Representing El Niño in coupled ocean–atmosphere GCMs: the dominant role of the atmospheric component

A systematic modular approach to investigate the respective roles of the ocean and atmosphere in setting El Niño characteristics in coupled general circulation models is presented. Several state-of-the-art coupled models sharing either the same atmosphere or the same ocean are compared. Major results include 1) the dominant role of the atmosphere model in setting El Niño characteristics (periodicity and base amplitude) and errors (regularity) and 2) the considerable improvement of simulated El Niño power spectra—toward lower frequency—when the atmosphere resolution is significantly increased. Likely reasons for such behavior are briefly discussed. It is argued that this new modular strategy represents a generic approach to identifying the source of both coupled mechanisms and model error and will provide a methodology for guiding model improvement.

KNIME: the Konstanz Information Miner

The Konstanz Information Miner is a modular environment which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables easy integration of new algorithms, data manipulation or visualization methods as new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture and briefly sketch how new nodes can be incorporated.

Self-assembled biomimetic [2Fe2S]-hydrogenase-based photocatalyst for molecular hydrogen evolution

The large-scale production of clean energy is one of the major challenges society is currently facing. Molecular hydrogen is envisaged as a key green fuel for the future, but it becomes a sustainable alternative for classical fuels only if it is also produced in a clean fashion. Here, we report a supramolecular biomimetic approach to form a catalyst that produces molecular hydrogen using light as the energy source. It is composed of an assembly of chromophores to a bis(thiolate)-bridged diiron ([2Fe2S]) based hydrogenase catalyst. The supramolecular building block approach introduced in this article enabled the easy formation of a series of complexes, which are all thoroughly characterized, revealing that the photoactivity of the catalyst assembly strongly depends on its nature. The active species, formed from different complexes, appears to be the [Fe-2(mu-pdt)(CO)(4){PPh2(4-py)}(2)] (3) with 2 different types of porphyrins (5a and 5b) coordinated to it. The modular supramolecular approach was important in this study as with a limited number of building blocks several different complexes were generated.

Characterization of the 23S and 5S rRNA genes and 23S-5S intergenic spacer region (ITS-2) of Photobacterium damselae

The 23S ribosomal RNA (rRNA) gene has been sequenced in strains of the fish pathogens Photobacterium damselae subsp. damselae (ATCC 33539) and subsp. piscicida (ATCC 29690), showing that 3 nucleotide positions are clearly different between subspecies. In addition, the 5S rRNA gene plus the intergenic spacer region between the 23S and 5S rRNA genes (ITS-2) were amplified, cloned and sequenced for the 2 reference strains as well as the field isolates RG91 (subsp. damselae) and DI21 (subsp. piscicida). A 100% similarity was found for the consensus 5S rRNA gene sequence in the 2 subspecies, although some microheterogeneity was detected as inter-cistronic variability within the same chromosome. Sequence analysis of the spacer region between the 23S and 5S rRNA genes revealed 2 conserved and 3 variable nucleotide sequence blocks, and 4 different modular organizations were found. The ITS-2 spacer region exhibited both inter-subspecies and inter-cistronic polymorphism, with a mosaic-like structure. The EMBL accession numbers for the 23S, 5S and ITS-2 sequences are: P. damselae subsp. piscicida 5S gene (AJ274379), P. damselae subsp. damselae 23S gene (Y18520), subsp. piscicida 23S gene (Y17901), R damselae subsp. piscicida ITS-2 (AJ250695, AJ250696), P. damselae subsp. damselae ITS-2 (AJ250697, AJ250698).

A case of anxiety disorder not otherwise specified (ADNOS): a transdiagnostic approach

The transdiagnostic approach to eating disorders has led to significant benefit for the research and treatment of "eating disorder not otherwise specified" (EDNOS). There is currently almost no research on "anxiety disorder not otherwise specified (ADNOS)." This case report describes a transdiagnostic approach to the treatment of ADNOS, using a modular framework. The treatment was successful in the short term but not in the longer term. It is concluded that increasing the evidence base for transdiagnostic treatment of anxiety disorders is a clinical and research priority.

The BOLD response during Stroop task-like inhibition paradigms: effects of task difficulty and task-relevant modality

Previous studies of the Stroop task propose two key mediators: the prefrontal and cingulate cortices but hints exist of functional specialization within these regions. This study aimed to examine the effect of task modality upon the prefrontal and cingulate response by examining the response to colour, number, and shape Stroop tasks whilst BOLD fMRI images were acquired on a Siemens 3 T MRI scanner. Behavioural analyses indicated facilitation and interference effects and a noticeable effect of task difficulty. Some modular effects of modality were observed in the prefrontal cortex that survived exclusion of task difficulty related activations. No effect of task-relevant information was observed in the anterior cingulate. Future comparisons of the mediation of selective attention need to consider the effects of task context and task difficulty. (c) 2005 Elsevier Inc. All rights reserved.

