The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the...The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.展开更多
In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergen...In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.展开更多
Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways.Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at...Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways.Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at these sites.One such site in the cold seep ecosystem of Krishna-Godavari basin(K-G basin)along the east coast of India,discovered in Feb 2018 at a depth of 1800 m was assessed for its bacterial diversity.The seep bacterial communities were dominated by phylum Proteobacteria(57%),Firmicutes(16%)and unclassified species belonging to the family Helicobacteriaceae.The surface sediments of the seep had maximum OTUs(operational taxonomic units)(2.27×10^(3))with a Shannon alpha diversity index of 8.06.In general,environmental parameters like total organic carbon(p<0.01),sulfate(p<0.001),sulfide(p<0.05)and methane(p<0.01)were responsible for shaping the bacterial community of the cold seep ecosystem in the K-G Basin.Environmental parameters play a significant role in changing the bacterial diversity richness between different cold seep environments in the oceans.展开更多
Accurately mapping the spatial distribution of soil organic carbon(SOC)is crucial for guiding agricultural management and improving soil carbon sequestration,especially in fragmented agricultural landscapes.Although r...Accurately mapping the spatial distribution of soil organic carbon(SOC)is crucial for guiding agricultural management and improving soil carbon sequestration,especially in fragmented agricultural landscapes.Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes,its relationship with SOC remains unclear.In this study,we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions.Taking the Qilu Lake watershed in Yunnan,China,as a case study area and based on 216 topsoil samples collected from irrigation areas,we applied the extreme gradient boosting(XGBoost)model to investigate the contributions of vegetation indices(VI),brightness indices(BI),moisture indices(MI),and spectral transformations(ST,principal component analysis and tasseled cap transformation)to SOC mapping.The results showed that ST contributed the most to SOC prediction accuracy,followed by MI,VI,and BI,with improvements in R2 of 29.27,26.83,19.51,and 14.43%,respectively.The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information.The optimal SOC prediction model integrated soil properties,topographic factors,location factors,and landscape metrics,as well as remote sensing-derived variables,and achieved RMSE and MAE of 15.05 and 11.42 g kg-1,and R2 and CCC of 0.57 and 0.72,respectively.The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture,vegetation status,soil brightness and SOC.Compared with traditional linear regression models,interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC.Overall,this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy.Through interpretable machine learning,we can further elucidate the causes of SOC variation,which is important for sustainable soil management and agricultural practices.展开更多
In this paper,we establish characterizations of α-Bloch functions and little α-Bloch functions on the unit ball as well as the unit polydisk of C^(m),which generalize and improve results of Aulaskari-Lappan,Minda,Au...In this paper,we establish characterizations of α-Bloch functions and little α-Bloch functions on the unit ball as well as the unit polydisk of C^(m),which generalize and improve results of Aulaskari-Lappan,Minda,Aulaskari-Wulan,and Wu.Some examples are also given to complement our theory.展开更多
Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+...Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+n),n≥1}under some proper conditions,where{Yi,-∞<i<∞}is a doubly infinite sequence of negatively dependent random variables under sub-linear expectations.These results extend and complement the relevant results in probability space.展开更多
In the current paper,we present a study of the spatial distribution of luminous blue variables(LBVs)and various LBV candidates(c LBVs)with respect to OB associations in the galaxy M33.The identification of blue star g...In the current paper,we present a study of the spatial distribution of luminous blue variables(LBVs)and various LBV candidates(c LBVs)with respect to OB associations in the galaxy M33.The identification of blue star groups was based on the LGGS data and was carried out by two clustering algorithms with initial parameters determined during simulations of random stellar fields.We have found that the distribution of distances to the nearest OB association obtained for the LBV/c LBV sample is close to that for massive stars with Minit>20 M⊙and WolfRayet stars.This result is in good agreement with the standard assumption that LBVs represent an intermediate stage in the evolution of the most massive stars.However,some objects from the LBV/cLBV sample,particularly Fe II-emission stars,demonstrated severe isolation compared to other massive stars,which,together with certain features of their spectra,implicitly indicates that the nature of these objects and other LBVs/cLBVs may differ radically.展开更多
