Understanding communication and information processing in nervous systems is a central
goal of neuroscience. Over the past two decades, advances in connectomics and network …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Learning from graph-structured data is an important task in machine learning and artificial
intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated …

Abstract Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, state-of-the-art embedding methods …

After two decades of development, cavity quantum electrodynamics with superconducting
circuits has emerged as a rich platform for quantum computation and simulation. Lattices of …

Daily experience suggests that we perceive distances near us linearly. However, the actual
geometry of spatial representation in the brain is unknown. Here we report that neurons in …

Networks are finite metric spaces, with distances defined by the shortest paths between
nodes. However, this is not the only form of network geometry: two others are the geometry …

Named Data Networking (NDN) is one of five projects funded by the US National Science
Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier …

Many systems on our planet shift abruptly and irreversibly from the desired state to an
undesired state when forced across a “tipping point”. Some examples are mass extinctions …

Complex systems are very often organized under the form of networks where nodes and
edges are embedded in space. Transportation and mobility networks, Internet, mobile phone …