Bond offerings in China : The role of ownership

Peer reviewedPostprin

Spectral Index as a Function of Mass Accretion Rate in Black Hole Sources. Monte-Carlo Simulations and an Analytical Description

In this Paper, we present theoretical arguments that the observationally established index saturation effect vs mass accretion rate is a signature of the bulk (converging) flow onto the black hole. We demonstrate that the index saturation value depends on the plasma temperature of converging flow. We self-consistently calculate the Compton cloud (CC) plasma temperature as a function of mass accretion rate using the energy balance between energy dissipation and Compton cooling. We explain the observable phenomenon, index- mdot correlations using a Monte-Carlo simulation of radiative processes in the innermost part (CC) of a BH source and we account for the Comptonization processes in the presence of thermal and bulk motions, as basic types of plasma motion. We show that, when mdot increases, BH sources evolve to high and very soft states (HSS and VSS, respectively), in which the strong blackbody-like and steep power-law components are formed in the resulting X-ray spectrum. The simultaneous detections of these two components strongly depends on sensitivity of high energy instruments, given that the relative contribution of the hard power-law tail in the resulting VSS spectrum can be very low, which is why, to date {\it RXTE} observations of the VSS X-ray spectrum has been characterized by the presence of the strong BB-like component only. We also predict specific patterns for high-energy efold (cutoff) energy (E_{fold}) evolution with mdot for thermal and dynamical (bulk) Comptonization cases. For the former case, E_{fold} monotonically decreases with mdot, in the latter case, the E_{fold}-decrease is followed by its increase at high values of mdot. The observational evolution of E_{fold} vs mdot can be one more test for the presence of a converging flow effect in the formation of the resulting spectra in the close vicinity of BHs.Comment: 15 pages, 11 figures, accepted for the publication in the Astrophysical Journa

Inverse reinforcement learning to control a robotic arm using a Brain-Computer Interface

The goal of this project is to use inverse reinforce- ment learning to better control a JACO robotic arm developed by Kinova in a Brain-Computer Interface (BCI). A self-paced BCI such as a motor imagery based-BCI allows the subject to give orders at any time to freely control a device. But using this paradigm, even after a long training, the accuracy of the classifier used to recognize the order is not 100%. While a lot of studies try to improve the accuracy using a preprocessing stage that improves the feature extraction, we work on a post- processing solution. The classifier used to recognize the mental commands will provide as outputs a value for each command such as the posterior probability. But the executed action will not only depend on this information. A decision process will also take into account the position of the robotic arm and previous trajectories. More precisely, the decision process will be obtained applying an inverse reinforcement learning (IRL) on a subset of trajectories specified by an expert. At the end of the workshop, the convergence of the inverse reinforcement algorithm has not been achieved. Nevertheless, we developed a whole processing chain based on OpenViBE for controlling 2D- movements and we present how to deal with this high dimensional time series problem with a lot of noise which is unusual for the IRL community

Persistent and Transient Inefficiency : Explaining the Low Efficiency of Chinese Big Banks

We thank Ifthekar Hasan, Chun-Yu Ho, Iikka Korhonen, Jun Wu, Yanrui Wu, the participants of the Conference on China’s Financial Markets and Growth Rebalancing in New York (October 2017), AEA/ASSA meeting in Philadelphia (January 2018), Finnish Economic Association XL Annual Meeting in Turku (February 2018), ESCB China Expert Network workshop in Helsinki (March 2018), XIX April International Academic Conference in Moscow (April 2018), China Financial and Banking System Workshop in Aberdeen (June 2018), CEA Annual Meeting in Edinburgh (June 2018) and the research seminars at the Bank of Finland Institute for Economies in Transition (October 2017), Aix-Marseille School of Economics (January 2018), Leibniz‐Institut für Ost‐ und Südosteuropaforschung in Regensburg (June 2018) for their valuable comments and suggestions.Peer reviewedPostprin

Religiosity vs. well-being effects on investor behavior

The authors thank Mustafa Disli, the Editor of the journal and the reviewers for their precious remarksPeer reviewedPostprin

Downregulation of FGF Signaling by Spry4 Overexpression Leads to Shape Impairment, Enamel Irregularities, and Delayed Signaling Center Formation in the Mouse Molar.

FGF signaling plays a critical role in tooth development, and mutations in modulators of this pathway produce a number of striking phenotypes. However, many aspects of the role of the FGF pathway in regulating the morphological features and the mineral quality of the dentition remain unknown. Here, we used transgenic mice overexpressing the FGF negative feedback regulator Sprouty4 under the epithelial keratin 14 promoter (K14-Spry4) to achieve downregulation of signaling in the epithelium. This led to highly penetrant defects affecting both cusp morphology and the enamel layer. We characterized the phenotype of erupted molars, identified a developmental delay in K14-Spry4 transgenic embryos, and linked this with changes in the tooth developmental sequence. These data further delineate the role of FGF signaling in the development of the dentition and implicate the pathway in the regulation of tooth mineralization. © 2019 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research

Estimation of the Sobol indices in a linear functional multidimensional model

International audienceWe consider a functional linear model where the explicative variables are stochastic processes taking values in a Hilbert space, the main example is given by Gaussian processes in L2([0; 1]). We propose estimators of the Sobol indices in this functional linear model. Our estimators are based on Ustatistics. We prove the asymptotic normality and the efficiency of our estimators and we compare them from a theoretical and practical point of view with classical estimators of Sobol indices