Plasticity of feature-based selection in triple-conjunction search.

Two experiments examined the disruption of feature-based selection in triple-conjunction search at multiple target transfers. In Experiment 1, after 10 training sessions, a new target possessing previous distractor features was introduced. This produced disruption in RT and fixation number, but no disruption in feature-based selection. Specifically, there was a tendency to fixate objects sharing the target's contrast polarity and shape and this did not change even upon transfer to the new target. In Experiment 2, 30 training sessions were provided with three target transfers. At the first transfer, the results replicated Experiment 1. Subsequent transfers did not produce disruption on any measure. These findings are discussed in terms of strength theory, Guided Search, rule-based approaches to perceptual learning, and the area activation model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Failures of retrieval and comparison constrain change detection in natural scenes.

In a change detection paradigm, a target object in a natural scene either rotated in depth, was replaced by another object token, or remained the same. Change detection performance was reliably higher when a target postcue allowed participants to restrict retrieval and comparison processes to the target object (Experiment 1). Change detection performance remained excellent when the target object was not attended at change (Experiment 2) and when a concurrent verbal working memory load minimized the possibility of verbal encoding. (Experiment 3). Together, these data demonstrate that visual representations accumulate in memory from attended objects as the eyes and attention are oriented within a scene and that change blindness derives, at least in part, from retrieval and comparison failure. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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.     

A sensing strategy for the reverse engineering of machined parts

The reverse engineering of machined parts requires sensing an existing part and producing a design (and perhaps a manufacturing process) for it. We have developed a reverse engineering system that has proven effective with a set of machined parts. This paper describes the system, presents some results, and discusses strategy for a new system.This work was supported by ARPA under ARO grant number DAAH04-93-G-0420, DARPA grant N00014-91-J-4123, NSF grant CDA 9024721, and a University of Utah Research Committee grant. All opinions, findings, conclusions or recommendations expressed in this document are those of the authors and do not necessarily reflect the views of the sponsoring agencies.     

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。     

Nonlinear suboptimal filtration of pseudorandom sequences

The critical importance of studies of alternative methods of representation and processing of signals constructed from pseudorandom sequences of maximal period is demonstrated. For the first time, it is proposed that a multidimensional nonlinear finite-difference representation of an arbitrary pseudorandom sequence be employed. A suboptimal algorithm for nonlinear discrete Kalman filtration of a pseudorandom sequence is obtained. A numerical example of filtration of a sequence formed on the basis of a given recurrence rule is considered and its efficiency is demonstrated.     

An approach to integrating shape and biomedical attributes in vascular models

Computational models have been used widely in tissue engineering research and have proven to be powerful tools for bio-mechanical analysis (i.e., blood flow, growth models, drug delivery, etc). This paper focuses on developing higher-fidelity models for vascular structures and blood vessels that integrate computational shape representations with biomedical properties and features. Previous work in computer-aided vascular modeling comes from two communities. For those in biomedical imaging, the goal of past research has been to develop image understanding techniques for the interpretation of x-ray, magnetic resonance imaging (MRI), or other radiological data. These representations are predominantly discrete shape models that are not tied to physiological properties. The other corpus of existing work comes from those interested in developing physiological models for vascular growth and behavior based on bio-medical attributes. These models usually either have a highly simplified shape representation, or lack one entirely. Further, neither of these representations are suitable for the kind of interactive modeling required by tissue engineering applications.This paper aims to bridge these two approaches and develop a set of mathematical tools and algorithms for feature-based representation and computer-aided modeling of vascular trees for use in computer-aided tissue engineering applications. The paper offers a multi-scale representation based on swept volumes and a feature-based representation that can attribute the geometric representation with information about blood flow, pressure, and other biomedical properties. The paper shows how the resulting representation can be used as part of an overall approach for designing and visualizing vascular scaffolds. As a real-world example, we show how this computational model can be used to develop a tissue scaffold for liver tissue engineering. Such scaffolds may prove useful in a number of biomedical applications, including the growth of replacement tissue grafts and in vitro study of the pharmacological affects of new drugs on tissue cultures.     

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.