A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of s...Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.展开更多
Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribu...Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.展开更多
The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typic...The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.展开更多
We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induc...We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induced by a probability bi-sequence.We also establish the Katok’s entropy formula for conditional entropy for ergodic measures in the case of the new family of metrics.展开更多
Background Conditional relative survival(CRS),the probability of survival given that an individual has already survived a certain period post-diagnosis,is a more clinically relevant measure for long-term survival than...Background Conditional relative survival(CRS),the probability of survival given that an individual has already survived a certain period post-diagnosis,is a more clinically relevant measure for long-term survival than standard relative survival(RS).This study aims to evaluate the 5-year CRS among adolescent and young adult(AYA)breast cancer patients by age,tumor stage,and receptor subtype to guide disclosure periods for insurance.Methods Data of all females aged 18–39 years and diagnosed with invasive breast cancer between 2003 and 2021(n=13,075)were obtained from The Netherlands Cancer Registry(NCR).The five-year CRS was calculated annually up to 10 years post-diagnosis using a hybrid analysis approach.Results For the total AYA breast cancer study population the 5-year CRS exceeded 90%from diagnosis and increased beyond 95%7 years post-diagnosis.Patients aged 18–24 reached 95%9 years post-diagnosis,those aged 25–29 after 5 years,and those aged 30–34 and 35–39 after 8 years.For stage I,the 5-year CRS reached 95%from diagnosis,for stage II after 6 years,while the 5-year CRS for stages III and IV did not reach the 95%threshold during the 10-year follow-up.Triple-negative tumors exceeded 95%after 4 years,human epidermal growth factor receptor 2(HER2)positive tumors after 6 years,while hormone receptor(HR)positive tumors did not reach 95%.Conclusion Excess mortality among AYA breast cancer patients tends to be little(CRS 90%–95%)from diagnosis and becomes minimal(CRS>95%)over time compared to the general population.These results can enhance expectation management and inform policymakers,suggesting a shorter disclosure period.展开更多
The likelihood of extreme heat occurrence is continuously increasing with global warming.Under high temperatures,humidity may exacerbate the heat impact on humanity.As atmospheric humidity depends on moisture availabi...The likelihood of extreme heat occurrence is continuously increasing with global warming.Under high temperatures,humidity may exacerbate the heat impact on humanity.As atmospheric humidity depends on moisture availability and is constrained by air temperature,it is important to project the changes in the distribution of atmospheric humidity conditional on air temperature as the climate continuously warms.Here,a non-crossing quantile smoothing spline is employed to build quantile regression models emulating conditional distributions of dew point(a measure of humidity)on local temperature evolving with escalating global mean surface temperature.By applying these models to 297 weather stations in seven regions in China,the study analyzes historical trends of humid-heat and dry-hot days,and projects their changes under global warming of 2.0℃ and 4.5℃.In response to global warming,rising trends of humid-heat extremes,while weakening trends of dry-hot extremes,are observed at most stations in Northeast China.Additionally,results indicate an increasing trend in dry-hot extremes at numerous stations across central China,but a rise in humid-heat extremes over Northwest China and coastal regions.These trends found in the current climate state are projected to intensify under 2.0℃ and 4.5℃ warming,possibly influenced by the heterogeneous variations in precipitation,soil moisture,and water vapor fluxes.Requiring much lower computational resources than coupled climate models,these quantile regression models can further project compound humidity and temperature extremes in response to different levels of global warming,potentially informing the risk management of compound humid-heat extremes on a local scale.展开更多
The conditional kernel correlation is proposed to measure the relationship between two random variables under covariates for multivariate data.Relying on the framework of reproducing kernel Hilbert spaces,we give the ...The conditional kernel correlation is proposed to measure the relationship between two random variables under covariates for multivariate data.Relying on the framework of reproducing kernel Hilbert spaces,we give the definitions of the conditional kernel covariance and conditional kernel correlation.We also provide their respective sample estimators and give the asymptotic properties,which help us construct a conditional independence test.According to the numerical results,the proposed test is more effective compared to the existing one under the considered scenarios.A real data is further analyzed to illustrate the efficacy of the proposed method.展开更多
Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate b...Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.展开更多
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The eff...In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.展开更多
Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstru...Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.展开更多
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef...In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.展开更多
The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regressi...The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.展开更多
Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challe...Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.展开更多
Learning to handle hypothetical situations in a new language is always difficult(Catford,et al.,1974).This rule holds true for Moroccan Arabic(henceforth MA)speakers learning English because grammatical devices in the...Learning to handle hypothetical situations in a new language is always difficult(Catford,et al.,1974).This rule holds true for Moroccan Arabic(henceforth MA)speakers learning English because grammatical devices in the two languages differ in almost all equivalent situations.For instance,while English verb forms are used to indicate tense in conditional sentences,MA uses them to indicate aspect.Adopting the typology of conditional constructions suggested by Dancygier(1999)and Dancygier&Sweetser(2005),this study provides a contrastive analysis of conditionals in English and MA to predict the possible errors EFL/ESL learners are likely to make while learning English.The analysis shows that the main discrepancy between English conditionals and MA conditionals lies in the verb form used by the two systems.Accordingly,if EFL/ESL learners are influenced by verb form in their L1,they are likely to face some challenges while learning English conditionals.That is,they are likely to use the past tense in the protases of English predictive conditionals and generic conditionals since the perfective form of the verb is used in the protases of these two types in MA.Concerning the protases of English non-predictive conditionals,Moroccan EFL/ESL learners are likely to use either the past tense or the present tense since both the perfective and the imperfective forms of the verb are possible in the protases of MA non-predictive conditionals.However,due to the fact that the perfective form is the prototypical form in the protases of conditionals in MA,EFL/ESL learners are likely to use the past tense more often than the present tense.The analysis also shows that EFL/ESL learners tend to use the present tense in the apodoses of English conditionals since the prevalent form in the apodoses of MA conditionals is the imperfective.展开更多
Conditionally t-diagnosable and t-diagnosable are important in system level diagnosis. Therefore,it is valuable to identify whether the system is conditionally t-diagnosable or t-diagnosable and derive the correspondi...Conditionally t-diagnosable and t-diagnosable are important in system level diagnosis. Therefore,it is valuable to identify whether the system is conditionally t-diagnosable or t-diagnosable and derive the corresponding conditional diagnosability and diagnosability. In the paper,distinguishable measures of pairs of distinct faulty sets with a new perspective on establishing functions are focused.Applying distinguishable function and decision function,it is determined whether a system is conditionally t-diagnosable( or t-diagnosable) or not under the PMC( Preparata,Metze,and Chien)model directly. Based on the decision function,a novel conditional diagnosability algorithm under the PMC model is introduced which can calculate conditional diagnosability rapidly.展开更多
Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow ...Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow syntactic parsing as the foundation, phrases or named entities as the labeled units, and the CRFs model is trained to label the predicates＇ semantic roles in a sentence. The key of the method is parameter estimation and feature selection for the CRFs model. The L-BFGS algorithm was employed for parameter estimation, and three category features： features based on sentence constituents, features based on predicate, and predicate-constituent features as a set of features for the model were selected. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the method can obtain better performance than the maximum entropy model, and can achieve 80. 43 % precision and 63. 55 % recall for semantic role labeling.展开更多
A factorial mating design in two environments was conducted using 7 cytoplasmic male sterile lines （A） and 5 restorer lines （R） along with their F1 （A × R） and F2 populations. The unconditional and conditio...A factorial mating design in two environments was conducted using 7 cytoplasmic male sterile lines （A） and 5 restorer lines （R） along with their F1 （A × R） and F2 populations. The unconditional and conditional analyses of genetic models and the corresponding statistic methods, including endospermic, cytoplasmic, and maternal plant genetic systems, were used to analyze the genetic relationships between protein content （PC） and the appearance quality traits of indica rice （Oryza sativa L.）. The results from unconditional analysis indicated that PC was significantly correlated with the appearance quality traits of rice, except for the brown rice thickness （BRT）. Only the genetic covariance between PC and the brown rice width （BRW） was positively correlative, whereas all the other pairwise traits were negatively correlative. The results from conditional analysis revealed that the weight of brown rice （WBR） or the amylose content （AC） could significantly affect the relationships between PC and the appearance quality traits of indica rice. The conditional analysis showed that WBR might negatively affect the relationships between PC and the brown rice length （BRL）, BRW, or BRT through the geuotype x environmental （GE） interaction effects, but positively affected the relationships between PC and the ratio of brown rice length to width （RLW） or the ratio of brown rice length to thickness （RLT）. The amylase content could positively affect the relationships between PC and BRL, RLW, RLT through the cytoplasmic effects and maternal additive effects, but negatively affected the relationships between PC and BRW.展开更多
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
文摘Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.
