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Study of light-meson resonances decaying to KS0Kπ in the B→(KS0Kπ)K channels

Physical Review D 2025年第9期111卷 092009-092009页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellan Beteta F. Abudinén T. Ackernley A. A. Adefisoye B. Adeva M. Adinolfi P. Adlarson C. Agapopoulou C. A. Aidala Z. Ajaltouni S. Akar K. Akiba P. Albicocco J. Albrecht F. Alessio M. Alexander Z. Aliouche P. Alvarez Cartelle R. Amalric S. Amato J. L. Amey Y. Amhis L. An L. Anderlini M. Andersson A. Andreianov P. Andreola M. Andreotti D. Andreou A. Anelli D. Ao F. Archilli M. Argenton S. Arguedas Cuendis A. Artamonov M. Artuso E. Aslanides R. Ataíde Da Silva M. Atzeni B. Audurier D. Bacher I. Bachiller Perea S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura A. Balboni W. Baldini L. Balzani H. Bao J. Baptista de Souza Leite C. Barbero Pretel M. Barbetti I. R. Barbosa R. J. Barlow M. Barnyakov S. Barsuk W. Barter J. Bartz J. M. Basels S. Bashir G. Bassi B. Batsukh P. B. Battista A. Bay A. Beck M. Becker F. Bedeschi I. B. Bediaga N. A. Behling S. Belin K. Belous I. Belov I. Belyaev G. Benane G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet S. Bernet Andres A. Bertolin C. Betancourt F. Betti J. Bex Ia. Bezshyiko J. Bhom M. S. Bieker N. V. Biesuz P. Billoir A. Biolchini M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti T. Blake F. Blanc J. E. Blank S. Blusk V. Bocharnikov J. A. Boelhauve O. Boente Garcia T. Boettcher A. Bohare A. Boldyrev C. S. Bolognani R. Bolzonella R. B. Bonacci N. Bondar A. Bordelius F. Borgato S. Borghi M. Borsato J. T. Borsuk E. Bottalico S. A. Bouchiba M. Bovill T. J. V. Bowcock A. Boyer C. Bozzi J. D. Brandenburg A. Brea Rodriguez N. Breer J. Brodzicka A. Brossa Gonzalo J. Brown D. Brundu E. Buchanan L. Buonincontri M. Burgos Marcos A. T. Burke C. Burr J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai A. Caillet R. Calabrese S. Calderon Ramirez L. Calefice S. Cali M. Calvi M. Calvo Gomez P. Camargo Magalhaes J. I. Cambon Bouzas P. Campana D. H. Campora Perez A. F. Campoverde Quezada S. Capelli L. Capriotti R. Caravaca-Mora A. Carbone L. Carcedo Salgado R. Cardinale A. Cardini P. Carniti L. Carus A. Casais Vidal R. Caspary G. Casse M. C Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Syracuse University Syracuse New York USA Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Department of Physics and Astronomy Uppsala University Uppsala Sweden (associated with School of Physics and Astronomy University of Glasgow Glasgow United Kingdom) Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany Also at Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany. European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China INFN Sezione di Firenze Firenze Italy Authors affiliated with an institute formerly covered by a cooperation agreement with CERN INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Milano-Bicocca Milano Italy Also at Università degli Studi di Milano-Bicocca Milano Italy. University of Chinese Academy o

A study is presented of B+→KS0K−π+K+ and B+→KS0K+π−K+ decays based on the analysis of proton-proton collision data collected with the LHCb detector at center-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb−1. The KS0Kπ invariant-mass distributions of both B+ decay modes show, in the m(KS0Kπ)<1.85 GeV mass region, large activity which is resolved using an amplitude analysis. A simple model, where JPC amplitudes are described by multiple Breit-Wigner functions with appropriate angular distributions, provides a good description of the experimental data. In this approach a complex mixture of JPC=0−+, 1++ and 1+− amplitudes is observed that is dominated by η(1405), η(1470), η(1760), f1(1285), f1(1420) and h1(1405) resonances. The KS0Kπ Dalitz plots are dominated by asymmetric crossing K*K¯ bands which are different for the two B+ decay modes. This is due to a different interference pattern between the 1++ and 1+− amplitudes in the two channels. Branching fractions are measured for each resonant contribution.

