Testing Embedded Memories in Telecommunication Systems

Extensive system testing is mandatory nowadays to achieve high product quality. Telecommunication systems are particularly sensitive to such a requirement; to maintain market competitiveness, manufacturers need to combine reduced costs, shorter life cycles, advanced technologies, and high quality. Moreover, strict reliability constraints usually impose very low fault latencies and a high degree of fault detection for both permanent and transient faults. This article analyzes major problems related to testing complex telecommunication systems, with particular emphasis on their memory modules, often so critical from the reliability point of view. In particular, advanced BIST-based solutions are analyzed, and two significant industrial case studies presente

Prompting the data transformation activities for cluster analysis on collections of documents

In this work we argue towards a new self-learning engine able to suggest to the analyst good transformation methods and weighting schemas for a given data collection. This new generation of systems, named SELF-DATA (SELF-learning DAta TrAnsformation) relies on an engine capable of exploring different data weighting schemas (e.g., normalized term frequencies, logarithmic entropy) and data transformation methods (e.g., PCA, LSI) before applying a given data mining algorithm (e.g., cluster analysis), evaluating and comparing solutions through different quality indices (e.g., weighted Silhouette), and presenting the 3-top solutions to the analyst. SELF-DATA will also include a knowledge database storing results of experiments on previously processed datasets, and a classification algorithm trained on the knowledge base content to forecast the best methods for future analyses. SELF-DATA’s current implementation runs on Apache Spark, a state-of-the-art distributed computing framework. The preliminary validation performed on 4 collections of documents highlights that the TF-IDF and logarithmic entropy weighting methods are effective to measure item relevance with sparse datasets, and the LSI method outperforms PCA in the presence of a larger feature domain

A System for Series Magnetic Measurements of the LHC Main Quadrupoles

More than 400 twin aperture lattice quadrupoles are needed for the Large Hadron Collider (LHC) which is under construction at CERN. The main quadrupole is assembled with correction magnets in a common cryostat called the Short Straight Section (SSS). We plan to measure all SSS's in cold conditions with an unprecedented accuracy: integrated gradient of the field within 150 ppm, harmonics in a range of 1 to 5 ppm, magnetic axis of all elements within 0.1 mm and their field direction within 0.2 mrad. In this paper we describe the automatic measurement system that we have designed, built and calibrated. Based on the results obtained on the two first prototypes of the SSS's (SSS3 and SSS4) we show that this system meets all above requirements

NEMICO: Mining network data through cloud-based data mining techniques

Thanks to the rapid advances in Internet-based applications, data acquisition and storage technologies, petabyte-sized network data collections are becoming more and more common, thus prompting the need for scalable data analysis solutions. By leveraging today’s ubiquitous many-core computer architectures and the increasingly popular cloud computing paradigm, the applicability of data mining algorithms to these large volumes of network data can be scaled up to gain interesting insights. This paper proposes NEMICO, a comprehensive Big Data mining system targeted to network traffic flow analyses (e.g., traffic flow characterization, anomaly detection, multiplelevel pattern mining). NEMICO comprises new approaches that contribute to a paradigm-shift in distributed data mining by addressing most challenging issues related to Big Data, such as data sparsity, horizontal scaling, and parallel computation

Editorial: hypotheses about protein folding - the proteomic code and wonderfolds

Theoretical biology journals can contribute in many ways to the progress of knowledge. They are particularly well-placed to encourage dialogue and debate about hypotheses addressing problematical areas of research. An online journal provides an especially useful forum for such debate because of the option of posting comments within days of the publication of a contentious article

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers

Arabidopsis thaliana response to extracellular dna: Self versus nonself exposure

The inhibitory effect of extracellular DNA (exDNA) on the growth of conspecific individuals was demonstrated in different kingdoms. In plants, the inhibition has been observed on root growth and seed germination, demonstrating its role in plant\u2013soil negative feedback. Several hypotheses have been proposed to explain the early response to exDNA and the inhibitory effect of conspecific exDNA. We here contribute with a whole-plant transcriptome profiling in the model species Arabidopsis thaliana exposed to extracellular self-(conspecific) and nonself-(heterologous) DNA. The results highlight that cells distinguish self-from nonself-DNA. Moreover, confocal microscopy analyses reveal that nonself-DNA enters root tissues and cells, while self-DNA remains outside. Specifically, exposure to self-DNA limits cell permeability, affecting chloroplast functioning and reactive oxygen species (ROS) production, eventually causing cell cycle arrest, consistently with macroscopic observations of root apex necrosis, increased root hair density and leaf chlorosis. In contrast, nonself-DNA enters the cells triggering the activation of a hypersensitive response and evolving into systemic acquired resistance. Complex and different cascades of events emerge from exposure to extracellular selfor nonself-DNA and are discussed in the context of Damage-and Pathogen-Associated Molecular Patterns (DAMP and PAMP, respectively) responses

Extracellular DNA secreted in yeast cultures is metabolism-specific and inhibits cell proliferation

Extracellular DNA (exDNA) can be actively released by living cells and different putative functions have been attributed to it. Further, homolo-gous exDNA has been reported to exert species-specific inhibitory effects on several organisms. Here, we demonstrate by different experimental evidence, including 1H-NMR metabolomic fingerprint, that the growth rate decline in Saccharomyces cerevisiae fed-batch cultures is determined by the accumula-tion of exDNA in the medium. Sequencing of such secreted exDNA represents a portion of the entire genome, showing a great similarity with extrachromo-somal circular DNA (eccDNA) already reported inside yeast cells. The recov-ered DNA molecules were mostly single strands and specifically associated to the yeast metabolism displayed during cell growth. Flow cytometric analysis showed that the observed growth inhibition by exDNA corresponded to an arrest in the S phase of the cell cycle. These unprecedented findings open a new scenario on the functional role of exDNA produced by living cells

ISOL@: an Italian SOLAnaceae genomics resource

BACKGROUND: Present-day '-omics' technologies produce overwhelming amounts of data which include genome sequences, information on gene expression (transcripts and proteins) and on cell metabolic status. These data represent multiple aspects of a biological system and need to be investigated as a whole to shed light on the mechanisms which underpin the system functionality.The gathering and convergence of data generated by high-throughput technologies, the effective integration of different data-sources and the analysis of the information content based on comparative approaches are key methods for meaningful biological interpretations.In the frame of the International Solanaceae Genome Project, we propose here ISOLA, an Italian SOLAnaceae genomics resource. RESULTS: ISOLA (available at http://biosrv.cab.unina.it/isola) represents a trial platform and it is conceived as a multi-level computational environment.ISOLA currently consists of two main levels: the genome and the expression level. The cornerstone of the genome level is represented by the Solanum lycopersicum genome draft sequences generated by the International Tomato Genome Sequencing Consortium. Instead, the basic element of the expression level is the transcriptome information from different Solanaceae species, mainly in the form of species-specific comprehensive collections of Expressed Sequence Tags (ESTs).The cross-talk between the genome and the expression levels is based on data source sharing and on tools that enhance data quality, that extract information content from the levels' under parts and produce value-added biological knowledge. CONCLUSIONS: ISOLA is the result of a bioinformatics effort that addresses the challenges of the post-genomics era. It is designed to exploit '-omics' data based on effective integration to acquire biological knowledge and to approach a systems biology view. Beyond providing experimental biologists with a preliminary annotation of the tomato genome, this effort aims to produce a trial computational environment where different aspects and details are maintained as they are relevant for the analysis of the organization, the functionality and the evolution of the Solanaceae family