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

Parallel implementation of the SHYFEM (System of HydrodYnamic Finite Element Modules) model

This paper presents the message passing interface (MPI)-based parallelization of the three-dimensional hydrodynamic model SHYFEM (System of HydrodYnamic Finite Element Modules). The original sequential version of the code was parallelized in order to reduce the execution time of high-resolution configurations using state-of-the-art high-performance computing (HPC) systems. A distributed memory approach was used, based on the MPI. Optimized numerical libraries were used to partition the unstructured grid (with a focus on load balancing) and to solve the sparse linear system of equations in parallel in the case of semi-to-fully implicit time stepping. The parallel implementation of the model was validated by comparing the outputs with those obtained from the sequential version. The performance assessment demonstrates a good level of scalability with a realistic configuration used as benchmark

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

Searches for Ultra-High-Energy Photons at the Pierre Auger Observatory

The Pierre Auger Observatory, being the largest air-shower experiment in the world, offers an unprecedented exposure to neutral particles at the highest energies. Since the start of data taking more than 18 years ago, various searches for ultra-high-energy (UHE, $E\gtrsim10^{17}\,\text{eV}$) photons have been performed: either for a diffuse flux of UHE photons, for point sources of UHE photons or for UHE photons associated with transient events like gravitational wave events. In the present paper, we summarize these searches and review the current results obtained using the wealth of data collected by the Pierre Auger Observatory.Comment: Review article accepted for publication in Universe (special issue on ultra-high energy photons

The UHECR dipole and quadrupole in the latest data from the original Auger and TA surface detectors

The sources of ultra-high-energy cosmic rays are still unknown, but assuming standard physics, they are expected to lie within a few hundred megaparsecs from us. Indeed, over cosmological distances cosmic rays lose energy to interactions with background photons, at a rate depending on their mass number and energy and properties of photonuclear interactions and photon backgrounds. The universe is not homogeneous at such scales, hence the distribution of the arrival directions of cosmic rays is expected to reflect the inhomogeneities in the distribution of galaxies; the shorter the energy loss lengths, the stronger the expected anisotropies. Galactic and intergalactic magnetic fields can blur and distort the picture, but the magnitudes of the largest-scale anisotropies, namely the dipole and quadrupole moments, are the most robust to their effects. Measuring them with no bias regardless of any higher-order multipoles is not possible except with full-sky coverage. In this work, we achieve this in three energy ranges (approximately 8--16 EeV, 16--32 EeV, and 32--∞ EeV) by combining surface-detector data collected at the Pierre Auger Observatory until 2020 and at the Telescope Array (TA) until 2019, before the completion of the upgrades of the arrays with new scintillator detectors. We find that the full-sky coverage achieved by combining Auger and TA data reduces the uncertainties on the north-south components of the dipole and quadrupole in half compared to Auger-only results

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

A Catalog of the Highest-energy Cosmic Rays Recorded during Phase I of Operation of the Pierre Auger Observatory

A catalog containing details of the highest-energy cosmic rays recorded through the detection of extensive air-showers at the Pierre Auger Observatory is presented with the aim of opening the data to detailed examination. Descriptions of the 100 showers created by the highest-energy particles recorded between 1 January 2004 and 31 December 2020 are given for cosmic rays that have energies in the range 78 EeV to 166 EeV. Details are also given of a further nine very-energetic events that have been used in the calibration procedure adopted to determine the energy of each primary. A sky plot of the arrival directions of the most energetic particles is shown. No interpretations of the data are offered

Constraining the sources of ultra-high-energy cosmic rays across and above the ankle with the spectrum and composition data measured at the Pierre Auger Observatory

In this work we present the interpretation of the energy spectrum and mass composition data as measured by the Pierre Auger Collaboration above $6 \times 10^{17}$ eV. We use an astrophysical model with two extragalactic source populations to model the hardening of the cosmic-ray flux at around $5\times 10^{18}$ eV (the so-called "ankle" feature) as a transition between these two components. We find our data to be well reproduced if sources above the ankle emit a mixed composition with a hard spectrum and a low rigidity cutoff. The component below the ankle is required to have a very soft spectrum and a mix of protons and intermediate-mass nuclei. The origin of this intermediate-mass component is not well constrained and it could originate from either Galactic or extragalactic sources. To the aim of evaluating our capability to constrain astrophysical models, we discuss the impact on the fit results of the main experimental systematic uncertainties and of the assumptions about quantities affecting the air shower development as well as the propagation and redshift distribution of injected ultra-high-energy cosmic rays (UHECRs).Comment: Submitted to JCA