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arXiv 2021年

作者： Pandey, Santosh Wang, Zhibin Zhong, Sheng Tian, Chen Zheng, Bolong Li, Xiaoye Li, Lingda Hoisie, Adolfy Ding, Caiwen Li, Dong Liu, Hang Electrical and Computer Engineering Stevens Institute of Technology United States State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu210008 China Huazhong University of Science and Technology China Computational Research Division Lawrence Berkeley National Laboratory United States Brookhaven National Laboratory United States Department of Computer Science & Engineering University of Connecticut United States Department of Electrical Engineering and Computer Science University of California Merced United States

Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric design reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hashing-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST which performs triangle counting with the hash operation and vertex-centric mechanism at the core. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting. © 2021, CC BY.

关键词： Program processors

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Cache-aware response time analysis for real-time tasks with fixed preemption points

Cache-aware response time analysis for real-time tasks with ...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Filip Marković Jan Carlson Radu Dobrin Division of Computer Science and Software Engineering (CSE) Mälardalen University V#x00E4 sterås Sweden

ISBN: (数字)9781728154992

ISBN: (纸本)9781728155005

In real-time systems that employ preemptive scheduling and cache architecture, it is essential to account as precisely as possible for cache-related preemption delays in the schedulability analysis, as an imprecise estimation may falsely deem the system unschedulable. In the current state of the art for preemptive scheduling of tasks with fixed preemption points, the existing schedulability analysis considers overly pessimistic estimation of cache-related preemption delay, which eventually leads to overly pessimistic schedulability results. In this paper, we propose a novel response time analysis for real-time tasks with fixed preemption points, accounting for a more precise estimation of cache-related preemption delays. The evaluation shows that the proposed analysis significantly dominates the existing approach by being able to always identify more schedulable tasksets.

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Correction to: Pose pattern mining using transformer for motion classification

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Applied Intelligence 2024年第22期54卷 12036-12036页

作者： Lee, Seo-El Yoo, Hyun Chung, Kyungyong Department of Public Safety Bigdata Kyonggi University Suwon-si South Korea Contents Convergence Software Research Institute Kyonggi University Suwon-si South Korea Division of AI Computer Science and Engineering Kyonggi University Suwon-si South Korea

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Long-short Term Prediction for Occluded Multiple Object Tracking

Long-short Term Prediction for Occluded Multiple Object Trac...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Zhihong Sun Jun Chen Mithun Mukherjee Weijian Ruan Chao Liang Yi Yu Dan Zhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China College of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Digital Content and Media Sciences Research Division National Institute of Informatics Japan School of Computer Science Wuhan University China

ISBN: (纸本)9781728181059

Online multiple object tracking (MOT) is a challenging problem in complex scenes due to frequent occlusions. Most of the existing MOT methods tend to focus on addressing an individual type of occlusion, which cannot meet the requirements of real complex scenes. In this paper, we propose a unified MOT framework that combines long- and short-term prediction models for online multiple object tracking. Basically, The short-term prediction model consists of an appearance-based model and a motion-based model, aiming at exploiting the appearance and motion of objects to handle different types of occlusions jointly. Furthermore, we adopt a cubic spline interpolation as a long-term prediction model to estimate the trajectory of the target in occluded frames. To handle different lengths of occlusions, an adaptive weighted fusion model is proposed to combine the short-term prediction model, and the long-term prediction model. Experimental results on several challenging datasets demonstrate that the proposed method outperforms state-of-the-art methods.

关键词： Adaptation models Interpolation Target tracking Conferences Video sequences Predictive models Trajectory

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Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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Transmission Opportunity-Based Distributed OBSS/PD Determination Method in IEEE 802.11ax Networks

Transmission Opportunity-Based Distributed OBSS/PD Determina...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yonggang Kim Gyungmin Kim Taewoon Kim Wooyeol Choi Network Division Samsung Electronics School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology School of Software Hallym University Chosun University

ISBN: (数字)9781728149851

ISBN: (纸本)9781728149868

As one of the most widely used wireless networking technologies, the IEEE 802.11 or Wi-Fi has been attracting enormous attention, and as a result, the access points (APs) are becoming densely deployed to support heavy traffic demands. However, such densely deployed APs also cause significant interference problems. For interference management, overlapped basic service set/packet detection (OBSS/PD) is recently added to the IEEE 802.11ax standard. In this paper, we propose a transmission opportunity-based distributed OBSS/PD threshold determination method to enhance the fairness among nodes. The simulation results show that the proposed method improves the fairness performance while mitigating interference.

