Zobrazeno 1 - 10
of 10 789
pro vyhledávání: '"Sneha, P."'
Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely on the fundamental concept of p
Externí odkaz:
http://arxiv.org/abs/2409.13977
Rheumatoid arthritis (RA) has an intricate etiology that includes environmental factors as well as genetics. Organophosphate esters (OPEs) are frequently used as chemical additives in many personal care products and household items. However, there ha
Externí odkaz:
http://arxiv.org/abs/2409.00745
Examining the Interplay Between Privacy and Fairness for Speech Processing: A Review and Perspective
Autor:
Leschanowsky, Anna, Das, Sneha
Speech technology has been increasingly deployed in various areas of daily life including sensitive domains such as healthcare and law enforcement. For these technologies to be effective, they must work reliably for all users while preserving individ
Externí odkaz:
http://arxiv.org/abs/2408.15391
We prove a Hankel-variant commutant lifting theorem. This also uncovers the complete structure of the Beurling-type reducing and invariant subspaces of Hankel operators. Kernel spaces of Hankel operators play a key role in the analysis.
Comment:
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Externí odkaz:
http://arxiv.org/abs/2408.13753
Adversarial attacks, particularly the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms
Externí odkaz:
http://arxiv.org/abs/2408.13274
We explore the prospects of measurements of spectral moments of inclusive charm decays with BESIII. The rich and uniquely clean data set of charm mesons and baryons at BESIII offers a unique laboratory to study the evolution of Heavy Quark Expansion
Externí odkaz:
http://arxiv.org/abs/2408.10063
Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on loc
Externí odkaz:
http://arxiv.org/abs/2408.08904
Publikováno v:
Physics of the Dark Universe 46 (2024) 101620
A crucial aspect of wormhole (WH) physics is the inclusion of exotic matter, which requires violating the null energy condition. Here, we explore the potential for WHs to be sustained by quark matter under conditions of extreme density along with the
Externí odkaz:
http://arxiv.org/abs/2408.06605
In our investigation, we pioneer the development of geometrically deformed strange stars within the framework of $f(\mathcal{T})$ gravity theory through gravitational decoupling via the complete geometric deformation (CGD) technique. The significant
Externí odkaz:
http://arxiv.org/abs/2408.03967
The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing
Externí odkaz:
http://arxiv.org/abs/2408.02793