Realistic Galaxy Models for Lensing Statistics

2005 ◽  
Vol 201 ◽  
pp. 292-295
Author(s):  
David. Rusin ◽  
Max. Tegmark
Keyword(s):  
Cosmological Constant ◽  
Optical Depth ◽  
Gravitational Lensing ◽  
Significant Progress ◽  
Data Set ◽  
Cosmological Constraints ◽  
Lens Galaxies

The gravitational lensing rate in a well-defined sample of sources can place strong bounds on the cosmological constant, but only if the lensing optical depth is robustly calculated. Significant progress is likely to be achieved by employing more realistic models to describe the population of lens galaxies. Here we investigate the role of elliptical deflectors in lensing statistics, and their effect on cosmological constraints derived from the JVAS survey. We also evaluate the prospects for constraining the cosmological constant using the much larger CLASS data set.

CONSTRAINT ON THE COSMOLOGICAL CONSTANT FROM GRAVITATIONAL LENSING IN AN EVOLUTIONARY MODEL OF GALAXIES

1998 ◽  
Vol 13 (24) ◽  
pp. 4227-4236 ◽  
Author(s):  
DEEPAK JAIN ◽  
N. PANCHAPAKESAN ◽  
S. MAHAJAN ◽  
V. B. BHATIA
Keyword(s):  
Cosmological Constant ◽  
Galaxy Evolution ◽  
Optical Depth ◽  
Gravitational Lensing ◽  
Statistical Study ◽  
Evolutionary Model ◽  
Building Blocks ◽  
Lens Model ◽  
Gravitational Lenses ◽  
Luminosity Evolution

We study the effect of the cosmological constant on the statistical properties of gravitational lenses in flat cosmologies (Ω0+λ0=1). It is shown that some of the lens observables are strongly affected by the cosmological constant, especially in a low-density universe, and its existence might be inferred by a statistical study of the lenses. In particular, the optical depth of the lens distribution may be used best for this purpose without depending much on the lens model. We calculate the optical depth (probability of a beam encountering a lens event) for a source in a new picture of galaxy evolution based on number evolution in addition to pure luminosity evolution. It seem that present-day galaxies result from the merging of a large number of building blocks. We have tried to put a limit on the cosmological constant in this new picture of galaxy evolution. This evolutionary model of galaxies permits a larger value of the cosmological constant.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Vocational Development in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Turning Point ◽  
Life Stories ◽  
Life Story ◽  
Emerging Adult ◽  
Vocational Development ◽  
Data Set ◽  
The World ◽  
Key Concepts

Chapter 6 reviews research on the topic of vocational/occupational development in relation to the McAdams and Pals tripartite personality framework of traits, goals, and life stories. Distinctions between types of motivations for the work role (as a job, career, or calling) are particularly highlighted. The authors then turn to research from the Futures Study on work motivations and their links to personality traits, identity, generativity, and the life story, drawing on analyses and quotes from the data set. To illustrate the key concepts from this vocation chapter, the authors end with a case study on Charles Darwin’s pivotal turning point, his round-the-world voyage as naturalist for the HMS Beagle. Darwin was an emerging adult in his 20s at the time, and we highlight the role of this journey as a turning point in his adult vocational development.

scholarly journals Scale dependence of galaxy biasing investigated by weak gravitational lensing: An assessment using semi-analytic galaxies and simulated lensing data

2018 ◽  
Vol 613 ◽  
pp. A15 ◽  
Author(s):  
Patrick Simon ◽  
Stefan Hilbert
Keyword(s):  
Gravitational Lensing ◽  
Spatial Scales ◽  
Scale Dependence ◽  
Reconstruction Technique ◽  
Weak Gravitational Lensing ◽  
Physical Scale ◽  
The Galaxy ◽  
Galaxy Bias ◽  
The Impact ◽  
Lens Galaxies

Galaxies are biased tracers of the matter density on cosmological scales. For future tests of galaxy models, we refine and assess a method to measure galaxy biasing as a function of physical scalekwith weak gravitational lensing. This method enables us to reconstruct the galaxy bias factorb(k) as well as the galaxy-matter correlationr(k) on spatial scales between 0.01hMpc−1≲k≲ 10hMpc−1for redshift-binned lens galaxies below redshiftz≲ 0.6. In the refinement, we account for an intrinsic alignment of source ellipticities, and we correct for the magnification bias of the lens galaxies, relevant for the galaxy-galaxy lensing signal, to improve the accuracy of the reconstructedr(k). For simulated data, the reconstructions achieve an accuracy of 3–7% (68% confidence level) over the abovek-range for a survey area and a typical depth of contemporary ground-based surveys. Realistically the accuracy is, however, probably reduced to about 10–15%, mainly by systematic uncertainties in the assumed intrinsic source alignment, the fiducial cosmology, and the redshift distributions of lens and source galaxies (in that order). Furthermore, our reconstruction technique employs physical templates forb(k) andr(k) that elucidate the impact of central galaxies and the halo-occupation statistics of satellite galaxies on the scale-dependence of galaxy bias, which we discuss in the paper. In a first demonstration, we apply this method to previous measurements in the Garching-Bonn Deep Survey and give a physical interpretation of the lens population.