Loop optimization plays an important role in compiler optimization and program transformation. Many sophisticated techniques such as loop invariance code motion have been developed. Loop peeling is a technique to ass...Loop optimization plays an important role in compiler optimization and program transformation. Many sophisticated techniques such as loop invariance code motion have been developed. Loop peeling is a technique to assist parallelization of loops by unfolding loops a few times. This paper introduces a novel technique called loop peeling based on quasi invariance/induction variables. It aims at finding a general and automatic method to derive how many times a given loop should be peeled. Our technique allows for a number of iterations before some variables assigned inside a given loop become invariance or induction variables. In this paper we define the notion of quasi invariance/induction variables, present an algorithm for statically computing the optimal peeling length of a given loop. Our technique can increase the accuracy of program analyses, improve the effectiveness of loop peeling and is well suited as supporting other optimization techniques in the context of supercomputers.展开更多
In this paper,we establish a Rosenthal-type inequality of partial sums for ρ~mixing random variables.As its applications,we get the complete convergence rates in the strong laws for ρ^-mixing random variables.The re...In this paper,we establish a Rosenthal-type inequality of partial sums for ρ~mixing random variables.As its applications,we get the complete convergence rates in the strong laws for ρ^-mixing random variables.The result obtained extends the corresponding result.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variable...The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variables（TFLV）, is studied. The formula of the degree of possibility between two TFLVs is defined, and some of its characteristics are studied. Based on the degree of possibility of fuzzy linguistic variables, an approach to ranking the decision alternatives in multiple attribute decision making with TFLV is developed. The trapezoid fuzzy linguistic weighted averaging （TFLWA） operator method is utilized to aggregate the decision information, and then all the alternatives are ranked by comparing the degree of possibility of TFLV. The method can carry out linguistic computation processes easily without loss of linguistic information, and thus makes the decision results reasonable and effective. Finally, the implementation process of the proposed method is illustrated and analyzed by a practical example.展开更多
Strong limit theorems are established for weighted sums of widely orthant dependent（WOD） random variables. As corollaries, the strong limit theorems for weighted sums of extended negatively orthant dependent（ENOD）...Strong limit theorems are established for weighted sums of widely orthant dependent（WOD） random variables. As corollaries, the strong limit theorems for weighted sums of extended negatively orthant dependent（ENOD） random variables are also obtained, which extend and improve the related known works in the literature.展开更多
In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomi...In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.展开更多
In this article, the strong laws of large numbers for array of rowwise asymptotically almost negatively associated(AANA) random variables are studied. Some sufficient conditions for strong laws of large numbers for ar...In this article, the strong laws of large numbers for array of rowwise asymptotically almost negatively associated(AANA) random variables are studied. Some sufficient conditions for strong laws of large numbers for array of rowwise AANA random variables are presented without assumption of identical distribution. Our results extend the corresponding ones for independent random variables to case of AANA random variables.展开更多
In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological d...In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological datasets are not more accurate than synoptic stations,but their various advantages,such as spatial coverage,time coverage,accessibility,and free use,have made these techniques superior,and sometimes we can use them instead of synoptic stations.In this study,we used four meteorological datasets,including Climatic Research Unit gridded Time Series(CRU TS),Global Precipitation Climatology Centre(GPCC),Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications(AgMERRA),Agricultural Climate Forecast System Reanalysis(AgCFSR),to estimate climate variables,i.e.,precipitation,maximum temperature,and minimum temperature,and crop variables,i.e.,reference evapotranspiration,irrigation requirement,biomass,and yield of maize,in Qazvin Province of Iran during 1980-2009.At first,data were gathered from the four meteorological datasets and synoptic station in this province,and climate variables were calculated.Then,after using the AquaCrop model to calculate the crop variables,we compared the results of the synoptic station and meteorological datasets.All the four meteorological datasets showed strong performance for estimating climate variables.AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature.However,their normalized root mean square error was inferior to CRU for minimum temperature.Furthermore,they were all very efficient for estimating the biomass and yield of maize in this province.For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR.But for the estimation of biomass and yield,all the four meteorological datasets were reliable.To sum up,GPCC and AgCFSR were the two best datasets in this study.This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.展开更多