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Search for D0 meson decays to π+π−e+e− and K+K−e+e− final states

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Physical Review D 2025年第9期111卷 L091101-L091101页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellan Beteta F. Abudinén T. Ackernley A. A. Adefisoye B. Adeva M. Adinolfi P. Adlarson C. Agapopoulou C. A. Aidala Z. Ajaltouni S. Akar K. Akiba P. Albicocco J. Albrecht F. Alessio M. Alexander Z. Aliouche P. Alvarez Cartelle R. Amalric S. Amato J. L. Amey Y. Amhis L. An L. Anderlini M. Andersson A. Andreianov P. Andreola M. Andreotti D. Andreou A. Anelli D. Ao F. Archilli M. Argenton S. Arguedas Cuendis A. Artamonov M. Artuso E. Aslanides R. Ataíde Da Silva M. Atzeni B. Audurier D. Bacher I. Bachiller Perea S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura A. Balboni W. Baldini L. Balzani H. Bao J. Baptista de Souza Leite C. Barbero Pretel M. Barbetti I. R. Barbosa R. J. Barlow M. Barnyakov S. Barsuk W. Barter J. Bartz J. M. Basels S. Bashir G. Bassi B. Batsukh P. B. Battista A. Bay A. Beck M. Becker F. Bedeschi I. B. Bediaga N. A. Behling S. Belin K. Belous I. Belov I. Belyaev G. Benane G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet S. Bernet Andres A. Bertolin C. Betancourt F. Betti J. Bex Ia. Bezshyiko J. Bhom M. S. Bieker N. V. Biesuz P. Billoir A. Biolchini M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti T. Blake F. Blanc J. E. Blank S. Blusk V. Bocharnikov J. A. Boelhauve O. Boente Garcia T. Boettcher A. Bohare A. Boldyrev C. S. Bolognani R. Bolzonella R. B. Bonacci N. Bondar A. Bordelius F. Borgato S. Borghi M. Borsato J. T. Borsuk E. Bottalico S. A. Bouchiba M. Bovill T. J. V. Bowcock A. Boyer C. Bozzi J. D. Brandenburg A. Brea Rodriguez N. Breer J. Brodzicka A. Brossa Gonzalo J. Brown D. Brundu E. Buchanan L. Buonincontri M. Burgos Marcos A. T. Burke C. Burr J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai A. C. Caillet R. Calabrese S. Calderon Ramirez L. Calefice S. Cali M. Calvi M. Calvo Gomez P. Camargo Magalhaes J. I. Cambon Bouzas P. Campana D. H. Campora Perez A. F. Campoverde Quezada S. Capelli L. Capriotti R. Caravaca-Mora A. Carbone L. Carcedo Salgado R. Cardinale A. Cardini P. Carniti L. Carus A. Casais Vidal R. Caspary G. Casse M Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Syracuse University Syracuse New York USA Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Department of Physics and Astronomy Uppsala University Uppsala Sweden (associated with School of Physics and Astronomy University of Glasgow Glasgow United Kingdom) Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany Also at Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany. European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China INFN Sezione di Firenze Firenze Italy Authors affiliated with an institute formerly covered by a cooperation agreement with CERN INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Milano-Bicocca Milano Italy Also at Università degli Studi di Milano-Bicocca Milano Italy. University of Chinese Academy o

A search for D0 meson decays to the π+π−e+e− and K+K−e+e− final states is reported using a sample of proton-proton collisions collected by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb−1. The decay D0→π+π−e+e− is observed for the first time when requiring that the two electrons are consistent with coming from the decay of a ϕ or ρ0/ω meson. The corresponding branching fractions are measured relative to the D0→K−π−[e+e−]ρ0/ω decay, where the two electrons are consistent with coming from the decay of a ρ0 or ω meson. No evidence is found for the D0→K+K−e+e− decay and world-best limits are set on its branching fraction. The results are compared to, and found to be consistent with, the branching fractions of the D0→π+π−μ+μ− and D0→K+K−μ+μ− decays recently measured by LHCb and confirm lepton universality at the current precision.

