Predictive fine granularity successive elimination for fast optimal block-matching motion estimation.

Given the number of checking points, the speed of block motion estimation depends on how fast the block matching is. In this paper, a new framework, fine granularity successive elimination (FGSE), is proposed for fast optimal block matching in motion estimation. The FGSE features providing a sequence of nondecreasing fine-grained boundary levels to reject a checking point using as little computation as possible, where block complexity is utilized to determine the order of partitioning larger sub-blocks into smaller subblocks in the creation of the fine-grained boundary levels. It is shown that the well-known successive elimination algorithm (SEA) and multilevel successive elimination algorithm (MSEA) are just two special cases in the FGSE framework. Moreover, in view that two adjacent checking points (blocks) share most of the block pixels with just one pixel shifting horizontally or vertically, we develop a scheme to predict the rejection level for a candidate by exploiting the correlation of matching errors between two adjacent checking points. The resulting predictive FGSE algorithm can further reduce computation load by skipping some redundant boundary levels. Experimental results are presented to verify substantial computational savings of the proposed algorithm in comparison with the SEA/MSEA.     

用户级通信在软件分布式共享存储系统中的应用与分析

通过信能不高是影响软件分布式共享存储系统性能的主要因素之一,用户级通信技术能够充分发挥高速网络的硬件性能,减少数据拷贝次数,降低软件件开发销,明显改善了带宽和延迟,为软件分布式共享存储系统性能的提高开避了新的途径,设计并实现了一个面向软件分布式存储系统的用户级通信库,它不仅改善了系统的通禽性能,同时也使得系统的并行计算性能得到改善,从而十分显著地提高了软件分布式共享存储系统的整体性能。     

Constrained adaptive optimal control using a reinforcement learning agent

To synthesize the optimal control strategies of nonlinear systems on infinite horizon while subject to mixed equality and inequality constraints has been a challenge to control engineers. This paper regards it as a problem of finite-time optimization in infinite-horizon control then devises a reinforcement learning agent, termed as the Adaptive Optimal Control (AOC) agent, to carry out the finite-time optimization procedures. Adaptive optimal control is in the sense of activating the finite-time optimization procedure whenever needed to improve the control strategy or adapt to a real-world environment. The Nonlinear Quadratic Regulator (NQR) is shown a typical example that the AOC agent can find out. The optimality conditions and adaptation rules for the AOC agent are deduced from Pontryagin’s minimum principle. The requirements for convergence and stability of the AOC system are shown.     

TPS型四自由度并联机器人位置分析

针对TPS型并联机械手的设计问题,提出一种退化结构:在三自由度并联机构基础上,采用退化结构对动平台的X、Y两方向进行约束,得到新型的具有3R1T四自由度的TPS型并联机构,并在该机构基础上,采用了螺旋理论对其运动特性进行分析,进一步给出了该类型机构的正解、反解位置特性。结果表明:该机构具有计算简单、易实现实时控制等特点,为该类型机构进一步深入研究打下基础。     

Optimality and convergence of adaptive optimal control by reinforcement synthesis

Adaptive Optimal Control (AOC) by reinforcement synthesis is proposed to facilitate the application of optimal control theory in feedback controls. Reinforcement synthesis uses the critic–actor architecture of reinforcement learning to carry out sequential optimization. Optimality conditions for AOC are formulated using the discrete minimum principle. A proof of the convergence conditions for the reinforcement synthesis algorithm is presented. As the final time extends to infinity, the reinforcement synthesis algorithm is equivalent to the Dual Heuristic dynamic Programming (DHP) algorithm, a version of approximate dynamic programming. Thus, formulating DHP with the AOC approach has rigorous proofs of optimality and convergence. The efficacy of AOC by reinforcement synthesis is demonstrated by solving a linear quadratic regulator problem.     

A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be irreconcilable.High resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable performance.Plenty of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource efficiency.In this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC applications.MAGI is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural networks.The experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.     

Phosphonium Modification Leads to Ultrapermeable Antibacterial Polyamide Composite Membranes with Unreduced Thickness

Water transport rate in network membranes is inversely correlated to thickness, thus superior permeance is achievable with ultrathin membranes prepared by complicated methods circumventing nanofilm weakness and defects. Conferring ultrahigh permeance to easily prepared thicker membranes remains challenging. Here, a tetrakis(hydroxymethyl) phosphonium chloride (THPC) monomer is discovered that enables straightforward modification of polyamide composite membranes. Water permeance of the modified membrane is ≈6 times improved, give rising to permeability (permeance × thickness) one magnitude higher than state-of-the-art polymer nanofiltration membranes. Meanwhile, the membrane exhibits good rejection (R_Na2SO4 = 98%) and antibacterial properties under crossflow conditions. THPC modification not only improves membrane hydrophilicity, but also creates additional angstrom-scale channels in polyamide membranes for unimpeded transport of water. This unique mechanism provides a paradigm shift in facile preparation of ultrapermeable membranes with unreduced thickness for clean water and desalination.     

基于新型Cache一致性协议的共享虚拟存储系统

介绍了一个基于新型Ｃａｃｈｅ一致性协议的共享虚拟存储系统ＪＩＡＪＩＡ,与目前国际上具有代表性的共享虚拟存储系统相比,ＪＩＡＪＩＡ采用了基于ＵＮＭＡ的结构,能够把多个机器的物理地址空间组织成一个更大的共享虚拟地址空间,此外,ＪＩＡＪＩＡ实现了一种基于锁的新型一致性协议,通过附带在锁上的ｗｒｉｔｅ－ｎｏｔｉｃｅ来维护一致性,从而避免了传统的目录协议中由目录引起的存储开销和系统复杂度,利用一些被广泛使用     

A New Approach to Correlate the Frictional Performance of Fin-and-Tube Heat Exchangers in Wet Conditions

This study presents a new approach to correlate the frictional performance of fin-and-tube heat exchangers in "completely wet" and "partially wet" conditions by using "completely dry" data. A total of 22 samples of uncoated plain fin-and-tube heat exchangers are used to develop the associated frictional correlation. The developed correlation is capable of predicting the frictional performance of "completely wet" and "partially wet" conditions with a mean deviation of 7.2%.     

曙光1000A上消息传递与共享存储的比较

分布式共享存储虽然有易于编程的优点,但往往被认为效率不高、完全由软件实现的分布式共享存储系统（又称为虚拟共享存储系统）更是如此,文中以典型的消息传递系统ＰＶＭ与分布式共享存储系统ＪＩＡＪＩＡ粉列,报这两种并行程序设计环境的特点,并用７个应用程序在曙光１０００Ａ上分别比较了这两个系统的性能,实验３结果表明,ＪＩＡＪＩＡ的与ＰＶ玎当,但基于ＪＩＡＪＩＡ的并行程序设计却比ＰＶＮ简单得多。