PIXOR: Real-time 3D Object Detection from Point Clouds

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.Comment: Update of CVPR2018 paper: correct timing, fix typos, add acknowledgemen

Representation Learning for Scale-free Networks

Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored. Scale-free property depicts the fact that vertex degrees follow a heavy-tailed distribution (i.e., only a few vertexes have high degrees) and is a critical property of real-world networks, such as social networks. In this paper, we study the problem of learning representations for scale-free networks. We first theoretically analyze the difficulty of embedding and reconstructing a scale-free network in the Euclidean space, by converting our problem to the sphere packing problem. Then, we propose the "degree penalty" principle for designing scale-free property preserving network embedding algorithm: punishing the proximity between high-degree vertexes. We introduce two implementations of our principle by utilizing the spectral techniques and a skip-gram model respectively. Extensive experiments on six datasets show that our algorithms are able to not only reconstruct heavy-tailed distributed degree distribution, but also outperform state-of-the-art embedding models in various network mining tasks, such as vertex classification and link prediction.Comment: 8 figures; accepted by AAAI 201

Advanced Research in the development of green and sustainable processes for high-value chemical production from biomass.

This thesis presents the studies of controlling surface acidity of alumina-based catalysts for biomass conversion. Via the application of different synthesis approach, this thesis developed wet-chemistry method to establishes penta-coordinated aluminium specie based Brønsted acid site on varied mixed oxide and to precisely adjusts the spatial location of Brønsted acid site and Lewis acid site. In Chapter 3, for the first time, the development of AlV-BAS has been achieved on non-silica alumina material, and the principle of the research outcome can be applied to the AlV-BAS formation within many other mixed oxide systems. Because that the current development of AlV-BAS requires complex and expensive preparation method, which render the AlV-BAS based acidic catalyst less optimal to its counterparts, in this scenario, in Chapter 4, for the very first time, a simple and cheap wet-chemistry synthesis route has been reported in order to prepare silanol linked AlV-BAS for acidic catalysis. This achievement presents promising opportunities for the large-scale industrial implementation of mesoporous silica-alumina with AlV-BAS. Moreover, there are many acidic reactions requires both Brønsted acidity and Lewis acidity. Nevertheless, the currently applied bi-acidic catalysts has limitations, such as uncontrolled diffusion on acid sites and uncontrolled Brønsted-Lewis acid site synergy. With this in mind, we put efforts on preparing catalyst with cascade structure for active site separation. In Chapter 5, a bi-acidic solid acid catalyst with cascade architectural structured BAS and LAS has been reported. Due to the spatial separation, the synergy between BAS-LAS pair was limited, and which also showed the ability to direct the diffusion flow from BAS to LAS and contributed to enhanced cascade acid reaction performance. We reckon this work paves the way for designing bi-acidic catalyst with unique cascade architectural structure for efficient cascade reactions

Automated Neurovascular Tracing and Analysis of the Knife-Edge Scanning Microscope India Ink Data Set

The 3D reconstruction of neurovascular network plays an important role in understanding the functions of the blood vessels in different brain regions. Many techniques have been applied to acquire microscopic neurovascular data. The Knife-Edge Scanning Microscope (KESM) is a physical sectioning microscopy instrument developed by the Brain Network Lab in Texas A&M University which enables imaging of an entire mouse brain at sub-micrometer resolution. With the KESM image data, we can trace the neurovascular structure of the whole mouse brain. For the large neurovascular volume like the KESM data set, complicated tracing algorithm with template matching process is not fast enough. Also, KESM imaging might involve gaps and noise in data when acquiring the large volume of data. To solve these issues, a novel automated neurovascular tracing and data analysis method with less processing time and high accuracy is developed in this thesis. First, an automated seed point selection algorithm was described in my approach. The seed points on every outer boundary surface of the volume were selected as the start points of tracing. Second, a vector-based tracing method was developed to trace vascular network in 3D space. Third, the properties of the extracted vascular network were analyzed. Finally, the accuracy of the tracing method was evaluated using synthetic data. This approach is expected to help explore the entire vascular network of KESM automatically without human assistance

