The frequency spectrum of finite samples from the intermittent silence process

It has been argued that the actual distribution of word frequencies could be reproduced or explained by generating a random sequence of letters and spaces according to the so-called intermittent silence process. The same kind of process could reproduce or explain the counts of other kinds of units from a wide range of disciplines. Taking the linguistic metaphor, we focus on the frequency spectrum, i.e., the number of words with a certain frequency, and the vocabulary size, i.e., the number of different words of text generated by an intermittent silence process. We derive and explain how to calculate accurately and efficiently the expected frequency spectrum and the expected vocabulary size as a function of the text size.Peer ReviewedPostprint (author's final draft

Clustering Patients with Tensor Decomposition

In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferable for tasks such as clustering patient records, to more commonly used distance-based methods. We run the algorithm on two datasets of healthcare records, obtaining clinically meaningful results.Comment: Presented at 2017 Machine Learning for Healthcare Conference (MLHC 2017). Boston, M

Discontinuities in recurrent neural networks

This paper studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net-function and a sigmoid-like continuous activation-function. The authors introducePostprint (published version

Learning probability distributions generated by finite-state machines

We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft

Identifiability and transportability in dynamic causal networks

In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Networks, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the identification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the result of causal experiments in a source domain may be used for the identification of causal effects in a target domain.Preprin

Machine learning assists the classification of reports by citizens on disease-carrying mosquitoes

Mosquito Alert (www.mosquitoalert.com/en) is an expert-validated citizen science platform for tracking and controlling disease-carrying mosquitoes. Citizens download a free app and use their phones to send reports of presumed sightings of two world-wide disease vector mosquito species (the Asian Tiger and the Yellow Fever mosquito). These reports are then supervised by a team of entomologists and, once validated, added to a database. As the platform prepares to scale to much larger geographical areas and user bases, the expert validation by entomologists becomes the main bottleneck. In this paper we describe the use of machine learning on the citizen reports to automatically validate a fraction of them, therefore allowing the entomologists either to deal with larger report streams or to concentrate on those that are more strategic, such as reports from new areas (so that early warning protocols are activated) or from areas with high epidemiological risks (so that control actions to reduce mosquito populations are activated). The current prototype flags a third of the reports as “almost certainly positive” with high confidence. It is currently being integrated into the main workflow of the Mosquito Alert platform.Postprint (published version