Leraning Concrete Stragegies Through Interaction

We discuss learning and the adaptive generation of concrete strategies through interactive experience. The domain is the game Tictactoe. The knowledge structures embodying strategies we represent as having tree parts: a Goal, a sequence of Actions, and a set of Constraints on those actions (GAC). We simulate such structures in a program that plays Tictactoe against different kinds of opponents. Applying these strategies leads to moves that often result in winning or losing; which in turn leads to the creation of new structures, by modifying the current GACs. These modifications are controlled by a small set of specific rules, so that the GACs are related by the ways modifications can map from one to another. Subject to certain limitations, we do a complete exploration of certain classes of strategy. This learnability analysis takes guidance from previous cognitive studies of a human subject by Lawler. The simulations were performed on a Symbolics 3600 in LISP. This work avoids abstractions in order to explore learnin

Measurement of Stress Profiles by Phase Contrast Techniques

An acoustic wave pissing through a material has its velocity changed when stress is applied. This is due to changes in the third order elastic constant and the density of the material. By using a small diameter beam or a focused beam Incident on a metal and reflected from both its front and back surfaces, It is possible to measure the difference in phase of the two reflected waves; the beam Itself can be scanned over the surface of the material. Three kinds of measurements will be shown. The first relates the change of velocity of a compressional wave to the applied stress taken \u27in an MTS testing system. The second shows a scan of the profile of the velocity change around a circular defect. The third is an image of the stressed region around a circular defect obtained with a scanned electronically focused system operating in a phase contrast mode

Learning to represent visual input

One of the central problems in computational neuroscience is to understand how the object-recognition pathway of the cortex learns a deep hierarchy of nonlinear feature detectors. Recent progress in machine learning shows that it is possible to learn deep hierarchies without requiring any labelled data. The feature detectors are learned one layer at a time and the goal of the learning procedure is to form a good generative model of images, not to predict the class of each image. The learning procedure only requires the pairwise correlations between the activations of neuron-like processing units in adjacent layers. The original version of the learning procedure is derived from a quadratic ‘energy’ function but it can be extended to allow third-order, multiplicative interactions in which neurons gate the pairwise interactions between other neurons. A technique for factoring the third-order interactions leads to a learning module that again has a simple learning rule based on pairwise correlations. This module looks remarkably like modules that have been proposed by both biologists trying to explain the responses of neurons and engineers trying to create systems that can recognize objects

SALL4 controls cell fate in response to DNA base composition

Contains fulltext : 232790.pdf (Publisher’s version ) (Open Access

Magnetic-field dependence of the critical currents in a periodic coplanar array of narrow superconducting strip

We calculate the magnetic-field dependence of the critical current due to both geometrical edge barriers and bulk pinning in a periodic coplanar array of narrow superconducting strips. We find that in zero or low applied magnetic fields the critical current can be considerably enhanced by the edge barriers, but in modest applied magnetic fields the critical current reduces to that due to bulk pinning alone.Comment: 23 pages, 7 figure

Visual masking: past accomplishments, present status, future developments

Visual masking, throughout its history, has been used as an investigative tool in exploring the temporal dynamics of visual perception, beginning with retinal processes and ending in cortical processes concerned with the conscious registration of stimuli. However, visual masking also has been a phenomenon deemed worthy of study in its own right. Most of the recent uses of visual masking have focused on the study of central processes, particularly those involved in feature, object and scene representations, in attentional control mechanisms, and in phenomenal awareness. In recent years our understanding of the phenomenon and cortical mechanisms of visual masking also has benefited from several brain imaging techniques and from a number of sophisticated and neurophysiologically plausible neural network models. Key issues and problems are discussed with the aim of guiding future empirical and theoretical research

A large-strain radial consolidation theory for soft clays improved by vertical drains

A system of vertical drains with combined vacuum and surcharge preloading is an effective solution for promoting radial flow, accelerating consolidation. However, when a mixture of soil and water is deposited at a low initial density, a significant amount of deformation or surface settlement occurs. Therefore, it is necessary to introduce large-strain theory, which has been widely used to manage dredged disposal sites in one-dimensional theory, into radial consolidation theory. A governing equation based on Gibson's large-strain theory and Barron's free-strain theory incorporating the radial and vertical flows, the weight of the soil, variable hydraulic conductivity and compressibility during the consolidation process is therefore presented