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pro vyhledávání: '"P, Nair"'
Despite the growing adoption of video processing via Internet of Things (IoT) devices due to their cost-effectiveness, transmitting captured data to nearby servers poses challenges due to varying timing constraints and scarcity of network bandwidth.
Externí odkaz:
http://arxiv.org/abs/2409.02453
Autor:
Chen, Yuhao, He, Jiangpeng, Czarnecki, Chris, Vinod, Gautham, Mahmud, Talha Ibn, Raghavan, Siddeshwar, Ma, Jinge, Mao, Dayou, Nair, Saeejith, Xi, Pengcheng, Wong, Alexander, Delp, Edward, Zhu, Fengqing
Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets across various applications, ranging from classification and instance
Externí odkaz:
http://arxiv.org/abs/2409.01966
Demand for ADHD diagnosis and treatment is increasing significantly and the existing services are unable to meet the demand in a timely manner. In this work, we introduce a novel action recognition method for ADHD diagnosis by identifying and analysi
Externí odkaz:
http://arxiv.org/abs/2409.02274
The Maker-Breaker domination game (MBD game) is a two-player game played on a graph $G$ by Dominator and Staller. They alternately select unplayed vertices of $G$. The goal of Dominator is to form a dominating set with the set of vertices selected by
Externí odkaz:
http://arxiv.org/abs/2408.16297
We study the representative arm identification (RAI) problem in the multi-armed bandits (MAB) framework, wherein we have a collection of arms, each associated with an unknown reward distribution. An underlying instance is defined by a partitioning of
Externí odkaz:
http://arxiv.org/abs/2408.14195
Autor:
Nair, Bharath Rajiv
This paper explores the paradigm of Collaborative Perception (CP), where multiple robots and sensors in the environment share and integrate sensor data to construct a comprehensive representation of the surroundings. By aggregating data from various
Externí odkaz:
http://arxiv.org/abs/2408.14039
Chiral-odd gluon generalized parton distributions in the proton: A light-front quantization approach
Autor:
Lin, Bolang, Nair, Sreeraj, Mondal, Chandan, Xu, Siqi, Hu, Zhi, Zhang, Pengxiang, Zhao, Xingbo, Vary, James P.
Within the basis light-front quantization (BLFQ) framework, we evaluate the gluon chiral-odd generalized parton distributions (GPDs) inside the proton at zero skewness. We employ the light-front wave functions of the proton obtained from a light-fron
Externí odkaz:
http://arxiv.org/abs/2408.09988
Autor:
Li, Xiner, Zhao, Yulai, Wang, Chenyu, Scalia, Gabriele, Eraslan, Gokcen, Nair, Surag, Biancalani, Tommaso, Regev, Aviv, Levine, Sergey, Uehara, Masatoshi
Diffusion models excel at capturing the natural design spaces of images, molecules, DNA, RNA, and protein sequences. However, rather than merely generating designs that are natural, we often aim to optimize downstream reward functions while preservin
Externí odkaz:
http://arxiv.org/abs/2408.08252
Autor:
Nair, Arjun, Novikov, Roman
We consider a plane wave, a radiation solution, and the sum of these solutions (total solution) for the Helmholtz equation in an exterior region in $\mathbb R^2$. We consider a straight line in this region, such that the direction of propagation of t
Externí odkaz:
http://arxiv.org/abs/2408.08326
Autor:
Shang, Liang, Lou, Zhengyang, Alexander, Andrew L., Prabhakaran, Vivek, Sethares, William A., Nair, Veena A., Adluru, Nagesh
Deep neural networks have demonstrated exceptional efficacy in stroke lesion segmentation. However, the delineation of small lesions, critical for stroke diagnosis, remains a challenge. In this study, we propose two straightforward yet powerful appro
Externí odkaz:
http://arxiv.org/abs/2408.02929