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In view of the problems of data sparsity and the diversity in academic paper recom-mender systems,based on CONVNCF, this paper proposed an algorithm of convolution with word and heterogeneous information network for academic paper recommendation ( WN -APR) . Firstly, WN -APR algorithm learned user and paper' s diverse features from different semantics to alleviate the sparsity problem. Then it designed an outer product fusing way to seamlessly combine user features with paper features. Replacing of 2D CNN, this algorithm applied 3 D convolution to mine the influence of different features on the performance. Finally, it modified the BPR loss function to enhance diversity in recommendations. Experimental results on CiteULike-a and CiteULike-t datasets show that WN-APR improves the performance of accuracy and diversity over the baseline models. [ABSTRACT FROM AUTHOR]

Garment fit evaluation is one of the main bottlenecks in current fashion design and manufacturing. In the process of remote customization and online shopping the size of the clothes that does not fit has become the primary reason for users to return or exchange them which has seriously affected the intelligent upgrade of the clothing industry. Therefore how to judge the fit degree of a garment without actually trying it on is one of the main problems that needs to be solved urgently in the clothing industry. In previous studies garment fit was usually evaluated by some characteristic indexes such as ease allowance clothing wrinkle the spatial relationship between clothing and the human body and clothing pressure. However most of them have some problems such as the need to try on clothes and the complicated evaluation process which are not suitable for remote customization of clothing and online shopping. With the development of computer vision and machine learning technology a small number of scholars use 2D images to judge garment fit based on clothing wrinkle but only for specific garments and the generalization ability is weak. To solve the problem that garment fit cannot be discriminated in advance in the process of remote customization and online shopping this study took women' s suits as an example and put forward a method to discriminate the fitness of women's suits based on 2D human images. Firstly 2D images and 3D human body size of 462 young women aged 18 - 25 in northeast China were collected and a human body data set was established. Twenty-two samples were selected from this data set and virtual models were established for virtual try-on by using CLO3D software. According to the national clothing size standard of China 10 virtual women's suits with the same style and different sizes were made to establish the garment data set. Based on the principle of Kansei engineering the fitting evaluation labels of women' s suits namely tight fit and loose types were given by expert evaluation method combined with virtual fitting. Then a method to discriminate the fit of women' s suits by using garment features and 2D human image features was proposed. As for the garment features GFs the bust girth and length characteristics of women's suits the features of 2D human body image including the key point distance features DFs and overall features OFs were extracted. The specific extraction method is as follows. Firstly the 2D human body image was preprocessed and normalized and the human body contour was extracted by Canny edge detector and the five DFs in the 2D human body image were obtained namely the distance of chest thickness key points distance waist thickness key points distance hip thickness key points distance waist width key points distance and hip breadth key points distance. Then three OFs of the 2D human body image were extracted namely the body height pixel value feature H the feature of the projected unit area ρ and the feature of the projected area ratio of the front and side of the human body C. Finally the Bayesian classifier was used to establish the garment fit prediction model and the Fisher linear discriminant function was used to establish the women' s suit fit discriminant equation. In this paper computer vision technology was used to extract the feature indexes of garment fit evaluation and a machine learning algorithm was combined to realize the women's suit fit discrimination based on 2D human body images. Experimental results indicate that the garment features proposed in this paper and the 2D human body image features based on computer vision technology can be used to predict the fit of women' s suits and the discriminant model of women' s suits based on the Bayesian algorithm has achieved good discriminant accuracy and the cross-validation discriminant accuracy rate can reach 84. 8% so the model is valid. The women' s suit fit discrimination model based on 2D human image feature learning provides an effective and feasible method for garment fit in fashion design production manufacturing and online shopping. This method provides a theoretical basis for the quantitative evaluation of garment fit and is beneficial to the recommendation of clothing size and the improvement of patterns in clothing customization enterprises and online shopping. [ABSTRACT FROM AUTHOR]

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Traditional geological stratification methods usually rely on manual interpretation and empirical judgment, which have shortcomings such as cumbersome processes, heavy workload, and large impact from human factors. This paper aims to study the ideas and methods of regional engineering geological stratification based on machine learning, and convert the stratigraphic stratification problem into a sequence-to-sequence prediction task of geological body spatial units and a geological attribute feature classification task, so as to improve the accuracy of geological stratification. and efficiency. Based on in-depth research on the original data of engineering geology, this paper proposes to use deep learning methods to train and predict geological stratification of static cone test data, and use classification algorithms to stratify borrow hole soil samples and quantify them. The applicability of the relevant algorithms was evaluated. By comparing the results of traditional methods and machine learning methods, the advantages of machine learning in geological stratification are verified. This study provides a new geological layering idea for regional engineering geology research, which has important practical significance. [ABSTRACT FROM AUTHOR]

The modeling process and uncertainty of different machine learning models to predict landslide susceptibility are different. High-precision assessment of post-seismic landslide susceptibility is the key to effective disaster prevention and mitigation. This paper established the post-seismic landslide database of the Jiuzhaigou earthquake, and 12 causative factors were finally selected for landslide susceptibility modeling by eliminating two factors from 14 initial landslide factors through factor covariance diagnosis. Then selects logical regression（LR）, support vector machine（SVM）, random forest（RF）, and artificial neural network（ANN） machine learning models and 10-fold cross-validation data sampling methods. Finally, the receiver operating characteristic curve and the distribution characteristics of the susceptibility index（mean and standard deviation） are used to discuss the modeling of landslide susceptibility based on machine learning and its uncertainty. The results show that the high susceptibility areas of landslides after the earthquake are mainly developed and distributed along the epicenter and valleys. The AUC values of the four machine learning models all exceed 0.87, which has achieved good applicability for predicting potential post-seismic landslides in the study area. Among them, the mean value and standard deviation of the susceptibility index of SVM are small, indicating that the model has good recognition ability for landslides, and it has the highest prediction accuracy(CVmean=0.925), whereas the other models are ANN（0.920）, LR（0.894） and RF（0.877）. The results of this paper are of great significance for popularizing and promoting the development of machine-learning models in other regions. [ABSTRACT FROM AUTHOR]

In order to solve the problem that class-imbalance data of clinical medical tables tend to have an impact on the model and that the performance of deep learning framework is difficult to match that of traditional machine learning methods when processing scale data tasks, this paper proposed a layer by layer Transformer (LLT) network model based on cascaded under-sampling. LLT deleted the most types of data layer by layer by cascade under-sampling method to achieve the balance of data categories and reduced the impact of class-imbalance data on the classifier. Moreover, LLT used attention mechanism to carry out correlation evaluation on the features of the input data to achieve feature selection, refined the feature extraction abi-lity and improved the model performance. This paper used RA (rheumatoid arthritis) data as test samples. Experimental results show that, on the premise of not changing the sample distribution, the recognition rate of a few categories is increased by 6.1% by the proposed cascade under-sampling method, which is 1.4% and 10.4% higher than that of the commonly used NEARMISS and ADASYN respectively. The accuracy of the RA tabular data and the F1-score index of LLT reach 72.6% and 71.5%, the AUC value is 0.89, the mAP value is 0.79, and the performance exceeds the current mainstream tabular data classification models such as RF, XGBoost and GBDT. This paper also visualized the model process and analyzed the characteristics affecting RA. It has a good guiding significance for the clinical diagnosis of RA. [ABSTRACT FROM AUTHOR]

As a classification task, the detection of new malicious users in social networks has been facing the lack of datasets and labels of malicious users. With limited data, this paper proposed a method based on adaptive differential graph convolution to detect malicious users accurately. By extracting user features and social relationships in the social network, the method constructed the social network graph. After this, it calculated the similarities between node and its neighbors to prioritize the neighbors, and used the priority order to sample key neighbors. The node used adaptive weighted average to aggregate the features of key neighbors to itself, to update its features. After feature updating, by feature dimension reduction and normalization, the node got its malicious value, for malicious detection. The experiment results show that, compared to other methods, the method proposed in the paper achieves higher precision and overall accuracy on detection of new malicious users, with a satisfactory speed. Results also demonstrate that adaptive differential graph convolutional networks can effectively capture the key features of a small number of data samples. [ABSTRACT FROM AUTHOR]

Federated learning solved the data silo problem of machine learning. However, the dataset of each party may have large differences in the instance space and feature space, which led to the degradation of prediction accuracy of the federated model. To address the above problems, this paper proposed a federated learning method based on differential privacy protection knowledge transfer. The method used boundary-expanding locality-sensitive hashing to calculate the similarity between instances of each party, and carried out weighted training of instances according to the similarity to achieve instance-based federated transfer learning. In the above process, each party needs not to disclose their instances to other parties, preventing the direct leakage of privacy. Meanwhile, to reduce the indirect privacy leakage in the knowledge transfer process, the proposed method introduced differential privacy mechanism to perturb the gradient data transmitted between all parties, so as to achieve privacy protection in the process of knowledge transfer. Theoretical analysis shows that the knowledge transfer process satisfies ε-differential privacy protection. This paper implements the proposed method based on the XGBoost model. The experimental results show that, compared with the other method without knowledge transfer, the proposed method reduces the test error of the federated model by more than 6% on average. [ABSTRACT FROM AUTHOR]

In order to explore the correlation between the elastic energy index, stress coefficient, brittleness coefficient and burial depth of four rockburst indexes and the rockburst grade, and to solve the black box problem of complex machine learning algorithms. The LIME algorithm was introduced in this paper to improve the interpretability of machine learning in rock burst prediction. In this paper, 190 sets of rockburst engineering construction data sets at home and abroad were collected. After preprocessing, the optimal algorithm was obtained by comparing 9 kinds of machine learning algorithms. The optimal parameters of the algorithm were obtained by Bayesian optimization, and the rockburst prediction model was established. Four kinds of rockburst were analyzed based on LIME algorithm. The correlation analysis, regression analysis and threshold analysis were carried out on the indexes. Finally, the rockburst of Zhongnanshan tunnel shaft project was predicted by using two thresholds of elastic energy index and stress coefficient. The research results show as follows. The rockburst grade is linearly related to the elastic energy index and stress coefficient, and the linear relationship of the elastic energy index is more obvious. The rockburst grade is nonlinearly related to the brittleness coefficient and burial depth, and the brittleness coefficient. The nonlinear relationship is more obvious. the four rockburst indexes influence the rockburst grade in order: elastic energy index, stress coefficient, burial depth, and brittleness coefficient. The LIME algorithm can accurately express the relationship between rockburst grade and rockburst grade. The correlation between the explosion indicators and the obtained thresholds of the two indicators are consistent with the Zhongnanshan tunnel shaft project example. [ABSTRACT FROM AUTHOR]

Autonomous navigation is an important guarantee for intelligent agricultural machinery to complete harvesting operations. Navigation line recognition is an important step in achieving autonomous navigation of intelligent agricultural machinery. Real-time and accurate detection of harvesting navigation lines can effectively accelerate the progress of operations and reduce crop loss rates. Visual sensors have the advantages of low cost and rich information acquisition, making them widely used in route detection. Frequent changes in lighting under cloudy weather make it difficult for a single feature to cope with wheat field environments. In this study, a wheat harvesting boundary detection method based on multi texture feature fusion was proposed. In the harvested area, the characteristics of low stubble and no top wheat ears result in differences in uniformity, density, and other aspects when harvesting under both light and backlight compared to the non-harvested area. At the same time, due to the exposed wheat stubble in the harvested area, it appears more regular on the image, and its gradient direction is more consistent than that in the non-harvested area. Therefore, a two-dimensional feature vector composed of image entropy and directional gradient was constructed to classify the harvested and unharvested areas of the wheat field, and then the harvesting boundary was extracted. Due to the susceptibility of image entropy features to perspective phenomena, this paper compared the entropy features of different regions in the image when fixing the camera installation angle and height, and ultimately selected the middle region of the image as the region of interest. After analyzing the characteristics of image entropy feature extraction, a histogram statistical method based on sliding windows was proposed to accelerate the speed of image entropy feature extraction. By further dividing the window to be calculated into several sub windows, sliding and combining these sub windows significantly reduced the computation. The entropy feature extraction in this paper took 0.53 seconds, which is 49.52% faster than the traditional method of directly extracting entropy from the entire window. After extracting twodimensional features, based on the distribution characteristics of the feature histogram and combined with the maximum entropy threshold segmentation algorithm, the wheat field image was preliminarily classified. For entropy features, low noise points affected the threshold segmentation effect. By comparing the impact of discarding different proportions of low entropy points on the segmentation threshold, this paper discarded 10% of low entropy points, enhancing the threshold segmentation effect. The method of removing small connected regions was used to eliminate the misclassified regions in the initial classification binary image. Then, the Canny operator was used to extract edge contour points distributed near the harvesting boundary. Due to the consistent direction between the harvesting boundary and the agricultural machinery's forward direction, the Ransac algorithm was used to restrict the fitting line in the region, obtaining the accurate harvesting boundary. In order to verify the feasibility of the method proposed in this article, wheat field images under different lighting conditions were collected, including 2 200 weak light images, 758 local strong light images, and 1 134 strong light images. After selecting the region of interest, the size of the image to be processed was 240 pixels×1 280 pixels. The algorithm in this paper took an average of 0.88 s to process each image on laptops. The detection rates under weak light, local strong light, and strong light were 90.41%, 88.26%, and 89.68%, respectively, with an average of 89.45%. Compared with the traditional method of using Adaboost algorithm for machine learning, the detection speed of this algorithm was improved by 73.89%, and the detection rate was increased by 46.19 percentage points, 46.00 percentage points, and 49.64 percentage points under weak light, local strong light, and strong light, respectively. Compared with traditional algorithms, the method proposed in this paper significantly improved real-time performance and detection rate, which can provide reference for the detection of field agricultural machinery navigation lines. [ABSTRACT FROM AUTHOR]

