.1.HOG RepresentationWe follow the construction in[5]to define a dense repre-sentation of an image at a particular resolution.The image isfirst divided into8x8non-overlapping pixel regions,or cells.For each cell we accumulate a1D histogram of gra-dient orientations over pixels in that cell.These histograms capture local shape properties but are also somewhat invari-ant to small deformations.The gradient at each pixel is discretized into one of nine orientation bins,and each pixel“votes”for the orientation of its gradient,with a strength that depends on the gradient magnitude.For color images,we compute the gradient of each color channel and pick the channel with highest gradi-ent magnitude at each pixel.Finally,the histogram of each cell is normalized with respect to the gradient energy in a neighborhood around it.We look at the four2×2blocks of cells that contain a particular cell and normalize the his-togram of the given cell with respect to the total energy in each of these blocks.This leads to a vector of length9×4 representing the local gradient information inside a cell.We define a HOG feature pyramid by computing HOG features of each level of a standard image pyramid(see Fig-ure2).Features at the top of this pyramid capture coarse gradients histogrammed over fairly large areas of the input image while features at the bottom of the pyramid capture finer gradients histogrammed over small areas.2.2.FiltersFilters are rectangular templates specifying weights for subwindows of a HOG pyramid.A w by hfilter F is a vector with w×h×9×4weights.The score of afilter is defined by taking the dot product of the weight vector and the features in a w×h subwindow of a HOG pyramid.The system in[5]uses a singlefilter to define an object model.That system detects objects from a particular class by scoring every w×h subwindow of a HOG pyramid and thresholding the scores.Let H be a HOG pyramid and p=(x,y,l)be a cell in the l-th level of the pyramid.Letφ(H,p,w,h)denote the vector obtained by concatenating the HOG features in the w×h subwindow of H with top-left corner at p.The score of F on this detection window is F·φ(H,p,w,h).Below we useφ(H,p)to denoteφ(H,p,w,h)when the dimensions are clear from context.2.3.Deformable PartsHere we consider models defined by a coarse rootfilter that covers the entire object and higher resolution partfilters covering smaller parts of the object.Figure2illustrates a placement of such a model in a HOG pyramid.The rootfil-ter location defines the detection window(the pixels inside the cells covered by thefilter).The partfilters are placed several levels down in the pyramid,so the HOG cells at that level have half the size of cells in the rootfilter level.We have found that using higher resolution features for defining partfilters is essential for obtaining high recogni-tion performance.With this approach the partfilters repre-sentfiner resolution edges that are localized to greater ac-curacy when compared to the edges represented in the root filter.For example,consider building a model for a face. The rootfilter could capture coarse resolution edges such as the face boundary while the partfilters could capture details such as eyes,nose and mouth.The model for an object with n parts is formally defined by a rootfilter F0and a set of part models(P1,...,P n) where P i=(F i,v i,s i,a i,b i).Here F i is afilter for the i-th part,v i is a two-dimensional vector specifying the center for a box of possible positions for part i relative to the root po-sition,s i gives the size of this box,while a i and b i are two-dimensional vectors specifying coefficients of a quadratic function measuring a score for each possible placement of the i-th part.Figure1illustrates a person model.A placement of a model in a HOG pyramid is given by z=(p0,...,p n),where p i=(x i,y i,l i)is the location of the rootfilter when i=0and the location of the i-th part when i>0.We assume the level of each part is such that a HOG cell at that level has half the size of a HOG cell at the root level.The score of a placement is given by the scores of eachfilter(the data term)plus a score of the placement of each part relative to the root(the spatial term), ni=0F i·φ(H,p i)+ni=1a i·(˜x i,˜y i)+b i·(˜x2i,˜y2i),(1)where(˜x i,˜y i)=((x i,y i)−2(x,y)+v i)/s i gives the lo-cation of the i-th part relative to the root location.Both˜x i and˜y i should be between−1and1.There is a large(exponential)number of placements for a model in a HOG pyramid.We use dynamic programming and distance transforms techniques[9,10]to compute the best location for the parts of a model as a function of the root location.This takes O(nk)time,where n is the number of parts in the model and k is the number of cells in the HOG pyramid.To detect objects in an image we score root locations according to the best possible placement of the parts and threshold this score.The score of a placement z can be expressed in terms of the dot product,β·ψ(H,z),between a vector of model parametersβand a vectorψ(H,z),β=(F0,...,F n,a1,b1...,a n,b n).ψ(H,z)=(φ(H,p0),φ(H,p1),...φ(H,p n),˜x1,˜y1,˜x21,˜y21,...,˜x n,˜y n,˜x2n,˜y2n,). We use this representation for learning the model parame-ters as it makes a connection between our deformable mod-els and linear classifiers.On interesting aspect of the spatial models defined here is that we allow for the coefficients(a i,b i)to be negative. This is more general than the quadratic“spring”cost that has been used in previous work.3.LearningThe PASCAL training data consists of a large set of im-ages with bounding boxes around each instance of an ob-ject.We reduce the problem of learning a deformable part model with this data to a binary classification problem.Let D=( x1,y1 ,..., x n,y n )be a set of labeled exam-ples where y i∈{−1,1}and x i specifies a HOG pyramid, H(x i),together with a range,Z(x i),of valid placements for the root and partfilters.We construct a positive exam-ple from each bounding box in the training set.For these ex-amples we define Z(x i)so the rootfilter must be placed to overlap the bounding box by at least50%.Negative exam-ples come from images that do not contain the target object. Each placement of the rootfilter in such an image yields a negative training example.Note that for the positive examples we treat both the part locations and the exact location of the rootfilter as latent variables.We have found that allowing uncertainty in the root location during training significantly improves the per-formance of the system(see Section4).tent SVMsA latent SVM is defined as follows.We assume that each example x is scored by a function of the form,fβ(x)=maxz∈Z(x)β·Φ(x,z),(2)whereβis a vector of model parameters and z is a set of latent values.For our deformable models we define Φ(x,z)=ψ(H(x),z)so thatβ·Φ(x,z)is the score of placing the model according to z.In analogy to classical SVMs we would like to trainβfrom labeled examples D=( x1,y1 ,..., x n,y n )by optimizing the following objective function,β∗(D)=argminβλ||β||2+ni=1max(0,1−y i fβ(x i)).(3)By restricting the latent domains Z(x i)to a single choice, fβbecomes linear inβ,and we obtain linear SVMs as a special case of latent tent SVMs are instances of the general class of energy-based models[18].3.2.Semi-ConvexityNote that fβ(x)as defined in(2)is a maximum of func-tions each of which is linear inβ.Hence fβ(x)is convex inβ.This implies that the hinge loss max(0,1−y i fβ(x i)) is convex inβwhen y i=−1.That is,the loss function is convex inβfor negative examples.We call this property of the loss function semi-convexity.Consider an LSVM where the latent domains Z(x i)for the positive examples are restricted to a single choice.The loss due to each positive example is now bined with the semi-convexity property,(3)becomes convex inβ.If the labels for the positive examples are notfixed we can compute a local optimum of(3)using a coordinate de-scent algorithm:1.Holdingβfixed,optimize the latent values for the pos-itive examples z i=argmax z∈Z(xi )β·Φ(x,z).2.Holding{z i}fixed for positive examples,optimizeβby solving the convex problem defined above.It can be shown that both steps always improve or maintain the value of the objective function in(3).If both steps main-tain the value we have a strong local optimum of(3),in the sense that Step1searches over an exponentially large space of latent labels for positive examples while Step2simulta-neously searches over weight vectors and an exponentially large space of latent labels for negative examples.3.3.Data Mining Hard NegativesIn object detection the vast majority of training exam-ples are negative.This makes it infeasible to consider all negative examples at a time.Instead,it is common to con-struct training data consisting of the positive instances and “hard negative”instances,where the hard negatives are data mined from the very large set of possible negative examples.Here we describe a general method for data mining ex-amples for SVMs and latent SVMs.The method iteratively solves subproblems using only hard instances.The innova-tion of our approach is a theoretical guarantee that it leads to the exact solution of the training problem defined using the complete training set.Our results require the use of a margin-sensitive definition of hard examples.The results described here apply both to classical SVMs and to the problem defined by Step2of the coordinate de-scent algorithm for latent SVMs.We omit the proofs of the theorems due to lack of space.These results are related to working set methods[17].We define the hard instances of D relative toβas,M(β,D)={ x,y ∈D|yfβ(x)≤1}.(4)That is,M(β,D)are training examples that are incorrectly classified or near the margin of the classifier defined byβ. We can show thatβ∗(D)only depends on hard instances. Theorem1.Let C be a subset of the examples in D.If M(β∗(D),D)⊆C thenβ∗(C)=β∗(D).This implies that in principle we could train a model us-ing a small set of examples.However,this set is defined in terms of the optimal modelβ∗(D).Given afixedβwe can use M(β,D)to approximate M(β∗(D),D).This suggests an iterative algorithm where we repeatedly compute a model from the hard instances de-fined by the model from the last iteration.This is further justified by the followingfixed-point theorem.Theorem2.Ifβ∗(M(β,D))=βthenβ=β∗(D).Let C be an initial“cache”of examples.In practice we can take the positive examples together with random nega-tive examples.Consider the following iterative algorithm: 1.Letβ:=β∗(C).2.Shrink C by letting C:=M(β,C).3.Grow C by adding examples from M(β,D)up to amemory limit L.Theorem3.If|C|<L after each iteration of Step2,the algorithm will converge toβ=β∗(D)infinite time.3.4.Implementation detailsMany of the ideas discussed here are only approximately implemented in our current system.In practice,when train-ing a latent SVM we iteratively apply classical SVM train-ing to triples x1,z1,y1 ,..., x n,z n,y n where z i is se-lected to be the best scoring latent label for x i under themodel trained in the previous iteration.Each of these triples leads to an example Φ(x i,z i),y i for training a linear clas-sifier.This allows us to use a highly optimized SVM pack-age(SVMLight[17]).On a single CPU,the entire training process takes3to4hours per object class in the PASCAL datasets,including initialization of the parts.Root Filter Initialization:For each category,we auto-matically select the dimensions of the rootfilter by looking at statistics of the bounding boxes in the training data.1We train an initial rootfilter F0using an SVM with no latent variables.The positive examples are constructed from the unoccluded training examples(as labeled in the PASCAL data).These examples are anisotropically scaled to the size and aspect ratio of thefilter.We use random subwindows from negative images to generate negative examples.Root Filter Update:Given the initial rootfilter trained as above,for each bounding box in the training set wefind the best-scoring placement for thefilter that significantly overlaps with the bounding box.We do this using the orig-inal,un-scaled images.We retrain