High-Dynamic-Range Lighting Estimation From Face Portraits.

We present a CNN-based method for outdoor highdynamic-range (HDR) environment map prediction from low-dynamic-range (LDR) portrait images. Our method relies on two different CNN architectures, one for light encoding and another for face-to-light prediction. Outdoor lighting is characterised by an extremely high dynamic range, and thus our encoding splits the environment map data between low and high-intensity components, and encodes them using tailored representations. The combination of both network architectures constitutes an end-to-end method for accurate HDR light prediction from faces at real-time rates, inaccessible for previous methods which focused on low dynamic range lighting or relied on non-linear optimisation schemes. We train our networks using both real and synthetic images, we compare our light encoding with other methods for light representation, and we analyse our results for light prediction on real images. We show that our predicted HDR environment maps can be used as accurate illumination sources for scene renderings, with potential applications in 3D object insertion for augmented reality

Ischemic Heart Disease Incidence in Relation to Fine versus Total Particulate Matter Exposure in a U.S. Aluminum Industry Cohort.

Ischemic heart disease (IHD) has been linked to exposures to airborne particles with an aerodynamic diameter <2.5 μm (PM2.5) in the ambient environment and in occupational settings. Routine industrial exposure monitoring, however, has traditionally focused on total particulate matter (TPM). To assess potential benefits of PM2.5 monitoring, we compared the exposure-response relationships between both PM2.5 and TPM and incidence of IHD in a cohort of active aluminum industry workers. To account for the presence of time varying confounding by health status we applied marginal structural Cox models in a cohort followed with medical claims data for IHD incidence from 1998 to 2012. Analyses were stratified by work process into smelters (n = 6,579) and fabrication (n = 7,432). Binary exposure was defined by the 10th-percentile cut-off from the respective TPM and PM2.5 exposure distributions for each work process. Hazard Ratios (HR) comparing always exposed above the exposure cut-off to always exposed below the cut-off were higher for PM2.5, with HRs of 1.70 (95% confidence interval (CI): 1.11-2.60) and 1.48 (95% CI: 1.02-2.13) in smelters and fabrication, respectively. For TPM, the HRs were 1.25 (95% CI: 0.89-1.77) and 1.25 (95% CI: 0.88-1.77) for smelters and fabrication respectively. Although TPM and PM2.5 were highly correlated in this work environment, results indicate that, consistent with biologic plausibility, PM2.5 is a stronger predictor of IHD risk than TPM. Cardiovascular risk management in the aluminum industry, and other similar work environments, could be better guided by exposure surveillance programs monitoring PM2.5

Incident Ischemic Heart Disease After Long-Term Occupational Exposure to Fine Particulate Matter: Accounting for 2 Forms of Survivor Bias.

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 µm or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87, 18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m(3)-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias

Numerical study of the thermoelectric power factor in ultra-thin Si nanowires

Low dimensional structures have demonstrated improved thermoelectric (TE) performance because of a drastic reduction in their thermal conductivity, {\kappa}l. This has been observed for a variety of materials, even for traditionally poor thermoelectrics such as silicon. Other than the reduction in {\kappa}l, further improvements in the TE figure of merit ZT could potentially originate from the thermoelectric power factor. In this work, we couple the ballistic (Landauer) and diffusive linearized Boltzmann electron transport theory to the atomistic sp3d5s*-spin-orbit-coupled tight-binding (TB) electronic structure model. We calculate the room temperature electrical conductivity, Seebeck coefficient, and power factor of narrow 1D Si nanowires (NWs). We describe the numerical formulation of coupling TB to those transport formalisms, the approximations involved, and explain the differences in the conclusions obtained from each model. We investigate the effects of cross section size, transport orientation and confinement orientation, and the influence of the different scattering mechanisms. We show that such methodology can provide robust results for structures including thousands of atoms in the simulation domain and extending to length scales beyond 10nm, and point towards insightful design directions using the length scale and geometry as a design degree of freedom. We find that the effect of low dimensionality on the thermoelectric power factor of Si NWs can be observed at diameters below ~7nm, and that quantum confinement and different transport orientations offer the possibility for power factor optimization.Comment: 42 pages, 14 figures; Journal of Computational Electronics, 201