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ANNALES DE L INSTITUT HENRI POINCARE-PROBABILITES ET STATISTIQUES 2020年第4期56卷 2428-2444页

作者： Csoka, Endre Harangi, Viktor Virag, Balint MTA Alfred Renyi Inst Math Budapest Hungary Univ Toronto Dept Math Toronto ON Canada

Shearer's inequality bounds the sum of joint entropies of random variables in terms of the total joint entropy. We give another lower bound for the same sum in terms of the individual entropies when the variables are functions of independent random seeds. The inequality involves a constant characterizing the expansion properties of the system. Our results generalize to entropy inequalities used in recent work in invariant settings, including the edge-vertex inequality for factor-of-IID processes, Bowen's entropy inequalities, and Bollobas's entropy bounds in random regular graphs. The proof method yields inequalities for other measures of randomness, including covariance. As an application, we give upper bounds for independent sets in both finite and infinite graphs.

关键词： Entropy inequality Expansion Cheeger constant Graph isoperimetry Factor-of-IID local algorithm Independent set

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Fragmentary Structures in Discrete Optimization Problems

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CYBERNETICS AND SYSTEMS ANALYSIS 2017年第6期53卷 931-936页

作者： Kozin, I. V. Maksyshko, N. K. Perepelitsa, V. A. Zaporizhzhya Natl Univ Zaporizhzhya Ukraine

The paper considers a combinatorial object (a fragmentary structure) and investigates the properties of this object. It is shown that a number of discrete optimization problems can be considered as optimization problems on a fragmentary structure. Optimization problem reduces to an unconditional combinatorial optimization problem on a set of permutations. Variants of algorithms to find approximate solutions for optimization problems of fragmentary structure are proposed.

关键词： discrete optimization fragmentary structure local algorithm evolutionary algorithm ant algorithm

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Dynamic Representations of Sparse Distributed Networks: A locality-sensitive Approach

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ACM TRANSACTIONS ON PARALLEL COMPUTING 2021年第1期8卷 1–26页

作者： Kaplan, Haim Solomon, Shay Tel Aviv Univ IL-69978 Tel Aviv Israel

In 1999, Brodal and Fagerberg (BF) gave an algorithm for maintaining a low outdegree orientation of a dynamic uniformly sparse graph. Specifically, for a dynamic graph on n-vertices, with arboricity bounded by alpha at all times, the BF algorithm supports edge updates in O(log n) amortized update time, while keeping the maximum outdegree in the graph bounded by O(alpha). Such an orientation provides a basic data structure for uniformly sparse graphs, which found applications to several dynamic graph algorithms, including adjacency queries and labeling schemes, maximal and approximate matching, approximate vertex cover, forest decomposition, and distance oracles. A significant weakness of the BF algorithm is the possible temporary blowup of the maximum outdegree, following edge insertions. Although BF eventually reduces all outdegrees to O(alpha), some vertices may reach an outdegree of Omega(n) during the process, and hence local memory usage at the vertices, which is an important quality measure in distributed systems, cannot be bounded. We show how to modify the BF algorithm to guarantee that the outdegrees of all vertices are bounded by O(alpha) at all times, without hurting any of its other properties and present an efficient distributed implementation of the modified algorithm. This provides the first representation of distributed networks in which the local memory usage at all vertices is bounded by the arboricity (which is essentially the average degree of the densest subgraph) rather than the maximum degree. For settings where there is no strict limitation on the local memory, one may take the temporary outdegree blowup to the extreme and allow a permanent outdegree blowup. This allows us to address the second significant weakness of the BF algorithm-its inherently global nature: An insertion of an edge (u, v) may trigger changes in the orientations of edges that are arbitrarily far away from u and v. Such a non-local scheme may be prohibitively expensive in

关键词： arboricity distributed network dynamic graph algorithm edge orientation local algorithm

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Diverse local calibration approaches for chemometric predictive analysis of large near-infrared spectroscopy (NIRS) multi-product datasets

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 251卷

作者： Yang, Xueping Yang, Fuyu Lesnoff, Matthieu Berzaghi, Paolo Ferragina, Alessandro China Agr Univ Coll Grassland Sci & Technol Beijing 100193 Peoples R China Guizhou Univ Coll Anim Sci Guiyang 550025 Peoples R China Univ Padua Dept Anim Med Prod & Hlth I-35020 Legnaro Italy GraiNit Srl I-35020 Padua Italy Univ Montpellier Inst Agro SELMET CIRADINRAe F-34090 Montpellier France CIRAD UMR SELMET F-34090 Montpellier France ChemHouse Res Grp F-34000 Montpellier France Univ Parma Dept Vet Sci I-43126 Parma Italy

