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International Conference on Computing, Networking and Communications (ICNC)

作者： Md Nurul Absur Sourya Saha Sifat Nawrin Nova Kazi Fahim Ahmad Nasif Md Rahat Ul Nasib Department of Computer Science City University of New York New York USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia USA Samsung Austin Research Center Austin Texas USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments.

关键词： Content distribution networks Heuristic algorithms Scalability computer architecture Dynamic scheduling Fabrics Performance analysis Servers Resource management Optimization

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Kinematic analysis and position/force control of the anthrobot dextrous hand

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1997年第1期27卷 95-104页

作者： Kyriakopoulos, KJ VanRiper, J Zink, A Stephanou, HE New York State Center for Advanced Technology in Automation and Robotics and Electrical Department of Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

The increasing demand for robotic applications in unstructured environments is motivating the need for dextrous end-effecters which can cope with the wide variety of tasks and objects encountered in these environments, A new anthropomorphic robot hand (Anthrobot) which was designed for these environments is described. In this paper, the problem of developing the kinematics and unit consistent hybrid position/force control of the Anthrobot fingers is considered as the first step toward its utilization for either teleoperation or autonomous operation, A dynamic model of the fingers is identified and used to develop the control laws, The experimental results of applying the control to the Anthrobot are discussed.

关键词： Kinematics Force control Fingers Robotics and automation Robots Humans Thumb Anthropomorphism Vehicle dynamics Power system modeling

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Optical neural network models applied to logic program execution

Optical neural network models applied to logic program execu...

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Neural Network Models for Optical Computing 1988

作者： Stormon, Charles D. New York State Center for Advanced Technology in Computer Applications and Software Engineering 120 Hinds Hall Syracuse University SyracuseNY13244-1190 United States

Logic programming is being used extensively by Artificial Intelligence researchers to solve problems including natural language processing and expert systems. These languages, of which Prolog is the most widely used, promise to revolutionize software engineering, but much greater performance is needed. Researchers have demonstrated the applicability of neural network models to the solution of certain NP-complete problems, but these methods are not obviously applicable to the execution of logic programs. This paper outlines the use of neural networks in four aspects of the logic program execution cycle, and discusses results of a simulation of three of these. Four neural network functional units are described, called the substitution agent, the clause filter, the structure processor, and the heuristics generator, respectively. Simulation results suggest that the system described may provide several orders of magnitude improvement in execution speed for large logic programs. However, practical implementation of the proposed architecture will require the application of optical computing techniques due to the large number of neurons required, and the need for massive, adaptive connectivity. © 1988 SPIE. All rights reserved.

关键词： Neural network models

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Emerging applications for Cyber Transportation Systems

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Journal of computer Science & technology 2014年第4期29卷 562-575页

作者： Aditya Wagh 侯云飞乔春明张龙飞李旭 Adel Sadek Kevin Hulme 吴昌旭徐宏力黄刘生 IEEE Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY 14260-2000 U.S.A. Department of Civil Structural and Environmental Engineering State University of New York at Buffalo Buffalo NY 14260-2000 U.S.A. The New York State Center for Engineering Design and Industrial Innovation Buffalo NY 14260 U.S.A. Department of Industrial and System Engineering State University of New York at Buffalo Buffalo NY 14260-2000 U.S.A. School of Computer Science and Technology Suzhou Institute for Advanced Study University of Science and Technology of China

Recent advances in connected vehicles and autonomous driving are going to change the face of ground trans- portation as we know it. This paper describes the design and evaluation of several emerging applications for such a cyber transportation system （CTS）. These applications have been designed using holistic approaches, which consider the unique roles played by the human drivers, the transportation system, and the communication network. They can improve driver safety and provide on-road infotainment. They can also improve transportation operations and efficiency, thereby benefiting travelers and attracting investment from both government agencies and private businesses to deploy infrastructures and bootstrap the evolutionary process of CTS.

关键词： emerging technology application algorithm/protocol design and analysis cyber transportation system

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Chip-scale spontaneous quasi-phase matched second harmonic generation in a micro-racetrack resonator

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Science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 66-74页

作者： Tingge Yuan Jiangwei Wu Yi'an Liu Xiongshuo Yan Haowei Jiang Hao Li Zhaokang Liang Qiang Lin Yuping Chen Xianfeng Chen State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and Astronomy.Shanghai Jiao Tong UniversityShanghai 200240China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal UniversityJinan 250358China Department of Electrical and Computer Engineering University of RochesterNew York 14627USA

In this paper,we demonstrate efficient spontaneous quasi-phase matched(SQPM)second harmonic generation(SHG)in a microracetrack resonator on X-cut thin film lithium *** approach does not involve poling,but exploits the anisotropy of the crystals to allow the phase-matching condition to be fulfilled spontaneously as the TE-polarized light circulates in a specifically designed racetrack *** experiment,normalized on-chip conversion efficiencies of 1.01×10-4/W and 0.43×10-4/W are achieved by 37th-order and 111th-order SQPM,*** configurable SQPM will benefit the application of nonlinear frequency conversion and quantum source generation in chip-scale integrated photonics compatible with standard CMOS fabrication processes.

