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Physical Review C 2020年第1期101卷 014613-014613页

作者： Gao-Feng Wei Xi-Guang Cao Qi-Jun Zhi Xin-Wei Cao Zheng-Wen Long School of Physics and Electronic Science Guizhou Normal University Guiyang 550025 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang 550025 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China Shanghai Synchrotron Radiation Facility Zhangjiang Laboratory Shanghai 201210 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China School of Mechanical and Material Engineering Xi'an University of Arts and Sciences Xi'an 710065 China College of Physics Guizhou University Guiyang 550025 China

Within an improved transport model, we examine effects of the high momentum tail of the nucleon momentum distribution induced by short-range correlations on the proton-proton momentum correlation function in Au197+Au197 collisions at 400 MeV/nucleon. It is found that the proton-proton momentum correlation function from preequilibrium emissions responds sensitively to the presence as well as fraction of nucleons in the high momentum tail of the nucleon momentum distribution but is almost robustly insensitive to other factors, including the symmetry energy and the uncertainty of cutoff value of nucleon effective high momentum. In terms of the sensitivity and clearness, we propose that the proton-proton momentum correlation function from preequilibrium emissions can be as an effective probe of the high momentum tail of the nucleon momentum distribution.

关键词： Low & intermediate energy heavy-ion reactions Nucleon distribution

An Early Stage Researcher's Primer on Systems Medicine Terminology

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Network and Systems Medicine

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains

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arXiv 2020年

作者： Guo, Jianxiong Ding, Xingjian Wu, Weili Du, Ding-Zhu Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Blockchain

Erratum to “Entropy-based fuzzy support vector machine for imbalanced datasets” [Knowl.-Based Syst. 115 (2017) 87–99]

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Knowledge-Based Systems 2020年 192卷

作者： Salim Rezvani Xizhao Wang Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen 518060 Guangdong China

In this note, we show that the calculation of statistics X F 2 and F F in sections 4.5 and 4.6 of the paper (Fan et al., 2017) is not correct. Also, based on the calculation of Critical Difference (CD) of Bonferroni–Dunn test, we show that the conclusion on ”significantly outperforming the compared algorithms”, drawn by the authors using the P -value test, is not convincing.

关键词： Information entropy fuzzy support vector machine Bonferroni–Dunn test Friedman test

Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

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Localization of Deep Inpainting Using High-Pass Fully Convol...

International Conference on Computer Vision (ICCV)

作者： Haodong Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： Forensics Feature extraction Convolution Kernel Machine learning Computer vision Probability

Evidence of mini-jet emission in a large emission zone from a magnetically-dominated gamma-ray burst jet

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arXiv 2023年

作者： Yi, S.-X. Wang, C.-W. Shao, X. Moradi, R. Gao, H. Zhang, B. Xiong, S.-L. Zhang, S.-N. Tan, W.-J. Liu, J.-C. Xue, W.-C. Zhang, Y.-Q. Zheng, C. Wang, Y. Zhang, P. An, Z.-H. Cai, C. Feng, P.-Y. Gong, K. Guo, D.-Y. Huang, Y. Li, B. Li, X.-B. Li, X.-Q. Liu, X.-J. Liu, Y.-Q. Ma, X. Peng, W.-X. Qiao, R. Song, L.-M. Wang, J. Wang, P. Wang, Y. Wen, X.-Y. Xiao, S. Xu, Y.-B. Yang, S. Yi, Q.-B. Zhang, D.-L. Zhang, F. Zhang, H.-M. Zhang, J.-P. Zhang, Z. Zhao, X.-Y. Zhao, Y. Zheng, S.-J. Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Department of Astronomy Beijing Normal University Beijing100875 China Nevada Center for Astrophysics University of Nevada Las Vegas NV89154 United States Department of Physics and Astronomy University of Nevada Las Vegas NV89154 United States ICRANet Piazza della Repubblica 10 PescaraI-65122 Italy ICRA Dipartamento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 RomeI-00185 Italy INAF Osservatorio Astronomico d’Abruzzo Via M. Maggini snc TeramoI-64100 Italy College of Electronic and Information Engineering Tongji University Shanghai201804 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan411105 China School of Computer and Information Dezhou University Dezhou253023 China

The second brightest GRB in history, GRB230307A, provides an ideal laboratory to study the mechanism of GRB prompt emission thanks to its extraordinarily high photon statistics and its single episode activity. Here we demonstrate that the rapidly variable components of its prompt emission compose an overall broad single pulse-like profile. Although these individual rapid components are aligned in time across all energy bands, this overall profile conspires to show a well-defined energy-dependent behavior which is typically seen in single GRB pulses. Such a feature demonstrates that the prompt emission of this burst is from many individual emitting units that are casually linked in a emission site at a large distance from the central engine. Such a scenario is in natural consistency with the internal-collision-induced magnetic reconnection and turbulence framework, which invokes many mini-jets due to local magnetic reconnection that constantly appear and disappear in a global magnetically-dominated jet. © 2023, CC BY.

