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International Joint Conference on Neural Networks (IJCNN)

作者： Prayas Jain Mridula Verma K.K Shukla Indian Institute of Technology (BHU) Varanasi Institute for Development and Research in Banking Technology (IDRBT) Hyderabad

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Proximal algorithms are known to be popular in solving non-smooth convex loss minimization framework due to their low iteration costs and good performance. Convergence rate analysis is an essential part in the process of designing new proximal methods. In this paper, we present a viscosity-approximation-based proximal gradient algorithm and prove its linear convergence rate. We also present its accelerated variant and discuss the condition for the improved convergence rate. These algorithms are applied to solve the problem of multiclass image classification problem. CIFAR10, a popular publicly available benchmark real image classification dataset is used to experimentally validate our theoretical proofs, and the classification performances are compared with that of the state-of-the-art algorithms. To the best of our knowledge, it is the first time that the viscosity-approximation concept is applied to a multiclass classification problem.

关键词： Convergence Acceleration approximation algorithms Viscosity Minimization Benchmark testing Machine learning

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Neural Adaptive Contextual Video Streaming

Neural Adaptive Contextual Video Streaming

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianchao Yang Mufan Liu Puyue Hou Yiling Xu Jun Sun Department of Electronic Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video streaming services typically employ traditional codecs, such as H.264, to encode videos into multiple bitrate representations. These codecs are tightly limited by discrete quantization parameters (QPs), resulting in encoded rates that do not align with the target bitrate. Additionally, the subpar video quality produced by conventional codecs does not meet the demands of high-resolution communication. Considering the limitations of traditional codecs, we take a fresh new approach to video streaming by leveraging advanced deep learning-based video codecs. Specifically, we develop a neural adaptive contextual video streaming framework that incorporates: 1) an ensemble deep reinforcement learning based adaptive bitrate algorithm named TSAC that enables continuous bitrate adjustment to varying network conditions 2) a two-stage proportional-integral-derivative-based rate control module that dynamically fine-tunes QPs to ensure the encoded bitrate aligning with the target bitrate. Furthermore, we implement intra-GoP and inter-GoP techniques to accelerate the inference process of the contextual video codec for real-time processing needs. Our experiments demonstrate that the average relative error in bitrate remains below 2%, the quality of experience provided by our TSAC agents surpasses that of existing discrete algorithms by 13%-20%. Our optimization techniques enable real-time decoding at approximately 24 frames per second for quad high definition videos.

关键词： Adaptive systems Bit rate Signal processing algorithms Streaming media approximation algorithms Real-time systems Inference algorithms Decoding Video codecs Speech processing

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Real-Time Traffic Sign Recognition Based on Threshold Segmentation and Neural Networks

Real-Time Traffic Sign Recognition Based on Threshold Segmen...

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Information and Network Technologies (ICINT), International Conference on

作者： Yuanzhou Wei Meiyan Gao Jun Xiao Bolin Huang Yeyubei Zhang Yuanhao Tian College of Engineering and Computing Florida International University Miami USA Viterbi School of Engineering University of Southern California California USA School of Engineering and Applied Science University of Pennsylvania Philadelphia United States Steven J. Green School Florida International University Miami USA

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

This paper constructs a traffic sign recognition model by comparing traffic sign detection and classification algorithms, utilizing threshold segmentation and neural network algorithms. The model achieves an error rate of less than 5% for traffic sign recognition on the GTSRB dataset, with the recognition process for each frame taking approximately 150ms. This effectively realizes real-time detection and classification of traffic signs. It provides convenient management technology for relevant traffic management departments.

关键词： Deep learning Error analysis Neural networks approximation algorithms Real-time systems Classification algorithms

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Towards Classical Software Verification using Quantum Computers

Towards Classical Software Verification using Quantum Comput...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Sebastian Issel Kilian Tscharke Pascal Debus Fraunhofer AISEC Garching Germany

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

We explore the possibility of accelerating the formal verification of classical programs with a quantum computer.A common source of security flaws stems from the existence of common programming errors like use after free, null-pointer dereference, or division by zero. To aid in the discovery of such errors, we try to verify that no such flaws *** our approach, for some code snippet and undesired behavior, a SAT instance is generated, which is satisfiable precisely if the behavior is present in the code. It is in turn converted to an optimization problem, that is solved on a quantum computer. This approach holds the potential of an asymptotically polynomial *** examples of common errors, like out-of-bounds and overflows, but also synthetic instances with special properties, specific number of solutions, or structure, are tested with different solvers and tried on a quantum *** use the near-standard Quantum approximation Optimization Algorithm, an application of the Grover algorithm, and the Quantum Singular Value Transformation to find the optimal solution, and with it a satisfying assignment.

关键词： Quantum computing Codes Software algorithms Noise approximation algorithms Hardware Encoding Software Optimization Formal verification

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Massively parallel algorithms for distance approximation and spanners

arXiv

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arXiv 2020年

作者： Biswas, Amartya Shankha Mitrović, Slobodan Dory, Michal Ghaffari, Mohsen Nazari, Yasamin CSAIL MIT ETH Zurich Johns Hopkins University

Over the past decade, there has been increasing interest in distributed/parallel algorithms for processing large-scale graphs. By now, we have quite fast algorithms—usually sublogarithmic-time and often poly(log log n)-time, or even faster—for a number of fundamental graph problems in the massively parallel computation (MPC) model. This model is a widely-adopted theoretical abstraction of MapReduce style settings, where a number of machines communicate in an all-to-all manner to process large-scale data. Contributing to this line of work on MPC graph algorithms, we present poly(log k) ∈ poly(log log n) round MPC algorithms for computing O(k1+o(1))-spanners in the strongly sublinear regime of local memory. To the best of our knowledge, these are the first sublogarithmic-time MPC algorithms for spanner construction. © 2020, CC BY.

