Character Segmentation System Based on C# Design and Implementation

AbstractAt present, most of the OCR recognizing through individual character, thus the quality of character segmentation is the key point to affect the quality of OCR recognition system. This paper introduces the formula of projective method in analysis of preliminary segmentation for images. Moreover it applied analysis for connected spatial domain, the correct results shows that writing image well matched. After two analyses and segmentation, characters can be segmented correctly. In order to provide useful solutions to these two problems that characters keying must be performed rapidly and documents digitizing can be conserved for a long time. Therefore, we must place emphasis on the research and development of the character segmentation

Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind

When reading a story, humans can rapidly understand new fictional characters with a few observations, mainly by drawing analogy to fictional and real people they met before in their lives. This reflects the few-shot and meta-learning essence of humans' inference of characters' mental states, i.e., humans' theory-of-mind (ToM), which is largely ignored in existing research. We fill this gap with a novel NLP benchmark, TOM-IN-AMC, the first assessment of models' ability of meta-learning of ToM in a realistic narrative understanding scenario. Our benchmark consists of $\sim$1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task; and requires models to mimic humans' ability of fast digesting characters with a few starting scenes in a new movie. Our human study verified that humans can solve our problem by inferring characters' mental states based on their previously seen movies; while the state-of-the-art metric-learning and meta-learning approaches adapted to our task lags 30% behind

Association of Age of Metabolic Syndrome Onset With Cardiovascular Diseases:The Kailuan Study

BACKGROUND: Metabolic syndrome (MetS) is associated with an increased risk of incident cardiovascular diseases (CVD), but the association between the new-onset MetS at different ages and the CVD risk remain unclear. METHODS: This was a prospective study comprising a total of 72,986 participants without MetS and CVD who participated in the Kailuan study baseline survey (July 2006 to October 2007). All participants received the biennial follow-up visit until December 31, 2019. In addition, 26,411 patients with new-onset MetS were identified from follow-up, and one control participant was randomly selected for each of them as a match for age ( ± 1 year) and sex. In the end, a total of 25,125 case-control pairs were involved. Moreover, the Cox proportional hazard model was established to calculate the hazard ratios (HR) for incident CVD across the onset age groups. RESULTS: According to the median follow-up for 8.47 years, 2,319 cases of incident CVD occurred. As MetS onset age increased, CVD hazards gradually decreased after adjusting for potential confounders. Compared with non-MetS controls, the HR and the 95% confidence interval (CI) for CVD were 1.84 (1.31–2.57) in the MetS onset age <45 years group, 1.67 (1.42–1.95) for the 45–54 years group, 1.36 (1.18–1.58) for the 55–64 years group, and 1.28 (1.10–1.50) for the ≥65 years group, respectively (p for interaction = 0.03). CONCLUSIONS: The relative risks of CVD differed across MetS onset age groups, and the associations was more intense in the MetS onset group at a younger age

Changes in Impaired Fasting Glucose and Borderline High Low-Density Lipoprotein-Cholesterol Status Alter the Risk of Cardiovascular Disease:A 9-Year Prospective Cohort Study

BackgroundWe aimed to characterize the relationships of the changes in impaired fasting glucose (IFG) and borderline high low-density lipoprotein-cholesterol (LDL-C) status with cardiovascular disease (CVD). MethodsA total of 36,537 participants who did not have previous CVD, diabetes mellitus, or high LDL-C (>= 4.1 mmol/L), nor were taking lipid-lowering drugs were recruited from the Kailuan study. The participants were allocated to six groups according to their baseline and follow-up fasting blood glucose (FBG) and LDL-C concentrations: (1) both were normal; (2) both normal at baseline, one abnormality subsequently; (3) both normal at baseline, both abnormal subsequently; (4) at least one abnormality that became normal; (5) at least one abnormality at baseline, a single abnormality subsequently; and (6) at least one abnormality, two abnormalities subsequently. The outcomes were CVD and subtypes of CVD (myocardial infarction and stroke). Multiple Cox regression models were used to calculate adjusted hazard ratio (HR) and confidence interval (95% CI). ResultsDuring a median follow-up period of 9.00 years, 1,753 participants experienced a CVD event. After adjustment for covariates, participants with IFG in combination with a borderline high LDL-C status at baseline and follow-up had higher risks of CVD (HR: 1.52; 95% CI: 1.04-2.23 and HR: 1.38, 95% CI: 1.13-1.70, respectively) compared with those with normal fasting blood glucose and LDL-C. Compared with participants that remained normal, those who changed from normality to having two abnormalities were at a higher risk of CVD (HR: 1.26; 95% CI: 0.98-1.61), as were those who changed from at least one abnormality to two abnormalities (HR: 1.48, 95% CI: 1.02-2.15). ConclusionChanges in IFG and borderline high LDL-C status alter the risk of CVD and its subtype, implying that it is important to focus on such individuals for the prevention and control of CVD

Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations

Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, so that they are neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES in almost all situations. Further analyses show that the machine-learned SES is incredibly stable in terms of rotational variation of tested molecules. Our timing analysis shows that the machine-learned SES is roughly 2.5 times as efficient as the classical SES routine implemented in Amber/PBSA on a tested central processing unit (CPU) platform. We expect further performance gain on massively parallel platforms such as graphics processing units (GPUs) given the ease in converting the machine-learned SES to a parallel procedure. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with a 1% deviation on average. Given its level set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms

Distributed Path Tracking for Autonomous Underwater Vehicles Based on Pseudo Position Feedback

