Graph-based Semi-supervised Learning: Algorithms and Applications.

114 p.Graph-based semi-supervised learning have attracted large numbers of researchers and it is an important part of semi-supervised learning. Graph construction and semi-supervised embedding are two main steps in graph-based semi-supervised learning algorithms. In this thesis, we proposed two graph construction algorithms and two semi-supervised embedding algorithms. The main work of this thesis is summarized as follows:1. A new graph construction algorithm named Graph construction based on self-representativeness and Laplacian smoothness (SRLS) and several variants are proposed. Researches show that the coefficients obtained by data representation algorithms reflect the similarity between data samples and can be considered as a measurement of the similarity. This kind of measurement can be used for the weights of the edges between data samples in graph construction. Each column of the coefficient matrix obtained by data self-representation algorithms can be regarded as a new representation of original data. The new representations should have common features as the original data samples. Thus, if two data samples are close to each other in the original space, the corresponding representations should be highly similar. This constraint is called Laplacian smoothness.SRLS graph is based on l2-norm minimized data self-representation and Laplacian smoothness. Since the representation matrix obtained by l2 minimization is dense, a two phrase SRLS method (TPSRLS) is proposed to increase the sparsity of graph matrix. By extending the linear space to Hilbert space, two kernelized versions of SRLS are proposed. Besides, a direct solution to kernelized SRLS algorithm is also introduced.2. A new sparse graph construction algorithm named Sparse graph with Laplacian smoothness (SGLS) and several variants are proposed. SGLS graph algorithm is based on sparse representation and use Laplacian smoothness as a constraint (SGLS). A kernelized version of the SGLS algorithm and a direct solution to kernelized SGLS algorithm are also proposed. 3. SPP is a successful unsupervised learning method. To extend SPP to a semi-supervised embedding method, we introduce the idea of in-class constraints in CGE into SPP and propose a new semi-supervised method for data embedding named Constrained Sparsity Preserving Embedding (CSPE).4. The weakness of CSPE is that it cannot handle the new coming samples which means a cascade regression should be performed after the non-linear mapping is obtained by CSPE over the whole training samples. Inspired by FME, we add a regression term in the objective function to obtain an approximate linear projection simultaneously when non-linear embedding is estimated and proposed Flexible Constrained Sparsity Preserving Embedding (FCSPE).Extensive experiments on several datasets (including facial images, handwriting digits images and objects images) prove that the proposed algorithms can improve the state-of-the-art results

Graph-based Semi-supervised Learning: Algorithms and Applications.

114 p.Graph-based semi-supervised learning have attracted large numbers of researchers and it is an important part of semi-supervised learning. Graph construction and semi-supervised embedding are two main steps in graph-based semi-supervised learning algorithms. In this thesis, we proposed two graph construction algorithms and two semi-supervised embedding algorithms. The main work of this thesis is summarized as follows:1. A new graph construction algorithm named Graph construction based on self-representativeness and Laplacian smoothness (SRLS) and several variants are proposed. Researches show that the coefficients obtained by data representation algorithms reflect the similarity between data samples and can be considered as a measurement of the similarity. This kind of measurement can be used for the weights of the edges between data samples in graph construction. Each column of the coefficient matrix obtained by data self-representation algorithms can be regarded as a new representation of original data. The new representations should have common features as the original data samples. Thus, if two data samples are close to each other in the original space, the corresponding representations should be highly similar. This constraint is called Laplacian smoothness.SRLS graph is based on l2-norm minimized data self-representation and Laplacian smoothness. Since the representation matrix obtained by l2 minimization is dense, a two phrase SRLS method (TPSRLS) is proposed to increase the sparsity of graph matrix. By extending the linear space to Hilbert space, two kernelized versions of SRLS are proposed. Besides, a direct solution to kernelized SRLS algorithm is also introduced.2. A new sparse graph construction algorithm named Sparse graph with Laplacian smoothness (SGLS) and several variants are proposed. SGLS graph algorithm is based on sparse representation and use Laplacian smoothness as a constraint (SGLS). A kernelized version of the SGLS algorithm and a direct solution to kernelized SGLS algorithm are also proposed. 3. SPP is a successful unsupervised learning method. To extend SPP to a semi-supervised embedding method, we introduce the idea of in-class constraints in CGE into SPP and propose a new semi-supervised method for data embedding named Constrained Sparsity Preserving Embedding (CSPE).4. The weakness of CSPE is that it cannot handle the new coming samples which means a cascade regression should be performed after the non-linear mapping is obtained by CSPE over the whole training samples. Inspired by FME, we add a regression term in the objective function to obtain an approximate linear projection simultaneously when non-linear embedding is estimated and proposed Flexible Constrained Sparsity Preserving Embedding (FCSPE).Extensive experiments on several datasets (including facial images, handwriting digits images and objects images) prove that the proposed algorithms can improve the state-of-the-art results

