冷轧对Cu-Al-Mn合金双程形状记忆效应及马氏体相变的影响

利用热膨胀仪、X射线衍射仪和示差扫描热分析装置等对冷轧Cu 18Al 10.5Mn合金的双程形状记忆效应的各向异性及其产生机制进行了研究·结果表明,冷轧可造成马氏体织构,且马氏体织构按最大程度缓和外加应力的方式产生,并在特定方向上产生形状记忆效应·通过对压下量的控制,可以在一定范围内任意获得正、负的自发形状变化和热膨胀性·随着加工量的增加,马氏体相变温度区间大幅度扩大·随着温度变化,具有择优取向的应力诱发马氏体晶核的生长和消失是导致可逆的自发形状变化各向异性的根本原因·     

基于工作记忆内容的注意导向的影响因素

工作记忆内容对视觉搜索中注意选择的影响到底如何,与工作记忆内容匹配的项目是否总是会自动捕获注意,至今仍存在争议。本研究针对前人研究中存在的差异和不足,统一采用"记忆-搜索"双任务范式来探讨搜索任务难度、搜索项目刺激强度以及记忆任务呈现时间对基于工作记忆内容的注意导向的影响。结果发现:当采用困难搜索任务时,干扰条件下被试反应时与中性条件下反应时无显著差异,说明难度会影响工作记忆内容对注意选择的自动导向效应,但这种影响只有在搜索任务难度足够大的情况下才能体现出来。而在本研究中并未发现刺激强度和记忆任务呈现时间这两个因素对基于工作记忆内容的注意导向的影响,即无论刺激强度高低,或记忆任务呈现时间长或短,与工作记忆内容匹配的搜索项目还是会"自动捕获注意",导致干扰条件下被试反应时显著慢于中性条件。     

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.     

心算加工策略的认知科学研究综述

工作记忆和练习效应都通过多方面对心算过程中的加工策略产生较大的影响。在工作记忆资源被占用的情况下,心算策略的执行与选择都受到了较大的影响。工作记忆与策略复杂性的交互作用,工作记忆与问题难度的交互作用都对策略执行有影响。工作记忆资源的被占用会对策略选择造成较大的影响。练习对策略选择,策略效能、策略迁移、策略的问题大小效应都有影响。练习效应能提高直接提取策略的使用频率,同时降低了运算策略的使用频率。练习有效地缩短了提取策略的完成时间和运算策略的完成时间。练习在部分情况下会缩小问题大小效应,但不能完全消除问题大小效应。练习的结果仅在乘法中会出现部分迁移。不同的理论对练习效应的影响,有着不同的解释。     

Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range

The paper considers the return and range model with dynamic conditional correlations (DCC). The paper suggests the new specifications for the asymmetric effects on log‐volatilities and dynamic correlations, combined with long‐run dependences. The new DCC model can be estimated by the quasi‐maximum likelihood method. Empirical analysis on Nikkei 225, Hang Seng and Straits Times indices shows the daily, weekly and monthly pattern of asymmetric effects. For the period including the global financial crisis, the new DCC model provides plausible one‐step‐ahead forecasts of the VaR thresholds, and yields positive economic values of switching from other DCC models. Copyright © 2013 John Wiley & Sons, Ltd.     

Using Quantum Associative Memory to Simulate the Brain Functions

To simulate the brain functions, a quantum associative memory combined with information preprocessing by a sparse coding model is presented. The sparse coding scheme is used to simulate the information transformation from retina up to primary visual cortex (V1) along the visual path and the quantum associative memory is used to simulate the pattern processing functions of the brain such as the pattern storing, forgetting and retrieving. Experimental results show that the model exhibits good associative ability on face recognition. Considering the huge storage capacity, mass parailel-distributed processing ability and oscillatory phenomena of the quantum system, this model might he a biological plausible implementation.     

软件事务存储中冲突检测算法研究

为了改进事务存储在并行程序中执行的性能，选取RSTM作为实验系统.根据具体应用的不同，使用不同的库实现来对其进行优化.实验挑选了斯坦福大学的STAMP基准测试程序中的6种典型应用程序，使用基于字的STM中的9种库实现对其进行比较，评估冲突检测中数据版本管理对性能产生的影响.实验结果显示，TML在各个应用程序上面运行的效果相对最好；而SGLA的效果相对最差.另外，在比较TML与TML+Lazy之后，采用急切版本管理取得了较好的效果，这主要是通过急切版本管理及时检测并避免了冲突.但是如果出现较多的放弃或活锁的情况，系统的性能可能有所下降.     

局部自相似过程的小波估计

首先, 针对时变自相似参数提出了一种基于最大重叠离散小波变换的估计方法; 对此进行了蒙特卡洛模拟研究, 发现提高了估计的精度; 最后, 将研究结果应用于海洋垂直切变序列.     

Parallelization and Performance Optimization on Face Detection Algorithm with OpenCL: A Case Study

Face detect application has a real time need in nature. Although Viola-Jones algorithm can handle it elegantly, today’s bigger and bigger high quality images and videos still bring in the new challenge of real time needs. It is a good idea to parallel the Viola-Jones algorithm with OpenCL to achieve high performance across both AMD and NVidia GPU platforms without bringing up new algorithms. This paper presents the bottleneck of this application and discusses how to optimize the face detection step by step from a very nave implementation. Some brilliant tricks and methods like CPU execution time hidden, stubbles usage of local memory as high speed scratchpad and manual cache, and variable granularity were used to improve the performance. Those technologies result in 4-13 times speedup varying with the image size. Furthermore, those ideas may throw on some light on the way to parallel applications efficiently with OpenCL. Taking face detection as an example, this paper also summarizes some universal advice on how to optimize OpenCL program, trying to help other applications do better on GPU.