The silicon stable isotope distribution along the GEOVIDE section (GEOTRACES GA-01) of the North Atlantic Ocean

The stable isotope composition of dissolved silicon in seawater (δ30SiDSi) was examined at 10 stations along the GEOVIDE section (GEOTRACES GA-01), spanning the North Atlantic Ocean (40–60∘ N) and Labrador Sea. Variations in δ30SiDSi below 500 m were closely tied to the distribution of water masses. Higher δ30SiDSi values are associated with intermediate and deep water masses of northern Atlantic or Arctic Ocean origin, whilst lower δ30SiDSi values are associated with DSi-rich waters sourced ultimately from the Southern Ocean. Correspondingly, the lowest δ30SiDSi values were observed in the deep and abyssal eastern North Atlantic, where dense southern-sourced waters dominate. The extent to which the spreading of water masses influences the δ30SiDSi distribution is marked clearly by Labrador Sea Water (LSW), whose high δ30SiDSi signature is visible not only within its region of formation within the Labrador and Irminger seas, but also throughout the mid-depth western and eastern North Atlantic Ocean. Both δ30SiDSi and hydrographic parameters document the circulation of LSW into the eastern North Atlantic, where it overlies southern-sourced Lower Deep Water. The GEOVIDE δ30SiDSi distribution thus provides a clear view of the direct interaction between subpolar/polar water masses of northern and southern origin, and allow examination of the extent to which these far-field signals influence the local δ30SiDSi distribution

Aiming higher : the Plymouth and Peninsula Tri-Level Model (PPM) for school/HE links : putting the university into school and community

"This report outlines an innovative, effective model of school/higher education (HE) liaison, the Plymouth & Peninsula Model (PPM). The PPM is of major national and international importance. The defining quality of PPM is that it is a genuine partnership, with parity of esteem between HEIs, schools and local authorities (LAs), supported by other major stakeholders. The PPM is based upon firm research evidence, is highly cost effective and could be rolled out nationally to cover geographically all primary and secondary schools and college grouped in consortia" - page iii

Cooperative mixing induced surface roughening in bilayer metals: a possible novel surface damage mechanism

Molecular dynamics simulations have been used to study a collective atomic transport phenomenon by repeated Ar$^+$ irradiations in the Ti/Pt interfacial system. The ion-induced injection of surface atoms to the bulk, the ejection of bulk atoms to the top layers together with surface erosion is strongly enhanced by interfacial mixing. This process leads to a dense interfacial material, and broadening of the interface region. The process scales with the relative difference of the atomic masses. We find that surface roughening and interfacial mixing is strongly coupled via an enhanced counterflow material transport normal to the surface which might be a novel surface damage mechanism. This cooperative phenomenon is active when the bilayer system is subjected to a high dose ion irradiation (multiple ion irradiations) and leads to surface cavity growth.Comment: 6 pages, 6 figures. accepted in Nucl. Instrum. Meth.

Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm

Mixed integer linear programs are commonly solved by Branch and Bound algorithms. A key factor of the efficiency of the most successful commercial solvers is their fine-tuned heuristics. In this paper, we leverage patterns in real-world instances to learn from scratch a new branching strategy optimised for a given problem and compare it with a commercial solver. We propose FMSTS, a novel Reinforcement Learning approach specifically designed for this task. The strength of our method lies in the consistency between a local value function and a global metric of interest. In addition, we provide insights for adapting known RL techniques to the Branch and Bound setting, and present a new neural network architecture inspired from the literature. To our knowledge, it is the first time Reinforcement Learning has been used to fully optimise the branching strategy. Computational experiments show that our method is appropriate and able to generalise well to new instances