In the practical application of recommendation systems, item popularity bias can be amplified by feedback loops, machine learning training models, and some external factors. It results in a phenomenon where a large number of long-tail items do not get a fair chance to be recommended. To address the fairness problem caused by the feedback loop amplifying the popularity bias, this paper conducted the first fairness analysis and enhancement study by means of a stochastic model checking method. It modeled the traditional recommendation system framework based on popularity bias and feedback loops as a DTMC and verified the fairness properties. The experiment revealed that as the number of feedback loop rounds increased, the Matthew effect intensified and fairness significantly diminished. This paper presented a fairness-enhanced recommendation system framework (FERSF) guided by stochastic model checking. It added a dynamic fairness threshold detection process to the feedback loop of the traditional framework to monitor the fairness. Also, it made a fairness-enhanced adjustment of the feedback influence factor to mitigate the impact of popularity bias on the system. The experimental analysis shows that the fairness of FERSF is significantly improved compared to the traditional recommendation system. Compared with the methods based on utility functions for fairness improvement, FERSF fundamentally inhibits the amplification of popularity bias due to the dynamic nature of the combined feedback loop. Compared with other algorithm-specific fairness improvements, FERSF is highly compatible because it is modeled based on the recommendation system framework. [ABSTRACT FROM AUTHOR]

In recent years, various human action recognition algorithms have achieved excellent recognition performance based on a large number of labeled samples. However, in practical applications, acquiring training samples and their corresponding labels is time-consuming and laborious, which limits the actual implementation of the algorithm. This paper summarized the deep learning algorithms for action recognition under weak supervision and few-shot learning. Firstly, in the case of weak supervision, it classified and summarized the semi-supervised action recognition methods and unsupervised domain adaptation video action recognition methods separately. Then, it reviewed video action recognition algorithms based on few-shot learning in detail. Further, it summarized the relevant human behavior datasets and analyzed and compared the performance of various relevant video action recognition algorithms on these datasets. Finally, the paper summarized the full text and discusses the future development trend of human action recognition. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Methods: Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS signal-to-noise ratio observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN). Results: Comparing the inversion effects of above three models, BPNN model has the best inversion effect, followed by RF model, and LSSVM model is the worst. The results show that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, and BPNN models are 0.830, 0.953, and 0.980, respectively, and the corresponding root mean square errors are 0.045, 0.035 and 0.032 cm³ /cm³ Conclusions: Compared with the single feature inversion method, both the accuracy and correlation coefficient of soil moisture inversion increase significantly. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method. [ABSTRACT FROM AUTHOR]

In the era of big data, with the continuous popularization of social media, various text data are growing in the network and in life. It is of great significance to analyze and manage text data using text classification technology. Text classification is a basic research field in the field of artificial intelligence natural language processing. Under the given criteria, it classifies text according to content. The application scenarios of text classification are very extensive, such as sentiment analysis, topic classification, relationship classification, etc. Deep learning is a method of representation learning based on data in machine learning, and it shows good classification effect in text data processing. Chinese text and English text have differences in form, sound, and image. Focusing on the uniqueness of Chinese text classification, this paper analyzes and expounds the deep learning methods used for Chinese text classification, and finally sorts out commonly used datasets for Chinese text classification. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy and efficiency of predicted compressible flowfields arounds airfoils at high Reynolds number, large angle of attack (AoA), and high Mach numbers, a machine learning inference method based on coordinate transformation method and UNet neural network is propesed. Firstly, a novel coordinate transformation method for data pre-processing is developed. This method transforms physical quantities and grid information in computational fluid dynamics into spatial information of neural network, making the distribution of flowfield information more in line with the input requirements of the neural network. Secondly, an improved deep UNet neural network is established, allowing the model to learn the fine and complex localized flow characteristics of the airfoil flowfield. The two innovative methods are combined to establish a machine learning inference method for the compressible flowfield of airfoils, and a fast and high-precision inference model is obtained. Finally, the flowfields and aerodynamic forces of different types of airfoils are predicted and analyzed, and results are compared with those from traditional machine learning method. The results show that the machine learning inference method proposed in this paper can better predict the compressible flowfield of airfoils, and can better capture the complex flow behavior at high Reynolds numbers, and predict the flow separation and shock waves phenomena under high Mach numbers with large AoA. [ABSTRACT FROM AUTHOR]

