Hybridizing Non-dominated Sorting Algorithms: Divide-and-Conquer Meets Best Order Sort

Many production-grade algorithms benefit from combining an asymptotically efficient algorithm for solving big problem instances, by splitting them into smaller ones, and an asymptotically inefficient algorithm with a very small implementation constant for solving small subproblems. A well-known example is stable sorting, where mergesort is often combined with insertion sort to achieve a constant but noticeable speed-up. We apply this idea to non-dominated sorting. Namely, we combine the divide-and-conquer algorithm, which has the currently best known asymptotic runtime of $O(N (\log N)^{M - 1})$, with the Best Order Sort algorithm, which has the runtime of $O(N^2 M)$ but demonstrates the best practical performance out of quadratic algorithms. Empirical evaluation shows that the hybrid's running time is typically not worse than of both original algorithms, while for large numbers of points it outperforms them by at least 20%. For smaller numbers of objectives, the speedup can be as large as four times.Comment: A two-page abstract of this paper will appear in the proceedings companion of the 2017 Genetic and Evolutionary Computation Conference (GECCO 2017

Topological Signals of Singularities in Ricci Flow

We implement methods from computational homology to obtain a topological signal of singularity formation in a selection of geometries evolved numerically by Ricci flow. Our approach, based on persistent homology, produces precise, quantitative measures describing the behavior of an entire collection of data across a discrete sample of times. We analyze the topological signals of geometric criticality obtained numerically from the application of persistent homology to models manifesting singularities under Ricci flow. The results we obtain for these numerical models suggest that the topological signals distinguish global singularity formation (collapse to a round point) from local singularity formation (neckpinch). Finally, we discuss the interpretation and implication of these results and future applications.Comment: 24 pages, 14 figure

In silico synchronization reveals regulators of nuclear ruptures in lamin A/C deficient model cells

The nuclear lamina is a critical regulator of nuclear structure and function. Nuclei from laminopathy patient cells experience repetitive disruptions of the nuclear envelope, causing transient intermingling of nuclear and cytoplasmic components. The exact causes and consequences of these events are not fully understood, but their stochastic occurrence complicates in-depth analyses. To resolve this, we have established a method that enables quantitative investigation of spontaneous nuclear ruptures, based on co-expression of a rmly bound nuclear reference marker and a uorescent protein that shuttles between the nucleus and cytoplasm during ruptures. Minimally invasive imaging of both reporters, combined with automated tracking and in silico synchronization of individual rupture events, allowed extracting information on rupture frequency and recovery kinetics. Using this approach, we found that rupture frequency correlates inversely with lamin A/C levels, and can be reduced in genome- edited LMNA knockout cells by blocking actomyosin contractility or inhibiting the acetyl-transferase protein NAT10. Nuclear signal recovery followed a kinetic that is co-determined by the severity of the rupture event, and could be prolonged by knockdown of the ESCRT-III complex component CHMP4B. In conclusion, our approach reveals regulators of nuclear rupture induction and repair, which may have critical roles in disease development

Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization

Functional brain imaging is a source of spatio-temporal data mining problems. A new framework hybridizing multi-objective and multi-modal optimization is proposed to formalize these data mining problems, and addressed through Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining are demonstrated as the approach facilitates the modelling of the experts' requirements, and flexibly accommodates their changing goals

Using landscape topology to compare continuous metaheuristics: a framework and case study on EDAs and ridge structure

In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behavior of continuous metaheuristic optimization algorithms. In particular, we generate twodimensional landscapes with parameterized, linear ridge structure, and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another.We apply thismethodology to investigate the specific issue of explicit dependency modeling in simple continuous estimation of distribution algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modeling is useful, harmful, or has little impact on mean algorithm performance. Heat maps are used to compare algorithm performance over a large number of landscape instances and algorithm trials. Finally, we perform ameta-search in the landscape parameter space to find landscapes which maximize the performance between algorithms. The results are related to some previous intuition about the behavior of these algorithms, but at the same time lead to new insights into the relationship between dependency modeling in EDAs and the structure of the problem landscape. The landscape generator and overall methodology are quite general and extendable and can be used to examine specific features of other algorithms

External validation of a prediction model and decision tree for sickness absence due to mental disorders

