cometadata/test-preprints-extracted-funding huggingface dataset:
S. Taheri and V. Kekatos are with the Bradley Dept. of ECE, Virginia Tech, Blacksburg, VA 24061, USA. S. Veeramachaneni is with NextEra Analytics, Saint Paul, MN 55107, USA. B. Zhang is with the ECE Dept., University of Washington, Seattle, WA 98195. Emails: st92@vt.edu, kekatos@vt.edu, Sriharsha.Veeramachaneni@nexteraanalytics.com, and zhangbao@uw.edu. This work was supported by a grant from NextEra Analytics.
The research of Yuyan Li and Ye Duan were partially supported by the National Science Foundation under award CNS-2018850, National Institute of Health under awards NIBIB-R03-EB028427 and NIBIB-R01-EB02943, and U.S. Army Research Laboratory W911NF2120275. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U. S. Government or agency thereof.
The authors thank Timothy C. Steimle for sharing critical equipment and for useful discussions. MO visualizations were performed using Multiwfn [52]. This work was supported by grants from the Keck Foundation, the CUA, the DOE, National Science Foundation (Grants No. PHY-1255526, No. PHY-1415560, No. PHY1912555, No. CHE-1900555, and No. DGE-1650604) and Army Research Office (Grants No. W911NF-15-10121, No. W911NF-14-1-0378, No. W911NF-13-1-0213 and W911NF-17-1-0071) grants. C.E.D. would like to acknowledge NSF GFRP grant No. DGE-2034835.
The authors are grateful to the anonymous reviewers, Nadia Ansini (Sapienza University of Rome), Maria Bruna (University of Cambridge), José Antonio Carrillo (University of Oxford), and Francesco S. Patacchini (IFP Energies Nouvelles) for fruitful discussions. This work was carried out while AE was a postdoctoral researcher at FAU Erlangen-Nürnberg. The authors thankfully acknowledge support by the German Science Foundation (DFG) through CRC TR 154 'Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks'. AE also acknowledges support by the Advanced Grant Nonlocal-CPD (Nonlocal PDEs for Complex Particle Dynamics: Phase Transitions, Patterns and Synchronization) of the European Research Council Executive Agency (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 883363).
The authors thank Shayan Aziznejad, Adrian Jarret, Matthieu Simeoni, and Michael Unser for interesting discussions. Julien Fageot is supported by the Swiss National Science Foundation (SNSF) under Grant P400P2_194364. The work of Thomas Debarre is supported by the SNSF under Grant 200020_184646 / 1. Quentin Denoyelle is supported by the European Research Council (ERC) under Grant 692726-GlobalBioIm.
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2021YJS001, in part by National Key R&amp;D Program of China under Grant 2020YFB1807201, in part by National Natural Science Foundation of China under Grants 61971027, and 61961130391, in part by Beijing Natural Science Foundation under Grant L202013, in part by Natural Science Foundation of Jiangsu Province, Major Project under Grant BK20212002, in part by the Royal Society Newton Advanced Fellowship under Grant NA191006, in part by Frontiers Science Center for Smart Highspeed Railway System. The work of Z. Chen was supported in part by the Center for Industrial Information Technology (CENIIT) and the Excellence Center at Link¨ oping-Lund in Information Technology (ELLIIT).
This work was supported by National Key R&amp;D Program of China (No. 2019YFA0705000 and 2019YFA0308700), the Key R&amp;D Program of Guangdong Province (Grant No. 2018B030329001), National Natural Science Foundation of China (Grants No. 51890861, No. 11547031, No. 11705096 No. 11674169, No. 11627810, No. 61705033, No. 11690031, and No. 11974178).
This work was supported by the U.S. National Science Foundation under grant DMS-2111159. † University of Texas at Austin; Department of Mathematics, C1200; Austin, TX 78712-1202 and Oden Institute for Computational Engineering and Sciences, C0200; Austin, TX 78712-1229 (ORCID ID 0000-0001-9692-5478, arbogast@oden.utexas.edu)
R.Sharma's work has received funding from the EU H2020 program under the SoBigData++ project (grant agreement No. 871042), by the CHIST-ERA grant CHIST-ERA-19-XAI-010, ETAg (grant No. SLTAT21096), and partially funded by HOLISTIC ANALYSIS OF ORGANISED MISINFORMATION ACTIVITY IN SOCIAL NETWORKS project (PCI2022-135026-2).
