The magnitude homology of a hypergraph

The magnitude homology, introduced by R. Hepworth and S. Willerton, offers a topological invariant that enables the study of graph properties. Hypergraphs, being a generalization of graphs, serve as popular mathematical models for data with higher-order structures. In this paper, we focus on describing the topological characteristics of hypergraphs by considering their magnitude homology. We begin by examining the distances between hyperedges in a hypergraph and establish the magnitude homology of hypergraphs. Additionally, we explore the relationship between the magnitude and the magnitude homology of hypergraphs. Furthermore, we derive several functorial properties of the magnitude homology for hypergraphs. Lastly, we present the K\"{u}nneth theorem for the simple magnitude homology of hypergraphs

Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images

Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated table detection and tabular data extraction from financial PDF documents. We proposed a method that consists of three main processes, which are detecting table areas with a Faster R-CNN (Region-based Convolutional Neural Network) model with Feature Pyramid Network (FPN) on each page image, extracting contents and structures by a compounded layout segmentation technique based on optical character recognition (OCR) and formulating regular expression rules for table header separation. The tabular data extraction feature is embedded with rule-based filtering and restructuring functions that are highly scalable. We annotate a new Financial Documents dataset with table regions for the experiment. The excellent table detection performance of the detection model is obtained from our customized dataset. The main contributions of this paper are proposing the Financial Documents dataset with table-area annotations, the superior detection model and the rule-based layout segmentation technique for the tabular data extraction from PDF files

Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection

Arbitrary-oriented object detection is a relatively emerging but challenging task. Although remarkable progress has been made, there still remain many unsolved issues due to the large diversity of patterns in orientation, scale, aspect ratio, and visual appearance of objects in aerial images. Most of the existing methods adopt a coarse-grained fixed label assignment strategy and suffer from the inconsistency between the classification score and localization accuracy. First, to align the metric inconsistency between sample selection and regression loss calculation caused by fixed IoU strategy, we introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy. The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects. Second, to further address the inconsistency between classification and localization, we propose a critical feature sampling (CFS) module, which performs localization refinement on the sampling location for classification task to extract critical features accurately. Third, we present a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high quality samples by changing the form of regression loss function based on the statistics of proposals during training. Extensive experiments are conducted on four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016, and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed detector

A Two-Wheeled Self-Balancing Robot with the Fuzzy PD Control Method

A two-wheeled self-balancing robot with a fuzzy PD control method is described and analyzed as an example of a high-order, multiple-variable, nonlinear, strong-coupling, and unstable system. Based on a system structure model, a kinetic equation is constructed using Newtonian dynamics and mechanics. After a number of simulation experiments, we get the best , , and state-feedback matrices. Then a fuzzy PD controller is designed for which the position and speed of the robot are inputs and for which the angle and angle rate of the robot are controlled by a PD controller. Finally, this paper describes a real-time control platform for the two-wheeled self-balancing robot that controls the robot effectively, after some parameter debugging. The result indicates that the fuzzy PD control algorithm can successfully achieve self-balanced control of the two-wheeled robot and prevent the robot from falling

Implementing Natural Infrastructure in the Upper Mississippi River Basin: Lessons from Iowa

The Upper Mississippi River Basin (UMRB) suffers from poor water quality due to high nutrient runoff from the over-application of fertilizers in industrial agriculture and the increasing frequency of flooding (America’s Watershed Initiative, 2020). A promising solution to address these issues is construction of natural infrastructure, such as restored wetlands, that reduce both flood risk and nutrient pollution. The state of Iowa in particular has struggled with increasing flooding and nitrogen pollution, and shows great potential for the benefits of natural infrastructure. However, implementing natural infrastructure in Iowa - and the region more broadly - has been slow due to knowledge gaps, policy conflicts, and institutional barriers. In order to fill knowledge gaps and explore barriers, the central questions of this project are: how can natural infrastructure implementation be improved and how can natural infrastructure benefit socially vulnerable communities? To answer these questions, the project has five specific objectives: (1) evaluate the potential for hydric soil proxies to help identify key locations for natural infrastructure interventions, (2) evaluate the flooding and nitrogen pollution exposure of socially vulnerable communities in Iowa, (3) understand the social and political conditions for successful natural infrastructure implementation in Iowa, (4) identify policy opportunities for expanding natural infrastructure in Iowa, and (5) propose priorities for future natural infrastructure research and advocacy.Ph.D.School for Environment and SustainabilityUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/167381/1/Final Report_ImplementingNIinUMRB_P08EDF.pd

Interactions of the apolipoprotein C-III 3238C>G polymorphism and alcohol consumption on serum triglyceride levels

