cometadata/test-preprints-extracted-funding huggingface dataset:
† School of Mathematical Sciences, Peking University, 100871, China ( snwu@math.pku.edu.cn ). The work of this author was partially supported by the National Natural Science Foundation of China grant No. 11901016.
‡ Department of Mathematics, The Ohio State University, Columbus, OH 43210, U.S.A. ( xing.205@osu.edu ). The work of this author was partially supported by the NSF grant DMS1753581.
∗ Department of Mathematics, University of Central Florida, Orlando, FL 32816, U.S.A. ( yukun.li@ucf.edu ). The work of this author was partially supported by the NSF grant DMS2110728.
Acknowledgement: RKP acknowledges support through PMRF scheme. TG was supported by the Hong Kong Research Grant Council through grant 17307719 and though the Senior Research Fellowship Scheme SRFS 2021 -7 S 02 , by the Croucher Foundation, and by the John Templeton Foundation through grant 61466 , The Quantum Information Structure of Spacetime (qiss.fr). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation. SSB acknowledges partial support by the Foundation for Polish Science (IRAP project, ICTQT, Contract No. MAB/ 2018 / 5 , cofinanced by EU within Smart Growth Operational Programme). MA and MB acknowledge support through the research grant of INSPIRE Faculty fellowship from the Department of Science and Technology, Government of India. MB acknowledges funding from the National Mission in Interdisciplinary Cyber-Physical systems from the Department of Science and Technology through the I-HUB Quantum Technology Foundation (Grant No. I-HUB/PDF/ 2021 -22 / 008 ) and the start-up research grant from SERB, Department of Science and Technology (Grant No. SRG/ 2021 / 000267 ).
This work was supported by the Government of Ireland Postdoctoral Fellowship from the Irish Research Council (No. GOIPD/2019/65), the National Natural Science Foundation of China (Nos. 11872329 and 11621062) and the China Scholarship Council (CSC). Partial supports from the Fundamental Research Funds for the Central Universities, PR China (No. 2016XZZX001-05) and the Shenzhen Scientific and Technological Fund for R&amp;D, PR China (No. JCYJ20170816172316775) are also acknowledged.
Acknowledgments. The authors are grateful to Mikhail Ershov, Dawid Kielak, Rob Kropholler, Kevin Schreve, and Stefan Witzel for a number of helpful discussions. Thanks are also due to the anonymous referee for many helpful suggestions. The first author was partially supported by the DFG grant WI 4079/4 within the SPP 2026 Geometry at Infinity. The second author is supported by grant #635763 from the Simons Foundation.
This work is equally and mainly supported by the National Key Research and Development Program of China (No. 2019YFC1521102, No. 2019YFC1521103) and the Key Research and Development Program of Shaanxi Province (No. 2020KW-068). Besides, this work is also partly supported by the Key Research and Development Program of Shaanxi Province (No.2019GY-215, No.2019ZDLSF07-02, No.2019ZDLGY10-01), the Major research and development project of Qinghai(No. 2020-SF-143), the National Natural Science Foundation of China under Grant (No.61701403,No.61731015) and the Young Talent Support Program of the Shaanxi Association for Science and Technology under Grant (No.20190107).
Mutation testing is one of the most challenging criteria to meet, given the significant effort required to apply all of its steps, as shown in Figure 3. Given this process and the large number of mutants that can be generated, it is essential that mutation testing be supported by a tool that automates all or part of the process. It is impractical to apply it manually in production.
This study was partially funded by the Coordenac ¸˜ ao de Aperfeic ¸oamento de Pessoal de N´ ıvel Superior Brasil (CAPES) - Finance Code 001.
This work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery Grant program, by the The Fonds de recherche du Quebec - Nature et technologies (FRQNT) grant, the Canada Research Chair on Shape Analysis in Medical Imaging, the ETS Research Chair on Artificial Intelligence in Medical Imaging, and NVIDIA with a donation of a GPU. The authors would like to thank the MICCAI 2018 IVDM3Seg, NCI-ISBI 2013, and MICCAI 2017 MMWHS organizers for providing the data.
