Binarized Simplicial Convolutional Neural Networks.

Graph Neural Networks have the limitation of processing features solely on graph nodes, neglecting data on high-dimensional structures such as edges and triangles. Simplicial Convolutional Neural Networks (SCNN) represent high-order structures using simplicial complexes to break this limitation but still lack time efficiency. In this paper, a novel neural network architecture named Binarized Simplicial Convolutional Neural Networks (Bi-SCNN) is proposed based on the combination of simplicial convolution with a weighted binary-sign forward propagation strategy.

Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo.

Geodetic observations through high-rate GPS time-series data allow the precise modeling of slow ground deformation at the millimeter level. However, significant attention has been devoted to utilizing these data for various earth science applications, including to determine crustal velocity fields and to detect significant displacement from earthquakes. The relationships inherent in these GPS displacement observations have not been fully explored.

The Relationship Between Obesity and Internet Addiction in University Students: A Cross-Sectional Study.

Purpose: The purpose of this study was to investigate the relationship between obesity and internet addiction in first-year university students (aged 18-25 years).

Design: The study was designed using a cross-sectional analytic model.

Setting: Data collection took place at Dokuz Eylul University, involving a diverse group of first-year students from different faculties.

Non-homogeneous Poisson and renewal processes as spatial models for cancer mutation.

Advances in sequencing technology assisted biologists in revealing signatures of DNA cancer mutation process and in demonstrating the mutagenesis behind. However, most of these signatures proposed by majority of work focus only on the type and frequency of mutations, without considering spatial information which is non-negligible in exploring mechanisms of mutation occurrence, e.g.

Beyond Photometric Consistency: Geometry-Based Occlusion-Aware Unsupervised Light Field Disparity Estimation.

Although learning-based light field disparity estimation has achieved great progress in the most recent years, the performance of unsupervised light field learning is still hindered by occlusions and noises. By analyzing the overall strategy underlying the unsupervised methodology and the light field geometry implied in epipolar plane images (EPIs), we look beyond the photometric consistency assumption, and design an occlusion-aware unsupervised framework to deal with the situations of photometric consistency conflict. Specifically, we present a geometry-based light field occlusion modeling, which predicts a group of visibility masks and occlusion maps, respectively, by forward warping and backward EPI-line tracing.