F0with the new positive set and the original random negative set,iterating twice.Part Initialization:We employ a simple heuristic to ini-tialize six parts from the rootfilter trained above.First,we select an area a such that6a equals80%of the area of the rootfilter.We greedily select the rectangular region of area a from the rootfilter that has the most positive energy.We zero out the weights in this region and repeat until six parts are selected.The partfilters are initialized from the rootfil-ter values in the subwindow selected for the part,butfilled in to handle the higher spatial resolution of the part.The initial deformation costs measure the squared norm of a dis-placement with a i=(0,0)and b i=−(1,1).Model Update:To update a model we construct new training data triples.For each positive bounding box in the training data,we apply the existing detector at all positions and scales with at least a50%overlap with the given bound-ing box.Among these we select the highest scoring place-ment as the positive example corresponding to this training bounding box(Figure3).Negative examples are selected byfinding high scoring detections in images not containing the target object.We add negative examples to a cache un-til we encounterfile size limits.A new model is trained by running SVMLight on the positive and negative examples, each labeled with part placements.We update the model10 times using the cache scheme described above.In each it-eration we keep the hard instances from the previous cache and add as many new hard instances as possible within the memory limit.Toward thefinal iterations,we are able to include all hard instances,M(β,D),in the cache.1We picked a simple heuristic by cross-validating over5object classes. We set the model aspect to be the most common(mode)aspect in the data. We set the model size to be the largest size not larger than80%of thedata.Figure3.The image on the left shows the optimization of the la-tent variables for a positive example.The dotted box is the bound-ing box label provided in the PASCAL training set.The large solid box shows the placement of the detection window while the smaller solid boxes show the placements of the parts.The image on the right shows a hard-negative example.4.ResultsWe evaluated our system using the PASCAL VOC2006 and2007comp3challenge datasets and protocol.We refer to[7,8]for details,but emphasize that both challenges are widely acknowledged as difficult testbeds for object detec-tion.Each dataset contains several thousand images of real-world scenes.The datasets specify ground-truth bounding boxes for several object classes,and a detection is consid-ered correct when it overlaps more than50%with a ground-truth bounding box.One scores a system by the average precision(AP)of its precision-recall curve across a testset.Recent work in pedestrian detection has tended to report detection rates versus false positives per window,measured with cropped positive examples and negative images with-out objects of interest.These scores are tied to the reso-lution of the scanning window search and ignore effects of non-maximum suppression,making it difficult to compare different systems.We believe the PASCAL scoring method gives a more reliable measure of performance.The2007challenge has20object categories.We entered a preliminary version of our system in the official competi-tion,and obtained the best score in6categories.Our current system obtains the highest score in10categories,and the second highest score in6categories.Table1summarizes the results.Our system performs well on rigid objects such as cars and sofas as well as highly deformable objects such as per-sons and horses.We also note that our system is successful when given a large or small amount of training data.There are roughly4700positive training examples in the person category but only250in the sofa category.Figure4shows some of the models we learned.Figure5shows some ex-ample detections.We evaluated different components of our system on the longer-established2006person dataset.The top AP scoreaero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tvOur rank 31211224111422112141Our score .180.411.092.098.249.349.396.110.155.165.110.062.301.337.267.140.141.156.206.336Darmstadt .301INRIA Normal .092.246.012.002.068.197.265.018.097.039.017.016.225.153.121.093.002.102.157.242INRIA Plus.136.287.041.025.077.279.294.132.106.127.067.071.335.249.092.072.011.092.242.275IRISA .281.318.026.097.119.289.227.221.175.253MPI Center .060.110.028.031.000.164.172.208.002.044.049.141.198.170.091.004.091.034.237.051MPI ESSOL.152.157.098.016.001.186.120.240.007.061.098.162.034.208.117.002.046.147.110.054Oxford .262.409.393.432.375.334TKK .186.078.043.072.002.116.184.050.028.100.086.126.186.135.061.019.036.058.067.090Table 1.PASCAL VOC 2007results.Average precision scores of our system and other systems that entered the competition [7].Empty boxes indicate that a method was not tested in the corresponding class.The best score in each class is shown in bold.Our current system ranks first in 10out of 20classes.A preliminary version of our system ranked first in 6classes in the official competition.BottleCarBicycleSofaFigure 4.Some models learned from the PASCAL VOC 2007dataset.We show the total energy in each orientation of the HOG cells in the root and part filters,with the part filters placed at the center of the allowable displacements.We also show the spatial model for each part,where bright values represent “cheap”placements,and dark values represent “expensive”placements.in the PASCAL competition was .16,obtained using a rigid template model of HOG features [5].The best previous re-sult of.19adds a segmentation-based verification step [20].Figure 6summarizes the performance of several models we trained.Our root-only model is equivalent to the model from [5]and it scores slightly higher at .18.Performance jumps to .24when the model is trained with a LSVM that selects a latent position and scale for each positive example.This suggests LSVMs are useful even for rigid templates because they allow for self-adjustment of the detection win-dow in the training examples.Adding deformable parts in-creases performance to .34AP —a factor of two above the best previous score.Finally,we trained a model with partsbut no root filter and obtained .29AP.This illustrates the advantage of using a multiscale representation.We also investigated the effect of the spatial model and allowable deformations on the 2006person dataset.Recall that s i is the allowable displacement of a part,measured in HOG cells.We trained a rigid model with high-resolution parts by setting s i to 0.This model outperforms the root-only system by .27to .24.If we increase the amount of allowable displacements without using a deformation cost,we start to approach a bag-of-features.Performance peaks at s i =1,suggesting it is useful to constrain the part dis-placements.The optimal strategy allows for larger displace-ments while using an explicit deformation cost.The follow-Figure 5.Some results from the PASCAL 2007dataset.Each row shows detections using a model for a specific class (Person,Bottle,Car,Sofa,Bicycle,Horse).The first three columns show correct detections while the last column shows false positives.Our system is able to detect objects over a wide range of scales (such as the cars)and poses (such as the horses).The system can also detect partially occluded objects such as a person behind a bush.Note how the false detections are often quite reasonable,for example detecting a bus with the car model,a bicycle sign with the bicycle model,or a dog with the horse model.In general the part filters represent meaningful object parts that are well localized in each detection such as the head in the person model.Figure6.Evaluation of our system on the PASCAL VOC2006 person dataset.Root uses only a rootfilter and no latent place-ment of the detection windows on positive examples.Root+Latent uses a rootfilter with latent placement of the detection windows. Parts+Latent is a part-based system with latent detection windows but no rootfilter.Root+Parts+Latent includes both root and part filters,and latent placement of the detection windows.ing table shows AP as a function of freely allowable defor-mation in thefirst three columns.The last column gives the performance when using a quadratic deformation cost and an allowable displacement of2HOG cells.s i01232+quadratic costAP.27.33.31.31.345.DiscussionWe introduced a general framework for training SVMs with latent structure.We used it to build a recognition sys-tem based on multiscale,deformable models.Experimental results on difficult benchmark data suggests our system is the current state-of-the-art in object detection.LSVMs allow for exploration of additional latent struc-ture for recognition.One can consider deeper part hierar-chies(parts with parts),mixture models(frontal vs.side cars),and three-dimensional pose.We would like to train and detect multiple classes together using a shared vocab-ulary of parts(perhaps visual words).We also plan to use A*search[11]to efficiently search over latent parameters during detection.References[1]Y.Amit and A.Trouve.POP:Patchwork of parts models forobject recognition.IJCV,75(2):267–282,November2007.[2]M.Burl,M.Weber,and P.Perona.A probabilistic approachto object recognition using local photometry and global ge-ometry.In ECCV,pages II:628–641,1998.[3] D.Crandall,P.Felzenszwalb,and D.Huttenlocher.Spatialpriors for part-based recognition using statistical models.In CVPR,pages10–17,2005.[4] D.Crandall and D.Huttenlocher.Weakly supervised learn-ing of part-based spatial models for visual object recognition.In ECCV,pages I:16–29,2006.[5]N.Dalal and B.Triggs.Histograms of oriented gradients forhuman detection.In CVPR,pages I:886–893,2005.[6] B.Epshtein and S.Ullman.Semantic hierarchies for recog-nizing objects and parts.In CVPR,2007.[7]M.Everingham,L.Van Gool,C.K.I.Williams,J.Winn,and A.Zisserman.The PASCAL Visual Object Classes Challenge2007(VOC2007)Results./challenges/VOC/voc2007/workshop.[8]M.Everingham, A.Zisserman, C.K.I.Williams,andL.Van Gool.The PASCAL Visual Object Classes Challenge2006(VOC2006)Results./challenges/VOC/voc2006/results.pdf.[9]P.Felzenszwalb and D.Huttenlocher.Distance transformsof sampled functions.Cornell Computing and Information Science Technical Report TR2004-1963,September2004.[10]P.Felzenszwalb and D.Huttenlocher.Pictorial structures forobject recognition.IJCV,61(1),2005.[11]P.Felzenszwalb and D.McAllester.The generalized A*ar-chitecture.JAIR,29:153–190,2007.[12]R.Fergus,P.Perona,and A.Zisserman.Object class recog-nition by unsupervised scale-invariant learning.In CVPR, 2003.[13]M.Fischler and R.Elschlager.The representation andmatching of pictorial structures.IEEE Transactions on Com-puter,22(1):67–92,January1973.[14] A.Holub and P.Perona.A discriminative framework formodelling object classes.In CVPR,pages I:664–671,2005.[15]S.Ioffe and D.Forsyth.Probabilistic methods forfindingpeople.IJCV,43(1):45–68,June2001.[16]Y.Jin and S.Geman.Context and hierarchy in a probabilisticimage model.In CVPR,pages II:2145–2152,2006.[17]T.Joachims.Making large-scale svm learning practical.InB.Sch¨o lkopf,C.Burges,and A.Smola,editors,Advances inKernel Methods-Support Vector Learning.MIT Press,1999.[18]Y.LeCun,S.Chopra,R.Hadsell,R.Marc’Aurelio,andF.Huang.A tutorial on energy-based learning.InG.Bakir,T.Hofman,B.Sch¨o lkopf,A.Smola,and B.Taskar,editors, Predicting Structured Data.MIT Press,2006.[19] A.Quattoni,S.Wang,L.Morency,M.Collins,and T.Dar-rell.Hidden conditional randomfields.PAMI,29(10):1848–1852,October2007.[20] ing segmentation to verify object hypothe-ses.In CVPR,pages1–8,2007.[21] D.Ramanan and C.Sminchisescu.Training deformablemodels for localization.In CVPR,pages I:206–213,2006.[22]H.Schneiderman and T.Kanade.Object detection using thestatistics of parts.IJCV,56(3):151–177,February2004. [23]J.Zhang,M.Marszalek,zebnik,and C.Schmid.Localfeatures and kernels for classification of texture and object categories:A comprehensive study.IJCV,73(2):213–238, June2007.。