Development of Fatal Intestinal Inflammation in MyD88 Deficient Mice Co-infected with Helminth and Bacterial Enteropathogens

Infections with intestinal helminth and bacterial pathogens, such as enteropathogenic Escherichia coli, continue to be a major global health threat for children. To determine whether and how an intestinal helminth parasite, Heligomosomoides polygyrus, might impact the TLR signaling pathway during the response to a bacterial enteropathogen, MyD88 knockout and wild-type C57BL/6 mice were infected with H. polygyrus, the bacterial enteropathogen Citrobacter rodentium, or both. We found that MyD88 knockout mice co-infected with H. polygyrus and C. rodentium developed more severe intestinal inflammation and elevated mortality compared to the wild-type mice. The enhanced susceptibility to C. rodentium, intestinal injury and mortality of the co-infected MyD88 knockout mice were found to be associated with markedly reduced intestinal phagocyte recruitment, decreased expression of the chemoattractant KC, and a significant increase in bacterial translocation. Moreover, the increase in bacterial infection and disease severity were found to be correlated with a significant downregulation of antimicrobial peptide expression in the intestinal tissue in co-infected MyD88 knockout mice. Our results suggest that the MyD88 signaling pathway plays a critical role for host defense and survival during helminth and enteric bacterial co-infection

Numerical Investigation of Storage Behaviors of A Liquid CO2 Tank

The dynamic behavior of heat transfer induced by flow of the storage tank during the storage process was investigated using the computational fluid dynamics (CFD) approach, with the target of the liquid CO2 storage tank in a CO2 injection station in an oilfield. The flow field distribution outside the tank was simulated, exhibiting the patterns of air flow near the tank wall. The effect of progressive cooling leakage in the tank under various conditions was determined through simulation of the dynamic of flow heat transfer under various storage settings, with the result indicating that tank pressure has a beneficial effect on cooling capacity. The medium level, on the other hand, had a negative impact on cooling capacity. Finally, the impact of environmental variables on fluid loss was evaluated. This finding supports the safety and cost-benefit analysis of liquid CO2 storage systems

LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set

Most object detection methods use rectangular bounding boxes to represent the object, while the representative points network (RepPoints) employs a point set to describe the object. The RepPoints can provide more fine-grained localization and facilitates classification. However, it ignores the difference between localization and classification tasks. Therefore, a lightweight RepPoints with decoupling of the sampling point set (LRP-DS) is proposed in this paper. Firstly, the lightweight MobileNet-V2 and Feature Pyramid Networks (FPN) is employed as the backbone network to realize the lightweight network, rather than the Resnet. Secondly, considering the difference between classification and localization tasks, the sampling points of classification and localization are decoupled, by introducing classification free sampling method. Finally, due to the introduction of the classification free sampling method, the problem of the mismatch between the localization accuracy and the classification confidence is highlighted, so the localization score is employed to describe the localization accuracy independently. The final network structure of this paper achieves 73.3% mean average precision (mAP) on the VOC07 test dataset, which is 1.9% higher than original RepPoints with the same backbone network MobileNetV2 and FPN. Our LRP-DS has a detection speed of 20FPS for the input image of (1000, 600), on RTX2060 GPU, which is nearly twice as fast as the backbone network of ResNet50 and FPN. Experimental results show the effectiveness of our method

