University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval

Abstract: Given a query instance from one modality (e.g., image), cross-modal retrieval aims to find semantically similar instances from another modality (e.g., text). To perform cross-modal retrieval, existing approaches typically learn a common semantic space from a labeled source set and directly produce common representations in the learned space for the instances in a target set. These methods commonly require that the instances of both two sets share the same classes. Intuitively, they may not generalize well on a more practical scenario of zero-shot cross-modal retrieval , that is, the instances of the target set contain unseen classes that have inconsistent semantics with the seen classes in the source set. Inspired by zero-shot learning, we propose a novel model called ternary adversarial networks with self-supervision (TANSS) in this paper, to overcome the limitation of the existing methods on this challenging task. Our TANSS approach consists of three paralleled subnetworks: 1) two semantic feature learning subnetworks that capture the intrinsic data structures of different modalities and preserve the modality relationships via semantic features in the common semantic space; 2) a self-supervised semantic subnetwork that leverages the word vectors of both seen and unseen labels as guidance to supervise the semantic feature learning and enhances the knowledge transfer to unseen labels; and 3) we also utilize the adversarial learning scheme in our TANSS to maximize the consistency and correlation of the semantic features between different modalities. The three subnetworks are integrated in our TANSS to formulate an end-to-end network architecture which enables efficient iterative parameter optimization. Comprehensive experiments on three cross-modal datasets show the effectiveness of our TANSS approach compared with the state-of-the-art methods for zero-shot cross-modal retrieval.

Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation

Abstract: Image segmentation refers to the process to divide an image into nonoverlapping meaningful regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. However, while many segmentation algorithms exist, yet there are only a few sparse and outdated summarizations available, an overview of the recent achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in this field. Covering 180 publications, we give an overview of broad areas of segmentation topics including not only the classic bottom-up approaches, but also the recent development in superpixel, interactive methods, object proposals, semantic image parsing and image cosegmentation. In addition, we also review the existing influential datasets and evaluation metrics. Finally, we suggest some design flavors and research directions for future research in image segmentation.

Switching of the Microglial Activation Phenotype Is a Possible Treatment for Depression Disorder.

Abstract: Major depressive disorder (MDD) is a common emotional cognitive disorder that seriously affects people's physical and mental health and their quality of life Due to its clinical and etiological heterogeneity, the molecular mechanisms underpinning MDD are complex and they are not fully understood In addition, the effects of traditional drug therapy are not ideal However, postmortem and animal studies have shown that overactivated microglia can inhibit neurogenesis in the hippocampus and induce depressive-like behaviors Nonetheless, the molecular mechanisms by which microglia regulate nerve regeneration and determine depressive-like behaviors remain unclear As the immune cells of the central nervous system (CNS), microglia could influence neurogenesis through the M1 and M2 subtypes, and these may promote depressive-like behaviors Microglia may be divided into four main states or phenotypes Under stress, microglial cells are induced into the M1 type, releasing inflammatory factors and causing neuroinflammatory responses After the inflammation fades away, microglia shift into the alternative activated M2 phenotypes that play a role in neuroprotection These activated M2 subtypes consist of M2a, M2b and M2c and their functions are different in the CNS In this article, we mainly introduce the relationship between microglia and MDD Importantly, this article elucidates a plausible mechanism by which microglia regulate inflammation and neurogenesis in ameliorating MDD This could provide a reliable basis for the treatment of MDD in the future

A Differential-Private Framework for Urban Traffic Flows Estimation via Taxi Companies

Abstract: Due to the prominent development of public transportation systems, the taxi flows could nowadays work as a reasonable reference to the trend of urban population. Being aware of this knowledge will significantly benefit regular individuals, city planners, and the taxi companies themselves. However, to mindlessly publish such contents will severely threaten the private information of taxi companies. Both their own market ratios and the sensitive information of passengers and drivers will be revealed. Consequently, we propose in this paper a novel framework for privacy-preserved traffic sharing among taxi companies, which jointly considers the privacy, profits, and fairness for participants. The framework allows companies to share scales of their taxi flows, and common knowledge will be derived from these statistics. Two algorithms are proposed for the derivation of sharing schemes in different scenarios, depending on whether the common knowledge can be accessed by third parties like individuals and governments. The differential privacy is utilized in both cases to preserve the sensitive information for taxi companies. Finally, both algorithms are validated on real-world data traces under multiple market distributions.