人脸识别研究综述*

人脸识别是一种重要的身份鉴别技术,具有广泛的应用前景。给出了人脸识别发展历程中的技术特点;根据人脸检测定位、面部特征提取和人脸确认识别三个关键的人脸识别过程,阐述了目前已成熟的核心技术和方法以及这些技术和方法的优缺点;展望了人脸识别未来的研究趋势。     

Extraction of Metal(Ti,V,Mo)-oxyl Cations by Monoacidic Phosphates and Phosphonates

The extraction of TiO~(2+),VO~(2+),VO_2~+ and MoO_2~(2+)by HDEHP,HEHEHP and HDTMPP was studiedrespectively.The orders of extraction ability for the three extractant systems are as follows:for TiO~(2+),VO~(2+):HDEHP>HEHEHP>HDTMPP;for VO_2~+, MoO_2~(2+):HDEHP< HEHEHP< HDTMPP.The experimental results were discussed from the point of view of extractant structures.     

A framework for variable content document generation with multiple actors

ContextAdvances in customization have highlighted the need for tools supporting variable content document management and generation in many domains. Current tools allow the generation of highly customized documents that are variable in both content and layout. However, most frameworks are technology-oriented, and their use requires advanced skills in implementation-related tools, which means their use by end users (i.e. document designers) is severely limited.ObjectiveStarting from past and current trends for customized document authoring, our goal is to provide a document generation alternative in which variants are specified at a high level of abstraction and content reuse can be maximized in high variability scenarios.MethodBased on our experience in Document Engineering, we identified areas in the variable content document management and generation field open to further improvement. We first classified the primary sources of variability in document composition processes and then developed a methodology, which we called DPL – based on Software Product Lines principles – to support document generation in high variability scenarios.ResultsIn order to validate the applicability of our methodology we implemented a tool – DPLfw – to carry out DPL processes. After using this in different scenarios, we compared our proposal with other state-of-the-art tools for variable content document management and generation.ConclusionThe DPLfw showed a good capacity for the automatic generation of variable content documents equal to or in some cases surpassing other currently available approaches. To the best of our knowledge, DPLfw is the only framework that combines variable content and document workflow facilities, easing the generation of variable content documents in which multiple actors play different roles.     

用D_2EHPA-煤油溶剂萃取分离L-异亮氨酸

本文研究了从发酵液中用D2EHPA－煤油溶剂萃取分离L－异亮氨酸工艺．考察了萃取时pH值、萃取剂浓度和异亮氨酸浓度对于异亮氨酸和缬氨酸的萃取分配比的影响．在计算机上进行回归，得到以下分配模型：当1≤pH≤3．5时lgDIle＝0．09906pH2＋0．8625pH＋2．2696lgCHR－0．09188lg2CIle＋0．1766lgCIle－1．366lgDrmVal＝－0．0293pH2＋0．550pH＋1．2659lgCHR－0．2420lg2CIle＋0．4549lgCIle－1．696用此模型，应用分馏萃取理论，进行逐级计算，求取了萃取工艺的主要参数，并在φ20mm的多级离心萃取器上对上述结果进行了验证．结果表明：当CHR浓度为1．5mol／L，相比V：L：L’＝2：1：2，N’＝3，N＝6时，异亮氨酸的收率达到90％以上，纯度＞99％．实验取得了较为满意的结果．     

从四氢呋喃、甲醇、水三元混合物系中分离四氢呋喃的研究

叙述了以液—液萃取、间歇精馏为手段，从四氢呋喃、甲醇、水三元混合物系中分离四氢呋喃的方法。四氢呋喃纯度超过９８％，收率６４．８％。     

深共熔溶剂-水混合物同步提取红景天苷和酪醇的响应面优化及其分离回收

以深共熔溶剂-水混合物（LAEG40）作为提取溶剂，红景天苷提取率为主要指标，酪醇提取率为辅助指标，在前期单因素实验的基础上，利用响应面法考察了液料比、提取温度、提取时间三个主要因素对红景天中红景天苷和酪醇同步提取的影响。得到的最佳提取条件为: 液料比12.05:1(mL/g)，提取温度60 ℃，提取时间35 min。在此条件下LAEG40对红景天苷的提取率可达到19.3552?0.6604 mg/g，酪醇提取率可达到1.7211?0.0585 mg/g，远高于传统溶剂水、乙醇对红景天苷和酪醇的提取率。为实现LAEG40提取红景天苷和酪醇的回收，进行了大孔树脂吸附分离的研究。经过一系列优化，获得的最佳吸附分离条件为：选用SP-825树脂装柱，每10g树脂上样30 mL LAEG40提取液，洗脱剂选用80 %乙醇，洗脱体积40 mL，洗脱流速1 mL/min，在此条件下红景天苷和酪醇的回收率分别可达60.47 %和85.07 %。     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

THE ROLE OF INTERFACIAL PHENOMENA IN A LIQUID-LIQUID REACTION SYSTEM: EXTRACTION OF PROPANOIC ACID BY TRIOCTYLAMINE

The kinetics of extraction of propanoic acid from water to isobulanol by reaction with trioctylamine have been investigated. Mass transfer was modelled by extending the approach used in absorption with chemical reaction, as it is usually done for liquid-liquid reactions. The presence of the amine, however, gave rise to phenomena that cause this method to fail, producing inconsistent results. In order to explain these data, the presence of relevant interfacial phenomena must be supposed.     

Text classification using genetic algorithm oriented latent semantic features

In this paper, genetic algorithm oriented latent semantic features (GALSF) are proposed to obtain better representation of documents in text classification. The proposed approach consists of feature selection and feature transformation stages. The first stage is carried out using the state-of-the-art filter-based methods. The second stage employs latent semantic indexing (LSI) empowered by genetic algorithm such that a better projection is attained using appropriate singular vectors, which are not limited to the ones corresponding to the largest singular values, unlike standard LSI approach. In this way, the singular vectors with small singular values may also be used for projection whereas the vectors with large singular values may be eliminated as well to obtain better discrimination. Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions.