Low resolution face recognition using a two-branch deep convolutional neural network architecture

We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality

Pressure Gradients Driving Ion Transport in the Topside Martian Atmosphere

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Magnetic and thermal pressure gradient forces drive plasma flow in the topside ionosphere of Mars. Some of this flow can contribute to ion loss from the planet and thus affect atmospheric evolution. MAVEN measurements of the magnetic field, electron density, and electron temperature, taken over a 3‐year time period, are used to obtain averaged magnetic and thermal pressures in the topside ionosphere versus altitude, solar zenith angle, and latitude. Magnetic pressures are several times greater than thermal pressures for altitudes greater than about 300 km; that is, the plasma beta is less than one. The total pressure increases with altitude in the ionosphere and decreases with increasing solar zenith angle. Using these pressure patterns in the dayside ionosphere to estimate the pressure gradient force in the fluid momentum equation, we estimate horizontal day‐to‐night plasma flow speeds of a few kilometers per second near 400 km

Wake and power prediction of horizontal-axis wind farm under yaw-controlled conditions with machine learning

The main objective of this study is to employ the Extreme Gradient Boosting (XGBoost) machine learning algorithm to predict the power, wake, and turbulent characteristics of horizontal-axis wind farms under yaw-controlled conditions. For this purpose, a series of high-fidelity numerical simulations using LES method are performed over tandem NREL-5 MW wind turbines to generate the input data for training and testing in machine learning analysis. It is observed that XGBoost is more accurate for wake prediction of the yaw-controlled wind farms compared to ANN, which was used in previous studies. The results illustrate that XGBoost can predict the power with a mean deviation of 0.94 % for different yaw angles, while ANN can estimate the power generation with a mean deviation of 2.15 % for various tested yaw angles. At far wake regions (X > 2000 m) of the second wind turbine, the deviations reach below 1 %. Moreover, XGBoost requires a much shorter training time, 87.5 % faster than ANN. The power production of both wind turbines can be predicted more accurately with XGBoost compared to ANN. The wake prediction time of XGBoost is just 0.105 sec, while this time is 4.480 for the ANN model. In conclusion, XGBoost provides a significant reduction in error and training time compared to ANN and deep learning algorithms over yaw-misaligned wind farms

A scaling procedure for straightforward computation of sorptivity

This research has been supported by the Agence Nationale de la Recherche (grant no. ANR-17-CE04-010).Sorptivity is a parameter of primary importance in the study of unsaturated flow in soils. This hydraulic parameter is required to model water infiltration into vertical soil profiles. Sorptivity can be directly estimated from the soil hydraulic functions (water retention and hydraulic conductivity curves), using the integral formulation of Parlange (1975). However, calculating sorptivity in this manner requires the prior determination of the soil hydraulic diffusivity and its numerical integration between initial and final saturation degrees, which may be difficult in some situations (e.g., coarse soil with diffusivity functions that are quasi-infinite close to saturation). In this paper, we present a procedure to compute sorptivity using a scaling parameter, cp, that corresponds to the sorptivity of a unit soil (i.e., unit values for all parameters and zero residual water content) that is utterly dry at the initial state and saturated at the final state. The cp parameter was computed numerically and analytically for five hydraulic models: delta (i.e., Green and Ampt), Brooks and Corey, van Genuchten–Mualem, van Genuchten–Burdine, and Kosugi. Based on the results, we proposed brand new analytical expressions for some of the models and validated previous formulations for the other models. We also tabulated the output values so that they can easily be used to determine the actual sorptivity value for any case. At the same time, our numerical results showed that the relation between cp and the hydraulic shape parameters strongly depends on the chosen model. These results highlight the need for careful selection of the proper model for the description of the water retention and hydraulic conductivity functions when estimating sorptivity.French National Research Agency (ANR) European Commission ANR-17-CE04-01

SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors

© 2019 China University of Geosciences (Beijing) and Peking University The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources. Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner. Hence, this study aimed at developing a user-friendly geographic information system (GIS) tool, Sub-Watershed Prioritization Tool (SWPT), using the Python programming language to decrease any possible uncertainty. It used geospatial–statistical techniques for analyzing morphometric and topo-hydrological factors and automatically identifying critical and priority sub-watersheds. In order to assess the capability and reliability of the SWPT tool, it was successfully applied in a watershed in the Golestan Province, Northern Iran. Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds. It provided a cost-effective approach for prioritization of sub-watersheds. Therefore, the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed

Hot oxygen escape from Mars: Simple scaling with solar EUV irradiance

The evolution of the atmosphere of Mars and the loss of volatiles over the lifetime of the solar system is a key topic in planetary science. An important loss process for atomic species, such as oxygen, is ionospheric photochemical escape. Dissociative recombination of O2+ ions (the major ion species) produces fast oxygen atoms, some of which can escape from the planet. Many theoretical hot O models have been constructed over the years, although a number of uncertainties are present in these models, particularly concerning the elastic cross sections of O atoms with CO2. Recently, the Mars Atmosphere and Volatile Evolution mission has been rapidly improving our understanding of the upper atmosphere and ionosphere of Mars and its interaction with the external environment (e.g., solar wind), allowing a new assessment of this important loss process. The purpose of the current paper is to take a simple analytical approach to the oxygen escape problem in order to (1) study the role that variations in solar radiation or solar wind fluxes could have on escape in a transparent fashion and (2) isolate the effects of uncertainties in oxygen cross sections on the derived oxygen escape rates. In agreement with several more elaborate numerical models, we find that the escape flux is directly proportional to the incident solar extreme ultraviolet irradiance and is inversely proportional to the backscatter elastic cross section. The amount of O lost due to ion transport in the topside ionosphere is found to be about 5–10% of the total.Key PointsPhotochemistry dominates oxygen escape from MarsMartian oxygen escape rate scales linearly with solar activityDependence of O escape rate from Mars on elastic cross section is describedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136315/1/jgra53155.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136315/2/jgra53155_am.pd

