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arXiv 2019年

作者： Yasuda, Muneki Obuchi, Tomoyuki Graduate School of Science and Engineering Yamagata University Japan Department of Mathematical and Computing Science Tokyo Institute of Technology Japan

In this study, we consider an empirical Bayes method for Boltzmann machines and propose an algorithm for it. The empirical Bayes method allows estimation of the values of the hyperparameters of the Boltzmann machine by maximizing a specific likelihood function referred to as the empirical Bayes likelihood function in this study. However, the maximization is computationally hard because the empirical Bayes likelihood function involves intractable integrations of the partition function. The proposed algorithm avoids this computational problem by using the replica method and the Plefka expansion. Our method does not require any iterative procedures and is quite simple and fast, though it introduces a bias to the estimate, which exhibits an unnatural behavior with respect to the size of the dataset. This peculiar behavior is supposed to be due to the approximate treatment by the Plefka expansion. A possible extension to overcome this behavior is also discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

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Unified description of cuprate superconductors using a four-band d−p model

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Physical Review Research 2021年第3期3卷 033157-033157页

作者： Hiroshi Watanabe Tomonori Shirakawa Kazuhiro Seki Hirofumi Sakakibara Takao Kotani Hiroaki Ikeda Seiji Yunoki Research Organization of Science and Technology Ritsumeikan University Shiga 525-8577 Japan Computational Condensed Matter Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Saitama 351-0198 Japan Computational Materials Science Research Team RIKEN Center for Computational Science (R-CCS) Hyogo 650-0047 Japan Quantum Computational Science Research Team RIKEN Center for Quantum Computing (RQC) Saitama 351-0198 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Saitama 351-0198 Japan Advanced Mechanical and Electronic System Research Center (AMES) Faculty of Engineering Tottori University Tottori 680-8552 Japan Center of Spintronics Research Network (CSRN) Graduate School of Engineering Science Osaka University Osaka 560-8531 Japan Department of Physics Ritsumeikan University Shiga 525-8577 Japan

In the 35 years since the discovery of cuprate superconductors, we have not yet reached a unified understanding of their properties, including their material dependence of the superconducting transition temperature Tc. The preceding theoretical and experimental studies have provided an overall picture of the phase diagram, and some important parameters for the Tc, such as the contribution of the Cu dz2 orbital to the Fermi surface and the site-energy difference Δdp between the Cu dx2−y2 and O p orbitals. However, they are somewhat empirical and limited in scope, always including exceptions, and do not provide a comprehensive view of the series of cuprates. Here we propose a four-band d−p model as a minimal model to study material dependence in cuprates. Using the variational Monte Carlo method, we theoretically investigate the phase diagram for the La2CuO4 and HgBa2CuO4 systems and the correlation between the key parameters and the superconductivity. Our results comprehensively account for the empirical correlation between Tc and model parameters, and thus can provide a guideline for new material design. We also show that the effect of the nearest-neighbor d−d Coulomb interaction Vdd is actually quite important for the stability of superconductivity and phase competition.

关键词： Pairing mechanisms Superconductivity High-temperature superconductors Mott insulators Strongly correlated systems Two-dimensional electron system Monte Carlo methods

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Quantifying symmetric exchange in ultrathin ferromagnetic films with chirality

arXiv

引用

arXiv 2021年

作者： Böttcher, Tobias Suraj, T.S. Chen, Xiaoye Sinha, Banibrato Tan, Hui Ru Tan, Hang Khume Laskowski, Robert Hillebrands, Burkard Kostylev, Mikhail Khoo, Khoong Hong Soumyanarayanan, Anjan Pirro, Philipp Fachbereich Physik and Landesforschungszentrum OPTIMAS Technische Universität Kaiserslautern Kaiserslautern67663 Germany MAINZ Graduate School of Excellence Mainz55128 Germany Department of Physics National University of Singapore 117551 Singapore Institute of Materials Research & Engineering Agency for Science Technology & Research 138634 Singapore Institute of High Performance Computing Agency for Science Technology & Research 138632 Singapore School of Physics M013 University of Western Australia Perth6009 Australia

