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检索条件"任意字段=4th Workshop on Algorithms and Data Structures, WADS 1995"

共 84 条记录，以下是1-10 订阅

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2020 4th International workshop on Advanced algorithms and Control Engineering, IWAACE 2020 - algorithms and data structures

2020 4th International Workshop on Advanced Algorithms and C...

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2020 4th International workshop on Advanced algorithms and Control Engineering, IWAACE 2020

the proceedings contain 339 papers. the topics discussed include: solving the optimization of physical distribution routing problem with hybrid genetic algorithm;design and construction of information-based teaching cloud space in colleges and universities under the framework of TPACK;the design and implementation of the smart trash can based on the Internet of things;design and hydrodynamic simulation analysis on debris cleaning robot underwater;initial training samples selection using clustering algorithm for audio recognition;research and application of data mining technology in library office information construction;experimental research on efficient irrigation system with mixed fertilizer in integration of water and fertilizer;research on assembly path planning method of low speed diesel engine based on hoisting characteristics;investment demand forecasting model of power grid based on different development paths;and multi-objective distribution network reconfiguration optimization considering space allocation of electric vehicles.

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Streaming Process Mining with Beamline (Extended Abstract) 4

Streaming Process Mining with Beamline (Extended Abstract)

引用

Doctoral Consortium and the Tool Demonstration Track of the 4th International Conference on Process Mining, ICPM-D 2022

作者： Burattin, Andrea DTU Compute Technical University of Denmark Kgs. Lyngby Denmark

Beamline is a Java framework designed to facilitate the prototyping and development of streaming process mining algorithms. the framework is designed on top of Apache Flink which makes it suitable for extremely efficient computation due to its distributed and stateful nature. Beamline consists of both algorithms as well as data structures, sources, and sinks to facilitate the development of process mining applications. the framework is licensed with Apache-2.0 and its companion website https://*** contains real-life examples on actual live data and all the system’s documentation. © 2022 Copyright for this paper by its authors.

关键词： data mining

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3rd International workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International workshop on Breast Image Analysis, BIA 2018, and 1st International workshop on thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018

3rd International Workshop on Reconstruction and Analysis of...

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3rd International workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International workshop on Breast Image Analysis, BIA 2018, and 1st International workshop on thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018

ISBN: (纸本)9783030009458

the proceedings contain 34 papers. the special focus in this conference is on Reconstruction and Analysis of Moving Body Organs. the topics include: Resection-based demons regularization for breast tumor bed propagation;improving breast cancer detection using symmetry information with deep learning;Conditional infilling GANs for data augmentation in mammogram classification;a unified mammogram analysis method via hybrid deep supervision;structure-aware staging for breast cancer metastases;reproducible evaluation of registration algorithms for movement correction in dynamic contrast enhancing magnetic resonance imaging for breast cancer diagnosis;robust windowed harmonic phase analysis with a single acquisition;Lung structures enhancement in chest radiographs via CT based FCNN training;improving the segmentation of anatomical structures in chest radiographs using U-net with an imagenet pre-trained encoder;Tuberculosis histopathology on X ray CT;A CT scan harmonization technique to detect emphysema and small airway diseases;linear and deformable image registration with 3D convolutional neural networks;transfer learning for segmentation of injured lungs using coarse-to-fine convolutional neural networks;High throughput lung and lobar segmentation by 2D and 3D CNN on chest CT with diffuse lung disease;Multi-structure segmentation from partially labeled datasets. Application to body composition measurements on CT scans;3D pulmonary artery segmentation from CTA scans using deep learning with realistic data augmentation;Automatic airway segmentation in chest CT using convolutional neural networks;Detecting out-of-phase ventilation using 4DCT to improve radiation therapy for lung cancer;XeMRI to CT lung image registration enhanced with personalized 4DCT-derived motion model;Diffeomorphic lung registration using deep CNNs and reinforced learning.

