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International Conference on Intelligent Transportation

作者： Chao Zhang Yechen Li Neha Arora Damien Pierce Carolina Osorio Google Research Mountain View CA USA HEC Montreal Mountain View QC Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional instances in a sample-efficient way. For the first time, case studies on 6 metropolitan highway networks are carried out, considering a total of 54 calibration scenarios. This is the first work to show the ability of a calibration algorithm to systematically scale across networks. Compared to the state-of-the-art simultaneous perturbation stochastic approximation (SPSA) algorithm, the proposed approach enhances fit to field data by an average 43.5% with a maximum improvement of 80.0%, and does so within fewer simulation calls.

关键词： Road transportation Perturbation methods Urban areas Traffic control Benchmark testing approximation algorithms Robustness Calibration Standards Intelligent transportation systems

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Beamforming Design of ISAC System for Enhancing Sensing Distance

Beamforming Design of ISAC System for Enhancing Sensing Dist...

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Future Communications and Networks (FCN), International Conference on

作者： Yuan Liu Ruihong Jiang Yongdong Zhu Bin Yang Shuyuan Zhao Research Center for Space Computing System Zhejiang Laboratory Hangzhou China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

In this paper, a dual-functionality base station (BS) integrated sensing and communication (ISAC) is exploited to sense one intended target and communicate with multiple users simultaneously. Meanwhile, rate splitting multiple access (RSMA) technology is used to improve communication quality. We formalize a problem aimed at maximizing sensing distance by optimizing the beamforming design of the BS and the allocation of common rate portions, where the signal to interference plus noise ratio (SINR) gain constraint of sensing echo signal and the individual communication rate requirements of each user are satisfied. Due to the non-convexity of the formulated problem, a successive convex approximation (SCA)-based algorithm is proposed to tackle it. Simulation results manifest that as the transmit power increases, the achievable sensing distance increases, and as the SINR threshold of the sensing echo signal increases, the achievable sensing distance decrease. Furthermore, it indicates that the RSMA-based protocol offers superior sensing distance gain compared to the SDMA-based protocol. This performance advantage becomes more pronounced when the communication rate threshold is high, resulting in a relatively large performance gap between the two protocols.

关键词： Base stations Protocols Array signal processing Simulation Interference Integrated sensing and communication approximation algorithms Resource management Iterative methods Signal to noise ratio

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Multi-Objective Derivative-Free Optimization Based on Hessian-Aware Gaussian Smoothing Method

Multi-Objective Derivative-Free Optimization Based on Hessia...

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International Conference on Control and Automation (ICCA)

作者： Ran Chen Yuzhe Li Tianyou Chai State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Most deterministic multi-objective optimization algorithms assume that the analytical objective functions and multi-objective gradients are available. However, there may be no available or explicit mathematical model for many real-world optimization problems. These problems are difficult to handle since the evaluations of gradients are inexact or even infeasible, which are termed derivative-free (or zeroth-order) optimization problems. To tackle the issues, a multi-objective derivative-free optimization based on Hessian-aware Gaussian smoothing method is proposed. We evaluate the multi-objective descent direction and produce new candidates for searching the Pareto front. Furthermore, we analyze the corresponding convergence rate of candidates produced by the Hessian-aware Gaussian smoothing method explicitly. Finally, we compared different multi-objective optimization algorithms and illustrated their performance on numerical benchmarks to show the proposed algorithm's effectiveness.

关键词： Measurement Sufficient conditions Smoothing methods Heuristic algorithms Estimation approximation algorithms Linear programming

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Reliability-Aware Network Slicing based on Multi-Objective Optimization

Reliability-Aware Network Slicing based on Multi-Objective O...

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Congress on Evolutionary Computation

作者： Qiqi Xia Jin Wang Chengqiang Huang Xin Yao Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Reliability Technology Lab Huawei Technologies Co. Ltd. Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong SAR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric.

