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Heavy tetraquarks in the relativistic quark model

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arXiv 2021年

作者： Faustov, Rudolf N. Galkin, Vladimir O. Savchenko, Elena M. Institute of Cybernetics and Informatics in Education Federal Research Center Computer Science and Control Russian Academy of Sciences Vavilov Street 40 Moscow119333 Russia Faculty of Physics M.V. Lomonosov Moscow State University Moscow119991 Russia

We give a review of the calculations of the masses of tetraquarks with two and four heavy quarks in the framework of the relativistic quark model based on the quasipotential approach and QCD. The diquark-antidiquark picture of heavy tetraquarks is used. The quasipotentials of the quark-quark and diquark-antidiquark interactions are constructed similarly to the previous consideration of mesons and baryons. Diquarks are considered in the colour triplet state. It is assumed that the diquark and antidiquark interact in the tetraquark as a whole and the internal structure of the diquarks is taken into account by the calculated form factor of the diquark-gluon interaction. All parameters of the model are kept fixed from our previous calculations of meson and baryon properties. A detailed comparison of the obtained predictions for heavy tetraquark masses with available experimental data is given. Many candidates for tetraquarks are found. It is argued that the structures in the di-J/φ mass spectrum observed recently by the LHCb Collaboration can be interpreted as cccc tetraquarks. © 2021, CC BY.

关键词： Hadrons

Forbidden induced subgraphs for graphs and signed graphs with eigenvalues bounded from below

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arXiv 2021年

作者： Jiang, Zilin Polyanskii, Alexandr School of Mathematical and Statistical Sciences School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Institute of Mathematics and Informatics The Bulgarian Academy of Sciences Department of Mathematics and Computer Science Saint Petersburg State University Russia

The smallest eigenvalue of a graph is the smallest eigenvalue of its adjacency matrix. We show that the family of graphs with smallest eigenvalue at least −λ can be defined by a finite set of forbidden induced subgraphs if and only if λ ∗, where (Equation presented), and ρ is the unique real root of x3 = x + 1. This resolves a question raised by Bussemaker and Neumaier. As a byproduct, we find all the limit points of smallest eigenvalues of graphs, supplementing Hoffman's work on those limit points in [−2, ∞). We also prove that the same conclusion about forbidden subgraph characterization holds for signed graphs. Our impetus for the study of signed graphs is to determine the maximum cardinality of a spherical two-distance set with two fixed angles (one acute and one obtuse) in high dimensions. Denote by Nα,β(n) the maximum number of unit vectors in Rd where all pairwise inner products lie in {α, β} with −1 ≤ β α,β(d)/d as d → ∞ when α + 2β ∗MSC Codes 05C50, 05D10, 52C35 © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

LOWER ESTIMATES OF THE KOBAYASHI DISTANCE AND LIMITS OF COMPLEX GEODESICS

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arXiv 2022年

作者： KosińSki, Lukasz Nikolov, Nikolai Institute of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Kraków30-348 Poland Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. G. Bonchev 8 Sofia1113 Bulgaria Faculty of Information Sciences State University of Library Studies and Information Technologies Shipchenski prohod 69A Sofia1574 Bulgaria

It is proved for a strongly pseudoconvex domain D in Cd with C2,α-smooth boundary that any complex geodesic through every two close points of D sufficiently close to ∂D and whose difference is non-tangential to ∂D intersect a compact subset of D that depends only on the rate of non-tangentiality. As an application, a lower bound for the Kobayashi distance is *** Codes 32F45 © 2022, CC BY.

关键词： Geodesy

Feasibility of computerized adaptive testing evaluated by Monte-Carlo and post-hoc simulations 15

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Feasibility of computerized adaptive testing evaluated by Mo...

15th Federated Conference on computer science and Information Systems, FedCSIS 2020

作者： Stepanek, Lubomir Martinkova, Patricia Institute of Biophysics and Informatics First Faculty of Medicine Charles University Salmovská 1 Czech Republic Institute of Computer Science of the Czech Academy of Sciences Pod Vodarenskou Vezi 2 Praha 8 Faculty of Education Charles University Myslíkova 7 Praha 1 Czech Republic

ISBN: (纸本)9788395541674

computerized adaptive testing (CAT) is a modern alternative to classical paper and pencil testing. CAT is based on an automated selection of optimal item corresponding to current estimate of test-taker's ability, which is in contrast to fixed predefined items assigned in linear test. Advantages of CAT include lowered test anxiety and shortened test length, increased precision of estimates of test-takers' abilities, and lowered level of item exposure thus better security. Challenges are high technical demands on the whole test work-flow and need of large item banks. In this study, we analyze feasibility and advantages of computerized adaptive testing using a Monte-Carlo simulation and post-hoc analysis based on a real linear admission test administrated at a medical college. We compare various settings of the adaptive test in terms of precision of ability estimates and test length. We find out that with adaptive item selection, the test length can be reduced to 40 out of 100 items while keeping the precision of ability estimates within the prescribed range and obtaining ability estimates highly correlated to estimates based on complete linear test (Pearson's rho=0.96). We also demonstrate positive effect of content balancing and item exposure rate control on item composition. © 2020 Polish Information Processing Society - as it is since 2011.

