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TechRxiv 2025年

作者： Hadi, Muhammad Usman Al-Tashi, Qasem Qureshi, Rizwan Shah, Abbas Muneer, Amgad Irfan, Muhammad Zafar, Anas Shaikh, Muhammad Bilal Akhtar, Naveed Al-Garadi, Mohammed Ali Hassan, Syed Zohaib Shoman, Maged Wu, Jia Mirjalili, Seyedali Shah, Mubarak School of Engineering Ulster University BelfastBT15 1AP United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Electronics Engineering Mehran University of Engineering and Technology Jamshoro76062 Pakistan of Engineering Sciences and Technology Swabi23460 Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Edith Cowan University 270 Joondalup Drive Joondalup PerthWA6027 Australia Computing and Information Systems The University of Melbourne 700 Swanston Street CarltonVIC3010 Australia Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Civil Environmental and Construction Engineering The University of Central Florida OrlandoFL United States Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest1034 Hungary Center for Research in Computer Vision The University of Central Florida OrlandoFL United States

Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. LLMs are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, vision applications, and question-answering. This survey provides a comprehensive overview of LLMs, including their history, architecture, datasets, training methods, applications, challenges, and future prospects. We begin by discussing the fundamental concepts of generative AI and the architecture of generative pre-trained transformers (GPT). We then provide an overview of the history of LLMs, their evolution over time, and the different training methods. We also present benchmark dataset for training and fine-tuning and evaluating LLMs. We then discuss the wide range of tasks where they are used and also discuss applications of LLMs in different domains, including medicine, education, finance, engineering, agriculture, media, entertainment, politics, and law. We also discuss how LLMs are shaping the future of AI and their increasing role in scientific discovery, and how they can be used to solve real-world problems. Next, we explore the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, privacy concerns, and computational resource requirements. This survey also highlights techniques for enhancing the robustness and controllability of LLMs and addressing bias, fairness, and quality issues in Generative AI. Finally, we conclude by highlighting the future of LLM research and the challenges that need to be addressed in order to make this technology more reliable and useful. This survey is intended to provide researchers, practitioners, and ent

关键词： Natural language processing systems

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Migration and Tuning of Software Prefetching for Sunway Multi-Core Processor 7

Migration and Tuning of Software Prefetching for Sunway Mult...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Gao, Xiuwu Jiang, Jun Huang, Liangming Wei, Hongmei Jiangnan Institute of Computing Technology Wuxi China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665478571

Data prefetching is a widely used technique to alleviate "memory wall"problem by fetching the data that may be touched in the near future in advance. Generally, data prefetching is classified into hardware prefetching and software prefetching. Compared to hardware prefetching, software prefetching is more flexible, and typically achieves higher prefetch accuracy. Currently, Sunway multiple-core processor only supports hardware prefetching. To study how software prefetching perform on Sunway processor, in this paper, we first migrate the software data prefetching in the GCC complier to Sunway processor. Then we tune the loop-level prefetching cost model according to Sunway processor's hardware features. Finally, we conduct experiments to evaluate the effectiveness of the tuned software prefetching. Results show that, compared to the baseline where no prefetching is applied, software prefetching delivers an average speedup of 1.08x (up to 2.46x) and 1.16x (up to 1.88x) for SPECint 2006 and SPECfp 2006 benchmark suite, respectively. Moreover, software prefetching outperforms hardware prefetching for both benchmark suites. This demonstrates the efficacy of software data prefetching. © 2022 IEEE.