non significant kruskal-wallis ns p值 意思

non significant kruskal-wallis ns p值 意思

non significant kruskal-wallis ns p值意思“Non significant Kruskal-Wallis ns p 值”是一种统计学结果的表达方式,用于描述 Kruskal-Wallis 检验的结果。

下面是对该结果的解释:1. Kruskal-Wallis 检验:这是一种非参数统计方法,用于比较多个独立样本的总体分布是否存在显著差异。

它不要求数据服从特定的分布形状,可以用于分析无序分类数据或等级数据。

2. 非显著(Non significant):这表示经过 Kruskal-Wallis 检验后,没有发现足够的证据来拒绝零假设。

零假设通常是指各个样本的总体分布相同或没有差异。

因此,“非显著”意味着我们不能得出这些样本之间存在显著差异的结论。

3. p 值:p 值是用于判断统计显著性的指标。

它表示在零假设为真的情况下,观察到当前结果或更极端结果的概率。

通常,p 值小于或等于显著性水平(通常为 0.05 或 0.01)时,我们可以拒绝零假设,认为存在显著差异。

4. ns:"ns"是"not significant"的缩写,意思是不显著。

它是对 p 值结果的一种简洁表示方式。

综上所述,“Non significant Kruskal-Wallis ns p 值”表示在 Kruskal-Wallis 检验中,没有发现样本之间存在显著差异,因此我们不能拒绝零假设。

p 值被表示为"ns",意味着结果不显著。

这可能表明在所比较的样本中,总体分布可能是相似的,或者差异可能是由于随机因素所致。

需要注意的是,这只是对统计结果的一种描述,具体解释还需要结合实际研究背景和数据特征进行综合分析。

Anderson Localization of Polar Eigenmodes in Random Planar Composites

Anderson Localization of Polar Eigenmodes in Random Planar Composites
arXiv:physics/0602098v3 [physics.optics] 31 Oct 2006
Anderson Localization of Polar Eigenmodes in Random Planar Composites
Vadim A. Markel†
Departments of Radiology and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 Abstract. Anderson localization of classical waves in disordered media is a fundamental physical phenomenon that has attracted attention in the past three decades. More recently, localization of polar excitations in nanostructured metaldielectric films (also known as random planar composite) has been subject of intense studies. Potential applications of planar composites include local near-field microscopy and spectroscopy. A number of previous studies have relied on the quasistatic approximation and a direct analogy with localization of electrons in disordered solids. Here I consider the localization problem without the quasistatic approximation. I show that localization of polar excitations is characterized by algebraic rather than by exponential spatial confinement. This result is also valid in two and three dimensions. I also show that the previously used localization criterion based on the gyration radius of eigenmodes is inconsistent with both exponential and algebraic localization. An alternative criterion based on the dipole participation number is proposed. Numerical demonstration of a localization-delocalization transition is given. Finally, it is shown that, contrary to the previous belief, localized modes can be effectively coupled to running waves.

Ordinarydifferentialequation

Ordinarydifferentialequation

Ordinary differential equationIn mathematics, an ordinary differential equation (or ODE ) is a relation that contains functions of only one independent variable, and one or more of their derivatives with respect to that variable.A simple example is Newton's second law of motion, which leads to the differential equationfor the motion of a particle of constant mass m . In general, the force F depends upon the position x(t) of the particle at time t , and thus the unknown function x(t) appears on both sides of the differential equation, as is indicated in the notation F (x (t )).Ordinary differential equations are distinguished from partial differential equations, which involve partial derivatives of functions of several variables.Ordinary differential equations arise in many different contexts including geometry, mechanics, astronomy and population modelling. Many famous mathematicians have studied differential equations and contributed to the field,including Newton, Leibniz, the Bernoulli family, Riccati, Clairaut, d'Alembert and Euler.Much study has been devoted to the solution of ordinary differential equations. In the case where the equation is linear, it can be solved by analytical methods. Unfortunately, most of the interesting differential equations are non-linear and, with a few exceptions, cannot be solved exactly. Approximate solutions are arrived at using computer approximations (see numerical ordinary differential equations).The trajectory of a projectile launched from a cannon follows a curve determined by an ordinary differential equation that is derived fromNewton's second law.Existence and uniqueness of solutionsThere are several theorems that establish existence anduniqueness of solutions to initial value problemsinvolving ODEs both locally and globally. SeePicard –Lindelöf theorem for a brief discussion of thisissue.DefinitionsOrdinary differential equationLet ybe an unknown function in x with the n th derivative of y , and let Fbe a given functionthen an equation of the formis called an ordinary differential equation (ODE) of order n . If y is an unknown vector valued function,it is called a system of ordinary differential equations of dimension m (in this case, F : ℝmn +1→ ℝm ).More generally, an implicit ordinary differential equation of order nhas the formwhere F : ℝn+2→ ℝ depends on y(n). To distinguish the above case from this one, an equation of the formis called an explicit differential equation.A differential equation not depending on x is called autonomous.A differential equation is said to be linear if F can be written as a linear combination of the derivatives of y together with a constant term, all possibly depending on x:(x) and r(x) continuous functions in x. The function r(x) is called the source term; if r(x)=0 then the linear with aidifferential equation is called homogeneous, otherwise it is called non-homogeneous or inhomogeneous. SolutionsGiven a differential equationa function u: I⊂ R→ R is called the solution or integral curve for F, if u is n-times differentiable on I, andGiven two solutions u: J⊂ R→ R and v: I⊂ R→ R, u is called an extension of v if I⊂ J andA solution which has no extension is called a global solution.A general solution of an n-th order equation is a solution containing n arbitrary variables, corresponding to n constants of integration. A particular solution is derived from the general solution by setting the constants to particular values, often chosen to fulfill set 'initial conditions or boundary conditions'. A singular solution is a solution that can't be derived from the general solution.Reduction to a first order systemAny differential equation of order n can be written as a system of n first-order differential equations. Given an explicit ordinary differential equation of order n (and dimension 1),define a new family of unknown functionsfor i from 1 to n.The original differential equation can be rewritten as the system of differential equations with order 1 and dimension n given bywhich can be written concisely in vector notation aswithandLinear ordinary differential equationsA well understood particular class of differential equations is linear differential equations. We can always reduce an explicit linear differential equation of any order to a system of differential equation of order 1which we can write concisely using matrix and vector notation aswithHomogeneous equationsThe set of solutions for a system of homogeneous linear differential equations of order 1 and dimension nforms an n-dimensional vector space. Given a basis for this vector space , which is called a fundamental system, every solution can be written asThe n × n matrixis called fundamental matrix. In general there is no method to explicitly construct a fundamental system, but if one solution is known d'Alembert reduction can be used to reduce the dimension of the differential equation by one.Nonhomogeneous equationsThe set of solutions for a system of inhomogeneous linear differential equations of order 1 and dimension ncan be constructed by finding the fundamental system to the corresponding homogeneous equation and one particular solution to the inhomogeneous equation. Every solution to nonhomogeneous equation can then be written asA particular solution to the nonhomogeneous equation can be found by the method of undetermined coefficients or the method of variation of parameters.Concerning second order linear ordinary differential equations, it is well known thatSo, if is a solution of: , then such that:So, if is a solution of: ; then a particular solution of , isgiven by:. [1]Fundamental systems for homogeneous equations with constant coefficientsIf a system of homogeneous linear differential equations has constant coefficientsthen we can explicitly construct a fundamental system. The fundamental system can be written as a matrix differential equationwith solution as a matrix exponentialwhich is a fundamental matrix for the original differential equation. To explicitly calculate this expression we first transform A into Jordan normal formand then evaluate the Jordan blocksof J separately asTheories of ODEsSingular solutionsThe theory of singular solutions of ordinary and partial differential equations was a subject of research from the time of Leibniz, but only since the middle of the nineteenth century did it receive special attention. A valuable but little-known work on the subject is that of Houtain (1854). Darboux (starting in 1873) was a leader in the theory, and in the geometric interpretation of these solutions he opened a field which was worked by various writers, notably Casorati and Cayley. To the latter is due (1872) the theory of singular solutions of differential equations of the first order as accepted circa 1900.Reduction to quadraturesThe primitive attempt in dealing with differential equations had in view a reduction to quadratures. As it had been the hope of eighteenth-century algebraists to find a method for solving the general equation of the th degree, so it was the hope of analysts to find a general method for integrating any differential equation. Gauss (1799) showed, however, that the differential equation meets its limitations very soon unless complex numbers are introduced. Hence analysts began to substitute the study of functions, thus opening a new and fertile field. Cauchy was the first to appreciate the importance of this view. Thereafter the real question was to be, not whether a solution is possible by means of known functions or their integrals, but whether a given differential equation suffices for the definition of a function of the independent variable or variables, and if so, what are the characteristic properties of this function.Fuchsian theoryTwo memoirs by Fuchs (Crelle, 1866, 1868), inspired a novel approach, subsequently elaborated by Thomé and Frobenius. Collet was a prominent contributor beginning in 1869, although his method for integrating a non-linear system was communicated to Bertrand in 1868. Clebsch (1873) attacked the theory along lines parallel to those followed in his theory of Abelian integrals. As the latter can be classified according to the properties of the fundamental curve which remains unchanged under a rational transformation, so Clebsch proposed to classify the transcendent functions defined by the differential equations according to the invariant properties of the corresponding surfaces f = 0 under rational one-to-one transformations.Lie's theoryFrom 1870 Sophus Lie's work put the theory of differential equations on a more satisfactory foundation. He showed that the integration theories of the older mathematicians can, by the introduction of what are now called Lie groups, be referred to a common source; and that ordinary differential equations which admit the same infinitesimal transformations present comparable difficulties of integration. He also emphasized the subject of transformations of contact.A general approach to solve DE's uses the symmetry property of differential equations, the continuous infinitesimal transformations of solutions to solutions (Lie theory). Continuous group theory, Lie algebras and differential geometry are used to understand the structure of linear and nonlinear (partial) differential equations for generating integrable equations, to find its Lax pairs, recursion operators, Bäcklund transform and finally finding exact analytic solutions to the DE.Symmetry methods have been recognized to study differential equations arising in mathematics, physics, engineering, and many other disciplines.Sturm–Liouville theorySturm–Liouville theory is a theory of eigenvalues and eigenfunctions of linear operators defined in terms of second-order homogeneous linear equations, and is useful in the analysis of certain partial differential equations.Software for ODE solving•FuncDesigner (free license: BSD, uses Automatic differentiation, also can be used online via Sage-server [2])•VisSim [3] - a visual language for differential equation solving•Mathematical Assistant on Web [4] online solving first order (linear and with separated variables) and second order linear differential equations (with constant coefficients), including intermediate steps in the solution.•DotNumerics: Ordinary Differential Equations for C# and [5] Initial-value problem for nonstiff and stiff ordinary differential equations (explicit Runge-Kutta, implicit Runge-Kutta, Gear’s BDF and Adams-Moulton).•Online experiments with JSXGraph [6]References[1]Polyanin, Andrei D.; Valentin F. Zaitsev (2003). Handbook of Exact Solutions for Ordinary Differential Equations, 2nd. Ed.. Chapman &Hall/CRC. ISBN 1-5848-8297-2.[2]/welcome[3][4]http://user.mendelu.cz/marik/maw/index.php?lang=en&form=ode[5]/NumericalLibraries/DifferentialEquations/[6]http://jsxgraph.uni-bayreuth.de/wiki/index.php/Differential_equationsBibliography• A. D. Polyanin and V. F. Zaitsev, Handbook of Exact Solutions for Ordinary Differential Equations (2nd edition)", Chapman & Hall/CRC Press, Boca Raton, 2003. ISBN 1-58488-297-2• A. D. Polyanin, V. F. Zaitsev, and A. Moussiaux, Handbook of First Order Partial Differential Equations, Taylor & Francis, London, 2002. ISBN 0-415-27267-X• D. Zwillinger, Handbook of Differential Equations (3rd edition), Academic Press, Boston, 1997.•Hartman, Philip, Ordinary Differential Equations, 2nd Ed., Society for Industrial & Applied Math, 2002. ISBN 0-89871-510-5.•W. Johnson, A Treatise on Ordinary and Partial Differential Equations (/cgi/b/bib/ bibperm?q1=abv5010.0001.001), John Wiley and Sons, 1913, in University of Michigan Historical Math Collection (/u/umhistmath/)• E.L. Ince, Ordinary Differential Equations, Dover Publications, 1958, ISBN 0486603490•Witold Hurewicz, Lectures on Ordinary Differential Equations, Dover Publications, ISBN 0-486-49510-8•Ibragimov, Nail H (1993), CRC Handbook of Lie Group Analysis of Differential Equations Vol. 1-3, Providence: CRC-Press, ISBN 0849344883.External links•Differential Equations (/Science/Math/Differential_Equations//) at the Open Directory Project (includes a list of software for solving differential equations).•EqWorld: The World of Mathematical Equations (http://eqworld.ipmnet.ru/index.htm), containing a list of ordinary differential equations with their solutions.•Online Notes / Differential Equations (/classes/de/de.aspx) by Paul Dawkins, Lamar University.•Differential Equations (/diffeq/diffeq.html), S.O.S. Mathematics.• A primer on analytical solution of differential equations (/mws/gen/ 08ode/mws_gen_ode_bck_primer.pdf) from the Holistic Numerical Methods Institute, University of South Florida.•Ordinary Differential Equations and Dynamical Systems (http://www.mat.univie.ac.at/~gerald/ftp/book-ode/ ) lecture notes by Gerald Teschl.•Notes on Diffy Qs: Differential Equations for Engineers (/diffyqs/) An introductory textbook on differential equations by Jiri Lebl of UIUC.Article Sources and Contributors7Article Sources and ContributorsOrdinary differential equation Source: /w/index.php?oldid=433160713 Contributors: 48v, A. di M., Absurdburger, AdamSmithee, After Midnight, Ahadley,Ahoerstemeier,AlfyAlf,Alll,AndreiPolyanin,Anetode,Ap,Arthena,ArthurRubin,BL,BMF81,********************,Bemoeial,BenFrantzDale,Benjamin.friedrich,BereanHunter,Bernhard Bauer, Beve, Bloodshedder, Bo Jacoby, Bogdangiusca, Bryan Derksen, Charles Matthews, Chilti, Chris in denmark, ChrisUK, Christian List, Cloudmichael, Cmdrjameson, Cmprince, Conversion script, Cpuwhiz11, Cutler, Delaszk, Dicklyon, DiegoPG, Dmitrey, Dmr2, DominiqueNC, Dominus, Donludwig, Doradus, Dysprosia, Ed Poor, Ekotkie, Emperorbma, Enochlau, Fintor, Fruge, Fzix info, Gauge, Gene s, Gerbrant, Giftlite, Gombang, HappyCamper, Heuwitt, Hongsichuan, Ht686rg90, Icairns, Isilanes, Iulianu, Jack in the box, Jak86, Jao, JeLuF, Jitse Niesen, Jni, JoanneB, John C PI, Jokes Free4Me, JonMcLoone, Josevellezcaldas, Juansempere, Kawautar, Kdmckale, Krakhan, Kwantus, L-H, LachlanA, Lethe, Linas, Lingwitt, Liquider, Lupo, MarkGallagher,MathMartin, Matusz, Melikamp, Michael Hardy, Mikez, Moskvax, MrOllie, Msh210, Mtness, Niteowlneils, Oleg Alexandrov, Patrick, Paul August, Paul Matthews, PaulTanenbaum, Pdenapo, PenguiN42, Phil Bastian, PizzaMargherita, Pm215, Poor Yorick, Pt, Rasterfahrer, Raven in Orbit, Recentchanges, RedWolf, Rich Farmbrough, Rl, RobHar, Rogper, Romanm, Rpm, Ruakh, Salix alba, Sbyrnes321, Sekky, Shandris, Shirt58, SilverSurfer314, Ssd, Starlight37, Stevertigo, Stw, Susvolans, Sverdrup, Tarquin, Tbsmith, Technopilgrim, Telso, Template namespace initialisation script, The Anome, Tobias Hoevekamp, TomyDuby, TotientDragooned, Tristanreid, Twin Bird, Tyagi, Ulner, Vadimvadim, Waltpohl, Wclxlus, Whommighter, Wideofthemark, WriterHound, Xrchz, Yhkhoo, 今古庸龍, 176 anonymous editsImage Sources, Licenses and ContributorsImage:Parabolic trajectory.svg Source: /w/index.php?title=File:Parabolic_trajectory.svg License: Public Domain Contributors: Oleg AlexandrovLicenseCreative Commons Attribution-Share Alike 3.0 Unported/licenses/by-sa/3.0/。

