nils-herrmann/clean_scirepeval huggingface dataset:
3-D Reconstruction of Multipart Self-Occluding Objects

In this paper we present a method for reconstruction of multipart objects from several arbitrary views using deformable superquadrics as the models of the object's parts. Two visual cues are used: occluding contours and stereo (possibly aided by projected patterns). The object can be relatively complex and can exhibit numerous self occlusions from some or all views. Our preliminary experiments on a human body and a tailor's mannequin show that the reconstruction is more complete than in purely stereo or structured light based methods and more precise than the reconstruction from occluding contours only.
Computer modeling, analysis and synthesis of dressed humans

In this paper we present a method for 3-D reconstruction of human bodies with application in CAD systems for garment design. The reconstruction scheme uses image information from several arbitrary views and deformable superquadrics as the models of the body parts. Two visual cues are used: occluding contours and stereo (possibly aided by projected patterns). Our preliminary experiments show that the reconstruction is more complete than in purely stereo or structured light based methods and more precise than the reconstruction from occluding contours only. From the reconstructed human body, the body measurements can be taken automatically, and used in garment design. We give an example of draping of virtual garment over the photo-realistic 3D model of the imaged human. One can easily envision the use of the described algorithms in the development of custom-fit garment retail software over the Internet, which would include the possibility of trying the garment on in virtual reality.
On Analysis of Cloth Drape Range Data

In this paper we present an algorithm for analysis of the range data of cloth drapes. The goal of the analysis is estimation of parameters for modeling and the geometry of the underlying object. In an analysis-by-synthesis manner, the algorithm compares the drape of the model with the range data and searches for the best fit. It can be applied to any physics-based cloth model. The motivating application is fashion design using CAD systems, but the ability of the algorithm to estimate the shape of the object supporting the scanned cloth indicates the possibility of utilizing cloth models to overcome problems in human tracking algorithms, caused by clothing.
On the use of the Karhunen-Loeve transform and expansion matching for generalized feature detection

A novel generalized feature extraction method based on the expansion matching (EXM) method and the Karhunen-Loeve (KL) transform is presented. This yields an efficient method to locate a large variety of features with a single pass of parallel filtering operations. The EXM method is used to design optimal detectors for different features. The KL representation is used to define an optimal basis for representing these EXM feature detectors with minimum truncation error. Input images are then analyzed with the resulting KL bases. The KL coefficients obtained from the analysis are used to efficiently reconstruct the response due to any combination of feature detectors. The method is successfully applied to real images and extracts a variety of arc and edge features as well as more complex junction features formed by combining two or more arcs or line features.
A generalized expansion matching based feature extractor

A novel and efficient generalized feature extraction method is presented based on the expansion matching (EXM) method and the Karhunen-Loueve (KL) transform. The EXM method is used to design optimal detectors for different features. The KL representation is used to define an optimal basis for representing these EXM feature detectors with minimum truncation error. Input images are then analyzed with the resulting KL basis set. The KL coefficients obtained from the analysis are used to efficiently reconstruct the response due to any combination of feature detectors. The method is applied to real images and successfully extracts a variety of arc and edge features as well as complex junction features formed by combining two or more arc or line features.
Good Word Attacks on Statistical Spam Filters

Unsolicited commercial email is a significant problem for users and providers of email services. While statistical spam filters have proven useful, senders of spam are learning to bypass these filters by systematically modifying their email messages. In a good word attack, one of the most common techniques, a spammer modifies a spam message by inserting or appending words indicative of legitimate email. In this paper, we describe and evaluate the effectiveness of active and passive good word attacks against two types of statistical spam filters: naive Bayes and maximum entropy filters. We find that in passive attacks without any filter feedback, an attacker can get 50% of currently blocked spam past either filter by adding 150 words or fewer. In active attacks allowing test queries to the target filter, 30 words will get half of blocked spam past either filter.
Adversarial learning

Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2]. In this paper, we introduce the adversarial classifier reverse engineering (ACRE) learning problem, the task of learning sufficient information about a classifier to construct adversarial attacks. We present efficient algorithms for reverse engineering linear classifiers with either continuous or Boolean features and demonstrate their effectiveness using real data from the domain of spam filtering.
Structural Periodic Measures for Time-Series Data