关键词： Monte Carlo methods

Retraction Note to: Design of ensemble classifier using Statistical Gradient and Dynamic Weight LogitBoost for malicious tumor detection

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Journal of Ambient Intelligence and Humanized Computing 2022年第1期14卷 33-33页

作者： Bose, S. Subash Chandra Sivanandam, Natarajan Sundar, P. V. Praveen Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Department of Computer Science School of Informatics Wolaita Soda University Wolaita Soda Addis Ababa Ethiopia Department of Computer Science Adhiparasakthi College of Arts and Science Kalavai Ranipet India

Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

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arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

OncoReg: Medical Image Registration for Oncological Challenges

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arXiv 2025年

作者： Heyer, Wiebke Elser, Yannic Berkel, Lennart Song, Xinrui Xu, Xuanang Yan, Pingkun Jia, Xi Duan, Jinming Li, Zi Mok, Tony C.W. Li, BoWen Staackmann, Christian Großbröhmer, Christoph Hansen, Lasse Hering, Alessa Sieren, Malte M. Heinrich, Mattias P. Institute of Medical Informatics University of Lübeck Lübeck Germany Institute of Radiology and Nuclear Medicine University Hospital Schleswig-Holstein Lübeck Germany Department of Biomedical Engineering Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States School of Computer Science University of Birmingham Birmingham United Kingdom Division of Informatics Imaging and Data Sciences University of Manchester United Kingdom DAMO Academy Alibaba Group Hangzhou China Hangzhou Shengshi Technology Co. Ltd Hangzhou China Department of Radiation Oncology University Hospital Schleswig-Holstein Lübeck Germany EchoScout GmbH Lübeck Germany Radboud University Medical Center Nijmegen Netherlands Institute of Radiology and Nuclear Medicine Institute of Interventional Radiology University Hospital Schleswig-Holstein Lübeck Germany

In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue by enabling researchers to develop and validate image registration methods through a two-phase framework that ensures patient privacy while fostering the development of more generalisable AI models. Phase one involves working with a publicly available dataset, while phase two focuses on training models on a private dataset within secure hospital networks. OncoReg builds upon the foundation established by the Learn2Reg Challenge by incorporating the registration of interventional cone-beam computed tomography (CBCT) with standard planning fan-beam CT (FBCT) images in radiotherapy. Accurate image registration is crucial in oncology, particularly for dynamic treatment adjustments in image-guided radiotherapy, where precise alignment is necessary to minimise radiation exposure to healthy tissues while effectively targeting tumours. This work details the methodology and data behind the OncoReg Challenge and provides a comprehensive analysis of the competition entries and results. Findings reveal that feature extraction plays a pivotal role in this registration task. A new method emerging from this challenge demonstrated its versatility, while established approaches continue to perform comparably to newer techniques. Both deep learning and classical approaches still play significant roles in image registration, with the combination of methods—particularly in feature extraction—proving most effective. © 2025, CC BY.

关键词： Deep learning

Precise estimates of invariant distances on strongly pseudoconvex domains

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Advances in Mathematics 2025年 478卷

作者： Kosiński, Łukasz Nikolov, Nikolai Ökten, Ahmed Yekta Institute of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Krakow 30-348 Poland Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. G. Bonchev Str. Block 8 Sofia 1113 Bulgaria Faculty of Information Sciences State University of Library Studies and Information Technologies 69A Shipchenski prohod Str. Sofia 1574 Bulgaria Dipartimento di Matematica Universitá di Roma “Tor Vergata” Via della Ricerca Scientifica 1 Roma 00133 Italy

Studying the behavior of real and complex geodesics we provide sharp estimates for the Kobayashi distance, the Lempert function, and the Carathéodory distance on strongly pseudoconvex domain with C^2,α-smooth boundary. Similar estimates are also provided for the Bergman distance on strongly pseudoconvex domains with C^3,1-smooth boundary. © 2025 Elsevier Inc.

关键词： Carathéodory and Bergman distances Carnot-Carathéodory metric Kobayashi Lempert function Strongly pseudoconvex domains, (complex) geodesics

Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach

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arXiv 2024年

作者： Liu, Xian-Xian Xu, Mingkun Wei, Yuanyuan Qin, Huafeng Song, Qun Fong, Simon Tien, Feng Luo, Wei Gao, Juntao Zhang, Zhihua Siu, Shirley The Department of Computer and Information Science University of Macau 999078 China Guangdong Institute of Intelligence Science and Technology Zhuhai519031 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 China Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China Institute of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China The director of the Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou510060 China Tsinghua University California Beijing100084 China Beijing Institute of Genomics Chinese Academy of Sciences Beijing100101 China Centre for Artificial Intelligence Driven Drug Discovery Macao Polytechnic University Rua de Luís Gonzaga Gomes 999078 China

Timely and precise classification and segmentation of gastric bleeding in endoscopic imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is critical in life-saving medical procedures. Traditional methods grapple with the challenge posed by the indistinguishable intensity values of bleeding tissues adjacent to other gastric structures. Our study seeks to revolutionize this domain by introducing a novel deep learning model, the Dual Spatial Kernelized Constrained Fuzzy C-Means (Deep DuS-KFCM) clustering algorithm. This Hybrid Neuro-Fuzzy system synergizes Neural Networks with Fuzzy Logic to offer a highly precise and efficient identification of bleeding regions. Implementing a two-fold coarse-to-fine strategy for segmentation, this model initially employs the Spatial Kernelized Fuzzy C-Means (SKFCM) algorithm enhanced with spatial intensity profiles and subsequently harnesses the state-of-the-art DeepLabv3+ with ResNet50 architecture to refine the segmentation output. Through extensive experiments across mainstream gastric bleeding and red spots datasets, our Deep DuS-KFCM model demonstrated unprecedented accuracy rates of 87.95%, coupled with a specificity of 96.33%, outperforming contemporary segmentation methods. The findings underscore the model’s robustness against noise and its outstanding segmentation capabilities, particularly for identifying subtle bleeding symptoms, thereby presenting a significant leap forward in medical image processing. © 2024, CC BY.

关键词： Medical image processing