The Imprint of Warm Dark Matter on the Cosmological 21-cm Signal

We investigate the effects of warm dark matter (WDM) on the cosmic 21-cm signal. If dark matter exists as WDM instead of cold dark matter (CDM), its non-negligible velocities can inhibit the formation of low-mass halos that normally form first in CDM models, therefore delaying star-formation. The absence of early sources delays the build-up of UV and X-ray backgrounds that affect the 21-cm radiation signal produced by neutral hydrogen. With use of the 21CMFAST, code, we demonstrate that the pre-reionization 21-cm signal can be changed significantly in WDM models with a free-streaming length equivalent to that of a thermal relic with mass mx of up to ~10-20 keV. In such a WDM cosmology, the 21-cm signal traces the growth of more massive halos, resulting in a delay of the 21-cm absorption signature and followed by accelerated X-ray heating. CDM models where astrophysical sources have a suppressed photon-production efficiency can delay the 21-cm signal as well, although its subsequent evolution is not as rapid as compared to WDM. This motivates using the gradient of the global 21-cm signal to differentiate between some CDM and WDM models. Finally, we show that the degeneracy between the astrophysics and mx can be broken with the 21-cm power spectrum, as WDM models should have a bias-induced excess of power on large scales. This boost in power should be detectable with current interferometers for models with mx < 3 keV, while next generation instruments will easily be able to measure this difference for all relevant WDM models.Comment: 9 pages, 7 figure

Tourism impact on gender and minority equality: comparison of survey, census, and industry data

Tourism is widely credited as an economic driver for communities, but the role of tourism industry in promoting social and cultural human development goals is less researched and frequently disputed. The majority of existing studies are following the coarse scale. The goal of this study is to investigate the relationship between the development of tourism sector and progress towards the UNWTO equality goals on a fine scale of one US state with the objective of comparing the available indicators of gender and minority equality on a county basis. The study combines the objective data from industry and the US census with survey data on local community perceptions on tourism industry. We found that overall the equality indicators follow the Kuznets type U-shaped model. This result is supported with the survey of public perceptions on tourism which suggests a positive outlook of the local communities on tourism industry effects on gender and race equality. This positive outlook is however not fully shared by women, Black minority, and the youngest population, suggesting a need for future studies those groups

Robust artificial neural networks and outlier detection. Technical report

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks to contaminated data using least trimmed squares criterion. We introduce a penalized least trimmed squares criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry- and LiDAR intensity-based localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensity-based approaches.Comment: 8 pages, 4 figures, 4 tables, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

The endoplasmic reticulum plays a key role in α-cell intracellular Ca2+ dynamics and glucose-regulated glucagon secretion in mouse islets

Glucagon is secreted by pancreatic α-cells to counteract hypoglycaemia. How glucose regulates glucagon secretion remains unclear. Here, using mouse islets, we studied the role of transmembrane and endoplasmic reticulum (ER) Ca2+ on intrinsic α-cell glucagon secretion. Blocking isradipine-sensitive L-type voltage-gated Ca2+ (Cav) channels abolished α-cell electrical activity but had little impact on its cytosolic Ca2+ oscillations or low-glucose-stimulated glucagon secretion. In contrast, depleting ER Ca2+ with cyclopiazonic acid or blocking ER Ca2+-releasing ryanodine receptors abolished α-cell glucose sensitivity and low-glucose-stimulated glucagon secretion. ER Ca2+ mobilization in α-cells is regulated by intracellular ATP and likely to be coupled to Ca2+ influx through P/Q-type Cav channels. ω-Agatoxin IVA blocked α-cell ER Ca2+ release and cell exocytosis, but had no additive effect on glucagon secretion when combined with ryanodine. We conclude that glucose regulates glucagon secretion through the control of ER Ca2+ mobilization, a mechanism that can be independent of α-cell electrical activity

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing.

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover variations in bi-temporal HSIs. Relying on the image difference, existing HSI change detection methods fail to preserve the spectral characteristics and suffer from high data dimensionality, making them extremely challenging to deal with changing areas of various sizes. To tackle these challenges, we propose a cross-band 2-D self-attention Network (CBANet) for end-to-end HSI change detection. By embedding a cross-band feature extraction module into a 2-D spatial-spectral self-attention module, CBANet is highly capable of extracting the spectral difference of matching pixels by considering the correlation between adjacent pixels. The CBANet has shown three key advantages: 1) less parameters and high efficiency; 2) high efficacy of extracting representative spectral information from bi-temporal images; and 3) high stability and accuracy for identifying both sparse sporadic changing pixels and large changing areas whilst preserving the edges. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and efficiency of the proposed methodology

Expectations and Investment

Using micro data from Duke University quarterly survey of Chief Financial Officers, we show that corporate investment plans as well as actual investment are well explained by CFOs’ expectations of earnings growth. The information in expectations data is not subsumed by traditional variables, such as Tobin’s Q or discount rates. We also show that errors in CFO expectations of earnings growth are predictable from past earnings and other data, pointing to extrapolative structure of expectations and suggesting that expectations may not be rational. This evidence, like earlier findings in finance, points to the usefulness of data on actual expectations for understanding economic behavior.Economic