Model endgame analysis

A reference model of Fallible Endgame Play has been implemented and exercised with the chess engine WILHELM. Various experiments have demonstrated the value of the model and the robustness of decisions based on it. Experimental results have also been compared with the theoretical predictions of a Markov model of the endgame and found to be in close agreement.

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

Isolation of culturable yeasts from market wines and evaluation of the 5 center dot 8S-ITS rDNA sequence analysis for identification purposes

Aims: To test the possibility that wines available in the marketplace may contain culturable yeasts and to evaluate the 5.8S-ITS rDNA sequence analysis as adequate means for the identification of isolates. Methods and Results: As a case study, typical Greek wines were surveyed. Sequence analysis of the 5.8S-ITS rDNA was tested for its robustness in species or strain identification. Sixteen isolates could be assigned into the species Brettanomyces bruxellensis, Saccharomyces cerevisiae and Rhodotorula pinicola, whereas four isolates could not be safely identified. B. bruxellensis was the dominant species present in house wines, while non-Saccharomyces sp. were viable in aged wines of high alcohol content. Conclusions: Yeast population depends on postfermentation procedures or storage conditions. Although 5.8S-ITS rDNA sequence analysis is generally a rapid method to identify wine yeast isolates at the species level, or even below that, it may not be sufficient for some genera. Significance and Impact of the Study: This is the first report to show that commercial wines may possess diverse and potentially harmful yeast populations. The knowledge of yeasts able to reside in this niche environment is essential towards integrated quality assurance programmes. For selected species, the 5.8S-ITS rDNA sequence analysis is a rapid and accurate means.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

Robustness and applicability of Markov chain Monte Carlo algorithms for eigenvalue problems

In this paper we analyse applicability and robustness of Markov chain Monte Carlo algorithms for eigenvalue problems. We restrict our consideration to real symmetric matrices. Almost Optimal Monte Carlo (MAO) algorithms for solving eigenvalue problems are formulated. Results for the structure of both - systematic and probability error are presented. It is shown that the values of both errors can be controlled independently by different algorithmic parameters. The results present how the systematic error depends on the matrix spectrum. The analysis of the probability error is presented. It shows that the close (in some sense) the matrix under consideration is to the stochastic matrix the smaller is this error. Sufficient conditions for constructing robust and interpolation Monte Carlo algorithms are obtained. For stochastic matrices an interpolation Monte Carlo algorithm is constructed. A number of numerical tests for large symmetric dense matrices are performed in order to study experimentally the dependence of the systematic error from the structure of matrix spectrum. We also study how the probability error depends on the balancing of the matrix. (c) 2007 Elsevier Inc. All rights reserved.

The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland

A first step in interpreting the wide variation in trace gas concentrations measured over time at a given site is to classify the data according to the prevailing weather conditions. In order to classify measurements made during two intensive field campaigns at Mace Head, on the west coast of Ireland, an objective method of assigning data to different weather types has been developed. Air-mass back trajectories calculated using winds from ECMWF analyses, arriving at the site in 1995–1997, were allocated to clusters based on a statistical analysis of the latitude, longitude and pressure of the trajectory at 12 h intervals over 5 days. The robustness of the analysis was assessed by using an ensemble of back trajectories calculated for four points around Mace Head. Separate analyses were made for each of the 3 years, and for four 3-month periods. The use of these clusters in classifying ground-based ozone measurements at Mace Head is described, including the need to exclude data which have been influenced by local perturbations to the regional flow pattern, for example, by sea breezes. Even with a limited data set, based on 2 months of intensive field measurements in 1996 and 1997, there are statistically significant differences in ozone concentrations in air from the different clusters. The limitations of this type of analysis for classification and interpretation of ground-based chemistry measurements are discussed.

