Recognition of overlap graphs

Overlap graphs occur in computational biology and computer science, and have applications in genome sequencing, string compression, and machine scheduling. Given two strings \(s_{i}\) and \(s_{j}\) , their overlap string is defined as the longest string \(v\) such that \(s_{i} = uv\) and \(s_{j} = vw\) , for some non empty strings \(u,w\) , and its length is called the overlap between these two strings. A weighted directed graph is an overlap graph if there exists a set of strings with one-to-one correspondence to the vertices of the graph, such that each arc weight in the graph equals the overlap between the corresponding strings. In this paper, we characterize the class of overlap graphs, and we present a polynomial time recognition algorithm as a direct consequence. Given a weighted directed graph \(G\) , the algorithm constructs a set of strings that has \(G\) as its overlap graph, or decides that this is not possible.     

Low-status work consequences on immigrant workers' organization: the cases of five immigrant groups in Athens

The article centres on the repercussions low-prestige work has on the collective organization and representation of immigrant workers. This micro-sociological analysis focuses on the cases of Egyptian, Albanian, Bangladeshi, Palestinian, and Philippine immigrants in Athens and how the frame of their work and their employment affects their participation in their immigrant work associations. Evidence from in-depth interviews proves that the majority of immigrants do not claim established workers' rights and do not seek membership in any unions. On the contrary, they rely on a network of friends and relatives for support and develop individual behaviours and alternative solutions to achieve survival and protection.     

Bivariate prior distributions via branching exchangeable sequences

This paper provides a novel approach to constructing bivariate prior distributions. The idea is based on the notion of partial exchangeability. In particular, in a simple extension of the exchangeable sequence, we create two dependent exchangeable sequences via a branching mechanism. This implies the existence of a bivariate prior distribution.     

The Formation of the Cypriot Thalassaemia Prevention System: The ‘Slow’ Assembly and Construction of a Problem (1944‐1984)

Thalassaemia is one of the most widespread recessive blood disorders in the world. This article focuses the historical trajectory by which the Cypriot thalassaemia prevention system, one of the most successful of its kind, achieved full prevention rate. By tracing the history of decision‐making of medical practitioners central to the construction of the prevention system, my objective is to further elucidate underlying logics of policy‐making and health governance which can account for its success. As I suggest, the Cypriot thalassaemia prevention system achieved a full prevention rate because it operated according to a ‘slow’ modality of problematisation and decision‐making, which accounted for the cultural, social and ethical dimensions specific to the Cypriot public.     

Bayesian nonparametric density estimation under length bias

A density estimation method in a Bayesian nonparametric framework is presented when recorded data are not coming directly from the distribution of interest, but from a length biased version. From a Bayesian perspective, efforts to computationally evaluate posterior quantities conditionally on length biased data were hindered by the inability to circumvent the problem of a normalizing constant. In this article, we present a novel Bayesian nonparametric approach to the length bias sampling problem that circumvents the issue of the normalizing constant. Numerical illustrations as well as a real data example are presented and the estimator is compared against its frequentist counterpart, the kernel density estimator for indirect data of Jones.     

The effect of aging on pacing strategies of cross-country skiers and the role of performance level

Background

The participation of master cross-country (XC) skiers in training and competition has increased during the last decades; however, little is known yet about whether these athletes differ from their younger counterparts in aspects of performance such as pacing. Therefore, the aim of the present study was to examine the combined effect of age and performance (race time) on pacing in cross-country (XC) skiing. We analyzed all finishers (n?=?79,722) in ‘Vasaloppet’ from 2012 to 2017, the largest cross-country skiing race in the world, classified according to their race time into 10 groups: 3-4 h, 4-5 h, ..., 12-13 h.

Results

A trivial main effect of sex on total pace range was observed (p?<?0.001, η²?=?0.002), where women (44.1?±?10.2%) had larger total pace range than men (40.9?±?11.8%). A large main effect of performance group on total pace range was shown (p?<?0.001, η²?=?0.160), where the smallest total pace range was 21.8?±?1.9% (3-4 h group) and the largest 50.1?±?9.9% (10-11 h group). A trivial sex×performance group interaction on total pace range was found (p?<?0.001, η²?=?0.001) with the largest sex difference in pacing shown in 9-10 h group. A trivial and small main effect of age was found in women (p?<?0.001, η²?=?0.005) and men (p?<?0.001, η²?=?0.011), respectively, where the masters had smaller total pace range than their younger counterparts. A trivial age group×performance group interaction on total pace range was observed in both women (p?<?0.001, η²?=?0.008) and men (p?<?0.001, η²?=?0.006) with smaller differences among age groups in the faster performance groups.

Conclusions

In summary, master XC skiers adopted a relatively even pacing independently from their race time and the differences in pacing from the younger XC skiers were more pronounced in the slower masters. These findings suggest that exercise attenuates the decline of performance in master XC skiers as shown by the similar pacing strategies between fast master XC skiers and their younger counterparts.

     

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

Hidden Markov models (HMMs) are flexible, well-established models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity.