Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …

Recently, deep learning (DL) approaches have become the main research frontier for
biological image reconstruction and enhancement problems thanks to their high …

Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in
determining the structures of rigid macromolecules. However, many imaged protein …

In most existing learning systems, images are typically viewed as 2D pixel arrays. However,
in another paradigm gaining popularity, a 2D image is represented as an implicit neural …

Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in
their native, spatially contextualized cellular environment. Cryo-ET processing software to …

Attention-based models, exemplified by the Transformer, can effectively model long range
dependency, but suffer from the quadratic complexity of self-attention operation, making …

Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights
into the dynamics of proteins and other building blocks of life. The algorithmic challenge of …

Image-based cell phenoty** relies on quantitative measurements as encoded
representations of cells; however, defining suitable representations that capture complex …

Cell and organelle shape are driven by diverse genetic and environmental factors and thus
accurate quantification of cellular morphology is essential to experimental cell biology …

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of
proteins and other macromolecular complexes at near-atomic resolution. In single particle …