关键词： Gamma rays

A Fabric Defect Detection System Based Improved YOLOv5 Detector

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Journal of Physics: Conference Series 2021年第1期2010卷

作者： Ying Wang Zhengyang Hao Fang Zuo Shanshan Pan Institute of Intelligence Networks System Henan University Kaifeng Henan 475001 China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng Henan 475001 China Intelligent Data Processing Engineering Research Center of Henan Province Kaifeng Henan University Kaifeng Henan 475001 China

Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet's bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.

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Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

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Journal of Harbin institute of Technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

关键词： expression profiling hidden conditional random field miRNA-disease association network

Unveiling the Multifaceted GRB 200613A: Prompt Emission Dynamics, Afterglow Evolution, and the Host Galaxy’s Properties

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arXiv 2024年

作者： Fu, Shao-Yu Xu, Dong Lei, Wei-Hua de Ugarte Postigo, Antonio Kann, D. Alexander Thöne, Christina C. Fernández, José Feliciano Agüí Shuang-Xi, Yi Xie, Wei Zou, Yuan-Chuan Liu, Xing Jiang, Shuai-Qing Lu, Tian-Hua An, Jie Zhu, Zi-Pei Zheng, Jie Tang, Qing-Wen Zhao, Peng-Wei Xin, Li-Ping Wei, Jian-Yan Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy and Space Science University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China Department of Astronomy School of Physics Huazhong University of Science and Technology Wuhan430074 China Université de la Côte d’Azur Observatoire de la Côte d’Azur CNRS Artemis Nice06304 France Aix Marseille Univ. CNRS LAM Marseille France Hessian Research Cluster ELEMENTS Giersch Science Center Goethe University Frankfurt Max-von-Laue-Straße 12 Campus Riedberg Frankfurt am MainD-60438 Germany Astronomical Institute Czech Academy of Sciences Fričova Ondřejov298 Czech Republic Centro Astronómico Hispano en Andalucía Observatorio de Calar Alto Sierra de los Filabres Gérgal Almería04550 Spain School of Physics and Physical Engineering Qufu Normal University Qufu273165 China Department of Astronomy School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy Data Processing Guizhou Normal University Guiyang550001 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Physics School of Science Nanchang University Nanchang330031 China

We present our optical observations and multi-wavelength analysis of the GRB 200613A detected by Fermi satellite. Time-resolved spectral analysis of the prompt γ-ray emission was conducted utilizing the Bayesian block method to determine statistically optimal time bins. Based on the Bayesian Information Criterion (BIC), the data generally favor the Band+Blackbody (short as BB) model. We speculate that the main Band component comes from the Blandford-Znajek mechanism, while the additional BB component comes from the neutrino annihilation process. The BB component becomes significant for a low-spin, high-accretion rate black hole central engine, as evidenced by our model comparison with the data. The afterglow light curve exhibits typical power-law decay, and its behavior can be explained by the collision between the ejecta and constant interstellar medium (ISM). Model fitting yields the following parameters: EK,iso = (2.04+11.8-1.50) × 1053 erg, Γ0 = 354+578-217, p = 2.09+0.02-0.03, n18 = (2.04+9.71-1.87)×102 cm-3, θj = 24.0-5.54+6.50 degree, ϵe = 1.66+4.09-1.39)×10-1 and ϵB = (7.76+48.5-5.9)×10-6. In addition, we employed the public Python package Prospector perform a spectral energy distribution (SED) modeling of the host galaxy. The results suggest that the host galaxy is a massive galaxy (log(M∗/M☉) = 11.75-0.09+0.10) with moderate star formation rate (SFR = 22.58+13.63-7.22 M☉/yr). This SFR is consistent with the SFR of ∼ 34.2M☉ yr-1 derived from the [OII] emission line in the observed spectrum. © 2024, CC0.

