James M. Goodwin

Cultural Heritage Preservation Using Constructive Shape Modeling

Issues of digital preservation of shapes and internal structures of historical cultural objects are discussed. An overview of existing approaches to digital preservation related to shape modeling is presented and corresponding problems are considered. We propose a new digital preservation paradigm based on both constructive modeling reflecting the logical structure of the objects and open standards and procedures. Constructive Solid Geometry (CSG) and Function Representation (FRep) are examined and practically applied as mathematical representations producing compressed yet precise data structures, thus providing inter‐operability between current and future computer platforms crucial to archiving. Examples of CSG reconstruction of historical temples and FRep modeling of traditional lacquer ware are given. We examine the application of fitting of a parameterized FRep model to a cloud of data points as a step towards automation of the modeling process. Virtual venues for public access to cultural heritage objects including real time interactive simulation of cultural heritage sites over the Web are discussed and illustrated.

Process control with adaptive range coding

Dynamical control with adaptive range coding eliminates fundamental shortcomings found in earlier applications of range (course) coding which used fixed partitioning. Adaptive range coding has the advantages of efficient implementation, execution and generalization. With the adaptive algorithm, region shapes are continually adjusted during operation and will self-organize to reflect the global dynamics of the system and the environment. The system progressively develops a control map relating environmental states, control actions, and future reinforcements.

Machine operant conditioning

This research investigates learning of machine reflexes by applying punishment and reward reinforcement to teach artificial neuronlike systems a prescribed behavior. Stochastic neuronlike elements based on the classical weighted sum of inputs and threshold model can learn stimulus-response associations by emulated classical Pavlovian conditioning, i.e. make associations between conditioned and unconditioned stimuli and later responses. Several mathematical models have been developed which apply abstractions of classical conditioning to such threshold logic devices. Temporal sequences of stimulus-response associations can be dynamically learned by using operant conditioning when only aggregate external reinforcement is available.<<ETX>>

IMAGE RECOGNITION AND RECONSTRUCTION USING ASSOCIATIVE MAGNETIC PROCESSING

This paper presents a technique for image recognition, reconstruction, and processing using a novel massively parallel system. This device is a physical implementation of a Boltzmann machine type of neural network based on the use of magnetic thin films and opto-magnetic control. Images or patterns in the form of pixel arrays are imposed on the magnetic film using a laser in an external magnetic field. These images are learned and can be recalled later when a similar image is presented. A stored image is recallable even when a partial, noisy, or corrupted version of that image is imposed on the film. The system can also be used for feature detection and image compression. The operation and construction of the physical system is described, together with a discussion of the physical basis for its operation. The authors have developed Monte Carlo style computer simulations of the system for a variety of platforms, including serial workstations and hypercube configured parallel systems. They describe here some of the factors involved in computer simulations of the system, which can be fast and relatively simple in implementation. Simulation results are presented and, in particular, the behavior of the model under simulated annealing in the light of statistical physics is discussed. The simulation itself can be used as a neural network model capable of the functions ascribed to the physical device.

The Meaning of Life — Real and/or Artificial

The boundaries between life and inanimate but complex systems are not obvious, even though people commonly think that anyone can instantly distinguish living from non-living systems. This paper attempts to identify those features which “define” life, but presents a number of examples of ambiguous lifelike systems, emphasizing presence or absence of these critical features. Thus we try to understand the meaning of life “as it could be” not only life as we know it. The paper discussses what the purpose to life, if any, might be. It examines the “artificial” nature of artificial life. Noting that it is not always evident how to separate “real” (“natural”) life from “artificial” life, we consider whether several systems — both actual and fictional — qualify as life forms, so that we may clarify the issues suggested. New social, political, and legal issues to be considered in light of the probable existence (development?) of artificial life are discussed, referring to examples from current fiction. The paper pays particular attention to computer based life forms, and the use of so-called genetic operators to support synthetic evolution. It then discusses the possible applications of artificial life forms and methods, using goal directed synthetic evolution, to problems for the benefit of humanity. It finally examines the limitations of current methods and the benefits to be obtained by the use of life-derived computer methodology, both in the construction of synthetic worlds and in the real world.

Training hard to learn networks using advanced simulated annealing methods

We describe a method for avoiding local minima by combining Very Fast Simulated Reannealing (VFSR) with BEP. While convergence to the best. training weights can be slower than gradient descent, methods, it is faster than other SA network training methods. More importantly, convergence to the optimal weight set is statistically guaranteed. We demonstrate VFSR network training on learning a set of difficult but. linearly separable logic functions and a set. of nonlinearly separable parity problems, and compare performances of VFSR network training with conjugate gradient trained backpropagation networks. I n t r o d u c t i o n Backpropagation networks [1] are nonlinear methods for mapping a set of multidimensional input patterns x = xl ..... xo, to a corresponding set of dimensional output patterns y = Yl,...,YM. While they have a variety of networks architectures, they are typified by a set of input, hidden, and outpul units taking on different values. The units are arranged in feed forward layers in the network• Units between layers have connections to each other with weights w associated with each connection. A typical architecture is the three layer network that has its input units connected to its hidden units, and its hidden units connected to its output.• Additionally, there may are often direct, connections between the input and output units. Bias unit. may be included to reflect constant input values. Networks operate by first, assigning an input pattern x to the input units. Then, the values of the hidden units h and output unit o values are calculated as follows: h~.(~.) = f(w~,0 + ~ u,~,jzj)

An associative magnetic processor for image reconstruction

A novel, massively parallel system is presented for image memorization, processing, and reconstruction based on the use of magnetic thin films and optomagnetic control. Images in the form of pixel arrays are imposed on the film by locally heating it in the regions of high image intensity, using a laser with an external magnetic field. One of these stored images is recalled when a partial or corrupted version of that image is imposed on the film. The system can also be used for feature detection and image compression.<<ETX>>

The author is dead, long live the author!

John Caughie. Theories of Authorship: A Reader. London and Boston: Routledge & Kegan Paul, 1981. 316 pp.

Eisenstein: Ideology and intellectual cinema

28.00, cloth;

Dancing Buddhas: new graphical tools for digital cultural heritage preservation

14.00, paper. Andrew Horton and Joan Magretta, eds. Modern European Filmmakers and the Art of Adaptation. New York: Frederick Ungar Publishing, 1981. 383 pp.