Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct...Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.展开更多
In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these chall...In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
Accurate identification of surrounding rock quality is critical for safe and efficient tunneling.A cost-sensitive bagging framework is developed to map engineering risk preference into the learning objective through a...Accurate identification of surrounding rock quality is critical for safe and efficient tunneling.A cost-sensitive bagging framework is developed to map engineering risk preference into the learning objective through an asymmetric cost matrix,with a confidence-gating rule to defer low-confidence predictions.Measurement-while-drilling(MWD)records from 1,115 boreholes are aggregated at the hole level into a 64-dimensional representation derived from six drilling channels and two indicators,each summarized by eight robust statistics.Stratified K-fold evaluation under class imbalance is conducted against RUSBoost,logistic regression,and weighted SVM;feature interpretation is performed via importance ranking and partial dependence.Results show ROC-AUC 0.958 and PR-AP 0.588,with reduced under-support at practitionerfavored operating points;the expected misclassication cost is minimized near t≈0.50.Penetration rate is negatively associated with poor rock,whereas pressure-related variables and derived indicators are positively associated.In summary,the framework provides accurate,interpretable,and risk-aware predictions that support real-time tunnel support planning under variable geology.展开更多
Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with ...Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with Class Ⅰ/Ⅱ reservoirs remaining underrepresented due to the difficulties in simulating the geothermal gradient and interlayer interactions.This study investigates depressurization performance across all three classes using a novel 360°rotatable reactor with segmented temperature control,enabling precise simulation of reservoir conditions.Results reveal:(i)Class Ⅰ shows two-stage gas production,with 50%from early free gas enabling rapid depressurization,followed by dissociated gas dominance.They achieve 38.4%-78.3%higher cumulative production and superior gas-to-water ratios due to efficient energy use.(ii)The free gas layer in Class Ⅰ accelerates pressure and heat transfer.Class Ⅱ’s water layer provides sensible heat but causes water blocking,impairing heat flow.Class Ⅲ exhibits rapid initial dissociation but a quick decline without fluid support.(iii)Low temperature,low hydrate saturation,and high production pressure collectively reduce efficiency by increasing flow resistance,limiting gas supply,and reducing dissociation drive.Over-depressurization risks hydrate reformation and ice blockage.This work bridges experimental gaps for Class Ⅰ/Ⅱ reservoirs,offering key insights for optimizing recovery.展开更多
Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is...Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings.展开更多
Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the...Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the“Five-Dimensional Education”model as an example,the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology,leaders of value creation,companions on the growth journey,practitioners of lifelong learning,and connectors of human efforts,forming a comprehensive and multi-dimensional framework for student education management.This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management.By implementing effective assessment mechanisms,it ensures that class advisors can perform ideological and political education and academic guidance in an efficient,high-quality,and orderly manner.This study not only helps to cultivate medical talents with both moral integrity and professional competence,but also provides valuable theoretical and practical references for reforming student management in medical institutions,thereby promoting the sustainable development of medical education.展开更多
师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同...师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同源性与实践契合性。CLASS视域下师幼互动面临情感支持的缺乏、课堂组织的失衡、教学支持的脱节的现实困境。基于此,提出情感交融,以情育情;协调秩序,灵活组织;回应需求,弥合引导的创新路径。The quality of teacher-child interaction is not only a key element of the quality of kindergarten education, but also an important aspect of the connotative development of pre-school education in China. Using literature, logical analysis and other methods, we analyze the associated logic, dilemma and innovative path of teacher-child interaction from the perspective of CLASS theory. The study found that the logic of CLASS theory and teacher-child interaction lies in the homology of the theory and the fit of practice, and that teacher-child interaction in the CLASS perspective faces the dilemmas of lack of emotional support, imbalance of classroom organization, and disconnection of pedagogical support. Based on this, we propose innovative paths of emotional integration, nurturing emotions with emotions, coordinating order, flexible organization, responding to needs, and bridging guidance.展开更多