A New Supersymmetric Index

A New Supersymmetric Index

Lyman Laboratory of Physics
Harvard University, Cambridge, MA 02138, USA
dimensions, ependent of almost all deformations of the theory. This index is related to the geometry of the vacua (Berry’s curvature) and satisfies an exact differential equation as a function of β . For integrable theories we can also compute the index thermodynamically, using the exact S -matrix. The equivalence of these two results implies a highly non-trivial equivalence of a set of coupled integral equations with these differential equations, among them Painleve III and the affine Toda equations.
HUTP-92/A021 SISSA 68/92/EP BUHEP-92-14
arXiv:hep-th/9204102v1 30 Apr 1992
A New Supersymmetric Index
Sergio Cecotti† , Paul Fendley⋆ , Ken Intriligator∞ and Cumrun Vafa∞
partition function Tr e−βH . This powerful method is known as the thermodynamic Bethe ansatz (TBA)[10]. In particular, the TBA analysis for a large class of N =2 integrable theories in two dimensions was carried out in [11,12], confirming the conjectured S -matrices as in particular reproducing the correct central charges in the UV limit. One can extend the usual TBA analysis by allowing arbitrary chemical potentials, and in particular one can compute objects such as TreiαF e−βH . This allows us, as a special case, to compute Tr(−1)F F e−βH in these theories in terms of integral equations. Thus for integrable theories we seem to have two inequivalent methods to compute the

Two-dimensional Quantum Field Theory, examples and applications

Two-dimensional Quantum Field Theory, examples and applications

Abstract The main principles of two-dimensional quantum field theories, in particular two-dimensional QCD and gravity are reviewed. We study non-perturbative aspects of these theories which make them particularly valuable for testing ideas of four-dimensional quantum field theory. The dynamics of confinement and theta vacuum are explained by using the non-perturbative methods developed in two dimensions. We describe in detail how the effective action of string theory in non-critical dimensions can be represented by Liouville gravity. By comparing the helicity amplitudes in four-dimensional QCD to those of integrable self-dual Yang-Mills theory, we extract a four dimensional version of two dimensional integrability.
2 48 49 52 54 56
5 Four-dimensional analogies and consequences 6 Conclusions and Final Remarks

#x #

#x #
Abstract We consider uniqueness theorems in classical analysis having the form (+) ∀u ∈ U, v1 , v2 ∈ Vu G(u, v1 ) = 0 = G(u, v2 ) → v1 = v2 , where U, V are complete separable metric spaces, Vu is compact in V and G : U × V → IR is a constructive function. If (+) is proved by arithmetical means from analytical assumptions (++) ∀x ∈ X ∃y ∈ Yx ∀z ∈ Z F (x, y, z ) = 0 only (where X, Y, Z are complete separable metric spaces, Yx ⊂ Y is compact and F : X × Y × Z → IR constructive), then we can extract from the proof of (++) → (+) an effective modulus of uniqueness, i.e. (+ + +) ∀u ∈ U, v1 , v2 ∈ Vu , k ∈ IN |G(u, v1 )|, |G(u, v2 )| ≤ 2−Φuk → dV (v1 , v2 ) ≤ 2−k . Such a modulus Φ can e.g. be used to give a finite algorithm which computes the (uniquely determined) zero of G(u, ·) on Vu with prescribed precision if it exists classically. The extraction of Φ uses a proof–theoretic combination of functional interpretation and pointwise majorization. If the proof of (++) → (+) uses only simple instances of induction, then Φ is a simple mathematical operation (on a convenient standard representation of X , e.g. on f together with a modulus of uniform continuity for X = C [0, 1]). Various uniqueness theorems in best approximation theory have the form (+) and are proved using only analytical tools of the form (++). We analyse the most common proof of uniqueness for the best Chebycheff approximation of f ∈ C [0, 1] by polynomials of degree ≤ n given by de La Vall´ ee Poussin and obtain explicit moduli of uniqueness and uniform constants of strong unicity. In a subsequent paper two further proofs of this uniqueness will be analysed yielding better estimates (due to the fact that mainly (++)–lemmas are used) which allow us to improve results obtained prior by D. Bridges significantly.