Capturing Evolution Genes for Time Series Data

The modeling of time series is becoming increasingly critical in a wide variety of applications. Overall, data evolves by following different patterns, which are generally caused by different user behaviors. Given a time series, we define the evolution gene to capture the latent user behaviors and to describe how the behaviors lead to the generation of time series. In particular, we propose a uniform framework that recognizes different evolution genes of segments by learning a classifier, and adopt an adversarial generator to implement the evolution gene by estimating the segments' distribution. Experimental results based on a synthetic dataset and five real-world datasets show that our approach can not only achieve a good prediction results (e.g., averagely +10.56% in terms of F1), but is also able to provide explanations of the results.Comment: a preprint version. arXiv admin note: text overlap with arXiv:1703.10155 by other author

Design of a Planar Eleven Antenna for Optimal MIMO Performance as a Wideband Micro Base-station Antenna

A new low-profile planar Eleven antenna is designed for optimal MIMO performance as a wideband MIMO antenna for micro base-stations in future wireless communication systems. The design objective has been to optimize both the reflection coefficient at the input port of the antenna and the 1-bitstream and 2-bitstream MIMO efficiency of the antenna at the same time, in both the Rich Isotropic MultiPath (RIMP) and Random Line-of-Sight (Random-LOS) environments. The planar Eleven antenna can be operated in 2-, 4-, and 8-port modes with slight modifications. The optimization is performed using genetic algorithms. The effects of polarization deficiencies and antenna total embedded efficiency on the MIMO performance of the antenna are further studied. A prototype of the antenna has been fabricated and the design has been verified by measurements against the simulations.Comment: 7 pages, 15 figures, 15 reference

Challenges and framework of life cycle management of small WEEE in China

As the biggest ITC manufacturers and consumers in the world market, China’s management strategy of WEEE (Waste Electric and Electronic Equipment) will definitely affect the global WEEE flows. The improper treatment activities by the informal sectors in China had led to some environmental damages and resources lost. This study is trying to develop a systematic framework of sustainable management of small WEEE to identify the main challenges of WEEE management in China from the life cycle perspective. This framework, covering the whole life cycle of small WEEE from discard to final treatment, consists of such aspects as the definition, scope, classification, and material flow analysis, as well as the environmental risk assessment. The Chinese government had established the WEEE management mechanism based on the EPR principle. The laws and regulations associated with WEEE constitute a policy system for promoting the sustainable management of e-waste in China and cover the entire life cycles of e-products, from design, production and use, to recycling and disposal. However, it only focuses on five large-sized product categories as TV set, air conditioner, refrigerator, wash machines and personal computers. The large quantity of small WEEE was not on the list of WEEE management. Some important life cycle stages of WEEE like eco-design, use/reuse, recycling and final disposal was reviewed and analyzed based on the China’s domestic WEEE flow. It is shown that eco-design of ITC products in China is just in the very young age. There is no available data and tools for EEE designers and manufacturers to implement eco-design. Reuse of EEE products and component is very common. It is also mixed with the recycling practices, which makes the WEEE flow pathway more complicated. There are no specific disposal facilities for the final residuals from WEEE treatment. The eco-efficiency method and life cycle assessment tools are used to model and compare different strategies within the life span of small WEEE like retired mobile phone. The results show that the informal manual collection and components-reuse strategy is higher in eco-efficiency than the formal motor powered vehicle collection and disassembling strategy. The main challenges of managing small WEEEs were identified as five aspects. 1) The discordant policies between environmental protection and resources reservation led to losing of some valuable and hazardous materials, as well as residuals in recycling. 2) Collection system is not linked with formal recycling sector, thus main small WEEE stream went to informal recyclers without pollution control facilities. 3) The formal recyclers can’t make profitable business without WEEE fund, because they have to buy the small WEEE from the collectors by higher price. 4) Conflict of reusing and recycling. Reuse of parts or components of WEEE in China is quite popular and out of control. 5) WEEE fund implementation. In conclusion, the sustainable management of small WEEE should integrate all life cycle stages and consider the efficiency of materials recovery and the environmental risk