Automated Mapping of the roof damage in historic buildings in seismic areas with UAV photogrammetry

The paper presents a fast methodology to quantify the damage to the roof in historic buildings, suggested soon after a light seismic event occurs, in order to evaluate the necessity of provisional interventions to prevent further damages. The survey is based on UAV photogrammetry, a well-known technique that allows inspection and digital documentation even in hardly accessible or dangerous areas. The research aims to analyze the feasibility of the automated mapping of roof damage using an image classification procedure based on supervised machine learning. The procedure is summed up in an efficient workflow, where UAV photogrammetry is combined with other 3D survey techniques, such as terrestrial photogrammetry and laser scanning, to provide comprehensive documentation and quantitative data on a historical building. The methodology was validated on a large historical building, now suffering from a serious state of neglect, which roof was never surveyed before and with different damage types. The output orthoimage of the tiled roof allowed us to understand the past interventions and the current serious damage state with promising outcomes regarding the speed of the survey method

A new generation photodetector for astroparticle physics: the VSiPMT

The VSiPMT (Vacuum Silicon PhotoMultiplier Tube) is an innovative design we proposed for a revolutionary photon detector. The main idea is to replace the classical dynode chain of a PMT with a SiPM (G-APD), the latter acting as an electron detector and amplifier. The aim is to match the large sensitive area of a photocathode with the performance of the SiPM technology. The VSiPMT has many attractive features. In particular, a low power consumption and an excellent photon counting capability. To prove the feasibility of the idea we first tested the performance of a special non-windowed SiPM by Hamamatsu (MPPC) as electron detector and current amplifier. Thanks to this result Hamamatsu realized two VSiPMT industrial prototypes. In this work, we present the results of a full characterization of the VSiPMT prototype

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored

Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review.

PURPOSE This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses. PATIENTS AND METHODS A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician. RESULTS U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement. CONCLUSION Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings

Comparison analysis between standard polysomnographic data and in-ear-EEG signals: A preliminary study

Study Objectives: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in sleep quality assessment. Hence, less invasive, cost-effective, and portable alternatives need to be explored. One promising contender is the in-ear-EEG sensor. This study aims to establish a methodology to assess the similarity between the single-channel in-ear-EEG and standard PSG derivations. Methods: The study involves four-hour signals recorded from ten healthy subjects aged 18 to 60 years. Recordings are analyzed following two complementary approaches: (i) a hypnogram-based analysis aimed at assessing the agreement between PSG and in-ear-EEG-derived hypnograms; and (ii) a feature-based analysis based on time- and frequency- domain feature extraction, unsupervised feature selection, and definition of Feature-based Similarity Index via Jensen-Shannon Divergence (JSD-FSI). Results: We find large variability between PSG and in-ear-EEG hypnograms scored by the same sleep expert according to Cohen's kappa metric, with significantly greater agreements for PSG scorers than for in-ear-EEG scorers (p < 0.001) based on Fleiss' kappa metric. On average, we demonstrate a high similarity between PSG and in-ear-EEG signals in terms of JSD-FSI (0.79 +/- 0.06 -awake, 0.77 +/- 0.07 -NREM, and 0.67 +/- 0.10 -REM) and in line with the similarity values computed independently on standard PSG-channel-combinations. Conclusions: In-ear-EEG is a valuable solution for home-based sleep monitoring, however further studies with a larger and more heterogeneous dataset are needed.Comment: 20 figures, 6 table

U-Sleep's resilience to AASM guidelines

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications,e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Darkside latest results and the future liquid argon dark matter program

DarkSide uses a dual-phase Liquid Argon Time Projection Chamber running since mid 2015 with a 50-kg-active-mass target from an underground source, to search for WIMP dark matter. The talk will present the latest result on the search for low mass WIMPs (M_{WIMP} <20GeV/c^2 ) extending the exclusion region below previous limits in the range $1.8-6 GeV/c^2$, as well as a search for high mass WIMPs (M_{WIMP}>100GeV/c^2) with 0 background reported in the signal region giving the most competitive result obtained with an Argon target. The next stage of the Darkside program will be a new generation experiment involving a global collaboration from all the current Argon based experiments. DarkSide-20k is designed as a &gt;20-tonne fiducial mass TPC with SiPM based photosensors, expected to achieve an instrumental background well below that from coherent scattering of solar and atmospheric neutrinos. Like its predecessor DarkSide-20k will be housed at the Gran Sasso (LNGS) underground laboratory, and it is expected to attain a WIMP-nucleon cross section exclusion sensitivity of $10^{-47}\, cm^2$ for a WIMP mass of $1 TeV/c^2$ in a 5 yr run.</p