关键词： Measurement 6G mobile communication Costs Network topology Network slicing Evolutionary computation approximation algorithms

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Well-Balanced Convex Limiting for Finite Element Discretizations of Steady Convection-Diffusion-Reaction Equations

SSRN

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SSRN 2024年

作者： Knobloch, Petr Kuzmin, Dmitri Jha, Abhinav Department of Numerical Mathematics Faculty of Mathematics and Physics Charles University Sokolovská 83 Praha 818675 Czech Republic TU Dortmund University Vogelpothsweg 87 DortmundD-44227 Germany Institute of Applied Analysis and Numerical Simulation University of Stuttgart Pfaffenwaldring 57 Stuttgart70569 Germany

We address the numerical treatment of source terms in algebraic flux correction schemes for steady convection-diffusion-reaction (CDR) equations. The proposed algorithm constrains a continuous piecewise-linear finite element approximation using a monolithic convex limiting (MCL) strategy. Failure to discretize the convective derivatives and source terms in a compatible manner produces spurious ripples, e.g., in regions where the coefficients of the continuous problem are constant and the exact solution is linear. We cure this deficiency by incorporating source term components into the fluxes and intermediate states of the MCL procedure. The design of our new limiter is motivated by the desire to preserve simple steady-state equilibria exactly, as in well-balanced schemes for the shallow water equations. The results of our numerical experiments for two-dimensional CDR problems illustrate potential benefits of well-balanced flux limiting in the scalar case. © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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ENAO: Evolutionary Neural Architecture Optimization in the Approximate Continuous Latent Space of a Deep Generative Model

ENAO: Evolutionary Neural Architecture Optimization in the A...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zheng Li Xuan Rao Shaojie Liu Bo Zhao Derong Liu Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China School of Systems Science Beijing Normal University Beijing China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Neural architecture search (NAS) has emerged as a transformative approach for automating the design of neural networks, demonstrating exceptional performance across a variety of tasks. Numerous NAS methods aim to optimize neural architectures within discrete or continuous search spaces, but each method possesses its own inherent limitations. Additionally, the search efficiency is notably impeded by suboptimal encoding methods, presenting an ongoing challenge. In response to these obstacles, this paper introduces a novel approach, evolutionary neural architecture optimization (ENAO), which optimizes architectures in an approximate continuous search space. ENAO begins with training a deep generative model to embed discrete architectures into a condensed latent space, leveraging unsupervised representation learning. Subsequently, evolutionary algorithm is employed to refine neural architectures within this approximate continuous latent space. Empirical comparisons against several NAS benchmarks underscore the effectiveness of the ENAO method. Thanks to its foundation in deep unsupervised representation learning, ENAO demonstrates a distinguished ability to identify high-quality architectures with fewer evaluations and achieve state-of-the-art result in NAS-Bench-201 dataset. Overall, the ENAO method is a promising approach for optimizing neural network architectures in an approximate continuous search space with evolutionary algorithms and may be a useful tool for researchers and practitioners in the field of NAS.

关键词： Representation learning Training Neural networks Evolutionary computation Benchmark testing approximation algorithms Encoding

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Direction Finding Using Null Steering Technique

Direction Finding Using Null Steering Technique

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Sumit Kumar Rishi Mishra Yallanki Sai Sreenija Sylvie Rana A.R. Harish Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur Uttar Pradesh

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

This paper proposes an idea of direction finding using null steering methodology. A reconfigurable intelligent surface (RIS) working at 2.4 GHz is designed and capable of generating as well as steering null when placed in front of a linearly polarized antenna. This null steering can be used to make an estimate of the direction of waves reaching the receiver. An algorithm has been proposed to mathematically estimate the direction of target. Along with that a comparison between estimated and true direction is presented in a tabular fashion.

