Zobrazeno 1 - 10
of 1 537
pro vyhledávání: '"Huerta E"'
Autor:
Li Haoyang, Duarte Javier, Roy Avik, Zhu Ruike, Huerta E. A., Diaz Daniel, Harris Philip, Kansal Raghav, Katz Daniel S., Kavoori Ishaan H., Kindratenko Volodymyr V., Mokhtar Farouk, Neubauer Mark S., Park Sang Eon, Quinnan Melissa, Rusack Roger, Zhao Zhizhen
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09017 (2024)
The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine
Externí odkaz:
https://doaj.org/article/4ab127e3bc5b41e2a36e37e44ea0a8e1
Autor:
Hoang, Trung-Hieu, Fuhrman, Jordan, Madduri, Ravi, Li, Miao, Chaturvedi, Pranshu, Li, Zilinghan, Kim, Kibaek, Ryu, Minseok, Chard, Ryan, Huerta, E. A., Giger, Maryellen
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is
Externí odkaz:
http://arxiv.org/abs/2312.08701
Autor:
LISA Consortium Waveform Working Group, Afshordi, Niayesh, Akçay, Sarp, Seoane, Pau Amaro, Antonelli, Andrea, Aurrekoetxea, Josu C., Barack, Leor, Barausse, Enrico, Benkel, Robert, Bernard, Laura, Bernuzzi, Sebastiano, Berti, Emanuele, Bonetti, Matteo, Bonga, Béatrice, Bozzola, Gabriele, Brito, Richard, Buonanno, Alessandra, Cárdenas-Avendaño, Alejandro, Casals, Marc, Chernoff, David F., Chua, Alvin J. K., Clough, Katy, Colleoni, Marta, Dhesi, Mekhi, Druart, Adrien, Durkan, Leanne, Faye, Guillaume, Ferguson, Deborah, Field, Scott E., Gabella, William E., García-Bellido, Juan, Gracia-Linares, Miguel, Gerosa, Davide, Green, Stephen R., Haney, Maria, Hannam, Mark, Heffernan, Anna, Hinderer, Tanja, Helfer, Thomas, Hughes, Scott A., Husa, Sascha, Isoyama, Soichiro, Katz, Michael L., Kavanagh, Chris, Khanna, Gaurav, Kidder, Larry E., Korol, Valeriya, Küchler, Lorenzo, Laguna, Pablo, Larrouturou, François, Tiec, Alexandre Le, Leather, Benjamin, Lim, Eugene A., Lim, Hyun, Littenberg, Tyson B., Long, Oliver, Lousto, Carlos O., Lovelace, Geoffrey, Lukes-Gerakopoulos, Georgios, Lynch, Philip, Macedo, Rodrigo P., Markakis, Charalampos, Maggio, Elisa, Mandel, Ilya, Maselli, Andrea, Mathews, Josh, Mourier, Pierre, Neilsen, David, Nagar, Alessandro, Nichols, David A., Novák, Jan, Okounkova, Maria, O'Shaughnessy, Richard, Oshita, Naritaka, O'Toole, Conor, Pan, Zhen, Pani, Paolo, Pappas, George, Paschalidis, Vasileios, Pfeiffer, Harald P., Pompili, Lorenzo, Pound, Adam, Pratten, Geraint, Rüter, Hannes R., Ruiz, Milton, Sam, Zeyd, Sberna, Laura, Shapiro, Stuart L., Shoemaker, Deirdre M., Sopuerta, Carlos F., Spiers, Andrew, Sundar, Hari, Tamanini, Nicola, Thompson, Jonathan E., Toubiana, Alexandre, Tsokaros, Antonios, Upton, Samuel D., van de Meent, Maarten, Vernieri, Daniele, Wachter, Jeremy M., Warburton, Niels, Wardell, Barry, Witek, Helvi, Witzany, Vojtěch, Yang, Huan, Zilhão, Miguel, Albertini, Angelica, Arun, K. G., Bezares, Miguel, Bonilla, Alexander, Chapman-Bird, Christian, Cownden, Bradley, Cunningham, Kevin, Devitt, Chris, Dolan, Sam, Duque, Francisco, Dyson, Conor, Fryer, Chris L., Gair, Jonathan R., Giacomazzo, Bruno, Gupta, Priti, Han, Wen-Biao, Haas, Roland, Hirschmann, Eric W., Huerta, E. A., Jetzer, Philippe, Kelly, Bernard, Khalil, Mohammed, Lewis, Jack, Lloyd-Ronning, Nicole, Marsat, Sylvain, Nardini, Germano, Neef, Jakob, Ottewill, Adrian, Pantelidou, Christiana, Piovano, Gabriel Andres, Redondo-Yuste, Jaime, Sagunski, Laura, Stein, Leo C., Skoupý, Viktor, Sperhake, Ulrich, Speri, Lorenzo, Spieksma, Thomas F. M., Stevens, Chris, Trestini, David, Vañó-Viñuales, Alex
LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectac
Externí odkaz:
http://arxiv.org/abs/2311.01300
We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spin
Externí odkaz:
http://arxiv.org/abs/2310.00052
Autor:
Li, Zilinghan, Chaturvedi, Pranshu, He, Shilan, Chen, Han, Singh, Gagandeep, Kindratenko, Volodymyr, Huerta, E. A., Kim, Kibaek, Madduri, Ravi
Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralize
Externí odkaz:
http://arxiv.org/abs/2309.14675
Autor:
Li, Zilinghan, He, Shilan, Chaturvedi, Pranshu, Hoang, Trung-Hieu, Ryu, Minseok, Huerta, E. A., Kindratenko, Volodymyr, Fuhrman, Jordan, Giger, Maryellen, Chard, Ryan, Kim, Kibaek, Madduri, Ravi
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the a
Externí odkaz:
http://arxiv.org/abs/2308.08786
Publikováno v:
Proceedings of the National Academy of Sciences, 121, 27, (2024)
The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI m
Externí odkaz:
http://arxiv.org/abs/2308.07954
Autor:
Hannon, Stephen, Whitmore, Bradley C., Lee, Janice C., Thilker, David A., Deger, Sinan, Huerta, E. A., Wei, Wei, Mobasher, Bahram, Klessen, Ralf, Boquien, Mederic, Dale, Daniel A., Chevance, Melanie, Grasha, Kathryn, Sanchez-Blazquez, Patricia, Williams, Thomas, Scheuermann, Fabian, Groves, Brent, Kim, Hwihyun, Kruijssen, J. M. Diederick, Team, the PHANGS-HST
Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues an
Externí odkaz:
http://arxiv.org/abs/2307.15133
Publikováno v:
Mach. Learn.: Sci. Technol. 5 (2024) 025056
We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes $(l, |m|)=\{(2, 2), (2, 1), (3, 3), (3, 2), (4, 4)\}$, and m
Externí odkaz:
http://arxiv.org/abs/2306.15728
Autor:
Park, Hyun, Yan, Xiaoli, Zhu, Ruijie, Huerta, E. A., Chaudhuri, Santanu, Cooper, Donny, Foster, Ian, Tajkhorshid, Emad
Publikováno v:
Commun Chem 7, 21 (2024)
Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we intr
Externí odkaz:
http://arxiv.org/abs/2306.08695