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arXiv 2025年

作者： Littauer, Richard Wilson, Greg Ainali, Jan AlOmar, Eman Abdullah Arabas, Sylwester Saibene, Yanina Bellini Bubendorfer, Kris Champion, Kaylea Dillon, Clare Helske, Jouni Huybrechts, Pieter Katz, Daniel S. Liao, Chang Lippert, David Liu, Fang Marshall, Pierre McCloy, Daniel R. McInerney, Ian Mkaouer, Mohamed Wiem Ojha, Priyanka Treude, Christoph White, Ethan P. CURIOSS SustainOSS GNOME Foundation Te Herenga Waka Victoria University of Wellington Te Whanganui-a-Tara Aotearoa Wellington New Zealand Third Bit TorontoON Canada Open By Default Italy Department of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States AGH University of Krakow Poland rOpenSci LP Santa Rosa Argentina Te Herenga Waka Victoria University of Wellington Wellington New Zealand Computing and Software Systems University of Washington Bothell BothellWA United States CURIOSS Lero University of Galway Ireland INVEST Research Flagship Centre University of Turku Finland Brussels Belgium University of Illinois Urbana-ChampaignIL United States Pacific Northwest National Laboratory RichlandWA United States Open Source Program Office George Washington University WashingtonDC United States Georgia Institute of Technology GA United States Oxford University United Kingdom Institute for Learning & Brain Sciences University of Washington SeattleWA United States Department of Mechanical Engineering Imperial College London London United Kingdom Department of Computer Science University of Michigan-Flint FlintMI United States Singapore Management University Singapore Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL United States

Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job—whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes. © 2025, CC BY-SA.

关键词： Human engineering

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Correction to: Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

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Archives of Computational Methods in engineering 2021年第7期28卷 5039-5039页

作者： Ismail, Leila Materwala, Huned Tayefi, Maryam Ngo, Phuong Karduck, Achim P. Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Norwegian Centre for E-Health Research Tromsø Norway Faculty of Informatics Furtwangen University Furtwangen Germany

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Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Are Smell-Based Metrics Actually Useful in Effort-Aware Stru...

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Asia-Pacific Conference on software engineering

作者： Huihui Liu Yijun Yu Bixin Li Yibiao Yang Ru Jia School of Computer Science and Engineering Southeast University Nanjing China Centre for Research in Computing The Open University UK State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9781728119717;9781728119700

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smell-based metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANOS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort.

关键词： Measurement Tools Predictive models Java software Data collection Logistics

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Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding

Guiding Cross-lingual Entity Alignment via Adversarial Knowl...

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IEEE International Conference on Data Mining (ICDM)

作者： Xixun Lin Hong Yang Jia Wu Chuan Zhou Bin Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence School of Software FEIT University of Technology Sydney Sydney Australia Department of Computing Macquarie University Sydney Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Xiaomi AI Lab Beijing China

Cross-lingual Entity Alignment (CEA) aims at identifying entities with their counterparts in different language knowledge graphs. Knowledge embedding alignment plays an important role in CEA due to its advantages of easy implementation and run-time robustness. However, existing embedding alignment methods haven't considered the problem of embedding distribution alignment which refers to the alignment of spatial shapes of embedding spaces. To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an end-to-end manner. By reducing the discrepancy of embedding distributions, AKE can approximately preserve an isomorphism between source and target embeddings. In addition, we introduce two new orthogonality constraints into mapping to obtain the self-consistency and numerical stability of transformation. Experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines.

