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Prediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping‐based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow‐related hydrological signatures. Key Points: A classification‐based training strategy is introduced for the regional long short‐term memory (LSTM) modelThe influence of static attributes on the performance of the regional LSTM model in ungauged basins is investigatedThere is a high level of consistency in the enhancement achieved by the two training strategies, either incorporated with static catchment attributes or based on classification [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study is focused on revealing and analysis of external influences that affect the process of teaching the Russian language for specific purposes - business/professional communication to bilingual learners. The analysis is correlated with the description of external factors that cause a shift of emphasis from teaching the language of profession to teaching business communication. This comprehensive analysis makes it possible to build a uniform teaching course model based on a holistic approach. The study defines the general strategy of training, formulates tasks and outlines approaches to their solution, as well as delineates the contours of a branched out multi-component practical training course.

Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, ASNet, Swin-Unet, and TransUNet). [ABSTRACT FROM AUTHOR]

Copyright of Opuntia Brava is the property of Universidad de Ciencias Pedagogicas de Las Tunas, Centro de Documentacion e Informacion Pedagogica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective is to enhance an understanding of how the KASH model can be used as a training tool to help managers achieve their goals of increasing individual commitment and organizational effectiveness. This study employed a qualitative method using a semi-structured interview to examine the application of the KASH model in Malaysia's automotive industry. Thus, the population of this study consists of employees, trainers, and training managers from various departments in Malaysia's automotive industry, with a population of approximately 709,000 predicted in 2023. The KASH model is a strategy and degree of innovation that assesses a person's competence to do an individual activity with various elements or characteristics that can be categorized might make a little more sense as their level of professional commitment by constructing an efficient training program. The KASH model should be recommended and implemented in all industries to foster employee commitment to their professions. [ABSTRACT FROM AUTHOR]

Background: With the increasing demands aced by teenage athletes in mixed martial arts (MMA) competitions, there is a pressing need for scientists to develop innovative training. This study aims to formulate a strategy for training teenage athletes to excel in high-level MMA competitions, leveraging the physiological characteristics of neuromuscular system adaptation to stress-inducing stimuli. Additionally, the practical efficacy of this strategy is be assessed. Materials and methods: The research encompassed 24 male athletes, representing various age and weight categories of the Ukrainian national youth team in MMA: U14 (G02-G05), U16 (F01-F04) and U18 (E03-E06). At first, the results of athletes' performance at the 2022 MMA World Championships were analyzed. The training procedures for these competitions were analyzed. In the second stage, the strategy for sportsmen's preparation for the World MMA Championship 2023 was developed. It employed physiological features of the neuromuscular system adaptation to stressful stimuli. An innovative system of training management was implemented. The mechanisms for improving the functional support of technical actions in fights were developed. In the third stage of the study, a comparative analysis of the athletes' performance at the World MMA Championships in 2022 and 2023 was carried out. Results: The 2nd place at the 2022 MMA World Championship was won by the study participants due to the successful implementation of suffocation and painful techniques. 20% of the victories in fights depended on the referees' decision. The strategy for training athletes for similar MMA competitions in 2023 was based on maximizing the adaptive potential of the neuromuscular system. The strategy implementation allowed for winning first place at the World MMA Championship 2023. The dynamics primarily increased owing to the growth of the maximum strength and adaptive body reserves, as well as a change in the priority of using technical actions. The number of victories by knockout and technical knockout doubled compared to similar competitions of the previous year. The number of victories obtained by referees' decisions decreased by 3.5 times, which reduced the possibility of errors during judging. Conclusions: Developing a strategy for training teenage MMA athletes requires a detailed study of the physiological processes of the neuromuscular system adaptation to different load modes. The efficiency of performed technical actions and their priority depends on the functional capabilities to carry out these tasks. An important factor of the training system is the validity of using power load regimes depending on the expediency of increasing adaptive reserves of priority muscle groups. [ABSTRACT FROM AUTHOR]

The existing open-source infrared dim and small target dataset have limited data capacity and insufficient scenarios. To improve the generalization and robustness of the network, it is usually needed to establish a new dataset for the specific application scenario. When using the conventional training method in the new dataset, there are problems such as low target-to-background proportion in full-sized images, inaccurate labels due to nonexpert annotators, and inadequate diversity of data, which affect the training efficiency and network performance. This article proposes a training strategy based on the newly established dataset to overcome these problems. Specifically, small-sized image transfer learning is proposed to increase the small target proportion, shorten training time, and improve training efficiency. Moreover, a label refinement method based on loss evaluation metrics is adopted to reduce the impact of inaccurate labeling on network training. In addition, an iterative training method is proposed by supplementing new false alarm and miss detection data into the dataset between each iteration to further improve the training performance. The experiments are carried out and the results show that the above method can effectively shorten training time, improve training efficiency and performance of infrared dim and small target detection networks.

