Algorithmic Discrimination and Privacy Protection

Objective: emergence of digital technologies such as Artificial intelligence became a challenge for states across the world. It brought many risks of the violations of human rights, including right to privacy and the dignity of the person. That is why it is highly relevant to research in this area. That is why this article aims to analyse the role played by algorithms in discriminatory cases. It focuses on how algorithms may implement biased decisions using personal data. This analysis helps assess how the Artificial Intelligence Act proposal can regulate the matter to prevent the discriminatory effects of using algorithms.Methods: the methods used were empirical and comparative analysis. Comparative analysis allowed to compare regulation of and provisions of Artificial Intelligence Act proposal. Empirical analysis allowed to analyse existing cases that demonstrate us algorithmic discrimination.Results: the study’s results show that the Artificial Intelligence Act needs to be revised because it remains on a definitional level and needs to be sufficiently empirical. Author offers the ideas of how to improve it to make more empirical.Scientific novelty: the innovation granted by this contribution concerns the multidisciplinary study between discrimination, data protection and impact on empirical reality in the sphere of algorithmic discrimination and privacy protection.Practical significance: the beneficial impact of the article is to focus on the fact that algorithms obey instructions that are given based on the data that feeds them. Lacking abductive capabilities, algorithms merely act as obedient executors of the orders. Results of the research can be used as a basis for further research in this area as well as in law-making process

MSR32 COVID-19 Beds’ Occupancy and Hospital Complaints: A Predictive Model

Objectives COVID-19 pandemic limited the number of patients that could be promptly and adequately taken in charge. The proposed research aims at predicting the number of patients requiring any type of hospitalizations, considering not only patients affected by COVID-19, but also other severe viral diseases, including untreated chronic and frail patients, and also oncological ones, to estimate potential hospital lawsuits and complaints. Methods An unsupervised learning approach of artificial neural network’s called Self-Organizing Maps (SOM), grounding on the prediction of the existence of specific clusters and useful to predict hospital behavioral changes, has been designed to forecast the hospital beds’ occupancy, using pre and post COVID-19 time-series, and supporting the prompt prediction of litigations and potential lawsuits, so that hospital managers and public institutions could perform an impacts’ analysis to decide whether to invest resources to increase or allocate differentially hospital beds and humans capacity. Data came from the UK National Health Service (NHS) statistic and digital portals, concerning a 4-year time horizon, related to 2 pre and 2 post COVID-19 years. Results Clusters revealed two principal behaviors in selecting the resources allocation. In case of increase of non-COVID hospitalized patients, a reduction in the number of complaints (-55%) emerged. A higher number of complaints was registered (+17%) against a considerable reduction in the number of beds occupied (-26%). Based on the above, the management of hospital beds is a crucial factor which can influence the complaints trend. Conclusions The model could significantly support in the management of hospital capacity, helping decision-makers in taking rational decisions under conditions of uncertainty. In addition, this model is highly replicable also in the estimation of current hospital beds, healthcare professionals, equipment, and other resources, extremely scarce during emergency or pandemic crises, being able to be adapted for different local and national settings

Vitamin D binding protein gene polymorphisms and baseline vitamin D levels as predictors of antiviral response in chronic hepatitis C

Vitamin D deficiency seems to predict the unsuccessful achievement of sustained viral response (SVR) after anti-viral treatment in hepatitis C virus (HCV) difficult to treat genotypes. Vitamin D binding protein (GC) gene polymorphisms are known to influence vitamin D levels. This study was performed to assess whether the interaction between basal circulating vitamin D and the GC polymorphism plays a role in influencing the rate of anti viral responses in patients affected by chronic hepatitis C. Two hundred six HCV patients treated with a combination therapy of PEGinterferon plus ribavirin were retrospectively evaluated. GC rs7041 G>T, GC rs4588 C>A and IL- 28B rs12979860 C>T polymorphisms were genotyped. Frequencies of GC rs7041 G>T and rs4588 C>A polymorphisms were: G/G=64 (31.1%), G/T=100 (48.5%), T/T=42 (20.4%) and C/C=108 (52.4%), C/A=84 (40.8%), A/A=14 (6.8%). Patients were divided into those carrying 653 major alleles (WT+: G-C/G-C, G-C/T-C, G-C/G-A, N=100) and the remaining (WT-: G-C/T-A, T-A/T-C, T-A/T-A, T-C/T-C, N=106). Four groups were identified: vitamin D 6420 ng/mL and WT-, vitamin D 6420 and WT+, vitamin D>20 and WT-, vitamin D>20 and WT+. In difficult to treat HCV genotypes the proportion of patients achieving SVR significantly increased with a linear trend from the first to the last group: 6/25 (24.0%), 9/24 (37.5%), 12/29 (41.4%), 19/29 (65.5%) (p=0.003). At multivariate analysis having basal vitamin D >20 ng/mL plus the carriage of GC WT+ was found to be an independent predictor of SVR (O.R. 4.52, p=0.015). Conclusions: in difficult to treat HCV genotypes, simultaneous pre treatment normal serum vitamin D levels and the carriage of GCglobulin wild type isoform strongly predicts the achievement of SVR after PEG-interferon plus ribavirin antiviral therapy. Page 3 of 28 Hepatolog

