BTLD+:A BAYESIAN APPROACH TO TRACKING LEARNING DETECTION BY PARTS

The contribution proposed in this thesis focuses on this particular instance of the visual tracking problem, referred as Adaptive Ap- iv \ufffcpearance Tracking. We proposed different approaches based on the Tracking Learning Detection (TLD) decomposition proposed in [55]. TLD decomposes visual tracking into three components, namely the tracker, the learner and detector. The tracker and the detector are two competitive processes for target localization based on comple- mentary sources of informations. The former searches for local fea- tures between consecutive frames in order to localize the target; the latter exploits an on-line appearance model to detect confident hy- pothesis over the entire image. The learner selects the final solution among the provided hypothesis. It updates the target appearance model, if necessary, reinitialize the tracker and bootstraps the detec- tor\u2019s appearance model. In particular, we investigated different ap- proaches to enforce the TLD stability. First, we replaced the tracker component with a novel one based on mcmc particle filtering; after- wards, we proposed a robust appearance modeling component able to characterize deformable objects in static images; after all, we inte- grated a modeling component able to integrate local visual features learning into the whole approach, lying to a couple layered represen- tation of the target appearance

A QR based approach for the nonlinear eigenvalue problem

We describe a fast and numerically robust approach based on the structured QR eigenvalue algorithm for computing approximations of the eigenvalues of a holomorphic matrix-valued function inside the unit circle. Numerical experiments confirm the effectiveness of the proposed method

The ECAPS Experiment for Solar Cell Characterization in the Stratosphere

The ECAPS project (Experimental Characterization of Advanced Photovoltaics in the Stratosphere) aims at the characterization of performance of a number of different solar cells in the stratospheric environment. ECAPS has been selected to fly as a zero-pressure balloon payload in the frame of the HEMERA H2020 project. Flight is scheduled for August 2022 from CNES’ base in Timmins, Canada. Testing solar cells in the stratosphere is of great interest for the development of High-Altitude Pseudo Satellite (HAPS) platforms, which will be equipped with high efficiency, flexible solar cells capable to operate at 20-30 km altitude for weeks or months, as well as to perform high-quality calibration of spacecraft solar cells in a near-air mass zero environment. The experiment includes a panel with up to 4 solar cells of different kinds (multi-junction GaAs, CIGS, perovskite, etc.), a dedicated I/V curve recording circuit, temperature and irradiance sensors, and an inertial measurement unit to monitor the instantaneous attitude of the gondola. During the ascent part of the flight, the I/V characteristic curves of the cells will be continuously recorded so to allow for comparison of performance of the different photovoltaic technologies in identical, real stratospheric flight conditions, as well as to detect performance changes with external temperature, irradiance and altitude. Upon recovery of the experiment, post-flight inspection will also yield useful information on the solar cell compatibility with the high altitude environment

Reconstructing individual responses to direct questions: a new method for reconstructing malingered responses

Introduction: The false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers’ responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual’s response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection. Method: We have validated the procedure of reconstructing individual responses from peers’ estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results. Results: According to the results, individual responses to a single question requiring a “yes” or “no” response are predicted with 70–80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7–0.77 with actual results. Discussion: The application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing

Secondary traumatic stress and burnout in healthcare workers during COVID-19 outbreak

Background: The present study aims to assess the level of professional burnout and secondary traumatic stress (STS), and to identify potential risk or protective factors among health care workers (HCWs) during the coronavirus disease 2019 (COVID-19) outbreak.; (2) Methods: This cross-sectional study, based on an online survey, collected demographic data and mental distress outcomes from 184 HCWs from 1 May 2020, to 15 June 2020, from 45 different countries. The degree of STS, perceived stress and burnout was assessed using the Secondary Traumatic Stress Scale (STSS), the Perceived Stress Scale (PSS) and Maslach Burnout Inventory Human Service Survey (MBI-HSS) respectively. Stepwise multiple regression analysis was performed to identify potential risk and protective factors for STS; (3) Results: 184 HCWs (M = 90; Age mean: 46.45; SD: 11.02) completed the survey. A considerable proportion of HCWs had symptoms of STS (41.3%), emotional exhaustion (56.0%), and depersonalization (48.9%). The prevalence of STS was 47.5% in frontline HCWs while in HCWs working in other units it was 30.3% (p < 0.023); 67.1% for the HCWs exposed to patients’ death and 32.9% for those HCWs which were not exposed to the same condition (p < 0.001). In stepwise multiple regression analysis, perceived stress, emotional exhaustion, and exposure to patients’ death remained as significant predictors in the final model for STS (adjusted R2 = 0.537, p < 0.001); (4) Conclusions: During the current COVID-19 pandemic, HCWs facing patients’ physical pain, psychological suffering, and death are more likely to develop STS. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Poor sleep quality may independently predict suicidal risk in COVID-19 survivors: a 2-year longitudinal study

Objective: Multiple symptoms of psychiatric, neurological, and physical illnesses may be part of Post-COVID conditions and may pose COVID-19 survivors a high suicidal risk. Accordingly, we aimed to study factors contributing to suicidal risk in Post COVID-19 patients. Method: Consecutive patients with post COVID-19 conditions were followed for 2 years at the University Hospital of Ferrara at baseline (T0), 6 (T1), 12 (T2), and 24 (T3) months. Demographics, and clinical data for all patients included: disease severity, hospital length of stay, comorbidity, clinical complications, sleep quality, cognitive complaints, anxiety and stress-related symptoms, depressive symptoms, and suicidal ideation. Results: The final sample included 81 patients with post COVID survivors. The mean age was 64 + 10,6 years, 35,8% were females, 65,4% had medical comorbidities, and 69,1% had WHO severe form of COVID forms. At T0 more than 90% of patients showed poor sleep quality, 59.3% reported moderate/severe depressive symptoms, and 51.% experienced anxiety, 25.9% experienced post-traumatic stress symptoms. At T0 suicidal ideation, interested 6.1% and at T3 it increased to 7.4%. In the regression analysis, suicidal ideation at baseline was best predicted by poor sleep quality (O.R. 1.71, p=0.044) and, after 2 years, suicidal ideation was best predicted by poor sleep quality experienced at baseline (OR 67.3, p=0.001). Conclusions: Poor sleep quality may play as an independent predictor of suicidal risk in post-COVID survivors. Evaluating and targeting sleep disturbances in COVID survivors is important to prevent the consequences of disrupted sleep in mental health