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Flexible data Views: Design and Implementation 4

Flexible Data Views: Design and Implementation

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4th ACM SIGPLAN International workshop on Libraries, Languages, and Compilers for Array Programming (ARRAY) / 1st ACM SIGPLAN International workshop on Machine Learning and Programming Languages (MAPL)

作者： Osvald, Leo Rompf, Tiark Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450350693

In this paper, we present a library-based framework of data views over chunks of memory segments. Such views not only enable a uniform treatment of references and arrays, but they provide a more general abstraction in the sense that parts of arrays, references, or even views, can be combined into hierarchies to form new logical data structures. To provide efficient implementations in widely used industrial languages such as C++ and Scala, we employ static and dynamic multi-staging techniques, respectively. through staging and code specialization, the overhead of traversal and tracking of such view hierarchies is mostly eliminated. thus, our data views can be used as building blocks for creating data structures for which programmers need not pick a specific representation but can rely on code generation and specialization to provide the right implementation that meets asymptotic running time and space guarantees. We apply our approach in case studies in which two-dimensional array views are used to efficiently encode real-world matrices, showing performance on par with specialized data structures such as sparse matrices from popular linear algebra libraries (Armadillo and Eigen), or hand-tuned dense representations. We also show the practicality of specializing data views at run-time on the JVM via Lightweight Modular Staging, a Scala framework for dynamic multi-stage programming, by designing a user-friendly API that hides the underlying compilation through lazy evaluation and a uniform access principle.

关键词： arrays specialization algorithms memory model

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Discovering and Tracking Organizational structures in Event Logs 4th

Discovering and Tracking Organizational Structures in Event ...

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4th International workshop on New Frontiers in Mining Complex Patterns (NFMCP)

作者： Appice, Annalisa Di Pietro, Marco Greco, Claudio Malerba, Donato Univ Bari Aldo Moro Dipartimento Informat Via Orabona 4 I-70126 Bari Italy

ISBN: (纸本)9783319393155;9783319393148

the goal of process mining is to extract process-related information by observing events recorded in event logs. An event is an activity initiated or completed by a resource at a certain time point. Organizational mining is a subfield of process mining that focuses on the organizational perspective of a business process. It considers the resource attribute and derives a profile that characterizes the behavior of a resource in a specific business process. By relating resources associated with correlated profiles, it is possible to define a social network. this paper focuses on the idea of performing organizational mining of event logs via social network mining. It presents a framework that resorts to a stream representation of an event log. It adapts the time-based window model to process this stream, so that window-based social resource networks can be constructed, in order to represent interactions between resources operating at the data window level. Finally, it integrates specific algorithms, in order to discover (overlapping) communities of resources and track the evolution of these communities over consecutive windows. this paper applies the defined framework to two real event logs.

关键词： data mining

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Discovering hierarchical structure in normal relational data

Discovering hierarchical structure in normal relational data

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International workshop on Cognitive Information Processing (CIP)

作者： Mikkel N. Schmidt Tue Herlau Morten Mørup Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

Hierarchical clustering is a widely used tool for structuring and visualizing complex data using similarity. Traditionally, hierarchical clustering is based on local heuristics that do not explicitly provide assessment of the statistical saliency of the extracted hierarchy. We propose a non-parametric generative model for hierarchical clustering of similarity based on multifurcating Gibbs fragmentation trees. this allows us to infer and display the posterior distribution of hierarchical structures that comply with the data. We demonstrate the utility of our method on synthetic data and data of functional brain connectivity.

关键词： Couplings Correlation Clustering algorithms Gaussian distribution Computational modeling data models Image color analysis

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Measurement data Aggregation for Active Distribution Networks

Measurement Data Aggregation for Active Distribution Network...

