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Information science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Zhijun Luo Zhibin Zhu School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi China School of Mathematics and Computing Science and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

Magnetic resonance imaging is an essential tool for clinical diagnosis, but acquisition time and reconstruction errors have always been bottleneck issues limiting the development of this field. In this paper, we use SCAD nonconvex function to construct a new MRI reconstruction nonconvex model, which can overcome the shortcomings of the traditional convex model. In model solving, considering the symmetry ADMM method improves the image reconstruction performance while increasing the reconstruction speed. The experiment shows that the method proposed in this paper has significant improvements over traditional TV methods.

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On the analysis of a higher-order Lotka-Volterra model: an application of S-tensors and the polynomial complementarity problem

arXiv

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arXiv 2024年

作者： Cui, Shaoxuan Zhao, Qi Zhang, Guofeng Jardón-Kojakhmetov, Hildeberto Cao, Ming The Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands The School of Data Science Qingdao University of Science and Technology Qingdao266061 China The Engineering and Technology Institute Groningen University of Groningen Groningen9747 AG Netherlands

It is known that the effect of species’ density on species’ growth is non-additive in real ecological systems. This challenges the conventional Lotka-Volterra model, where the interactions are always pairwise and their effects are additive. To address this challenge, we introduce HOIs (Higher-Order Interactions) which are able to capture, for example, the indirect effect of one species on a second one correlating to a third species. Towards this end, we study a general higher-order Lotka-Volterra model. We provide an existence result of a positive equilibrium for a non-homogeneous polynomial equation system with the help of S-tensors. Afterward, by utilizing the latter result, as well as the theory of monotone systems and results from the polynomial complementarity problem, we provide comprehensive results regarding the existence, uniqueness, and stability of the corresponding equilibrium. These results can be regarded as natural extensions of many analogous ones for the classical Lotka-Volterra model, especially in the case of full cooperation, competition among two factions, and pure competition. Finally, illustrative numerical examples are provided to highlight our contributions. © 2024, CC BY.

关键词： Tensors

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Entanglement Detection Length of Multipartite Quantum States

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Physical Review Letters 2025年第5期134卷 050201-050201页

作者： Fei Shi Lin Chen Giulio Chiribella Qi Zhao QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong LMIB(Beihang University) Ministry of Education and School of Mathematical Sciences Beihang University Beijing 100191 China Department of Computer Science Parks Road Oxford OX1 3QD United Kingdom Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Multipartite entanglement is a valuable resource for quantum technologies. However, detecting this resource can be challenging: for genuine multipartite entanglement, the detection may require global measurements that are hard to implement experimentally. Here we introduce the concept of entanglement detection length, defined as the minimum number of particles that have to be jointly measured in order to detect genuine multipartite entanglement. For symmetric states, we show that the entanglement detection length can be determined by testing separability of the marginal states. For general states, we provide an upper bound on the entanglement detection length based on semidefinite programming. We show that the entanglement detection length is generally smaller than the minimum observable length needed to uniquely determine a multipartite state, and we provide examples achieving the maximum gap between these two quantities.

关键词： Quantum entanglement

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Evaluation and Inference of a Proposed, Improved and Efficient W Model for Software Testing

Evaluation and Inference of a Proposed, Improved and Efficie...

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International Conference on computing and Networking Technology (ICCNT)

作者： S Senthil Velan Sivaranjani Senthil Velan P Rubini K Sudhakar Information Science and Engineering CMR Institute of Technology Bengaluru India Computer Science and Engineering MVJ College of Engineering Bengaluru India School of Engineering and Technology CMR University Bengaluru India Artificial Intelligence and Data Science NITTE Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Software Testing is an important and measured/outcome-oriented field that requires an in-depth analysis for developing new methodologies. This enables the development of high-quality end product resulting in fewer maintenance schedules. Several models have been proposed by research teams of different companies leading to improved development and testing schedules. Even with the availability of these models, a significant allocation of the budget has been dedicated purely on the maintenance of software developed by the IT industry. In order to address this need for improved process models of testing, a new exclusive and efficient W model has been proposed and explained in this paper. This model can improve the quality of end product, namely the high-quality software and further reduce the cost involved for the maintenance. Based on the quantitative measurement the proposed W software testing model, it was found the small scale software developed by adopting this method produces high-quality software with 50% less errors/faults compared to development using classical life cycle model.