There exist various methods for transferring knowledge between neural networks, such as parameter transfer, feature sharing, and knowledge distillation. However, these methods are typically applied when transferring knowledge between networks of equal size or from larger networks to smaller ones. Currently, there is a lack of methods for transferring knowledge from shallower networks to deeper ones, which is crucial in real-world scenarios such as system upgrades where network size increases for better performance. End-to-end training is the commonly used method for network training. However, in this training strategy, the deeper network cannot inherit the knowledge from the existing shallower network. As a result, not only is the flexibility of the network limited but there is also a significant waste of computing power and time. Therefore, it is imperative to develop new methods that enable the transfer of knowledge from shallower to deeper networks. To address the aforementioned issue, we propose an depth incremental learning strategy (DILS). It starts from a shallower net and deepens the net gradually by inserting new layers each time until reaching requested performance.We also derive an analyticalmethod and a network approximationmethod for training new added parameters to guarantee the new deeper net can inherit the knowledge learned by the old shallower net. It enables knowledge transfer fromsmaller to larger networks and provides good initialization of layers in the larger network to stabilize the performance of large models and accelerate their training process. Its reasonability can be guaranteed by information projection theory and is verified by a series of synthetic and real-data experiments. [ABSTRACT FROM AUTHOR]

Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the performance and training speed of the model in CNNs. This study employed six training strategies, including Adam, SGD, Adam + StepLR, SGD + StepLR, Warm-up + Cosine annealing + SGD, Warm-up + Cosine, and annealing + Adam, with three initial learning rates (0.05, 0.01, and 0.001). Using the wheat stripe rust, wheat powdery mildew, and healthy wheat datasets, five lightweight CNN models, namely MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2, were evaluated. The results showed that upon combining the SGD + StepLR with the initial learning rate of 0.001, the MnasNet obtained the highest recognition accuracy of 98.65%. The accuracy increased by 1.1% as compared to that obtained with the training strategy with a fixed learning rate, and the size of the parameters was only 19.09 M. The above results indicated that the MnasNet was appropriate for porting to the mobile terminal and efficient for automatically identifying wheat leaf diseases. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Administrative & Legal Sciences is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on the Meta-Baseline few-shot learning method, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application.

Introduction: Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by armpostures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks. Method: This work focuses on twomajor functional wristmovements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation. Result: The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks. [ABSTRACT FROM AUTHOR]

To comprehensively understand the repetitive jump ability, this paper explores the research progress of repetitive jump ability abroad by reviewing related foreign literature, including the influencing factors, testing methods, and training strategy. The results show that: (1) in terms of influencing factors, physiological factors include anaerobic capacity, thermal environment, age and gender; biomechanical factors include inter-limb coordination, vertical stiffness, peak force, flight and ground contact times, lower-extremity joint angles, and muscle activation degree; training factors include lower-limb explosive force, relative strength and reaction strength. (2) in terms of testing methods, the differences and connections between various methods focus on movement pattern, duration, interval and jump frequency; the power index has high reliability and validity, but the jump height and fatigue index in some methods lack of test with reliability and validity. The testing methods are commonly applied to sports primarily involving in jump and to sports that emphasizes on conducting repeated explosive lower-limb movements. These methods can used to evaluate lower-limb explosive power endurance and anaerobic capacity. (3) Regarding training strategy, acute training includes squat exercises and cold therapy recovery; long-term training includes specific practical training, plyometrics, combination training of plyometrics and resistance, and repeated-sprint training. The future research should be focus on followings: (1) exploring the influence of aerobic capacity on interval jump performance, as well as the changes in biomechanical parameters of upper-limb swing motion and their impacts on jump performance during repetitive jump; (2) optimizing and developing testing methods and indicators that are consistent with the characteristics about the sports; (3) further enriching acute and long-term training strategy. [ABSTRACT FROM AUTHOR]