基金Programs for Science and Technology Development of Henan Province,grant number 242102210152The Fundamental Research Funds for the Universities of Henan Province,grant number NSFRF240620+1 种基金Key Scientific Research Project of Henan Higher Education Institutions,grant number 24A520015Henan Key Laboratory of Network Cryptography Technology,grant number LNCT2022-A11.
文摘Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.
基金supported by the National Natural Science Foundation of China (Grant No. 42288101, 42375062, 42476192, 42275158)the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)the GHfund C (202407036001)
文摘The Conditional Nonlinear Optimal Perturbation(CNOP)method works essentially for conventional numerical models;however,it is not fully applicable to the commonly used deep-learning forecasting models(DLMs),which typically input multiple time slices without deterministic dependencies.In this study,the CNOP for DLMs(CNOP-DL)is proposed as an extension of the CNOP in the time dimension.This method is useful for targeted observations as it indicates not only where but also when to deploy additional observations.The CNOP-DL is calculated for a forecast case of sea surface temperature in the South China Sea with a DLM.The CNOP-DL identifies a sensitive area northwest of Palawan Island at the last input time.Sensitivity experiments demonstrate that the sensitive area identified by the CNOP-DL is effective not only for the CNOP-DL itself,but also for random perturbations.Therefore,this approach holds potential for guiding practical field campaigns.Notably,forecast errors are more sensitive to time than to location in the sensitive area.It highlights the crucial role of identifying the time of the sensitive area in targeted observations,corroborating the usefulness of extending the CNOP in the time dimension.
文摘We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induced by a probability bi-sequence.We also establish the Katok’s entropy formula for conditional entropy for ergodic measures in the case of the new family of metrics.
基金supported by The Netherlands Organization for Scientific Research VIDI(grant number:198.007).
文摘Background Conditional relative survival(CRS),the probability of survival given that an individual has already survived a certain period post-diagnosis,is a more clinically relevant measure for long-term survival than standard relative survival(RS).This study aims to evaluate the 5-year CRS among adolescent and young adult(AYA)breast cancer patients by age,tumor stage,and receptor subtype to guide disclosure periods for insurance.Methods Data of all females aged 18–39 years and diagnosed with invasive breast cancer between 2003 and 2021(n=13,075)were obtained from The Netherlands Cancer Registry(NCR).The five-year CRS was calculated annually up to 10 years post-diagnosis using a hybrid analysis approach.Results For the total AYA breast cancer study population the 5-year CRS exceeded 90%from diagnosis and increased beyond 95%7 years post-diagnosis.Patients aged 18–24 reached 95%9 years post-diagnosis,those aged 25–29 after 5 years,and those aged 30–34 and 35–39 after 8 years.For stage I,the 5-year CRS reached 95%from diagnosis,for stage II after 6 years,while the 5-year CRS for stages III and IV did not reach the 95%threshold during the 10-year follow-up.Triple-negative tumors exceeded 95%after 4 years,human epidermal growth factor receptor 2(HER2)positive tumors after 6 years,while hormone receptor(HR)positive tumors did not reach 95%.Conclusion Excess mortality among AYA breast cancer patients tends to be little(CRS 90%–95%)from diagnosis and becomes minimal(CRS>95%)over time compared to the general population.These results can enhance expectation management and inform policymakers,suggesting a shorter disclosure period.
