We study simple lattice systems to demonstrate the influence of interpenetrating bond networks on phase behavior. We promote interpenetration by using a Hamiltonian with a weakly repulsive interaction with nearest neighbors and an attractive interaction with second-nearest neighbors. In this way, bond networks will form between second-nearest neighbors, allowing for two (locally) distinct netwo...

Deep neural networks (DNNs) enable innovative applications of machine learning like image recognition, machine translation, or malware detection. However, deep learning is often criticized for its lack of robustness in adversarial settings (e.g., vulnerability to adversarial inputs) and general inability to rationalize its predictions. In this work, we exploit the structure of deep learning to ...

In nearest neighbor searching we are given a set of n data points in real d-dimensional space, R, and the problem is to preprocess these points into a data structure, so that given a query point, the nearest data point to the query point can be reported efficiently. Because data sets can be quite large, we are interested in data structures that use optimal O(dn) storage. Given the limitation of...

Given a set S of n points in d-dimensional Euclidean metric space X and a small positive real number ǫ, we present an algorithm to preprocess S and answer queries that require finding a set S′ ⊆ S of ǫ-approximate nearest neighbors (ANNs) to a given query point q ∈ X . The following are the characteristics of points belonging to set S′: ∀s ∈ S′, ∃ a point p ∈ X such that |pq| ≤ ǫ and the neares...

We present a new regular grid search algorithm for quick fixed-radius nearest-neighbor lookup developed in Python. This module indexes set of k-dimensional points grid, with optional periodic conditions, providing fast approach nearest neighbors queries. In this first installment, we provide three types queries: bubble, shell and the nth-nearest. For these queries include different metrics inte...

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and ...

The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and outliers. Many of these issues become even more significant when the data is of very high dimensionality, such as text or time series data. In this paper we present a novel clustering technique that addresses these issues...

CONTEXT Ambulance diversion, a practice in which emergency departments (EDs) are temporarily closed to ambulance traffic, might be problematic for patients experiencing time-sensitive conditions, such as acute myocardial infarction (AMI). However, there is little empirical evidence to show whether diversion is associated with worse patient outcomes. OBJECTIVE To analyze whether temporary ED c...

Finding the k nearest neighbours of each point in a point cloud forms an integral part of many point-cloud processing tasks. One common approach is to build a kd-tree over the points and then iteratively query the k nearest neighbors of each point. We introduce a simple modification to these queries to exploit the coherence between successive points; no changes are required to the kd-tree data ...