Zobrazeno 1 - 10
of 374
pro vyhledávání: '"B. Aditya"'
Autor:
Sarabeth M. Mathis, Alexander E. Webber, Tomás M. León, Erin L. Murray, Monica Sun, Lauren A. White, Logan C. Brooks, Alden Green, Addison J. Hu, Roni Rosenfeld, Dmitry Shemetov, Ryan J. Tibshirani, Daniel J. McDonald, Sasikiran Kandula, Sen Pei, Rami Yaari, Teresa K. Yamana, Jeffrey Shaman, Pulak Agarwal, Srikar Balusu, Gautham Gururajan, Harshavardhan Kamarthi, B. Aditya Prakash, Rishi Raman, Zhiyuan Zhao, Alexander Rodríguez, Akilan Meiyappan, Shalina Omar, Prasith Baccam, Heidi L. Gurung, Brad T. Suchoski, Steve A. Stage, Marco Ajelli, Allisandra G. Kummer, Maria Litvinova, Paulo C. Ventura, Spencer Wadsworth, Jarad Niemi, Erica Carcelen, Alison L. Hill, Sara L. Loo, Clifton D. McKee, Koji Sato, Claire Smith, Shaun Truelove, Sung-mok Jung, Joseph C. Lemaitre, Justin Lessler, Thomas McAndrew, Wenxuan Ye, Nikos Bosse, William S. Hlavacek, Yen Ting Lin, Abhishek Mallela, Graham C. Gibson, Ye Chen, Shelby M. Lamm, Jaechoul Lee, Richard G. Posner, Amanda C. Perofsky, Cécile Viboud, Leonardo Clemente, Fred Lu, Austin G. Meyer, Mauricio Santillana, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Michal Ben-Nun, Pete Riley, James Turtle, Chis Hulme-Lowe, Shakeel Jessa, V. P. Nagraj, Stephen D. Turner, Desiree Williams, Avranil Basu, John M. Drake, Spencer J. Fox, Ehsan Suez, Monica G. Cojocaru, Edward W. Thommes, Estee Y. Cramer, Aaron Gerding, Ariane Stark, Evan L. Ray, Nicholas G. Reich, Li Shandross, Nutcha Wattanachit, Yijin Wang, Martha W. Zorn, Majd Al Aawar, Ajitesh Srivastava, Lauren A. Meyers, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan L. Lewis, Madhav Marathe, Srinivasan Venkatramanan, Patrick Butler, Andrew Farabow, Naren Ramakrishnan, Nikhil Muralidhar, Carrie Reed, Matthew Biggerstaff, Rebecca K. Borchering
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predi
Externí odkaz:
https://doaj.org/article/b22341a399b644b886f816887477dc5b
Autor:
Jiaming Cui, Sungjun Cho, Methun Kamruzzaman, Matthew Bielskas, Anil Vullikanti, B. Aditya Prakash
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Healthcare-associated infections (HAIs) are a major problem in hospital infection control. Although HAIs can be suppressed using contact precautions, such precautions are expensive, and we can only apply them to a small fraction of patients
Externí odkaz:
https://doaj.org/article/f087efb80031448a86437548c474471a
Autor:
Vedant Das Swain, Jiajia Xie, Maanit Madan, Sonia Sargolzaei, James Cai, Munmun De Choudhury, Gregory D. Abowd, Lauren N. Steimle, B. Aditya Prakash
Publikováno v:
Frontiers in Digital Health, Vol 5 (2023)
Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote in
Externí odkaz:
https://doaj.org/article/3d530c62fc95406aa5c2fd8bd25ffd65
Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the real world. In this work, we propose TimerBed, the first comprehensive
Externí odkaz:
http://arxiv.org/abs/2411.06018
Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nat
Externí odkaz:
http://arxiv.org/abs/2410.00049
Autor:
Kamarthi, Harshavardhan, Sasanur, Aditya B., Tong, Xinjie, Zhou, Xingyu, Peters, James, Czyzyk, Joe, Prakash, B. Aditya
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works,
Externí odkaz:
http://arxiv.org/abs/2407.02657
Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time serie
Externí odkaz:
http://arxiv.org/abs/2407.02641
Autor:
Du, Wenjie, Wang, Jun, Qian, Linglong, Yang, Yiyuan, Ibrahim, Zina, Liu, Fanxing, Wang, Zepu, Liu, Haoxin, Zhao, Zhiyuan, Zhou, Yingjie, Wang, Wenjia, Ding, Kaize, Liang, Yuxuan, Prakash, B. Aditya, Wen, Qingsong
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectiv
Externí odkaz:
http://arxiv.org/abs/2406.12747
Autor:
Liu, Haoxin, Kamarthi, Harshavardhan, Kong, Lingkai, Zhao, Zhiyuan, Zhang, Chao, Prakash, B. Aditya
Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial to equip TSF models with out-of-distribution (OOD) generalization abilities, as historical training data and
Externí odkaz:
http://arxiv.org/abs/2406.09130
Autor:
Liu, Haoxin, Xu, Shangqing, Zhao, Zhiyuan, Kong, Lingkai, Kamarthi, Harshavardhan, Sasanur, Aditya B., Sharma, Megha, Cui, Jiaming, Wen, Qingsong, Zhang, Chao, Prakash, B. Aditya
Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely
Externí odkaz:
http://arxiv.org/abs/2406.08627