An Introduction to Non-diffusive Transport Models

The process of diffusion is the most elementary stochastic transport process. Brownian motion, the representative model of diffusion, played a important role in the advancement of scientific fields such as physics, chemistry, biology and finance. However, in recent decades, non-diffusive transport processes with non-Brownian statistics were observed experimentally in a multitude of scientific fields. Examples include human travel, in-cell dynamics, the motion of bright points on the solar surface, the transport of charge carriers in amorphous semiconductors, the propagation of contaminants in groundwater, the search patterns of foraging animals and the transport of energetic particles in turbulent plasmas. These examples showed that the assumptions of the classical diffusion paradigm, assuming an underlying uncorrelated (Markovian), Gaussian stochastic process, need to be relaxed to describe transport processes exhibiting a non-local character and exhibiting long-range correlations. This article does not aim at presenting a complete review of non-diffusive transport, but rather an introduction for readers not familiar with the topic. For more in depth reviews, we recommend some references in the following. First, we recall the basics of the classical diffusion model and then we present two approaches of possible generalizations of this model: the Continuous-Time-Random-Walk (CTRW) and the fractional L\'evy motion (fLm)

Picture recognition in animals and in humans : a review

The question of object–picture recognition has received relatively little attention in both human and comparative psychology; a paradoxical situation given the important use of image technology (e.g. slides, digitised pictures) made by neuroscientists in their experimental investigation of visual cognition. The present review examines the relevant literature pertaining to the question of the correspondence between and:or equivalence of real objects and their pictorial representations in animals and humans. Two classes of reactions towards pictures will be considered in turn: acquired responses in picture recognition experiments and spontaneous responses to pictures of biologically relevant objects (e.g. prey or conspecifics). Our survey will lead to the conclusion that humans show evidence of picture recognition from an early age; this recognition is, however, facilitated by prior exposure to pictures. This same exposure or training effect appears also to be necessary in nonhuman primates as well as in other mammals and in birds. Other factors are also identified as playing a role in the acquired responses to pictures: familiarity with and nature of the stimulus objects, presence of motion in the image, etc. Spontaneous and adapted reactions to pictures are a wide phenomenon present in different phyla including invertebrates but in most instances, this phenomenon is more likely to express confusion between objects and pictures than discrimination and active correspondence between the two. Finally, given the nature of a picture (e.g. bi-dimensionality, reduction of cues related to depth), it is suggested that object–picture recognition be envisioned in various levels, with true equivalence being a limited case, rarely observed in the behaviour of animals and even humans

Judgement of conceptual identity in monkeys

Baboons (Papio anubis) were tested on categorization tasks at two different conceptual levels. The monkeys showed their ability (1) to judge as identical or different the objects belonging to two categories, on a perceptual basis, and (2) to perform a judgment of conceptual identity—that is, to use the same/different relation between two previously learned categories. This latter experiment represents the first demonstration of judgment of conceptual identity in a monkey specie

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

[Protocol] Visual feedback of the individual's medical imaging results for changing health behaviours in clinical and non-clinical populations

Primary objective To assess the extent to which presentation to the individual of images of their own body created during medical imaging procedures increases or decreases health behaviours such as: 1. dietary fat intake; 2. physical activity levels; 3. smoking; 4. alcohol use; 5. damaging exposure to sunlight or other sources of ultraviolet radiation. This will be considered in comparison to the impact of communicating the same findings in a way which does not involve showing the person the source images derived from the imaging procedure (such as solely through oral feedback, or a written report). Secondary objective A secondary objective is to determine the impact of this feedback on consumers': 1. understanding of the relevant condition and of the risk information they have been given; 2. perceived severity and risk of disease; 3. perceived control over the disease risk; 4. perceived effectiveness of the risk-reducing behaviour; 5. emotional response, including general anxiety and condition-specific worry