The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can serio...The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore,early orthodontic treatment for Class Ⅲ malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class Ⅲ malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class Ⅲ malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class Ⅲ malocclusion through early orthodontic treatment.展开更多
In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a to...In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a total of 19,126 solar flares were observed regardless the class: 3548 flares in solar cycle 23 (SC23) and 15,668 flares in solar cycle 24 (SC24). Our findings show that the cycle 23 has observed the highest occurrences of M-class and X-class flares, whereas cycle 24 has pointed out a predominance of B-class and C-class flares throughout its different phases. The results indicate that the cycle 23 was magnetically more intense than cycle 24, leading to more powerful solar flares and more frequent geomagnetic storms, capable of generating significant electromagnetic emissions that can affect satellites and GPS signals. The decrease in intense solar flares during cycle 24 compared to cycle 23 reflects an evolution in solar activity patterns over time.展开更多
Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in ...Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.展开更多
This paper addresses a fundamental question in rock mechanics:Are there Class Ⅱ rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing ...This paper addresses a fundamental question in rock mechanics:Are there Class Ⅱ rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing machines.The pioneering work of some researchers is reviewed,and the misconception of classifying rocks into Class Ⅰ and Class Ⅱ is discussed.The mechanism of post-peak Class Ⅱ behavior is discussed based on some recent test results.When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading,violent failure can occur when the peak strength is reached,and the post-peak stress-strain curve cannot be obtained.However,a Class Ⅱ post-peak stress-strain curve can be obtained when the rock is tested under lateral-strain-controlled loading.If a stiff testing machine is used,Class Ⅰ and Class Ⅱ post-peak stress-strain curves will be obtained under axial-and lateral-strain-controlled loadings,respectively.It is therefore not appropriate to classify rocks into Class Ⅰ or Class Ⅱ rocks.The influences of other conditions,such as rock type,confinement,and specimen height-to-diameter ratio,on the type(Class Ⅰ or Class Ⅱ)of post-peak stress-strain curves are also discussed.Finally,some misconceptions in the rock mechanics community,stemming from the concept of“Class Ⅱ rock”,are discussed.By clarifying these concepts related to Class Ⅰ and Class Ⅱ behaviors,this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field.展开更多
文摘Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.
基金supported by the European Research Council(ERC)under Grant Agreement No.951424(Water-Futures)by the Republic of Cyprus through the Deputy Ministry of Research,Innovation and Digital Policy.
文摘In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金supported by the National Science Foundation for Young Scientists of China(grant numbers ZR2024QE402).
文摘Accurate identification of surrounding rock quality is critical for safe and efficient tunneling.A cost-sensitive bagging framework is developed to map engineering risk preference into the learning objective through an asymmetric cost matrix,with a confidence-gating rule to defer low-confidence predictions.Measurement-while-drilling(MWD)records from 1,115 boreholes are aggregated at the hole level into a 64-dimensional representation derived from six drilling channels and two indicators,each summarized by eight robust statistics.Stratified K-fold evaluation under class imbalance is conducted against RUSBoost,logistic regression,and weighted SVM;feature interpretation is performed via importance ranking and partial dependence.Results show ROC-AUC 0.958 and PR-AP 0.588,with reduced under-support at practitionerfavored operating points;the expected misclassication cost is minimized near t≈0.50.Penetration rate is negatively associated with poor rock,whereas pressure-related variables and derived indicators are positively associated.In summary,the framework provides accurate,interpretable,and risk-aware predictions that support real-time tunnel support planning under variable geology.
基金partially funded by Shenzhen Science and Technology Program(No.JCYJ20240813112038050)the National Natural Science Foundation of China(No.52404059)+1 种基金the Economy Trade and Information Commission of Shenzhen Municipality,China(No.HYCYPT20140507010002)the Key Program of Marine Economy Development(Six Marine Industries)Special Foundation of the Department of Natural Resources of Guangdong Province,China(No.GDOE[2021]55).
