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arXiv 2022年

作者： Chung, Joaquin Eastman, Ely M. Kanter, Gregory S. Kapoor, Keshav Lauk, Nikolai Peña, Cristián Plunkett, Robert Sinclair, Neil Thomas, Jordan M. Valivarthi, Raju Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States The University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt Llc 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metropolitan scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-Nodes) representing the communication parties in a quantum network. Q-Nodes generate or measure quantum signals such as entangled photons and communicate the measurement results via standard, classical signals and conventional networking processes. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via transparent optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages softwaredefined networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-Nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely implemented in the classical domain. We also discuss the IEQNET processes that address issues associated with synchronization, calibration, network monitoring, and scheduling. An important goal of IEQNET is to demonstrate the extent to which the control plane classical signals can co-propagate with the data plane quantum signals in the same fiber lines (quantum-classical signal "coexistence"). This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Daily rainfall modeling using Neural Network

引用

Journal of Physics: Conference Series 2021年第1期1988卷

作者： S D Permai M Ohyver M K B M Aziz Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Centre for Mathematical Sciences College of Computing & Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia

In the early 2020, Indonesia experienced flooding in several areas. This disaster caused a lot of damage and losses. One of the causes of flooding in Indonesia is due to high rainfall. This was not anticipated beforehand so there was a flood. Therefore, research on rainfall in Indonesia is very important to anticipate floods. If it is predicted that rainfall is very high and conditions do not allow it to accommodate, the government can prepare watersheds so that rainwater can flow and not be trapped. In this research, the rainfall data were obtained from Meteorological, Climatological, and Geophysical Agency (BMKG Indonesia), then the analysis of rainfall data in Indonesia was carried out. There are several statistical methods that can be used. There are ARIMA and Neural Network. In this research, the results of ARIMA model are used as input variables in the Neural Network model. Then there are several numbers of hidden layer in the Neural Network model that are compared. The results of ARIMA model and Neural Network model showed that Neural Network model is better than ARIMA model, because the mean square error (MSE) value of Neural Network model is smaller than ARIMA model.

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Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

TechRxiv

引用

TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

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Crystalline phase discriminating neutron tomography using advanced reconstruction methods

arXiv

引用

arXiv 2021年

作者： Ametova, Evelina Burca, Genoveva Chilingaryan, Suren Fardell, Gemma Jørgensen, Jakob S. Papoutsellis, Evangelos Pasca, Edoardo Warr, Ryan Turner, Martin Lionheart, William R.B. Withers, Philip J. Henry Royce Institute Department of Materials The University of Manchester M13 9PL United Kingdom Laboratory for Application of Synchrotron Radiation Karlsruhe Institute of Technology Germany ISIS Pulsed Neutron and Muon Source STFC UKRI Rutherford Appleton Laboratory United Kingdom Department of Mathematics The University of Manchester M13 9PL United Kingdom Institute for Data Processing and Electronics Karlsruhe Institute of Technology Germany Scientific Computing Department STFC UKRI Rutherford Appleton Laboratory United Kingdom Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Research IT Services The University of Manchester M13 9PL United Kingdom

Time-of-flight neutron imaging offers complementary attenuation contrast to X-ray computed tomography (CT), coupled with the ability to extract additional information from the variation in attenuation as a function of neutron energy (time of flight) at every point (voxel) in the image. In particular Bragg edge positions provide crystallographic information and therefore enable the identification of crystalline phases directly. Here we demonstrate Bragg edge tomography with high spatial and spectral resolution. We propose a new iterative tomographic reconstruction method with a tailored regularisation term to achieve high quality reconstruction from low-count data, where conventional filtered back-projection (FBP) fails. The regularisation acts in a separated mode for spatial and spectral dimensions and favours characteristic piece-wise constant and piecewise smooth behaviour in the respective dimensions. The proposed method is compared against FBP and a state-of-the-art regulariser for multi-channel tomography on a multi-material phantom. The proposed new regulariser which accommodates specific image properties outperforms both conventional and state-of-the-art methods and therefore facilitates Bragg edge fitting at the voxel level. The proposed method requires significantly shorter exposure to retrieve features of interest. This in turn facilitates more efficient usage of expensive neutron beamline time and enables the full utilisation of state-of-the-art high resolution detectors. © 2021, CC BY.

关键词： Iterative methods

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The algebra, logic and topology of system-of-systems 20

The algebra, logic and topology of system-of-systems

引用

20th International Dependency and Structure Modeling Conference, DSM 2018

作者： Johansson, Mats Eklund, Patrik Kortelainen, Jari Winter, Michael Umeå University Department of Computing Science Umeå Sweden South-Eastern Finland University of Applied Sciences Mikkeli Finland Brock University Department of Computer Science St. CatharinesON Canada

ISBN: (纸本)9783000574924

In this paper we propose an information structure enrichment of relational models underlying design structure models typically used in System-of-Systems. Such design structures are algebraically, logically and topologically mostly unstructured relations as treated within naïve set theory. The paper also aims to show how an enriched information structure can be applied to monitor the health status of a System-of-System as an alternative to fault trees. © 2018 Lehrstuhl für Produktentwicklung und Leichtbau.

