Surface energy and surface proton order of the ice Ih basal and prism surfaces

Density-functional theory (DFT) is used to examine the basal and prism surfaces of ice Ih. Similar surface energies are obtained for the two surfaces; however, in each case a strong dependence of the surface energy on surface proton order is identified. This dependence, which can be as much as 50% of the absolute surface energy, is significantly larger than the bulk dependence (< 1%) on proton order, suggesting that the thermodynamic ground state of the ice surface will remain proton ordered well above the bulk order-disorder temperature of about 72 K. On the basal surface this suggestion is supported by Monte Carlo simulations with an empirical potential and solution of a 2D Ising model with nearest neighbor interactions taken from DFT. Order parameters that define the surface energy of each surface in terms of nearest neighbor interactions between dangling OH bonds (those which point out of the surface into vacuum) have been identified and are discussed. Overall, these results suggest that proton order-disorder effects have a profound impact on the stability of ice surfaces and will most likely have an effect on ice surface reactivity as well as ice crystal growth and morphology. S Supplementary data are available from stacks.iop.org/JPhysCM/22/074209/mmedia

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)

Woman's Club Yearbooks - Accession 39 - M14 (24)

The Woman's Club Yearbooks Collection consists of a Woman’s Club of Rock Hill Yearbook (1950-1951) and Woman's Club of Gaffney Yearbook (1935-1936).

Structures of Cage, Prism, and Book Isomers of Water Hexamer from Broadband Rotational Spectroscopy

Theory predicts the water hexamer to be the smallest water cluster with a three-dimensional hydrogen-bonding network as its minimum energy structure. There are several possible low-energy isomers, and calculations with different methods and basis sets assign them different relative stabilities. Previous experimental work has provided evidence for the cage, book, and cyclic isomers, but no experiment has identified multiple coexisting structures. Here, we report that broadband rotational spectroscopy in a pulsed supersonic expansion unambiguously identifies all three isomers; we determined their oxygen framework structures by means of oxygen-18–substituted water (H218O). Relative isomer populations at different expansion conditions establish that the cage isomer is the minimum energy structure. Rotational spectra consistent with predicted heptamer and nonamer structures have also been identified.

Poema of Lieutenant Schmidt’s End: Pasternak’s Dialogue with Tsvetaeva through the Prism of Genre.

Boris Pasternak’s poemy are acutely self-conscious of their place in the epic tradition. Lieutenant Schmidt (LS) represents one attempt at exploring the parameters of the poema itself as the poet makes a “difficult” transition from “lyric thinking” to “the epic.” In this article I examine this transition against a contemporaneous example in the genre, Tsvetaeva’s Poema of the End (PE). In LS, structural elements of the poema are counterposed to those of PE. While PE amplifies the individual voice, LS muffles what is personal for the sake of the public voice. While PE is atemporal, LS is historical. While PE unfolds on symbolic planes, with elements of plot kept to a bare minimum (a single moment of separation), LS is a plot-driven account based on concrete, documentary material. Finally, while PE is an “overgrown lyric”—representing the “lyric thinking” that Pasternak hopes to transcend— LS is an exploration of the possibilities that a more traditional model of the poema can offer. Although in the present analysis I draw on several theories of poetic genres, this is by no means an exhaustive study of epic versus lyric forms of poetry. Instead, my analysis focuses on those structural and thematic features of the poema that the poets themselves perceived as central to their texts. Pasternak, for his part, develops the structure and thematics of his poema in ways that are inspired by PE, but also, as we will see, in more significant ways, contrast with it.