关键词： approximation algorithms

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Joint Position and Orientation Optimization for 6DMA Enhanced Multi-Access Point Coordination

Joint Position and Orientation Optimization for 6DMA Enhance...

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IEEE Conference on Wireless Communications and Networking

作者： Xiangyu Pi Lipeng Zhu Haobin Mao Zhenyu Xiao School of Electronic and Information Engineering Beihang University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we develop a six-dimensional movable antenna (6DMA) enhanced multi-access point (AP) coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and can attain considerable performance improvement compared to the conventional fixed antenna (FA)-based schemes.

关键词： Wireless communication Three-dimensional displays Simulation Transmitting antennas Receiving antennas approximation algorithms Vectors Uplink Optimization Antenna arrays

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Low-Complexity Cramér-Rao Lower Bound and Sum Rate Optimization in ISAC Systems

Low-Complexity Cramér-Rao Lower Bound and Sum Rate Optimiza...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tianyu Fang Nhan Thanh Nguyen Markku Juntti Centre for Wireless Communications University of Oulu Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While Cramér-Rao lower bound is an important metric in sensing functions in integrated sensing and communications (ISAC) designs, its optimization usually involves a computationally expensive solution such as semidefinite relaxation. In this paper, we aim to develop a low-complexity yet efficient algorithm for CRLB optimization. We focus on a beamforming design that maximizes the weighted sum between the communications sum rate and the sensing CRLB, subject to a transmit power constraint. Given the non-convexity of this problem, we propose a novel method that combines successive convex approximation (SCA) with a shifted generalized power iteration (SGPI) approach, termed SCA-SGPI. The SCA technique is utilized to approximate the non-convex objective function with convex surrogates, while the SGPI efficiently solves the resulting quadratic subproblems. Simulation results demonstrate that the proposed SCA-SGPI algorithm not only achieves superior tradeoff performance compared to existing method but also significantly reduces computational time, making it a promising solution for practical ISAC applications.

关键词： Measurement Lower bound Array signal processing Simulation Signal processing algorithms Integrated sensing and communication approximation algorithms Linear programming Speech processing Optimization

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Synthesis of the Reference Model Based on the Generative Model Method

Synthesis of the Reference Model Based on the Generative Mod...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexander V. Finoshin Nikita A. Martynov Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper describes the Generative Model method along with its advantages over traditional approaches to reference model construction. The modification of algorithm of the Generative model parameters calculation is proposed. The sample step problem is investigated. The algorithm of sample step verification is proposed and successfully applied for the signal with aliasing. Application of the Generative Model design for a complicated signal for which polynomial approximation or Fourier series is either difficult or not optimal is presented.

关键词： Power engineering Filtering Signal processing algorithms Signal processing approximation algorithms Polynomials Trajectory Fourier series

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Exploring the Effects of Dimensionality Reduction Techniques on Topic Classification of an Arabic News Article Dataset

Exploring the Effects of Dimensionality Reduction Techniques...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Mouaad Errami Mohamed Amine Ouassil Rabia Rachidi Mohammed Jebbari Bouchaib Cherradi Abdelhadi Raihani EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco LaROSERI Laboratory Faculty of Science Chouaib Doukali University El Jadida Morocco STIE Team CRMEF Casablanca-Settat EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

this study examines the difficulties of classifying text in Arabic using advanced machine learning (ML) algorithms with dimension reduction methods like Principal Component Analysis (PCA) and Uniform Manifold approximation and Projection (UMAP). Keeping in view the unique complexities of text in news articles written in Arabic, we employed diverse ML algorithms like Support Vector Machines (SVM), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Random Forest. In comparative research, we examine the effect of PCA and UMAP on model performance with regard to accuracy and processing time. The findings indicate that PCA increases accuracy in all models with a maximum accuracy of 87.23% using SVM with PCA. Along with this, PCA reduces processing time significantly compared to processing raw text and is thus a good candidate to consider in text classification in large datasets. This research not only emphasizes the importance of dimension reduction in text classification in Arabic but also offers insights to enhance ML workflows in other languages with complex structures.

关键词： Support vector machines Dimensionality reduction Accuracy Machine learning algorithms Text categorization Feature extraction approximation algorithms Classification algorithms Random forests Principal component analysis

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Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges

arXiv

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arXiv 2025年

作者： Crane, Alex Stanley, Thomas Sullivan, Blair D. Veldt, Nate University of Utah United States Texas A&M University United States

We consider a framework for clustering edge-colored hypergraphs, where the goal is to cluster (equivalently, to color) objects based on the primary type of multiway interactions they participate in. One well-studied objective is to color nodes to minimize the number of unsatisfied hyperedges – those containing one or more nodes whose color does not match the hyperedge color. We motivate and present advances for several directions that extend beyond this minimization problem. We first provide new algorithms for maximizing satisfied edges, which is the same at optimality but is much more challenging to approximate, with all prior work restricted to graphs. We develop the first approximation algorithm for hypergraphs, and then refine it to improve the best-known approximation factor for graphs. We then introduce new objective functions that incorporate notions of balance and fairness, and provide new hardness results, approximations, and fixed-parameter tractability results. Copyright © 2025, The Authors. All rights reserved.

关键词： approximation algorithms

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