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Akademický článek
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This work investigates the potential of tailoring Large Language Models (LLMs), specifically GPT3.5 and GPT4, for the domain of chip testing. A key aspect of chip design is functional testing, which relies on testbenches to evaluate the functionality
Externí odkaz:
http://arxiv.org/abs/2406.17132
Autor:
Rajiv Agarwal, Sanjeev Agarwal
Publikováno v:
Indian Journal of Plastic Surgery, Vol 55, Iss 03, Pp 221-223 (2022)
Externí odkaz:
https://doaj.org/article/95c8da0158c549b585fd4f51cb1e1fa2
Akademický článek
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Autor:
Naug, Avisek, Guillen, Antonio, Luna, Ricardo, Gundecha, Vineet, Rengarajan, Desik, Ghorbanpour, Sahand, Mousavi, Sajad, Babu, Ashwin Ramesh, Markovikj, Dejan, Kashyap, Lekhapriya D, Sarkar, Soumyendu
Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate change. This makes sustainable data center control a priority. In this p
Externí odkaz:
http://arxiv.org/abs/2408.07841
Autor:
Godala, Bhargav Reddy, Ramesh, Sankara Prasad, Tibrewala, Krishnam, Pepi, Chrysanthos, Chacon, Gino, Kanev, Svilen, Pokam, Gilles A., Jiménez, Daniel A., Gratz, Paul V., August, David I.
Modern OOO CPUs have very deep pipelines with large branch misprediction recovery penalties. Speculatively executed instructions on the wrong path can significantly change cache state, depending on speculation levels. Architects often employ trace-dr
Externí odkaz:
http://arxiv.org/abs/2408.05912
Surgical procedures are often not "standardised" (i.e., defined in a unique and unambiguous way), but rather exist as implicit knowledge in the minds of the surgeon and the surgical team. This reliance extends to pre-surgery planning and effective co
Externí odkaz:
http://arxiv.org/abs/2408.05001
This paper introduces LLDif, a novel diffusion-based facial expression recognition (FER) framework tailored for extremely low-light (LL) environments. Images captured under such conditions often suffer from low brightness and significantly reduced co
Externí odkaz:
http://arxiv.org/abs/2408.04235
Autor:
Kumar, Shachi H, Sahay, Saurav, Mazumder, Sahisnu, Okur, Eda, Manuvinakurike, Ramesh, Beckage, Nicole, Su, Hsuan, Lee, Hung-yi, Nachman, Lama
Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the m
Externí odkaz:
http://arxiv.org/abs/2408.03907
Land Use Land Cover (LULC) mapping is a vital tool for urban and resource planning, playing a key role in the development of innovative and sustainable cities. This study introduces a semi-supervised segmentation model for LULC prediction using high-
Externí odkaz:
http://arxiv.org/abs/2408.02382