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Quantum invariants and the graph isomorphism problem

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Physical Review A 2019年第5期100卷 052317-052317页

作者： P. W. Mills R. P. Rundle J. H. Samson Simon J. Devitt Todd Tilma V. M. Dwyer Mark J. Everitt Quantum Systems Engineering Research Group Department of Physics Loughborough University Leicestershire LE11 3TU United Kingdom The Wolfson School Loughborough University Loughborough Leicestershire LE11 3TU England United Kingdom Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia Turing Inc. Berkeley California 94701 USA Department of Physics College of Science Tokyo Institute of Technology H-63 2-12-1 Oookayama Meguro-ku Tokyo 152-8550 Japan Quantum Computing Unit Institute of Innovative Research Tokyo Institute of Technology S1-16 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan

Three graph invariants are introduced which may be measured from a quantum graph state and form examples of a framework under which other graph invariants can be constructed. Each invariant is based on distinguishing a different number of qubits. This is done by applying different measurements to the qubits to be distinguished. The performance of these invariants is evaluated and compared to classical invariants. We verify that the invariants can distinguish all nonisomorphic graphs with nine or fewer nodes. The invariants have also been applied to “classically hard” strongly regular graphs, successfully distinguishing all strongly regular graphs of up to 29 nodes, and preliminarily to weighted graphs. We have found that, although it is possible to prepare states with a polynomial number of operations, the average number of preparations required to distinguish nonisomorphic graph states scales exponentially with the number of nodes. We have so far been unable to find operators which reliably compare graphs and reduce the required number of preparations to feasible levels.

关键词： Quantum algorithms Graph theory Phase space methods

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A Flexible Keyphrase Extraction Technique for Academic Literature

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Procedia Computer Science 2018年 135卷 553-563页

作者： Gollam Rabby Saiful Azad “ Mufti Mahmud Kamal Z. Zamli Mohammed Mostafizur Rahman Faculty of Computer Systems & Software Engineering University Malaysia Pahang Gambang Kuantan Malaysia IBM Centre of Excellence Gambang Kuantan Malaysia Department of Computing and Technology Nottingham Trent University Nottingham United Kingdom Department of Mathematics American International University - Bangladesh (AIUB) Dhaka Bangladesh

A keyphrase extraction technique endeavors to extract quality keyphrases from a given document, which provide a high-level summary of that document. Except statistical keyphrase extraction approaches, all other approaches are either domain-dependent or require a sufficient amount of training data, which are rare at present. Therefore, in this paper, a new tree-based automatic keyphrase extraction technique is proposed, which is domain-independent and employs nominal statistical knowledge; but no train data are required. The proposed technique extracts a quality keyphrase through forming a tree from a candidate keyphrase; and later, it is expanded or shrunk or remained in the same state depending on other similar candidate keyphrases. At the end, keyphrases are extracted from the resultant trees based on a value, µ (which is the Maturity Index (MI) of a node in the tree), which enables flexibility in this process. A small µ value would yield many and/or lengthy keyphrases (greedy approach); whereas, a large µ value would yield lower and/or abbreviated keyphrases (conservative approach). Thereby, a user can extract his/her desired-level of keyphrases through tuning µ value. The effectiveness of the proposed technique is evaluated on an actual corpus, and compared with Rapid Automatic Keyphrase Extraction (RAKE) technique.

关键词： Candidate keyphrase keyphrase automatic keyphrase extraction technique tree data structure

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Population of Merging Compact Binaries Inferred Using Gravitational Waves through GWTC-3

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Physical Review X 2023年第1期13卷 011048-011048页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu P. F. de Alarcón S. Akcay S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier F. Antonini S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud M. Arogeti S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi C. P. L. Berry D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. Brandt J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Universitat de les Illes Balears IAC3–IEEC E-07122 Palma de Mallorca Spain University College Dublin Dublin 4 Ireland INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli

We report on the population properties of compact binary mergers inferred from gravitational-wave observations of these systems during the first three LIGO-Virgo observing runs. The Gravitational-Wave Transient Catalog 3 (GWTC-3) contains signals consistent with three classes of binary mergers: binary black hole, binary neutron star, and neutron star–black hole mergers. We infer the binary neutron star merger rate to be between 10 and 1700 Gpc−3 yr−1 and the neutron star–black hole merger rate to be between 7.8 and 140 Gpc−3 yr−1, assuming a constant rate density in the comoving frame and taking the union of 90% credible intervals for methods used in this work. We infer the binary black hole merger rate, allowing for evolution with redshift, to be between 17.9 and 44 Gpc−3 yr−1 at a fiducial redshift (z=0.2). The rate of binary black hole mergers is observed to increase with redshift at a rate proportional to (1+z)κ with κ=2.9−1.8+1.7 for z≲1. Using both binary neutron star and neutron star–black hole binaries, we obtain a broad, relatively flat neutron star mass distribution extending from 1.2−0.2+0.1 to 2.0−0.3+0.3M⊙. We confidently determine that the merger rate as a function of mass sharply declines after the expected maximum neutron star mass, but cannot yet confirm or rule out the existence of a lower mass gap between neutron stars and black holes. We also find the binary black hole mass distribution has localized over- and underdensities relative to a power-law distribution, with peaks emerging at chirp masses of 8.3−0.5+0.3 and 27.9−1.8+1.9M⊙. While we continue to find that the mass distribution of a binary’s more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above approximately 60M⊙, which would indicate the presence of a upper mass gap. Observed black hole spins are small, with half of spin magnitudes below χi≈0.25. While the majority of spins are preferentially aligned wi

关键词： Gravitational wave sources Gravitational waves Gravitational wave detection

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GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo during the Second Part of the Third Observing Run

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Physical Review X 2023年第4期13卷 041039-041039页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith S. Akcay T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud M. Arogeti S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi C. P. L. Berry D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. Brandt J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer G. S. Cabourn Davies L. Cadonati G. Cagn LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India University College Dublin Dublin 4 Ireland Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut L

The third Gravitational-Wave Transient Catalog (GWTC-3) describes signals detected with Advanced LIGO and Advanced Virgo up to the end of their third observing run. Updating the previous GWTC-2.1, we present candidate gravitational waves from compact binary coalescences during the second half of the third observing run (O3b) between 1 November 2019, 15∶00 Coordinated Universal Time (UTC) and 27 March 2020, 17∶00 UTC. There are 35 compact binary coalescence candidates identified by at least one of our search algorithms with a probability of astrophysical origin pastro>0.5. Of these, 18 were previously reported as low-latency public alerts, and 17 are reported here for the first time. Based upon estimates for the component masses, our O3b candidates with pastro>0.5 are consistent with gravitational-wave signals from binary black holes or neutron-star–black-hole binaries, and we identify none from binary neutron stars. However, from the gravitational-wave data alone, we are not able to measure matter effects that distinguish whether the binary components are neutron stars or black holes. The range of inferred component masses is similar to that found with previous catalogs, but the O3b candidates include the first confident observations of neutron-star–black-hole binaries. Including the 35 candidates from O3b in addition to those from GWTC-2.1, GWTC-3 contains 90 candidates found by our analysis with pastro>0.5 across the first three observing runs. These observations of compact binary coalescences present an unprecedented view of the properties of black holes and neutron stars.

关键词： Gravitational waves

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A Direct Proof of Improved Biased Random Walk with Gastric Cancer Dataset

A Direct Proof of Improved Biased Random Walk with Gastric C...

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International Conference on Applied Mathematics & Computer Science

作者： Choon Sen Seah Shahreen Kasim Mohd Farhan Md Fudzee Mohd Saberi Mohamad Rd Rohmat Saedudin Deden Witarsyah Rohayanti Hassan Mohd Arfian Ismail Rodziah Atan Soft Computing and Data Mining Centre Faculty of Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Faculty of Creative Technology and Heritage Universiti Malaysia Kelantan School of Industrial Engineering Telkom University Laboratory of Biodiversity and Bioinformatics Universiti Teknologi Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Department of Software Engineering & Information System Faculty of Computer Science and Information Technology University Putra Malaysia (UPM)

ISBN: (数字)9781728132587

ISBN: (纸本)9781728132594

The relationship between the weight of a single genes and the connection between the genes had been studied. Chemistry and physical sciences have proved the attraction between molecules. Molecules are attracted to each other by bond. This paper is presented to disclose the relationship between weight and connectivity of nodes with biased random walk. An equation of biased random walk which named as significant directed random walk is formed to enhance the connectivity of nodes in directed graph via weigh. To be completely biased to the random walk, references data is implement as directed graph. Weight of genes will be used as one of the parameter in the formula. While the adjacency matrix is further enhanced by Warshall's algorithm to increases the accessibility of nodes via vector. The evolution of random walk is disclosed in this paper as well. Significant directed random walk will be used to prove the importance of weight in this paper. Comparison of the result between biased random walk is presented to prove the enhancement of random walk.

