Structured Epipolar Matcher for Local Feature Matching

Local feature matching is challenging due to the textureless and repetitive pattern. Existing methods foucs on using appearance features and global interaction and matching, while the importance of geometry prior in local feature matching has not been fully exploited. Different from these methods, in this paper, we delve into the importance of geometry prior and propose Structured Epipolar Matcher (SEM) for local feature matching, which can leverage the geometric information in a iterative matching way. The proposed model enjoys several merits. First, our proposed Structured Feature Extractor can model the relative positional relationship between pixels and high-confidence anchor points. Second, our proposed Epipolar Attention and Matching can filter out irrelevant areas by utilizing the epipolar constraint. Extensive experimental results on five standard benchmarks demonstrate the superior performance of our SEM compared to state-of-the-art methods

Adaptive Spot-Guided Transformer for Consistent Local Feature Matching

Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods. Our code will be released on https://astr2023.github.io.Comment: Accepted to CVPR 2023. Project page: https://astr2023.github.io

Effect of aramid core-spun yarn on impact resistance of aramid/epoxy composite

Introduction: The surface of aramid filament is smooth, which is a great defect for impact resistance and composite molding of aramid/epoxy composite. In this study, a new type of yarn—aramid core-spun yarn is introduced to the fabrication of compositematerials. It increases the friction among yarns and optimizes the performance of yarns.Methods: To verify the improvement of yarn in the composite material, the hand lay-up process is used, and the first layer and the fourth layer are replaced by core-spun yarns in a four-layer composite configuration.Results and Discussion: The energy absorption, and the damage of the impacted surface and the back surface are evaluated through the drop weight impact test. The yarn pull-out test can reflect the internal friction of fabric. The results show that the average energy absorption of new yarn in the first layer is 10 J cm2/g more than that in the fourth layer at a 90°/45°/-45°/0° configuration after the normalization, but the conclusion is contrary when the structure is -45°/0°/90°/45°. Under the structure of 90°/45°/-45°/0°, the damaged area of the fabric is larger when the aramid core-spun yarn is laid on the first layer, while a contrary result can be found for the structure of -45°/0°/90°/45°. The fundamental research will provide design ideas and supports for aramid composite

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https://github.com/violet-sto/HN-GFN.Comment: NeurIPS 202

Research on oil-based plugging technology in a horizontal well section of the Fuling shale gas field

To address the serious issues of leakage, challenging plugging, and significant friction related to the long horizontal section construction in the Fuling shale gas field, a study was conducted to develop a suitable leak-proof agent OBMCP for oil-based drilling fluid. The difficulties of preventing leakage and plugging oil-based drilling fluid were analyzed, and the amount of reactively consolidated polymer HY-1 was controlled at 6.25 – 7.5% while considering the plugging strength requirements and plugging costs. The enhancer ZQJ was controlled at 0.4 – 0.6% to achieve high-strength rapid plugging, and the gelation time was controlled under the premise of safe construction to achieve a controllable curing time. Consequently, the oil-based drilling fluid leakage prevention and plugging technology were formed and applied in the J1 and J2 wells. The field test showed that the optimized drilling fluid had stable performance, a good plugging effect from the consolidation plugging agent, a stable borehole wall, small leakage, and strong anti-pollution ability, thereby achieving high-strength rapid plugging and a controllable curing time

Anthropogenic Noise Aggravates the Toxicity of Cadmium on Some Physiological Characteristics of the Blood Clam Tegillarca granosa

