The 3DP: a processor architecture for three-dimensionalapplications

The 3DP (3-Dimensional Processor), a parallel-computing architecture that targets problems that have a 3-D numerical structure and require numerous calculations on 3-D vectors, is described. The 3DP architecture differs from traditional scalar architectures in that it operates directly on vectors. It differs from general parallel architectures in that it can solve problems that predict the behavior of highly coupled systems, and it differs from vector architectures in that it runs efficiently on length-3 vectors. Object-oriented programming on the 3DP and programming the 3DP in C++ are discussed. 3DP performance is reviewed, and the current implementation of the 3DP architecture, as an attached processor that plugs directly into Sun host VMEbus, is described     

The Epigenesis of Meaning in Human Beings,and Possibly in Robots

Minds and Machines - This article addresses a classical question: Can a machine use language meaningfully and if so, how can this be achieved? The first part of the paper is mainly philosophical....     

Implementing a dynamic processor allocation policy for multiprogrammed parallel applications in the SolarisTM

Parallel applications typically do not perform well in a multiprogrammed environment that uses time‐sharing to allocate processor resources to the applications' parallel threads. Co‐scheduling related parallel threads, or statically partitioning the system, often can reduce the applications' execution times, but at the expense of reducing the overall system utilization. To address this problem, there has been increasing interest in dynamically allocating processors to applications based on their resource demands and the dynamically varying system load. The Loop‐Level Process Control (LLPC) policy (Yue K, Lilja D. Efficient execution of parallel applications in multiprogrammed multiprocessor systems. 10th International Parallel Processing Symposium, 1996; 448–456) dynamically adjusts the number of threads an application is allowed to execute based on the application's available parallelism and the overall system load. This study demonstrates the feasibility of incorporating the LLPC strategy into an existing commercial operating system and parallelizing compiler and provides further evidence of the performance improvement that is possible using this dynamic allocation strategy. In this implementation, applications are automatically parallelized and enhanced with the appropriate LLPC hooks so that each application interacts with the modified version of the Solaris operating system. The parallelism of the applications are then dynamically adjusted automatically when they are executed in a multiprogrammed environment so that all applications obtain a fair share of the total processing resources. Copyright © 2001 John Wiley & Sons, Ltd.     

Abductive Theorem Proving for Analyzing Student Explanations to Guide Feedback in Intelligent Tutoring Systems

The Why2-Atlas tutoring system presents students with qualitative physics questions and encourages them to explain their answers through natural language. Although there are inexpensive techniques for analyzing explanations, we claim that better understanding is necessary for use within tutoring systems. In this paper we motivate and describe how the system creates and uses a deeper proof-based representation of student essays in order to provide students with substantive feedback on their explanations. We describe in detail the abductive reasoner, Tacitus-lite+, that we use within the tutoring system. We also discuss evaluation results for an early version of the Why2-Atlas system and a subsequent evaluation of the theorem-proving module. We conclude with the discussion of work in progress and additional future work for deriving more benefits from a proof-based approach for tutoring applications.     

Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking

Application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchange has recently gained research attention. The basic aim is to guide design and presentation of products to optimize them to be as compatible as possible with consumer preferences. This paper investigates physiological decision processes while participants undertook a choice task designed to elicit preferences for a product. The task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping). The two main research objectives were (1) to observe and evaluate the cortical activity of the different brain regions and the interdependencies among the Electroencephalogram (EEG) signals from these regions; and (2) unlike most research in this area that has focused mainly on liking/disliking certain products, we provide a way to quantify the importance of different cracker features that contribute to the product design based on mutual information. We used the commercial Emotiv EPOC wireless EEG headset with 14 channels to collect EEG signals from participants. We also used a Tobii-Studio eye tracker system to relate the EEG data to the specific choice options (crackers). Subjects were shown 57 choice sets; each choice set described three choice options (crackers). The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0–4 Hz), Theta (3–7 Hz), Alpha (8–12 Hz), Beta (13–30 Hz), and Gamma (30–40 Hz). There was a clear phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the chosen task for the 18 participants. Results also indicated that there was a clear and significant change (p < 0.01) in the EEG power spectral activities taking a place mainly in the frontal (delta, alpha and beta across F3, F4, FC5 and FC6), temporal (alpha, beta, gamma across T7), and occipital (theta, alpha, and beta across O1) regions when participants indicated their preferences for their preferred crackers. Additionally, our mutual information analysis indicated that the various cracker flavors and toppings of the crackers were more important factors affecting the buying decision than the shapes of the crackers.     

A survey on enhanced subspace clustering

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-dimensional datasets, and has been successfully applied in many domains. In recent years, a new breed of subspace clustering algorithms, which we denote as enhanced subspace clustering algorithms, have been proposed to (1) handle the increasing abundance and complexity of data and to (2) improve the clustering results. In this survey, we present these enhanced approaches to subspace clustering by discussing the problems they are solving, their cluster definitions and algorithms. Besides enhanced subspace clustering, we also present the basic subspace clustering and the related works in high-dimensional clustering.     

Oscillatory Metastability in Optical Network Synchronizer Circuits

Synchronization of signal packets is required in networks in order to route the packets. Due to changes in the interarrival times of the packets the inputs to the routing circuits may exhibit oscillatory metastability. Oscillatory metastability can be eliminated in optical circuits by the addition of logic signal stretch. This can be done because the propagation time of optics can be precisely controlled. An algorithm for inserting logic signal stretch to eliminate oscillatory metastability is presented herein.     

Model Selection Using Information Criteria and Genetic Algorithms

Automated model searches using information criteria are used for the estimation of linear single equation models. Genetic algorithms are described and used for this purpose. These algorithms are shown to be a practical method for model selection when the number of sub-models are very large. Several examples are presented including tests for bivariate Granger causality and seasonal unit roots. Automated selection of an autoregressive distributed lag model for the consumption function in the US is also undertaken.JEL classifications: C32, C69     

Computer-based extraction of the inferior alveolar nerve canal in 3-D space

Computed tomography (CT) is increasingly employed in orthodontic treatment. One of the most successful applications is in dental implantology, in which an artificial root is surgically inserted into the jawbone to provide anchorage for a dental prosthesis. For successful implant surgery, it is crucial to locate internal structures such as the inferior alveolar nerve canal (IAC). This paper presents a computerized technique for extracting the IAC. To facilitate the extraction, we first generate panoramic CT images (panoramics) by reformatting the original CT images. The panoramics are a series of cross-sectional images along curved planes through the mandible (lower jawbone). Hollow canals are subsequently detected by analyzing the voxel intensities and 3-D gradient orientations in the panoramics. The axis of the IAC is then traced out by a novel 3-D line-tracking technique. The method is effective for extracting the IAC despite the open structure of the surrounding bone.