Convergence analysis of stochastic diffusion search

In this paper we present a connectionist searching technique - the Stochastic Diffusion Search (SDS), capable of rapidly locating a specified pattern in a noisy search space. In operation SDS finds the position of the pre-specified pattern or if it does not exist - its best instantiation in the search space. This is achieved via parallel exploration of the whole search space by an ensemble of agents searching in a competitive cooperative manner. We prove mathematically the convergence of stochastic diffusion search. SDS converges to a statistical equilibrium when it locates the best instantiation of the object in the search space. Experiments presented in this paper indicate the high robustness of SDS and show good scalability with problem size. The convergence characteristic of SDS makes it a fully adaptive algorithm and suggests applications in dynamically changing environments.

Does increased hydrochemical model complexity decrease robustness?

The aim of this study was, within a sensitivity analysis framework, to determine if additional model complexity gives a better capability to model the hydrology and nitrogen dynamics of a small Mediterranean forested catchment or if the additional parameters cause over-fitting. Three nitrogen-models of varying hydrological complexity were considered. For each model, general sensitivity analysis (GSA) and Generalized Likelihood Uncertainty Estimation (GLUE) were applied, each based on 100,000 Monte Carlo simulations. The results highlighted the most complex structure as the most appropriate, providing the best representation of the non-linear patterns observed in the flow and streamwater nitrate concentrations between 1999 and 2002. Its 5% and 95% GLUE bounds, obtained considering a multi-objective approach, provide the narrowest band for streamwater nitrogen, which suggests increased model robustness, though all models exhibit periods of inconsistent good and poor fits between simulated outcomes and observed data. The results confirm the importance of the riparian zone in controlling the short-term (daily) streamwater nitrogen dynamics in this catchment but not the overall flux of nitrogen from the catchment. It was also shown that as the complexity of a hydrological model increases over-parameterisation occurs, but the converse is true for a water quality model where additional process representation leads to additional acceptable model simulations. Water quality data help constrain the hydrological representation in process-based models. Increased complexity was justifiable for modelling river-system hydrochemistry. Increased complexity was justifiable for modelling river-system hydrochemistry.

Performance evaluation of crowd image analysis using the PETS2009 dataset

This paper presents the PETS2009 outdoor crowd image analysis surveillance dataset and the performance evaluation of people counting, detection and tracking results using the dataset submitted to five IEEE Performance Evaluation of Tracking and Surveillance (PETS) workshops. The evaluation was carried out using well established metrics developed in the Video Analysis and Content Extraction (VACE) programme and the CLassification of Events, Activities, and Relationships (CLEAR) consortium. The comparative evaluation highlights the detection and tracking performance of the authors’ systems in areas such as precision, accuracy and robustness and provides a brief analysis of the metrics themselves to provide further insights into the performance of the authors’ systems.

New-on-line method for water isotope analysis of speleothem fluid inclusions using laser absorption spectroscopy (WS-CRDS)

A new online method to analyse water isotopes of speleothem fluid inclusions using a wavelength scanned cavity ring down spectroscopy (WS-CRDS) instrument is presented. This novel technique allows us simultaneously to measure hydrogen and oxygen isotopes for a released aliquot of water. To do so, we designed a new simple line that allows the online water extraction and isotope analysis of speleothem samples. The specificity of the method lies in the fact that fluid inclusions release is made on a standard water background, which mainly improves the δ D robustness. To saturate the line, a peristaltic pump continuously injects standard water into the line that is permanently heated to 140 °C and flushed with dry nitrogen gas. This permits instantaneous and complete vaporisation of the standard water, resulting in an artificial water background with well-known δ D and δ18O values. The speleothem sample is placed in a copper tube, attached to the line, and after system stabilisation it is crushed using a simple hydraulic device to liberate speleothem fluid inclusions water. The released water is carried by the nitrogen/standard water gas stream directly to a Picarro L1102-i for isotope determination. To test the accuracy and reproducibility of the line and to measure standard water during speleothem measurements, a syringe injection unit was added to the line. Peak evaluation is done similarly as in gas chromatography to obtain &delta D; and δ18O isotopic compositions of measured water aliquots. Precision is better than 1.5 ‰ for δ D and 0.4 ‰ for δ18O for water measurements for an extended range (−210 to 0 ‰ for δ D and −27 to 0 ‰ for δ18O) primarily dependent on the amount of water released from speleothem fluid inclusions and secondarily on the isotopic composition of the sample. The results show that WS-CRDS technology is suitable for speleothem fluid inclusion measurements and gives results that are comparable to the isotope ratio mass spectrometry (IRMS) technique.

