155 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Porównanie Japonii i krajów OECD pod względem zasobów związanych z dobrostanem
While evaluating the concept of well-being for sustainability, which is defined as the feeling of having the physical and psychological resources necessary for a good life, it is essential to benefit from different perspectives referring to socio-psychological factors or their possible effects as well as financial and economic data. The aim of this study, which deals with the well-being level in terms of sustainability resources, is to evaluate the OECD countries and examine the differences and similarities in Japan, one of the G8 countries. According to the results of the multidimensional scaling analysis conducted for this purpose, Japan is in the same cluster as Luxembourg, which has the highest positive value, while Germany is one of the countries with the highest rate of divergence from other G8 countries in the difference matrix.Oceniając koncepcję dobrostanu pod kątem zrównoważoności, którą definiuje się jako poczucie posiadania zasobów fizycznych i psychicznych niezbędnych do dobrego życia, istotne jest skorzystanie z różnych perspektyw odnoszących się do czynników społeczno-psychologicznych lub ich możliwych skutków a także danych finansowych i gospodarczych. Przeprowadzona analiza umożliwiła na wskazanie poziomu dobrobytu pod względem zrównoważonych zasobów w krajach OECD, a także określenie różnic i podobieństw pomiędzy tymi państwami a Japonią, jednym z krajów grupy G8. Zgodnie z wynikami analizy skalowania wielowymiarowego Japonia znajduje się w tym samym klastrze co mający najwyższą wartość dodatnią Luksemburg, podczas gdy Niemcy należą do jednego z krajów o najwyższym wskaźniku rozbieżności w stosunku do innych państwo G8 w macierzy różnic
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
Quantitative contraction rates for Sinkhorn algorithm: beyond bounded costs and compact marginals
We show non-asymptotic geometric convergence of Sinkhorn iterates to the
Schr\"odinger potentials, solutions of the quadratic Entropic Optimal Transport
problem on . Our results hold under mild assumptions on the
marginal inputs: in particular, we only assume that they admit an
asymptotically positive log-concavity profile, covering as special cases
log-concave distributions and bounded smooth perturbations of quadratic
potentials. More precisely, we provide exponential and pointwise
convergence of the iterates (resp. their gradient and Hessian) to Schr\"odinger
potentials (resp. their gradient and Hessian) for large enough values of the
regularization parameter. As a corollary, we establish exponential convergence
of Sinkhorn plans and bridges w.r.t. a symmetric relative entropy. Up to the
authors' knowledge, these are the first results which establish geometric
convergence of Sinkhorn algorithm in a general setting without assuming bounded
cost functions or compactly supported marginals. Our results are proven
following a probabilistic approach that rests on integrated semiconvexity
estimates for Sinkhorn iterates that are of independent interest.Comment: 51 page
Principled methods for mixtures processing
This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the shortterm research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and αstable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences
High-dimensional non-Gaussian data analysis based on sample relationship
High-dimensional data are omnipresent. Although many statistical methods developed for analysing high-dimensional data adopt the normality assumption, the Gaussian distribution could be a poor approximation of real data in many applications. In this thesis, we investigate how to properly analyse such high-dimensional non-Gaussian data. As quantifying sample relationships, such as measuring the inter-sample proximity and determining neighbours for samples, is an important step in numerous statistical approaches, this thesis develops three methods for analysing different high-dimensional non-Gaussian data types based on the sample relationship: dimension reduction for single cell RNA-sequencing data with missingness with a proposed proximity measure, dimension reduction for data of small counts with a developed proximity measure, and modelling skewed survival data with a proposed procedure of identifying neighbours for samples. In chapter 3, I develop an unbiased estimator of the Gram matrix, which characterises the proximity between samples. The proposed estimator improves a broad spectrum of dimension reduction methods when applied to single cell RNA-sequencing data with missingness. In addition, the consequences of directly applying existing dimension reduction methods to data with missingness are empirically and theoretically clarified. In chapter 4, I develop a dissimilarity measure for count data with an excess of zeros based on the Kullback-Leibler divergence and the empirical Bayes estimators. The proposed measure is shown to have better discriminative power compared with other popular measures. The proposed measure boosts the performance of standard dimension reduction methods on count data containing many zeros. In chapter 5, I clarify that graphs derived from features themselves can be beneficial for the analysis of high-dimensional survival data when used in graph convolutional networks. Besides, a sequential forward floating selection algorithm is proposed to simultaneously perform survival analysis and unveil the local neighbourhoods of samples with the aid of graph convolutional networks
Understanding biodiversity responses to global change: Populations, communities, and species distributions
Human influence on global ecosystems is pervasive. To mitigate the effects of climate change and land use change, there is a need for developing a predictive understanding of how global biodiversity has been impacted. Identifying ecological traits of species associated with species that are vulnerable to, tolerant of, or benefitting from anthropogenic change can help predict ecological communities of the future. In this dissertation, I investigated the ecological impacts of global change at three levels: populations, communities, and range distributions.Population responses to anthropogenic change may be context dependent: climate change effects may be exacerbated by simultaneous land use changes, or intraspecific population response to climate change may depend on whether the population is in a warmer or colder portion of the species’ range. To address these questions, I modeled how forest fragmentation and climate change predict changes in population trends of 67 forest breeding bird species throughout the United States and Canada. Secondly, I determined whether ecological traits such as migratory strategy, habitat specialization, and thermal niche width can predict the susceptibility of species to the impacts of forest fragmentation and climate change. As a result of ongoing anthropogenic change, ecological communities have reshuffled. Understanding how communities are changing requires consideration of compositional shifts in species identity and abundance and how they are related to global change. I examined the compositional change in bird communities, comparing the relative contribution of land use and climate change variables from local to regional scales over the past 25 years in the United States and Canada. Additionally, I measured how species traits may explain turnover in response to climate and land use change. Impacts from local climate and land use change on populations and communities ultimately scale up to impact species range distributions. In response, species may undergo shifts in population size, sites occupied within their range, and shifts in range extent. Niche breadth on various axes may influence the direction and magnitude of these responses. Using annual survey data on breeding birds over forty years, I characterized the relative importance of niche breadth in explaining changes in species range responses.Doctor of Philosoph
ECOS 2012
The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology
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