关键词： lithium niobate on insulator quasi-phase matching second harmonic generation

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A practical guide to machine learning interatomic potentials – Status and future

arXiv

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

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User Behaviour based Insider Threat Detection using a Hybrid Learning Approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4573-4593页

作者： Singh, Malvika Mehtre, B.M. Sangeetha, S. Govindaraju, Venu Centre of Excellence in Cyber Security Institute for Development and Research in Banking Technology Established by Reserve bank of India Telangana Hyderabad500057 India Department of Computer Applications National Institute of Technology Trichy Tamil Nadu Tiruchirappalli620015 India Center for Unified Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo New York BuffaloNY14260 United States

Insider threats constitute a major cause of security breaches in organizations. They are the employees/users of an organization, causing harm by performing any malicious activity. Most of the existing methods to detect insider threats are based on machine and deep learning and have the following limitations: they use predefined rules or stored signatures and fail to detect new or unknown threats;they require explicit feature engineering, which results in more false positives;they require a large amount of training data, and are computationally expensive. In this paper, an improved user behavior-based insider threat detection method is proposed using a hybrid learning approach that overcomes the above limitations. It uses bi-directional long-short-term memory for feature extraction, a feed-forward artificial neural network (using distance measurements) for feature selection, and a support vector machine for classification-normal user or malicious user. The genetic algorithm’s fast global search strategy is used for the support vector machine’s initial kernel selection. Finally, alerts are generated for each user based on their combined anomaly score. The proposed method is tested using the CMU-CERT r4.2 insider threat dataset, and its performance is evaluated using the following parameters: accuracy, precision, recall, f-measure, and area under curve-receiver operating characteristic curve. The results show a significant improvement over the existing methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Support vector machines

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Conformal mesh parameterization using discrete Calabi flow

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computer AIDED GEOMETRIC DESIGN 2018年 63卷 96-108页

作者： Zhao, Hui Li, Xuan Ge, Huabin Lei, Na Zhang, Min Wang, Xiaoling Gu, Xianfeng [a]Tsinghua University China [b]Beijing Advanced Innovation Center for Imaging Technology Capital Normal University China [c]Department of Mathematics Beijing Jiaotong University China [d]DUT-RU International School of Information and Software Engineering Dalian University of Technology China [e]Computer Science Department State University of New York at Stony Brook USA [f]Brigham and Women's Hospital Harvard Medical School USA [g]Center of Mathematical Sciences and Applications Harvard University USA [h]Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China [i]School of Mechanical Engineering University of Science and Technology Beijing China

In this paper, we introduce discrete Calabi flow to the graphics research community and present a novel conformal mesh parameterization algorithm. Calabi energy has a succinct and explicit format. Its corresponding flow is conformal and convergent under certain conditions. Our method is based on the Calabi energy and Calabi flow with solid theoretical and mathematical base. We demonstrate our approach on dozens of models and compare it with other related flow based methods, such as the well-known Ricci flow and conformal equivalence of triangle meshes (CETM). Our experiments show that the performance of our algorithm is comparably the same with other methods. The discrete Calabi flow in our method provides another perspective on conformal flow and conformal parameterization. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Calabi flow Mesh parameterization Conformal mapping

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben york, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Nonhysteretic phenomena in the metal- Semiconductor phasetransition loop of VO2 films for bolometric sensor applications

Nonhysteretic phenomena in the metal- Semiconductor phasetra...

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作者： Gurvitch, Michael Luryi, Serge Polyakov, Aleksandr Shabalov, Alexander Department of Physics and Astronomy State University of New York Stony Brook NY 11794 United States Stony Brook United States Department of Electrical and Computer Engineering State University of New York Stony Brook NY 11794 United States New York State Center for Advanced Sensor Technology State University of New York Stony Brook NY 11794 United States

Hysteresis observed in the resistive semiconductorto- metal phase transition in VO2 causes problems in bolometric readout, and thus is an obstacle in utilizing this strong phase transition in bolometric sensor applications. It is possible to avoid the unwanted hysteresis when operating in limited temperature ranges within the hysteresis loop of VO2. Nonhysteretic branches (NHBs) traced in such limited temperature intervals turned out to have much higher temperature coefficient of resistance (TCR) than VO2 at room temperature: while TCR at 25 ?C in VO2 is close to 3%, peak TCR values in NHB-s reach 6% in VO2 films on Si/SiOi2 substrates and 21% in films on crystalline sapphire substrates. At the same time, the nanoscopic-scale mixture of semiconducting and metallic phases in VO2 within its hysteresis loop provides forpartially shunted low resistivity, thus creating an unprecedented combination of record high semiconducting TCR and metal-like low resistance. This combination may benefit the uncooled ocal plane array microbolometer IR visualization technology.

关键词： Hysteresis

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