CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion

Channel and spatial attention mechanism has proven to provide an evident performance boost of deep convolution neural networks (CNNs). Most existing methods focus on one or run them parallel (series), neglecting the collaboration between the two attentions. In order to better establish the feature interaction between the two types of attention, we propose a plug-and-play attention module, which we term "CAT"-activating the Collaboration between spatial and channel Attentions based on learned Traits. Specifically, we represent traits as trainable coefficients (i.e., colla-factors) to adaptively combine contributions of different attention modules to fit different image hierarchies and tasks better. Moreover, we propose the global entropy pooling (GEP) apart from global average pooling (GAP) and global maximum pooling (GMP) operators, an effective component in suppressing noise signals by measuring the information disorder of feature maps. We introduce a three-way pooling operation into attention modules and apply the adaptive mechanism to fuse their outcomes. Extensive experiments on MS COCO, Pascal-VOC, Cifar-100, and ImageNet show that our CAT outperforms existing state-of-the-art attention mechanisms in object detection, instance segmentation, and image classification. The model and code will be released soon.Comment: 8 pages, 5 figure

Activation of Interleukin-1β Release by the Classical Swine Fever Virus Is Dependent on the NLRP3 Inflammasome, Which Affects Virus Growth in Monocytes

Classical swine fever virus (CSFV) is a classic Flavivirus that causes the acute, febrile, and highly contagious disease known as classical swine fever (CSF). Inflammasomes are molecular platforms that trigger the maturation of proinflammatory cytokines to engage innate immune defenses that are induced upon cellular infection or stress. However, the relationship between the inflammasome and CSFV infection has not been thoroughly characterized. To understand the function of the inflammasome response to CSFV infection, we infected porcine peripheral blood monocytes (PBMCs) with CSFV. Our results indicated that CSFV infection induced both the generation of pro-interleukin-1β (pro-IL-1β) and its processing in monocytes, leading to the maturation and secretion of IL-1β through the activation of caspase 1. Moreover, CSFV infection in PBMCs induced the production and cleavage of gasdermin D (GSDMD), which is an inducer of pyroptosis. Additional studies showed that CSFV-induced IL-1β secretion was mediated by NLRP3 and that CSFV infection could sufficiently activate the assembly of the NLRP3 inflammasome in monocytes. These results revealed that CSFV infection inhibited the expression of NLRP3, and knockdown of NLRP3 enhanced the replication of CSFV. In conclusion, these findings demonstrate that the NLRP3 inflammasome plays an important role in the innate immune response to CSFV infection

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Creative Destruction and Stock Price Informativeness in Emerging Economies

It is generally accepted that creative destruction can increase stock price informativeness, for innovative companies tend to behave more surprisingly. However, we believe the rising of stock price informativeness by enterprise innovation in emerging or developing markets is, in some sense, the result of executive ownership and insider trading. To investigate our proposition, we build a rational expectation framework model and define stock price informativeness (SPI) as the Kolmogorov-Smirnov distance between expected distribution and actual distribution of stock prices. Then we use Chinese listed company data to perform benchmark and mediation effects regressions, along with instrumental variable regression in the empirical sector. After that, we use Thailand and Indonesia listed company data for robustness tests. Finally, we divide Chinese listed companies into developed-economy funded and others to do grouping regression. The main conclusion is: Creative destruction can raise stock price informativeness, while executive ownership plays a partial mediating effect in the path of such influence. However, that mechanism is not significant when we use developed-country-funded enterprises listed in China as the sample for regression. Thus, the effects of creative destruction on stock price informativeness are uneven across countries, and executive ownership plays a vital role in that impact in emerging economies

The research status of flash flood warning in China

The article discusses from the disaster mechanism of flash flood to the current situation of early warning system. The formation of flash flood is closely related to rainfall intensity, underlying surface conditions and antecedent soil moisture content, and analysis of the physical process of flash flood disasters is crucial for the study of flash flood warning. Flash flood disaster warning indexes are mainly divided into two types: rainfall warning index and water level warning index. Data-driven statistical induction method and hydro-hydraulic methods based on physical mechanisms are used to determine rainfall warning index; The water level warning index can be directly determined by the upstream and downstream corresponding water level method or by the disaster water level. And summed up the current situation and development trend of China's flash flood warning research

ELCD: Efficient Lunar Crater Detection Based on Attention Mechanisms and Multiscale Feature Fusion Networks from Digital Elevation Models

The detection and counting of lunar impact craters are crucial for the selection of detector landing sites and the estimation of the age of the Moon. However, traditional crater detection methods are based on machine learning and image processing technologies. These are inefficient for situations with different distributions, overlaps, and crater sizes, and most of them mainly focus on the accuracy of detection and ignore the efficiency. In this paper, we propose an efficient lunar crater detection (ELCD) algorithm based on a novel crater edge segmentation network (AFNet) to detect lunar craters from digital elevation model (DEM) data. First, in AFNet, a lightweight attention mechanism module is introduced to enhance the feature extract capabilities of networks, and a new multiscale feature fusion module is designed by fusing different multi-level feature maps to reduce the information loss of the output map. Then, considering the imbalance in the classification and the distributions of the crater data, an efficient crater edge segmentation loss function (CESL) is designed to improve the network optimization performance. Lastly, the crater positions are obtained from the network output map by the crater edge extraction (CEA) algorithm. The experiment was conducted on the PyTorch platform using two lunar crater catalogs to evaluate the ELCD. The experimental results show that ELCD has a superior detection accuracy and inference speed compared with other state-of-the-art crater detection algorithms. As with most crater detection models that use DEM data, some small craters may be considered to be noise that cannot be detected. The proposed algorithm can be used to improve the accuracy and speed of deep space probes in detecting candidate landing sites, and the discovery of new craters can increase the size of the original data set