文摘Natural gas hydrate in Class Ⅰ reservoirs holds significant commercial potential,as demonstrated by production trials in the South China Sea.However,experimental studies have focused largely on Class Ⅲ systems,with Class Ⅰ/Ⅱ reservoirs remaining underrepresented due to the difficulties in simulating the geothermal gradient and interlayer interactions.This study investigates depressurization performance across all three classes using a novel 360°rotatable reactor with segmented temperature control,enabling precise simulation of reservoir conditions.Results reveal:(i)Class Ⅰ shows two-stage gas production,with 50%from early free gas enabling rapid depressurization,followed by dissociated gas dominance.They achieve 38.4%-78.3%higher cumulative production and superior gas-to-water ratios due to efficient energy use.(ii)The free gas layer in Class Ⅰ accelerates pressure and heat transfer.Class Ⅱ’s water layer provides sensible heat but causes water blocking,impairing heat flow.Class Ⅲ exhibits rapid initial dissociation but a quick decline without fluid support.(iii)Low temperature,low hydrate saturation,and high production pressure collectively reduce efficiency by increasing flow resistance,limiting gas supply,and reducing dissociation drive.Over-depressurization risks hydrate reformation and ice blockage.This work bridges experimental gaps for Class Ⅰ/Ⅱ reservoirs,offering key insights for optimizing recovery.
基金supported by Basic Science Research Program to Research Institute for Basic Sciences(RIBS)of Jeju National University through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2019-NR040080)This research was also carried out with the support of the Jeju RISE Center,funded by the Ministry of Education and Jeju Special Self-Governing Province in 2025,as part of the“Regional Innovation System&Education(RISE):Glocal University 30”initiative.
文摘Given the growing number of vehicle accidents caused by unintended acceleration and braking failure,verifying Sudden Unintended Acceleration(SUA)incidents has become a persistent challenge.A central issue of debate is whether such events stem frommechanical malfunctions or driver pedalmisapplications.However,existing verification procedures implemented by vehiclemanufacturers often involve closed tests after vehicle recalls;thus raising ongoing concerns about reliability and transparency.Consequently,there is a growing need for a user-driven framework that enables independent data acquisition and verification.Although previous studies have addressed SUA detection using deep learning,few have explored howclass granularity optimization affects power efficiency and inference performance in real-time Edge AI systems.To address this problem,this work presents a cloud-assisted artificial intelligence(AI)solution for the reliable verification of SUA occurrences.The proposed system integrates multimodal sensor streams including camera-based foot images,On-Board Diagnostics II(OBD-II)signals,and six-axismeasurements to determine whether the brake pedal was actually engaged at themoment of a suspected SUA.Beyond image acquisition,convolutional neural network(CNN)models perform real-time inference to classify the driver’s pedal operation states with the resulting outputs transmitted and archived in the cloud.A dedicated dataset of brake and accelerator pedal images was collected from 15 vehicles produced by 6 domestic and international manufacturers.Using this dataset,transfer learning techniques were applied to compare and analyze model performance and generalization as the CNN class granularity varied from coarse to fine levels.Furthermore,classification performance was evaluated in terms of latency and power efficiency under different class configurations.The experimental results demonstrated that the proposed solution identified the driver’s pedal behavior accurately and promptly,with the two-class model achieving the highest F1-score and accuracy among all granularity settings.
基金supported by Anhui Teaching Quality and Teaching Reform Project(2022jyxm1698)Special Fund Project for Party Construction of Wannan Medical College(WK2024DJ06).
文摘Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation.Taking the“Five-Dimensional Education”model as an example,the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology,leaders of value creation,companions on the growth journey,practitioners of lifelong learning,and connectors of human efforts,forming a comprehensive and multi-dimensional framework for student education management.This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management.By implementing effective assessment mechanisms,it ensures that class advisors can perform ideological and political education and academic guidance in an efficient,high-quality,and orderly manner.This study not only helps to cultivate medical talents with both moral integrity and professional competence,but also provides valuable theoretical and practical references for reforming student management in medical institutions,thereby promoting the sustainable development of medical education.
