Geometrically Interlocking Space-Filling Tiling Based on Fabric Weaves.

In this article, we introduce a framework for the geometric design and fabrication of a family of geometrically interlocking space-filling shapes, which we call woven tiles. Our framework is based on a unique combination of (1) Voronoi partitioning of space using curve segments as the Voronoi sites and (2) the design of these curve segments based on weave patterns closed under symmetry operations. The underlying weave geometry provides an interlocking property to the tiles and the closure property under symmetry operations ensure single tile can fill space. In order to demonstrate this general framework, we focus on specific symmetry operations induced by fabric weaving patterns. We specifically showcase the design and fabrication of woven tiles on flat and curved domains by using the most common 2-fold fabrics, namely, plain, twill, and satin weaves. We further evaluate and compare the mechanical behavior of the so created woven tiles through finite element analysis.

Parallel Low-Loss Measurement of Multiple Atomic Qubits.

We demonstrate low-loss measurement of the hyperfine ground state of rubidium atoms by state dependent fluorescence detection in a dipole trap array of five sites. The presence of atoms and their internal states are minimally altered by utilizing circularly polarized probe light and a strictly controlled quantization axis. We achieve mean state detection fidelity of 97% without correcting for imperfect state preparation or background losses, and 98.7% when corrected. After state detection and correction for background losses, the probability of atom loss due to the state measurement is <2% and the initial hyperfine state is preserved with >98% probability.

Atomic Fock state preparation using Rydberg blockade.

We use coherent excitation of 3-16 atom ensembles to demonstrate collective Rabi flopping mediated by Rydberg blockade. Using calibrated atom number measurements, we quantitatively confirm the expected √N Rabi frequency enhancement to within 4%. The resulting atom number distributions are consistent with an essentially perfect blockade. We then use collective Rabi π pulses to produce N=1, 2 atom number Fock states with fidelities of 62% and 48%, respectively. The N=2 Fock state shows the collective Rabi frequency enhancement without corruption from atom number fluctuations.

Ontology-based tools to expedite predictive model construction.

Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.

An ontology-driven, diagnostic modeling system.

OBJECTIVES: To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. MATERIALS AND METHODS: We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. RESULTS: The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. DISCUSSION: Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. CONCLUSIONS: The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.