Quantum control and information processing

We review the recent developments in quantum control and its contribution to quantum information processing.     

Measurement of information processing load and visual load on a dynamic information processing task

A dynamic visual information processing task was designed to investigate time-based and intensity-based factors on an operator's information processing load as measured by reaction time, pupil diameter, and eye movement parameters. The time-based factor was manipulated by the target rate and scanning rate while the intensity-based factor was manipulated by the difference between a simple reaction task and a physical matching (choice reaction) task. Nine participants tracked the scanning line at two different scanning rates and were required to respond to two designated targets presented singly at two different temporal frequencies. The results indicated that task difficulty (the intensity-based factor) had a significant effect on the reaction time. Target rate and scanning rate were integrated as one time-based factor in terms of three sweeping angles. The time-based factor was found to have a significant effect on the fixation time, saccade amplitude, fixation frequency, eye movement speed, reaction time and hit rate. No interaction effect was found between time-based and intensity-based factors. The time pressure (defined by the time required divided by the time available) based on a model human processor was positively related to scanning rate, target rate and task difficulty. It was found to be the most objective and reliable if time required can be reliably predicted based on a predictive model approach.     

Metabolically efficient information processing

Energy-efficient information transmission may be relevant to biological sensory signal processing as well as to low-power electronic devices. We explore its consequences in two different regimes. In an "immediate" regime, we argue that the information rate should be maximized subject to a power constraint, and in an "exploratory" regime, the transmission rate per power cost should be maximized. In the absence of noise, discrete inputs are optimally encoded into Boltzmann distributed output symbols. In the exploratory regime, the partition function of this distribution is numerically equal to 1. The structure of the optimal code is strongly affected by noise in the transmission channel. The Arimoto-Blahut algorithm, generalized for cost constraints, can be used to derive and interpret the distribution of symbols for optimal energy-efficient coding in the presence of noise. We outline the possibilities and problems in extending our results to information coding and transmission in neurobiological systems.     

Notationality and the information processing mind

Cognitive science uses the notion of computational information processing to explain cognitive information processing. Some philosophers have argued that anything can be described as doing computational information processing; if so, it is a vacuous notion for explanatory purposes.An attempt is made to explicate the notions of cognitive information processing and computational information processing and to specify the relationship between them. It is demonstrated that the resulting notion of computational information processing can only be realized in a restrictive class of dynamical systems called physical notational systems (after Goodman's theory of notationality), and that the systems generally appealed to by cognitive science-physical symbol systems-are indeed such systems. Furthermore, it turns out that other alternative conceptions of computational information processing, Fodor's (1975) Language of Thought and Cummins' (1989) Interpretational Semantics appeal to substantially the same restrictive class of systems.The necessary connection of computational information processing with notationality saves the enterprise from charges of vacuousness and has some interesting implications for connectionism. But, unfortunately, it distorts the subject matter and entails some troubling consequences for a cognitive science which tries to make notationality do the work of genuine mental representations.     

TMS for multimodal information processing

Many working processes are complex and composed by heterogeneous atomic tasks, e.g. editing, assembling data from different sources (as databases or laboratory's devices) with texts, images or learning objects, or submitting them to software components to retrieve information, to render them, re-format, submit to computations, and other types of information processing. All these processes heavily require procedural knowledge which is tacit as owned by experts of the working activity; they are complex and are extremely difficult to be modeled and automatized without having a flexible, multimodular evolutionary system in place. Support to information from different modalities increases the performance of a computer system originally designed for a task with a unimodular nature. In this paper, we discuss the idea of task management system (TMS) as a component-based system which offers a virtual workbench to search, acquire, describe and assemble computational agents performing single autonomous tasks into working processes. We sustain that TMS is a cutting edge platform to develop software solutions for problems related to workflow automatization and design. The architecture we propose follows the conceptual track of the TMS to allow composition and arrangement of atomic modules into a complex system. A configuration of the workflow can be implemented and extended with a set of task/components, chunks of activities which are considered basic elements of the workflow. By interacting with the TMS in editing mode, the workflow designer selects the relevant chunks from system repositories, drags them into a working system area and assembles them into a working process. As the main actor of the system, the workflow designer is provided with an environment resembling an artisan’s workshop, to let her/him select the relevant chunks from system repositories, drags them into a working area and assembles them into a working TMS instance, which represents the working process. Global interaction modality of the TMS instance is moulded and specialized on the base of the specific modalities of the task/components which have been retrieved from the system repositories and each time negotiated. Complex activities could be formally described, implemented and applied with a consequent advantage for personnel re-organization toward more conceptual activities.     