The increase of IC design complexity and the continuous reduction of process feature size bring new severe challenges to static timing analysis (STA) and chip design cycle. In order to improve the efficiency of STA and shorten the chip design cycle, this paper fully considers the FinFET process characteristics and the principle of STA, and predicts the timing characteristics of another part of corners by introducing machine learning methods based on the timing characteristics of some corners. The experiment is based on an industrial design, and the results show that the proposed method uses 5 corners to predict the timing of other 31 corners, which can achieve an average absolute error of less than 2 ps, far better than the 21 process angles required by traditional methods. Thus, the proposed method significantly improves the prediction accuracy and significantly reduces the workload of static time series analysis. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is one of the main problems affecting the health of middle-aged and elderly people, and its most serious complication is osteoporotic fracture (OPF), which also has a high disability and mortality rate. The accurate screening and diagnosis of OP mainly rely on imaging analysis, and the diagnosis result rely on the subjective judgment of doctors. Therefore, the traditional imaging method of OP have certain challenges to the accurate screening, diagnosis and differentiation of OP. The cross-fusion of machine learning (ML) and imaging images effectively improves the diagnostic efficiency of clinicians, while realizing the prediction of unknown diseases (such as OPF). Recently, significant progress has been made in the screening and diagnosis of OP and in the diagnosis and prediction of OPF. This paper will review the recent related studies on ML model-assisted screening and diagnosis of OP based on imaging data, so as to provide new ideas for accurate clinical diagnosis and treatment of OP. [ABSTRACT FROM AUTHOR]

Continual learning, as a special machine learning paradigm that continuously learns new tasks in non-stationary data streams and can maintain the performance of old tasks, is a hot research topic in fields such as visual computing and autonomous robotics, but at this stage, the catastrophic forgetting problem is still a great challenge for continuous learning. This paper conducted a review study on the catastrophic forgetting problem of continual learning, analyzed the mechanism of catastrophic forgetting problem mitigation and explored the catastrophic forgetting problem solving strategies at three levels, included regularization strategy, replay strategy, dynamic architecture strategy and joint strategy, in terms of model parameters, training data and network architecture. According to the existing literature, this paper condensed the evaluation index of the catastrophic forgetting method and compared the performance of solving strategies for different catastrophic forgetting problems. Finally, it pointed out the future research direction of continual learning, to provide references for the study of continuous learning catastrophic forgetting problems. [ABSTRACT FROM AUTHOR]

In this paper, the effects of different task allocation strategies on the performance of sparse matrix and dense vector multiplication are discussed. It is observed that the selection of task allocation strategy can significantly affect the performance of sparse matrix, and there is no fixed task allocation strategy that can obtain the best performance for all sparse matrices. Therefore, this paper proposes an optimal task allocation strategy selection method based on machine learning. Its training process only uses sparse matrix features to characterize the input data set, and can automatically train the model for a given data set and target platform. Experiments show that, compared with the default block allocation method, the task allocation method selected by this model can achieve an average performance improvement of about 35%. [ABSTRACT FROM AUTHOR]

Aiming at the hybrid flow shop scheduling problem with the objective of minimizing the makespan, this paper proposed an opposite artificial bee colony algorithm with opposition-based learning to solve the problem. First, according to the characteristics of hybrid flow shop scheduling problem, this paper established the corresponding mathematical model and simulation optimization model. Secondly, in order to avoid the problem of local optima in the process of optimization, this paper added the opposition-based learning strategy in the three stages of population initialization, employment bee and observation bee, used the reverse order strategy between two points and the element exchange strategy to speed up the optimization, then used the elitist preservation strategy to retain the optimal solution. Finally, the simulation experiment selected two examples and 21 benchmark problems of different scales, the results of simulation experiment verified that proposed algorithm is effective in solving such problems by comparison with the experimental of related algorithms. [ABSTRACT FROM AUTHOR]

The study of entanglement propagation in the dynamics of quantum multibody systems is an important problem in non-equilibrium physics. This paper optimizes the magnetic field to maximize the entanglement entropy and studies its influence on the entanglement propagation properties of quantum states. On the quantum Ising chain with the nearest neighbor coupling, the magnetic field obtained by variational optimization is applied to maximize the entanglement entropy of the final state and improve the entanglement growth rate in the ballistic transport region. At the same time, the entanglement entropy of the short chain can reach the entanglement saturation value of the long chain under a fixed magnetic field. When the chain length is increased, the saturation value of entanglement entropy increases, but the growth rate is almost the same. Specifically, by controlling the magnetic field, the entanglement growth rate of Heisenberg antiferromagnetic chain with only the nearest neighbor interaction can be increased to the same rate as that of the system with long-range interaction. The research in this paper reveals the ability of magnetic field to control entanglement propagation in non-equilibrium process, which can be applied to quantum control and information processing in the future. [ABSTRACT FROM AUTHOR]

Objectives: To address the problem of internal inconsistency of classification targets in existing three dimensional(3D) point cloud data segmentation and classification methods. we propose a high-precision classification method for indoor point cloud jointly optimized by super voxel random forest and long short-term memory (LSTM) neural network. Methods: The method takes into account that the super voxel structure has the characteristics of internal feature consistency, divides the original point cloud into super voxels, and uses super voxels as the basic unit for multivariate feature calculation to build a super voxel random forest classification model for indoor point cloud to achieve coarse classification of point cloud data. On this basis, LSTM is introduced to train and predict the neural network model for the hyper voxel neighborhood connectivity of coarse classification to achieve the optimization of hyper voxel coarse classification results. The validity and accuracy of the proposed classification method are verified based on the open dataset. Results: The results show that the classification accuracy of the proposed classification method can reach 83.2% for 13 types of elements in the open dataset. The training data of the LSTM optimization network proposed in this paper used only the label information of region 1 for model training, while other deep learning frameworks used regions 1-5 for model training, so from the perspective of training data requirements, the point cloud data classification framework proposed in this paper can achieve a relatively better prediction result with a small portion of the training data set. The super voxel-based LSTM optimization method approach has high classification accuracy on objects with obvious set features such as ceiling, floor and wall, however, it is inferior to the deep learning algorithm RandLA-Net in classifying objects with complex structures such as chair, sofa and bookcase. Conclusions: In this paper, we consider the association characteristics between different types of elements embedded in the connection relations among super voxels, and introduce LSTM to train and predict the model for the coarse classification of super voxel neighborhood connection relations to achieve the optimization of coarse classification results of super voxels. The proposed method can achieve better classification accuracy when trained with small samples compared with the classical deep learning framework. [ABSTRACT FROM AUTHOR]