This research was partly supported by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (No. 2021-0-01696) supervised by the IITP (Institute for Information &amp; Communications Technology Planning &amp; Evaluation), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2003760), and the Institute for Information &amp; Communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01361: Artificial Intelligence Graduate School Program (YONSEI UNIVERSITY)). This project was also supported by Microsoft Research Asia.
Acknowledgements. The authors would like to thank their advisor Efstratia Kalfagianni for guidance and helpful discussions. In addition, the authors would like to thank Renaud Detcherry and Tian Yang for their comments and suggestions on this paper. Finally, the authors would like to thank the referee for their time and careful reading, as well as providing valuable suggestions to improve the overall exposition of the paper. Part of this research was supported in the form of graduate Research Assistantships by NSF Grants DMS-1708249 and DMS-2004155. For the second author, this research was partially supported by a Herbert T. Graham Scholarship through the Department of Mathematics at Michigan State University.
§ Data Science Institute, Shandong University, China, Email: dlyang@sdu.edu.cn . D.Y. was supported by the China Postdoctoral Science Foundation (2021T140413), Natural Science Foundation of China (12101365) and Natural Science Foundation of Shandong Province (ZR2021QA029).
† School of Mathematics, Shandong University, China. Email: ghwang@sdu.edu.cn . G.W. was supported by National Key R&amp;D Program of China (2020YFA0712400), Natural Science Foundation of China (11871311, 11631014) and seed fund program for international research cooperation of Shandong University.
∗ Extremal Combinatorics and Probability Group (ECOPRO), Institute for Basic Science (IBS), Daejeon, South Korea, Email: hongliu@ibs.re.kr . H.L. was supported by the Institute for Basic Science (IBS-R029C4).
Acknowledgements. We thank Henning Fernau and Dietrich Kuske for interesting discussions at CSR 2020. Alexsander A. de Melo acknowledges support from the Brazilian agencies CNPq/GD 140399/2017-8 and CAPES/PDSE 88881.187636/2018-01. Mateus de O. Oliveira acknowledges support from the Trond Mohn Foundation and from the Research Council of Norway (Grant Nr. 288761).
This work is supported by National Natural Science Foundation of China (Grant No. 62002177), Science and Technology Planning Project of Tianjin, China (Grant No. 20YDTPJC01810), Tianjin Natural Science Foundation (Grant No. 19JCQNJC00300) and Research and Innovation Project for Postgraduates in Tianjin (Artificial Intelligence) (Grant No. 2020YJSZXB01).
- P. Nghe acknowledges support from Institut Pierre-Gilles de Gennes (laboratoire d'excellence, 'Investissements d'avenir' program ANR-10-IDEX-0001-02 PSL and ANR-10-LABX-31) and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. [101002075]).
This document was prepared by the DUNE collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, IPP and NSERC, Canada; CERN; Mˇ SMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; CAM, Fundacion 'La Caixa', Junta de Andaluc´ ıa-FEDER, and MICINN, Spain; SERI and SNSF, Switzerland; T ¨ UB ˙ ITAK, Turkey; The Royal Society and UKRI/STFC, United Kingdom; DOE and NSF, United States of America. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC0205CH11231.
Research reported in this publication was supported by the National Institutes of Health (award R01AI27271, R01CA222147, R01GM139926, R01AG065276, to Eric Tchetgen Tchetgen). This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgement is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining Data Files from Add Health should contact Add Health, The University of North Carolina at Chapel Hill, Carolina Population Center, Carolina Square, Suite 210, 123 W. Franklin Street, Chapel Hill, NC 27516 (addhealth contracts@unc.edu). No direct support was received from grant P01-HD31921 for this analysis.
