Zobrazeno 1 - 10
of 92 157
pro vyhledávání: '"Sahu AN"'
Autor:
Warwick, B., Lyman, J., Pursiainen, M., Coppejans, D. L., Galbany, L., Jones, G. T., Killestein, T. L., Kumar, A., Oates, S. R., Ackley, K., Anderson, J. P., Aryan, A., Breton, R. P., Chen, T. W., Clark, P., Dhillon, V. S., Dyer, M. J., Gal-Yam, A., Galloway, D. K., Gutiérrez, C. P., Gromadzki, M., Inserra, C., Jiménez-Ibarra, F., Kelsey, L., Kotak, R., Kravtsov, T., Kuncarayakti, H., Magee, M. R., Matilainen, K., Mattila, S., Müller-Bravo, T. E., Nicholl, M., Noysena, K., Nuttall, L. K., O'Brien, P., O'Neill, D., Pallé, E., Pessi, T., Petrushevska, T., Pignata, G., Pollacco, D., Ragosta, F., Ramsay, G., Sahu, A., Sahu, D. K., Singh, A., Sollerman, J., Stanway, E., Starling, R., Steeghs, D., Teja, R. S., Ulaczyk, K.
SN 2023tsz is a Type Ibn supernova (SNe Ibn) discovered in an extremely low-mass host. SNe Ibn are an uncommon subtype of stripped-envelope core-collapse SNe. They are characterised by narrow helium emission lines in their spectra and are believed to
Externí odkaz:
http://arxiv.org/abs/2409.14147
Autor:
Cotton, R. James, Seamon, Bryant A., Segal, Richard L., Davis, Randal D., Sahu, Amrita, McLeod, Michelle M., Celnik, Pablo, Ramey, Sharon L.
Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding
Externí odkaz:
http://arxiv.org/abs/2411.03919
Autor:
Sahu, Subhajit
Community detection in graphs identifies groups of nodes with denser connections within the groups than between them, and while existing studies often focus on optimizing detection performance, memory constraints become critical when processing large
Externí odkaz:
http://arxiv.org/abs/2411.02268
Autor:
Paul, Debjoty, Yadav, Shivesh, Gupta, Shikhar, Patra, Bikash, Kulkarni, Nilesh, Mondal, Debashis, Gavankar, Kaushal, Samanta, Saheli, Sahu, Sourav K., Satpati, Biswarup, Singh, Bahadur, Benton, Owen, Chatterjee, Shouvik
Applying strain to epitaxial thin films has proven to be an effective approach for controlling material properties, paving the way for "materials by design". In this study, we explore this concept in the context of topological Kagome antiferromagnets
Externí odkaz:
http://arxiv.org/abs/2411.01824
Autor:
Singh, Utsav, Chakraborty, Souradip, Suttle, Wesley A., Sadler, Brian M., Sahu, Anit Kumar, Shah, Mubarak, Namboodiri, Vinay P., Bedi, Amrit Singh
This work introduces Hierarchical Preference Optimization (HPO), a novel approach to hierarchical reinforcement learning (HRL) that addresses non-stationarity and infeasible subgoal generation issues when solving complex robotic control tasks. HPO le
Externí odkaz:
http://arxiv.org/abs/2411.00361
The \textsc{Capacitated $d$-Hitting Set} problem involves a universe $U$ with a capacity function $\mathsf{cap}: U \rightarrow \mathbb{N}$ and a collection $\mathcal{A}$ of subsets of $U$, each of size at most $d$. The goal is to find a minimum subse
Externí odkaz:
http://arxiv.org/abs/2410.20900
Autor:
Sahu, Soumyadip
This article aims to extend the Eichler-Shimura isomorphism theorem for cusp forms on a congruence subgroup of $\text{SL}_2(\mathbb{Z})$ to the whole space of modular forms. To regularize the Eichler-Shimura integral at infinity we reconstruct the st
Externí odkaz:
http://arxiv.org/abs/2410.20385
We establish Trudinger-type inequality in the context of fractional boundary Hardy-type inequality for the case $sp=d$, where $p>1, ~ s \in (0,1)$ on a bounded Lipschitz domain $\Omega \subset \mathbb{R}^d$. In particular, we establish fractional ver
Externí odkaz:
http://arxiv.org/abs/2410.19362
The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadsid
Externí odkaz:
http://arxiv.org/abs/2410.16724
Autor:
Sahu, Siddharth, Altahhan, Abdulrahman
Capsule Networks outperform Convolutional Neural Networks in learning the part-whole relationships with viewpoint invariance, and the credit goes to their multidimensional capsules. It was assumed that increasing the number of capsule layers in the c
Externí odkaz:
http://arxiv.org/abs/2410.16908