Hepatic encephalopathy is a clinical syndrome of central nervous system dysfunction caused by liver insufficiency. It severely affects the quality of life of patients and may lead to death. Accurate prediction of the risk of developing hepatic encephalopathy is crucial for early intervention and treatment. In order to identify the risk of hepatic encephalopathy in patients in advance, many studies have been devoted to efforts to develop tools and methods to identify the risk of hepatic encephalopathy as early as possible, so as to develop preventive and early management strategies. Most conventional hepatic encephalopathy risk prediction models currently assess the probability of a patient developing hepatic encephalopathy by analysing factors such as clinical data and biochemical indicators, however, their accuracy, sensitivity and positive predictive value are not high. The application of artificial intelligence to clinical predictive modelling is a very hot and promising area, which can use large amounts of data and complex algorithms to improve the accuracy and efficiency of diagnosis and prognosis. To date, there have been few studies using AI techniques to predict hepatic encephalopathy. Therefore, this paper reviews the research progress of hepatic encephalopathy risk prediction models, and also discusses the prospect of AI application in hepatic encephalopathy risk prediction models. It also points out the challenges and future research directions of AI in HE risk prediction model research in order to promote the development and clinical application of hepatic encephalopathy risk prediction models. [ABSTRACT FROM AUTHOR]

Exploring the particle size distribution of suspended load and bed material load in river channels helps to analyze the overall sedimentation situation of river sediment and provide feedback to guide the optimization of reservoir operation. In order to systematically grasp the distribution pattern of suspended load and bed material load particle size in the lower Yellow River, this paper collected and organized the water and sediment series data from 6 key sections such as Huayuankou in the lower Yellow River. Recursive feature elimination algorithm was used for variable selection, and a prediction model for sediment particle size in the lower Yellow River was built based on different machine learning algorithms. The results show that the variable selection algorithm can effectively extract the main influencing factors in the model, and when using machine learning models to predict the sediment particle size of different sections, the overall performance is good on the test set. The R² values of suspended load and bed material load particle size prediction under each section optimization model are between 0.64 and 0.89, and 0.37 and 0.72 respectively. At the same time, when using 2020 data to further verify the predictive effect of the model, the predicted and actual values of suspended load and bed material load particle sizes R² are also up to 0.609 7 and 0.445 6 respectively, which indicates that the built model can effectively achieve the prediction of sediment particle sizes in the lower Yellow River channel. [ABSTRACT FROM AUTHOR]

With the emergence of various delay-sensitive applications, how to improve the system's delay performance has be come a hot topic in academic. However, it is difficult for existing multi-domain embedding algorithms to meet the delay per- formance requirements of virtual networks. Therefore, to solve this problem, this paper proposed a TDS-VNE algorithm. In the node embedding phase, it defined a propagation delay evaluation function(D). In the link embedding phase, it defined a path delay sensing parameter. Simulation results show that the proposed embedding algorithm reduces the average network propaga tion delay and has good results on the virtual network request acceptance rate and long-term benefit-cost ratio [ABSTRACT FROM AUTHOR]

The compressive strength of concrete is a crucial parameter in the design and evaluation of building structures,as it directly impacts the quality and safety of buildings. To solve the current problem that the prediction of concrete compressive strength by the existing machine learning models suffers from issues such as long prediction times and low precision, which cannot meet the real-time and accuracy requirements of the prediction on construction sites, this paper proposes a novel method for predicting concrete compressive strength based on the Tuna Swarm Optimization Algorithm（TSO-ELM）. The proposed method utilizes TSO to optimize the offset value of the connection weight in the initial parameters of the ELM hidden layer, thereby enhancing the prediction accuracy of ELM. In the simulation experiment, the prediction speed of ELM, the optimization ability of TSO, and the generalization of the TSO-ELM model are verified using two sets of concrete data. The results demonstrate that the proposed method can significantly improve prediction speed and accuracy with fewer iterations and better generalization, providing a new approach for the timely prediction of concrete compressive strength in field construction. [ABSTRACT FROM AUTHOR]

With the increasing diversity and complexity of material requirements for underground water conservancy and water pipeline networks, the efficient and convenient design of special materials to meet individual needs through machine learning has become a hot topic of concern. Traditional supervised learning methods are all based on a large dataset to train models, but obtaining large datasets for special materials required in deeply buried underground water pipeline networks and high-end military equipment, such as rare and high-entropy alloys, etc. requires extremely high cost and a long period. To solve this problem, we propose a small sample expansion model-RX-SMOGN, using XGBoost and RFECV algorithms for feature screening. We enrich the dataset with the SMOGN algorithm. In this paper, the phase structure of high-entropy alloys is used as the research object, and traditional machine learning models are trained to predict them to verify the effectiveness of the RX-SMOGN model. From the results of 5-fold cross-verification and 4 evaluation indicators, it can be seen that the RX-SMOGN model fully improves the performance of the machine learning model, provides a more convenient method for alloy material design, and fully improves the efficiency of alloy material design. [ABSTRACT FROM AUTHOR]

As a result of the development of industrial intelligence, intelligent control systems have been equipped in industrial production systems, and one of the important requirements is intelligent anomaly detection. However, achieving intelligent anomaly detection is often difficult due to the streaming data format of dynamic runtime parameters and the coupling of high-dimensional data, which make it challenging to perform reliable and efficient anomaly detection. To this end, this paper proposes an anomaly detection method based on joint distribution. Firstly, the dynamic operating data was sampled from both real-time detection and overall detection perspectives. Then, the joint distribution was modeled using empirical Copula. Finally, the anomaly score was determined based on the model. The dataset of the drainage system in a hydropower station on Dadu River was used to investigate the effectiveness of the proposed method, and the results show that this method is more efficient than traditional anomaly detection methods and improves area under curve(AUC) value and average precision. Meanwhile, the interpretability of this method also provides a reliable basis for subsequent troubleshooting and maintenance of the drainage system. [ABSTRACT FROM AUTHOR]

