The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.Advanced Research Projects Agency (ONR-N00014-92-J-4015, AFOSR 90-0083); British Petroleum (89-A-1204); National Science Foundation (IRI-90-00530, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100, N00014-95-1-0409, N00014-95-1-0657); Air Force Office of Scientific Research (F49620-92-J-0499, F49620-92-J-0334

A What-and-Where Neural Network for Invariant Image Preprocessing

A feedforward neural network for invariant image preprocessing is proposed that represents the position1 orientation and size of an image figure (where it is) in a multiplexed spatial map. This map is used to generate an invariant representation of the figure that is insensitive to position1 orientation, and size for purposes of pattern recognition (what it is). A multiscale array of oriented filters followed by competition between orientations and scales is used to define the Where filter.British Petroleum (89-A-1024); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N0014-91-J-4100); Air Force Office of Scientific Research (90-0175); NSF Graduate Fellowshi

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.Defense Advanced Research Projects Agency (ONR N0014-92-J-401J, AFOSR 90-0083, ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530, IRI-90-24877, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100); British Petroleum (89-A-1204); Air Force Office of Scientific Research (F49620-92-J-0334

Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate

Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.Defense Advanced Projects Research Agency SyNaPSE program (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC #801881-BS under HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (NSF SBE-0354378

Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.Advanced Research Projects Agency (ONR N00014-92-J-401J, ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530, IRI-90-24877, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100); British Petroleum (89-A-1204); Air Force Office of Scientific Research (F49620-92-J-0334

The Effect of Adding Features on Product Attractiveness: the Role of Product Perceived Congruity

This paper investigates the effect of adding more features on product evaluation. We argue that product evaluation as the number of features increases depends on the congruity of the features added with the product. We show that adding features leads to increased product attractiveness if these features are congruent with the product, but not if these features are moderately or extremely incongruent. However, the manipulation of two factors, task involvement and temporal construal, has been shown to make product evaluation increase as more moderately (but not extremely) incongruent features are added to the product

PERMEABILITY TESTING in the TRIAXIAL CELL.

It Has Been Suggested that a Triaxial Shear Test Chamber Can Be Used to Measure the Permeability of Low Permeability Soils. to Verify This, the Influence of a Number of Test Parameters on the Measured Coefficient of Permeability Was Investigated. Results Indicate Such Permeability Tests Should Be Performed on Samples Having a Minimum Diameter of 71. 1 Mm (2. 8 In.) and a Length to Diameter Ratio of 0. 5 to 1. 0. It Was Found that a Permeant Consisting of 1 G of Magnesium Sulfate Heptahydrate (Epsom Salt) Dissolved in 1 L of Deaired, Distilled Water is Adequate for General Permeability Testing. the Triaxial, Falling Head Permeability Test Should Be Conducted at a Gradient that Results in an Applied Effective Stress at the Outflow End of the Sample Less Than the Preconsolidation Stress of the Material. It Was Found that with Very Careful Trimming, the Influence of the Smear Zones Created at the Ends of the Samples during the Trimming Process Can Be Minimized

Modeling the Effects of Genetic Manipulations of Calsequestrin on Local Calcium Release and Depletion in Cardiac Myocytes

Cardiac calsequestrin (CASQ2), a Ca buffer localized to the junctional SR (jSR) of cardiac myocytes, is known to bind to the RyR-triadin-junctin complex, participate in the luminal regulation of RyRs, and modulate Ca spark activity..

The origin of formation of the amphibolite-granulite transition facies

The origin of formation of the amphibolitegranulite transition facies may be from deep burial in the earth's crust or it may come from the tectonic process of an orogenic event related to continental collision zones. Temperatures needed to form this transition facies are on the order of 600°c to 800°c, with the accompanying pressures of 5 to almost 8 kbar. These conditions of temperature and pressure can be met by each above hypothesis for the transition facies. In the deep crustal model, it is known that upon reaching the depths of 10-35 km the temperatures and pressures at this depth are suitable to form this facies. Similarly, in the hypothesis of the orogenic event, these temperature and pressure conditions can be created by a descending slab of continental material into the earth's interior. In order to come to a conclusive opinion on which hypotheses formed the transition facies, a study of the facies involved must be completed. This includes: defining the facies involved by their mineral compositions, evaluating the temperature and pressure conditions which affect the formation of the facies involved, and taking into account the content and composition of H20 and C02 activities in fluid inclusions found in these facies. Finally, a description and study of the two previous mentioned hypotheses has been carried out and a conclusion as to which process formed the amphibolite-granulite transition facies is stated.No embarg