A Memory Model for Concept Hierarchy Representation and Commonsense Reasoning

Most associative memory models perform one level mapping between predefined sets of input and output patterns1 and are unable to represent hierarchical knowledge. Complex AI systems allow hierarchical representation of concepts, but generally do not have learning capabilities. In this paper, a memory model is proposed which forms concept hierarchy by learning sample relations between concepts. All concepts are represented in a concept layer. Relations between a concept and its defining lower level concepts, are chunked as cognitive codes represented in a coding layer. By updating memory contents in the concept layer through code firing in the coding layer, the system is able to perform an important class of commonsense reasoning, namely recognition and inheritance.

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.

A Fuzzy ARTMAP Nonparametric Probability Estimator For Nonstationary Pattern Recognition Problem

An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is introduced. In slow-learning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In max-nodes mode, the network initially learns a fixed number of categories, and weights are then adjusted gradually.

Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views

The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system that classifies the preprocessed representations into 2-D view categories whose outputs arc combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence from 3-D object category nodes as multiple 2-D views are experienced. The simplest VIEWNET achieves high recognition scores without the need to explicitly code the temporal order of 2-D views in working memory. Working memories are also discussed that save memory resources by implicitly coding temporal order in terms of the relative activity of 2-D view category nodes, rather than as explicit 2-D view transitions. Variants of the VIEWNET architecture may also be used for scene understanding by using a preprocessor and classifier that can determine both What objects are in a scene and Where they are located. The present VIEWNET preprocessor includes the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and suppresses image noise. This boundary segmentation is rendered invariant under 2-D translation, rotation, and dilation by use of a log-polar transform. The invariant spectra undergo Gaussian coarse coding to further reduce noise and 3-D foreshortening effects, and to increase generalization. These compressed codes are input into the classifier, a supervised learning system based on the fuzzy ARTMAP algorithm. Fuzzy ARTMAP learns 2-D view categories that are invariant under 2-D image translation, rotation, and dilation as well as 3-D image transformations that do not cause a predictive error. Evidence from sequence of 2-D view categories converges at 3-D object nodes that generate a response invariant under changes of 2-D view. These 3-D object nodes input to a working memory that accumulates evidence over time to improve object recognition. ln the simplest working memory, each occurrence (nonoccurrence) of a 2-D view category increases (decreases) the corresponding node's activity in working memory. The maximally active node is used to predict the 3-D object. Recognition is studied with noisy and clean image using slow and fast learning. Slow learning at the fuzzy ARTMAP map field is adapted to learn the conditional probability of the 3-D object given the selected 2-D view category. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of l28x128 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex.

An Integrated Neural Network-Event-Related Potentials Model of Temporal and Probability Context Effects on Event Categorization

We present a neural network that adapts and integrates several preexisting or new modules to categorize events in short term memory (STM), encode temporal order in working memory, evaluate timing and probability context in medium and long term memory. The model shows how processed contextual information modulates event recognition and categorization, focal attention and incentive motivation. The model is based on a compendium of Event Related Potentials (ERPs) and behavioral results either collected by the authors or compiled from the classical ERP literature. Its hallmark is, at the functional level, the interplay of memory registers endowed with widely different dynamical ranges, and at the structural level, the attempt to relate the different modules to known anatomical structures.

Genetically Generated Neural Networks II: Searching for an Optimal Representation

Genetic Algorithms (GAs) make use of an internal representation of a given system in order to perform optimization functions. The actual structural layout of this representation, called a genome, has a crucial impact on the outcome of the optimization process. The purpose of this paper is to study the effects of different internal representations in a GA, which generates neural networks. A second GA was used to optimize the genome structure. This structure produces an optimized system within a shorter time interval.

Financial modelling with 2-EPT probability density functions

The class of all Exponential-Polynomial-Trigonometric (EPT) functions is classical and equal to the Euler-d’Alembert class of solutions of linear differential equations with constant coefficients. The class of non-negative EPT functions defined on [0;1) was discussed in Hanzon and Holland (2010) of which EPT probability density functions are an important subclass. EPT functions can be represented as ceAxb, where A is a square matrix, b a column vector and c a row vector where the triple (A; b; c) is the minimal realization of the EPT function. The minimal triple is only unique up to a basis transformation. Here the class of 2-EPT probability density functions on R is defined and shown to be closed under a variety of operations. The class is also generalised to include mixtures with the pointmass at zero. This class coincides with the class of probability density functions with rational characteristic functions. It is illustrated that the Variance Gamma density is a 2-EPT density under a parameter restriction. A discrete 2-EPT process is a process which has stochastically independent 2-EPT random variables as increments. It is shown that the distribution of the minimum and maximum of such a process is an EPT density mixed with a pointmass at zero. The Laplace Transform of these distributions correspond to the discrete time Wiener-Hopf factors of the discrete time 2-EPT process. A distribution of daily log-returns, observed over the period 1931-2011 from a prominent US index, is approximated with a 2-EPT density function. Without the non-negativity condition, it is illustrated how this problem is transformed into a discrete time rational approximation problem. The rational approximation software RARL2 is used to carry out this approximation. The non-negativity constraint is then imposed via a convex optimisation procedure after the unconstrained approximation. Sufficient and necessary conditions are derived to characterise infinitely divisible EPT and 2-EPT functions. Infinitely divisible 2-EPT density functions generate 2-EPT Lévy processes. An assets log returns can be modelled as a 2-EPT Lévy process. Closed form pricing formulae are then derived for European Options with specific times to maturity. Formulae for discretely monitored Lookback Options and 2-Period Bermudan Options are also provided. Certain Greeks, including Delta and Gamma, of these options are also computed analytically. MATLAB scripts are provided for calculations involving 2-EPT functions. Numerical option pricing examples illustrate the effectiveness of the 2-EPT approach to financial modelling.

