Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Liu, Mia"'
This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics. Addressing the limitations of graph neural networks and standard transformers,
Externí odkaz:
http://arxiv.org/abs/2402.12535
Autor:
Gutsche, Oliver, Bose, Tulika, Votava, Margaret, Mason, David, Melo, Andrew, Liu, Mia, Hufnagel, Dirk, Gray, Lindsey, Hildreth, Mike, Holzman, Burt, Lannon, Kevin, Sehrish, Saba, Sperka, David, Letts, James, Bauerdick, Lothar, Bloom, Kenneth
The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission of the U.S. CMS Software & Computing Operations Program is to develop a
Externí odkaz:
http://arxiv.org/abs/2312.00772
Autor:
Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, Yeh, Shu-Wei
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient w
Externí odkaz:
http://arxiv.org/abs/2306.08106
Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses concerns to
Externí odkaz:
http://arxiv.org/abs/2210.16966
Autor:
Benelli, Gabriele, Chen, Thomas Y., Duarte, Javier, Feickert, Matthew, Graham, Matthew, Gray, Lindsey, Hackett, Dan, Harris, Phil, Hsu, Shih-Chieh, Kasieczka, Gregor, Khoda, Elham E., Komm, Matthias, Liu, Mia, Neubauer, Mark S., Norberg, Scarlet, Perloff, Alexx, Rieger, Marcel, Savard, Claire, Terao, Kazuhiro, Thais, Savannah, Roy, Avik, Vlimant, Jean-Roch, Chachamis, Grigorios
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting
Externí odkaz:
http://arxiv.org/abs/2207.09060
Autor:
Harris, Philip, Katsavounidis, Erik, McCormack, William Patrick, Rankin, Dylan, Feng, Yongbin, Gandrakota, Abhijith, Herwig, Christian, Holzman, Burt, Pedro, Kevin, Tran, Nhan, Yang, Tingjun, Ngadiuba, Jennifer, Coughlin, Michael, Hauck, Scott, Hsu, Shih-Chieh, Khoda, Elham E, Chen, Deming, Neubauer, Mark, Duarte, Javier, Karagiorgi, Georgia, Liu, Mia
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML
Externí odkaz:
http://arxiv.org/abs/2203.16255
Autor:
Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E, Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia
Publikováno v:
Front. Big Data 5, 787421 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Externí odkaz:
http://arxiv.org/abs/2110.13041
Autor:
Fahim, Farah, Hawks, Benjamin, Herwig, Christian, Hirschauer, James, Jindariani, Sergo, Tran, Nhan, Carloni, Luca P., Di Guglielmo, Giuseppe, Harris, Philip, Krupa, Jeffrey, Rankin, Dylan, Valentin, Manuel Blanco, Hester, Josiah, Luo, Yingyi, Mamish, John, Orgrenci-Memik, Seda, Aarrestad, Thea, Javed, Hamza, Loncar, Vladimir, Pierini, Maurizio, Pol, Adrian Alan, Summers, Sioni, Duarte, Javier, Hauck, Scott, Hsu, Shih-Chieh, Ngadiuba, Jennifer, Liu, Mia, Hoang, Duc, Kreinar, Edward, Wu, Zhenbin
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drasticall
Externí odkaz:
http://arxiv.org/abs/2103.05579
Autor:
Aarrestad, Thea, Loncar, Vladimir, Ghielmetti, Nicolò, Pierini, Maurizio, Summers, Sioni, Ngadiuba, Jennifer, Petersson, Christoffer, Linander, Hampus, Iiyama, Yutaro, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Rankin, Dylan, Jindariani, Sergo, Pedro, Kevin, Tran, Nhan, Liu, Mia, Kreinar, Edward, Wu, Zhenbin, Hoang, Duc
Publikováno v:
Mach. Learn.: Sci. Technol. 2 045015 (2021)
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional architectures, ta
Externí odkaz:
http://arxiv.org/abs/2101.05108