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IEEE International Conference on Networking, Sensing and Control

作者： Song Wang Sheng He Yishuang Ning Xiang Gao Zhijun Ding Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Tongji University Shanghai China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions; second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost; and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms.

关键词： Costs Heuristic algorithms Redundancy Buildings Microservice architectures Sensors

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Generalized representative structures for atomistic systems

arXiv

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arXiv 2024年

作者： Goff, James M. Mullen, Coreen Yang, Shizhong Starovoytov, Oleg N. Wood, Mitchell A. Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States Department of Mathematics Computer Science and Engineering Technology Elizabeth City State University Elizabeth CityNC27909 United States Computer Science Department Southern University Baton RougeLA70807 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g., the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted. © 2024, CC BY.

关键词： Crystal lattices

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Strong Current in Carbon Nanoconductors: Mechanical and Magnetic Stability

SSRN

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SSRN 2024年

作者： Brandbyge, Mads Leitherer, Susanne Papior, Nick Department of Chemistry University of Copenhagen CopenhagenDK-2100 Denmark DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens LyngbyDK-2800 Denmark Department of Physics Technical University of Denmark Kongens LyngbyDK-2800 Denmark

Carbon nanoconductors are known to have extraordinary mechanical strength and interestingmagnetic properties. Moreover, nanoconductors based on one- or two-dimensional carbon allotropesdisplay a very high current-carrying capacity and ballistic transport. Here, we employa recent, simple approach based on density functional theory to analyze the impact of strongcurrent on the mechanical and magnetic properties of carbon nanoconductors. We find that theinfluence of the current itself on the bond-strength of carbon in general is remarkably low comparedto e.g. typical metals. This is demonstrated for carbon chains, carbon nanotubes, grapheneand polyacetylene. We can trace this to the strong binding and electronic bandstructure. On theother hand, we find that the current significanly change the magnetic properties. In particular, wefind that currents in graphene zig-zag edge states quench the magnetism. © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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Strong current in carbon nanoconductors: Mechanical and magnetic stability

arXiv

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arXiv 2024年

作者： Leitherer, Susanne Papior, Nick R. Brandbyge, Mads Department of Chemistry University of Copenhagen CopenhagenDK-2100 Denmark DTU Computing Center Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens LyngbyDK-2800 Denmark Department of Physics Technical University of Denmark Kongens LyngbyDK-2800 Denmark

Carbon nanoconductors are known to have extraordinary mechanical strength and interesting magnetic properties. Moreover, nanoconductors based on one- or two-dimensional carbon allotropes display a very high current-carrying capacity and ballistic transport. Here, we employ a recent, simple approach based on density functional theory to analyze the impact of strong current on the mechanical and magnetic properties of carbon nanoconductors. We find that the influence of the current itself on the bond-strength of carbon in general is remarkably low compared to e.g. typical metals. This is demonstrated for carbon chains, carbon nanotubes, graphene and polyacetylene. We can trace this to the strong binding and electronic bandstructure. On the other hand, we find that the current significanly change the magnetic properties. In particular, we find that currents in graphene zig-zag edge states quench the magnetism. © 2024, CC BY.

关键词： Magnetic properties

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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Real-time 3D-aware Portrait Video Relighting

arXiv

引用

arXiv 2024年

作者： Cai, Ziqi Jiang, Kaiwen Chen, Shu-Yu Lai, Yu-Kun Fu, Hongbo Shi, Boxin Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China Beijing Jiaotong University China University of California San Diego United States Cardiff University United Kingdom City University of Hong Kong Hong Kong The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However, most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper, we present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF). Given an input portrait video, our method can synthesize talking faces under both novel views and novel lighting conditions with a photo-realistic and disentangled 3D representation. Specifically, we infer an albedo tri-plane, as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. We also leverage a temporal consistency network to ensure smooth transitions and reduce flickering artifacts. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality, lighting error, lighting instability, temporal consistency and inference speed. We demonstrate the effectiveness and interactivity of our method on various portrait videos with diverse lighting and viewing conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Video conferencing

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT

QoS Improvement using Federated Learning-based Position Pred...

引用

IEEE World Forum on Internet of Things (WF-IoT)

作者： Saniya Zafar Mohammed S. Alshehri Syed Aziz Shah Jawad Ahmad Adnan Zafar Aizaz Ahmad Khattak Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University KSA Research Centre for Intelligent Healthcare Coventry University UK Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism.