This work is supported by the Center for Research and Development in Mathematics and Applications (CIDMA) through the Portuguese Foundation for Science and Technology (FCT - Fundação para a Ciência e a Tecnologia), references UIDB/04106/2020 and, UIDP/04106/2020, and by national funds (OE), through FCT, I.P., in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. This work is also supported by CFTC-UL through FCT, references UIDB/00618/2020
and UIDP/00618/2020. The author(s) gratefully acknowledges the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV). We acknowledge support from the projects PTDC/FIS-OUT/28407/2017, PTDC/FIS-PAR/31000/2017, CERN/FIS-PAR/0027/2019, CERN/FIS-PAR/0002/2019 and PTDC/FIS-AST/3041/2020. This work has further been supported by the European Union's Horizon 2020 research and innovation (RISE) programme H2020-MSCARISE-2017 Grant No. FunFiCO-777740. NSG was also supported by the Spanish Ministerio de Universidades, reference UP2021-044, within the European Union-Next Generation EU. This work is also supported by FCT under contracts UIDB/00618/2020, UIDP/00618/2020, CERN/FISPAR/0002/2017 and CERN/FIS-PAR/0014/2019. This work has also been supported in part by the Swedish Research Council grant, contract number 2016-05996 and by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 668679).
This work has been supported by the Czech Science Foundation (GA ˇ CR) through projects 22-08633J (for ˇ S.N. and J.S.) and GJ19-11707Y (for B.B.). Also, the work of B.B. and J.S. was supported by the Charles University, Prague via project PRIMUS/19/SCI/01. Moreover, ˇ S. N. has been supported by Praemium Academiæ of ˇ S. Neˇ casova. Finally, the Institute of Mathematics, CAS is supported by RVO:67985840.
This publication is part of the project "DAEDALUS - Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability" with project number 18373 of the research programme Open Technology Programme which is (partly) financed by the Dutch Research Council (NWO). Other financial contributions as part of this project have been provided by Elekta AB and Ortec Logiqcare B.V.. Special thanks go out to Arkadiy Dushatskiy for providing the source code for DSMGA-II with the Interleaved Multi-start Scheme, as well as the source code of the most recent version of GOMEA from [4].
NSF/EPSCoR RII Track-1: Emergent Quantum Materials and Technologies (EQUATE) under (Grant No. OIA-2044049). The authors would like to thank Ms. Martina Rodriguez Sala for her assistance with the IR-1048 dye absorption measurement.
This work is supported by the National Science Foundation (NSF), DMR-1709612 (BK, TH and CA), by the Ralph E. Powe Junior Faculty Enhancement Awards from Oak Ridge Associated Universities (to TH). TH acknowledges the National Institute of Health (NIH Grant # R15 CA238890-01A1) for its support. CA acknowledges partially support from the Office of Naval Research Young Investigator Program (ONR-YIP) (Grant No. N00014-19-1-2384) and
∗ R.J. is a FNRS honorary Research Associate. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No 864017 - L2C. R.J. is also supported by the Innoviris Foundation and the FNRS (Chist-Era Druid-net). The first and second authors contributed equally. The Authors are with the ICTEAM, UCLouvain, 4 Av. G. Lemaˆ ıtre, 1348 Louvain-la-Neuve, Belgium. ). { matteo.dellarossa,lucas.egidio, raphael.jungers } @uclouvain.be
We thank Laurent Sanchez-Palencia, Dan Arovas, and Andre Eckardt for helpful conversations. We acknowledge support from the Air Force Office of Scientific Research (FA9550-20-1-0240), the Army Research Office (MURI W911NF1710323), and the Eddleman Center for Quantum Innovation, and from the NSF QLCI program through grant number OMA-2016245. We acknowledge support via the UC Santa Barbara NSF Quantum Foundry funded via the Q-AMASE-i program under Grant DMR1906325. This material is based in part upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center. T. Grover is supported by an Alfred P. Sloan Research Fellowship and National Science Foundation under Grant No. DMR1752417. T.-C. Lu acknowledges support from Perimeter Institute for Theoretical Physics. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Colleges and Universities.
