The Grid[Way] Job Template Manager, a tool for parameter sweeping

Parameter sweeping is a widely used algorithmic technique in computational science. It is specially suited for high-throughput computing since the jobs evaluating the parameter space are loosely coupled or independent. A tool that integrates the modeling of a parameter study with the control of jobs in a distributed architecture is presented. The main task is to facilitate the creation and deletion of job templates, which are the elements describing the jobs to be run. Extra functionality relies upon the GridWay Metascheduler, acting as the middleware layer for job submission and control. It supports interesting features like multi-dimensional sweeping space, wildcarding of parameters, functional evaluation of ranges, value-skipping and job template automatic indexation. The use of this tool increases the reliability of the parameter sweep study thanks to the systematic bookkeping of job templates and respective job statuses. Furthermore, it simplifies the porting of the target application to the grid reducing the required amount of time and effort.Comment: 26 pages, 1 figure

Oscillation of generalized differences of H\"older and Zygmund functions

In this paper we analyze the oscillation of functions having derivatives in the H\"older or Zygmund class in terms of generalized differences and prove that its growth is governed by a version of the classical Kolmogorov's Law of the Iterated Logarithm. A better behavior is obtained for functions in the Lipschitz class via an interesting connection with Calder\'on-Zygmund operators.Comment: 16 page

Predicting human preferences using the block structure of complex social networks

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups

Pseudo-Probabilistic Design for High-Resolution Tsunami Simulations in the Southwestern Spanish Coast

The application of simulation software has proven to be a crucial tool for tsunami hazard assessment studies. Understanding the potentially devastating effects of tsunamis leads to the development of safety and resilience measures, such as the design of evacuation plans or the planning of the economic investment necessary to quickly mitigate their consequences. This article introduces a pseudo-probabilistic seismic-triggered tsunami simulation approach to investigate the potential impact of tsunamis in the southwestern coast of Spain, in the provinces of Huelva and Cádiz. Selected faults, probabilistic distributions and sampling methods are presented as well as some results for the nearly 900 Atlantic-origin tsunamis computed along the 250 km-long coast.This work has being carried out under a project funded by a public mutual agreement of understanding between the CN-IGME (CSIC) and the CCS (Law reference: BOE 103, 30/04/2019). This project is supported by an agreement of understanding between CN-IGME and UMA, creating a cooperative entity INGEA (Law reference: BOE 332, 22/12/2020). The numerical results presented in this work have been performed with the computational resources allocated by the Spanish Network for Supercomputing (RES) grants AECT-2020-3-0023 and AECT-2021-2-0018. Further support has also been received from the Spanish Government research project MEGAFLOW (RTI2018-096064-B-C21) and ChEESE project (EU Horizon 2020, grant agreement No. 823844, https://cheese-coe.eu/) due to the synergies found between the projects. Partial funding for open access charge: Universidad de Málag

Mobility and interaction patterns in social networks

The question of analyzing the predictability of human behavior has been widely studied in literature, to unveil how individuals move, how they can be mobilized and, more philosophically, to understand to what extent our decisions are random or whether we are free to choose. As a consequence of humans relate to each other, we also tend to live in groups at different hierarchies in a social way so it is interesting to analyze how individual features and choices affect the global structure of a society. In this work, we explore the limits of human predictability in terms of shopping behavior, observing that, even when we are constrained to a limited set of possible places where we can make a purchase, predicting where the next purchase will happen is not accurately possible to do by only observing the past. The next question is to study how individual decisions affect emergent phenomena such as the economy or information diffusion across a country. We analyze the contents, temporal and mobility patterns extracted from users’ social media publications to build a profile of the geographical regions that allow to predict the unemployment rate. Finally, we also use a mobile phone call dataset to test whether the dynamics at the urban level, how people create and destroy links within a city, affect the inter-urban diffusion of diseases, virus or rumors. Our results suggest that inter-regional structure is robust and does not vary significantly on time so diffusion processes can be well modeled in terms of static properties of the inter-urban network.Programa Oficial de Doctorado en Ingeniería MatemáticaPresidente: Javier Borge Holthoefer.- Secretario: Rubén Cuevas Rumín.- Vocal: Josep Perelló Palo

Winning the War for Talent: An Experimental Evaluation of Online Recruitment Campaigns Using Twitter

Organizations have moved rapidly from traditional recruitment methods to online recruiting. The present study argues that the fierce demand for labor in technology-related industries —“second war for talent”— besieges workers in competitive environments to the point of lowering their propensity to engage in online recruiting campaigns. Collecting data from the social media platform Twitter, we take an experimental approach to investigate the effectiveness of online recruitment processes in attracting the attention of potential job candidates from different occupational categories. The findings reveal that workers in technology, engineering, and mathematical occupations (TEM) are less likely to react to recruitment processes than workers in other professional jobs. However, motivated advertisement designed according to individual group interests significantly increase the rate of participation of TEM, while these ads have no effect on workers from other sectors. Our experiment helps to explain pre-hiring outcomes. The findings have important implications for organizations seeking to boost their talent acquisition strategies

Social Media Fingerprints of Unemployment

Anexo: Supporting Information. This file contains Figures A-I, Tables A-F and Sections A-I.Recent widespread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and interpersonal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 19 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.Partial funding came from the Spanish Ministry of Economy and Competitiveness through grant FIS2013-47532-C3-3-P, the Australian Government, and the Australian Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Plyometric exercise and bone health in children and adolescents: a systematic review

Background: Many jumping interventions have been performed in children and adolescents in order to improve bone-related variables and thus, ensure a healthy bone development during these periods and later in life. This systematic review aims to summarize and update present knowledge regarding the effects that jumping interventions may have on bone mass, structure and metabolism in order to ascertain the efficacy and durability (duration of the effects caused by the intervention) of the interventions. Methods: Identification of studies was performed by searching in the database MEDLINE/PubMed and SportDiscus. Additional studies were identified by contacting clinical experts and searching bibliographies and abstracts. Search terms included “bone and bones”, “jump*”, “weight-bearing”, “resistance training” and “school intervention”. The search was conducted up to October 2014. Only studies that had performed a specific jumping intervention in under 18-year olds and had measured bone mass were included. Independent extraction of articles was done by 2 authors using predefined data fields. Results: A total of 26 studies were included in this review. Twenty-four studies found positive results as subjects included in the intervention groups showed higher bone mineral density, bone mineral content and bone structure improvements than controls. Only two studies found no effects on bone mass after a 10-week and 9-month intervention. Moreover, those studies that evaluated the durability of the effects found that some of the increases in the intervention groups were maintained after several years. Conclusion: Jumping interventions during childhood and adolescence improve bone mineral content, density and structural properties without side effects. These type of interventions should be therefore implemented when possible in order to increase bone mass in early stages of life, which may have a direct preventive effect on bone diseases like osteoporosis later in life