Nonlinear Dirac Equations

Nonlinear Dirac Equations

with F ∝ I, n = 1 23 . . . . . . . . . . . . . 24 . . . . . . . . . . . . . 24 . . . . . . . . . . . . . 25 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 26 26 27 27
with F ∝ I, n = 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Abstract We study nonlinear extensions of Dirac’s relativistic electron equation that preserve its other desirable properties such as locality, separability, conservation of probability and Poincar´ e invariance. We determine the constraints that the nonlinear term must obey and classify the resultant non-polynomial nonlinearities in a double expansion in the degree of nonlinearity and number of derivatives. We give explicit examples of such nonlinear equations, studying their discrete symmetries, plane wave solutions, modified dispersion relations and the non-relativistic limit. Motivated by some previously suggested applications such as neutrino oscillations, we then consider nonlinear terms that simultaneously violate Lorentz covariance and again study various explicit examples together with their associated properties. We contrast our construction procedure and results with others in the literature and also outline various physical applications we envisage for these equations, ranging from use as effective equations at low energies to probes of possible quantum nonlinearities at high energies.

基于Non-Local means滤波的雾天降质图像恢复算法

基于Non-Local means滤波的雾天降质图像恢复算法
( R 处 理 效 果 较 好 , 易 导 致 边 缘 光 晕 现 象 、 易 出 现 MS ) 但 容 偏 色 问 题 。 第 2类 是 基 于 大气 退 化 物 理 模 型 的 方 法 , 些 有
雾 相似时 , 没有 阴影 投射 到这 个物 体上 , 导致求 解黑 暗通
道失败。文献 [ ] 8 中提出的去雾方法 在景深 大的地方边缘 轮廓及 景物特征都 比较模糊 , 同时也可能 导致边缘 晕环效
借助于专用的 已经标定的雷 达装置 获得深度 信息 , 然后 利
较清楚 , 而且可有效抑制边缘晕 环效 应。
用图像数据和深度信息来求解 模型 参数 , 再把参 数代入 退
化模型 , 得估计图像 ; 求 文献 [ ] 3 中通过处理至少两 幅雾 天
恶劣天气下 的降质 图像来 获得场景 结构 和深度信息 , 得 求
摘要: 针对 目 前去雾算法易导致边缘晕环效应、 边缘轮廓及景物特征比较模糊问题 , 提出了一种景深等先验信息
未知条件下基于 N nLcl en 滤波 的雾天降质图像恢复算法 。首先 , o-oa m as 根据大气散射模型将经典 的场景深度估 计转化为大气面纱以及天空亮度估计 , 避免难求 的场景深度 图 ; 然后 , 对雾 天降质 图像进行 雾气平 均化预处 理 ,
恢复 图像 , 这些 处理方 法 的要 求都 比较 苛刻 。此 外 , 献 文 [] 4 中提 出利用光 偏振 特性 的 图像 恢 复模型 , 它不 需要 知 道景物信息 , 其需要通过机 械转动调 整偏振 片角度来 获 但 得两幅极化方 向 图像 , 从而 使获 取 图像 时间过 长 , 不利 于 工程 应 用 。由 此 , 幅 雾 天 降 质 图 像 恢 复 引 起 广 泛 关 单

无核边界积分 英文俊

无核边界积分 英文俊

无核边界积分英文俊"无核边界积分"的英文翻译是 "non-nuclear boundary integral."The term "non-nuclear boundary integral" refers to a mathematical concept used in the field of boundary integral equations, particularly in the context of problems related to potential theory, elasticity, and fluid dynamics. In these problems, the boundary integral equation is often used to relate the values of a function on the boundary of a domain to the values of the function inside the domain. The "non-nuclear" aspect may refer to the absence of nuclear singularities in the integral, which can have implications for the convergence and behavior of the integral equation.From a mathematical perspective, the study of non-nuclear boundary integrals involves understanding the properties of the underlying functions and their behavior near the boundary of the domain. It also involves thedevelopment of numerical methods and techniques for solving boundary integral equations that arise in practical applications.In summary, the term "non-nuclear boundary integral" encompasses a specific type of boundary integral equation that arises in various mathematical and physical contexts, and its study involves both theoretical analysis and practical computational methods.。

nonlocal结构

nonlocal结构

nonlocal结构
Nonlocal结构是指结构中存在着非局部效应的现象。

在传统的局部效应结构中,结构的受力和变形只与其周围的局部构件有关,而非局部效应结构中则存在着信息的非局部传递和作用,也就是说,结构的受力和变形不仅与其周围的局部构件有关,也与结构内部和远离结构的区域有关。

非局部效应的存在使得结构的设计和分析变得更为复杂。

在材料的选择和结构的组装中,需要考虑非局部效应对结构的影响,同时在结构的分析和优化中也需要考虑非局部效应的影响。

因此,非局部效应的研究和理解对于结构设计和分析具有重要的意义。

目前,非局部效应的研究主要涉及到物理学、数学和工程学等领域。

在物理学中,非局部效应主要涉及量子力学和相对论等领域;在数学中,则涉及到非局部微积分和非局部偏微分方程等内容;在工程学中,非局部效应的研究主要集中在结构力学、材料力学和计算力学等领域。

尽管非局部效应的研究还存在着一些挑战和困难,但其在结构设计和分析中的重要性已经得到了广泛的认识和重视。

未来,随着技术的不断进步和理论的不断完善,非局部效应的研究将会取得更加深入和广泛的进展。

- 1 -。

混凝土细观裂缝超真实仿真模拟

混凝土细观裂缝超真实仿真模拟

Unlike the conventional rigid-body mechanisms, which attain mobility from hinges, bearings and sliders, compliant mechanisms gain their mobility from relative flexibility of the constituents [24]. The synthesis of compliant mechanisms is mainly used to control the ratios between output and input displacements or forces, which can be described by geometrical advantage and/or the mechanical advantage. Compared to the rigid-body counterparts, compliant mechanisms can be built using fewer parts with less wear, friction, noise and backlash, and require less assembly processes. Due to these advantages, compliant mechanisms have been widely used in precision control devices and Micro Electro Mechanical Systems (MEMS).
Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction

Nonlocal Invariants in Index Theory

Nonlocal Invariants in Index Theory
Partially supported by the NSF.
1
generic n 2 N is the formal di erence Ker Dn ? Coker Dn. For example, for a family of Dirac operators associated to a family of metrics gn on M parametrized by n 2 N , ^ ^ ^ we get ch(Ind D) = A(M=N ), where A(M=N ) is the A-polynomial of the bundle over N whose ber at n is the bundle of spinors associated to gn; and : M N ! N is the projection. This example becomes less trivial if we replace M N by a manifold X bered by M (cf. x4). Among the basic examples of elliptic operators are the so-called geometric operators, i.e. operators canonically associated to a Riemannian metric on M . For these operators, Hodge theory identi es the index with the Euler characteristic of de Rham-type cohomology groups. For example, the Gauss-Bonnet operator is given by D = d + d taking even forms to odd forms, where d is exterior di erentiation and d is its adjoint with respect to the Hodge inner product on forms induced by the metric. The index of D is the Euler characteristic (M ). The Dolbeault operator @ + @ , the analog of the Gauss-Bonnet operator for a complex Kahler manifold, has the arithemetic genus as its index, and the signature operator has (M ), the s (The index of the Dirac operator on a spin manifold does not have a Hodge theoretic topological interpretation.) The simplest geometric example is the Gauss-Bonnet operator on a closed surface. The characteristic number given by the Index Theorem is he(M ); M ]i, the value of the Euler class of TM evaluated on the fundamental class of M . By Chern-Weil theory, R this characteristic number equals (1=2 ) M KdA, the integral of the Gaussian curvature. Thus the Index RTheorem reduces in this case to the classical Gauss-Bonnet theorem: (M ) = (1=2 ) M KdA: In particular, we have expressed the index as the integral of a local invariant, a top dimensional form (or density) computed pointwise canonically from the metric (i.e. independent of choice of chart). This derivation of the Gauss-Bonnet theorem is somewhat indirect, as it uses Hodge theory to equate the analytic term, the index, with a topological expression, and then appeals to Chern-Weil theory to obtain a geometric expression for the topological side of the Index Theorem. Similar remarks apply to other geometric operators. Beginning with 1], heat equation proofs of the Index Theorem were developed which expressed the index of a geometrically de ned operator directly as the integral of the correct ChernWeil expression. In brief, it is easily shown that the supertrace trse?tD D = tr(e?tD D ) ? tr(e?tDD ) of the heat operator is time independent, with D = D +D acting on sections of E F: As t ! 1, the supertrace converges to the index of D by a Hodge theory argument. By parabolic regularity theory, the heat operator e?tD D has a smooth integral kernel. The pointwise supertrace of the integral kernel of e?tD D is nonlocal, as it is built from the eigensections of D D, which are solutions of a global equation. However, by more or less standard pseudodi erential operator techniques, the pointwise supertrace has the asymptotic expansion trse?tD D(t; x; x) = P (x) + O(t); as t ! 0, for some local curvature invariant P (x) = P (x; g), where g is the Riemannian R metric. Equating the long and short time behavior gives Ind D = M P (x)dvol. 2

