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11th International Conference on Traffic and Transportation Studies, ICTTS 2024

作者： Du, Xinyi Zhang, Yaqin Wang, Li School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China Beijing Engineering Research Center of Urban Traffic Information Intelligent Sensing andService Technologies Beijing100044 China Key Laboratory of Railway Industry for Operational Active Safety Assurance and Risk Prevention and Control Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796397

The "one-bill coverage" of China-Laos railway multimodal transport is an effective way to promote the growth of bilateral trade between China and Laos railways, and it is an inevitable requirement for the construction of a modern comprehensive transport system. In order to enhance the efficiency and economic benefits of cargo transport on China-Laos Railway, this paper combines the current situation of freight transport on China-Laos Railway and divides the life cycle of "one-bill coverage" of China-Laos Railway multimodal transport into five phases: order submission, domestic consolidation, cross-border transport, foreign evacuation and order signing, etc., and builds a "one-bill coverage" covering all the links, such as loading, transport, transshipment, warehousing, connecting, and customs clearance. This paper can provide a reference for the development of "one-bill coverage" transport service of China-Laos Railway multimodal transport. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Multimodal transportation

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Production of exotic hadrons in pp and nuclear collisions

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Nuclear Science and Techniques 2025年第4期36卷 1-22页

作者： Jin-Hui Chen Feng-Kun Guo Yu-Gang Ma Cheng-Ping Shen Qi-Ye Shou Qian Wang Jia-Jun Wu Bing-Song Zou Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan UniversityShanghai 200438China CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China Southern Center for Nuclear-Science Theory(SCNT) Institute of Modern PhysicsChinese Academy of SciencesHuizhou 516000China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear ScienceSouthern Nuclear Science Computing CenterSouth China Normal UniversityGuangzhou 510006China Key Laboratory of Atomic and Subatomic Structure and Quantum Control(MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of MatterInstitute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Department of Physics Tsinghua UniversityBeijing 100084China

Exotic hadrons,beyond the conventional quark model,have been discovered over the past two *** these states can lead to a deeper understanding of the nonperturbative dynamics of the strong *** this review,we focus on the production of exotic hadrons in pp,PP^(-),and nuclear *** observations of light and hypernuclei as prototypes of hadronic molecules in heavy-ion collisions are also briefly discussed.

关键词： Exotic hadrons Hadron-hadron collision Heavy-ion collision

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on Computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Role of J/ψ production in electron-ion collisions

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Physical Review D 2025年第1期111卷 L011501-L011501页

作者： Zexuan Chu Jinhui Chen Xiang-Peng Wang Hongxi Xing Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) and Institute of Modern Physics Fudan University Shanghai 200433 China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan University Shanghai 200438 China Technical University of Munich TUM School of Natural Sciences Physics Department James-Franck-Strasse 1 85748 Garching Germany Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China

Within the framework of nonrelativistic QCD (NRQCD) effective field theory, we study the leptoproduction of J/ψ at next-to-leading order in perturbative QCD for both unpolarized and polarized electron-ion collisions. We demonstrate that the J/ψ-tagged deep inelastic scattering in the future Electron-Ion Collider can be served as a golden channel for reasons including constraining NRQCD long-distance matrix elements, probing the nuclear gluon distribution functions, as well as investigating the gluon helicity distribution inside a longitudinal polarized proton.

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Railway Cross-Modal Data Retrieval System Based on Cross-Modal Technology

Railway Cross-Modal Data Retrieval System Based on Cross-Mod...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Lu, Mengting Xie, Zhengyu Ma, Xiaoning Liu, Min Mao, Junyan School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Haidian Beijing100081 China

ISBN: (纸本)9789819620791

With the exponential growth of cross-modal information in railway data-to-service platforms, including accumulated text, images, files, and audiovisual materials, efficiently managing and utilizing these valuable resources has become an important issue for optimizing data organization and improving retrieval efficiency. This paper innovatively proposes a design scheme for an intelligent cross-modal data retrieval system tailored to the railway domain. The system aims to overcome the limitations of traditional single-modal retrieval by integrating multiple data types such as images and files, using names as core indices, to achieve seamless retrieval and integrated presentation of cross-modal information. This system not only accurately retrieves all modal information related to specific individuals, such as academic papers, on-site photos, research reports, and related news links, but also deeply extracts and categorizes key information, such as providing file paths, report titles, and enabling download operations, greatly enhancing the efficiency and precision of railway data utilization and retrieval. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Content based retrieval

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Quantum frontiers in high energy physics

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Science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 53-75页

作者： Yaquan Fang Christina Gao Ying-Ying Li Jing Shu Yusheng Wu Hongxi Xing Bin Xu Lailin Xu Chen Zhou Institute of High Energy Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Physics Southern University of Science and Technology Interdisciplinary Center for Theoretical Study University of Science and Technology of China Peng Huanwu Center for Fundamental Theory State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Center for High Energy Physics Peking University Beijing Laser Acceleration Innovation Center State Key Laboratory of Particle Detection and Electronics Department of Modern Physics University of Science and Technology of China Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum MatterSouth China Normal University Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear ScienceSouthern Nuclear Science Computing Center South China Normal University Southern Center for Nuclear-Science Theory Institute of Modern Physics Chinese Academy of Sciences

