On the clustering of winter storm loss events over Germany

During the last decades, several windstorm series hit Europe leading to large aggregated losses. Such storm series are examples of serial clustering of extreme cyclones, presenting a considerable risk for the insurance industry. Clustering of events and return periods of storm series for Germany are quantified based on potential losses using empirical models. Two reanalysis data sets and observations from German weather stations are considered for 30 winters. Histograms of events exceeding selected return levels (1-, 2- and 5-year) are derived. Return periods of historical storm series are estimated based on the Poisson and the negative binomial distributions. Over 4000 years of general circulation model (GCM) simulations forced with current climate conditions are analysed to provide a better assessment of historical return periods. Estimations differ between distributions, for example 40 to 65 years for the 1990 series. For such less frequent series, estimates obtained with the Poisson distribution clearly deviate from empirical data. The negative binomial distribution provides better estimates, even though a sensitivity to return level and data set is identified. The consideration of GCM data permits a strong reduction of uncertainties. The present results support the importance of considering explicitly clustering of losses for an adequate risk assessment for economical applications.

A prolegomenon to the construct of the native speaker: heritage speaker bilinguals are natives too!

This Forum challenges the generally accepted position in the linguistic sciences—conscious or not—that monolingualism and nativeness are essentially synonymous in an exclusive way. We discuss two consequences of our position that naturalistic bilinguals and multilinguals exposed to a language in early childhood are also native speakers: (i) that bi-/multilinguals have multiple native languages;and (ii) nativeness can be applicable to a state of linguistic knowledge that is characterized by significant differences to the monolingual baseline.

Large-scale dynamics associated with clustering of extratropical cyclones affecting Western Europe

Some recent winters in Western Europe have been characterized by the occurrence of multiple extratropical cyclones following a similar path. The occurrence of such cyclone clusters leads to large socio-economic impacts due to damaging winds, storm surges, and floods. Recent studies have statistically characterized the clustering of extratropical cyclones over the North Atlantic and Europe and hypothesized potential physical mechanisms responsible for their formation. Here we analyze 4 months characterized by multiple cyclones over Western Europe (February 1990, January 1993, December 1999, and January 2007). The evolution of the eddy driven jet stream, Rossby wave-breaking, and upstream/downstream cyclone development are investigated to infer the role of the large-scale flow and to determine if clustered cyclones are related to each other. Results suggest that optimal conditions for the occurrence of cyclone clusters are provided by a recurrent extension of an intensified eddy driven jet toward Western Europe lasting at least 1 week. Multiple Rossby wave-breaking occurrences on both the poleward and equatorward flanks of the jet contribute to the development of these anomalous large-scale conditions. The analysis of the daily weather charts reveals that upstream cyclone development (secondary cyclogenesis, where new cyclones are generated on the trailing fronts of mature cyclones) is strongly related to cyclone clustering, with multiple cyclones developing on a single jet streak. The present analysis permits a deeper understanding of the physical reasons leading to the occurrence of cyclone families over the North Atlantic, enabling a better estimation of the associated cumulative risk over Europe.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Serial clustering of extratropical cyclones in a multi-model ensemble of historical and future simulations

This study has investigated serial (temporal) clustering of extra-tropical cyclones simulated by 17 climate models that participated in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December-February counts of Atlantic storm tracks passing nearby each grid point. Results from single historical simulations of 1975-2005 were compared to those from historical ERA40 reanalyses from 1958-2001 ERA40 and single future model projections of 2069-2099 under the RCP4.5 climate change scenario. Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability. Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30 year sample, is prone to large amounts of sampling uncertainty that obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion that are discernible above natural sampling variations.

What drives ICT clustering in European Cities?

ICT clusters have attracted much attention because of their rapid growth and their value for other economic activities. Using a nested multi-level model, we examine how conditions at the country level and at the city level affect ICT clustering activity in 227 cities across 22 European countries. We test for the influence of three country regulations (starting a business, registering property, enforcing contracts) and two city conditions (proximity to university, network density) on ICT clustering. We consider heterogeneity within the sector and study two types of ICT activities: ICT product firms and ICT content firms. Our results indicate that country conditions and city conditions each have idiosyncratic implications for ICT clustering, and further, that these can vary by activities in ICT products or ICT content manufacturing.

