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Title: SparseCodePicking: feature extraction in mass spectrometry using sparse coding algorithms |
Abstract: Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest i... |
Title: A Numerical Approach to Performance Analysis of Quickest Change-Point Detection Procedures |
Abstract: For the most popular sequential change detection rules such as CUSUM, EWMA, and the Shiryaev-Roberts test, we develop integral equations and a concise numerical method to compute a number of performance metrics, including average detection delay and average time to false alarm. We pay special attention to the... |
Title: Message Passing Algorithms for Compressed Sensing |
Abstract: Compressed sensing aims to undersample certain high-dimensional signals, yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis. Currently, the best known sparsity-undersampling tradeoff ... |
Title: Image Sampling with Quasicrystals |
Abstract: We investigate the use of quasicrystals in image sampling. Quasicrystals produce space-filling, non-periodic point sets that are uniformly discrete and relatively dense, thereby ensuring the sample sites are evenly spread out throughout the sampled image. Their self-similar structure can be attractive for cre... |
Title: Empirical Bernstein Bounds and Sample Variance Penalization |
Abstract: We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functionswhose growth function is polynomial in the sample size n. The bounds lead us to consider sample variance penalizat... |
Title: Un syst\`eme modulaire d'acquisition automatique de traductions \`a partir du Web |
Abstract: We present a method of automatic translation (French/English) of Complex Lexical Units (CLU) for aiming at extracting a bilingual lexicon. Our modular system is based on linguistic properties (compositionality, polysemy, etc.). Different aspects of the multilingual Web are used to validate candidate translati... |
Title: Self-adaptive web intrusion detection system |
Abstract: The evolution of the web server contents and the emergence of new kinds of intrusions make necessary the adaptation of the intrusion detection systems (IDS). Nowadays, the adaptation of the IDS requires manual -- tedious and unreactive -- actions from system administrators. In this paper, we present a self-ad... |
Title: Gamma-based clustering via ordered means with application to gene-expression analysis |
Abstract: Discrete mixture models provide a well-known basis for effective clustering algorithms, although technical challenges have limited their scope. In the context of gene-expression data analysis, a model is presented that mixes over a finite catalog of structures, each one representing equality and inequality co... |
Title: Artificial Dendritic Cells: Multi-faceted Perspectives |
Abstract: Dendritic cells are the crime scene investigators of the human immune system. Their function is to correlate potentially anomalous invading entities with observed damage to the body. The detection of such invaders by dendritic cells results in the activation of the adaptive immune system, eventually leading t... |
Title: The Utility of Reliability and Survival |
Abstract: Reliability (survival analysis, to biostatisticians) is a key ingredient for mak- ing decisions that mitigate the risk of failure. The other key ingredient is utility. A decision theoretic framework harnesses the two, but to invoke this framework we must distinguish between chance and probability. We describe... |
Title: Contextual Bandits with Similarity Information |
Abstract: In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a time-invariant set of alternatives and receives the payoff associated with this alternative. While the case of small strategy sets is by now well-understood, a lot of recent work has focused... |
Title: Simulation of truncated normal variables |
Abstract: We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables with restricted parameter space for any covariance structure. |
Title: Simulating Events of Unknown Probabilities via Reverse Time Martingales |
Abstract: Assume that one aims to simulate an event of unknown probability $s\in (0,1)$ which is uniquely determined, however only its approximations can be obtained using a finite computational effort. Such settings are often encountered in statistical simulations. We consider two specific examples. First, the exact s... |
Title: A fifth order expansion for the distribution function of the maximum likelihood estimator |
Abstract: In this paper, expansions for the maximum likelihood estimator of location and its distribution funtion are extended to fifth order. Since the proofs are straightforward extentions of proofs given in earlier papers for orders less than the fifth, they are not given here. The purpose of the paper is mainly to ... |
Title: Beyond Turing Machines |
Abstract: This paper discusses "computational" systems capable of "computing" functions not computable by predefined Turing machines if the systems are not isolated from their environment. Roughly speaking, these systems can change their finite descriptions by interacting with their environment. |
Title: Relativized hyperequivalence of logic programs for modular programming |
Abstract: A recent framework of relativized hyperequivalence of programs offers a unifying generalization of strong and uniform equivalence. It seems to be especially well suited for applications in program optimization and modular programming due to its flexibility that allows us to restrict, independently of each oth... |
Title: Notes on Using Control Variates for Estimation with Reversible MCMC Samplers |
