Bipartite stable Poisson graphs on R

Let red and blue points be distributed on $\mathbb{R}$ according to two independent Poisson processes $\mathcal{R}$ and $\mathcal{B}$ and let each red (blue) point independently be equipped with a random number of half-edges according to a probability distribution $\nu$ ($\mu$). We consider translation-invariant bipartite random graphs with vertex classes defined by the point sets of $\mathcal{R}$ and $\mathcal{B}$, respectively, generated by a scheme based on the Gale-Shapley stable marriage for perfectly matching the half-edges. Our main result is that, when all vertices have degree 2 almost surely, then the resulting graph does not contain an infinite component. The two-color model is hence qualitatively different from the one-color model, where Deijfen, Holroyd and Peres have given strong evidence that there is an infinite component. We also present simulation results for other degree distributions

Comparing Exchange Market Pressure in West and Southern African Countries

We compare the performance of Cape Verde and Mozambique concerning financial credibility as measured by Exchange Market Pressure, an institutional feature that has often been overlooked in the literature as a relevant institution for economies. Drawing on previous research by Macedo et al. (2009), we expand their analysis and, using several definitions of “financial credibility”, all related to different angles on Exchange Market Pressure indices, we conclude that - against reasonable benchmarks in their respective regions - financial credibility has been very good for Cape Verde and fairly good for Mozambique. JEL codes: C22, E44, F31, F33Exchange Rate Regime, Exchange Market Pressure, EGARCH

Competing frogs on Z^d

A two-type version of the frog model on Z^d is formulated, where active type i particles move according to lazy random walks with probability pi of jumping in each time step (i = 1; 2). Each site is independently assigned a random number of particles. At time 0, the particles at the origin are activated and assigned type 1 and the particles at one other site are activated and assigned type 2, while all other particles are sleeping.When an active type i particle moves to a new site, any sleeping particles there are activated and assigned type i, with an arbitrary tie-breaker deciding the type if the site is hit by particles of both types in the same time step. Let G_i denote the event that type i activates infinitely many particles. We show that the events G_1 \cap G_2^c and G_1^c \cap G_2 both have positive probability for all 0< p_1, p_2 <=1. Furthermore, if p_1 = p_2, then the types can coexist in the sense that the event G_1 \cap G_2 has positive probability.We also formulate several open problems. For instance, we conjecture that, when the initial number of particles per site has a heavy tail, the types can coexist also when p_1 does not equal p_2

Material Palette: Extraction of Materials from a Single Image

In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image. We do so in two steps: first, we map regions of the image to material concepts using a diffusion model, which allows the sampling of texture images resembling each material in the scene. Second, we benefit from a separate network to decompose the generated textures into Spatially Varying BRDFs (SVBRDFs), providing us with materials ready to be used in rendering applications. Our approach builds on existing synthetic material libraries with SVBRDF ground truth, but also exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. The code and models will be made open-source. Project page: https://astra-vision.github.io/MaterialPalette/Comment: 8 pages, 11 figures, 2 tables. Webpage https://astra-vision.github.io/MaterialPalette