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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Measurement of CP asymmetries in Λb0→ph− decays

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Physical Review D 2025年第9期111卷 092004-092004页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellan Beteta F. Abudinén T. Ackernley A. A. Adefisoye B. Adeva M. Adinolfi P. Adlarson C. Agapopoulou C. A. Aidala Z. Ajaltouni S. Akar K. Akiba P. Albicocco J. Albrecht F. Alessio M. Alexander Z. Aliouche P. Alvarez Cartelle R. Amalric S. Amato J. L. Amey Y. Amhis L. An L. Anderlini M. Andersson A. Andreianov P. Andreola M. Andreotti D. Andreou A. Anelli D. Ao F. Archilli M. Argenton S. Arguedas Cuendis A. Artamonov M. Artuso E. Aslanides R. Ataíde Da Silva M. Atzeni B. Audurier D. Bacher I. Bachiller Perea S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura A. Balboni W. Baldini L. Balzani H. Bao J. Baptista de Souza Leite C. Barbero Pretel M. Barbetti I. R. Barbosa R. J. Barlow M. Barnyakov S. Barsuk W. Barter J. Bartz J. M. Basels S. Bashir G. Bassi B. Batsukh P. B. Battista A. Bay A. Beck M. Becker F. Bedeschi I. B. Bediaga N. A. Behling S. Belin K. Belous I. Belov I. Belyaev G. Benane G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet S. Bernet Andres A. Bertolin C. Betancourt F. Betti J. Bex Ia. Bezshyiko J. Bhom M. S. Bieker N. V. Biesuz P. Billoir A. Biolchini M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti T. Blake F. Blanc J. E. Blank S. Blusk V. Bocharnikov J. A. Boelhauve O. Boente Garcia T. Boettcher A. Bohare A. Boldyrev C. S. Bolognani R. Bolzonella R. B. Bonacci N. Bondar A. Bordelius F. Borgato S. Borghi M. Borsato J. T. Borsuk E. Bottalico S. A. Bouchiba M. Bovill T. J. V. Bowcock A. Boyer C. Bozzi J. D. Brandenburg A. Brea Rodriguez N. Breer J. Brodzicka A. Brossa Gonzalo J. Brown D. Brundu E. Buchanan L. Buonincontri M. Burgos Marcos A. T. Burke C. Burr J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai A. C. Caillet R. Calabrese S. Calderon Ramirez L. Calefice S. Cali M. Calvi M. Calvo Gomez P. Camargo Magalhaes J. I. Cambon Bouzas P. Campana D. H. Campora Perez A. F. Campoverde Quezada S. Capelli L. Capriotti R. Caravaca-Mora A. Carbone L. Carcedo Salgado R. Cardinale A. Cardini P. Carniti L. Carus A. Casais Vidal R. Caspary G. Casse M Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Syracuse University Syracuse New York USA Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Department of Physics and Astronomy Uppsala University Uppsala Sweden (associated with School of Physics and Astronomy University of Glasgow Glasgow United Kingdom) Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany Also at Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany. European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China INFN Sezione di Firenze Firenze Italy Affiliated with an institute formerly covered by a cooperation agreement with CERN INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Milano-Bicocca Milano Italy Also at Università degli Studi di Milano-Bicocca Milano Italy. University of Chinese Academy of Scienc

A search for CP violation in Λb0→pK− and Λb0→pπ− decays is presented using the full Run 1 and Run 2 data samples of pp collisions collected with the LHCb detector, corresponding to an integrated luminosity of 9 fb−1 at center-of-mass energies of 7, 8, and 13 TeV. For the Run 2 data sample, the CP-violating asymmetries are measured to be ACPpK−=(−1.4±0.7±0.4)% and ACPpπ−=(0.4±0.9±0.4)%, where the first uncertainty is statistical and the second is systematic. Following significant improvements in the evaluation of systematic uncertainties compared to the previous LHCb measurement, the Run 1 dataset is reanalyzed to update the corresponding results. When combining the Run 2 and updated Run 1 measurements, the final results are found to be ACPpK−=(−1.1±0.7±0.4)% and ACPpπ−=(0.2±0.8±0.4)%, constituting the most precise measurements of these asymmetries to date.

