摘要： The statolith in cephalopods has a stable morphology and contains important ecological information. Influenced by genetic structures and environmental variability, statolith shapes often vary among different stocks and are ideal indices for stock discrimination. In the present study, wavelet analysis was used to explore the statolith shape variations in Dosidicus gigas (D'Orbigny, 1835 in 1834-1847) among four geographic stocks obtained by Chinese jigging fleets in the eastern tropical Pacific Ocean (ETP). In addition, machine learning methods were compared with traditional classification methods to improve the stock classification results of D. gigas. According to our analyses, statolith shapes of D. gigas differed significantly among the four stocks. Wavelet coefficients extracted from the statolith images by computer software were used to reconstruct the mean statolith shape for every stock. The rostrum and wing of the statolith are two main components determining the variances among stocks. Canonical analysis of principal coordinates dearly separated Costa Rican from other stocks. Machine learning methods performed better than the traditional method of statolith shape dassification. The results of our study supported the geographical separation of D. gigas stocks (Costa Rican and equatorial stock in the northern hemisphere, and Peruvian and Chilean stock in the southern hemisphere) reported in previous studies. Wavelet analysis is an appropriate method for stock classification and machine learning methods can effectively improve the classification accuracy and is a promising method for determining the stock structure.

关键词： Machine learning   Ore grade estimation   Ordinary kriging   Indicator kriging   Neural networks   Random forests   Gaussian processes  

摘要： In this study, machine learning methods such as neural networks, random forests, and Gaussian processes are applied to the estimation of copper grade in a mineral deposit. The performance of these methods is compared to geostatistical techniques, such as ordinary kriging and indicator kriging. To ensure that these comparisons are realistic and relevant, the predictive accuracy is estimated on test instances located in drill holes that are different from the training data. The results of an extensive empirical study in the Sarcheshmeh porphyry copper deposit in Southeastern Iran illustrate that specially designed Gaussian processes with a symmetric standardization of the spatial location inputs and an anisotropic kernel yield the most accurate predictions. Furthermore, significant improvements are obtained when, besides location, information on the rock type is included in the set of predictor variables. This observation highlights the importance of carrying out detailed studies of the geological composition of the deposit to obtain more accurate ore grade predictions.

关键词： fractional vegetation cover   VIIRS surface reflectance   machine learning method   global  

摘要： Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R-2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

关键词： Multiuser data transmission   Spatial coupling   Constellation rotation   Power allocation   Coordinate ascent algorithm   Genetic algorithm  

摘要： The rapid development of mobile Internet triggered massive data exchange among multiple sources, which then results in challenges in the efficiency of multi-user data transmission. Spatial coupling multiple access, where different users share the same resource blocks by superimposing data streams with different time offsets, has been proposed to obtain higher spectral efficiency. The optimization of the system can be carried out through for example constellation rotation or power allocation, which mitigates the interferences and distinguishes users/data streams better. The optimization aims for the maximization of the average mutual information between the transmitted data symbols and the received signal. To solve the optimization problem, we employ machine learning based methods to locate the possibly global optimal solution. Simulation results show that the optimization clearly contributes to the performance improvement of the system.

关键词： Formal verification   classification ML   algorithm selection  

摘要： Typical modem HW designs include many blocks associated with thousands of design properties. Having today's commercial formal verifiers utilize a complementary set of state-of-art formal algorithms is a key in enabling the formal verification tools to successfully cope with verification problems of different sizes, types, and complexities. Formal engines orchestration is the methodology used to pick the most appropriate formal engine for a specific verification problem. It assures proper scheduling of the formal engines to minimize the time consumed to solve individual design verification problems, hence highly impacts the time required to verify the overall design properties. This work proposes the utilization of supervised machine learning classification techniques to guide the orchestration step by predicting the formal engines that should be assigned to a design property. Up to 16,500 formal verification runs on RTL designs and their properties are used to train the classifier to create a prediction model. The classifier assigns any new verification problem to an appropriate list of formal engines associated with a probability distribution over the set of engines classes. Our results indicate how the proposed model is able to improve the formal suite total run-time by up to 59% of its maximum allowable time improvement using multi-classification-based orchestration and to nominate with 88% accuracy the appropriate formal engines for new-to-verify HW designs.

关键词： PM2.5   Aerosol optical depth   Random forests   Machine learning   China  

摘要： Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter <= 2.5 mu m) at daily time scale in China at a national level. Objectives: To estimate daily concentrations of PM2.5 across China during 2005-2016. Methods: Daily ground-level PM2.5 data were obtained from 1479 stations across China during 2014-2016. Data on aerosol optical depth (AOD), meteorological conditions and other predictors were downloaded. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed to estimate ground-level PM2.5 concentrations. The best-fit model was then utilized to estimate the daily concentrations of PM2.5 across China with a resolution of 0.1 degrees (approximate to 10 km) during 2005-2016. Results: The daily random forests model showed much higher predictive accuracy than the other two traditional regression models, explaining the majority of spatial variability in daily PM2.5 [10-fold cross-validation (CV) R-2 = 83%, root mean squared prediction error (RMSE) = 28.1 mu g/m(3)]. At the monthly and annual time-scale, the explained variability of average PM2.5 increased up to 86% (RMSE = 10.7 mu g/m(3) and 6.9 mu g/m(3), respectively). Conclusions: Taking advantage of a novel application of modeling framework and the most recent ground-level PM2.5 observations, the machine learning method showed higher predictive ability than previous studies. Capsule: Random forests approach can be used to estimate historical exposure to PM2.5 in China with high accuracy. (C) 2018 Elsevier B.V. All rights reserved.

