Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects. Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views. Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods.

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Retinal vessel segmentation based on deep learning requires a lot of manual labeled data. That's time-consuming, laborious and professional. In this paper, we propose a data-efficient semi-supervised learning framework, which effectively combines the existing deep learning network with generative adversarial networks (GANs) and self-training ideas. In view of the difficulty of tuning hyper-parameters of semi-supervised learning, we propose a method for hyper-parameters selection based on particle swarm optimization (PSO) algorithm. This work is the first demonstration that combines intelligent optimization with semi-supervised learning for achieving the best performance. Under the collaboration of adversarial learning, self-training and PSO, we obtain the performance of retinal vessel segmentation approximate to or even better than representative supervised learning using only one tenth of the labeled data from DRIVE.

Person re-identification (re-ID) aims to match pedestrian pairs captured from different cameras. Recently, various attribute based models have been proposed to combine the pedestrian attribute as an auxiliary semantic information in order to learn a more discriminative pedestrian representation. However, these methods usually directly concatenate the visual branch and attribute branch embeddings as the final pedestrian representation, which ignores the semantic relation between the pedestrian revealed by attribute similarity. To capture and explore such semantic relation, we propose an unified pedestrian representation framework, called Visual Attribute Graph Embedding Network (VAGEN), to simultaneously learn attribute and visual representation. We unify the visual embedding and attribute similarity into a Visual Attribute Graph (VAG), where pedestrian is considered as a node and attribute similarity as an edge. Then, we learn graph node embedding to generate pedestrian representation through Graph Neural Network. Except for this unified representation for visual and attribute embeddings, VAGEN also conducts implicitly hard example mining for visual similar false positive results, which has not been explored yet among existing attribute based methods. We conduct extensive empirical studies on several person re-ID datasets to evaluate our proposed algorithm from different aspects. The results show that our proposed method outperforms state-of-the-art techniques with considerable margins.

This work presents a novel general compact model for 7-nm technology node devices like FinFETs as an extension of previous conventional compact model that based on some less accurate elements including one-dimensional Poisson equation for three-dimensional devices and analytical equations for short channel effects, quantum effects and other physical effects. The general compact model exhibits efficient extraction, high accuracy, strong scaling capability and excellent transfer capability. As a demo application, two key design knobs of FinFET and their multiple impacts on RC control electrostatic discharge (ESD) power clamp circuit are systematically evaluated with implementation of the newly proposed general compact model, accounting for device design, circuit performance optimization and variation control. The performance of ESD power clamp can be improved extremely. This framework is also suitable for path-finding researches on 5-nm node gate-all-around devices, like nanowire (NW) FETs, nanosheet (NSH) FETs and beyond.

Recently, person re-identification (re-ID) with weakly labeled or unlabeled data has drawn much attention in open-set and cross-domain re-ID systems especially for the attribute auxiliary weakly supervised person re-ID. Although state-of-the-art clustering-based methods have achieved good performance, the pseudo labels generated through clustering are often low-quality and noisy. To address this problem, we propose a graph neural network based Attribute Auxiliary structured Grouping (A 2 G) to improve the confidence of the pseudo labels. Different from the existing clustering-based approaches that only utilize the similarity in feature space, we learn the feature representation from the similarities in both attribute space and feature space by graph learning on the pedestrian attribute graph. Specifically, we first utilize the pair-wise attribute similarity to represent the relation between two pedestrians to construct a pedestrian attribute graph. Furthermore, we aggregate the features from their neighborhood on a pedestrian attribute graph by the graph neural network, which would make the attribute similar pairs closer and simultaneously take the dissimilar pairs further in the feature space. Finally, to avoid the over-confidence of the hard pseudo labels, we regularize the learning of the embedding model with the smoothed pseudo label (SPL) in the optimization of the feature embedding network. We conduct extensive experiments on several person re-ID datasets to validate the efficacy of our proposed method. The results demonstrate that our technique is effective and promising for person re-ID tasks.INDEX TERMS Unsupervised person re-identification, attribute-auxiliary structured grouping, graph neural network.

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