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Efficient image representation for object recognition via pivots selection

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Frontiers of computer science 2015年第3期9卷 383-391页

作者： Bojun XIE Yi LIU HuiZHANG Jian YU Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well- known pre-defined patch-level descriptors such as scalein- variant feature transform （SIFT） and histogram of oriented gradient （HOG）, the kernel descriptor （KD） method [1] of- fers a new way to ＂grow-up＂ features from a match-kernel defined over image patch pairs using kernel principal compo- nent analysis （KPCA） and yields impressive results. In this paper, we present efficient kernel descriptor （EKD） and efficient hierarchical kernel descriptor （EHKD）, which are built upon incomplete Cholesky decomposition. EKD au- tomatically selects a small number of pivot features for gener- ating patch-level features to achieve better computational effi- ciency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD.

关键词： efficient kernel descriptor efficient hierarchi-cal kernel descriptor incomplete Cholesky decomposition,patch-level features image-level features

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Euclid: Early Release Observations – Interplay between dwarf galaxies and their globular clusters in the Perseus galaxy cluster

arXiv

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arXiv 2025年

作者： Saifollahi, T. Lançon, A. Cantiello, Michele Cuillandre, J.-C. Bethermin, M. Carollo, D. Duc, P.-A. Ferré-Mateu, A. Hatch, N.A. Hilker, M. Hunt, L.K. Marleau, F.R. Román, J. Sánchez-Janssen, R. Tortora, C. Urbano, M. Voggel, K. Bolzonella, M. Bouy, H. Kluge, M. Schirmer, M. Stone, C. Giocoli, C. Knapen, J.H. Le, M.N. Mondelin, M. Aghanim, N. Altieri, B. Andreon, S. Auricchio, N. Baccigalupi, C. Bagot, D. Baldi, M. Balestra, A. Bardelli, S. Basset, A. Battaglia, P. Biviano, A. Bonchi, A. Bonino, D. Bon, W. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Capobianco, V. Carbone, C. Carretero, J. Casas, S. Castellano, M. Castignani, G. Cavuoti, S. Chambers, K.C. Cimatti, A. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Courbin, F. Courtois, H.M. Cropper, M. Da Silva, A. Degaudenzi, H. De Lucia, G. Dole, H. Douspis, M. Dubath, F. Duncan, C.A.J. Dupac, X. Dusini, S. Escoffier, S. Farina, M. Farinelli, R. Faustini, F. Ferriol, S. Fotopoulou, S. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. George, K. Gillis, B. Gracia-Carpio, J. Grazian, A. Grupp, F. Haugan, S.V.H. Hoar, J. Hoekstra, H. Holmes, W. Hook, I.M. Hormuth, F. Hornstrup, A. Jahnke, K. Jhabvala, M. Keihänen, E. Kermiche, S. Kiessling, A. Kubik, B. Kümmel, M. Kunz, M. Kurki-Suonio, H. Lahav, O. Laureijs, R. Le Brun, A.M.C. Le Mignant, D. Ligori, S. Lilje, P.B. Lindholm, V. Lloro, I. Maino, D. Maiorano, E. Mansutti, O. Marggraf, O. Martinelli, M. Martinet, N. Marulli, F. Massey, R. Maurogordato, S. Medinaceli, E. Mei, S. Mellier, Y. Meneghetti, M. Merlin, E. Meylan, G. Mora, A. Moresco, M. Moscardini, L. Nakajima, R. Neissner, C. Niemi, S.-M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Percival, W.J. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L.A. Pozzetti, L. Raison, F. Rebolo, R. Renzi, A. Rhodes, J. Riccio, G. Romelli, E. Roncarelli, M. Saglia, R. Sakr, Z. Sánchez, A.G. Université de Strasbourg CNRS Observatoire astronomique de Strasbourg UMR 7550 Strasbourg67000 France INAF - Osservatorio Astronomico d’Abruzzo Via Maggini Teramo64100 Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy Instituto de Astrofísica de Canarias Vía Láctea Tenerife La Laguna38205 Spain Universidad de La Laguna Departamento de Astrofísica Tenerife La Laguna38206 Spain School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom European Southern Observatory Karl-Schwarzschild-Str. 2 Garching85748 Germany INAF-Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 Firenze50125 Italy Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria Departamento de Física de la Tierra y Astrofísica Universidad Complutense de Madrid Plaza de las Ciencias 2 MadridE-28040 Spain UK Astronomy Technology Centre Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy 1 rue Descartes Paris75231 CEDEX 05 France Laboratoire d’Astrophysique de Bordeaux CNRS Université de Bordeaux Allée Geoffroy St. Hilaire Pessac33165 France Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 Garching85748 Germany Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany Department of Physics Université de Montréal 2900 Edouard Montpetit Blvd MontréalQCH3T 1J4 Canada Ciela Institute - Montréal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Mila - Québec Artificial Intelligence Institute MontréalQC Canada INFN Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Universi

