Optimal choice among a class of nonparametric estimators of the jump rate for piecewise-deterministic Markov processes

A piecewise-deterministic Markov process is a stochastic process whose behavior is governed by an ordinary differential equation punctuated by random jumps occurring at random times. We focus on the nonparametric estimation problem of the jump rate for such a stochastic model observed within a long time interval under an ergodicity condition. We introduce an uncountable class (indexed by the deterministic flow) of recursive kernel estimates of the jump rate and we establish their strong pointwise consistency as well as their asymptotic normality. We propose to choose among this class the estimator with the minimal variance, which is unfortunately unknown and thus remains to be estimated. We also discuss the choice of the bandwidth parameters by cross-validation methods.Comment: 36 pages, 18 figure

Spike-Timing Dependent Plasticity and Feed-Forward Input Oscillations Produce Precise and Invariant Spike Phase-Locking

In the hippocampus and the neocortex, the coupling between local field potential (LFP) oscillations and the spiking of single neurons can be highly precise, across neuronal populations and cell types. Spike phase (i.e., the spike time with respect to a reference oscillation) is known to carry reliable information, both with phase-locking behavior and with more complex phase relationships, such as phase precession. How this precision is achieved by neuronal populations, whose membrane properties and total input may be quite heterogeneous, is nevertheless unknown. In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks – the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2–150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses. These points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule. For a given input oscillation, the balance of potentiation and de-potentiation in the STDP rule is the critical parameter that determines the phase at which an output neuron will learn to spike. These findings are robust to changes in intrinsic post-synaptic properties. Finally, we discuss implications of this mechanism for stable learning of spike-timing in the hippocampus

Self-Healing Distributed Scheduling Platform

International audienceDistributed systems require effective mechanisms to manage the reliable provisioning of computational resources from different and distributed providers. Moreover, the dynamic environment that affects the behaviour of such systems and the complexity of these dynamics demand autonomous capabilities to ensure the behaviour of distributed scheduling platforms and to achieve business and user objectives. In this paper we propose a self-adaptive distributed scheduling platform composed of multiple agents implemented as intelligent feedback control loops to support policy-based scheduling and expose self-healing capabilities. Our platform leverages distributed scheduling processes by (i) allowing each provider to maintain its own internal scheduling process, and (ii) implementing self-healing capabilities based on agent module recovery. Simulated tests are performed to determine the optimal number of agents to be used in the negotiation phase without affecting the scheduling cost function. Test results on a real-life platform are presented to evaluate recovery times and optimize platform parameters

Estimation non paramétrique optimale du taux de saut d'un processus markovien déterministe par morceaux

International audienceUn processus markovien déterministe par morceaux est un processus stochastique dont la trajectoire est décrite par une équation différentielle perturbée par des sauts aléatoires en des instants aléatoires. Nous nous intéressons à l'estimation du taux de saut d'un tel processus observé en temps long sous une hypothèse d'ergodicité. Nous introduisons une classe d'estimateurs non paramétriques consistants et asymptotiquement gaussiens. Nous proposons de choisir l'estimateur de variance minimale, variance qui est elle-même à estimer

Optimal choice among a class of nonparametric estimators of the jump rate for piecewise-deterministic Markov processes

International audienceA piecewise-deterministic Markov process is a stochastic process whose behavior is governed by an ordinary differential equation punctuated by random jumps occurring at random times. We focus on the nonparametric estimation problem of the jump rate for such a stochastic model observed within a long time interval under an ergodicity condition. We introduce an uncountable class (indexed by the deterministic flow) of recursive kernel estimates of the jump rate and we establish their strong pointwise consistency as well as their asymptotic normality. We propose to choose among this class the estimator with the minimal variance, which is unfortunately unknown and thus remains to be estimated. We also discuss the choice of the bandwidth parameters by cross-validation methods

Influence of the Lockdown on PM2.5 Concentrations around an Urban School in the South of Belgium

