Zobrazeno 1 - 10
of 4 373
pro vyhledávání: '"Shah, P N"'
Autor:
Patel, Rameshri V., Shah, Manan N.
The spectroscopy of $\Xi^0$ is performed within the relativistic framework of independent quark model. The equal mixture of scalar and vector components in the potential having Martin-like form is considered for the confinement. With the suitable pot
Externí odkaz:
http://arxiv.org/abs/2407.16409
There is a growing attention given to utilizing Lagrangian and Hamiltonian mechanics with network training in order to incorporate physics into the network. Most commonly, conservative systems are modeled, in which there are no frictional losses, so
Externí odkaz:
http://arxiv.org/abs/2405.14645
Autor:
Chung, Philip, Fong, Christine T, Walters, Andrew M, Aghaeepour, Nima, Yetisgen, Meliha, O'Reilly-Shah, Vikas N
We investigate whether general-domain large language models such as GPT-4 Turbo can perform risk stratification and predict post-operative outcome measures using a description of the procedure and a patient's clinical notes derived from the electroni
Externí odkaz:
http://arxiv.org/abs/2401.01620
Autor:
Pokhariya, Chandradeep, Shah, Ishaan N, Xing, Angela, Li, Zekun, Chen, Kefan, Sharma, Avinash, Sridhar, Srinath
Understanding how we grasp objects with our hands has important applications in areas like robotics and mixed reality. However, this challenging problem requires accurate modeling of the contact between hands and objects. To capture grasps, existing
Externí odkaz:
http://arxiv.org/abs/2312.02137
In this paper we show an effective means of integrating data driven frameworks to sampling based optimal control to vastly reduce the compute time for easy adoption and adaptation to real time applications such as on-road autonomous driving in the pr
Externí odkaz:
http://arxiv.org/abs/2310.13077
The vast combination of material properties seen in nature are achieved by the complexity of the material microstructure. Advanced characterization and physics based simulation techniques have led to generation of extremely large microstructural data
Externí odkaz:
http://arxiv.org/abs/2301.04261
Autor:
Lloret-Talavera, Guillermo, Jorda, Marc, Servat, Harald, Boemer, Fabian, Chauhan, Chetan, Tomishima, Shigeki, Shah, Nilesh N., Peña, Antonio J.
The proliferation of machine learning services in the last few years has raised data privacy concerns. Homomorphic encryption (HE) enables inference using encrypted data but it incurs 100x-10,000x memory and runtime overheads. Secure deep neural netw
Externí odkaz:
http://arxiv.org/abs/2103.16139
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