Utilizing Deep Neural Networks for Brain–Computer Interface-Based Prosthesis Control

Limb amputations affect a significant portion of the world’s population every year. The necessity for these operations can be associated with related health conditions or a traumatic event. Currently, prosthetic devices intended to alleviate the burden of amputation lack many of the premier features possessed by their biological counterparts. The foremost of these features are agility and tactile function. In an effort to address the former, researchers here investigate the fundamental connection between agile finger movement and brain signaling. In this study each subject was asked to move his or her right index finger in sync with a time-aligned finger movement demonstration while each movement was labeled and the subject’s brain waves were recorded via a single-channel electroencephalograph. This data was subsequently used to train and test a deep neural network in an effort to classify each subject’s intention to rest and intention to extend his or her right index finger. On average, the employed model yielded an accuracy of 63.3%, where the most predictable subject’s movements were classified with an accuracy of 70.5%

\u3ci\u3eCryptopygus Bipunctatus\u3c/i\u3e (Collembola: Isotomidae) in North America, and \u3ci\u3eC. Posteroculatus\u3c/i\u3e N. Comb.

Specimens of Cryptopygus bipunctatus are reported and described from North America (Michigan) for the first time. The species is easily recognized by its lack of color, one pair of ocelli on black eyespots, and one flair of ventral manubrial setae. Michigan and European specimens are very· similar. A very similar Polish species, Isotomina posteroculata, is transferred to Cryptopygus

Recent Extreme Ultraviolet Solar Spectra and Spectroheliograms

Extreme ultraviolet solar spectra and spectroheliogram analyse

Classical Limit of Demagnetization in a Field Gradient

We calculate the rate of decrease of the expectation value of the transverse component of spin for spin-1/2 particles in a magnetic field with a spatial gradient, to determine the conditions under which a previous classical description is valid. A density matrix treatment is required for two reasons. The first arises because the particles initially are not in a pure state due to thermal motion. The second reason is that each particle interacts with the magnetic field and the other particles, with the latter taken to be via a 2-body central force. The equations for the 1-body Wigner distribution functions are written in a general manner, and the places where quantum mechanical effects can play a role are identified. One that may not have been considered previously concerns the momentum associated with the magnetic field gradient, which is proportional to the time integral of the gradient. Its relative magnitude compared with the important momenta in the problem is a significant parameter, and if their ratio is not small some non-classical effects contribute to the solution. Assuming the field gradient is sufficiently small, and a number of other inequalities are satisfied involving the mean wavelength, range of the force, and the mean separation between particles, we solve the integro- partial differential equations for the Wigner functions to second order in the strength of the gradient. When the same reasoning is applied to a different problem with no field gradient, but having instead a gradient to the z-component of polarization, the connection with the diffusion coefficient is established, and we find agreement with the classical result for the rate of decrease of the transverse component of magnetization.Comment: 22 pages, no figure

Optical and Infrared Photometry of the Unusual Type Ia Supernova 2000cx

We present optical and infrared photometry of the unusual Type Ia supernova 2000cx. With the data of Li et al. (2001) and Jha (2002), this comprises the largest dataset ever assembled for a Type Ia SN, more than 600 points in UBVRIJHK. We confirm the finding of Li et al. regarding the unusually blue B-V colors as SN 2000cx entered the nebular phase. Its I-band secondary hump was extremely weak given its B-band decline rate. The V minus near infrared colors likewise do not match loci based on other slowly declining Type Ia SNe, though V-K is the least ``abnormal''. In several ways SN 2000cx resembles other slow decliners, given its B-band decline rate (Delta m_15(B) = 0.93), the appearance of Fe III lines and weakness of Si II in its pre-maximum spectrum, the V-K colors and post-maximum V-H colors. If the distance modulus derived from Surface Brightness Fluctuations of the host galaxy is correct, we find that the rate of light increase prior to maximum, the characteristics of the bolometric light curve, and the implied absolute magnitude at maximum are all consistent with a sub-luminous object with Delta m_15(B) ~ 1.6-1.7 having a higher than normal kinetic energy.Comment: 46 pages, 17 figures, to be published in Publications of the Astronomical Society of the Pacifi

