Zobrazeno 1 - 10
of 91 971
pro vyhledávání: '"Malhotra, A"'
This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is meticulously crafted to strike a balance between computational efficiency a
Externí odkaz:
http://arxiv.org/abs/2407.21370
Autor:
Khostovan, Ali Ahmad, Malhotra, Sangeeta, Rhoads, James E., Sobral, David, Harish, Santosh, Tilvi, Vithal, Coughlin, Alicia, Rezaee, Saeed
We investigate the `intrinsic' H$\alpha$ EW distributions of $z \sim 0.4 - 2.2$ narrowband-selected H$\alpha$ samples from HiZELS and DAWN using a forward modeling approach. We find an EW - stellar mass anti-correlation with steepening slopes $-0.18\
Externí odkaz:
http://arxiv.org/abs/2408.00080
Strong emission from doubly ionized oxygen is a beacon for some of the most intensely star forming galaxies known. JWST enables the search for this beacon in the early universe with unprecedented sensitivity. In this work, we extend the study of fain
Externí odkaz:
http://arxiv.org/abs/2407.19023
Air pollution remains one of the most formidable environmental threats to human health globally, particularly in urban areas, contributing to nearly 7 million premature deaths annually. Megacities, defined as cities with populations exceeding 10 mill
Externí odkaz:
http://arxiv.org/abs/2407.11283
Autor:
Schwöbel, Pola, Franceschi, Luca, Zafar, Muhammad Bilal, Vasist, Keerthan, Malhotra, Aman, Shenhar, Tomer, Tailor, Pinal, Yilmaz, Pinar, Diamond, Michael, Donini, Michele
fmeval is an open source library to evaluate large language models (LLMs) in a range of tasks. It helps practitioners evaluate their model for task performance and along multiple responsible AI dimensions. This paper presents the library and exposes
Externí odkaz:
http://arxiv.org/abs/2407.12872
Autor:
Khalatyan, A., Anders, F., Chiappini, C., Queiroz, A. B. A., Nepal, S., Ponte, M. dal, Jordi, C., Guiglion, G., Valentini, M., Elipe, G. Torralba, Steinmetz, M., Pantaleoni-González, M., Malhotra, S., Jiménez-Arranz, Ó., Enke, H., Casamiquela, L., Ardèvol, J.
We explore the feasibility of using machine-learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions, given spectro-photometric data. To this end, we build a stable gradient-boosted random-forest regressor
Externí odkaz:
http://arxiv.org/abs/2407.06963
Autor:
Cheung, Yin Bun, Ma, Xiangmei, Chaudhry, Isha, Liu, Nan, Zhuang, Qingyuan, Yang, Grace Meijuan, Malhotra, Chetna, Finkelstein, Eric Andrew
Background: Incidence of adverse outcome events rises as patients with advanced illness approach end-of-life. Exposures that tend to occur near end-of-life, e.g., use of wheelchair, oxygen therapy and palliative care, may therefore be found associate
Externí odkaz:
http://arxiv.org/abs/2407.02178
Autor:
Malhotra, Garima, Fuller-Rowell, Timothy, Fang, Tzu-Wei, Yudin, Valery, Karol, Svetlana, Becker, Erich, Kubaryk, Adam Marshall
This paper presents a study of the global medium-scale (scales<620 km) gravity wave (GW) activity (in terms of zonal wind variance) and its seasonal, local time and longitudinal variations by employing the enhanced-resolution (~50 km) Whole Atmospher
Externí odkaz:
http://arxiv.org/abs/2407.00234
Autor:
Malhotra, Isha, Löwen, Hartmut
The Mpemba effect describes the phenomenon that a system at a hot initial temperature cools faster than at an initial warm temperature in the same environment. Such an anomalous cooling has recently been predicted and realized for trapped colloids. H
Externí odkaz:
http://arxiv.org/abs/2406.19098
Autor:
Singha, Mainak, Sarmiento, Julissa, Malhotra, Sangeeta, Rhoads, James E., Yung, L. Y. Aaron, Wang, Junxian, Zheng, Zhen-Ya, Lin, Ruqiu, Kim, Keunho, Kang, Jialai, Harish, Santosh
We searched the Chandra and XMM archives for observations of 900 green pea galaxies to find AGN signatures. Green peas are low-mass galaxies with prominent emission lines, similar in size and star formation rate to high-redshift dwarf galaxies. Of th
Externí odkaz:
http://arxiv.org/abs/2406.18730