Search results for "Regular"

showing 10 items of 855 documents

Migration and the Mediterranean: The EU’s Response to the “European Refugee Crisis”

2020

This chapter describes and examines the origin, nature and development of the so-called “European refugee crisis” and particularly analyzes the internal and external measures taken by the EU and its Member States in response to it. Our inquiry focuses on the following measures: (1) hotspots and emergency support for affected Member States, (2) relocation, (3) resettlement and other legal ways of entry, (4) the CEAS reform, (5) addressing irregular migration through border controls and countering smuggling and trafficking, (6) return and readmission, (7) the EU–Turkey Statement, (8) additional cooperation with third countries as well as (8) (trust) funds to support regions of origin and tran…

ExternalizationHuman rightsEconomic policyRestructuringRefugeePolitical sciencemedia_common.quotation_subjectRefugee crisisIrregular migrationSecuritizationRelocationmedia_common
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A robust blind 3-D mesh watermarking based on wavelet transform for copyright protection

2019

Nowadays, three-dimensional meshes have been extensively used in several applications such as, industrial, medical, computer-aided design (CAD) and entertainment due to the processing capability improvement of computers and the development of the network infrastructure. Unfortunately, like digital images and videos, 3-D meshes can be easily modified, duplicated and redistributed by unauthorized users. Digital watermarking came up while trying to solve this problem. In this paper, we propose a blind robust watermarking scheme for three-dimensional semiregular meshes for Copyright protection. The watermark is embedded by modifying the norm of the wavelet coefficient vectors associated with th…

FOS: Computer and information sciences0209 industrial biotechnologyComputer sciencevideo watermarking02 engineering and technologyWatermarkingimage watermarking020901 industrial engineering & automationWaveletcopy protectionvectorsRobustness (computer science)Computer Science::Multimedia0202 electrical engineering electronic engineering information engineeringwavelet coefficient vectorsControlled IndexingComputer visionPolygon meshQuantization (image processing)RobustnessDigital watermarkingComputingMilieux_MISCELLANEOUSComputer Science::Cryptography and SecurityQuantization (signal)digital watermarkingbusiness.industrycopyrightedge normal normsWavelet transformunauthorized usersWatermarkThree-dimensional meshesMultimedia (cs.MM)mesh generationwavelet transformssynchronizing primitives3D semiregular meshesSolid modelingrobust blind 3D mesh watermarking020201 artificial intelligence & image processingArtificial intelligenceLaplacian smoothingbusinessCopyright protection[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingComputer Science - Multimediaimage resolutionDigital images
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P2D: a self-supervised method for depth estimation from polarimetry

2021

Monocular depth estimation is a recurring subject in the field of computer vision. Its ability to describe scenes via a depth map while reducing the constraints related to the formulation of perspective geometry tends to favor its use. However, despite the constant improvement of algorithms, most methods exploit only colorimetric information. Consequently, robustness to events to which the modality is not sensitive to, like specularity or transparency, is neglected. In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network. Therefore, we propose exploiting polarization cues to encourage accurate reconstruction of scenes. Furthermore, we…

FOS: Computer and information sciences0209 industrial biotechnologyMonocularComputer sciencebusiness.industryComputer Vision and Pattern Recognition (cs.CV)PolarimetryComputer Science - Computer Vision and Pattern RecognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]02 engineering and technology010501 environmental sciences01 natural sciencesRegularization (mathematics)Term (time)020901 industrial engineering & automation[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]SpecularityRobustness (computer science)Depth mapComputer visionArtificial intelligenceTransparency (data compression)business0105 earth and related environmental sciences
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Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

2022

Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and unde…

FOS: Computer and information sciencesComputer Science - Machine LearningArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceGeneral Earth and Planetary SciencesAI in banking; personalized services; prosperity management; explainable AI; reinforcement learning; policy regularisationVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550General Environmental ScienceMachine Learning (cs.LG)AI; Volume 3; Issue 2; Pages: 526-537
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Efficient Nonlinear RX Anomaly Detectors

2020

Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks t…

FOS: Computer and information sciencesComputer Science - Machine LearningBasis (linear algebra)Computer scienceComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern Recognition0211 other engineering and technologiesApproximation algorithmHyperspectral imaging02 engineering and technologyElectrical Engineering and Systems Science - Image and Video ProcessingGeotechnical Engineering and Engineering GeologyRegularization (mathematics)Machine Learning (cs.LG)Nonlinear systemKernel (linear algebra)Kernel (statistics)FOS: Electrical engineering electronic engineering information engineeringAnomaly detectionElectrical and Electronic EngineeringAnomaly (physics)Algorithm021101 geological & geomatics engineeringIEEE Geoscience and Remote Sensing Letters
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Anti-powers in infinite words

2018

In combinatorics of words, a concatenation of $k$ consecutive equal blocks is called a power of order $k$. In this paper we take a different point of view and define an anti-power of order $k$ as a concatenation of $k$ consecutive pairwise distinct blocks of the same length. As a main result, we show that every infinite word contains powers of any order or anti-powers of any order. That is, the existence of powers or anti-powers is an unavoidable regularity. Indeed, we prove a stronger result, which relates the density of anti-powers to the existence of a factor that occurs with arbitrary exponent. As a consequence, we show that in every aperiodic uniformly recurrent word, anti-powers of ev…

