Results 51 to 60 of about 622,624 (287)
Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a
Xiaoying Song, Li Chai
doaj +1 more source
Multi-Kernel Regression for Graph Signal Processing [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions.
Venkitaraman, Arun +2 more
openaire +2 more sources
A Connectedness Constraint for Learning Sparse Graphs
, 2017 Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing.
friedman +8 more
core +1 more source
Time after time – circadian clocks through the lens of oscillator theory
FEBS Letters, EarlyView.Oscillator theory bridges physics and circadian biology. Damped oscillators require external drivers, while limit cycles emerge from delayed feedback and nonlinearities. Coupling enables tissue‐level coherence, and entrainment aligns internal clocks with environmental cues.
Marta del Olmo +2 more
wiley +1 more source
FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley +1 more source
From Time–Frequency to Vertex–Frequency and Back
Mathematics, 2021 The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided.
Ljubiša Stanković +5 more
doaj +1 more source
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
, 2017 The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices.
Banelli, Paolo +4 more
core +1 more source
FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
pygrank: A Python package for graph node ranking
SoftwareX, 2022 We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithmic components, such as graph filters, post-processors, measures, benchmarks, and ...
Emmanouil Krasanakis +3 more
doaj +1 more source
Signal processing on kernel-based random graphs [PDF]
2017 25th European Signal Processing Conference (EUSIPCO), 2017 Publication in the conference proceedings of EUSIPCO, Kos island, Greece ...
Leus, Geert, Morency, Matthew
openaire +1 more source