Zobrazeno 1 - 10
of 44 475
pro vyhledávání: '"P. Arora"'
Autor:
P. Arora, L. Ceferino
Publikováno v:
Natural Hazards and Earth System Sciences, Vol 23, Pp 1665-1683 (2023)
Strong hurricane winds damage power grids and cause cascading power failures. Statistical and machine learning models have been proposed to predict the extent of power disruptions due to hurricanes. Existing outage models use inputs including power s
Externí odkaz:
https://doaj.org/article/b0ed5e120e7e4656a29980749d1cf80d
Autor:
Ferland, Matthew, Rao, Varun Nagaraj, Arora, Arushi, van der Poel, Drew, Luu, Michael, Huynh, Randy, Reiber, Freddy, Ossman, Sandra, Poulsen, Seth, Shindler, Michael
Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic programming
Externí odkaz:
http://arxiv.org/abs/2411.14655
Linearity testing has been a focal problem in property testing of functions. We combine different known techniques and observations about linearity testing in order to resolve two recent versions of this task. First, we focus on the online manipulati
Externí odkaz:
http://arxiv.org/abs/2411.14431
Spoken term detection (STD) is often hindered by reliance on frame-level features and the computationally intensive DTW-based template matching, limiting its practicality. To address these challenges, we propose a novel approach that encodes speech i
Externí odkaz:
http://arxiv.org/abs/2411.14100
Autor:
Khindkar, Vaishnavi, Balasubramanian, Vineeth, Arora, Chetan, Subramanian, Anbumani, Jawahar, C. V.
With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting pedestrian intent is one such challenging task, where prior work predicts
Externí odkaz:
http://arxiv.org/abs/2411.13302
Machine unlearning algorithms are increasingly important as legal concerns arise around the provenance of training data, but verifying the success of unlearning is often difficult. Provable guarantees for unlearning are often limited to supervised le
Externí odkaz:
http://arxiv.org/abs/2411.12600
Autor:
Arora, Sunil, Hastings, John
This paper presents a multi-cloud networking architecture built on zero trust principles and micro-segmentation to provide secure connectivity with authentication, authorization, and encryption in transit. The proposed design includes the multi-cloud
Externí odkaz:
http://arxiv.org/abs/2411.12162
This manuscript presents the Quantum Finite Element Method (Q-FEM) developed for use in noisy intermediate-scale quantum (NISQ) computers, and employs the variational quantum linear solver (VQLS) algorithm. The proposed method leverages the classical
Externí odkaz:
http://arxiv.org/abs/2411.09038
We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLMS
Externí odkaz:
http://arxiv.org/abs/2411.08862
Publikováno v:
Foundations and Trends in Signal Processing 2024: Vol. 18: No. 4, pp 310-389
This monograph presents a theoretical background and a broad introduction to the Min-Max Framework for Majorization-Minimization (MM4MM), an algorithmic methodology for solving minimization problems by formulating them as min-max problems and then em
Externí odkaz:
http://arxiv.org/abs/2411.07561