Exceptionally high genetic variance of the doubled haploid(DH)population of poplar

Doubled haploid(DH)plants have been widely used for breeding and biological research in crops.Pop ulus spp.have been used as model woody plant species for biological research.However,the induction of DH poplar plants is onerous,and limited biological or breeding work has been carried out on DH individuals or populations.In this study,we provide an effective protocol for poplar haploid induction based on an anther culture method.A total of 96 whole DH plant lines were obtained using an F1hybrid of Populus simonii×P.nigra as a donor tree.The phenotypes of the DH population showed exceptionally high variance when compared to those of half-sib progeny of the donor tree.Each DH line displayed distinct features compared to those of the other DH lines or the donor tree.Additionally,some excellent homozygous lines have the potential to be model plants in genetic and breeding studies.

Variational Quantum Eigensolver with Mutual Variance-Hamiltonian Optimization

The zero-energy variance principle can be exploited in variational quantum eigensolvers for solving general eigenstates but its capacity for obtaining a specified eigenstate,such as ground state,is limited as all eigenstates are of zero energy variance.We propose a variance-based variational quantum eigensolver for solving the ground state by searching in an enlarged space of wavefunction and Hamiltonian.With a mutual variance-Hamiltonian optimization procedure,the Hamiltonian is iteratively updated to guild the state towards to the ground state of the target Hamiltonian by minimizing the energy variance in each iteration.We demonstrate the performance and properties of the algorithm with numeral simulations.Our work suggests an avenue for utilizing guided Hamiltonian in hybrid quantum-classical algorithms.

Mesh‑free semi‑quantitative variance underestimation elimination method in Monte Caro algorithm

The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This study provides a mesh-free semiquantitative variance underestimation elimination method to obtain a credible confidence interval for the tallied results.This method comprises two procedures:Estimation and Elimination.The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm.The batch method was then used in the elimination procedure.The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem.We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model.The results showed that the average variance underestimation ratios of local tallies declined from 37 to 87%to within±5%in these models.

Performance of CMIP6 models in simulating the dynamic sea level:Mean and interannual variance

本研究采用卫星测高数据与第六次国际耦合模式比较计划(CMIP6)海平面动力进行对比,重点针对40S-40N地区的动力海平面(DSL),评估了模式对其平均态与年际变率的综合模拟能力,结果表明,对于DSL平均态的模拟,模式与观测结果非常吻合,模式之间的差异较小.其中,副热带北大西洋是模拟偏差和模式间差异较为显著的区域,对于DSL年际变率的模拟,模式之间保持较高的一致性,但是,模式与观测结果存在明显差异,模式普遍低估了DSL的年际方差;其中,误差大值区域出现在副热带西边界流附近,模式分辨率会影响CMIP6对中小尺度海洋过程的重现能力,这可能是导致CMIP6历史模拟出现误差的原因之一.

Manifold Structure Analysis of Tactical Network Traffic Matrix Based on Maximum Variance Unfolding Algorithm

As modern weapons and equipment undergo increasing levels of informatization,intelligence,and networking,the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex.In this paper,we employ a traffic matrix to model the tactical data link network.We propose a method that utilizes the Maximum Variance Unfolding(MVU)algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets.This approach introduces novel ideas and methods for future applications,including traffic prediction and anomaly analysis in real battlefield network environments.

