Zobrazeno 1 - 10
of 33 437
pro vyhledávání: '"A. Selvaraj"'
Conventional route planning services typically offer the same routes to all drivers, focusing primarily on a few standardized factors such as travel distance or time, overlooking individual driver preferences. With the inception of autonomous vehicle
Externí odkaz:
http://arxiv.org/abs/2407.17980
The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that
Externí odkaz:
http://arxiv.org/abs/2407.18923
Autor:
Selvaraj, Dinesh Cyril, Vitale, Christian, Panayiotou, Tania, Kolios, Panayiotis, Chiasserini, Carla Fabiana, Ellinas, Georgios
In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to
Externí odkaz:
http://arxiv.org/abs/2407.02546
With the increasing prevalence of autonomous vehicles, it is essential for computer vision algorithms to accurately assess road features in real-time. This study explores the LaneSegNet architecture, a new approach to lane topology prediction which i
Externí odkaz:
http://arxiv.org/abs/2406.15946
Autor:
Wei, Changshuai, Zelditch, Benjamin, Chen, Joyce, Ribeiro, Andre Assuncao Silva T, Tay, Jingyi Kenneth, Elizondo, Borja Ocejo, Selvaraj, Keerthi, Gupta, Aman, De Almeida, Licurgo Benemann
Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limited marketing budgets. In this paper, we propose a general framework fo
Externí odkaz:
http://arxiv.org/abs/2405.10490
Autor:
Guo, Xiaobao, Yu, Zitong, Selvaraj, Nithish Muthuchamy, Shen, Bingquan, Kong, Adams Wai-Kin, Kot, Alex C.
Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior. Conventional contact-based techniques, like polygraph devices, rely on physiological signals to determine the authe
Externí odkaz:
http://arxiv.org/abs/2405.06995
Concept Bottleneck Models (CBM) map images to human-interpretable concepts before making class predictions. Recent approaches automate CBM construction by prompting Large Language Models (LLMs) to generate text concepts and employing Vision Language
Externí odkaz:
http://arxiv.org/abs/2405.01825
Autor:
Selvaraj, Dinesh Cyril, Vitale, Christian, Panayiotou, Tania, Kolios, Panayiotis, Chiasserini, Carla Fabiana, Ellinas, Georgios
Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively col
Externí odkaz:
http://arxiv.org/abs/2404.14523
Exploring Li-ion Transport Properties of Li$_3$TiCl$_6$: A Machine Learning Molecular Dynamics Study
Publikováno v:
Journal of the Electrochemical Society 171 (2024) 050544
We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li$_3$TiCl$_6$ cathode at six different temperatures, ranging from 25$^\mathrm{o
Externí odkaz:
http://arxiv.org/abs/2403.01077
Autor:
Liu, Zirui, Song, Qingquan, Xiao, Qiang Charles, Selvaraj, Sathiya Keerthi, Mazumder, Rahul, Gupta, Aman, Hu, Xia
The large number of parameters in Pretrained Language Models enhance their performance, but also make them resource-intensive, making it challenging to deploy them on commodity hardware like a single GPU. Due to the memory and power limitations of th
Externí odkaz:
http://arxiv.org/abs/2401.04044