Abstract: A general methodology is presented for the construction and effective use of control variates for reversible MCMC samplers. The values of the coefficients of the optimal linear combination of the control variates are computed, and adaptive, consistent MCMC estimators are derived for these optimal coefficients... |
Title: Learning Object Location Predictors with Boosting and Grammar-Guided Feature Extraction |
Abstract: We present BEAMER: a new spatially exploitative approach to learning object detectors which shows excellent results when applied to the task of detecting objects in greyscale aerial imagery in the presence of ambiguous and noisy data. There are four main contributions used to produce these results. First, we ... |
Title: Precision Measurements of the Cluster Red Sequence using an Error Corrected Gaussian Mixture Model |
Abstract: The red sequence is an important feature of galaxy clusters and plays a crucial role in optical cluster detection. Measurement of the slope and scatter of the red sequence are affected both by selection of red sequence galaxies and measurement errors. In this paper, we describe a new error corrected Gaussian ... |
Title: The Cost of Stability in Coalitional Games |
Abstract: A key question in cooperative game theory is that of coalitional stability, usually captured by the notion of the --the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we ... |
Title: Graphical Probabilistic Routing Model for OBS Networks with Realistic Traffic Scenario |
Abstract: Burst contention is a well-known challenging problem in Optical Burst Switching (OBS) networks. Contention resolution approaches are always reactive and attempt to minimize the BLR based on local information available at the core node. On the other hand, a proactive approach that avoids burst losses before th... |
Title: Pattern Recognition Theory of Mind |
Abstract: I propose that pattern recognition, memorization and processing are key concepts that can be a principle set for the theoretical modeling of the mind function. Most of the questions about the mind functioning can be answered by a descriptive modeling and definitions from these principles. An understandable co... |
Title: Fact Sheet on Semantic Web |
Abstract: The report gives an overview about activities on the topic Semantic Web. It has been released as technical report for the project "KTweb -- Connecting Knowledge Technologies Communities" in 2003. |
Title: Mean-Field Theory of Meta-Learning |
Abstract: We discuss here the mean-field theory for a cellular automata model of meta-learning. The meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemb... |
Title: Shrinkage Algorithms for MMSE Covariance Estimation |
Abstract: We address covariance estimation in the sense of minimum mean-squared error (MMSE) for Gaussian samples. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with a small number of samples (large p small n). First, we improve on the Ledoit-Wolf (LW) method by conditioni... |
Title: Regeneration and Fixed-Width Analysis of Markov Chain Monte Carlo Algorithms |
Abstract: In the thesis we take the split chain approach to analyzing Markov chains and use it to establish fixed-width results for estimators obtained via Markov chain Monte Carlo procedures (MCMC). Theoretical results include necessary and sufficient conditions in terms of regeneration for central limit theorems for ... |
Title: A survey of cross-validation procedures for model selection |
Abstract: Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the ... |
Title: Spectral estimation of the L\'evy density in partially observed affine models |
Abstract: The problem of estimating the L\'evy density of a partially observed multidimensional affine process from low-frequency and mixed-frequency data is considered. The estimation methodology is based on the log-affine representation of the conditional characteristic function of an affine process and local linear ... |
Title: Nonasymptotic bounds on the estimation error for regenerative MCMC algorithms |
Abstract: MCMC methods are used in Bayesian statistics not only to sample from posterior distributions but also to estimate expectations. Underlying functions are most often defined on a continuous state space and can be unbounded. We consider a regenerative setting and Monte Carlo estimators based on i.i.d. blocks of ... |
Title: Ezhil: A Tamil Programming Language |
Abstract: Ezhil is a Tamil language based interpreted procedural programming language. Tamil keywords and grammar are chosen to make the native Tamil speaker write programs in the Ezhil system. Ezhil allows easy representation of computer program closer to the Tamil language logical constructs equivalent to the conditi... |
Title: Automatic local Gabor Features extraction for face recognition |
Abstract: We present in this paper a biometric system of face detection and recognition in color images. The face detection technique is based on skin color information and fuzzy classification. A new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their correspon... |
Title: Restart Strategy Selection using Machine Learning Techniques |
Abstract: Restart strategies are an important factor in the performance of conflict-driven Davis Putnam style SAT solvers. Selecting a good restart strategy for a problem instance can enhance the performance of a solver. Inspired by recent success applying machine learning techniques to predict the runtime of SAT solve... |
Title: Online Search Cost Estimation for SAT Solvers |
Abstract: We present two different methods for estimating the cost of solving SAT problems. The methods focus on the online behaviour of the backtracking solver, as well as the structure of the problem. Modern SAT solvers present several challenges to estimate search cost including coping with nonchronological backtrac... |
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