基于mean-variance的服务集群负载均衡方法

大量并发请求任务进行分配时,负载调度机制是通过最小化响应时间及最大化节点利用率实现网络中节点的负载均衡,在基于遗传算法的负载均衡算法中,适应度函数设计对服务集群负载均衡效率产生重要的影响。对此提出了一种基于mean-variance的服务集群负载均衡方法对适应度函数进行优化,采用投资组合选择模型mean-variance进行最小化响应时间,以得到每个服务器资源利用率的权重,从而获得最优的分配组合,进而提高适应度函数的准确性和有效性。在不同服务环境下与其他模型进行比较,仿真结果表明,本文的负载均衡算法在节点利用率和响应时间方面使服务集群得到了更好的均衡。

Variance Gamma过程与股票期权定价中的波动率偏度的纠正

针对 Black- Scholes期权定价模型在股票期权定价中的波动率偏度的定价偏差 ,介绍一种新的改进模型来纠正波动率偏度 ,这种改进模型是通过将 Black- Scholes期权定价模型中的布朗运动过程替换为 variance gamma过程来实现的。在给出相应欧式看涨期权价格的解析解的基础上 ,对改进模型的定价性能进行实证检验。

摩擦市场条件下的Mean-Variance-Skewness模型

在投资组合选择模型中考虑了资产收益率分布中正的偏度水平,并通过引入一些市场摩擦因素建立了摩擦市场条件下的Mean-Variance-Skewness模型.提出了一个新的遗传算法加速其搜索收敛过程,解决了该模型的计算复杂性问题.在该模型框架内对交易费用和税收等市场摩擦因素进行了敏感性分析.研究证明资产收益率分布的偏度水平是与投资者的决策相关的,市场摩擦因素对投资者的决策行为也有直接的影响.因此,考虑摩擦市场条件下基于正偏度水平偏好的最优投资组合模型对投资者有很强的实践指导价值.

Turbulent Variance Characteristics of Temperature and Humidity over a Non-uniform Land Surface for an Agricultural Ecosystem in China

This paper describes the application of the variance method for flux estimation over a mixed agricultural region in China. Eddy covariance and flux variance measurements were conducted in a near-surface layer over a non-uniform land surface in the central plain of China from 7 June to 20 July 2002. During this period, the mean canopy height was about 0.50 m. The study site consisted of grass （10% of area）, beans （15%）, corn （15%） and rice （60%）. Under unstable conditions, the standard deviations of temperature and water vapor density （normalized by appropriate scaling parameters）, observed by a single instrument, followed the Monin-Obukhov similarity theory. The similarity constants for heat （CT） and water vapor （Cq） were 1.09 and 1.49, respectively. In comparison with direct measurements using eddy covariance techniques, the flux variance method, on average, underestimated sensible heat flux by 21% and latent heat flux by 24%, which may be attributed to the fact that the observed slight deviations （20% or 30% at most） of the similarity ＂constants＂ may be within the expected range of variation of a single instrument from the generally-valid relations.

Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models

Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,the site-to-fault distance and the site foundation properties.In the present study,the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an effi ciency-robustness balanced formula is proposed.For this purpose,a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship.In this approach,each model class(a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data.The one with the highest plausibility is robust since it possesses the optimal balance between the data fi tting capability and the sensitivity to noise.A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis.The optimal predictive formula is proposed based on this database.It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore,Joyner and Fumal(1993).

Variance-based fingerprint distance adjustment algorithm for indoor localization

The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication （RSSI） and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm （VFDA）. Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs.

OPTIMAL PROPORTIONAL REINSURANCE AND INVESTMENT FOR A CONSTANT ELASTICITY OF VARIANCE MODEL UNDER VARIANCE PRINCIPLE

This article studies the optimal proportional reinsurance and investment problem under a constant elasticity of variance （CEV） model. Assume that the insurer＇s surplus process follows a jump-diffusion process, the insurer can purchase proportional reinsurance from the reinsurer via the variance principle and invest in a risk-free asset and a risky asset whose price is modeled by a CEV model. The diffusion term can explain the uncertainty associated with the surplus of the insurer or the additional small claims. The objective of the insurer is to maximize the expected exponential utility of terminal wealth. This optimization problem is studied in two cases depending on the diffusion term＇s explanation. In all cases, by using techniques of stochastic control theory, closed-form expressions for the value functions and optimal strategies are obtained.

