Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.     

基于北斗定位导航系统的士兵状态监视与指挥控制系统

战场救护中,及时发现士兵伤情并迅速找到伤员是挽救伤员的重要条件,因此,对受伤士兵的定位与获取需救护伤员的准确个人信息对于救护的有效性具有非常重要的意义。基于北斗卫星的定位导航系统设计战场士兵信息采集与指挥控制系统,利用我国自主研发的北斗卫星导航定位通信系统对战场士兵进行定位,通过由基于蓝牙的士兵状态监视终端对士兵生理状态参数进行连续监测,当发现士兵伤情时实时由通信终端向指挥控制中心传递伤员个人信息和生理状态参数,指挥中心接收伤员数据后,通过GIS系统、根据地形等信息可实时规划救援路线和救援方案,以实现对伤员的及时救治。分析了北斗卫星导航系统的功能与特点,介绍了士兵信息采集与指挥控制系统的组成及功能,详细阐述了士兵信息采集终端的设计、生命体征信息监测方法、指挥控制系统的组成与功能以及地理信息系统(GIS)设计。     

铝合金凝固晶粒尺寸的人工神经网络研究

建立了强脉冲电磁场作用下铝合金凝固组织晶粒尺寸的人工神经网络BP算法模型。用该模型进行的模拟结果和实验数据吻合得较好。研究表明，用这一方法可对脉冲电磁场作用下的凝固组织晶粒尺寸进行预测，为优化实验设计提供了简便实用的方法和手段。     

miRNA regulation of Sdf1 chemokine signaling provides genetic robustness to germ cell migration

microRNAs (miRNAs) function as genetic rheostats to control gene output. Based on their role as modulators, it has been postulated that miRNAs canalize development and provide genetic robustness. Here, we uncover a previously unidentified regulatory layer of chemokine signaling by miRNAs that confers genetic robustness on primordial germ cell (PGC) migration. In zebrafish, PGCs are guided to the gonad by the ligand Sdf1a, which is regulated by the sequestration receptor Cxcr7b. We find that miR-430 regulates sdf1a and cxcr7 mRNAs. Using target protectors, we demonstrate that miR-430-mediated regulation of endogenous sdf1a (also known as cxcl12a) and cxcr7b (i) facilitates dynamic expression of sdf1a by clearing its mRNA from previous expression domains, (ii) modulates the levels of the decoy receptor Cxcr7b to avoid excessive depletion of Sdf1a and (iii) buffers against variation in gene dosage of chemokine signaling components to ensure accurate PGC migration. Our results indicate that losing miRNA-mediated regulation can expose otherwise buffered genetic lesions leading to developmental defects.     

A New Dimension Reduction Method: Factor Discriminant K-means

Reduced K-means (RKM) and Factorial K-means (FKM) are two data reduction techniques incorporating principal component analysis and K-means into a unified methodology to obtain a reduced set of components for variables and an optimal partition for objects. RKM finds clusters in a reduced space by maximizing the between-clusters deviance without imposing any condition on the within-clusters deviance, so that clusters are isolated but they might be heterogeneous. On the other hand, FKM identifies clusters in a reduced space by minimizing the within-clusters deviance without imposing any condition on the between-clusters deviance. Thus, clusters are homogeneous, but they might not be isolated. The two techniques give different results because the total deviance in the reduced space for the two methodologies is not constant; hence the minimization of the within-clusters deviance is not equivalent to the maximization of the between-clusters deviance. In this paper a modification of the two techniques is introduced to avoid the afore mentioned weaknesses. It is shown that the two modified methods give the same results, thus merging RKM and FKM into a new methodology. It is called Factor Discriminant K-means (FDKM), because it combines Linear Discriminant Analysis and K-means. The paper examines several theoretical properties of FDKM and its performances with a simulation study. An application on real-world data is presented to show the features of FDKM.     

深化改革 探索毕业实践教学新途径

本文通过校企合作实践,探讨了产学结合进行毕业实践教学的新途径.     

A network analysis of the 2010 FIFA world cup champion team play

This paper analyzes the network of passes among the players of the Spanish team during the last FIFA World Cup 2010, where they emerged as the champion, with the objective of explaining the results obtained from the behavior at the complex network level. The team is considered a network with players as nodes and passes as (directed) edges. A temporal analysis of the resulting passes network is also done, looking at the number of passes, length of the chain of passes, and to network measures such as player centrality and clustering coefficient. Results of the last three matches (the decisive ones) indicate that the clustering coefficient of the pass network remains high, indicating the elaborate style of the Spanish team. The effectiveness of the opposing team in negating the Spanish game is reflected in the change of several network measures over time, most importantly in drops of the clustering coefficient and passing length/speed, as well as in their being able in removing the most talented players from the central positions of the network. Spain’s ability to restore their combinative game and move the focus of the game to offensive positions and talented players is shown to tilt the balance in favor of the Spanish team.     

Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm

Framework of this paper is statistical data editing, specifically how to edit or impute missing or contradictory data and how to merge two independent data sets presenting some lack of information. Assuming a missing at random mechanism, this paper provides an accurate tree-based methodology for both missing data imputation and data fusion that is justified within the Statistical Learning Theory of Vapnik. It considers both an incremental variable imputation method to improve computational efficiency as well as boosted trees to gain in prediction accuracy with respect to other methods. As a result, the best approximation of the structural risk (also known as irreducible error) is reached, thus reducing at minimum the generalization (or prediction) error of imputation. Moreover, it is distribution free, it holds independently of the underlying probability law generating missing data values. Performance analysis is discussed considering simulation case studies and real world applications.     

Requirements for leukocyte transmigration via the transmembrane chemokine CX3CL1

The surface-expressed transmembrane CX3C chemokine ligand 1 (CX3CL1/fractalkine) induces firm adhesion of leukocytes expressing its receptor CX3CR1. After shedding by the disintegrins and metalloproteinases (ADAM) 10 and 17, CX3CL1 also acts as soluble leukocyte chemoattractant. Here, we demonstrate that transmembrane CX3CL1 expressed on both endothelial and epithelial cells induces leukocyte transmigration. To investigate the underlying mechanism, we generated CX3CR1 variants lacking the intracellular aspartate-arginine-tyrosine (DRY) motif or the intracellular C-terminus which led to a defect in intracellular calcium response and impaired ligand uptake, respectively. While both variants effectively mediated firm cell adhesion, they failed to induce transmigration and rather mediated retention of leukocytes on the CX3CL1-expressing cell layer. Targeting of ADAM10 led to increased adhesion but reduced transmigration in response to transmembrane CX3CL1, while transmigration towards soluble CX3CL1 was not affected. Thus, transmembrane CX3CL1 mediates leukocyte transmigration via the DRY motif and C-terminus of CX3CR1 and the activity of ADAM10.