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.

Salt marsh morphodynamics: An investigation of tidal flows and marsh channel equilibrium

Although numerous field studies have evaluated flow and transport processes in salt marsh channels, the overall role of channels in delivering and removing material from salt marsh platforms is still poorly characterised. In this paper, we consider this issue based on a numerical hydrodynamic model for a prototype marsh system and on a field survey of the cross-sectional geometry of a marsh channel network. Results of the numerical simulations indicate that the channel transfers approximately three times the volume of water that would be estimated from mass balance considerations alone. Marsh platform roughness exerts a significant influence on the partitioning of discharge between the channel and the marsh platform edge, alters flow patterns on the marsh platform due to its effects on channel-to-platform transfer and also controls the timing of peak discharge relative to marsh-edge overtopping. Although peak channel discharges and velocities are associated with the flood tide and marsh inundation, a larger volume of water is transferred by the channel during ebb flows, a portion of which transfer takes place after the tidal height is below the marsh platform. Detailed surveys of the marsh channels crossing a series of transects at Upper Stiffkey Marsh, north Norfolk, England, show that the total channel cross-sectional area increases linearly with catchment area in the inner part of the marsh, which is consistent with the increase in shoreward tidal prism removed by the channels. Toward the marsh edge, however, a deficit in the total cross-sectional area develops, suggesting that discharge partitioning between the marsh channels and the marsh platform edge may also be expressed in the morphology of marsh channel systems.

Vibrational intensities IX: C2F6, extension and revision

The intensity of the low fundamental of C2F6 at 219 cm—1 was measured using a CsI prism. This completed earlier studies on the other fundamentals, and permits extension and revision of the interpretation. Effective bond moments are compared with those of other fluorocarbons.

Function of the hydration layer around an antifreeze protein revealed by atomistic molecular dynamics simulations

Atomistic molecular dynamics simulations are used to investigate the mechanism by which the antifreeze protein from the spruce budworm, Choristoneura fumiferana, binds to ice. Comparison of structural and dynamic properties of the water around the three faces of the triangular prism-shaped protein in aqueous solution reveals that at low temperature the water structure is ordered and the dynamics slowed down around the ice-binding face of the protein, with a disordering effect observed around the other two faces. These results suggest a dual role for the solvation water around the protein. The preconfigured solvation shell around the ice-binding face is involved in the initial recognition and binding of the antifreeze protein to ice by lowering the barrier for binding and consolidation of the protein:ice interaction surface. Thus, the antifreeze protein can bind to the molecularly rough ice surface by becoming actively involved in the formation of its own binding site. Also, the disruption of water structure around the rest of the protein helps prevent the adsorbed protein becoming covered by further ice growth.

Origin of the Parry arc

Laboratory experiments to determine the preferred orientation of free-falling hexagonal prisms were performed at Reynolds numbers appropriate to falling ice crystals in the atmosphere. Hexagonal plates orient with their c axis vertical for aspect ratios < 0.9, whilst hexagonal columns fall with their c axis horizontal. A secondary alignment is also observed: regular hexagonal columns fall preferentially with two prism facets aligned vertically and not horizontally – the latter scenario was previously assumed to be responsible for the rare Parry arc. However, if the column is made scalene in its cross-section, it can orient such that a pair of prism facets is horizontal. This finding indicates that the development of scalene crystals may be key to the production of certain ice-crystal optical phenomena

Use of in situ FT-Raman spectroscopy to study the kinetics of the transformation of carbamazepine polymorphs

The solid-state transformation of carbamazepine from form III to form I was examined by Fourier Transform Raman spectroscopy. Using a novel environmental chamber, the isothermal conversion was monitored in situ at 130◦C, 138◦C, 140◦C and 150◦C. The rate of transformation was monitored by taking the relative intensities of peaks arising from two C H bending modes; this approach minimised errors due to thermal artefacts and variations in power intensities or scattering efficiencies from the samples in which crystal habit changed from a characteristic prism morphology (form III) to whiskers (form I). The solid-state transformation at the different temperatures was fitted to various solid-state kinetic models of which four gave good fits, thus indicating the complexity of the process which is known to occur via a solid–gas–solid mechanism. Arrhenius plots from the kinetic models yielded activation energies from 344 kJ mol−1 to 368 kJ mol−1 for the transformation. The study demonstrates the value of a rapid in situ analysis of drug polymorphic type which can be of value for at-line in-process control.

The civic zeitgeist: nationalism and liberal values in the European radical right

This article examines the relationship between nationalism and liberal values, and more specifically the redefinition of boundaries between national communities and others in the rhetoric of radical right parties in Europe. The aim is to examine the tension between radical right party discourse and the increasing need to shape this discourse in liberal terms. We argue that the radical right parties that successfully operate within the democratic system tend to be those best able to tailor their discourse to the liberal and civic characteristics of national identity so as to present themselves and their ideologies as the true authentic defenders of the nation's unique reputation for democracy, diversity and tolerance. Comparing the success of a number of European radical right parties ranging from the most electorally successful SVP to the more mixed BNP, FN and NPD, we show that the parties that effectively deploy the symbolic resources of national identity through a predominantly voluntaristic prism tend to be the ones that fare better within their respective political systems. In doing so, we challenge the conventional view in the study of nationalism which expects civic values to shield countries from radicalism and extremism.

Non-display uses of digital works: Google Books and beyond

With the advent of mass digitization projects, such as the Google Book Search, a peculiar shift has occurred in the way that copyright works are dealt with. Contrary to what has so far been the case, works are turned into machine-readable data to be automatically processed for various purposes without the expression of works being displayed to the public. In the Google Book Settlement Agreement, this new kind of usage is referred to as ‘non-display uses’ of digital works. The legitimacy of these uses has not yet been tested by Courts and does not comfortably fit in the current copyright doctrine, plainly because the works are not used as works but as something else, namely as data. Since non-display uses may prove to be a very lucrative market in the near future, with the potential to affect the way people use copyright works, we examine non-display uses under the prism of copyright principles to determine the boundaries of their legitimacy. Through this examination, we provide a categorization of the activities carried out under the heading of ‘non-display uses’, we examine their lawfulness under the current copyright doctrine and approach the phenomenon from the spectrum of data protection law that could apply, by analogy, to the use of copyright works as processable data.