创新科技携籽亿干细胞技术回归中国

<正>作为"世界多媒体之父",沈望傅及其创新科技(Creative Technology Ltd.)曾给业界带来无数惊喜,如一炮而红的SoundBlaster声霸卡、以红外遥控的光驱以及多媒体音箱等。有人说:"在上世纪90年代,作为多媒体PC的先驱,Intel卖出多少个CPU,创新就卖出了多少块声霸卡!"随着PC主板集成度的提高,     

A multicore periodical preemption virtual machine scheduling scheme to improve the performance of computational tasks

In virtualized environments, the VMM (virtual machine monitor) scheduler is critical to overall performance, as it allocates the physical resources. However, traditional schedulers have poor I/O performance of mixed workloads. Although recent research significantly improves I/O performance, they degrade the performance of computational tasks by shortening time slices and reducing cache efficiency. In order to eliminate these problems while guaranteeing I/O performance, this paper presents a multicore periodical preemption scheduling scheme with three optimization techniques: (1) periodically coalescing and handling I/O events to reduce the preemption rate and scheduling latency, which guarantees I/O performance; (2) taking advantage of multicore environments and centrally handling I/O events on different cores in a Round-Robin manner to lengthen time slices, which improves the performance of computational tasks; (3) using a dedicated priority for I/O event handling to keep the CPU fairness. We implement a Xen-based prototype and evaluate the performance of I/O workloads and computation-intensive workloads. The experimental results demonstrate that our scheduling scheme efficiently lengthens time slices and improves the performance of computational tasks, achieving the same I/O performance as the existing approaches optimized for I/O.     

Effective RED: An algorithm to improve RED's performance by reducing packet loss rate

Random early detection (RED) is an effective congestion control mechanism acting on the intermediate gateways. We describe a new active queue management scheme, Effective RED (ERED) that aims to reduce packet loss rates in a simple and scalable manner. We made a few change to packet drop function of existing RED scheme. The rest of the original RED remains unchanged. We achieve this by making several refinements and by controlling packet dropping function both with average queue size and instantaneous queue size. Simulations demonstrate that ERED achieves a highest throughput and lowest packet drops than RED, Blue, REM, FRED, LDC and SRED. Since ERED is fully compatible with RED, we can easily upgrade/replace the existing RED implementations by ERED.     

Multi-objective genetic algorithms: A way to improve the convergence rate

Multi-objective optimization is generally a time consuming step of the design process. In this paper, a Pareto based multi-objective genetic algorithm is proposed, which enables a faster convergence without degrading the estimated set of solutions. Indeed, the population diversity is correctly conserved during the optimization process; moreover, the solutions belonging to the frontier are equally distributed along the frontier. This improvement is due to an extension function based on a natural phenomenon, which is similar to a cyclical epidemic which happens every N generations (eN-MOGA). The use of this function enables a faster convergence of the algorithm by reducing the necessary number of generations.     

Reorganizing knowledge to improve performance

This paper presents a method to reorganize rules in knowledge bases with the objective of improving their performance. Knowledge reorganization is achieved through the combination of rule compression and abstraction techniques. The effectiveness of this methodology is evaluated in terms of pattern matching activity and execution times using knowledge bases from several application areas     

改善iSCSI存储系统性能的优化策略

针对iSCSI在磁盘I/O和网络传输速度上存在的瓶颈问题,提出了一种改进的iSCSI存储架构.根据请求包大小动态的选择存储策略,目标端采用提出的并行算法对包进行处理,并且消除了写冗余并减少了对同一扇区多次读操作的冗余.实验结果表明,该策略有效地缩短了系统的响应时间,提高了系统的吞吐量,从而达到优化iSCSI存储系统性能的目的.     

Chaotic sequences to improve the performance of evolutionary algorithms

This paper proposes an experimental analysis on the convergence of evolutionary algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during all the phases of the evolution process is investigated. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences. Several numerical examples are reported in order to compare the performance of the EA using random and chaotic generators as regards to both the results and the convergence speed. The results obtained show that some chaotic sequences are always able to increase the value of some measured algorithm-performance indexes with respect to random sequences. Moreover, it is shown that EAs can be extremely sensitive to different RNGs. Some t-tests were performed to confirm the improvements introduced by the proposed strategy.     

