Zobrazeno 1 - 10
of 194
pro vyhledávání: '"HALAPPANAVAR, MAHANTESH"'
The emergence of the COVID-19 pandemic resulted in a significant rise in the spread of misinformation on online platforms such as Twitter. Oftentimes this growth is blamed on the idea of the "echo chamber." However, the behavior said to characterize
Externí odkaz:
http://arxiv.org/abs/2412.09578
Owing to the ongoing COVID-19 pandemic and other recent global epidemics, epidemic simulation frameworks are gaining rapid significance. In this work, we present a workflow that will allow researchers to simulate the spread of an infectious disease u
Externí odkaz:
http://arxiv.org/abs/2411.05243
Autor:
Ferracina, Fabiana, Beeler, Payton, Halappanavar, Mahantesh, Krishnamoorthy, Bala, Minutoli, Marco, Fierce, Laura
Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physioc
Externí odkaz:
http://arxiv.org/abs/2409.13861
Autor:
Hussain, Md Taufique, Halappanavar, Mahantesh, Chatterjee, Samrat, Radicchi, Filippo, Fortunato, Santo, Azad, Ariful
We develop an algorithm that finds the consensus of many different clustering solutions of a graph. We formulate the problem as a median set partitioning problem and propose a greedy optimization technique. Unlike other approaches that find median se
Externí odkaz:
http://arxiv.org/abs/2408.11331
Autor:
Meyur, Rounak, Phan, Hung, Wagle, Sridevi, Strube, Jan, Halappanavar, Mahantesh, Horawalavithana, Sameera, Acharya, Anurag, Munikoti, Sai
In the rapidly evolving landscape of Natural Language Processing (NLP) and text generation, the emergence of Retrieval Augmented Generation (RAG) presents a promising avenue for improving the quality and reliability of generated text by leveraging in
Externí odkaz:
http://arxiv.org/abs/2408.11800
Autor:
Barik, Reet, Cappa, Wade, Ferdous, S M, Minutoli, Marco, Halappanavar, Mahantesh, Kalyanaraman, Ananth
Influence maximization--the problem of identifying a subset of k influential seeds (vertices) in a network--is a classical problem in network science with numerous applications. The problem is NP-hard, but there exist efficient polynomial time approx
Externí odkaz:
http://arxiv.org/abs/2408.10982
Autor:
Phan, Hung, Acharya, Anurag, Meyur, Rounak, Chaturvedi, Sarthak, Sharma, Shivam, Parker, Mike, Nally, Dan, Jannesari, Ali, Pazdernik, Karl, Halappanavar, Mahantesh, Munikoti, Sai, Horawalavithana, Sameera
As LLMs become increasingly ubiquitous, researchers have tried various techniques to augment the knowledge provided to these models. Long context and retrieval-augmented generation (RAG) are two such methods that have recently gained popularity. In t
Externí odkaz:
http://arxiv.org/abs/2407.07321
Autor:
Das, Siddhartha Shankar, Ferdous, S M, Halappanavar, Mahantesh M, Serra, Edoardo, Pothen, Alex
We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophi
Externí odkaz:
http://arxiv.org/abs/2405.15218
Autor:
Ferracina, Fabiana, Krishnamoorthy, Bala, Halappanavar, Mahantesh, Hu, Shengwei, Sathuvalli, Vidyasagar
We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently iden
Externí odkaz:
http://arxiv.org/abs/2404.03701
We study the problem of comparing a pair of geometric networks that may not be similarly defined, i.e., when they do not have one-to-one correspondences between their nodes and edges. Our motivating application is to compare power distribution networ
Externí odkaz:
http://arxiv.org/abs/2403.12334