Numerous challenges persist in high energy physics(HEP), the addressing of which requires advancements in detection technology, computational methods, data analysis frameworks, and phenomenological designs. We provide a concise yet comprehensive overview of recent progress across these areas, in line with advances in quantum technology. We will discuss the potential of quantum devices in detecting subtle effects indicative of new physics BSM, the transformative role of quantum algorithms and large-scale quantum computers in studying real-time non-perturbative dynamics in the early universe and at colliders, as well as in analyzing complex HEP data. Additionally, we emphasize the importance of integrating quantum properties into HEP experiments to test quantum mechanics at unprecedented high-energy scales and search for hints of new physics. Looking ahead, the continued integration of resources to fully harness these evolving technologies will enhance our efforts to deepen our understanding of the fundamental laws of nature.

关键词： high energy physics quantum sensing quantum simulations quantum machine learning quantum information

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Curiosity and Affect-Driven Cognitive Architecture for HRI

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IEEE Transactions on Affective computing 2025年

作者： Berto, Leticia Tanevska, Ana Cirne, Azamor Costa, Paula Simoes, Alexandre Gudwin, Ricardo Rea, Francesco Colombini, Esther Sciutti, Alessandra State University of Campinas Institute of Computing Campinas Brazil Italian Institute of Technology CONTACT Unit Genoa Italy Uppsala University Department of Information Technology Uppsala Sweden Federal University of Paraiba Department of Finance and Accounting João Pessoa Brazil State University of Campinas School of Electrical and Computer Engineering Campinas Brazil São Paulo State University Department of Control and Automation Engineering Sorocaba Brazil

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants. © 2025 IEEE. All rights reserved.

关键词： Robot learning

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SHAA: Spatial Hybrid Attention Network with Adaptive Cross-Entropy Loss Function for UAV-view Geo-localization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Nanhua Zhang, Dongshuo Jiang, Kai Yu, Meng Zhu, Yeqing Lou, Tai-Shan Zhao, Liangyu Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Zhengzhou University of Light Industry School of Electrical and Information Engineering Zhengzhou450002 China Beijing Institute of Technology School of Aerospace Engineering Beijing100081 China Beijing Institute of Technology Zhengzhou Research Institute Zhengzhou450000 China Beijing Institute of Technology National Key Laboratory of Land and Air Based Information Perception and Control Beijing100081 China

Cross-view geo-localization provides an offline visual positioning strategy for unmanned aerial vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, it still faces the following challenges, leading to suboptimal localization performance: 1) Existing methods primarily focus on extracting global features or local features by partitioning feature maps, neglecting the exploration of spatial information, which is essential for extracting consistent feature representations and aligning images of identical targets across different views. 2) Cross-view geo-localization encounters the challenge of data imbalance between UAV and satellite images. To address these challenges, the Spatial Hybrid Attention Network with Adaptive Cross-Entropy Loss Function (SHAA) is proposed. To tackle the first issue, the Spatial Hybrid Attention (SHA) method employs a Spatial Shift-MLP (SSM) to focus on the spatial geometric correspondences in feature maps across different views, extracting both global features and fine-grained features. Additionally, the SHA method utilizes a Hybrid Attention (HA) mechanism to enhance feature extraction diversity and robustness by capturing interactions between spatial and channel dimensions, thereby extracting consistent cross-view features and aligning images. For the second challenge, the Adaptive Cross-Entropy (ACE) loss function incorporates adaptive weights to emphasize hard samples, alleviating data imbalance issues and improving training effectiveness. Extensive experiments on widely recognized benchmarks, including University-1652, SUES-200, and DenseUAV, demonstrate that SHAA achieves state-of-the-art performance, outperforming existing methods by over 3.92%. Code will be released at: https://***/chennanhua001/SHAA. © 1991-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Riemann–Liouville integrals in Besov spaces

arXiv

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arXiv 2025年

作者： Ushakova, Elena P. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences 65 Profsoyuznaya str. Moscow117997 Russia Steklov Mathematical Institute of Russian Academy of Sciences 8 Gubkina str. Moscow119991 Russia Computing Center of Far Eastern Branch of Russian Academy of Sciences 65 Kim Yu Chena str. Khabarovsk680000 Russia

Criteria for the fulfillment of inequalities in weighted smoothness function spaces of Besov type with Riemann–Liouville operators of natural orders on the real axis and semi–axes are found. The obtained estimates are refined under additional conditions on *** Codes 44A15, 41A15, 26A33 Copyright © 2025, The Authors. All rights reserved.

关键词： Choquet integral

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