一种改善稳态性能的模糊控制器

针对常规模糊控制存在静差的缺陷，提出一种变结构自适应模糊控制器，该控制器能够根据控制过程的不同阶段进行变结构控制和自适应控制，且不存在平滑过渡问题，有效地改善了系统的动态性能并克服了静差，为大时滞系统和许多未知模型系统控制提供了一种新的思路，仿真和实际应用证明了该控制器的有效性。     

Optimizing the stretch of independent tasks on a cluster: From sequential tasks to moldable tasks

This paper addresses the problem of scheduling non-preemptive moldable tasks to minimize the stretch of the tasks in an online non-clairvoyant setting. To the best of the authors’ knowledge, this problem has never been studied before. To tackle this problem, first the sequential subproblem is studied through the lens of the approximation theory. An algorithm, called DASEDF, is proposed and, through simulations, it is shown to outperform the first-come, first-served scheme. Furthermore, it is observed that machine availability is the key to getting good stretch values. Then, the moldable task scheduling problem is considered, and, by leveraging the results from the sequential case, another algorithm, DBOS, is proposed to optimize the stretch while scheduling moldable tasks. This work is motivated by a task scheduling problem in the context of parallel short sequence mapping which has important applications in biology and genetics. The proposed DBOS algorithm is evaluated both on synthetic data sets that represent short sequence mapping requests and on data sets generated using log files of real production clusters. The results show that the DBOS algorithm significantly outperforms the two state-of-the-art task scheduling algorithms on stretch optimization.     

Caching strategies to improve disk system performance

I/O subsystem manufacturers attempt to reduce latency by increasing disk rotation speeds, incorporating more intelligent disk scheduling algorithms, increasing I/O bus speed, using solid-state disks, and implementing caches at various places in the I/O stream. In this article, we examine the use of caching as a means to increase system response time and improve the data throughput of the disk subsystem. Caching can help to alleviate I/O subsystem bottlenecks caused by mechanical latencies. This article describes a caching strategy that offers the performance of caches twice its size. After explaining some basic caching issues, we examine some popular caching strategies and cache replacement algorithms, as well as the advantages and disadvantages of caching at different levels of the computer system hierarchy. Finally, we investigate the performance of three cache replacement algorithms: random replacement (RR), least recently used (LRU), and a frequency-based variation of LRU known as segmented LRU (SLRU)     

Spying on real-time computers to improve performance

The sampled program-counter histogram, an established technique for shortening the execution times of programs, is described for a real-time computer. The use of a real-time clock allows particularly easy implementation.     

Methods to improve prediction performance of ANN models

Artificial neural network (ANN) is a powerful tool and applied successfully in numerous fields. But there are still two limitations on its use. One is over-training, which occurs when the capacity of the ANN for training is too great because it is allowed too many training iterations. The other is that ANNs are not effective for extrapolation, which is sometimes very important because the existing data used to train an ANN do not necessarily cover the entire range. The two limitations degrade seriously the prediction performance of ANN models. In this paper, two practices are introduced to alleviate or overcome the negative effect of the limitations. Demonstrations based on these practices indicate that they are general and useful practices and can improve greatly the prediction performance of the resulting ANN models to make them really suitable for engineering applications.     

Aging and performance of home tasks

Household behaviors must be understood in terms of a hierarchy of behavioral competence within which complexity determines the location of behaviors that can be evaluated in terms of basic health and social-normative criteria. Older people's household behaviors as well as higher-order behaviors are schematized in terms of physical and instrumental activities of daily living (ADL). Data on impairment rates for ADLs and time budget studies are cited to emphasize the importance of the home environment. Other behaviors that represent needs for autonomy, support, and proactivity are discussed, as are research needs, both for development of measures of microbehaviors and for the study of behaviors designed to achieve alternative routes to an instrumental goal whose preferred means of attainment has been disturbed by physical impairment.     

