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Addressing the traveling salesperson problem with frequency fitness assignment and hybrid algorithms

Soft computing 2024年第17-18期28卷 9495-9508页

作者： Liang, Tianyu Wu, Zhize Lässig, Jörg Berg, Daan van den Thomson, Sarah L. Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China Fakultät Elektrotechnik und Informatik Hochschule Zittau/Görlitz Brückenstraße 1 Saxony Görlitz02826 Germany Abteilung Kognitive Energiesysteme Fraunhofer IOSB-AST Wilhelmsplatz 11 Saxony Görlitz02826 Germany Department of Computer Science Vrije Universiteit Amsterdam De Boelelaan 1111 Amsterdam1081 HV Netherlands School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Jiulong Road 111 Anhui Hefei230601 China

The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to optimization has recently been proposed: instead of directing the search towards better solutions, the optimization process prefers those with rarely encountered objective values. FFA can be plugged into existing algorithms and can improve their performance on some problems that are hard for them. However, problems like the TSP, where the number of possible objective values (here: tour lengths) is high, cause the performance of FFA-based algorithms to deteriorate. We choose 56 symmetric instances from TSPLib as a testbed for our experiments. We plug FFA both into the (1+1) EA and simulated annealing (SA) and obtain the FEA and FSA, respectively. We find that the resulting FEA substantially outperforms the (1+1) EA and that FSA can reach the global optima more often than SA within our computational budget. We propose two ways to hybridize FFA and traditional optimization in a round-robin scheme where objective function evaluations are alternatingly assigned to both components. The FFA-based parts of the algorithms sometimes send a solution over to the traditional-optimization-based ones. This can lead to further performance improvements. In particular, the hybrids combining simulated annealing with the FEA solve most problems and find close-to-optimal solutions on other instances. Even for several instances with many possible different objective values, which are therefore not suited to FFA, hybrids combining FFA and traditional objective-guided optimization algorithms can offer substantial performance improvements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

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Scalable and verifiable quantum secret sharing with photonic efficiency via quasicausal cones in the multiscale entanglement renormalization ansatz framework

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Physical Review A 2025年第2期111卷 022603-022603页

作者： Hong Lai Zhongrui Huang Li Ren Josef Pieprzyk Qibin Zhao Leongchuan Kwek School of Computer and Information Science Southwest University Chongqing 400715 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore Data61 CSIRO Sydney New South Wales 2122 Australia Institute of Computer Science Polish Academy of Sciences Warsaw 01-248 Poland RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan MajuLab UMI No. 3654 CNRS-UNS-NUS-NTU International Joint Research Unit Singapore 117543 Singapore National Institute of Education Nanyang Technological University Singapore 637616 Singapore Quantum Science and Engineering Centre Nanyang Technological University Singapore 639798 Singapore

Quantum secret sharing (QSS) is set to revolutionize secure communication. However, achieving practical implementations that balance efficiency, security, and flexibility remains a significant challenge. To address this issue, we propose an innovative QSS scheme that integrates a verification mechanism for the distribution process by leveraging quasicausal cones within the multiscale entanglement renormalization ansatz framework. This approach ensures the secure dissemination of entangled photons across a multiuser network while enhancing the integrity of the quantum state against disturbances. A key feature of our scheme is the application of quasicausal cones, which improve the system's fault tolerance and recovery precision, preserving quantum state coherence and stability even in the presence of errors or losses. The recursive and hierarchical design of our framework enables it to dynamically adapt to fluctuations in network size and the number of participants, making it well suited for large-scale quantum networks. Furthermore, our method reduces the costs associated with photon transmission and storage, enhancing resource allocation within expansive quantum networks. The strategic incorporation of quasicausal cones represents a major advancement, streamlining the flow of quantum information and minimizing the overall resource footprint. By flexibly accommodating varying network sizes and configurations, our scheme marks a significant step towards efficient large-scale QSS, underscoring the critical role of quasicausal cones in advancing quantum communication technologies.

关键词： Photons

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China in the eyes of news media:a case study under COVID-19 epidemic

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Frontiers of information Technology & Electronic Engineering 2021年第11期22卷 1443-1457页

作者： Hong HUANG Zhexue CHEN Xuanhua SHI Chenxu WANG Zepeng HE Hai JIN Mingxin ZHANG Zongya LI National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhan430074China

As one of the early COVID-19 epidemic outbreak areas,China attracted the global news media’s attention at the beginning of *** the epidemic period,Chinese people united and actively fought against the ***,in the eyes of the international public,the situation reported about China is not *** better understand how the international public portrays China,especially during the epidemic,we present a case study with big data *** aim to answer three questions:(1)What has the international media focused on during the COVID-19 epidemic period?(2)What is the media’s tone when they report China?(3)What is the media’s attitude when talking about China?In detail,we crawled more than 280000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting *** example,international media paid more attention to Chinese livelihood during the COVID-19 epidemic *** March and April,“progress of Chinese vaccines,”“specific drugs and treatments,”and“virus outbreak in U.S.”became the media’s most common *** terms of news attitude,Cuba,Malaysia,and Venezuela had a positive attitude toward China,while France,Canada,and the united Kingdom had a negative *** study can help understand China’s image in the eyes of the international media and provide a sound basis for image analysis.

关键词： Country image COVID-19 epidemic Topic mining Entity Tone of news Emotion

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Reinforcement Learning Based Control Domain Division in LEO Satellite Networks

Reinforcement Learning Based Control Domain Division in LEO ...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Feilong Tang Xue Li Long Chen Jiacheng Liu Ming Gao Yanqin Yang Wenchao Xu Heteng Zhang School of Data Science and Engineering East China Normal University Shanghai China School of Computing Science Simon Fraser University Vancouver Canada Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Communication and Electric Engineering East China Normal University Shanghai China Suzhou All-Space-Time Information Technology Co. Ltd. Suzhou China

The performance of SDN-based LEO satellite networks significantly depends on the control domain division approaches. The dynamical topology in LEO networks results in the time-varying delay for network management. Therefore, the control domain division is very challenging and should be dynamical. In this paper, we propose a control domain division approach based on reinforcement learning (RL), which aims at reducing the average management delay of controllers during the control period, and as well as minimizing the number of control domain handovers. Firstly, we analyze the factors that need to be paid attention to in controller deployment, such as delay, load and controller switching cost, and formulate the controller deployment. By relaxing the constraints, we prove that the problem is NP-hard. Secondly, we propose an approach that considers future motion trajectory based on reinforcement learning (FMT _ RL) to deploy controllers. Since the dimension of the neural network in reinforcement learning is fixed, we use the improved K-Means algorithm to select the set of mechanisms to determine the dimension of the neural network. Particularly, in order to obtain the node composition control domain, the observation including both future motion trajectory and history controller selection information are input into the neural network to calculate the control domain division results. The experimental results demonstrate that our approach significantly outperforms related approaches, with better stability and average control cost.

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Virtual quantum resource distillation: General framework and applications

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Physical Review A 2024年第2期109卷 022403-022403页

作者： Ryuji Takagi Xiao Yuan Bartosz Regula Mile Gu Department of Basic Science The University of Tokyo Tokyo 153-8902 Japan Nanyang Quantum Hub School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Center on Frontiers of Computing Studies Peking University Beijing 100871 China School of Computer Science Peking University Beijing 100871 China Mathematical Quantum Information RIKEN Hakubi Research Team RIKEN Cluster for Pioneering Research and RIKEN Center for Quantum Computing Wako Saitama 351-0198 Japan Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore CNRS-UNS-NUS-NTU International Joint Research Unit UMI No. 3654 Singapore 117543 Singapore

We develop the general framework of virtual resource distillation, an alternative distillation strategy proposed in Phys. Rev. Lett. 132, 050203 (2024), which extends conventional quantum resource distillation by integrating the power of classical postprocessing. The framework presented here is applicable not only to quantum states, but also to dynamical quantum objects such as quantum channels and higher-order processes. We provide a general characterization and benchmarks for the performance of virtual resource distillation in the form of computable semidefinite programs as well as several operationally motivated quantities. We apply our general framework to various concrete settings of interest, including standard resource theories such as entanglement, coherence, and magic, as well as settings involving dynamical resources such as quantum memory, quantum communication, and non-Markovian dynamics. The framework of probabilistic distillation is also discussed.

关键词： Quantum coherence & coherence measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Quantum information theory

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Enhancing The Felsenstein Method To Reinvigorate Phylogenetic Starting In The Big data

Enhancing The Felsenstein Method To Reinvigorate Phylogeneti...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Ashendra Kumar Saxena Priyanka Vishwkarma Sandeep Kumar N. Thangarasu K. S. Bhuvaneshwari Bhandari Mahesh Ashok College of Computing Science and Information Technology Teerthankar Magaveer University Moradabad Uttar Pradesh India Department of Computer Science & Engineering Invertis University Bareilly Uttar Pradesh India School of Commerce and Management IIMT University Meerut Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Tamilnadu India Vishwakarma Institute of Information Technology Pune Maharashtra India

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic.

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CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell data Analysis

arXiv

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arXiv 2024年

作者： Xiao, Yihang Liu, Jinyi Zheng, Yan Xie, Xiaohan Hao, Jianye Li, Mingzhi Wang, Ruitao Ni, Fei Li, Yuxiao Luo, Jintian Jiao, Shaoqing Peng, Jiajie AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University No.1 Dongxiang Road Xi’an China College of Intelligence and Computing Tianjin University No.92 Weijin Road Tianjin China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Ministry of Industry and Information Technology No.1 Dongxiang Road Xi’an China

Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manipulation of various tools to achieve desired outcomes can be labor-intensive for researchers. To address this, we introduce CellAgent (http://***/), an LLM-driven multi-agent framework, specifically designed for the automatic processing and execution of scRNA-seq data analysis tasks, providing high-quality results with no human intervention. Firstly, to adapt general LLMs to the biological field, CellAgent constructs LLM-driven biological expert roles—planner, executor, and evaluator—each with specific responsibilities. Then, CellAgent introduces a hierarchical decision-making mechanism to coordinate these biological experts, effectively driving the planning and step-by-step execution of complex data analysis tasks. Furthermore, we propose a self-iterative optimization mechanism, enabling CellAgent to autonomously evaluate and optimize solutions, thereby guaranteeing output quality. We evaluate CellAgent on a comprehensive benchmark dataset encompassing dozens of tissues and hundreds of distinct cell types. Evaluation results consistently show that CellAgent effectively identifies the most suitable tools and hyperparameters for single-cell analysis tasks, achieving optimal performance. This automated framework dramatically reduces the workload for science data analyses, bringing us into the "Agent for science" era. Copyright © 2024, The Authors. All rights reserved.

关键词： Cells

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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