基于GPGPU-sim的多kernel场景下GPGPU性能优化实验方法

该文介绍了基于GPGPU-sim的多kernel环境下GPGPU性能优化实验方法,旨在为初学者开展多kernenl场景下GPGPU性能优化研究提供实验方法参考,也能为计算机系统结构教学提供案例。文中重点分析讨论了基于GPGPU-sim模拟器、多kernel场景下的一种自适应线程块调度方法的改进思想、实验方法及过程,还对GPGPU的微系统结构、GPGPU-sim模拟器及源代码结构进行了介绍。实验结果表明,该文阐述的实验方法可行,相对于基准方法,该文提出的改进策略可以提升多kernel场景下GPGPU的执行效率。

Fine mapping and validation of a stable QTL for thousand-kernel weight in wheat(Triticum aestivum L.)

Thousand-kernel weight(TKW)is a measure of grain weight,a target of wheat breeding.The object of this study was to fine-map a stable quantitative trait loci(QTL)for TKW and identify its candidate gene in a recombinant inbred line(RIL)population derived from the cross of Kenong 9204(KN9204)and Jing411(J411).On a high-density genetic linkage map,24,26 and 25 QTL were associated with TKW,kernel length(KL),and kernel width(KW),respectively.A major and stable QTL,QTkw-2D,was mapped to an8.3 cM interval on chromosome arm 2DL.By saturation of polymorphic markers in its target region,QTkw-2D was confined to a 9.13 Mb physical interval using a secondary mapping population derived from a residually heterozygous line(F6:7).This interval was further narrowed to 2.52 Mb using QTkw-2D near-isogenic lines(NILs).NILs~(KN9204)had higher fresh and dry weights than NILsJ411at various grain-filling stages.The TKW and KW of NILs~(KN9204)were much higher than those of NILsJ411in field trials.By comparison of both DNA sequence and expression between KN9204 and J411,TraesCS2D02G460300.1(TraesKN2D01HG49350)was assigned as a candidate gene for QTkw-2D.This was confirmed by RNA sequencing(RNA-seq)of QTkw-2D NILs.These results provide the basis of map-based cloning of QTkw-2D,and DNA markers linked to the candidate gene may be used in marker-assisted selection.

LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

Genetic dissection and validation of a major QTL for grain weight on chromosome 3B in bread wheat(Triticum aestivum L.)

Grain weight is one of the key components of wheat(Triticum aestivum L.)yield.Genetic manipulation of grain weight is an efficient approach for improving yield potential in breeding programs.A recombinant inbred line(RIL)population derived from a cross between W7268 and Chuanyu 12(CY12)was employed to detect quantitative trait loci(QTLs)for thousand-grain weight(TGW),grain length(GL),grain width(GW),and the ratio of grain length to width(GLW)in six environments.Seven major QTLs,QGl.cib-2D,QGw.cib-2D,QGw.cib-3B,QGw.cib-4B.1,QGlw.cib-2D.1,QTgw.cib-2D.1 and QTgw.cib-3B.1,were consistently identified in at least four environments and the best linear unbiased estimation(BLUE)datasets,and they explained 2.61 to 34.85%of the phenotypic variance.Significant interactions were detected between the two major TGW QTLs and three major GW loci.In addition,QTgw.cib-3B.1 and QGw.cib-3B were co-located,and the improved TGW at this locus was contributed by GW.Unlike other loci,QTgw.cib-3B.1/QGw.cib-3B had no effect on grain number per spike(GNS).They were further validated in advanced lines using Kompetitive Allele Specific PCR(KASP)markers,and a comparison analysis indicated that QTgw.cib-3B.1/QGw.cib-3B is likely a novel locus.Six haplotypes were identified in the region of this QTL and their distribution frequencies varied between the landraces and cultivars.According to gene annotation,spatial expression patterns,ortholog analysis and sequence variation,the candidate gene of QTgw.cib-3B.1/QGw.cib-3B was predicted.Collectively,the major QTLs and KASP markers reported here provide valuable information for elucidating the genetic architecture of grain weight and for molecular marker-assisted breeding in grain yield improvement.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

The cytosolic isoform of triosephosphate isomerase,ZmTPI4,is required for kernel development and starch synthesis in maize(Zea mays L.)

