11 citations
- Texas A&M UniversityUS12 papers
- College Station Medical CenterUS3 papers
- Mitchell InstituteUS2 papers
- Texas A&M University SystemUS2 papers
- University of North TexasUS2 papers
- Aarhus UniversityDK1 paper
- Ames Research CenterUS1 paper
- Australian Mathematical Sciences InstituteAU1 paper
- Autonomous University of QueretaroMX1 paper
- BC Research (Canada)CA1 paper
- Carnegie ObservatoriesUS1 paper
- Center for Engineering and Industrial DevelopmentMX1 paper
15 papers
AI Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages
Miguel Esparza, Bo Li, Junwei Ma +1
Our society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (…
Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system
Mark M. Iskarous, Zan Chaudhry, Fangjie Li +7
Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations o…
MR.RGM: An R Package for Fitting Bayesian Multivariate Bidirectional Mendelian Randomization Networks
Bitan Sarkar, Yang Ni
Motivation: Mendelian randomization (MR) infers causal relationships between exposures and outcomes using genetic variants as instrumental variables. Typically, MR considers only a…
Type Ia Supernova Progenitor Properties and Their Host Galaxies
Sudeshna Chakraborty, Benjamin Sadler, Peter Hoeflich +16
We present an eigenfunction method to analyze 161 visual light curves (LCs) of Type Ia supernovae (SNe Ia) obtained by the Carnegie Supernova Project to characterize their diversit…
Neural Parametric Surfaces for Shape Modeling
Lei Yang, Yongqing Liang, Xin Li +6
The recent surge of utilizing deep neural networks for geometric processing and shape modeling has opened up exciting avenues. However, there is a conspicuous lack of research effo…
Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data
Hieu Le, Jian Tao
Lossy compression has become an important technique to reduce data size in many domains. This type of compression is especially valuable for large-scale scientific data, whose size…