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Abstract: Preoperative templating in Total Hip Replacement (THR) is a method to estimate the optimal size and position of the implant. Today, observational (manual) size recognition techniques are still used to find a suitable implant for the patient. Therefore, a digital and automated technique should be developed so ... |
Title: Methods of Hierarchical Clustering |
Abstract: We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finall... |
Title: SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity |
Abstract: We propose a general information-theoretic approach called Seraph (SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize the entropy of that probability on la... |
Title: Deviance Information Criteria for Model Selection in Approximate Bayesian Computation |
Abstract: Approximate Bayesian computation (ABC) is a class of algorithmic methods in Bayesian inference using statistical summaries and computer simulations. ABC has become popular in evolutionary genetics and in other branches of biology. However model selection under ABC algorithms has been a subject of intense deba... |
Title: Splitting and Updating Hybrid Knowledge Bases (Extended Version) |
Abstract: Over the years, nonmonotonic rules have proven to be a very expressive and useful knowledge representation paradigm. They have recently been used to complement the expressive power of Description Logics (DLs), leading to the study of integrative formal frameworks, generally referred to as hybrid knowledge bas... |
Title: Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity |
Abstract: Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse in... |
Title: Rapid Learning with Stochastic Focus of Attention |
Abstract: We present a method to stop the evaluation of a decision making process when the result of the full evaluation is obvious. This trait is highly desirable for online margin-based machine learning algorithms where a classifier traditionally evaluates all the features for every example. We observe that some exam... |
Title: Suboptimal Solution Path Algorithm for Support Vector Machine |
Abstract: We consider a suboptimal solution path algorithm for the Support Vector Machine. The solution path algorithm is an effective tool for solving a sequence of a parametrized optimization problems in machine learning. The path of the solutions provided by this algorithm are very accurate and they satisfy the opti... |
Title: Pruning nearest neighbor cluster trees |
Abstract: Nearest neighbor (k-NN) graphs are widely used in machine learning and data mining applications, and our aim is to better understand what they reveal about the cluster structure of the unknown underlying distribution of points. Moreover, is it possible to identify spurious structures that might arise due to s... |
Title: Metamodel-based importance sampling for structural reliability analysis |
Abstract: Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation met... |
Title: Complexity of Unconstrained L_2-L_p Minimization |
Abstract: We consider the unconstrained $L_2$-$L_p$ minimization: find a minimizer of $\|Ax-b\|^2_2+\lambda \|x\|^p_p$ for given $A \in R^m\times n$, $b\in R^m$ and parameters $\lambda>0$, $p\in [0,1)$. This problem has been studied extensively in variable selection and sparse least squares fitting for high dimensional... |
Title: Transition Systems for Model Generators - A Unifying Approach |
Abstract: A fundamental task for propositional logic is to compute models of propositional formulas. Programs developed for this task are called satisfiability solvers. We show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for solvers... |
Title: Mark My Words! Linguistic Style Accommodation in Social Media |
Abstract: The psycholinguistic theory of communication accommodation accounts for the general observation that participants in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In it... |
Title: Parameterized Complexity of Problems in Coalitional Resource Games |
Abstract: Coalition formation is a key topic in multi-agent systems. Coalitions enable agents to achieve goals that they may not have been able to achieve on their own. Previous work has shown problems in coalitional games to be computationally hard. Wooldridge and Dunne (Artificial Intelligence 2006) studied the class... |
Title: Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity |
Abstract: A trend in compressed sensing (CS) is to exploit structure for improved reconstruction performance. In the basic CS model, exploiting the clustering structure among nonzero elements in the solution vector has drawn much attention, and many algorithms have been proposed. However, few algorithms explicitly cons... |
Title: Structured Sparsity via Alternating Direction Methods |
Abstract: We consider a class of sparse learning problems in high dimensional feature space regularized by a structured sparsity-inducing norm which incorporates prior knowledge of the group structure of the features. Such problems often pose a considerable challenge to optimization algorithms due to the non-smoothness... |
Title: Variational Bayes approach for model aggregation in unsupervised classification with Markovian dependency |
Abstract: We consider a binary unsupervised classification problem where each observation is associated with an unobserved label that we want to retrieve. More precisely, we assume that there are two groups of observation: normal and abnormal. The `normal' observations are coming from a known distribution whereas the d... |