Triosephosphate isomerase(TPI)is an enzyme that functions in plant energy production,accumulation,and conversion.To understand its function in maize,we characterized a maize TPI mutant,zmtpi4.In comparison to the wild type,zmtpi4 mutants showed altered ear development,reduced kernel weight and starch content,modified starch granule morphology,and altered amylose and amylopectin content.Protein,ATP,and pyruvate contents were reduced,indicating ZmTPI4 was involved in glycolysis.Although subcellular localization confirmed ZmTPI4 as a cytosolic rather than a plastid isoform of TPI,the zmtpi4 mutant showed reduced leaf size and chlorophyll content.Overexpression of ZmTPI4 in Arabidopsis led to enlarged leaves and increased seed weight,suggesting a positive regulatory role of ZmTPI4 in kernel weight and starch content.We conclude that ZmTPI4 functions in maize kernel development,starch synthesis,glycolysis,and photosynthesis.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

我国护理人力资源区域差异的演变特征——基于Dagum基尼系数分解和Kernel核密度估计的实证研究

目的分析我国护理人力资源的区域差异及分布动态演进,为我国护理人力资源的合理配置和规划提供参考。方法基于2011-2022年省级护理人力资源面板数据,通过测算Kernel密度和Dagum基尼系数对我国护理人力资源的区域差异及分布动态演进进行分析评价。结果2011-2022年,在空间分布上,全国及各地区护理人力资源总量呈增加趋势,各区域差异逐步降低,且两极化特征明显;在区域差异上,我国护理人力资源总体差异均值为0.1149;区域内呈东部>西部>中部>东北区域的梯度逐步递增趋势;区域间差异占总体差异的40.61%。结论全国护理人力资源总体差异处于相对合理状态,区域间差异是主要来源,均等化水平逐步提升;政府应针对各区域精准施策,进一步稳定护理人力资源队伍,完善护理人力资源结构以促进护理人力资源的优质均衡发展。

Longitudinal changes in body weight of breastfeeding mothers in the first year after delivery and its relationship with human milk composition:a combined longitudinal and cross-sectional cohort study

Objective:Postpartum weight retention(PPWR)is a common problem among women after childbirth.The main objectives of this study are to understand the changes in body weight of breastfeeding mothers during long-term follow-up and preliminarily explore the relationship between maternal body weight and human milk composition,including macronutrients,leptin,and adiponectin.Methods:The study included a longitudinal cohort(122 mothers),and a cross-sectional cohort(37 mothers).The human milk,maternal weight,and dietary surveys were collected in the longitudinal cohort at different follow-up time points(1-14 days postpartum,2-4 months postpartum,5-7 months postpartum,and 12-17 months postpartum).The maternal body weight was analyzed using the responses in the survey questionnaires.A milk analyzer based on the mid-infrared spectroscopy(MIRS)was used to determine milk composition,and nutrition analysis software evaluated dietary intakes.In the cross-sectional cohort,participating mothers were asked to provide blood and human milk samples and pertinent information related to maternal body composition.Maternal body composition was measured by bioelectrical impedance analysis(BIA),while ELISA analyzed leptin and adiponectin in milk and serum.Results:At 5-7 months postpartum,the PPWR of breastfeeding mothers was(2.46±3.59)kg.At 12-17 months postpartum,the PPWR was(0.98±4.06)kg.PPWR was found to be negatively correlated with milk fat content within 14 days postpartum and positively correlated at 2-4 months postpartum.In addition,the maternal weight and body muscle mass were positively correlated with leptin and adiponectin in milk.Plasma leptin was positively correlated with the mother’s body weight,body mass index(BMI),FAT percentage,and body fat mass,while plasma adiponectin did not correlate with any parameter.The results also indicate that the PPWR did not correlate with leptin and adiponectin in plasma or milk.Conclusions:Breastfeeding mothers may retain considerable weight gain one year after delivery.Human milk composition may be related to changes in maternal body weight.Leptin and adiponectin in breast milk and leptin in plasma are associated with the maternal body composition.This study supports the notion that maternal nutritional status may affect offspring health through lactation,and future research should focus on exploring weight management of postpartum mothers.