Ontological representation of design science research publications

A search result provided by existing digital library and web search systems typically comprises only a prioritised list of possible publications or web pages that meet the search criteria, possibly with excerpts and possibly with search terms highlighted. The research in progress reported in this poster contributes to a larger research effort to provide a readable summary of search results that synthesise relevant publications or web pages to provide results that meet four C’s: comprehensive, concise, coherent, and correct, as a more useful alternative to un-synthesised result lists. The scope of this research is limited to searching for and synthesising Design Science Research (DSR) publications that present the results of DSR, as an example problem domain.

Coevolution of capitalism and political representation: The choice of electoral systems

Protocorporatist West European countries in which economic interests were collectively organized adopted PR in the first quarter of the twentieth century, whereas liberal countries in which economic interests were not collectively organized did not. Political parties, as Marcus Kreuzer points out, choose electoral systems. So how do economic interests translate into party political incentives to adopt electoral reform? We argue that parties in protocorporatist countries were representative of and closely linked to economic interests. As electoral competition in single member districts increased sharply up to World War I, great difficulties resulted for the representative parties whose leaders were seen as interest committed. They could not credibly compete for votes outside their interest without leadership changes or reductions in interest influence. Proportional representation offered an obvious solution, allowing parties to target their own voters and their organized interest to continue effective influence in the legislature. In each respect, the opposite was true of liberal countries. Data on party preferences strongly confirm this model. (Kreuzer's historical criticisms are largely incorrect, as shown in detail in the online supplementary Appendix.). © 2010 American Political Science Association.

Scale-wise evolution of rainfall probability density functions fingerprints the rainfall generation mechanism

© 2010 by the American Geophysical Union.The cross-scale probabilistic structure of rainfall intensity records collected over time scales ranging from hours to decades at sites dominated by both convective and frontal systems is investigated. Across these sites, intermittency build-up from slow to fast time-scales is analyzed in terms of heavy tailed and asymmetric signatures in the scale-wise evolution of rainfall probability density functions (pdfs). The analysis demonstrates that rainfall records dominated by convective storms develop heavier-Tailed power law pdfs toward finer scales when compared with their frontal systems counterpart. Also, a concomitant marked asymmetry build-up emerges at such finer time scales. A scale-dependent probabilistic description of such fat tails and asymmetry appearance is proposed based on a modified q-Gaussian model, able to describe the cross-scale rainfall pdfs in terms of the nonextensivity parameter q, a lacunarity (intermittency) correction and a tail asymmetry coefficient, linked to the rainfall generation mechanism.

Standardizing clinical trials workflow representation in UML for international site comparison.

BACKGROUND: With the globalization of clinical trials, a growing emphasis has been placed on the standardization of the workflow in order to ensure the reproducibility and reliability of the overall trial. Despite the importance of workflow evaluation, to our knowledge no previous studies have attempted to adapt existing modeling languages to standardize the representation of clinical trials. Unified Modeling Language (UML) is a computational language that can be used to model operational workflow, and a UML profile can be developed to standardize UML models within a given domain. This paper's objective is to develop a UML profile to extend the UML Activity Diagram schema into the clinical trials domain, defining a standard representation for clinical trial workflow diagrams in UML. METHODS: Two Brazilian clinical trial sites in rheumatology and oncology were examined to model their workflow and collect time-motion data. UML modeling was conducted in Eclipse, and a UML profile was developed to incorporate information used in discrete event simulation software. RESULTS: Ethnographic observation revealed bottlenecks in workflow: these included tasks requiring full commitment of CRCs, transferring notes from paper to computers, deviations from standard operating procedures, and conflicts between different IT systems. Time-motion analysis revealed that nurses' activities took up the most time in the workflow and contained a high frequency of shorter duration activities. Administrative assistants performed more activities near the beginning and end of the workflow. Overall, clinical trial tasks had a greater frequency than clinic routines or other general activities. CONCLUSIONS: This paper describes a method for modeling clinical trial workflow in UML and standardizing these workflow diagrams through a UML profile. In the increasingly global environment of clinical trials, the standardization of workflow modeling is a necessary precursor to conducting a comparative analysis of international clinical trials workflows.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).