关键词： Biased random walk Cancer classification Gene expression dataset Random walk cancer classification Biased Ganglia one-parameter Mentoring Physical science

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Ultralight vector dark matter search using data from the KAGRA O3GK run

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Physical Review D 2024年第4期110卷 042001页

作者： A. G. Abac R. Abbott H. Abe I. Abouelfettouh F. Acernese K. Ackley C. Adamcewicz S. Adhicary N. Adhikari R. X. Adhikari V. K. Adkins V. B. Adya C. Affeldt D. Agarwal M. Agathos O. D. Aguiar I. Aguilar L. Aiello A. Ain P. Ajith T. Akutsu S. Albanesi R. A. Alfaidi A. Al-Jodah C. Alléné A. Allocca S. Al-Shammari P. A. Altin S. Alvarez-Lopez A. Amato L. Amez-Droz A. Amorosi C. Amra S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Andia M. Ando T. Andrade N. Andres M. Andrés-Carcasona T. Andrić J. Anglin S. Ansoldi J. M. Antelis S. Antier M. Aoumi E. Z. Appavuravther S. Appert S. K. Apple K. Arai A. Araya M. C. Araya J. S. Areeda N. Aritomi F. Armato N. Arnaud M. Arogeti S. M. Aronson K. G. Arun G. Ashton Y. Aso M. Assiduo S. Assis de Souza Melo S. M. Aston P. Astone F. Aubin K. AultONeal G. Avallone S. Babak F. Badaracco C. Badger S. Bae S. Bagnasco E. Bagui Y. Bai J. G. Baier R. Bajpai T. Baka M. Ball G. Ballardin S. W. Ballmer S. Banagiri B. Banerjee D. Bankar P. Baral J. C. Barayoga B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta S. D. Barthelmy M. A. Barton I. Bartos S. Basak A. Basalaev R. Bassiri A. Basti M. Bawaj P. Baxi J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale F. Beirnaert M. Bejger D. Belardinelli A. S. Bell V. Benedetto D. Beniwal W. Benoit J. D. Bentley M. Ben Yaala S. Bera M. Berbel F. Bergamin B. K. Berger S. Bernuzzi M. Beroiz D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge N. Bevins R. Bhandare U. Bhardwaj R. Bhatt D. Bhattacharjee S. Bhaumik S. Bhowmick A. Bianchi I. A. Bilenko G. Billingsley A. Binetti S. Bini O. Birnholtz S. Biscoveanu A. Bisht M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair F. Bobba N. Bode G. Bogaert G. Boileau M. Boldrini G. N. Bolingbroke A. Bolliand L. D. Bonavena R. Bondarescu F. Bondu E. Bonilla M. S. Bonilla A. Bonino R. Bonnand P. Booker A. Borchers V. Boschi S. Bose V. Bossilkov V. Boudart A. Boumerdassi A. Bozzi C. Bradaschia P. R. Brady M. Braglia A. Branch M. Branchesi M. Breschi T. B Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan LIGO Hanford Observatory Richland Washington 99352 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli I-80126 Napoli Italy University of Warwick Coventry CV4 7AL United Kingdom OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia The Pennsylvania State University University Park Pennsylvania 16802 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA Louisiana State University Baton Rouge Louisiana 70803 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Stanford University Stanford California 94305 USA Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy INFN Sezione di Torino I-10125 Torino Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Austral

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation timescale of DM.

关键词： Dark matter Particle dark matter Gravitational wave detectors

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