AFQN: approximate Qn estimation in data streams

We present afqn (Approximate Fast Qn), a novel algorithm for approximate computation of the Qn scale estimator in a streaming setting, in the sliding window model. It is well-known that computing the Qn estimator exactly may be too costly for some applications, and the problem is a fortiori exacerbated in the streaming setting, in which the time available to process incoming data stream items is short. In this paper we show how to efficiently and accurately approximate the Qn estimator. As an application, we show the use of afqn for fast detection of outliers in data streams. In particular, the outliers are detected in the sliding window model, with a simple check based on the Qn scale estimator. Extensive experimental results on synthetic and real datasets confirm the validity of our approach by showing up to three times faster updates per second. Our contributions are the following ones: (i) to the best of our knowledge, we present the first approximation algorithm for online computation of the Qn scale estimator in a streaming setting and in the sliding window model; (ii) we show how to take advantage of our UDDSketch algorithm for quantile estimation in order to quickly compute the Qn scale estimator; (iii) as an example of a possible application of the Qn scale estimator, we discuss how to detect outliers in an input data stream

Fast online computation of the Qn estimator with applications to the detection of outliers in data streams

We present FQN (Fast Qn), a novel algorithm for online computation of the Qn scale estimator. The algorithm works in the sliding window model, cleverly computing the Qn scale estimator in the current window. We thoroughly compare our algorithm for online Qn with the state of the art competing algorithm by Nunkesser et al., and show that FQN (i) is faster, requiring only O(s) time in the worst case where s is the length of the window (ii) its computational complexity does not depend on the input distribution and (iii) it requires less space. To the best of our knowledge, our algorithm is the first that allows online computation of the Qn scale estimator in worst case time linear in the size of the window. As an example of a possible application, besides its use as a robust measure of statistical dispersion, we show how to use the Qn estimator for fast detection of outliers in data streams. Extensive experimental results on both synthetic and real datasets confirm the validity of our approach

High Throughput Protein Similarity Searches in the LIBI Grid Problem Solving Environment

Bioinformatics applications are naturally distributed, due to distribution of involved data sets, experimental data and biological databases. They require high computing power, owing to the large size of data sets and the complexity of basic computations, may access heterogeneous data, where heterogeneity is in data format, access policy, distribution, etc., and require a secure infrastructure, because they could access private data owned by different organizations. The Problem Solving Environment (PSE) is an approach and a technology that can fulfil such bioinformatics requirements. The PSE can be used for the definition and composition of complex applications, hiding programming and configuration details to the user that can concentrate only on the specific problem. Moreover, Grids can be used for building geographically distributed collaborative problem solving environments and Grid aware PSEs can search and use dispersed high performance computing, networking, and data resources. In this work, the PSE solution has been chosen as the integration platform of bioinformatics tools and data sources. In particular an experiment of multiple sequence alignment on large scale, supported by the LIBIPSE, is presented

The PURPLE mystery: Semantic meaning of three purple terms in French speakers from Algeria, France, and Switzerland

Studies on the colour category PURPLE yielded inconsistent category boundaries, focal colours, and colour-emotion associations. In French, there are at least three colour terms referring to the shades of purple, potentially weighing on these inconsistencies. Thus, we tested the semantic breadth and richness in semantic meaning of violet (basic term), lilas (non-basic), and pourpre (non-basic). We collected free associations in 274 French speakers from Algeria, France, and Switzerland, yielding 2,079 responses, of which 436 were discrete and 275 were unique. Frequency analyses and semantic coding supported the basicness status of violet in French, within a hierarchically structured semantic system. Moreover, the meaning of the three terms was not synonymous. Violet had the most abstract meaning. Lilas had the narrowest meaning, mainly referring to Natural Entities. Pourpre seemed close to RED. We found no differences between the countries. Future studies should extend this approach to other languages and other colour terms

SARS-CoV-2 infection among hospitalised pregnant women and impact of different viral strains on COVID-19 severity in Italy: a national prospective population-based cohort study

