Human action recognition via skeletal and depth based feature fusion

This paper addresses the problem of recognizing human actions captured with depth cameras. Human action recognition is a challenging task as the articulated action data is high dimensional in both spatial and temporal domains. An effective approach to handle this complexity is to divide human body into different body parts according to human skeletal joint positions, and performs recognition based on these part-based feature descriptors. Since different types of features could share some similar hidden structures, and different actions may be well characterized by properties common to all features (sharable structure) and those specific to a feature (specific structure), we propose a joint group sparse regression-based learning method to model each action. Our method can mine the sharable and specific structures among its part-based multiple features meanwhile imposing the importance of these part-based feature structures by joint group sparse regularization, in favor of discriminative part-based feature structure selection. To represent the dynamics and appearance of the human body parts, we employ part-based multiple features extracted from skeleton and depth data respectively. Then, using the group sparse regularization techniques, we have derived an algorithm for mining the key part-based features in the proposed learning framework. The resulting features derived from the learnt weight matrices are more discriminative for multi-task classification. Through extensive experiments on three public datasets, we demonstrate that our approach outperforms existing methods

Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand. Metasurfaces are versatile, novel platforms for manipulating the scattering, color, phase, or intensity of light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters, such as various materials’ properties and coupling effects, as well as the geometrical parameters. Ideally, this would require multidimensional space optimization through direct numerical simulations. Recently, an alternative, popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted method. We utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude, and spectral position. We exploit such high-Q resonances for enhanced light–matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation; at the same time, they also contribute to 100-fold enhancement of the amplitude of optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses

Chromophore Ordering by Confinement into Carbon Nanotubes

International audienceWe report an experimental study on the confinement of oligothiophene derivatives into single-walled carbon nanotubes over a large range of diameter (from 0.68 to 1.93 nm). We evidence by means of Raman spectroscopy and transmission electron microscopy that the supramolecular organizations of the confined oligothiophenes depend on the nanocontainer size. The Raman Radial Breathing Mode frequency is shown to be monitored by both the number of confined molecules into a nanotube section and the competition between oligothiophene/oligothiophene and oligothiophene/tube wall interactions. We finally propose simple Raman criteria to characterize oligothiophene supramolecular organization at the nanoscale

Landsat-8, advanced spaceborne thermal emission and reflection radiometer, and WorldView-3 multispectral satellite imagery for prospecting copper-gold mineralization in the northeastern Inglefield Mobile Belt (IMB), northwest Greenland

© 2019 by the authors. Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/biotite were identified with the documented Cu-Au occurrences in central and southwestern sectors of the Cu-Au mineralization belt. The calculation of confusion matrix and Kappa Coefficient proved appropriate overall accuracy and good rate of agreement for alteration mineral mapping. This investigation accomplished the application of multispectral/multi-sensor satellite imagery as a valuable and economical tool for reconnaissance stages of systematic mineral exploration projects in remote and inaccessible metallogenic provinces around the world, particularly in the High Arctic regions

Simulating MOS science on the ELT: Lyα forest tomography

Mapping the large-scale structure through cosmic time has numerous applications in studies of cosmology and galaxy evolution. At z ≳ 2, the structure can be traced by the neutral intergalactic medium (IGM) by way of observing the Lyα forest towards densely sampled lines of sight of bright background sources, such as quasars and star-forming galaxies. We investigate the scientific potential of MOSAIC, a planned multi-object spectrograph on the European Extremely Large Telescope (ELT), for the 3D mapping of the IGM at z ≳ 3. We simulated a survey of 3 ≲ z ≲ 4 galaxies down to a limiting magnitude of mr ∼ 25.5 mag in an area of 1 degree2 in the sky. Galaxies and their spectra (including the line-of-sight Lyα absorption) were taken from the lightcone extracted from the Horizon-AGN cosmological hydrodynamical simulation. The quality of the reconstruction of the original density field was studied for different spectral resolutions (R = 1000 and R = 2000, corresponding to the transverse typical scales of 2.5 and 4 Mpc) and signal-to-noise ratios (S/N) of the spectra. We demonstrate that the minimum S/N (per resolution element) of the faintest galaxies that a survey like this has to reach is S/N = 4. We show that a survey with this sensitivity enables a robust extraction of cosmic filaments and the detection of the theoretically predicted galaxy stellar mass and star-formation rate gradients towards filaments. By simulating the realistic performance of MOSAIC, we obtain S/N(Tobs, R, mr) scaling relations. We estimate that ≲35 (65) nights of observation time are required to carry out the survey with the instrument’s high multiplex mode and with a spectral resolution of R = 1000 (2000). A survey with a MOSAIC-concept instrument on the ELT is found to enable the mapping of the IGM at z >  3 on Mpc scales, and as such will be complementary to and competitive with other planned IGM tomography surveys