This research is partially supported by the National Key R&amp;D Programmes of China (No. 2019AAA0104301), National Natural Science Foundation of China (No. 61902395), National Research Foundation, Singapore under its the AI Singapore Programme (AISG2-RP-2020-019), the National Research Foundation, Prime Ministers Office, Singapore under its National Cybersecurity R&amp;D Program (Award No. NRF2018NCR-NCR005-0001), NRF Investigatorship NRF-NRFI06-2020-0001, the National Research Foundation through its National Satellite of Excellence in Trustworthy Software Systems (NSOE-TSS) project under the National Cybersecurity R&amp;D (NCR) Grant award no. NRF2018NCRNSOE003-0001.
This work was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764850 'SAGEX' . The research of P.V. has received funding from the ANR grant 'Amplitude' ANR-17CE31-0001-01, and the ANR grant 'SMAGP' ANR-20-CE40-0026-01. The work of P.H.D. was supported in part by DFF grant 0135-00089A. The work of N.E.J.B.-B. was supported in part by the Carlsberg Foundation as well as DFF grant 1026-00077B.
In this work, we focused on the continual test-time adaptation in non-stationary environments where the target domain distribution can continually change over time. To tackle the error accumulation and catastrophic forgetting in this setup, we proposed a novel method CoTTA which comprises two parts. Firstly, we reduced the error accumulation by using weight-averaged and augmentation-averaged predictions which are often more accurate. Secondly, to preserve the knowledge from the source model, we stochastically restored a small part of the weights to the source pre-trained weights. The proposed method can be incorporated in off-the-shelf pre-trained models without requiring any access to source data. The effectiveness of CoTTA was validated on four classification and one segmentation tasks. Acknowledgement The contributions of Qin Wang and Olga Fink were funded by the Swiss National Science Foundation Grant PP00P2 176878. This work is also funded by Toyota Motor Europe via the project TRACE-Zurich.
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61871262, 62071284, and 61901251, the National Key R&amp;D Program of China grants 2017YFE0121400 and 2019YFE0196600, the Innovation Program of Shanghai Municipal Science and Technology Commission grant 20JC1416400, Pudong New Area Science &amp; Technology Development Fund, Key-Area Research and Development Program of Guangdong Province grant 2020B0101130012, Foshan Science and Technology Innovation Team Project grant FS0AA-KJ919-4402-0060, and research funds from Shanghai Institute for Advanced Communication and Data Science (SICS).
DSRwas funded by the UK Medical Research Council (MC UU 00002/6) and the Biometrika Trust. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care (DHSC). This work was funded by UKRI grant MC UU 00002/6. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Manuscript received August 3, 2021; revised November 28, 2021; accepted January 6, 2022. This work was supported by the National Science Foundation of China under Grants 61906085, 62172208, 61972192, 61802169, 41972111; the Second Tibetan Plateau Scientific Expedition and Research Program under Grant 2019QZKK0204. This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. (Corresponding author: Zhiwei Jiang.)
We thank A. S. Christensen for discussions. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 772834). This result only reflects the author's view and the EU is not responsible for any use that may be made of the information it contains. This research was also supported by the NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 182892).
- † Both authors contributed equally to the work. - ∗ Corresponding author. - This work was supported by Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2021-0-00511, Robust AI and Distributed Attack Detection for Edge AI Security). - C. Lee is with the Samsung Electronics, South Korea (e-mail: chaenn.lee@samsung.com). - H. Park and Y.-G. Shin are with the Department of Computer Science and Engineering, Seoul National University, South Korea. - *M. Chung is with the School of Software, Soongsil University, South Korea (e-mail: chungmy@ssu.ac.kr).