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. We combine these new measures with an effective metric tree index structure for efficiently answering k-Nearest-Neighbor queries. The goal of these tools and techniques are to assist in detecting, monitoring and visualizing structural periodic changes. It is our belief that these methods can be directly applicable in the manufacturing industry for preventive maintenance and in the medical sciences for accurate classification and anomaly detection.
Identifying similarities, periodicities and bursts for online search queries

We present several methods for mining knowledge from the query logs of the MSN search engine. Using the query logs, we build a time series for each query word or phrase (e.g., 'Thanksgiving' or 'Christmas gifts') where the elements of the time series are the number of times that a query is issued on a day. All of the methods we describe use sequences of this form and can be applied to time series data generally. Our primary goal is the discovery of semantically similar queries and we do so by identifying queries with similar demand patterns. Utilizing the best Fourier coefficients and the energy of the omitted components, we improve upon the state-of-the-art in time-series similarity matching. The extracted sequence features are then organized in an efficient metric tree index structure. We also demonstrate how to efficiently and accurately discover the important periods in a time-series. Finally we propose a simple but effective method for identification of bursts (long or short-term). Using the burst information extracted from a sequence, we are able to efficiently perform 'query-by-burst' on the database of time-series. We conclude the presentation with the description of a tool that uses the described methods, and serves as an interactive exploratory data discovery tool for the MSN query database.
Chapter 24 Causal Inference

A principal aim of many sciences is to model causal systems well enough to provide insight into their structures and mechanisms and to provide reliable predictions about the effects of policy interventions. To succeed in either of these aims, in general, one must specify a model at least approximately correctly. In the social and behavioral sciences, causal models are often described within any of a variety of statistical formalisms: categorical data models, logistic regression models, linear regression models, factor analysis models, principal components models, structural equation models, and so on. In practice, these models are obtained by a variety of methods: experimentation, investigator’s convictions, uncontested background knowledge, automated search procedures such as stepwise regression and factor analysis, and any of a wide range of ad hoc model selection procedures. To understand the assumptions built into these and other classes of models, as well as their limitations, one
Practically Perfect

We prove that perfect distributions exist when using a finite number of bits to represent the parameters of a Bayesian network. In addition, we provide an upper bound on the probability of sampling a non-perfect distribution when using a fixed number of bits for the parameters and that the upper bound approaches zero exponentially fast as one increases the number of bits. We also provide an upper bound on the number of bits needed to guarantee that a distribution sampled from a uniform Dirichlet distribution is perfect with probability greater than 1/2.
Finding Optimal Bayesian Networks

In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use asymptotically consistent scoring criteria. Our results extend those of Meek (1997) and Chickering (2002), who demonstrate that in the limit of large datasets, if the generative distribution is perfect with respect to a DAG defined over the observable variables, such search algorithms will identify this optimal (i.e. generative) DAG model. We relax their assumption about the generative distribution, and assume only that this distribution satisfies the composition property over the observable variables, which is a more realistic assumption for real domains. Under this assumption, we guarantee that the search algorithms identify an inclusion-optimal model; that is, a model that (1) contains the generative distribution and (2) has no sub-model that contains this distribution. In addition, we show that the composition property is guaranteed to hold whenever the dependence relationships in the generative distribution can be characterized by paths between singleton elements in some generative graphical model (e.g. a DAG, a chain graph, or a Markov network) even when the generative model includes unobserved variables, and even when the observed data is subject to selection bias.
Using Temporal Data for Making Recommendations

We treat collaborative filtering as a univariate time series problem: given a user's previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of collaborative filtering without ordering information. The improvements in both predictive accuracy and in recommendation quality that we realize advocate the use of predictive algorithms exploiting the temporal order of data.
Efficient determination of dynamic split points in a decision tree

