Zobrazeno 1 - 10
of 4 434
pro vyhledávání: '"Guna A"'
Publikováno v:
Applied Water Science, Vol 13, Iss 12, Pp 1-16 (2023)
Abstract Urbanization, changes in land use and land cover (LULC), and an increase in population collectively have significant impacts on urban catchments. However, a vast majority of LULC studies have been conducted using readily available satellite
Externí odkaz:
https://doaj.org/article/91df0eca5d8a426b910fd18d92e45db2
Publikováno v:
Land, Vol 12, Iss 7, p 1396 (2023)
Soil erosion and sediment transport have significant consequences, including decreased agricultural production, water quality degradation, and modification to stream channels. Understanding these processes and their interactions with contributing fac
Externí odkaz:
https://doaj.org/article/dbe27fa3b27942b5923894971adcecd0
Autor:
Gurazada, Sai Guna Ranjan, Kennedy, Hannah M., Braatz, Richard D., Mehrman, Steven J., Polson, Shawn W., Rombel, Irene T.
Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. However, this requires robust, scalable, industrialized production to meet demand and provide affordable p
Externí odkaz:
http://arxiv.org/abs/2408.13374
Autor:
Nagar, Aishik, Liu, Yutong, Liu, Andy T., Schlegel, Viktor, Dwivedi, Vijay Prakash, Kaliya-Perumal, Arun-Kumar, Kalanchiam, Guna Pratheep, Tang, Yili, Tan, Robby T.
Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informativeness. While recent advancem
Externí odkaz:
http://arxiv.org/abs/2408.12095
Autor:
Dubey, Abhimanyu, Jauhri, Abhinav, Pandey, Abhinav, Kadian, Abhishek, Al-Dahle, Ahmad, Letman, Aiesha, Mathur, Akhil, Schelten, Alan, Yang, Amy, Fan, Angela, Goyal, Anirudh, Hartshorn, Anthony, Yang, Aobo, Mitra, Archi, Sravankumar, Archie, Korenev, Artem, Hinsvark, Arthur, Rao, Arun, Zhang, Aston, Rodriguez, Aurelien, Gregerson, Austen, Spataru, Ava, Roziere, Baptiste, Biron, Bethany, Tang, Binh, Chern, Bobbie, Caucheteux, Charlotte, Nayak, Chaya, Bi, Chloe, Marra, Chris, McConnell, Chris, Keller, Christian, Touret, Christophe, Wu, Chunyang, Wong, Corinne, Ferrer, Cristian Canton, Nikolaidis, Cyrus, Allonsius, Damien, Song, Daniel, Pintz, Danielle, Livshits, Danny, Esiobu, David, Choudhary, Dhruv, Mahajan, Dhruv, Garcia-Olano, Diego, Perino, Diego, Hupkes, Dieuwke, Lakomkin, Egor, AlBadawy, Ehab, Lobanova, Elina, Dinan, Emily, Smith, Eric Michael, Radenovic, Filip, Zhang, Frank, Synnaeve, Gabriel, Lee, Gabrielle, Anderson, Georgia Lewis, Nail, Graeme, Mialon, Gregoire, Pang, Guan, Cucurell, Guillem, Nguyen, Hailey, Korevaar, Hannah, Xu, Hu, Touvron, Hugo, Zarov, Iliyan, Ibarra, Imanol Arrieta, Kloumann, Isabel, Misra, Ishan, Evtimov, Ivan, Copet, Jade, Lee, Jaewon, Geffert, Jan, Vranes, Jana, Park, Jason, Mahadeokar, Jay, Shah, Jeet, van der Linde, Jelmer, Billock, Jennifer, Hong, Jenny, Lee, Jenya, Fu, Jeremy, Chi, Jianfeng, Huang, Jianyu, Liu, Jiawen, Wang, Jie, Yu, Jiecao, Bitton, Joanna, Spisak, Joe, Park, Jongsoo, Rocca, Joseph, Johnstun, Joshua, Saxe, Joshua, Jia, Junteng, Alwala, Kalyan