We present an analysis of globular clusters (GCs) of dwarf galaxies in the Perseus galaxy cluster to explore the relationship between dwarf galaxy properties and their GCs, which offer important clues on the origin and subsequent evolution of dwarf galaxies. Our focus is on GC numbers (NGC) and GC half-number radii (RGC) around dwarf galaxies, and their relations with host galaxy stellar masses (M∗), central surface brightnesses (µ0), and effective radii (Re). Interestingly, we find that at a given stellar mass, RGC is almost independent of the host galaxy µ0 and Re, while RGC/Re depends on µ0 and Re;lower surface brightness and diffuse dwarf galaxies show RGC/Re ≈ 1 while higher surface brightness and compact dwarf galaxies show RGC/Re ≈ 1.5–2. This means that for dwarf galaxies of similar stellar mass, the GCs have a similar median extent;however, their distribution is different from the field stars of their host. Additionally, low surface brightness and diffuse dwarf galaxies on average have a higher NGC than high surface brightness and compact dwarf galaxies at any given stellar mass. We also find that UDGs (ultra-diffuse galaxies) and non-UDGs in the sample have similar RGC, while UDGs have smaller RGC/Re (typically less than 1) and 3–4 times higher NGC than non-UDGs. Furthermore, examining nucleated versus not-nucleated dwarf galaxies, we find that for M∗ > 108M☉, nucleated dwarf galaxies seem to have smaller RGC and RGC/Re, with no significant differences seen between their NGC, except at M∗ 8M☉ where the nucleated dwarf galaxies tend to have a higher NGC. Lastly, we explore the stellar-to-halo mass ratio (SHMR) of dwarf galaxies (halo mass based on NGC) and conclude that the Perseus cluster dwarf galaxies follow the expected SHMR at z = 0 extrapolated down to M∗ = 106M☉ © 2025, CC BY.

关键词： Planetary surface analysis

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Index construction for mathematical expression retrieval based on Trie tree 4th

Index construction for mathematical expression retrieval bas...

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4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Sun, Jing Tian, Xuedong Liu, Dazhong Zhang, Zhiming School of Computer Science and Technology Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781138028302

With two-dimensional structures, mathematical expressions deliver more information than normal text not only with symbols but also their spatial arrangements. Users of mathematical retrieval systems need more search modes, such as fuzzy matching mode, rather than only exact matching mode to fit the features of mathematical expressions. Therefore, more and more researchers focus on how to retrieve mathematical expressions on the levels of structural similarity matching and synonyms matching. In this paper, an index structure based on Trie tree is designed oriented to measure the structural similarity of mathematical expressions. Firstly, the existing math indexing and matching method are introduced. Then, a stack algorithm of extracting information of math operators in LaTeX format is given. Finally, an index method based on Trie Tree is proposed for structural matching of mathematical expressions. The experimental analysis shows the effectiveness of the index method in this paper. © 2015 Taylor & Francis Group, London.

