Average Interest Rate Caps

There exist a number of approximation methods for the price of average rate options, when the underlying asset is a currency or equity. Realistic pricing models for average interest rate caps based on interbank offered rates have not yet been published. In this paper we propose to adapt the methods of Levy (1992), Vorst (1992) and Rogers and Shi (1995) for average rate options to price average interest rate caps, and we compare their computational efficiencies. All three methods are very fast, compared to the Monte Carlo simulation. Two of them are fast enough for on-the-fly calculations. The underlying interest rate model we use is consistent with the observed term structure of the interest rate. Hence, the models developed here are suitable for practical implementation.     

Speed Scaling on Parallel Processors

In this paper we investigate dynamic speed scaling, a technique to reduce energy consumption in variable-speed microprocessors. While prior research has focused mostly on single processor environments, in this paper we investigate multiprocessor settings. We study the basic problem of scheduling a set of jobs, each specified by a release date, a deadline and a processing volume, on variable-speed processors so as to minimize the total energy consumption. We first settle the problem complexity if unit size jobs have to be scheduled. More specifically, we devise a polynomial time algorithm for jobs with agreeable deadlines and prove NP-hardness results if jobs have arbitrary deadlines. For the latter setting we also develop a polynomial time algorithm achieving a constant factor approximation guarantee. Additionally, we study problem settings where jobs have arbitrary processing requirements and, again, develop constant factor approximation algorithms. We finally transform our offline algorithms into constant competitive online strategies.     

Speed Scaling for Maximum Lateness

We consider the power-aware problem of scheduling non-preemptively a set of jobs on a single speed-scalable processor so as to minimize the maximum lateness, under a given budget of energy. In the offline setting, our main contribution is a combinatorial polynomial time algorithm for the case in which the jobs have common release dates. In the presence of arbitrary release dates, we show that the problem becomes strongly \(\mathcal {N}\mathcal {P}\)-hard. Moreover, we show that there is no O(1)-competitive deterministic algorithm for the online setting in which the jobs arrive over time. Then, we turn our attention to an aggregated variant of the problem, where the objective is to find a schedule minimizing a linear combination of maximum lateness and energy. As we show, our results for the budget variant can be adapted to derive a similar polynomial time algorithm and an \(\mathcal {N}\mathcal {P}\)-hardness proof for the aggregated variant in the offline setting, with common and arbitrary release dates respectively. More interestingly, for the online case, we propose a 2-competitive algorithm.     

Speed Scaling of Tasks with Precedence Constraints

We consider the problem of speed scaling to conserve energy in a multiprocessor setting where there are precedence constraints between tasks, and where the performance measure is the makespan. That is, we consider an energy bounded version of the classic problem Pm | prec | C _max. We extend the standard 3-field notation and denote this problem as Sm | prec, energy | C _max. We show that, without loss of generality, one need only consider constant power schedules. We then show how to reduce this problem to the problem Qm | prec | C _max to obtain a poly-log(m)-approximation algorithm. A preliminary version of this paper appears in Proceedings of the 3rd Workshop on Approximation and Online Algorithms (WAOA 2005). Research of K. Pruhs supported in part by NSF grants CCR-0098752, ANI-0123705, CNS-0325353, CCF-0448196, CCF-0514058, and IIS-0534531. Research of R. van Stee supported by Alexander von Humboldt-Stiftung.     

Nonclairvoyant Speed Scaling for Flow and Energy

We give three results related to online nonclairvoyant speed scaling to minimize total flow time plus energy. We give a nonclairvoyant algorithm LAPS, and show that for every power function of the form P(s)=s ^α , LAPS is O(1)-competitive; more precisely, the competitive ratio is 8 for α=2, 13 for α=3, and frac2a²lnafrac{2alpha^{2}}{lnalpha} for α>3. We then show that there is no constant c, and no deterministic nonclairvoyant algorithm A, such that A is c-competitive for every power function of the form P(s)=s ^α . So necessarily the achievable competitive ratio increases as the steepness of the power function increases. Finally we show that there is a fixed, very steep, power function for which no nonclairvoyant algorithm can be O(1)-competitive.     

