Computational prediction, characterization, and methodology development for two-dimensional nanostructures: phosphorene and phosphide binary compounds.

In this thesis, a comprehensive computational simulation was carried out for predicting, characterizing, and applications of two-dimensional (2D) materials. The newly discovered GaP and InP layers were selected as an example to demonstrate how to explore new 2D materials using computational simulations. The performance of phosphorene as the anode material of Lithium-ion battery was discussed as the example of the application of 2D material. Furthermore, the semi-empirical Hamiltonian for phosphorous and lithium elements have been developed for our future work on the application of phosphorus and lithium-based systems. The novel 2D materials of GaP and InP binary compounds were found to possess unique anisotropic structural, electronic, and mechanical properties. Their crystalline structures show orthorhombic lattices symmetry and high buckling of 2.14 Å-2.46 Å. They have strong directional dependence of Young’s moduli and effective nonlinear elastic moduli. They have wide fundamental bandgaps which were also found to be tunable under the strain. In particular, a direct-indirect bandgap transition was found under certain strains, reflecting their promising applications for the strain-induced bandgap engineering in nanoelectronics and photovoltaics. To completely understand the performance of phosphorene as the anode material of Li-ion battery, the lithium adsorption energy landscape, diffusion mobility, intercalation, and capacity of phosphorene were studied. The calculations show the anisotropic diffusivity and the ultrafast diffusion mobility of lithium along the zigzag direction. Phosphorene could accommodate up to the ratio of one Li per P atom (i.e., Li16P16). In particular, there was no lithium clustering even at the high Li concentration. The structure of phosphorene, when it was fractured at high concentration, is reversible during the lithium intercalation. The theoretical value of the lithium capacity for a monolayer phosphorene is predicted to be above 433 mAh/g. The SCED-LCAO Hamiltonians for phosphorus and lithium were developed in this thesis. The optimized parameters were obtained by fitting the structural and electronic properties of small clusters and bulk phases, which were calculated by the ab-initial methods. The robustness tests of phosphorus parameters were executed by relaxing the back phosphorus, phosphorene, and blue phosphorene with SCED-LCAO-MD code. The energy order and band gap of black phosphorus, phosphorene and blue phosphorene are all consistent with the DFT calculations and experimental measurements. The robustness tests of Li parameters were executed for the BCC bulk of Li and its stability was proved

Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (Re-ID) across multiple datasets is a challenging yet important task due to the possibly large distinctions between different datasets and the lack of training samples in practical applications. This work proposes a novel unsupervised domain adaption framework which transfers discriminative representations from the labeled source domain (dataset) to the unlabeled target domain (dataset). We propose to formulate the domain adaption task as an one-class classification problem with a novel domain similarity loss. Given the feature map of any image from a backbone network, a novel domain adaptive attention model (DAAM) first automatically learns to separate the feature map of an image to a domain-shared feature (DSH) map and a domain-specific feature (DSP) map simultaneously. Specially, the residual attention mechanism is designed to model DSP feature map for avoiding negative transfer. Then, a DSH branch and a DSP branch are introduced to learn DSH and DSP feature maps respectively. To reduce domain divergence caused by that the source and target datasets are collected from different environments, we force to project the DSH feature maps from different domains to a new nominal domain, and a novel domain similarity loss is proposed based on one-class classification. In addition, a novel unsupervised person Re-ID loss is proposed to take full use of unlabeled target data. Extensive experiments on the Market-1501 and DukeMTMC-reID benchmarks demonstrate state-of-the-art performance of the proposed method. Code will be released to facilitate further studies on the cross-domain person re-identification task

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This work reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlook challenges between current academic research and real-world applications. The project page is https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-DrivingComment: 18 pages, 6 figures. Accepted by IEEE Intelligent Transportation Systems Magazine. URL: https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Drivin