4 citations · 9 across the 6 of their papers we have counts for
6 papers
Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models
Nina Cao, Pavan Ravindra, Shubha R. Kharel +6
In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for x-ray absorption spectroscopy (XAS) analysis. Compare…
OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
Shubha R. Kharel, Fanchen Meng, Xiaohui Qu +2
X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents sig…
Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence
Shubha R. Kharel, Prashansa Mukim, Piotr Maj +4
Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt po…
The sequence of prime gaps is graphic
Péter L. Erdős, Gergely Harcos, Shubha R. Kharel +3
Let us call a simple graph on vertices a prime gap graph if its vertex degrees are and the first prime gaps. We show that such a graph exists for every large $n…
New results on graph matching from degree preserving growth
Péter L. Erdős, Shubha R. Kharel, Tamás Róbert Mezei +1
The recently introduced \emph{Degree Preserving Growth} model (Nature Physics, \DOI{10.1038/s41567-021-01417-7}) uses matchings to insert new vertices of prescribed degrees into th…
Degree-preserving graph dynamics -- a versatile process to construct random networks
Péter L. Erdős, Shubha R. Kharel, Tamás R. Mezei +1
Real-world networks evolve over time via additions or removals of vertices and edges. In current network evolution models, vertex degree varies or grows arbitrarily. A recently int…