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.     

行业股票日收益率的长记忆性研究

运用修正R/S分析和V/S检验两种分析方法,对我国股票市场13个行业日收益率序列的长期记忆性进行了比较研究,得出了以下结论：在5%的显著水平下,农林牧渔业的日收益率在两种方法下均不具有长记忆性;传播与文化产业的日收益率在修正R/S分析中不具有长记忆性,而交通运输仓储业的日收益率在V/S分析下不具有长记忆性;除上述提到的行业外,其余行业的日收益率在两种分析结果下,均具有明显的长记忆性。     

Forecasting high‐frequency financial data with the ARFIMA–ARCH model

Financial data series are often described as exhibiting two non‐standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA–ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid ( 1987 ). For daily data on the Swiss 1‐month Euromarket interest rate during the period 1986–1989, the ARFIMA–ARCH (5,d,2/4) model with non‐integer d is selected by AIC. Model‐based out‐of‐sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (τ > 40). Regarding volatility forecasts, however, the selected ARFIMA–ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd.     

树立科学理财观全面提升理财能力

财政工作是发展、调节社会分配关系、促进公正与公平、构建和谐社会的物质基础和重要政策工具。所谓科学的理财观，就是指坚持以“三个代表”为指导，坚持以人为本，坚持以发展为中心，统筹生财、聚财、分财、管财、用财等各个环节，树立全面安全、协调型的理财观，从而提升政府理财能力。     

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。     

Wavelet Estimation of a Long Memory Parameter In the Stock Market

I 1ntroductlonThe potential presence of stochastic long memory in financial asset returns has been animportant subject of both theoretical and empirical research. If asset returns display longmemory, or long--term dependence, they exhibit significant autocorrelation betweenobservations widely separated in time. In economics and finance, long--range dependencehas a long history and has remained a topic of active research is the study of economic andfinancial time series (see Lo [5j and Mandelb…     

集成电路的计算机辅助测试

以9001测试系统为例，介绍计算机在集成电路测试技术中的应用及测试原理，以及现代测试中利用软件进行数据处理，提高测试精度的一些方法。     

扣槽式干挂建筑瓷板幕墙试验研究

通过对１６件建筑瓷板的破坏性试验，探讨了瓷板幕墙扣槽式干挂支承的破坏形态、承载力、以及抗剪切和抗弯的计算公式 。     

如何提高英语的记忆效率

提高英语的记忆效率是学好英语的关键。教师应注意运用适当的教学策略,并指导学生掌握几种英语的学习策略,以提高英语的学习效果。     

谈大学英语听力课中多种能力的培养

针对听力课信息来源广泛，练习性强的特点，提出在不同的学习阶段对学生分别进行听读结合，听说结合，听写结合，视听结合的训练，以确保学生在初级阶段打好基础，中级阶段勇于表达，高级阶段扩大视野，提高层次。强调培养学生的记忆能力，理解能力，表达能力和良好的英语思维能力，并提出了具体的实施步骤和范例。