He was enrolled in the Thousand Young Talents Program of China in 2012, and received the Excellent Young Scientists Fund from the National Natural Science Foundation of China (NSFC) in 2013. He was supported by the National Key R&amp;D Program of China in 2016 and 2020. He received several awards including Chinese Society of Biomedical Engineering (CSMBE) Huang Jiasi Award, China Society of Image and Graphics Technological Invention, Jiangsu Province Science and Technology Award and Beijing Science and Technology Award. He has authored or coauthored over 170 peer-reviewed articles in international journals, 100 conference proceedings papers, and 200 conference abstracts. His research interests include ultrasound imaging, fluorescence imaging, and photoacoustic imaging.
This work was supported in part by the National Natural Science Foundation of China (61871251 and 62027901), Beijing Natural Science Foundation (No.
Funding. The authors express their gratitude for the support from the Austrian Science Fund (FWF) grant P29190-N32 (H. Gfrerer, M. Mandlmayr) and the Czech Science Foundation (GACR) grant GA22-15524S (J.V.Outrata), grant GF21-06569K and the MSMT CR grant 8J21AT001 (J.Valdman). Conflict of interest. The authors have no competing interests to declare that are relevant to the content of this article.
We thank Professor Jin-Xin Zhou and Wenying Zhu for reading the first draft of this paper and making valuable comments. We are also grateful to the anonymous referee for helpful suggestions to improve the paper. We acknowledge Magma [6] and the support and resources from the Center for High Performance Computing at Beijing Jiaotong University for computation. This work was supported by the National Natural Science Foundation of China (12161141005, 12271024, 12071023,11971054), the 111 Project of China (B16002) and Yunnan Applied Basic Research Projects (202101AT070137).
This work was partially supported by National Natural Science Foundation of China (NSFC Grant No. 62122089), Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098, and Intelligent Social Governance Platform, Major Innovation &amp; Planning Interdisciplinary Platform for the 'Double-First Class' Initiative, Renmin University of China, the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA), the Department of Defense, or the United States Government. This work was supported partially by a Defense Advanced Research Projects Agency (DARPA) Grant No. FA8750-19-2-0502, PNNL Grant No. 528678, ICB Grant No. W911NF-19-D-0001 and No. W911NF-19-F-0037, and ROE Young Investigator Grant No. W911NF-20-10165.
This paper was recommended for publication by Editor S. Behnke upon evaluation of the Associate Editor and Reviewers' comments. This work received funding from the Australian Government, via grant AUSMURIB000001 associated with ONR MURI grant N00014-19-1-2571, Intel via grant RV3.248.Fischer, and by funding from ARC Laureate Fellowship FL210100156 to MM. The authors acknowledge continued support from the Queensland University of Technology (QUT) through the Centre for Robotics.
A.V.A. acknowledges financial support from UNAM-DGAPA-PAPIIT-IN111919 .
Financial support from the Swedish Research Council (2019-02376) is acknowledged.
I wish to acknowledge Icrea Academia prizes 2018 (Catalan Government) and the Spanish Research Council Grant No PID2019-111148GB-I00 for financial support.
T. L. is supported by the Office of Naval Research under Grant No. N00014-18-1-2371, and NSF under Grant No. PHY-2110591.
The work is partially supported by Boğaziçi University Research Fund, Start-Up project 21B03SUP3 awarded to PE. FK acknowledges support from the Novo Nordisk Foundation (grant no NNF20OC0061673)
This work is partly supported by the Israeli Science Foundation grants No. 998/17 and 385/21, and partly by European Union's Horizon 2020 research and innovation programme (grant agreement No. [101002392]).
This work is supported by an NSERC (Canada) Discovery Grant RGPIN-2017-03830.
This work was funded by the Knut and Alice Wallenberg Foundation.
This work was supported by OIST Graduate University and the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) Grant Number 19K05316, Grant-in-Aid for JSPS Fellows Grant Number 18F1836, and International Research Fellowship (Standard) P18367.
We thank the Indian Institute of Technology, Madras, India for their seed and initiation grants. This work was also supported by the DBT/Wellcome Trust India Alliance Fellowship IA/I/20/1/504900 awarded to Basudev Roy.
