Semi-Supervised Generation with Cluster-aware Generative Models

Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster-aware Generative Model, that uses unlabelled information to infer a latent representation that models the natural clustering of the data, and additional labelled data points to refine this clustering. The generative performances of the model significantly improve when labelled information is exploited, obtaining a log-likelihood of -79.38 nats on permutation invariant MNIST, while also achieving competitive semi-supervised classification accuracies. The model can also be trained fully unsupervised, and still improve the log-likelihood performance with respect to related methods

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework for unsupervised learning of sequential data that disentangles two latent representations: an object's representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate high dimensional frames at each time step. The model is trained end-to-end on videos of a variety of simulated physical systems, and outperforms competing methods in generative and missing data imputation tasks.Comment: NIPS 201

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data. In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures. We introduce the Bidirectional-Inference Variational Autoencoder (BIVA), characterized by a skip-connected generative model and an inference network formed by a bidirectional stochastic inference path. We show that BIVA reaches state-of-the-art test likelihoods, generates sharp and coherent natural images, and uses the hierarchy of latent variables to capture different aspects of the data distribution. We observe that BIVA, in contrast to recent results, can be used for anomaly detection. We attribute this to the hierarchy of latent variables which is able to extract high-level semantic features. Finally, we extend BIVA to semi-supervised classification tasks and show that it performs comparably to state-of-the-art results by generative adversarial networks

Generative Temporal Models with Spatial Memory for Partially Observed Environments

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.Comment: ICML 201

Early and mid-term outcome of patients with low-flow-low-gradient aortic stenosis treated with newer-generation transcatheter aortic valves

Patients with non-paradoxical low-flow-low-gradient (LFLG) aortic stenosis (AS) are at increased surgical risk, and thus, they may particularly benefit from transcatheter aortic valve replacement (TAVR). However, data on this issue are still limited and based on the results with older-generation transcatheter heart valves (THVs). The aim of this study was to investigate early and mid-term outcome of TAVR with newer-generation THVs in the setting of LFLG AS. Data for the present analysis were gathered from the OBSERVANT II dataset, a national Italian observational, prospective, multicenter cohort study that enrolled 2,989 consecutive AS patients who underwent TAVR at 30 Italian centers between December 2016 and September 2018, using newer-generation THVs. Overall, 420 patients with LVEF <= 50% and mean aortic gradient <40 mmHg were included in this analysis. The primary outcomes were 1-year all-cause mortality and a combined endpoint including all-cause mortality and hospital readmission due to congestive heart failure (CHF) at 1 year. A risk-adjusted analysis was performed to compare the outcome of LFLG AS patients treated with TAVR (n = 389) with those who underwent surgical aortic valve replacement (SAVR, n = 401) from the OBSERVANT I study. Patients with LFLG AS undergoing TAVR were old (mean age, 80.8 +/- 6.7 years) and with increased operative risk (mean EuroSCORE II, 11.5 +/- 10.2%). VARC-3 device success was 83.3% with 7.6% of moderate/severe paravalvular leak. Thirty-day mortality was 3.1%. One-year all-cause mortality was 17.4%, and the composite endpoint was 34.8%. Chronic obstructive pulmonary disease (HR 1.78) and EuroSCORE II (HR 1.02) were independent predictors of 1-year mortality, while diabetes (HR 1.53) and class NYHA IV (HR 2.38) were independent predictors of 1-year mortality or CHF. Compared with LFLG AS treated with SAVR, TAVR patients had a higher rate of major vascular complications and permanent pacemaker, while SAVR patients underwent more frequently to blood transfusion, cardiogenic shock, AKI, and MI. However, 30-day and 1-year outcomes were similar between groups. Patients with non-paradoxical LFLG AS treated by TAVR were older and with higher surgical risk compared with SAVR patients. Notwithstanding, TAVR was safe and effective with a similar outcome to SAVR at both early and mid-term

Стан та перспективи конкурентоспроможності галузі національного господарства в умовах глобалізації

Метою дослідження є узагальнення нових теоретичних положень розвитку галузей економіки в умовах глобалізації, визначення загальних конкурентних переваг хімічної галузі України та практичних напрямів сучасного розвитку економіки країни

Prolonged higher dose methylprednisolone vs. conventional dexamethasone in COVID-19 pneumonia: a randomised controlled trial (MEDEAS)

Dysregulated systemic inflammation is the primary driver of mortality in severe COVID-19 pneumonia. Current guidelines favor a 7-10-day course of any glucocorticoid equivalent to dexamethasone 6 mg·day-1. A comparative RCT with a higher dose and a longer duration of intervention was lacking