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)Formal Languages and Automata Theory (cs.FL)ConcatenationComputer Science - Formal Languages and Automata Theory68R150102 computer and information sciences01 natural sciencesTheoretical Computer ScienceCombinatoricsUnavoidable regularityPosition (vector)Infinite wordAvoidability[MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO]FOS: MathematicsMathematics - CombinatoricsDiscrete Mathematics and CombinatoricsOrder (group theory)Point (geometry)0101 mathematicsDiscrete Mathematics and CombinatoricMathematicsDiscrete mathematics000 Computer science knowledge general worksAnti-power010101 applied mathematicsComputational Theory and Mathematics010201 computation theory & mathematicsAperiodic graphComputer ScienceExponentPairwise comparisonCombinatorics (math.CO)SoftwareWord (group theory)Computer Science - Discrete Mathematics
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Splicing Systems from Past to Future: Old and New Challenges

2014

A splicing system is a formal model of a recombinant behaviour of sets of double stranded DNA molecules when acted on by restriction enzymes and ligase. In this survey we will concentrate on a specific behaviour of a type of splicing systems, introduced by P\u{a}un and subsequently developed by many researchers in both linear and circular case of splicing definition. In particular, we will present recent results on this topic and how they stimulate new challenging investigations.

FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)[INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]Formal Languages and Automata Theory (cs.FL)Splicing Systems Formal Languages.ACM: F.: Theory of Computation/F.4: MATHEMATICAL LOGIC AND FORMAL LANGUAGES/F.4.3: Formal LanguagesACM: F.: Theory of Computation/F.4: MATHEMATICAL LOGIC AND FORMAL LANGUAGES/F.4.2: Grammars and Other Rewriting SystemsComputer Science - Formal Languages and Automata TheorySplicing Systems Formal languages Regular languages DNA computingComputingMilieux_MISCELLANEOUS[INFO.INFO-FL] Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]Computer Science - Discrete Mathematics
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CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration

2017

International audience; In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor a…

FOS: Computer and information sciencesInverse problemsMathematical optimization[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer Vision and Pattern Recognition (cs.CV)General MathematicsComputer Science - Computer Vision and Pattern RecognitionMachine Learning (stat.ML)Mathematics - Statistics TheoryImage processingStatistics Theory (math.ST)02 engineering and technologyDebiasing[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciencesRegularization (mathematics)Boosting010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]Variational methods[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Statistics - Machine LearningRefittingMSC: 49N45 65K10 68U10[ INFO.INFO-TI ] Computer Science [cs]/Image ProcessingFOS: Mathematics0202 electrical engineering electronic engineering information engineeringCovariant transformation[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicsImage restoration[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]MathematicsApplied Mathematics[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]EstimatorInverse problem[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Jacobian matrix and determinantsymbolsTwicing020201 artificial intelligence & image processingAffine transformationAlgorithm
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On the family of $r$-regular graphs with Grundy number $r+1$

2014

International audience; The Grundy number of a graph $G$, denoted by $\Gamma(G)$, is the largest $k$ such that there exists a partition of $V(G)$, into $k$ independent sets $V_1,\ldots, V_k$ and every vertex of $V_i$ is adjacent to at least one vertex in $V_j$, for every $j < i$. The objects which are studied in this article are families of $r$-regular graphs such that $\Gamma(G) = r + 1$. Using the notion of independent module, a characterization of this family is given for $r=3$. Moreover, we determine classes of graphs in this family, in particular the class of $r$-regular graphs without induced $C_4$, for $r \le 4$. Furthermore, our propositions imply results on partial Grundy number.

FOS: Computer and information sciencesPartial Grundy numberDiscrete Mathematics (cs.DM)Regular graphFalse twinsFOS: MathematicsGrundy numberMathematics - Combinatorics[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]Combinatorics (math.CO)[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]Computer Science - Discrete Mathematics
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Fast Graph Filters for Decentralized Subspace Projection

2020

A number of inference problems with sensor networks involve projecting a measured signal onto a given subspace. In existing decentralized approaches, sensors communicate with their local neighbors to obtain a sequence of iterates that asymptotically converges to the desired projection. In contrast, the present paper develops methods that produce these projections in a finite and approximately minimal number of iterations. Building upon tools from graph signal processing, the problem is cast as the design of a graph filter which, in turn, is reduced to the design of a suitable graph shift operator. Exploiting the eigenstructure of the projection and shift matrices leads to an objective whose…

FOS: Computer and information sciencesSignal processingComputer scienceMatrix normConvex relaxationRegular polygon020206 networking & telecommunications02 engineering and technologyShift operatorStatistics - ComputationGraphsymbols.namesakeMatrix (mathematics)Approximation errorKronecker deltaSignal Processing0202 electrical engineering electronic engineering information engineeringsymbolsGraph (abstract data type)Electrical and Electronic EngineeringAlgorithmComputation (stat.CO)Subspace topologyEigenvalues and eigenvectorsIEEE Transactions on Signal Processing
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