Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.     

Structural Cues for Understanding eEF1A2 Moonlighting

Spontaneous mutations in the EEF1A2 gene cause epilepsy and severe neurological disabilities in children. The crystal structure of eEF1A2 protein purified from rabbit skeletal muscle reveals a post-translationally modified dimer that provides information about the sites of interaction with numerous binding partners, including itself, and maps these mutations onto the dimer and tetramer interfaces. The spatial locations of the side chain carboxylates of Glu301 and Glu374, to which phosphatidylethanolamine is uniquely attached via an amide bond, define the anchoring points of eEF1A2 to cellular membranes and interorganellar membrane contact sites. Additional bioinformatic and molecular modeling results provide novel structural insight into the demonstrated binding of eEF1A2 to SH3 domains, the common MAPK docking groove, filamentous actin, and phosphatidylinositol-4 kinase IIIβ. In this new light, the role of eEF1A2 as an ancient, multifaceted, and articulated G protein at the crossroads of autophagy, oncogenesis and viral replication appears very distant from the “canonical” one of delivering aminoacyl-tRNAs to the ribosome that has dominated the scene and much of the thinking for many decades.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

The factorial structure of the Dutch translation of the Inventory of Interpersonal Problems: A test of the long and short versions.

In this article, the authors study the factorial structure of 2 versions (64 items and 32 items) of the Dutch translation of the Inventory of Interpersonal Problems (IIP; L. M. Horowitz, L. E. Alden, J. S. Wiggins, & A. L. Pincus, 2000) in both a clinical sample (n = 382) and a student sample (n = 287). First, the authors test whether both inventories are constituted by correlated uni-dimensional subscales, and they conclude that this is true only for the short version. Second, the authors study whether the correlations between the subscales follow a circumplex pattern. This proves to be true for both versions. Coefficients alpha and correlations between the IIP-64 and IIP-32 subscales are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

网络展开图与符号网络分析

采用封闭网络模型,按照电路元件参数,采用开路、短路和着色运算,将电路图逐层分解,得到网络展开图.给出网络展开图的概念和运算规则,指出网络展开图中从根到末稍每个路径的值就是网络行列式展开式中的一个有效项,从而由网络展开图得到符号网络函数.该方法直接对电路图进行运算,不需要建立任何形式的电路方程和行列式,适用于一般的有源电路,而且能生成各种类型的网络函数.     

A Symbolic Pole/Zero Extraction Methodology Based on Analysis of Circuit Time-Constants

This paper introduces a methodology for symbolic pole/zero extraction based on the formulation of the time-constant matrix of the circuits. This methodology incorporates approximation techniques specifically devoted to achieve an optimum trade-off between accuracy and complexity of the symbolic root expressions. The capability to efficiently handle even large circuits will be demonstrated through several practical circuits.     

基于软交换的NAT/防火墙穿透技术研究

通过对目前几种解决NAT(network addres stranslation)／防火墙穿逢方案的分析，提出了一种较易实现的穿透技术，并给出具体实现方案。     

凸轮机构基圆半径的确定方法及其分析

基圆半径的确定方法，除了用图解法和试算法外，还可用解析法和图解分析法求解。４种方法各有优缺点，设计时要根据具体情况选择。     

符号运算在求解车辆传动系扭振中的应用

介绍符号运算方法在用拉普拉斯变换方法求解车辆传动系扭振过程中的作用．针对弹性联轴器的匹配计算，给出了具体求解实例．实践表明，与传统的纯数值计算方法相比，符号运算方法可以方便准确地处理车辆传动系统扭振分析中的非线性等复杂问题，具有推广应用价值．     

Symbolic control of visual attention: The role of working memory and attentional control settings.

This study examined how 1 symbol is selected to control the allocation of attention when several symbols appear in the visual field. In Experiments 1-3, the critical target feature was color, and it was found that uninformative central arrows that matched the color of the target were selected and produced unintentional shifts of attention (i.e., involuntary, initiated slowly, producing long-lasting facilitatory effects). Experiment 4 tested whether such selection is the result of an attentional filter or of a competition bias due to a match of incoming information against integrated object representations stored in working memory. Here, the critical feature was shape and color was irrelevant, but matching color arrows were still selected. Thus, features of objects in working memory will bias the selection of symbols in the visual field, and such selected symbols are capable of producing unintentional shifts of attention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)