Cognitive stimulation through task-oriented telerobotics

The project presented, iCognos, consists of a flexible platform to assist end-users in performing a series of mental tasks with a sensitized mobile telerobotic platform aimed at mitigating the problems associated to cognitive disorders with an ecological cognition approach.

PhD Students’ Research Group Social Capital in Two Countries: A Clustering Approach with Duocentred Network Measures

The article examines the structure of the collaboration networks of research groups where Slovenian and Spanish PhD students are pursuing their doctorate. The units of analysis are student-supervisor dyads. We use duocentred networks, a novel network structure appropriate for networks which are centred around a dyad. A cluster analysis reveals three typical clusters of research groups. Those which are large and belong to several institutions are labelled under a bridging social capital label. Those which are small, centred in a single institution but have high cohesion are labelled as bonding social capital. Those which are small and with low cohesion are called weak social capital groups. Academic performance of both PhD students and supervisors are highest in bridging groups and lowest in weak groups. Other variables are also found to differ according to the type of research group. At the end, some recommendations regarding academic and research policy are drawn

Clustering in complex networks. I. General formalism

We develop a full theoretical approach to clustering in complex networks. A key concept is introduced, the edge multiplicity, that measures the number of triangles passing through an edge. This quantity extends the clustering coefficient in that it involves the properties of two¿and not just one¿vertices. The formalism is completed with the definition of a three-vertex correlation function, which is the fundamental quantity describing the properties of clustered networks. The formalism suggests different metrics that are able to thoroughly characterize transitive relations. A rigorous analysis of several real networks, which makes use of this formalism and the metrics, is also provided. It is also found that clustered networks can be classified into two main groups: the weak and the strong transitivity classes. In the first class, edge multiplicity is small, with triangles being disjoint. In the second class, edge multiplicity is high and so triangles share many edges. As we shall see in the following paper, the class a network belongs to has strong implications in its percolation properties.

Clustering in complex networks. II. Percolation properties

The percolation properties of clustered networks are analyzed in detail. In the case of weak clustering, we present an analytical approach that allows us to find the critical threshold and the size of the giant component. Numerical simulations confirm the accuracy of our results. In more general terms, we show that weak clustering hinders the onset of the giant component whereas strong clustering favors its appearance. This is a direct consequence of the differences in the k-core structure of the networks, which are found to be totally different depending on the level of clustering. An empirical analysis of a real social network confirms our predictions.

Tuning clustering in random networks with arbitrary degree distributions

We present a generator of random networks where both the degree-dependent clustering coefficient and the degree distribution are tunable. Following the same philosophy as in the configuration model, the degree distribution and the clustering coefficient for each class of nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding topologies by applying first a closure of triangles and second the classical closure of remaining free stubs. The procedure unveils an universal relation among clustering and degree-degree correlations for all networks, where the level of assortativity establishes an upper limit to the level of clustering. Maximum assortativity ensures no restriction on the decay of the clustering coefficient whereas disassortativity sets a stronger constraint on its behavior. Correlation measures in real networks are seen to observe this structural bound.

Conserved chromosomal clustering of genes governed by chromatin regulators in Drosophila

Background: The trithorax group (trxG) and Polycomb group (PcG) proteins are responsible for the maintenance of stable transcriptional patterns of many developmental regulators. They bind to specific regions of DNA and direct the post-translational modifications of histones, playing a role in the dynamics of chromatin structure.Results: We have performed genome-wide expression studies of trx and ash2 mutants in Drosophila melanogaster. Using computational analysis of our microarray data, we have identified 25 clusters of genes potentially regulated by TRX. Most of these clusters consist of genes that encode structural proteins involved in cuticle formation. This organization appears to be a distinctive feature of the regulatory networks of TRX and other chromatin regulators, since we have observed the same arrangement in clusters after experiments performed with ASH2, as well as in experiments performed by others with NURF, dMyc, and ASH1. We have also found many of these clusters to be significantly conserved in D. simulans, D. yakuba, D. pseudoobscura and partially in Anopheles gambiae.Conclusion: The analysis of genes governed by chromatin regulators has led to the identification of clusters of functionally related genes conserved in other insect species, suggesting this chromosomal organization is biologically important. Moreover, our results indicate that TRX and other chromatin regulators may act globally on chromatin domains that contain transcriptionally co-regulated genes.

