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Retraction Note: Smart explainable artificial intelligence for sustainable secure healthcare application based on quantum optical neural network

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Optical and Quantum Electronics 2024年第11期56卷 1-1页

作者： Suhasini, S. Tatini, Narendra Babu Arslan, Farrukh Bansal, Sushil Kumar Babu, Suresh Umaralievich, Mekhmonov Sultonali Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Vijayawada India Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India School of Electrical and Computer Engineering Purdue University West Lafayette USA Department of Electrical Engineering University of Engineering and Technology Lahore Lahore Pakistan Computer Science and Engineering Maharaja Agrasen University Baddi India Department of Biochemistry Symbiosis Medical College for Women Symbiosis International (Deemed University) Pune India Tashkent Institute of Finance Tashkent Uzbekistan

Observation of room-temperature ferroelectricity in elemental Te nanowires

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arXiv 2022年

作者： Zhang, Jinlei Zhang, Jiayong Qi, Yaping Gong, Shuainan Zhao, Run Yang, Hongbin Wu, Zhenping Cui, Dapeng Wang, Lin Ma, Chunlan Gao, Ju Chen, Yong P. Jiang, Yucheng Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Institute of Materials Science & Devices Suzhou University of Science and Technology Suzhou215009 China State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China Department of Physics Faculty of Science National University of Singapore Singapore117551 Singapore School of Materials Science and Engineering Shanghai University Shanghai200444 China School for Optoelectronic Engineering Zaozhuang University Shandong277160 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University Aarhus-C8000 Denmark

Ferroelectrics are essential in low-dimensional memory devices for multi-bit storage and high-density integration. A polar structure is a necessary premise for ferroelectricity, mainly existing in compounds. However, it is usually rare in elemental materials, causing a lack of spontaneous electric polarization. Here, we report an unexpected room-temperature ferroelectricity in few-chain Te nanowires. Out-of-plane ferroelectric loops and domain reversal are observed by piezoresponse force microscopy. Through density functional theory, we attribute the ferroelectricity to the ion-displacement created by the interlayer interaction between lone pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, supporting a self-gated field-effect transistor. It enables a nonvolatile memory with high in-plane mobility, zero supply voltage, multilevel resistive states, and a high on/off ratio. Our work provides new opportunities for elemental ferroelectrics with polar structures and paves a way towards applications such as low-power dissipation electronics and computing-in-memory devices. © 2022, CC BY.

关键词： Ferroelectricity

Roadmap for Unconventional Computing with Nanotechnology

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arXiv 2023年

作者： Finocchio, Giovanni Incorvia, Jean Anne C. Friedman, Joseph S. Yang, Qu Giordano, Anna Grollier, Julie Yang, Hyunsoo Ciubotaru, Florin Chumak, Andrii Naeemi, Azad J. Cotofana, Sorin D. Tomasello, Riccardo Panagopoulos, Christos Carpentieri, Mario Lin, Peng Pan, Gang Joshua Yang, J. Todri-Sanial, Aida Boschetto, Gabriele Makasheva, Kremena Sangwan, Vinod K. Trivedi, Amit Ranjan Hersam, Mark C. Camsari, Kerem Y. McMahon, Peter L. Datta, Supriyo Koiller, Belita Aguilar, Gabriel H. Temporão, Guilherme P. Rodrigues, Davi R. Sunada, Satoshi Everschor-Sitte, Karin Tatsumura, Kosuke Goto, Hayato Puliafito, Vito Åkerman, Johan Takesue, Hiroki Ventra, Massimiliano Di Pershin, Yuriy V. Mukhopadhyay, Saibal Roy, Kaushik Wang, I-Ting Kang, Wang Zhu, Yao Kaushik, Brajesh Kumar Hasler, Jennifer Ganguly, Samiran Ghosh, Avik W. Levy, William Roychowdhury, Vwani Bandyopadhyay, Supriyo Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Engineering University of Messina Messina98166 Italy Laboratoire Albert Fert CNRS Thales Université Paris-Saclay Palaiseau91767 France IMEC Leuven3001 Belgium Faculty of Physics University of Vienna Vienna1090 Austria School of Electrical & Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Quantum and Computer Engineering Department Delft University of Technology DelftCD 2628 Netherlands Department of Electrical and Information Engineering Politecnico di Bari Bari70125 Italy Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore639798 Singapore College of Computer Science and Technology Zhejiang University Hangzhou310013 China Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Electrical Engineering Department Eindhoven University of Technology AZ Eindhoven5612 Netherlands Microelectronics Department LIRMM Université de Montpellier Montpellier34095 France Laplace CNRS UT3 INPT University of Toulouse Toulouse31062 France Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering University of Illinois at Chicago ChicagoIL60607 United States Department of Electrical and Computer Engineering University of California at Santa Barbara Santa BarbaraCA93106 United States School of Applied and

