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arXiv 2023年

作者： Liu, Zhengliang Zhong, Tianyang Li, Yiwei Zhang, Yutong Pan, Yi Zhao, Zihao Dong, Peixin Cao, Chao Liu, Yuxiao Shu, Peng Wei, Yaonai Wu, Zihao Ma, Chong Wang, Jiaqi Wang, Sheng Zhou, Mengyue Jiang, Zuowei Li, Chunlin Holmes, Jason Xu, Shaochen Zhang, Lu Dai, Haixing Zhang, Kai Zhao, Lin Chen, Yuanhao Liu, Xu Wang, Peilong Yan, Pingkun Liu, Jun Ge, Bao Sun, Lichao Zhu, Dajiang Li, Xiang Liu, Wei Cai, Xiaoyan Hu, Xintao Jiang, Xi Zhang, Shu Zhang, Xin Zhang, Tuo Zhao, Shijie Li, Quanzheng Zhu, Hongtu Shen, Dinggang Liu, Tianming School of Computing University of Georgia GA United States School of Automation Northwestern Polytechnical University Xi'An710072 China Department of Radiology Massachusetts General Hospital Harvard Medical School MA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Institute of Medical Research Northwestern Polytechnical University Xi'An710072 China School of Physics and Information Technology Shaanxi Normal University Xi'An710119 China School of Computer Science Northwestern Polytechnical University Xi'An710072 China School of Biomedical Engineering ShanghaiTech University Shanghai Clinical Research and Trial Center Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. China Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Biomedical Engineering Center for Biotechnology Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY12180 United States Department of Radiology Second Xiangya Hospital Changsha410011 China Shanghai Clinical Research Trial Center China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering Lehigh University PA United States Department of Linguistics Department of Computer Science Dartmouth College NH United States Lingang Laboratory Shanghai200031 China

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain. © 2023, CC BY.

关键词： Radiology

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A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization

arXiv

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arXiv 2022年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, Dicheng Gao, Yuncheng Dong, Jiyang Guo, Di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in DMF. We prove that, for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading 8

Secure and Privacy-Preserving Lightweight Blockchain for Ene...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

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Procedia computer science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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Initial Evaluation of a TOF, Open-ring PET Scanner with Continuous Variable Diameter

Initial Evaluation of a TOF, Open-ring PET Scanner with Cont...

引用

2021 IEEE Nuclear science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Li, Bingxuan Zhang, Bo Fang, Lei Yang, Lingli Xiao, Peng Chen, Xiaolin Xie, Qingguo Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Huazhong University of Science and Technology Biomedical Engineering Department Wuhan430074 China Hubei RaySolution Digital Medical Imaging Technology Co. Ltd. Ezhou436000 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Chuxiong Normal University School of Math and Computer Science Chuxiong675000 China

ISBN: (纸本)9781665421133

We propose a TOF, open-ring PET scanner with continuously variable diameter for in-situ imaging. The detectors of the scanner could be spread or shrunk along circumference direction, provides an adaptive FOV for bodies of different sizes. The annular shape contributes to higher sensitivity and better response homogeneity than the panel scanner composed with the same detector number and internal diameter. The simulation results show the image quality and the response homogeneity of the open-ring scanners get worse than those of the full-ring scanner (with coincidence time resolution of 380 ps), but the fluctuation of the background variance (improved 4.5%~6.1%) and the hot lesion contrast (reduced 3.9%~11.8%) is acceptable even the open angle reaches up to 180°. © 2021 IEEE.

关键词： Image reconstruction

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IoT-Edge-Cloud Computing Framework for QoS-Aware Computation Offloading in Autonomous Mobile Agents: Modeling and Simulation 1

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6th International Conference on Mobile, Secure and Programmable networking, MSPN 2020

作者： Ismail, Leila Materwala, Huned Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

ISBN: (数字)9783030675509

ISBN: (纸本)9783030675493

Edge-cloud computing is an emerging computational model that allows offloading of service requests by the autonomous mobile agents from the edge-server to the cloud-server. This is to reduce the network latency prevalent in the cloud-IoT model. However, Quality-of-Service (QoS)-Aware computation offloading in a heterogeneous and dynamic edge-cloud environment remains an open problem. In this paper, we propose a queuing theory-based edge-cloud computing framework for QoS-aware offloading in mobile autonomous agents. This framework model decides whether to execute an incoming request to the edge-server on the edge itself or to offload to one of the heterogeneous cloud servers such that the request’s execution time is the minimum. To model a request’s execution time, we consider the processing capabilities and the queues overheads of the edge and cloud servers, and the edge-cloud communications’ time. The details of the evaluation results, using dataset generated from real-life scenarios, are presented in the paper. © 2021, Springer Nature Switzerland AG.