We consider the problem of choosing split points for continuous predictor variables in a decision tree. Previous approaches to this problem typically either: (1) discretize the continuous predictor values prior to learning, or (2) apply a dynamic method that considers all possible split points for each potential split. We describe a number of alternative approaches that generate a small number of candidate split points dynamically with little overhead. We argue that these approaches are preferable to pre-discretization, and provide experimental evidence that they yield probabilistic decision trees with the same prediction accuracy as the traditional dynamic approach. Furthermore, because the time to grow a decision tree is proportional to the number of split points evaluated, our approach is significantly faster than the traditional dynamic approach.
Challenges of the Email Domain for Text Classification

Interactive classi cation of email into a userde ned hierarchy of folders is a natural domain for application of text classi cation methods. This domain presents several challenges. First, the user's changing mailling habits mandate classi cation technology adapt in a dynamic environment. Second, the classi cation technology needs to be able to handle heterogeneity in folder content and folder size. Performance when there are only a small number of messages in a folder is especially important. Third, methods must meet the processing and memory requirements of a software implementation. We study three promising methods and present an analysis of their behavior with respect to these domain-speci c challenges.
Singling out Ill-fit Items in a Classification. Application to the Taxonomy of Enterobacteriaceae

We address the problem of evaluating the quality of cluster assignment of in- dividual items in a classification. The problem can also be viewed as outlier detection in classifications. We describe simple methods for this task based on the use of Naive Bayes classification. Applied to two existing classifica- tions of 5313 strains of bacteria the method indicated that one classification is far more robust than the other. The observations that fit badly to their clusters are typically items whose classification is suspect also from other considerations. Removing these elements from the data set, performing clus- tering on the reduced data set, and adding the outliers back one-by-one yielded a clustering that has a higher likelihood than the previous accepted classifications. Investigation of this new clustering lead to suggested changes in the classification of 69 strains in the material.
Global partial orders from sequential data

Sequences of events arise in many applications, such as web browsing, e-commerce, and monitoring of processes. An important problem in mining sets of sequences of events is to get an overview of the ordering relationships in the data. We present a method for nding partial orders that describe the ordering relationships between the events in a collection of sequences. The method is based on viewing a partial order as a generative model for a set of sequences, and applying mixture modeling techniques to obtain a descriptive set of partial orders. Runtimes for our algorithm scale linearly in the number of sequences and polynomially in the number of di erent event types. Thus, the methods scales to handle large data sets and can be used for reasonable numbers of di erent types of events. We illustrate our technique by applying it to student enrollment data and web browsing data.
Quantifier Elimination for Statistical Problems

Recent improvements on Tarski's procedure for quantifier elimination in the first order theory of real numbers makes it feasible to solve small instances of the following problems completely automatically: 1. listing all equality and inequality constraints implied by a graphical model with hidden variables. 2. Comparing graphical models with hidden variables (i.e., model equivalence, inclusion, and overlap). 3. Answering questions about the identification of a model or portion of a model, and about bounds on quantities derived from a model. 4. Determining whether an independence assertion is implied from a given set of independence assertions. We discuss the foundations of quantifier elimination and demonstrate its application to these problems.
Using Path Diagrams as a Structural Equation Modeling Tool

A linear structural equation model (SEM) without free parameters has two parts: a probability distribution and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. This article shows how path diagrams can be used to solve a number of important problems in structural equation modeling; for example, How much do sample data underdetermine the correct model specification? Given that there are equivalent models, is it possible to extract the features common to those models? When a modeler draws conclusions about coefficients in an unknown underlying SEM from a multivariate regression, precisely what assumptions are being made about the SEM? The authors explain how the path diagram provides much more than heuristics for special cases; the theory of path diagrams helps to clarify several of the issues just noted.
The dimensionality of mixed ancestral graphs

First we will introduce some graph terminology. The concepts defined here are illustrated in Figure 1. A graph consists of two parts, a set of vertices V and a set of edges E. Each edge in E is between two distinct vertices in V. There are two kinds of edges in E, directed edges A → B or A ← B, and double-headed edges A ↔ B; in either case A and B are endpoints of the edge; further, A and B are said to be adjacent. In Figure 1 the set of vertices is {A,B,C,D,E} and the set of edges is {A ↔ B, B → C, C → D, E → D}. For a directed edge A → B, A is the tail of the edge and B is the head of the edge, A is a parent of B, and B is a child of A.
Heuristic greedy search algorithms for latent variable models

