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arXiv 2024年

作者： Hashemi, Diba Wrzos-Kaminska, Weronika EPFL China

We study the maximum weight matching problem in the random-order semi-streaming model and in the robust communication model. Unlike many other sublinear models, in these two frameworks, there is a large gap between the guarantees of the best known algorithms for the unweighted and weighted versions of the problem. In the random-order semi-streaming setting, the edges of an n-vertex graph arrive in a stream in a random order. The goal is to compute an approximate maximum weight matching with a single pass over the stream using O(n polylog n) space. Our main result is a (2/3 − ǫ)approximation algorithm for maximum weight matching in random-order streams, using space O(n log n log R), where R is the ratio between the heaviest and the lightest edge in the graph. Our result nearly matches the best known unweighted (2/3 + ǫ0)-approximation (where ǫ0 ∼ 10−14 is a small constant) achieved by Assadi and Behnezhad [ICALP 2021], and significantly improves upon previous weighted results. Our techniques also extend to the related robust communication model, in which the edges of a graph are partitioned randomly between Alice and Bob. Alice sends a single message of size O(n polylog n) to Bob, who must compute an approximate maximum weight matching. We achieve a (5/6 − ǫ)-approximation using O(n log n log R) words of communication, matching the results of Azarmehr and Behnezhad [ICALP 2023] for unweighted graphs. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Exploring the Feasibility of Autonomous Diagnosis with AI/ML for Smart Health Care

Exploring the Feasibility of Autonomous Diagnosis with AI/ML...

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Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Atul Dadhich Babitha B.S. Rajkumari Ghosh Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of English ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Self-reliant diagnosis with synthetic Intelligence (AI) and gadget mastering (ML) has the potential to revolutionize healthcare by way of improving diagnosis accuracy and growing performance. AI/ML can be used to hit upon sicknesses from imaging scans, analyze massive datasets to become aware of traits in crucial fitness signs, and provide customized chance assessments. This study explores the feasibility of the use of AI and ML for autonomous prognosis, along with the opportunities, capability packages, challenges, and boundaries of such an era. A key consideration whilst comparing AI/ML for diagnosis is accuracy. AI/ML algorithms require access to categorized datasets, which consist of statistics points with correct descriptions of what they represent. In any other case, the algorithms can't appropriately recognize which styles in a dataset indicate a sure diagnosis. To expand an AI/ML device for self-reliant analysis properly, the accuracy of the dataset used to train the AI/ML algorithms needs to be of the highest nice. Similarly, AI/ML algorithms ought to be capable of accounting for variability in datasets which come from one-of-a-kind hospitals or medical imaging systems. Moreover, AI/ML algorithms ought to make decisions approximately interpretations of information in situations in which a definitive answer cannot be reached. In these cases, the AI/ML device should be able to evaluate the available fact points, decide the most in all likelihood analysis.

关键词： Hospitals Smart healthcare Machine learning approximation algorithms Internet of Things Prognostics and health management Biomedical imaging

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PhD Forum Abstract: Diffusion-based Task Scheduling for Efficient AI-Generated Content in Edge Networks

PhD Forum Abstract: Diffusion-based Task Scheduling for Effi...

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International Symposium on Information Processing in Sensor Networks (IPSN)

作者： Changfu Xu Hong Kong and BNU-HKBU United International College Hong Kong Baptist University Zhuhai China

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

The Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services is a centralized framework, thus leading to high response times. To address this issue, we propose a diffusion-based task scheduling method that considers the integration of the diffusion model, Deep Reinforcement Learning (DRL), and Mobile Edge Computing (MEC) technique to improve the AIGC efficiency. This challenges efficient server selection without prior information in dynamic MEC systems. We formulate our problem as an online integer linear programming problem aiming to minimize task offloading delay. Furthermore, we propose a novel AIGC Task Scheduling (DDRL-ATS) algorithm based on Diffusion DRL (DDRL) that effectively addresses this problem. The DDRL-ATS algorithm achieves efficient AIGC tailored for heterogeneous MEC environments. Additionally, an online Adaptive Multi-server Selection and Allocation (DDRL-AMSA) algorithm based on DDRL is proposed to further enhance the AIGC efficiency. Moreover, our DDRL-AMSA algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least a reduction of 13.54% in task offloading delay compared to state-of-the-art methods.