In 2020, the world was affected by an unprecedented health crisis. Europe had to close its internal and external borders, and the majority of countries had to impose lockdowns on their people. Shops, restaurants, building sites, and industries had to close, and working from home became the rule. This paper reflects a study conducted from 17 March to 25 June 2020, in which homemade low-cost devices measured PM2.5 concentrations at three different locations around a Belgian school and background concentrations. The period monitored covered seven reopening stages from lockdown to the reopening of borders. The overall analysis did not show any correlation between traffic and PM2.5 concentrations in the streets in any of the phases. However, the analysis of each reopening showed that it was possible to observe significant differences in the background concentrations measured in a rural town and on urban streets

Taking Language out of the Equation: The Assessment of Basic Math Competence Without Language

While numerical skills are fundamental in modern societies, some estimated 5–7% of children suffer from mathematical learning difficulties (MLD) that need to be assessed early to ensure successful remediation. Universally employable diagnostic tools are yet lacking, as current test batteries for basic mathematics assessment are based on verbal instructions. However, prior research has shown that performance in mathematics assessment is often dependent on the testee's proficiency in the language of instruction which might lead to unfair bias in test scores. Furthermore, language-dependent assessment tools produce results that are not easily comparable across countries. Here we present results of a study that aims to develop tasks allowing to test for basic math competence without relying on verbal instructions or task content. We implemented video and animation-based task instructions on touchscreen devices that require no verbal explanation. We administered these experimental tasks to two samples of children attending the first grade of primary school. One group completed the tasks with verbal instructions while another group received video instructions showing a person successfully completing the task. We assessed task comprehension and usability aspects both directly and indirectly. Our results suggest that the non-verbal instructions were generally well understood as the absence of explicit verbal instructions did not influence task performance. Thus we found that it is possible to assess basic math competence without verbal instructions. It also appeared that in some cases a single word in a verbal instruction can lead to the failure of a task that is successfully completed with non-verbal instruction. However, special care must be taken during task design because on rare occasions non-verbal video instructions fail to convey task instructions as clearly as spoken language and thus the latter do not provide a panacea to non-verbal assessment. Nevertheless, our findings provide an encouraging proof of concept for the further development of non-verbal assessment tools for basic math competence

Correlation of structural and optical properties using virtual materials analysis

Thin film growth of TiO2 by physical vapor deposition processes is simulated in the Virtual Coater framework resulting in virtual thin films. The simulations are carried out for artificial, simplified deposition conditions as well as for conditions representing a real coating process. The study focuses on porous films which exhibit a significant anisotropy regarding the atomistic structure and consequently, to the index of refraction. A method how to determine the effective anisotropic index of refraction of virtual thin films by the effective medium theory is developed. The simulation applies both, classical molecular dynamics as well as kinetic Monte Carlo calculations, and finally the properties of the virtual films are compared to experimentally grown films especially analyzing the birefringence in the evaluation

Organellar inheritance in the green lineage: insights from Ostreococcus tauri

Along the green lineage (Chlorophyta and Streptophyta), mitochondria and chloroplast are mainly uniparentally transmitted and their evolution is thus clonal. The mode of organellar inheritance in their ancestor is less certain. The inability to make clear phylogenetic inference is partly due to a lack of information for deep branching organisms in this lineage. Here, we investigate organellar evolution in the early branching green alga Ostreococcus tauri using population genomics data from the complete mitochondrial and chloroplast genomes. The haplotype structure is consistent with clonal evolution in mitochondria, while we find evidence for recombination in the chloroplast genome. The number of recombination events in the genealogy of the chloroplast suggests that recombination, and thus biparental inheritance, is not rare. Consistent with the evidence of recombination, we find that the ratio of the number of nonsynonymous to the synonymous polymorphisms per site is lower in chloroplast than in the mitochondria genome. We also find evidence for the segregation of two selfish genetic elements in the chloroplast. These results shed light on the role of recombination and the evolutionary history of organellar inheritance in the green lineage