Surfactant effect in heteroepitaxial growth. The Pb - Co/Cu(111) case

A MonteCarlo simulations study has been performed in order to study the effect of Pb as surfactant on the initial growth stage of Co/Cu(111). The main characteristics of Co growing over Cu(111) face, i.e. the decorated double layer steps, the multiple layer islands and the pools of vacancies, disappear with the pre-evaporation of a Pb monolayer. Through MC simulations, a full picture of these complex processes is obtained. Co quickly diffuses through the Pb monolayer exchanging place with Cu atoms at the substrate. The exchange process diffusion inhibits the formation of pure Co islands, reducing the surface stress and then the formation of multilayer islands and the pools of vacancies. On the other hand, the random exchange also suppress the nucleation preferential sites generated by Co atoms at Cu steps, responsible of the step decoration.Comment: 4 pages, latex, 2 figures embedded in the tex

Continuous-action reinforcement learning for memory allocation in virtualized servers

In a virtualized computing server (node) with multiple Virtual Machines (VMs), it is necessary to dynamically allocate memory among the VMs. In many cases, this is done only considering the memory demand of each VM without having a node-wide view. There are many solutions for the dynamic memory allocation problem, some of which use machine learning in some form. This paper introduces CAVMem (Continuous-Action Algorithm for Virtualized Memory Management), a proof-of-concept mechanism for a decentralized dynamic memory allocation solution in virtualized nodes that applies a continuous-action reinforcement learning (RL) algorithm called Deep Deterministic Policy Gradient (DDPG). CAVMem with DDPG is compared with other RL algorithms such as Q-Learning (QL) and Deep Q-Learning (DQL) in an environment that models a virtualized node. In order to obtain linear scaling and be able to dynamically add and remove VMs, CAVMem has one agent per VM connected via a lightweight coordination mechanism. The agents learn how much memory to bid for or return, in a given state, so that each VM obtains a fair level of performance subject to the available memory resources. Our results show that CAVMem with DDPG performs better than QL and a static allocation case, but it is competitive with DQL. However, CAVMem incurs significant less training overheads than DQL, making the continuous-action approach a more cost-effective solution.This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 754337 (EuroEXA) and the European Union’s 7th Framework Programme under grant agreement number 610456 (Euroserver). It also received funding from the Spanish Ministry of Science and Technology (project TIN2015-65316-P), Generalitat de Catalunya (contract 2014-SGR-1272), and the Severo Ochoa Programme (SEV-2015-0493) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Like trainer, like bot? Inheritance of bias in algorithmic content moderation

The internet has become a central medium through which `networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.Comment: 12 pages, 3 figures, 9th International Conference on Social Informatics (SocInfo 2017), Oxford, UK, 13--15 September 2017 (forthcoming in Springer Lecture Notes in Computer Science

Daily sitting time and its association with non-communicable diseases and multimorbidity in Catalonia

Background: Non-communicable diseases (NCDs) account for 71% of deaths worldwide and individual behaviours such as sedentariness play an important role on their development and management. However, the detrimental effect of daily sitting on multiple NCDs has rarely been studied. This study sought (i) to investigate the association between sitting time and main NCDs and multimorbidity in the population of Catalonia and (ii) to explore the effect of physical activity as a modifier of the associations between sitting time and health outcomes. Methods: Cross-sectional data from the 2016 National Health Survey of Catalonia were analyzed, and multivariable logistic regression, adjusting for socio-demographics and individual risk factors (tobacco and alcohol consumption, diet, hyperlipidaemia, hypertension, body mass index) was used to estimated odds ratios (ORs) and 95% confidence intervals (CIs) of the association between sitting time and NCDs. Results: A total of 3320 people 15 years old were included in the study. Sitting more than 5 h/day was associated with a higher risk of cardiovascular disease (OR 1.90, 95% CI: 1.21-2.97), respiratory disease (OR 1.61, 95% CI: 1.13-2.30) and multimorbidity (OR 2.80, 95% CI: 1.53-5.15). Sitting more than 3 h/day was also associated with a higher risk of multimorbidity (OR 2.26, 95% CI: 1.23-4.16). Physical activity did not modify the associations between sitting time and any of the outcomes. Conclusions: Daily sitting time might be an independent risk factor for some NCDs, such as cardiovascular disease, respiratory disease and multimorbidity, independently of the level risk of physical inactivity