The relations of change rate of an independent variable, volumetric strain of the porous skeleton, with the change rates of a kind of constitutive variables, such as porosity, volumetric strain of the solid matrix, ar...The relations of change rate of an independent variable, volumetric strain of the porous skeleton, with the change rates of a kind of constitutive variables, such as porosity, volumetric strain of the solid matrix, are derived from the definition of the porosity of water saturated porous media; and the relations of the change rates of another two independent variables, pressure of the pore liquid water and temperature, with the change rates of another kind of constitutive variables, such as pressure of the pore ice, average pressure of the pore liquid water and ice, and average stress of the solid matrix, are obtained from the Clausius Clapeyron relation in the process of freezing or thawing, definitions of the average pore pressure and effective stress. Based on the hypothesis of linear thermoelasticity, principle of effective stress and these relations, the change rates of all constitutive variables may be described with the change rates of the three independent variables.展开更多
Convergence properties for arrays of rowwise φ-mixing random variables are studied. As an application, the Chung-type strong law of large numbers for arrays of rowwise φ-mixing random variables is obtained. Our resu...Convergence properties for arrays of rowwise φ-mixing random variables are studied. As an application, the Chung-type strong law of large numbers for arrays of rowwise φ-mixing random variables is obtained. Our results extend the corresponding ones for independent random variables to the case of φ-mixing random variables.展开更多
Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect ...Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.展开更多
In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status ev...In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.展开更多
Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nation...Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.展开更多
基金supported by Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)Re-accompanying Funding Project of Academic Achievements of Jingdezhen Ceramic University(Grant Nos.215/20506277,215/20506341)。
文摘The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.
基金supported by the National Social Science Fundation(Grant No.21BTJ040)the Project of Outstanding Young People in University of Anhui Province(Grant Nos.2023AH020037,SLXY2024A001).
文摘In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.
文摘Cold seeps are oases for biological communities on the sea floor around hydrocarbon emission pathways.Microbial utilization of methane and other hydrocarbons yield products that fuel rich chemosynthetic communities at these sites.One such site in the cold seep ecosystem of Krishna-Godavari basin(K-G basin)along the east coast of India,discovered in Feb 2018 at a depth of 1800 m was assessed for its bacterial diversity.The seep bacterial communities were dominated by phylum Proteobacteria(57%),Firmicutes(16%)and unclassified species belonging to the family Helicobacteriaceae.The surface sediments of the seep had maximum OTUs(operational taxonomic units)(2.27×10^(3))with a Shannon alpha diversity index of 8.06.In general,environmental parameters like total organic carbon(p<0.01),sulfate(p<0.001),sulfide(p<0.05)and methane(p<0.01)were responsible for shaping the bacterial community of the cold seep ecosystem in the K-G Basin.Environmental parameters play a significant role in changing the bacterial diversity richness between different cold seep environments in the oceans.
基金supported by the National Key Research and Development Program of China(2022YFB3903302).
文摘Accurately mapping the spatial distribution of soil organic carbon(SOC)is crucial for guiding agricultural management and improving soil carbon sequestration,especially in fragmented agricultural landscapes.Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes,its relationship with SOC remains unclear.In this study,we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions.Taking the Qilu Lake watershed in Yunnan,China,as a case study area and based on 216 topsoil samples collected from irrigation areas,we applied the extreme gradient boosting(XGBoost)model to investigate the contributions of vegetation indices(VI),brightness indices(BI),moisture indices(MI),and spectral transformations(ST,principal component analysis and tasseled cap transformation)to SOC mapping.The results showed that ST contributed the most to SOC prediction accuracy,followed by MI,VI,and BI,with improvements in R2 of 29.27,26.83,19.51,and 14.43%,respectively.The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information.The optimal SOC prediction model integrated soil properties,topographic factors,location factors,and landscape metrics,as well as remote sensing-derived variables,and achieved RMSE and MAE of 15.05 and 11.42 g kg-1,and R2 and CCC of 0.57 and 0.72,respectively.The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture,vegetation status,soil brightness and SOC.Compared with traditional linear regression models,interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC.Overall,this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy.Through interpretable machine learning,we can further elucidate the causes of SOC variation,which is important for sustainable soil management and agricultural practices.
