Using earlier measures in a longitudinal sequence as a potential surrogate for a later one

The number of potential surrogate markers for clinical-trial endpoints is increasing rapidly, not in the least owing to the availability of biomarkers. At the same time, considerable development has taken place regarding statistical evaluation paradigms for such markers. As a consequence, such endpoints are given more extensive consideration for practice than previously had been the case. A particular but important instance is where the true endpoint is the ultimate assessment in a sequence of repeated measures. It is then appealing to consider earlier measures, either in isolation or several combined, as a potential surrogate endpoint. The length and cost reducing potential has to be weighed carefully against loss in precision and the risks of an inappropriate decision regarding a new compound’s fate. Quantitative criteria to do so are developed, embedded in a meta-analytic framework. The methodology’s behavior is assessed through simulations and applied to data from a pair of clinical trials, one in ophthalmology and one in schizophrenia.     

On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks

In traditional event-driven strategies, spike timings are analytically given or calculated with arbitrary precision (up to machine precision). Exact computation is possible only for simplified neuron models, mainly the leaky integrate-and-fire model. In a recent paper, Zheng, Tonnelier, and Martinez (2009) introduced an approximate event-driven strategy, named voltage stepping, that allows the generic simulation of nonlinear spiking neurons. Promising results were achieved in the simulation of single quadratic integrate-and-fire neurons. Here, we assess the performance of voltage stepping in network simulations by considering more complex neurons (quadratic integrate-and-fire neurons with adaptation) coupled with multiple synapses. To handle the discrete nature of synaptic interactions, we recast voltage stepping in a general framework, the discrete event system specification. The efficiency of the method is assessed through simulations and comparisons with a modified time-stepping scheme of the Runge-Kutta type. We demonstrated numerically that the original order of voltage stepping is preserved when simulating connected spiking neurons, independent of the network activity and connectivity.     

果蝇耦合均匀设计算法及其优化SVM参数

支持向量机的参数选择仍无系统的理论指导,且参数优化一直是支持向量机的一个重要研究方向。传统果蝇优化算法能够较快寻得一个较优的近似最优解,随后在该解的邻域继续迭代而造成寻优时间的严重增加。针对该问题构建了果蝇优化算法与均匀设计相耦合的果蝇耦合均匀设计算法,并将其用于支持向量机的参数优化。该算法首先利用果蝇优化算法并行寻优以快速得到所研究问题的一个较优近似最优解,然后跳转执行均匀设计的局部寻优,以获得一个更优的近似最优解。数值实验结果表明:该算法具有较快的寻优效率和较高的分类精度,验证了其在支持向量机参数优化中的有效性和可行性。     

A novel excitation controller using support vector machines and approximate models

This paper proposes a novel excitation controller using support vector machines （SVM） and approximate models. The nonlinear control law is derived directly based on an input-output approximation method via Taylor expansion, which not only avoids complex control development and intensive computation, but also avoids online learning or adjustment. Only a general SVM modelling technique is involved in both model identification and controller implementation. The robustness of the stability is rigorously established using the Lyapunov method. Several simulations demonstrate the effectiveness of the proposed excitation controller.     

基于小波和支持向量机的多尺度时间序列预测

介绍了相空间重构和基于支持向量机的时间序列预测建模技术,提出了基于小波和支持向量机的复杂时间序列预测方法,利用小波对复杂时间序列进行多尺度分解,对重构后的近似序列和细节序列分别利用支持向量机进行回归预测并将结果融合。对股票数据进行预测,试验结果表明该方法预测精度高于单尺度支持向量机和神经网络预测方法,可用于复杂非平稳时间序列的预测。     

A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output

Reliability analysis of a multidisciplinary system is computationally intensive due to the involvement of multiple disciplinary models and coupling between the individual models. When the system inputs and outputs are varying over time and space, the reliability analysis is even more challenging. This paper proposes a surrogate model-based method for the reliability analysis of a multidisciplinary system with spatio-temporal output. The transient characteristics of the multidisciplinary system output under time-dependent variability are analyzed first. Based on the transient analysis, surrogate models are built for individual disciplinary analyses instead of a single surrogate model for the fully coupled analysis. To address the challenge introduced by the high-dimensionality of spatially varying inter-disciplinary coupling variables, a data compression method is first employed to convert the high-dimensional coupling variables into low-dimensional latent space. Kriging surrogate modeling is then used to build surrogates for the individual disciplinary models in the latent space. Based on the individual disciplinary surrogate models, reliability analysis of the coupled multidisciplinary system under time-dependent uncertainty is investigated. Further, epistemic uncertainty sources, such as data uncertainty and model uncertainty, lead to uncertainty in the reliability estimate. Therefore, an auxiliary variable approach is used to efficiently include the epistemic uncertainty sources within the reliability analysis. An aircraft panel subjected to hypersonic flow conditions is used to demonstrate the proposed method. The analysis involves four interacting disciplinary models, namely, aerodynamics, aerothermal analysis, heat transfer, and structural analysis. The results show that the proposed method is able to effectively perform reliability analysis of a multidisciplinary system with spatio-temporal output.     

Global magnetohydrodynamic simulations on multiple GPUs

Global magnetohydrodynamic (MHD) models play the major role in investigating the solar wind–magnetosphere interaction. However, the huge computation requirement in global MHD simulations is also the main problem that needs to be solved. With the recent development of modern graphics processing units (GPUs) and the Compute Unified Device Architecture (CUDA), it is possible to perform global MHD simulations in a more efficient manner. In this paper, we present a global magnetohydrodynamic (MHD) simulator on multiple GPUs using CUDA 4.0 with GPUDirect 2.0. Our implementation is based on the modified leapfrog scheme, which is a combination of the leapfrog scheme and the two-step Lax–Wendroff scheme. GPUDirect 2.0 is used in our implementation to drive multiple GPUs. All data transferring and kernel processing are managed with CUDA 4.0 API instead of using MPI or OpenMP. Performance measurements are made on a multi-GPU system with eight NVIDIA Tesla M2050 (Fermi architecture) graphics cards. These measurements show that our multi-GPU implementation achieves a peak performance of 97.36 GFLOPS in double precision.     

Kernel Function in SVM-RFE based Hyperspectral Data band Selection

Supporting vector machine recursive feature elimination (SVM-RFE) has a low efficiency when it is applied to band selection for hyperspectral dada,since it usually uses a non-linear kernel and trains SVM every time after deleting a band.Recent research shows that SVM with non-linear kernel doesn’t always perform better than linear one for SVM classification.Similarly,there is some uncertainty on which kernel is better in SVM-RFE based band selection.This paper compares the classification results in SVM-RFE using two SVMs,then designs two optimization strategies for accelerating the band selection process:the percentage accelerated method and the fixed accelerated method.Through an experiment on AVIRIS hyperspectral data,this paper found:① Classification precision of SVM will slightly decrease with the increasing of redundant bands,which means SVM classification needs feature selection in terms of classification accuracy;② The best band collection selected by SVM-RFE with linear SVM that has higher classification accuracy and less effective bands than that with non-linear SVM;③ Both two optimization strategies improved the efficiency of the feature selection,and percentage eliminating performed better than fixed eliminating method in terms of computational efficiency and classification accuracy.     

Sequential modeling of a low noise amplifier with neural networks and active learning

The use of global surrogate models has become commonplace as a cost effective alternative for performing complex high fidelity computer simulations. Due to their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, what-if analysis, optimization, prototyping, visualization, and sensitivity analysis. Neural networks have been proven particularly useful in this respect due to their ability to model high dimensional, non-linear responses accurately. In this article, we present the results of an extensive study on the performance of neural networks as compared to other modeling techniques in the context of active learning. We investigate the scalability and accuracy in function of the number design variables and number of datapoints. The case study under consideration is a high dimensional, parametrized low noise amplifier RF circuit block.     

