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. However, it still suffers from some problems such as low accuracy, overfitting, or a large number of parameters. In order to address these problems, we propose a fusion method of a neural network and linear coordinate solver (NN-LCS). We use two FC layers to extract the distance feature and received signal strength (RSS) feature, respectively, and a multi-layer perceptron (MLP) to estimate the distances with the fusion of these two features. We prove that the least square method which supports error loss backpropagation in the neural network is feasible for distance correcting learning. Therefore, our model is end-to-end and directly outputs the localization results. The results show that the proposed method is high-accuracy and with small model size which could be easily deployed on embedded devices with low computing ability.

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. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein-protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.

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. Herein, we have developed an innovative synthesis method that can overcome the particle size limitation by utilizing a sacrificial solvent for pore formation and a recondensation inhibitor. Pseudo-state Ostwald ripening was realized by selecting the sacrificial solvent with less affinity to the copolymer of hollow polymer particles, thus inhibiting the particle growth during polymerization. We have successfully obtained 120 nm diameter hollow PS-PMMA copolymer particles in large quantity via the single-step preparation of emulsion polymerization.

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. They stipulate that only those primary entities (representing components) fulfilling the criteria of inter-part diversity and intra-object cohesiveness can update senior entities (representing the whole/intermediate composites). To further boost effects, GAEMRA is defined to shield the redundant voting signals of conventional routing agreement. With CSASs and GAEMRA, FCPN gradually parses objective semantic patterns by clustering highly associated secondary entities in a bottom-up "part backtracking" manner. Quantitative and ablation experiments surrounding face and human parsing demonstrate the superiority of FCPN over the state-of-the-arts, especially for the definition of fine-grained semantic boundaries.

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. Senior entities can only be updated by primary entities that meet the requirements of inter-part diversity and intra-object cohesiveness. In order to enhance results, CARA is designed to protect against the unnecessary voting signals of traditional routing protocols. Experiments involving facial and human segmentation show that RAEN is better than current remarkable methods, particularly for defining detailed semantic boundaries.

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. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO.

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. A simple improvement idea is to directly substitute it with self-attention, which has an excellent ability to learn global dependencies. Since self-attention is not friendly to GPU memory, this simple substitution will make the new attention mechanism unable to be applied to high-resolution low-level feature maps, which contain considerable geometric information and are also important for improving the performance of attention mechanism in BTS. METHOD: In this paper, we propose a hierarchical fully connected module, named H-FC, to learn global dependencies. H-FC learns local dependencies at different feature map scales through fully connected layers hierarchically, and then combines these local dependencies as approximations of the global dependencies. H-FC requires very little GPU memory and can easily replace spatial attention module based on convolution operation, such as Attention Gate and SAM (in CBAM), to improve the performance of attention mechanisms in BTS. RESULTS: Adequate comparative experiments illustrate that H-FC performs better than Attention Gate and SAM (in CBAM), which lack the ability to learn global dependencies, in BTS, with improvements in most metrics and a larger improvement in Hausdorff Distance. By comparing the amount of calculation and parameters of the model before and after adding H-FC, it is prove that H-FC is light-weight. CONCLUSION: In this paper, we propose a novel H-FC to learn global dependencies. We illustrate the effectiveness of H-FC through experiments on BraTS2020 dataset. We mainly explore the influence of the region size and the number of steps on the performance of H-FC. We also confirm that the global dependencies of low-level feature maps are also important to BTS. We show that H-FC is light-weight through a time and space complexity analysis and the experimental results.

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. The learned band attention vector was adopted as the basis of a band importance indicator to select effective bands. The proposed network was evaluated by two datasets, a regression dataset for predicting the relative chlorophyll content (soil and plant analyzer development, SPAD) of basil leaves and a classification dataset for detecting the drought stress of pepper leaves. A number of calibration models, including SVM, 1D-CNN, 2B-CNN (two-branch CNN), 3D ResNet and the developed network were established for performance comparison. Results showed that the effective bands selected by the proposed attention-based model achieved higher regression R 2 values and classification accuracies not only than the full-spectrum data, but also than the comparative band selection methods, including traditional SPA (successive projections algorithm) and GA (genetic algorithm) methods and the latest 2B-CNN algorithm. In addition, different from the traditional methods, the proposed band selection algorithm can effectively select bands while carrying out model training and can simultaneously take advantage of the original spectral-spatial information. The results confirmed the usefulness of the proposed attention mechanism-based convolutional network for selecting the most effective band combination of hyperspectral images.

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. The diameter size of the hollow copolymer (styrene-co-methyl methacrylate) changes from 400 to 141 nm and the average core-vacancy sizes changes from 330 to 71 nm as increasing the feed ratio of the cross-linker, divinyl benzene, from 0.07 to 0.43. Cross-linkers in polymerization precipitates a polymerization reaction to produce seed copolymer particles quickly. The seed copolymer is a more transferrable medium through the surfactants across emulsion droplets and inhibits emulsion growth by unstable concentration variations of seed copolymers in emulsions. Therefore, Ostwald ripening was reduced by a higher feeding ratio of the cross-linker in the copolymer, which tends to produce smaller sized hollow nanoparticles.