Zobrazeno 1 - 10
of 4 508
pro vyhledávání: '"Gupta Rajesh"'
Publikováno v:
E3S Web of Conferences, Vol 559, p 04023 (2024)
These days, building sustainable infrastructure requires human endurance. In this sense, the idea of combining PET (polyethylene terephthalate) with pervious concrete is an intriguing field of study. On the one hand, by enabling water to percolate th
Externí odkaz:
https://doaj.org/article/28a8c18f71af4accb6ddb7d18aa0e99a
Autor:
Zhang, Xiyuan, Teng, Diyan, Chowdhury, Ranak Roy, Li, Shuheng, Hong, Dezhi, Gupta, Rajesh K., Shang, Jingbo
Motion time series collected from mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR due to their low-power, alw
Externí odkaz:
http://arxiv.org/abs/2410.19818
Autor:
Fu, Xiaohan, Li, Shuheng, Wang, Zihan, Liu, Yihao, Gupta, Rajesh K., Berg-Kirkpatrick, Taylor, Fernandes, Earlence
Large Language Model (LLM) Agents are an emerging computing paradigm that blends generative machine learning with tools such as code interpreters, web browsing, email, and more generally, external resources. These agent-based systems represent an eme
Externí odkaz:
http://arxiv.org/abs/2410.14923
In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency. We validate the performance of the proposed framework in system model learning via two case
Externí odkaz:
http://arxiv.org/abs/2403.08996
Autor:
Gupta, Rajesh Kumar, Meenu
Non-relativistic conformal field theory describes many-body physics at unitarity. The correlation functions of the system are fixed by the requirement of conformal invariance. In this article, we discuss the correlation functions of scalar operators
Externí odkaz:
http://arxiv.org/abs/2403.01933
Autor:
Guo, Han, Hosseini, Ramtin, Zhang, Ruiyi, Somayajula, Sai Ashish, Chowdhury, Ranak Roy, Gupta, Rajesh K., Xie, Pengtao
Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning. It operates by randomly masking image patches and reconstructing these masked patches using the unmasked ones. A key limitation of MAE lie
Externí odkaz:
http://arxiv.org/abs/2402.18128
Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domain
Externí odkaz:
http://arxiv.org/abs/2402.01801
Autor:
Zhang, Xiyuan, Fu, Xiaohan, Teng, Diyan, Dong, Chengyu, Vijayakumar, Keerthivasan, Zhang, Jiayun, Chowdhury, Ranak Roy, Han, Junsheng, Hong, Dezhi, Kulkarni, Rashmi, Shang, Jingbo, Gupta, Rajesh
Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approache
Externí odkaz:
http://arxiv.org/abs/2311.06968
Autor:
Fu, Xiaohan, Wang, Zihan, Li, Shuheng, Gupta, Rajesh K., Mireshghallah, Niloofar, Berg-Kirkpatrick, Taylor, Fernandes, Earlence
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities. These new capabilities bring new benefits and also new security risks. In this work, we show that an attacker can use visual adversarial
Externí odkaz:
http://arxiv.org/abs/2310.03185