Robust estimation for range image segmentation and fitting

In the dissertation a new robust estimation technique for range image segmentation and fitting has been developed. The performance of the algorithm has been considerably improved by incorporating the genetic algorithm. The new robust estimation method randomly samples range image points and solves equations determined by these points for parameters of selected primitive type. From K samples we measure RESidual Consensus (RESC) to choose one set of sample points which determines an equation best fitting the largest homogeneous surface patch in the current processing region. The residual consensus is measured by a compressed histogram method which can be used at various noise levels. After obtaining surface parameters of the best fitting and the residuals of each point in the current processing region, a boundary list searching method is used to extract this surface patch out of the processing region and to avoid further computation. Since the RESC method can tolerate more than 80% of outliers, it is a substantial improvement over the least median squares method. The method segments range image into planar and quadratic surfaces, and works very well even in smoothly connected curve regions. A genetic algorithm is used to accelerate the random search. A large number of offline average performance experiments on GA are carried out to investigate different types of GAs and the influence of control parameters. A steady state GA works better than a generational replacement GA. The algorithms have been validated on the large set of synthetic and real range images

C2B Orders Decision-making in Multiple Supply Chains Under Cloud Manufacturing

Considering the background of cloud manufacturing and cluster supply chain, we build the basic model to assign the orders priority within each capacity. Then, considering the inter-chain horizontal cooperation, the extended model is proposed to parallel allocation of cross-chain orders as the orders exceeding one single- chain’s capacity. Lagrange algorithm is implemented, and the simulation analysis shown that the opportunity cost of rejected orders factor and cross-chain orders manufacturing cost factor have significant impacts on orders’ allocation decision, and there is a critical point in the combinations of those two factors. Through combinations, the cluster supply chain can make the acceptance decisions policy and production schedules of priority orders and cross- chain orders, so that customers’ satisfaction and the cluster supply chain’s total profits achieve the best situations

Learning to Count Isomorphisms with Graph Neural Networks

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on an input graph, the space of possible query graphs is enormous, and different parts of the input graph will be triggered to match different queries. Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. At the edge level, given that an edge is an atomic unit of encoding graph structures, we propose an edge-centric message passing scheme, where messages on edges are propagated and aggregated based on the edge adjacency to preserve fine-grained structural information. At the graph level, we modulate the input graph representation conditioned on the query, so that the input graph can be adapted to each query individually to improve their matching. Finally, we conduct extensive experiments on a number of benchmark datasets to demonstrate the superior performance of Count-GNN.Comment: AAAI-23 main trac

Comparative analysis of variation in the quality and completeness of local outbreak control plans for SARS-CoV-2 in English local authorities.

BACKGROUND: Local outbreak control plans (LOCPs) are statutory documents produced by local authorities (LAs) across England. LOCPs outline LAs' response to Coronavirus Disease 19 (COVID19) outbreaks and the coordination of local resources, data and communication to support outbreak response. LOCPs are therefore crucial in the nation's response to COVID-19. However, there has been no previous systematic assessment of these documents. We performed this study to systematically assess the quality of LOCPs and to offer recommendations of good practice. METHODS: All published LOCPs were assessed for basic characteristics. A framework based on Department of Health and Social Care guidelines was used to assess a random sample of LOCPs. Qualitative analysis was undertaken for LOCPs with highest completeness. RESULTS: Hundred and thirty-seven of 150 LAs publicly published a full LOCP; 9 were drafts. Statistical analysis demonstrated the significant difference between reporting of mainstream schools, care homes and the homeless population and other educational settings, high-risk settings and other vulnerable groups. LOCPs varied in approach when structuring outbreak response information and focused on different areas of outbreak management. CONCLUSIONS: The majority of LAs are publicly accessible. There is significant variation between the reporting of high-risk settings and groups. Suggested recommendations may help to improve future LOCP updates

The pathophysiology of degenerative cervical myelopathy and the physiology of recovery following decompression