关键词： Degradation Costs Multicore processing Prefetching Computational modeling Benchmark testing Signal processing

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Big Data Mining of Maritime Shipping Flows Based on Dependency Relationships 9

Big Data Mining of Maritime Shipping Flows Based on Dependen...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Fu, Yude Li, Xiang Lu, Xiongyi Yu, Mengjun Huangpeng, Qizi Duan, Xiaojun College of Science National University of Defense Technology Changsha China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9798350376548

This research utilizes higher-order dependency relationships for the modeling of higher-order dependency networks from voluminous maritime flow data and introduces higher-order dependency mining to uncover critical port information in the China-Southeast Asia region as well as the coopetition relationships between ports. Analysis of maritime data from the years 2018 and 2023 indicates that global ports maintain stability and competitiveness through their evolution, with Singapore serving as the central hub connecting China and Southeast Asia. Subsequent analysis employing dependency paths indicates that in recent years, the dependency relationships among ports between China and Southeast Asia have achieved higher orders and greater complexity. © 2024 IEEE.

关键词： competition and cooperation dependency relationships flow data maritime network

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Quantum simulation of QC2D on a 2-dimensional small lattice

arXiv

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arXiv 2025年

作者： Yang, Zhen-Xuan Matsuda, Hidefumi Huang, Xu-Guang Kashiwa, Kouji Physics Department Center for Particle Physics and Field Theory Fudan University Shanghai200438 China Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Hangzhou310027 China Fudan University Shanghai200433 China Shanghai Research Center for Theoretical Nuclear Physics National Natural Science Foundation of China Fudan University Shanghai200438 China Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan

We study the Hamiltonian formulation of SU(2) Yang-Mills theory with staggered fermions in a (2+1)-dimensional small lattice system. We construct a gauge-invariant and finite-dimensional Hilbert space for the theory by applying the loop-string-hadron formulation and specifically map the model to a spin system. We classically emulate digital quantum simulation and observe the real-time evolution of the single-site entanglement entropy, the fermion entanglement entropy, and the fermion pair production. Copyright © 2025, The Authors. All rights reserved.

关键词： Fermions

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data Data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Hydrodynamics and the Eigenstate Thermalization Hypothesis

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Physical Review X 2025年第1期15卷 011059-011059页

作者： Luca Capizzi Jiaozi Wang Xiansong Xu Leonardo Mazza Dario Poletti Université Paris-Saclay CNRS LPTMS 91405 Orsay France Department of Mathematics/Computer Science/Physics University of Osnabrück D-49076 Osnabrück Germany Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore College of Physics and Electronic Engineering and Center for Computational Sciences Sichuan Normal University Chengdu 610068 China Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore

The eigenstate thermalization hypothesis (ETH) describes the properties of diagonal and off-diagonal matrix elements of local operators in the eigenenergy basis. In this work, we propose a relation between (i) the singular behavior of the off-diagonal part of ETH at small energy differences and (ii) the smooth profile of the diagonal part of ETH as a function of the energy density. We establish this connection from the decay of the autocorrelation functions of local operators, which is constrained by the presence of local conserved quantities whose evolution is described by hydrodynamics. We corroborate our predictions through numerical simulations of two distinct nonintegrable spin-1 Ising models, exhibiting diffusive and superdiffusive transport behaviors. The simulations are performed using dynamical quantum typicality up to 18 spins, for both infinite- and finite-temperature regimes.

关键词： Ising model

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Recurrent 3D attentional networks for end-to-end active object recognition

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Computational Visual Media 2019年第1期5卷 91-103页

作者： Min Liu Yifei Shi Lintao Zheng Kai Xu Hui Huang Dinesh Manocha School of Computer National University of Defense Technology Department of Computer Science and Electrical & Computer Engineering University of Maryland Visual Computing Research Center Shenzhen University

Active vision is inherently attention-driven:an agent actively selects views to attend in order to rapidly perform a vision task while improving its internal representation of the scene being *** by the recent success of attention-based models in 2D vision tasks based on single RGB images, we address multi-view depth-based active object recognition using an attention mechanism, by use of an end-to-end recurrent 3D attentional network. The architecture takes advantage of a recurrent neural network to store and update an internal representation. Our model,trained with 3D shape datasets, is able to iteratively attend the best views targeting an object of interest for recognizing it. To realize 3D view selection, we derive a 3D spatial transformer network. It is dierentiable,allowing training with backpropagation, and so achieving much faster convergence than the reinforcement learning employed by most existing attention-based models. Experiments show that our method, with only depth input, achieves state-of-the-art next-best-view performance both in terms of time taken and recognition accuracy.