scholarly journals Actors, Partisan Inclination, and Emotions: An Analysis of Government Shutdown News Stories Shared on Twitter

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110088
Author(s):  
Colin Agur ◽  
Lanhuizi Gan
Keyword(s):  
Emotional Responses ◽  
The United States ◽  
Online Information ◽  
News Frames ◽  
Data Set ◽  
Political Issues ◽  
News Stories ◽  
News Sharing ◽  
Government Shutdown

Scholars have recognized emotion as an increasingly important element in the reception and retransmission of online information. In the United States, because of existing differences in ideology, among both audiences and producers of news stories, political issues are prone to spark considerable emotional responses online. While much research has explored emotional responses during election campaigns, this study focuses on the role of online emotion in social media posts related to day-to-day governance in between election periods. Specifically, this study takes the 2018–2019 government shutdown as its subject of investigation. The data set shows the prominence of journalistic and political figures in leading the discussion of news stories, the nuance of emotions employed in the news frames, and the choice of pro-attitudinal news sharing.

Do the Heterogeneous Determinants of Repayment Affect Differently across Borrowers of Diverse Credit Sources in Rural Assam? A Double Hurdle Approach

2021 ◽  
pp. 245513332110316
Author(s):  
Tiken Das ◽  
Pradyut Guha ◽  
Diganta Das
Keyword(s):  
Rural Areas ◽  
Probit Model ◽  
Original Data ◽  
Community Based ◽  
Data Set ◽  
Brahmaputra Valley ◽  
Double Hurdle ◽  
Instrumental Variable Probit ◽  
Instrumental Variable Probit Model

This study made an attempt to answer the question: Do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently? The study is based on data collected from 240 households from three districts in the lower Brahmaputra valley of Assam through a carefully designed primary survey. Besides, the study uses the double hurdle approach and the instrumental variable probit model to reduce possible selection bias. It observes better repayment performance among formal borrowers, followed by semiformal borrowers, while occupation wise it is prominent among organised employees. It has been found that in general, the household characteristics, loan characteristics and location-specific characteristics significantly affect repayment performance of borrowers. However, the nature of impact of the factors influencing repayment performance is remarkably different across credit sources. It ignores the role of traditional community-based organisations in rural Assam while analysing the determinants of repayment performance. The study also recommends for ensuring productive opportunities and efficient market linkages in rural areas of Assam. The study is based on an original data set that has specially been collected to examine question that—do the heterogeneous determinants of repayment affect the borrowers of diverse credit sources differently in the lower Brahmaputra valley of Assam—which has not been studied before.

scholarly journals Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

2020 ◽  
Vol 499 (4) ◽  
pp. 5641-5652
Author(s):  
Georgios Vernardos ◽  
Grigorios Tsagkatakis ◽  
Yannis Pantazis
Keyword(s):  
Confidence Intervals ◽  
Galaxy Evolution ◽  
Gravitational Lensing ◽  
Probability Distributions ◽  
Mass Density ◽  
Ground Truth ◽  
Gaussian Random Fields ◽  
Training Data ◽  
Gravitational Lens ◽  
Data Set

ABSTRACT Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.

scholarly journals The Pivotal Player: Components of NF-κB Pathway as Promising Biomarkers in Colorectal Cancer

2021 ◽  
Vol 22 (14) ◽  
pp. 7429
Author(s):  
Matthew Martin ◽  
Mengyao Sun ◽  
Aishat Motolani ◽  
Tao Lu
Keyword(s):  
Colorectal Cancer ◽  
Biological Response ◽  
Significant Progress ◽  
Therapeutic Approaches ◽  
Successful Development ◽  
Diagnostic Screening ◽  
Proteomic Data ◽  
Large Groups ◽  
Made In

Over the last several decades, colorectal cancer (CRC) has been one of the most prevalent cancers. While significant progress has been made in both diagnostic screening and therapeutic approaches, a large knowledge gap still remains regarding the early identification and treatment of CRC. Specifically, identification of CRC biomarkers that can help with the creation of targeted therapies as well as increasing the ability for clinicians to predict the biological response of a patient to therapeutics, is of particular importance. This review provides an overview of CRC and its progression stages, as well as the basic types of CRC biomarkers. We then lay out the synopsis of signaling pathways related to CRC, and further highlight the pivotal and multifaceted role of nuclear factor (NF) κB signaling in CRC. Particularly, we bring forth knowledge regarding the tumor microenvironment (TME) in CRC, and its complex interaction with cancer cells. We also provide examples of NF-κB signaling-related CRC biomarkers, and ongoing efforts made at targeting NF-κB signaling in CRC treatment. We conclude and anticipate that with more emerging novel regulators of the NF-κB pathway being discovered, together with their in-depth characterization and the integration of large groups of genomic, transcriptomic and proteomic data, the day of successful development of more ideal NF-κB inhibitors is fast approaching.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Sign in / Sign up

Export Citation Format

Share Document