关键词： System of systems

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A survey on studying the social networks of students

arXiv

引用

arXiv 2019年

作者： Saxena, Akrati Saxena, Pratishtha Reddy, Harita Gera, Ralucca Department of Computer Science School of Computing National University of Singapore Singapore Singapore Department of Computer Science and Engineering Center for Advanced Studies Lucknow India Department of Computer Science and Engineering National Institute of Technology Karnataka India Department of Applied Mathematics Associate Provost for Graduate Education Teaching and Learning Commons Naval Postgraduate School MontereyCA United States

Do studies show that physical and online students’ social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students’ social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students’ social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

arXiv

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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Quantum Circuits for Exact Unitary t-Designs and Applications to Higher-Order Randomized Benchmarking

引用

PRX Quantum 2021年第3期2卷 030339-030339页

作者： Yoshifumi Nakata Da Zhao Takayuki Okuda Eiichi Bannai Yasunari Suzuki Shiro Tamiya Kentaro Heya Zhiguang Yan Kun Zuo Shuhei Tamate Yutaka Tabuchi Yasunobu Nakamura Photon Science Center Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113–8656 Japan JST PRESTO 4–1–8 Honcho Kawaguchi Saitama 332–0012 Japan School of Mathematical Sciences Shanghai Jiao Tong University Shanghai 400240 China Department of Mathematics Hiroshima University 1-3-1 Kagamiyama Higashihiroshima 739-8562 Japan Faculty of Mathematics Kyushu University (emeritus) Fukuoka 819-0385 Japan Mathematics Division National Center for Theoretical Sciences National Taiwan University Taipei 10617 Taiwan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bynkyo-ku Tokyo 113-8656 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro-ku Tokyo 153-8904 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan

A unitary t-design is a powerful tool in quantum information science and fundamental physics. Despite its usefulness, only approximate implementations were known for general t. In this paper, we provide quantum circuits that generate exact unitary t-designs for any t on an arbitrary number of qubits. Our construction is inductive and is of practical use in small systems. We then introduce a tth-order generalization of randomized benchmarking (t-RB) as an application of exact 2t-designs. We particularly study the 2-RB in detail and show that it reveals self-adjointness of quantum noise, a metric related to the feasibility of quantum error correction (QEC). We numerically demonstrate that the 2-RB in one- and two-qubit systems is feasible, and experimentally characterize background noise of a superconducting qubit by the 2-RB. It is shown from the experiment that interactions with adjacent qubits induce the noise that may result in an obstacle toward a realization of QEC.

关键词： Quantum algorithms Quantum benchmarking Quantum gates Quantum protocols

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Slip and thermal effects on MHD fractional Jeffrey nanofluid flow

引用

Journal of Radiation Research and applied sciences 2025年第3期18卷

作者： Shajar Abbas S. Saleem Maryam Fatima Ioan-Lucian Popa Mudassar Nazar Ghada Ragheb Elnaggar Rashid Jan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan AS (Ayub Shah) Institute of Mathematics and Research Centre Multan Pakistan Department of Mathematics College of Science King Khalid University Abha 61413 Saudi Arabia Center for Artificial Intelligence (CAI) King Khalid University Abha 61421 Saudi Arabia Department of Computing Mathematics and Electronics “1 Decembrie 1918” University of Alba Iulia 15 510009 Alba Iulia Romania Faculty of Mathematics and Computer Science Transilvania University of Brasov Iuliu Maniu Street 50 500091 Brasov Romania Industrial and Systems Engineering Department College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mathematics Saveetha School of Engineering (SIMATS) Thandalam 600124 Chennai Tamil Nadu India Department of Mathematics Khazar University AZ1096 Baku Azerbaijan

Nanofluids with superior thermal characteristics are increasingly important in radiation-influenced environments across industrial and biomedical domains. This work develops a fractional-order model describing the mixed convection flow of human blood-based nanofluids embedded with single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes (MWCNTs) along an infinite vertical surface. To capture memory and hereditary effects, the constant proportional Caputo (CPC) operator is utilized, while the Laplace technique yields semi-analytical solutions for temperature, velocity, and concentration profiles, and its inverse Laplace transform is obtained using the Stehfest and Tzou algorithm. The results show that thermal radiation significantly enhances heat transfer, with a 12 % increase in temperature for blood-SWCNT nanofluid due to superior thermal conductivity, while blood-MWCNT nanofluid exhibits higher velocity due to lower density. These findings contribute to the development and optimization of nanofluid systems for use in industrial engineering applications, including high-performance heat exchangers, solar collectors, electronic cooling systems, and biomedical thermal therapies where precise thermal diffusion control over is essential.

关键词： Thermal flow Laplace transform Carbon nanotubes MHD Jeffrey fluid

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Exact bounds for dynamical critical exponents of transverse-field Ising chains with a correlated disorder

arXiv

引用

arXiv 2020年

作者： Shirai, Tatsuhiko Tanaka, Shu Department of Computer Science and Communications Engineering Waseda University 3-4-1 Ookubo Shinjuku-ku Tokyo169-8555 Japan Department of Applied Physics and Physico-Informatics Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Green Computing System Research Organization Waseda University Wasedamachi-27 Shinjuku-ku Tokyo162-0042 Japan

This study investigates the dynamical critical exponent of disordered Ising chains under transverse fields to examine the effect of a correlated disorder on quantum phase transitions. The correlated disorder, where the on-site transverse field depends on the nearest-neighbor coupling strengths connecting the site, gives a qualitatively different result from the uncorrelated disorder. In the uncorrelated disorder cases where the transverse field is either homogeneous over sites or random independently of the nearest-neighbor coupling strengths, the dynamical critical exponent is infinite. In contrast, in the presence of the correlated disorder, we analytically show that the dynamical critical exponent is finite. We also show that the dynamical critical exponent depends on the tuning process of the transverse field strengths. Copyright © 2020, The Authors. All rights reserved.

关键词： Phase transitions

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