关键词： Direction-of-arrival estimation Accuracy Receivers Reconfigurable intelligent surfaces approximation algorithms Antennas

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Phase distribution in 1D localization and phase transitions in single-mode waveguides

arXiv

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arXiv 2024年

作者： Suslov, I.M. P.L.Kapitza Institute for Physical Problems Moscow119334 Russia

Localization of electrons in 1D disordered systems is usually described in the random phase approximation, when distributions of phases φ and θ, entering the transfer matrix, are considered as uniform. In the general case, the random phase approximation is violated, and the evolution equations (when the system length L is increased) contain three independent variables, i.e. the Landauer resistance ρ and the combined phases ψ = θ - φ and χ = θ + φ. The phase χ does not affect the evolution of ρ and was not considered in previous papers. The distribution of the phase ψ is found to exhibit an unusual phase transition at the point ϵ0when changing the electron energy ϵ, which manifests itself in the appearance of the imaginary part of ψ. The distribution of resistance P(ρ) has no singularity at the point ϵ0, and the transition looks unobservable in the electron disordered systems. However, the theory of 1D localization is immediately applicable to propagation of waves in single-mode optical waveguides. The optical methods are more efficient and provide possibility to measure phases ψ and χ. On the one hand, it makes observable the phase transition in the distribution P(ψ), which can be considered as a φtraceφ of the mobility edge remaining in 1D systems. On the other hand, observability of the phase χ makes actual derivation of its evolution equation, which is presented below. Relaxation of the distribution P(χ) to the limiting distribution P∞(χ) at L → 1 is described by two exponents, whose exponentials have jumps of the second derivative, when the energy E is changed. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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A Bregman-Sinkhorn Algorithm for the Maximum Weight Independent Set Problem

arXiv

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arXiv 2024年

作者： Haller, Stefan Savchynskyy, Bogdan Heidelberg University Germany

We propose a scalable approximate algorithm for the NP-hard maximum-weight independent set problem. The core of our algorithm is a dual coordinate descent applied to a smoothed LP relaxation of the problem. This technique is referred to as Bregman method or Sinkhorn algorithm in the literature. Our algorithm addresses a family of clique cover LP relaxations, where the constraints are determined by the set of cliques covering the underlying graph. The objective function of the relaxation is smoothed with an entropy term. An important question determining efficiency of our algorithm is the control of the smoothing level over the course of optimization. While several dedicated techniques have been considered in the literature to this end, we propose a new one based on estimation of the relaxed duality gap. To make this estimation possible, we developed a new projection method to the feasible set of the considered LP relaxation. We experimentally show that our technique notably outperforms the standard one based on feasibility estimation. Additionally to solving the relaxed dual, we utilize a simple and very efficient primal heuristic to obtain feasible integer solutions of the initial non-relaxed problem. Our heuristic is a combination of the greedy generation and optimal recombination applied to the reduced costs computed by the dual optimization. Our experimental validation considers two datasets based on real-life applications, where our method shows competitive results being able to find high quality approximate solutions within 10 seconds for graphs with hundreds of thousands of nodes and dozens of millions of edges. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Comparison of quantum computing algorithms for network traffic partitioning and mitigation of DoS and DDoS attacks

Comparison of quantum computing algorithms for network traff...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Casimer DeCusatis Evan Spillane Dominick Foti Megan O’Loughlin School of Computer Science and Mathematics Marist College Poughkeepsie NY USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

We investigate application of quantum computing algorithms to classifying network traffic and mitigating denial of service and distributed denial of service attacks. Using actual honeynet data, we compare solutions of the Max Cut problem using both the quantum approximate optimization algorithm (QAOA) and Variational Quantum Eigensolver (VQE). Experimental results from a 6-qubit quantum computer show that optimal solutions can be found with about 98% accuracy in polynomial execution time. We found that VQE is more accurate than QAOA (compared with classical brute force solutions) but also requires significantly longer execution times. Simulations predict this approach can scale to much larger quantum computing hardware (when available) and may achieve quantum advantage on hardware with 30-35 qubits.

关键词： Accuracy Quantum advantage Prevention and mitigation Qubit Telecommunication traffic Predictive models approximation algorithms Prediction algorithms Hardware Classification algorithms

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