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arXiv 2021年

作者： Wang, Samson Czarnik, Piotr Arrasmith, Andrew Cerezo, M. Cincio, Lukasz Coles, Patrick J. Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics Imperial College London LondonSW7 2AZ United Kingdom Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków Poland Mark Kac Center for Complex Systems Research Jagiellonian University Kraków Poland Quantum Science Center Oak RidgeTN37931 United States Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States

Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the trainability of VQAs, e.g., by exponentially flattening the cost landscape and suppressing the magnitudes of cost gradients. Error Mitigation (EM) shows promise in reducing the impact of noise on near-term devices. Thus, it is natural to ask whether EM can improve the trainability of VQAs. In this work, we first show that, for a broad class of EM strategies, exponential cost concentration cannot be resolved without committing exponential resources elsewhere. This class of strategies includes as special cases Zero-Noise Extrapolation, Virtual Distillation, Probabilistic Error Cancellation, and Clifford Data Regression. Second, we perform analytical and numerical analysis of these EM protocols, and we find that some of them (e.g., Virtual Distillation) can make it harder to resolve cost function values compared to running no EM at all. As a positive result, we do find numerical evidence that Clifford Data Regression (CDR) can aid the training process in certain settings where cost concentration is not too severe. Our results show that care should be taken in applying EM protocols as they can either worsen or not improve trainability. On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability. © 2021, CC BY.

关键词： Distillation

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JUNO sensitivity on proton decay p→νK^(+)searches

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Chinese Physics C 2023年第11期47卷 7-22页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram 安丰鹏安琪 Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Antonio Bergnoli Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Mario Buscemi Jose Busto Ilya Butorov Anatael Cabrera Barbara Caccianiga 蔡浩蔡啸蔡严克蔡志岩 Riccardo Callegari Antonio Cammi Agustin Campeny 曹传亚曹国富曹俊 Rossella Caruso Cédric Cerna 常劲帆 Yun Chang 陈平平 Po-An Chen 陈少敏陈旭荣 Yi-Wen Chen 陈义学陈羽陈长程捷程雅苹 Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Flavio Dal Corso Olivia Dalager Christophe De La Taille 邓智邓子艳 Wilfried Depnering Marco Diaz Xuefeng Ding 丁雅韵 Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko 董建蒙 Evgeny Doroshkevich Marcos Dracos Frédéric Druillole 杜然杜书先 Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Ulrike Fahrendholz 范东华樊磊方建方文兴 Marco Fargetta Dmitry Fedoseev Li-Cheng Feng 冯启春 Richard Ford Amélie Fournier 甘浩男 Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Agnese Giaz Nunzio Giudice Maxim Gonchar 龚光华宫辉 Yuri Gornushkin Alexandre Göttel Marco Grassi Christian Grewing Vasily Gromov 顾旻皓谷肖飞古宇关梦云 Nunzio Guardone Maria Gul 郭聪郭竞渊郭万磊郭新恒郭宇航 Paul Hackspacher Caren Hagner 韩然 Yang Han Muhammad Sohaib Hassan 何苗何伟 Tobias Heinz Patrick Hellmuth 衡月昆 Rafael Herrera 贺远强侯少静 Yee Hsiung Bei-Zhen Hu 胡航胡健润胡俊胡守扬胡涛胡宇翔胡焯钧黄春豪黄桂鸿黄翰雄黄文昊黄鑫黄性涛黄永波惠加琪霍雷霍文驹 Cédric Huss Safeer Hussain Ara Ioannisian Roberto Isocrate Beatrice Jelmini Kuo-Lun Jen Ignacio Jeria 季筱璐吉星曌贾慧慧贾俊基蹇司玉蒋荻蒋炜江晓山金如意荆小平 Cécile Jollet Jari Joutsenvaara Sirichok Jungthawan Leonidas Kalousis Philipp Ka Pontificia Universidad Católica de Chile SantiagoChile IPHC Universitéde StrasbourgCNRS/IN2P3F-67037 StrasbourgFrance Pakistan Institute of Nuclear Science and Technology IslamabadPakistan INFN Catania and Dipartimento di Fisica e Astronomia dell Universitàdi Catania CataniaItaly East China University of Science and Technology ShanghaiChina University of Science and Technology of China HefeiChina Joint Institute for Nuclear Research DubnaRussia INFN Sezione di Milano and Dipartimento di Fisica dell Universitàdi Milano MilanoItaly Department of Physics Faculty of ScienceChulalongkorn UniversityBangkokThailand IJCLab UniversitéParis-SaclayCNRS/IN2P391405 OrsayFrance Department of Physics and Earth Science University of Ferrara and INFN Sezione di FerraraFerraraItaly INFN Milano Bicocca and University of Milano Bicocca MilanoItaly INFN Sezione di Padova PadovaItaly III.Physikalisches Institut B RWTH Aachen UniversityAachenGermany Eberhard Karls Universität Tübingen Physikalisches InstitutTübingenGermany Institute of High Energy Physics BeijingChina SUBATECH Universitéde NantesIMT AtlantiqueCNRS-IN2P3NantesFrance Univ.Bordeaux CNRSLP2IUMR 5797F-33170 GradignanFrance Department of Physics National Taiwan UniversityTaipei Dipartimento di Fisica e Astronomia dell’Universitàdi Padova and INFN Sezione di Padova PadovaItaly University of Roma Tre and INFN Sezione Roma Tre RomaItaly Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance Wuhan University WuhanChina INFN Milano Bicocca and Politecnico of Milano MilanoItaly National United University Miao-Li Dongguan University of Technology DongguanChina Tsinghua University BeijingChina Institute of Modern Physics Chinese Academy of SciencesLanzhouChina Institute of Physics National Yang Ming Chiao Tung UniversityHsinchu North China Electric Power University BeijingChina Sun Yat-Sen University GuangzhouChina Beijing Institute of Spacecraft Environment Engineering BeijingChina Universidade Estadual de Londrina LondrinaBrazil INFN Sez

