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arXiv 2024年

作者： Chen, Zihao Xiao, Zhili Akl, Mahmoud Leugring, Johannes Olajide, Omowuyi Malik, Adil Dennler, Nik Harper, Chad Bose, Subhankar Gonzalez, Hector A. Eshraghian, Jason Pignari, Riccardo Urgese, Gianvito Andreou, Andreas G. Shankar, Sadasivan Mayr, Christian Cauwenberghs, Gert Chakrabartty, Shantanu Department of Electrical and Systems Engineering Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States Department of Bioengineering University of California 9500 Gilman Dr La Jolla San DiegoCA92093 United States SpiNNcloud Systems GmbH Freibergerstr. 37 Dresden01067 Germany Department of Electrical and computer engineering Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Electrical and Electronic Engineering Imperial College London Exhibition Rd LondonSW7 2AZ United Kingdom International Centre for Neuromorphic Engineering Western Sydney University Penrith Second Ave KingswoodNSW2747 Australia Department of Physics University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Department of Electrical and Computer Engineering University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States Politecnico di Torino Address: Corso Duca degli Abruzzi 24 Torino10129 Italy Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute University of California Berkeley University Avenue and Oxford St BerkeleyCA94720 United States Biocomputation Group University of Hertfordshire Exhibition Rd LondonSW7 2AZ United Kingdom Materials Science and Engineering Stanford University 450 Jane Stanford Way StanfordCA94305 United States Highly-Parallel VLSI-Systems and Neuro-Microelectronics Technische Universität Dresden Mommsenstraße 12 Dresden01069 Germany Scads.AI: Center for scalable data analytics and artificial intelligence Strehlener Street 12 14 Dresden01069 Germany

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-Nordheim (FN). The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing (SA) dynamics onto a network of integrate-and-fire (IF) neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer which replicates the optimal escape mechanism and convergence of SA, particularly at low temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved various benchmark MAX-CUT combinatorial optimization problems. Across multiple runs, NeuroSA consistently generates solutions that approach the state-of-the-art level with high accuracy (greater than 99%), and without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform. © 2024, CC BY-NC-SA.

关键词： Simulated annealing

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High-Fidelity Simulation of the Effects of Street Trees, Green Roofs and Walls on the Distribution of Thermal Exposure in Prague-Dejvice

SSRN

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SSRN 2022年

作者： Geletič, Jan Lehnert, Michal Resler, Jaroslav Krč, Pavel Middel, Ariane Krayenhoff, Eric Scott Krüger, Eduardo L. Department of Complex Systems Institute of Computer Science the Czech Academy of Sciences Pod Vodárenskou věží 271/2 Prague182 07 Czech Republic Department of Geography Faculty of Science Palacký University in Olomouc 17. Listopadu 12 Olomouc771 46 Czech Republic Arizona State University 950 S. Forest Mall Stauffer B TempeAZ85281 United States School of Environmental Sciences University of Guelph GuelphONN1G 2W1 Canada Departamento de Construção Civil Universidade Tecnológica Federal do Paraná - UTFPR Campus Curitiba - Sede Ecoville Rua Deputado Heitor Alencar Furtado 4900 Curitiba81280-340 Brazil

We investigate the heat stress mitigation potential of greening strategies in Prague using the PALM model that has been rigorously validated. For an evaluation of three greening scenarios the Universal Thermal Climate Index (UTCI) was used. The UTCI reduction effect of broad-leaf or coniferous trees in a complex urban environment was found to be localized with minor domain-average UTCI reductions;–4.1K under tree crowns and –0.6K on average in the neighbourhood as a day-time average, peaking at about twice this value near midday. During daytime the cooling potential of trees increases with the intensity and duration of solar exposure;–15.1K is the maximum for scenarios. For trees fully shaded by buildings, UTCI reduction was low as well (–0.5K as maximum). Tree planting reduces air temperature by more than 5K in some locations under trees, and reduces neighbourhood-average air temperature by up to 0.3K, with cooling peaking in the early evening about eight hours after the corresponding peak in UTCI reduction. Results emphasize the highly localized microclimate effects of trees for pedestrian thermal exposure reduction. The combination of green walls and roofs yielded negligible results in terms of UTCI reduction and only small air temperature effects. © 2022, The Authors. All rights reserved.