OBJECTIVE: The primary aim of this article was to describe SARS-CoV-2 infection among pregnant women during the wild-type and Alpha-variant periods in Italy. The secondary aim was to compare the impact of the virus variants on the severity of maternal and perinatal outcomes. DESIGN: National population-based prospective cohort study. SETTING: A total of 315 Italian maternity hospitals. SAMPLE: A cohort of 3306 women with SARS-CoV-2 infection confirmed within 7 days of hospital admission. METHODS: Cases were prospectively reported by trained clinicians for each participating maternity unit. Data were described by univariate and multivariate analyses. MAIN OUTCOME MEASURES: COVID-19 pneumonia, ventilatory support, intensive care unit (ICU) admission, mode of delivery, preterm birth, stillbirth, and maternal and neonatal mortality. RESULTS: We found that 64.3% of the cohort was asymptomatic, 12.8% developed COVID-19 pneumonia and 3.3% required ventilatory support and/or ICU admission. Maternal age of 30-34 years (OR 1.43, 95% CI 1.09-1.87) and ≥35 years (OR 1.62, 95% CI 1.23-2.13), citizenship of countries with high migration pressure (OR 1.75, 95% CI 1.36-2.25), previous comorbidities (OR 1.49, 95% CI 1.13-1.98) and obesity (OR 1.72, 95% CI 1.29-2.27) were all associated with a higher occurrence of pneumonia. The preterm birth rate was 11.1%. In comparison with the pre-pandemic period, stillbirths and maternal and neonatal deaths remained stable. The need for ventilatory support and/or ICU admission among women with pneumonia increased during the Alpha-variant period compared with the wild-type period (OR 3.24, 95% CI 1.99-5.28). CONCLUSIONS: Our results are consistent with a low risk of severe COVID-19 disease among pregnant women and with rare adverse perinatal outcomes. During the Alpha-variant period there was a significant increase of severe COVID-19 illness. Further research is needed to describe the impact of different SARS-CoV-2 viral strains on maternal and perinatal outcomes

A comparative analysis of colour–emotion associations in 16–88‐year‐old adults from 31 countries

As people age, they tend to spend more time indoors, and the colours in their surroundings may significantly impact their mood and overall well-being. However, there is a lack of empirical evidence to provide informed guidance on colour choices, irrespective of age group. To work towards informed choices, we investigated whether the associations between colours and emotions observed in younger individuals also apply to older adults. We recruited 7,393 participants, aged between 16 and 88 years and coming from 31 countries. Each participant associated 12 colour terms with 20 emotion concepts and rated the intensity of each associated emotion. Different age groups exhibited highly similar patterns of colour-emotion associations (average similarity coefficient of 0.97), with subtle yet meaningful age-related differences. Adolescents associated the greatest number but the least positively biased emotions with colours. Older participants associated a smaller number but more intense and more positive emotions with all colour terms, displaying a positivity effect. Age also predicted arousal and power biases, varying by colour. Findings suggest parallels in colour-emotion associations between younger and older adults, with subtle but significant age-related variations. Future studies should next assess whether colour-emotion associations reflect what people actually feel when exposed to colour

Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds

Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars

The computational and energy cost of simulation and storage for climate science: lessons from CMIP6

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature

Design, upgrade and characterization of the silicon photomultiplier front-end for the AMIGA detector at the Pierre Auger Observatory

AMIGA (Auger Muons and Infill for the Ground Array) is an upgrade of the Pierre Auger Observatory to complement the study of ultra-high-energy cosmic rays (UHECR) by measuring the muon content of extensive air showers (EAS). It consists of an array of 61 water Cherenkov detectors on a denser spacing in combination with underground scintillation detectors used for muon density measurement. Each detector is composed of three scintillation modules, with 10 m$^2$ detection area per module, buried at 2.3 m depth, resulting in a total detection area of 30 m$^2$. Silicon photomultiplier sensors (SiPM) measure the amount of scintillation light generated by charged particles traversing the modules. In this paper, the design of the front-end electronics to process the signals of those SiPMs and test results from the laboratory and from the Pierre Auger Observatory are described. Compared to our previous prototype, the new electronics shows a higher performance, higher efficiency and lower power consumption, and it has a new acquisition system with increased dynamic range that allows measurements closer to the shower core. The new acquisition system is based on the measurement of the total charge signal that the muonic component of the cosmic ray shower generates in the detector.Comment: 40 pages, 33 figure

Direct measurement of the muonic content of extensive air showers between 2×1017\mathbf { 2\times 10^{17}} and 2×1018 \mathbf {2\times 10^{18}}~eV at the Pierre Auger Observatory

The hybrid design of the Pierre Auger Observatory allows for the measurement of the properties of extensive air showers initiated by ultra-high energy cosmic rays with unprecedented precision. By using an array of prototype underground muon detectors, we have performed the first direct measurement, by the Auger Collaboration, of the muon content of air showers between 2×10$^{17}$ and 2×10$^{18}$ eV. We have studied the energy evolution of the attenuation-corrected muon density, and compared it to predictions from air shower simulations. The observed densities are found to be larger than those predicted by models. We quantify this discrepancy by combining the measurements from the muon detector with those from the Auger fluorescence detector at 10$^{17.5}$eV and 10$^{18}$eV. We find that, for the models to explain the data, an increase in the muon density of 38% ±4%(12%) ± (21%)¦(18%) for EPOS-LHC, and of 50%(53%) ±4%(13%) ± (23%)¦(20%) for QGSJetII-04, is respectively needed