Zobrazeno 1 - 10
of 57 221
pro vyhledávání: '"A. Ganesan"'
Autor:
H. Chawner, E. Saboya, K. E. Adcock, T. Arnold, Y. Artioli, C. Dylag, G. L. Forster, A. Ganesan, H. Graven, G. Lessin, P. Levy, I. T. Luijkx, A. Manning, P. A. Pickers, C. Rennick, C. Rödenbeck, M. Rigby
Publikováno v:
Atmospheric Chemistry and Physics, Vol 24, Pp 4231-4252 (2024)
We investigate the use of atmospheric oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimizing
Externí odkaz:
https://doaj.org/article/84c71da9e5184a1aa58c3d520e1c5aaa
Publikováno v:
Molecules, Vol 29, Iss 15, p 3613 (2024)
The tropical Garcinia genus of flowering plants is a prolific producer of aromatic natural products including polyphenols, flavonoids, and xanthones. In this study, we report the first phytochemical investigation of Garcinia caudiculata Ridl. from th
Externí odkaz:
https://doaj.org/article/0b6dfa62559a4cf4bfaa6921f10cec06
Autor:
C.-Y. Lau, R. Gorelick, J. Earhart, J. Higgins, R. Dewar, D. McMahon, A. Ganesan, B. Luke, F. Maldarelli
Publikováno v:
Journal of Virus Eradication, Vol 8, Iss , Pp 100210- (2022)
Externí odkaz:
https://doaj.org/article/5d36b655160041e595f3b622294a0028
Autor:
Anandh, Thivin, Ghose, Divij, Jain, Himanshu, Sunkad, Pratham, Ganesan, Sashikumaar, John, Volker
This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG
Externí odkaz:
http://arxiv.org/abs/2411.09329
Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and req
Externí odkaz:
http://arxiv.org/abs/2411.05961
Autor:
S, Vatchala, C, Yogesh, Govindarajan, Yeshwanth, M, Krithik Raja, Ganesan, Vishal Pramav Amirtha, A, Aashish Vinod, Ramesh, Dharun
In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Netwo
Externí odkaz:
http://arxiv.org/abs/2411.02112
Autor:
Lei, Hongbin, Zhang, Qian, Xie, Hongqiang, Meng, Congsen, Peng, Zhaoyang, Liu, Jinlei, Bai, Guangru, Ganesan, Adarsh, Zhao, Zengxiu
The mechanical analog of optical frequency combs, phononic frequency combs (PFCs), has recently been demonstrated in mechanical resonators via nonlinear coupling among multiple phonon modes. However, for exciting phononic combs in molecules, the requ
Externí odkaz:
http://arxiv.org/abs/2409.19607
Wildlife monitoring via camera traps has become an essential tool in ecology, but the deployment of machine learning models for on-device animal classification faces significant challenges due to domain shifts and resource constraints. This paper int
Externí odkaz:
http://arxiv.org/abs/2409.07796
Autor:
T V, Arunima Dev, K, Anagha P., C. V, Midhun, Musthafa, M. M, T, Vafiya Thaslim T., Akbar, Shaima, B, Swapna, Thomas, Nicemon, Joseph, Antony, Ganesan, S.
The capability of standard Geant4 PhysicsLists to address the fragmentation of $^{12}$C$-^{12}$C was assessed through a comparative analysis with experimental cross sections reported by Divay et al. and Dudouet et al. The standard PhysicsLists were f
Externí odkaz:
http://arxiv.org/abs/2409.07090
Autor:
Anandh, Thivin, Ghose, Divij, Tyagi, Ankit, Gupta, Abhineet, Sarkar, Suranjan, Ganesan, Sashikumaar
Physics-informed neural networks (PINNs) are able to solve partial differential equations (PDEs) by incorporating the residuals of the PDEs into their loss functions. Variational Physics-Informed Neural Networks (VPINNs) and hp-VPINNs use the variati
Externí odkaz:
http://arxiv.org/abs/2409.04143