Local SGD is used in distributed machine learning to alleviate communication bottleneck. However, it increases the time gap between heterogeneous cluster workers in local computation with the characteristics of local multi-round iteration, which bring about large synchronization wait time and the problem of stale parameters. Regarding the above problems, based on the Local SGD algorithm, this paper proposed a dynamic partial synchronization communication method（LPSP）. This method makes full use of the advantage of Local SGD with two-layer decision. After each worker finishes each epoch of local gradient calculation, it determines its communication possibility based on local training situation. And parameter server divides the synchronization worker sets to minimize the synchronization wait time. Based on above strategies, the communication overhead of Local SGD method is reduced and the negative influence of straggler is effectively controlled. Experiments show that LPSP can achieve up to 0.75 to 1.26 times acceleration without loss of training accuracy, and up to 5.14% accuracy improvement, which can effectively speed up the convergence of model training. [ABSTRACT FROM AUTHOR]

Aiming at improving the performance of the default loop scheduling and tile size of Pluto, a commonly used polyhedral compiler, this paper proposes a method to compute a variety of legal permutations for its default scheduling and auto-tune its performance according to the configuration space composed of permutations and tile sizes. Through the processing of scalar dimension that defines loop fusion, both intra and inter permutations for imperfect loop nest are realized. Four machine learning driven auto-tuning strategies are proposed to find the optimized combination of permutation order and tile size for a loop with a given problem size. Under the default tile size, the optimal permutation generated by the extended Pluto compiler in a parallel environment achieves a maximum speedup of 4.02 and a geometric mean of 2.12 compared with the default scheduling of Pluto. By further searching for a better combination of permutation order and tile size, the best auto-tuning strategy achieves a maximum speedup of 5.48 and a geometric mean of 2.86 compared with Pluto's default optimization in a parallel environment. In addition, the best configuration and the learned model obtained by auto-tuning for a particular problem size, when being applied to similar problem sizes, also outperform the default optimization of Pluto in various degrees. [ABSTRACT FROM AUTHOR]

In recent years, the deep learning network model based on UNet and GAN (generative adversarial network) has shown unique advantages in the field of image restoration. But, there are still artifacts, blurring, degradation of texture details, difficult to repair large-area damage, and the repaired holes are incompatible with the background image. In order to solve the problems that the current existing models are not friendly to the repair of large-area damaged images and the degraded images after repair, this paper improved the Shift-UNet model by studying the existing methods. On the basis of U-Net and GAN, this paper added an improved attention mechanism Attention-UNet between each layer of encoder and decoder and integrated it into the Shift-UNet to form Attention-Shift-UNet. And through research, it changed the activation function of the original down-sampling part from Leaky＿ ReLU to SiLU. The improved model not only achieved good results on the 64×64 central cover, but also realized the random cover, and the cover area increased from 20 % to 80 %. The experimental results show that the repair effect of the proposed model is better, especially for the repair of large-area damaged images. After testing on CelebA, Paris Architecture and Paris Stree View datasets, the evaluation indexes have been significantly improved, and SSIM has increased from 0.944 5 to 0.947 1. PSNR is increased from 27.992 7 to 28.553 6. L2 loss is decreased from 0.001 7 to 0.001 5. [ABSTRACT FROM AUTHOR]

Aspect and opinion pair extraction aims to extract aspect and opinion items and match relations from a given sentence. However, related studies typically extract aspects and opinions independently without identifying the relationships. To identify the relationships of aspect and opinion item, this paper proposed a knowledge-augmented multi-task learning model for aspect and opinion pair extraction. First, it used the pre-trained language model to generate word vectors with semantic information for the text. In order to achieve the effect of knowledge enhancement, it used the masked attention mechanism to integrate the semantic information of the knowledge graph into the word vectors, and used the sequence labeling method based on the distance-based attention and conditional random fields to extract aspects and opinions. Finally, it matched the extracted aspects and opinions to predict the corresponding relationship. In order to strengthen the connection between the aspect and opinion extraction module and the matching module, the model adopted a shared coding layer to achieve joint training. In addition, in the training process, the matching module used the real labels as input, and used the result of the extraction module as input in the testing process. Finally, to demonstrate the effectiveness of the model, this paper used three general domain datasets for comparative experiments. The model achieves F, values of 66. 99%, 75. 17% and 67. 30% in aspect and opinion matching tasks respectively, and outperforms other comparative models. [ABSTRACT FROM AUTHOR]

Comparing the similarity of different enhancements of the same image is the key to achieving remarkable results in contrastive learning. The traditional contrastive learning method uses two different views of the image. In order to learn more information of the image to improve the classification accuracy, based on MoCo, this paper presented multi-view momentum contrast learning algorithms. In each iteration, this paper used a query encoder and multiple momentum encoders to extract feature for multiple data enhancements of the image, so that it could use more data enhancements and negative samples in this iteration. And it used the optimized noise contrast estimation(InfoNCE) to calculate the loss, so that the query encoder could obtain feature representations which was more beneficial to downstream tasks. The query encoder network updated with gradient backhaul, and each momentum encoder updated with an improved momentum update formula to improve the generalization ability of the model. Experimental results show that using multi-view momentum contrastive learning can effectively improve the classification accuracy of the model. [ABSTRACT FROM AUTHOR]

In order to further mine the changelog information, this paper proposed a siamese Bi-lSTM network model based on the attention mechanism to classify the changelog to realize topic annotation and assist in locating the location of code defects. The model proposed a word segmentation tool with security features to realize changelog preprocessing, used Bi-lSTM network to learn changelog contextual semantic information, and solved the problem of overfilling mode existing in changelog itself through siamese neural network and expanded the data set with high quality to improve generalization ability. It carried out serialization training for the changelog composed of multiple sentences, and the influence of sentences was distinguished through the attention mechanism. For some changelogs of the defect repair class, this paper improved the LLVM tool, generated a mapping table to search the log content, and located the location of the defect module in the source code. A large number of experimental results show that the classification effect of the model in this paper has strong generalization ability, and is nearly 10% higher than the general methods in text classification methods in terms of accuracy, F, value and other indicators, has ideal log classification effect and topic learning effect. [ABSTRACT FROM AUTHOR]

