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Advances in Neural Networks - ISNN 2006 1

丛书名： Lecture Notes in computer science

1000年

作者： Jun Wang Zhang Yi Jacek M. Zurada Bao-Liang Lu Hujun Yin

ISBN: (数字)9783540344407

ISBN: (纸本)9783540344391

This book and its sister volumes constitute the Proceedings of the Third International Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China during May 28–31, 2006. After a successful ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN became a well-established series of conferences on neural computation in the region with growing popularity and improving quality. ISNN 2006 received 2472 submissions from authors in 43 countries and regions (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, UK, Ireland, Canada, USA, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews, 616 high-quality papers were selected for publication in the proceedings with the acceptance rate being less than 25%. The papers are organized in 27 cohesive sections covering all major topics of neural network research and development. In addition to the numerous contributed papers, ten distinguished scholars gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).

关键词： Artificial Intelligence Computation by Abstract Devices computer Communication Networks Algorithm Analysis and Problem Complexity Discrete Mathematics in computer science Pattern Recognition

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Ferroelectric-Gate Field Effect Transistor Memories 2

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丛书名： Topics in Applied Physics

1000年

作者： Byung-Eun Park Hiroshi Ishiwara Masanori Okuyama Shigeki Sakai Sung-Min Yoon

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Comparing analytical, micromagnetic and statistical channel models at 4 Tcbpsi patterned media recording

Comparing analytical, micromagnetic and statistical channel ...

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作者： Chua, M. Elidrissi, M.R. Eason, K. Zhang, S.H. Qin, Z.L. Chai, K.S. Tan, K.P. Cai, K. Chan, K.S. Goh, W.L. Dong, Y. Victora, R.H. Data Storage Institute (DSI) Agency for Science Technology and Research (A STAR) 117608 Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University 637457 Singapore Singapore Center for Micromagnetics and Information Technologies Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN United States

Bit patterned media recording (BPMR) is a candidate technology proposed to extend the areal density growth capability of magnetic recording systems. In conventional granular magnetic recording (CGMR), bits of information are recorded onto a number of randomly distributed magnetic grains. The difficulty for the granular media approach is that due to the randomness of the grains, a certain minimum number of grains are required in order to maintain the media signal to noise ratio (SNR). This requires smaller grains as the bits are shrunk and we end up with either unstable, or unwriteable grains as per the well-known media-trilemma. In BPMR, instead of random positions, the ''grains'' are ordered into a well-defined lattice of magnetic islands. Because of this ordering, the media SNR is no longer determined by the number of grains per bit as in CGMR, but is defined by the geometrical and magnetic distributions of the fabrication process of the media. Analytical models exist for BPMR, that flip grains in the media based on the switching field distribution (SFD) and the knowledge of the applied head field. On the other side, a statistical model for CGMR has been proposed that measures probabilities of grains flipping based on micromagnetic simulations, named the grain flipping probability (GFP) model. In this work, we adapt the GFP model for BPMR and perform a comparison between densities predicted via the analytical and GFP models, by processing the signals from these models with the appropriate detectors and LDPC (low density parity check) decoders. © 2012 IEEE.

关键词： Signal to noise ratio

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Advances in Neural Networks - ISNN 2006 1

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丛书名： Lecture Notes in computer science

1000年

作者： Jun Wang Zhang Yi Jacek M. Zurada Bao-Liang Lu Hujun Yin

ISBN: (数字)9783540344834

ISBN: (纸本)9783540344827

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Computational Neuroscience: Mathematical and Statistical Perspectives

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Statistics and Its Application 1000年第1期5卷 183-214页

作者： Robert E. Kass Shun-Ichi Amari Kensuke Arai Emery N. Brown Casey O. Diekman Markus Diesmann Brent Doiron Uri T. Eden Adrienne L. Fairhall Grant M. Fiddyment Tomoki Fukai Sonja Grün Matthew T. Harrison Moritz Helias Hiroyuki Nakahara Jun-nosuke Teramae Peter J. Thomas Mark Reimers Jordan Rodu Horacio G. Rotstein Eric Shea-Brown Hideaki Shimazaki Shigeru Shinomoto Byron M. Yu Mark A. Kramer 1Department of Statistics Machine Learning Department and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA email: kass@stat.cmu.edu 2Mathematical Neuroscience Laboratory RIKEN Brain Science Institute Wako Saitama Prefecture 351-0198 Japan 3Department of Mathematics and Statistics Boston University Boston Massachusetts 02215 USA 5Department of Anesthesia Harvard Medical School Boston Massachusetts 02115 USA 4Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematical Sciences New Jersey Institute of Technology Newark New Jersey 07102 USA 8Department of Theoretical Systems Neurobiology Institute of Biology RWTH Aachen University 52062 Aachen Germany 7Institute of Neuroscience and Medicine Jülich Research Centre 52428 Jülich Germany 9Department of Mathematics University of Pittsburgh Pittsburgh Pennsylvania 15260 USA 10Department of Physiology and Biophysics University of Washington Seattle Washington 98105 USA 11Division of Applied Mathematics Brown University Providence Rhode Island 02912 USA 12Department of Integrated Theoretical Neuroscience Osaka University Suita Osaka Prefecture 565-0871 Japan 13Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University Cleveland Ohio 44106 USA 14Department of Neuroscience Michigan State University East Lansing Michigan 48824 USA 15Department of Statistics University of Virginia Charlottesville Virginia 22904 USA 17Institute for Brain and Neuroscience Research New Jersey Institute of Technology Newark New Jersey 07102 USA 16Federated Department of Biological Sciences Rutgers University/New Jersey Institute of Technology Newark New Jersey 07102 USA 19Department of Physics Kyoto University Kyoto Kyoto Prefecture 606-8502 Japan 18Honda Research Institute Japan Wako Saitama Prefecture 351-0188 Japan 20Department of Electrical and Compute