A Posteriori analysis of discontinuous Galerkin schemes for systems of hyperbolic conservation laws

In this work we construct reliable a posteriori estimates for some semi- (spatially) discrete discontinuous Galerkin schemes applied to nonlinear systems of hyperbolic conservation laws. We make use of appropriate reconstructions of the discrete solution together with the relative entropy stability framework, which leads to error control in the case of smooth solutions. The methodology we use is quite general and allows for a posteriori control of discontinuous Galerkin schemes with standard flux choices which appear in the approximation of conservation laws. In addition to the analysis, we conduct some numerical benchmarking to test the robustness of the resultant estimator.

Robustness despite uncertainty: regional climate data reveal the dominant role of humans in explaining global extinctions of Late Quaternary megafauna

Debate over the late Quaternary megafaunal extinctions has focussed on whether human colonisation or climatic changes were more important drivers of extinction, with few extinctions being unambiguously attributable to either. Most analyses have been geographically or taxonomically restricted and the few quantitative global analyses have been limited by coarse temporal resolution or overly simplified climate reconstructions or proxies. We present a global analysis of the causes of these extinctions which uses high-resolution climate reconstructions and explicitly investigates the sensitivity of our results to uncertainty in the palaeological record. Our results show that human colonisation was the dominant driver of megafaunal extinction across the world but that climatic factors were also important. We identify the geographic regions where future research is likely to have the most impact, with our models reliably predicting extinctions across most of the world, with the notable exception of mainland Asia where we fail to explain the apparently low rate of extinction found in in the fossil record. Our results are highly robust to uncertainties in the palaeological record, and our main conclusions are unlikely to change qualitatively following minor improvements or changes in the dates of extinctions and human colonisation.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

El Niño 2015/2016: impact analysis of past El Niños

This is a DfID (Department for International Development) commissioned report on the impact of historical El Niño events on low- and middle-income countries across Africa and elsewhere. The report identifies El Niño events in the past 35 years and highlights regions and countries vulnerable to their impacts. The impacts on rainfall and temperature are broken down by season and country as the El Niño develops, peaks and decays and are represented in at-a-glance Impact Tables. The Impact Tables also include an extensive review of literature (e.g., peer-reviewed, grey literature and media reports) to identify potential socio-economic impacts in vulnerable sectors such as water, infrastructure, energy and health. The risk of such impacts are graded as high, medium or potential depending on the meteorological signal and the robustness of evidence available.

The role of predator overlap in the robustness and extinction of a four species predator-prey network

Predators and preys often form species networks with asymmetric patterns of interaction. We study the dynamics of a four species network consisting of two weakly connected predator-prey pairs. We focus our analysis on the effects of the cross interaction between the predator of the first pair and the prey of the second pair. This is an example where the predator overlap, which is the proportion of predators that a given prey shares with other preys, is not uniform across the network due to asymmetries in patterns of interaction. We explore the behavior of the system under different interaction strengths and study the dynamics of survival and extinction. In particular, we consider situations in which the four species have initial populations lower than their long-term equilibrium, simulating catastrophic situations in which their abundances are reduced due to human action or environmental change. We show that, under these reduced initial conditions, and depending on the strength of the cross interaction, the populations tend to oscillate before re-equilibrating, disturbing the community equilibrium and sometimes reaching values that are only a small fraction of the equilibrium population, potentially leading to their extinction. We predict that, contrary to one`s intuition, the most likely scenario is the extinction of the less predated preys. (C) 2010 Elsevier B.V. All rights reserved.