On (Enriched) Left Bousfield Localization of Model Categories

On (Enriched) Left Bousfield Localization of Model Categories

a rXiv:078.267v2[mat h.A T]29Nov27Abstract .—I verify the existence of left Bousfield localizations and of enriched left Bousfield localizations,and I prove a collection of useful technical results char-acterizing certain fibrations of (enriched)left Bousfield localizations.I also use such Bousfield localizations to construct a number of new model categories,including models for the homotopy limit of right Quillen presheaves,for Postnikov towers in model categories,and for presheaves valued in a symmetric monoidal model category satisfying a homotopy-coherent descent condition.A class of maps H in a model category M specifies a class of H -local objects,which are those objects X with the property that the morphism R Mor M (f,X )is a weak equivalence of simplicial sets for any f ∈H .The left Bousfield localization of M with respect to H is a model for the homotopy theory of H -local objects.Similarly,if M is enriched over a symmetric monoidal model category V ,the class H specifies a class of (H/V )-local objects,which are those objects X with the property that the morphism RMor V M (f,X )of derived mapping objects is a weak equivalence of V for any f ∈H .The V -enriched left Bousfield localization of M is a model for the homotopy theory of (H/V )-local objects.The (enriched)left Bousfield localization is described as a new “H -local”model cat-egory structure on the underlying category of M .The H -local cofibrations of the (en-riched)left Bousfield localization are precisely those of M ,the H -local fibrant objects are the (enriched)H -local objects that are fibrant in M ,and the H -local weak equiv-alences are those morphisms f of M such that R Mor M (f,X )(resp.,RMor VM (f,X ))is a weak equivalence.This is enough to specify H -local fibrations,but it can be dif-ficult to get explicit control over them.Luckily,is frequently possible to characterize some of the H -local fibrations as fibrations that are in addition homotopy pullbacks of fibrations between H -local fibrant objects.2CLARK BAR WICKThe(enriched)Bousfield localization gives an effective way of constructing new model categories from old.In particular,one can use this to construct models for the homotopy limit of a right Quillen presheaf,for Postnikov towers in model cate-gories,and for presheaves valued in a symmetric monoidal model category satisfying a homotopy-coherent descent condition.The aim of this short note is to demonstrate the existence of(enriched)left Bous-field localizations,to give some useful technical results concerning thefibrations thereof,and to use this machinery to provide a number of interesting examples of model categories,including those described in the previous paragraph.Plan.—In thefirst section,I give a brief review of the general theory of combina-torial and tractable model categories.This material is all well-known.There one may find two familiar but important examples:model structures on diagram categories and model structures on section categories.In the second section,I define the left Bousfield localization and give the well-known existence theorem due to Smith.Following this,I continue with a small collection of results that permit one to cope with the fact that left Bousfield localization ruins right properness,as well as a characterization of a certain class of H-localfibrations. Ifinish the section withfive simple applications of the technique of left Bousfield localization:Dugger’s presentation theorem,the existence of homotopy images,the existence of resolutions of model categories,the construction of homotopy limits of diagrams of model categories,and the existence of Postnikov towers for simplicial model categories.In thefinal section,I review the notions of symmetric monoidal and enriched model categories.I describe the enriched left Bousfield localization and prove an existence theorem.I then give two applications:first,the existence of enriched Postnikov towers, and second,the existence of local model structures on presheaves valued in symmetric monoidal model categories.Thanks to J.Bergner,P.A.Østvær,and B.To¨e n for persistent encouragement and hours of interesting discussion.Thanks also to J.Rosick´y for pointing out a careless omission.Thanks especially to M.Spitzweck for a profound and lasting impact on my work;were it not for his insights and questions,there would be nothing for me to report here or anywhere else.ContentsPlan (2)1.Tractable model categories (3)Combinatorial and tractable model categories (3)Application I:Model structures on diagram categories (9)Application II:Model structures on section categories (10)2.Left Bousfield localization (14)Definition and existence of left Bousfield localizations (14)The failure of right properness (18)Application I:Presentations of combinatorial model categories..23ON(ENRICHED)LEFT BOUSFIELD LOCALIZATIONS3 Application II:Homotopy images (23)Application III:Resolutions of combinatorial model categories (24)Application IV:Homotopy limits of right Quillen presheaves (24)Application V:Postnikov towers for simplicial model categories.263.Enriched model categories and enriched left Bousfield localization28Symmetric monoidal model categories and enrichments (28)The enriched left Bousfield localization (32)Application I:Postnikov towers for spectral model categories (35)Application II:Local model structures (36)References (39)1.Tractable model categoriesCombinatorial and tractable model categories.—Combinatorial model cat-egories are those whose homotopy theory is controlled by the homotopy theory of a small subcategory of presentable objects.A variety of algebraic applications require that the sets of(trivial)cofibrations can be generated(as a saturated set)by a given small set of(trivial)cofibrations with cofibrant domain.This leads to the notion of tractable model categories.Many of the results have satisfactory proofs in print;the first section of[3]in particular is a very nice reference.(1)1.1.—Suppose here X a universe.Notation1.2.—Suppose C a model X-category.(1.2.1)Write w C(respectively,cof C,fib C)for the lluf subcategories comprised ofweak equivalences(resp.,cofibrations,fibrations).(1.2.2)Write C c(respectively,C f)for the full subcategories of cofibrant(resp.,fi-brant)objects.(1.2.3)For any X-small regular cardinalλ,write Cλfor the full subcategory of Ccomprised ofλ-presentable objects.Definition1.3.—Suppose C a model X-category.Suppose,in addition,thatλisa regular X-small cardinal.(1.3.1)One says that C isλ-combinatorial(respectively,λ-tractable)if its underlyingX-category is locallyλ-presentable,and if there exist X-small sets I and J of morphisms of Cλ(resp.,of Cλ∩C c)such that the following hold.(1.3.1.1)A morphism satisfies the right lifting property with respect to I ifand only if it is a trivialfibration.(1.3.1.2)A morphism satisfies the right lifting property with respect to J ifand only if it is afibration.(1.3.2)An X-small full subcategory C0of C is homotopyλ-generating if every objectof C is weakly equivalent to aλ-filtered homotopy colimit of objects of C0.4CLARK BAR WICK(1.3.3)One says that C is X-combinatorial(respectively,X-tractable)just in casethere exists a regular X-small cardinalκfor which it isκ-combinatorial(resp.,κ-tractable).(1.3.4)Likewise,an X-small full subcategory C0of C is homotopy X-generating ifand only if for some regular X-small cardinalκ,C0is homotopyκ-generating. Notation1.4.—Suppose C an X-category,and suppose I an X-small set of mor-phisms of C.Denote by inj I the set of all morphisms with the right lifting property with respect to I,denote by cof I the set of all morphisms with the left lifting prop-erty with respect to inj I,and denote by cell I the set of all transfinite compositions of pushouts of morphisms of I.Lemma1.5(Transfinite small object argument,[3,Proposition1.3]) Supposeλa regular X-small cardinal,C a locallyλ-presentable X-category,and I an X-small set of morphisms of Cλ.(1.5.1)There is an accessible functorial factorization of every morphism f as p◦i,wherein p∈inj I,and i∈cell I.(1.5.2)A morphism q∈inj I if and only if it has the right lifting property with respectto all retracts of morphisms of cell I.(1.5.3)A morphism j is a retract of morphisms of cell I if and only j∈cof I. Proof.—Supposeκa regular cardinal strictly greater thanλ.For any morphism f:XLL(I/f)be the coproduct∐i∈(I/f)i.Define a section P of d1:C2X⊔L(I/f)K(I/f)Y.For any regular cardinalα,set Pα:=colimβ<αPβ. This provides a functorial factorization Pκwith the required properties.The remaining parts follow from the existence of this factorization and the retract argument.1.6.—J.Smith’s insight is that the transfinite small object argument and the solu-tion set condition on weak equivalences together provide a good recognition principle for combinatorial model categories.In effect,one requires only two-thirds of the data normally required to produce cofibrantly generated model structures.Proposition1.7(Smith,[3,Theorem1.7and Propositions1.15and1.19]) Suppose C a locally X-presentable X-category,W an accessibly embedded,accessible subcategory of C1,and I an X-small set of morphisms of C.Suppose in addition that the following conditions are satisfied.(1.7.1)W satisfies the two-out-of-three axiom.ON(ENRICHED)LEFT BOUSFIELD LOCALIZATIONS5 (1.7.2)The set inj I is contained in W.(1.7.3)The intersection W∩cof I is closed under pushouts and transfinite composi-tion.Then C is a combinatorial model category with weak equivalences W,cofibrations cof I,andfibrations inj(W∩cof I).Proof.—The usual small object and retract arguments apply once one constructs an X-small set J such that cof J=W∩cof I.The following pair of lemmas complete the proof.Lemma1.8(Smith,[3,Lemma1.8]).—Under the hypotheses of proposition1.7, suppose J⊂W∩cof I a set such that any commutative squareK MNin which[K N]∈W can be factored as a commutative dia-gramK M′MN′N′]∈J.Then cof J=W∩cof I.Proof.—To prove this,one need only factor any element of W as an element of cell J followed by an element of inj I.The result then follows from the retract argument.Supposeκan X-small regular cardinal such that every codomain of I isκ-presentable.For any morphism[f:XLL Y,and let M(I/f)Y]6CLARK BAR WICKfor any morphism[f:XY]∈W,a functorial factorizationX Ywith the desired properties.Lemma1.9(Smith,[3,Lemma1.9]).—Under the hypotheses of proposition1.7,an X-small set J satisfying the conditions of lemma1.8can be found. Proof.—Since W is an accessibly embedded accessible subcategory of C1,it follows that for any morphism[i:KLN]∈W,there exist a morphism[PL N.It thus suffices tofind,for every square of the type on the left,an element of W∩cof I factoring it.For every[i:K Q]∈W(i),and every commutative squarePKiQ,factor the morphism L⊔K P R]∈cell I followedby an element of[RPRR]∈W∩cof I.Proposition1.10(Smith,[5,Propositions7.1–3]).—Suppose C an X-combinatorial left model X-category.For any sufficient large X-small regular cardinalκ,the follow-ing hold.(1.10.1)There exists aκ-accessible functorial factorization C1ON(ENRICHED)LEFT BOUSFIELD LOCALIZATIONS7 (1.10.2)There exists aκ-accessible functorial factorization C1G an objectwise weak equivalence in C A.Theκ-accessible func-torial factorizations in C permit one to give aκ-accessible factorization of FH followed by an objectwisefibration (which is therefore an objectwise trivialfibration)Hcolim G is afibration,and it remains only to show that it is also a trivialfibration;for this one need only show that for any morphism f:KLcolim F exists.This follows from theκ-presentability of K and L.To verify(1.10.7),let us note that it follows from the existence of aκ-accessible functorial factorization that it suffices to verify that the full subcategory of C1com-prised of trivialfibrations is aκ-accessibly embedded,κ-accessible subcategory.For this,consider the functorMor C,2:C1Mor C(K,Y)])[K→L]∈I.([Mor C(L,Y)→Mor C(L,X)×MorC(K,X)Since the domains and codomains of I areκ-presentable,this is aκ-accessible functor. The trivialfibrations are by definition the inverse image of the full subcategory of I-tuples of surjective morphisms under Mor C,2.Corollary1.11.—Any X-combinatorial model X-category satisfies the hypothesesof1.7.Corollary1.12.—An X-combinatorial model X-category C is X-tractable if and only if the X-small set I of generating cofibrations can be chosen with cofibrant do-mains.8CLARK BAR WICKProof.—Suppose I is an X-small set of generating cofibrations with cofibrant do-mains,and suppose J an X-small set of trivial cofibrations satisfying the conditions of1.8.To give another such X-small set of trivial cofibrations with cofibrant domains, it suffices to show that any commutative squareK MNin which[K N]∈J can be factored as a a commutative diagramK M′MN′N′]∈W∩cof I.To construct this factor-ization,factor the morphism K M′followed by a weak equivalence M′N as a cofibra-tion L⊔K M′N.Then the composite M′DY of D is said to be a projec-tive weak equivalence(respectively,a projectivefibration,a projective trivialfibration)if Uf:UXY of D is said to be a projective cofibration if it sat-isfies the left lifting property with respect to any projective trivialfibration;f is said to be a projective trivial cofibration if it is,in addition,a projectiveweak equivalence.(1.14.3)If the projective weak equivalences,projective cofibrations,and projectivefibrations define a model structure on D,then I call this model structure theprojective model structure.Lemma1.15.—Suppose that in D,transfinite compositions and pushouts of pro-jective trivial cofibrations of D are projective weak equivalences.Then the projective model structure on D exists;it is X-combinatorial,and it is X-tractable if C is. Furthermore the adjunction(F,U)is a Quillen adjunction.ON(ENRICHED)LEFT BOUSFIELD LOCALIZATIONS9 Proof.