关键词： Interference Throughput Wireless communication Sensors Color IEEE 802.11 Standard Uplink

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication

Towards Cluster-Based Split Federated Learning Approach for ...

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Cyber Security in Networking Conference (CSNet)

作者： Mohamad Wazzeh Mohamad Arafeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT Engineering Ecole de Technologie Supérieure (ÉTS) Montreal Canada Department of Mathematics and Computer Science Université du Québec à Trois-Riviéres (UQTR) Trois-Rivières Canada Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security.

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Three-Dimensional (3D) Animation and Calculation for the Assessment of Engaging Hill–Sachs Lesions With Computed Tomography 3D Reconstruction

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Arthroscopy, Sports Medicine, and Rehabilitation 2021年第1期3卷 e89-e96页

作者： Tat, Jimmy Crawford, Jordan Chong, Jaron Powell, Tom Fevens, Thomas G. Popa, Tiberiu Martineau, Paul A. Division of Orthopedic Surgery University of Toronto Toronto ON Canada Department of Radiology McGill University Health Centre Montreal QC Canada Division of Orthopaedic Surgery McGill University Health Centre Montreal QC Canada Department of Computer Science and Software Engineering Concordia University Montreal QC Canada

Purpose: To dynamically assess for Hill–Sachs engagement with animated 3-dimensional (3D) shoulder models. Methods: We created 3D shoulder models from reconstructed computed tomography (CT) images from a consecutive series of patients with recurrent anterior dislocation. They were divided into 2 groups based on the perceived Hill–Sachs severity. For our cohort of 14 patients with recurrent anterior dislocation, 4 patients had undergone osteoarticular allografting of Hill–Sachs lesions and 10 control patients had undergone CT scanning to quantify bone loss but no treatment for bony pathology. A biomechanical analysis was performed to rotate each 3D model using local coordinate systems to the classical vulnerable position of the shoulder (abduction = 90°, external rotation = 0-135°) and through a functional range. A Hill–Sachs lesion was considered “dynamically” engaging if the angle between the lesion's long axis and anterior glenoid was parallel. Results: In the vulnerable position of the shoulder, none of the Hill–Sachs lesions aligned with the anterior glenoid in any of our patients. However, in our simulated physiological shoulder range, all allograft patients and 70% of controls had positions producing alignment. Conclusions: The technique offers a visual representation of an engaging Hill–Sachs using 3D-animated reconstructions with open-source software and CT images. In our series of patients, we found multiple shoulder positions that align the Hill–Sachs and glenoid axes that do not necessarily meet the traditional definition of engagement. Identifying all shoulder positions at risk of “engaging,” in a broader physiological range, may have critical implications toward selecting the appropriate surgical management of bony defects. Level of Evidence: level III, case-control study. © 2020 Arthroscopy Association of North America. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

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A regularized CNN approach for detecting cervical cancer

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AIP Conference Proceedings 2024年第1期3220卷

作者： Jesse Jeremiah Tanimu Mohamed Hamada Mohammed Hassan Tsentob Joy Samson Jane Simon Parah Department of computer Science Bayero Unoiversity Kano Kano Nigeria Division of information systems University of Aizu Aizu Japan Department of Software Engineering Bayero University Kano Kano Nigeria Department of computer Science Air Force Institute of Technology Kaduna Kaduna Nigeria Department of Mathematical and Physical Science Glorious Vision University Ogwa Benin Nigeria

This study delved into leveraging deep learning techniques to classify different types of cervical cancer images, recognizing the disease’s prevalence and criticality. We employed a dataset comprising cervical images and investigated the utilization of the VGG16 model as a foundational architecture. To enhance performance, we incorporated various improvements, such as L2 regularization and data augmentation. The outcomes underscored the efficacy of these enhancements, with the refined model achieving a remarkable accuracy of 94%, surpassing the VGG16 model’s baseline accuracy of 89%. These findings demonstrate the potential of deep learning to improve the classification of cervical cancer.

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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