关键词： Software testing Industries Schedules Costs Computational modeling Measurement uncertainty Software Maintenance Software measurement Resource management

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Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins

arXiv

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arXiv 2024年

作者： Yin, Yanlei Wang, Lihua Hoang, Dinh Thai Wang, Wenbo Niyato, Dusit The School of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Australia The School of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In the process industry, long-term and efficient optimization of production lines requires real-time monitoring and analysis of operational states to fine-tune production line parameters. However, complexity in operational logic and intricate coupling of production process parameters make it difficult to develop an accurate mathematical model for the entire process, thus hindering the deployment of efficient optimization mechanisms. In view of these difficulties, we propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. By iteratively mapping the real-world data reflecting equipment operation status and product quality indicators in the digital twin, we adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. This model enables the data-driven state evolution of the digital twin. The digital twin takes a role of aggregating the information of actual operating conditions and the results of quality-sensitive analysis, which facilitates the optimization of process production with virtual-reality evolution. Leveraging the digital twin as an information-flow carrier, we extract temporal features from key process indicators and establish a production process quality prediction model based on the proposed deep neural network. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines. This integration achieves an average operating status prediction accuracy of over 98% and a product quality acceptance rate of over 96%. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Searching for gravitational-wave signals from precessing black hole binaries with the GstLAL pipeline

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Physical Review D 2024年第2期110卷 023038-023038页

作者： Stefano Schmidt Sarah Caudill Jolien D. E. Creighton Ryan Magee Leo Tsukada Shomik Adhicary Pratyusava Baral Amanda Baylor Kipp Cannon Bryce Cousins Becca Ewing Heather Fong Richard N. George Patrick Godwin Chad Hanna Reiko Harada Yun-Jing Huang Rachael Huxford Prathamesh Joshi James Kennington Soichiro Kuwahara Alvin K. Y. Li Duncan Meacher Cody Messick Soichiro Morisaki Debnandini Mukherjee Wanting Niu Alex Pace Cort Posnansky Anarya Ray Surabhi Sachdev Shio Sakon Divya Singh Ron Tapia Takuya Tsutsui Koh Ueno Aaron Viets Leslie Wade Madeline Wade Science Park 105 1098 XG Amsterdam The Netherlands Institute for Gravitational and Subatomic Physics (GRASP) Department of Physics Center for Scientific Computing and Data Science Research Leonard E. Parker Center for Gravitation Cosmology and Astrophysics LIGO Laboratory Institute for Gravitation and the Cosmos RESCEU Department of Physics and Astronomy Graduate School of Science Center for Gravitational Physics Department of Astronomy and Astrophysics Institute for Computational and Data Sciences MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute for Cosmic Ray Research Huntsville Alabama 35811 USA Center for Space Plasma and Aeronomic Research School of Physics Mequon Wisconsin 53097 USA Department of Physics Hayes Hall

