检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

The distance backbone of complex networks

arXiv

引用

arXiv 2021年

作者： Simas, Tiago Correia, Rion Brattig Rocha, Luis M. Universidade Lusófona Lisboa Portugal Center for Social and Biomedical Complexity Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN United States Instituto Gulbenkian de Ciência Oeiras Portugal CAPES Foundation Ministry of Education of Brazil DF Brasília Brazil Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY13902 United States

Redundancy needs more precise characterization as it is a major factor in the evolution and robustness of networks of multivariate interactions. We investigate the complexity of such interactions by inferring a connection transitivity that includes all possible measures of path length for weighted graphs. The result, without breaking the graph into smaller components, is a distance backbone subgraph sufficient to compute all shortest paths. This is important for understanding the dynamics of spread and communication phenomena in real-world networks. The general methodology we formally derive yields a principled graph reduction technique and provides a finer characterization of the triangular geometry of all edges-those that contribute to shortest paths and those that do not but are involved in other network phenomena. We demonstrate that the distance backbone is very small in large networks across domains ranging from air traffic to the human brain connectome, revealing that network robustness to attacks and failures seems to stem from surprisingly vast amounts of *** Codes 05C12 (Primary) 05C22, 05C82, 91D30 (Secondary) © 2021, CC BY-SA.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease 1st

Multi-task Dictionary Learning Based on Convolutional Neura...

引用

1st International Workshop on Human Brain and Artificial Intelligence, HBAI 2019, held in conjunction with the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Dong, Qunxi Zhang, Jie Li, Qingyang Thompson, Pau M. Caselli, Richard J. Ye, Jieping Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Imaging Genetics Center Institute for Neuroimaging and Informatics University of Southern California Los AngelesCA United States Department of Neurology Mayo Clinic Arizona ScottsdaleAZ United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States

ISBN: (纸本)9789811513978

Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimer’s disease (AD). The current state-of-the-art models for many images analyzing tasks are based on Convolutional Neural Networks (CNN). However, the lack of training data is a common challenge in applying CNN to the diagnosis of AD and its prodromal stages. Another challenge for CAD applications is the controversy between the requiring of longitudinal cortical structural information for higher diagnosis/prognosis accuracy and the computing ability for processing varied imaging features. To address these two challenges, we propose a novel computer-aided AD diagnosis system CNN-Stochastic Coordinate Coding (MSCC) which integrates CNN with transfer learning strategy, a novel MSCC algorithm and our effective AD-related biomarkers–multivariate morphometry statistics (MMS). We applied the novel CNN-MSCC system on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to predict future cognitive clinical measures with baseline Hippocampal/Ventricle MMS features and cortical thickness. The experimental results showed that CNN-MSCC achieved superior results. The proposed system may aid in expediting the diagnosis of AD progress, facilitating earlier clinical intervention, and resulting in improved clinical outcomes. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Quantum reservoir computing with repeated measurements on superconducting devices

arXiv

引用

arXiv 2023年

作者： Yasuda, Toshiki Suzuki, Yudai Kubota, Tomoyuki Nakajima, Kohei Gao, Qi Zhang, Wenlong Shimono, Satoshi Nurdin, Hendra I. Yamamoto, Naoki Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku Yokohama223-8522 Japan Department of Mechanical engineering Keio University 3-14-1 Hiyoshi Kohoku Yokohama223-8522 Japan Gradurate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8556 Japan Mitsubishi Chemical Corporation Science & Innovation Center 1000 Kamoshida-cho Aoba-ku Yokohama227-8502 Japan Quantum Computing Center Keio University Hiyoshi 3-14-1 Kohoku Yokohama223-8522 Japan School of Manufacturing Systems and Networks Arizona State University MesaAZ85212 United States The Global KAITEKI Center Arizona State University TempeAZ85281 United States School of Electrical Engineering and Telecommunications The University of New South Wales SydneyNSW2052 Australia

Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dynamical systems. Quantum systems are considered as promising reservoirs, but the conventional quantum reservoir computing (QRC) models have problems in the execution time. In this paper, we develop a quantum reservoir (QR) system that exploits repeated measurement to generate a time-series, which can effectively reduce the execution time. We experimentally implement the proposed QRC on the IBM’s quantum superconducting device and show that it achieves higher accuracy as well as shorter execution time than the conventional QRC method. Furthermore, we study the temporal information processing capacity to quantify the computational capability of the proposed QRC;in particular, we use this quantity to identify the measurement strength that best tradeoffs the amount of available information and the strength of dissipation. An experimental demonstration with soft robot is also provided, where the repeated measurement over 1000 timesteps was effectively applied. Finally, a preliminary result with 120 qubits device is discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

来源：评论

学校读者我要写书评

暂无评论

Reporting of Scope2 Co2-Emissions and Electricity Consumption in 111 Indian Ocean Cities between 2015-2022

