Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

The emergence of the information age in the last few decades brought with it an explosion of
biomedical data. But with great power comes great responsibility: there is now a pressing …

We address the problem of crowd localization, ie, the prediction of dots corresponding to
people in a crowded scene. Due to various challenges, a localization method is prone to …

Highly resolved spatial data of complex systems encode rich and nonlinear information.
Quantification of heterogeneous and noisy data—often with outliers, artifacts, and …

In the last decade, there has been increasing interest in topological data analysis, a new
methodology for using geometric structures in data for inference and learning. A central …

In digital pathology, both detection and classification of cells are important for automatic
diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells …

Pathology images contain rich information of cell appearance, microenvironment, and
topology features for cancer analysis and diagnosis. Among such features, topology …

In digital pathology, the spatial context of cells is important for cell classification, cancer
diagnosis and prognosis. To model such complex cell context, however, is challenging …

In recent years, algebraic topology and its modern development, the theory of persistent
homology, has shown great potential in graph representation learning. In this paper, based …

This research finds that when a single gain has strong associations with multiple costs,
consumers often mentally deduct that gain from perceived costs multiple times. For example …