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Title: Estimation of the autocovariance function with missing observations |
Abstract: We propose a novel estimator of the autocorrelation function in presence of missing observations. We establish the consistency, the asymptotic normality, and we derive deviation bounds for various classes of weakly dependent stationary time series, including causal or non causal models. In addition, we introd... |
Title: Using a priori knowledge to construct copulas |
Abstract: Our purpose is to model the dependence between two random variables, taking into account a priori knowledge on these variables. For example, in many applications (oceanography, finance...), there exists an order relation between the two variables; when one takes high values, the other cannot take low values, ... |
Title: On Practical Algorithms for Entropy Estimation and the Improved Sample Complexity of Compressed Counting |
Abstract: Estimating the p-th frequency moment of data stream is a very heavily studied problem. The problem is actually trivial when p = 1, assuming the strict Turnstile model. The sample complexity of our proposed algorithm is essentially O(1) near p=1. This is a very large improvement over the previously believed O(... |
Title: Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations |
Abstract: Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent bas... |
Title: Bregman Distance to L1 Regularized Logistic Regression |
Abstract: In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We present a detailed study of Bregman Distance minimization, a family of generalized entropy measures associated with convex functions. We convert the L1-regularized logistic regression into this... |
Title: Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model |
Abstract: This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropoli... |
Title: Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models |
Abstract: Stochastic differential equations (SDEs) are established tools to model physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dy... |
Title: Oil Price Trackers Inspired by Immune Memory |
Abstract: We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term m... |
Title: Motif Detection Inspired by Immune Memory |
Abstract: The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorit... |
Title: Optimally Robust Kalman Filtering at Work: AO-, IO-, and Simultaneously IO- and AO- Robust Filters |
Abstract: We take up optimality results for robust Kalman filtering from Ruckdeschel[2001,2010] where robustness is understood in a distributional sense, i.e.; we enlarge the distribution assumptions made in the ideal model by suitable neighborhoods, allowing for outliers which in our context may be system-endogenous/p... |
Title: Performance Evaluation of DCA and SRC on a Single Bot Detection |
Abstract: Malicious users try to compromise systems using new techniques. One of the recent techniques used by the attacker is to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These compromised machines are said to be infected with malicious sof... |
Title: Classification using distance nearest neighbours |
Abstract: This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, a class label should depend more on class labels which are closer in the feature space,... |
Title: Modelling Immunological Memory |
Abstract: Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the autho... |
Title: Price Trackers Inspired by Immune Memory |
Abstract: In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful tr... |
Title: Hashing Image Patches for Zooming |
Abstract: In this paper we present a Bayesian image zooming/super-resolution algorithm based on a patch based representation. We work on a patch based model with overlap and employ a Locally Linear Embedding (LLE) based approach as our data fidelity term in the Bayesian inference. The image prior imposes continuity con... |
Title: Optimization Under Unknown Constraints |
Abstract: Optimization of complex functions, such as the output of computer simulators, is a difficult task that has received much attention in the literature. A less studied problem is that of optimization under unknown constraints, i.e., when the simulator must be invoked both to determine the typical real-valued res... |
Title: Pooling Design and Bias Correction in DNA Library Screening |
Abstract: We study the group test for DNA library screening based on probabilistic approach. Group test is a method of detecting a few positive items from among a large number of items, and has wide range of applications. In DNA library screening, positive item corresponds to the clone having a specified DNA segment, a... |
Title: Real-Time Alert Correlation with Type Graphs |
Abstract: The premise of automated alert correlation is to accept that false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in an understandable way. Several algorithms exist for this purpose which use attack graphs to model the ways in which attacks can be... |
Title: STORM - A Novel Information Fusion and Cluster Interpretation Technique |
Abstract: Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many... |
Title: Assessing molecular variability in cancer genomes |
Abstract: The dynamics of tumour evolution are not well understood. In this paper we provide a statistical framework for evaluating the molecular variation observed in different parts of a colorectal tumour. A multi-sample version of the Ewens Sampling Formula forms the basis for our modelling of the data, and we provi... |
Title: Displacement Calculus |
Abstract: The Lambek calculus provides a foundation for categorial grammar in the form of a logic of concatenation. But natural language is characterized by dependencies which may also be discontinuous. In this paper we introduce the displacement calculus, a generalization of Lambek calculus, which preserves its good p... |
Title: Settling the Polynomial Learnability of Mixtures of Gaussians |
Abstract: Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumption... |
Title: Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version) |
Abstract: The need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantics for hybrid knowledge bases composed of both an ontology (expressed in a fragment of first-o... |
Title: Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems |
Abstract: In segmentation problems, inference on change-point position and model selection are two difficult issues due to the discrete nature of change-points. In a Bayesian context, we derive exact, non-asymptotic, explicit and tractable formulae for the posterior distribution of variables such as the number of chang... |
Title: Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning |
Abstract: We propose a direct reconstruction algorithm for Computed Tomography, based on a local fusion of a few preliminary image estimates by means of a non-linear fusion rule. One such rule is based on a signal denoising technique which is spatially adaptive to the unknown local smoothness. Another, more powerful fu... |
Title: Locally most powerful sequential tests of a simple hypothesis vs. One-sided alternatives for independent observations |
Abstract: Let $X_1,X_2,..., X_n,...$ be a stochastic process with independent values whose distribution $P_\theta$ depends on an unknown parameter $\theta$, $\theta\in\Theta$, where $\Theta$ is an open subset of the real line. The problem of testing $H_0:$ $\theta=\theta_0$ vs. a composite alternative $H_1:$ $\theta>\t... |
Title: Efficient Learning with Partially Observed Attributes |
Abstract: We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the case, for instance, in medical research, where each patient participating in the experiment is only willing to go through a small num... |
Title: Deblured Gaussian Blurred Images |
Abstract: This paper attempts to undertake the study of Restored Gaussian Blurred Images. by using four types of techniques of deblurring image as Wiener filter, Regularized filter, Lucy Richardson deconvlutin algorithm and Blind deconvlution algorithm with an information of the Point Spread Function (PSF) corrupted bl... |
Title: An Efficient Watermarking Algorithm to Improve Payload and Robustness without Affecting Image Perceptual Quality |
Abstract: Capacity, Robustness, & Perceptual quality of watermark data are very important issues to be considered. A lot of research is going on to increase these parameters for watermarking of the digital images, as there is always a tradeoff among them. . In this paper an efficient watermarking algorithm to improve p... |
Title: Toy Model for Large Non-Symmetric Random Matrices |
Abstract: Non-symmetric rectangular correlation matrices occur in many problems in economics. We test the method of extracting statistically meaningful correlations between input and output variables of large dimensionality and build a toy model for artificially included correlations in large random time series.The res... |
Title: Evolutionary Inference for Function-valued Traits: Gaussian Process Regression on Phylogenies |
Abstract: Biological data objects often have both of the following features: (i) they are functions rather than single numbers or vectors, and (ii) they are correlated due to phylogenetic relationships. In this paper we give a flexible statistical model for such data, by combining assumptions from phylogenetics with Ga... |
Title: On the comparison of plans: Proposition of an instability measure for dynamic machine scheduling |
Abstract: On the basis of an analysis of previous research, we present a generalized approach for measuring the difference of plans with an exemplary application to machine scheduling. Our work is motivated by the need for such measures, which are used in dynamic scheduling and planning situations. In this context, qua... |
Title: Logical methods of object recognition on satellite images using spatial constraints |
Abstract: A logical approach to object recognition on image is proposed. The main idea of the approach is to perform the object recognition as a logical inference on a set of rules describing an object shape. |
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