Practical service placement approach for microservices architecture

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft

30 años conviviendo con el SIDA

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Deformation of Gabor systems

We introduce a new notion for the deformation of Gabor systems. Such deformations are in general nonlinear and, in particular, include the standard jitter error and linear deformations of phase space. With this new notion we prove a strong deformation result for Gabor frames and Gabor Riesz sequences that covers the known perturbation and deformation results. Our proof of the deformation theorem requires a new characterization of Gabor frames and Gabor Riesz sequences. It is in the style of Beurling's characterization of sets of sampling for bandlimited functions and extends significantly the known characterization of Gabor frames 'without inequalities' from lattices to non-uniform sets

Performance evaluation of a distributed storage service in community network clouds

Community networks are self-organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user-provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end-user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least-authority file system (Tahoe-LAFS), a decentralized storage system with provider-independent security that guarantees privacy to the users. We evaluate how the Tahoe-LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe-LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe-LAFS. We observed that the write operation of Tahoe-LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe-LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe-LAFS can run on community network clouds with suitable performance for the needed end-user experience.Peer ReviewedPreprin

Strict density inequalities for sampling and interpolation in weighted spaces of holomorphic functions

Answering a question of Lindholm, we prove strict density inequalities for sampling and interpolation in Fock spaces of entire functions in several complex variables defined by a plurisubharmonic weight. In particular, these spaces do not admit a set that is simultaneously sampling and interpolating. To prove optimality of the density conditions, we construct sampling sets with a density arbitrarily close to the critical density. The techniques combine methods from several complex variables (estimates for $\bar \partial$) and the theory of localized frames in general reproducing kernel Hilbert spaces (with no analyticity assumed). The abstract results on Fekete points and deformation of frames may be of independent interest

SUGAR. Sustainable Urban Goods Logistics Achieved by Regional and Local Policies. City Logistics Best Practices: a Handbook for Authorities

This publication is one of the main results of the SUGAR project and it is focused on the Best Practices analysis, a tool for the involvement of the community of experts in the emerging field of city logistics. The handbook proposes a quick overview on the project, a detailed collection of best practice synthesis, a synthesis of transferrability issues of such practices and the methodology for applying some of them to different cities and fields

A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

[EN] There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies. Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.This study was funded by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020 vertical bar RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494.Cerdà Alberich, L.; Sangüesa Nebot, C.; Alberich-Bayarri, A.; Carot Sierra, JM.; Martinez De Las Heras, B.; Veiga Canuto, D.; Cañete, A.... (2020). A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors. Cancers. 12(12):1-14. https://doi.org/10.3390/cancers12123858114121

Recovery of phenolic compounds from wine lees using green processing: Identifying target molecules and assessing membrane ultrafiltration performance

Winery wastes are rich in polyphenols with high added value to be used in cosmetics, pharmaceuticals, and food products. This work aims at recovering and purifying the polyphenolic fraction occurring in the malolactic fermentation lees generated during the production of Albariño wines. Phenolic acids, flavonoids, and related compounds were recovered from this oenological waste by green liquid extraction using water as the solvent. The resulting extract solution was microfiltered to remove microparticles and further treated by ultrafiltration (UF) using membranes of 30 kDa and 5 kDa molecular weight cut-offs (MWCOs). The feed sample and the filtrate and retentate solutions from each membrane system were analyzed by reversed-phase liquid chromatography (HPLC) with UV and mass spectrometric (MS) detection. The most abundant polyphenols in the extracts were identified and quantified, namely: caftaric acid with a concentration of 200 µg g−1 and trans-coutaric acid, cis-coutaric acid, gallic acid, and astilbin with concentrations between 15 and 40 µg g−1. Other minor phenolic acids and flavanols were also found. The UF process using the 30 kDa membrane did not modify the extract composition, but filtration through the 5 kDa poly-acrylonitrile membrane elicited a decrease in polyphenolic content. Hence, the 30 kDa membrane was recommended to further pre-process the extracts. The combined extraction and purification process presented here is environmentally friendly and demonstrates that malolactic fermentation lees of Albariño wines are a valuable source of phenolic compounds, especially phenolic acids

Extracting Parallel Corpora from Wikipedia on the basis of Phrase Level Bilingual Alignment

[EN] This paper presents a proposal for extracting parallel corpora from Wikipedia on the basis of statistical machine translation techniques. We have used word-level alignment models from IBM in order to obtain phrase-level bilingual alignments between documents pairs. We have manually annotated a set of test English-Spanish comparable documents in order to evaluate the model. The obtained results are encouraging.[ES] Este art'¿culo presenta una nueva t'ecnica de extracci'on de corpus paralelos de la Wikipedia mediante la aplicaci'on de t'ecnicas de traducci'on autom'atica estad'¿stica. En concreto, se han utilizado los modelos de alineamiento basados en palabras de IBM para obtener alineamientos biling¿ues a nivel de frase entre pares de documentos. Para su evaluaci'on se ha generado manualmente un conjunto de test formado por pares de documentos ingl'es-espa¿nol, obteni'endose resultados prometedores.Este trabajo se ha llevado a cabo en el marco del VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems, financiado parcialmente por parte de la EC (FEDER/FSE; WIQEI IRSES no. 269180 / FP 7 Marie Curie People), por el MICINN como parte del proyecto Text-Enterprise 2.0 (TIN2009-13391-C04-03) en el Plan I+D+i, y por la beca 192021 del CONACyT. Tambi´en ha recibido apoyo por parte del EC (FEDER/FSE) y del MEC/MICINN bajo el programa MIPRCV “Consolider Ingenio 2010” (CSD2007-00018) y el proyecto iTrans2 (TIN2009-14511), por el MITyC en el marco del proyecto erudito.com (TSI-020110-2009-439), por la Generalitat Valenciana con las ayudas Prometeo/2009/014 y GV/2010/067, y por el “Vicerrectorado de Investigaci´on de la UPV” con la ayuda 20091027.Silvestre Cerdà, JA.; Garcia Martinez, MM.; Barrón Cedeño, LA.; Civera Saiz, J.; Rosso ., P. (2011). Extracción de Corpus Paralelos de la Wikipedia basada en la Obtención de Alineamientos Bilingües a Nivel de Frase. CEUR Workshop Proceedings. 824:14-21. http://hdl.handle.net/10251/27930S142182