基金supported by the National Natural Science Foundation of China[grant number 42175066]the Shanghai International Science and Technology Partnership Project[grant number 21230780200].
文摘The likelihood of extreme heat occurrence is continuously increasing with global warming.Under high temperatures,humidity may exacerbate the heat impact on humanity.As atmospheric humidity depends on moisture availability and is constrained by air temperature,it is important to project the changes in the distribution of atmospheric humidity conditional on air temperature as the climate continuously warms.Here,a non-crossing quantile smoothing spline is employed to build quantile regression models emulating conditional distributions of dew point(a measure of humidity)on local temperature evolving with escalating global mean surface temperature.By applying these models to 297 weather stations in seven regions in China,the study analyzes historical trends of humid-heat and dry-hot days,and projects their changes under global warming of 2.0℃ and 4.5℃.In response to global warming,rising trends of humid-heat extremes,while weakening trends of dry-hot extremes,are observed at most stations in Northeast China.Additionally,results indicate an increasing trend in dry-hot extremes at numerous stations across central China,but a rise in humid-heat extremes over Northwest China and coastal regions.These trends found in the current climate state are projected to intensify under 2.0℃ and 4.5℃ warming,possibly influenced by the heterogeneous variations in precipitation,soil moisture,and water vapor fluxes.Requiring much lower computational resources than coupled climate models,these quantile regression models can further project compound humidity and temperature extremes in response to different levels of global warming,potentially informing the risk management of compound humid-heat extremes on a local scale.
基金partially supported by Knowledge Innovation Program of Hubei Province(No.2019CFB810)partially supported by NSFC(No.12325110)the CAS Project for Young Scientists in Basic Research(No.YSBR-034)。
文摘The conditional kernel correlation is proposed to measure the relationship between two random variables under covariates for multivariate data.Relying on the framework of reproducing kernel Hilbert spaces,we give the definitions of the conditional kernel covariance and conditional kernel correlation.We also provide their respective sample estimators and give the asymptotic properties,which help us construct a conditional independence test.According to the numerical results,the proposed test is more effective compared to the existing one under the considered scenarios.A real data is further analyzed to illustrate the efficacy of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant No.52109010)the Postdoctoral Science Foundation of China(Grant No.2021M701047)the China National Postdoctoral Program for Innovative Talents(Grant No.BX20200113).
文摘Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
基金supported by the National Natural Science Foundation of China(Grant Nos.42225501 and 42105059)the National Key Scientific and Tech-nological Infrastructure project“Earth System Numerical Simula-tion Facility”(EarthLab).
文摘In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.
基金the support from the National Key R&D Program of China underGrant(Grant No.2020YFA0711700)the National Natural Science Foundation of China(Grant Nos.52122801,11925206,51978609,U22A20254,and U23A20659)G.W.is supported by the National Natural Science Foundation of China(Nos.12002303,12192210 and 12192214).
文摘Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.
基金supported in part by the National Key Research and Development Program of China(2019YFB1503700)the Hunan Natural Science Foundation-Science and Education Joint Project(2019JJ70063)。
文摘The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.
基金supported by the National Key Research and Development Projects (Grant Nos.2021YFB3300601,2021YFB3300603,2021YFB3300604)Fundamental Research Funds for the Central Universities (No.DUT22QN241).
文摘Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.
