Search results for "hidden"

A Hidden Curriculum? Coeducation and Gender Identity

2000

Different subjects, in particular, are shown to be gendered, and it is a feet that children invest and excel in subject matters in accordance with their sex. Findings cm the reinforcement of gender stereotypes are more convergent with studies showing much more clear-cut differences in attitude between boys and girls in mixed groups. Coeducation holds back intellectual and personal development because it gives particular cogency to the cognitive processes of gender categorisation not only of fields and professions, but also of one's self and of others. Most importantly, probably, is the socialisation process that takes place simply through the cohabitation of the two groups, with their suppo…

Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation

2016

The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…

Learning from Errors: Detecting ZigBee Interference in WiFi Networks

2014

In this work we show how to detect ZigBee interference on commodity WiFi cards by monitoring the reception errors, such as synchronization errors, invalid header formats, too long frames, etc., caused by ZigBee transmissions. Indeed, in presence of non-WiFi modulated signals, the occurrence of these types of errors follows statistics that can be easily recognized. Moreover, the duration of the error bursts depends on the transmission interval of the interference source, while the error spacing depends on the receiver implementation. On the basis of these considerations, we propose the adoption of hidden Markov chains for characterizing the behavior of WiFi receivers in presence of controlle…

Dark, Cold, and Noisy: Constraining Secluded Hidden Sectors with Gravitational Waves

2018

We explore gravitational wave signals arising from first-order phase transitions occurring in a secluded hidden sector, allowing for the possibility that the hidden sector may have a different temperature than the Standard Model sector. We present the sensitivity to such scenarios for both current and future gravitational wave detectors in a model-independent fashion. Since secluded hidden sectors are of particular interest for dark matter models at the MeV scale or below, we pay special attention to the reach of pulsar timing arrays. Cosmological constraints on light degrees of freedom restrict the number of sub-MeV particles in a hidden sector, as well as the hidden sector temperature. Ne…

CP symmetry and thermal effects on Dirac bi-spinor spin–parity local correlations

2018

Intrinsic quantum correlations supported by the $SU(2)\otimes SU(2)$ structure of the Dirac equation used to describe particle/antiparticle states, optical ion traps and bilayer graphene are investigated and connected to the description of local properties of Dirac bi-spinors. For quantum states driven by Dirac-like Hamiltonians, quantum entanglement and geometric discord between spin and parity degrees of freedom - sometimes mapped into equivalent low energy internal degrees of freedom - are obtained. Such \textit{spin-parity} quantum correlations and the corresponding nonlocal intrinsic structures of bi-spinor fermionic states can be classified in order to relate quantum observables to th…

Bayesian Markov switching models for the early detection of influenza epidemics

2008

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, t…

Analytical-numerical methods for finding hidden oscillations in dynamical systems

2012

ASR performance prediction on unseen broadcast programs using convolutional neural networks

2018

In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably …

Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest

2020

Eye tracking is used to analyze and compare user behaviour within numerous domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making th ...

Model selection procedure for mixture hidden Markov models

2021

This paper proposes a model selection procedure to identify the number of clusters and hidden states in discrete Mixture Hidden Markov models (MHMMs). The model selection is based on a step-wise approach that uses, as score, information criteria and an entropy criterion. By means of a simulation study, we show that our procedure performs better than classical model selection methods in identifying the correct number of clusters and hidden states or an approximation of them