关键词： Reforestation

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Observation of high-energy neutrinos from the Galactic plane

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science 2023年第6652期380卷 1338-1343页

作者： IceCube Collaboration R. Abbasi M. Ackermann J. Adams J. A. Aguilar M. Ahlers M. Ahrens J. M. Alameddine A. A. Alves N. M. Amin K. Andeen T. Anderson G. Anton C. Argüelles Y. Ashida S. Athanasiadou S. Axani X. Bai A. Balagopal V. S. W. Barwick V. Basu S. Baur R. Bay J. J. Beatty K.-H. Becker J. Becker Tjus J. Beise C. Bellenghi S. Benda S. BenZvi D. Berley E. Bernardini D. Z. Besson G. Binder D. Bindig E. Blaufuss S. Blot M. Boddenberg F. Bontempo J. Y. Book J. Borowka S. Böser O. Botner J. Böttcher E. Bourbeau F. Bradascio J. Braun B. Brinson S. Bron J. Brostean-Kaiser R. T. Burley R. S. Busse M. A. Campana E. G. Carnie-Bronca C. Chen Z. Chen D. Chirkin K. Choi B. A. Clark K. Clark L. Classen A. Coleman G. H. Collin A. Connolly J. M. Conrad P. Coppin P. Correa D. F. Cowen R. Cross C. Dappen P. Dave C. De Clercq J. J. DeLaunay D. Delgado López H. Dembinski K. Deoskar A. Desai P. Desiati K. D. de Vries G. de Wasseige T. DeYoung A. Diaz J. C. Díaz-Vélez M. Dittmer H. Dujmovic M. Dunkman M. A. DuVernois T. Ehrhardt P. Eller R. Engel H. Erpenbeck J. Evans P. A. Evenson K. L. Fan A. R. Fazely A. Fedynitch N. Feigl S. Fiedlschuster A. T. Fienberg C. Finley L. Fischer D. Fox A. Franckowiak E. Friedman A. Fritz P. Fürst T. K. Gaisser J. Gallagher E. Ganster A. Garcia S. Garrappa L. Gerhardt A. Ghadimi C. Glaser T. Glauch T. Glüsenkamp N. Goehlke A. Goldschmidt J. G. Gonzalez S. Goswami D. Grant T. Grégoire S. Griswold C. Günther P. Gutjahr C. Haack A. Hallgren R. Halliday L. Halve F. Halzen M. Ha Minh K. Hanson J. Hardin A. A. Harnisch A. Haungs K. Helbing F. Henningsen E. C. Hettinger S. Hickford J. Hignight C. Hill G. C. Hill K. D. Hoffman K. Hoshina W. Hou F. Huang M. Huber T. Huber K. Hultqvist M. Hünnefeld R. Hussain K. Hymon S. In N. Iovine A. Ishihara M. Jansson G. S. Japaridze M. Jeong M. Jin B. J. P. Jones D. Kang W. Kang X. Kang A. Kappes D. Kappesser L. Kardum T. Karg M. Karl A. Karle U. Katz M. Kauer M. Kellermann J. L. Kelley A. Kheirandish K. Kin J. Kiryluk S. R. Klein A. Kochocki R. Koirala H. Kolanoski T. Department of Physics Loyola University Chicago Chicago IL 60660 USA. Deutsches Elektronen-Synchrotron D-15738 Zeuthen Germany. Department of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand. Université Libre de Bruxelles Science Faculty CP230 B-1050 Brussels Belgium. Niels Bohr Institute University of Copenhagen DK-2100 Copenhagen Denmark. Oskar Klein Centre and Department of Physics Stockholm University SE-10691 Stockholm Sweden. Department of Physics TU Dortmund University D-44221 Dortmund Germany. Karlsruhe Institute of Technology Institute for Astroparticle Physics D-76021 Karlsruhe Germany. Bartol Research Institute and Department of Physics and Astronomy University of Delaware Newark DE 19716 USA. Department of Physics Marquette University Milwaukee WI 53201 USA. Department of Physics and Astronomy Uppsala University Box 516 S-75120 Uppsala Sweden. Erlangen Centre for Astroparticle Physics Friedrich-Alexander-Universität Erlangen-Nürnberg D-91058 Erlangen Germany. Department of Physics and Laboratory for Particle Physics and Cosmology Harvard University Cambridge MA 02138 USA. Department of Physics and Wisconsin IceCube Particle Astrophysics Center University of Wisconsin–Madison Madison WI 53706 USA. Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA. Physics Department South Dakota School of Mines and Technology Rapid City SD 57701 USA. Department of Physics and Astronomy University of California Irvine CA 92697 USA. Department of Physics University of California Berkeley CA 94720 USA. Department of Astronomy Ohio State University Columbus OH 43210 USA. Department of Physics and Center for Cosmology and Astro-Particle Physics Ohio State University Columbus OH 43210 USA. Department of Physics University of Wuppertal D-42119 Wuppertal Germany. Fakultät für Physik & Astronomie Ruhr-Universität Bochum D-44780 Bochum Germany. Physik-department Technische Unive

