Implicit QR for Companion-like Pencils

A fast implicit QR algorithm for eigenvalue computation of low rank corrections of unitary matrices is adjusted to work with matrix pencils arising from polynomial zerofinding problems . The modified QZ algorithm computes the generalized eigenvalues of certain NxN rank structured matrix pencils using O(N^2) ops and O(N) memory storage. Numerical experiments and comparisons confirm the effectiveness and the stability of the proposed method

Scaling and intermittency of brain events as a manifestation of consciousness

We discuss the critical brain hypothesis and its relationship with intermittent renewal processes displaying power-law decay in the distribution of waiting times between two consecutive renewal events. In particular, studies on complex systems in a "critical" condition show that macroscopic variables, integrating the activities of many individual functional units, undergo fluctuations with an intermittent serial structure characterized by avalanches with inverse-power-law (scale-free) distribution densities of sizes and inter-event times. This condition, which is denoted as "fractal intermittency", was found in the electroencephalograms of subjects observed during a resting state wake condition. It remained unsolved whether fractal intermittency correlates with the stream of consciousness or with a non-task-driven default mode activity, also present in non-conscious states, like deep sleep. After reviewing a method of scaling analysis of intermittent systems based of event-driven random walks, we show that during deep sleep fractal intermittency breaks down, and re-establishes during REM (Rapid Eye Movement) sleep, with essentially the same anomalous scaling of the pre-sleep wake condition. From the comparison of the pre-sleep wake, deep sleep and REM conditions we argue that the scaling features of intermittent brain events are related to the level of consciousness and, consequently, could be exploited as a possible indicator of consciousness in clinical applications

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

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

Adaptive filtering for removing nonstationary physiological noise from resting state fMRI BOLD signals

fMRI is used to investigate brain functional connectivity after removing nonneural components by General Linear Model (GLM) approach with a reference ventricle-derived signal as covariate. Ventricle signals are related to low-frequency modulations of cardiac and respiratory rhythms, which are nonstationary activities. Herein, we employed an adaptive filtering approach to improve removing physiological noise from BOLD signals. Comparisons between filtering approaches were performed by evaluating the amount of removed signal variance and the connectivity between homologous contralateral regions of interest (ROIs). The global connectivity between ROIs was estimated with a generalized correlation named RV coefficient. The mean ROI decrease of variance was -52% and -11%, for adaptive filtering and GLM, respectively. Adaptive filtering led to higher connectivity between grey matter ROIs than that obtained with GLM. Thus, adaptive filtering is a feasible method for removing the physiological noise in the low frequency band and to highlight resting state functional networks

A nosocomial measles outbreak in Italy, February-April 2017

We describe a nosocomial outbreak of measles that occurred in an Italian hospital during the first months of 2017, involving 35 persons and including healthcare workers, support personnel working in the hospital, visitors and community contacts. Late diagnosis of the first case, support personnel not being promptly recognised as hospital workers and diffusion of the infection in the emergency department had a major role in sustaining this outbreak

Implicit QR with compression

AbstractIn this paper, we elaborate on the implicit shifted QR eigenvalue algorithm given in [D.A. Bini, P. Boito, Y. Eidelman, L. Gemignani, I. Gohberg, A fast implicit QR eigenvalue algorithm for companion matrices, Linear Algebra Appl. 432 (2010), 2006–2031]. The algorithm is substantially simplified and speeded up while preserving its numerical robustness. This allows us to obtain a potentially important advance towards a proof of its backward stability together with both cost reductions and implementative benefits