Title: Considerations and Results in Multimedia and DVB Application Development on Philips Nexperia Platform |
Abstract: This paper presents some experiments regarding applications development on high performance media processors included in Philips Nexperia Family. The PNX1302 dedicated DVB-T kit used has some limitations. Our work has succeeded to overcome these limitations and to make possible a general-purpose use of this k... |
Title: Streaming Multimedia Information Using the Features of the DVB-S Card |
Abstract: This paper presents a study of audio-video streaming using the additional possibilities of a DVB-S card. The board used for experiments (Technisat SkyStar 2) is one of the most frequently used cards for this purpose. Using the main blocks of the board's software support it is possible the implement a really u... |
Title: MissForest - nonparametric missing value imputation for mixed-type data |
Abstract: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation m... |
Title: Domain Adaptation: Overfitting and Small Sample Statistics |
Abstract: We study the prevalent problem when a test distribution differs from the training distribution. We consider a setting where our training set consists of a small number of sample domains, but where we have many samples in each domain. Our goal is to generalize to a new domain. For example, we may want to learn... |
Title: Bounding rare event probabilities in computer experiments |
Abstract: We are interested in bounding probabilities of rare events in the context of computer experiments. These rare events depend on the output of a physical model with random input variables. Since the model is only known through an expensive black box function, standard efficient Monte Carlo methods designed for ... |
Title: A Risk Comparison of Ordinary Least Squares vs Ridge Regression |
Abstract: We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note s... |
Title: Modeling Network Evolution Using Graph Motifs |
Abstract: Network structures are extremely important to the study of political science. Much of the data in its subfields are naturally represented as networks. This includes trade, diplomatic and conflict relationships. The social structure of several organization is also of interest to many researchers, such as the a... |
Title: Rapid Feature Learning with Stacked Linear Denoisers |
Abstract: We investigate unsupervised pre-training of deep architectures as feature generators for "shallow" classifiers. Stacked Denoising Autoencoders (SdA), when used as feature pre-processing tools for SVM classification, can lead to significant improvements in accuracy - however, at the price of a substantial incr... |
Title: GANC: Greedy Agglomerative Normalized Cut |
Abstract: This paper describes a graph clustering algorithm that aims to minimize the normalized cut criterion and has a model order selection procedure. The performance of the proposed algorithm is comparable to spectral approaches in terms of minimizing normalized cut. However, unlike spectral approaches, the propose... |
Title: Patient-Specific Prosthetic Fingers by Remote Collaboration - A Case Study |
Abstract: The concealment of amputation through prosthesis usage can shield an amputee from social stigma and help improve the emotional healing process especially at the early stages of hand or finger loss. However, the traditional techniques in prosthesis fabrication defy this as the patients need numerous visits to ... |
Title: Adaptively Learning the Crowd Kernel |
Abstract: We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is object 'a' more similar to 'b' or to 'c'?" and is chosen to ... |
Title: English-Lithuanian-English Machine Translation lexicon and engine: current state and future work |
Abstract: This article overviews the current state of the English-Lithuanian-English machine translation system. The first part of the article describes the problems that system poses today and what actions will be taken to solve them in the future. The second part of the article tackles the main issue of the translati... |
Title: Interpreting Graph Cuts as a Max-Product Algorithm |
Abstract: The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexample where... |
Title: Sampling-based Algorithms for Optimal Motion Planning |
Abstract: During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal ... |
Title: Multilingual lexicon design tool and database management system for MT |
Abstract: The paper presents the design and development of English-Lithuanian-English dictionarylexicon tool and lexicon database management system for MT. The system is oriented to support two main requirements: to be open to the user and to describe much more attributes of speech parts as a regular dictionary that ar... |
Title: Machine-Part cell formation through visual decipherable clustering of Self Organizing Map |
Abstract: Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine... |
Title: CHICOM: A code of tests for comparing unweighted and weighted histograms and two weighted histograms |
Abstract: A self-contained Fortran-77 program for calculating test statistics to compare weighted histogram with unweighted histogram and two histograms with weighted entries is presented. The code calculates test statistics for cases of histograms with normalized weights of events and unnormalized weights of events. |
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