Evolution of Helping and Harming in Viscous Populations When Group Size Varies

Funding: Balliol College and the Royal Society.Recent years have seen huge interest in understanding how demographic factors mediate the evolution of social behavior in viscous populations. Here we study the impact of variation in group size on the evolution of helping and harming behavior. Although variation in group size influences the degree of relatedness and the degree of competition between groupmates, we find that these effects often exactly cancel, so as to give no net impact of variation in group size on the evolution of helping and harming. Specifically, (1) obligate helping and harming are never mediated by variation in group size, (2) facultative helping and harming are not mediated by variation in group size when this variation is spatial only, (3) facultative helping and harming are mediated by variation in group size only when this variation is temporal or both spatial and temporal, and (4) when there is an effect of variation in group size, facultative helping is favored in big groups and facultative harming is favored in little groups. Moreover, we find that spatial and temporal heterogeneity in individual fecundity may interact with patch-size heterogeneity to change these predictions, promoting the evolution of harming in big patches and of helping in little patches.Publisher PDFPeer reviewe

On the Small Sample Properties of Dickey Fuller and Maximum Likelihood Unit Root Tests on Discrete-Sampled Short-Term Interest Rates

Testing for unit roots in short-term interest rates plays a key role in the empirical modelling of these series. It is widely assumed that the volatility of interest rates follows some time-varying function which is dependent of the level of the series. This may cause distortions in the performance of conventional tests for unit root nonstationarity since these are typically derived under the assumption of homoskedasticity. Given the relative unfamiliarity on the issue, we conducted an extensive Monte Carlo investigation in order to assess the performance of the DF unit root tests, and examined the effects on the limiting distributions of test procedures (t- and likelihood ratio tests) based on maximum likelihood estimation of models for short-term rates with a linear drift.Unit root, interest rates, CKLS model.

Multicolor optical polarimetry of reddened stars in the small Magellanic cloud

First results of an on-going program to determine the wavelength dependence of the interstellar optical polarization of reddened stars in the Small Magellanic Cloud (SMC) are presented. IUE observations of reddened stars in the SMC (Bouchet et al. 1985) generally show marked differences in the extinction law as compared to both the Galaxy and the Large Megallanic Cloud. The aim here is to determine the wavelength dependence of the optical linear polarization in the direction of several such stars in the SMC in order to further constrain the dust composition and size distribution in that galaxy

Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions

In this paper, a quality evaluation of three point cloud coding solutions based on machine learning technology is presented, notably, ADLPCC, PCC_GEO_CNN, and PCGC, as well as LUT_SR, which uses multi-resolution Look-Up Tables. Moreover, the MPEG G-PCC was used as an anchor. A set of six point clouds, representing both landscapes and objects were coded using the five encoders at different bit rates, and a subjective test, where the distorted and reference point clouds were rotated in a video sequence side by side, is carried out to assess their performance. Furthermore, the performance of point cloud objective quality metrics that usually provide a good representation of the coded content is analyzed against the subjective evaluation results. The obtained results suggest that some of these metrics fail to provide a good representation of the perceived quality, and thus are not suitable to evaluate some distortions created by machine learning-based solutions. A comparison between the analyzed metrics and the type of represented scene or codec is also presented.This research was funded by the Portuguese FCT-Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, PLive X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competencias em Cloud Computing.info:eu-repo/semantics/acceptedVersio

On the stability of point cloud machine learning based coding

This paper analyses the performance of two of the most well known deep learning-based point cloud coding solutions, considering the training conditions. Several works have recently been published on point cloud machine learning-based coding, following the recent tendency on image coding. These codecs are typically seen as a set of predefined trained machines. However, the performance of such models is usually very dependent of their training, and little work has been considered on the stability of the codecs’ performance, as well as the possible influence of the loss function parameters, and the increasing number of training epochs. The evaluation experiments are supported in a generic test set with point clouds representing objects and also more complex scenes, using the point to point metric (PSNR D1), as several studies revealed the good quality representation of this geometry-only point cloud metric.Research funded by the Portuguese FCT-Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, PLive X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competencias em Cloud Computing.info:eu-repo/semantics/acceptedVersio

Subjective Quality Evaluation of Point Clouds Using a Head Mounted Display

This paper reports on a subjective quality evaluation of static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the deep learning-based codec RS-DLPCC, and the popular Draco codec. 18 subjects visualized 3D representations of distorted point clouds using a Head Mounted Display, which allowed for a direct comparison with their reference. The Mean Opinion Scores (MOS) obtained in this subjective evaluation were compared with the MOS from two previous studies, where the same content was visualized either on a 2D display or a 3D stereoscopic display, through the Pearson Correlation, Spearman Rank Order Correlation, Root Mean Square Error, and the Outlier Ratio. The results indicate that the three studies are highly correlated with one another. Moreover, a statistical analysis between all evaluations showed no significant differences between them

GC-TOF-MS metabolite profiling of drought tolerant Quercus ilex

Perfil metabólico primario de hojas de encina en respuesta al estrés hídrico por sequía