A fast and flexible machine learning approach to data quality monitoring

We present a machine learning based approach for real-time monitoring of particle detectors. The proposed strategy evaluates the compatibility between incoming batches of experimental data and a reference sample representing the data behavior in normal conditions by implementing a likelihood-ratio hypothesis test. The core model is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. The resulting algorithm is fast, efficient and agnostic about the type of potential anomaly in the data. We show the performance of the model on multivariate data from a drift tube chambers muon detector

Echinoderm larvae as bioindicators for the assessment of marine pollution: Sea urchin and sea cucumber responsiveness and future perspectives

Echinoderms play a crucial role in the functioning of marine ecosystems and due to their extensive distribution, rapid response, and the high sensitivity of their planktonic larvae to a large range of stressors, some species are widely used as biological indicators. In addition to sea urchins, sea cucumbers have recently been implemented in embryotoxicity bioassays showing high potential in ecotoxicological studies. However, the use of this species is still hindered by a lack of knowledge regarding their comparative responsiveness. The present study aimed to investigate the responsiveness of different echinoderm species to environmental pollution in order to develop their integration in batteries of ecotoxicological bioassays. To this end, the embryos of two sea urchins (Paracentrotus lividus and Arbacia lixula) and two sea cucumbers (Holothuria polii and Holothuria tubulosa) were incubated with inorganic and organic toxicants (cadmium, copper, mercury, lead, sodium dodecyl sulphate and 4-n-Nonhyphenol) and elutriates from contaminated marine sediments, chosen as a case study model. The results obtained, expressed through the percentage of abnormal embryos and Integrative Toxicity Indices (ITI), indicated species-specific sensitivities to pollutants, with comparable and correlated responsiveness between sea urchins and sea cucumbers. More specifically, sea cucumber larvae exposed to elutriates appear to be more sensitive than sea urchins, especially when incubated with samples containing trace metals, PCB and TBT. These results indicate that toxic responses in embryos exposed to environmental matrices are probably modulated by interactions between different variables, including additive, synergistic and antagonistic effects. These findings suggest that performing a larval test using different echinoderm classes can integrate the interactive effects of bioavailable fraction of contaminants on various levels, providing sensitive, representative and all year-round batteries of bioassays to apply in ecotoxicological studies

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.Comment: 22 pages, 13 figure

Making eco-sustainable floating offshore wind farms: Siting, mitigations, and compensations

Floating Offshore Wind Farms (FOWFs) are the most promising renewable energy resource. Floating turbines are installed at progressively increasing water depths, interacting with offshore and deep-sea ecosystems. Thus, specific criteria to enable a sound and accurate Environmental Impact Assessment (EIA) are required. The still limited understanding of the impacts of FOWFs, and the concerns for the conflicts in the use of maritime space (e.g., fisheries), might lead to a more precautionary approach and constrain their development. Here we describe the characteristics of the deep habitats potentially impacted and identify a set of comprehensive and standardized criteria, response variables and approaches for a reliable EIA based on an Ecosystem-based approach. These analyses will support an appropriate design and site prioritization to respect the “Do No Significant Harm" principle. Considering the wide heterogeneity among habitats and geographic regions, we examined the potential interactions of FOWFs with i) Vulnerable Marine Ecosystems; ii) critical habitats; iii) migratory routes of large marine vertebrates; iv) habitat-forming species, benthic/pelagic organisms, v) migratory routes of birds/chiropters; vi) other human uses leading to cumulative/synergistic effects and any other potential interference. We identified mitigation and compensation measures and explored the potential of wind-farm areas as “Other Effective Conservation Measures” to support sustainable fisheries and passive restoration. Adequate siting, EIA and systematic monitoring can minimize FOWFs’ environmental interactions, with final negligible, or even positive effects on marine ecosystems. Standardized criteria could significantly reduce the bottlenecks in permitting while offering a strategic vision for the sustainable use of the maritime space

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe

Probing effective field theory operators in the associated production of top quarks with a Z boson in multilepton final states at root s=13 TeV

Peer reviewe