Outcomes from studies of Antineutrophil Cytoplasm Antibody Associated Vasculitis: a systematic review by the European League Against Rheumatism systemic vasculitis task force.

Objectives: We undertook a systematic literature review as a background to the European League Against Rheumatism (EULAR) recommendations for conducting clinical trials in anti-neutrophil cytoplasm antibody associated vasculitis (AAV), and to assess the quality of evidence for outcome measures in AAV. Methods: Using a systematic Medline search, we categorised the identified studies according to diagnoses. Factors affecting remission, relapse, renal function and overall survival were identified. Results: A total of 44 papers were reviewed from 502 identified by our search criteria. There was considerable inconsistency in definitions of end points. Remission rates varied from 30% to 93% in Wegener granulomatosis (WG), 75% to 89% in microscopic polyangiitis (MPA) and 81% to 91% in Churg¿Strauss syndrome (CSS). The 5-year survival for WG, MPA and CSS was 74¿91%, 45¿76% and 60¿97%. Relapse (variably defined) was common in the first 2 years but the frequency varied: 18% to 60% in WG, 8% in MPA, and 35% in CSS. The rate of renal survival in WG varied from 23% at 15 months to 23% at 120 months. Methods used to assess morbidity varied between studies. Ignoring the variations in definitions of the stage of disease, factors influencing remission, relapse, renal and overall survival included immunosuppressive therapy used, type of organ involvement, presence of ANCA, older age and male ender. Conclusions: Factors influencing remission, relapse, renal and overall survival include the type of immunosuppressive therapy used, pattern of organ involvement, presence of ANCA, older age and male gender. Methodological variations between studies highlight the need for a consensus on terminology and definitions for future conduct of clinical studies in AAV.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

Quality of Learners' Time and Learning Performance Beyond Quantitative Time-on-Task

Peer-reviewed

Clustering of grape yield maps to delineate site-specific management zones

Zonal management in vineyards requires the prior delineation of stable yield zones within the parcel. Among the different methodologies used for zone delineation, cluster analysis of yield data from several years is one of the possibilities cited in scientific literature. However, there exist reasonable doubts concerning the cluster algorithm to be used and the number of zones that have to be delineated within a field. In this paper two different cluster algorithms have been compared (k-means and fuzzy c-means) using the grape yield data corresponding to three successive years (2002, 2003 and 2004), for a ‘Pinot Noir’ vineyard parcel. Final choice of the most recommendable algorithm has been linked to obtaining a stable pattern of spatial yield distribution and to allowing for the delineation of compact and average sized areas. The general recommendation is to use reclassified maps of two clusters or yield classes (low yield zone and high yield zone) and, consequently, the site-specific vineyard management should be based on the prior delineation of just two different zones or sub-parcels. The two tested algorithms are good options for this purpose. However, the fuzzy c-means algorithm allows for a better zoning of the parcel, forming more compact areas and with more equilibrated zonal differences over time.

Task-induced deactivation from rest extends beyond the default mode brain network

Activity decreases, or deactivations, of midline and parietal cortical brain regions are routinely observed in human functional neuroimaging studies that compare periods of task-based cognitive performance with passive states, such as rest. It is now widely held that such task-induced deactivations index a highly organized"default-mode network" (DMN): a large-scale brain system whose discovery has had broad implications in the study of human brain function and behavior. In this work, we show that common task-induced deactivations from rest also occur outside of the DMN as a function of increased task demand. Fifty healthy adult subjects performed two distinct functional magnetic resonance imaging tasks that were designed to reliably map deactivations from a resting baseline. As primary findings, increases in task demand consistently modulated the regional anatomy of DMN deactivation. At high levels of task demand, robust deactivation was observed in non-DMN regions, most notably, the posterior insular cortex. Deactivation of this region was directly implicated in a performance-based analysis of experienced task difficulty. Together, these findings suggest that task-induced deactivations from rest are not limited to the DMN and extend to brain regions typically associated with integrative sensory and interoceptive processes.

