Automatic Composition Recommendations for Portrait Photography

A user with no training in photography that takes pictures using a smartphone or other camera is often not able to capture attractive portrait photographs. This disclosure describes techniques to automatically determine optimal camera view-angles and frame elements, and to generate instructions to guide users to capture better composed photographs. An ultra-wide (UW) image is obtained via a stream parallel to a wide (W) image stream that the user previews during the capture of a photograph. The UW image is used as a guide to determine an optimal field of view (FoV) for the W-image, e.g., to determine an optimal foreground and background composition; to add elements that enhance artistic value; to omit elements that detract from artistic value; etc. Standard techniques of good photography, e.g., rule of thirds, optimal head orientation, etc. can be used to guide the user to obtain an optimal FoV that results in an attractive photograph

The impact of anti-diabetic drugs on colorectal cancer risk in a large cohort of women with diabetes

No Abstrac

Prevalence of Dyslipidemia in Patients Receiving Health Checkups: A Hospital-Based Study

We used the dataset from one medical center in Taiwan to explore the prevalence of dyslipidemia, which included 2695 subjects receiving private health checkups in 2003-2004. The overall prevalence of hypercholesterolemia was 53.3% in men and 48.2% in women (P = 0.008). The overall prevalence of hypertriglyceridemia was 29.3% in men and 13.7% in women (P < 0.001). The overall prevalence of elevated LDL level was 50.7% in men and 37.9% in women (P < 0.001). The overall prevalence of low HDL level was 47.4% in men and 53% in women (P = 0.004)

Multi-wavelength Stellar Polarimetry of the Filamentary Cloud IC5146: I. Dust Properties

We present optical and near-infrared stellar polarization observations toward the dark filamentary clouds associated with IC5146. The data allow us to investigate the dust properties (this paper) and the magnetic field structure (Paper II). A total of 2022 background stars were detected in $R_{c}$-, $i'$-, $H$-, and/or $K$-bands to $A_V \lesssim 25$ mag. The ratio of the polarization percentage at different wavelengths provides an estimate of $\lambda_{max}$, the wavelength of peak polarization, which is an indicator of the small-size cutoff of the grain size distribution. The grain size distribution seems to significantly change at $A_V \sim$ 3 mag, where both the average and dispersion of $P_{R_c}/P_{H}$ decrease. In addition, we found $\lambda_{max}$ $\sim$ 0.6-0.9 $\mu$m for $A_V>2.5$ mag, which is larger than the $\sim$ 0.55 $\mu$m in the general ISM, suggesting that grain growth has already started in low $A_V$ regions. Our data also reveal that polarization efficiency (PE $\equiv P_{\lambda}/A_V$) decreases with $A_V$ as a power-law in $R_c$-, $i'$-, and $K$-bands with indices of -0.71$\pm$0.10, -1.23$\pm$0.10 and -0.53$\pm$0.09. However, $H$-band data show a power index change; the PE varies with $A_V$ steeply (index of -0.95$\pm$0.30) when $A_V < 2.88\pm0.67$ mag but softly (index of -0.25$\pm$0.06) for greater $A_V$ values. The soft decay of PE in high $A_V$ regions is consistent with the Radiative Aligned Torque model, suggesting that our data trace the magnetic field to $A_V \sim 20$ mag. Furthermore, the breakpoint found in $H$-band is similar to the $A_V$ where we found the $P_{R_c}/P_{H}$ dispersion significantly decreased. Therefore, the flat PE-$A_V$ in high $A_V$ regions implies that the power index changes result from additional grain growth.Comment: 31 pages, 17 figures, and 3 tables; accepted for publication in Ap

Web-based computer adaptive assessment of individual perceptions of job satisfaction for hospital workplace employees

<p>Abstract</p> <p>Background</p> <p>To develop a web-based computer adaptive testing (CAT) application for efficiently collecting data regarding workers' perceptions of job satisfaction, we examined whether a 37-item Job Content Questionnaire (JCQ-37) could evaluate the job satisfaction of individual employees as a single construct.</p> <p>Methods</p> <p>The JCQ-37 makes data collection via CAT on the internet easy, viable and fast. A Rasch rating scale model was applied to analyze data from 300 randomly selected hospital employees who participated in job-satisfaction surveys in 2008 and 2009 via non-adaptive and computer-adaptive testing, respectively.</p> <p>Results</p> <p>Of the 37 items on the questionnaire, 24 items fit the model fairly well. Person-separation reliability for the 2008 surveys was 0.88. Measures from both years and item-8 job satisfaction for groups were successfully evaluated through item-by-item analyses by using <it>t</it>-test. Workers aged 26 - 35 felt that job satisfaction was significantly worse in 2009 than in 2008.</p> <p>Conclusions</p> <p>A Web-CAT developed in the present paper was shown to be more efficient than traditional computer-based or pen-and-paper assessments at collecting data regarding workers' perceptions of job content.</p

A shallow physics-informed neural network for solving partial differential equations on surfaces

In this paper, we introduce a shallow (one-hidden-layer) physics-informed neural network for solving partial differential equations on static and evolving surfaces. For the static surface case, with the aid of level set function, the surface normal and mean curvature used in the surface differential expressions can be computed easily. So instead of imposing the normal extension constraints used in literature, we write the surface differential operators in the form of traditional Cartesian differential operators and use them in the loss function directly. We perform a series of performance study for the present methodology by solving Laplace-Beltrami equation and surface diffusion equation on complex static surfaces. With just a moderate number of neurons used in the hidden layer, we are able to attain satisfactory prediction results. Then we extend the present methodology to solve the advection-diffusion equation on an evolving surface with given velocity. To track the surface, we additionally introduce a prescribed hidden layer to enforce the topological structure of the surface and use the network to learn the homeomorphism between the surface and the prescribed topology. The proposed network structure is designed to track the surface and solve the equation simultaneously. Again, the numerical results show comparable accuracy as the static cases. As an application, we simulate the surfactant transport on the droplet surface under shear flow and obtain some physically plausible results