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science 2022年第6615期378卷 22-22页

作者： Sills, Jennifer Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230027 China. Department of Surgery Harvard University Brigham and Women’s Hospital Boston MA 02115 USA. Faculty of Applied Science Macao Polytechnic University Macao SR China. School of Liberal Education Chengdu Jincheng College Chengdu Sichuan 611731 China. Translational Health Science and Technology Institute Faridabad Haryana 121001 India. Department of Chemical Engineering University of Washington Seattle WA 98105 USA. Johns Hopkins University Baltimore MD 21218 USA. The Affiliated Hospital Guizhou Medical University Guiyang Guizhou 550004 China. Department of Infectious Disease Epidemiology Imperial College London London W2 1PG UK. Manchester Fungal Infection Group University of Manchester Manchester UK. Philadelphia PA USA. Harrisburg PA 17102 USA. Department of Chemistry University of Pennsylvania Philadelphia PA 19104 USA. University of Kansas Lawrence KS 66045 USA. Mott MacDonald Noida UP 201010 India. Trudeau Institute Saranac Lake NY 12983 USA. US Department of Agriculture Parlier CA 93648 USA. Vagelos Molecular Life Sciences Program University of Pennsylvania Philadelphia PA 19104 USA. School of Information Systems and Technology Management University of New South Wales Sydney NSW 2052 Australia.

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"So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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International Journal of information Management 2023年 71卷

作者： Dwivedi, Yogesh K. Kshetri, Nir Hughes, Laurie Slade, Emma Louise Jeyaraj, Anand Kar, Arpan Kumar Baabdullah, Abdullah M. Koohang, Alex Raghavan, Vishnupriya Ahuja, Manju Albanna, Hanaa Albashrawi, Mousa Ahmad Al-Busaidi, Adil S. Balakrishnan, Janarthanan Barlette, Yves Basu, Sriparna Bose, Indranil Brooks, Laurence Buhalis, Dimitrios Carter, Lemuria Chowdhury, Soumyadeb Crick, Tom Cunningham, Scott W. Davies, Gareth H. Davison, Robert M. Dé, Rahul Dennehy, Denis Duan, Yanqing Dubey, Rameshwar Dwivedi, Rohita Edwards, John S. Flavián, Carlos Gauld, Robin Grover, Varun Hu, Mei-Chih Janssen, Marijn Jones, Paul Junglas, Iris Khorana, Sangeeta Kraus, Sascha Larsen, Kai R. Latreille, Paul Laumer, Sven Malik, F. Tegwen Mardani, Abbas Mariani, Marcello Mithas, Sunil Mogaji, Emmanuel Nord, Jeretta Horn O'Connor, Siobhan Okumus, Fevzi Pagani, Margherita Pandey, Neeraj Papagiannidis, Savvas Pappas, Ilias O. Pathak, Nishith Pries-Heje, Jan Raman, Ramakrishnan Rana, Nripendra P. Rehm, Sven-Volker Ribeiro-Navarrete, Samuel Richter, Alexander Rowe, Frantz Sarker, Suprateek Stahl, Bernd Carsten Tiwari, Manoj Kumar van der Aalst, Wil Venkatesh, Viswanath Viglia, Giampaolo Wade, Michael Walton, Paul Wirtz, Jochen Wright, Ryan Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Fabian Bay Wales Swansea United Kingdom Maharashtra Pune India Bryan School of Business and Economics University of North Carolina at Greensboro United States University of Bristol Business School University of Bristol BS8 1SD United Kingdom Professor of Information Systems Raj Soin College of Business Wright State University 3640 Colonel Glenn Highway DaytonOH45435 United States School of Artificial Intelligence Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Studies Indian Institute of Technology Delhi Hauz Khas New Delhi India Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia School of Computing Middle Georgia State University MaconGA United States Client Advisory and Transformation Stackroute NIIT Limited India Department of Information Systems Analytics and Operations College of Business University of Louisville United States Northumbria University London United Kingdom IRC-FDE KFUPM Saudi Arabia ISOM KFUPM Business School KFUPM Saudi Arabia Innovation and Technology Transfer Center Sultan Qaboos University Oman Department of Business Communication Sultan Qaboos University Oman Department of Management Studies National Institute of Technology Tiruchirappalli India Montpellier France FORE School of Management New Delhi India Indian Institute of Management Ahmedabad Vastrapur Ahmedabad380015 India Information School University of Sheffield United Kingdom Bournemouth University Business School Poole United Kingdom School of Information Systems and Technology Management University of New South Wales Sydney Australia Information Operations and Management Sciences Department TBS Business School 1 Place Alphonse Jourdain Toulouse31068 France Department of Education & Childhood Studies Swansea Univers

