Zobrazeno 1 - 10
of 3 978
pro vyhledávání: '"Manivannan P"'
Autor:
Manivannan Prasanth, Gurusamy Muruganandam, Krishnasamy Ravichandran, Gnanasekar Dayana Jeyaleela, Krishnasamy Shanthaseelan, Baskaran Pradhiba Priyadharshini
Publikováno v:
Biomedical and Biotechnology Research Journal, Vol 6, Iss 3, Pp 337-340 (2022)
Background: The aim of the study is to investigate the anticancer potential of tin oxide (SnO2) and different concentrations (2%, 4%, 6%, and 8%) of cerium-doped tin oxide nanoparticles (Ce-SnO2 NPs) using Ipomoea carnea flower extract. The synthesiz
Externí odkaz:
https://doaj.org/article/983fc2719ebe489398f0d355a859fcef
Autor:
Manivannan, Veeramakali Vignesh, Jafari, Yasaman, Eranky, Srikar, Ho, Spencer, Yu, Rose, Watson-Parris, Duncan, Ma, Yian, Bergen, Leon, Berg-Kirkpatrick, Taylor
The use of foundation models in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs.
Externí odkaz:
http://arxiv.org/abs/2410.16701
Recent progress in audio-language modeling, such as automated audio captioning, has benefited from training on synthetic data generated with the aid of large-language models. However, such approaches for environmental sound captioning have primarily
Externí odkaz:
http://arxiv.org/abs/2410.12028
Autor:
Yershova, Anna, Uotila, Elmeri, Mimnaugh, Katherine J., Prencipe, Nicoletta, Manivannan, M., Ojala, Timo, LaValle, Steven M.
This paper introduces a novel interaction method for virtual and augmented reality called look-and-twist, which is directly analogous to point-and-click operations using a mouse and desktop. It is based on head rotation alone and is straightforward t
Externí odkaz:
http://arxiv.org/abs/2410.09820
Autor:
Dana, Asaf, Benson, Christian, Kalairaj, Manivannan Sivaperuman, Hellikson, Kayla, George, Sasha M., Chimene, David C., Gibson, Jared A., Tasmim, Seelay, Kohl, Phillip A., Li, Youli, Abdelrahman, Mustafa K., Patil, Vishal P., Ware, Taylor H.
Interactions between active individuals in animal collectives lead to emergent responses that remain elusive in synthetic soft matter. Here, shape-morphing polymers are used to create bio-inspired transient solids that self-assemble with controlled m
Externí odkaz:
http://arxiv.org/abs/2409.19094
Autor:
Distelzweig, Aron, Kosman, Eitan, Look, Andreas, Janjoš, Faris, Manivannan, Denesh K., Valada, Abhinav
Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has improved prediction accuracy in various fields, its application in traj
Externí odkaz:
http://arxiv.org/abs/2409.10585
Autor:
Surasinghe, Sudam, Manivannan, Swathi Nachiar, Scarpino, Samuel V., Crawford, Lorin, Ogbunugafor, C. Brandon
Mathematical modelling has served a central role in studying how infectious disease transmission manifests at the population level. These models have demonstrated the importance of population-level factors like social network heterogeneity on structu
Externí odkaz:
http://arxiv.org/abs/2409.09096
Publikováno v:
Microorganisms, Vol 11, Iss 1, p 42 (2022)
Microorganisms are exceptional at producing several volatile substances called microbial volatile organic compounds (mVOCs). The mVOCs allow the microorganism to communicate with other organisms via both inter and intracellular signaling pathways. Re
Externí odkaz:
https://doaj.org/article/157ec92f5be74b908bec56511369b097
Autor:
Suri, Siddharth, Counts, Scott, Wang, Leijie, Chen, Chacha, Wan, Mengting, Safavi, Tara, Neville, Jennifer, Shah, Chirag, White, Ryen W., Andersen, Reid, Buscher, Georg, Manivannan, Sathish, Rangan, Nagu, Yang, Longqi
Until recently, search engines were the predominant method for people to access online information. The recent emergence of large language models (LLMs) has given machines new capabilities such as the ability to generate new digital artifacts like te
Externí odkaz:
http://arxiv.org/abs/2404.04268
Uncertainty Quantification aims to determine when the prediction from a Machine Learning model is likely to be wrong. Computer Vision research has explored methods for determining epistemic uncertainty (also known as model uncertainty), which should
Externí odkaz:
http://arxiv.org/abs/2403.09228