Zobrazeno 1 - 10
of 11 401
pro vyhledávání: '"A, Roshni"'
Publikováno v:
Modern Physics Letters A Vol. 38, Nos. 22 & 23 (2023) 2350109 (15 pages)
In the background of homogeneous and isotropic flat FLRW space-time, both classical and quantum cosmology has been studied for teleparallel dark energy (DE) model. Using Noether symmetry analysis, not only the symmetry vector but also the coupling fu
Externí odkaz:
http://arxiv.org/abs/2407.08217
Publikováno v:
International Journal of Modern Physics A Vol. 38, Nos. 12 & 13 (2023) 2350064 (14 pages)
The present work deals with Einstein-aether Scalar tensor gravity in the background of homogeneous and isotropic flat FLRW space-time model. The Noether symmetry vector identifies a transformation in the augmented space so that the field equations be
Externí odkaz:
http://arxiv.org/abs/2407.08207
In recent years, the proliferation of misinformation on social media platforms has become a significant concern. Initially designed for sharing information and fostering social connections, platforms like Twitter (now rebranded as X) have also unfort
Externí odkaz:
http://arxiv.org/abs/2406.12444
Autor:
Le, Khiem, Guo, Zhichun, Dong, Kaiwen, Huang, Xiaobao, Nan, Bozhao, Iyer, Roshni, Zhang, Xiangliang, Wiest, Olaf, Wang, Wei, Chawla, Nitesh V.
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding. However, their proficiency within the chemistry domain remains res
Externí odkaz:
http://arxiv.org/abs/2406.06777
Online social media platforms, such as Twitter, provide valuable information during disaster events. Existing tweet disaster summarization approaches provide a summary of these events to aid government agencies, humanitarian organizations, etc., to e
Externí odkaz:
http://arxiv.org/abs/2405.06551
The abundance of situational information on Twitter poses a challenge for users to manually discern vital and relevant information during disasters. A concise and human-interpretable overview of this information helps decision-makers in implementing
Externí odkaz:
http://arxiv.org/abs/2405.06541
Optimal decision-making is key to efficient allocation and scheduling of repair resources (e.g., crews) to service affected nodes of large power grid networks. Traditional manual restoration methods are inadequate for modern smart grids sprawling acr
Externí odkaz:
http://arxiv.org/abs/2404.13422
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relational graph convolutions, to effectively operate on highly multi-relationa
Externí odkaz:
http://arxiv.org/abs/2404.09365
Autor:
Busch, Florian Peter, Kamath, Roshni, Mitchell, Rupert, Stammer, Wolfgang, Kersting, Kristian, Mundt, Martin
A dataset is confounded if it is most easily solved via a spurious correlation, which fails to generalize to new data. In this work, we show that, in a continual learning setting where confounders may vary in time across tasks, the challenge of mitig
Externí odkaz:
http://arxiv.org/abs/2402.06434
The quest to improve scalar performance numbers on predetermined benchmarks seems to be deeply engraved in deep learning. However, the real world is seldom carefully curated and applications are seldom limited to excelling on test sets. A practical s
Externí odkaz:
http://arxiv.org/abs/2402.04814