In this paper, we consider the distributed polynomial path tracking problem for a swarm of autonomous underwater vehicles (AUVs) modeled by second-order uncertain multi-agent systems. The application scenario of this paper has three distinguished characteristics. First, the communication network for the multi-agent system is unreliable and switching. Under the jointly connected condition, the communication network can be disconnected the entire time. Second, it is supposed that only the relative position between AUVs can be obtained for trajectory tracking control. Third, the AUV dynamics are subject to uncertain system parameters. By applying the cooperative output regulation control framework, a novel distributed robust control scheme is proposed to solve the distributed path tracking problem, which consists of three parts. First, to cope with communication network uncertainty, the distributed observer was invoked to recover the polynomial path for each AUV. Second, based on the relative position measurement between AUVs, a pseudo position estimator was adopted to generate the pseudo position for each AUV. Finally, based on the estimated polynomial path and the pseudo position, a certainty equivalent robust internal model control law was synthesized to achieve asymptotic reference trajectory tracking, where the internal model compensator aims to tackle uncertain system parameters. Numerical simulations are provided to validate the effectiveness of the proposed control scheme

Development of a Lightweight Crop Disease Image Identification Model Based on Attentional Feature Fusion

Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in time, and reduce crop losses. Inspired by the application of convolutional neural networks in image identification, we propose a lightweight crop disease image identification model based on attentional feature fusion named DSGIResNet_AFF, which introduces self-built lightweight residual blocks, inverted residuals blocks, and attentional feature fusion modules on the basis of ResNet18. We apply the model to the identification of rice and corn diseases, and the results show the effectiveness of the model on the real dataset. Additionally, the model is compared with other convolutional neural networks (AlexNet, VGG16, ShuffleNetV2, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large), and the experimental results show that the accuracy, sensitivity, F1-score, AUC of the proposed model DSGIResNet_AFF are 98.30%, 98.23%, 98.24%, 99.97%, respectively, which are better than other network models, while the complexity of the model is significantly reduced (compared with the basic model ResNet18, the number of parameters is reduced by 94.10%, and the floating point of operations(FLOPs) is reduced by 86.13%). The network model DSGIResNet_AFF can be applied to mobile devices and become a useful tool for identifying crop diseases

Characteristics of sagittal curvature and mobility of the thoracolumbar segment of the spine in students specialising in physical dance

Aims: To understand the sagittal and mobility characteristics of the thoracolumbar segment of the spine in students specialising in physical dance. METHODS: Sagittal curvature and mobility of the thoracolumbar segment of the spine was measured in 35 standard dance students, 41 Latin dance students and 43 general university students using the Spinal Mouse spine electrometer. Results: (1) In the upright position, the sagittal curvature of the T9/10 to L4/5 segment was significantly less in standard dance-specific girls than in regular female university students (p<0.05). (2) The sagittal curvature of the T1/2~T5/6 and L1/2-L5~S1 segments in the upright position was significantly less than the spinal curvature in the shoe position for boys in the standard dance specialization (p<0.05, respectively); the sagittal curvature of the T1/2~T7/8 and L1/2~L5/S1 segments of the thoracic spine in the upright position for boys in the Latin dance specialization was significantly less than the spinal curvature in the shoe position ( p<0.05, respectively); sagittal curvature in the T1/2~T6/7 and T11/12~L5/S1 segments was significantly less than the spinal curvature in the shoe-wearing position for girls in standard dance (p<0.05, respectively); sagittal curvature in the T1/2~T7/8 and T12/L1~L5/S1 segments was significantly less than the spinal curvature in the shoe-wearing position for girls in Latin dance in the upright position (p<0.05, respectively). curvature (p<0.05, respectively). (3) In the upright to maximum forward flexion position, sagittal mobility in the T1/2 to T4/5 segments was significantly less in the standard dance-specific male students than in the general male students (p<0.05). (4) In the upright to maximum posterior extension position, the sagittal mobility of the T1/2 to T5/6 segments of the thoracic spine was significantly greater in the standard dance-specific girls than in the general female university students (p<0.05). Conclusions: (1) In the upright position, the sagittal plane thoracic posterior convexity decreased and the anterior convexity of the lumbar spine increased in girls with standard dance specialties. (2) Reduced sagittal thoracic and lumbar spine curvature in the shoe position for students of the same gender in Latin and standard dance. (3) Standard dance specific girls have better spinal-thoracic stability and standard dance boys have less flexibility in the spinal-thoracic spine

Effects of Acceptors on the Charge Photogeneration Dynamics of PM6-Based Solar Cells

In this work, we investigated the effects of different acceptors (IT−4F and PC71BM) on the charge dynamics in PM6-based solar cells. The correlation between different acceptors and the performance of organic solar cells was studied by atomic force microscope, steady-state absorption spectrum, transient absorption spectrum, and electrical measurements. Optical absorption exhibited that IT−4F has strong absorption in the near-infrared region for the active layer. Transient absorption measurements showed that different acceptors (IT−4F and PC71BM) had a significant influence on the behaviors of PM6 excitons and charge dynamics. That is, the exciton dissociation rate and delocalized polaron transport in the PM6:IT−4F active layer were significantly faster than that in the PM6:PC71BM active layer. The lifetime of localized polaron in the PM6:PC71BM active layer was longer than that in the PM6:IT−4F active layer. Conversely, the lifetime of delocalized polaron in the PM6:IT−4F active layer was longer than that in the PM6:PC71BM active layer. Electrical measurement analysis indicated that lower bimolecular recombination, higher charge transport, and charge collection ability were shown in the PM6:IT−4F device compared with the PM6:PC71BM device. Therefore, PM6:IT−4F solar cells achieved a higher power conversion efficiency (12.82%) than PM6:PC71BM solar cells (8.78%)