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Title: Combining Symmetry Breaking and Global Constraints |
Abstract: We propose a new family of constraints which combine together lexicographical ordering constraints for symmetry breaking with other common global constraints. We give a general purpose propagator for this family of constraints, and show how to improve its complexity by exploiting properties of the included gl... |
Title: A pseudo empirical likelihood approach for stratified samples with nonresponse |
Abstract: Nonresponse is common in surveys. When the response probability of a survey variable $Y$ depends on $Y$ through an observed auxiliary categorical variable $Z$ (i.e., the response probability of $Y$ is conditionally independent of $Y$ given $Z$), a simple method often used in practice is to use $Z$ categories ... |
Title: Real-time Texture Error Detection |
Abstract: This paper advocates an improved solution for real-time error detection of texture errors that occurs in the production process in textile industry. The research is focused on the mono-color products with 3D texture model (Jaquard fabrics). This is a more difficult task than, for example, 2D multicolor textur... |
Title: A new approach to Cholesky-based covariance regularization in high dimensions |
Abstract: In this paper we propose a new regression interpretation of the Cholesky factor of the covariance matrix, as opposed to the well known regression interpretation of the Cholesky factor of the inverse covariance, which leads to a new class of regularized covariance estimators suitable for high-dimensional probl... |
Title: The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs |
Abstract: Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula--or "nonparanormal"--for high dimensional inference. Just as additive models extend linear models by replaci... |
Title: Compressive Sensing Using Low Density Frames |
Abstract: We consider the compressive sensing of a sparse or compressible signal $\bf x \in \mathbb R^M$. We explicitly construct a class of measurement matrices, referred to as the low density frames, and develop decoding algorithms that produce an accurate estimate $$ even in the presence of additive noise. Low densi... |
Title: Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition |
Abstract: It is common practice in Markov chain Monte Carlo to update the simulation one variable (or sub-block of variables) at a time, rather than conduct a single full-dimensional update. When it is possible to draw from each full-conditional distribution associated with the target this is just a Gibbs sampler. Ofte... |
Title: Online Estimation of SAT Solving Runtime |
Abstract: We present an online method for estimating the cost of solving SAT problems. Modern SAT solvers present several challenges to estimate search cost including non-chronological backtracking, learning and restarts. Our method uses a linear model trained on data gathered at the start of search. We show the effect... |
Title: Support points of locally optimal designs for nonlinear models with two parameters |
Abstract: We propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, we use an approach based on algebraic tools. Considerations are restricted to models with two parameters, and the general results a... |
Title: Modeling the Experience of Emotion |
Abstract: Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers resulting in work that is widely published. The majority of this work consists of computational models of emotion recognition, computational modeling of causal factors of emotion and em... |
Title: On Requirements for Programming Exercises from an E-learning Perspective |
Abstract: In this work, we deal with the question of modeling programming exercises for novices pointing to an e-learning scenario. Our purpose is to identify basic requirements, raise some key questions and propose potential answers from a conceptual perspective. Presented as a general picture, we hypothetically situa... |
Title: Tagging multimedia stimuli with ontologies |
Abstract: Successful management of emotional stimuli is a pivotal issue concerning Affective Computing (AC) and the related research. As a subfield of Artificial Intelligence, AC is concerned not only with the design of computer systems and the accompanying hardware that can recognize, interpret, and process human emot... |
Title: Estimation of cosmological parameters using adaptive importance sampling |
Abstract: We present a Bayesian sampling algorithm called adaptive importance sampling or Population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the pe... |
Title: Algorithms for Weighted Boolean Optimization |
Abstract: The Pseudo-Boolean Optimization (PBO) and Maximum Satisfiability (MaxSAT) problems are natural optimization extensions of Boolean Satisfiability (SAT). In the recent past, different algorithms have been proposed for PBO and for MaxSAT, despite the existence of straightforward mappings from PBO to MaxSAT and v... |
Title: Decomposition, Reformulation, and Diving in University Course Timetabling |
Abstract: In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of resource. Often, each component is associated with different sets of soft constraints, and so with different measures of soft cons... |
Title: Efficient Human Computation |
Abstract: Collecting large labeled data sets is a laborious and expensive task, whose scaling up requires division of the labeling workload between many teachers. When the number of classes is large, miscorrespondences between the labels given by the different teachers are likely to occur, which, in the extreme case, m... |
Title: Symmetry Breaking Using Value Precedence |