Purpose: A previously developed prediction model and decision tree were externally validated for their ability to identify occupational health survey participants at increased risk of long-term sickness absence (LTSA) due to mental disorders. Methods: The study population consisted of N = 3415 employees in mobility services who were invited in 2016 for an occupational health survey, consisting of an online questionnaire measuring the health status and working conditions, followed by a preventive consultation with an occupational health provider (OHP). The survey variables of the previously developed prediction model and decision tree were used for predicting mental LTSA (no = 0, yes = 1) at 1-year follow-up. Discrimination between survey participants with and without mental LTSA was investigated with the area under the receiver operating characteristic curve (AUC). Results: A total of n = 1736 (51%) non-sick-listed employees participated in the survey and 51 (3%) of them had mental LTSA during follow-up. The prediction model discriminated (AUC = 0.700; 95% CI 0.628–0.773) between participants with and without mental LTSA during follow-up. Discrimination by the decision tree (AUC = 0.671; 95% CI 0.589–0.753) did not differ significantly (p = 0.62) from discrimination by the prediction model. Conclusion: At external validation, the prediction model and the decision tree both poorly identified occupational health survey participants at increased risk of mental LTSA. OHPs could use the decision tree to determine if mental LTSA risk factors should be explored in the preventive consultation which follows after completing the survey questionnaire

Differential activation of killer cells in the circulation and the lung: a study of current smoking status and chronic obstructive pulmonary disease (COPD)

Background:CD8+ T-lymphocytes, natural killer T-like cells (NKT-like cells, CD56+CD3+) and natural killer cells (NK cells, CD56+CD3−) are the three main classes of human killer cells and they are implicated in the pathogenesis of chronic obstructive pulmonary disease (COPD). Activation of these cells can initiate immune responses by virtue of their production of inflammatory cytokines and chemokines that cause lung tissue damage, mucus hypersecretion and emphysema. The objective of the current study was to investigate the activation levels of human killer cells in healthy non-smokers, healthy smokers, ex-smokers with COPD and current smokers with COPD, in both peripheral blood and induced sputum. Methods/Principal Findings:After informed consent, 124 participants were recruited into the study and peripheral blood or induced sputum was taken. The activation states and receptor expression of killer cells were measured by flow cytometry. In peripheral blood, current smokers, regardless of disease state, have the highest proportion of activated CD8+ T-lymphocytes, NKT-like cells and NK cells compared with ex-smokers with COPD and healthy non-smokers. Furthermore, CD8+ T-lymphocyte and NK cell activation is positively correlated with the number of cigarettes currently smoked. Conversely, in induced sputum, the proportion of activated killer cells was related to disease state rather than current smoking status, with current and ex-smokers with COPD having significantly higher rates of activation than healthy smokers and healthy non-smokers. Conclusions: A differential effect in systemic and lung activation of killer cells in COPD is evident. Systemic activation appears to be related to current smoking whereas lung activation is related to the presence or absence of COPD, irrespective of current smoking status. These findings suggest that modulating killer cell activation may be a new target for the treatment of COPD

Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population

PurposeThis study investigated if and how occupational health survey variables can be used to identify workers at risk of long-term sickness absence (LTSA) due to mental disorders.MethodsCohort study including 53,833 non-sicklisted participants in occupational health surveys between 2010 and 2013. Twenty-seven survey variables were included in a backward stepwise logistic regression analysis with mental LTSA at 1-year follow-up as outcome variable. The same variables were also used for decision tree analysis. Discrimination between participants with and without mental LTSA during follow-up was investigated by using the area under the receiver operating characteristic curve (AUC); the AUC was internally validated in 100 bootstrap samples.Results30,857 (57%) participants had complete data for analysis; 450 (1.5%) participants had mental LTSA during follow-up. Discrimination by an 11-predictor logistic regression model (gender, marital status, economic sector, years employed at the company, role clarity, cognitive demands, learning opportunities, co-worker support, social support from family/friends, work satisfaction, and distress) was AUC = 0.713 (95% CI 0.692-0.732). A 3-node decision tree (distress, gender, work satisfaction, and work pace) also discriminated between participants with and without mental LTSA at follow-up (AUC = 0.709; 95% CI 0.615-0.804).ConclusionsAn 11-predictor regression model and a 3-node decision tree equally well identified workers at risk of mental LTSA. The decision tree provides better insight into the mental LTSA risk groups and is easier to use in occupational health care practice