Scalarons mimicking dark matter in the Hu–Sawicki model of f(R ) gravity

In this paper, we conduct a study on the scalar field obtained from [Formula: see text] gravity via Weyl transformation of the spacetime metric [Formula: see text] from the Jordan frame to the Einstein frame. The scalar field is obtained as a result of the modification in the geometrical part of Einstein’s field equation of General Relativity. For the Hu–Sawicki model of [Formula: see text] gravity, we find the effective potential of the scalar field and calculate its mass. Our study shows that the scalar field (also named as scalaron) obtained from this model has the chameleonic property, i.e. the scalaron becomes light in the low-density region, while it becomes heavy in the high-density region of matter. Then it is found that the scalaron can be regarded as a dark matter (DM) candidate since the scalaron mass is found to be quite close to the mass of ultralight axions, a prime DM candidate. Thus, the scalaron in the Hu–Sawicki model of [Formula: see text] gravity behaves as DM. Further, a study on the evolution of the scalaron mass with the redshift is also carried out, which depicts that scalaron becomes light with expansion of the Universe and with different rates at different stages of the Universe.

Spectral Embedded Deep Clustering

We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by using the trained model, we obtain the estimated cluster labels of all given unlabeled data points. The advantage of our method is that it does not require key conditions. Existing clustering methods with deep neural networks assume that the cluster balance of a given dataset is uniform. Moreover, it also can be applied to various data domains as long as the data is expressed by a feature vector. In addition, it is observed that our method is robust against outliers. Therefore, the proposed method is expected to perform, on average, better than previous methods. We conducted numerical experiments on five commonly used datasets to confirm the effectiveness of the proposed method.

Cooperation mitigates diversity loss in a spatially expanding microbial population

The evolution and potentially even the survival of a spatially expanding population depends on its genetic diversity, which can decrease rapidly due to a serial founder effect. The strength of the founder effect is predicted to depend strongly on the details of the growth dynamics. Here, we probe this dependence experimentally using a single microbial species, Saccharomyces cerevisiae, expanding in multiple environments that induce varying levels of cooperativity during growth. We observe a drastic reduction in diversity during expansions when yeast grows non-cooperatively on simple sugars, but almost no loss of diversity when cooperation is required to digest complex metabolites. These results are consistent with theoretical expectations. When cells grow independently from each other, the expansion proceeds as a pulled wave driven by the growth at the low-density tip of the expansion front. Such populations lose diversity rapidly because of the strong genetic drift at the expansion edge. In contrast, diversity loss is substantially reduced in pushed waves that arise due to cooperative growth. In such expansions, the low-density tip of the front grows much more slowly and is often reseeded from the genetically diverse population core. Additionally, in both pulled and pushed expansions, we observe a few instances of abrupt changes in allele fractions due to rare fluctuations of the expansion front and show how to distinguish such rapid genetic drift from selective sweeps.Significance statementSpatially expanding populations lose genetic diversity rapidly because of the repeated bottlenecks formed at the front as a result of the serial founder effect. However, the rate of diversity loss depends on the specifics of the expanding population, such as its growth and dispersal dynamics. We have previously demonstrated that changing the amount of within-species cooperation leads to a qualitative transition in the nature of expansion from pulled (driven by migration at the low density tip) to pushed (driven by migration from the high density region at the front, but behind the tip). Here we demonstrate experimentally that pushed waves, which emerge in the presence of sufficiently strong cooperation, result in strongly reduced genetic drift during range expansions, thus preserving genetic diversity in the newly colonized region.

Detection of Outliers in Pollutant Emissions from the Soto de Ribera Coal-Fired Plant Using Functional Data Analysis: A Case Study in Northern Spain

The present research uses two different functional data analysis methods called functional high-density region (HDR) boxplot and functional bagplot. Both methodologies were applied for the outlier detection in the time pollutant emissions curves that were built using as inputs the discrete information available from an air quality monitoring data record station. Although the record of pollutant emissions is made in a discrete way, these methodologies consider pollutant emissions over time as curves, with outliers obtained by a comparison of curves instead of vectors. Then the concept of outlier passes from been a point to a curve that employed the functional depth as the indicator of curve distances. In this study, the referred methodologies are applied to the detection of outliers in pollutant emissions from the Soto de Ribera coal-fired plant which is in the nearby of the city of Oviedo, located in the Principality of Asturias, Spain. Finally, the advantages of the functional method are reported.

The impact of Lyman-α radiative transfer on large-scale clustering in the Illustris simulation

Context. Lyman-α emitters (LAEs) are a promising probe of the large-scale structure at high redshift, z ≳ 2. In particular, the Hobby-Eberly Telescope Dark Energy Experiment aims at observing LAEs at 1.9 < z < 3.5 to measure the baryon acoustic oscillation (BAO) scale and the redshift-space distortion (RSD). However, it has been pointed out that the complicated radiative transfer (RT) of the resonant Lyman-α emission line generates an anisotropic selection bias in the LAE clustering on large scales, s ≳ 10 Mpc. This effect could potentially induce a systematic error in the BAO and RSD measurements. Also, there exists a recent claim to have observational evidence of the effect in the Lyman-α intensity map, albeit statistically insignificant. Aims. We aim at quantifying the impact of the Lyman-α RT on the large-scale galaxy clustering in detail. For this purpose, we study the correlations between the large-scale environment and the ratio of an apparent Lyman-α luminosity to an intrinsic one, which we call the “observed fraction”, at 2 < z < 6. Methods. We apply our Lyman-α RT code by post-processing the full Illustris simulations. We simply assume that the intrinsic luminosity of the Lyman-α emission is proportional to the star formation rate of galaxies in Illustris, yielding a sufficiently large sample of LAEs to measure the anisotropic selection bias. Results. We find little correlation between large-scale environment and the observed fraction induced by the RT, and hence a smaller anisotropic selection bias than has previously been claimed. We argue that the anisotropy was overestimated in previous work due to insufficient spatial resolution; it is important to keep the resolution such that it resolves the high-density region down to the scale of the interstellar medium, that is, ~1 physical kpc. We also find that the correlation can be further enhanced by assumptions in modeling intrinsic Lyman-α emission.

Fundamental Diagrams for Specific Very High Density Crowds

An experimental campaign was undertaken to measure the pedestrian flow in the region close to the Kaaba during the Hajj pilgrimages of 2014 and 2015. High resolution video and photographs were used. The space was divided into areas of 10 sqm. The pedestrians were counted, and the velocity measured from video clips. The results were surprising: the velocity in the very high density region increases, which implies also an increase of the flux. The flux in this region (with more than 8 p/sqm) reaches values that exceed 3.5 p/m/sec, much higher than previously recorded under more `standard conditions' in corridors and passages.

Bayesian reconstruction of images of objects with high-density inclusions with suppression of artifacts

The technology of three-dimensional Bayesian tomographic reconstruction of homogeneous objects with high-density inclusions is developed. The approach is based on preliminary correction of projections by extracting the data corresponding to X-rays passing through a high-density region, and replacing it with synthesized data obtained by two-dimensional interpolation. An original method for selecting interpolation points is proposed and a mathematical algorithm is described that ensures the implementation of two-dimensional interpolation correction of projections.