—The full accessible inverse image of an accessibly embedded accessible full subcategory is again an accessibly embedded accessible full subcategory;hence the projective weak equivalences are an accessibly embedded accessible subcategory of D1.Choose now an X-small set I of C(respectively,C c)of generating cofibrations.One now applies the recognition lemma1.7to the set W of projective weak equiva-lences and the X-small set F I.It is clear that inj I⊂W,and by assumption it follows that W∩cof I is closed under pushouts and transfinite compositions.One now verifies easily that thefibrations are the projective ones and that the adjunction(F,U)is a Quillen adjunction.Since F is left Quillen,the set F I has cofibrant domains if I does.Application I:Model structures on diagram categories.—Suppose X a uni-verse,K an X-small category,and C an X-combinatorial(respectively,X-tractable) model X-category.The category C(K)of C-valued presheaves on K has two X-combinatorial(resp.X-tractable)model structures,to which we now turn.Definition1.16.—A morphism XY k is a weak equivalence orfibration of C.Theorem1.17.—The category C(K)of C-valued presheaves on K has an X-combinatorial(resp.,X-tractable)model structure—the projective model structure C(K)proj—,in which the weak equivalences andfibrations are the projective weak equivalences andfibrations.Proof.—Consider the functor e:Obj KC(K)Y of C-valued presheaves on K is an injec-tive weak equivalence or injective cofibration if,for any object k of K,the morphism X k10CLARK BAR WICKcardinality argument,which proceeds almost exactly as for s Set-functors from an s Set-category to a simplicial model category.For this cardinality argument I refer to[13,A.3.3.12-14]of J.Lurie,whose proofs and exposition I am unable to improve upon.Since colimits are formed objectwise,it follows that the injective trivial cofibrations are closed under pushouts and transfinite composition.Proposition1.20.—The identity functor is a Quillen equivalenceC(K)proj.Proof.—Projective cofibrations are in particular objectwise cofibrations,and weak equivalences are identical sets.Proposition1.21.—If C is left or right proper,then so are C(K)proj and C(K)inj.Proof.—Pullbacks and pushouts are defined objectwise;hence it suffices to note that in both model structures,weak equivalences are defined objectwise,and any cofibration orfibration is in particular an objectwise cofibration orfibration.Proposition1.22.—A functor f:KC(L)proj C(K)injCat Y for some universe Y with X∈Y such that for every k∈Obj K,the category F k is a model X-category,and for every morphismf:ℓFℓis left(resp.,right)Quillen.(1.24.2)A left or right Quillen presheaf F on K is said to be X-combinatorial(re-spectively,X-tractable,left proper,right proper,...)if for every k∈Obj K,the model X-category F k is so.ON (ENRICHED)LEFT BOUSFIELD LOCALIZATIONS 11(1.24.3)A left (respectively,right )morphism Θ:FCat Y suchthat for any k ∈Obj K ,the functor Θk :F k X k (resp.,φf :X kk ]∈K ,such that for any composable pair[mg k ]∈K,one has the identityφg ◦(g ⋆φf )=φf ◦g :(f ◦g )⋆X k (f ◦g )⋆X k ).(1.24.5)A morphism of left (respectively,right )sections r :(X,φ)Y of k ∈Obj K F k such that the diagramf ⋆X k φf f ⋆Y kψfY ℓin F ℓ(resp.,the diagramX kφf Y k ψff ⋆Y ℓin F k )commutes for any morphism f :ℓG of left (resp.,right)Quillen presheaves on an X -small category K induces a left adjointΘ!:Sect L FSect L G ).12CLARK BAR WICKProof.—IfΘis a left morphism of left Quillen presheaves,then defineΘ!by the formulaΘ!(X,φ):=((Θk X k)k∈Obj K,((Θφf)◦θf)f∈Obj(K1))for any left section(X,φ)=((X k)k∈Obj K,(φf)f∈Obj(K1)),in which the morphismθf:f⋆Θk X kSect L Fis defined by the formulaΘ⋆(Y,ψ):=((H k Y k)k∈Obj K,(ηf)f∈Obj(K1))for any left section(Y,ψ)=((Y k)k∈Obj K,(ψf)f∈Obj(K1)),in which the morphismηf:f⋆H k Y k⋆cf⋆Θk H k Y k fY k is the counit of the adjunction(Θk,H k).The corresponding statement for right morphisms follows by duality.1.26.—The previous lemma suggests that the most natural model structure on the category of left(respectively,right)sections of a left(resp.,right)Quillen presheaf F is an injective(resp.,projective)one,in which the weak equivalences and cofibrations (resp.,fibrations)are defined objectwise.This idea is borne out by the observation that these model categories can be thought of as good models for the(∞,1)-categorical lax limit(resp.,(∞,1)-categorical colax limit)of F.Lemma1.27.—If F is a left(respectively,right)X-combinatorial Quillen presheaf on an X-small category K,then the category Sect L F(resp.,Sect R F)is locally X-presentable.Proof.—It is a simple matter to verify that Sect L F and Sect R F are complete and cocomplete.The category of left sections is the lax limit of F,and the category of right sections is a colax limit of F;so the result follows from the fact that the2-category of X-accessible categories is closed under arbitrary X-small weighted bilimits in which all functors are accessible[14,Theorem5.1.6].Lemma1.28.—Suppose a:L:a⋆(resp.,a!,a⋆:Sect R(F◦a)Sect R FON(ENRICHED)LEFT BOUSFIELD LOCALIZATIONS13Proof.—If F is a left Quillen presheaf,then the functora⋆:Sect L(F◦a)Y of right sections of F is a projective weak equivalence or projectivefibration if,for any object k of K,the morphism X kK,which induces an adjunctione!: k∈Obj K F k:e⋆.The condition of1.15follows from the observation that e⋆preserves all colimits.Definition1.31.—Suppose K an X-small category,F a left Quillen presheaf on K.A morphism XY k is a weak equivalence or cofibration of F k.Theorem1.32.—The category Sect L F of left sections of an X-combinatorial(re-spectively,X-tractable)left Quillen presheaf F on an X-small category K has an X-combinatorial(resp.,X-tractable)model structure—the injective model structure Sect L inj F—,in which the weak equivalences and cofibrations are the injective weak equivalences and cofibrations.Proof.—Supposeκan X-small regular cardinal such that K isκ-small,each F kis locallyκ-presentable,and a set of generating cofibrations I Fk for each F k can bechosen from F k,κ(resp.,from F k,κ∩F k,c);without loss of generality,we may assume that I Fkis the X-small set of all cofibrations in F k,κ(resp.,in F k,κ∩F k,c).Denote by I Sect L F the set of injective cofibrations betweenκ-presentable objects of Sect L F (resp.,betweenκ-presentable objects of Sect L F that are in addition objectwise cofi-brant).This set contains a generating set of cofibrations for the projective model structure,so it follows that inj I Sect L F⊂W.The argument given for the existence of the injective model structure on presheaf categories applies almost verbatim here to demonstrate that any injective cofibration can be written as a retract of transfinite composition of pushouts of elements of I Sect L F.14CLARK BAR WICKSince colimits are formed objectwise,it follows that the injective trivial cofibrations are closed under pushouts and transfinite composition.Proposition1.33.—Suppose K an X-small category,F an X-combinatorial left (respectively,right)Quillen presheaf on K.If each F k is left or right proper,then so is Sect L inj F(resp.,Sect R proj F).Proof.—Pullbacks and pushouts are defined objectwise;hence it suffices to note that in both model structures,weak equivalences are defined objectwise,and any cofibration orfibration is in particular an objectwise cofibration orfibration. Proposition1.34.—A left(resp.,right)morphismΘ:FSect L inj G Sect R proj Fsuch that each morphism X2i...X n−1X′2...X′n−1,wherein the vertical maps are in w M.Recall that the hammock localization of M is the s Set-category L H M whose objects are exactly those of M,withMor L H M(X,Y)=colim nν•(w Mor n M(X,Y))for any objects X and Y.ON (ENRICHED)LEFT BOUSFIELD LOCALIZATIONS 15The standard references on the hammock localization are the triple of papers [6],[7],and [8]of W.G.Dwyer and D.Kan.A more modern treatment can be found in[9].Scholium 2.3(Dwyer–Kan).—Suppose Q :MM f a fibrant replacement functor,Γ•:M(s M )f a simplicial resolution functor;thenthere are natural weak equivalences of the simplicial setsMor M (Γ•X,RY )S S S S S S S S S S S Mor M (QX,Λ•Y )k k k k k k k k k k kdiag Mor M (Γ•X,Λ•Y )hocolim (p,q )∈∆op ×∆op Mor M (Γp X,Λq Y )ν•w Mor 3M (X,Y )Ho s Set X(X,Y )Bof M .(2.5.1)The morphism AR Mor M (A,Z )is an isomorphism of Ho s Set X .(2.5.3)For any cofibrant object X of M ,the induced morphismR Mor M (X,A )L H M that is initial amongleft Quillen functors F :M16CLARK BAR WICK(2.6.2)An object Z of M is H-local if for any morphism AR Mor M(A,Z)is an isomorphism of Ho s Set X.(2.6.3)A morphism AR Mor M(A,Z)is an isomorphism of Ho s Set X.Lemma2.7.—When it exists,the left Bousfield localization of M with respect to H is unique up to a unique isomorphism of model X-categories.Proof.—Initial objects are essentially unique.2.8.—Left Bousfield localizations of left proper,X-combinatorial model X-categories with respect to X-small sets of homotopy classes of morphisms are guaranteed to exist,as I shall now demonstrate.For the remainder of this section, suppose H a set of homotopy classes of morphisms of M.The uniqueness,charac-terization,and existence of left Bousfield localizations are the central objectives of the next few results.Uniqueness is a simple matter,and it is a familiar fact that if a model structure exists on M with the same cofibrations whose weak equivalences are the H-local weak equivalences,then this is the left Bousfield localization.The central point is thus to determine the existence of such a model structure.Smith’s existence theorem2.11hinges on the recognition principle1.7and the following pair of technical lemmata.Lemma2.9.—If M is X-combinatorial,and H is X-small,then the set of H-local objects of M comprise an accessibly embedded,accessible subcategory of M1. Proof.—Choose an accessiblefibrant replacement functor R for M,a functorial cosimplicial resolution functorΓ•:M(s Set X)1ZMor M(Γ•X,RZ)is the mor-phism of simplicial sets induced by f:XON(ENRICHED)LEFT BOUSFIELD LOCALIZATIONS17 colimits.Then for any H-local object Z,a colimit colim AR Mor M(colim A,Z)is a homotopy limit of weak equivalences in s Set X,hence a weak equivalence. Theorem2.11(Smith,[16]).—If M is X-combinatorial,and H is an X-small set of homotopy classes of morphisms of M,the left Bousfield localization L H M of M along any set representing H exists and satisfies the following conditions.(2.11.1)The model category L H M is left proper and X-combinatorial.(2.11.2)As a category,L H M is simply M.(2.11.3)The cofibrations of L H M are exactly those of M.(2.11.4)Thefibrant objects of L H M are thefibrant H-local objects Z of M.(2.11.5)The weak equivalences of L H M are the H-local equivalences. Proof.—The aim is to guarantee that a cofibrantly generated model structure on M exists sstisfying conditions(2.11.3)–(2.11.5)using1.7.The combinatoriality is then automatic,and the universal property and the left properness are then verified in[10, Theorem3.3.19and Proposition3.4.4].Fix an X-small set I M of generating cofibrations of M,and let wL H M denote the set of the weak equivalences described in(2.11.5).By2.10,we can now apply 1.7:observe that since I M-injectives are trivialfibrations of M,they are in particular weak equivalences of M,and hence are among the elements of wL H M.It thus remains only to show that pushouts and transfinite compositions of mor-phisms of cof M∩wL H M are H-local weak equivalences.Supposefirst that KK′L′is an element of wL H M is equivalent to the assertion that,for any H-local object Z,the diagramR Mor M(K′,Z)R Mor M(K′,Z)R Mor M(K,Z)is a homotopy pullback diagram in s Set,and this follows immediately from the fact that R Mor M(L,Z)18CLARK BAR WICKlocalization L H M is quasifibrant if somefibrant replacement R M X of X in M is fibrant in L H M.Lemma2.13.—If M is left proper and X-combinatorial,and H is an X-small set of homotopy classes of morphisms of M,then an object X of the left Bousfield localization L H M is quasifibrant if and only if it is H-local.Proof.—H-locality is closed under weak equivalences in M;hence if X is quasifibrant it is surely H-local,and thefibrant replacement in M of an H-local object is H-local.2.14.—As a rule,one has essentially no control in a left Bousfield localization over the generating trivial cofibrations.The following proposition(originally—with a different proof—due to M.Hovey)is one of the very few results on the trivial cofibrations of left Bousfield localizations;it is critical for the forthcoming existence theorem3.18for enriched left Bousfield localizations.Proposition2.15(Hovey,[12,Proposition4.3]).—Suppose that M is left proper and X-combinatorial,and suppose that H is X-small.Then the left Bousfield local-ization L H M is X-tractable if M is.Proof.—Immediate from1.12.The failure of right properness.—Left Bousfield localizations inherit left properness,but in general they destroy right properness.This is because there is very little control over thefibrations.Nevertheless,there are often full subcategories that are in a sense right proper, and this form of right properness is inherited by the quasifibrant objects contained in these subcategories in the left Bousfield localization.In this case,there exist functorial factorizations of morphisms of these quasifibrant objects through quasifibrant objects, so homotopy pullbacks of these quasifibrant objects can be computed effectively.This also provides a nice recognition principle forfibrations of L H M with a quasifibrant codomain that lies in such a subcategory.One can think of this subsection as an enlargement of Reedy’s observation that homotopy pullbacks offibrant objects can be computed by replacing on only one side,or,alternatively,one can think of this subsection as a collection of techniques for coping with the reality that many important combinatorial model categories are simply not right proper.There are,of course,dual conditions and results to many of those of this section,but they are not needed here,essentially because left properness is a relatively common condition in practice.2.16.—Suppose X a universe,M a model X-category.Definition2.17.—(2.17.1)If E is any full subcategory of M,an E-placement functor is a pair(r E,ǫE)consisting of a functor r E:MM denotes the inclusion.。