SSRN

引用

SSRN 2024年

作者： Nematchoua, Modeste Kameni Buratti, Cinza Orosa, Jose A. Attia, Shady Teller, Jacques Bemanana, Muriel Akinola, Olatunji Bernard, Andrianirina Charles Sendrahasina, Rakotomalala Minoson Roy, Sambatra Eric Jean Falisoa, Rafanotsimiva Liva Jean-Pierre, Messina Reiter, Sigrid School of Informatics Computing and Cyber Systems Northern Arizona University 1295 S. Knoles Dr. Building 90 Room 320 FlagstaffAZ86011 United States Department of Engineering University of Perugia Via G. Duranti 63 Perugia06125 Italy Department of N. S. and Marine Engineering Escuela Técnica Superior de N. y M University of A Coruña Paseo de Ronda 51 A Coruna15011 Spain Sustainable Building Design Lab Dept. UEE Applied Sciences University of Liege Belgium LEMA ArGEnCo Department University of Liege Belgium Higher Polytechnic School University of Antsiranana Antsiranana Madagascar Science and Technology’s Higher Institute University of Mahajanga Mahajanga Madagascar University of Antananarivo Po Box 566 Antananarivo101 Madagascar Department of Industrial Engineering Higher Institute of Technology Antsiranana Antsiranana201 Madagascar Faculty of Architecture and Construction Notre-Dame du Kasaï University Congo

Several sources related that the electricity sector emits almost a quarter of greenhouse gases each year in the world. It is therefore one of the important sectors to take into account to limit global warming. Indian Ocean cities produce significant CO2 emissions during electricity consumption. Their volume and accuracy remain practically unknown and untested. Indeed, until now, there is no methodology suggested by the researchers to evaluate Fossil Fuel carbon dioxide (FFCO2) emission, and electricity consumption in this region. Aware of these crucial problems, this study was carried out to assess and analyse CO2 emissions coming from Electricity consumption (called Scope2) in 111 cities located in the Indian Ocean from 55 Power plants between 2015-2022 (08 years) and in four sectors (Residential, Commercial, Industrial, and On-road). To carry out a good comparison, all the data were grouped into three categories, Before COVID (2015-2018);During COVID (2019-2020);and After COVID (2021-2022)). The results showed that the CO2 emission difference is significant in the residential and commercial sectors. It was observed that CO2 emissions increased in 2019-2022 in the residential, industrial, and on-road sectors whereas, simultaneously during the same period, it decreased in the commercial sector. During the three periods, the CO2 emissions rate was the highest in the residential sector (around 52%), and the least on-road (around 1%). The significant difference in the commercial sector suggests a decrease in electricity consumption during the peak of the pandemic due to reduced business activities. Businesses adapted to new operating conditions, such as reduced hours or enhanced energy efficiency measures, which also contributed to the change in consumption patterns. © 2024, The Authors. All rights reserved.

关键词： Low emission

来源：评论

学校读者我要写书评

暂无评论

Assessing responsible innovation training

引用

Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

来源：评论

学校读者我要写书评

暂无评论

REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices

arXiv

引用

arXiv 2019年

作者： Bhat, Ganapati Bagewadi, Kunal Lee, Hyung Gyu Ogras, Umit Y. School of Electrical Computer and Energy Engineering Asu TempeAZ United States School of Computing Informatics and Decision Systems Engineering Asu TempeAZ United States Deagu University Daegu

The use of wearable and mobile devices for health monitoring and activity recognition applications is increasing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and small form-factor constraints. This paper considers energy harvesting devices that run on a limited energy budget to recognize user activities over a given period. We propose a technique to co-optimize the accuracy and active time by utilizing multiple design points with different energy-Accuracy trade-offs. The proposed technique switches between these design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. We evaluate the proposed approach experimentally using a custom hardware prototype and fourteen user studies. The proposed approach achieves both 46% higher expected accuracy and 66% longer active time compared to the highest performance design point. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Abductive Causal Reasoning for Internet of Things

Abductive Causal Reasoning for Internet of Things

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Adrienne Raglin Anshuman Venkateswaran Huan Liu Information Science Division US Army Research Laboratory Adelphi USA School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe USA

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

The Internet of Things (IoT) is defined as a "global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies [1]." This network of heterogeneous devices that are connected can interact and share data as well as aid in decision making. Abductive causal reasoning (ACR) offers a new perspective to learn from data with the capability to support decisions by explaining the context or environment in which the choices are made. In this paper, we propose to use ACR in an IoT environment to infer sensible hypotheses from IoT data and explain how decisions can be made or improved. Examples are given to illustrate how ACR can support decisions from the available data.

关键词： Cognition Engines Smart cities decision making Sensors Automobiles Internet of Things

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

引用

arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Modulated Model Predictive Control of a Two-Level Inverter with Even-Order Harmonic Elimination

Modulated Model Predictive Control of a Two-Level Inverter w...

引用

Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Alexander Dahlmann Venkata Yaramasu Apparao Dekka Kishore Yadlapati School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff USA Department of Electrical Engineering Lakehead University Thunder Bay Canada Department of Electrical and Electronics Engineering Jawaharlal Nehru Technological University Narasaraopet India

ISBN: (数字)9781728165752

ISBN: (纸本)9781728165769

The even-order harmonics have detrimental effect on the grid compared with the triplen harmonics. In this paper, a novel modulated model predictive current control strategy capable of eliminating even-order harmonic components from the two-level inverter output voltage/current waveforms is proposed. The proposed control strategy combines the features of fast dynamic response from the classical model predictive control and fixed switching frequency operation from the linear control. The proposed control strategy regulates output currents through the duty cycle based cost function optimization. The optimal voltage vectors are used to form a P-type and N-type switching sequences depending on the sector number and generate the gating signals for the inverter. The performance of the proposed control strategy is validated through MATLAB simulations.

关键词： dynamic response electric current control harmonics suppression invertors linear systems power system control predictive control switching systems (control) voltage control Inventors Harmonic reduction control strategy Predictive control electric current control Power system control Dynamic response Voltage regulation Linear system Model predictive control output current

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：