文摘师幼互动质量不仅是幼儿园教育质量的关键要素,还是我国学前教育内涵式发展的重要方面。运用文献资料、逻辑分析等方法,从CLASS理论视域下探析师幼互动的关联逻辑、困境与创新路径。研究发现:CLASS理论与师幼互动的关联逻辑在于理论同源性与实践契合性。CLASS视域下师幼互动面临情感支持的缺乏、课堂组织的失衡、教学支持的脱节的现实困境。基于此,提出情感交融,以情育情;协调秩序,灵活组织;回应需求,弥合引导的创新路径。The quality of teacher-child interaction is not only a key element of the quality of kindergarten education, but also an important aspect of the connotative development of pre-school education in China. Using literature, logical analysis and other methods, we analyze the associated logic, dilemma and innovative path of teacher-child interaction from the perspective of CLASS theory. The study found that the logic of CLASS theory and teacher-child interaction lies in the homology of the theory and the fit of practice, and that teacher-child interaction in the CLASS perspective faces the dilemmas of lack of emotional support, imbalance of classroom organization, and disconnection of pedagogical support. Based on this, we propose innovative paths of emotional integration, nurturing emotions with emotions, coordinating order, flexible organization, responding to needs, and bridging guidance.
文摘The prevalence of Class Ⅲ malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore,early orthodontic treatment for Class Ⅲ malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class Ⅲ malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class Ⅲ malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class Ⅲ malocclusion through early orthodontic treatment.
文摘In this study we review the occurrence of different types (A, B, C, M, and X classes) of solar flares during different solar cycle phases from 1996 to 2019 covering the solar cycles 23 and 24. During this period, a total of 19,126 solar flares were observed regardless the class: 3548 flares in solar cycle 23 (SC23) and 15,668 flares in solar cycle 24 (SC24). Our findings show that the cycle 23 has observed the highest occurrences of M-class and X-class flares, whereas cycle 24 has pointed out a predominance of B-class and C-class flares throughout its different phases. The results indicate that the cycle 23 was magnetically more intense than cycle 24, leading to more powerful solar flares and more frequent geomagnetic storms, capable of generating significant electromagnetic emissions that can affect satellites and GPS signals. The decrease in intense solar flares during cycle 24 compared to cycle 23 reflects an evolution in solar activity patterns over time.
基金supported by National Key R&D Program of China(2022YFA1008000)the National Natural Science Foundation of China(12571297,12101585)+1 种基金the CAS Talent Introduction Program(Category B)the Young Elite Scientist Sponsorship Program by CAST(YESS20220125).
文摘Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.
基金the Natural Science and Engineering Research Council of Canada(RGPIN/4052-16,ALLRP 560390-20).
文摘This paper addresses a fundamental question in rock mechanics:Are there Class Ⅱ rocks?The historical development of servo-controlled rock testing machines is reviewed,followed by a brief review of some stiff testing machines.The pioneering work of some researchers is reviewed,and the misconception of classifying rocks into Class Ⅰ and Class Ⅱ is discussed.The mechanism of post-peak Class Ⅱ behavior is discussed based on some recent test results.When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading,violent failure can occur when the peak strength is reached,and the post-peak stress-strain curve cannot be obtained.However,a Class Ⅱ post-peak stress-strain curve can be obtained when the rock is tested under lateral-strain-controlled loading.If a stiff testing machine is used,Class Ⅰ and Class Ⅱ post-peak stress-strain curves will be obtained under axial-and lateral-strain-controlled loadings,respectively.It is therefore not appropriate to classify rocks into Class Ⅰ or Class Ⅱ rocks.The influences of other conditions,such as rock type,confinement,and specimen height-to-diameter ratio,on the type(Class Ⅰ or Class Ⅱ)of post-peak stress-strain curves are also discussed.Finally,some misconceptions in the rock mechanics community,stemming from the concept of“Class Ⅱ rock”,are discussed.By clarifying these concepts related to Class Ⅰ and Class Ⅱ behaviors,this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field.
