Geocoding Data Analysis and Processing in Relational Databases

Geocoding data are widely used in applications to support geographical locations and compute spatial relationships, such as distances. It is a common practice to capture geospatial data in relational database management systems (RDBMS), where most business data are stored and processed. However, RDBMS’s lack of ability in geocoding analysis and processing has prevented applications from handling spatial data effectively. Relational databases need to support geocoding data analysis and processing, such as geocoding data sorting, searching, geo-positioning, and distance computing. More advanced requirements may include the analysis of an area based on geo-location and area coverage of a geo-location. This paper provides analysis of some of the requirements on geocoding data processing in RDBMS from business application perspectives. The challenges in design and implementation will be discussed. The requirements will be further transformed to modeling, analysis, and processing on geocoding support in relational databases. When database volume increases, performance issues need to be addressed in order to provide efficient data queries and data processing. Database objects, such as auxiliary tables, views, and indexes, can be designed to provide performance improvement

Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table