A Framework for Genetic Algorithms Based on Hadoop

Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in many real-world applications. The sequential execution of GAs requires considerable computational power both in time and resources. Nevertheless, GAs are naturally parallel and accessing a parallel platform such as Cloud is easy and cheap. Apache Hadoop is one of the common services that can be used for parallel applications. However, using Hadoop to develop a parallel version of GAs is not simple without facing its inner workings. Even though some sequential frameworks for GAs already exist, there is no framework supporting the development of GA applications that can be executed in parallel. In this paper is described a framework for parallel GAs on the Hadoop platform, following the paradigm of MapReduce. The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance. The framework has been also exploited to develop an application for Feature Subset Selection problem. A preliminary analysis of the performance of the developed GA application has been performed using three datasets and shown very promising performance

On Redundancy Elimination Tolerant Scheduling Rules

In (Ferrucci, Pacini and Sessa, 1995) an extended form of resolution, called Reduced SLD resolution (RSLD), is introduced. In essence, an RSLD derivation is an SLD derivation such that redundancy elimination from resolvents is performed after each rewriting step. It is intuitive that redundancy elimination may have positive effects on derivation process. However, undesiderable effects are also possible. In particular, as shown in this paper, program termination as well as completeness of loop checking mechanisms via a given selection rule may be lost. The study of such effects has led us to an analysis of selection rule basic concepts, so that we have found convenient to move the attention from rules of atom selection to rules of atom scheduling. A priority mechanism for atom scheduling is built, where a priority is assigned to each atom in a resolvent, and primary importance is given to the event of arrival of new atoms from the body of the applied clause at rewriting time. This new computational model proves able to address the study of redundancy elimination effects, giving at the same time interesting insights into general properties of selection rules. As a matter of fact, a class of scheduling rules, namely the specialisation independent ones, is defined in the paper by using not trivial semantic arguments. As a quite surprising result, specialisation independent scheduling rules turn out to coincide with a class of rules which have an immediate structural characterisation (named stack-queue rules). Then we prove that such scheduling rules are tolerant to redundancy elimination, in the sense that neither program termination nor completeness of equality loop check is lost passing from SLD to RSLD.Comment: 53 pages, to appear on TPL

A Real-Time, Space Borne Volcano Observatory to Support Decision Making during Eruptive Crises: European Volcano Observatory Space Services

Within the Global Monitoring for Environment and Security (GMES) framework of the European Commission, the EVOSS consortium of academic and industrial partners has created a satellite-based volcano observatory, designed to provide the real-time information support to crisis management. Data from 8 satellite payloads acquired at 6 different down-link stations, are split and automatically processed at 5 locations (in Italy, the Netherlands, Belgium and Germany). The results are sent, in four separate data streams (thermal, volcanic SO2, volcanic ash and ground deformation), to a central system called VVO, the “Virtual Volcano Observatory”. The system operates 24H/24-7D/7 since October 2011 on all volcanoes in Europe, Africa, the Lesser Antilles, and the oceans around them, and during this interval has detected and monitored all eruptions that occurred in this region. EVOSS services are delivered to a group of 14 qualified users in Cabo Verde, Comoros, Congo, Djibouti, Ethiopia, France, Iceland, Montserrat, Tanzania, Uganda and the United Kingdom. Physical modelling of erupive phenomena, with an emphasis on rapid numerical calculations, underpins the satellite monitoring system

In vivo bioluminescence imaging using orthotopic xenografts towards patient's derived-xenograft Medulloblastoma models

BACKGROUND: Medulloblastoma is a cerebellar neoplasia of the central nervous system. Four molecular subgrups have been identified (MBWNT, MBSHH, MBgroup3 and MBgroup4) with distinct genetics and clinical outcome. Among these, MBgroup3-4 are highly metastatic with the worst prognosis. The current standard therapy includes surgery, radiation and chemotherapy. Thus, specific treatments adapted to cure those different molecular subgroups are needed. The use of orthotopic xenograft models, together with the non-invasive in vivo biolumiscence imaging (BLI) technology, is emerging during preclinical studies to test novel therapeutics for medulloblastoma treatment. METHODS: Orthotopic MB xenografts were performed by injection of Daoy-luc cells, that had been previously infected with lentiviral particles to stably express luciferase gene, into the fourth right ventricle of the cerebellum of ten nude mice. For the implantation, specific stereotactic coordinates were used. Seven days after the implantation the mice were imaged by acquisitions of bioluminescence imaging (BLI) using IVIS 3D Illumina Imaging System (Xenogen). Tumor growth was evaluated by quantifying the bioluminescence signals using the integrated fluxes of photons within each area of interest using the Living Images Software Package 3.2 (Xenogen-Perkin Elmer). Finally, histological analysis using hematoxylin-eosin staining was performed to confirm the presence of tumorigenic cells into the cerebellum of the mice. RESULTS: We describe a method to use the in vivo bioluminescent imaging (BLI) showing the potential to be used to investigate the potential antitumorigenic effects of a drug for in vivo medulloblastoma treatment. We also discuss other studies in which this technology has been applied to obtain a more comprehensive knowledge of medulloblastoma using orthotopic xenograft mouse models. CONCLUSIONS: There is a need to develop patient's derived-xenograft (PDX) model systems to test novel drugs for medulloblastoma treatment within each molecular sub-groups with a higher predictive value. Here we show how this technology should be applied with hopes on generations of new treatments to be applied then in human

Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?

In this keynote I introduce the use of Predictive Analytics for Software Engineering (SE) and then focus on the use of search-based heuristics to tackle long-standing SE prediction problems including (but not limited to) software development effort estimation and software defect prediction. I review recent research in Search-Based Predictive Modelling for SE in order to assess the maturity of the field and point out promising research directions. I conclude my keynote by discussing best practices for a rigorous and realistic empirical evaluation of search-based predictive models, a condicio sine qua non to facilitate the adoption of prediction models in software industry practices.Predictive analytics Predictive modelling Search-based software engineering Machine learning Software analytic

Editorial: The functional anatomy of the reticular formation

Editorial on the research topic of a special issue on the functional anatomy of the reticular formatio

Exploiting Visual Languages Generation and UML Meta Modeling to Construct Meta-CASE Workbenches

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Exploiting XPG for Visual Languages Definition, Analysis and Development

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