基于K-means聚类算法的公交行程速度计算模型

定位与无线装置在公交系统中的广泛应用使得获取实时公交数据成为可能。为挖掘这些数据中蕴含的道路交通状况信息,提出了一种基于K-means聚类算法的数据融合模型,来计算相邻公交站点间的平均行程速度。首先对K-means聚类算法进行改进:(1)聚类数K不是预先设定的固定值,而是不重复样本数的平方根,不同路段不同时段K值不同;(2)初始聚类中心不是随机选取,而是根据K值按一定规则选取。其次利用改进的算法对样本数据进行聚类,然后对各类数据进行加权融合,计算出平均行程速度。最后通过折线图对青岛市4个城区的行程速度进行分析,挖掘交通流的演变规律。研究结果为交通管理、居民出行等提供了强有力的支持。     

快速自适应调频机制及其在NetFPGA上的实现

降低网络设备能耗已经成为当前研究的热点.文中,作者设计了一种能够实时适应流量变化的动态自适应频率调整方法FASS,它能根据处理模块的负载,实时地调整模块的工作频率,从而有效降低模块的能耗.同时,作者通过修改NetFPGA的参考路由器设计,将FASS添加到数据包处理模块中来验证FASS的性能.马尔可夫模型分析结果和实际实验的测试结果表明,在仅增加可容忍的延迟的情况下,FASS在多种不同的负载情况下,都能有效地降低模块功耗.另外,作者的工作说明FASS可以应用于实际的物理设备中以实现节能.     

Speed Scaling of Processes with Arbitrary Speedup Curves on a Multiprocessor

We consider the setting of a multiprocessor where the speeds of the m processors can be individually scaled. Jobs arrive over time and have varying degrees of parallelizability. A nonclairvoyant scheduler must assign the processes to processors, and scale the speeds of the processors. We consider the objective of energy plus flow time. We assume that a processor running at speed s uses power s ^α for some constant α>1. For processes that may have side effects or that are not checkpointable, we show an W(m^(a-1)/a2)\Omega(m^{(\alpha -1)/\alpha^{2}}) bound on the competitive ratio of any randomized algorithm. For checkpointable processes without side effects, we give an O(log m)-competitive algorithm. Thus for processes that may have side effects or that are not checkpointable, the achievable competitive ratio grows quickly with the number of processors, but for checkpointable processes without side effects, the achievable competitive ratio grows slowly with the number of processors. We then show a lower bound of Ω(log ^1/α m) on the competitive ratio of any randomized algorithm for checkpointable processes without side effects.     

高速率时代网络安全技术

介绍了计算机网络安全存在的各种问题以及新的应用带来的新安全问题,最后从几个方面阐述了在高速率时代如何架构新一代网络安全平台。     

高速率时代网络安全技术

介绍了计算机网络安全存在的各种问题以及新的应用带来的新安全问题。最后从几个方面阐述了在高速率时代如何架构新一代网络安全平台。     

Online Speed Scaling Based on Active Job Count to Minimize Flow Plus Energy

This paper is concerned with online scheduling algorithms that aim at minimizing the total flow time plus energy usage. The results are divided into two parts. First, we consider the well-studied “simple” speed scaling model and show how to analyze a speed scaling algorithm (called AJC) that changes speed discretely. This is in contrast to the previous algorithms which change the speed continuously. More interestingly, AJC admits a better competitive ratio, and without using extra speed. In the second part, we extend the study to a more general speed scaling model where the processor can enter a sleep state to further save energy. A new sleep management algorithm called IdleLonger is presented. This algorithm, when coupled with AJC, gives the first competitive algorithm for minimizing total flow time plus energy in the general model.     

基于波速驱动的机器海豚平均推进速度控制方法

通过分析海豚豚体波推进特点,提出了波速与平均推进速度的匹配方法和工程化的速度匹配方程(Speed-velocity matching equation, SVME), 并进一步设计了基于速度匹配方程的机器海豚平均速度控制实现方法.首先,分析了海豚尾部摆动时呈现的正弦豚体波特征, 指出某一豚体波波速与相应海豚推进平均速度存在严格对应关系,据此给出了速度匹配系数(Speed-velocity matching coefficient, SVMC)定义及速度匹配方程. 然后,以三关节尾部机器海豚为例,根据速度匹配关系特征,建立了三关节尾部摆动豚体波波速与推进平均速度的数学关系. 最后,基于已知的速度匹配系数分布状况,采用分区线性化处理策略分别设计了开环控制方法和自校正控制实现方法. 通过速度匹配系数的取值对机器海豚进行驱动与控制,机器海豚可到达目标平均速度. 实验结果表明,豚体波波速与海豚平均速度存在严格对应关系, 基于速度匹配系数这一数据驱动的机器海豚速度控制方法是可行的.     

