DNA-decorated graphene chemical sensors

Graphene is a true two dimensional material with exceptional electronic properties and enormous potential for practical applications. Graphene's promise as a chemical sensor material has been noted but there has been relatively little work on practical chemical sensing using graphene, and in particular how chemical functionalization may be used to sensitize graphene to chemical vapors. Here we show one route towards improving the ability of graphene to work as a chemical sensor by using single stranded DNA as a sensitizing agent. The resulting broad response devices show fast response times, complete and rapid recovery to baseline at room temperature, and discrimination between several similar vapor analytes.Comment: 7 pages, To appear in Applied Physics Letter

Hyperspectral Point Cloud Projection for the Semantic Segmentation of Multimodal Hyperspectral and Lidar Data with Point Convolution-Based Deep Fusion Neural Networks

The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which enables ingestion and exploitation with point cloud processing neural network methods. Additionally, we present a composite fusion-style, point convolution-based neural network architecture for the semantic segmentation of HSPC and lidar point cloud data. We investigate the effects of the proposed HSPC representation for both unimodal and multimodal networks ingesting a variety of hyperspectral and lidar data representations. Finally, we compare the performance of these networks against each other and previous approaches. This study paves the way for innovative approaches to multimodal remote sensing data fusion, unlocking new possibilities for enhanced data analysis and interpretation

Model Atmospheres for Irradiated Stars in pre-Cataclysmic Variables

Model atmospheres have been computed for M dwarfs that are strongly irradiated by nearby hot companions. A variety of primary and secondary spectral types are explored in addition to models specific to four known systems: GD 245, NN Ser, AA Dor, and UU Sge. This work demonstrates that a dramatic temperature inversion is possible on at least one hemisphere of an irradiated M dwarf and the emergent spectrum will be significantly different from an isolated M dwarf or a black body flux distribution. For the first time, synthetic spectra suitable for direct comparison to high-resolution observations of irradiated M dwarfs in non-mass transferring post-common envelope binaries are presented. The effects of departures from local thermodynamic equilibrium on the Balmer line profiles are also discussed.Comment: Accepted for publication in ApJ; 12 pages, 10 figure

Dust in the Photospheric Environment: Unified Cloudy Models of M, L, and T Dwarfs

We address the problem of how dust forms and how it could be sustained in the static photospheres of cool dwarfs for a long time. In the cool and dense gas, dust forms easily at the condensation temperature, T_cond, and the dust can be in detailed balance with the ambient gas so long as it remains smaller than the critical radius, r_cr. However, dust will grow larger and segregate from the gas when it will be larger than r_cr somewhere at the lower temperature, which we refer to as the critical temperature, T_cr. Then, the large dust grains will precipitate below the photosphere and only the small dust grains in the region of T_cr < T < T_cond can be sustained in the photosphere. Thus a dust cloud is formed. Incorporating the dust cloud, non-grey model photo- spheres in radiative-convective equilibrium are extended to T_eff as low as 800K. Observed colors and spectra of cool dwarfs can consistently be accounted for by a single grid of our cloudy models. This fact in turn can be regarded as supporting evidence for our basic assumption on the cloud formation.Comment: 50 pages with 14 postscript figures, to be published in Astrophys.

Tempo and intensity of pre-task music modulate neural activity during reactive task performance

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 The Authors.Research has shown that not only do young athletes purposively use music to manage their emotional state (Bishop, Karageorghis, & Loizou, 2007), but also that brief periods of music listening may facilitate their subsequent reactive performance (Bishop, Karageorghis, & Kinrade, 2009). We report an fMRI study in which young athletes lay in an MRI scanner and listened to a popular music track immediately prior to performance of a three-choice reaction time task; intensity and tempo were modified such that six excerpts (2 intensities × 3 tempi) were created. Neural activity was measured throughout. Faster tempi and higher intensity collectively yielded activation in structures integral to visual perception (inferior temporal gyrus), allocation of attention (cuneus, inferior parietal lobule, supramarginal gyrus), and motor control (putamen), during reactive performance. The implications for music listening as a pre-competition strategy in sport are discussed

Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network

Use of small specimen creep data in component life management: a review

Small specimen creep testing techniques are novel mechanical test techniques that have been developed over the past 25 years. They mainly include the sub-size uniaxial test, the small punch creep test, the impression creep test, the small ring creep test and the two-bar creep test. This paper outlines the current methods in practice for data interpretation as well as the state-of-the-art procedures for conducting the tests. Case studies for the use of impression creep testing and material strength ranking of creep resistant steels are reviewed along with the requirement for the standardisation of the impression creep test method. A database of small specimen creep testing is required to prove the validity of the tests

A Multi-Wavelength, Multi-Epoch Study of the Soft X-Ray Transient Prototype, V616 Mon (A0620-00)

We have obtained optical and infrared photometry of the soft x-ray transient prototype V616 Mon (A0620-00). From this photometry, we find a spectral type of K4 for the secondary star in the system, which is consistent with spectroscopic observations. We present J-, H-, and K-band light curves modeled with WD98 and ELC. Combining detailed, independently run models for ellipsoidal variations due to a spotted, non-spherical secondary star, and the observed ultraviolet to infrared spectral energy distribution of the system, we show that the most likely value for the orbital inclination is 40.75 +/- 3 deg. This inclination angle implies a primary black hole mass of 11.0 +/- 1.9 solar masses.Comment: 29 pages (preprint format), including 7 figures and 4 tables, accepted for publication in the Nov 2001 issue of A