Radical polymerization of butyl acrylate and random copolymerization of styrene and butyl acrylate and styrene and methyl methacrylate mediated by monospiro- and dispiropiperidinyl-N-oxyl radicals

Radical polymerization of butyl acrylate (BA) and random copolymerizations of styrene (St) and BA and St and methyl methacrylate (MMA) in the presence of 7-aza-15-hydroxydispiro[5.1.5.3]hexadecane-7-yloxyl (1) and 1-aza-2,2-dimethyl-4-hydroxyspiro[5.6]dodecane-1-yloxyl (2) were carried out. Radical polymerization of BA at 120 °C in the presence of 1 gave poly(BA) with M_n=20200 and M_w/M_n=1.30 at 23% conversion. The termination of polymerization observed around ∼20% conversion was solved to a certain extent by an addition of small amounts of dicumyl peroxide, and poly(BA) with M_n=37400 and M_w/M_n=1.33 was obtained in 46% yield. Random copolymerizations of St and BA and St and MMA in the presence of 1 and 2 at 80 °C gave the corresponding random copolymers with narrow polydispersities of 1.12-1.38 at the molar fraction above 0.30 of St in feed. The kinetic study for the NO-C bond homolysis of the corresponding alkoxyamines prepared from 1 and 2 were carried out, and evaluation of the preexponential factors (A_act) and the activation parameters (E_act) showed that the steric factors of the nitroxides are reflected mainly on E_act.     

基于GLR算法的维吾尔语句法分析研究

采用GLR算法对维吾尔语句子进行句法分析,并且与平行LR算法进行比较,比较它们的分析过程,针对于维吾尔语在单词集上进行句法分析。分析结果采用最优规则,选取最优的句法分析树,对下一步的句法分析研究提供很大的帮助。     

试谈Tcl的闭包

为在Tcl编程语言中加入闭包（匿名函数）,先对Tcl和闭包进行必要的了解,结合两者的特点,提出了针对Tcl的闭包解决方案,通过在闭包代码段前增加上下文环境信息以及组成,以列表参数和代码段为基础的闭包结构,成功地模拟出了闭包这一编程特性,给简化Tcl代码和优化Tcl代码结构提供了一个简单而高效的工具。     

Learning to learn: From smart machines to intelligent machines

Since its birth, more than five decades ago, one of the biggest challenges of artificial intelligence remained the building of intelligent machines. Despite amazing advancements, we are still far from having machines that reach human intelligence level. The current paper tries to offer a possible explanation of this situation. For this purpose, we make a review of different learning strategies and context types that are involved in the learning process. We also present the results of a study on cognitive development applied to the problem of face recognition for social robotics.     

Inductive transfer with context-sensitive neural networks

Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models.     

A JESS-enabled context elicitation system for providing context-aware Web services

Providing context-aware Web services is an adaptive process of delivering contextually matched Web services to meet service requesters’ needs. We define the term “context” from two perspectives: one from service requesters; and the other from Web services. From the former perspective, context is defined as the surrounding environment affecting requesters’ services discovery and access, such as requesters’ preferences, locations, activities, and accessible network and devices. From the latter perspective, context is defined as the surrounding environment affecting Web services delivery and execution, such as networks and protocols for service binding, devices and platforms for service execution, and so on. This paper presents a Java Expert System Shell (JESS)-enabled context elicitation system featuring an ontology-based context model that formally describes and acquires contextual information pertaining to service requesters and Web services. Based on the context elicitation system, we present a context-aware services-oriented architecture for providing context-aware Web service request, publication, and discovery. Implementation details of the context elicitation system and the evaluation results of context-aware service provision are also reported.     

海量层次信息的Focus+Context交互式可视化技术

综述了海量层次信息可视化与Focus Context技术的相关工作,针对海量层次信息可视化的交互问题,在嵌套圆可视化技术的基础上提出了基于上下文感知的Focus Context交互式可视化技术.首先,基于外切圆排列方法提出对圆心进行三角网格剖分的方法,为变形计算建立上下文;然后,针对变形计算前后上下文一致性问题,在三角网格邻居跟踪方法的基础上,提出了用于同层兄弟节点上下文感知的外切圆变形排列方法,以及用于父子节点上下文感知的嵌套圆迭代排列方法.实验结果表明。上述方法在实现焦点突出的鱼眼视图的同时,能够有效地解决Focus Context交互式可视化的上下文感知问题.上述方法应用于文件系统海量层次信息的交互式可视化问题,提供了交互式可视化工具.     

The Use of Context-Aware Policies and Ontologies to Facilitate Business-Aware Network Management

The purpose of autonomic networking is to manage the business and technical complexity of networked components and systems. However, existing network management data has no link to business concepts. This makes it very difficult to ensure that services offered by the network are meeting business objectives. This paper describes a novel context-aware policy model that uses a combination of modeled and ontological data to determine the current context, which policies are applicable to that context, and what services and resources should be offered to which users and applications.

Operator context scanning to support high segmentation rates for real time license plate recognition

Introducing high definition videos and images in object recognition has provided new possibilities in the field of intelligent image processing and pattern recognition. However, due to the large amount of information that needs to be processed, the computational costs are high, making the HD systems slow. To this end, a novel algorithm applied to sliding window analysis, namely Operator Context Scanning (OCS), is proposed and tested on the license plate detection module of a License Plate Recognition (LPR) system. In the LPR system, the OCS algorithm is applied on the Sliding Concentric Windows pixel operator and has been found to improve the LPR system’s performance in terms of speed by rapidly scanning input images focusing only on regions of interest, while at the same time it does not reduce the system effectiveness. Additionally, a novel characteristic is presented, namely, the context of the image based on a sliding windows operator. This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. The algorithm is tested on a data set that includes images of various resolutions, acquired under a variety of environmental conditions.