一种自适应尺度与学习速率调整的背景感知相关滤波跟踪算法

针对跟踪过程中遮挡因素以及目标尺度变化因素导致的目标跟踪漂移问题,文中提出了一种自适应尺度与学习速率调整的背景感知相关滤波跟踪算法.该算法首先通过背景感知相关滤波器获得目标的初步位置信息;其次在背景感知相关滤波器的基础框架下训练尺度相关滤波器,以有效估计目标尺度变化,从而准确调整搜索区域的大小;然后根据响应图波动情况进...     

非线性学习中学习路径变化率对群体平均知识水平的影响研究

本文运用非线性动力学原理,通过分析非线性学习中学习者的学习路径与知识水平之间的数学关系,引出非线性学习的动力学模型.并在随机网络、小世界网络和规则网络三种环境下,针对学习路径变换率对某一群体平均知识水平存在的影响进行了仿真分析.仿真结果表明,学习路径变化率越大,群体的平均知识水平越容易达到平衡且趋于稳定,并且三种网络环境下小世界网络能较好的模拟真实网络的拓扑结构,这对于在群体的学习中合理优化的调控学习者的学习方向和学习质量有着科学的指导意义.     

基于PCI-E总线的高速SDH信号存储系统

SDH光纤信号传输速率高、数据量大的特点是对该数据实时连续存储的主要难点:针对信号的这一特点,文章提出采用PCI-E总线作为数据传输总线,保证了数据存储的总线带宽;采用磁盘阵列作为数据存储的物理介质,解决了单个磁盘速率低容量小的问题;在此基础上给出了硬件结构配置和软件设计,实现了高速SDH信号的存储;测试结果表明,该系统运行稳定可靠,满足2.5Gb/sSDH信号的存储需求。     

基于短时平均能量和短时过零率的藏语语音端点检测研究

语音端点检测是语音处理分析过程中的重要环节之一,该文介绍了语音端点检测的两个传统算法即短时平均能量和短时过零率,并将这两种算法结合起来进行藏语语音进行端点检测。运用Matlab编程和仿真验证了双门限判断法在藏语语音端点检测中的准确性。这种方法降低了藏语语音处理的时间、提高了处理的质量、可用来进行一些藏语语音识别的特征参数的提取。     

计算机通信网络中一种基于速率的PDFF拥塞控制方案*

从最优策略的角度出发,将控制理论中的控制方法引入主动队列管理的研究中,提出了一种基于控制理论的主动队列管理PDFF拥塞控制方案。该方案以快速延迟响应和缓冲队列稳定为优化目标,根据网络动态时延调节源发送端发送速率,使得队列长度能够很快收敛到目标值,并且抖动很小。通过不同网络环境下的仿真显示,与PI控制方案相比,该PDFF控制方案具有较低的链路延迟和数据丢失率,并能更快地使整个网络系统达到稳定,具有较好的网络性能。     

MOS Scaling

Basic MOS feature dimensions may fall to the 0.5μm level by the mid-1980's. Such scaling will create new challenges related to registration accuracy, dimensional control, and defect density.     

Scaling agile methods

Using agile methods to develop large systems presents a thorny set of issues. If large teams are to produce lots of software functionality quickly, the agile methods involved must scale to meet the task. After all, a small team could create the software if the functionality to be delivered was small and, conversely, could be delivered given we had the time. Scaling agile teams thus becomes an issue if the only option for meeting a system delivery deadline is to have many developers working concurrently.     

Scaling step-wise refinement

Step-wise refinement is a powerful paradigm for developing a complex program from a simple program by adding features incrementally. We present the AHEAD (algebraic hierarchical equations for application design) model that shows how step-wise refinement scales to synthesize multiple programs and multiple noncode representations. AHEAD shows that software can have an elegant, hierarchical mathematical structure that is expressible as nested sets of equations. We review a tool set that supports AHEAD. As a demonstration of its viability, we have bootstrapped AHEAD tools from equational specifications, refining Java and nonJava artifacts automatically; a task that was accomplished only by ad hoc means previously.