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics;digital transformation of organisations and societies;and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI;examining biases of generative AI attributable to training datasets and processes;exploring business and societal contexts best suited for generative AI implementation;determining optimal combinations of human and generative AI for various tasks;identifying ways to assess accuracy of text produced by generative AI;and uncovering the ethical and legal issues in using generative AI across different contexts. © 2023 The Authors

关键词： Marketing

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Surface reconstruction and band alignment of nonmetallic A(II)B(IV)O3 perovskites

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Physical Review Materials 2020年第4期4卷 044606-044606页

作者： Ha-Jun Sung Yasuhide Mochizuki Fumiyasu Oba Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated Systems National Institute for Materials Science Tsukuba 305-0047 Japan

Understanding the band alignment at oxide surfaces is of great importance for designing oxide-surface-based electronic, catalytic, and photocatalytic applications. We systematically investigate the band alignment of (001) surfaces of A(II)B(IV)O3 perovskites (A=Ca, Sr, Ba, Pb; B=Ti, Zr, Hf, Ge, Sn) through first-principles calculations using semilocal and hybrid functionals. The results are discussed with an emphasis on the effects of surface reconstruction on the band alignment. Reconstructed stoichiometric surfaces are generated by an evolutionary algorithm with surface energy minimization for various surface periodicity units by taking the orthorhombic phase of CaTiO3, the tetragonal phase of SrTiO3, and the cubic phases of SrTiO3 and BaSnO3 as representatives. Two types of reconstruction patterns are obtained as energetically favorable configurations common to these phases, which are composed of half-AO and -BO2 topmost layers, respectively. These reconstructed stoichiometric surfaces have energies comparable to those of nonstoichiometric surfaces, with full AO or BO2 termination under specific chemical potential conditions in CaTiO3 and SrTiO3. We systematically calculate the positions of the valence-band maxima and the conduction-band minima with respect to the vacuum level, namely, the ionization potentials and the electron affinities, for the reconstructed stoichiometric and the nonstoichiometric surfaces of A(II)B(IV)O3 perovskites. The ionization potentials and electron affinities at the reconstructed surfaces well describe the termination-plane dependencies of experimentally reported values. The surface band positions are found to show an approximately linear trend against the Goldschmidt tolerance factor, with the sign of slopes opposite to each other for the two types of reconstruction patterns. This tendency is explained by the tolerance-factor dependency of surface rumpling that significantly modifies the surface dipole, although the band positions of

关键词： Composition Surface reconstruction Perovskites Semiconductor compounds First-principles calculations Genetic algorithm

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Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor

arXiv

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arXiv 2024年

作者： Akahori, Shizuka Iizuka, Satoshi Mawatari, Ken Fukui, Kazuhiro Graduate School of Science and Technology University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Center for Artificial Intelligence Research University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Division of Physics Faculty of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Faculty of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan

We propose an effective unsupervised 3D point cloud novelty detection approach, leveraging a general point cloud feature extractor and a one-class classifier. The general feature extractor consists of a graph-based autoencoder and is trained once on a point cloud dataset such as a mathematically generated fractal 3D point cloud dataset that is independent of normal/abnormal categories. The input point clouds are first converted into latent vectors by the general feature extractor, and then one-class classification is performed on the latent vectors. Compared to existing methods measuring the reconstruction error in 3D coordinate space, our approach utilizes latent representations where the shape information is condensed, which allows more direct and effective novelty detection. We confirm that our general feature extractor can extract shape features of unseen categories, eliminating the need for autoencoder re-training and reducing the computational burden. We validate the performance of our method through experiments on several subsets of the ShapeNet dataset and demonstrate that our latent-based approach outperforms the existing methods. Copyright © 2024, The Authors. All rights reserved.