Measuring Vehicle Profile Size: Lidar-Based System and K-Frame-Based Methodology

At present, light curtain is a widely-used method to measure the vehicle profile size. However, it is sensitive to temperature, humidity, dust and other weather factors. In this paper, a lidar-based system with a K-frame-based algorithm is proposed for measuring vehicle profile size. The system is composed of left lidar, right lidar, front lidar, control box and industry controlling computer. Within the system, a K-frame-based methodology is investigated, which include several probable algorithm combinations. Three groups of experiments are conducted. An optimal algorithm combination, A16, is determined through the first group experiments. In the second group experiments, various types of vehicles are chosen to verify the generality and repeatability of the proposed system and methodology. The third group experiments are implemented to compare with vision-based methods and other lidar-based methods. The experimental results show that the proposed K-frame-based methodology is far more accurate than the comparative methods

Numerical Investigation of Storage Behaviors of A Liquid CO

The dynamic behavior of heat transfer induced by flow of the storage tank during the storage process was investigated using the computational fluid dynamics (CFD) approach, with the target of the liquid CO2 storage tank in a CO2 injection station in an oilfield. The flow field distribution outside the tank was simulated, exhibiting the patterns of air flow near the tank wall. The effect of progressive cooling leakage in the tank under various conditions was determined through simulation of the dynamic of flow heat transfer under various storage settings, with the result indicating that tank pressure has a beneficial effect on cooling capacity. The medium level, on the other hand, had a negative impact on cooling capacity. Finally, the impact of environmental variables on fluid loss was evaluated. This finding supports the safety and cost-benefit analysis of liquid CO2 storage systems

Recognition of Bimolecular Logic Operation Pattern Based on a Solid-State Nanopore

Nanopores have a unique advantage for detecting biomolecules in a label-free fashion, such as DNA that can be synthesized into specific structures to perform computations. This method has been considered for the detection of diseased molecules. Here, we propose a novel marker molecule detection method based on DNA logic gate by deciphering a variable DNA tetrahedron structure using a nanopore. We designed two types of probes containing a tetrahedron and a single-strand DNA tail which paired with different parts of the target molecule. In the presence of the target, the two probes formed a double tetrahedron structure. As translocation of the single and the double tetrahedron structures under bias voltage produced different blockage signals, the events could be assigned into four different operations, i.e., (0, 0), (0, 1), (1, 0), (1, 1), according to the predefined structure by logic gate. The pattern signal produced by the AND operation is obviously different from the signal of the other three operations. This pattern recognition method has been differentiated from simple detection methods based on DNA self-assembly and nanopore technologies

Development of fatal intestinal inflammation in MyD88 deficient mice co-infected with helminth and bacterial enteropathogens.

Infections with intestinal helminth and bacterial pathogens, such as enteropathogenic Escherichia coli, continue to be a major global health threat for children. To determine whether and how an intestinal helminth parasite, Heligomosomoides polygyrus, might impact the TLR signaling pathway during the response to a bacterial enteropathogen, MyD88 knockout and wild-type C57BL/6 mice were infected with H. polygyrus, the bacterial enteropathogen Citrobacter rodentium, or both. We found that MyD88 knockout mice co-infected with H. polygyrus and C. rodentium developed more severe intestinal inflammation and elevated mortality compared to the wild-type mice. The enhanced susceptibility to C. rodentium, intestinal injury and mortality of the co-infected MyD88 knockout mice were found to be associated with markedly reduced intestinal phagocyte recruitment, decreased expression of the chemoattractant KC, and a significant increase in bacterial translocation. Moreover, the increase in bacterial infection and disease severity were found to be correlated with a significant downregulation of antimicrobial peptide expression in the intestinal tissue in co-infected MyD88 knockout mice. Our results suggest that the MyD88 signaling pathway plays a critical role for host defense and survival during helminth and enteric bacterial co-infection