基金Supported by Natural Science Research Project for Colleges and Universities of Anhui Province(Grant No.2022AH050329)Yunnan Provincial Department of Education Fund(Grant No.2025J0376).
文摘In this paper,we establish characterizations of α-Bloch functions and little α-Bloch functions on the unit ball as well as the unit polydisk of C^(m),which generalize and improve results of Aulaskari-Lappan,Minda,Aulaskari-Wulan,and Wu.Some examples are also given to complement our theory.
基金Supported by the Academic Achievement Re-cultivation Projects of Jingdezhen Ceramic University(Grant Nos.215/20506341215/20506277)the Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)。
文摘Assume that{a_(i),−∞<i<∞}is an absolutely summable sequence of real numbers.We establish the complete q-order moment convergence for the partial sums of moving average processes{X_(n)=Σ_(i=−∞)^(∞)a_(i)Y_(i+n),n≥1}under some proper conditions,where{Yi,-∞<i<∞}is a doubly infinite sequence of negatively dependent random variables under sub-linear expectations.These results extend and complement the relevant results in probability space.
文摘In the current paper,we present a study of the spatial distribution of luminous blue variables(LBVs)and various LBV candidates(c LBVs)with respect to OB associations in the galaxy M33.The identification of blue star groups was based on the LGGS data and was carried out by two clustering algorithms with initial parameters determined during simulations of random stellar fields.We have found that the distribution of distances to the nearest OB association obtained for the LBV/c LBV sample is close to that for massive stars with Minit>20 M⊙and WolfRayet stars.This result is in good agreement with the standard assumption that LBVs represent an intermediate stage in the evolution of the most massive stars.However,some objects from the LBV/cLBV sample,particularly Fe II-emission stars,demonstrated severe isolation compared to other massive stars,which,together with certain features of their spectra,implicitly indicates that the nature of these objects and other LBVs/cLBVs may differ radically.
基金Supported by the Japan Society for the Promotion of Science(JSPS96 P0 0 5 0 4)
文摘Loop optimization plays an important role in compiler optimization and program transformation. Many sophisticated techniques such as loop invariance code motion have been developed. Loop peeling is a technique to assist parallelization of loops by unfolding loops a few times. This paper introduces a novel technique called loop peeling based on quasi invariance/induction variables. It aims at finding a general and automatic method to derive how many times a given loop should be peeled. Our technique allows for a number of iterations before some variables assigned inside a given loop become invariance or induction variables. In this paper we define the notion of quasi invariance/induction variables, present an algorithm for statically computing the optimal peeling length of a given loop. Our technique can increase the accuracy of program analyses, improve the effectiveness of loop peeling and is well suited as supporting other optimization techniques in the context of supercomputers.
基金Supported by the National Science Foundation（10661006） Supported by Innovation Project of Guangxi Graduate Education（2007105960812M18）
文摘In this paper,we establish a Rosenthal-type inequality of partial sums for ρ~mixing random variables.As its applications,we get the complete convergence rates in the strong laws for ρ^-mixing random variables.The result obtained extends the corresponding result.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金2008 Soft Science Program of Jiangsu Science and Technology Department (No.BR2008098)
文摘The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variables（TFLV）, is studied. The formula of the degree of possibility between two TFLVs is defined, and some of its characteristics are studied. Based on the degree of possibility of fuzzy linguistic variables, an approach to ranking the decision alternatives in multiple attribute decision making with TFLV is developed. The trapezoid fuzzy linguistic weighted averaging （TFLWA） operator method is utilized to aggregate the decision information, and then all the alternatives are ranked by comparing the degree of possibility of TFLV. The method can carry out linguistic computation processes easily without loss of linguistic information, and thus makes the decision results reasonable and effective. Finally, the implementation process of the proposed method is illustrated and analyzed by a practical example.
