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Jun 30, 2018
06/18

by
Lorenzo Livi; Alireza Sadeghian

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Researches in granular modeling produced a variety of mathematical models, such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets, which are all suitable to characterize the so-called information granules. Modeling of the input data uncertainty is recognized as a crucial aspect in information granulation. Moreover, the uncertainty is a well-studied concept in many mathematical settings, such as those of probability theory, fuzzy set theory, and possibility theory. This fact...

Topics: Computing Research Repository, Artificial Intelligence

Source: http://arxiv.org/abs/1407.7138

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3.0

Jun 30, 2018
06/18

by
Lorenzo Livi; Antonello Rizzi; Alireza Sadeghian

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We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful...

Topics: Physics, Quantitative Biology, Computing Research Repository, Computer Vision and Pattern...

Source: http://arxiv.org/abs/1408.3873

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9.0

Jun 27, 2018
06/18

by
Lorenzo Livi; Alireza Sadeghian; Hamid Sadeghian

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In this paper, we analyze 48 signals of rest tremor velocity related to 12 distinct subjects affected by Parkinson's disease. The subjects belong to two different groups, formed by four and eight subjects with, respectively, high- and low-amplitude rest tremors. Each subject is tested in four settings, given by combining the use of deep brain stimulation and L-DOPA medication. We develop two main feature-based representations of such signals, which are obtained by considering (i) the long-term...

Topics: Computer Vision and Pattern Recognition, Computing Research Repository, Data Analysis, Statistics...

Source: http://arxiv.org/abs/1504.02756

2
2.0

Jun 30, 2018
06/18

by
Lorenzo Livi; Alireza Sadeghian; Witold Pedrycz

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The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed non-target, and therefore they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of...

Topics: Machine Learning, Computing Research Repository, Computer Vision and Pattern Recognition, Learning,...

Source: http://arxiv.org/abs/1407.7556

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4.0

Jun 30, 2018
06/18

by
Lorenzo Livi; Alessandro Giuliani; Alireza Sadeghian

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This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists in representing the available E.Coli proteins in terms of graphs. We first analyze those graphs by considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the...

Topics: Physics, Data Analysis, Statistics and Probability, Quantitative Biology, Molecular Networks,...

Source: http://arxiv.org/abs/1407.8033

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7.0

Jun 27, 2018
06/18

by
Lorenzo Livi; Enrico Maiorino; Antonello Rizzi; Alireza Sadeghian

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In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals coming from an industrial electric arc furnace plant. Implicit sinusoidal trends are suitably detected by considering the scaling of the fluctuation functions. Time series are then filtered via a Fourier-based analysis, removing hence such strong periodicities. In the filtered time series we detected long-term, positive correlations. The presence of positive...

Topics: Data Analysis, Statistics and Probability, Physics

Source: http://arxiv.org/abs/1503.03332

4
4.0

Jun 30, 2018
06/18

by
Enrico De Santis; Lorenzo Livi; Alireza Sadeghian; Antonello Rizzi

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Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are...

Topics: Computing Research Repository, Artificial Intelligence

Source: http://arxiv.org/abs/1407.7008

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10.0

Jun 26, 2018
06/18

by
Francesca Possemato; Maurizio Paschero; Lorenzo Livi; Antonello Rizzi; Alireza Sadeghian

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Power losses reduction is one of the main targets for any electrical energy distribution company. In this paper, we face the problem of joint optimization of both topology and network parameters in a real smart grid. We consider a portion of the Italian electric distribution network managed by the ACEA Distribuzione S.p.A. located in Rome. We perform both the power factor correction (PFC) for tuning the generators and the distributed feeder reconfiguration (DFR) to set the state of the...

Topics: Neural and Evolutionary Computing, Computing Research Repository, Computational Engineering,...

Source: http://arxiv.org/abs/1501.04659

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3.0

Jun 30, 2018
06/18

by
Enrico Maiorino; Lorenzo Livi; Alessandro Giuliani; Alireza Sadeghian; Antonello Rizzi

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The multifractal detrended fluctuation analysis of time series is able to reveal the presence of long-range correlations and, at the same time, to characterize the self-similarity of the series. The rich information derivable from the characteristic exponents and the multifractal spectrum can be further analyzed to discover important insights about the underlying dynamical process. In this paper, we employ multifractal analysis techniques in the study of protein contact networks. To this end,...

Topics: Physics, Biomolecules, Data Analysis, Statistics and Probability, Quantitative Biology

Source: http://arxiv.org/abs/1410.0890

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8.0

Jun 27, 2018
06/18

by
Lorenzo Livi; Enrico Maiorino; Alessandro Giuliani; Antonello Rizzi; Alireza Sadeghian

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In this paper we present a generative model for protein contact networks. The soundness of the proposed model is investigated by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian. To complement the analysis, we study also classical topological descriptors, such as statistics of the shortest paths and the important feature of modularity. Our experiments show that the proposed model results in a considerable improvement with respect to two suitably...

Topics: Molecular Networks, Data Analysis, Statistics and Probability, Biomolecules, Quantitative Biology,...

Source: http://arxiv.org/abs/1503.02336

3
3.0

Jun 30, 2018
06/18

by
Filippo Maria Bianchi; Enrico Maiorino; Lorenzo Livi; Antonello Rizzi; Alireza Sadeghian

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We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity...

Topics: Distributed, Parallel, and Cluster Computing, Multiagent Systems, Computing Research Repository,...

Source: http://arxiv.org/abs/1409.4988

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Jun 28, 2018
06/18

by
Enrico Maiorino; Filippo Maria Bianchi; Lorenzo Livi; Antonello Rizzi; Alireza Sadeghian

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In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time series relative to measurements of an underlying dynamical system that evolves through time. We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo State Network (ESN), which are capable to model a generic dynamical process. In order to isolate the...

Topics: Learning, Neural and Evolutionary Computing, Data Analysis, Statistics and Probability, Computing...

Source: http://arxiv.org/abs/1510.07146

3
3.0

Jun 30, 2018
06/18

by
Lorenzo Livi; Enrico Maiorino; Andrea Pinna; Alireza Sadeghian; Antonello Rizzi; Alessandro Giuliani

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In this paper, we study the structure and dynamical properties of protein contact networks with respect to other biological networks, together with simulated archetypal models acting as probes. We consider both classical topological descriptors, such as the modularity and statistics of the shortest paths, and different interpretations in terms of diffusion provided by the discrete heat kernel, which is elaborated from the normalized graph Laplacians. A principal component analysis shows high...

Topics: Biological Physics, Biomolecules, Physics, Data Analysis, Statistics and Probability, Quantitative...

Source: http://arxiv.org/abs/1409.1819

3
3.0

Jun 28, 2018
06/18

by
Filippo Maria Bianchi; Enrico De Santis; Hedieh Montazeri; Parisa Naraei; Alireza Sadeghian

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In this position paper we describe a general framework for applying machine learning and pattern recognition techniques in healthcare. In particular, we are interested in providing an automated tool for monitoring and incrementing the level of awareness in the operating room and for identifying human errors which occur during the laparoscopy surgical operation. The framework that we present is divided in three different layers: each layer implements algorithms which have an increasing level of...

Topics: Computers and Society, Learning, Computing Research Repository

Source: http://arxiv.org/abs/1511.09099