This study aimed to assess the predictive accuracy of Near-Infrared Spectroscopy (NIRS) across a large multiproduct library, employing novel local calibration methodologies. Three local strategies were examined: local algorithm, locally Weighted Regression predicted on k-nearest neighbor selection (kNN-LWPLSR), along with a newly proposed algorithm within this study called Hybrid local. These strategies were applied to an extensive multi-product dataset. When compared with Global PLS models, the results exhibited significant reductions in RMSEP values for all local strategies. Particularly, the kNN-LWPLSR demonstrated proficient prediction for the constituents of ADF and DM. The newly proposed method [Hybrid local] exhibits comparable performance to the local algorithm;however, it notably reduces the prediction time by half compared to the latter, representing a significant advancement for the practical implementation of NIRS technology within industrial processing scenarios.

关键词： Near infrared spectroscopy (NIRS) Chemometrics local calibration Large multi -products dataset local algorithm

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A self-adaptive local metric learning method for classification

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PATTERN RECOGNITION 2019年 96卷 106994-000页

作者： Taheri, Mahsa Moslehi, Zahra Mirzaei, Abdolreza Safayani, Mehran Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Measuring distance among data point pairs is a necessary step among numerous counts of algorithms in machine learning, pattern recognition and data mining. In the local perspective, the emphasis of all existing supervised metric learning algorithms is to shrink similar data points and to separate dissimilar ones in the local neighborhoods. This provides learning more appropriate distance metric in dealing with the within-class multi modal data. In this article, a new supervised local metric learning method named Self-Adaptive local Metric Learning Method (SA-LM2) has been proposed. The contribution of this method is in five aspects. First, in this method, learning an appropriate metric and defining the radius of local neighborhood are integrated in a joint formulation. Second, unlike the traditional approaches, SA-LM2 learns the parameter of local neighborhood automatically thorough its formulation. As a result, it is a parameter free method, where it does not require any parameters that would need to be tuned. Third, SA-LM2 is formulated as a SemiDefinite Program (SDP) with a global convergence guarantee. Fourth, this method does not need the similar set S, the focus here is on local areas' data points and their separation from dissimilar ones. Finally, results of SA-LM2 are less influenced by noisy input data points than the other compared global and local algorithms. Results obtained from different experiments indicate the outperformance of this algorithm over its counterparts. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Supervised metric learning Linear metric learning local algorithm Neighborhood learning Multimodal data

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local Construction of Connected Plane Subgraphs in Graphs Satisfying Redundancy and Coexistence ⁎

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Procedia Computer Science 2021年 195卷 108-117页

作者： Lucas Böltz Benjamin Becker Hannes Frey Department of Computer Science University of Koblenz-Landau Koblenz Germany Multimedia Communications Lab Technical University of Darmstadt Darmstadt Germany

Connected plane graphs enable many local algorithmic solutions for data communication, task coordination and network maintenance in wireless sensor networks, sensor-actuator networks and distributed robotics. We study construction of such graphs by removing edges from a given network graph. We assume redundancy and coexistence, a graph structure which holds in realistic wireless network models with high probability and which assures that a connected plane graph can always be constructed with local edge removal rules. We present a local algorithm to construct such a connected plane graph. We prove algorithm correctness under redundancy and coexistence assumption. Furthermore, we discuss how far redundancy and coexistence could be weakened while still assuring correctness of the algorithm.

关键词： Plane spanning subgraph local algorithm Redundancy Coexistence Correctness analysis

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Freedom of speech for all critically ill patients: work in progress

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CRITICAL CARE 2017年第1期21卷 1-2页

作者： Tuinman, P. R. ten Hoorn, S. VU Univ Med Ctr Amsterdam Dept Intens Care Med & Res VUmc Intens Care REVIV Room ZH-7D-166De Boelelaan 1117POB 7057 NL-1007 MB Amsterdam Netherlands VU Univ Med Ctr Amsterdam Inst Cardiovasc Res VU ICaR VU Amsterdam Netherlands

See related Letter by Sutt et al., https://***/articles/10.1186/s13054-016-1588-7 and related Letter by Egbers et al., https://***/articles/10.1186/s13054-016-1587-8

关键词： local algorithm Speech Enhancement Cuff Deflation Simple Communication Intensive Care Unit Staff

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IEEE 11th Conference on Industrial Electronics and Applications (ICIEA)

作者： Li, Baoping Commun Univ China Key Lab Media Audio & Video Minist Educ Beijing Peoples R China Henan Polytech Univ Jiaozuo Peoples R China

ISBN: (纸本)9781467386449

Cost initialization is an important step in both local and global stereo matching algorithms. Different similarity measures may perform differently in a particular location depending on the image structure. Different from the traditional similarity fusion strategies, we present a new fusion strategy which fuses similarity measures adaptively based on their performance on different image regions. The experimental results demonstrate that the proposed adaptive similarity fusion algorithm used in both local and global stereo matching algorithms is capable of providing high-quality disparity maps comparable to other fusion strategies.

