Fake News, Immigration, and Opinion Polarization

Nowadays, it is hard to venture online without coming across a heated discussion over “Fake News”; as a result, people are finding hardE times moving through an entirely new distorted era of misinfor-mation and biased news. In this paper, we investigate the effect of fake news on people’s opinion polarization on a hot topic – such as immigration – through an experiment. We show that “Backfire Effect” and a cognitive bias we named “Validation Myopia” occur when people read Fake news in-dependently if they believe them or not. In addition, we show how Fake news affect opinion polariza-tion and we provide evidence that the Backfire Effect has a higher magnitude than its counterpart (i.e. validation myopia). Finally we show that the emotion-driven effect of fake news can be neutralized thanks to ex-ante signaling of the inaccuracy of fake news

Fake News, Immigration, and Opinion Polarization

Nowadays, it is hard to venture online without coming across a heated discussion over “Fake News”; as a result, people are finding hardE times moving through an entirely new distorted era of misinfor-mation and biased news. In this paper, we investigate the effect of fake news on people’s opinion polarization on a hot topic – such as immigration – through an experiment. We show that “Backfire Effect” and a cognitive bias we named “Validation Myopia” occur when people read Fake news in-dependently if they believe them or not. In addition, we show how Fake news affect opinion polariza-tion and we provide evidence that the Backfire Effect has a higher magnitude than its counterpart (i.e. validation myopia). Finally we show that the emotion-driven effect of fake news can be neutralized thanks to ex-ante signaling of the inaccuracy of fake news

An open and parallel multiresolution framework using block-based adaptive grids

A numerical approach for solving evolutionary partial differential equations in two and three space dimensions on block-based adaptive grids is presented. The numerical discretization is based on high-order, central finite-differences and explicit time integration. Grid refinement and coarsening are triggered by multiresolution analysis, i.e. thresholding of wavelet coefficients, which allow controlling the precision of the adaptive approximation of the solution with respect to uniform grid computations. The implementation of the scheme is fully parallel using MPI with a hybrid data structure. Load balancing relies on space filling curves techniques. Validation tests for 2D advection equations allow to assess the precision and performance of the developed code. Computations of the compressible Navier-Stokes equations for a temporally developing 2D mixing layer illustrate the properties of the code for nonlinear multi-scale problems. The code is open source

Neural parameters estimation for brain tumor growth modeling

Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression

Scalable Simulation of Realistic Volume Fraction Red Blood Cell Flows through Vascular Networks

High-resolution blood flow simulations have potential for developing better understanding biophysical phenomena at the microscale, such as vasodilation, vasoconstriction and overall vascular resistance. To this end, we present a scalable platform for the simulation of red blood cell (RBC) flows through complex capillaries by modeling the physical system as a viscous fluid with immersed deformable particles. We describe a parallel boundary integral equation solver for general elliptic partial differential equations, which we apply to Stokes flow through blood vessels. We also detail a parallel collision avoiding algorithm to ensure RBCs and the blood vessel remain contact-free. We have scaled our code on Stampede2 at the Texas Advanced Computing Center up to 34,816 cores. Our largest simulation enforces a contact-free state between four billion surface elements and solves for three billion degrees of freedom on one million RBCs and a blood vessel composed from two million patches

Identifying reactive organo-selenium precursors in the synthesis of CdSe nanoplatelets

In the synthesis of CdSe nanoplatelets, the selenium-to-selenide reduction pathway is unknown. We study solvent-free growth of CdSe nanoplatelets and identify bis(acyl) selenides as key reactive intermediates. Based on our findings, we prepare a series of bis(acyl) selenides that provide useful precursors with tailored reactivity for liquid-phase syntheses of nanoplatelets.ISSN:1359-7345ISSN:1364-548