基金Supported by the National Natural Science Foundation of China (Grant No.11271161)
文摘Strong limit theorems are established for weighted sums of widely orthant dependent（WOD） random variables. As corollaries, the strong limit theorems for weighted sums of extended negatively orthant dependent（ENOD） random variables are also obtained, which extend and improve the related known works in the literature.
基金The National Natural Science Foundation of China(No.51408229,51278202)the Program of the Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University(No.K201204)the Science and Technology Program of Guangdong Communication Department(No.2013-02-068)
文摘In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.
基金Supported by the National Natural Science Foundation of China（lilT1001, 11201001） Supported by the Natural Science Foundation of Anhui Province（1208085QA03）+1 种基金 Supported by the Talents Youth Fund of Anhui Province Universities（2012SQRL204） Supported by th Doctoral Research Start-up Funds Projects of Anhui University（33190250）
文摘In this article, the strong laws of large numbers for array of rowwise asymptotically almost negatively associated(AANA) random variables are studied. Some sufficient conditions for strong laws of large numbers for array of rowwise AANA random variables are presented without assumption of identical distribution. Our results extend the corresponding ones for independent random variables to case of AANA random variables.
文摘In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological datasets are not more accurate than synoptic stations,but their various advantages,such as spatial coverage,time coverage,accessibility,and free use,have made these techniques superior,and sometimes we can use them instead of synoptic stations.In this study,we used four meteorological datasets,including Climatic Research Unit gridded Time Series(CRU TS),Global Precipitation Climatology Centre(GPCC),Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications(AgMERRA),Agricultural Climate Forecast System Reanalysis(AgCFSR),to estimate climate variables,i.e.,precipitation,maximum temperature,and minimum temperature,and crop variables,i.e.,reference evapotranspiration,irrigation requirement,biomass,and yield of maize,in Qazvin Province of Iran during 1980-2009.At first,data were gathered from the four meteorological datasets and synoptic station in this province,and climate variables were calculated.Then,after using the AquaCrop model to calculate the crop variables,we compared the results of the synoptic station and meteorological datasets.All the four meteorological datasets showed strong performance for estimating climate variables.AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature.However,their normalized root mean square error was inferior to CRU for minimum temperature.Furthermore,they were all very efficient for estimating the biomass and yield of maize in this province.For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR.But for the estimation of biomass and yield,all the four meteorological datasets were reliable.To sum up,GPCC and AgCFSR were the two best datasets in this study.This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.
文摘The relations of change rate of an independent variable, volumetric strain of the porous skeleton, with the change rates of a kind of constitutive variables, such as porosity, volumetric strain of the solid matrix, are derived from the definition of the porosity of water saturated porous media; and the relations of the change rates of another two independent variables, pressure of the pore liquid water and temperature, with the change rates of another kind of constitutive variables, such as pressure of the pore ice, average pressure of the pore liquid water and ice, and average stress of the solid matrix, are obtained from the Clausius Clapeyron relation in the process of freezing or thawing, definitions of the average pore pressure and effective stress. Based on the hypothesis of linear thermoelasticity, principle of effective stress and these relations, the change rates of all constitutive variables may be described with the change rates of the three independent variables.
基金the National Natural Science Foundation of China(Grant Nos.1117100111301004+5 种基金11326172)the Natural Science Foundation of Anhui Province(Grant No.1408085QA02)Talents Youth Fund of Anhui ProvinceUniversities(Grant No.2012SQRL204)the Students Science Research Training Program of Anhui University(Grant Nos.KYXL2014013KYXL2014016)Doctoral Research Start-up Funds Projects of Anhui University
文摘Convergence properties for arrays of rowwise φ-mixing random variables are studied. As an application, the Chung-type strong law of large numbers for arrays of rowwise φ-mixing random variables is obtained. Our results extend the corresponding ones for independent random variables to the case of φ-mixing random variables.
文摘Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.
文摘In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.
基金National Science and Technology Major Projects for Major New Drugs Innovation and Development(2014ZX09J14102-02A)Special Topic on Military Health Care(17bjz41)National Natural Science Foundation of China(81170249 and 30700305).
文摘Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.