To address the problem of poor recommendation accuracy in the session recommendation model, this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation (CGCD) method.Specifically, it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels. Then, it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding.In addition, it used a multi-head attention mechanism to assign weights to each item in the session. Finally, it aggregated the item embedding with other items in the current session according to the assigned weights, and then generated an accurate session representation to achieve item recommendation. Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10, Pre@20, MRR@10 and MRR@20 by 5.17%, 2.99%, 6.56% and 2.94% on average of the recommended performance, which verifies the effectiveness and efficiency of the method in this paper. [ABSTRACT FROM AUTHOR]

Aiming at the deficiency that K2 algorithm depends on the maximum number of parent nodes and node of order, this paper proposed an improved Bayesian network structure learning algorithm (MWST-CS-K2). The algorithm firstly obtained the maximum number of parent nodes by constructing the maximum support tree (MWST). Then it used the correlation degree and updated coefficient between variables to set rules for adding, subtracting and reversing edges. The paper used improved cuckoo algorithm to optimize the nest location, and applied the breadth first search strategy to search and traverse to get the node order. Finally, the paper used maximum number of parent nodes and the order of nodes as the input of K2 algorithm to get the final network. Experiments show that in the standard ASIA,SACHS and CHILD network data tests, the average correct edge ratio of MWST-CS-K2 algorithm reaches 97.3%, 87.7 % and 95.6% respectively. The learning effect is better than other corresponding comparison algorithms, and the network structure obtains the most similar to the standard network structure. [ABSTRACT FROM AUTHOR]

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low accuracy of 6D pose estimation for textured models under occlusion, this paper proposes an end-to-end 6D pose estimation algorithm based on local feature representation. Firstly, this paper proposes a Spatial and Coordinate Attention mechanism to obtain accurate localization information. A YOLOv5-CBE detection network forms by adding the attention mechanism to the backbone network and introducing a weighted Bidirectional Feature Pyramid Network in the detection layer. The Precision, Recall and mAP@0.5 of YOLOV5-CBE algorithm rise by 3.6%, 2.8% and 2.5% respectively, and the coordinate error of local feature center point decreases by 25% at most. Secondly, the YOLOv5-CBE network detects the local feature key points and calculates 6D pose of the model with 3 D Harris key points by Singular Value Decomposition, and the algorithm can guarantee 2D reprojection Accuracy and ADD Accuracy above 95% with 70% occlusion, which has a strong robustness. [ABSTRACT FROM AUTHOR]

Most of the existing image tampering detection methods are only for a certain tampering method, and there is a problem that the detection accuracy of the tampered area boundary is not high. In this regard, this paper proposed a U-shaped network based on dual-stream encoder-decoder architecture for image tampering. Firstly, the method used the skip connection between the encoder and the decoder to fuse the low-level and high-level features in the tampered image, and used the atrous convolution and CBAM attention mechanism to fuse the features output by the encoder, so that the network had better localization performance for tampered regions of different scales; Secondly, in order to improve the network’s detection accuracy of the boundary of the tampered area, this algorithm used the image morphological method to make a tampered boundary dataset; Finally, it used multiple loss functions to optimize the performance of the network simultaneously, namely it used cross-entropy and root-mean-square loss functions to measure the tampered region loss and tampered boundary loss of the prediction graph respectively. The experimental results on 4 public datasets: CASIA, Columbia, NIST16, Coverage show that the method proposed in this paper can effectively detect the tampering area of the fake image with splicing and copy-paste tampering methods, and output pixel-level tampering region location map. Compared with other mainstream tampering detection methods, the proposed method achieves the highest AUC values on the CASIA and Columbia dataset and the highest F1 values on the Columbia dataset. [ABSTRACT FROM AUTHOR]

In reality, the data generated in many fields usually have multiple classes and are imbalanced. In multi-class imbalance classification, problems such as class overlap, noise and multiple minority classes reduce the capability of classifiers, and effective solution of multi-class imbalance problem has become an important research topic in the field of machine learning and data mining. Based on the recent literature on multi-class imbalance classification methods, this paper analyzed and summarized both data preprocessing and algorithm-level classification methods, and conducted a detailed analysis of all algorithms in terms of advantages, disadvantages and data sets. The data preprocessing methods introduced oversampling, under-sampling, hybrid sampling and feature selection methods to compare the performance of the algorithms using the same datasets. In addition, the algorithm-level classification methods described and analyzed base classifier optimization, ensemble learning and multi-class decomposition techniques. Finally, this paper summarizes the future development directions of the multi-class imbalanced data classification research field. [ABSTRACT FROM AUTHOR]

Network fault management aims to detect, identify and correct error conditions occurring in the network, providing users with reliable and stable network services. Recently, the utilization of machine learning methods for network fault management has attracted widespread attention. In this regard, this paper first introduced the research background of cell network fault management and explain the process and function of network fault management, and then introduced the existing cell network fault management frameworks. Subsequently, this paper reviewed the existing research on machine learning methods in cell network fault management. Referring to the fault management lifecycle, this paper introduced, summarized and compared the machine learning methods for fault detection, fault diagnosis and fault prediction for the purpose of providing reference for research in related fields. [ABSTRACT FROM AUTHOR]

Multiple random empirical kernel learning machine(MREKLM) selects a small number of samples to construct the empirical feature space, but does not take into account the distribution of data when projecting, and takes a long time to select samples. In order to utilize the distribution information of samples, this paper introduced the intraclass dispersion matrix, and proposed an intra-class variance based multiple empirical kernel learning(ICVMEKL), which enabled samples to take into account the intra-class information when projecting. The method enhanced the classification boundary and improved the classification accuracy. Further, in order to reduce the selection time of samples, this paper proposed an improved ICVMEKL(ICVMEKL＿I) based on locality-sensitive hashing algorithm by using the border point extraction based on locality-sensitive hashing(BPLSH) to select samples, so that the samples for constructing the empirical kernel no longer need to be obtained from the candidate set, and the method reduced the training time. Experiments on multiple datasets show that ICVMEKL can effectively improve the accuracy, ICVMEKL＿I can significantly reduce the training time, both of which show good generalization performance. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection accuracy in massive unbalanced data and poor model generalization ability caused by data heterogeneity in traditional anomaly detection methods in IoT scenarios, this paper proposed an adversarial dual-coding anomaly detection network based on federated learning(GB-AEnet-FL). Firstly, this paper proposed an active feature distribution learning algorithm based on abnormal data, which actively learns the potential feature distribution of data, expands abnormal data through data reconstruction, and balances the proportion of positive and negative samples. Secondly, this paper introduced an adversarial training mechanism in the latent feature layer adding consistency enhancement constraints and shrinkage constraints to improve the accuracy of feature extraction. Finally, this paper designed a federated learning algorithm based on dynamic model selection which compares the confidence scores of the local model and the global model, dynamically selects part of the federated bodies to participate, accelerates the aggregation of models, and protects user privacy to a certain extent. Validated on four different datasets, the results show that the proposed algorithm is better than the traditional algorithm in detection accuracy, and the generalization ability is improved accordingly. [ABSTRACT FROM AUTHOR]

