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Schizophrenia is marked by a variety of cognitive and biological abnormalities. In the first part of this paper we describe schizophrenic cognitive deficits in three experimental tasks which tap attention and language processing abilities. We also review biological disturbances that have been reported involving the frontal lobes and the mesocortical dopamine system. In the second part of the paper we present three computer models, each of which simulates normal performance in one of the cognitive tasks described initially. These models were developed within the connectionist (or parallel distributed processing) framework. At the behavioral level, the models suggest that a disturbance in the processing of context can account for schizophrenic patterns of performance in both the attention and language-related tasks. At the same time, the models incorporate features of biological computation that address the biological processes underlying cognitive deficits. All three models incorporate a mechanism for processing context that can be identified with frontal lobe function, and a parameter that corresponds to the effects of dopamine on frontal cortex. A disturbance in this parameter is sufficient to account for schizophrenic patterns of performance in all three of the cognitive tasks simulated. Thus, the models offer an explanatory mechanism linking performance deficits to a disturbance in the processing of context which, in turn, is attributed to a reduction of dopaminergic activity in prefrontal cortex. In the General Discussion, we consider the implications of these models for our understanding of both normal and schizophrenic cognition. We conclude with a discussion of some of the general issues surrounding the modelling endeavor itself.

Talk by Marcin Cieslik, University of Virginia. Presented at the Python for Scientific Computing Conference (Scipy 2009) held August 18-23, 2009 at CalTech in Pasadena CA. Information about the conference is at: http://conference.scipy.org

Talk by Marcin Cieslik, University of Virginia. Presented at the Python for Scientific Computing Conference (Scipy 2009) held August 18-23, 2009 at CalTech in Pasadena CA. Information about the conference is at: http://conference.scipy.org

Typical desktop workstations can be a severe bottleneck in the viewing and enhancement of imagery data. Due to the nature of many image processing algorithms, an effective method for alleviating this problem is through parallelism. Parallel hardware can come in many forms, from small clusters of workstations and workstations with many processors to dedicated hardware containing 10's, 100's and 1000's of processing nodes. One of the challenges is developing a portable parallel image processing library in such a potentially diverse environment. These issues led to the development of a Parallel Image Processing Toolkit (PIPT). The toolkit hides the detail of parallelization from the users of the PIPT and provides a uniform programming interface. In developing the toolkit the issues of advanced data handling, load, balancing and parallel visualization were addressed. In addition a specific computationally expensive High Resolution Video Stills algorithm was implemented within the PIPT. The important contributions that resulted from this work transcend the individual tasks that were undertaken. First, the general approach that was taken to implement the PIPT illustrates several important principles for the design and implementation of general purpose parallel libraries. in this regard, the PIPT design can serve as a design pattern for an extensible parallel library. Second, the design pattern of the current implementation of the PIPT contains some notable attempts at programming image processing tasks in a generic fashion.

The demand for very high speed data processing coupled with falling hardware costs has made large-scale parallel and distributed computer systems both desirable and feasible. Two modes of parallel processing are single instruction stream-multiple data stream (SIMD) and multiple instruction stream - multiple data stream (MIMD). PASM, a partitionable SIMD/MIMD system, is a reconfigurable multimicroprocessor system being designed for image processing and pattern recognition. An important component of these systems is the interconnection network, the mechanism for communication among the computation nodes and memories. Assuring high reliability for such complex systems is a significant task. Thus, a crucial practical aspect of an interconnection network is fault tolerance. In answer to this need, the Extra Stage Cube (ESC), a fault-tolerant, multistage cube-type interconnection network, is defined. The fault tolerance of the ESC is explored for both single and multiple faults, routing tags are defined, and consideration is given to permuting data and partitioning the ESC in the presence of faults. The ESC is compared with other fault-tolerant multistage networks. Finally, reliability of the ESC and an enhanced version of it are investigated. Keywords: Theses.

The demand for very high speed data processing coupled with falling hardware costs has made large-scale parallel and distributed computer systems both desirable and feasible. Two modes of parallel processing are single instruction stream-multiple data stream (SIMD) and multiple instruction stream - multiple data stream (MIMD). PASM, a partitionable SIMD/MIMD system, is a reconfigurable multimicroprocessor system being designed for image processing and pattern recognition. An important component of these systems is the interconnection network, the mechanism for communication among the computation nodes and memories. Assuring high reliability for such complex systems is a significant task. Thus, a crucial practical aspect of an interconnection network is fault tolerance. In answer to this need, the Extra Stage Cube (ESC), a fault-tolerant, multistage cube-type interconnection network, is defined. The fault tolerance of the ESC is explored for both single and multiple faults, routing tags are defined, and consideration is given to permuting data and partitioning the ESC in the presence of faults. The ESC is compared with other fault-tolerant multistage networks. Finally, reliability of the ESC and an enhanced version of it are investigated. Keywords: Theses.

