FCPN: Pruning redundant part-whole relations for more streamlined pattern parsing.

Cropping-and-segmenting pattern parsers often combine diverse inner correlations into a single metric/scheme, resulting in over-generalizations and redundant representations. It is proposed to streamline pattern parsing by using presenting a redundant association elimination network (RAEN) with capsule attention twisters (CATs) and capsule-attention routing agreement (CARA). CATs trim delicate relationships between parts and wholes that are weak and interchangeable.

FCPN: Pruning redundant part-whole relations for more streamlined pattern parsing.

Most cropping-and-segmenting pattern parsers typically establish a single metric/scheme to reason diverse inner correlations, resulting in over-general and redundant representations. To make pattern parsing more streamlined and efficient, a fragile correlation pruner network (FCPN) with correlation-steered attention shifters (CSASs) and graph attention expectation-maximum routing agreement (GAEMRA) is proposed. CSASs prune fragile (weak and substitutable) part-to-whole correlations.

NN-LCS: Neural Network and Linear Coordinate Solver Fusion Method for UWB Localization in Car Keyless Entry System.

Nowadays, ultra-wideband (UWB) technology is becoming a new approach to localize keyfobs in the car keyless entry system (KES), because it provides precise localization and secure communication. However, for vehicles the distance ranging suffers from great errors because of none-line-of-sight (NLOS) which is raised by the car. Regarding the NLOS problem, efforts have been made to mitigate the point-to-point ranging error or to estimate the tag coordinate by neural networks.

TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution.

The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics.

Graph Neural Network for Protein-Protein Interaction Prediction: A Comparative Study.

Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein-protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system.

Learning global dependencies based on hierarchical full connection for brain tumor segmentation.

Background And Objective: Because the appearance, shape and location of brain tumors vary greatly among different patients, brain tumor segmentation (BTS) is extremely challenging. Recently, many studies have used attention mechanisms to solve this problem, which can be roughly divided into two categories: the spatial attention based on convolution (with or without channel attention) and self-attention. Due to the limitation of convolution operations, the spatial attention based on convolution cannot learn global dependencies very well, resulting in poor performance in BTS.

Effective band selection of hyperspectral image by an attention mechanism-based convolutional network.

The selection of effective and representative spectral bands is extremely important in eliminating redundant information and reducing the computational burden for the potential real-time applications of hyperspectral imaging. However, current band selection methods act as a separate procedure before model training and are implemented merely based on extracted average spectra without incorporating spatial information. In this paper, an end-to-end trainable network framework that combines band selection, feature extraction, and model training was proposed based on a 3D CNN (convolutional neural network, CNN) with the attention mechanism embedded in its first layer.

Cross-Linker-Controlled Ostwald Ripening in Emulsion Polymerization of Hollow Copolymer Nanoparticles.

We propose a synthesis method for hollow copolymer nanoparticles, in which the size is controllable by the wettability of the materials designed by relative energy difference (RED). We investigated the influence of cross-linkers in RED and the hollow polymer nanoparticle synthesis. The size of the nanoparticles was characterized by scanning electron microscopy and transmission electron microscopy images.

Size-Controllable, Single-Step, and Scalable Synthesis of Hollow Polymer Nanoparticles.

Hollow polymer nanoparticles are of great importance in various industrial fields such as drug delivery vehicles in pharmaceutics, high thermal insulation materials for heat flow blocking and energy savings, and materials with unique optical properties. While the fabrication methods for hollow polymer nanoparticles have been studied and developed by numerous researchers, most synthesis methods require a rather complicated process, including a thorough core-washing step to formulate pores inside the particles. Single-step synthesis methods were developed to overcome this practical issue by utilizing the sacrificial solvent filling the pores temporarily and having it naturally evaporate without further process; however, such processes could not produce sub-200 nm diameter particles, which limit the application for high surface area applications.