Vasuden, Upasani, Kartikeya, Plawiak, Kate, Li, Ke, Heafield, Kenneth, Stone, Kevin, El-Arini, Khalid, Iyer, Krithika, Malik, Kshitiz, Chiu, Kuenley, Bhalla, Kunal, Rantala-Yeary, Lauren, van der Maaten, Laurens, Chen, Lawrence, Tan, Liang, Jenkins, Liz, Martin, Louis, Madaan, Lovish, Malo, Lubo, Blecher, Lukas, Landzaat, Lukas, de Oliveira, Luke, Muzzi, Madeline, Pasupuleti, Mahesh, Singh, Mannat, Paluri, Manohar, Kardas, Marcin, Oldham, Mathew, Rita, Mathieu, Pavlova, Maya, Kambadur, Melanie, Lewis, Mike, Si, Min, Singh, Mitesh Kumar, Hassan, Mona, Goyal, Naman, Torabi, Narjes, Bashlykov, Nikolay, Bogoychev, Nikolay, Chatterji, Niladri, Duchenne, Olivier, Çelebi, Onur, Alrassy, Patrick, Zhang, Pengchuan, Li, Pengwei, Vasic, Petar, Weng, Peter, Bhargava, Prajjwal, Dubal, Pratik, Krishnan, Praveen, Koura, Punit Singh, Xu, Puxin, He, Qing, Dong, Qingxiao, Srinivasan, Ragavan, Ganapathy, Raj, Calderer, Ramon, Cabral, Ricardo Silveira, Stojnic, Robert, Raileanu, Roberta, Girdhar, Rohit, Patel, Rohit, Sauvestre, Romain, Polidoro, Ronnie, Sumbaly, Roshan, Taylor, Ross, Silva, Ruan, Hou, Rui, Wang, Rui, Hosseini, Saghar, Chennabasappa, Sahana, Singh, Sanjay, Bell, Sean, Kim, Seohyun Sonia, Edunov, Sergey, Nie, Shaoliang, Narang, Sharan, Raparthy, Sharath, Shen, Sheng, Wan, Shengye, Bhosale, Shruti, Zhang, Shun, Vandenhende, Simon, Batra, Soumya, Whitman, Spencer, Sootla, Sten, Collot, Stephane, Gururangan, Suchin, Borodinsky, Sydney, Herman, Tamar, Fowler, Tara, Sheasha, Tarek, Georgiou, Thomas, Scialom, Thomas, Speckbacher, Tobias, Mihaylov, Todor, Xiao, Tong, Karn, Ujjwal, Goswami, Vedanuj, Gupta, Vibhor, Ramanathan, Vignesh, Kerkez, Viktor, Gonguet, Vincent, Do, Virginie, Vogeti, Vish, Petrovic, Vladan, Chu, Weiwei, Xiong, Wenhan, Fu, Wenyin, Meers, Whitney, Martinet, Xavier, Wang, Xiaodong, Tan, Xiaoqing Ellen, Xie, Xinfeng, Jia, Xuchao, Wang, Xuewei, Goldschlag, Yaelle, Gaur, Yashesh, Babaei, Yasmine, Wen, Yi, Song, Yiwen, Zhang, Yuchen, Li, Yue, Mao, Yuning, Coudert, Zacharie Delpierre, Yan, Zheng, Chen, Zhengxing, Papakipos, Zoe, Singh, Aaditya, Grattafiori, Aaron, Jain, Abha, Kelsey, Adam, Shajnfeld, Adam, Gangidi, Adithya, Victoria, Adolfo, Goldstand, Ahuva, Menon, Ajay, Sharma, Ajay, Boesenberg, Alex, Vaughan, Alex, Baevski, Alexei, Feinstein, Allie, Kallet, Amanda, Sangani, Amit, Yunus, Anam, Lupu, Andrei, Alvarado, Andres, Caples, Andrew, Gu, Andrew, Ho, Andrew, Poulton, Andrew, Ryan, Andrew, Ramchandani, Ankit, Franco, Annie, Saraf, Aparajita, Chowdhury, Arkabandhu, Gabriel, Ashley, Bharambe, Ashwin, Eisenman, Assaf, Yazdan, Azadeh, James, Beau, Maurer, Ben, Leonhardi, Benjamin, Huang, Bernie, Loyd, Beth, De