Symbolic Sequences and Tsallis Entropy

We address this work to investigate symbolic sequences with long-range correlations by using computational simulation. We analyze sequences with two, three and four symbols that could be repeated $l$ times, with the probability distribution $p(l)\propto 1/ l^{\mu}$. For these sequences, we verified that the usual entropy increases more slowly when the symbols are correlated and the Tsallis entropy exhibits, for a suitable choice of $q$, a linear behavior. We also study the chain as a random walk-like process and observe a nonusual diffusive behavior depending on the values of the parameter $\mu$.Comment: Published in the Brazilian Journal of Physic

An extended space approach for particle Markov chain Monte Carlo methods

In this paper we consider fully Bayesian inference in general state space models. Existing particle Markov chain Monte Carlo (MCMC) algorithms use an augmented model that takes into account all the variable sampled in a sequential Monte Carlo algorithm. This paper describes an approach that also uses sequential Monte Carlo to construct an approximation to the state space, but generates extra states using MCMC runs at each time point. We construct an augmented model for our extended space with the marginal distribution of the sampled states matching the posterior distribution of the state vector. We show how our method may be combined with particle independent Metropolis-Hastings or particle Gibbs steps to obtain a smoothing algorithm. All the Metropolis acceptance probabilities are identical to those obtained in existing approaches, so there is no extra cost in term of Metropolis-Hastings rejections when using our approach. The number of MCMC iterates at each time point is chosen by the used and our augmented model collapses back to the model in Olsson and Ryden (2011) when the number of MCMC iterations reduces. We show empirically that our approach works well on applied examples and can outperform existing methods.Comment: 35 pages, 2 figures, Typos corrected from Version

'It started with this one post’:# MeToo, India and higher education

In October 2017, Raya Sarkar, a 24-year-old law student from India, posted a crowdsourced list on Facebook of male Indian academics who allegedly harassed women. This led to the start of the #MeToo movement in India, where universities became key spaces of discussion, debate and activism. Due to failures of both the criminal justice system and the described capitalist, patriarchal, casteist structures of Indian academia, hundreds of survivors who had experienced sexual violence at universities came forward online, disclosing their stories of harassment and abuse. Drawing from interviews with seven sexual violence survivors who disclosed their experiences online, this paper provides insight into reasons why survivors choose to bypass formal reporting mechanisms in HEIs, and instead turn to online spaces in their search for justice and healing. We argue that students are wary of university processes and often seek alternative forms of justice beyond the ‘punishment’ that HEIs are often unable or unwilling to provide. As such, this article provides compelling empirical evidence of the urgent need for universities to adopt survivor-centred approaches in their processes and conceptualization of justice, as well as how online spaces enable healing, catharsis and new means of informal justice

Experiencing discrimination increases risk taking.

Prior research has revealed racial disparities in health outcomes and health-compromising behaviors, such as smoking and drug abuse. It has been suggested that discrimination contributes to such disparities, but the mechanisms through which this might occur are not well understood. In the research reported here, we examined whether the experience of discrimination affects acute physiological stress responses and increases risk-taking behavior. Black and White participants each received rejecting feedback from partners who were either of their own race (in-group rejection) or of a different race (out-group rejection, which could be interpreted as discrimination). Physiological (cardiovascular and neuroendocrine) changes, cognition (memory and attentional bias), affect, and risk-taking behavior were assessed. Significant participant race × partner race interactions were observed. Cross-race rejection, compared with same-race rejection, was associated with lower levels of cortisol, increased cardiac output, decreased vascular resistance, greater anger, increased attentional bias, and more risk-taking behavior. These data suggest that perceived discrimination is associated with distinct profiles of physiological reactivity, affect, cognitive processing, and risk taking, implicating direct and indirect pathways to health disparities

Extensive Characterization of Seismic Laws in Acoustic Emissions of Crumpled Plastic Sheets

Statistical similarities between earthquakes and other systems that emit cracking noises have been explored in diverse contexts, ranging from materials science to financial and social systems. Such analogies give promise of a unified and universal theory for describing the complex responses of those systems. There are, however, very few attempts to simultaneously characterize the most fundamental seismic laws in such systems. Here we present a complete description of the Gutenberg-Richter law, the recurrence times, Omori's law, the productivity law, and Bath's law for the acoustic emissions that happen in the relaxation process of uncrumpling thin plastic sheets. Our results show that these laws also appear in this phenomenon, but (for most cases) with different parameters from those reported for earthquakes and fracture experiments. This study thus contributes to elucidate the parallel between seismic laws and cracking noises in uncrumpling processes, revealing striking qualitative similarities but also showing that these processes display unique features.Comment: Accepted for publication in EP