Text-based Person Search in Full Images via Semantic-Driven Proposal Generation

Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images. To close the gap, we study the problem of text-based person search in full images by proposing a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals. Besides, a cross-scale visual-semantic embedding mechanism is utilized to improve the performance. To validate the proposed method, we collect and annotate two large-scale benchmark datasets based on the widely adopted image-based person search datasets CUHK-SYSU and PRW. Comprehensive experiments are conducted on the two datasets and compared with the baseline methods, our method achieves the state-of-the-art performance

Towards a high-intensity muon source at CiADS

The proposal of a high-intensity muon source driven by the CiADS linac, which has the potential to be one of the state-of-the-art facilities, is presented in this paper. We briefly introduce the development progress of the superconducting linac of CiADS. Then the consideration of challenges related to the high-power muon production target is given and the liquid lithium jet muon production target concept is proposed, for the first time. The exploration of the optimal target geometry for surface muon production efficiency and the investigation into the performance of liquid lithium jet target in muon rate are given. Based on the comparison between the liquid lithium jet target and the rotation graphite target, from perspectives of surface muon production efficiency, heat processing ability and target geometry compactness, the advantages of the new target concept are demonstrated and described comprehensively. The technical challenges and the feasibility of the free-surface liquid lithium target are discussed

A Large-Scale Invariant Matching Method Based on DeepSpace-ScaleNet for Small Celestial Body Exploration

Small Celestial Body (SCB) image matching is essential for deep space exploration missions. In this paper, a large-scale invariant method is proposed to improve the matching accuracy of SCB images under large-scale variations. Specifically, we designed a novel network named DeepSpace-ScaleNet, which employs an attention mechanism for estimating the scale ratio to overcome the significant variation between two images. Firstly, the Global Attention-DenseASPP (GA-DenseASPP) module is proposed to refine feature extraction in deep space backgrounds. Secondly, the Correlation-Aware Distribution Predictor (CADP) module is built to capture the connections between correlation maps and improve the accuracy of the scale distribution estimation. To the best of our knowledge, this is the first work to explore large-scale SCB image matching using Transformer-based neural networks rather than traditional handcrafted feature descriptors. We also analysed the effects of different scale and illumination changes on SCB image matching in the experiment. To train the network and verify its effectiveness, we created a simulation dataset containing light variations and scale variations named Virtual SCB Dataset. Experimental results show that the DeepSpace-ScaleNet achieves a current state-of-the-art SCB image scale estimation performance. It also shows the best accuracy and robustness in image matching and relative pose estimation

A Large-Scale Invariant Matching Method Based on DeepSpace-ScaleNet for Small Celestial Body Exploration

Small Celestial Body (SCB) image matching is essential for deep space exploration missions. In this paper, a large-scale invariant method is proposed to improve the matching accuracy of SCB images under large-scale variations. Specifically, we designed a novel network named DeepSpace-ScaleNet, which employs an attention mechanism for estimating the scale ratio to overcome the significant variation between two images. Firstly, the Global Attention-DenseASPP (GA-DenseASPP) module is proposed to refine feature extraction in deep space backgrounds. Secondly, the Correlation-Aware Distribution Predictor (CADP) module is built to capture the connections between correlation maps and improve the accuracy of the scale distribution estimation. To the best of our knowledge, this is the first work to explore large-scale SCB image matching using Transformer-based neural networks rather than traditional handcrafted feature descriptors. We also analysed the effects of different scale and illumination changes on SCB image matching in the experiment. To train the network and verify its effectiveness, we created a simulation dataset containing light variations and scale variations named Virtual SCB Dataset. Experimental results show that the DeepSpace-ScaleNet achieves a current state-of-the-art SCB image scale estimation performance. It also shows the best accuracy and robustness in image matching and relative pose estimation

Spatio-Temporal Pattern and Influence Mechanism of Cultivated Land System Resilience: Case from China

The study of cultivated land systems from the perspective of resilience is of great significance for the innovation of the research paradigm of cultivated land use and the rational utilization and protection of cultivated land. This study aims to explain the theoretical connotations of cultivated land system resilience (CLSR), construct an evaluation system and zoning rules for CLSR, and take 30 provinces of China as case study areas to explore the influencing factors of CLSR, so as to provide a reliable governance plan for the sustainable development of cultivated land. The results show that: (1) CLSR refers to a sustainable development ability that CLS—by adjusting the structure and scale of internal elements—absorbs and adapts to internal and external disturbances and shocks to the maximum possible extent, abandons the original inapplicable state, creates a new recovery path, achieves a new balance, and avoids system recession. (2) The overall CLSR of the 30 provinces showed an upward trend, and the degree of polarization of the distribution pattern was gradually intensified and experienced a transition process from “leading by resource and ecological resilience—equilibrium of each resilience—leading by production and scale structural resilience”. (3) In the north, east, and south coastal areas of China, CLSR mainly consists of the major evolution areas and the stable development areas; the potential excitation areas of CLSR are mainly concentrated in the central and western regions of China; the CLSR-sensitive lag areas and degraded vulnerable areas are mainly distributed in the northwest and southwest of China. (4) Water resource endowment has a strong influence on CLSR, while social economy mainly influences CLSR through ‘economic foundation-superstructures’ and ‘economic development-factor agglomeration’. (5) According to the different CLSR zones, CLSR was strengthened mainly from the aspects of driving factor agglomeration, building factor free-flow systems, and multi-means support

