Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method.

AMAR: A Computational Model of Autosegmental Phonology

This report describes a computational system with which phonologists may describe a natural language in terms of autosegmental phonology, currently the most advanced theory pertaining to the sound systems of human languages. This system allows linguists to easily test autosegmental hypotheses against a large corpus of data. The system was designed primarily with tonal systems in mind, but also provides support for tree or feature matrix representation of phonemes (as in The Sound Pattern of English), as well as syllable structures and other aspects of phonological theory. Underspecification is allowed, and trees may be specified before, during, and after rule application. The association convention is automatically applied, and other principles such as the conjunctivity condition are supported. The method of representation was designed such that rules are designated in as close a fashion as possible to the existing conventions of autosegmental theory while adhering to a textual constraint for maximum portability.

On Directional Selectivity in Vertebrate Retina: An Experimental and Computational Study

This thesis describes an investigation of retinal directional selectivity. We show intracellular (whole-cell patch) recordings in turtle retina which indicate that this computation occurs prior to the ganglion cell, and we describe a pre-ganglionic circuit model to account for this and other findings which places the non-linear spatio-temporal filter at individual, oriented amacrine cell dendrites. The key non-linearity is provided by interactions between excitatory and inhibitory synaptic inputs onto the dendrites, and their distal tips provide directionally selective excitatory outputs onto ganglion cells. Detailed simulations of putative cells support this model, given reasonable parameter constraints. The performance of the model also suggests that this computational substructure may be relevant within the dendritic trees of CNS neurons in general.

Computational Structure of GPSG Models: Revised Generalized Phrase Structure Grammar

The primary goal of this report is to demonstrate how considerations from computational complexity theory can inform grammatical theorizing. To this end, generalized phrase structure grammar (GPSG) linguistic theory is revised so that its power more closely matches the limited ability of an ideal speaker--hearer: GPSG Recognition is EXP-POLY time hard, while Revised GPSG Recognition is NP-complete. A second goal is to provide a theoretical framework within which to better understand the wide range of existing GPSG models, embodied in formal definitions as well as in implemented computer programs. A grammar for English and an informal explanation of the GPSG/RGPSG syntactic features are included in appendices.

Towards a Computational Theory of Definite Anaphora Comprehension in English Discourse

This report investigates the process of focussing as a description and explanation of the comprehension of certain anaphoric expressions in English discourse. The investigation centers on the interpretation of definite anaphora, that is, on the personal pronouns, and noun phrases used with a definite article the, this or that. Focussing is formalized as a process in which a speaker centers attention on a particular aspect of the discourse. An algorithmic description specifies what the speaker can focus on and how the speaker may change the focus of the discourse as the discourse unfolds. The algorithm allows for a simple focussing mechanism to be constructed: and element in focus, an ordered collection of alternate foci, and a stack of old foci. The data structure for the element in focus is a representation which encodes a limted set of associations between it and other elements from teh discourse as well as from general knowledge.

A Computational Model of Skill Acquisition

This thesis confronts the nature of the process of learning an intellectual skill, the ability to solve problems efficiently in a particular domain of discourse. The investigation is synthetic; a computational performance model, HACKER, is displayed. Hacker is a computer problem-solving system whose performance improves with practice. HACKER maintains performance knowledge as a library of procedures indexed by descriptions of the problem types for which the procedures are appropriate. When applied to a problem, HACKER tries to use a procedure from this "Answer Library". If no procedure is found to be applicable, HACKER writes one using more general knowledge of the problem domain and of programming techniques. This new program may be generalized and added to the Answer Library.

Computational Intelligence and Feature Selection : Rough and Fuzzy Approaches

Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations. It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selections described through the use of real-world applications and worked examples. Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web.

DIP: Density Inference Protocol for wireless sensor networks and its application to density-unbiased statistics

Wireless sensor networks have recently emerged as enablers of important applications such as environmental, chemical and nuclear sensing systems. Such applications have sophisticated spatial-temporal semantics that set them aside from traditional wireless networks. For example, the computation of temperature averaged over the sensor field must take into account local densities. This is crucial since otherwise the estimated average temperature can be biased by over-sampling areas where a lot more sensors exist. Thus, we envision that a fundamental service that a wireless sensor network should provide is that of estimating local densities. In this paper, we propose a lightweight probabilistic density inference protocol, we call DIP, which allows each sensor node to implicitly estimate its neighborhood size without the explicit exchange of node identifiers as in existing density discovery schemes. The theoretical basis of DIP is a probabilistic analysis which gives the relationship between the number of sensor nodes contending in the neighborhood of a node and the level of contention measured by that node. Extensive simulations confirm the premise of DIP: it can provide statistically reliable and accurate estimates of local density at a very low energy cost and constant running time. We demonstrate how applications could be built on top of our DIP-based service by computing density-unbiased statistics from estimated local densities.

A Short History of Computational Complexity

National Science Foundation (CCR-998310); Army Research Office (DAAD19-02-1-0058)

An Information Theoretic Framework for Field Monitoring Using Autonomously Mobile Sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.

Computational Properties of SNAFU

Sensor applications in Sensoria [1] are expressed using STEP (Sensorium Task Execution Plan). SNAFU (Sensor-Net Applications as Functional Units) serves as a high-level sensor-programming language, which is compiled into STEP. In SNAFU’s current form, its differences with STEP are relatively minor, as they are limited to shorthands and macros not available in STEP. We show that, however restrictive it may seem, SNAFU has in fact universal power; technically, it is a Turing-complete language, i.e., any Turing program can be written in SNAFU (though not always conveniently). Although STEP may be allowed to have universal power, as a low-level language not directly available to Sensorium users, SNAFU programmers may use this power for malicious purposes or inadvertently introduce errors with destructive consequences. In future developments of SNAFU, we plan to introduce restrictions and highlevel features with safety guards, such as those provided by a type system, which will make SNAFU programming safer.

Fixational Instability and Natural Image Statistics: Implications for Early Visual Representations

Under natural viewing conditions small movements of the eye, head, and body prevent the maintenance of a steady direction of gaze. It is known that stimuli tend to fade when they a restabilized on the retina for several seconds. However; it is unclear whether the physiological motion of the retinal image serves a visual purpose during the brief periods of natural visual fixation. This study examines the impact of fixational instability on the statistics of the visua1 input to the retina and on the structure of neural activity in the early visual system. We show that fixational instability introduces a component in the retinal input signals that in the presence of natural images, lacks spatial correlations. This component strongly influences neural activity in a model of the LGN. It decorrelates cell responses even if the contrast sensitivity functions of simulated cells arc not perfectly tuned to counterbalance the power-law spectrum of natural images. A decorrelation of neural activity at the early stages of the visual system has been proposed to be beneficial for discarding statistical redundancies in the input signals. The results of this study suggest that fixational instability might contribute to establishing efficient representations of natural stimuli.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.