A Bayesian network consists of two distinct parts: a directed acyclic graph (DAG or belief-network structure) and a set of parameters for the DAG. The DAG in a Bayesian network can be used to represent both causal hypotheses and sets of probability distributions. Under the causal interpretation, a DAG represents the causal relations in a given population with a set of vertices V when there is an edge from A to B if and only if A is a direct cause of B relative to V. (We adopt the convention that sets of variables are capitalized and boldfaced, and individual variables are capitalized and italicized.) Under the statistical interpretation a DAG G can be taken to represent a set of all distributions all of which share a set of conditional independence relations that are entailed by satisfying a local directed Markov property (defined below).
Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors

It has been shown in Spirtes(1995) that X and Y are d-separated given Z in a directed graph associated with a recursive or non-recursive linear model without correlated errors if and only if the model entails that ρ XY.Z = 0. This result cannot be directly applied to a linear model with correlated errors, however, because the standard graphical representation of a linear model with correlated errors is not a directed graph. The main result of this paper is to show how to associate a directed graph with a linear model L with correlated errors, and then use d-separation in the associated directed graph to determine whether L entails that a particular partial correlation is zero. In a linear structural equation model (SEM) some partial correlations may be equal to zero for all values of the model’s free parameters (for which the partial correlation is defined). (When we refer to “all values” of the free parameters, we assume that there are no constraints upon the models parameters except for the coefficients and the correlations among the error variables that are fixed at zero.) In this case we will say that the SEM linearly entails that the partial correlation is zero. It has been shown in Spirtes(1995) that X and Y are d-separated given Z in a directed graph associated with a recursive or nonrecursive linear model with uncorrelated errors if and only if the model linearly entails that ρ XY.Z = 0. This result cannot be directly applied to a linear model with correlated errors, however, because the standard graphical representation of a linear model with correlated errors is not a directed graph. The main result of this paper is to show how to associate a directed graph with a linear model L with correlated errors, and then use d-separation in the associated directed graph to determine whether L linearly entails that a particular partial
Causal Inference in the Presence of Latent Variables and Selection Bias

We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.
Relating graphical frameworks : undirected, directed acyclic and chain graph models

Researchers have systematically explored the Markov equivalence relationships among models from three classes of graphical models; the undirected, the directed acyclic, and the chain graph or block-recursive models. This paper considers the Markov equivalence relationships between models of different classes of graphical models and gives conditions for the existence of a model from a given class which is "almost Markov equivalent" to a given model from another class. An example of such a condition which is well known in the literature is the Wennuth condition. In addition, for each of the classes of models correct algorithms for learning models from conditional independence facts are given. While correct algorithms for learning undirected and directed acyclic graphs from only conditional independence facts exist in the literature, no such algorithms exists for chain graphs. This paper presents algorithms for learning undirected and directed acyclic graphs to highlight the relationship between the learning problems for these classes of models and the learning problem for chain graphs. The learning algorithms are proved correct and are shown to run in polynomial time in the number of vertices for fixed degree graphs except for one algorithm for learning undirected graphs which is polynomial in the number of vertices regardless of the degree of the graph. 1 Research for this paper was supported by the Office of Navel Research grant ONR
Bayesian integration of visual and auditory signals for spatial localization.

Human observers localize events in the world by using sensory signals from multiple modalities. We evaluated two theories of spatial localization that predict how visual and auditory information are weighted when these signals specify different locations in space. According to one theory (visual capture), the signal that is typically most reliable dominates in a winner-take-all competition, whereas the other theory (maximum-likelihood estimation) proposes that perceptual judgments are based on a weighted average of the sensory signals in proportion to each signal's relative reliability. Our results indicate that both theories are partially correct, in that relative signal reliability significantly altered judgments of spatial location, but these judgments were also characterized by an overall bias to rely on visual over auditory information. These results have important implications for the development of cue integration and for neural plasticity in the adult brain that enables humans to optimally integrate multimodal information.