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Title: Embedding Data within Knowledge Spaces |
Abstract: The promise of e-Science will only be realized when data is discoverable, accessible, and comprehensible within distributed teams, across disciplines, and over the long-term--without reliance on out-of-band (non-digital) means. We have developed the open-source Tupelo semantic content management framework and... |
Title: Alleviating Media Bias Through Intelligent Agent Blogging |
Abstract: Consumers of mass media must have a comprehensive, balanced and plural selection of news to get an unbiased perspective; but achieving this goal can be very challenging, laborious and time consuming. News stories development over time, its (in)consistency, and different level of coverage across the media outl... |
Title: Comparative concept similarity over Minspaces: Axiomatisation and Tableaux Calculus |
Abstract: We study the logic of comparative concept similarity $\CSL$ introduced by Sheremet, Tishkovsky, Wolter and Zakharyaschev to capture a form of qualitative similarity comparison. In this logic we can formulate assertions of the form " objects A are more similar to B than to C". The semantics of this logic is de... |
Title: Directional Clustering Tests Based on Nearest Neighbor Contingency Tables |
Abstract: Spatial interaction between two or more classes or species has important implications in various fields and causes multivariate patterns such as segregation or association. Segregation occurs when members of a class or species are more likely to be found near members of the same class or conspecifics; while a... |
Title: New Confidence Measures for Statistical Machine Translation |
Abstract: A confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not... |
Title: A Model for Managing Collections of Patterns |
Abstract: Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns t... |
Title: Discovering general partial orders in event streams |
Abstract: Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Efficient (and separate) algorithms exist for episode discovery when the associated partial order is total (serial episode) and... |
Title: Extraction de concepts sous contraintes dans des donn\'ees d'expression de g\`enes |
Abstract: In this paper, we propose a technique to extract constrained formal concepts. |
Title: Database Transposition for Constrained (Closed) Pattern Mining |
Abstract: Recently, different works proposed a new way to mine patterns in databases with pathological size. For example, experiments in genome biology usually provide databases with thousands of attributes (genes) but only tens of objects (experiments). In this case, mining the "transposed" database runs through a sma... |
Title: Multi-Label Prediction via Compressed Sensing |
Abstract: We consider multi-label prediction problems with large output spaces under the assumption of output sparsity -- that the target (label) vectors have small support. We develop a general theory for a variant of the popular error correcting output code scheme, using ideas from compressed sensing for exploiting t... |
Title: Sparse partial least squares for on-line variable selection in multivariate data streams |
Abstract: In this paper we propose a computationally efficient algorithm for on-line variable selection in multivariate regression problems involving high dimensional data streams. The algorithm recursively extracts all the latent factors of a partial least squares solution and selects the most important variables for ... |
Title: Using the Eigenvalue Relaxation for Binary Least-Squares Estimation Problems |
Abstract: The goal of this paper is to survey the properties of the eigenvalue relaxation for least squares binary problems. This relaxation is a convex program which is obtained as the Lagrangian dual of the original problem with an implicit compact constraint and as such, is a convex problem with polynomial time comp... |
Title: Hierarchical Bayesian Modeling of Hitting Performance in Baseball |
Abstract: We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm provides a principled method for balancing past performance with crucial covariates, such as player age and position. We share information across time and across p... |
Title: Optimal designs for dose-finding experiments in toxicity studies |
Abstract: We construct optimal designs for estimating fetal malformation rate, prenatal death rate and an overall toxicity index in a toxicology study under a broad range of model assumptions. We use Weibull distributions to model these rates and assume that the number of implants depend on the dose level. We study pro... |
Title: Needles in the Haystack: Identifying Individuals Present in Pooled Genomic Data |
Abstract: Recent publications have described and applied a novel metric that quantifies the genetic distance of an individual with respect to two population samples, and have suggested that the metric makes it possible to infer the presence of an individual of known genotype in a sample for which only the marginal alle... |
Title: p-order rounded integer-valued autoregressive (RINAR(p)) process |
Abstract: An extension of the RINAR(1) process for modelling discrete-time dependent counting processes is considered. The model RINAR(p) investigated here is a direct and natural extension of the real AR(p) model. Compared to classical INAR(p) models based on the thinning operator, the new models have several advantag... |
Title: Improvements of real coded genetic algorithms based on differential operators preventing premature convergence |
Abstract: This paper presents several types of evolutionary algorithms (EAs) used for global optimization on real domains. The interest has been focused on multimodal problems, where the difficulties of a premature convergence usually occurs. First the standard genetic algorithm (SGA) using binary encoding of real valu... |