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

This paper provides a brief overview of the connectionist or parallel distributed processing framework for modeling cognitive processes, and considers the application of the connectionist framework to problems of cognitive development. Several aspects of cognitive development might result from the process of learning as it occurs in multi-layer networks. This learning process has the characteristic that it reduces the discrepancy between expected and observed events. As it does this, representations develop on hidden units which dramatically change both the way in which the network represents the environment from which it learns and the expectations that the network generates about environmental events. The learning process exhibits relatively abrupt transitions corresponding to stage shifts in cognitive development. These points are illustrated using a network that learns to anticipate which side of a balance beam will go down, based on the number of weights on each side of the fulcrum and their distance from the fulcrum on each side of the beam. The network is trained in an environment in which weight more frequently governs which side will go down. It recapitulates the states of development seen in children, as well as the stage transitions, as it learns to represent weight and distance information. Keywords: Parallel processing; Data processing.

This paper provides a brief overview of the connectionist or parallel distributed processing framework for modeling cognitive processes, and considers the application of the connectionist framework to problems of cognitive development. Several aspects of cognitive development might result from the process of learning as it occurs in multi-layer networks. This learning process has the characteristic that it reduces the discrepancy between expected and observed events. As it does this, representations develop on hidden units which dramatically change both the way in which the network represents the environment from which it learns and the expectations that the network generates about environmental events. The learning process exhibits relatively abrupt transitions corresponding to stage shifts in cognitive development. These points are illustrated using a network that learns to anticipate which side of a balance beam will go down, based on the number of weights on each side of the fulcrum and their distance from the fulcrum on each side of the beam. The network is trained in an environment in which weight more frequently governs which side will go down. It recapitulates the states of development seen in children, as well as the stage transitions, as it learns to represent weight and distance information. Keywords: Parallel processing; Data processing.

This paper provides a brief overview of the connectionist or parallel distributed processing framework for modeling cognitive processes, and considers the application of the connectionist framework to problems of cognitive development. Several aspects of cognitive development might result from the process of learning as it occurs in multi-layer networks. This learning process has the characteristic that it reduces the discrepancy between expected and observed events. As it does this, representations develop on hidden units which dramatically change both the way in which the network represents the environment from which it learns and the expectations that the network generates about environmental events. The learning process exhibits relatively abrupt transitions corresponding to stage shifts in cognitive development. These points are illustrated using a network that learns to anticipate which side of a balance beam will go down, based on the number of weights on each side of the fulcrum and their distance from the fulcrum on each side of the beam. The network is trained in an environment in which weight more frequently governs which side will go down. It recapitulates the states of development seen in children, as well as the stage transitions, as it learns to represent weight and distance information. Keywords: Parallel processing; Data processing.

This paper provides a brief overview of the connectionist or parallel distributed processing framework for modeling cognitive processes, and considers the application of the connectionist framework to problems of cognitive development. Several aspects of cognitive development might result from the process of learning as it occurs in multi-layer networks. This learning process has the characteristic that it reduces the discrepancy between expected and observed events. As it does this, representations develop on hidden units which dramatically change both the way in which the network represents the environment from which it learns and the expectations that the network generates about environmental events. The learning process exhibits relatively abrupt transitions corresponding to stage shifts in cognitive development. These points are illustrated using a network that learns to anticipate which side of a balance beam will go down, based on the number of weights on each side of the fulcrum and their distance from the fulcrum on each side of the beam. The network is trained in an environment in which weight more frequently governs which side will go down. It recapitulates the states of development seen in children, as well as the stage transitions, as it learns to represent weight and distance information. Keywords: Parallel processing; Data processing.

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Mind and brain provide two quite different perspectives for viewing cognition. Yet both perspectives are informed by the study of parallel distributed processing. This duality creates a certain ambiguity about the interpretation of a particular PDP model of a cognitive process: Is each processing unit to be interpreted as a neuron? Is the model supposed to relate to the neural implementation of the process in some less direct way? A closely related set of questions arises when it is observed that PDP models of cognitive processing divide broadly into two classes. In local models, the activity of a single unit represents the degree of participation in the processing of a known conceptual entity a word, a word sense, a phoneme, a motor program. In distributed models, the strength of patterns of activity over many units determine the degree of participation of these conceptual entities. In some models, these patterns are chosen in a deliberately arbitrary way, so that the activity of a single unit has no apparent meaning whatever -- no discernible relation to the conceptual entities involved in the cognitive process. On the surface, at least, these two types of models seem quite different. Are they as different as they seem? How are they related? This chapter begins with a brief consideration of the neural interpretation of PDP models of cognition. These considerations serve mostly to lay out a certain perspective on the PDP modeling world, to make some distinctions I have found to be valuable, to introduce some terminology and to lead into the main question of this chapter: How are distributed and local PDP models related? The chapter ends with a discussion of how, using the framework of PDP models, we might forge a mathematical relationship between the principles of mind and brain.

Many modern command and control applications are driven by imagery, and imagery, in general, may originate from diverse geographically distributed sources. A rapid increase in image sizes and processing capabilities has resulted from improved hardware for collection and display. Moreover, techniques for parallel processing are keeping pace with military requirements by harnessing powerful parallel processors to facilitate the analysis of images. In addition, tools for distributed processing are under development that can be used to coordinate the processing effort across a network of parallel processing systems. This paper describes a system that can be used to request image analysis over distributed parallel processing facilities, and integrate the results into command and control systems.

Many modern command and control applications are driven by imagery, and imagery, in general, may originate from diverse geographically distributed sources. A rapid increase in image sizes and processing capabilities has resulted from improved hardware for collection and display. Moreover, techniques for parallel processing are keeping pace with military requirements by harnessing powerful parallel processors to facilitate the analysis of images. In addition, tools for distributed processing are under development that can be used to coordinate the processing effort across a network of parallel processing systems. This paper describes a system that can be used to request image analysis over distributed parallel processing facilities, and integrate the results into command and control systems.

Many modern command and control applications are driven by imagery, and imagery, in general, may originate from diverse geographically distributed sources. A rapid increase in image sizes and processing capabilities has resulted from improved hardware for collection and display. Moreover, techniques for parallel processing are keeping pace with military requirements by harnessing powerful parallel processors to facilitate the analysis of images. In addition, tools for distributed processing are under development that can be used to coordinate the processing effort across a network of parallel processing systems. This paper describes a system that can be used to request image analysis over distributed parallel processing facilities, and integrate the results into command and control systems.

Many modern command and control applications are driven by imagery, and imagery, in general, may originate from diverse geographically distributed sources. A rapid increase in image sizes and processing capabilities has resulted from improved hardware for collection and display. Moreover, techniques for parallel processing are keeping pace with military requirements by harnessing powerful parallel processors to facilitate the analysis of images. In addition, tools for distributed processing are under development that can be used to coordinate the processing effort across a network of parallel processing systems. This paper describes a system that can be used to request image analysis over distributed parallel processing facilities, and integrate the results into command and control systems.

Many modern command and control applications are driven by imagery, and imagery, in general, may originate from diverse geographically distributed sources. A rapid increase in image sizes and processing capabilities has resulted from improved hardware for collection and display. Moreover, techniques for parallel processing are keeping pace with military requirements by harnessing powerful parallel processors to facilitate the analysis of images. In addition, tools for distributed processing are under development that can be used to coordinate the processing effort across a network of parallel processing systems. This paper describes a system that can be used to request image analysis over distributed parallel processing facilities, and integrate the results into command and control systems.

As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware.

As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware.

As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware.

As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware.

As the capabilities of computing machinery grow, so does the diverse variety of their applications. The feasibility of many approaches to these applications depends solely upon the existence of computing machinery capable of performing these tasks within a given time constraint. Because the majority of the available computing machinery is general purpose in nature, tasks that do not require purpose facilities, but that do require high throughput, are condemned to execution on expensive general purpose hardware. This research describes several tasks that require fast computing machinery. These tasks do not require general purpose facilities in the sense that the computing machinery used will only perform a fixed set of tasks. Some of the tasks are simple in nature, but are required to execute on very large data sets. Other tasks are computationally intensive in addition to possibly involving large data sets. Both simple and complex algorithms are considered. The discussion includes a description of the tasks. All of the above tasks are useful; however, their value is determined in part by the time required to perform them. This work discusses three architectures for performing remote sensing tasks. These architectures can execute the described tasks more quickly than conventionally available hardware.