关键词： Quality of service

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Exact and sampling methods for mining higher-order motifs in large hypergraphs

arXiv

引用

arXiv 2022年

作者： Lotito, Quintino Francesco Musciotto, Federico Battiston, Federico Montresor, Alberto Department of Information Engineering and Computer Science University of Trento via Sommarive 9 TrentoI-38123 Italy Dipartimento di Fisica e Chimica Emilio Segrè Università di Palermo Viale delle Scienze PalermoI-90128 Italy Department of Network and Data Science Central European University Quellenstraße 51 ViennaA-1100 Austria

network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very large hypergraphs, where higher-order interactions connect arbitrary numbers of system units. We show how directly exploiting higher-order structures speeds up the counting process compared to traditional data mining techniques for exact motif discovery. Moreover, with hyperedge sampling, performance is further improved at the cost of small errors in the estimation of motif frequency. We evaluate our method on several real-world datasets describing face-to-face interactions, co-authorship and human communication. We show that our approximated algorithm allows us to extract higher-order motifs faster and on a larger scale, beyond the computational limits of an exact approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Complex networks

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Efficient carpooling and toll pricing for autonomous transportation

arXiv

引用

arXiv 2021年

作者： Amin, Saurabh Jaillet, Patrick Wu, Manxi Laboratory for Information and Decision Systems United States Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems and Operations Research Center United States CambridgeMA United States

In this paper, we address the existence and computation of competitive equilibrium in the transportation market for autonomous carpooling first proposed by [18]. At equilibrium, the market organizes carpooled trips over a transportation network in a socially optimal manner and sets the corresponding payments for individual riders and toll prices on edges. The market outcome ensures individual rationality, stability of carpooled trips, budget balance, and market clearing properties under heterogeneous rider preferences. We show that the question of market’s existence can be resolved by proving the existence of an integer optimal solution of a linear programming problem. We characterize conditions on the network topology and riders’ disutility for carpooling under which a market equilibrium can be computed in polynomial time. This characterization relies on ideas from the theory of combinatorial auctions and minimum cost network flow problem. Finally, we characterize a market equilibrium that achieves strategyproofness and maximizes welfare of individual riders. © 2021, CC BY.

关键词： Polynomial approximation

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A Progressive Secret Image Sharing Scheme Based on GEMD Data Hiding 3rd

A Progressive Secret Image Sharing Scheme Based on GEMD Data...

引用

3rd International Conference on Security with Intelligent Computing and Big-data Services, SICBS 2019

作者： Kuo, Wen-Chung Chen, Yi-Jiun Wang, Chun-Cheng Huang, Yu-Chih Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan National Center for High-Performance Computing National Applied Research Laboratories Tainan744 Taiwan Department of Information Management Tainan University of Technology Tainan71002 Taiwan

ISBN: (纸本)9783030468279

With the development of the Internet, people spread digital information through the Internet. However, the Internet is a public channel. Avoiding digital information be tampered with to keep the security of data is the most important thing today. The main method of data hiding is to embed secret information into multimedia and then transmit it to the recipient. The stego multimedia would not attract someone and is protected from tampering during transmission. In addition to data hiding technology, this paper is combined with secret image sharing. We use Progressive Secret Image Sharing (PSIS) that the secret image is divided into multiple regions and hidden in N stego images. If the recipient wants to extract the secret image, the recipient must have a part of or all stego images to extract a similar secret image or a complete secret image. In this paper, we propose the method of secret image sharing based on GEMD data hiding. The secret image is not in sequence, but can be overlapped and divided. The experimental results present that the stego images are PSNR ≥ 50dB. The recipient can extract be similar or complete with stego images and judge whether stego images is tampered or not. © Springer Nature Switzerland AG 2020.

关键词： Steganography

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