Zobrazeno 1 - 10
of 64 806
pro vyhledávání: '"A. Harish"'
The growing threat of deepfakes and manipulated media necessitates a radical rethinking of media authentication. Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered, and current deepfake detection alg
Externí odkaz:
http://arxiv.org/abs/2411.17684
We present Asymmetric Dexterity (AsymDex), a novel reinforcement learning (RL) framework that can efficiently learn asymmetric bimanual skills for multi-fingered hands without relying on demonstrations, which can be cumbersome to collect. Two crucial
Externí odkaz:
http://arxiv.org/abs/2411.13020
Autor:
Chen, Zhixin, Deng, Jie-Ren, Wang, Mengyun, Farmakidis, Nikolaos, Baugh, Jonathan, Bhaskaran, Harish, Mol, Jan A., Anderson, Harry L., Bogani, Lapo, Thomas, James O.
Interferometry has underpinned a century of discoveries, ranging from the disproval of the ether theory to the detection of gravitational waves, offering insights into wave dynamics with unrivalled precision through the measurement of phase relations
Externí odkaz:
http://arxiv.org/abs/2411.11243
Autor:
Khostovan, Ali Ahmad, Kartaltepe, Jeyhan S., Brinch, Malte, Casey, Caitlin, Faisst, Andreas, Harish, Santosh, Gozaliasl, Ghassem, Onodera, Masato, Yabe, Kiyoto
We present a detailed analysis of EELG1002: a $z = 0.8275$ EELG identified within archival Gemini/GMOS spectroscopy as part of the COSMOS Spectroscopic Archive. Combining GMOS spectra and available multi-wavelength photometry, we find EELG1002 is a l
Externí odkaz:
http://arxiv.org/abs/2411.10537
In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since the days o
Externí odkaz:
http://arxiv.org/abs/2411.14452
Neural networks trained with stochastic gradient descent exhibit an inductive bias towards simpler decision boundaries, typically converging to a narrow family of functions, and often fail to capture more complex features. This phenomenon raises conc
Externí odkaz:
http://arxiv.org/abs/2411.04569
Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the singl
Externí odkaz:
http://arxiv.org/abs/2410.22303
Autor:
Harish, Ishita, Mishra, Saurav, Bhadoria, Neha, Kumar, Rithik, Arora, Madhav, Zahra, Syed Rameem, Gupta, Ankur
In this study, we explore an ensemble-based approach to improve classification accuracy in complex image datasets. Utilizing a Convolutional Block Attention Module (CBAM) alongside a Deep Neural Network (DNN) we leverage the unique feature-extraction
Externí odkaz:
http://arxiv.org/abs/2410.20231
Autor:
Dureppagari, Harish K., Saha, Chiranjib, Krishnamurthy, Harikumar, Wang, Xiao Feng, Rico-Alvariño, Alberto, Buehrer, R. Michael, Dhillon, Harpreet S.
The integration of non-terrestrial networks (NTN) into 5G new radio (NR) has opened up the possibility of developing a new positioning infrastructure using NR signals from Low-Earth Orbit (LEO) satellites. LEO-based cellular positioning offers severa
Externí odkaz:
http://arxiv.org/abs/2410.18301
At the sub-stellar boundary, signatures of magnetic fields begin to manifest at radio wavelengths, analogous to the auroral emission of the magnetised solar system planets. This emission provides a singular avenue for measuring magnetic fields at pla
Externí odkaz:
http://arxiv.org/abs/2410.18073