27 papers
How Architecture and Training Affect TPC Representations Across Experiments
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +11
Deep-learning efforts have increasingly shifted toward foundation model approaches. In experimental physics, this allows models and learned representations to be reused beyond the…
ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
Yixuan Jia, Siyi Chen, Yida Pan +9
Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and clim…
Trajectory Constraints for Imaging Inverse Problems
Chaoyan Huang, Haijie Yuan, Saiprasad Ravishankar
Diffusion-based and iterative methods have become effective tools for solving imaging inverse problems. Their reconstruction process naturally forms a trajectory of intermediate es…
Fractional-gradient Sparsity with Autoencoding Sequential Deep Image Prior for 3D CT Reconstruction
Haijie Yuan, Chaoyan Huang, Srijita Bandopadhyay +2
3D volumetric reconstruction from incomplete or noisy measurements is a fundamental problem in medical imaging and computational tomography. Deep image prior (DIP)-based methods ha…
L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark
Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri +3
We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to foster research into the neural ef…
Dynamic MRI Reconstruction Via Dual Deep Priors and Low-Rank Plus Sparse Modeling
Yongliang Sun, Siddhant Gautam, Chaoyan Huang +3
Dynamic MRI reconstruction from undersampled measurements is a challenging inverse problem that requires preserving both spatial reconstruction quality and temporal consistency acr…