Polling systems in the critical regime

We study polling systems with Poisson arrival streams and exhaustive service discipline in the critical load case [rho]=1. We show that the system may exhibit null-recurrence or transient behavior and give the corresponding conditions in terms of the first two moments of the distribution of service and switching times. For models with two nodes we establish existence of moments of small orders for the return time to the origin of the corresponding embedded Markov chain in the case of null-recurrence.Polling systems Greedy server Transience Null-recurrence Lyapunov functions Markov walk

A note on matrix multiplicative cascades and bindweeds

International audienceWe study the problem of Mandelbrot's multiplicative cascades, but with random matrices instead of random variables. Then, we introduce a new model (which we call the bindweed model), which can be viewed as a random string in a random environment on a tree, and show that the classification of this model from the point of view of positive recurrence can be obtained from the corresponding classification of the matrix-valued multiplicative cascades

A Mixture of the exclusion process and the Voter model

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7 Rome / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Abstract Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003–2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN). We found that the supervised approaches produce better chronological age estimations at the expense of a loss of the association between the aging acceleration and all-cause mortality. Consequently, we turned to the NHANES death register directly and introduced a novel way to train parametric proportional hazards models suitable for out-of-the-box implementation with any modern machine learning software. As a demonstration, we produced a separate deep CNN for mortality risks prediction that outperformed any of the biological age or a simple linear proportional hazards model. Altogether, our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers