Zobrazeno 1 - 10
of 45 875
pro vyhledávání: '"A, Soumya"'
SPACE-SUIT: An Artificial Intelligence based chromospheric feature extractor and classifier for SUIT
Autor:
Seth, Pranava, Upendran, Vishal, Anand, Megha, Sarkar, Janmejoy, Roy, Soumya, Chaki, Priyadarshan, Chowdhury, Pratyay, Ghosh, Borishan, Tripathi, Durgesh
The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynami
Externí odkaz:
http://arxiv.org/abs/2412.08589
Gravitational wave (GW) searches using pulsar timing arrays (PTAs) are commonly assumed to be limited to a GW frequency of $\lesssim 4\times 10^{-7}$Hz given by the Nyquist rate associated with the average observational cadence of $2$ weeks for a sin
Externí odkaz:
http://arxiv.org/abs/2412.07615
Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert assessment of X-ray images and is commonly based on the Kellgren-Lawrence grading system, a time-intensive
Externí odkaz:
http://arxiv.org/abs/2412.07526
Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the mo
Externí odkaz:
http://arxiv.org/abs/2412.05710
Autor:
Ahamed, Sayyed Farid, Banerjee, Soumya, Roy, Sandip, Kapoor, Aayush, Vucovich, Marc, Choi, Kevin, Rahman, Abdul, Bowen, Edward, Shetty, Sachin
In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This paper introduces the concept of ``privacy drift", an innovative framew
Externí odkaz:
http://arxiv.org/abs/2412.05183
The spectra of particles in disordered lattices can either be completely extended or localized or can be intermediate which hosts both the localized and extended states separated from each other. In this work, however, we show that in the case of a o
Externí odkaz:
http://arxiv.org/abs/2412.04344
Autor:
Wei, Dennis, Padhi, Inkit, Ghosh, Soumya, Dhurandhar, Amit, Ramamurthy, Karthikeyan Natesan, Chang, Maria
Training data attribution (TDA) is the task of attributing model behavior to elements in the training data. This paper draws attention to the common setting where one has access only to the final trained model, and not the training algorithm or inter
Externí odkaz:
http://arxiv.org/abs/2412.03906
The hot circumgalactic medium in the eROSITA All-Sky Survey III. Star-forming and quiescent galaxies
Autor:
Zhang, Yi, Comparat, Johan, Ponti, Gabriele, Merloni, Andrea, Nandra, Kirpal, Haberl, Frank, Truong, Nhut, Pillepich, Annalisa, Popesso, Paola, Locatelli, Nicola, Zhang, Xiaoyuan, Sanders, Jeremy, Zheng, Xueying, Liu, Ang, Liu, Teng, Predehl, Peter, Salvato, Mara, Bruggen, Marcus, Shreeram, Soumya, Yeung, Michael C. H.
The circumgalactic medium (CGM), as the gas repository for star formation, might contain the answer to the mysterious galaxy quenching and bimodal galaxy population origin. We measured the X-ray emission of the hot CGM around star-forming and quiesce
Externí odkaz:
http://arxiv.org/abs/2411.19945
Autor:
Diamond, N'yoma, Banerjee, Soumya
The Generative Agents framework recently developed by Park et al. has enabled numerous new technical solutions and problem-solving approaches. Academic and industrial interest in generative agents has been explosive as a result of the effectiveness o
Externí odkaz:
http://arxiv.org/abs/2411.19211
Autor:
Ghosal, Soumya Suvra, Chakraborty, Souradip, Singh, Vaibhav, Guan, Tianrui, Wang, Mengdi, Beirami, Ahmad, Huang, Furong, Velasquez, Alvaro, Manocha, Dinesh, Bedi, Amrit Singh
With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to
Externí odkaz:
http://arxiv.org/abs/2411.18688