The symmetric (Heisenberg) exchange interaction is fundamental to magnetism and assumes critical importance in designing magnetic materials for novel emergent phenomena and device applications. However, quantifying exchange is extremely challenging for ultrathin (∼ 1 nm) magnetic films, as techniques and approximations reliably used for bulk materials are largely inapplicable in the two-dimensional (2D) limit. Here we present and contrast the measurement of exchange stiffness, A, by several methods on a series of five Co/Pt-based ultrathin (1 − 2 nm) films. We compare results from (a) spin-wave spectroscopy by Brillouin light scattering (BLS), (b) three analytical models describing the temperature dependence of magnetization obtained by magnetometry, (c) microscopic domain periodicity measurements and simulations, and (d) ab initio density functional theory (DFT) calculations. While different methods present some qualitatively consistent trends across samples, we note, for any given sample, considerable differences (up to 5×) in the absolute values of A across the techniques, consistent with discrepancies of A reported in literature for nominally similar samples. We analyze possible sources of the discrepancies across various methods, notably including their relationship to the spin-wave dispersion, and the wave-vector ranges probed. We compare the strengths and limitations of the techniques, and outline directions for their future use in characterizing exchange interactions in ultrathin films. Copyright © 2021, The Authors. All rights reserved.

关键词： Spin waves

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Learning Analytics of the Relationships among Learning Behaviors, Learning Performance, and Motivation

Learning Analytics of the Relationships among Learning Behav...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Xuewang Geng Yufan Xu Li Chen Hiroaki Ogata Atsushi Shimada Masanori Yamada Graduate School of Human-Environment Studies Kyushu University Fukuoka Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan Faculty of Information Science & Electrical Engineering Kyushu University Fukuoka Japan Faculty of Arts and Science Kyushu University Fukuoka Japan

ISBN: (数字)9781728160900

ISBN: (纸本)9781728160917

Previous research has established that motivation has a positive impact on the learning processes and behaviors [1] [2]. Learning analytics (LA) can play an important role in addressing the issue of collecting learning behaviors. In this study, we observed the teaching activities of three classes and examined the relationships among learning motivation, learning performance, and learning behaviors of students in digital learning material readers.

关键词： Education Turning Correlation Data collection Analysis of variance Analytical models Correlation coefficient

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Do different instructional styles affect students’ learning on summer assignments?

Do different instructional styles affect students’ learning...

引用

International Conference on Advanced Learning Technologies (ICALT)

作者： Li Chen Yufan Xu Xuewang Geng Hiroaki Ogata Atsushi Shimada Masanori Yamada Graduate School of Human-Environment Studies Kyushu University Fukuoka Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan Faculty of Information Science & Electrical Engineering Kyushu University Fukuoka Japan Faculty of Arts and Science Kyushu University Fukuoka Japan

ISBN: (数字)9781728160900

ISBN: (纸本)9781728160917

Summer vacation is considered a cause of loss in students' learning performance. In this study, we investigated the differences in learning behaviors in reading learning materials and time-related behavior patterns regarding summer assignments among three classes under different instructional styles. The results showed that students' learning behaviors in summer were correlated with instructional styles in school.

关键词： Tools Task analysis Analysis of variance Electronic learning Turning

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Optimization of laser-driven quantum beam generation and the applications with artificial intelligence

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Physics of Plasmas 2024年第5期31卷