Acknowledgments. EGJ and JGS were partially supported by the ERC Starting Grant ERC-StGCAPA-852741. EGJ was partially supported by the ERC Starting Grant project H2020-EU.1.1.-639227. HQN was partially supported by NSF grant DMS-1907776. BP was supported by NSF grant DMS1700282 and the CY-Advanced Studies fellow program. We thank Princeton University for computing facilities (Polar Cluster). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement CAMINFLOW No 101031111.
Acknowledgements The authors acknowledge the partial support of FCT R&amp;D Unit SYSTEC - POCI-01-0145-FEDER006933, and R&amp; D Projects STRIDE - NORTE-01-0145-FEDER-000033, MAGIC - PTDC/POCI-01-0145-FEDER-032485, SNAP - NORTE-01-0145-FEDER-000085, HARMONY - NORTE-01-0145-FEDER-031411 funded by ERDF - COMPETE2020 - FCT/MEC - PT2020 - NORTE 2020, POCI and PIDDAC.
This work was supported by the São Paulo State Foundation (FAPESP, grant nos. 2012/50259-8, 2015/11779-4 and 2017/01817-1), the Brazilian Nanocarbon Institute of Science and Technology (INCT/Nanocarbono), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and CAPES - PRINT (Programa Institucional de Internacionalização; Grant no. 88887.310281/2018-00). DG acknowledges financial support from Rio de Janeiro State Foundation (FAPERJ - grant nos. E-26/010.0022672019 and E-26/211.281/2019) and Serrapilheira Institute (grant number Serra - R- 2012-37959). This work was partly supported by the Air Force Office of Scientific Research (AFOSR) through grant No. FA9550-18-1-0072. LS acknowledges financial support from CNPq (Grant No. 408525/2018-5), and high-performance computing facilities from LoboC/NACAD/UFRJ.
Andrew Parnell's work was supported by: a Science Foundation Ireland Career Development Award (17/CDA/4695); an investigator award (16/IA/4520); a Marine Research Programme funded by the Irish Government, co-financed by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01); European Union's Horizon 2020 research and innovation programme under grant agreement No 818144; SFI Centre for Research Training 18/CRT/6049, and SFI Research Centre awards 16/RC/3872 and 12/RC/2289P2.
Estevão Prado's work was supported by a Science Foundation Ireland Career Development Award grant number 17/CDA/4695 and SFI research centre 12/RC/2289P2.
This material is based upon work supported by the National Science Foundation under grant #CMMI-1943900 and #ECCS-1924723 as well as the U.S. Army Research Office under grant #W911NF-19-1-0080. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, the Army Research Office, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Financial support from the National Supercomputing Mission (Subgroup Materials and Computational Chemistry) and Science and Engineering Research Board (India) under the MATRICS scheme (Ref. No. MTR/2019/000359) and from the German Research Foundation (DFG) through Research Unit FOR 1878 (funCOS) and Collaborative Research Center SFB 953 (project number 182849149) are gratefully acknowledged. RK thanks the Council of Scientific &amp; Industrial Research (CSIR), India for her Junior Research Fellowship (JRF). Computational resources were provided by SuperMUC-NG (project pn98fa) at Leibniz Supercomputing Centre (LRZ).
Proof. I first figure out the behavior of the T 1 -consumer that is supported by a personal equilibrium (PE). Recall that a plan is consistent with a PE if any deviation from it yields a weakly lower expected utility.
∗ China Center For Behavioral Economics and Finance, Southwestern University of Finance and Economics, 555 Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, China. Email: senranlin@outlook.com. I am grateful to Martin Dufwenberg for his guidance and support. I also thank Soo Hong Chew, Shuya He, Gavin Kader, RC Lim, Joshua Lanier, Rachel Mannahan, and Jin Sohn for helpful comments, and audiences at China Center For Behavioral Economics and Finance, Antai College of Economics &amp; Management. Financial supports from the National Natural Science Foundation of China (No. 72033006) are gratefully acknowledged.
