Situation-aware technologies enabled by multitarget tracking will lead to new services and
applications in fields such as autonomous driving, indoor localization, robotic networks, and …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …

Compressed sensing aims to undersample certain high-dimensional signals yet accurately
reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible …

Data association, the problem of reasoning over correspondence between targets and
measurements, is a fundamental problem in tracking. This paper presents a graphical model …

Bitcoin and other cryptocurrencies have surged in popularity over the last decade. Although
Bitcoin does not claim to provide anonymity for its users, it enjoys a public perception of …

Multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm
for accurate indoor localization constrained by limited navigation resources. The goal of …

Compressed sensing refers to a growing body of techniques that'undersample'high-
dimensional signals and yet recover them accurately. Such techniques make fewer …

The main problem in multiple object tracking is data association, which has a natural
representation as a graph. This paper reviews two different graph approaches for solving the …

Cytoplasmic flows are widely emerging as key functional players in development. In early
Drosophila embryos, flows drive the spreading of nuclei across the embryo. Here, we …

A set is an unordered collection of unique elements--and yet many machine learning models
that generate sets impose an implicit or explicit ordering. Since model performance can …