Troposphere-to-mesosphere microphysics of carbon dioxide ice clouds in a Mars Global Climate Model

We have implemented full CO ice cloud microphysics into the LMD Mars Global Climate Model (MGCM) and we have conducted the first global simulations. The microphysical model implementation follows the modal scheme used for water ice cloud microphysics in the MGCM, but includes specific aspects that need to be accounted for when dealing with CO ice clouds. These include nucleation of CO on water ice crystals and CO condensation theory adapted for the Martian conditions. The model results are compared to available observations globally, and separately for polar regions and equatorial mesosphere. The observed seasonal and latitudinal variability of the CO ice clouds is in general reproduced. The polar regions are covered by CO ice clouds during the winter as observed. Instead of forming only in the lowest 10–15 km of the atmosphere, they extend up to several tens of kilometers above the surface in the model, dictated by the modeled temperature structure. We have also quantified the contribution of the cloud microphysics to the surface CO ice deposits. Snowfall from these clouds contributes up to 10% of the atmosphere–surface ice flux in the polar regions in our simulations, in the range that has been indirectly deduced from observations. In the mesosphere, notable amounts of CO ice clouds form only when water ice crystals are used as condensation nuclei in addition to dust particles, and their spatial distribution is in agreement with observations. The mesospheric temperature structure, dominated by tides, dictates the longitudinal and seasonal distribution of these clouds. The seasonal and local time variations of the clouds are not fully reproduced by the model. There is a long pause in CO ice cloud formation in the model around the aphelion season, but clouds have been observed during this period, although with a lower apparition frequency. Modeled mesospheric clouds form mainly during the night and in the morning, whereas during the daytime, when most of the cloud observations have been made, the model rarely predicts clouds. These discrepancies could be explained by the strong dependence of the cloud formation process on mesospheric temperatures that are themselves challenging to reproduce and sensitive to the MGCM processes and parameters. The rare possibilities for nighttime observations might also bias the observational climatologies towards daytime detections. Future developments of the model consist in the inclusion of a possible exogenous condensation nucleus source in the mesosphere and the radiative effect of CO ice clouds. © 2022 Elsevier Inc. All rights reserved.This paper presents the results of ten years of development that has been supported by funding from several sources. We thank the Agence National de la Recherche for funding (project MECCOM, ANR-18-CE31-0013). We are also grateful for the financial support by the LabEx (Laboratoire d’Excellence) ESEP, by the French space agency CNES and the European Space Agency ESA. We acknowledge the support of the French national planetology programme (PNP) as well. F.G.-G. is funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, the Agencia Estatal de Investigación and EC FEDER funds under project RTI2018-100920-J-I00, and acknowledges financial support from the State Agency for Research of the Spanish MCIU through the Center of Excellence Severo Ochoa” award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). This work was performed using HPC computing resources from GENCI-CINES (Grant 2021-A0100110391), and resources at the ESPRI mesocentre of the IPSL institute .Peer reviewe

In-flight measurement of the absolute energy scale of the Fermi Large Area Telescope

The Large Area Telescope (LAT) on-board the Fermi Gamma-ray Space Telescope is a pair-conversion telescope designed to survey the gamma-ray sky from 20 MeV to several hundreds of GeV. In this energy band there are no astronomical sources with sufficiently well known and sharp spectral features to allow an absolute calibration of the LAT energy scale. However, the geomagnetic cutoff in the cosmic ray electron-plus-positron (CRE) spectrum in low earth orbit does provide such a spectral feature. The energy and spectral shape of this cutoff can be calculated with the aid of a numerical code tracing charged particles in the Earth's magnetic field. By comparing the cutoff value with that measured by the LAT in different geomagnetic positions, we have obtained several calibration points between ~6 and ~13 GeV with an estimated uncertainty of ~2%. An energy calibration with such high accuracy reduces the systematic uncertainty in LAT measurements of, for example, the spectral cutoff in the emission from gamma ray pulsars.Comment: 11 pages, 7 figures, submitted to Astroparticle Physic

The Second Fermi Large Area Telescope Catalog of Gamma-Ray Pulsars

This catalog summarizes 117 high-confidence 0.1 GeV gamma-ray pulsar detections using three years of data acquired by the Large Area Telescope (LAT) on the Fermi satellite. Half are neutron stars discovered using LAT data through periodicity searches in gamma-ray and radio data around LAT unassociated source positions. The 117 pulsars are evenly divided into three groups: millisecond pulsars, young radio-loud pulsars, and young radio-quiet pulsars. We characterize the pulse profiles and energy spectra and derive luminosities when distance information exists. Spectral analysis of the off-peak phase intervals indicates probable pulsar wind nebula emission for four pulsars, and off-peak magnetospheric emission for several young and millisecond pulsars.We compare the gammaray properties with those in the radio, optical, and X-ray bands.We provide flux limits for pulsars with no observed gamma-ray emission, highlighting a small number of gamma-faint, radio-loud pulsars. The large, varied gamma-ray pulsar sample constrains emission models. Fermiﾒs selection biases complement those of radio surveys, enhancing comparisons with predicted population distributions