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries. © 2023, CC BY-NC-SA.

关键词： Nanotechnology

SHREC 2022: Protein-ligand binding site recognition

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arXiv 2022年

作者： Gagliardi, Luca Raffo, Andrea Fugacci, Ulderico Biasotti, Silvia Rocchia, Walter Huang, Hao Ben Amor, Boulbaba Fang, Yi Zhang, Yuanyuan Wang, Xiao Christoffer, Charles Kihara, Daisuke Axenopoulos, Apostolos Mylonas, Stelios Daras, Petros Via Enrico Melen 83 Genova16152 Italy Istituto di Matematica Applicata e Tecnologie Informatiche E. Magenes Consiglio Nazionale delle Ricerche Via de Marini 6 Genova16149 Italy Computer Science and Engineering Tandon School of Engineering New York University 6 MetroTech Center BrooklynNY United States Inception Institute of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates Electrical and Computer Engineering New York University Abu Dhabi Tandon School of Engineering New York University Saadiyat Island Abu Dhabi United Arab Emirates Department of Computer Science Purdue University 249 S. Martin Jischke Dr. West LafayetteIN47907 United States Department of Biological Sciences Department of Computer Science Purdue University 249 S. Martin Jischke Dr. West LafayetteIN47907 United States Information Technologies Institute Centre for Research and Technology Hellas57001 Greece

This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein- ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem). © 2022, CC BY-NC-ND.

关键词： Ligands

Asymptotic Boundary Shrink Control with Multi-robot Systems

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arXiv 2020年

作者： Min, Byung-Cheol Kim, Jonghoek Luo, Shaocheng SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States Electrical and Electronic Convergence Department Hongik University Sejong-City Korea Republic of

—Harmful marine spills, such as algae blooms and oil spills, damage ecosystems and threaten public health tremendously. Hence, an effective spill coverage and removal strategy will play a significant role in environmental protection. In recent years, low-cost water surface robots have emerged as a solution, with their efficacy verified at small scale. However, practical limitations such as connectivity, scalability, and sensing and operation ranges significantly impair their large-scale use. To circumvent these limitations, we propose a novel asymptotic boundary shrink control strategy that enables collective coverage of a spill by autonomous robots featuring customized operation ranges. For each robot, a novel controller is implemented that relies only on local vision sensors with limited vision range. Moreover, the distributedness of this strategy allows any number of robots to be employed without inter-robot collisions. Finally, features of this approach including the convergence of robot motion during boundary shrink control, spill clearance rate, and the capability to work under limited ranges of vision and wireless connectivity are validated through extensive experiments with simulation. Copyright © 2020, The Authors. All rights reserved.

关键词： Oil spills

Mitigating query-flooding parameter duplication attack on regression models with high-dimensional gaussian mechanism

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arXiv 2020年

作者： Xiaoguang, L.I. Hui, L.I. Haonan, Y.A.N. Cheng, Zelei Wenhai, S.U.N. Hui, Z.H.U. State Key Laboratory of Integrated Service Network School of Cyber Engineering Xidian University Xi’an China Department of Computer and Information Technology Purdue University West Lafayette United States

—Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries [36]. Differential privacy (DP) has been considered a promising technique to mitigate this attack. However, we find that the vulnerability persists when regression models are being protected by current DP solutions. We show that the adversary can launch a query-flooding parameter duplication (QPD) attack to infer the model information by repeated queries. To defend against the QPD attack on logistic and linear regression models, we propose a novel High-Dimensional Gaussian (HDG) mechanism to prevent unauthorized information disclosure without interrupting the intended services. In contrast to prior work, the proposed HDG mechanism will dynamically generate the privacy budget and random noise for different queries and their results to enhance the obfuscation. Besides, for the first time, HDG enables an optimal privacy budget allocation that automatically determines the minimum amount of noise to be added per user-desired privacy level on each dimension. We comprehensively evaluate the performance of HDG using real-world datasets and shows that HDG effectively mitigates the QPD attack while satisfying the privacy requirements. We also prepare to open-source the relevant codes to the community for further research. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Kernel regression imputation in manifolds via bi-linear modeling: The dynamic-MRI case

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TechRxiv

TechRxiv 2021年

作者： Slavakis, Konstantinos Shetty, Gaurav N. Cannelli, Loris Scutari, Gesualdo Nakarmi, Ukash Ying, Leslie The Department of Information and Communications Engineering of Tokyo Institute of Technology Tokyo Japan PA United States The Dalle Molle Institute for Artificial Intelligence IDSIA/SUPSI Lugano Switzerland The Department of Industrial Engineering of Purdue University West LafayetteIN United States The Department of Computer Science and Computer Engineering University of Arkansas FayettevilleAR United States The Departments of Electrical Engineering and Biomedical Engineering of University at Buffalo SUNY BuffaloNY United States

This paper introduces a non-parametric approximation framework for imputation-by-regression on data with missing entries. The proposed framework, coined kernel regression imputation in manifolds (KRIM), is built on the hypothesis that features, generated by the measured data, lie close to an unknown-to-the-user smooth manifold. The feature space, where the smooth manifold is embedded in, takes the form of a reproducing kernel Hilbert space (RKHS). Aiming at concise data descriptions, KRIM identifies a small number of "landmark points" to define approximating "linear patches" in the feature space which mimic tangent spaces to smooth manifolds. This geometric information is infused into the design through a novel bi-linear model that allows for multiple approximating RKHSs. To effect imputation-by-regression, a bi-linear inverse problem is solved by an iterative algorithm with guaranteed convergence to a stationary point of a non-convex loss function. To showcase KRIM’s modularity, the application of KRIM to dynamic magnetic resonance imaging (dMRI) is detailed, where reconstruction of images from severely under-sampled dMRI data is desired. Extensive numerical tests on synthetic and real dMRI data demonstrate the superior performance of KRIM over state-of-the-art approaches under several metrics and with a small computational footprint. © 2021, CC BY.

关键词： Magnetic resonance imaging

Multi-fidelity Gaussian process based empirical potential development for Si:H nanowires

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arXiv 2020年

作者： Kim, Moonseop Yin, Huayi Lin, Guang School of Mechanical Engineering Purdue University West LafayetteIN47906-2045 United States School of Computer and Information Engineering Xiamen University of Technology XiamenFujian361024 China Department of Mathematics Purdue University West LafayetteIN47906-2045 United States

In material modeling, the calculation speed using the empirical potentials is fast compared to the first principle calculations, but the results are not as accurate as of the first principle calculations. First principle calculations are accurate but slow and very expensive to calculate. In this work, first, the H-H binding energy and H2-H2 interaction energy are calculated using the first principle calculations which can be applied to the Tersoff empirical potential. Second, the H-H parameters are estimated. After fitting H-H parameters, the mechanical properties are obtained. Finally, to integrate both the low-fidelity empirical potential data and the data from the high-fidelity first-principle calculations, the multi-fidelity Gaussian process regression is employed to predict the H-H binding energy and the H2-H2 interaction energy. Numerical results demonstrate the accuracy of the developed empirical potentials. Copyright © 2020, The Authors. All rights reserved.

关键词： Binding energy

Cyber Security of Smart Grid: Attacks and Defenses

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Cyber Security of Smart Grid: Attacks and Defenses

IEEE International Conference on Communications (ICC)

作者： Tu N. Nguyen Bing-Hong Liu Nam P. Nguyen Jung-Te Chou Department of Computer Science Purdue University Fort Wayne IN U.S.A. Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer and Information Sciences Towson University MD U.S.A.

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

关键词： Smart grids Power transmission lines Power system faults Power system protection Mathematical model Peer-to-peer computing