关键词： Search engines

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DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets

DEEP-CARVING: Discovering Visual Attributes by Carving Deep ...

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IEEE Conference on computer Vision and Pattern Recognition

作者： Sukrit Shankar Vikas K. Garg Roberto Cipolla Machine Intelligence Lab (MIL) Cambridge University Computer Science & Artificial Intelligence Lab (CSAIL) MIT

ISBN: (纸本)9781467369657

Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines. For instance, given a noun (say forest) and its associated attributes (say dense, sunlit, autumn), search engines can now generate many valid images for any attribute-noun pair (dense forests, autumn forests, etc). However, images for an attribute-noun pair do not contain any information about other attributes (like which forests in the autumn are dense too). Thus, a weakly supervised scenario occurs: each of the M attributes corresponds to a class such that a training image in class m ∈ {1,...,M} contains a single label that indicates the presence of the m~(th) attribute only. The task is to discover all the attributes present in a test image. Deep Convolutional Neural Networks (CNNs) [20] have enjoyed remarkable success in vision applications recently. However, in a weakly supervised scenario, widely used CNN training procedures do not learn a robust model for predicting multiple attribute labels simultaneously. The primary reason is that the attributes highly co-occur within the training data, and unlike objects, do not generally exist as well-defined spatial boundaries within the image. To ameliorate this limitation, we propose Deep-Carving, a novel training procedure with CNNs, that helps the net efficiently carve itself for the task of multiple attribute prediction. During training, the responses of the feature maps are exploited in an ingenious way to provide the net with multiple pseudo-labels (for training images) for subsequent iterations. The process is repeated periodically after a fixed number of iterations, and enables the net carve itself iteratively for efficiently disentangling features. Additionally, we contribute a noun-adjective pairing inspired Natural Scenes Attribute

关键词： feature map Dataset images Search engines Neural network iterative methods Sculpture Exploration Sun

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Contextual Relative Entropy Policy Search with Covariance Matrix Adaptation

Contextual Relative Entropy Policy Search with Covariance Ma...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Abbas Abdolmaleki David Simões Nuno Lau Luís Paulo Reis Gerhard Neumann DETIIUA - Electronics University of Aveiro Aveiro Portugal LIACC - Artificial Intelligence and Computer Science Laboratory University of Porto Porto Portugal IEETA - Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Aveiro Portugal DSI - Department of Information Systems University of Minho Braga Portugal CLAS - Computational Learning for Autonomous Systems Group Technische Universität Darmstadt Darmstadt Germany

Stochastic search algorithms are black-box optimizers of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. However, with slightly different tasks or objective functions, many stochastic search algorithms require complete re-learning in order to adapt the solution to the new objective function or the new context. As such, we consider the contextual stochastic search paradigm. Here, we want to find good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation. Contextual algorithms have been investigated in the field of policy search. However, contextual policy search algorithms typically suffer from premature convergence and perform unfavourably in comparison with state of the art stochastic search methods. In this paper, we investigate a contextual stochastic search algorithm known as Contextual Relative Entropy Policy Search (CREPS), an informationtheoretic algorithm that can learn for multiple tasks simultaneously. We extend that algorithm with a covariance matrix adaptation technique that alleviates the premature convergence problem. We call the new algorithm Contextual Relative Entropy Policy Search with Covariance Matrix Adaptation (CREPS-CMA). We will show that CREPS-CMA outperforms the original CREPS by orders of magnitude. We illustrate the performance of CREPS-CMA on several contextual tasks, including a complex simulated robot kick task.

关键词： Context Covariance matrices Linear programming Convergence Entropy Robots Search methods

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