KJF is supported by funding for the Wellcome Centre for Human Neuroimaging (Ref: 205103/Z/16/Z) and a Canada-UK Artificial Intelligence Initiative (Ref: ES/T01279X/1). RER is supported by funding from the Wellcome Trust (209164/Z/17/Z). KJF and RER are supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
Manuscript received October 1, 2022; revised March 1, 2023; accepted May 2, 2023. This work is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project No. A18A2b0046), and IAF, A*STAR, SOITEC, NXP and National University of Singapore under FD-fAbrICS: Joint Lab for FD-SOI Always-on Intelligent &amp; Connected Systems (Award I2001E0053).
SM thanks Dr Raja Chakaraborti, Tripura Institute of Technology, Agartala, India, for an enlightening discussion on Masubuchi and Martin's solution [34] for a welded plate. The research of MD was supported by a 111 Project for International Collaboration (Chinese Government, PR China) No. B21034 and by a grant from the Seagull Program (Zhejiang Province, PR China). AG gratefully acknowledges partial funding provided by IN2TRACK3 European Commission - Horison Europe and Network Rail LTD and FRA - Federal Rail Association of America (FR21RPD31000000039).
The authors would like to thank all crew members who agreed to participate in this study. Special thanks to Professor Xi Zheng for his constructive advice and guidance. This work was fully supported by the 2020 Science and Technology Project of the Maritime Safety Administration of the Ministry of Transport of China (No. 07452041CCIEC016), National Natural Science Foundation of China (No. 91846107) and scholarship funding from the China Scholarship Council (No.202106690030).
Acknowledgments. The authors would like to thank B. Traxler, A. Nourmohammad, J. Mougous, and J. Mittler for many useful conversations. We would like to thank P. Levin, J. Wang and L. Simmons for advice on our manuscript. We thank S. B. Peterson and A. Schaefer for help with V. cholerae . Wewouldlike to thank J. Wang, C. Possoz, F.-X. Barre, C. Rudolph for detailed conversations about their data. This work was supported by NIH grant R01-GM128191.
Acknowledgments. The authors warmly thank the hospitality of the departments of mathematics of Carnegie Mellon University and of Politecnico di Torino, where part of this research was developed. MM and EZ are members of the Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica 'F. Severi' (INdAM). They also acknowledge partial funding from the GNAMPA Project 2019 Analysis and optimisation of thin structures .
The research of ACB was partially supported by National Funding from FCT-Fundação para a Ciência e a Tecnologia, through project UIDB/04561/2020. The research of JM is partially supported by FCT/Portugal through CAMGSD, IST-ID, projects UIDB/04459/2020 and UIDP/04459/2020. The work of MM is partially supported by the MIUR project Dipartimenti di Eccellenza 2018-2022 (CUP E11G18000350001) and by the Starting grant per giovani ricercatori of Politecnico di Torino. EZ is indebted to IST-Universidade de Lisboa for their kind support and hospitality. She also acknowledges GNAMPA support for travel expenses in 2022.
This research was funded in part by the National Natural Science Foundation of China grant number 61902058, in part by the Central University Basic Research Fund of China grant number N2019002, in part by the Natural Science Foundation of Liaoning Province grant number No. 2019-ZD-0751, in part by the Central University Basic Research Fund of China grant number No. JC2019025, in part by the Medical Imaging Intelligence Research grant number N2124006-3, in part by the National Natural Science Foundation of China grant number 61872075.
We thank Brandon Reichl at GFDL and the reviewers for valuable feedback and suggestions. This research was funded by US DOE grant DE-SC0020229. Partial support from US NSF grants PHY-2020249, OCE-2123496, US NASA grant 80NSSC18K0772, and US NNSA grant DE-NA0003856 is acknowledged. HA was also supported by US DOE grants DESC0014318, DE-SC0019329, NSF grant PHY-2206380, and US NNSA grant DE-NA0003914. JKS was also supported by US DOE grant DE-SC0019329, US NNSA grant DE-NA0003914, and US NSF grant CBET-2143702. Computing time was provided by NERSC under Contract No. DE-AC02-05CH11231 and NASA's HEC Program through NCCS at Goddard Space Flight Center. MOM6 is publicly available and can be accessed at https://github. com/mom-ocean/MOM6 . The coarse-graining analysis was performed using codes similar to FlowSieve, which is publicly available at https://github.com/husseinaluie/FlowSieve . Other data used here may be obtained from the corresponding author upon reasonable request.