作者： Kuramitsu, Y. Taguchi, T. Nikaido, F. Minami, T. Hihara, T. Suzuki, S. Oda, K. Kuramoto, K. Yasui, T. Abe, Y. Ibano, K. Takabe, H. Chu, C.M. Wu, K.T. Woon, W.Y. Chen, S.H. Jao, C.S. Chen, Y.C. Liu, Y.L. Morace, A. Yogo, A. Arikawa, Y. Kohri, H. Tokiyasu, A. Kodaira, S. Kusumoto, T. Kanasaki, M. Asai, T. Fukuda, Y. Kondo, K. Kiriyama, H. Hayakawa, T. Tanaka, S.J. Isayama, S. Watamura, N. Suzuki, H. Kumar, H.S. Ohnishi, N. Pikuz, T. Filippov, E. Sakai, K. Yasuhara, R. Nakata, M. Ishikawa, R. Hoshi, T. Mizuta, A. Bolouki, N. Saura, N. Benkadda, S. Koenig, M. Hamaguchi, S. Graduate School of Engineering Osaka University 2-1 Yamadaoka Osaka Suita565-0871 Japan Institute of Laser Engineering Osaka University 2-6 Yamadaoka Osaka Suita565-0871 Japan 8-1-7 Umemidai Kyoto Kizugawa619-0215 Japan Department of Physics National Central University No. 300 Jhongda Rd. Taoyuan Jhongli320 Taiwan Department of Physics National Cheng Kung University Tainan701 Taiwan National Center for High-Performance Computing National Applied Research Laboratories Tainan City711010 Taiwan Institute of Space and Plasma Sciences National Cheng Kung University Tainan701 Taiwan Research Center for Nuclear Physics Osaka University 10-1 Mihogaoka Osaka Ibaraki567-0047 Japan Research Center for Accelerator and Radioisotope Science Tohoku University 1-2-1 Mikamine Taihaku-ku Miyagi Sendai982-0826 Japan Inage Chiba263-8555 Japan Graduate School of Maritime Sciences Kobe University Kobe658-0022 Japan Department of Physical Sciences Aoyama Gakuin University 5-10-1 Fuchinobe Kanagawa Sagamihara252-5258 Japan Department of Advanced Environmental Science and Engineering Kyushu University 6-1 Kasuga-Kohen Fukuoka Kasuga816-8580 Japan Graduate School of Information Science and Technology Osaka University 2-1 Yamadaoka Suita565-0871 Japan Department of Aerospace Engineering Tohoku University 6-6-01 Aramakiazaaoba Aoba-ku Sendai980-8579 Japan Institute for Open and Transdisciplinary Research Initiatives Osaka University 2-1 Yamadaoka Osaka Suita565-0871 Japan Villamayor Salamanca37185 Spain National Institute for Fusion Science 322-6 Oroshicho Gifu Toki509-5292 Japan Faculty of Arts and Sciences Komazawa University 1-23-1 Komazawa Setagaya-ku Tokyo154-8525 Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako351-0198 Japan Department of Plasma Physics and Technology Faculty of Science Masaryk University Kotlářská 2 Brno611 37 Czech Republic Aix-Marseille Université CNRS PIIM UMR 7345 Marseille France LULI CN

We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions. © 2024 Author(s).

关键词： Ions

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Fast Local Map Construction of Robot Using Semantic Priors

Fast Local Map Construction of Robot Using Semantic Priors

引用

2020 International Conference on Communications, computing, Cybersecurity, and Informatics (CCCI)

作者： Nan Yang Zhenqiang Mi Yu Guo Balqies Sadoun Mohammad S. Obaidat University of Science and Technology Beijing Beijing China Shunde Graduate School of University Of Science and Technology Beijing Foshan China College of Engineering University of Sharjah UAE College of Engineering Al-Balqa’ Applied University Jordan College of Computing and Informatics University of Sharjah King Abdullah II School of Information Technology University of Jordan Jordan

ISBN: (数字)9781728173153

ISBN: (纸本)9781728173160

When carrying out search or rescue missions, robots often need to explore in an unfamiliar environment. Inspired by the fact that human beings can use their semantic prior knowledge to find objects, robots can also perform search tasks independently according to the semantic relevance of objects in the new environment. In this paper, we propose a scheme that combines SLAM (Simultaneous Localization and Mapping), map construction and object detection to perform the task of target search. By presetting the semantic association rules of common indoor things, the robot can quickly build a local map of the exploration environment on the search path, which is convenient for subsequent use.

关键词： Robots Semantics Task analysis Cameras Object detection Three-dimensional displays Simultaneous localization and mapping

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Temporal information processing induced by quantum noise

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Physical Review Research 2023年第2期5卷 023057-023057页

作者： Tomoyuki Kubota Yudai Suzuki Shumpei Kobayashi Quoc Hoan Tran Naoki Yamamoto Kohei Nakajima Next Generation Artificial Intelligence Research Center (AI Center) Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Mechanical Engineering Keio University 3-14-1 Hiyoshi Kohoku Yokohama 223-8522 Japan Department of Creative Informatics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku Yokohama 223-8522 Japan Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku Yokohama 223-8522 Japan Department of Mechano-Informatics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on their correction and mitigation techniques. But do these quantum noises always provide disadvantages? We show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data in a framework called quantum reservoir computing. We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile. We verified our perspective by implementing the framework in a number of IBM quantum processors and obtained similar characteristic memory profiles with model analyses. As a surprising result, information processing capacity increased with quantum devices’ higher noise levels and error rates. Our study opens up a path for diverting useful information from quantum computer noises into a more sophisticated information processor.

关键词： Quantum computation Quantum information processing

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Multimodal deep learning framework for image popularity prediction on social media

arXiv

引用

arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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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

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