The Jiangmen Underground Neutrino Observatory(JUNO)is a large liquid scintillator detector designed to explore many topics in fundamental *** this study,the potential of searching for proton decay in the p→νK^(+)mode with JUNO is *** kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for ***,the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed *** on these advantages,the detection efficiency for the proton decay via p→νK^(+)is 36.9%±4.9%with a background level of 0.2±0.05(syst)±0.2(stat)events after 10 years of data *** estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 years,which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.

关键词： proton decay grand unified theories JUNO liquid scintillator detector

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Machine Learning in Earth Observation Operations: A review

Machine Learning in Earth Observation Operations: A review

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IAF Earth Observation Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Miralles, Pablo Scannapieco, Antonio Fulvio Jagadam, Nitya Baranwal, Prerna Faldu, Bhavin Abhang, Ruchita Bhatia, Sahil Bonnart, Sebastien Bhatnagar, Ishita Batul, Beenish Prasad, Pallavi Ortega-González, Héctor Joseph, Harrish More, Harshal Morchedi, Sondes Panda, Aman Kumar Di Fraia, Marco Zaccaria Wischert, Daniel Stepanova, Daria GTD International GTD Group 99 Route d’Espagne Toulouse31100 France c/o European Space Policy Institute Schwarzenbergplatz 6 Vienna1030 Austria Rajasthan Pilani333031 India Department of Computer Science Engineering Savitribai Phule Pune University Maharashtra Pune411052 India Department of Aerospace Engineering University of Petroleum and Energy Studies Energy Acres Bidholi via Premnagar Uttarakhand Dehradun248007 India Rajasthan Pilani333031 India C II Johar Town Lahore Pakistan Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy ISAE-École Nationale Supérieure de Mécanique et d’Aérotechnique Téléport 2 – 1 avenue Clément Ader BP 40109 Futuroscope Chasseneuil Cedex Poitiers86961 France Centre for Electronic Warfare Information and Cyber Cranfield University The Defence Academy Of The UK ShrivenhamSN6 8LA United Kingdom German Orbital Systems GmbH Manfred-Fuchs-Platz 2-4 Bremen28359 Germany

ISBN: (纸本)9781713843016

Analysis of downlinked satellite imagery has undeniably benefited greatly from the ongoing Machine Learning (ML) revolution. Other aspects of the Earth Observation industry, despite being less prone to an extensive application of ML, are also following this trend. This work aims at presenting - in the form of a review of Machine Learning applied to Earth Observation Operations - such applications, the existing use cases, potential opportunities and pitfalls, and perceived gaps in research. A wide range of topics are discussed including mission planning, diagnosis, prognosis, and repair of faults, optimization of telecommunications, enhanced GNC, on-board image processing, and usage of Machine Learning models within platforms with limited compute and power capabilities. The review tackles all on-board and off-board applications of machine learning to Earth Observation with one notable exception: it omits all post-processing of payload data on the ground, a topic that has been studied extensively by past authors. This research was produced by a team of volunteers from the Small Satellite Project Group of the Space Generation Advisory Council. © [IAC 2021] by Mr. Pablo Miralles.

关键词： Remote sensing

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A patchy model for the transmission dynamics of tuberculosis in sub-Saharan Africa

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International Journal of Dynamics and Control 2018年第1期6卷 122-139页

作者： Moualeu, D.P. Bowong, S. Tsanou, B. Temgoua, A. Institute for Horticultural Production Systems Vegetable Systems Modelling Section Faculty of Natural Sciences Leibniz Universität Hannover Herrenhäuser Str. 2 Hannover30419 Germany Department of Mathematics and Computer Science Faculty of Science University of Douala PO Box 24157 Douala Cameroon Department of Mathematics and Computer Science Faculty of Science University of Dschang PO Box 47 Dschang Cameroon University of Yaounde 1 Yaounde Cameroon

Tuberculosis (TB) spreads through contact between a susceptible person and smear positive pulmonary TB case (TPM+). The spread of TB is highly dependent on people migration between cities or regions that may have different contact rates and different environmental parameters, leading to different disease spread speed in the population. In this work, a metapopulation model, i.e., networks of populations connected by migratory flows, which overcomes the assumption of homogeneous mixing between different regions was constructed. The TB model was combined to a simple demographic structure for the population living in a multi-patch environment (cities, towns, regions or countries). The model consist of a system of differential equations coupling TB epidemic at different strength and mobility between the patches. Constant recruitment rate, slow and fast progression to the disease, effective chemoprophylaxis, diagnostic and treatment are taken into account to make the model including the reality of people in the sub-Saharan African countries. The basic reproduction number (R0) was computed and it was demonstrated that the disease-free equilibrium is globally asymptotically stable if R00> 1 , the disease-free equilibrium is unstable and there exists one endemic equilibrium. Moreover, the impact of increasing migration rate between patches on the TB spread was quantified using numerical implementation of the model. Using an example on 15 inter-connected patches on the same road, we demonstrated that most people was most likely to get infected if the disease starts in a patch in the middle than in border patches. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Nonlinear dynamical systems

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Front Cover: Micrometer-Sized Liposome-Based systems: A Hierarchical Breakdown (ChemsystemsChem 3/2025)

ChemSystemsChem

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ChemsystemsChem 2025年第3期7卷

作者： Prof. Dr. Shogo Hamada Dr. Hironori Sugiyama Prof. Dr. Yiting Zhang Prof. Dr. Shoji Iwabuchi Soichiro Hiroi Toshiki Maruyama Yuktesh Balaji Sota Kumagai Prof. Dr. Satoshi Murata Prof. Dr. Taro Toyota Department of Computer Science School of Computing & Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo 4259 Nagatsuta-cho Midori-ku Yokohama-city Kanagawa 226-8501 Japan These authors contributed equally. Department of Applied Chemistry Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemistry College of Science Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo 171-8501 Japan Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho Koganei-shi Tokyo 185-8588 Japan Department of Basic Science Graduate School of Arts and Sciences The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Robotics Graduate School of Engineering Tohoku University 6-6-01 Aramaki-Aza Aoba Aoba-ku Sendai Miyagi 980-8579 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific Reports 2025年第1期15卷 1-23页

作者： Adnan, Khan Muhammad Ghazal, Taher M. Saleem, Muhammad Farooq, Muhammad Sajid Yeun, Chan Yeob Ahmad, Munir Lee, Sang-Woong Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 South Korea Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates University College Korea University Seoul 02841 South Korea

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model’s predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered “black box” systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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Modeling a population of switches via chaotic dynamics

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IFAC-PapersOnLine 2020年第2期53卷 16791-16795页

作者： A. Buscarino L. Belhamel M. Bucolo P. Palumbo C. Manes DIEEI Università degli Studi di Catania Catania Italy Department of Biotechnology and Biosciences University of Milano-Bicocca Piazza della Scienza 2 20126 Milan Italy DISIM Università degli Studi dell’Aquila Via Vetoio CNR-IASI Italian National Research Council Institute for Systems Analysis and Computer Science “A. Ruberti” Rome Italy

This note investigates the behavior of a population of interconnected irreversible switches, whose switching mechanism is triggered by a chaotic map. Motivation comes from the fact that many emergent properties of biological systems are ruled by the activation of a population of biochemical switches, whose timing and fluctuations may lead to different cell fates. They have been usually investigated according to a stochastic approach, and in this work we show how some properties could be similarly explained in terms of the emergent properties of a chaotic system. With respect to noise, chaos comes out from a deterministic framework, thereby allowing the implementation of experimental procedures directed to investigate the behavior of the system according to different scenarios by means of a rigorous deterministic passage from mathematics to simulation, that cannot be honestly rendered for the stochastic framework, since pseudo-random sequences are usually invoked.