Cortical architecture and self-referential control for brain-like processing in artificial neural systems

Progress in understanding the way the brain processes information while it is constantly interacting with the sensory environment is hampered by inadequate models and theories. Current models and theories of brain computing are, obviously, still not completely correct when confronted with so-called real-world problems. Sensory recognition and the subsequent selection and optimization of a proper behavior are basically constraint satisfaction problems. Both conventional AI and current formal neural network systems operate with set constraints: the architecture and parameters are defined a priori and then the input data are structured according to these set constraints on the learning process. However, as long as the constraints are set from outside the system (by the programmer, designer), the system has no ability for self-organization. There is the ability for adaptation within these a priori defined limits, but not the ability to include new knowledge into the consistent relational framework of existing knowledge beyond the prespecified constraints. Therefore, self-organization of constraints in complex systems is the key problem for getting self-organization of knowledge representation under real-world conditions. We show that a value system and self-referential control in a modular architecture are crucial prerequisites for both robust recognition of sensory input and the ability to integrate new knowledge into the already acquired knowledge representation. Finally, we outline a philosophy and propose a model approach that is a first step toward implementing those capabilities in artificial neural systems.     

Parallel systems in financial information processing

This paper reports on an ongoing investigation into the applicability of parallel systems in the processing of financial information. For the past five years a wide spectrum of issues has been investigated; this paper attempts to draw together this work as a coherent whole, and discusses past, present and planned activity. A number of case studies applying parallel systems to typical financial applications are described and, where appropriate, results presented. The commonality between the case studies is also discussed.     

Knowledge information processing language: ShapeUp

A new logic programming language, ShapeUp, is developed. ShapeUp is an expanded Prolog system with string matching facilities. The language has been developed to give programmers a new computer programming environment, especially for knowledge information processing. This area includes natural language comprehension and intelligent text processing systems with better man-machine interfaces. For this kind of application, character string data play a principal part rather than conventional numerical data. In ShapeUp, string patterns are introduced as Prolog ‘terms’. Their matching process is performed inside the unification. Thus, a program is far simpler and easier to write and read in ShapeUp, than in conventional Prolog systems, and program size is extremely reduced.     

Time-and-frequency approach to navigation information processing

The relation between the time-varying optimal algorithms of Kalman filtering and the time-invariant algorithms obtained within the framework of the frequency approach using the approximate method of local approximation of spectral densities was revealed. Introduced was the notion of time-and-frequency approach lying in combined use of the Kalman and frequency approaches, including the method of local approximation. Consideration was given to the examples of processing the navigation information, and the practical importance of the results obtained was discussed.     

Neural information processing with feedback modulations

Descending feedback connections, together with ascending feedforward ones, are the indispensable parts of the sensory pathways in the central nervous system. This study investigates the potential roles of feedback interactions in neural information processing. We consider a two-layer continuous attractor neural network (CANN), in which neurons in the first layer receive feedback inputs from those in the second one. By utilizing the intrinsic property of a CANN, we use a projection method to reduce the dimensionality of the network dynamics significantly. The simplified dynamics allows us to elucidate the effects of feedback modulation analytically. We find that positive feedback enhances the stability of the network state, leading to an improved population decoding performance, whereas negative feedback increases the mobility of the network state, inducing spontaneously moving bumps. For strong, negative feedback interaction, the network response to a moving stimulus can lead the actual stimulus position, achieving an anticipative behavior. The biological implications of these findings are discussed. The simulation results agree well with our theoretical analysis.