Abstract: We analyze several temporal aspects of a large dataset of crimes and their suspects. The dataset covers all crimes recorded by the Swedish police from 1992 to 2012, it covers 460,846 crimes involving 1,250,535 individuals. The crimes are categorized into about 400 categories and are time stamped. We extract several types of temporal structures, from interevent times to the more long-scale structures related to criminal careers. We discuss the connections between the various extracted time scales and to the social phenomenon of crime. Some quantities are even scale-free (with exponent around 2.5)?like the distribution of collaboration trees (where a crime is connected to the previous if there is a significant overlap of criminals involved (see the figurebelow). Most quantities do, however, have scales. We discuss how these temporal effects can help preventive police works.

Abstract: Several biological and natural systems appear to operate close to a critical point, as evidenced by the absence of a characteristic size in the phenomenon. Indeed, the existence of power law distributions has been detected in several contexts, as different as earthquakes, solar flares or spontaneous brain activity, and, surprisingly, with similar scaling behaviour. We propose that the specific features of each phenomenon are imbedded in the temporal organization of events in time. A detailed analysis of time-energy correlations detrending statistical noise is able to enlighten the difference between the physical mechanisms controlling different phenomena, as for instance earthquakes and solar flares. Conversely, the temporal organization of neuronal avalanches in the rat cortex in vitro exhibits a distribution of waiting times between successive events with a non-monotonic behavior, not usually found in other natural processes. Numerical simulations provide evidence that this behavior is a consequence of the alternation between states of high and low activity, leading to a dynamic balance between excitation and inhibition. This behavior is also detected at a larger scale, i.e., on fMRI data from resting patients. By monitoring temporal correlations in high amplitude BOLD signal, we find that the activity variations with opposite sign are correlated over a temporal scale of few seconds, suggesting a critical balance between activity excitation and depression in the brain.

Abstract: Particle-laden flows experience deposition and erosion when passing through a porous medium, a common situation in many fields, ranging from environmental sciences to industrial filters and petroleum recovery. We experimentally study dense suspensions during deep bed filtration and find that the time evolution of pressure losses through the filter is characterized by jumps separated bytime delays. These jumps are related to erosive events inside the porous medium and are preceded and followed by deposition. A statistical analysis shows that the events are independent whose size distribution scales with a power law. The detection of such jumps provides new insight into the dynamics of particle-laden flows through porous media, specifically as they can be considered analogous to sand avalanches occurring in petroleum wells. The above phenomenon can be reproduced in an electrical network of fuse-anti- fuse devices, which become insulators within acertain finite interval of local applied voltages. As a consequence, the macroscopic current exhibits temporal fluctuations which increase with system size. We determine the conditions under which this itinerant conduction appears by establishing a phase diagram as a function of the applied field and the size of the insulating window.

Abstract: In the traditional ?bare-bones? network approach, nodes are nodes and links and links, and that is all there is. For social networks, this means that individuals are distinguishable only on the basis of their network characteristics (degree, centrality, etc). However, we all know that people are different and behave in different ways. These differences can be approached with more fine-grained behavioural data, in particular with the help of data on time-stamped interactions that allow constructing dynamic and temporal social networks. In this talk, I will focus on exploring individual differences with the help of temporal data on electronic interactions (calls, emails, etc). I will first talk about longer timescales and the similarities and differences in how we maintain our personal networks. Then, I will focus on shorter timescales of circadian patterns, and show how various data sets reveal chronotypes of individuals (morning/evening-active persons) and chronotype compositions of populations.

Abstract: Bursty dynamical patterns characterise not only individual human behaviour but also appear on the level of dyadic interactions and even in case of collective phenomena. The first observations of human bursty patterns were commonly addressed the activity of individuals, although many observations were made on interaction datasets. All these studies reported heterogeneous non-Poissonian dynamical patterns characterised by broad inter-event time distributions, which emergence was explained in various ways: due to intrinsic correlations via decision mechanisms; due to independent actions influenced by circadian patterns; or other underlying mechanisms. In addition several combinations of these modelling directions were proposed together with phenomenological models aiming at simply reproducing signals with similar temporal features. In this talk our aim is to give a brief introduction to these modelling efforts and to provide an overview about their development during the last decay.

Abstract: Over the past two decades, school shootings within the United States have repeatedly devastated communities and shaken public opinion. Many of these attacksappear to be ?lone wolf? ones driven by specific individual motivations, and the identification of precursor signals and hence actionable policy measures would thusseem highly unlikely. Here, we take a system-wide view and investigate the timing of school attacks and the dynamical feedback with social media. We identifya trend divergence in which college attacks have continued to accelerate over the last 25 years while those carried out on K-12 schools have slowed down. We estab-lish the copycat effect in school shootings and use a Hawkes process to model the statistical association between social media chatter and the probability of an attackin the following days. While hinting at causality, this relationship may also help mitigate the frequency and intensity of future attacks.