检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Wang, Xinke Meng, Qingyan Liu, Xingzhao Zhang, Linlin Hu, Xinli Bi, Yaxin Jancsó, Tamás College Landscape Architecture Fujian Agriculture and Forestry University Fuzhou350002 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Earth Observation of Hainan Province Hainan Aerospace Information Research Institute Sanya572029 China Innovation Center of Engineering Technology for Monitoring and Restoration of Ecological Fragile Areas in Southeast China Ministry of Natural Resources Fuzhou350013 China School of Computing Ulster University York Street BelfastBT15 1ED United Kingdom Alba Regia Technical Faculty Óbuda University Budai ut 45. SzekesfehervarH-80001 Hungary

Social justice reflects social members’ perception of fairness in the distribution of public resources. Whether urban park green spaces (UPGS) are equitably distributed among different social groups is gradually attracting the attention of governments and scholars. Taking the central urban district of Beijing as an example, combining the quantity, attraction, and proximity of parks, the measurement model of supply of UPGS had been improved. Subsequently, this study evaluated disparities in access opportunities to UPGS for different social groups and whether the supply of UPGS matched the demands of residents within the central urban area of Beijing. Finally, this study put forward some suggestions for the improvement of the fairness of UPGS. The research results suggested that: a) The improved model clarified the evaluation method of park attractiveness and explored the supply of different types of UPGS. b) There is spatial heterogeneity in the supply of UPGS enjoyed by residents, and different social groups have different access to UPGS, for example, sub-districts with higher proportion of young adults and children, men, rural populations, Han Chinese, highly educated residents, and migrants tend to have a greater supply of UPGS. On the contrary, sub-districts with a larger proportion of the elderly, women, urban population, ethnic minorities, and locals show the less supply of UPGS. To a certain extent, this reduced access does not imply simple environmental inequality, but can be considered as an environmental injustice. c) The mismatch between supply of UPGS and residents’ demands varies spatially, indicating potential problems of resource shortage, and underutilization. The fairness assessment model provides a reference for the evaluation of the fairness of UPGS in other cities;the evaluation results can provide a guidance for optimizing the layout of UPGS in Beijing. © 2022, The Authors. All rights reserved.

关键词： Parks

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Sparse code multiple access for 6G wireless communication networks: Recent advances and future directions

arXiv

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arXiv 2021年

作者： Yu, Lisu Liu, Zilong Wen, Miaowen Cai, Donghong Dang, Shuping Wang, Yuhao Xiao, Pei School of Information Engineering Nanchang University Nanchang330031 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China College of Cyber Security Jinan University Guangzhou510632 China Thuwal23955-6900 Saudi Arabia Institute for Communication Systems 5G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom

As 5G networks rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity. We propose to apply SCMA to a massively distributed access system (MDAS), whose architecture is based on fiber-based visible light communication (FVLC), ultra-dense network (UDN), and NOMA. Under this framework, we consider the interactions between optical front-hauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks. © 2021, CC BY-NC-ND.

关键词： Visible light communication

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Optimizing Multi-Dimensional Packet Classification for Multi-Core Systems

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Journal of Computer Science & Technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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A Pipelining Loop Optimization Method for Dataflow architecture

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Journal of Computer Science & Technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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Smart Holistic Model for Children and Youth with Special Educational Needs and Disabilities

Smart Holistic Model for Children and Youth with Special Edu...

引用

International Conference on computing, Electronics & Communications engineering (iCCECE)

作者： Rasha Hafidh Mhd Saeed Sharif Muna Alsallal School of Architecture Computing and Engineering College of Arts Technology and Innovation UEL University Way Dockland Campus London UK Science Technology and Innovation ATU Technical University

ISBN: (纸本)9781728121390

The introduction of the children and families act in 2014 (CFA 2014), claimed to make a significant change to the assessment and support for children with special educational needs and disabilities (SEND) in the UK. As well as enhancing the multi-professional collaboration between education, health and social care services. However, the Department for Education has not produced a national `blueprint' for Education, Health and Care-plan (EHCP) or supporting processes. As it is up to individual pathfinder areas (Local Authorities of different boroughs) to design, pilot and implement their own tools and processes. Therefore, this research addresses an apparent gap between the theoretical approach and the practical implementation of the new SEND code of practice. This research work proposes and develops an innovative generic smart “School Care Coordination System” (SCCS) knowledge-base, based on a holistic six-layered data management model. Which adopts a methodology of ontology engineering so that information is given in a well-defined meaning that translates the given unstructured data into a knowledgeable representation data to bring education, health and social care services into a single application to meet the needs of children and young people with SEND, thus such model can significantly facilitate the cooperative work between the education, health and care provider. The developed model infostructure has solved the huge difficulties schools' are facing on a daily bases. It enabled the coordination of care and integration of information about a child with an EHCP from different departments involved and have received countless attention from all the schools and the local authorities involved with great feedback.