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5s level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

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RFBLS: A robust rough fuzzy broad learning system with local neighborhood structure

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Neurocomputing 2025年 647卷

作者： Chu, Yonghe Guo, Yanlong Li, Peng Ding, Weiping Pedrycz, Witold Cao, Heling School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Ministry of Education Zhengzhou450001 China Centre for Complexity Science Henan University of Technology Zhengzhou450001 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology Macau SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

As an extension of random vector functional link neural network (RVFLNN), the broad learning system (BLS) has gradually been applied in various fields due to its advantages of simple network structure, rapid training, and fast model updating. Despite its remarkable achievements, there are still some difficulties and challenges that restrict its further application in practical tasks. In BLS, all samples equally contribute to constructing the optimal classification boundary. However, when training samples contain noise or outliers, it will result in the obtained classification boundary not being the true optimal classification boundary. In other words, BLS is sensitive to noise points or outliers. To address it, we propose a robust rough fuzzy broad learning system with local neighborhood structure (RFBLS). In RFBLS, fuzzy sets are utilized by associating a membership degree with each sample. This incorporation ensures diverse contributions of different samples during the minimization of the objective function. Samples with noise or outliers are assigned smaller weights, effectively eliminating the influence of noise and outlier samples in the learning process. We employ the rough set theory to explore the influence-decision degree of each conditional attribute on the decision attribute, aiming to eliminate the impact of redundant or interfering features. Subsequently, we comprehensively consider the distance between samples and class centers, as well as the local neighborhood structure relationship between samples. Initially, we calculate the initial membership degree of each sample based on the distance to class centers. Then, utilizing fuzzy K-nearest neighbors, we calculate and rank the membership degrees of K nearest neighbors for each sample. These rankings are then weighted with the initial membership degree, achieving the fusion of relationships and membership degree calculations between samples. Finally, extensive comparative experiments are conducted on var

关键词： Fuzzy set theory

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Accelerating NATO Transformation with SnTEE: Experiments with Network Security Function Virtualization in Coalition Networks

Accelerating NATO Transformation with SnTEE: Experiments wit...

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International Conference on Military Communications and Information systems (ICMCIS)

作者： Roberto Rigolin F. Lopes Johannes F. Loevenich Konrad Wrona Paulo H. L. Rettore Jerry Falkcrona Joseph Mathews Nils Nordbotten Bogdan Vasilache Thorsten Lampe Olwen L. Worthington Juha Röning Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany Department of Mathematics/Computer Science University of Osnabrück Osnabrück Germany NATO Cyber Security Centre The Hague Netherlands Military University of Technology Warsaw Poland Communication Systems (KOM) Fraunhofer FKIE Bonn Germany Cyber Defence and C2 Technology Swedish Defence Research Agency Linköping Sweden Information Technology Division U.S. Naval Research Laboratory Washington DC USA Thales Norway Oslo Norway WonderBit B.V. Almere The Netherlands Defence Science and Technology Laboratory (Dstl) UK Ministry of Defence London United Kingdom Information Technology and Electrical Engineering University of Oulu Oulu Finland