Complementary product recommendation is to recommend products to users that can be used together. Existing product recommendation methods consider all features of product’s images and texts, but do not consider the relationship between visual and textual modalities, and not all features contribute equally to the complementary relationship. Based on this situation, this paper proposes a complementary product recommendation model fusing with attention mechanism, dubbed as CPRFA. The model first enriches its feature representation with the product’s image and text information, and secondly, in order to unify heterogeneous products with multimodal information, it further converts them into image and text vector representations, and then uses the attention mechanism to adaptively assign weights to different features of the product, and finally learns the relationship between the product and the candidate products for the recommendation of complementary products. In the experiments based on the Amazon dataset, the CPRFA model proposed in this paper outperforms other baseline models in all indicators. Experiments show that the CPRFA model can recommend complementary products to users more accurately. [ABSTRACT FROM AUTHOR]

The U-turn task is one of the contents of autonomous driving research, and most of the solutions under the standard roads in cities cannot be implemented on non-standard roads. Aiming at solving this problem, this paper establishes a vehicle U-turn dynamical model and designs a multi-scale convolutional neural network to extract feature maps as the input of the agent. In addition, for the sparse reward problem in the U-turn task, this paper proposes a hierarchical proximal policy optimization algorithm that combines hierarchical reinforcement learning and proximal policy optimization algorithm. In experiments with simple and complex scenarios, this algorithm learns policies faster and has a higher success rate of U-turn compared to other algorithms. [ABSTRACT FROM AUTHOR]

In order to overcome the challenges of policy learning in Multi-Agents Systems（MASs）, such as the unstable environment and the interaction of agent decisions, this paper proposes a method named Observation Relation Extraction（ORE）, which uses a complete graph to model the relationship between different parts of each agent’s observation, and takes advantage of the attention mechanism to calculate the importance of the relationship between different parts of each agent’s observation. By applying the above method to multi-agent reinforcement learning algorithms based on value decomposition, this paper proposes multi-agent reinforcement learning algorithms based on observation relation extraction. Experimental results on StarCraft Multi-Agent Challenge（SMAC） show the proposed algorithms with ORE leads to better performance than the original algorithms in terms of both convergence speed and final performance [ABSTRACT FROM AUTHOR]

The study of asperities is of great importance for seismic hazard analysis. This paper introduces the concepts of asperities, barriers and their source models, and reviews the progress of research on the determination of asperities by seismic geodesy, mainly discusses the technique and method for determining asperities from the aspects of medium structure analysis （high-intensity anomalies）, seismic activity analysis, the fault slip pattern, and the problems are analyzed. This paper also prospects the determination of the distribution of asperities on active faults, future researches will focus on joint inversion of geodetic data, joint geodetic and seismic geophysical data, and machine learning based methods to determinate the distribution of asperities. The study provides a strong theoretical basis for seismic safety evaluation. [ABSTRACT FROM AUTHOR]

Facing the current situation of load uncertainty, new energy grid integration, and “dual carbon” target in the power system, the paper established a real-time pricing model considering load uncertainty and carbon trading in the context of the smart grid with full consideration of the welfare of both supply side and user side. Based on the advantages that reinforcement learning can handle variable complexity, non-convex, and nonlinear problems, this paper used the Q-learning algorithm in reinforcement learning to solve the model iteratively. Firstly, this paper transformed the real-time interaction process between the user and the power supplier into a Markov decision process corresponding to the reinforcement learning framework. Secondly, the process represented the information interaction between the user and the power supplier as the iterative exploration of the agent in a dynamic environment. Finally, this paper found the optimal value by the Q-learning algorithm in reinforcement learning, i. e., the maximal social welfare value. The simulation results show that the proposed real-time pricing strategy can effectively enhance social welfare and reduce total carbon emissions, which verifies the feasibility and effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Machine learning, as a new technique combining statistics and computer science, has been widely used in crop information acquisition tasks in recent years. Traditional methods for obtaining crop information mainly rely on chemical detection methods, which is time-consuming and labor-intensive. Based on machine learning algorithms and hyperspectral remote sensing techniques, crop appearance and internal physical and chemical parameters can be quickly sensed in a non-destructive way, which has obvious application advantages and development prospects. First, the researches related to the hyperspectral remote sensing of crop information were systematically reviewed in this paper. Second, the application, advantages and disadvantages and uncertainties of different machine learning algorithms in hyperspectral sensing crop information were summarized. Finally, it was pointed out that the future development trends of hyperspectral sensing crop information were as follows: (1) complementary crop information acquisition methods could be realized through multi-source remote sensing collaborative observations. (2) The assimilation technologies of hyperspectral remote sensing and crop model as well as the deep integration technologies of hyperspectral remote sensing and artificial intelligence could be developed. (3) The intelligent acquisition of key information oriented to the whole growth period of crops and decision-making could be realized. [ABSTRACT FROM AUTHOR]

The complexity of modern war is increasing，and the rapid operational mission planning is of great importance to improve the efficiency of command and control. This paper presents a joint operational Task Matrix （TM） model，which is a theoretical method for mission planning; A belief network model is put forward to describe the relationship among the elements in TM model ;A naive bayesian learning method for belief network is designed; A mechanism of imagination is put forward to speed up the learning process ; A search algorithm named Deep Minimum Threat Generation Tree（DMTGT） is proposed, which can efficiently calculate task priority by balancing search error and search speed. Finally, the validity of above models and algorithms is verified by simulation experiments. [ABSTRACT FROM AUTHOR]