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4th IEEE International workshop on Applied Measurements for Power Systems (AMPS)

作者： Calin, Mihai Dumitrescu, Ana Maria Asprou, Markos Kyriakides, Elias Albu, Mihaela Univ Politehn Bucuresti Bucharest Romania Univ Cyprus Nicosia Cyprus

ISBN: (纸本)9781467355711

Power Quality indicators are still conveyed into a classical paradigm of power systems where the steady state operation is governed by quasi-constant frequency. Moreover, distribution grids are considered as network structures with solely load behavior. the standards proposing power quality descriptors are built on models, which seem to limit the possibilities of achieving real time control targeting highly efficient energy transfer, at least in all-electric grids. In this paper the measurement data aggregation algorithms proposed in the well-known standard IEC 61000-4-30 are discussed based on an analysis performed on voltages and frequency measurements. Raw data are obtained from PMUs installed in various nodes of active distribution grids with reporting rate of 50 frames per second. New aggregation algorithms for frequency, rate of change of frequency and unbalance coefficient appear to be needed.

关键词： Statistical analysis power quality phasor measurement units voltage measurements root mean square data aggregation algorithm

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A Hybrid Graph Representation for Recursive Backtracking algorithms

A Hybrid Graph Representation for Recursive Backtracking Alg...

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4th International Frontiers of Algorithmics workshop

作者： Abu-Khzam, Faisal N. Langston, Michael A. Mouawad, Amer E. Nolan, Clinton P. Lebanese Amer Univ Dept Math & Comp Sci Beirut Lebanon Univ Tennessee Dept Elect & Comp Engn Knoxville TN 37996 USA

ISBN: (纸本)9783642145520

Many exact;algorithms for NP-hard graph problems adopt the old Davis-Putman branch-and-reduce paradigm. the performance of these algorithms often suffers from the increasing number of graph modifications, such as deletions, that reduce the problem instance and have to be "taken back" frequently during the search process. the use of efficient data structures is necessary for fast graph modification modules as well as fast take-back procedures. In this paper, we investigate practical implementation-based aspects of exact algorithms by providing a hybrid graph representation that addresses the take-back challenge and combines the advantage of O(1) adjacency-queries in adjacency-matrices with the advantage of efficient neighborhood traversal in adjacency-lists.

关键词： data structures exact algorithms recursive backtracking vertex cover dominating set

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Efficient Approximation of Combinatorial Problems by Moderately Exponential algorithms

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11th International workshop on algorithms and data structures (wads 2009)

作者： Bourgeois, Nicolas Escoffier, Bruno Paschos, Vangelis th. CNRS FRE 3234 LAMSADE F-75700 Paris France

ISBN: (纸本)9783642033667

We design approximation algorithms for several NP-hard combinatorial problems achieving ratios that cannot be achieved in polynomial time (unless a very unlikely complexity conjecture is confirmed) with worst-case complexity much lower (though super-polynomial) than that of an exact computation. We study in particular MAX INDEPENDENT SET, MIN VERTEX COVER and MIN SET COVER and then extend our results to MAX CLIQUE, MAX BIPARTITE SUBGRAPH and MAX SET PACKING.

关键词： Approximation algorithms

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Dynamic Graph Clustering Using Minimum-Cut Trees

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11th International workshop on algorithms and data structures (wads 2009)

作者： Goerke, Robert Hartmann, Tanja Wagner, Dorothea Univ Karlsruhe TH Karlsruhe Inst Technol Fac Informat Karlsruhe Germany

ISBN: (纸本)9783642033667

algorithms or target functions for graph clustering rarely admit quality guarantees or optimal results in general. Based on properties of minimum-cut trees, a clustering algorithm by Flake et al. does however yield such a provable guarantee. We show that the structure of minimum-s-t-cuts in a graph allows for an efficient dynamic update of minimum-cut trees, and present a dynamic graph clustering algorithm that maintains a clustering fulfilling this quality quarantee, and that effectively avoids changing the clustering. Experiments on real-world dynamic graphs complement our theoretical results.

关键词： Clustering algorithms

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