Zobrazeno 1 - 10
of 7 975
pro vyhledávání: '"Niraj, P."'
Autor:
Bhatta, Gopal, Chaudhary, Suvas C., Dhital, Niraj, Adhikari, Tek P., Mohorian, Maksym, Pánis, Radim, Neupane, Raghav, Maharjan, Yogesh Singh
Blazars, a class of active galactic nuclei (AGN) powered by supermassive black holes, are known for their remarkable variability across multiple timescales and wavelengths. With advancements in both ground- and space-based telescopes, our understandi
Externí odkaz:
http://arxiv.org/abs/2410.01278
Autor:
Nepal, Niraj K., Wang, Lin-Lin
We present a workflow that iteratively combines \textit{ab-initio} calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which ha
Externí odkaz:
http://arxiv.org/abs/2409.18441
Optimizing Deep Learning-based Simultaneous Localization and Mapping (DL-SLAM) algorithms is essential for efficient implementation on resource-constrained embedded platforms, enabling real-time on-board computation in autonomous mobile robots. This
Externí odkaz:
http://arxiv.org/abs/2409.14515
Autor:
Li, Chia-Hao, Jha, Niraj K.
Wearable medical sensors (WMSs) are revolutionizing smart healthcare by enabling continuous, real-time monitoring of user physiological signals, especially in the field of consumer healthcare. The integration of WMSs and modern machine learning (ML)
Externí odkaz:
http://arxiv.org/abs/2409.09549
Accurate and efficient calculation of optical response properties of solid materials is still challenging. We present a meta-generalized gradient approximation (metaGGA) density functional based time-dependent and dielectric function dependent method
Externí odkaz:
http://arxiv.org/abs/2409.04904
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which stochastic Mo
Externí odkaz:
http://arxiv.org/abs/2409.01523
Autor:
Nepal, Niraj K., Slade, Tyler J., Blawat, Joanna M., Eaton, Andrew, Palmstrom, Johanna C., Ueland, Benjamin G., Kaminski, Adam, McQueeney, Robert J., McDonald, Ross D., Canfield, Paul C., Wang, Lin-Lin
Quantum materials with stacking van der Waals (vdW) layers that can host non-trivial band structure topology and magnetism have shown many interesting properties. Here using high-throughput density functional theory calculations, we design and predic
Externí odkaz:
http://arxiv.org/abs/2407.20938
Autor:
Yue, Chang, Jha, Niraj K.
The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with notable disadv
Externí odkaz:
http://arxiv.org/abs/2407.04168
Autor:
Lala, Sayeri, Jha, Niraj K.
Clinical randomized controlled trials (RCTs) collect hundreds of measurements spanning various metric types (e.g., laboratory tests, cognitive/motor assessments, etc.) across 100s-1000s of subjects to evaluate the effect of a treatment, but do so at
Externí odkaz:
http://arxiv.org/abs/2406.16351
Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data to maintain model accuracy. This is particularly important in applicat
Externí odkaz:
http://arxiv.org/abs/2406.12008