Closely coupled behaviour in networked robot systems

Multiple cooperating robot systems may be required to take up a closely coupled configuration in order to perform a task. An example is extended baseline stereo (EBS), requiring that two robots must establish and maintain for a certain period of time a constrained kinematic relationship to each other. In this paper we report on the development of a networked robotics framework for modular, distributed robot systems that supports the creation of such configurations. The framework incorporates a query mechanism to locate modules distributed across the two robot systems. The work presented in this paper introduces special mechanisms to model the kinematic constraint and its instantiation. The EBS configuration is used as a case study and experimental implementation to demonstrate the approach.

Empirical impact of cache effects for vision system design

Computer vision applications generally split their problem into multiple simpler tasks. Likewise research often combines algorithms into systems for evaluation purposes. Frameworks for modular vision provide interfaces and mechanisms for algorithm combination and network transparency. However, these don’t provide interfaces efficiently utilising the slow memory in modern PCs. We investigate quantitatively how system performance varies with different patterns of memory usage by the framework for an example vision system.

A framework for comparing pollinator performance: effectiveness and efficiency

Measuring pollinator performance has become increasingly important with emerging needs for risk assessment in conservation and sustainable agriculture that require multi-year and multi-site comparisons across studies. However, comparing pollinator performance across studies is difficult because of the diversity of concepts and disparate methods in use. Our review of the literature shows many unresolved ambiguities. Two different assessment concepts predominate: the first estimates stigmatic pollen deposition and the underlying pollinator behaviour parameters, while the second estimates the pollinator’s contribution to plant reproductive success, for example in terms of seed set. Both concepts include a number of parameters combined in diverse ways and named under a diversity of synonyms and homonyms. However, these concepts are overlapping because pollen deposition success is the most frequently used proxy for assessing the pollinator’s contribution to plant reproductive success. We analyse the diverse concepts and methods in the context of a new proposed conceptual framework with a modular approach based on pollen deposition, visit frequency, and contribution to seed set relative to the plant’s maximum female reproductive potential. A system of equations is proposed to optimize the balance between idealised theoretical concepts and practical operational methods. Our framework permits comparisons over a range of floral phenotypes, and spatial and temporal scales, because scaling up is based on the same fundamental unit of analysis, the single visit.

Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations

This paper describes a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation models using the extended Kalman filter. The method involves the use of a time-varying linearisation of a semi-explicit index one differential-algebraic equation. The estimation technique consists of a simplified extended Kalman filter that is integrated with the differential-algebraic equation model. The paper describes a simulation study using a model of a batch chemical reactor. It also reports a study based on experimental data obtained from a mixing process, where the model of the system is solved using the sequential modular method and the estimation involves a bank of extended Kalman filters.

Intact automatic imitation of human and robot actions in autism spectrum disorders

The existence of a specialized imitation module in humans is hotly debated. Studies suggesting a specific imitation impairment in individuals with autism spectrum disorders (ASD) support a modular view. However, the voluntary imitation tasks used in these studies (which require socio-cognitive abilities in addition to imitation for successful performance) cannot support claims of a specific impairment. Accordingly, an automatic imitation paradigm (a ‘cleaner’ measure of imitative ability) was used to assess the imitative ability of 16 adults with ASD and 16 non-autistic matched control participants. Participants performed a prespecified hand action in response to observed hand actions performed either by a human or a robotic hand. On compatible trials the stimulus and response actions matched, while on incompatible trials the two actions did not match. Replicating previous findings, the Control group showed an automatic imitation effect: responses on compatible trials were faster than those on incompatible trials. This effect was greater when responses were made to human than to robotic actions (‘animacy bias’). The ASD group also showed an automatic imitation effect and a larger animacy bias than the Control group. We discuss these findings with reference to the literature on imitation in ASD and theories of imitation.

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.

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.

J-measure based hybrid pruning for complexity reduction in classification rules

Prism is a modular classification rule generation method based on the ‘separate and conquer’ approach that is alternative to the rule induction approach using decision trees also known as ‘divide and conquer’. Prism often achieves a similar level of classification accuracy compared with decision trees, but tends to produce a more compact noise tolerant set of classification rules. As with other classification rule generation methods, a principle problem arising with Prism is that of overfitting due to over-specialised rules. In addition, over-specialised rules increase the associated computational complexity. These problems can be solved by pruning methods. For the Prism method, two pruning algorithms have been introduced recently for reducing overfitting of classification rules - J-pruning and Jmax-pruning. Both algorithms are based on the J-measure, an information theoretic means for quantifying the theoretical information content of a rule. Jmax-pruning attempts to exploit the J-measure to its full potential because J-pruning does not actually achieve this and may even lead to underfitting. A series of experiments have proved that Jmax-pruning may outperform J-pruning in reducing overfitting. However, Jmax-pruning is computationally relatively expensive and may also lead to underfitting. This paper reviews the Prism method and the two existing pruning algorithms above. It also proposes a novel pruning algorithm called Jmid-pruning. The latter is based on the J-measure and it reduces overfitting to a similar level as the other two algorithms but is better in avoiding underfitting and unnecessary computational effort. The authors conduct an experimental study on the performance of the Jmid-pruning algorithm in terms of classification accuracy and computational efficiency. The algorithm is also evaluated comparatively with the J-pruning and Jmax-pruning algorithms.