Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery

In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpretation and differs significantly from the pinhole imaging model; hence, it cannot be directly applied to learning-based 3D reconstruction networks and to more novel reconstruction pipelines in computer vision. Hence, in this study, we introduce a method in which the RFM is equivalent to the pinhole camera model (PCM), meaning that the internal and external parameters of the pinhole camera are used instead of the rational polynomial coefficient parameters. We then derive an error formula for this equivalent pinhole model for the first time, demonstrating the influence of the image size on the accuracy of the reconstruction. In addition, we propose a polynomial image refinement model that minimizes equivalent errors via the least squares method. The experiments were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7. The results demonstrated that the reconstruction accuracy was proportional to the image size. Our polynomial image refinement model significantly enhanced the accuracy and completeness of the reconstruction, and achieved more significant improvements for larger-scale images.Comment: 24 page

Reward prediction error in learning-related behaviors

Learning is a complex process, during which our opinions and decisions are easily changed due to unexpected information. But the neural mechanism underlying revision and correction during the learning process remains unclear. For decades, prediction error has been regarded as the core of changes to perception in learning, even driving the learning progress. In this article, we reviewed the concept of reward prediction error, and the encoding mechanism of dopaminergic neurons and the related neural circuities. We also discussed the relationship between reward prediction error and learning-related behaviors, including reversal learning. We then demonstrated the evidence of reward prediction error signals in several neurological diseases, including Parkinson’s disease and addiction. These observations may help to better understand the regulatory mechanism of reward prediction error in learning-related behaviors

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

Multi-Task Learning for Building Extraction and Change Detection from Remote Sensing Images

Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images is a crucial step toward practical BE and CD solutions, as in other DL-based applications. To better exploit the available labeled training data for representation learning, we propose a multi-task learning (MTL) network for simultaneous BE and CD, comprising the state-of-the-art (SOTA) powerful Swin transformer as a shared backbone network and multiple heads for predicting building labels and changes. Using the popular CD dataset the Wuhan University building change detection dataset (WHU-CD), we benchmarked detailed designs of the MTL network, including backbone and pre-training choices. With a selected optimal setting, the intersection over union (IoU) score was improved from 70 to 81 for the WHU-CD. The experimental results of different settings demonstrated the effectiveness of the proposed MTL method. In particular, we achieved top scores in BE and CD from optical images in the 2021 Gaofen Challenge. Our method also shows transferable performance on an unseen CD dataset, indicating high label efficiency

Unified building change detection pre-training method with masked semantic annotations

Building change detection (CD) using remote sensing images plays a vital role in urban development, and deep learning models attracted attention for their potential to accomplish CD tasks automatically. However, most methods are still facing challenges, such as the costly and time-consuming process of constructing CD datasets and the severely imbalanced distribution of positive and negative samples preventing loss functions from functioning as desired in the training process. Inspired by weak supervision have demonstrated excellent performance in solving the above-mentioned problems, a unified change detection pre-training paradigm is proposed to accomplish the CD task using a small number of samples and improve the inference accuracy of building change detection. The keys of this proposed method are as follows. First, the pre-training paradigm detects building changes using pseudo-labels and them generated by highly available semantic segmentation datasets. Second, a balanced sample distribution is ensured by using the proposed method for semantic masked building areas and controlling the proportion of the areas. Third, multi-task networks for simultaneous building extraction and change detection are used in the proposed unified paradigm, owing to the semantic information can be employed as an effective supervision signal to assist with the CD training to solve the problem that pseudo-labels adversely affect the ability of the algorithm to converge. In particular, experiments were performed on three challenging datasets. For aerial WHU-CD and satellite Gaofen Challenge-CD datasets, our pre-trained weights generated with pseudo-bitemporal samples were applied to subsets containing different proportions of ground truth for fine-tuning, respectively. Notably, 10% of ground truth for fine-tuning with our pre-trained weights obtained intersection over union (IoU) comparable to that obtained using 100% of the CD ground truth without our pre-trained weights, whereas an even greater IoU was achieved using 30% of ground truth with our pre-trained weights. Experiments demonstrated that the performance of our method based on a small number of samples was superior to that of conventional supervised learning methods. When conducting experiments with 100% ground truth, the results showed that the use of our pre-trained weights yields IoU that substantially exceeds that of conventional supervised learning methods. The results of experiments conducted on the LEVIR-CD dataset proved the excellent transferable performance of our method. The proposed method can greatly reduce the need for high-quality CD labels and alleviate bottlenecks caused by an imbalanced distribution. Moreover, our method can improve CD accuracy and has excellent generalization capabilities

Effects of pituitary-specific overexpression of FSHα/β on reproductive traits in transgenic boars