This paper introduces a virtual laboratory for experiments with cybersecurity functions in coalition networks. The motivation comes from the NATO Allied Command Transformation (ACT) initiative on science and Technology Experimentation Environment (SnTEE), which supports the transfer of scientific and technical activities carried out by the NATO science and Technology Organization (STO) research task groups towards NATO operations. The goal of the experiments reported in this paper is to emulate a network topology inspired by Protected Core Networking (PCN), which defines two interfaces. One interface between two protected core segments, from different nations, and another interface between a protected core segment and a colored cloud within a particular nation. The experiments are intended to support the investigation performed by the STO IST-196 RTG on Cyber Security in Virtualized Networks. In particular, the starting hypothesis of IST-196 is that security function virtualization can support/speedup PCN topology changes when links are added/removed. We define our experimental setup as Infrastructure as Code (IaC) and introduce experiments that emulate topology changes. We also sketch a solution for the deployment of two chains of virtualized cybersecurity functions at lower protocol layers (physical and IP) and at higher protocol layers (transport and application). This paper also lists future experiments for the NATO SnTEE lab, namely risk-aware routing, cross-layer enforcement of policies, information exchange functions, and federate monitoring and detection of cyber incidents/attacks.

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Student procrastination analysis in virtual learning environments

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AIP Conference Proceedings 2022年第1期2433卷

作者： Manzoor Ahmed Hashmani Mehak Maqbool Memon Mansoor Ebrahim Kamran Raza Adnan Ashraf Arain 1Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia 2Faculty of Engineering Science and Technology Iqra University Karachi Pakistan 3Department of Computer Systems Engineering Mehran University of Engineering Technology Jamshoro Pakistan

With the increase in Massive Open Online Courses (MOOC) and Virtual Learning Environments (VLE), a general intuition about the student performance degradation is always attributed to the incorporation of technology in academic system. Procrastination is one of the perennial challenges faced by students that affects the overall academic performance. computer-based learning environments have enhanced the overall academic system, but it sometimes a challenge for students to stick to assigned tasks and avoid procrastination. This is a comprehensive study conducted to uncover students’ behavior based on their interactions with the VLE. Information of students from versatile educational arenas including science, technology, engineering, mathematics, and social sciences have been included to gain understanding of student behavior to classify student behavior as probable to procrastinate or not. The study uses different unsupervised machine learning techniques including k-means, mini-batch k-means, agglomerative and hierarchical clustering to identify the correlations. Astonishing facts revealed by the study, representing little to absolutely no correlation between the final scores and the activities performed while interacting with the computer-based learning environments. The study also emphasizes on the need of detailed logs of students’ activity and properly annotated data to get valued insights for procrastination and academic performance of an individual, which would be helpful to identify students at risk of adverse academic performances and would lead to assist such students to improve their performance before off-track.

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Optical data transmissionwith high spectral efficiency at44Terabits/s with a soliton crystal micro-comb

Optical data transmissionwith high spectral efficiency at44T...

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Signal Processing in Photonic Communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021

作者： Tan, M. Corcoran, B. Xu, X. Wu, J. Boes, A. Nguyen, T. Chu, S. Little, B. Morandotti, R. Mitchell, A. Moss, D.J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering Rmit University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of Cas Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology Chengdu China

We report data transmission over 75km in the lab and a field trial, at 44.2 Tb/s over standard optical fibre using a single integrated chip source, reaching a spectral efficiency of 10.4 bits/s/Hz. © 2021 OSA - T...

ISBN: (纸本)9781557528209

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Cognitive Intelligence with Adaptive MAC Vehicular Ad-Hoc Network Optimization 2

Cognitive Intelligence with Adaptive MAC Vehicular Ad-Hoc Ne...