Optimization of the new Saab 9-3 exposed to impact load using a space mapping technique

The aim of this work is to illustrate how a space mapping technique using surrogate models together with response surfaces can be used for structural optimization of crashworthiness problems. To determine the response surfaces, several functional evaluations must be performed and each evaluation can be computationally demanding. The space mapping technique uses surrogate models, i.e. less costly models, to determine these surfaces and their associated gradients. The full model is used to correct the gradients from the surrogate model for the next iteration. Thus, the space mapping technique makes it possible to reduce the total computing time needed to find the optimal solution. First, two analytical functions and one analytical structural optimization problem are presented to exemplify the idea of space mapping and to compare the efficiency of space mapping to traditional response surface optimization. Secondly, a sub-model of a complete vehicle finite element (FE) model is used to study different objective functions in vehicle crashworthiness optimization. Finally, the space mapping technique is applied to a structural optimization problem of a large industrial FE vehicle model, consisting of 350.000 shell elements and a computing time of 100 h. In this problem the intrusion in the passenger compartment area was reduced by 32% without compromising other crashworthiness parameters.     

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

Ensemble of metamodels with optimized weight factors

Approximate mathematical models (metamodels) are often used as surrogates for more computationally intensive simulations. The common practice is to construct multiple metamodels based on a common training data set, evaluate their accuracy, and then to use only a single model perceived as the best while discarding the rest. This practice has some shortcomings as it does not take full advantage of the resources devoted to constructing different metamodels, and it is based on the assumption that changes in the training data set will not jeopardize the accuracy of the selected model. It is possible to overcome these drawbacks and to improve the prediction accuracy of the surrogate model if the separate stand-alone metamodels are combined to form an ensemble. Motivated by previous research on committee of neural networks and ensemble of surrogate models, a technique for developing a more accurate ensemble of multiple metamodels is presented in this paper. Here, the selection of weight factors in the general weighted-sum formulation of an ensemble is treated as an optimization problem with the desired solution being one that minimizes a selected error metric. The proposed technique is evaluated by considering one industrial and four benchmark problems. The effect of different metrics for estimating the prediction error at either the training data set or a few validation points is also explored. The results show that the optimized ensemble provides more accurate predictions than the stand-alone metamodels and for most problems even surpassing the previously reported ensemble approaches.     

Surrogate modeling of impedance matching transformers by means of variable‐fidelity electromagnetic simulations and nested cokriging

Accurate performance evaluation of microwave components can be carried out using full‐wave electromagnetic (EM) simulation tools, routinely employed for circuit verification but also in the design process itself. Unfortunately, the computational cost of EM‐driven design may be high. This is especially pertinent to tasks entailing considerable number of simulations (eg, parametric optimization, statistical analysis). A possible way of alleviating these difficulties is utilization of fast replacement models, also referred to as surrogates. Notwithstanding, conventional modeling methods exhibit serious limitations when it comes to handling microwave components. The principal challenges include large number of geometry and material parameters, highly nonlinear characteristics, as well as the necessity of covering wide ranges of operating conditions. The latter is mandatory from the point of view of the surrogate model utility. This article presents a novel modeling approach that incorporates variable‐fidelity EM simulations into the recently reported nested kriging framework. A combination of domain confinement due to nested kriging, and low‐/high‐fidelity EM data blending through cokriging, enables the construction of reliable surrogates at a fraction of cost required by single‐fidelity nested kriging. Our technique is validated using a three‐section miniaturized impedance matching transformer with its surrogate model rendered over wide range of operating frequencies. Comprehensive benchmarking demonstrates superiority of the proposed method over both conventional models and nested kriging.     

