Generalised Estimators for Seasonal Forecasting by Combining Grouping with Shrinkage Approaches

In this paper, generalised estimators are proposed to estimate seasonal indices for certain forms of additive and mixed seasonality. The estimators combine one of two group seasonal indices methods—Dalhart's group method and Withycombe's group method—with a shrinkage method in different ways. Optimal shrinkage parameters are derived to maximise the performance of the estimators. Then, the generalised estimators, with the optimal shrinkage parameters, are evaluated based on forecasting accuracy. Moreover, the effects of three factors are examined, namely, the length of data history, variance of random components and the number of series. Finally, a simulation experiment is conducted to support the evaluation. Copyright © 2011 John Wiley & Sons, Ltd.     

Control of cell growth: Rag GTPases in activation of TORC1

The target of rapamycin (TOR) is a central regulator controlling cell growth. TOR is highly conserved from yeast to mammals, and is deregulated in human cancers and diabetes. TOR complex 1 (TORC1) integrates signals from growth factors, cellular energy status, stress, and amino acids to control cell growth, mitochondrial metabolism, and lipid biosynthesis. The mechanisms of growth factors and cellular energy status in regulating TORC1 have been well established, whereas the mechanism by which amino acid induces TORC1 remains largely unknown. Recent studies revealed that Rag GTPases play a central role in the regulation of TORC1 activation in response to amino acids. In this review, we will discuss the recent progress in our understanding of Rag GTPase-regulated TORC1 activation in response to amino acids. Particular focus will be given to the function of Rag GTPases in TORC1 activation and how Rag GTPases are regulated by amino acids.     

MetaOJ: A Massive Distributed Online Judge System

Online Judge(OJ) systems are a basic and important component of computer education. Here, we present MetaOJ, an OJ system that can be used for holding massive programming tests online. MetaOJ is designed to create a distributed, fault-tolerant, and easy-to-scale OJ system from an existing ordinary OJ system by adding several interfaces into it and creating multiple instances of it. Our case on modifying the TUOJ system shows that the modification adds no more than 3% lines of code and the performance loss on a single OJ instance is no more than 12%. We also introduce mechanisms to integrate the system with cloud infrastructure to automate the deployment process. MetaOJ provides a solution for those OJ systems that are designed for a specific programming contest and are now facing performance bottlenecks.     

CADCSC软件系统人机界面设计

本文论述了CADCSC软件系统的界面设计。提出了适合各种用户的、由多种交互方式组成的混合人机交互界面;为控制系统的计算机辅助设计提供了统一的命令表达方式和统一的运行界面;介绍了其中界面设计一致性与灵活性。     

Model Averaging Multistep Prediction in an Infinite Order Autoregressive Process

The key issue in the frequentist model averaging is the choice of weights. In this paper, the authors advocate an asymptotic framework of mean-squared prediction error(MSPE) and develop a model averaging criterion for multistep prediction in an infinite order autoregressive(AR(∞)) process.Under the assumption that the order of the candidate model is bounded, this criterion is proved to be asymptotically optimal, in the sense of achieving the lowest out of sample MSPE for the samerealization pred...     

Single-Index Quantile Regression with Left Truncated Data

The purpose of this paper is two fold. First, the authors investigate quantile regression(QR) estimation for single-index QR models when the response is subject to random left truncation.The random weights are introduced to deal with left truncated data and the associated iteration estimation method is proposed. The asymptotic properties for the proposed QR estimates of the index parameter and unknown link function are both obtained. Further, by combining the QR loss function and the adaptive LA...     

低温微生物的研究进展概述

综述了低温微生物的生态分布、适冷机制的研究进展及其在工业、生物修复等多个领域中的应用及前景.