Task complexity, the Cognition Hypothesis and second language learning and performance

In this paper we describe a taxonomy of task demands which distinguishes between Task Complexity, Task Condition and Task Difficulty. We then describe three theoretical claims and predictions of the Cognition Hypothesis (Robinson 2001, 2003b, 2005a) concerning the effects of task complexity on: (a) language production; (b) interaction and uptake of information available in the input to tasks; and (c) individual differences-task interactions. Finally we summarize the findings of the empirical studies in this special issue which all address one or more of these predictions and point to some directions for continuing, future research into the effects of task complexity on learning and performance.

Effects of manipulating Task Complexity on self-repairs during L2 oral production

This paper analyses the effects of manipulating the cognitive complexity of L2 oral tasks on language production. It specifically focuses on self-repairs, which are taken as a measure of accuracy since they denote both attention to form and an attempt at being accurate. By means of a repeated measures de- sign, 42 lower-intermediate students were asked to perform three different tasks types (a narrative, and instruction-giving task, and a decision-making task) for which two degrees of cognitive complexity were established. The narrative task was manipulated along +/− Here-and-Now, an instruction-giving task ma- nipulated along +/− elements, and the decision-making task which is manipu- lated along +/− reasoning demands. Repeated measures ANOVAs are used for the calculation of differences between degrees of complexity and among task types. One-way ANOVA are used to detect potential differences between low- proficiency and high-proficiency participants. Results show an overall effect of Task Complexity on self-repairs behavior across task types, with different be- haviors existing among the three task types. No differences are found between the self-repair behavior between low and high proficiency groups. Results are discussed in the light of theories of cognition and L2 performance (Robin- son 2001a, 2001b, 2003, 2005, 2007), L1 and L2 language production models (Levelt 1989, 1993; Kormos 2000, 2006), and attention during L2 performance (Skehan 1998; Robinson, 2002).

Task Trade and its determinants in Spain: a national and regional analysis

Globalisation and technological advances have made possible to offshore specific productive tasks (that do not require physical proximity to the actual location of the work unit) to foreign countries where these are usually performed at lower costs. We analyse the effect of task trade (i.e. task offshorability) on Spanish regional and national employment levels correlating a newly built index of task-delocalisation index to key variables such as the region’s wealth, the worker’s age and level of education, the importance of the service sector and the technological level of the economic activities undertaken in that particular geographical area. We conclude that approximately 25 per cent of Spanish occupations are potentially affected by task trade / offshoring and that this is likely to benefit Spanish economy (and the performance of specific regions, categories of workers and sectors) being Spain a potential recipient of tasks offshored from abroad. Also we obtain that Spain’s trade in tasks correlates strongly with the above variables, presenting significant regional differences.

Language Learning in Task Management and Task Accomplishment

In this paper we study student interaction in English and Swedish courses at a Finnish university. We focus on language choices made in task-related activities in small group interaction. Our research interests arose from the change in the teaching curriculum, in which content and language courses were integrated at Tampere University of Technology in 2013. Using conversation analysis, we analysed groups of 4-5 students who worked collaboratively on a task via a video conference programme. The results show how language alternation has different functions in 1) situations where students orient to managing the task, e.g., in transitions into task, or where they orient to technical problems, and 2) situations where students accomplish the task. With the results, we aim to show how language alternation can provide interactional opportunities for language learning. The findings will be useful in designing tasks in the future.