Fuzzy ART Choice Functions

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic intersection (∩) with the fuzzy intersection(∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric theory in which the fuzzy intersection and the fuzzy union (∨), or component-wise maximum, play complementary roles. A geometric interpretation of fuzzy ART represents each category as a box that increases in size as weights decrease. This paper analyzes fuzzy ART models that employ various choice functions for category selection. One such function minimizes total weight change during learning. Benchmark simulations compare peformance of fuzzy ARTMAP systems that use different choice functions.

3-D Object Recognition by the ART-EMAP Evidence Accumulation Network

ART-EMAP synthesizes adaptive resonance theory (AHT) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage I introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Simulations of the four ART-EMAP stages demonstrate performance on a difficult 3-D object recognition problem.

The Art of Seeing and Painting

The human urge to represent the three-dimensional world using two-dimensional pictorial representations dates back at least to Paleolithic times. Artists from ancient to modern times have struggled to understand how a few contours or color patches on a flat surface can induce mental representations of a three-dimensional scene. This article summarizes some of the recent breakthroughs in scientifically understanding how the brain sees that shed light on these struggles. These breakthroughs illustrate how various artists have intuitively understand paradoxical properties about how the brain sees, and have used that understanding to create great art. These paradoxical properties arise from how the brain forms the units of conscious visual perception; namely, representations of three-dimensional boundaries and surfaces. Boundaries and surfaces are computed in parallel cortical processing streams that obey computationally complementary properties. These streams interact at multiple levels to overcome their complementary weaknesses and to transform their complementary properties into consistent percepts. The article describes how properties of complementary consistency have guided the creation of many great works of art.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.

The architecture of influence: Paul Rudolph's Art and Architecture Building at Yale

Focussing on Paul Rudolph’s Art & Architecture Building at Yale, this thesis demonstrates how the building synthesises the architect’s attitude to architectural education, urbanism and materiality. It tracks the evolution of the building from its origins – which bear a relationship to Rudolph’s pedagogical ideas – to later moments when its occupants and others reacted to it in a series of ways that could never have been foreseen. The A&A became the epicentre of the university’s counter culture movement before it was ravaged by a fire of undetermined origins. Arguably, it represents the last of its kind in American architecture, a turning point at the threshold of postmodernism. Using an archive that was only made available to researchers in 2009, this is the first study to draw extensively on the research files of the late architectural writer and educator, C. Ray Smith. Smith’s 1981 manuscript about the A&A entitled “The Biography of a Building,” was never published. The associated research files and transcripts of discussions with some thirty interviewees, including Rudolph, provide a previously unavailable wealth of information. Following Smith’s methodology, meetings were recorded with those involved in the A&A including, where possible, some of Smith’s original interviewees. When placed within other significant contexts – the physicality of the building itself as well as the literature which surrounds it – these previously untold accounts provide new perspectives and details, which deepen the understanding of the building and its place within architectural discourse. Issues revealed include the importance of the influence of Louis Kahn’s Yale Art Gallery and Yale’s Collegiate Gothic Campus on the building’s design. Following a tumultuous first fifty years, the A&A remains an integral part of the architectural education of Yale students and, furthermore, constitutes an important didactic tool for all students of architecture.

Religion, the study of religion and other products of transnational and colonial imaginings

The category of ‘religion’ as contemporary scholarship has demonstrated is a fairly recent innovation, dating back only a few hundred years in Western thought, and ‘world religions’ as we think of it and as we teach it is an even more recent category, emerging out of European colonialism. Thus the academic study of religion is both the product and, at times, the agent of colonial modes of knowledge. And yet, it is perhaps because ‘religion’ continues to be invented and reinvented through connections across cultures that investigating the work of religious ideas and practices offers such fruitful possibilities for understanding the work of culture and power. This article investigates religion and the study of religion as a mode of anti-colonial practice, seeking to understand how each have the potential to cross boundaries, build bridges and produce critical insights into assumptions and worldviews too often taken for granted.

Art markets

SCOPUS: ch.b

Léo Strauss: art d'écrire, politique, philosophie

L'ouvrage examine la pensée de Léo Strauss (1899-1973) et étudie à partir d'elle les stratégies d'exposition et de dissimulation de la philosophie. Les études qu'il réunit mesurent la portée de l'hypothèse d'un "art d'écrire oublié" et examinent la fécondité et les limites de la conception straussienne de l'écriture philosophique.

De l'art d'écrire à l'art de lire: les enjeux juridico-politiques de l'interprétation

info:eu-repo/semantics/published

BOULANGER, SATIE, AND DEBUSSY: THEIR SPHERES OF INFLUENCE ON FRENCH MELODIE AND AMERICAN ART SONG

This dissertation project explored the spheres of influence on art song by Nadia Boulanger, Erik Satie, and Claude Debussy within Boulangeries, Les Six, and Les Apaches. After World War I, American composers flocked to Paris to study with Boulanger. Boulanger gave her students the confidence to explore their native talents instead of mimicking foreign models. Works by Aaron Copland, Virgil Thomson, Theodore Chanler, John Duke, and Richard Hundley were included in the first dissertation recital on January 31, 2010: The Legacy of Nadia Boulanger: Her Influence on American Song Composers. Satie established a new modern French musical style, and was a catalyst for the formation of Les Six. Ned Rorem came to Paris, and had a close association with Les Six. Works by Satie, and three members of Les Six, Francis Poulenc, Arthur Honegger, Darius Milhaud; and Rorem were featured in the second recital on September 1, 2010: Satie, Selected Members of Les Six, and Rorem in Paris. Debussy was one of the most significant French composers in the late nineteenth century, predating Boulanger and Satie. Young composers exploring new directions were inspired by Debussy, forming the group Les Apaches. The final recital, April 7, 2011, featured works by Debussy and two members of Les Apaches, Maurice Ravel and Manuel de Falla: Debussy: A Catalyst for Les Apaches, Ravel and Falla. Falla‘s less well-known repertoire was presented. This dissertation showed the influence of these three major figures and that they embraced innovation in their own time, along with their followers. Recordings of these three performances may be obtained from the Michelle Smith Performing Arts Library in Clarice Smith Performing Arts Center at the University of Maryland, College Park.

THE ART SONG OF SOUTH AMERICA: AN EXPLORATION THROUGH PERFORMANCE

The repertoire included in this dissertation was presented over the course of three recitals, The Songs of Argentina, The Songs of Brazil, Chile and Venezuela, and The Songs of Perú and Colombia. Each recital was supplemented by written program notes and English translations of the Spanish, Portuguese and Quechua texts. The selections presented in this study was chosen in an effort to pair the works of internationally renowned composers like Argentine composers Alberto Ginastera and Carlos Guastavino, and Brazilian composer Heitor Villa-Lobos, with those of lesser-known composers, including Venezuelan composer Juan Bautista Plaza, Peruvian composers Edgar Valcárcel, Theodoro Valcárcel, and Rosa Mercedes Ayarza de Morales, and Colombian composer Jaime Léon. Each composer represents a milestone in the development of art song composition in South America. All three recitals were recorded and are available on compact discs in the Digital Repository at the University of Maryland (DRUM). This dissertation was completed in May, 2011.