40 research outputs found
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