Perceptually Grounded Language Learning: Part 2—DETE: A Neural/Procedural Model

In Part 1 of this two-part series, we introduced Katamic memory—a neural network architecture capable of robust sequence learning and recognition. In Part 2, we introduce the Blobs World taskjdomain for language learning and describe the DETE language learning system, which is composed of over 50 Katamic memory modules. DETE currently learns small subsets of English and Spanish via association with perceptual! motor inputs. In addition to Kaiamic memory, DETE employs several other novel features: (1) use of feature planes, to encode visual shapes, spatial relationships and the motions of objects, (2) phase-locking of neural firing, in order to represent focus of atention and to bind objects across multiple feature planes, and (3) a method for encoding temporal relationships, so that DETE can learn utterances involving the immediate past and future. We compare DETE to related models and discuss the implications of this approach for language-learning research.     

动态过程监视的最佳显示方式研究

文中讨论了人机界面的视学工效问题，通过实验评价了用于动态过程监视与控制的三种ＣＲＴ显示方式的工效，并由此得出人类视学系统反应对图形方式更优于对数字方式，图形与数字的复合方式。     

Radiosity with Well Distributed Ray Sets

In this paper we present a new radiosity algorithm, based on the notion of a well distributed ray set (WDRS). A WDRS is a set of rays, connecting mutually visible points and patches, that forms an approximate representation of the radiosity operator and the radiosity distribution. We propose an algorithm that constructs an optimal WDRS for a given accuracy and mesh. The construction is based on discrete importance sampling as in previously proposed stochastic radiosity algorithms, and on quasi Monte Carlo sampling. Quasi Monte Carlo sampling leads to faster convergence rates and the fact that the sampling is deterministic makes it possible to represent the well distributed ray set very efficiently in computer memory. Like previously proposed stochastic radiosity algorithms, the new algorithm is well suited for computing the radiance distribution in very complex diffuse scenes, when it is not feasible to explicitly compute and store form factors as in classical radiosity algorithms. Experiments show that the new algorithm is often more efficient than previously proposed Monte Carlo radiosity algorithms by half an order of magnitude and more.     

Automated Support for Building and Extending Expert Models

Building a knowledge-based system is like developing a scientific theory. Although a knowledge base does not constitute a theory of some natural phenomenon, it does represent a theory of how a class of professionals approaches an application task. As when scientists develop a natural theory, builders of expert systems first must formulate a model of the behavior that they wish to understand and then must corroborate and extend that model with the aid of specific examples. Thus there are two interrelated phases of knowledge-base construction: (1) model building and (2) model extension. Computer-based tools can assist developers with both phases of the knowledge-acquisition process. Workers in the area of knowledge acquisition have developed computer-based tools that emphasize either the building of new models or the extension of existing models. The PROTÉGÉ knowledge-acquisition system addresses these two activities individually and facilitates the construction of expert systems when the same general model can be applied to a variety of application tasks.     

Using formalized temporal message-flow diagrams

Temporal message-flow diagrams (TMFDs), alternatively called sequence charts, interaction diagrams, event traces, or actor diagrams, are illustrations of a system's global message-passing activity over time, and a pictorial aid to understanding the system's behavior. They are widely used for requirements and documentation for network protocols and object-oriented applications. We present a general formalism for TMFDs, describe a suite of tools we have designed that employs this formalism, and present our experiences with these tools. The formalism and tools described serve to support and broaden the use of TMFDs in developing communicating systems.     

公安110报警指挥系统组网设计

１１０是电话网中的匪警特服号，现代信息化社会的发展要求１１０接警响应迅速而准确，本文介绍了公安１１０报警指挥系统的组成原理，给出了该系统与公共市话网和公安专网的组网实现方法，并对工程设计中的系统可靠性问题进行了详细的讨论，该系统也可用于１１９报警等其待服业务。     

视觉技术在YP19装封箱机中的设计与应用

介绍视觉技术在YP19装封箱机的装箱过程中的应用,简述视觉技术系统的基本组成、工作原理及软件设计。     

视错觉及其应用

视觉足人类获取外部信息的最重要方式。但由于人类自身的生理、心理因素以及外界光、形、色等因素的干扰,会产生视觉误差,即视错觉。本章系统的分析了视错觉产生原因,按不同的现象和成因把视错觉分成病理视错觉、动态视错觉、几何视错觉、颜色视错觉、轮廓错觉等5类。在此基础上介绍了视错觉的应用,特别介绍了其在图像融合中的应用,并用matlab实现了图像的融合。     

基于多模态推理图神经网络的场景文本视觉问答模型

文本阅读能力差和视觉推理能力不足是现有视觉问答(visual question answering, VQA)模型效果不好的主要原因,针对以上问题,设计了一个基于图神经网络的多模态推理(multi-modal reasoning graph neural network, MRGNN)模型。利用图像中多种形式的信息帮助理解场景文本内容,将场景文本图片分别预处理成视觉对象图和文本图的形式,并且在问题自注意力模块下过滤多余的信息;使用加入注意力的聚合器完善子图之间相互的节点特征,从而融合不同模态之间的信息,更新后的节点利用不同模态的上下文信息为答疑模块提供了更好的功能。在ST-VQA和TextVQA数据集上验证了有效性,实验结果表明,相比较此任务的一些其他模型,MRGNN模型在此任务上有明显的提升。