A Preliminary Study on the Relationship Between Arctic Oscillation and Daily SLP Variance in the Northern Hemisphere During Wintertime

In the present study, the authors investigated the relationship between the Arctic Oscillation (AO) and the high-frequency variability of daily sea level pressures in the Northern Hemisphere in winter (November through March), using NCEP/NCAR reanalysis datasets for the time period of 1948/49-2000/01. High-frequency signals are defined as those with timescales shorter than three weeks and measured in terms of variance, for each winter for each grid. The correlations between monthly mean AO index and high-frequency variance are conducted. A predominant feature is that several regional centers with high correlation show up in the middle to high latitudes. Significant areas include mid- to high-latitude Asia centered at Siberia, northern Europe and the middle-latitude North Atlantic east of northern Africa. Their strong correlations can also be confirmed by the singular value decomposition analysis of covariance between mean SLP and high-frequency variance. This indicates that the relationship of AO with daily Sea Level Pressure (SLP) is confined to some specific regions in association with the inherent atmospheric dynamics. In middle-latitude Asia, there is a significant (at the 95% level) trend of variance of-2.26% (10 yr)-1. Another region that displays a strong trend is the northwestern Pacific with a significant rate of change of 0.80% (10 yr)-1. If the winter of 1948/49, an apparent outlier, is excluded, a steady linear trend of +1.51% (10 yr)-1 shows up in northern Europe. The variance probability density functions (PDFs) are found to change in association with different AO phases. The changes corresponding to high and low AO phases, however, are asymmetric in these regions. Some regions such as northern Europe display much stronger changes in high AO years, whereas some other regions such as Siberia show a stronger connection to low AO conditions. These features are supported by ECMWF reanalysis data. However, the dynamical mechanisms involved in the AO-high frequency SLP variance connection have not been well understood, and this needs further study.

The low-frequency variance of the ocean surface wave field in the area of the Antarctic Circumpolar Current

The low-frequency variance of the surface wave in the area of the Antarctic Circumpolar Current （ACC） and its correlation with the antarctic circumpolar wave （ACW） are focused on. The analysis of the series of 44 a significant wave height （SWH） interannual anomalies reveals that the SWH anomalies have a strong periodicity of about 4-5 a and this signal propagates eastward obviously from 1985 to 1995, which needs about 8 a to complete a mimacircle around the earth. The method of empirical orthogonal function （EOF） is used to analyze the filtered monthly SWH anomalies to study the spatio-temporal distributions and the propagation characteristics of the low-frequency signals in the wave field. Both the dominant wavenumber- 2 pattern in space and the propagation feature in the south Pacific, the south Atlantic and the south Indian ocean show strong consistency with the ACW. So it is reasonable to conclude that the ACW signal also exists in the wave field. The ACW is important for the climate in the Southern Ocean, so it is worth to pay more attention to the large- scale effect of the surface wave, which may also be important for climate studies.

EMPIRICAL BAYES TEST PROBLEMS OF VARIANCE COMPONENTS IN RANDOM EFFECTS MODEL

Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree (VQT) Method

The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.

Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis

Suppression effect in multiple regression analysis may be more common in research than what is currently recognized. We have reviewed several literatures of interest which treats the concept and types of suppressor variables. Also, we have highlighted systematic ways to identify suppression effect in multiple regressions using statistics such as: R2, sum of squares, regression weight and comparing zero-order correlations with Variance Inflation Factor (VIF) respectively. We also establish that suppression effect is a function of multicollinearity;however, a suppressor variable should only be allowed in a regression analysis if its VIF is less than five (5).

Adaptive diagonal loaded minimum variance beamforming applied to medical ultrasound imaging

In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.

ONE-WAY MULTIVARIATE REPEATED MEASUREMENTS ANALYSIS OF VARIANCE MODEL

The one-way multivariate repeated measurements analysis of variance (1-way MRM ANOVA) model for complete data and the sphericity test are studied.

ROBUST ESTIMATION OF VARIANCE COMPONENTS MODEL

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