关键词： Stereo Matching Adaptive Similarity Fusion local algorithm Global algorithm

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Finding Sparse Cuts locally Using Evolving Sets

Finding Sparse Cuts Locally Using Evolving Sets

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41st Annual ACM Symposium on Theory of Computing

作者： Andersen, Reid Peres, Yuval Microsoft Live Labs Redmond WA 98052 USA

ISBN: (纸本)9781605586137

A local graph partitioning algorithm finds a set of vertices with small conductance (i.e. a sparse cut) by adaptively exploring a large graph G, starting from a specified vertex. For the algorithm to he local, its complexity must be bounded in terms of the size of the set it outputs. with at most a, weak dependence on n, the number of vertices in G. Previous local partitioning algorithms find sparse cuts using random walks and personalized PageRank. In this paper, we introduce a randomized local partitioning algorithm that finds a sparse cut by simulating the volume-based evolving set which is a Markov chain oil sets of vertices. We, prove that For any set of vertices A that has conductance Lit most phi, and for at least half of the starting vertices in A our algorithm will output (with probability at least half) a set of conductance O (phi(1/2) log(1/2) n). The complexity of a local partitioning algorithm is measured by, its work/volume ratio, which is the ratio between the computational complexity of the algorithm oil a given run, and the volume of the Set, output. We prove that for our algorithm, the expected value of the work/volume ratio is O(phi(-1/2) polylog(n)). The best previous local partitioning algorithm, due to Andersen, Chung, and Lang, has the same approximation guarantee but, a, larger work/volume ratio of O(phi(-1/2) polylog(n)). As an application of our local partitioning algorithm, we construct a fast algorithm for finding balanced cuts. The resulting algorithm Likes as input a graph and a fixed value of phi, has complexity (m + n phi(-1/2)). O(phi(-1/2) polylog(n)), and returns a cut with conductance O (phi(1/2) log(1/2) n) and volume at least nu(phi)/2, where nu(phi) is the volume of the largest set in the graph with conductance at most phi.

关键词： Evolving Sets Graph Partitioning local algorithm

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Dynamic Representations of Sparse Distributed Networks: A locality-Sensitive Approach 18

Dynamic Representations of Sparse Distributed Networks: A Lo...

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30th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Kaplan, Haim Solomon, Shay Tel Aviv Univ Tel Aviv Israel IBM Res TJ Watson Res Ctr New York NY USA

ISBN: (纸本)9781450357999

In 1999, Brodal and Fagerberg (BF) gave an algorithm for maintaining a low outdegree orientation of a dynamic uniformly sparse graph. Specifically, for a dynamic graph on n-vertices, with arboricity bounded by alpha at all times, the BF algorithm supports edge updates in O(log n) amortized update time, while keeping the maximum outdegree in the graph bounded by O(alpha). Such an orientation provides a basic data structure for uniformly sparse graphs, which found applications to a plethora of dynamic graph algorithms. A significant weakness of the BF algorithm is the possible temporary blowup of the maximum outdegree, following edge insertions. Although BF eventually reduces all outdegrees to O(alpha), some vertices may reach an outdegree of Omega(n) during the process, hence local memory usage at the vertices-an important quality measure in distributed systems-cannot be bounded. We show how to modify the BF algorithm to guarantee that the outdegrees of all vertices are bounded by O(alpha) at all times, without hurting any of its other properties, and present an efficient distributed implementation of the modified algorithm. This provides the first representation of distributed networks in which the local memory usage at vertices is bounded by the arboricity (which is essentially the average degree of the densest subgraph) rather than the maximum degree. For settings where there is no local memory constraints, one may take the temporary outdegree blowup to the extreme and allow a permanent outdegree blowup. This allows us to address the second significant weakness of the BF algorithm - its inherently global nature: An insertion of an edge (u, v) may trigger changes in the orientations of edges that are arbitrarily far away from u and v. We suggest an alternative local scheme, which does not guarantee any outdegree bound on the vertices, yet is just as efficient as the BF scheme for some applications. For example, we obtain a local dynamic algorithm for maintaining a

关键词： arboricity dynamic network local algorithm local memory

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