FEASIBILITY OF AN OKLAHOMA FRESH GREENS AND COWPEAS PACKING COOPERATIVE

Oklahoma's green producers are not benefiting from a growing fresh market. In order to seize the opportunities offered by the growing fresh market for leafy greens, investment in packing facilities have been evaluated. To make use of these facilities during summer months, the addition of a cowpea shelling enterprise is considered. A business plan for a new generation cooperative is estimated using an updated version of "The Packing Simulation Model" (PACKSIM) The business associates PACKSIM with @RISK®, to incorporate risks in the financial analysis.Agribusiness,

Scattering for the Zakharov system in 3 dimensions

We prove global existence and scattering for small localized solutions of the Cauchy problem for the Zakharov system in 3 space dimensions. The wave component is shown to decay pointwise at the optimal rate of t^{-1}, whereas the Schr\"odinger component decays almost at a rate of t^{-7/6}.Comment: Minor changes and referee's comments include

Efficiency of antenatal care and childbirth services in selected primary health care facilities in rural Tanzania : a cross-sectional study

Background: Cost studies are paramount for demonstrating how resources have been spent and identifying opportunities for more efficient use of resources. The main objective of this study was to assess the actual dimension and distribution of the costs of providing antenatal care (ANC) and childbirth services in selected rural primary health care facilities in Tanzania. In addition, the study analyzed determining factors of service provision efficiency in order to inform health policy and planning. Methods: This was a retrospective quantitative cross-sectional study conducted in 11 health centers and dispensaries in Lindi and Mtwara rural districts. Cost analysis was carried out using step down cost accounting technique. Unit costs reflected efficiency of service provision. Multivariate regression analysis on the drivers of observed relative efficiency in service provision between the study facilities was conducted. Reported personnel workload was also described. Results: The health facilities spent on average 7 USD per capita in 2009. As expected, fewer resources were spent for service provision at dispensaries than at health centers. Personnel costs contributed a high approximate 44% to total costs. ANC and childbirth consumed approximately 11% and 12% of total costs; and 8% and 10% of reported service provision time respectively. On average, unit costs were rather high, 16 USD per ANC visit and 79.4 USD per childbirth. The unit costs showed variation in relative efficiency in providing the services between the health facilities. The results showed that efficiency in ANC depended on the number of staff, structural quality of care, process quality of care and perceived quality of care. Population-staff ratio and structural quality of basic emergency obstetric care services highly influenced childbirth efficiency. Conclusions: Differences in the efficiency of service provision present an opportunity for efficiency improvement. Taking into consideration client heterogeneity, quality improvements are possible and necessary. This will stimulate utilization of ANC and childbirth services in resource-constrained health facilities. Efficiency analyses through simple techniques such as measurement of unit costs should be made standard in health care provision, health managers can then use the performance results to gauge progress and reward efficiency through performance based incentives

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound

We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective.The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives -- both with uninformed (data-independent) and informed (data-dependent) priors

Shrinkers, expanders, and the unique continuation beyond generic blowup in the heat flow for harmonic maps between spheres

Using mixed analytical and numerical methods we investigate the development of singularities in the heat flow for corotational harmonic maps from the $d$-dimensional sphere to itself for $3\leq d\leq 6$. By gluing together shrinking and expanding asymptotically self-similar solutions we construct global weak solutions which are smooth everywhere except for a sequence of times $T_1<T_2<...<T_k<\infty$ at which there occurs the type I blow-up at one of the poles of the sphere. We show that in the generic case the continuation beyond blow-up is unique, the topological degree of the map changes by one at each blow-up time $T_i$, and eventually the solution comes to rest at the zero energy constant map.Comment: 24 pages, 8 figures, minor corrections, matches published versio

Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases

Phenotypic characteristics of the p.Asn215Ser (p.N215S) GLA mutation in male and female patients with Fabry disease: A multicenter Fabry Registry study.

BackgroundThe p.Asn215Ser or p.N215S GLA variant has been associated with late-onset cardiac variant of Fabry disease.MethodsTo expand on the scarce phenotype data, we analyzed natural history data from 125 p.N215S patients (66 females, 59 males) enrolled in the Fabry Registry (NCT00196742) and compared it with data from 401 patients (237 females, 164 males) harboring mutations associated with classic Fabry disease. We evaluated interventricular septum thickness (IVST), left ventricular posterior wall thickness (LVPWT), estimated glomerular filtration rate and severe clinical events.ResultsIn p.N215S males, mildly abnormal mean IVST and LVPWT values were observed in patients aged 25-34 years, and values gradually increased with advancing age. Mean values were similar to those of classic males. In p.N215S females, these abnormalities occurred primarily in patients aged 55-64 years. Severe clinical events in p.N215S patients were mainly cardiac (males 31%, females 8%) while renal and cerebrovascular events were rare. Renal impairment occurred in 17% of p.N215S males (mostly in patients aged 65-74 years), and rarely in females (3%).Conclusionp.N215S is a disease-causing mutation with severe clinical manifestations found primarily in the heart. Cardiac involvement may become as severe as in classic Fabry patients, especially in males

Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure

The Full-sky Astrometric Mapping Explorer -- Astrometry for the New Millennium

FAME is designed to perform an all-sky, astrometric survey with unprecedented accuracy. It will create a rigid astrometric catalog of 4x10^7 stars with 5 < m_V < 15. For bright stars, 5 < m_V < 9, FAME will determine positions and parallaxes accurate to < 50 microarcseconds, with proper motion errors < 50 microarcseconds/year. For fainter stars, 9 < m_V < 15, FAME will determine positions and parallaxes accurate to < 500 microarcseconds, with proper motion errors < 500 microarcseconds/year. It will also collect photometric data on these 4 x 10^7 stars in four Sloan DSS colors.Comment: 6 pages, 4 figures, to appear in "Working on the Fringe