Ocular scanning and perceptual size distortion in hemispatial neglect: effects of prism adaptation and sequential stimulus presentation

When asked to compare two lateralized shapes for horizontal size, neglect patients often indicate the left stimulus to be smaller. Gainotti and Tiacci (1971) hypothesized that this phenomenon might be related to a rightward bias in the patients' gaze. This study aimed to assess the relation between this size underestimation and oculomotor asymmetries. Eye movements were recorded while three neglect patients judged the horizontal extent of two rectangles. Two experimental manipulations were performed to increase the likelihood of symmetrical scanning of the stimulus display. The first manipulation entailed a sequential, rather than simultaneous presentation of the two rectangles. The second required adaptation to rightward displacing prisms, which is known to reduce many manifestations of neglect. All patients consistently underestimated the left rectangle, but the pattern of verbal responses and eye movements suggested different underlying causes. These include a distortion of space perception without ocular asymmetry, a failure to view the full leftward extent of the left stimulus, and a high-level response bias. Sequential presentation of the rectangles and prism adaptation reduced ocular asymmetries without affecting size underestimation. Overall, the results suggest that leftward size underestimation in neglect can arise for a number of different reasons. Incomplete leftward scanning may perhaps be sufficient to induce perceptual size distortion, but it is not a necessary prerequisite.

Europe and the Petersberg Tasks – Nation-State behavior through the prism of Strategic Culture

Les missions Petersberg són l'operatiu militar més ambiciós organitzat per la Unió Europea en el desenvolupament de la CSDP, Política Europea de Seguretat i Defensa. Amb l'objectiu d'aconseguir una organització efectiva y funcional d'aquestes missions, és desitjable que les cultures estratègiques dels diferents Estats membres siguin, en gran mesura, compatibles en benefici d'una cultura estratègica europea amb directrius clares. Aquest estudi compara les cultures estratègiques d'Alemanya, el Regne Unit i França en referència al seu nivell de compatibilitat contrastant-les amb dos casos recents, exemples paradigmàtics de cultures estratègiques integrals. D'aquesta manera, pretenem descriure les circumstàncies en què es desenvolupen les missions Petersberg.

Periodismo de filtración: el caso PRISM

Este trabajo final de máster se centra en describir la realidad que afecta al periodismo de filtración. Además se analiza detalladamente el caso Planning Tool for Resource Integration, Synchronization, and Management (PRISM). Se ha averiguado qué han hecho los medios con ésta información, identificado cada elemento clave de esta trama, descrito la situación mediática actual y se han expuesto las repercusiones que ha tenido esta noticia a nivel internacional y a nivel personal. Se analizan además las noticias, artículos o ensayos que se han escrito sobre el tema para averiguar qué se ha conseguido con éste filtraje y finalmente, se han observado las consecuencias de tal acto.

PRISM and ENES: a European approach to Earth system modelling

Europe's widely distributed climate modelling expertise, now organized in the European Network for Earth System Modelling (ENES), is both a strength and a challenge. Recognizing this, the European Union's Program for Integrated Earth System Modelling (PRISM) infrastructure project aims at designing a flexible and friendly user environment to assemble, run and post-process Earth System models. PRISM was started in December 2001 with a duration of three years. This paper presents the major stages of PRISM, including: (1) the definition and promotion of scientific and technical standards to increase component modularity; (2) the development of an end-to-end software environment (graphical user interface, coupling and I/O system, diagnostics, visualization) to launch, monitor and analyse complex Earth system models built around state-of-art community component models (atmosphere, ocean, atmospheric chemistry, ocean bio-chemistry, sea-ice, land-surface); and (3) testing and quality standards to ensure high-performance computing performance on a variety of platforms. PRISM is emerging as a core strategic software infrastructure for building the European research area in Earth system sciences. Copyright (c) 2005 John Wiley & Sons, Ltd.

Calibration and use of a cesium-iodide prism in the infrared

With a cesium-iodide prism the long wavelength range of an infrared spectrometer may be extended to 55µ The use of such a prism, the choice of optical system, and the problems of stray radiation are all discussed. Accurate data are assembled for calibration in this region, and sample calibration traces are shown. A simple gas absorption cell is described for use at long wavelengths.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Vortex-Antivortex Dynamics in a Mesoscopic Superconducting Prism with a Centered Antidot

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Properties of Mortar Using Cubes, Prism Halves, and Cylinder Specimens

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)