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We propose to measure the effects of urban riots on the labour market prospects of workers residing in affected areas through the channel of labour market discrimination based on locality. We investigate the case of the French riots of 2007, which were very geographically concentrated. The town of Villiers-le-Bel is selected as the treatment unit because it received a uniquely high degree of unfavourable exposure in the media. Two other towns serve control groups: i) Sarcelles, which is contiguous to Villiers-le-Bel, has a similar socio-economic-demographic profile, and did experience some rioting activity, and ii) Enghien-les-Bains, which is considered to be economically advantaged and did not experience rioting activity. Using the technique of correspondence testing, we are able to discern disparities in call-back rates for fictitious candidates who respond to actual job postings over four dimensions: gender, ethnic origin, locality of residence (advantaged vs. disadvantaged), and the degree of media exposure during the riots. We implement an empirical approach to measure discrimination across several dimensions that integrates a set of relevant parameters into one unified system of equations. We decomposed the probability of receiving a callback for any candidate of given characteristics as a function of several parameters. We find statistically significant negative effects of a pure media exposure effect. All other factors held constant, people residing in the area which received negative publicity were 3.2 percentage points less likely to receive a callback. The group of workers who tend to be the most associated with the riots, i.e. men of North African origin (at least in terms of perceptions), are the least affected by potential discrimination by region of residence, while women of French origin are the most affected.

We study the geometry of the Thurston metric on the Teichm\"uller space $\mathcal{T}(S)$ of hyperbolic structures on a surface $S$. Some of our results on the coarse geometry of this metric apply to arbitrary surfaces $S$ of finite type; however, we focus particular attention on the case where the surface is a once-punctured torus, $S_{1,1}$. In that case, our results provide a detailed picture of the infinitesimal, local, and global behavior of the geodesics of the Thurston metric, as well as an analogue of Royden's theorem. Comment: 50 pages, 14 figures. v6: Minor change in introduction. v5: Remark 5.5 adds info about Figure 0. v4: Minor correction in Thm 3.10. v3: Revised according to referee report. v2: Minor corrections

Let $ \{P_{n}\}_{n\geq 0} $ be the sequence of Padovan numbers defined by $ P_0=0 $, $ P_1 = P_2=1$, and $ P_{n+3}= P_{n+1} +P_n$ for all $ n\geq 0 $. In this paper, we find all positive square-free integers $ d \ge 2$ such that the Pell equations $ x^2-dy^2 = \ell$, where $ \ell\in\{\pm 1, \pm 4\} $, have at least two positive integer solutions $ (x,y) $ and $(x^{\prime}, y^{\prime})$ such that each of $ x$ and $x^{\prime}$ is a product of two Padovan numbers.

In the last decade, computer-aided early diagnostics of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. The network is designed with an emphasis on the interior resource utilization and uses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with the conventional AlexNet-based network using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates significantly better performance of the proposed 3D Inception-based CNN. Comment: arXiv admin note: substantial text overlap with arXiv:1801.05968

Abstract Context Adrenal venous sampling (AVS) is the key test for subtyping primary aldosteronism (PA), but its interpretation varies widely across referral centers and this can adversely affect the management of PA patients. Objectives To investigate in a real-life study the rate of bilateral success and identification of unilateral aldosteronism and their impact on blood pressure outcomes in PA subtyped by AVS. Design and settings In a retrospective analysis of the largest international registry of individual AVS data (AVIS-2 study), we investigated how different cut-off values of the selectivity index (SI) and lateralization index (LI) affected rate of bilateral success, identification of unilateral aldosteronism, and blood pressure outcomes. Results AVIS-2 recruited 1625 individual AVS studies performed between 2000 and 2015 in 19 tertiary referral centers. Under unstimulated conditions, the rate of biochemically confirmed bilateral AVS success progressively decreased with increasing SI cut-offs; furthermore, with currently used LI cut-offs, the rate of identified unilateral PA leading to adrenalectomy was as low as <25%. A within-patient pairwise comparison of 402 AVS performed both under unstimulated and cosyntropin-stimulated conditions showed that cosyntropin increased the confirmed rate of bilateral selectivity for SI cut-offs ≥ 2.0, but reduced lateralization rates (P < 0.001). Post-adrenalectomy outcomes were not improved by use of cosyntropin or more restrictive diagnostic criteria. Conclusion Commonly used SI and LI cut-offs are associated with disappointingly low rates of biochemically defined AVS success and identified unilateral PA. Evidence-based protocols entailing less restrictive interpretative cut-offs might optimize the clinical use of this costly and invasive test. (J Clin Endocrinol Metab XX: 0-0, 2020)

In human and nonhuman primates, sex differences typically explain much interindividual variability. Male and female behaviors may have played unique roles in the likely coevolution of increasing brain volume and more complex social dynamics. To explore possible divergence in social brain morphology between men and women living in different social environments, we applied probabilistic generative modeling to ~10,000 UK Biobank participants. We observed strong volume effects especially in the limbic system but also in regions of the sensory, intermediate, and higher association networks. Sex-specific brain volume effects in the limbic system were linked to the frequency and intensity of social contact, such as indexed by loneliness, household size, and social support. Across the processing hierarchy of neural networks, different conditions for social interplay may resonate in and be influenced by brain anatomy in sex-dependent ways. Population variability in social lifestyle is reflected in brain morphology in sex-dependent ways.

International audience; We overview the papers of this special issue of the Journal of Corporate Finance and explain how they fit within the different segments of the entrepreneurial finance literature, including equity crowdfunding, angel investors, debt, venture capital, and private equity. We point to the growing importance of different sources of capital for entrepreneurs and emerging research trends pertinent to academics, practitioners, and policymakers. We explain common questions and suggest scope in future work for combining segments.