The Effects of Selenium Supplementation on Gene Expression Related to Insulin and Lipid in Infertile Polycystic Ovary Syndrome Women Candidate for In Vitro Fertilization: a Randomized, Double-Blind, Placebo-Controlled Trial

Abstract This study was conducted to evaluate the effects of selenium supplementation on gene expression related to insulin and lipid in infertile women with polycystic ovary syndrome (PCOS) candidate for in vitro fertilization (IVF). This randomized double-blind, placebo-controlled trial was conducted among 40 infertile women with PCOS candidate for IVF. Subjects were randomly allocated into two groups to intake either 200-μg selenium (n = 20) or placebo (n = 20) per day for 8 weeks. Gene expression levels related to insulin and lipid were quantified in lymphocytes of women with PCOS candidate for IVF with RT-PCR method. Results of RT-PCR demonstrated that after the 8-week intervention, compared with the placebo, selenium supplementation upregulated gene expression of peroxisome proliferator-activated receptor gamma (PPAR-γ) (1.06 ± 0.15-fold increase vs. 0.94 ± 0.18-fold reduction, P = 0.02) and glucose transporter 1 (GLUT-1) (1.07 ± 0.20-fold increase vs. 0.87 ± 0.18-fold reduction, P = 0.003) in lymphocytes of women with PCOS candidate for IVF. In addition, compared with the placebo, selenium supplementation downregulated gene expression of low-density lipoprotein receptor (LDLR) (0.88 ± 0.17-fold reduction vs. 1.05 ± 0.22-fold increase, P = 0.01) in lymphocytes of women with PCOS candidate for IVF. We did not observe any significant effect of selenium supplementation on gene expression levels of lipoprotein(a) [LP(a)] in lymphocytes of women with PCOS candidate for IVF. Overall, selenium supplementation for 8 weeks in lymphocytes of women with infertile PCOS candidate for IVF significantly increased gene expression levels of PPAR-γ and GLUT-1 and significantly decreased gene expression levels of LDLR, but did not affect LP(a). Keywords Selenium supplementation Gene expression Insulin Lipid Polycystic ovary syndrom

Mixed formulation for an easy and robust numerical computation of sorptivity

Sorptivity is one of the most important parameters for the quantification of water infiltration into soils. proposed a specific formulation to derive sorptivity as a function of the soil water retention and hydraulic conductivity functions, as well as initial and final soil water contents. However, this formulation requires the integration of a function involving hydraulic diffusivity, which may be undefined or present numerical difficulties that cause numerical misestimations. In this study, we propose a mixed formulation that scales sorptivity and splits the integrals into two parts: the first term involves the scaled degree of saturation, while the second involves the scaled water pressure head. The new mixed formulation is shown to be robust and well-suited to any type of hydraulic function - even with infinite hydraulic diffusivity or positive air-entry water pressure heads - and any boundary condition, including infinite initial water pressure head, h→-∞. Lastly, we show the benefits of using the proposed formulation for modeling water into soil with analytical models that use sorptivity. Copyright

The source counts of submillimetre galaxies detected at 1.1 mm

The source counts of galaxies discovered at sub-millimetre and millimetre wavelengths provide important information on the evolution of infrared-bright galaxies. We combine the data from six blank-field surveys carried out at 1.1 mm with AzTEC, totalling 1.6 square degrees in area with root-mean-square depths ranging from 0.4 to 1.7 mJy, and derive the strongest constraints to date on the 1.1 mm source counts at flux densities S(1100) = 1-12 mJy. Using additional data from the AzTEC Cluster Environment Survey to extend the counts to S(1100) ~ 20 mJy, we see tentative evidence for an enhancement relative to the exponential drop in the counts at S(1100) ~ 13 mJy and a smooth connection to the bright source counts at >20 mJy measured by the South Pole Telescope; this excess may be due to strong lensing effects. We compare these counts to predictions from several semi-analytical and phenomenological models and find that for most the agreement is quite good at flux densities > 4 mJy; however, we find significant discrepancies (>3sigma) between the models and the observed 1.1 mm counts at lower flux densities, and none of them are consistent with the observed turnover in the Euclidean-normalised counts at S(1100) < 2 mJy. Our new results therefore may require modifications to existing evolutionary models for low luminosity galaxies. Alternatively, the discrepancy between the measured counts at the faint end and predictions from phenomenological models could arise from limited knowledge of the spectral energy distributions of faint galaxies in the local Universe.Comment: 16 pages, 3 figures, 4 tables; accepted for publication in MNRA