关键词： Mathematical modeling Chaotic systems Stochastic systems Synthetic Biology

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Nonlinear compact localized modes in flux-dressed octagonal-diamond photonic lattice

arXiv

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arXiv 2021年

作者： Stojanović, M.G. Gündoğdu, S. Leykam, D. Angelakis, D.G. Krasić, M. Stojanović Stepić, M. Maluckov, Aleksandra COHERENCE Vinča Institute of Nuclear Sciences University of Belgrade National Institute of the Republic of Serbia P.O.B. 522 Belgrade11001 Serbia Center for Theoretical Physics of Complex Systems Institute for Basic Science Daejeon34126 Korea Republic of Department of Physics Humboldt-Universität zu Berlin Newtonstrasse 15 Berlin12489 Germany Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece Faculty of Technology University of Niš Leskovac16000 Serbia

Tuning the values of artificial flux in the two-dimensional octagonal-diamond lattice drives topological phase transitions, including between singular and non-singular flatbands. We study the dynamical properties of nonlinear compact localized modes that can be continued from linear flatband modes. We show how the stability of the compact localized modes can be tuned by the nonlinearity strength or the applied artificial flux. Our model can be realized using ring resonator lattices or nonlinear waveguide arrays. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical lattices

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Global patterns and mechanisms of decoupling municipal solid waste generation from economic growth

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Environmental Impact Assessment Review 2025年 115卷

作者： Chenyi Zhang Huijuan Dong Yong Zeng Yong Geng Xiao Liu Wen Chen Minoru Fujii International Business Strategy Institute University of International Business and Economics Beijing 100029 China School of Environmental Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery Shanghai Jiao Tong University Shanghai 200240 China Concordia Institute for Information Systems Gina Cody School of Engineering and Computer Science Concordia University Montreal QC H3G 2W1 Canada School of International and Public Affairs Shanghai Jiao Tong University Shanghai 200030 China China Institute for Urban Governance Shanghai Jiao Tong University Shanghai 200030 China Department of Environment Systems Graduate School of Frontier Sciences The University of Tokyo Kashiwanoha 277-8563 Japan Center for Social and Environmental Systems Research National Institute for Environmental Studies Tsukuba 305-8506 Japan

To explore the driving forces and seek solutions to the municipal solid waste (MSW) challenge, this study aims to uncover the decoupling patterns and mechanisms of global MSW generation by applying Tapio decoupling analysis and the environmental Kuznets curve (EKC) model. Thirty countries with different development levels were selected as research samples. The results show that the decoupling of MSW from economic development has deteriorated since 2010, with middle-income countries exhibiting worse performance than high-income countries. Several countries, including Australia, Belgium, Spain, the United Kingdom, Hungary, Japan, Bulgaria, Costa Rica, and Ukraine, have achieved continuous decoupling. Furthermore, an inverted U-shaped EKC model was validated between per capita MSW generation and per capita national income, with the EKC turning point at 32,626 USD per capita national income. This indicates that high-income countries have a relatively easier pathway to achieve the decoupling of per capita MSW generation. Finally, we proposed several practical solutions for achieving global MSW decoupling and MSW peaks.

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Residual-sparse fuzzy c-means clustering incorporating morphological reconstruction and wavelet frames

arXiv

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arXiv 2020年

作者： Wang, Cong Pedrycz, Witold Li, ZhiWu Zhou, MengChu Zhao, Jun School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Institute of Systems Engineering Macau University of Science and Technology Macau China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States School of Computer Science and Engineering Nanyang Technological University Singapore

Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an 0 regularization-based Fuzzy C-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morphological reconstruction is used to filter an observed image. By combining the observed and filtered images, a weighted sum image is generated. Since a tight wavelet frame system has sparse representations of an image, it is employed to decompose the weighted sum image, thus forming its corresponding feature set. Taking it as data for clustering, we present an improved FCM algorithm by imposing an 0 regularization term on the residual between the feature set and its ideal value, which implies that the favorable estimation of the residual is obtained and the ideal value participates in clustering. Spatial information is also introduced into clustering since it is naturally encountered in image segmentation. Furthermore, it makes the estimation of the residual more reliable. To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other *** codes 62H30 Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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