关键词： active object recognition recurrent neural network next-best-view 3D attention

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Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2130 days

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Acta Biomaterialia 2025年 198卷 188-206页

作者： Bjånes, David A. Kellis, Spencer Nickl, Robert Baker, Brian Aflalo, Tyson Bashford, Luke Chivukula, Srinivas Fifer, Matthew S. Osborn, Luke E. Christie, Breanne Wester, Brock A. Celnik, Pablo A. Kramer, Daniel Pejsa, Kelsie Crone, Nathan E. Anderson, William S. Pouratian, Nadar Lee, Brian Liu, Charles Y. Tenore, Francesco V. Rieth, Loren Andersen, Richard A. Department of Biology and Biological Engineering California Institute of Technology PasadenaCA United States Department of Neurological Surgery Keck School of Medicine of USC Los AngelesCA90033 United States Department of Physical Medicine and Rehabilitation Johns Hopkins University School of Medicine BaltimoreMD United States Electrical and Computer Engineering Univ. of Utah Salt Lake CityUT United States Faculty of Medical Sciences Newcastle University Newcastle upon Tyne United Kingdom Department of Neurosurgery Kaiser Permanente Los Angeles Medical Center Los AngelesCA90027 United States Research and Exploratory Development Department Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States Department of Biomedical Engineering Case Western Reserve University ClevelandOH44106 United States Shirley Ryan AbilityLab ChicagoIL60611 United States Department of Neurological Surgery University of Colorado Hospital CO80045 United States Department of Neurology Johns Hopkins University School of Medicine BaltimoreMD21287 United States Department of Neurosurgery Johns Hopkins University School of Medicine BaltimoreMD21287 United States Department of Neurological Surgery UT Southwestern Medical Center DallasTX75390 United States USC Neurorestoration Center Department of Neurological Surgery Keck School of Medicine of USC Los AngelesCA90033 United States Mechanical Materials and Aerospace Engineering West Virginia University MorgantownWV United States Rancho Los Amigos National Rehabilitation Center DowneyCA United States Tianqiao and Chrissy Chen Institute for Neuroscience CA United States

The clinical success of brain computer interfaces (BCI) depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. This study systematically quantified damage that microelectrodes sustained during chronical implantation in three people with tetraplegia for 956–2130 days. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from eleven Neuroport arrays tipped with platinum (Pt, n = 8) and sputtered iridium oxide film (SIROF, n = 3). Arrays were implanted/explanted from posterior parietal, motor and somatosensory cortices across three clinical sites (Caltech/UCLA, Caltech/USC, APL/Johns Hopkins). From the electron micrographs, we quantified and correlated physical damage with functional outcomes measured in vivo, prior to explant (recording quality, noise, impedance and stimulation ability). Despite greater physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt (measured by SNR). For SIROF, 1 kHz impedance significantly correlated with all physical damage metrics, recording metrics, and stimulation performance, suggesting a reliable measurement of in vivo degradation. We observed a new degradation type, primarily on stimulated electrodes ("pockmarked" vs "cracked") electrodes;however, no significant degradation due to stimulation or amount of charge delivered. We hypothesize erosion of the silicon shank accelerates damage to the electrode / tissue interface, following damage to the tip metal. These findings link quantitative measurements to the microelectrodes’ physical condition and their capacity to record/stimulate. These data could lead to improved manufacturing processes or novel electrode designs to improve long-term performance of BCIs, making them vitally important as multi-year clinical trials of BCIs are becoming more common. Statement of significance: Long-term performance stability of the electrode-tissue interface is e

关键词： Oxide films

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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