Biofilter aquaponic system for nutrients removal from fresh market wastewater

Aquaponics is a significant wastewater treatment system which refers to the combination of conventional aquaculture (raising aquatic organism) with hydroponics (cultivating plants in water) in a symbiotic environment. This system has a high ability in removing nutrients compared to conventional methods because it is a natural and environmentally friendly system (aquaponics). The current chapter aimed to review the possible application of aquaponics system to treat fresh market wastewater with the intention to highlight the mechanism of phytoremediation occurs in aquaponic system. The literature revealed that aquaponic system was able to remove nutrients in terms of nitrogen and phosphorus

Synthetic plasmonic nanocircuits and the evolution of their correlated spatial arrangement and resonance spectrum

Optical nanocircuits, inspired by electrical nanocircuits, provide a versatile platform for tailoring and manipulating optical fields at the subwavelength scale, which is vital for developing various innovative optical nanodevices and integrated nanosystems. Plasmonic nanoparticles can be employed as promising building blocks for optical nanocircuits with unprecedentedly high integration capacity. Among various plasmonic systems, aggregated metallic nanoparticle, known as oligomers, possess great potential in constructing functional metatronic circuits. Here, the optical nanocircuits comprising special plasmonic oligomers, such as trimers with D3h symmetry, quadrumers with D2h symmetry, and their variants with reduced symmetry, are systematically investigated in the metatronic paradigm, both theoretically and experimentally. Our proposed circuit models, based on the displacement current in the oligomers, not only reproduce the resonance spectral details, but also retrieve many hidden physical quantities associated with their optical responses. Guided by the metatronic circuits, the spectral engineering of the oligomers with reduced geometric symmetry is predicted, and subgroup decomposition of several plasmonic quadrumers is examined. Our investigation has revealed a close correlation between the metatronic circuitry and strongly coupled plasmonic oligomers. The observed correlation of spatial arrangement and frequency response in oligomers provides a metatronic guide to modulate plasmonic responses via geometric variation

Biomarker-predicted sugars intake compared with self-reported measures in US Hispanics/Latinos: results from the HCHS/SOL SOLNAS study

Abstract Objective Measurement error in self-reported total sugars intake may obscure associations between sugars consumption and health outcomes, and the sum of 24 h urinary sucrose and fructose may serve as a predictive biomarker of total sugars intake. Design The Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS) was an ancillary study to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) cohort. Doubly labelled water and 24 h urinary sucrose and fructose were used as biomarkers of energy and sugars intake, respectively. Participants’ diets were assessed by up to three 24 h recalls (88 % had two or more recalls). Procedures were repeated approximately 6 months after the initial visit among a subset of ninety-six participants. Setting Four centres (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA) across the USA. Subjects Men and women ( n 477) aged 18–74 years. Results The geometric mean of total sugars was 167·5 (95 % CI 154·4, 181·7) g/d for the biomarker-predicted and 90·6 (95 % CI 87·6, 93·6) g/d for the self-reported total sugars intake. Self-reported total sugars intake was not correlated with biomarker-predicted sugars intake ( r =−0·06, P =0·20, n 450). Among the reliability sample ( n 90), the reproducibility coefficient was 0·59 for biomarker-predicted and 0·20 for self-reported total sugars intake. Conclusions Possible explanations for the lack of association between biomarker-predicted and self-reported sugars intake include measurement error in self-reported diet, high intra-individual variability in sugars intake, and/or urinary sucrose and fructose may not be a suitable proxy for total sugars intake in this study population