Paola, Beto, Paranjape, Bhargavi, Liu, Bing, Wu, Bo, Ni, Boyu, Hancock, Braden, Wasti, Bram, Spence, Brandon, Stojkovic, Brani, Gamido, Brian, Montalvo, Britt, Parker, Carl, Burton, Carly, Mejia, Catalina, Wang, Changhan, Kim, Changkyu, Zhou, Chao, Hu, Chester, Chu, Ching-Hsiang, Cai, Chris, Tindal, Chris, Feichtenhofer, Christoph, Civin, Damon, Beaty, Dana, Kreymer, Daniel, Li, Daniel, Wyatt, Danny, Adkins, David, Xu, David, Testuggine, Davide, David, Delia, Parikh, Devi, Liskovich, Diana, Foss, Didem, Wang, Dingkang, Le, Duc, Holland, Dustin, Dowling, Edward, Jamil, Eissa, Montgomery, Elaine, Presani, Eleonora, Hahn, Emily, Wood, Emily, Brinkman, Erik, Arcaute, Esteban, Dunbar, Evan, Smothers, Evan, Sun, Fei, Kreuk, Felix, Tian, Feng, Ozgenel, Firat, Caggioni, Francesco, Guzmán, Francisco, Kanayet, Frank, Seide, Frank, Florez, Gabriela Medina, Schwarz, Gabriella, Badeer, Gada, Swee, Georgia, Halpern, Gil, Thattai, Govind, Herman, Grant, Sizov, Grigory, Guangyi, Zhang, Lakshminarayanan, Guna, Shojanazeri, Hamid, Zou, Han, Wang, Hannah, Zha, Hanwen, Habeeb, Haroun, Rudolph, Harrison, Suk, Helen, Aspegren, Henry, Goldman, Hunter, Damlaj, Ibrahim, Molybog, Igor, Tufanov, Igor, Veliche, Irina-Elena, Gat, Itai, Weissman, Jake, Geboski, James, Kohli, James, Asher, Japhet, Gaya, Jean-Baptiste, Marcus, Jeff, Tang, Jeff, Chan, Jennifer, Zhen, Jenny, Reizenstein, Jeremy, Teboul, Jeremy, Zhong, Jessica, Jin, Jian, Yang, Jingyi, Cummings, Joe, Carvill, Jon, Shepard, Jon, McPhie, Jonathan, Torres, Jonathan, Ginsburg, Josh, Wang, Junjie, Wu, Kai, U, Kam Hou, Saxena, Karan, Prasad, Karthik, Khandelwal, Kartikay, Zand, Katayoun, Matosich, Kathy, Veeraraghavan, Kaushik, Michelena, Kelly, Li, Keqian, Huang, Kun, Chawla, Kunal, Lakhotia, Kushal, Huang, Kyle, Chen, Lailin, Garg, Lakshya, A, Lavender, Silva, Leandro, Bell, Lee, Zhang, Lei, Guo, Liangpeng, Yu, Licheng, Moshkovich, Liron, Wehrstedt, Luca, Khabsa, Madian, Avalani, Manav, Bhatt, Manish, Tsimpoukelli, Maria, Mankus, Martynas, Hasson, Matan, Lennie, Matthew, Reso, Matthias, Groshev, Maxim, Naumov, Maxim, Lathi, Maya, Keneally, Meghan, Seltzer, Michael L., Valko, Michal, Restrepo, Michelle, Patel, Mihir, Vyatskov, Mik, Samvelyan, Mikayel, Clark, Mike, Macey, Mike, Wang, Mike, Hermoso, Miquel Jubert, Metanat, Mo, Rastegari, Mohammad, Bansal, Munish, Santhanam, Nandhini, Parks, Natascha, White, Natasha, Bawa, Navyata, Singhal, Nayan, Egebo, Nick, Usunier, Nicolas, Laptev, Nikolay Pavlovich, Dong, Ning, Zhang, Ning, Cheng, Norman, Chernoguz, Oleg, Hart, Olivia, Salpekar, Omkar, Kalinli, Ozlem, Kent, Parkin, Parekh, Parth, Saab, Paul, Balaji, Pavan, Rittner, Pedro, Bontrager, Philip, Roux, Pierre, Dollar, Piotr, Zvyagina, Polina, Ratanchandani, Prashant, Yuvraj, Pritish, Liang, Qian, Alao, Rachad, Rodriguez, Rachel, Ayub, Rafi, Murthy, Raghotham, Nayani, Raghu, Mitra, Rahul, Li, Raymond, Hogan, Rebekkah, Battey, Robin, Wang, Rocky, Maheswari, Rohan, Howes, Russ, Rinott, Ruty, Bondu, Sai Jayesh, Datta, Samyak, Chugh, Sara, Hunt, Sara, Dhillon, Sargun, Sidorov, Sasha, Pan, Satadru, Verma, Saurabh, Yamamoto, Seiji, Ramaswamy, Sharadh, Lindsay, Shaun, Feng, Sheng, Lin, Shenghao, Zha, Shengxin