Title: A competitive comparison of different types of evolutionary algorithms |
Abstract: This paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in Civil Engineering. The introduced optimization methods are: the integer augmented simulated annealing (IASA), the real-coded augmented simulate... |
Title: Back analysis of microplane model parameters using soft computing methods |
Abstract: A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis... |
Title: Risk bounds in linear regression through PAC-Bayesian truncation |
Abstract: We consider the problem of predicting as well as the best linear combination of d given functions in least squares regression, and variants of this problem including constraints on the parameters of the linear combination. When the input distribution is known, there already exists an algorithm having an expec... |
Title: Optimal Probabilistic Ring Exploration by Asynchronous Oblivious Robots |
Abstract: We consider a team of $k$ identical, oblivious, asynchronous mobile robots that are able to sense (, view) their environment, yet are unable to communicate, and evolve on a constrained path. Previous results in this weak scenario show that initial symmetry yields high lower bounds when problems are to be solv... |
Title: Topological Centrality and Its Applications |
Abstract: Recent development of network structure analysis shows that it plays an important role in characterizing complex system of many branches of sciences. Different from previous network centrality measures, this paper proposes the notion of topological centrality (TC) reflecting the topological positions of nodes... |
Title: Sparse Conformal Predictors |
Abstract: Conformal predictors, introduced by Vovk et al. (2005), serve to build prediction intervals by exploiting a notion of conformity of the new data point with previously observed data. In the present paper, we propose a novel method for constructing prediction intervals for the response variable in multivariate ... |
Title: On the usefulness of Meyer wavelets for deconvolution and density estimation |
Abstract: The aim of this paper is to show the usefulness of Meyer wavelets for the classical problem of density estimation and for density deconvolution from noisy observations. By using such wavelets, the computation of the empirical wavelet coefficients relies on the fast Fourier transform of the data and on the fac... |
Title: A List of Household Objects for Robotic Retrieval Prioritized by People with ALS (Version 092008) |
Abstract: This technical report is designed to serve as a citable reference for the original prioritized object list that the Healthcare Robotics Lab at Georgia Tech released on its website in September of 2008. It is also expected to serve as the primary citable reference for the research associated with this list unt... |
Title: A Standalone Markerless 3D Tracker for Handheld Augmented Reality |
Abstract: This paper presents an implementation of a markerless tracking technique targeted to the Windows Mobile Pocket PC platform. The primary aim of this work is to allow the development of standalone augmented reality applications for handheld devices based on natural feature tracking. In order to achieve this goa... |
Title: Feature Hashing for Large Scale Multitask Learning |
Abstract: Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate... |
Title: BagPack: A general framework to represent semantic relations |
Abstract: We introduce a way to represent word pairs instantiating arbitrary semantic relations that keeps track of the contexts in which the words in the pair occur both together and independently. The resulting features are of sufficient generality to allow us, with the help of a standard supervised machine learning ... |
Title: Multiple testing via successive subdivision |
Abstract: A sequential multiple testing procedure recently introduced by Heinrich, Bach and Kornmeier allows to "zoom in" on, and thus identify regions with highly significant departures from null-hypotheses. The purpose of this note is to state a cognate of this procedure in general form and to prove that it controls ... |
Title: What's in a Message? |
Abstract: In this paper we present the first step in a larger series of experiments for the induction of predicate/argument structures. The structures that we are inducing are very similar to the conceptual structures that are used in Frame Semantics (such as FrameNet). Those structures are called messages and they wer... |
Title: XML Representation of Constraint Networks: Format XCSP 2.1 |
Abstract: We propose a new extended format to represent constraint networks using XML. This format allows us to represent constraints defined either in extension or in intension. It also allows us to reference global constraints. Any instance of the problems CSP (Constraint Satisfaction Problem), QCSP (Quantified CSP) ... |
Title: Object Classification by means of Multi-Feature Concept Learning in a Multi Expert-Agent System |
Abstract: Classification of some objects in classes of concepts is an essential and even breathtaking task in many applications. A solution is discussed here based on Multi-Agent systems. A kernel of some expert agents in several classes is to consult a central agent decide among the classification problem of a certain... |
Title: Using SLP Neural Network to Persian Handwritten Digits Recognition |
Abstract: This paper has been withdrawn by the author ali pourmohammad. |
Title: Ultrametric Wavelet Regression of Multivariate Time Series: Application to Colombian Conflict Analysis |
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