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.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

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.

Working Memory Networks for Learning Temporal Order, with Application to 3-D Visual Object Recognition

Working memory neural networks are characterized which encode the invariant temporal order of sequential events. Inputs to the networks, called Sustained Temporal Order REcurrent (STORE) models, may be presented at widely differing speeds, durations, and interstimulus intervals. The STORE temporal order code is designed to enable all emergent groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensory-motor planning, or 3-D visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described. The new model is based on the model of Seibert and Waxman (1990a), which builds a 3-D representation of an object from a temporally ordered sequence of its 2-D aspect graphs. The new model, called an ARTSTORE model, consists of the following cascade of processing modules: Invariant Preprocessor --> ART 2 --> STORE Model --> ART 2 --> Outstar Network.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. 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 logic 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. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

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.

Normal and Amnesis Learning, Recognition, and Memory by a Neural Model of Cortico-Hippocampal Interactions

The processes by which humans and other primates learn to recognize objects have been the subject of many models. Processes such as learning, categorization, attention, memory search, expectation, and novelty detection work together at different stages to realize object recognition. In this article, Gail Carpenter and Stephen Grossberg describe one such model class (Adaptive Resonance Theory, ART) and discuss how its structure and function might relate to known neurological learning and memory processes, such as how inferotemporal cortex can recognize both specialized and abstract information, and how medial temporal amnesia may be caused by lesions in the hippocampal formation. The model also suggests how hippocampal and inferotemporal processing may be linked during recognition learning.

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 of auscultation: evaluating a patient's respiratory pathology

This article considers national policy drivers promoting the development of advanced assessment skills and practical procedures for the safe and effective use of the stethoscope in the clinical area. The evidence base underpinning effective use of the stethoscope in clinical practice is explored, including the preparation of the patient and the environment, applying infection control policies, and placing an emphasis on privacy and dignity. This is followed by a practical guide to auscultation technique of the respiratory system for nurses developing advanced practice skills.

Welfare Effort and Poverty from the Monetary and Capabilities Approach: A Cross-Country Analysis in Latin American and the Caribbean (1990-2010)

There is abundant empirical evidence on the negative relationship between welfare effort and poverty. However, poverty indicators traditionally used have been representative of the monetary approach, excluding its multidimensional reality from the analysis. Using three regression techniques for the period 1990-2010 and controlling for demographic and cyclical factors, this paper examines the relationship between social spending per capita —as the indicator of welfare effort— and poverty in up to 21 countries of the region. The proportion of the population with an income below its national basic basket of goods and services (PM1) and the proportion of population with an income below 50% of the median income per capita (PM2) were the two poverty indicators considered from the monetarist approach to measure poverty. From the capability approach the proportion of the population with food inadequacy (PC1) and the proportion of the population without access to improved water sources or sanitation facilities (PC2) were used. The fi ndings confi rm that social spending is actually useful to explain changes in poverty (PM1, PC1 and PC2), as there is a high negative and signifi cant correlation between the variables before and after controlling for demographic and cyclical factors. In two regression techniques, social spending per capita did not show a negative relationship with the PM2. Countries with greater welfare effort for the period 1990-2010 were not necessarily those with the lowest level of poverty. Ultimately social spending per capita was more useful to explain changes in poverty from the capability approach.

Art as an educative and social tool

Since remote times, certain sectors of society have been exposed to inequality and vulnerability, where adequate intervention processes have become conspicuous because of their absence. Nowadays, current societies have the responsibility of contributing, based on their experience and knowledge, with more efficient policies and programs that improve the life quality of the most disadvantaged. It is here where art and its different tools play a very important role, not only on a physical level, but also as an education tool that allows the development of emotional, mental and communicative skills. The aim of this paper is to make clear the potential of art as an instrument of social and educational intervention. It starts by showing worldwide-collected experience related to education and arts, and then, it acquaints the reader with two parallel intervention projects that worked with youths under social vulnerability conditions. These interventions were developed based on a qualitative research (Grounded theory), using as methodology “The Artistic Mediation” with emphasis on body language. This methodology helped researchers to get close to the participants and to know their experiences and emotions. At the same time, it was possible to evidence the positive effects of educative interventions through art. These workshops were based on an artistic methodology especially focused on body language. Data in this work is qualitative, and as such, it permits a special approach to the personal and emotional experiences of the participants; clearly showing the positive effects of the referenced practice on them.

Art and Architecture of Ireland Volume Four:Architecture: 1600-2000

Three Entries: Peacelines; Public Housing in Northern Ireland in the Twentieth Century; Interpretive Centres, NI Peacelines, NI Social Housing

Women artists of the international avant-garde: the case of "Abstraction-Création" (1931-1936)

Women’s contribution to abstract art in the interwar period is a subject that, to date, has received very little attention. In this article we deal with the untold story of the participation of women artists in Abstraction-Création, the foremost international group dedicated to abstract art in the 1930s. Founded in Paris in 1931, the group took on the work of two previous collectives to become a platform for the dissemination and promotion of abstract art and consisted of around a hundred members. Twelve of these were women, whose writings and works were published in the group’s annual magazine, abstraction creátion art non figuratif (1932-1936), and who participated in a number of the group’s exhibitions. Compared to what had occurred in previous groups, the participation of women, although reduced in number, was comparable to that of the male artists and being members of the group had a generally positive impact on the women’s careers. However, all this came at the expense of relinquishing any gender specificity in their work and the public presentation of it, and demonstrates that the normalization of women’s contributions to the avant-garde could only be brought about alongside a questioning of the more dogmatic views of modernity.

Primitivismo y Feminismo en el arte contemporáneo

Para la mentalidad colonialista del siglo XIX la raza negra estaba provista de una extraordinaria sexualidad y lujuria. En la metrópolis el equivalente era la mujer sexuada, la prostituta. En los primeros años del siglo XX este tándem, prostituta-colonizado, sirve a los artistas vanguardistas como elemento de confrontación con la moral imperante. Son las artistas mujeres, con una intención feminista, quienes principalmente utilizan el primitivismo asociado a la mujer en su crítica a los estereotipos. Figura pionera en ello es Hannah Höch, especialmente en su serie De un Museo Etnográfico, en el que asocia figuras femeninas y objetos de arte primitivo. En los años 70 del siglo XX, algunas artistas de la performance, como Carolee Schneemann o Ana Mendieta reivindican el cuerpo femenino con el significado de Origen y Fertilidad que tiene en las culturas primitivas. En este mismo contexto puede inscribirse la escultura Maman de Louise Bourgeois. El elemento de crítica social, que está vigente aún en nuestros días, puede verse en las obras de las Guerrilla Girls.