Signal-Adapted Tight Frames on Graphs

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Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brai ...

We examine the connection of two graph parameters, the size of a minimum feedback arcs set and the acyclic disconnection. A feedback arc set of a directed graph is a subset of arcs such that after deletion the graph becomes acyclic. The acyclic disconnecti ...

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms ...

This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...

Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. This process is performed by means of local replacements that are extracted by matching sections of the subject graph to library ...

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...

Background: Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve thr ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across var ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despit ...

The European Union's Green Deal aims for a 55% reduction in greenhouse gas emissions by 2030. To reach this goal, a massive integration of Renewable Energy Sources (RES) into the power grid is necessary. As RES become a large part of the electricity genera ...

In ferroelectric switching, an applied electric field switches the system between two polar symmetry-equivalent states. In this work, we use first-principles calculations to explore the polar states of hydrogen-doped samarium nickelate (SNO) at a concentra ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

Signal-Adapted Tight Frames on Graphs

Chattez avec Graph Search

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

The connection of the acyclic disconnection and feedback arc sets - On an open problem of Figueroa et al

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Recovering Static and Time-Varying Communities Using Persistent Edges

In Medio Stat Virtus: Combining Boolean and Pattern Matching

Network-based kinetic models: Emergence of a statistical description of the graph topology

Graph generative deep learning models with an application to circuit topologies

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables

Advanced control strategies to exploit the hydropower potential enhancing ancillary services provision to the power system

'Fraternal-twin' ferroelectricity: competing polar states in hydrogen-doped samarium nickelate from first principles

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

A full characterization of invariant embeddability of unimodular planar graphs

Equivariant Neural Architectures for Representing and Generating Graphs

Discovering Influencers in Opinion Formation Over Social Graphs

Privatized graph federated learning