Zobrazeno 1 - 10
of 1 476 095
pro vyhledávání: '"P A, Lee"'
Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, and AR/VR use mesh-based representations to represent and render 3D sce
Externí odkaz:
http://arxiv.org/abs/2410.08941
Autor:
Andrews, Moira, Artale, M. Celeste, Kumar, Ankit, Lee, Kyoung-Soo, Florek, Tess, Anand, Kaustub, Cerdosino, Candela, Ciardullo, Robin, Firestone, Nicole, Gawiser, Eric, Gronwall, Caryl, Guaita, Lucia, Hong, Sungryong, Hwang, Ho Seong, Lee, Jaehyun, Lee, Seong-Kook, Padilla, Nelson, Park, Jaehong, Popescu, Roxana, Ramakrishnan, Vandana, Song, Hyunmi, Vivanco, Felipe, Vogelsberger, Mark
We investigate the physical properties and redshift evolution of simulated galaxies residing in protoclusters at cosmic noon, to understand the influence of the environment on galaxy formation. This work is to build clear expectations for the ongoing
Externí odkaz:
http://arxiv.org/abs/2410.08412
Autor:
Shah, Paul, Davis, Tamara M., Vincenzi, Maria, Armstrong, Patrick, Brout, Dillon, Camilleri, Ryan, Galbany, Lluis, Garcia-Bellido, Juan, Gill, Mandeep S. S., Lahav, Ofer, Lee, Jason, Lidman, Chris, Moeller, Anais, Sako, Masao, Sanchez, Bruno O., Sullivan, Mark, Whiteway, Lorne, Wiseman, Phillip, Allam, S., Aguena, M., Bocquet, S., Brooks, D., Burke, D. L., Rosell, A. Carnero, da Costa, L. N., Pereira, M. E. S., Desai, S., Dodelson, S., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., Gaztanaga, E., Gruen, D., Gruendl, R. A., Gutierrez, G., Herner, K., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lee, S., Marshall, J. L., Mena-Fernandez, J., Miquel, R., Myles, J., Palmese, A., Pieres, A., Malagon, A. A. Plazas, Roodman, A., Samuroff, S., Sanchez, E., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Vikram, V.
Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $\alpha$ of the total matter density in compact obj
Externí odkaz:
http://arxiv.org/abs/2410.07956
Autor:
Chen, Jie, Gruber, Susan, Lee, Hana, Chu, Haitao, Lee, Shiowjen, Tian, Haijun, Wang, Yan, He, Weili, Jemielita, Thomas, Song, Yang, Tamura, Roy, Tian, Lu, Zhao, Yihua, Chen, Yong, van der Laan, Mark, Nie, Lei
Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challen
Externí odkaz:
http://arxiv.org/abs/2410.06586
Autor:
Chen, Jie, Nie, Lei, Lee, Shiowjen, Chu, Haitao, Tian, Haijun, Wang, Yan, He, Weili, Jemielita, Thomas, Gruber, Susan, Song, Yang, Tamura, Roy, Tian, Lu, Zhao, Yihua, Chen, Yong, van der Laan, Mark, Lee, Hana
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phe
Externí odkaz:
http://arxiv.org/abs/2410.06585
Autor:
Gyawali, Gaurav, Cochran, Tyler, Lensky, Yuri, Rosenberg, Eliott, Karamlou, Amir H., Kechedzhi, Kostyantyn, Berndtsson, Julia, Westerhout, Tom, Asfaw, Abraham, Abanin, Dmitry, Acharya, Rajeev, Beni, Laleh Aghababaie, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Ballard, Brian, Bardin, Joseph C., Bengtsson, Andreas, Bilmes, Alexander, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Zijun, Chiaro, Ben, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Das, Sayan, Debroy, Dripto M., De Lorenzo, Laura, Barba, Alexander Del Toro, Demura, Sean, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Fatemi, Reza, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Gasca, Robert, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heslin, Stephen, Heu, Paula, Hill, Gordon, Hilton, Jeremy, Hoffmann, Markus R., Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kieferová, Mária, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Korotkov, Alexander N., Kostritsa, Fedor, Kreikebaum, John Mark, Kurilovich, Vladislav D., Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Joonho, Lee, Kenny, Lester, Brian J., Li, Wing Yan, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Maloney, Ashley, Mandrà, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Neill, Charles, Nersisyan, Ani, Newman, Michael, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, Niu, Murphy Yuezhen, Oliver, William D., Ottosson, Kristoffer, Pizzuto, Alex, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rocque, Charles, Rubin, Nicholas C., Saei, Negar, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smith, W. Clarke, Springer, Sofia, Sterling, George, Suchard, Jordan, Szalay, Marco, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torunbalci, M. Mert, Vaishnav, Abeer, Vdovichev, Sergey, Vidal, Guifré, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas, Boixo, Sergio, Kelly, Julian, Lucero, Erik, Chen, Yu, Smelyanskiy, Vadim, Neven, Hartmut, Kovrizhin, Dmitry, Knolle, Johannes, Halimeh, Jad C., Aleiner, Igor, Moessner, Roderich, Roushan, Pedram
One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient pro
Externí odkaz:
http://arxiv.org/abs/2410.06557
Recent advances in diffusion models have introduced a new era of text-guided image manipulation, enabling users to create realistic edited images with simple textual prompts. However, there is significant concern about the potential misuse of these m
Externí odkaz:
http://arxiv.org/abs/2410.05694
Autor:
Kang, Mingu, Lee, Dongseok, Cho, Woojin, Park, Jaehyeon, Lee, Kookjin, Gruber, Anthony, Hong, Youngjoon, Park, Noseong
Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and pre-training techniques. Motivated by this, we explore whether a simila
Externí odkaz:
http://arxiv.org/abs/2410.06442
Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we propose the Amortized Control of continuous State Space Model (ACSSM)
Externí odkaz:
http://arxiv.org/abs/2410.05602
Neural fields are an emerging paradigm that represent data as continuous functions parameterized by neural networks. Despite many advantages, neural fields often have a high training cost, which prevents a broader adoption. In this paper, we focus on
Externí odkaz:
http://arxiv.org/abs/2410.04779