Performing lifespan assays with Caenorhabditis elegans (C. elegans) nematodes manually is a time consuming and laborious task. Therefore, automation is necessary to increase productivity. In this paper, we propose a method to automate the counting of live C. elegans using deep learning. The survival curves of the experiment are obtained using a sequence formed by an image taken on each day of the assay. Solving this problem would require a very large labeled dataset; thus, to facilitate its generation, we propose a simplified image-based strategy. This simplification consists of transforming the real images of the nematodes in the Petri dish to a synthetic image, in which circular blobs are drawn on a constant background to mark the position of the C. elegans. To apply this simplification method, it is divided into two steps. First, a Faster R-CNN network detects the C. elegans, allowing its transformation into a synthetic image. Second, using the simplified image sequence as input, a regression neural network is in charge of predicting the count of live nematodes on each day of the experiment. In this way, the counting network was trained using a simple simulator, avoiding labeling a very large real dataset or developing a realistic simulator. Results showed that the differences between the curves obtained by the proposed method and the manual curves are not statistically significant for either short-lived N2 (p-value log rank test 0.45) or long-lived daf-2 (p-value log rank test 0.83) strains.

With the increasing globalization of football,the global player transfer market is becoming more and more prosperous.However,as the most important factor affecting player transfer transaction,the player’s transfer value lacks in-depth model and application research.In this paper,the FIFA’s official player database is taken as the research object.Firstly,on the premise of distinguishing different player positions,Box-Cox transformation,F-Score feature selection,etc.are used to perform feature processing on the original data set.Secondly,the player value prediction model is constructed by XGBoost,and compared with the main machine learning algorithms such as random forest,AdaBoost,GBDT and SVR for 10-fold cross validation experiments.Experimental results prove that the XGBoost model has a performance advantage over the existing models on the indicators of R2,MAE and RMSE.Finally,on the basis of constructing the value prediction model,this paper integrates the SHAP framework to analyze the important factors affecting the players’ value score in different positions,and provides decision support for some scenarios,such as player’s value score evaluation,comparative analysis,and training strategy formulation,etc.

The analysis and calculation of the armature electromagnetic force is the premise of studying the dynamic characteristics of the electromagnetic railgun. Aiming at the problem of the numerical solution "pseudo-oscillation" at high speed, an extrapolation prediction method of armature electromagnetic force based on the Deep Belief Network-Deep Neural Network (DBN-DNN) is proposed. Firstly, the electromagnetic field control equation and armature dynamics equation, considering the influence of armature movement, are given, and the finite element simulation model of the electromagnetic railgun is established to analyze the dynamic characteristics and numerical solution stability of the armature electromagnetic force. Then, based on the stable numerical simulation data under different armature conductivities, a DBN-DNN method is proposed to realize the extrapolation prediction of the armature electromagnetic force under the standard conductance. Finally, the extrapolation prediction performance of the proposed method is tested by two electromagnetic railgun cases. Additionally, we further propose the training strategy of DBN-DNN parameters from solving armature electromagnetic force at low conductivity to standard conductivity. The armature electromagnetic force extrapolation prediction method for the whole launch process from low speed to high speed provides a new idea for the dynamic characteristic analysis of the high-speed electromagnetic railgun. [ABSTRACT FROM AUTHOR]