FROM THE AETHIOPICA TO THE RENAISSANCE: RECOVERING A STAGE TRADITION OF POSITIVE REPRESENTATION OF AFRICANS IN EARLY MODERN ENGLAND

This dissertation looks at the connection between Heliodorus's fifth-century prose romance, An Aethiopian History, certain Renaissance texts, and how these texts helped influence an alternate representation of Africans in the early modern world. Through their portrayals of Africans, early modern English playwrights frequently give the impression that Africans, especially black Africans, were people without accomplishments, without culture. Previously, however, this was not the case. Africans were depicted with dignity, as a tradition existed for this kind of representation--and Renaissance Europe had long been acquainted with the achievements of Africans, dating back to antiquity. As the source of several lost plays, the Aethiopica is instrumental in dramatizing Africans favorably, especially on the early modern stage, and helped shape a stage tradition that runs alongside the stereotyping of Africans. This Heliodoran tradition can be seen in works of Greene, Heywood, Jonson, Shakespeare, and others in the motifs of crosscultural and transracial romance, male and female chastity, racial metamorphosis, lost or abandoned babies, wandering heroes, and bold heroines. In Jonson's Masque of Blackness and Masque of Beauty, I establish a connection between these two masques and Heliodorus's Aethiopica and argue for a Heliodoran stage tradition implicit in both masques through the conceit of blanching. In The English Moore, I explore how Richard Brome uses the Heliodoran and Jonsonian materials to create a negative quality of blackness that participates in the dramatic tradition of the degenerate African on the English Renaissance stage. With Othello, I contend that it is a drama that can be seen in the Heliodoran tradition by stressing certain motifs found in the play that derives from the Aethiopica. Reading Othello this way provides us with a more layered and historicized interpretation of Shakespeare's protagonists. Othello's nationality and faith make his exalted position in Venice and the Venetian army credible and logical. His nobility and heroic status become more sharply defined, giving us a fuller understanding of the emphasis he places on chastity--both for himself and for Desdemona. Instead of a traditional, compliant, and submissive Desdemona, a courageous, resourceful, witty, and pure heroine emerges--one who lives by the dictates of her conscience than by the constraints of societal norms. Recovering the tradition of positive portrayal of Africans that originated from the Aethiopica necessitated an examination of eleven plays that I contend helped to frame the dramatic tradition under investigation. Six of these plays are continental dramas, and five are English. Although three of the English plays are lost and the other two are seventeenth-century dramas, their titles and names of their protagonists, like those of the six extant continental plays, share the names of Heliodorus's hero and heroine, making an exploration of the continental plays imperative to facilitate their use as paradigms in reconstructing the three lost English plays. These continental dramas show that plays whose titles derive from the Aethiopica itself or reflect the names of its major characters follow Heliodorus's text closely, enabling an investigation of the Heliodoran tradition on the early modern English stage. Recovering the Heliodoran tradition adds to the exploration of racial politics and the understanding of the dramatic tradition that constrained and enabled Renaissance playwrights' representation of race and gender.

The Representation of Emotion in Autonomic and Central Nervous System Activity

Phenomenologically, humans effectively label and report feeling distinct emotions, yet the extent to which emotions are represented categorically in nervous system activity is controversial. Theoretical accounts differ in this regard, some positing distinct emotional experiences emerge from a dimensional representation (e.g., along axes of valence and arousal) whereas others propose emotions are natural categories, with dedicated neural bases and associated response profiles. This dissertation aims to empirically assess these theoretical accounts by examining how emotions are represented (either as disjoint categories or as points along continuous dimensions) in autonomic and central nervous system activity by integrating psychophysiological recording and functional neuroimaging with machine-learning based analytical methods. Results demonstrate that experientially, emotional events are well-characterized both along dimensional and categorical frameworks. Measures of central and peripheral responding discriminate among emotion categories, but are largely independent of valence and arousal. These findings suggest dimensional and categorical aspects of emotional experience are driven by separable neural substrates and demonstrate that emotional states can be objectively quantified on the basis of nervous system activity.

Graphical representation and generalization in sequences problems

In this paper we present different ways used by Secondary students to generalize when they try to solve problems involving sequences. 359 Spanish students solved generalization problems in a written test. These problems were posed through particular terms expressed in different representations. We present examples that illustrate different ways of achieving various types of generalization and how students express generalization. We identify graphical representation of generalization as a useful tool of getting other ways of expressing generalization, and we analyze its connection with other ways of expressing it.