Improving performance of repetitive computer-based tasks through visual stimuli tailored to the individual

When humans conduct simple and repetitive tasks, their task performance can decrease depending on time. At this time, by deciding stimulus timing and delivering stimuli to humans, speed and accuracy of the task can be improved. To clearly investigate the effectiveness of such stimuli, we designed this study as repetitive tasks on a PC that could be conducted simply and were not much affected by other environmental variables. For our experiment, a total of 20 participants were asked to perform mouse pointing and keyboard typing tasks; usual speed and accuracy of each participant on each task were determined after completing the task. Next, the participant’s level of concentration was assessed using several metrics. When concentration was deemed to have declined during the task, a stimulus was delivered to the participant in the form of a screen flash, which resulted in an increased focus on the task. Through the proposed approach, speed was boosted by 11.6% on a pointing task and 3.8% on a typing task. Moreover, these results were found to be statistically significant in analysis of variance and paired t test. Our experiments and studies focus on improvements in reaction time caused by effect of stimulus and our presented approach.     

Neuro-fuzzy chip to handle complex tasks with analog performance.

This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input-output delay, and precision, performs as a fully analog implementation. However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core. Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture are smaller than those of its purely analog counterparts simply because most rules are implemented through programming. The paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype. This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 /spl mu/m standard technology. It has two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided.     

DEMATEL-based model to improve the performance in a matrix organization

Carrying out a design project for a high-tech facility requires a large amount of special knowledge in order to deal with the specific requirements from the client. Often the client forms a matrix organization, structured with several interrelated functional and project divisions to dictate the various requirements and needs. However, an ill-defined matrix organization is likely to result in late and inadequate decisions. This in turn may result in a poorly performing design. Identifying those divisions within the organization that perform poorly in the way they provide information for the project is critical. Therefore, this study proposes an innovative model to identify those divisions that may be responsible for the poor performance of the design project. The proposed model integrates the satisfied importance analysis (SIA) and the decision making trial and evaluation laboratory technique (DEMATEL). The SIA evaluates the performance of each division, while the DEMATEL captures the causal relationships among divisions to generate an influence-relations map. The proposed model is applied to a real-world high-tech facility design project in Taiwan to demonstrate the strengths of the model.     

Exploiting performance counters to predict and improve energy performance of HPC systems

Hardware monitoring through performance counters is available on almost all modern processors. Although these counters are originally designed for performance tuning, they have also been used for evaluating power consumption. We propose two approaches for modelling and understanding the behaviour of high performance computing (HPC) systems relying on hardware monitoring counters. We evaluate the effectiveness of our system modelling approach considering both optimizing the energy usage of HPC systems and predicting HPC applications’ energy consumption as target objectives. Although hardware monitoring counters are used for modelling the system, other methods–including partial phase recognition and cross platform energy prediction–are used for energy optimization and prediction. Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimization indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24% of the overall HPC system’s energy consumption under benchmarks and real-life workloads.     

Age differences in the performance of information retrieval tasks

In two experiments younger (18-25 years) and older (60-70 years) participants performed an information retrieval task in which they searched for the answers to questions in a hierarchical menu structure. Participants' movement speed, spatial ability, spatial memory, working memory capacity and reasoning speed were measured. Results showed older participants to be slower than younger participants on overall latencies on the information retrieval task. This slowing increases with each consecutive step in the menu structure. Regression analysis showed that movement speed, reasoning speed and spatial ability predicted the overall latencies accurately. Modelling the consecutive steps showed that latencies on the first selection are predicted by movement speed and reasoning speed. Memory and spatial measures are predictors for latencies on steps further into the menu structure only. This finding is consistent with increased slowing of older participants for later selections and suggests that deep menu structures are less suited for older users.     

Applying category theory to improve the performance of a neural architecture

A recently developed mathematical semantic theory explains the relationship between knowledge and its representation in connectionist systems. The semantic theory is based upon category theory, the mathematical theory of structure. A product of its explanatory capability is a set of principles to guide the design of future neural architectures and enhancements to existing designs. We claim that this mathematical semantic approach to network design is an effective basis for advancing the state of the art. We offer two experiments to support this claim. One of these involves multispectral imaging using data from a satellite camera.     

一种改善入侵检测系统性能的新方法

针对高速网络环境下基于知识的入侵检测系统因搜索的知识库庞大，耗时过多，导致丢包而影响到系统的性能等问题，通过引入网络流量的反馈机制，采用随机投影算法，灵活调整模式匹配过程中知识库的规模，以提高入侵检测系统的实时性，减少系统的丢包率，从而有效降低系统的误漏报率，以此改善入侵检测系统的性能。