Tree species classification is an important and challenging task in image recognition and the management of forest resources. Moreover, the task of tree species classification based on remote sensing images can significantly improve the efficiency of the tree species survey and save costs. In recent years, many large models have achieved high accuracy in the task of tree species classification in an airborne remote-sensing manner, but due to their fixed geometric structure, traditional convolutional neural networks are inherently limited to the local receptive field and can only provide segmental context information. The limitation of insufficient context information greatly affects the segmentation accuracy. In this paper, a dual-attention residual network (AMDNet) and a re-parameterized model approach are proposed to capture the global context information, fuse the weight, reduce the model volume, and maintain the computational efficiency. Firstly, we propose MobileNetV2 as the backbone network for feature extraction to further improve the feature identification by modeling semantic dependencies in the spatial dimension and channel dimension and adding the output of the two attention modules. Then, the attention perception features are generated by stacking the attention modules, and the in-depth residual attention network is trained using attention residual learning, through which more accurate segmentation results can be obtained. Secondly, we adopt the approach of structure re-parameterization, use a multi-branch topology for training, carry out weighted averaging on multiple trained models, and fuse multiple branch modules into a completely equivalent module in inference. The proposed approach results in a reduction in the number of parameters and an accelerated inference speed while also achieving improved classification accuracy. In addition, the model training strategy is optimized based on Transformer to enhance the accuracy of segmentation. The model was used to conduct classification experiments on aerial orthophotos of Hongya Forest Farm in Sichuan, China, and the MIOU of tree species recognition using the test equipment reached 93.8%. Compared with current models such as UNet, our model exhibits a better performance in terms of both speed and accuracy, in addition to its enhanced deployment capacity, and its speed advantage is more conducive to real-time segmentation, thereby representing a novel approach for the classification of tree species in remote sensing imagery with significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Predicting the sequence pattern of the handwritten text images is a challenging problem due to various writing styles, insufficient training data, and also background noise appearing in the text images. The architecture of the combination between convolutional neural network (CNN) and recurrent neural network (RNN), called CRNN architecture, is the most successful sequence learning method for handwritten text recognition systems. For handwritten text recognition in historical Thai document images, we first trained nine different CRNN architectures with both training from scratch and transfer learning techniques to find out the most powerful technique. We discovered that the transfer learning technique does not significantly outperform scratch learning. Second, we examined training the CRNN model by applying the basic transformation data augmentation techniques: shifting, rotation, and shearing. Indeed, the data augmentation techniques provided more accurate performance than without applying data augmentation techniques. However, it did not show significant results. The original training strategy aimed to find the global minima value and not always solve the overfitting problems. Third, we proposed a cyclical data augmentation strategy, called CycleAugment, to discover many local minima values and prevent overfitting. In each cycle, it rapidly decreased the training loss to reach the local minima. The CycleAugment strategy allowed the CRNN model to learn the input images with and without applying data augmentation techniques to learn from many input patterns. Hence, the CycleAugment strategy consistently achieved the best performance when compared with other strategies. Finally, we prevented image distortion by applying a simple technique to the short word images and achieved better performance on the historical Thai document image dataset.

Digital competencies are those knowledge or skills that contribute to the cultural transformation and digitization of a company, allowing the safe use of all those tools and technology to streamline and improve the achievement of objectives. In the Public Company of Production and Strategic Development, of the State Technical University of Quevedo, insufficiencies are detected to visualize the products and services offered by the company, for which digital competencies are required to meet the demands of customers and the community. The bases and foundations of the research are determined, the digital skills system is designed and the strategy that allows training workers to raise the quality of their products and services. It is proved that the effectiveness of the training program mediated by information and learning technologies influences the development of digital competences. [ABSTRACT FROM AUTHOR]

Offline handwritten mathematical expression recognition is a challenging optical character recognition (OCR) task due to various ambiguities of handwritten symbols and complicated two-dimensional structures. Recent work in this area usually constructs deeper and deeper neural networks trained with end-to-end approaches to improve the performance. However, the higher the complexity of the network, the more the computing resources and time required. To improve the performance without more computing requirements, we concentrate on the training data and the training strategy in this paper. We propose a data augmentation method which can generate synthetic samples with new LaTeX notations by only using the official training data of CROHME. Moreover, we propose a novel training strategy called Shuffled Multi-Round Training (SMRT) to regularize the model. With the generated data and the shuffled multi-round training strategy, we achieve the state-of-the-art result in expression accuracy, i.e., 59.74% and 61.57% on CROHME 2014 and 2016, respectively, by using attention-based encoder-decoder models for offline handwritten mathematical expression recognition. [ABSTRACT FROM AUTHOR]