Widespread applications of cadmium (Cd) in various products have caused Cd contamination in marine ecosystems. Meanwhile, human activities in the ocean have also generated an increasing amount of noise in recent decades. Although anthropogenic noise and Cd contaminants could be present simultaneously in marine environments, the physiological responses of marine bivalve mollusks upon coexposure to anthropogenic noise and toxic metal contaminants, including Cd remain unclear. Therefore, the combined effects of anthropogenic noise and Cd on the physiological characteristics of the blood clam Tegillarca granosa were investigated in this study. The results showed that 10 days of coexposure to anthropogenic noise and Cd can enhance adverse impacts on metabolic processes, as indicated by the clearance rate, respiration rate, ammonium excretion rate, and O:N ratio of T. granosa. In addition, both the ATP content, ATP synthase activity and genes encoding important enzymes in ATP synthesis significantly declined after coexposures to anthropogenic noise and Cd, which have resulted from reduced feeding activity and respiration. Furthermore, the expressions of neurotransmitter-related genes (MAO, AChE, and mAChR3) were all significantly down-regulated after coexposure to anthropogenic noise and Cd, which suggests an enhanced neurotoxicity under coexposure. In conclusion, our study demonstrated that anthropogenic noise and Cd would have synergetic effects on the feeding activity, metabolism, and ATP synthesis of T. granosa, which may be due to the add-on of stress responses and neurotransmitter disturbances

Changes in grassland soil types lead to different characteristics of bacterial and fungal communities in Northwest Liaoning, China

IntroductionSoil microbial communities are critical in regulating grassland biogeochemical cycles and ecosystem functions, but the mechanisms of how environmental factors affect changes in the structural composition and diversity of soil microbial communities in different grassland soil types is not fully understood in northwest Liaoning, China.MethodsWe investigated the characteristics and drivers of bacterial and fungal communities in 4 grassland soil types with 11 sites across this region using high-throughput Illumina sequencing.Results and DiscussionActinobacteria and Ascomycota were the dominant phyla of bacterial and fungal communities, respectively, but their relative abundances were not significantly different among different grassland soil types. The abundance, number of OTUs, number of species and diversity of both bacterial and fungal communities in warm and temperate ecotone soil were the highest, while the warm-temperate shrub soil had the lowest microbial diversity. Besides, environmental factors were not significantly correlated with soil bacterial Alpha diversity index. However, there was a highly significant negative correlation between soil pH and Shannon index of fungal communities, and a highly significant positive correlation between plant cover and Chao1 index as well as Observed species of fungal communities. Analysis of similarities showed that the structural composition of microbial communities differed significantly among different grassland soil types. Meanwhile, the microbial community structure of temperate steppe-sandy soil was significantly different from that of other grassland soil types. Redundancy analysis revealed that soil total nitrogen content, pH and conductivity were important influencing factors causing changes in soil bacterial communities, while soil organic carbon, total nitrogen content and conductivity mainly drove the differentiation of soil fungal communities. In addition, the degree of connection in the soil bacterial network of grassland was much higher than that in the fungal network and soil bacterial and fungal communities were inconsistently limited by environmental factors. Our results showed that the microbial community structure, composition and diversity of different grassland soil types in northwest Liaoning differed significantly and were significantly influenced by environmental factors. Microbial community structure and the observation of soil total nitrogen and organic carbon content can predict the health changes of grassland ecosystems to a certain extent

X-ray Polarimetry of the accreting pulsar 1A~0535+262 in the supercritical state with PolarLight

The X-ray pulsar 1A 0535+262 exhibited a giant outburst in 2020, offering us a unique opportunity for X-ray polarimetry of an accreting pulsar in the supercritical state. Measurement with PolarLight yielded a non-detection in 3-8 keV; the 99% upper limit of the polarization fraction (PF) is found to be 0.34 averaged over spin phases, or 0.51 based on the rotating vector model. No useful constraint can be placed with phase resolved polarimetry. These upper limits are lower than a previous theoretical prediction of 0.6-0.8, but consistent with those found in other accreting pulsars, like Her X-1, Cen X-3, 4U 1626-67, and GRO J1008-57, which were in the subcritical state, or at least not confidently in the supercritical state, during the polarization measurements. Our results suggest that the relatively low PF seen in accreting pulsars cannot be attributed to the source not being in the supercritical state, but could be a general feature.Comment: accepted for publication in Ap