Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition

Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods. However, the most disadvantage of PINNs is that one neural network corresponds to one PDE. In practice, we usually need to solve a class of PDEs, not just one. With the explosive growth of deep learning, many useful techniques in general deep learning tasks are also suitable for PINNs. Transfer learning methods may reduce the cost for PINNs in solving a class of PDEs. In this paper, we proposed a transfer learning method of PINNs via keeping singular vectors and optimizing singular values (namely SVD-PINNs). Numerical experiments on high dimensional PDEs (10-d linear parabolic equations and 10-d Allen-Cahn equations) show that SVD-PINNs work for solving a class of PDEs with different but close right-hand-side functions.Comment: Accepted to The 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022

Resonant TMR inversion in LiF/EuS based spin-filter tunnel junctions

Resonant tunneling can lead to inverse tunnel magnetoresistance when impurity levels rather than direct tunneling dominate the transport process. We fabricated hybrid magnetic tunnel junctions of CoFe/LiF/EuS/Ti, with an epitaxial LiF energy barrier joined with a polycrystalline EuS spin-filter bar-rier. Due to the water solubility of LiF, the devices were fully packaged in situ. The devices showed sizeable positive TMR up to 16% at low bias voltages but clearly inverted TMR at higher bias voltages. The TMR inversion depends sensitively on the thickness of LiF, and the tendency of inversion disap-pears when LiF gets thick enough and recovers its intrinsic properties

Research on the Design Methods for Green Renovation of Existing Buildings in Lingnan Region

China’s urbanization has entered a new stage with the promotion of “Carbon Peaking and Carbon Neutrality Goals” and “Urban Renewal Strategy”. Problems such as poor comfort, high energy consumption and unreasonable functions of existing buildings have attracted extensive attention from society. The climate-adapted human environment created by traditional buildings in the Lingnan region offers insights for the green transformation of buildings in this area. This paper summarizes the wisdom from the climate-adaptive construction of traditional buildings in Lingnan region, and proposes a green transformation design scheme that meets the requirements of energy efficiency and comfort, which provides a reference for the green renovation design of existing buildings

Role of fractal dimension in random walks on scale-free networks

Fractal dimension is central to understanding dynamical processes occurring on networks; however, the relation between fractal dimension and random walks on fractal scale-free networks has been rarely addressed, despite the fact that such networks are ubiquitous in real-life world. In this paper, we study the trapping problem on two families of networks. The first is deterministic, often called $(x,y)$-flowers; the other is random, which is a combination of $(1,3)$-flower and $(2,4)$-flower and thus called hybrid networks. The two network families display rich behavior as observed in various real systems, as well as some unique topological properties not shared by other networks. We derive analytically the average trapping time for random walks on both the $(x,y)$-flowers and the hybrid networks with an immobile trap positioned at an initial node, i.e., a hub node with the highest degree in the networks. Based on these analytical formulae, we show how the average trapping time scales with the network size. Comparing the obtained results, we further uncover that fractal dimension plays a decisive role in the behavior of average trapping time on fractal scale-free networks, i.e., the average trapping time decreases with an increasing fractal dimension.Comment: Definitive version published in European Physical Journal

A green and environmentally benign route to synthesizing Z-scheme Bi2S3-TCN photocatalyst for efficient hydrogen production

Designing and developing photocatalysts with excellent performance in order to achieve efficient hydrogen production is an important strategy for addressing future energy and environmental challenges. Traditional single-phase photocatalytic materials either have a large bandgap and low visible light response or experience rapid recombination of the photogenerated carriers with low quantum efficiency, seriously hindering their photocatalytic applications. To solve these issues, an important solution is to construct well-matched heterojunctions with highly efficient charge separation capabilities. To this end, an in situ sulfurization reaction was adopted after the deposition of Bi3+ supramolecular complex on a layered supramolecular precursor of tubular carbon nitride (TCN). X-ray diffraction (XRD) patterns confirmed that the as-prepared sample has a good crystalline structure without any other impurities, while high-resolution transmission electron microscopy (HR-TEM) revealed that the heterojunction possesses a 2D structure with a layer of nano-array on its surface. Combined Fourier-transform infrared (FT-IR) spectra and energy-dispersive X-ray spectroscopy (EDX) revealed the interfacial interactions. Owing to the formation of the Z-scheme heterojunction, the visible light adsorption and the separation efficiency of the photo-generated carriers are both obviously enhanced, leaving the high energy electrons and high oxidative holes to participate in the photocatalytic reactions. As a result, the photocatalytic hydrogen evolution rate of Bi2S3-TCN achieves 65.2 μmol g-1·h-1. This proposed green and environmentally benign route can also be applied to construct other sulfides with 2D TCN, providing some important information for the design and optimization of novel carbon-nitride-based semiconductors