TOEPLITZ OPERATORS BETWEEN WEIGHTED BERGMAN SPACES OVER THE HALF-PLANE

In this paper,by characterizing Carleson measures,we investigate a class of bounded Toeplitz operator between weighted Bergman spaces with Békolléweights over the half-plane for all index choices.

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

HEAT KERNEL ON RICCI SHRINKERS(II)

This paper is the sequel to our study of heat kernel on Ricci shrinkers[29].In this paper,we improve many estimates in[29]and extend the recent progress of Bamler[2].In particular,we drop the compactness and curvature boundedness assumptions and show that the theory of F-convergence holds naturally on any Ricci flows induced by Ricci shrinkers.

Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.

A wealth distribution model with a non-Maxwellian collision kernel

A non-Maxwellian collision kernel is employed to study the evolution of wealth distribution in a multi-agent society.The collision kernel divides agents into two different groups under certain conditions. Applying the kinetic theory of rarefied gases, we construct a two-group kinetic model for the evolution of wealth distribution. Under the continuous trading limit, the Fokker–Planck equation is derived and its steady-state solution is obtained. For the non-Maxwellian collision kernel, we find a suitable redistribution operator to match the taxation. Our results illustrate that taxation and redistribution have the property to change the Pareto index.

Optimal investment based on relative performance and weighted utility

This paper studies the optimal portfolio allocation of a fund manager when he bases decisions on both the absolute level of terminal relative performance and the change value of terminal relative performance comparison to a predefined reference point. We find the optimal investment strategy by maximizing a weighted average utility of a concave utility and an Sshaped utility via a concavification technique and the martingale method. Numerical results are carried out to show the impact of the extent to which the manager pays attention to the change of relative performance related to the reference point on the optimal terminal relative performance.

Effect of Molecular Weight on Thermoelectric Performance of P3HT Analogues with 2-Propoxyethyl Side Chains

By replacing hexyl chains in poly(3-hexylthiophene)(P3HT)with 2-propoxyethyls,four poly(3-(2-propoxyethyl)thiophene)(P3POET)homopolymers with comparable polydispersity indexes(PDI)and regioregularities were prepared herein in addition with step increment of about 7 kDa on numberaverage molecular weight(M_(n))from around 11 to 32 kDa(accordingly denoted as P11k,P18k,P25k,and P32k).When doped in film by FeCl_(3)at the optimized conditions,the maximum power factor(PF_(max))increases greatly from 4.3μW·m^(-1)·K^(-2)for P11k to 8.8μW·m^(-1)·K^(-2)for P18k,and further to 9.7μW·m^(-1)·K^(-2)for P25k,followed by a slight decrease to 9.2μW·m^(-1)·K^(-2)for P32k.The close Seebeck coefficients(S)at PF_(max)are observed in these doped polymer films due to their consistent frontier orbital energy levels and Fermi levels.The main contribution to this PF_(max)evolution thus comes from the corresponding conductivities(σ).Theσvariation of the doped films can be rationally correlated with their microstructure evolution.

qGW11a/OsCAT8,encoding an amino acid permease,negatively regulates grain size and weight in rice

Grain size is a key factor influencing grain weight in rice.In this study,a chromosome segment substitution line(CSSL9-17)was identified,that exhibits a significant reduction in both grain size and weight compared to its donor parent 93-11.Further investigation identified two quantitative trait loci(QTL)on chromosome 11,designated qGW11a and qGW11b,which contribute to 1000-grain weight with an additive effect.LOC_Os11g05690,encoding the amino acid permease OsCAT8,is the target gene of qGW11a.Overexpression of OsCAT8 resulted in decreased grain weight,while OsCAT8 knockout mutants exhibited increased grain weight.The 287-bp located within the OsCAT8 promoter region of 93-11 negatively regulates its activity,which is subsequently correlated with an increase in grain size and weight.These results suggest that OsCAT8 functions as a negative regulator of grain size and grain weight in rice.

A redundant subspace weighting procedure for clock ensemble

A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.

Nuclear charge radius predictions by kernel ridge regression with odd-even effects

The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.

Curve Classification Based onMean-Variance Feature Weighting and Its Application

The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.