The research status of flash flood warning in China

The article discusses from the disaster mechanism of flash flood to the current situation of early warning system. The formation of flash flood is closely related to rainfall intensity, underlying surface conditions and antecedent soil moisture content, and analysis of the physical process of flash flood disasters is crucial for the study of flash flood warning. Flash flood disaster warning indexes are mainly divided into two types: rainfall warning index and water level warning index. Data-driven statistical induction method and hydro-hydraulic methods based on physical mechanisms are used to determine rainfall warning index; The water level warning index can be directly determined by the upstream and downstream corresponding water level method or by the disaster water level. And summed up the current situation and development trend of China's flash flood warning research

Prediction of Crack Resistance of LFSMA-13 with and without Anti-Rut Agent Using Parameters of FTIR Spectrum under Different Aging Degrees

This paper aims to better analyze the crack resistance of lignin fiber reinforced SMA-13 (LFSMA-13) asphalt mixtures, with and without polymer anti-rut agent (ARA), under different aging degrees. IDEAL-CT test and Fourier transform infrared (FTIR) spectroscopy were utilized to analyze the relationships between the crack resistance of LFSMA-13, with and without ARA, and the parameters of the FTIR spectrum of the asphalt extracted from the test samples. A convenient testing method to predict the anti-crack ability of the mixtures in a road was also derived in this study. The test samples were prepared using the specifications listed by AASHTO. The fracture formation work (Winitial) and cracking index (CTIndex) in the IDEAL-CT test were adopted to reflect the cracking ability of the asphalt mixtures in both the crack formation stage and the crack propagation stage. The peak areas of the FTIR spectrum were utilized to reveal the chemical properties of the asphalt material inside the SMA-13 asphalt mixtures, with and without ARA under different aging degrees. Grey correlation analysis was adopted to choose the most suitable FTIR spectrum parameters to derive the prediction models of Winitial and CTIndex under different aging degrees. After conducting a series of tests, the results showed that the aging process could well affect the crack resistance of the test samples and the peak areas of the asphalt extracted from the mixtures. The FTIR parameters selected from the grey correlation analysis could be used to well predict the anti-crack ability of the asphalt mixtures

RockSeg: A Novel Semantic Segmentation Network Based on a Hybrid Framework Combining a Convolutional Neural Network and Transformer for Deep Space Rock Images

Rock detection on the surface of celestial bodies is critical in the deep space environment for obstacle avoidance and path planning of space probes. However, in the remote and complex deep environment, rocks have the characteristics of irregular shape, being similar to the background, sparse pixel characteristics, and being easy for light and dust to affect. Most existing methods face significant challenges to attain high accuracy and low computational complexity in rock detection. In this paper, we propose a novel semantic segmentation network based on a hybrid framework combining CNN and transformer for deep space rock images, namely RockSeg. The network includes a multiscale low-level feature fusion (MSF) module and an efficient backbone network for feature extraction to achieve the effective segmentation of the rocks. Firstly, in the network encoder, we propose a new backbone network (Resnet-T) that combines the part of the Resnet backbone and the transformer block with a multi-headed attention mechanism to capture the global context information. Additionally, a simple and efficient multiscale feature fusion module is designed to fuse low-level features at different scales to generate richer and more detailed feature maps. In the network decoder, these feature maps are integrated with the output feature maps to obtain more precise semantic segmentation results. Finally, we conduct experiments on two deep space rock datasets: the MoonData and MarsData datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art rock detection algorithms under the conditions of low computational complexity and fast inference speed

The scenario-based variations and causes of future surface soil moisture across China in the twenty-first century

Abstract Surface soil moisture (SSM) is a key factor for water and heat exchanges between land surface and the atmosphere. It is also important to water resources, agriculture, and ecosystems. In the backdrop of global warming, SSM variations and potential causes are not well-known at regional scales. Based on soil moisture (SM) data from GLDAS-Noah and 16 global climate models (GCMs) selected from 25 GCMs in CMIP5, we analyzed spatial distribution and temporal changes of SSM in China and quantified fractional contributions of four meteorological factors to the SSM variations. The selected models have the same direction of historic trends in SSM during 1981–2005 as those in the GLDAS SSM data which were also further used to calibrate the trends simulated by the 16 GCMs. Based on the calibration results for the 16 GCMs, future SSMs for nine regions were analyzed in mainland China under four Intergovernmental Panel on Climate Change emission scenarios. No significant changes were identified in SSM across most regions of mainland China under RCP2.6 scenario. However, there is a general wetting tendency in the arid regions and drying tendency across the humid regions under all the scenarios except RCP2.6. In general, the higher the global temperature raises, the more grids with significant increase or significant decrease in SSM. These findings contradicted prevailing view that wet regions get wetter and dry regions get drier. Attribution analysis indicates that precipitation acts as the major driver for SSM variations and contributes up to 43.4% of SSM variations across China. These results provide new insights into future SSM response to climate warming and a scientific basis to mitigation and adaptation works related to SSM in the future