Error Propagation in the Hypercycle

We study analytically the steady-state regime of a network of n error-prone self-replicating templates forming an asymmetric hypercycle and its error tail. We show that the existence of a master template with a higher non-catalyzed self-replicative productivity, a, than the error tail ensures the stability of chains in which m<n-1 templates coexist with the master species. The stability of these chains against the error tail is guaranteed for catalytic coupling strengths (K) of order of a. We find that the hypercycle becomes more stable than the chains only for K of order of a2. Furthermore, we show that the minimal replication accuracy per template needed to maintain the hypercycle, the so-called error threshold, vanishes like sqrt(n/K) for large K and n<=4

Impact of COVID-19 on maxillofacial surgery practice: a worldwide survey

The outbreak of coronavirus disease 2019 (COVID-19) is rapidly changing our habits. To date, April 12, 2020, the virus has reached 209 nations, affecting 1.8 million people and causing more than 110,000 deaths. Maxillofacial surgery represents an example of a specialty that has had to adapt to this outbreak, because of the subspecialties of oncology and traumatology. The aim of this study was to examine the effect of this outbreak on the specialty of maxillofacial surgery and how the current situation is being managed on a worldwide scale. To achieve this goal, the authors developed an anonymous questionnaire which was posted on the internet and also sent to maxillofacial surgeons around the globe using membership lists from various subspecialty associations. The questionnaire asked for information about the COVID-19 situation in the respondent's country and in their workplace, and what changes they were facing in their practices in light of the outbreak. The objective was not only to collect and analyse data, but also to highlight what the specialty is facing and how it is handling the situation, in the hope that this information will be useful as a reference in the future, not only for this specialty, but also for others, should COVID-19 or a similar global threat arise again

Critical behavior in a cross-situational lexicon learning scenario

The associationist account for early word-learning is based on the co-occurrence between objects and words. Here we examine the performance of a simple associative learning algorithm for acquiring the referents of words in a cross-situational scenario affected by noise produced by out-of-context words. We find a critical value of the noise parameter $\gamma_c$ above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order $\tau^{-1/2}$ about $\gamma_c$, where $\tau$ is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point

A Population Genetic Approach to the Quasispecies Model

A population genetics formulation of Eigen's molecular quasispecies model is proposed and several simple replication landscapes are investigated analytically. Our results show a remarcable similarity to those obtained with the original kinetics formulation of the quasispecies model. However, due to the simplicity of our approach, the space of the parameters that define the model can be explored. In particular, for the simgle-sharp-peak landscape our analysis yelds some interesting predictions such as the existence of a maximum peak height and a mini- mum molecule length for the onset of the error threshold transition.Comment: 16 pages, 4 Postscript figures. Submited to Phy. Rev.

Gini estimation under infinite variance

We study the problems related to the estimation of the Gini index in presence of a fat-tailed data generating process, i.e. one in the stable distribution class with finite mean but infinite variance (i.e. with tail index α∈(1,2)). We show that, in such a case, the Gini coefficient cannot be reliably estimated using conventional nonparametric methods, because of a downward bias that emerges under fat tails. This has important implications for the ongoing discussion about economic inequality. We start by discussing how the nonparametric estimator of the Gini index undergoes a phase transition in the symmetry structure of its asymptotic distribution, as the data distribution shifts from the domain of attraction of a light-tailed distribution to that of a fat-tailed one, especially in the case of infinite variance. We also show how the nonparametric Gini bias increases with lower values of α. We then prove that maximum likelihood estimation outperforms nonparametric methods, requiring a much smaller sample size to reach efficiency. Finally, for fat-tailed data, we provide a simple correction mechanism to the small sample bias of the nonparametric estimator based on the distance between the mode and the mean of its asymptotic distribution

Mass media destabilizes the cultural homogeneous regime in Axelrod's model

An important feature of Axelrod's model for culture dissemination or social influence is the emergence of many multicultural absorbing states, despite the fact that the local rules that specify the agents interactions are explicitly designed to decrease the cultural differences between agents. Here we re-examine the problem of introducing an external, global interaction -- the mass media -- in the rules of Axelrod's model: in addition to their nearest-neighbors, each agent has a certain probability $p$ to interact with a virtual neighbor whose cultural features are fixed from the outset. Most surprisingly, this apparently homogenizing effect actually increases the cultural diversity of the population. We show that, contrary to previous claims in the literature, even a vanishingly small value of $p$ is sufficient to destabilize the homogeneous regime for very large lattice sizes