Bolder Together 2: Building Grassroots Movements for Change

California's demographics are changing fast, but rates of voting and civic participation haven't kept up. In four rapidly growing counties across the state, a group of funders is working with local organizations to support diverse communities to lift up their voice and exercise their power on issues that affect their rights and their quality of life. The work of the funders and their local partners is yielding important lessons as states and communities across the country begin to experience the dramatic demographic shifts that are transforming California. This new report documents key lessons for philanthropy from this work so far. The report is a follow-up to a 2011 report that told the story of the funders' early efforts. Now, after five years of grantmaking and intensive work in the four counties, California Civic Participation Funders tells a fuller story about how local organizations are coming together and working across issues to mobilize diverse communities to flex their democratic rights. The funders also reflect further on how philanthropy can work with local communities to create a nation where government acts in the interests of all of the people

Adversarial nets with perceptual losses for text-to-image synthesis

Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work

Does rationality matter to the central bank?

Rationality is one of the main assumptions in economics, and is represented as the rational expectation in macroeconomics. As such, it is important to note that the effectiveness of economic policy depends on the degree of economic agents’ rationality. According to this point of view, it is only natural to ask how the central bank views economic agents to be either rational or bounded rational. In implementing economic policies, it is possible to assume that the central bank views economic agents to be bounded rational. This is due to the fact that most theoretical arguments state that policy under rationality is not as effective as one under bounded rationality. Based on this argument, this paper employs bounded rational New Keynesian Model proposed by Gabaix to know if rationality matters to the central bank. As a result, as long as the central bank does not follow the full gradualism, it is possible to conclude that the rationality matters to the central bank. However, it is not anymore if the central bank employs full gradualism in monetary policy rule

Superfluid-insulator transition of the Josephson junction array model with commensurate frustration

We have studied the rationally frustrated Josephson-junction array model in the square lattice through Monte Carlo simulations of $(2+1)$D XY-model. For frustration $f=1/4$, the model at zero temperature shows a continuous superfluid-insulator transition. From the measurement of the correlation function and the superfluid stiffness, we obtain the dynamical critical exponent $z=1.0$ and the correlation length critical exponent $\nu=0.4 \pm 0.05$. While the dynamical critical exponent is the same as that for cases $f=0$, 1/2, and 1/3, the correlation length critical exponent is surprisingly quite different. When $f=1/5$, we have the nature of a first-order transition.Comment: RevTex 4, to appear in PR

Multimodal Sparse Coding for Event Detection

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such as GMM supervectors and sparse RBM. We report the cross-validated classification accuracy and mean average precision of the MED system trained on features learned from our unimodal and multimodal settings for a subset of the TRECVID MED 2014 dataset.Comment: Multimodal Machine Learning Workshop at NIPS 201

The spread of media content through blogs

Blogs are a popular way to share personal journals, discuss matters of public opinion, pursue collaborative conversations, and aggregate content on similar topics. Blogs can be also used to disseminate new content and novel ideas to communities of interest. In this paper, we present an analysis of the topological structure and the patterns of popular media content that is shared in blogs. By analyzing 8.7 million posts of 1.1 million blogs across 15 major blog hosting sites, we find that the network structure of blogs is “less social” compared to other online social networks: most links are unidirectional and the network is sparsely connected. The type of content that was popularly shared on blogs was surprisingly different from that from the mainstream media: user generated content, often in the form of videos or photos, was the most common type of content disseminated in blogs. The user-generated content showed interesting viral-spreading patterns within blogs. Topical content such as news and political commentary spreads quickly by the hour and then quickly disappears, while non-topical content such as music and entertainment propagates slowly over a much long period of time

Hydrodynamic simulations with the Godunov SPH

We present results based on an implementation of the Godunov Smoothed Particle Hydrodynamics (GSPH), originally developed by Inutsuka (2002), in the GADGET-3 hydrodynamic code. We first review the derivation of the GSPH discretization of the equations of moment and energy conservation, starting from the convolution of these equations with the interpolating kernel. The two most important aspects of the numerical implementation of these equations are (a) the appearance of fluid velocity and pressure obtained from the solution of the Riemann problem between each pair of particles, and (b the absence of an artificial viscosity term. We carry out three different controlled hydrodynamical three-dimensional tests, namely the Sod shock tube, the development of Kelvin-Helmholtz instabilities in a shear flow test, and the "blob" test describing the evolution of a cold cloud moving against a hot wind. The results of our tests confirm and extend in a number of aspects those recently obtained by Cha (2010): (i) GSPH provides a much improved description of contact discontinuities, with respect to SPH, thus avoiding the appearance of spurious pressure forces; (ii) GSPH is able to follow the development of gas-dynamical instabilities, such as the Kevin--Helmholtz and the Rayleigh-Taylor ones; (iii) as a result, GSPH describes the development of curl structures in the shear-flow test and the dissolution of the cold cloud in the "blob" test. We also discuss in detail the effect on the performances of GSPH of changing different aspects of its implementation. The results of our tests demonstrate that GSPH is in fact a highly promising hydrodynamic scheme, also to be coupled to an N-body solver, for astrophysical and cosmological applications. [abridged]Comment: 19 pages, 13 figures, MNRAS accepted, high resolution version can be obtained at http://adlibitum.oats.inaf.it/borgani/html/papers/gsph_hydrosim.pd