Zobrazeno 1 - 10
of 12 346
pro vyhledávání: '"Bajaj P"'
Autor:
Xie, Chengyan, Pascucci, Ilaria, Deng, Dingshan, Bajaj, Naman S., Alexander, Richard, Sellek, Andrew, Kospal, Agnes, Ballabio, Giulia, Gorti, Uma
We present JWST/MIRI observations of T~Cha, a highly variable ($\Delta V \sim$3-5\,mag) accreting Sun-like star surrounded by a disk with a large ($\sim 15$\,au) dust gap. We find that the JWST mid-infrared spectrum is signiticantly different from th
Externí odkaz:
http://arxiv.org/abs/2410.00136
Autor:
Xie, Quanting, Min, So Yeon, Zhang, Tianyi, Xu, Kedi, Bajaj, Aarav, Salakhutdinov, Ruslan, Johnson-Roberson, Matthew, Bisk, Yonatan
There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhouse of large-scale non-parametric know
Externí odkaz:
http://arxiv.org/abs/2409.18313
Autor:
Bajaj, Utkarsh
In this paper, we first study hypergraph rewriting in categorical terms in an attempt to define the notion of events and develop foundations of causality in graph rewriting. We introduce novel concepts within the framework of double-pushout rewriting
Externí odkaz:
http://arxiv.org/abs/2409.01006
Autor:
Bajaj, Bhavuk Sikka
This study addresses the use of Reed-Solomon error correction codes in QR codes to enhance resilience against failures. To fully grasp this approach, a basic cryptographic context is provided, necessary for understanding Reed-Solomon codes. The study
Externí odkaz:
http://arxiv.org/abs/2407.17364
Given the ubiquity of charts as a data analysis, visualization, and decision-making tool across industries and sciences, there has been a growing interest in developing pre-trained foundation models as well as general purpose instruction-tuned models
Externí odkaz:
http://arxiv.org/abs/2407.04172
This study investigates the nonlinear normal modes (NNMs) of a system comprising of two coupled Duffing oscillators, with one oscillator being grounded and with the coupling being both linear and nonlinear. The study utilizes the eigenfunctions of th
Externí odkaz:
http://arxiv.org/abs/2407.00854
Autor:
Cong, Xiaoyan, Yang, Haitao, Chen, Liyan, Zhang, Kaifeng, Yi, Li, Bajaj, Chandrajit, Huang, Qixing
This paper presents a novel approach 4DRecons that takes a single camera RGB-D sequence of a dynamic subject as input and outputs a complete textured deforming 3D model over time. 4DRecons encodes the output as a 4D neural implicit surface and presen
Externí odkaz:
http://arxiv.org/abs/2406.10167
Autor:
Calzetti, Daniela, Adamo, Angela, Linden, Sean T., Gregg, Benjamin, Krumholz, Mark R., Bajaj, Varun, Bik, Arjan, Cignoni, Michele, Correnti, Matteo, Elmegreen, Bruce, Vieira, Helena Faustino, Gallagher, John S., Grasha, Kathryn, Gutermuth, Robert A., Johnson, Kelsey E., Messa, Matteo, Melinder, Jens, Ostlin, Goran, Pedrini, Alex, Sabbi, Elena, Smith, Linda J., Tosi, Monica
New JWST near-infrared imaging of the nearby galaxy NGC 628 from the Cycle 1 program JWST-FEAST is combined with archival JWST mid-infrared imaging to calibrate the 21 $\mu$m emission as a star formation rate indicator (SFR) at $\sim$120 pc scales. T
Externí odkaz:
http://arxiv.org/abs/2406.01831
Autor:
Pedrini, Alex, Adamo, Angela, Calzetti, Daniela, Bik, Arjan, Gregg, Benjamin, Linden, Sean T., Bajaj, Varun, Ryon, Jenna E., Ali, Ahmad A., Bortolini, Giacomo, Correnti, Matteo, Elmegreen, Bruce G., Elmegreen, Debra Meloy, Gallagher, John S., Grasha, Kathryn, Gutermuth, Robert A., Johnson, Kelsey E., Melinder, Jens, Messa, Matteo, Östlin, Göran, Sabbi, Elena, Smith, Linda J., Tosi, Monica, Vieira, Helena Faustino
We investigate the emergence phase of young star clusters in the nearby spiral galaxy NGC 628. We use JWST NIRCam and MIRI observations to create spatially resolved maps of the Pa$\alpha$-1.87 $\mu$m and Br$\alpha$-4.05 $\mu$m hydrogen recombination
Externí odkaz:
http://arxiv.org/abs/2406.01666
Graph Neural Networks (GNNs) have gained significant attention in recent years due to their ability to learn representations of graph structured data. Two common methods for training GNNs are mini-batch training and full-graph training. Since these t
Externí odkaz:
http://arxiv.org/abs/2406.00552