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A regularized CNN approach for detecting cervical cancer

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AIP Conference Proceedings 2024年第1期3220卷

作者： Jesse Jeremiah Tanimu Mohamed Hamada Mohammed Hassan Tsentob Joy Samson Jane Simon Parah Department of computer Science Bayero Unoiversity Kano Kano Nigeria Division of information systems University of Aizu Aizu Japan Department of Software Engineering Bayero University Kano Kano Nigeria Department of computer Science Air Force Institute of Technology Kaduna Kaduna Nigeria Department of Mathematical and Physical Science Glorious Vision University Ogwa Benin Nigeria

This study delved into leveraging deep learning techniques to classify different types of cervical cancer images, recognizing the disease’s prevalence and criticality. We employed a dataset comprising cervical images and investigated the utilization of the VGG16 model as a foundational architecture. To enhance performance, we incorporated various improvements, such as L2 regularization and data augmentation. The outcomes underscored the efficacy of these enhancements, with the refined model achieving a remarkable accuracy of 94%, surpassing the VGG16 model’s baseline accuracy of 89%. These findings demonstrate the potential of deep learning to improve the classification of cervical cancer.

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Linearization-based quantized stabilization of nonlinear systems under DoS attacks

arXiv

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arXiv 2020年

作者： Kato, Rui Cetinkaya, Ahmet Ishii, Hideaki Department of Computer Science Tokyo Instutite of Technology Yokohama226-8502 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan

Motivated by recent security issues in cyber-physical systems, this technical note studies the stabilization problem of networked control systems under Denial-of-Service (DoS) attacks. In particular, we consider to stabilize a nonlinear system with limited data rate via linearization. We employ a deterministic DoS attack model constrained in terms of attacks’ frequency and duration, allowing us to cover a large class of potential attacks. To achieve asymptotic stabilization, we propose a resilient dynamic quantizer in the sense that it does not saturate in the presence of packet losses caused by DoS attacks. A sufficient condition for stability is derived by restricting the average DoS frequency and duration. In addition, because of the locality of linearization, we explicitly investigate an estimate of the region of attraction, which can be expected to be reduced depending on the strength of DoS attacks. A simulation example is presented for demonstration of our results. Copyright © 2020, The Authors. All rights reserved.

关键词： Denial-of-service attack

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ecUWB: A Energy-Centric Communication Scheme for Unstable WiFi Based Backscatter systems : (Invited Paper)

ecUWB: A Energy-Centric Communication Scheme for Unstable Wi...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Yue Xu Jingwen Zhang Hao Zhou Xiaoyan Wang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology Univ. of Science and Technology of China China College of Engineering Ibaraki University Japan Department of Mathematical and Systems Engineering Shizuoka University Japan Information Systems Architecture Research Division National Institute of Informatics Japan

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

By integrating both energy harvesting and backscatter communication technologies, so-called `battery-free tag' emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals. At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

关键词： Wireless fidelity Backscatter Energy harvesting Receivers systems architecture Power supplies Modulation

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Demonstration of broadband topological slow light

arXiv

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arXiv 2022年

作者： Chen, Fujia Xue, Haoran Pan, Yuang Wang, Maoren Hu, Yuanhang Zhang, Li Chen, Qiaolu Han, Song Liu, Gui-Geng Gao, Zhen Zhou, Peiheng Yin, Wenyan Chen, Hongsheng Zhang, Baile Yang, Yihao Interdisciplinary Center for Quantum Information State Key Lab. of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang ZJU-UIUC Institute Zhejiang University Hangzhou310027 China Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Centre for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China