文摘Learning to handle hypothetical situations in a new language is always difficult(Catford,et al.,1974).This rule holds true for Moroccan Arabic(henceforth MA)speakers learning English because grammatical devices in the two languages differ in almost all equivalent situations.For instance,while English verb forms are used to indicate tense in conditional sentences,MA uses them to indicate aspect.Adopting the typology of conditional constructions suggested by Dancygier(1999)and Dancygier&Sweetser(2005),this study provides a contrastive analysis of conditionals in English and MA to predict the possible errors EFL/ESL learners are likely to make while learning English.The analysis shows that the main discrepancy between English conditionals and MA conditionals lies in the verb form used by the two systems.Accordingly,if EFL/ESL learners are influenced by verb form in their L1,they are likely to face some challenges while learning English conditionals.That is,they are likely to use the past tense in the protases of English predictive conditionals and generic conditionals since the perfective form of the verb is used in the protases of these two types in MA.Concerning the protases of English non-predictive conditionals,Moroccan EFL/ESL learners are likely to use either the past tense or the present tense since both the perfective and the imperfective forms of the verb are possible in the protases of MA non-predictive conditionals.However,due to the fact that the perfective form is the prototypical form in the protases of conditionals in MA,EFL/ESL learners are likely to use the past tense more often than the present tense.The analysis also shows that EFL/ESL learners tend to use the present tense in the apodoses of English conditionals since the prevalent form in the apodoses of MA conditionals is the imperfective.
基金Supported by the National Natural Science Foundation of China(No.61562046)Science and Technology Project of Jiangxi Provincial Education Department(No.GJJ150777,GJJ160742)
文摘Conditionally t-diagnosable and t-diagnosable are important in system level diagnosis. Therefore,it is valuable to identify whether the system is conditionally t-diagnosable or t-diagnosable and derive the corresponding conditional diagnosability and diagnosability. In the paper,distinguishable measures of pairs of distinct faulty sets with a new perspective on establishing functions are focused.Applying distinguishable function and decision function,it is determined whether a system is conditionally t-diagnosable( or t-diagnosable) or not under the PMC( Preparata,Metze,and Chien)model directly. Based on the decision function,a novel conditional diagnosability algorithm under the PMC model is introduced which can calculate conditional diagnosability rapidly.
基金The National Natural Science Foundation of China(No60663004)the PhD Programs Foundation of Ministry of Educa-tion of China (No20050007023)
文摘Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow syntactic parsing as the foundation, phrases or named entities as the labeled units, and the CRFs model is trained to label the predicates＇ semantic roles in a sentence. The key of the method is parameter estimation and feature selection for the CRFs model. The L-BFGS algorithm was employed for parameter estimation, and three category features： features based on sentence constituents, features based on predicate, and predicate-constituent features as a set of features for the model were selected. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the method can obtain better performance than the maximum entropy model, and can achieve 80. 43 % precision and 63. 55 % recall for semantic role labeling.
基金This work was supported by National Natural Science Foundation of China (No. 30571198) and the Science and Technology Office of Zhejiang Province (No. 2004C2020-2 and No. 011102471).
文摘A factorial mating design in two environments was conducted using 7 cytoplasmic male sterile lines （A） and 5 restorer lines （R） along with their F1 （A × R） and F2 populations. The unconditional and conditional analyses of genetic models and the corresponding statistic methods, including endospermic, cytoplasmic, and maternal plant genetic systems, were used to analyze the genetic relationships between protein content （PC） and the appearance quality traits of indica rice （Oryza sativa L.）. The results from unconditional analysis indicated that PC was significantly correlated with the appearance quality traits of rice, except for the brown rice thickness （BRT）. Only the genetic covariance between PC and the brown rice width （BRW） was positively correlative, whereas all the other pairwise traits were negatively correlative. The results from conditional analysis revealed that the weight of brown rice （WBR） or the amylose content （AC） could significantly affect the relationships between PC and the appearance quality traits of indica rice. The conditional analysis showed that WBR might negatively affect the relationships between PC and the brown rice length （BRL）, BRW, or BRT through the geuotype x environmental （GE） interaction effects, but positively affected the relationships between PC and the ratio of brown rice length to width （RLW） or the ratio of brown rice length to thickness （RLT）. The amylase content could positively affect the relationships between PC and BRL, RLW, RLT through the cytoplasmic effects and maternal additive effects, but negatively affected the relationships between PC and BRW.
