On the density of sets of the Euclidean plane avoiding distance 1

A subset $A \subset \mathbb R^2$ is said to avoid distance $1$ if: $\forall x,y \in A, \left\| x-y \right\|_2 \neq 1.$ In this paper we study the number $m_1(\mathbb R^2)$ which is the supremum of the upper densities of measurable sets avoiding distance 1 in the Euclidean plane. Intuitively, $m_1(\mathbb R^2)$ represents the highest proportion of the plane that can be filled by a set avoiding distance 1. This parameter is related to the fractional chromatic number $\chi_f(\mathbb R^2)$ of the plane. We establish that $m_1(\mathbb R^2) \leq 0.25646$ and $\chi_f(\mathbb R^2) \geq 3.8992$.Comment: 11 pages, 5 figure

On the density of sets avoiding parallelohedron distance 1

The maximal density of a measurable subset of R^n avoiding Euclidean distance1 is unknown except in the trivial case of dimension 1. In this paper, we consider thecase of a distance associated to a polytope that tiles space, where it is likely that the setsavoiding distance 1 are of maximal density 2^-n, as conjectured by Bachoc and Robins. We prove that this is true for n = 2, and for the Vorono\"i regions of the lattices An, n >= 2

Projecting Ising Model Parameters for Fast Mixing

Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.Comment: Advances in Neural Information Processing Systems 201

Origin of Scaling Behavior of Protein Packing Density: A Sequential Monte Carlo Study of Compact Long Chain Polymers

Single domain proteins are thought to be tightly packed. The introduction of voids by mutations is often regarded as destabilizing. In this study we show that packing density for single domain proteins decreases with chain length. We find that the radius of gyration provides poor description of protein packing but the alpha contact number we introduce here characterize proteins well. We further demonstrate that protein-like scaling relationship between packing density and chain length is observed in off-lattice self-avoiding walks. A key problem in studying compact chain polymer is the attrition problem: It is difficult to generate independent samples of compact long self-avoiding walks. We develop an algorithm based on the framework of sequential Monte Carlo and succeed in generating populations of compact long chain off-lattice polymers up to length $N=2,000$. Results based on analysis of these chain polymers suggest that maintaining high packing density is only characteristic of short chain proteins. We found that the scaling behavior of packing density with chain length of proteins is a generic feature of random polymers satisfying loose constraint in compactness. We conclude that proteins are not optimized by evolution to eliminate packing voids.Comment: 9 pages, 10 figures. Accepted by J. Chem. Phy

From isomorphism to polymorphism: connecting interzeolite transformations to structural and graph similarity

Zeolites are nanoporous crystalline materials with abundant industrial applications. Despite sustained research, only 235 different zeolite frameworks have been realized out of millions of hypothetical ones predicted by computational enumeration. Structure-property relationships in zeolite synthesis are very complex and only marginally understood. Here, we apply structure and graph-based unsupervised machine learning to gain insight on zeolite frameworks and how they relate to experimentally observed polymorphism and phase transformations. We begin by describing zeolite structures using the Smooth Overlap of Atomic Positions method, which clusters crystals with similar cages and density in a way consistent with traditional hand-selected composite building units. To also account for topological differences, zeolite crystals are represented as multigraphs and compared by isomorphism tests. We find that fourteen different pairs and one trio of known frameworks are graph isomorphic. Based on experimental interzeolite conversions and occurrence of competing phases, we propose that the availability of kinetic-controlled transformations between metastable zeolite frameworks is related to their similarity in the graph space. When this description is applied to enumerated structures, over 3,400 hypothetical structures are found to be isomorphic to known frameworks, and thus might be realized from their experimental counterparts. Using a continuous similarity metric, the space of known zeolites shows additional overlaps with experimentally observed phase transformations. Hence, graph-based similarity approaches suggest a venue for realizing novel zeolites from existing ones by providing a relationship between pairwise structure similarity and experimental transformations.Comment: 11 pages, 6 figure

Spherical sets avoiding a prescribed set of angles

Let $X$ be any subset of the interval $[-1,1]$. A subset $I$ of the unit sphere in $R^n$ will be called \emph{$X$-avoiding} if $\notin X$ for any $u,v \in I$. The problem of determining the maximum surface measure of a $\{ 0 \}$-avoiding set was first stated in a 1974 note by Witsenhausen; there the upper bound of $1/n$ times the surface measure of the sphere is derived from a simple averaging argument. A consequence of the Frankl-Wilson theorem is that this fraction decreases exponentially, but until now the $1/3$ upper bound for the case $n=3$ has not moved. We improve this bound to $0.313$ using an approach inspired by Delsarte's linear programming bounds for codes, combined with some combinatorial reasoning. In the second part of the paper, we use harmonic analysis to show that for $n\geq 3$ there always exists an $X$-avoiding set of maximum measure. We also show with an example that a maximiser need not exist when $n=2$.Comment: 21 pages, 3 figure