Exploring the source of differences and similarities in L1 attrition and heritage speaker competence: evidence from pronominal resolution

Several studies of different bilingual groups including L2 learners, child bilinguals, heritage speakers and L1 attriters reveal similar performance on syntax-discourse interface properties such as anaphora resolution (Sorace, 2011 and references therein). Specifically, bilinguals seem to allow more optionality in the interpretation of overt subject pronouns in null subject languages, such as Greek, Italian and Spanish while the interpretation of null subject pronouns is indistinguishable from monolingual natives. Nevertheless, there is some evidence pointing to bilingualism effects on the interpretation of null subject pronouns too in heritage speakers’ grammars (Montrul, 2004) due to some form of ‘arrested’ development in this group of bilinguals. The present study seeks to investigate similarities and differences between two Greek–Swedish bilingual groups, heritage speakers and L1 attriters, in anaphora resolution of null and overt subject pronouns in Greek using a self-paced listening with a sentence-picture matching decision task at the end of each sentence. The two groups differ in crucial ways: heritage speakers were simultaneous or early bilinguals while the L1 attriters were adult learners of the second language, Swedish. Our findings reveal differences from monolingual preferences in the interpretation of the overt pronoun for both heritage and attrited speakers while the differences attested between the two groups in the interpretation of null subject pronouns affect only response times with heritage being faster than attrited speakers. We argue that our results do not support an age of onset or differential input effects on bilingual performance in pronoun resolution.

On L2 Clustering and Resetting the Null Subject Parameter in L2 Spanish: Implications and Observations.

In this article, along with others, we take the position that the Null-Subject Parameter (NSP) (Chomsky 1981; Rizzi 1982) cluster of properties is narrower in scope than some originally contended. We test for the resetting of the NSP by English L2 learners of Spanish at the intermediate level, including poverty-of-the stimulus knowledge of the Overt Pronoun Constraint (Montalbetti 1984). Our participants are tested before and after five months' residency in Spain in an effort to see if increased amounts of native exposure are particularly beneficial for parameter resetting. Although we demonstrate NSP resetting for some of the L2 learners, our data essentially demonstrate that even with the advent of time/exposure to native input, there is no immediate gainful effect for NSP resetting.

Eventive and stative passives and copula selection in Canadian and American heritage speaker Spanish

Spanish captures the difference between eventive and stative passives via an obligatory choice between two copula; verbal passives take the copula ser and adjectival passives take the copula estar. In this study, we compare and contrast US and Canadian heritage speakers of Spanish on their knowledge of this difference in relation to copula choice in Spanish. The backgrounds of the target groups differ significantly from each other in that only one of them, the Canadian group, has grown up in a societal multilingual environment. We discuss the results as being supportive of two non-mutually exclusive explanation factors: (a) French facilitates (bootstraps) the acquisition of eventive and stative passives and/or (b) the US/Canadian HS differences (e.g. status of bilingualism and the languages at stake) is a reflection of the uniqueness of the language contact situations and the effects this has on the input HSS receive.

Syk and Src family kinases regulate C-type lectin receptor 2 (CLEC-2)-mediated clustering of podoplanin and platelet adhesion to lymphatic endothelial cells

The interaction of C-type lectin receptor 2 (CLEC-2) on platelets with Podoplanin on lymphatic endothelial cells initiates platelet signaling events that are necessary for prevention of blood-lymph mixing during development. In the present study, we show that CLEC-2 signaling via Src family and Syk tyrosine kinases promotes platelet adhesion to primary mouse lymphatic endothelial cells at low shear. Using supported lipid bilayers containing mobile Podoplanin, we further show that activation of Src and Syk in platelets promotes clustering of CLEC-2 and Podoplanin. Clusters of CLEC-2-bound Podoplanin migrate rapidly to the center of the platelet to form a single structure. Fluorescence lifetime imaging demonstrates that molecules within these clusters are within 10 nm of one another and that the clusters are disrupted by inhibition of Src and Syk family kinases. CLEC-2 clusters are also seen in platelets adhered to immobilized Podoplanin using direct stochastic optical reconstruction microscopy. These findings provide mechanistic insight by which CLEC-2 signaling promotes adhesion to Podoplanin and regulation of Podoplanin signaling, thereby contributing to lymphatic vasculature development.

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.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.