关键词： Multi-access edge computing Processor scheduling Heuristic algorithms approximation algorithms Scheduling Delays Time factors Servers Resource management Time complexity

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Measurement-Based Quantum Approximate Optimization

Measurement-Based Quantum Approximate Optimization

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Tobias Stollenwerk Stuart Hadfield Jülich Research Center Institute for Quantum Computing Analytics (PGI-12) Jülich Germany Quantum Artificial Intelligence Lab (QuAIL) USRA Research Institute for Advanced Computer Science NASA Ames Research Center CA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Parameterized quantum circuits are attractive candidates for potential quantum advantage in the near term and beyond. At the same time, as quantum computing hardware not only continues to improve but also begins to incorporate new features such as mid-circuit measurement and adaptive control, opportunities arise for innovative algorithmic paradigms. In this work we focus on measurement-based quantum computing protocols for approximate optimization, in particular related to quantum alternating operator ansätze (QAOA), a popular quantum circuit model approach to combinatorial optimization. For the construction and analysis of our measurement-based protocols we demonstrate that diagrammatic approaches, specifically ZX-calculus and its extensions, are effective for adapting such algorithms to the measurement-based setting. In particular we derive measurement patterns for applying QAOA to the broad and important class of QUBO problems. We further outline how for constrained optimization, hard problem constraints may be directly incorporated into our protocol to guarantee the feasibility of the solution found and avoid the need for dealing with penalties. Finally we discuss the resource requirements and tradeoffs of our approach to that of more traditional quantum circuits.

关键词： Protocols Quantum advantage Focusing Logic gates approximation algorithms Time measurement Hardware

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Comparative Study on Reference Signal for Data Selection algorithms in PEM-AFC Framework

Comparative Study on Reference Signal for Data Selection Alg...

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IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Yu-Xiang Weng Ying-Ren Chien Felix Albu Dept. of Electrical Engineering National Ilan University NIU Yilan Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

In this paper, we explore an acoustic feedback cancellation (AFC) algorithm that integrates the correntropy-based data-selective algorithm within the prediction error method (PEM). The data selective approach compares the performance of using the error signal $e(n)$ and the pre-whitened error signal $e_{p}(n)$ as data samples. It is shown that using the error signal provides superior system performance and audio quality compared to the pre-whitened error signal, while closely approximating the performance of the original algorithm.

关键词： Communication systems System performance Signal processing algorithms Signal processing Prediction algorithms approximation algorithms Acoustics Multiple signal classification Speech processing

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A Relation-and-Regression-assisted Evolutionary Algorithm for Expensive Constrained Multi-objective Optimization

A Relation-and-Regression-assisted Evolutionary Algorithm fo...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Siyu Chen Jinyuan Zhang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Expensive constrained multi-objective optimization problems involve computationally expensive objectives and constraints, which impose stiff challenges on traditional evolutionary algorithms to optimize within limited function evaluations. To address this, some surrogate-assisted constrained multi-objective evolutionary algorithms have been proposed, where surrogate models are constructed to replace expensive function evaluations. However, most existing surrogate models are either regression or classification models, which are liable to poor reliability in approximating complicated constraints. In this paper, a relation-and-regression-assisted constrained multi-objective evolutionary algorithm, named RCMOEA, is proposed. In RCMOEA, each regression model is constructed to approximate each objective function, and each relation model is built to learn the relation of constraint values between any two solutions. Based on the constructed surrogate models, a relation-and-regression-based constrained Pareto dominance, denoted as RCPD, is proposed to compare solution pairs. By adopting RCPD as the dominance criterion, the RCPD-based selection strategy is proposed for selecting offspring solutions. Also, the distance-based infill sampling strategy is proposed to preserve the diversity of solutions. Experimental results demonstrate the superiority of RCMOEA over the compared algorithms.