Cindy, Shankar, Shiva, Zhang, Shuqiang, Wang, Sinong, Agarwal, Sneha, Sajuyigbe, Soji, Chintala, Soumith, Max, Stephanie, Chen, Stephen, Kehoe, Steve, Satterfield, Steve, Govindaprasad, Sudarshan, Gupta, Sumit, Cho, Sungmin, Virk, Sunny, Subramanian, Suraj, Choudhury, Sy, Goldman, Sydney, Remez, Tal, Glaser, Tamar, Best, Tamara, Kohler, Thilo, Robinson, Thomas, Li, Tianhe, Zhang, Tianjun, Matthews, Tim, Chou, Timothy, Shaked, Tzook, Vontimitta, Varun, Ajayi, Victoria, Montanez, Victoria, Mohan, Vijai, Kumar, Vinay Satish, Mangla, Vishal, Albiero, Vítor, Ionescu, Vlad, Poenaru, Vlad, Mihailescu, Vlad Tiberiu, Ivanov, Vladimir, Li, Wei, Wang, Wenchen, Jiang, Wenwen, Bouaziz, Wes, Constable, Will, Tang, Xiaocheng, Wang, Xiaofang, Wu, Xiaojian, Wang, Xiaolan, Xia, Xide, Wu, Xilun, Gao, Xinbo, Chen, Yanjun, Hu, Ye, Jia, Ye, Qi, Ye, Li, Yenda, Zhang, Yilin, Zhang, Ying, Adi, Yossi, Nam, Youngjin, Yu, Wang, Hao, Yuchen, Qian, Yundi, He, Yuzi, Rait, Zach, DeVito, Zachary, Rosnbrick, Zef, Wen, Zhaoduo, Yang, Zhenyu, Zhao, Zhiwei
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage
Externí odkaz:
http://arxiv.org/abs/2407.21783
Publikováno v:
Hydrology, Vol 9, Iss 9, p 152 (2022)
The Magnitude and occurrence of extreme low flow events are needed in setting minimum flows to protect the instream users. As the true distribution is not normally known, the identification of the most appropriate distribution function that describes
Externí odkaz:
https://doaj.org/article/555a1b16b77143e9b28187cace6d1c4e
Publikováno v:
Water, Vol 14, Iss 13, p 2021 (2022)
The conventional drinking water treatment process involves disinfecting water at the final stage of treatment to ensure water is microbiologically safe at customer taps. Monochloramine is a popular disinfectant used in many water distribution systems
Externí odkaz:
https://doaj.org/article/fac9a02fc3824b6b8d1f2f5fb5c4bee1
Autor:
Zhang, Buyun, Luo, Liang, Chen, Yuxin, Nie, Jade, Liu, Xi, Guo, Daifeng, Zhao, Yanli, Li, Shen, Hao, Yuchen, Yao, Yantao, Lakshminarayanan, Guna, Wen, Ellie Dingqiao, Park, Jongsoo, Naumov, Maxim, Chen, Wenlin
Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those observed in the domain of large language models, due to the inefficiencies
Externí odkaz:
http://arxiv.org/abs/2403.02545
Autor:
Luo, Liang, Zhang, Buyun, Tsang, Michael, Ma, Yinbin, Chu, Ching-Hsiang, Chen, Yuxin, Li, Shen, Hao, Yuchen, Zhao, Yanli, Lakshminarayanan, Guna, Wen, Ellie Dingqiao, Park, Jongsoo, Mudigere, Dheevatsa, Naumov, Maxim
We study a mismatch between the deep learning recommendation models' flat architecture, common distributed training paradigm and hierarchical data center topology. To address the associated inefficiencies, we propose Disaggregated Multi-Tower (DMT),
Externí odkaz:
http://arxiv.org/abs/2403.00877
Publikováno v:
Water, Vol 14, Iss 8, p 1313 (2022)
Quantifying hydrological losses in a catchment is crucial for developing an effective flood forecasting system and estimating design floods. This can be a complicated and challenging task when the catchment is urbanised as the interaction of pervious
Externí odkaz:
https://doaj.org/article/62920f50ba3341768d902752f4815e86