Abstract Background Follicle-stimulating hormone (FSH) is a gonadotropin synthesized and secreted by the pituitary gland. FSH stimulates follicle development and maturation in females. It also plays an important role in spermatogenesis in males, including humans and mice. However, the effects of FSH on male pigs are largely unknown. In this study, we generated transgenic pigs to investigate the effects of FSHα/β overexpression on reproductive traits in boars. Results After five transgenic F0 founders were crossed with wide-type pigs, 193 F1 animals were obtained. Of these, 96 were confirmed as transgenic. FSHα and FSHβ mRNAs were detected only in pituitary tissue. Transgenic boars exhibited significantly higher levels of FSHα and FSHβ mRNA, serum FSH, and serum testosterone, compared to full-sib non-transgenic boars. Significant increases in testis weight, vas deferens diameter, seminiferous tubule diameter, and the number of Leydig cells were observed, suggesting that the exogenous FSHα/β affects reproductive traits. Finally, transgenic and non-transgenic boars had similar growth performance and biochemical profiles. Conclusions Pituitary-specific overexpression of FSHα/β genes is likely to impact reproductive traits positively, as indicated by enhancements in serum testosterone level, testis weight, the development of vas deferens, seminiferous tubules, and Leydig cells in transgenic boars. A high level of serum FSH induces secretion of serum testosterone, possibly by boosting the number of Leydig cells, which presumably increases the libido and the frequency of sexual activity in transgenic boars. Our study provides a preliminary foundation for the genetic improvement of reproductive traits in male pigs

Comparative Studies on Enhanced Oil Recovery: Thermoviscosifying Polymer Versus Polyacrylamide

High-molecular-weight polyacrylamide (PAM) has been widely used in chemically enhanced oil recovery (EOR) processes under mild conditions, but its poor tolerance to high temperature and high salinity impeded the use in severe oil reservoirs. To overcome the inadequacies of PAM, thermoviscosifying polymers (TVPs) whose viscosity increases upon increasing temperature and salinity were developed in recent years. In this work, comparative studies with PAM and TVP, having more similar molecular weights, were performed with regard to their rheological behaviors, thermal stability, and core flooding feasibility. It was found that the TVP aqueous solution exhibited thermothickening ability, even at a polymer concentration of 0.2 wt % with a total dissolved solids ratio (TDS) of 101 000 mg L^–1 upon increasing temperature, while PAM only showed a monotonic decrease in viscosity under identical conditions. Remaining viscosity of TVP was higher than that of PAM after aging at 45 or 85 °C for one month. Core flooding tests demonstrated both polymers show good transportation in porous media, and a higher oil recovery of 16.4% and 15.5% can be attained by TVP at 45 and 85 °C, respectively, while those of PAM are only 12.0% and 9.20%

Flexibility Demand Analysis and Regulation Capacity Sharing Decisions Between Interconnected Power Systems Considering Differences in Regulation Performance

To cope with the demand for large amount of flexibility regulation caused by high penetration of intermittent renewable energy, it is necessary to classify and measure the demand capacity for different regulation performance, and to reasonably allocate flexibility resources for different regions and different regulation capacities. This study proposes a flexibility demand analysis and regulation capacity sharing decisions between interconnected power systems considering differences in regulation performance. Firstly, the empirical mode decomposition (EMD) method is used to decompose the historical operating load curves of each sub-region, and the demand capacities of different regulation performances are calculated based on the obtained decomposition results of trend components and fluctuation components. Then, the probability density and the regulation demand capacity interval at different confidence levels are calculated based on the regulation capacity statistics of the sample of historical operation days. Finally, the regulation capacity sharing decisions between the interconnected regions are made based on the cost of various regulation resources in different sub-regions and the confidence level requirements of internal resources in sub-regions to meet regulation demand. A scenario based on the interconnection operation of two regional grids and the self-sufficiency rate of regulation capacity in each sub-region is no less than 0.95 confidence level is used to verify the effectiveness and feasibility of the proposed method. The simulation results demonstrate that the regulation capacity demand considering the difference in regulation quality can provide a detailed basis for the cross-region deployment of different quality flexibility resources, and the total cost of regulation capacity of the regional grid after adopting the cross-region sharing decision model is reduced by about 4.51% compared with the system independent optimization model

A bibliometric and visualized analysis of research on air pollution and cardiovascular diseases

A large number of studies have shown that air pollution has a great impact on cardiovascular diseases (CVD). However, there are few bibliometric studies or visual analyses in this field. The objective of this study was to research trends and hotspots of air pollution and CVD. We used CiteSpace and VOSviewer software to retrieve relevant studies from the Web of Science Core Collection (WoSCC) over the past decade. Amount to 4284 documents on air pollution and CVD were included in this study. The past decade saw an upward trend in the number of studies. The analysis of national publications showed that the United States had the highest academic contribution in this field. Peking University, the University of Washington and Harvard University were the main institutions studying the effect of air pollution on CVD. The cooperation among institutions with high publications was very close. Cluster analysis of the keywords listed four categories as follow: (1) oxidative stress and the cardiovascular effects of air pollution; (2) the cardiovascular effects of pollution exposure sources; (3) the relationship between environmental stressors and CVD; (4) personal-level interventions. This study puts forward a comprehensive summary of the trends and development of air pollution and CVD, confirms the research frontier and hotspot direction and could give a meaningful reference for researchers in this field