关键词： Computational modeling Evolutionary computation Predictive models Linear programming Prediction algorithms approximation algorithms Manufacturing Reliability Complex systems Optimization

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Bayes Optimal Cardinality Filters for Streaming Count Data

Bayes Optimal Cardinality Filters for Streaming Count Data

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International Conference on Information Fusion

作者： R. L. Streit Metron 1818 Library St. Suite 600 Reston VA USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

A time sequence of counts of the number of sensor detections is the sum of the number of detections of objects and the number of false alarms. We model the count sequence as an insertion-deletion process, and model the time-varying number of objects as a birth-death process. Under these modeling assumptions, we derive the optimal recursive Bayesian posterior distribution for the number of objects conditioned only on the count sequence. The method is potentially applicable in management science to detect changes in demand in decisionindependent observed data streams and in social media to estimate the number of users who abuse hashtags. A maximum a posteriori (MAP) algorithm for estimating the parameters of the birth-death and insertion-deletion processes is presented.

关键词： Filters Social networking (online) Filtering algorithms Information filters approximation algorithms Bayes methods Streams

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Distributed Nash Equilibrium Seeking for Games with Nonlinear Players via Fuzzy Adaptive Control

Distributed Nash Equilibrium Seeking for Games with Nonlinea...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Ying Chen Qian Ma Dongrui Wu School of Automation Nanjing University of Science and Technology Nanjing China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

This paper explores the distributed Nash equilibrium seeking problem for games involving a class of players with unknown high-order nonlinear dynamics. Firstly, the fuzzy observer is employed to estimate the unknown nonlinearities and unmeasurable states. Next, an observer-based distributed Nash equilibrium seeking algorithm is developed using the fuzzy adaptive backstepping method. The algorithm ensures that the players' actions can be regulated within the neighbourhood of the Nash equilibrium, and all signals in the closed-loop systems remain bounded. Lastly, the effectiveness of the algorithm is demonstrated through a numerical example.

关键词： Backstepping Heuristic algorithms Process control Games Observers Nash equilibrium approximation algorithms

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The impact of inverse matrix computation on the results of the Capon algorithm

The impact of inverse matrix computation on the results of t...

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Telecommunications Forum (TELFOR)

作者： Nebojša Kolarić Miloš Bjelić Nataša Ćirović School of Electrical Engineering Belgrade Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

algorithms for estimating the direction of signal arrival are crucial in various applications, particularly in acoustics. They are essential for accurately locating sources of both desired and undesired sound signals. This research focuses on the performance analysis of the Capon algorithm using an approximated inverse covariance matrix. Instead of using direct methods for matrix inversion, iterative methods are employed, potentially leading to shorter processing times and reduced memory usage. Furthermore, the performance of these iterative methods is compared to the direct Cholesky method in terms of computational operations.

关键词： Sensitivity Program processors Heuristic algorithms Signal processing algorithms approximation algorithms Telecommunications Performance analysis Iterative methods Sparse matrices Covariance matrices

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A Bilayered Decomposition Technique for Handling Complex Constrained Multi-Objective Optimization Problems

A Bilayered Decomposition Technique for Handling Complex Con...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yusuke Yasuda Kenichi Tamura Keiichiro Yasuda Department of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Constrained multi-objective optimization problems (CMOPs) are prevalent in real-world applications, dealing with multi-objective and constraint functions. Multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) has proven effective for unconstrained multi-objective optimization problems with complex Pareto front (PF). However, in CMOPs, the conflicting nature between objectives and constraints often makes it challenging to appropriately manage constraints while ensuring convergence, diversity, and feasibility of the solution set towards the PF. To address this issue, this paper proposes a bilayered decomposition technique. The proposed algorithm treats constraints as an additional objective function, separately decomposing the objective space and the constraint violation space. This method allows for the simultaneous attainment of convergence, diversity, and feasibility in the solution set while efficiently exploring the PF. Experimental results demonstrate that, within the MOEA/D-DE framework, our algorithm efficiently navigates complex PFs on challenging problems such as LIR-CMOPs and DAS-CMOPs, matching the performance of state-of-the-art constrained multi-objective evolutionary algorithms.

关键词： Navigation Diversity reception Evolutionary computation approximation algorithms Search problems Linear programming Vectors Optimization Cybernetics Convergence

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