Precession in binary black holes (BBH) is caused by the failure of the black hole spins to be aligned and its study can open up new perspectives in gravitational wave astronomy, providing, among other advancements, a precise measure of distance and an accurate characterization of the BBH spins. However, detecting precessing signals is a highly nontrivial task, as standard matched filtering pipelines for gravitational wave searches are built on many assumptions that do not hold in the precessing case. This work details the upgrades made to the GstLAL pipeline to facilitate the search for precessing BBH signals. The implemented changes in the search statistics and in the signal consistency test are then described in detail. The performance of the upgraded pipeline is evaluated through two extensive searches of precessing signals, targeting two different regions in the mass space, and the consistency of the results is examined. Additionally, the benefits of the upgrades are assessed by comparing the sensitive volume of the precessing searches with two corresponding traditional aligned-spin searches. While no significant sensitivity improvement is observed for precessing binaries with mass ratio q≲6, a volume increase of up to 100% is attainable for heavily asymmetric systems with largely misaligned spins. Furthermore, our findings suggest that the primary cause of degraded performance in an aligned-spin search targeting precessing signals is not a poor signal-to-noise-ratio recovery but rather the failure of the ξ2 signal-consistency test. Our work paves the way for a large-scale search for precessing signals, which could potentially result in exciting future detections.

关键词： Gravitational waves data analysis

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Multi-agent Traffic Prediction via Denoised Endpoint Distribution

Multi-agent Traffic Prediction via Denoised Endpoint Distrib...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yao Liu Ruoyu Wang Yuanjiang Cao Quan Z. Sheng Lina Yao Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Data 61 CSIRO & School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatiotemporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents’ spatio-temporal features alongside their intrinsic intentions and un-certainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model’s efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.

关键词： Uncertainty Navigation Soft sensors Robot kinematics Predictive models Transformers Road traffic Trajectory Complexity theory Spatiotemporal phenomena

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BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning

arXiv

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arXiv 2023年

作者： Yang, Yunchao Zhou, Yipeng Hu, Miao Wu, Di Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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Controllable Protein Sequence Generation with LLM Preference Optimization 39

Controllable Protein Sequence Generation with LLM Preference...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Xiangyu Liu, Yi Chen, Silei Hu, Wei State Key Laboratory for Novel Software Technology Nanjing University China Medical School Nanjing University China National Institute of Healthcare Data Science Nanjing University China

ISBN: (纸本)157735897X

Designing proteins with specific attributes offers an important solution to address biomedical challenges. Pre-trained protein large language models (LLMs) have shown promising results on protein sequence generation. However, to control sequence generation for specific attributes, existing work still exhibits poor functionality and structural stability. In this paper, we propose a novel controllable protein design method called CtrlProt. We finetune a protein LLM with a new multi-listwise preference optimization strategy to improve generation quality and support multi-attribute controllable generation. Experiments demonstrate that CtrlProt can meet functionality and structural stability requirements effectively, achieving state-of-the-art performance in both single-attribute and multi-attribute protein sequence generation. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Joint Energy Harvesting, Semantic Transmission Selection, Channel Allocation and Power Control for Resource-Constrained IoT Networks

Joint Energy Harvesting, Semantic Transmission Selection, Ch...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nguyen Huu Sang Nguyen Duc Hai Nguyen Duc Duy Anh Nguyen Cong Luong Shimin Gong Dusit Niyato Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College of Computing and Data Sciene Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this work, we propose the use of energy harvesting and semantic communication for Internet of Things (IoT) systems. The system allows IoT devices to harvest energy from a base station and then uses the harvested energy for extracting and transmitting semantic information, i.e., scene graphs, to the base station. The proposed network thus copes with the energy and network resource constraints of the IoT devices. To maximize the total image data or scene graph transmitted to the base station, we formulate a problem that optimizes the energy harvesting duration, the selection of original image or portions of scene graphs, transmit power, and channel allocation to the IoT devices. Under the high dynamics and uncertainty of the context and size of the collected images as well as the wireless channels and computing resources, we propose two advanced deep reinforcement learning (DRL) algorithms, i.e., advantage Actor-Critic (A2C) and proximal policy optimization (PPO), to solve the problem. Simulation results are implemented on the real dataset clearly showing that the performance achieved by the proposed algorithms is much higher than that achieved by the baseline scheme. This implies that more original images or triplets are transmitted.

关键词： Wireless communication Base stations Uncertainty Heuristic algorithms Simulation Semantics Channel allocation

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