Spatio-Temporal Pattern and Influence Mechanism of Cultivated Land System Resilience: Case from China

The study of cultivated land systems from the perspective of resilience is of great significance for the innovation of the research paradigm of cultivated land use and the rational utilization and protection of cultivated land. This study aims to explain the theoretical connotations of cultivated land system resilience (CLSR), construct an evaluation system and zoning rules for CLSR, and take 30 provinces of China as case study areas to explore the influencing factors of CLSR, so as to provide a reliable governance plan for the sustainable development of cultivated land. The results show that: (1) CLSR refers to a sustainable development ability that CLS—by adjusting the structure and scale of internal elements—absorbs and adapts to internal and external disturbances and shocks to the maximum possible extent, abandons the original inapplicable state, creates a new recovery path, achieves a new balance, and avoids system recession. (2) The overall CLSR of the 30 provinces showed an upward trend, and the degree of polarization of the distribution pattern was gradually intensified and experienced a transition process from “leading by resource and ecological resilience—equilibrium of each resilience—leading by production and scale structural resilience”. (3) In the north, east, and south coastal areas of China, CLSR mainly consists of the major evolution areas and the stable development areas; the potential excitation areas of CLSR are mainly concentrated in the central and western regions of China; the CLSR-sensitive lag areas and degraded vulnerable areas are mainly distributed in the northwest and southwest of China. (4) Water resource endowment has a strong influence on CLSR, while social economy mainly influences CLSR through ‘economic foundation-superstructures’ and ‘economic development-factor agglomeration’. (5) According to the different CLSR zones, CLSR was strengthened mainly from the aspects of driving factor agglomeration, building factor free-flow systems, and multi-means support

Research on Neutronics Safety Parameters of the AP1000 Nuclear Reactor under Different Conditions

Changes in temperature during reactor operation may cause changes in physical parameters, leading to core overheating and accidents. It is essential to analyze and assess the safety parameters of the core under different operating conditions. This paper investigates the effects of fuel temperature, moderator density, boron concentration, and control rod state on AP1000 safety parameters. The study uses RMC and NJOY to calculate the changes in reactivity factor, effective delayed neutron fraction, and neutron generation time of the AP1000 reactor under different operating conditions. The changes in reactivity coefficients, neutron fluxes, and relative power densities of AP1000 reactors are analyzed for normal and accidental operating conditions. The results indicated that the reactivity coefficient remained negative under accident conditions, which ensured the safe operation of the reactor. The delayed neutron fraction, neutron flux, and power density distributions are affected by fuel temperature, moderator density, and control rod position. The control rod worth was sufficient for the emergency shutdown of the reactor under accidental conditions. It is demonstrated that the operation of the AP1000 reactor under study conditions is safe and controllable

UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy

Unmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher than that of the Proportional-Integral-Derivative (PID) method. As an indirect result, a state-of-the-art deep reinforcement learning-based UAV control method is validated, where the UAV can learn the optimal strategy of a continuously autonomous landing and perform properly in a simulation environment

15N tracer-based analysis of fertiliser nitrogen accumulation, utilisation and distribution in processing tomato at different growth stages

The unnecessary application of nitrogen could negatively impact both the yield and quality of crops, and could also lead to nitrate leaching and soil pollution. The objective of this study was to investigate the effects of the optimized nitrogen application on 15N accumulation, utilization and distribution of processing tomato in China. Through field experiments with 15N and consisted two treatments: optimized nitrogen application (NY) and no nitrogen application (N0). The results showed that the absorption efficiency of applied fertilizer nitrogen was 46%. In processing tomato plants, 128.9 kg ha−1 of nitrogen came from applied fertilizer, while the remainder of the nitrogen (177.6 kg ha−1) came from the soil. The processing tomato fully absorbed the soil nitrogen, which reduced the nitrogen loss rate and resulted in higher production of dry matter. In the harvest stage. The residual percentage in soil of applied fertilizer nitrogen was 34%, which was mainly distributed in the 0-40 cm soil layer. This reduced the leaching of nitrogen and was conducive to the reuse of crops in the next year. This study provides a reference for the development of precise nitrogen management techniques using drip irrigation methods in arid areas.

Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks

Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accuracy and prolong end-to-end delay when eliminating transmission overhead. In this paper, we propose a data aggregation scheme based on overlapping rate of sensing area, namely AggOR, aiming for energy-efficient data collection in wireless sensor networks with high information accuracy. According to aggregation rules, gathering nodes are selected from candidate parent nodes and appropriate neighbor nodes considering a preset threshold of overlapping rate of sensing area. Therefore, the collected data in a gathering area are highly correlated, and a large amount of redundant data could be cleaned. Meanwhile, AggOR keeps the original entropy by only deleting the duplicated data. Experiment results show that compared with others, AggOR has a high data accuracy and a short end-to-end delay with a similar network lifetime