Slow-light devices are able to significantly enhance light-matter interaction due to the reduced group velocity of light, but a very low group velocity is usually achieved in a narrow bandwidth, accompanied by extreme sensitivity to imperfections that causes increased disorder-induced attenuation. Recent theories have suggested an ideal solution to this problem—unidirectional chiral photonic states, previously discovered in structures known as photonic topological insulators, not only resist backscattering from imperfections but can also be slowed down in the entire topological bandgap with multiple windings in the Brillouin zone. Here, we report on the experimental demonstration of broadband topological slow light in a photonic topological insulator. When coupled with periodic resonators that form flat bands, the chiral photonic states can wind many times around the Brillouin zone, achieving an ultra-low group velocity in the entire topological bandgap. This demonstration extends the scope of topological photonics into slow light engineering, and opens a unique avenue in the dispersion manipulation of chiral photonic states. © 2022, CC BY.

关键词： Slow light

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Dynamical simulation via quantum machine learning with provable generalization

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Physical Review Research 2024年第1期6卷 013241-013241页

作者： Joe Gibbs Zoë Holmes Matthias C. Caro Nicholas Ezzell Hsin-Yuan Huang Lukasz Cincio Andrew T. Sornborger Patrick J. Coles Department of Physics University of Surrey Guildford GU2 7XH United Kingdom AWE Aldermaston Reading RG7 4PR United Kingdom Information Sciences Los Alamos National Laboratory Los Alamos New Mexico USA Institute of Physics Ecole Polytechnique Fédéderale de Lausanne (EPFL) CH-1015 Lausanne Switzerland Department of Mathematics Technical University of Munich Garching Germany Munich Center for Quantum Science and Technology (MCQST) Munich Germany Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin Germany Institute for Quantum Information and Matter Caltech Pasadena California USA Department of Physics & Astronomy University of Southern California Los Angeles California USA Department of Computing and Mathematical Sciences Caltech Pasadena California USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico USA

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.

关键词： Machine learning Quantum algorithms Quantum circuits Quantum computation Quantum information processing Quantum information theory Quantum simulation

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Charge-Sign Dependent Cosmic-Ray Modulation Observed with the Calorimetric Electron Telescope on the International Space Station

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Physical Review Letters 2023年第21期130卷 211001-211001页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka E. Berti G. Bigongiari W. R. Binns M. Bongi P. Brogi A. Bruno J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol G. A. de Nolfo K. Ebisawa A. W. Ficklin H. Fuke S. Gonzi T. G. Guzik T. Hams K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo K. Kobayashi K. Kohri H. S. Krawczynski J. F. Krizmanic P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake A. A. Moiseev M. Mori N. Mori H. M. Motz K. Munakata S. Nakahira J. Nishimura S. Okuno J. F. Ormes S. Ozawa L. Pacini P. Papini B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi P. Spillantini F. Stolzi S. Sugita A. Sulaj M. Takita T. Tamura T. Terasawa S. Torii Y. Tsunesada Y. Uchihori E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida W. V. Zober Department of Physics University of Florence Via Sansone 1 - 50019 Sesto Fiorentino Italy INFN Sezione di Firenze Via Sansone 1 - 50019 Sesto Fiorentino Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo 162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 - 56127 Pisa Italy Department of Physics and McDonnell Center for the Space Sciences Washington University One Brookings Drive Saint Louis Missouri 63130-4899 USA Heliospheric Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Research and Exploration in Space Sciences and Technology NASA/GSFC Greenbelt Maryland 20771 USA Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori 036

We present the observation of a charge-sign dependent solar modulation of galactic cosmic rays (GCRs) with the Calorimetric Electron Telescope onboard the International Space Station over 6 yr, corresponding to the positive polarity of the solar magnetic field. The observed variation of proton count rate is consistent with the neutron monitor count rate, validating our methods for determining the proton count rate. It is observed by the Calorimetric Electron Telescope that both GCR electron and proton count rates at the same average rigidity vary in anticorrelation with the tilt angle of the heliospheric current sheet, while the amplitude of the variation is significantly larger in the electron count rate than in the proton count rate. We show that this observed charge-sign dependence is reproduced by a numerical “drift model” of the GCR transport in the heliosphere. This is a clear signature of the drift effect on the long-term solar modulation observed with a single detector.

关键词： Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles

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