Machine Learning for Aggregate Computing: a Research Roadmap

Aggregate computing is a macro-approach for programming collective intelligence and self-organisation in distributed systems. In this paradigm, a single 'aggregate program' drives the collective behaviour of the system, provided that the agents follow an execution protocol consisting of asynchronous sense-compute-act rounds. For actual execution, a proper aggregate computing middleware or platform has to be deployed across the nodes of the target distributed system, to support the services needed for the execution of applications. Overall, the engineering of aggregate computing applications is a rich activity that spans multiple concerns including designing the aggregate program, developing reusable algorithms, detailing the execution model, and choosing a deployment based on available infrastructure. Traditionally, these activities have been carried out through ad-hoc designs and implementations tailored to specific contexts and goals. To overcome the complexity and cost of manually tailoring or fixing algorithms, execution details, and deployments, we propose to use machine learning techniques, to automatically create policies for applications and their management. To support such a goal, we detail a rich research roadmap, showing opportunities and challenges of integrating aggregate computing and learning

Towards Reinforcement Learning-based Aggregate Computing

Recent trends in pervasive computing promote the vision of Collective Adaptive Systems (CASs): large-scale collections of relatively simple agents that act and coordinate with no central orchestrator to support distributed applications. Engineering global behaviour out of local activity and interaction, however, is a difficult task, typically addressed by try-and-error approaches in simulation environments. In the context of Aggregate Computing (AC), a prominent functional programming approach for CASs based on field-based coordination, this difficulty is reflected in the design of versatile algorithms preserving efficiency in a variety of environments. To deal with this complexity, in this work we propose to apply Machine Learning techniques to automatically devise local actions to improve over manually-defined AC algorithms specifications. Most specifically, we adopt a Reinforcement Learning-based approach to let a collective learn local policies to improve over the standard gradient algorithm—a cornerstone brick of several higher-level self-organisation algorithms. Our evaluation shows that the learned policies can speed up the self-stabilisation of the gradient to external perturbations

Seven years of marine environmental changes monitoring at coastal OOCS stations (Catalan Sea, NW Mediterranean)

Since March 2009 up to the present (more than 7 years now), the Operational Observatory of the Catalan Sea (OOCS; http://www2.ceab.csic.es/ oceans/) remains a witness of persistent marine environmental changes. The OOCS has two fixed observation stations at the head of the Blanes Canyon (200 m depth, 41.66°N; 2.91°E) and at the Blanes bay (20 m depth, 41.67°N; 2.80°E) in the Catalan Sea, NW Mediterranean. At the canyon station, a multi-parametric buoy presently installed delivers high frequency (by 30 min) and multi-parametric oceanographic (i.e. salinity, temperature, chlorophyll, turbidity, as well as light intensity in the PAR range for the upper 50 m depth) and atmospheric (air temperature, relative humidity, wind speed and direction and PAR) data. Subsurface photos and videos by an IP high resolution fisheye camera attached to the buoy are also delivered at 4-hour basis. Data and multimedia are transmitted in near real time for public access, via combined GSM/GPRS and 3G connections. At both stations, CTD profiles and water samples (collected for nutrients and picoplankton analyses) are carried out on board a research vessel at fortnightly basis. Numerical simulations along with the time series of in-situ observations show inter-annual seasonality anomalies possibly linked to global environmental changes. The lower-atmosphere and upper-sea environmental time series data collected prove the occurrence of shifting patterns of heat and matter fluxes impacting pelagic and benthic organisms.Peer Reviewe

A field-based computing approach to sensing-driven clustering in robot swarms

Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also called clusters. In this work, we address the sensing-based swarm clustering problem, where clusters are defined based on both the values sensed from the environment and the spatial distribution of the values and the agents. Moreover, we address it in a setting characterised by decentralisation of computation and interaction, and dynamicity of values and mobility of agents. For the solution, we propose to use the field-based computing paradigm, where computation and interaction are expressed in terms of a functional manipulation of fields, distributed and evolving data structures mapping each individual of the system to values over time. We devise a solution to sensing-based swarm clustering leveraging multiple concurrent field computations with limited domain and evaluate the approach experimentally by means of simulations, showing that the programmed swarms form clusters that well reflect the underlying environmental phenomena dynamics

Prions and neuronal death

The present contribution is a Letter to the Editor (Correspondence) and, as a consequence, no abstract is available.[...

Evaluation of anti-inflammatory and immunoregulatory activities of Stimunex® and Stimunex D3® in human monocytes/macrophages stimulated with LPS or IL-4/IL-13

Macrophages exert an important role in maintaining and/or ameliorating the inflammatory response. They are involved in the activation of an immune response to pathogens, with a balance between the immunomodulatory role and tissue integrity maintenance, however, excessive macrophage activity promotes tissue injury and chronic disease pathogenesis. There is a high interest in evaluating the anti-inflammatory properties of new botanical preparations. Stimunex® and Stimunex D3® are two food supplements formulated as syrups, containing the extract of elderflower (Sambucus nigra, Caprifoliaceae), standardized in polyphenol (6%) and anthocyanins (4%), associated with wellmune WGP® β-glucan, with the addiction of vitamin D3 (in Stimunex D3® formulation). The aim of the work was the evaluation of Stimunex® and Stimunex D3® activity in human polarized-macrophages, in order to support their use as supplement for preventing and reducing the inflammatory processes. In primary human stimulated macrophages, both syrups were able to revert LPS- and IL-4/IL-13-mediated response, reducing the release of several pro-inflammatory cytokines. Results support that these standardized botanical preparations fortified with β-glucan, may have a potential use in the prevention and coadjuvant management of inflammatory process as respiratory recurrent infections and other similar conditions. Moreover, the addition of vitamin D3 revealed to be an advantage in Stimunex D3® for its important role in maintaining and enhancing the innate immune response

Diseño de una guía de prácticas de laboratorio de acuerdo con las orientaciones del EEES

La adaptación de la docencia universitaria al Espacio Europeo de Educación Superior (EEES) supone un cambio en los sistemas de enseñanza actual. En este sentido el desarrollo de guías de laboratorio capaces de informar al alumnado, y normalizar la confección y presentación de las prácticas de laboratorio, asegurando una mejor calidad de la docencia y coordinación entre grupos, viene a cumplir con algunos de los objetivos pretendidos en el contexto de la Convergencia Europea. La Guía que se presenta, destinada a su empleo en la docencia práctica de una asignatura troncal de la licenciatura en Farmacia en la Universidad de Granada, recoge las indicaciones necesarias para llevar a cabo un trabajo seguro y eficiente en los laboratorios

Acceptability with general orderings

We present a new approach to termination analysis of logic programs. The essence of the approach is that we make use of general orderings (instead of level mappings), like it is done in transformational approaches to logic program termination analysis, but we apply these orderings directly to the logic program and not to the term-rewrite system obtained through some transformation. We define some variants of acceptability, based on general orderings, and show how they are equivalent to LD-termination. We develop a demand driven, constraint-based approach to verify these acceptability-variants. The advantage of the approach over standard acceptability is that in some cases, where complex level mappings are needed, fairly simple orderings may be easily generated. The advantage over transformational approaches is that it avoids the transformation step all together. {\bf Keywords:} termination analysis, acceptability, orderings.Comment: To appear in "Computational Logic: From Logic Programming into the Future