Self-esteem among heinous and non-heinous male delinquent adolescents- A descriptive comparative study from East India

Autor: P Srinivasan, Sreevani Rentala, Pragya Kumar

Publikováno v: Industrial Psychiatry Journal, Vol 32, Iss 2, Pp 273-281 (2023)

Background: Adolescence is the most complex stage of development that causes the greatest difficulties in their life because of frequent physical and emotional issues including deviated character and behavioral disorders. Aim: This descriptive compar

Externí odkaz: https://doaj.org/article/4432f67f48764040a0be935b7fd5815e

Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks

Autor: He, Yuntian, Maneriker, Pranav, Srinivasan, Anutam, Vadlamani, Aditya T., Parthasarathy, Srinivasan

Conformal Prediction is a robust framework that ensures reliable coverage across machine learning tasks. Although recent studies have applied conformal prediction to graph neural networks, they have largely emphasized post-hoc prediction set generati

Externí odkaz: http://arxiv.org/abs/2410.21618

FloAt: Flow Warping of Self-Attention for Clothing Animation Generation

Autor: Mishra, Swasti Shreya, Kulkarni, Kuldeep, Ceylan, Duygu, Srinivasan, Balaji Vasan

We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of c

Externí odkaz: http://arxiv.org/abs/2411.15028

Depth-resolved measurement of solvation entropy, interfacial transport and charge-transfer kinetics of practical lithium-ion batteries

Autor: Chalise, Divya, Lubner, Sean D., Kaur, Sumanjeet, Srinivasan, Venkat, Prasher, Ravi S

Understanding the performance of electrochemical energy storage systems requires probing the electrochemical properties at each layer and interface during cell operation. While traditional onboard and operando methods can measure impedance, voltage,

Externí odkaz: http://arxiv.org/abs/2411.10920

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

Autor: John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language mode

Externí odkaz: http://arxiv.org/abs/2411.10548