关键词： Pediatrics Education Ontologies Planning Local government Semantic Web Monitoring

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Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices

arXiv

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arXiv 2020年

作者： Li, Guangli Ma, Xiu Wang, Xueying Liu, Lei Xue, Jingling Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural network pruning methods have been proposed to compress the models, prevailing approaches focus only on parametric operators (e.g., convolution), which may miss optimization opportunities. In this paper, we present a novel fusion-catalyzed pruning approach, called FUPRUNER, which simultaneously optimizes the parametric and non-parametric operators for accelerating neural networks. We introduce an aggressive fusion method to equivalently transform a model, which extends the optimization space of pruning and enables non-parametric operators to be pruned in a similar manner as parametric operators, and a dynamic filter pruning method is applied to decrease the computational cost of models while retaining the accuracy requirement. Moreover, FUPRUNER provides configurable optimization options for controlling fusion and pruning, allowing much more flexible performance-accuracy trade-offs to be made. Evaluation with state-of-the-art residual neural networks on five representative intelligent edge platforms, Jetson TX2, Jetson Nano, Edge TPU, NCS, and NCS2, demonstrates the effectiveness of our approach, which can accelerate the inference of models on CIFAR-10 and ImageNet datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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Environmental thermal influence over soundscape perception: a test room experimental campaign involving the psychological and physiological description of the indoor environment

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Journal of Physics: Conference Series 2021年第1期2042卷

作者： Ilaria Pigliautile Gioia Fusaro Jian Kang Wen-Shao Chang Anna Laura Pisello Engineering Department University of Studies of Perugia Italy CIRIAF - Interuniversity research centre University of Perugia Italy School of Architecture University of Sheffield UK Institute of High Performance Computing A*Star Singapore Institute for Environmental Design and Engineering (IEDE) The Bartlett University College London UK

Human environmental perception leads occupants' behaviour when interacting with buildings components, affecting the final building energy performance. A solid understanding of human comfort perception includes simultaneous multisensory stimuli and cross-modal interactions among different comfort domains. This study aims to explore the cross-modal effect between thermal and acoustic domains. Each of the 40 subjects took part in a multisensory survey under two different stationary environmental temperature settings. Results show that people in thermally warm conditions are less confident in describing the provided acoustic records. To perform the same procedure but providing a decreasing air temperature ramp would lead to a better interpretation of the results of this campaign.

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Applications and Advances in TiO2 Based Photocatalytic Building Materials

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Journal of Physics: Conference Series 2021年第1期2011卷

作者： Chongyang Liu Jitao Bai Simiao Zhang Zewen Yang Ming Luo School of Civil Engineering Tianjin University Tianjin 300072 China State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin University Tianjin 300072 China College of Intelligence and Computing Tianjin University Tianjin 300072 China Department of Naval Architecture and Ocean Engineering Tianjin University Tianjin 300072 China

TiO2 is currently the most widely used photocatalytic material. Compared with traditional building materials, TiO2-based photocatalytic building materials have unique and significant advantages, which endows them with great potential in application. By summarizing the current application and research status of photocatalytic building materials, the existing problems of photocatalytic building materials and corresponding solutions were pointed out, and current research progress of photocatalytic building materials in terms of carbon emission reduction in buildings was introduced. Finally, some prospects were made on photocatalytic building materials, which would provide a reference for the study on the next generation of photocatalytic building materials.

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Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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Author Correction: Aerial additive manufacturing with multiple autonomous robots

引用

Nature 2024年第7998期626卷 E5页

作者： Ketao Zhang Pisak Chermprayong Feng Xiao Dimos Tzoumanikas Barrie Dams Sebastian Kay Basaran Bahadir Kocer Alec Burns Lachlan Orr Talib Alhinai Christopher Choi Durgesh Dattatray Darekar Wenbin Li Steven Hirschmann Valentina Soana Shamsiah Awang Ngah Clément Grillot Sina Sareh Ashutosh Choubey Laura Margheri Vijay M Pawar Richard J Ball Chris Williams Paul Shepherd Stefan Leutenegger Robert Stuart-Smith Mirko Kovac Department of Aeronautics Imperial College London London UK. School of Engineering and Materials Science Queen Mary University of London London UK. Department of Computing Imperial College London London UK. Department of Architecture and Civil Engineering University of Bath Bath UK. Department of Computer Science University College London London UK. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. Department of Informatics Technical University of Munich Garching Germany. Stuart Weitzman School of Design University of Pennsylvania Philadelphia PA USA. Department of Aeronautics Imperial College London London UK. m.kovac@imperial.ac.uk. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. m.kovac@imperial.ac.uk.

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