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2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Lim, Joanne Mun-Yee Chang, Yoong Choon Patel, Dhaval K School of Engineering Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Department of Electrical and Computer Systems Engineering Subang Jaya Selangor47500 Malaysia Lee Kong Chian Faculty of Engineering Science UTAR Jalan Sungai Long Bandar Sungai Long Kajang Selangor43000 Malaysia School of Engineering and Applied Science Ahmedabad University AES Bg.No-2 Commerce Six Rd Navrangpura Ahmedabad Gujarat380009 India

ISBN: (纸本)9781728149851

Successful and timely data delivery in evolutionary Vehicular Ad-Hoc Network (VANET) for Intelligent Transportation System (ITS) are particularly challenging due to the unique characteristics of VANET, such as fast topology change and frequent disruptions. The current VANET schemes use static parameters in an ever changing VANET topology, which results in lack of adaptability. Due to the fast topology change and frequent disruptions in VANET, a cognitive and adaptive VANET Medium Access Control (MAC) scheduling optimization protocol scheme, which is able to change its transmission parameters, to suit different topology change in VANET is preferable. In the proposed Adaptive V ANET MAC, namely AdMAC, change of signal to noise and interference ratio (SINR) is proposed as an indicator to represent VANET transmission environment and traffic density. The change of SINR is then used to determine the V ANET MAC contention window size. Simulation is conducted under congested and non-congested traffics, in the urban map of Kuala Lumpur, Malaysia. Results show that the proposed AdMAC has better adaptability, in terms of packet success rate and average delay. © 2020 IEEE.

关键词： Vehicular ad hoc networks

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Software module clustering: An in-depth literature analysis

arXiv

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arXiv 2020年

作者： Sarhan, Qusay I. Ahmed, Bestoun S. Bures, Miroslav Zamli, Kamal Z. Department of Computer Science University of Duhok Duhok Kurdistan Region Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Karlovo nam. 13 Praha 2121 35 Czech Republic IBM Center of Excellence Faculty of Computer Systems and Software Engineering University Malaysia Pahang 26 300 Gambang KuantanPahang Malaysia

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Fast estimation of outcome probabilities for quantum circuits

arXiv

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arXiv 2021年

作者： Pashayan, Hakop Reardon-Smith, Oliver Korzekwa, Kamil Bartlett, Stephen D. Institute for Quantum Computing Department of Combinatorics and Optimization University of Waterloo ONN2L 3G1 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Centre for Engineered Quantum Systems School of Physics The University of Sydney SydneyNSW2006 Australia

We present two classical algorithms for the simulation of universal quantum circuits on n qubits constructed from c instances of Clifford gates and t arbitrary-angle Z-rotation gates such as T gates. Our algorithms complement each other by performing best in different parameter regimes. The Estimate algorithm produces an additive precision estimate of the Born rule probability of a chosen measurement outcome with the only source of run-time inefficiency being a linear dependence on the stabilizer extent (which scales like ≈ 1.17t for T gates). Our algorithm is state-of-the-art for this task: as an example, in approximately 13 hours (on a standard desktop computer), we estimated the Born rule probability to within an additive error of 0.03, for a 50-qubit, 60 non-Clifford gate quantum circuit with more than 2000 Clifford gates. Our second algorithm, Compute, calculates the probability of a chosen measurement outcome to machine precision with run-time O (2t−rt) where r is an efficiently computable, circuit-specific quantity. With high probability, r is very close to min {t, n − w} for random circuits with many Clifford gates, where w is the number of measured qubits. Compute can be effective in surprisingly challenging parameter regimes, e.g., we can randomly sample Clifford+T circuits with n = 55, w = 5, c = 105 and t = 80 T gates, and then compute the Born rule probability with a run-time consistently less than 10 minutes using a single core of a standard desktop computer. We provide a C+Python implementation of our algorithms and benchmark them using random circuits, the hidden shift algorithm and the quantum approximate optimization algorithm (QAOA). Copyright © 2021, The Authors. All rights reserved.

关键词： Probability

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