Generalized Multi-Output Gaussian Process Censored Regression

When modelling censored observations, a typical approach in current regression methods is to use a censored-Gaussian (i.e. Tobit) model to describe the conditional output distribution. In this paper, as in the case of missing data, we argue that exploiting correlations between multiple outputs can enable models to better address the bias introduced by censored data. To do so, we introduce a heteroscedastic multi-output Gaussian process model which combines the non-parametric flexibility of GPs with the ability to leverage information from correlated outputs under input-dependent noise conditions. To address the resulting inference intractability, we further devise a variational bound to the marginal log-likelihood suitable for stochastic optimization. We empirically evaluate our model against other generative models for censored data on both synthetic and real world tasks and further show how it can be generalized to deal with arbitrary likelihood functions. Results show how the added flexibility allows our model to better estimate the underlying non-censored (i.e. true) process under potentially complex censoring dynamics.Comment: 7 pages, 3 figures, 3 table

Optimizing heart failure treatment following cardiac resynchronization therapy

Device therapy in addition to medical treatment improves prognosis in a subset of patients with heart failure and reduced ejection fraction. However, some patients remain symptomatic or their heart failure even progresses despite cardiac resynchronization therapy (CRT). The aim of the study was to evaluate the proportion of patients who could benefit from optimization of medical therapy using sacubitril/valsartan, ivabradine, or both following CRT implantation. We conducted a post hoc analysis of a single-centre, patient and outcome-assessor blinded, randomized-controlled trial, in which patients scheduled for CRT were randomized to empiric (n = 93) or imaging-guided left-ventricular lead placement (n = 89). All patients underwent clinical evaluation and blood sampling at baseline and 6 months following CRT implantation. The proportion of patients meeting the indication for sacubitril/valsartan (irrespective of angiotensin-converting enzyme inhibitor or angiotensin 2 receptor blocker dosage) and/or ivabradine according to current guidelines was evaluated at baseline and after 6 months. Of 182 patients with an indication for CRT, 146 (80%) also had an indication for optimization of medical therapy at baseline by adding sacubitril/valsartan, ivabradine, or both. Of the 179 survivors at 6 months, 136 (76%) were still symptomatic after device implantation; of these, 51 (38%) patients had an indication for optimization of medical therapy: sacubitril/valsartan in 37 (27%), ivabradine in 7 (5%), and both drugs in 7 (5%) patients. Seven (18%) patients without indication at baseline developed an indication for medical optimization 6 months after CRT implantation. In the present study, 38% of those who remained symptomatic 6 months after CRT implantation were eligible for optimization of medical therapy with sacubitril/valsartan, ivabradine, or both. Patients with CRT may benefit from systematic follow-up including evaluation of medical treatment

Temperature coefficient of resistance and thermal boundary conductance determination of ruthenium thin films by micro four-point probe

Accurate characterization of the temperature coefficient of resistance (αTCR) of electrically conductive materials is pertinent for reducing self-heating in electronic devices. In-situ non-destructive measurements of αTCR using the micro four-point probe (M4PP) technique have previously been demonstrated on platinum (Pt) thin films deposited on fused silica, assuming the thermal conductivity of the substrate as known. In this study, we expand the M4PP method to obtain the αTCR on industrially relevant stacks, comprising ruthenium (Ru) thin films (3.3 nm and 5.2 nm thick) deposited on bulk silicon (Si), separated by a 90 nm SiO2 spacer. The new M4PP methodology allows simultaneous determination of both αTCR and the total thermal boundary conductance (GTBC) between the metallic film and its substrate. We measured the αTCR and the GTBC to be 542 ± 18 ppm K−1 and 15.6 ± 1.3 MW m−2K−1 for 3.3 nm Ru, and 982 ± 46 ppm K−1 and 19.3 ± 2.3 MW m−2K−1 for 5.2 nm Ru. This is in good agreement with independent measurements of αTCR. Our methodology demonstrates the potential of M4PP to characterize thermal properties of metallic thin films used in semiconductor technology