Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

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.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

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.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

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.

Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

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.

Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

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.

Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

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.

Characterizing large-scale weak interlayer shear zones using conditional random field theory

The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

Conditional survival probability of distant-metastatic hepatocellular carcinoma: A population-based study

BACKGROUND The prognosis of many patients with distant metastatic hepatocellular carcinoma(HCC)improved after they survived for several months.Compared with tradi-tional survival analysis,conditional survival(CS)which takes into account changes in survival risk could be used to describe dynamic survival probabilities.AIM To evaluate CS of distant metastatic HCC patients.METHODS Patients diagnosed with distant metastatic HCC between 2010 and 2015 were extracted from the Surveillance,Epidemiology and End Results database.Univariate and multivariate Cox regression analysis were used to identify factors for overall survival(OS),while competing risk model was used to identify risk factors for cancer-specific survival(CSS).Six-month CS was used to calculate the probability of survival for an additional 6 mo at a specific time after initial diagnosis,and standardized difference(d)was used to evaluate the survival differences between subgroups.Nomograms were constructed to predict CS.Positiveα-fetoprotein expression,higher T stage(T3 and T4),N1 stage,non-primary site surgery,non-chemotherapy,non-radiotherapy,and lung metastasis were independent risk factors for actual OS and CSS through univariate and multivariate analysis.Actual survival rates decreased over time,while CS rates gradually increased.As for the 6-month CS,the survival difference caused by chemotherapy and radiotherapy gradually disappeared over time,and the survival difference caused by lung metastasis reversed.Moreover,the influence of age and gender on survival gradually appeared.Nomograms were fitted for patients who have lived for 2,4 and 6 mo to predict 6-month conditional OS and CSS,respectively.The area under the curve(AUC)of nomograms for conditional OS decreased as time passed,and the AUC for conditional CSS gradually increased.CONCLUSION CS for distant metastatic HCC patients substantially increased over time.With dynamic risk factors,nomograms constructed at a specific time could predict more accurate survival rates.

An Analysis of Conditional Survival Rates for Ewing Sarcoma Patients

Using data from the Surveillance, Epidemiology, and End Results (SEER) Program based at the National Cancer Institute in the US, conditional survival rates are reported for 1,988 Ewing Sarcoma patients diagnosed during the period 2000-2015. These patients represent the experience of 26.5% of the US population. Specifically, 5-year conditional relative survival rates are calculated for these patients for the first eight years subsequent to diagnosis of their cancer by Extent of Disease (EOD) (Localized, Regional, and Distant as coded by the SEER Program), gender, and age (<18, 18 - 34, and 35+). Findings include showing how the conditional survival rate patterns improve over time and that there are differences by gender, age, and EOD.

A Contrastive Analysis of Conditionals in English and Moroccan Arabic: Potential problems when learning English conditionals by Moroccan EFL learners

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.

A novel conditional diagnosability algorithm under the PMC model

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.

Semantic role labeling based on conditional random fields

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.

Analysis of the Conditional Correlations from Different Genetic Systems Between the Protein Content and the Appearance Quality Traits of Indica Rice

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.

TONE MODELING BASED ON HIDDEN CONDITIONAL RANDOM FIELDS AND DISCRIMINATIVE MODEL WEIGHT TRAINING

The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training （DMWT） is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error （MPE） criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition （LVCSR） can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.

Identification of unconditional and conditional QTL for oil, protein and starch content in maize

Oil, protein and starch are key chemical components of maize kernels. A population of 245 recombinant inbred lines(RILs) derived from a cross between a high-oil inbred line, By804, and a regular inbred line, B73, was used to dissect the genetic interrelationships among oil, starch and protein content at the individual QTL level by unconditional and conditional QTL mapping. Combined phenotypic data over two years with a genetic linkage map constructed using 236 markers, nine, five and eight unconditional QTL were detected for oil, protein and starch content, respectively. Some QTL for oil, protein and starch content were clustered in the same genomic regions and the direction of their effects was consistent with the sign of their correlation. In conditional QTL mapping, 37(29/8) unconditional QTL were not detected or showed reduced effects, four QTL demonstrated similar effects under unconditional and conditional QTL mapping, and 17 additional QTL were identified by conditional QTL mapping. These results imply that there is a strong genetic relationship among oil, protein and starch content in maize kernels. The information generated in the present investigation could be helpful in marker-assisted breeding for maize varieties with desirable kernel quality traits.