- D. Liu is with the School of Cyber Science and Technology, Beihang University, Beijing 100191, China. (e-mail: dliu@buaa.edu.cn) - J. Cui, S. X. Ng, R, G. Maunder, and L. Hanzo are with the School of Electronics and Computer Science, the University of Southampton, Southampton SO17 1BJ, U.K. (email: jingj.cui@soton.ac.uk; sxn@ecs.soton.ac.uk; rm@ecs.soton.ac.uk; lh@ecs.soton.ac.uk). - J. Zhang is with the Department of Computing and Informatics, Bournemouth University, Bournemouth BH12 5BB, U.K. (e-mail: jzhang3@bournemouth.ac.uk). - L. Hanzo would like to acknowledge the financial support of the Engineering and Physical Sciences Research Council projects EP/P034284/1 and EP/P003990/1 (COALESCE) as well as of the European Research Council's Advanced Fellow Grant QuantCom (Grant No. 789028).
§ Department of Mathematics, University of Macau, Macao, People's Republic of China (xqjin@umac.edu.mo). The research of this author was supported by the research grants MYRG2019-00042-FST and CPG2021-00035FST from University of Macau and 0014/2019/A from FDCT.
† Department of Mathematics, School of Sciences, Zhejiang University of Science and Technology, Hangzhou 310023, People's Republic of China (yaotengteng718@163.com). The research of this author was supported by the National Natural Science Foundation of China (No. 11701514) and the Zhejiang Provincial Natural Science Foundation of China (No. LY21A010004).
‡ Corresponding author. School of Mathematical Sciences and Fujian Provincial Key Laboratory on Mathematical Modeling &amp; High Performance Scientific Computing, Xiamen University, Xiamen 361005, People's Republic of China (zjbai@xmu.edu.cn). The research of this author was partially supported by the National Natural Science Foundation of China (No. 11671337) and the Natural Science Foundation of Fujian Province of China (No. 2021J01033).
∗ Department of Mathematics, School of Sciences, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China (zhaozhi231@163.com). The research of this author was supported by the National Natural Science Foundation of China (No. 11601112) and the Zhejiang Provincial Natural Science Foundation of China (No. LY21A010010).
This research was performed at the AUTOLAB at UC Berkeley in affiliation with the Berkeley AI Research (BAIR) Lab, and the CITRIS 'People and Robots' (CPAR) Initiative. The authors were supported in part by donations from Google, Siemens, Toyota Research Institute and Autodesk. Michael Danielczuk is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1752814. We thank Jeffrey Ichnowski who introduced Incremental Potential Contact (IPC) to us. We also thank Minchen Li and Zachary Ferguson for support in using IPC.
The first and third author were partly supported the Project PIP N ◦ 0275 from CONICET-Universidad Austral. The first author was also partly supported by the projects Austral N ◦ 006-INV00020 (Rosario, Argentina) and European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement N ◦ 823731 CONMECH. The second author was partly supported by National Sciences Center, Poland through 2017/26/M/ST1/00700 Grant.
Funding Statement. We acknowledge funding from the Engineering and Physical Sciences Research Council, UK, through the Programme Grant PREMIERE (EP/T000414/1), as well as funding through the Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the Digital Twins for Complex Engineering Systems theme within that grant, and the Royal Academy of Engineering through their support of OKM's PETRONAS/RAEng Research Chair in Multiphase Fluid Dynamics. IP acknowledges funding from the NUAcT fellowship scheme at Newcastle University and Imperial College Research Fellowship scheme at Imperial College London.
The authors would like to thank Jason Atnip for useful suggestions regarding exponential decay of residence times, Christopher Rock for helpful discussions on the dynamic Buser inequality, and to the COSIMA consortium (www.cosima.org.au) for making available the ACCESS-OM2 suite of models (available at https://github.com/COSIMA/access-om2 .) This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. MCD is supported by an Australian Government Research Training Program Scholarship, GF is partially supported by an ARC Discovery Project, and SRK acknowledges the support of the Australian Research Council (ARC) through grants LP170100498 and DP210102745 and the ARC Centre of Excellence for Climate Extremes (CLEX; CE170100023).
