User Access Review and a UAR Supporting Tool for Improving Manual Access Review Process in Enterprise Environment

User Access Review is a process of re-evaluating the appropriateness of user access to systems or applications. It is a critical step of the user account management life cycle. Companies implement User Access Review processes to ensure that employees are given least privileges to access critical corporate IT systems, and segregation of duties (SoD) are enabled through effective access control to prevent fraud and error. User access review becomes mandatory for corporations that are in scope under federal regulations, industry standards, or compliances. With growing number of employees (users) and IT systems, the process of conducting user access review becomes increasingly complicated and time-consuming. Corporations often find it changeling to meet audit requirements with existing error prone manual review process and are searching for a better solution for delivering quality access review in a timely manner. A database based user access review tool (UAR Supporting Tool) is proposed in this thesis to improve the efficiency and accuracy of the manual review process

Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection

Millimeter-wave (MMW) imaging is emerging as a promising technique for safe security inspection. It achieves a delicate balance between imaging resolution, penetrability and human safety, resulting in higher resolution compared to low-frequency microwave, stronger penetrability compared to visible light, and stronger safety compared to X ray. Despite of recent advance in the last decades, the high cost of requisite large-scale antenna array hinders widespread adoption of MMW imaging in practice. To tackle this challenge, we report a large-scale single-shot MMW imaging framework using sparse antenna array, achieving low-cost but high-fidelity security inspection under an interpretable learning scheme. We first collected extensive full-sampled MMW echoes to study the statistical ranking of each element in the large-scale array. These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, which realizes robust and accurate image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at the same sample sampling ratio. The performance of the reported technique presents higher than 50% superiority over the existing MMW imaging schemes on various metrics including precision, recall, and mAP50. With such strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging, and could advocate its further practical applications

(Tn5-)fish-based imaging in the era of 3D/spatial genomics

3D genomics mainly focuses on the 3D position of single genes at the cell level, while spatial genomics focuses more on the tissue level. In this exciting new era of 3D/spatial genomics, half-century old FISH and its derivative methods, including Tn5-FISH, play important roles. In this review, we introduce the Tn5-FISH we developed recently, and present six different applications published by our collaborators and us, based on (Tn5-)FISH, which can be either general BAC clone-based FISH or Tn5-FISH. In these interesting cases, (Tn5-)FISH demonstrated its vigorous ability of targeting sub-chromosomal structures across different diseases and cell lines (leukemia, mESCs (mouse embryonic stem cells), and differentiation cell lines). Serving as an effective tool to image genomic structures at the kilobase level, Tn5-FISH holds great potential to detect chromosomal structures in a high-throughput manner, thus bringing the dawn for new discoveries in the great era of 3D/spatial genomics

Ellipse Crater Recognition for Lost-in-Space Scenario

In the field of deep space exploration, a crater recognition algorithm is key to landing navigation based on craters. When there is only visual information, determining how to identify the crater and provide the initial pose of the lander for a lost-in-space (LIS) scenario is a difficulty in terrain relative navigation (TRN). In this paper, a fast and robust crater recognition method for absolute pose estimation based on projective invariants is proposed, which can provide an accurate initial pose for tracking navigation. First, the method selects navigation craters to establish a small-capacity and high-efficiency crater database, and crater pair serial numbers and projective invariants are stored. Second, our method uses a dynamic threshold to solve the problem that the projective invariants are sensitive to noise. Then, an iterative pyramid algorithm is proposed to quickly filter redundancies. Using a dynamic threshold, the matching rate was increased by at least 10%, and the average processing speed was increased by 40%. When the detection errors of the major and minor axes of the ellipse reached 5%, the detection error of the center point reached 1 pixel, and the tilt angle error reached 5°; the matching rate was still >80%. Finally, the pose was estimated by solving the perspective-n-point (PNP) problem based on the recognized craters. The initial pose error in the simulation environment was less than 2°, and the position error was less than 44 m

Collateral

The theme of this dissertation is Collateral. The first chapter focuses on the meaning of collateral value. The main idea is that collateral value is the expected liquidation price conditional on the borrower's default, which is not necessarily the distressed value of the underlying asset. The second chapter compares the price movement of an asset before and after it has been used as collateral. A "V" shaped price path is documented for the asset used as collateral, suggesting a possible market instability