<p>Abstract</p> <p>Background</p> <p>Both apolipoprotein (Apo) C-III gene polymorphism and alcohol consumption have been associated with increased serum triglyceride (TG) levels, but their interactions on serum TG levels are not well known. The present study was undertaken to detect the interactions of the ApoC-III 3238C>G (rs5128) polymorphism and alcohol consumption on serum TG levels.</p> <p>Methods</p> <p>A total of 516 unrelated nondrinkers and 514 drinkers aged 15-89 were randomly selected from our previous stratified randomized cluster samples. Genotyping of the ApoC-III 3238C>G was performed by polymerase chain reaction and restriction fragment length polymorphism combined with gel electrophoresis, and then confirmed by direct sequencing. Interactions of the ApoC-III 3238C>G genotype and alcohol consumption was assessed by using a cross-product term between genotypes and the aforementioned factor.</p> <p>Results</p> <p>Serum total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), ApoA-I and ApoB levels were higher in drinkers than in nondrinkers (<it>P </it>< 0.05-0.001). There was no significant difference in the genotypic and allelic frequencies between the two groups. Serum TG levels in nondrinkers were higher in CG genotype than in CC genotype (<it>P </it>< 0.01). Serum TC, TG, low-density lipoprotein cholesterol (LDL-C) and ApoB levels in drinkers were higher in GG genotype than in CC or CG genotype (<it>P </it>< 0.01 for all). Serum HDL-C levels in drinkers were higher in CG genotype than in CC genotype (<it>P </it>< 0.01). Serum TC, TG, HDL-C and ApoA-I levels in CC genotype, TC, HDL-C, ApoA-I levels and the ratio of ApoA-I to ApoB in CG genotype, and TC, TG, LDL-C, ApoA-I and ApoB levels in GG genotype were higher in drinkers than in nondrinkers (<it>P </it>< 0.05-0.01). But the ratio of ApoA-I to ApoB in GG genotype was lower in drinkers than in nondrinkers (<it>P </it>< 0.01). Multivariate logistic regression analysis showed that the levels of TC, TG and ApoB were correlated with genotype in nondrinkers (<it>P </it>< 0.05 for all). The levels of TC, LDL-C and ApoB were associated with genotype in drinkers (<it>P </it>< 0.01 for all). Serum lipid parameters were also correlated with age, sex, alcohol consumption, cigarette smoking, blood pressure, body weight, and body mass index in both groups.</p> <p>Conclusions</p> <p>This study suggests that the ApoC-III 3238CG heterozygotes benefited more from alcohol consumption than CC and GG homozygotes in increasing serum levels of HDL-C, ApoA-I, and the ratio of ApoA-I to ApoB, and lowering serum levels of TC and TG.</p

Dietary patterns and risk for gastric cancer: A case-control study in residents of the Huaihe River Basin, China

AimEvidence linking dietary patterns and the risk of gastric cancer was limited, especially in Chinese populations. This study aimed to explore the association between dietary patterns and the risk of gastric cancer in residents of the Huaihe River Basin, China.MethodsThe association between dietary patterns and the risk of gastric cancer was investigated through a case-control study. Dietary patterns were identified with factor analysis based on responses to a food frequency questionnaire (FFQ). Gastric cancer was diagnosed according to the International Classification of Diseases, 10th Revision (ICD 10). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated across the tertiles of dietary pattern scores using unconditional logistic regression models.ResultsA total of 2,468 participants were included in this study. Six main dietary patterns were extracted, and those patterns explained 57.09% of the total variation in food intake. After adjusting for demographic characteristics, lifestyle factors, individual disease history, family history of cancer and Helicobacter. Pylori (H. pylori) infection, comparing the highest with the lowest tertiles of dietary pattern scores, the multivariable ORs (95% CIs) were 0.786 (0.488, 1.265; Ptrend &lt; 0.001) for the flavors, garlic and protein pattern, 2.133 (1.299, 3.502; Ptrend &lt; 0.001) for the fast food pattern, 1.050 (0.682, 1.617; Ptrend &lt; 0.001) for the vegetable and fruit pattern, 0.919 (0.659, 1.282; Ptrend &lt; 0.001) for the pickled food, processed meat products and soy products pattern, 1.149 (0.804, 1.642; Ptrend &lt; 0.001) for the non-staple food pattern and 0.690 (0.481, 0.989; Ptrend &lt; 0.001) for the coffee and dairy pattern.ConclusionsThe specific dietary patterns were associated with the risk of gastric cancer. This study has implications for the prevention of gastric cancer