Unconditional and Conditional QTL Mapping for Tiller Numbers at Various Stages with Single Segment Substitution Lines in Rice(Oryza sativa L.)

Tiller is one of the most important agronomic traits which influences quantity and quality of effective panicles and finally influences yield in rice. It is important to understand ＂static＂ and ＂dynamic＂ information of the QTLs for tillers in rice. This work was the first time to simultaneously map unconditional and conditional QTLs for tiller numbers at various stages by using single segment substitution lines in rice. Fourteen QTLs for tiller number, distributing on the corresponding substitution segments of chromosomes 1, 2, 3, 4, 6, 7 and 8 were detected. Both the number and the effect of the QTLs for tiller number were various at different stages, from 6 to 9 in the number and from 1.49 to 3.49 in the effect, respectively. Tiller number QTLs expressed in a time order, mainly detected at three stages of 0-7 d, 14-21 d and 35-42 d after transplanting with 6 positive, 9 random and 6 negative expressing QTLs, respectively. Each of the QTLs expressed one time at least during the whole duration of rice. The tiller number at a specific stage was determined by sum of QTL effects estimated by the unconditional method, while the increasing or decreasing number in a given time interval was controlled by the total of QTL effects estimated by the conditional method. These results demonstrated that it is highly effective and accurate for mapping of the QTLs by using single segment substitution lines and the conditional analysis methodology.

Conditional and unconditional QTLs mapping of gluten strength in common wheat (Triticum aestivum L.)

Dissecting the genetic relationships among gluten-related traits is important for high quality wheat breeding. Quantita- tive trait loci （QTLs） analysis for gluten strength, as measured by sedimentation volume （SV） and gluten index （GI）, was performed using the QTLNetwork 2.0 software. Recombinant inbred lines （RILs） derived from the winter wheat varieties Shannong 01-35xGaocheng 9411 were used for the study. A total of seven additive QTLs for gluten strength were identi- fied using an unconditional analysis. QGi1D-13 and QSv1D-14 were detected through unconditional and conditional QTLs mapping, which explained 9.15-45.08% of the phenotypic variation. QTLs only identified under conditional QTL mapping were located in three marker intervals： WPT-3743-GLU-D1 （1D）, WPT-7001-WMC258 （1B）, and WPT-8682-WPT-5562 （1B）. Six pairs of epistatic QTLs distributed nine chromosomes were identified. Of these, two main effect QTLs （QGi1D-13 and QSvlD-14） and 12 pairs of epistatic QTLs were involved in interactions with the environment. The results indicated that chromosomes 1B and 1D are important for the improvement of gluten strength in common wheat. The combination of conditional and unconditional QTLs mapping could be useful for a better understanding of the interdependence of different traits at the QTL molecular level.

Mining Applicable Elite Alleles of Growth Duration, Plant Height and Panicle Number per Plant by Conditional QTL Mapping in Japonica Rice

Unconditional and conditional QTL mapping were conducted for growth duration (GD), plant height (PH) and effective panicle number per plant (PN) using a recombinant inbred line (RIL) population derived from a cross between two japonica rice varieties Xiushui 79 and C Bao. The RIL population consisted of 254 lines was planted in two environments, Nanjing and Sihong, Jiangsu Province, China. Results showed that additive effects were major in all of QTLs for GD, PH and PN detected by the two methods, and the epistatic effects explained a small proportion of phenotypic variation. No interactions were detected between additive QTL and environment, and between epistatic QTL pairs and environment. After growth duration was adjusted to an identical level, RM80-160bp was detected as an applicable elite allele for PN, with an additive effect of 0.71. When effective panicle number per plant was adjusted to an identical level, RM448-240bp was detected as an applicable elite allele for GD, with an additive effect of 4.64. After plant height was adjusted to an identical level, RM80-160bp was detected as an applicable elite allele for PN, with an additive effect of 0.62, and RM448-240bp was detected as an applicable elite allele for GD, with an additive effect of 3.89. These applicable elite alleles could be used to improve target traits without influencing the other two traits.

SOLVABILITY RESULTS OF A CONDITIONAL INPUT-OUTPUT EQUATION BASED ON A TYPE OF NONLINEAR LEONTIEF MODEL

A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are presented. By approaches of nonlinear analysis some solvability results of this equation and continuous perturbation properties of the relative solution sets are obtained, and some economic significance are illustrated by the remark.

Nonparametric inferences for kurtosis and conditional kurtosis

Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S＆P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution＇s tails are little affected by the historical data.