Background: Degenerative cervical myelopathy (DCM), also known as cervical spondylotic myelopathy is the leading cause of spinal cord compression in adults. The mainstay of treatment is surgical decompression, which leads to partial recovery of symptoms, however, long term prognosis of the condition remains poor. Despite advances in treatment methods, the underlying pathobiology is not well-known. A better understanding of the disease is therefore required for the development of treatments to improve outcomes following surgery. Objective: To systematically evaluate the pathophysiology of DCM and the mechanism underlying recovery following decompression. Methods: A total of 13,808 published articles were identified in our systematic search of electronic databases (PUBMED, WEB OF SCIENCE). A total of 51 studies investigating the secondary injury mechanisms of DCM or physiology of recovery in animal models of disease underwent comprehensive review. Results: Forty-seven studies addressed the pathophysiology of DCM. Majority of the studies demonstrated evidence of neuronal loss following spinal cord compression. A number of studies provided further details of structural changes in neurons such as myelin damage and axon degeneration. The mechanisms of injury to cells included direct apoptosis and increased inflammation. Only four papers investigated the pathobiological changes that occur in spinal cords following decompression. One study demonstrated evidence of axonal plasticity following decompressive surgery. Another study demonstrated ischaemic-reperfusion injury following decompression, however this phenomenon was worse when decompression was delayed. Conclusions: In preclinical studies, the pathophysiology of DCM has been poorly studied and a number of questions remain unanswered. The physiological changes seen in the decompressed spinal cord has not been widely investigated and it is paramount that researchers investigate the decompressed spinal cord further to enable the development of therapeutic tools, to enhance recovery following surgery

The ancient function of RB-E2F Pathway: insights from its evolutionary history

<p>Abstract</p> <p>Background</p> <p>The RB-E2F pathway is conserved in most eukaryotic lineages, including animals and plants. E2F and RB family proteins perform crucial functions in cycle controlling, differentiation, development and apoptosis. However, there are two kinds of E2Fs (repressive E2Fs and active E2Fs) and three RB family members in human. Till now, the detail evolutionary history of these protein families and how RB-E2F pathway evolved in different organisms remain poorly explored.</p> <p>Results</p> <p>We performed a comprehensive evolutionary analysis of E2F, RB and DP (dimerization partners of E2Fs) protein family in representative eukaryotic organisms. Several interesting facts were revealed. First, orthologues of RB, E2F, and DP family are present in several representative unicellular organisms and all multicellular organisms we checked. Second, ancestral E2F, RB genes duplicated before placozoans and bilaterians diverged, thus E2F family was divided into E2F4/5 subgroup (including repressive E2Fs: E2F4 and E2F5) and E2F1/2/3 subgroup (including active E2Fs: E2F1, E2F2 and E2F3), RB family was divided into RB1 subgroup (including RB1) and RBL subgroup (including RBL1 and RBL2). Third, E2F4 and E2F5 share more sequence similarity with the predicted E2F ancestral sequence than E2F1, E2F2 and E2F3; E2F4 and E2F5 also possess lower evolutionary rates and higher purification selection pressures than E2F1, E2F2 and E2F3. Fourth, for RB family, the RBL subgroup proteins possess lower evolutionary rates and higher purification selection pressures compared with RB subgroup proteins in vertebrates,</p> <p>Conclusions</p> <p>Protein evolutionary rates and purification selection pressures are usually linked with protein functions. We speculated that function conducted by E2F4/5 subgroup and RBL subgroup proteins might mainly represent the ancient function of RB-E2F pathway, and the E2F1/2/3 subgroup proteins and RB1 protein might contribute more to functional diversification in RB-E2F pathway. Our results will enhance the current understanding of RB-E2F pathway and will also be useful to further functional studies in human and other model organisms.</p> <p>Reviewers</p> <p>This article was reviewed by Dr. Pierre Pontarotti, Dr. Arcady Mushegian and Dr. Zhenguo Lin (nominated by Dr. Neil Smalheiser).</p

Corrigendum: The Pathophysiology of Degenerative Cervical Myelopathy and the Physiology of Recovery Following Decompression.

[This corrects the article DOI: 10.3389/fnins.2020.00138.]

Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution

Current Scene text image super-resolution approaches primarily focus on extracting robust features, acquiring text information, and complex training strategies to generate super-resolution images. However, the upsampling module, which is crucial in the process of converting low-resolution images to high-resolution ones, has received little attention in existing works. To address this issue, we propose the Pixel Adapter Module (PAM) based on graph attention to address pixel distortion caused by upsampling. The PAM effectively captures local structural information by allowing each pixel to interact with its neighbors and update features. Unlike previous graph attention mechanisms, our approach achieves 2-3 orders of magnitude improvement in efficiency and memory utilization by eliminating the dependency on sparse adjacency matrices and introducing a sliding window approach for efficient parallel computation. Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details. Comprehensive experiments on TextZoom demonstrate that our proposed method generates high-quality super-resolution images, surpassing existing methods in recognition accuracy. For single-stage and multi-stage strategies, we achieved improvements of 0.7\% and 2.6\%, respectively, increasing the performance from 52.6\% and 53.7\% to 53.3\% and 56.3\%. The code is available at https://github.com/wenyu1009/RTSRN