This work has in part received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 860813 - TReSPAsS-ETN and the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE. This work is partially supported by ERCIM, who kindly enabled the internship of P. Terh¨ orst at NTNU, Gjøvik, Norway and the ARRS Project J2-1734 'Face deidentification with generative deep models' (FaceGEN).
This research work has been funded by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE. Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office. This work was carried out during the tenure of an ERCIM 'Alain Bensoussan' Fellowship Programme.
This work has been partially supported by National Key R&amp;D Program of China under Grant No.2018YFB1800302 and No.2018YFB1800805, National Natural Science Foundation of China under Grant No.61772345, Shenzhen Science and Technology Program under Grant No. RCYX20200714114645048, No. JCYJ20190808142207420 and No. GJHZ20190822095416463, and Pearl River Young Scholars funding of Shenzhen University, and Australia Research Council DE180100950, and the Major Key Project of PCL (PCL2021A08).
This research was supported in part through the Maxwell computational resources operated at Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany. E. Buhmann and W. Korcari are funded by the German Federal Ministry of Science and Research (BMBF) via Verbundprojekts 05H2018 - R&amp;D COMPUTING (Pilotmaßnahme ErUM-Data) Innovative Digitale Technologien f¨ ur die Erforschung von Universum und Materie . S. Diefenbacher is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2121 'Quantum Universe' - 390833306. E. Eren is funded through the Helmholtz Innovation Pool project ACCLAIM that provided a stimulating scientific environment for parts of the research done here. L. Rustige was supported by DESY and HamburgX grant LFF-HHX-03 to the Center for Data and Computing in Natural Sciences (CDCS) from the Hamburg Ministry of Science, Research, Equalities and Districts. This project has received funding from the European Union's Horizon 2020 Research and Innovation programme under Grant Agreement No 101004761.
Dr. Rao thanks the supports from the National Natural Science Foundation of China (Grant No. 52104017), and the Open Fund of Cooperative Innovation Center of Unconventional Oil and Gas (Ministry of Education &amp; Hubei Province, Yangtze University) (Grant No. UOG2022-14), and Open Fund of Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas (Yangtze University) (Grant Nos. YQZC202201), and thanks Prof. Hui Zhao and Mr. Wentao Zhan for help in the design of complexboundary domain.
PW wishes to acknowledge use of the computational resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. DC wishes to thank the Agence Nationale de la Recherche for support of her research under grant ANR-GIBBS-15-CE30-0016. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
plementation of the lepton PDFs, Rhorry Gauld for helpful comments about the leptonic PDFs of the proton, both Philip Bechtle and Admir Greljo for useful discussions about search strategies and other relevant limits, and lastly Simon Knapen for comments on the manuscript. The work of HKD and SN has been supported by BMBF project grant 05H18PDCA1. VML is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2121 'Quantum Universe' -39083330. VML also acknowledges the financial support by Ministerio de Universidades and 'European Union- NextGenerationEU/PRTR' under the Grant No. Mar´ ıa Zambrano ZA2021-081, and the Spanish Grants No. PID2020-113775GB-I00 (AEI) and No. CIPROM/ 2021/054 (Generalitat Valenciana).
This work was supported by the European Union's Horizon 2020 Research and Innovation Program through the 5G!Drones Project under Grant No. 857031, the Academy of Finland 6Genesis project under Grant No.318927, the Academy of Finland CSN project under Grant No. 311654, the NSF of China under Grant No. 61962033, the Anhui Province project under Grant No. 1808085MF165, gxgwfx2019060 and KJ2019A0643, and the Yunnan Province project under Grant No. 2018FH001-010.
A. E. and K. S. have been supported by the Hausdorff Center for Mathematics . Gef¨ ordert durch die Deutsche Forschungsgemeinschaft (DFG) im Rahmen der Exzellenzstrategie des Bundes und der L¨ ander - GZ 2047/1, Projekt-ID 390685813. The work of A.G. has been (partially) supported by the Project EFI ANR-17-CE40-0030 of the French National Research Agency A.D. acknowledges support of the Lagrange Mathematical and Computing Research Center.