关键词： Wireless communication Urban areas Interference Quality of service Predictive models Prediction algorithms Probabilistic logic Internet of Things Long short term memory Edge computing

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Energy grid management system with anomaly detection and Q-learning decision modules

引用

computers and Electrical Engineering 2023年第1期107卷

作者： Syu, Jia-Hao Srivastava, Gautam Fojcik, Marcin Cupek, Rafal Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Mathematics and Computer Science Brandon University Brandon Canada Research Center for Interneural Computing China Medical University Taichung Taiwan Department of Computer Science and Math Lebanese American University Beirut Lebanon Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management. © 2023 Elsevier Ltd

关键词： Energy management

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Nonlinear Impulsive Control Design for Biologically Grounded NSM Tumor Growth Model Using Exact Linearized Mapping

引用

IFAC-PapersOnLine 2023年第2期56卷 7228-7233页

作者： Zehor Belkhatir Soulaimane Berkane Larry Norton Allen Tannenbaum School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester LE1 9BH UK Department of Computer Science and Engineering University of Quebec in Outaouais Gatineau QC Canada Department of Medicine Memorial Sloan Kettering Cancer Center NY 10065 USA Department of Computer Science and Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY 11794 USA

This paper investigates the problem of personalized chemotherapy scheduling using a system-modeling approach based on a nonlinear impulsive control technique. In particular, this work deals with the so-called Norton-Simon-Massagué (NSM) model, a minimal biologically grounded model for controlled tumor growth, which has been accepted in clinical oncology for breast cancer treatment. In this paper, we continue this line of research, to advance the concept of closed-loop control a step further towards widespread clinical use. In the present work, the problem of chemotherapy drug scheduling is approached using an impulsive control strategy combined with a state-space observer. The technical challenges of the nonlinearity of the controlled system and the impulsive nature of control input are handled via the utilization of a novel mapping that transforms the control problem into a “simplified” linear discrete-time control problem. Theoretical guarantees regarding the sign and invertibility of the proposed transformation are demonstrated. Then, an observer-based state feedback controller is proposed for chemotherapy drug scheduling as a proof-of-concept control strategy. Simulation results showing the performance of the designed closed-loop controlled chemotherapy schedules in reducing and eradicating tumor volume are provided using model's parameters derived using real tumor data and Doxirubicin drug.

关键词： Controlled tumor growth Nonlinear impulsive control Norton-Simon-Massagué (NSM) model Exact linearized transformation observer-based state feedback

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Unlucky Explorer: A Complete non-Overlapping Map Exploration 3

Unlucky Explorer: A Complete non-Overlapping Map Exploration

引用

3rd World Symposium on Software Engineering, WSSE 2021

作者： Kiarostami, Mohammad Sina Khalaj Monfared, Saleh Daneshvaramoli, Mohammadreza Yousefian, Negar Massoud, Mahsa Visuri, Aku Hosio, Simo Rahmati, Dara Gorgin, Saeid Center for Ubiquitous Computing Faculty of ITEE University of Oulu Oulu Finland Tehran Iran Computer Science and Engineering Department Shahid Beheshti University Tehran Iran Tehran Iran

ISBN: (纸本)9781450384094

In this work, we introduce the Maze Dash puzzle as an exploration problem where the agent must find a Hamiltonian Path visiting all the cells with a minimum number of turnings for most cases. We also discuss the real-world application of the problem, such as 8 ball billiards and Snooker games. We investigate different methods by a focus on Monte-Carlo Tree Search (MCTS) and SAT to get an overview of which class of solutions solves the puzzle quickly and accurately. Also, we perform optimization to the proposed MCTS algorithm to prune the tree search. Also, since the prefabricated test cases of this puzzle are not large enough to assay the proposed method, we employ a technique to generate solvable test cases to evaluate the approaches. Eventually, our comparison indicates that the MCTS-based approach is an up-And-coming method that could cope with the test cases with small and medium sizes with faster run-Time than SAT. However, for specific discussed reasons, including the features of the problem, tree search organization, and also the approach of MCTS in the Simulation step, MCTS takes more time to execute in large size scenarios. Our results can be employed to choose a proper approach to create an AI to solve the Maze Dash, 8 ball billiards, and Snooker games. © 2021 ACM.

关键词： Monte Carlo methods

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