Scaling-laws of human broadcast communication enable distinction between human, corporate and robot Twitter users.

Human behaviour is highly individual by nature, yet statistical structures are emerging which seem to govern the actions of human beings collectively. Here we search for universal statistical laws dictating the timing of human actions in communication decisions. We focus on the distribution of the time interval between messages in human broadcast communication, as documented in Twitter, and study a collection of over 160,000 tweets for three user categories: personal (controlled by one person), managed (typically PR agency controlled) and bot-controlled (automated system). To test our hypothesis, we investigate whether it is possible to differentiate between user types based on tweet timing behaviour, independently of the content in messages. For this purpose, we developed a system to process a large amount of tweets for reality mining and implemented two simple probabilistic inference algorithms: 1. a naive Bayes classifier, which distinguishes between two and three account categories with classification performance of 84.6% and 75.8%, respectively and 2. a prediction algorithm to estimate the time of a users next tweet with an R2 ≈0.7. Our results show that we can reliably distinguish between the three user categories as well as predict the distribution of a users inter-message time with reasonable accuracy. More importantly, we identify a characteristic power-law decrease in the tail of inter-message time distribution by human users which is different from that obtained for managed and automated accounts. This result is evidence of a universal law that permeates the timing of human decisions in broadcast communication and extends the findings of several previous studies of peer-to-peer communication. © 2013 Tavares, Faisal

Inelastic Coulomb scattering rate of a multisubband Q1D electron gas

In this work, the Coulomb scattering lifetimes of electrons in two coupled quantum wires have been studied by calculating the quasiparticle self-energy within a multisubband model of quasi-one-dimensional (Q1D) electron system. We consider two strongly coupled quantum wires with two occupied subbands. The intrasubband and intersubband inelastic scattering rates are caculated for electrons in different subbands. Contributions of the intrasubband, intersubband plasmon excitations, as well as the quasiparticle excitations are investigated. Our results shows that the plasmon exictations of the first subband are the most important scattering mechanism for electrons in both subbands.Comment: 9 pages, REVTEX, 2 figure

Finite size and finite temperature studies of the osp(1∣2)osp(1|2) spin chain

We study a quantum spin chain invariant by the superalgebra $osp(1|2)$. We derived non-linear integral equations for the row-to-row transfer matrix eigenvalue in order to analyze its finite size scaling behaviour and we determined its central charge. We have also studied the thermodynamical properties of the obtained spin chain via the non-linear integral equations for the quantum transfer matrix eigenvalue. We numerically solved these NLIE and evaluated the specific heat and magnetic susceptibility. The analytical low temperature analysis was performed providing a different value for the effective central charge. The computed values are in agreement with the numerical predictions in the literature.Comment: 26 pages, 2 figure

Two-dimensional state sum models and spin structures

The state sum models in two dimensions introduced by Fukuma, Hosono and Kawai are generalised by allowing algebraic data from a non-symmetric Frobenius algebra. Without any further data, this leads to a state sum model on the sphere. When the data is augmented with a crossing map, the partition function is defined for any oriented surface with a spin structure. An algebraic condition that is necessary for the state sum model to be sensitive to spin structure is determined. Some examples of state sum models that distinguish topologically-inequivalent spin structures are calculated.Comment: 43 pages. Mathematica script in ancillary file. v2: nomenclature of models and their properties changed, some proofs simplified, more detailed explanations. v3: extended introduction, presentational improvements; final versio