Zobrazeno 1 - 10
of 11 837
pro vyhledávání: '"P Veena"'
Autor:
Shang, Liang, Lou, Zhengyang, Alexander, Andrew L., Prabhakaran, Vivek, Sethares, William A., Nair, Veena A., Adluru, Nagesh
Deep neural networks have demonstrated exceptional efficacy in stroke lesion segmentation. However, the delineation of small lesions, critical for stroke diagnosis, remains a challenge. In this study, we propose two straightforward yet powerful appro
Externí odkaz:
http://arxiv.org/abs/2408.02929
Autor:
Veena, V. S., Kim, W. -J., Sanchez-Monge, Alvaro, Schilke, P., Menten, K. M., Fuller, G. A., Sormani, M. C., Wyrowski, F., Banda-Barragan, W. E., Riquelme, D., Tarrio, P., de Vicente, P.
The expanding molecular ring (EMR) manifests itself as a parallelogram in the position-velocity diagram of spectral line emission from the Central Molecular Zone (CMZ) surrounding the Galacic centre (GC). Using multiwavelength data, we investigate th
Externí odkaz:
http://arxiv.org/abs/2407.14338
Autor:
Medina, S. -N. X., Dzib, S. A., Urquhart, J. S., Yang, A. Y., Brunthaler, A., Menten, K. M., Wyrowski, F., Cotton, W. D., Cheema, A., Dokara, R., Gong, Y., Khan, S., Nguyen, H., Ortiz-Leon, G. N., Rugel, M. R., Veena, V. S., Beuther, H., Csengeri, T., Pandian, J. D., Roy, N.
The GLOSTAR survey studies star formation with the VLA and the Effelsberg 100m telescope in the Galactic plane (-2d
Externí odkaz:
http://arxiv.org/abs/2407.12585
Autor:
Khan, S., Rugel, M. R., Brunthaler, A., Menten, K. M., Wyrowski, F., Urquhart, J. S., Gong, Y., Yang, A. Y., Nguyen, H., Dokara, R., Dzib, S. A., Medina, S. -N. X., Ortiz-León, G. N., Pandian, J. D., Beuther, H., Veena, V. S., Neupane, S., Cheema, A., Reich, W., Roy, N.
Studies of Galactic HII regions are of crucial importance for studying star formation and the evolution of the interstellar medium. Gaining an insight into their physical characteristics contributes to a more comprehensive understanding of these phen
Externí odkaz:
http://arxiv.org/abs/2407.05770
Autor:
Cirne, Dalmo, Calambur, Veena
Artificial Intelligence (AI) and Machine Learning (ML) providers have a responsibility to develop valid and reliable systems. Much has been discussed about trusting AI and ML inferences (the process of running live data through a trained AI model to
Externí odkaz:
http://arxiv.org/abs/2407.05919
Autor:
Christensen, I. Barlach, Wyrowski, F., Veena, V. S., Beuther, H., Semenov, D., Menten, K. M., Jacob, A. M., Kim, W. -J., Cunningham, N., Gieser, C., Hacar, A., Li, S., Schneider, N., Skretas, I., Winters, J. M.
Deuterated molecules and their molecular D/H-ratios (RD(D)) are important diagnostic tools to study the physical conditions of star-forming regions. The degree of deuteration, RD(D), can be significantly enhanced over the elemental D/H-ratio dependin
Externí odkaz:
http://arxiv.org/abs/2406.09145
Autor:
Chung, Moo K., Che, Ji Bi, Nair, Veena A., Ramos, Camille Garcia, Mathis, Jedidiah Ray, Prabhakaran, Vivek, Meyerand, Elizabeth, Hermann, Bruce P., Binder, Jeffrey R., Struck, Aaron F.
We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonan
Externí odkaz:
http://arxiv.org/abs/2405.07835
Autor:
Golshan, Roya Hamedani, Sánchez-Monge, Álvaro, Schilke, Peter, Sewiło, Marta, Möller, Thomas, Veena, V. S., Fuller, Gary A.
To study the impact of the initial effects of metallicity (i.e., the abundance of elements heavier than helium) on star formation and the formation of different molecular species, we searched for hot molecular cores in the sub-solar metallicity envir
Externí odkaz:
http://arxiv.org/abs/2405.01710
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most existing works have focused on horizontal or vertical data distributions,
Externí odkaz:
http://arxiv.org/abs/2404.09443
We reconstruct the 3D matter density and peculiar velocity fields in the local Universe up to a distance of $200\,h^{-1}\,\mathrm{Mpc}$ from the Two-Micron All-Sky Redshift Survey (2MRS), using a neural network (NN). We employ a NN with U-net autoenc
Externí odkaz:
http://arxiv.org/abs/2404.02278