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a r X i v :h e p -t h /9804144v 2 22 A p r 1998RU-98-16April 1998Non-Existence of Local Integrals of Motionin the Multi-Deformed Ising ModelPedro D.Fonseca 1Department of Physics and AstronomyRutgers University Piscataway,NJ 08855-0849AbstractWe confirm the non-integrability of the multi-deformed Ising Model,an already expected result.After deforming with the energy operator φ1,3we use the Majorana free fermionic representation for the massive theory to show that,besides the trivial one,no local integrals of motion can be built in the theory arising from perturbing with both energy and spin operators.1IntroductionAfter Zamolodchikov’s work[1]-[3],great advances have been achieved in the understanding of field theories,and in particular of integrablefield theories(IFT),arising from the perturbation of certain conformalfield theories(CFT).The simplest example is certainly given by the perturbed Ising ModelA=A CF T+τ ǫ(z,¯z)d2z+h σ(z,¯z)d2z,(1) where A CFT stands for the action of the two dimensional c=1/2CFT andσ(spin)and ǫ(energy)are,respectively,the relevant spinless primaryfieldsφ1,2andφ1,3with conformal dimensions(1/16,1/16)and(1/2,1/2).From dimensional analysis,we see that the coupling constants h∼(length)−15/8andτ∼(length)−1have conformal dimensions(15/16,15/16)and (1/2,1/2),respectively.When considered separately,these perturbations were studied in[1]-[5],and are known to yield IFT’ly,in the caseτ=0,h=0the corresponding perturbation has a realization as an Affine Toda Field Theory(ATFT)based on SU(2)(i.e.a sine-Gordon system)with an infinite set of local integrals of motion(IM)of spin s=1,3,5,7,9,...(the Coxeter exponents modulo the dual Coxeter number of SU(2)),while the caseτ=0,h=0also has an infinite set of local IM but with spin s=1,7,11,13,17,19,23,29(mod30)(the Coxeter exponents modulo the dual Coxeter number of E8),related to the fact that now it has a realization as an ATFT based on E8.Finally(the case that will interest us here),when both perturbations are turned on(see[7] for an extensive analysis),although there is the possibility of having conserved charges of spin s=1,7,11,...(mod30),in[6]it is shown that no low spin IM exist,leading to belief that(1) is no longer integrable,except for the above h=0orτ=0particular cases.In fact,a proof for it can be obtained by starting with thefiniteτtheory(with scattering matrix S=−1)and then,using perturbation theory in h to compute the corresponding S-matrix elements,showing that there exists particle production for h=0(see,for instance,[6]and[7]).In this letter we confirm this result by explicitly verifying the absence of local IM,with the argument being as follows:Given a minimal model,let T s+1be some descendant state of the identity operator with spin s+1satisfying the conservation law¯∂T s+1=0in the CFT andφkl represent some perturbing relevant spinless operator with operator product expansion(OPE)T s+1(z)φkl(w,¯w)∼...+A(s−1)klz−w+ (2)From the deformed Ward identities we then have¯∂Ts+1=λB(s)kl−∂(λA(s−1)kl)+∂(...),(3)whereλis the perturbation coupling,so that there exists a functionΘs−1such that¯∂Ts+1=∂Θs−1,(4)only if B (s )kl can be written as a partial derivative of some local field,i.e.B (s )kl =∂(...).In such case,we can build the conserved charge of spin sP s =T s +1dz +Θs −1d ¯z ,(5)plus its anti-holomorphic partner,thatwewill not write down in what follows.We will denotethe set formed by T s +1in (5)for all possible values of s by Λkl .Whenever Λkl has an infinite number of elements the φkl perturbed theory is said to be integrable.In the case of the unitary minimal models deformed by φ1,3,Λ1,3can be derived from the sine-Gordon model,with the first elements given by [4],[5]T 2=T ,T 4=(T T ),T 6=(T (T T ))−(c +2)2πiA (z )B (w )2,12πd 2zψ¯∂ψ+¯ψ∂¯ψ+mψ¯ψ ,(9)where m∝(T−T C).This theory has c=1/2,OPEψ(z)ψ(w)∼−1/(z−w)and stress-energy tensor given by T(z)=−1z n+1/2,(10) where n∈Z+12 n,m m+1z n+2,(11)where:···:denotes the usual mode normal ordering.Without loss of generality,T s+1∈Λ1,3(in(6))can be written asT2k+2(z)=(−1)k+1116 =σ(0,0)|0 .Using(10),(12)and the fact that a nσ=0for n>0we get the OPE T2k+2(z)σ(0,0)= ...+B(2k+1)1,2/z+...,whereB(2k+1) 1,2=−−k−1n=−2k−1c(k)n(a n a−2k−1−nσ),(13)with coefficientsc(k)n= n+12 ... n+2k+1L −1,which,from(11),can be written asL −1=−m<−1/2m +12a 1a 0σso that (15)is verified for d (0)0=4in (16)(using (17)B (0)1,2is directly seen to be L −1σ),implying that indeed an s =1IM exists.For k =1,B (3)1,2=32d (1)−2a −3a 0−132(63a −5a 0−7a −4a −1+3a −3a −2)σand L −1O (4)=92d (2)−3+12d (2)−3a −3a −2σ,so that (15)supplies three equations for d (2)−4and d (2)−3,again easily seen to be inconsistent.This agrees with the absence of local IM of spin s =3and s =5in the Ising Model perturbed by a magnetic field.Finally,for k ≥3some simple calculations yield∂O (2k )=L −1O (2k )=−−k −1 n =−2k −1e (k )na n a −2k −1−n σ+12d (k )n +1(1−δn,−k −1)−n +2k +18d (k )−2k δn,−2k .(19)The second term in (18)is obviously incompatible with the desired structure in (13),im-plying that O (2k )in (15)does not exist.Expressions other that the quadratic chosen in (16)would obviously have more incompatible terms,thus ending our proof.AcknowledgmentsThe author is most thankful to A.B.Zamolodchikov for having proposed this problem,reviewed the manuscript and for the all the enlightening and valuable conversations.This work was supported by JNICT -PRAXIS XXI (Portugal)under the grant BD 9102/96.References[1]A.B.Zamolodchikov,JETP Lett.46(1987)160.[2]A.B.Zamolodchikov,Advanced Studies in Pure Math.19(1989)641.[3]A.B.Zamolodchikov,Int.J.Mod.Phys.A4(1989)4235.[4]R.Sasaki,I.Yamanaka,Advanced Studies in Pure Math.16(1988)271.[5]T.Egushi,S.K.Yang,Phys.Lett.B224(1989)373.[6]G.Mussardo,Phys.Rev.218,5&6(1992)215.[7]G.Delfino,G.Mussardo,P.Simonetti,Nucl.Phys.B473(1996)469.。

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