Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR

Current-Induced Spin Polarization in Gallium Nitride

Electrically generated spin polarization is probed directly in bulk GaN using Kerr rotation spectroscopy. A series of n-type GaN epilayers are grown in the wurtzite phase both by molecular beam epitaxy (MBE) and metalorganic chemical vapor deposition (MOCVD) with a variety of doping densities chosen to broadly modulate the transverse spin lifetime, T2*. The spin polarization is characterized as a function of electrical excitation energy over a range of temperatures. Despite weak spin-orbit interactions in GaN, a current-induced spin polarization (CISP) is observed in the material at temperatures of up to 200 K.Comment: 16 pages, 3 figure

An investigation of the reaction of titanium with hydrogen Summary report

Hydrogen gas reaction with titanium and titanium alloys determined at low temperatures and pressure

Flexibility of Crab Chemosensory Hairs Enables Flicking Antennules to Sniff

The first step in smelling is capture of odorant molecules from the surrounding fluid. We used lateral flagella of olfactory antennules of crabs Callinectes sapidus to study the physical process of odor capture by antennae bearing dense tufts of hair-like chemosensory sensilla (aesthetascs). Fluid flow around and through aesthetasc arrays on dynamically scaled models of lateral flagella of C. sapidus was measured by particle image velocimetry to determine how antennules sample the surrounding water when they flick. Models enabled separate evaluation of the effects of flicking speed, aesthetasc spacing, and antennule orientation. We found that crab antennules, like those of other malacostracan crustaceans, take a discrete water sample during each flick by having a rapid downstroke, during which water flows into the aesthetasc array, and a slow recovery stroke, when water is trapped in the array and odorants have time to diffuse to aesthetascs. However, unlike antennules of crustaceans with sparse aesthetasc arrays, crabs enhance sniffing via additional mechanisms: 1) Aesthetascs are flexible and splay as a result of the hydrodynamic drag during downstrokes, then clump together during return strokes; and 2) antennules flick with aesthetascs on the upstream side of the stalk during downstrokes, but are hidden downstream during return strokes. Aiming aesthetascs into ambient flow maintains sniffing. When gaps between aesthetascs are wide, changes in antennule speed are more effective at altering flow through the array than when gaps are narrow. Nonetheless, if crabs had fixed gap widths, their ability to take discrete samples of their odorant environment would be diminished

The Principles of Social Order. Selected Essays of Lon L. Fuller, edited With an introduction by Kenneth I. Winston

The electron spins of semiconductor defects can have complex interactions with their host, particularly in polar materials like SiC where electrical and mechanical variables are intertwined. By combining pulsed spin resonance with ab initio simulations, we show that spin-spin interactions in 4H-SiC neutral divacancies give rise to spin states with a strong Stark effect, sub-10(-6) strain sensitivity, and highly spin-dependent photoluminescence with intensity contrasts of 15%-36%. These results establish SiC color centers as compelling systems for sensing nanoscale electric and strain fields

Initializing decadal climate predictions with the GECCO oceanic synthesis

This study aims at improving the forecast skill of climate predictions through the use of ocean synthesis data for initial conditions of a coupled climate model. For this purpose, the coupled model of the Max Planck Institute (MPI) for Meteorology, which consists of the atmosphere model ECHAM5 and the MPI Ocean Model (MPI-OM), is initialized with oceanic synthesis fields available from the German contribution to Estimating the Circulation and Climate of the Ocean (GECCO) project. The use of an anomaly coupling scheme during the initialization avoids the main problems with drift in the climate predictions. Thus, the coupled model is continuously forced to follow the density anomalies of the GECCO synthesis over the period 1952-2001. Hindcast experiments are initialized from this experiment at constant intervals. The results show predictive skill through the initialization up to the decadal time scale, particularly over the North Atlantic. Viewed over the time scales analyzed here (annual, 5-yr, and 10-yr mean), greater skill for the North Atlantic sea surface temperature (SST) is obtained in the hindcast experiments than in either a damped persistence or trend forecast. The Atlantic meridional overturning circulation hindcast closely follows that of the GECCO oceanic synthesis. Hindcasts of global-mean temperature do not obtain greater skill than either damped persistence or a trend forecast, owing to the SST errors in the GECCO synthesis, outside the North Atlantic. An ensemble of forecast experiments is subsequently performed over the period 2002-11. North Atlantic SST from the forecast experiment agrees well with observations until the year 2007, and it is higher than if simulated without the oceanic initialization (averaged over the forecast period). The results confirm that both the initial and the boundary conditions must be accounted for in decadal climate predictions

Polytype control of spin qubits in silicon carbide

Crystal defects can confine isolated electronic spins and are promising candidates for solid-state quantum information. Alongside research focusing on nitrogen vacancy centers in diamond, an alternative strategy seeks to identify new spin systems with an expanded set of technological capabilities, a materials driven approach that could ultimately lead to "designer" spins with tailored properties. Here, we show that the 4H, 6H and 3C polytypes of SiC all host coherent and optically addressable defect spin states, including spins in all three with room-temperature quantum coherence. The prevalence of this spin coherence shows that crystal polymorphism can be a degree of freedom for engineering spin qubits. Long spin coherence times allow us to use double electron-electron resonance to measure magnetic dipole interactions between spin ensembles in inequivalent lattice sites of the same crystal. Together with the distinct optical and spin transition energies of such inequivalent spins, these interactions provide a route to dipole-coupled networks of separately addressable spins.Comment: 28 pages, 5 figures, and supplementary information and figure

Self consistent determination of plasmonic resonances in ternary nanocomposites

We have developed a self consistent technique to predict the behavior of plasmon resonances in multi-component systems as a function of wavelength. This approach, based on the tight lower bounds of the Bergman-Milton formulation, is able to predict experimental optical data, including the positions, shifts and shapes of plasmonic peaks in ternary nanocomposites without using any ftting parameters. Our approach is based on viewing the mixing of 3 components as the mixing of 2 binary mixtures, each in the same host. We obtained excellent predictions of the experimental optical behavior for mixtures of Ag:Cu:SiO2 and alloys of Au-Cu:SiO2 and Ag-Au:H2 O, suggesting that the essential physics of plasmonic behavior is captured by this approach.Comment: 7 pages and 4 figure

MAO: a Multiple Alignment Ontology for nucleic acid and protein sequences

The application of high-throughput techniques such as genomics, proteomics or transcriptomics means that vast amounts of heterogeneous data are now available in the public databases. Bioinformatics is responding to the challenge with new integrated management systems for data collection, validation and analysis. Multiple alignments of genomic and protein sequences provide an ideal environment for the integration of this mass of information. In the context of the sequence family, structural and functional data can be evaluated and propagated from known to unknown sequences. However, effective integration is being hindered by syntactic and semantic differences between the different data resources and the alignment techniques employed. One solution to this problem is the development of an ontology that systematically defines the terms used in a specific domain. Ontologies are used to share data from different resources, to automatically analyse information and to represent domain knowledge for non-experts. Here, we present MAO, a new ontology for multiple alignments of nucleic and protein sequences. MAO is designed to improve interoperation and data sharing between different alignment protocols for the construction of a high quality, reliable multiple alignment in order to facilitate knowledge extraction and the presentation of the most pertinent information to the biologist