Zobrazeno 1 - 10
of 36 233
pro vyhledávání: '"P Ehsan"'
In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated
Externí odkaz:
http://arxiv.org/abs/2411.14592
Autor:
Rowland, Melanie J., Morley, Caroline V., Miles, Brittany E., Suárez, Genaro, Faherty, Jacqueline K., Skemer, Andrew J., Beiler, Samuel A., Line, Michael R., Bjoraker, Gordon L., Fortney, Jonathan J., Vos, Johanna M., Merchan, Sherelyn Alejandro, Marley, Mark, Burningham, Ben, Freedman, Richard, Gharib-Nezhad, Ehsan, Batalha, Natasha, Lupu, Roxana, Visscher, Channon, Schneider, Adam C., Geballe, T. R., Carter, Aarynn, Allers, Katelyn, Mang, James, Apai, Dániel, Limbach, Mary Anne, Wilson, Mikayla J.
The coldest Y spectral type brown dwarfs are similar in mass and temperature to cool and warm ($\sim$200 -- 400 K) giant exoplanets. We can therefore use their atmospheres as proxies for planetary atmospheres, testing our understanding of physics and
Externí odkaz:
http://arxiv.org/abs/2411.14541
Autor:
Ehsan, Mashaekh Tausif, Zafar, Saifuddin, Sarker, Apurba, Suvro, Sourav Das, Hasan, Mohammad Nasim
Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a graph-bas
Externí odkaz:
http://arxiv.org/abs/2411.13670
Medium and high-entropy alloys (M/HEAs) have garnered significant attention as potential nuclear structural materials due to their excellent stability at high temperatures and resistance to radiation. However, the common use of Co in M/HEAs, which ex
Externí odkaz:
http://arxiv.org/abs/2411.13665
Autor:
Sajedi, Ahmad, Khaki, Samir, Liu, Lucy Z., Amjadian, Ehsan, Lawryshyn, Yuri A., Plataniotis, Konstantinos N.
Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a model trained on the full dataset. Despite recent progress, existing data
Externí odkaz:
http://arxiv.org/abs/2411.12841
Autor:
Gąsieniec, Leszek, Kuszner, Łukasz, Latif, Ehsan, Parasuraman, Ramviyas, Spirakis, Paul, Stachowiak, Grzegorz
In the distributed localization problem (DLP), n anonymous robots (agents) A0, A1, ..., A(n-1) begin at arbitrary positions p0, ..., p(n-1) in S, where S is a Euclidean space. The primary goal in DLP is for agents to reach a consensus on a unified co
Externí odkaz:
http://arxiv.org/abs/2411.08434
Autor:
Nguyen, Minh, Shareghi, Ehsan
Language agents have shown promising adaptability in dynamic environments to perform complex tasks. However, despite the versatile knowledge embedded in large language models, these agents still fall short when it comes to tasks that require planning
Externí odkaz:
http://arxiv.org/abs/2411.08432
Autor:
McKnight, Shaun, Tunukovic, Vedran, Hifi, Amine, Pierce, Gareth, Mohseni, Ehsan, MacLeod, Charles, OHare, Tom
This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By employing this self-supervised method
Externí odkaz:
http://arxiv.org/abs/2411.07835
This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by ex
Externí odkaz:
http://arxiv.org/abs/2411.07407
Autor:
Ibarra-García-Padilla, Eduardo, Lange, Hannah, Melko, Roger G, Scalettar, Richard T, Carrasquilla, Juan, Bohrdt, Annabelle, Khatami, Ehsan
Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubb
Externí odkaz:
http://arxiv.org/abs/2411.07144