Zobrazeno 1 - 10
of 17 757
pro vyhledávání: '"P. Dwivedi"'
We propose a novel hybrid mode interferometer (HMI) leveraging the interference of hybridized TE-TM modes in a silicon-on-insulator (SOI) waveguide integrated with a GeSe phase change material (PCM) layer. The SOI waveguide's dimensions are optimized
Externí odkaz:
http://arxiv.org/abs/2410.19587
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This
Externí odkaz:
http://arxiv.org/abs/2410.19179
Autor:
Dwivedi, Vinod Kumar
In recent years, solid state magnetic cooling based on magnetocaloric effect (MCE) have drawn attention worldwide as a promising alternative potential candidate to the conventional gas compression-expansion cooling technique. In this chapter, the cur
Externí odkaz:
http://arxiv.org/abs/2410.16777
Let $a_1,\ldots,a_m$ be nonzero integers, $c \in \mathbb Z$ and $r \ge 2$. The Rado number for the equation \[ \sum_{i=1}^m a_ix_i = c \] in $r$ colours is the least positive integer $N$ such that any $r$-colouring of the integers in the interval $[1
Externí odkaz:
http://arxiv.org/abs/2410.16051
Deep neural networks trained on biased data often inadvertently learn unintended inference rules, particularly when labels are strongly correlated with biased features. Existing bias mitigation methods typically involve either a) predefining bias typ
Externí odkaz:
http://arxiv.org/abs/2410.15094
The kernel thinning algorithm of Dwivedi & Mackey (2024) provides a better-than-i.i.d. compression of a generic set of points. By generating high-fidelity coresets of size significantly smaller than the input points, KT is known to speed up unsupervi
Externí odkaz:
http://arxiv.org/abs/2410.13749
Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate distribution for each unit-outcome entry, such as the distribution of a
Externí odkaz:
http://arxiv.org/abs/2410.13381
Autor:
Dwivedi, Vijay Prakash, Schlegel, Viktor, Liu, Andy T., Nguyen, Thanh-Tung, Kashyap, Abhinav Ramesh, Wei, Jeng, Yin, Wei-Hsian, Winkler, Stefan, Tan, Robby T.
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records li
Externí odkaz:
http://arxiv.org/abs/2410.13351
We introduce the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both observed and unobserved matrix entries. This is a generalization
Externí odkaz:
http://arxiv.org/abs/2410.13112
Autor:
Babu, K. Victor Sam Moses, Dwivedi, Divyanshi, Valdes, Marcelo Esteban, Chakraborty, Pratyush, Panigrahi, Prasanta Kumar, Pal, Mayukha
Arcing faults in low voltage (LV) distribution systems associated with arc-flash risk and potentially significant equipment damage are notoriously difficult to detect under some conditions. Especially so when attempting to detect using sensing at the
Externí odkaz:
http://arxiv.org/abs/2410.10151