关键词： Gamma rays

Final results on the 0νββ decay half-life limit of 100Mo from the CUPID-Mo experiment

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arXiv 2022年

作者： Augier, C. Barabash, A.S. Bellini, F. Benato, G. Beretta, M. Bergé, L. Billard, J. Borovlev, Yu.A. Cardani, L. Casali, N. Cazes, A. Chapellier, M. Chiesa, D. Dafinei, I. Danevich, F.A. De Jesus, M. de Marcillac, P. Dixon, T. Dumoulin, L. Eitel, K. Ferri, F. Fujikawa, B.K. Gascon, J. Gironi, L. Giuliani, A. Grigorieva, V.D. Gros, M. Helis, D.L. Huang, H.Z. Huang, R. Imbert, L. Johnston, J. Juillard, A. Khalife, H. Kleifges, M. Kobychev, V.V. Kolomensky, Yu.G. Konovalov, S.I. Loaiza, P. Ma, L. Makarov, E.P. Mariam, R. Marini, L. Marnieros, S. Navick, X.-F. Nones, C. Norman, E.B. Olivieri, E. Ouellet, J.L. Pagnanini, L. Pattavina, L. Paul, B. Pavan, M. Peng, H. Pessina, G. Pirro, S. Poda, D.V. Polischuk, O.G. Pozzi, S. Previtali, E. Redon, Th. Rojas, A. Rozov, S. Sanglard, V. Scarpaci, J.A. Schmidt, B. Shen, Y. Shlegel, V.N. Singh, V. Tomei, C. Tretyak, V.I. Umatov, V.I. Vagneron, L. Velázquez, M. Welliver, B. Winslow, L. Xue, M. Yakushev, E. Zarytskyy, M. Zolotarova, A.S. Univ Lyon Université Lyon 1 CNRS IN2P3 IP2I-Lyon VilleurbanneF-69622 France National Research Centre Kurchatov Institute Institute of Theoretical and Experimental Physics Moscow117218 Russia Dipartimento di Fisica Sapienza Università di Roma P.le Aldo Moro 2 RomeI-00185 Italy INFN Sezione di Roma P.le Aldo Moro 2 RomeI-00185 Italy IRFU CEA Université Paris-Saclay Gif-sur-YvetteF-91191 France INFN Laboratori Nazionali del Gran Sasso AQ AssergiI-67100 Italy Department of Physics University of California BerkeleyCA94720 United States Université Paris-Saclay CNRS IN2P3 IJCLab Orsay91405 France Nikolaev Institute of Inorganic Chemistry Novosibirsk630090 Russia Dipartimento di Fisica Università di Milano-Bicocca MilanoI-20126 Italy INFN Sezione di Milano-Bicocca MilanoI-20126 Italy Institute for Nuclear Research Kyiv03028 Ukraine Institute for Astroparticle Physics Karlsruhe Institute of Technology Karlsruhe76021 Germany Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Gran Sasso Science Institute L'AquilaI-67100 Italy Fudan University Shanghai200433 China Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe76021 Germany Department of Nuclear Engineering University of California BerkeleyCA94720 United States Physik Department Technische Universität München GarchingD-85748 Germany Department of Modern Physics University of Science and Technology of China Hefei230027 China LSM Laboratoire Souterrain de Modane Modane73500 France Laboratory of Nuclear Problems JINR Moscow region Dubna141980 Russia Department of Physics & Astronomy Northwestern University EvanstonIL60208 United States Université Grenoble Alpes CNRS Grenoble INP SIMAP Saint Martin d'Héres38402 France

The CUPID-Mo experiment to search for 0νββ decay in 100Mo has been recently completed after about 1.5 years of operation at Laboratoire Souterrain de Modane (France). It served as a demonstrator for CUPID, a next generation 0νββ decay experiment. CUPID-Mo was comprised of 20 enriched Li2100MoO4 scintillating calorimeters, each with a mass of ∼ 0.2 kg, operated at ∼20 mK. We present here the final analysis with the full exposure of CUPID-Mo (100Mo exposure of 1.47 kg×year) used to search for lepton number violation via 0νββ decay. We report on various analysis improvements since the previous result on a subset of data, reprocessing all data with these new techniques. We observe zero events in the region of interest and set a new limit on the 100Mo 0νββ decay half-life of T1/20ν > 1.8 × 1024 year (stat.+syst.) at 90% CI. Under the light Majorana neutrino exchange mechanism this corresponds to an effective Majorana neutrino mass of 〈mββ〉 © 2022, CC BY.

关键词： Calorimeters