Hydrodynamical simulations of galaxy properties: Environmental effects

Using N-body+hydro simulations we study relations between the local environments of galaxies on 0.5 Mpc scale and properties of the luminous components of galaxies. Our numerical simulations include effects of star formation and supernova feedback in different cosmological scenarios: the standard Cold Dark Matter model, the Broken Scale Invariance model (BSI), and a model with cosmological constant (LCDM). In this paper, we concentrate on the effects of environment on colors and morphologies of galaxies, on the star formation rate and on the relation between the total luminosity of a galaxy and its circular velocity. We demonstrate a statistically significant theoretical relationship between morphology and environment. In particular, there is a strong tendency for high-mass galaxies and for elliptical galaxies to form in denser environments, in agreement with observations. We find that in models with denser environments (CDM scenario) ~ 13 % of the galactic halos can be identified as field ellipticals, according to their colors. In simulations with less clustering (BSI and LCDM), the fraction of ellipticals is considerably lower (~ 2-3 %). The strong sensitivity of morphological type to environment is rather remarkable because our results are applicable to ``field'' galaxies and small groups. If all galaxies in our simulations are included, we find a statistically significant dependence of the galaxy luminosity - circular velocity relation on dark matter overdensity within spheres of radius 0.5 Mpc, for the CDM simulations. But if we remove ``elliptical'' galaxies from our analysis to mimic the Tully-Fisher relation for spirals, then no dependence is found in any model.Comment: 44 pages, 21 figures (17 included). Submitted to New Astronomy. GIFF color plots and the complete paper in Postscript (including color figures) can be found at http://astrosg.ft.uam.es/~gustavo/newas

How robust are SVARs at measuring monetary policy in small open economies?

We study the ability of exclusion and sign restrictions to measure monetary policy shocks in small open economies. Our Monte Carlo experiments show that sign restrictions systematically overshoot inflation responses to the said shock, so we propose to add prior information to limit the number of economically implausible responses. This modified procedure robustly recovers the transmission of the shock, whereas exclusion restrictions show large sensitivity to the assumed monetary transmission mechanism of the model and the set of foreign variables included in the VAR. An application with Mexican data supports our findings

Immirzi ambiguity, boosts and conformal frames for black holes

We analyse changes of the Immirzi parameter in loop quantum gravity and compare their consequences with those of Lorentz boosts and constant conformal transformations in black-hole physics. We show that the effective value deduced for the Planck length in local measurements of vacuum black holes by an asymptotic observer may depend on its conformal or Lorentz frame. This introduces an apparent ambiguity in the expression of the black-hole entropy which is analogous to that produced by the Immirzi parameter. For quantities involving a notion of energy, the similarity between the implications of the Immirzi ambiguity and a conformal scaling disappears, but the parallelism with boosts is maintained

Behavior Management Techniques Used by Teachers of Emotionally/behaviorally Disordered Students in Various Educational Settings

The purpose of this study was to delineate the differences between the types of behavioral management techniques used by teachers of students with emotional/behavioral disorders

SOCIAL MEDIA, ANXIETY, AND READING ACHIEVEMENT IN ELEMENTARY STUDENTS

The purpose of this study is to explore the relationship between perceived social media usage, academic achievement in reading, and anxiety. This study looked at a second grade classroom in ASFM consisting of 21 bilingual Mexican students, 11 boys and 10 girls. Perceived social media usage was measured using a survey, academic reading achievement was measured using Fountas and Pinell reading levels and anxiety was measured using the Children’s Test Anxiety Scale (CTAS) by Wren and Benson (2004). Each variable was then analyzed on its own through the use of descriptive statistics and bar graphs; then, each dependent variable was compared against the independent variable using a t-test and scatter-plot. This study found that there is a positive moderate correlation between perceived social media usage and academic achievement in reading; as perceived social media usage increases so does the likelihood of higher reading scores. Additionally this study found a weak positive correlation between perceived social media usage and test anxiety scores; as perceived social media usage increases the likelihood of higher test anxiety scores increases as well

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images

Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. This enables customized and more precise cancer treatments. However, accurate cell instance segmentation, necessary for further cell tracking and behavior analysis, is still challenging in scenarios with high cell concentration and overlapping edges. Within this framework, we propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The DDT output is subsequently used to train a top-model. The following top-models are considered: a three-class (\emph{e.g.,} foreground, background and cell border) U-net, and a watershed transform. The obtained results suggest a performance boost over traditional U-Net architectures. This opens an interesting research line around the idea of injecting morphological information into a fully convolutional model.Comment: Accepted at the IWANN 2021 (International Work-Conference on Artificial and Natural Neural Networks

Application of data augmentation techniques towards metabolomics

Niemann–Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.e. the addition of noise, oversampling techniques and conditional generative adversarial networks. These techniques have been used to evaluate their predictive capacities on a set of urine samples donated by 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Results on the prediction have also been obtained using different machine learning classification models and the partial least squares techniques. These results provide strong evidence for the ability of DA techniques to generate good quality synthetic data. Results acquired show increases in sensitivity of 20%–50%, an F1 score of 6%–30%, and a predictive capacity of 0.3 (out of 1). Additionally, more conventional forms of multivariate data analysis have been employed. These have allowed the detection of unusual urinary metabolite profiles, and the identification of biomarkers through the use of synthetically augmented datasets. Results indicate that urinary branched-chain amino acids such as valine, 3-aminoisobutyrate and quinolinate, may be employable as valuable biomarkers for the diagnosis and prognostic monitoring of NPC1 diseaseThe authors acknowledge the support from MINECO (Spain) through grants TIN2017-88728-C2-1-R and PID2020-116898RB-I00 (MICINN), from Universidad de Málaga y Junta de Andalucía through grant UMA20-FEDERJA-045, and from Instituto de Investigación Biomédica de Málaga – IBIMA (all including FEDER funds). Funding for open access charge: Universidad de Málaga / CBUA

Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting

Skin lesions are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. These skin diseases have become a challenge in medical diagnosis due to visual similarities, where image classification is an essential task to achieve an adequate diagnostic of different lesions. Melanoma is one of the best-known types of skin lesions due to the vast majority of skin cancer deaths. In this work, we propose an ensemble of improved convolutional neural networks combined with a test-time regularly spaced shifting technique for skin lesion classification. The shifting technique builds several versions of the test input image, which are shifted by displacement vectors that lie on a regular lattice in the plane of possible shifts. These shifted versions of the test image are subsequently passed on to each of the classifiers of an ensemble. Finally, all the outputs from the classifiers are combined to yield the final result. Experiment results show a significant improvement on the well-known HAM10000 dataset in terms of accuracy and Fscore. In particular, it is demonstrated that our combination of ensembles with test-time regularly spaced shifting yields better performance than any of the two methods when applied alone.This work is partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary Environments, and B1-2019_01, project name Anomaly detection on roads by moving cameras. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. The authors acknowledge the funding from the Universidad de Málaga. Funding for open access charge: Universidad de Málaga / CBUA