Purpose: This paper intends to explore the influence of Human Resource (HR) analytics on the training and development skills in the Information technology (IT) sector concerning Kerala in India. Furthermore, this paper investigates the approaches and practices followed by the HR officials and the IT professionals in the organization. Also, this paper explains how HR analytics can be applied in IT sectors for the betterment of the skills of IT professionals. Design/Methodology/Approach: This paper presents a quantitative study by considering the sample of 213 expertise, qualified, and knowledgeable employees in the IT sector and HR people of Kerala. The hypotheses were verified using Cronbach's Alpha reliability test, and Bivariate Correlation. Findings: From the findings, it is understandable that the HR analytical practices achievea straight and positive influence on the performance of IT employeesthrough practicing training and development skills. Research Limitations/Implications: As for scholars and experts, this study point towardthe practices of HR analytics that help to enhance employee skills through training strategy. Originality/Value: The paper plans perceptions on training, and development practices followed by HR analytics and professionals. Also, it delivers HR analytics that can cope with the development and retention of IT employees. [ABSTRACT FROM AUTHOR]

Oceanic environmental factors have marked impacts on marine ecosystems and economic development. Variations in high-magnitude oceanic environmental factors often lead to marine disasters and ecological crises. Previous studies have demonstrated the success of deep learning in marine environmental prediction tasks; however, limited research has specifically targeted high-magnitude oceanic environmental factors. Here, we propose a deep-learning model training strategy that enhances the model's attention to high-magnitude oceanic environmental factors by incorporating an adaptive weight matrix, thereby improving prediction accuracy. We evaluated the proposed training strategy for two prediction tasks: time-series and physics-based predictions. Compared to the baseline model, the prediction error for high-magnitude oceanic environmental factors was reduced by up to 56.93 % using this training strategy, and the stability of the model performance in medium-to long-term forecasts was enhanced. In addition, the suitability of the strategy across diverse scenarios was examined by assessing its robustness and computational overhead. This study provides new insights for more accurate prediction of oceanic environmental factors. • A deep learning model training strategy is proposed to enhance the accuracy of forecasting high-magnitude ocean environmental elements. • The training strategy significantly enhances the forecasting accuracy of the model for high-magnitude ocean environmental elements, while also bolstering the stability of model performance in medium-to long-term forecasts. [ABSTRACT FROM AUTHOR]

This paper focuses on semi-supervised video object segmentation (VOS). Recently, several Space‐Time Memory based networks have effectively improved the performance of VOS. However, most methods predict the target object mask forwardly, which causes error propagation to mislead the future frame segmentation. Moreover, the rich multi-scale information of objects needs to be effectively exploited in videos to extract fine-grained multi-scale spatial information. To address these limitations, we present a network with a multi-scale attention module for semi-supervised VOS, which combines a new bidirectional strategy during training. Firstly, we propose the bidirectional strategy in which a backward flow combines the existing standard forward flow. With the strategy, we can rely on the first frame's ground-truth mask to mitigate the problem of error propagation. Secondly, a multi-scale attention module is designed to extracts multi-scale features by different weights and interacts with information between multi-scale channel attention. Especially the multi-scale attention module can effectively extract the fine-grained mask by the network during the bidirectional training. Experimental results show that our network achieves significant segmentation performance compared to state-of-the-art approaches on the YouTube-VOS and DAVIS datasets. • Propose a bidirectional strategy to alleviate the error propagation problem. • Propose a multi-scale module refine the prediction of object boundaries. • Non-local modules combined with channel attention. • Experimental results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Wheat leaf diseases are considered to be the foremost threat to wheat yield. In the realm of crop disease detection, convolutional neural networks (CNNs) have emerged as important tools. The training strategy and the initial learning rate are key factors that impact the performance and training speed of the model in CNNs. This study employed six training strategies, including Adam, SGD, Adam + StepLR, SGD + StepLR, Warm-up + Cosine annealing + SGD, Warm-up + Cosine, and annealing + Adam, with three initial learning rates (0.05, 0.01, and 0.001). Using the wheat stripe rust, wheat powdery mildew, and healthy wheat datasets, five lightweight CNN models, namely MobileNetV3, ShuffleNetV2, GhostNet, MnasNet, and EfficientNetV2, were evaluated. The results showed that upon combining the SGD + StepLR with the initial learning rate of 0.001, the MnasNet obtained the highest recognition accuracy of 98.65%. The accuracy increased by 1.1% as compared to that obtained with the training strategy with a fixed learning rate, and the size of the parameters was only 19.09 M. The above results indicated that the MnasNet was appropriate for porting to the mobile terminal and efficient for automatically identifying wheat leaf diseases.

Copyright of China Medical Education Technology is the property of China Medical Education Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learningmethods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on theMeta-Baseline few-shot learningmethod, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application. [ABSTRACT FROM AUTHOR]

Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenarios such as computer vision and natural language processing etc. In this survey article, we comprehensively review CL from various aspects including motivations, definitions, theories, and applications. We discuss works on curriculum learning within a general CL framework, elaborating on how to design a manually predefined curriculum or an automatic curriculum. In particular, we summarize existing CL designs based on the general framework of Difficulty Measurer $+$ + Training Scheduler and further categorize the methodologies for automatic CL into four groups, i.e., Self-paced Learning, Transfer Teacher, RL Teacher, and Other Automatic CL. We also analyze principles to select different CL designs that may benefit practical applications. Finally, we present our insights on the relationships connecting CL and other machine learning concepts including transfer learning, meta-learning, continual learning and active learning, etc., then point out challenges in CL as well as potential future research directions deserving further investigations. [ABSTRACT FROM AUTHOR]

Background: Since the 2016 WHO guidelines, glioma diagnosis has entered an era of integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has focused on promoting the application of molecular diagnosis in the classification of central nervous system tumors. Genetic information such as IDH1 and 1p/19q are important molecular markers, and pathological grading is also a key clinical indicator. However, obtaining genetic pathology labels is more costly than conventional MRI images, resulting in a large number of missing labels in realistic modeling., Method: We propose a training strategy based on label encoding and a corresponding loss function to enable the model to effectively utilize data with missing labels. Additionally, we integrate a graph model with genes and pathology-related clinical prior knowledge into the ResNet backbone to further improve the efficacy of diagnosis. Ten-fold cross-validation experiments were conducted on a large dataset of 1072 patients., Results: The classification area under the curve (AUC) values are 0.93, 0.91, and 0.90 for IDH1, 1p/19q status, and grade (LGG/HGG), respectively. When the label miss rate reached 59.3 %, the method improved the AUC by 0.09, 0.10, and 0.04 for IDH1, 1p/19q, and pathological grade, respectively, compared to the same backbone without the missing label strategy., Conclusions: Our method effectively utilizes data with missing labels and integrates clinical prior knowledge, resulting in improved diagnostic performance for glioma genetic and pathological markers, even with high rates of missing labels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Predicting the sequence pattern of the handwritten text images is a challenging problem due to various writing styles, insufficient training data, and also background noise appearing in the text images. The architecture of the combination between convolutional neural network (CNN) and recurrent neural network (RNN), called CRNN architecture, is the most successful sequence learning method for handwritten text recognition systems. For handwritten text recognition in historical Thai document images, we first trained nine different CRNN architectures with both training from scratch and transfer learning techniques to find out the most powerful technique. We discovered that the transfer learning technique does not significantly outperform scratch learning. Second, we examined training the CRNN model by applying the basic transformation data augmentation techniques: shifting, rotation, and shearing. Indeed, the data augmentation techniques provided more accurate performance than without applying data augmentation techniques. However, it did not show significant results. The original training strategy aimed to find the global minima value and not always solve the overfitting problems. Third, we proposed a cyclical data augmentation strategy, called CycleAugment, to discover many local minima values and prevent overfitting. In each cycle, it rapidly decreased the training loss to reach the local minima. The CycleAugment strategy allowed the CRNN model to learn the input images with and without applying data augmentation techniques to learn from many input patterns. Hence, the CycleAugment strategy consistently achieved the best performance when compared with other strategies. Finally, we prevented image distortion by applying a simple technique to the short word images and achieved better performance on the historical Thai document image dataset. [ABSTRACT FROM AUTHOR]

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Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm. [ABSTRACT FROM AUTHOR]

Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG's encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning. Availability: The source code is available in https://github.com/gu-yaowen/CurrMG. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the performance of convolutional neural networks (CNNs) that were trained with small datasets using different strategies in the detection of proximal caries at different levels of severity on periapical radiographs. Small datasets containing 800 periapical radiographs were randomly categorized into a training and validation dataset (n = 600) and a test dataset (n = 200). A pretrained Cifar-10Net CNN was used in the present study. Different training strategies were used to train the CNN model independently; these strategies were defined as image recognition (IR), edge extraction (EE), and image segmentation (IS). Different metrics, such as sensitivity and area under the receiver operating characteristic curve (AUC), for the trained CNN and human observers were analysed to evaluate the performance in detecting proximal caries. IR, EE, and IS recognition modes and human eyes achieved AUCs of 0.805, 0.860, 0.549, and 0.767, respectively, with the EE recognition mode having the highest values (p all < 0.05). The EE recognition mode was significantly more sensitive in detecting both enamel and dentin caries than human eyes (p all < 0.05). The CNN trained with the EE strategy, the best performer in the present study, showed potential utility in detecting proximal caries on periapical radiographs when using small datasets. [ABSTRACT FROM AUTHOR]

Tree species classification is an important and challenging task in image recognition and the management of forest resources. Moreover, the task of tree species classification based on remote sensing images can significantly improve the efficiency of the tree species survey and save costs. In recent years, many large models have achieved high accuracy in the task of tree species classification in an airborne remote-sensing manner, but due to their fixed geometric structure, traditional convolutional neural networks are inherently limited to the local receptive field and can only provide segmental context information. The limitation of insufficient context information greatly affects the segmentation accuracy. In this paper, a dual-attention residual network (AMDNet) and a re-parameterized model approach are proposed to capture the global context information, fuse the weight, reduce the model volume, and maintain the computational efficiency. Firstly, we propose MobileNetV2 as the backbone network for feature extraction to further improve the feature identification by modeling semantic dependencies in the spatial dimension and channel dimension and adding the output of the two attention modules. Then, the attention perception features are generated by stacking the attention modules, and the in-depth residual attention network is trained using attention residual learning, through which more accurate segmentation results can be obtained. Secondly, we adopt the approach of structure re-parameterization, use a multi-branch topology for training, carry out weighted averaging on multiple trained models, and fuse multiple branch modules into a completely equivalent module in inference. The proposed approach results in a reduction in the number of parameters and an accelerated inference speed while also achieving improved classification accuracy. In addition, the model training strategy is optimized based on Transformer to enhance the accuracy of segmentation. The model was used to conduct classification experiments on aerial orthophotos of Hongya Forest Farm in Sichuan, China, and the MIOU of tree species recognition using the test equipment reached 93.8%. Compared with current models such as UNet, our model exhibits a better performance in terms of both speed and accuracy, in addition to its enhanced deployment capacity, and its speed advantage is more conducive to real-time segmentation, thereby representing a novel approach for the classification of tree species in remote sensing imagery with significant potential for practical applications.

The analysis and calculation of the armature electromagnetic force is the premise of studying the dynamic characteristics of the electromagnetic railgun. Aiming at the problem of the numerical solution “pseudo-oscillation” at high speed, an extrapolation prediction method of armature electromagnetic force based on the Deep Belief Network-Deep Neural Network (DBN-DNN) is proposed. Firstly, the electromagnetic field control equation and armature dynamics equation, considering the influence of armature movement, are given, and the finite element simulation model of the electromagnetic railgun is established to analyze the dynamic characteristics and numerical solution stability of the armature electromagnetic force. Then, based on the stable numerical simulation data under different armature conductivities, a DBN-DNN method is proposed to realize the extrapolation prediction of the armature electromagnetic force under the standard conductance. Finally, the extrapolation prediction performance of the proposed method is tested by two electromagnetic railgun cases. Additionally, we further propose the training strategy of DBN-DNN parameters from solving armature electromagnetic force at low conductivity to standard conductivity. The armature electromagnetic force extrapolation prediction method for the whole launch process from low speed to high speed provides a new idea for the dynamic characteristic analysis of the high-speed electromagnetic railgun.

In this paper, we propose a novel training strategy named Feature Mining for convolutional neural networks (CNNs) that aims to strengthen the network's learning of the local features. Through experiments, we found that different parts of the feature contain different semantics, while the network will inevitably lose a large amount of local information during feedforward propagation. In order to enhance the learning of the local features, Feature Mining divides the complete feature into two complementary parts and reuses this divided feature to make the network capture different local information; we call the two steps Feature Segmentation and Feature Reusing. Feature Mining is a parameter-free method with a plug-and-play nature and can be applied to any CNN model. Extensive experiments demonstrated the wide applicability, versatility, and compatibility of our method. [ABSTRACT FROM AUTHOR]