The Search for Hamilton

In July and August of 2009 my wife and I went on a four-week trip to Scotland and Ireland. We would be visiting Dublin, so I decided that we should visit the famous bridge where William Rowan Hamilton carved the equations for the quaternions. The task was not as simple as I had assumed. This paper gives some details of the search

Digital Filtering and Smoothing: A Student Simulation Project

A bug tracking problem is used to introduce students to filtering and smoothing real-time data. A predictor-corrector filter/smoother algorithm is developed and a simulation platform is provided so that students can program and test implementations of the filter/smoother. The platform includes the ability to animate the simulation. The tracking problem: A small computer bug is traveling around the $x-y$ plane trying to avoid detection. We can eliminate the bug if we can produce a reasonably accurate approximation to its trajectory. We have a bug detecting device which can be pointed at the plane. It can measure the $x$ and $y$ distances to the bug from where it is pointing if the bug is not too far from where it is pointing. The bug does not want to be tracked, so it sends out jamming signals which corrupt the bug detecting device\u27s measurements in a random manner. Our task is to create a computer algorithm that uses the bug detecting device to: \begin{itemize} \item Screen out the measurement noise \item Point at the bug\u27s most likely next position in the plane \item Produce an accurate record of the bug\u27s path. \end{itemize

The Search for the Real Josephus Problem

Many of the problems that mathematicians and computer scientists dearly love have been around for a long time. One such problem is known as the Josephus Problem, named after the first century Jewish historian Flavius Josephus. Josephus did not invent the problem. Instead, an event from his life served as the inspiration for the problem statement. Many current books refer to Mathematical Recreations and Essays by W. W. Rouse Ball [originally published in 1892] for the problem statement. The problem is quite interesting (and will be solved here). However, the story, as quoted in Bell, is not completely accurate

Counting Tulips: Three Combinatorial Proofs

A gardener has r ≥ 1 red tulips and b ≥ 1 blue tulips, each in its own pot. She plans to plant them in a line along the edge of her driveway. In how many visually distinguishable ways can she arrange them

Introduction (2013)

Nineteenth Conference of the Association of Christians in the Mathematical Science

Designing for Mistrust

The 2014 ACM North Central Region programming contest contained a problem about a group of v bandits who want to use multiple locks to seal their treasure and distribute keys in such a way that no group of less than m bandits can open all the locks. The problem asks for an algorithm that will determine the number of locks needed for any set of parameters (v, m). I will present an analytic solution that produces a minimum number of locks, a recurrence relation solution, and a constructive algorithm that can print out a table showing the locks and which subset of bandits hold keys for each lock. Each table forms a balanced incomplete block design (BIBD). The parameters of the BIBD can be uniquely determined from v and m

AFLOW-SYM: Platform for the complete, automatic and self-consistent symmetry analysis of crystals

Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educated guesses. Here, we present a robust procedure for evaluating the complete suite of symmetry properties, featuring various representations for the point-, factor-, space groups, site symmetries, and Wyckoff positions. The protocol determines a system-specific mapping tolerance that yields symmetry operations entirely commensurate with fundamental crystallographic principles. The self consistent tolerance characterizes the effective spatial resolution of the reported atomic positions. The approach is compared with the most used programs and is successfully validated against the space group information provided for over 54,000 entries in the Inorganic Crystal Structure Database. Subsequently, a complete symmetry analysis is applied to all 1.7$+$ million entries of the AFLOW data repository. The AFLOW-SYM package has been implemented in, and made available for, public use through the automated, $\textit{ab-initio}$ framework AFLOW.Comment: 24 pages, 6 figure

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only at interesting materials \unicode{x2014} neglecting the non-synthesizable systems and those without the desired properties \unicode{x2014} thus reducing the amount of resources spent on expensive computations and/or time-consuming experimental synthesis. However, using these predictive models is not always straightforward. Often, they require a panoply of technical expertise, creating barriers for general users. AFLOW-ML (AFLOW $\underline{\mathrm{M}}$achine $\underline{\mathrm{L}}$earning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium. The framework provides an open RESTful API to directly access the continuously updated algorithms, which can be transparently integrated into any workflow to retrieve predictions of electronic, thermal and mechanical properties. These types of interconnected cloud-based applications are envisioned to be capable of further accelerating the adoption of machine learning methods into materials development.Comment: 10 pages, 2 figure

Universal fragment descriptors for predicting properties of inorganic crystals

Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules