In recent years, a new “fine-grained” theory of computational hardness has been developed,
based on “fine-grained reductions” that focus on exact running times for problems …

Inspired by a width invariant defined on permutations by Guillemot and Marx [SODA'14], we
introduce the notion of twin-width on graphs and on matrices. Proper minor-closed classes …

The edit distance (aka the Levenshtein distance) between two strings is defined as the
minimum number of insertions, deletions or substitutions of symbols needed to transform …

We consider several well-studied problems in dynamic algorithms and prove that sufficient
progress on any of them would imply a breakthrough on one of five major open problems in …

Two important similarity measures between sequences are the longest common
subsequence (LCS) and the dynamic time warping distance (DTWD). The computations of …

Over the last decade, deep neural networks have transformed our society, and they are
already widely applied in various machine learning applications. State-of-the-art deep …

Classic similarity measures of strings are longest common subsequence and Levenshtein
distance (ie, The classic edit distance). A classic similarity measure of curves is dynamic …

The Fréchet distance is a well-studied and very popular measure of similarity of two curves.
Many variants and extensions have been studied since Alt and Godau introduced this …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

The radius and diameter are fundamental graph parameters, with several natural definitions
for directed graphs. Each definition is well-motivated in a variety of applications. All versions …