关键词： EGFR抑制剂   机器学习方法   随机森林   分子描述符  

摘要： 表皮生长因子受体(Epidermal Growth Factor Receptor,EGFR)的扩增或突变与人类实体肿瘤密切相关。三种机器学习方法:随机森林(random forest,RF),k最近邻和C4.5决策树,被用于建立理论模型,来预测EGFR的抑制剂。结果表明,这些模型的预测结果均取得了令人满意的精度。又通过对比分析,发现RF模型具有较好的预测性能,并进一步修改和优化了RF模型的参数。此外,还采用特征选择程序筛选出了25个和EGFR的抑制最相关的分子描述符。最后对得到的最优RF模型进行了评价。

关键词： CRISPR/Cas9   机器学习   sgRNA   脱靶效应   基因敲除  

摘要： 基于CRISPR/Cas9系统介导的第三代基因组定点编辑技术,已被广泛应用于基因编辑和基因表达调控等研究领域。如何提高该技术对基因组编辑的效率与特异性、最大限度降低脱靶风险一直是该领域的难点。近年来,机器学习为解决CRISPR/Cas9系统所面临的问题提供了新思路,基于机器学习的CRISPR/Cas9系统已逐渐成为研究热点。本文阐述了CRISPR/Cas9的作用机理,总结了现阶段该技术面临的基因组编辑效率低、存在潜在的脱靶效应、前间区序列邻近基序(PAM)限制识别序列等问题,最后对机器学习应用于优化设计高效向导RNA(sg RNA)序列、预测sg RNA的活性、脱靶效应评估、基因敲除、高通量功能基因筛选等领域的研究现状与发展前景进行了展望,以期为基因组编辑领域的研究提供参考。

关键词： Building energy   Data-driven   Machine learning   Multi-scale   Simulation   Urban energy modeling   Urban context  

摘要： The world is rapidly urbanizing, and the energy intensive built environment is becoming increasingly responsible for the world's energy consumption and associated environmental emissions. As a result, significant efforts have been put forth to develop methods that can accurately model and characterize building energy consumption in cities. These models aim to utilize physics-based building energy simulations, reduced-order calculations and statistical learning methods to assess the energy performance of buildings within a dense urban area. However, current urban building energy models are limited in their ability to account for the inter-building energy dynamics and urban microclimate factors that can have a substantial impact on building energy use. To overcome these limitations, this paper proposes a novel Data-driven Urban Energy Simulation (DUE-S) framework that integrates a network-based machine learning algorithm (ResNet) with engineering simulation to better understand how buildings consume energy on multiple temporal (hourly, daily, monthly) and spatial scales in a city (single building, block, urban). We validate the proposed DUE-S framework on a proof of concept case study of 22 densely located university buildings in California, USA. Our results indicate that the DUE-S framework is able to accurately predict urban scale energy consumption at hourly, daily and monthly intervals. Moreover, our results also demonstrate that the integration of data-driven and engineering simulation approaches can partially capture the inter-building energy dynamics and impacts of the urban context and merits future work to explore how they can be improved to predict sub-urban scale energy predictions (single building, block). In the end, successfully predicting and modeling the energy performance of urban buildings has the potential to inform the decision-making of a wide variety of urban sustainability stakeholders including architects, engineers and policymakers.

关键词： 反向传播人工神经网络   随机森林   支持向量机   K折交叉验证   混淆矩阵  

摘要： 吉林地理标志大米因其品质优,口感好,营养价值高,在市场上享有较高的声誉,研究地理标志大米产地确证技术具有重要意义。以吉林省柳河县和辉南县为研究区域,分别采集柳河地理标志大米样本62个,辉南地理标志大米样本58个,共120个样本。通过检测大米样本中矿物质元素[铜(Cu)、锌(Zn)、铁(Fe)、锰(Mn)、钾(K)、钙(Ca)、钠(Na)、镁(Mg)、铅(Pb)、镉(Cd)],利用反向传播人工神经网络(BackPropagation Artificial Neural Network,BP-ANN)、随机森林(Random Forest,RF)和支持向量机(Support Vector Machines,SVM)三种机器学习方法建立确证模型,并通过F评分(F-score)方法对矿物质元素进行特征提取,采用10次10折交叉验证和混淆矩阵对研究区域建立的产地确证模型进行评估比较。结果表明:单个Cu元素可作为代表该地区空间特征的典型变量。三种机器学习方法建立的产地确证模型均达到了良好的预测性能,其中BP-ANN方法使用Cu元素和Zn元素建立的分类模型准确率为99. 7%; SVM方法使用Cu元素、Zn元素和Pb元素建立的分类模型准确率为100%; RF方法使用Cu、Zn、Pb、Ca、Cd、K 6种元素建立的分类准确率为100%。RF模型和SVM模型整体分类效果优于BP-ANN模型,模型更稳定,更适合建立研究区域的产地确证模型。