Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning

Abstract: Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD2L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD2L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD2L (MVSLD2L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.

Perovskite QLED with an external quantum efficiency of over 21% by modulating electronic transport

Abstract: Perovskite quantum-dot-based light-emitting diodes (QLEDs) are highly promising for future solid-state lightings and high-definition displays due to their excellent color purity. However, their device performance is easily affected by charge accumulation induced luminescence quenching due to imbalanced charge injection in the devices. Here we report green perovskite QLEDs with simultaneously improved efficiency and operational lifetime through balancing the charge injection with the employment of a bilayered electron transport structure. The charge-balanced QLEDs exhibit a color-saturated green emission with a full-width at half-maximum (FWHM) of 18 nm and a peak at 520 nm, a low turn-on voltage of 2.0 V and a champion external quantum efficiency (EQE) of 21.63%, representing one of the most efficient perovskite QLEDs so far. In addition, the devices with modulated charge balance demonstrate a nearly 20-fold improvement in the operational lifetime compared to the control device. Our results demonstrate the great potential of further improving the device performance of perovskite QLEDs toward practical applications in lightings and displays via rational device engineering.

Pricing and Resource Allocation via Game Theory for a Small-Cell Video Caching System

Abstract: Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

Survey on Multi-Output Learning

Abstract: The aim of multi-output learning is to simultaneously predict multiple outputs given an input. It is an important learning problem for decision-making since making decisions in the real world often involves multiple complex factors and criteria. In recent times, an increasing number of research studies have focused on ways to predict multiple outputs at once. Such efforts have transpired in different forms according to the particular multi-output learning problem under study. Classic cases of multi-output learning include multi-label learning, multi-dimensional learning, multi-target regression, and others. From our survey of the topic, we were struck by a lack in studies that generalize the different forms of multi-output learning into a common framework. This article fills that gap with a comprehensive review and analysis of the multi-output learning paradigm. In particular, we characterize the four Vs of multi-output learning, i.e., volume, velocity, variety, and veracity, and the ways in which the four Vs both benefit and bring challenges to multi-output learning by taking inspiration from big data. We analyze the life cycle of output labeling, present the main mathematical definitions of multi-output learning, and examine the field’s key challenges and corresponding solutions as found in the literature. Several model evaluation metrics and popular data repositories are also discussed. Last but not least, we highlight some emerging challenges with multi-output learning from the perspective of the four Vs as potential research directions worthy of further studies.

Observation of Various and Spontaneous Magnetic Skyrmionic Bubbles at Room-Temperature in a Frustrated Kagome Magnet with Uniaxial Magnetic Anisotropy

Abstract: Various and spontaneous magnetic skyrmionic bubbles are experimentally observed for the first time, at room temperature in a frustrated kagome magnet Fe3Sn2 with unixial magnetic anisotropy. The magnetization dynamics were investigated using in-situ Lorentz transmission electron microscopy, revealing that the transformation between different magnetic bubbles and domains are via the motion of Bloch lines driven by applied external magnetic field. The results demonstrate that Fe3Sn2 facilitates a unique magnetic control of topological spin textures at room temperature, making it a promising candidate for further skyrmion-based spintronic devices.