Evolution Strategies for Learning Sparse Matrix Representations of Gene Regulatory Networks

Currently, a massive amount of temporal gene expression data is available to researchers, which makes it possible to infer Gene Regulatory Networks (GRNs). Gene regulatory networks are theoretical models to represent excitatory and inhibitory interactions between genes. GRNs are useful in understanding how genes function, and hence they are also useful in pharmaceutical and other applications in biology and medicine. However, despite the importance of GRNs, the process of inferring GRNs from observational data is very difficult. This thesis applies evolutionary algorithms to the problem of GRN inference. We propose a novel evolutionary algorithm: hierarchical evolution strategy (HES) to target the specific difficulties in GRN inference. We propose a sparse matrix representation of GRN to account for sparse connectivity in biological gene interactions. Unlike traditional evolution strategies, we divide our optimization into two concurrent processes: connectivity construction and numerical optimization. In each generation, we first establish connectivity structure of the GRN. Inside the same generation, we apply a secondary ES to find the best numerical values with those fixed connections. We also propose a hybrid crowding method to maintain high population diversity while applying the evolutionary algorithms. High population diversity leads to broader exploration area in the search space, therefore preventing premature convergence. The results obtained show that the proposed HES outperforms other algorithms, and has the potential to scale up to realistic problems with thousands of genes

Palaeoenvironment and Its Control on the Formation of Miocene Marine Source Rocks in the Qiongdongnan Basin, Northern South China Sea

The main factors of the developmental environment of marine source rocks in continental margin basins have their specificality. This realization, in return, has led to the recognition that the developmental environment and pattern of marine source rocks, especially for the source rocks in continental margin basins, are still controversial or poorly understood. Through the analysis of the trace elements and maceral data, the developmental environment of Miocene marine source rocks in the Qiongdongnan Basin is reconstructed, and the developmental patterns of the Miocene marine source rocks are established. This paper attempts to reveal the hydrocarbon potential of the Miocene marine source rocks in different environment and speculate the quality of source rocks in bathyal region of the continental slope without exploratory well. Our results highlight the palaeoenvironment and its control on the formation of Miocene marine source rocks in the Qiongdongnan Basin of the northern South China Sea and speculate the hydrocarbon potential of the source rocks in the bathyal region. This study provides a window for better understanding the main factors influencing the marine source rocks in the continental margin basins, including productivity, preservation conditions, and the input of terrestrial organic matter

Using vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings

A novel seam detection approach based on vector building maps is presented for low-attitude aerial orthoimage mosaicking. The approach tracks the centerlines between vector buildings to generate the candidate seams that avoid crossing buildings existing in maps. The candidate seams are then refined by considering their surrounding pixels to minimize the visual transition between the images to be mosaicked. After the refinement of the candidate seams, the final seams further bypass most of the buildings that are not updated into vector maps. Finally, three groups of aerial imagery from different urban densities are employed to test the proposed approach. The experimental results illustrate the advantages of the proposed approach in avoiding the crossing of buildings. The computational efficiency of the proposed approach is also significantly higher than that of Dijkstra’s algorithm

Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS

Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency

Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy

An Automatic Morphological Attribute Building Extraction Approach for Satellite High Spatial Resolution Imagery

A new morphological attribute building index (MABI) and shadow index (MASI) are proposed here for automatically extracting building features from very high-resolution (VHR) remote sensing satellite images. By investigating the associated attributes in morphological attribute filters (AFs), the proposed method establishes a relationship between AFs and the characteristics of buildings/shadows in VHR images (e.g., high local contrast, internal homogeneity, shape, and size). In the pre-processing step of the proposed work, attribute filtering was conducted on the original VHR spectral reflectance data to obtain the input, which has a high homogeneity, and to suppress elongated objects (potential non-buildings). Then, the MABI and MASI were calculated by taking the obtained input as a base image. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. To better detect buildings from the MABI feature image, an object-oriented analysis and building-shadow concurrence relationships were utilized to further filter out non-building land covers, such as roads and bare ground, that are confused for buildings. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. In view of both the visual inspection and quantitative assessment, the results of the proposed work are superior to recent automatic building index and supervised binary classification approach results

A New Method for Continuous Monitoring of Black and Odorous Water Body Using Evaluation Parameters: A Case Study in Baoding

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management