Abstract: We present a comprehensive study of the use of value precedence constraints to break value symmetry. We first give a simple encoding of value precedence into ternary constraints that is both efficient and effective at breaking symmetry. We then extend value precedence to deal with a number of generalizations ... |
Title: Complexity of Terminating Preference Elicitation |
Abstract: Complexity theory is a useful tool to study computational issues surrounding the elicitation of preferences, as well as the strategic manipulation of elections aggregating together preferences of multiple agents. We study here the complexity of determining when we can terminate eliciting preferences, and prov... |
Title: The Complexity of Reasoning with Global Constraints |
Abstract: Constraint propagation is one of the techniques central to the success of constraint programming. To reduce search, fast algorithms associated with each constraint prune the domains of variables. With global (or non-binary) constraints, the cost of such propagation may be much greater than the quadratic cost ... |
Title: Breaking Value Symmetry |
Abstract: One common type of symmetry is when values are symmetric. For example, if we are assigning colours (values) to nodes (variables) in a graph colouring problem then we can uniformly interchange the colours throughout a colouring. For a problem with value symmetries, all symmetric solutions can be eliminated in ... |
Title: Tetravex is NP-complete |
Abstract: Tetravex is a widely played one person computer game in which you are given $n^2$ unit tiles, each edge of which is labelled with a number. The objective is to place each tile within a $n$ by $n$ square such that all neighbouring edges are labelled with an identical number. Unfortunately, playing Tetravex is ... |
Title: Stochastic Constraint Programming: A Scenario-Based Approach |
Abstract: To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochastic constraint programming. Stochastic constraint programs contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution. We provid... |
Title: Stochastic Constraint Programming |
Abstract: To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best f... |
Title: The Digital Restoration of Da Vinci's Sketches |
Abstract: A sketch, found in one of Leonardo da Vinci's notebooks and covered by the written notes of this genius, has been recently restored. The restoration reveals a possible self-portrait of the artist, drawn when he was young. Here, we discuss the discovery of this self-portrait and the procedure used for restorat... |
Title: Definition of evidence fusion rules on the basis of Referee Functions |
Abstract: This chapter defines a new concept and framework for constructing fusion rules for evidences. This framework is based on a referee function, which does a decisional arbitrament conditionally to basic decisions provided by the several sources of information. A simple sampling method is derived from this framew... |
Title: Taking Advantage of Sparsity in Multi-Task Learning |
Abstract: We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning Argyriou et al. [2008], we assume that the regression vectors share the same sparsity pattern. This means that the set of relevant pre... |
Title: Heuristic Reasoning on Graph and Game Complexity of Sudoku |
Abstract: The Sudoku puzzle has achieved worldwide popularity recently, and attracted great attention of the computational intelligence community. Sudoku is always considered as Satisfiability Problem or Constraint Satisfaction Problem. In this paper, we propose to focus on the essential graph structure underlying the ... |
Title: Expectations of Random Sets and Their Boundaries Using Oriented Distance Functions |
Abstract: Shape estimation and object reconstruction are common problems in image analysis. Mathematically, viewing objects in the image plane as random sets reduces the problem of shape estimation to inference about sets. Currently existing definitions of the expected set rely on different criteria to construct the ex... |
Title: Free actions and Grassmanian variety |
Abstract: An algebraic notion of representational consistency is defined. A theorem relating it to free actions is proved. A metrizability problem of the quotient (a shape space) is discussed. This leads to a new algebraic variety with a metrizability result. A concrete example is given from stereo vision. |
Title: Confidence Regions for Means of Random Sets using Oriented Distance Functions |
Abstract: Image analysis frequently deals with shape estimation and image reconstruction. The ob jects of interest in these problems may be thought of as random sets, and one is interested in finding a representative, or expected, set. We consider a definition of set expectation using oriented distance functions and st... |
Title: Contracting preference relations for database applications |
Abstract: The binary relation framework has been shown to be applicable to many real-life preference handling scenarios. Here we study preference contraction: the problem of discarding selected preferences. We argue that the property of minimality and the preservation of strict partial orders are crucial for contractio... |
Title: SMART: A statistical framework for optimal design matrix generation with application to fMRI |
Abstract: The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate fashion where the same design matrix is used for analyzing data from each voxel. A major limitation of this approach is the lo... |
Title: Feature selection in omics prediction problems using cat scores and false nondiscovery rate control |
Abstract: We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-ad... |
Title: Multiagent Learning in Large Anonymous Games |
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