Quasiparticle transport equation with collision delay. II. Microscopic Theory

For a system of non-interacting electrons scattered by neutral impurities, we derive a modified Boltzmann equation that includes quasiparticle and virial corrections. We start from quasiclassical transport equation for non-equilibrium Green's functions and apply limit of small scattering rates. Resulting transport equation for quasiparticles has gradient corrections to scattering integrals. These gradient corrections are rearranged into a form characteristic for virial corrections

Three-Dimensional Spectral Classification of Low-Metallicity Stars Using Artificial Neural Networks

We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity, and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict Teff with an accuracy of ~ 135-150 K over the range 4250 <= Teff <= 6500 K, logg with an accuracy of ~ 0.25-0.30 dex over the range 1.0 <= logg <= 5.0 dex, and [Fe/H] with an accuracy ~ 0.15-0.20 dex over the range -4.0 <= [Fe/H] <= +0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs, and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys.Comment: 51 pages, 11 eps figures, uses aastex; to appear in Ap

SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short-and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.Fil: Snider, G.. Dalhousie University Halifax; CanadáFil: Weagle, C. L.. Dalhousie University Halifax; CanadáFil: Martin, R. V.. Dalhousie University Halifax; Canadá. University of Cambridge; Reino UnidoFil: van Donkelaar, A.. Dalhousie University Halifax; CanadáFil: Conrad, K.. Dalhousie University Halifax; CanadáFil: Cunningham, D.. Dalhousie University Halifax; CanadáFil: Gordon, C.. Dalhousie University Halifax; CanadáFil: Zwicker, M.. Dalhousie University Halifax; CanadáFil: Akoshile, C.. University of Ilorin; NigeriaFil: Artaxo, P.. Governo Do Estado de Sao Paulo; BrasilFil: Anh, N. X.. Vietnam Academy of Science and Technology. Institute of Geophysics; VietnamFil: Brook, J.. University of Toronto; CanadáFil: Dong, J.. Tsinghua University; ChinaFil: Garland, R. M.. North-West University; SudáfricaFil: Greenwald, R.. Rollins School of Public Health; Estados UnidosFil: Griffith, D.. Council for Scientific and Industrial Research; SudáfricaFil: He, K.. Tsinghua University; ChinaFil: Holben, B. N.. NASA Goddard Space Flight Center; Estados UnidosFil: Kahn, R.. NASA Goddard Space Flight Center; Estados UnidosFil: Koren, I.. Weizmann Institute Of Science Israel; IsraelFil: Lagrosas, N.. Manila Observatory, Ateneo de Manila University campus; FilipinasFil: Lestari, P.. Institut Teknologi Bandung; IndonesiaFil: Ma, Z.. Rollins School of Public Health; Estados UnidosFil: Vanderlei Martins, J.. University of Maryland; Estados UnidosFil: Quel, Eduardo Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rudich, Y.. Weizmann Institute Of Science Israel; IsraelFil: Salam, A.. University Of Dhaka; BangladeshFil: Tripathi, S. N.. Indian Institute Of Technology, Kanpur; IndiaFil: Yu, C.. Rollins School of Public Health; Estados UnidosFil: Zhang, Q.. Tsinghua University; ChinaFil: Zhang, Y.. Tsinghua University; ChinaFil: Brauer, M.. University of British Columbia; CanadáFil: Cohen, A.. Health Effects Institute; Estados UnidosFil: Gibson, M. D.. Dalhousie University Halifax; CanadáFil: Liu, Y.. Rollins School of Public Health; Estados Unido

Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models

Global measurements of the elemental composition of fine particulate matter across several urban locations by the Surface Particulate Matter Network reveal an enhanced fraction of anthropogenic dust compared to natural dust sources, especially over Asia. We develop a global simulation of anthropogenic fugitive, combustion, and industrial dust which, to our knowledge, is partially missing or strongly underrepresented in global models. We estimate 2-16 μg/m3 increase in fine particulate mass concentration across East and South Asia by including anthropogenic fugitive, combustion, and industrial dust emissions. A simulation including anthropogenic fugitive, combustion, and industrial dust emissions increases the correlation from 0.06 to 0.66 of simulated fine dust in comparison with Surface Particulate Matter Network measurements at 13 globally dispersed locations, and reduces the low bias by 10% in total fine particulate mass in comparison with global in situ observations. Global population-weighted PM2.5 increases by 2.9 μg/m3 (10%). Our assessment ascertains the urgent need of including this underrepresented fine anthropogenic dust source into global bottom-up emission inventories and global models