Socio-Demographic and Economic Determinants of Food Deserts

In this paper we utilized a panel data set from 2004 to 2010 to identify and determine the demographic and economic drivers of food deserts in both urban and rural areas in Arkansas. We defined food deserts as areas where access to healthy foods such as fresh vegetables and fruits are limited. More specifically, separate distance measures from the census block centroid to the nearest supermarket or grocery store were used to determine if the area is an urban food desert (1 mile) or rural food desert (10 miles). These distance measures were then aggregated at the census block group level. Locations of supermarkets and big grocery stores that provide fresh produce were geocoded (latitude and longitude) accordingly. Socio-demographic and economic variables at the census block group level were then matched with the distance information. These variables were from Census 2000 Summary File 3. Finally, we employed multivariate regression approaches to model the relationship between socio-demographic and economic factors and the existence of urban and rural food deserts in Arkansas. We found that block groups with deprived situation, such as less per capita income, higher unemployment, and less educational attainment, will be more likely to be food deserts

Quantile regression in partially linear varying coefficient models

Semiparametric models are often considered for analyzing longitudinal data for a good balance between flexibility and parsimony. In this paper, we study a class of marginal partially linear quantile models with possibly varying coefficients. The functional coefficients are estimated by basis function approximations. The estimation procedure is easy to implement, and it requires no specification of the error distributions. The asymptotic properties of the proposed estimators are established for the varying coefficients as well as for the constant coefficients. We develop rank score tests for hypotheses on the coefficients, including the hypotheses on the constancy of a subset of the varying coefficients. Hypothesis testing of this type is theoretically challenging, as the dimensions of the parameter spaces under both the null and the alternative hypotheses are growing with the sample size. We assess the finite sample performance of the proposed method by Monte Carlo simulation studies, and demonstrate its value by the analysis of an AIDS data set, where the modeling of quantiles provides more comprehensive information than the usual least squares approach.Comment: Published in at http://dx.doi.org/10.1214/09-AOS695 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multi-Adversarial Domain Adaptation

Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators. The adaptation can be achieved by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets.Comment: AAAI 2018 Oral. arXiv admin note: substantial text overlap with arXiv:1705.10667, arXiv:1707.0790

Self-pollination by sliding pollen in Caulokaempferia coenobialis (Zingiberaceae)

Caulokaempferia coenobialis (Zingiberaceae) forms dense populations on steep cliffs in shady, humid monsoon forests in south China. It produces few consecutively opening bright yellow flowers that are 3 cm long and oriented parallel to the ground. Upon anther dehiscence at about 0600 hours, each pollen sac releases a drop of pollen onto the horizontally oriented style, and the two drops then merge to form an oily film that slowly flows toward the stigma, carrying out self-pollination between about 1500 and 0730 hours the next day. The distance covered by the pollen film is ca. 3 mm. There is no significant difference in fruit set between experimentally cross- and self-pollinated flowers or between naturally pollinated and bagged flowers. The low pollen/ovule ratio of 664 probably relates to the pollen grains being held together by pollen-connecting threads. The latter ensure that pollen grains always arrive as multiples, and this is the first report of such threads in the Zingiberaceae. During 35 h of observation at several locations and during three flowering periods, only three individual bees, five flies, and two butterflies visited single flowers. It remained unclear whether they affected pollination because no return visits were observed. The automatic selfing by pollen that reaches the stigma ca. 9 h after the onset of anthesis apparently constitutes a case of delayed selfing, providing reproductive reassurance in situations of low pollinator visitation

Ribosome profiling reveals principles of translatome and transcriptome evolution in mammalian organs

A primary goal in evolutionary biology is to understand the molecular basis responsible for phenotypic differences between species, most notably between humans and other species. Regulatory mutations affecting gene expression likely underlie most phenotypic changes. Re-cent evolutionary studies of mammalian transcriptomes have provided initial insights into mammalian gene expression evolution. However, mRNA levels are, in general, limited proxies for protein levels due to a sequence of regulations that succeed transcription. The fact that the evolution of mammalian translatomes or proteomes is essentially unexplored has severely lim-ited our understanding of gene expression evolution and its phenotypic implications. To fill this gap and explore the co-evolution of regulatory processes across the transcriptome and translatome layers of gene expression, we generated, in the framework of my thesis pro-ject, ribosome profiling (high-throughput sequencing of ribosome-protected fragments) and matched RNA sequencing data for three major mammalian organs (brain, liver, testis) from representatives of all major mammalian lineages (human, macaque, mouse, opossum, platy-pus) and a bird (chicken), which serves as an evolutionary outgroup. My analyses identified strong and highly differential patterns of translational buffering among organs, gene classes and chromosomes. Specifically, to assess the extent to which transcrip-tional changes of individual genes are reflected at the level of protein synthesis, we devised a "translational tuning index" (TTI), and found that translational forces frequently counteracted but rarely boosted transcriptional changes. Expression changes of functionally cooperating genes tend to be balanced by concerted (modular) translational changes to preserve ancestral cellular stoichiometries. Contrary to individual gene compensation, this concerted buffering is more pronounced in brain and liver than in testis. By contrasting the evolutionary dynamics of transcriptomes and translatomes, my analyses furthermore revealed that the widespread translational buffering more strongly preserved dosage-sensitive and, especially, housekeeping genes. I also found that translational upregulation acts to globally counterbalance the global dosage reduction that arose in the wake of mammalian sex chromosome differentiation; trans-lational buffering thus represents a novel mechanism for X chromosome dosage compensation. In summary, my PhD thesis work revealed that fine-tuned translational buffering substantially stabilized gene expression levels during mammalian evolution

Numerical simulation of the non-uniform flow in a full-annulus multi-stage axial compressor with the harmonic balance method

To improve the understanding of unsteady flow in modern advanced axial compressor, unsteady simulations on full-annulus multi-stage axial compressor are carried out with the harmonic balance method. Since the internal flow in turbomachinery is naturally periodic, the harmonic balance method can be used to reduce the computational cost. In order to verify the accuracy of the harmonic balance method, the numerical results are first compared with the experimental results. The results show that the internal flow field and the operating characteristics of the multi-stage axial compressor obtained by the harmonic balance method coincide with the experimental results with the relative error in the range of 3%. Through the analysis of the internal flow field of the axial compressor, it can be found that the airflow in the clearance of adjacent blade rows gradually changes from axisymmetric to non-axisymmetric and then returns to almost completely axisymmetric distribution before the downstream blade inlet, with only a slight non-axisymmetric distribution, which can be ignored. Moreover, the slight non-axisymmetric distribution will continue to accumulate with the development of the flow and, finally, form a distinct circumferential non-uniform flow field in latter stages, which may be the reason why the traditional single-passage numerical method will cause certain errors in multi-stage axial compressor simulations

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

DOI 10.1007/s10463-007-0158-9

Local influence analysis for penalized Gaussian likelihood estimation in partially linear single-index model