Lattice gases and parallel processors

This paper introduces the notion of lattice gas as a new medium to perform fluid dymanics simulations. After giving the definition of lattice gases, the results of statistical analyses of their macroscopic behaviour are reviewed. It is shown that Navier-Stokes equations can be simulated. Some information is given concerning the algorithms and their possible adaptation to parallel hard-ware, including cellular automata. Finally the construction of a specialized machine for lattice gas simulations will be presented.     

Ensemble Classifier Technique to Predict Gestational Diabetes Mellitus (GDM)

Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels namely, format conversion, class labeling, replacement of missing values, and normalization. Besides, four ML models such as Logistic Regression (LR), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) are used for classification. In addition to the above, RF, LR, KNN and SVM classifiers are integrated to perform the final classification in which a voting classifier is also used. In order to investigate the proficiency of the proposed model, the authors conducted extensive set of simulations and the results were examined under distinct aspects. Particularly, the ensemble model has outperformed the classical ML models with a precision of 94%, recall of 94%, accuracy of 94.24%, and F-score of 94%.     

工业过程稳态模型估计误差的渐近正态性分析:SISO情形

本文研究了在相当弱的条件下工业过程稳态模型估计误差的渐近正态性.参数估计采用 简单加权最小二乘法,并利用近似线性模型集.优化过程中正常的系统设定点阶跃变化作为 辨识信号.据此证明了系统稳态模型估计误差渐近正态分布的结论.同时,还研究了估计量 的收敛速度和对线性近似模型结构的渐近鲁棒性.仿真研究则探讨了在有限样本空间内,影 响估计精度的若干因素.     

Probabilistic approximations of ODEs based bio-pathway dynamics

Bio-chemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we propose a discrete probabilistic approximation of the ODEs dynamics. We do so by discretizing the value and the time domain and assuming a distribution of initial states w.r.t. the discretization. Then we sample a representative set of initial states according to the assumed initial distribution and generate a corresponding set of trajectories through numerical simulations. Finally, using the structure of the signaling pathway we encode these trajectories compactly as a dynamic Bayesian network.This approximation of the signaling pathway dynamics has several advantages. First, the discretized nature of the approximation helps to bridge the gap between the accuracy of the results obtained by ODE simulation and the limited precision of experimental data used for model construction and verification. Second and more importantly, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques instead of resorting to a large number of ODE simulations. We have tested our method on ODE models of the EGF-NGF signaling pathway [1] and the segmentation clock pathway [2]. The results are very promising in terms of accuracy and efficiency.     

Exploiting lookahead in parallel simulation

Lookahead is the ability of a process to predict its future behavior. The feasibility of implicit lookahead for non-FCFS stochastic queuing systems is demonstrated. Several lookahead exploiting techniques are proposed for round-robin (RR) system simulations. An algorithm that generates lookahead in O(1) time is described. Analytical models and experiments are constructed to evaluate these techniques. A lookahead technique for preemptive priority (PP) systems is evaluated using an analytical model. The performance metric for these techniques is the lookahead ratio, which is correlated with other performance measures of more direct interest, such as speedup. The analyses show that using implicit lookahead can significantly improve the lookahead ratios of RR and PP system simulations     

从近似超平面到SVR的算法研究

本文证明了SVM存在近似超平面;根据SV分布于SVM超平面附近,也必然分布于其近似超平面附近的特点,提出了从近似超平面出发,通过向量距近似超平面的距离的大小逐步搜索SV,建立SVR的算法思想;列举了基于该算法思想的一个算法实例——从多元回归平面构建LS-SVM;分析了其时空复杂度,并与LS-SVM的线性方程组解法和直接分解算法进行比较,其结果是该算法能够收敛到l个训练样本直接建立的SVR,并降低了计算时间复杂度和显著降低了计算空间复杂度。     

An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error

Structural and Multidisciplinary Optimization - Surrogate models are often used as surrogates for computationally intensive simulations. And there are a variety of surrogate models which are widely...