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

关键词： neural data analysis neural modeling neural networks theoretical neuroscience

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Advances in Natural Computation 1

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丛书名： Lecture Notes in computer science

1000年

作者： Licheng Jiao Lipo Wang Xinbo Gao Jing Liu Feng Wu

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Game Theory for Networks 1

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丛书名： Lecture Notes of the Institute for computer sciences, Social Informatics and Telecommunications engineering

1000年

作者： Lingjie Duan Anibal Sanjab Husheng Li Xu Chen Donatello Materassi Rachid Elazouzi

This book constitutes the refereed proceedings of the 7th EAI International Conference on Game Theory for Networks, GameNets 2017, held in Knoxville, Tennessee, USA, in May 2017.

ISBN: (数字)9783319675404

ISBN: (纸本)9783319675398

This book constitutes the refereed proceedings of the 7th EAI International Conference on Game Theory for Networks, GameNets 2017, held in Knoxville, Tennessee, USA, in May 2017.

关键词： computer Communication Networks Artificial Intelligence Numeric Computing Information Systems Applications (incl. Internet)

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The Social Cognition Ability Evaluation of LLMs: A Dynamic Gamified Assessment and Hierarchical Social Learning Measurement Approach

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ACM Transactions on Intelligent Systems and technology 1000年

作者： Qin Ni Yangze Yu Yiming Ma Xin Lin Ciping Deng Tingjiang Wei Mo Xuan Key Laboratory of Multilingual Education with AI Shanghai International Studies University China The College of Information Mechanical and Electrical Engineering SHNU China School of Computer Science and Technology East China Normal University China Shanghai Key Laboratory of Brain Functional Genomics Shanghai Changning-ECNU Mental Health Center School of Psychology and Cognitive Science East China Normal University China Songjiang Yunjian High School Affiliated to SFLS China

Large Language Model(LLM) has shown amazing abilities in reasoning tasks, theory of mind(ToM) has been tested in many studies as part of reasoning tasks, and social learning, which is closely related to theory of mind, are still lack of investigation. However, the test methods and materials make the test results unconvincing. We propose a dynamic gamified assessment(DGA) and hierarchical social learning measurement to test ToM and social learning capacities in LLMs. The test for ToM consists of five parts. First, we extract ToM tasks from ToM experiments and then design game rules to satisfy the ToM task requirement. After that, we design ToM questions to match the game’s rules and use these to generate test materials. Finally, we go through the above steps to test the model. To assess the social learning ability, we introduce a novel set of social rules (three in total). Experiment results demonstrate that, except GPT-4, LLMs performed poorly on the ToM test but showed a certain level of social learning ability in social learning measurement.

关键词： Large Language Model theory of mind social learning DGA and hierarchical social learning measurement

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Intelligent Computing and Optimization 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Pandian Vasant Mohammad Shamsul Arefin Vladimir Panchenko J. Joshua Thomas Elias Munapo Gerhard-Wilhelm Weber Roman Rodriguez-Aguilar

ISBN: (数字)9783031501517

ISBN: (纸本)9783031501500

The 6th edition of International Conference on Intelligent Computing and Optimization took place at G Hua Hin Resort & Mall on April 27–28, 2023, with tremendous support from the global research scholars across the planet. Objective is to celebrate “Research Novelty with Compassion and Wisdom” with researchers, scholars, experts, and investigators in Intelligent Computing and Optimization across the globe, to share knowledge, experience, and innovation—a marvelous opportunity for discourse and mutuality by novel research, invention, and creativity. This proceedings book of the 6th ICO’2023 is published by Springer Nature—Quality Label of Enlightenment.

关键词： Computational Intelligence Artificial Intelligence

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Privacy-Aware Offloading Strategy via Self-Supervised Feature Mapping in the End-Edge-Cloud System

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ACM Transactions on Sensor Networks 1000年

作者： Rui Zhang Xuemei Zhao Yajing Li Shipu Zheng Ruhui Ma Mengke Tian Youhua Xue Yong Wang Haibing Guan School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Equipment Project Management Center of Equipment Department of Aerospace System Department Shanghai China Aerospace System Engineering Shanghai Shanghai China China Electronics Corporation Hainan Joint Innovation Research Institute Co. Ltd Haikou China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Peking University Institute of Microelectronics Beijing China Beijing Microelectronic Technology Institute Beijing China

In the Internet of Things (IoT) era, the pervasive application of tremendous end devices puts forth an unprecedented demand for data processing. To address this challenge, the end-edge-cloud system has emerged as a solution, where task offloading plays a crucial role in efficiently allocating computing resources. Meanwhile, driven by the growing social awareness of privacy, privacy-aware task offloading methods have attracted significant attention. However, existing privacy-aware task offloading methods face various limitations, such as being applicable to specific scenarios, poor transfer ability of offloading strategies, etc. This paper studies the privacy-aware task offloading problem in the end-edge-cloud system and proposes PATO, a Privacy-Aware Task Offloading strategy. PATO consists of two core modules. Specifically, a novel self-supervised feature mapping module transforms sensitive information via complex unidirectional mapping. Subsequently, a DRL-based decision-making module is trained to utilize transformed information to make task offloading decisions. Subtly combining the self-supervised feature mapping module and the DRL-based decision-making module, the proposed PATO addresses both privacy protection and task offloading challenges. Furthermore, PATO is designed as a general solution for task offloading problems and exhibits good transfer ability.

关键词： task offloading strategy privacy-aware computing end-edge-cloud system

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