A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine

Machine learning methods are used today for most recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model. We use Artificial Immune System Principles to enhance small size of training data set. A layer of Clonal Selection is added to the local filtering and max pooling of CNN Architecture. The proposed Architecture is evaluated using the standard MNIST dataset by limiting the data size and also with a small personal data sample belonging to two different classes. Experimental results show that the proposed hybrid CNN-AIS based recognition engine works well when the size of training data is limited in siz

Partial Schauder estimates for second-order elliptic and parabolic equations

We establish Schauder estimates for both divergence and non-divergence form second-order elliptic and parabolic equations involving H\"older semi-norms not with respect to all, but only with respect to some of the independent variables.Comment: CVPDE, accepted (2010)

Missouri 2011 Soft Red Winter Wheat Performance Tests

This report is published by the MU Variety Testing Program, Division of Plant Sciences, University of Missouri. The work was supported by fees from companies and organizations submitting varieties for evaluation. The large number of varieties available makes selection of a superior variety difficult. To select intelligently, producers need a reliable, unbiased, up-to-date source of information that will permit valid comparisons among available varieties. The objective of the MU Variety Testing Program is to provide this information. Tests are conducted under as close to uniform conditions as possible. Small plots are used to reduce the chance of soil and other variations occurring among variety plots. Results obtained should aid individual growers in judging the relative merits of many of the commercial wheat varieties available in Missouri

Overlapping fern and Bryophyte hotspots: Assessing ferns as a predictor of Bryophyte diversity

Bryophytes are significant contributors to floristic diversity, but they are often neglected in field surveys and collections. Thus, in order to obtain more accurate estimates of plant richness, there must be reliable estimates of bryophyte diversity. To address this, we examined whether another plant group, namely the ferns, could be used as a surrogate for bryophytes. We used datasets spanning the entire Australian continent for mosses, liverworts, liverworts+hornworts, ferns, and conifers (hornworts were aggregated into the group liverworts+hornworts). Two measures of richness were examined across the continent (as 50 km × 50 km grid cells): uncorrected richness and sample-standardised richness. We calculated the correlations among richness of all of the groups to test the hypothesis that fern diversity predicts bryophyte diversity (because of shared ecological preferences) while conifer diversity does not. Conifers showed very little correlation to either of the four plant groups, whereas ferns were highly correlated to mosses and to a lesser extent to liverworts and liverworts+hornworts. Liverworts, as well as liverworts+hornworts, and mosses were also strongly correlated. These results indicate that surrogates can assist in estimating the diversity and the conservation of other poorly collected plant groups

Prevalence and Determinants of Obesity among Primary School Children in Dar es Salaam, Tanzania.

Childhood obesity has increased dramatically and has become a public health concern worldwide. Childhood obesity is likely to persist through adulthood and may lead to early onset of NCDs. However, there is paucity of data on obesity among primary school children in Tanzania. This study assessed the prevalence and determinants of obesity among primary school children in Dar es Salaam. A cross sectional study was conducted among school age children in randomly selected schools in Dar es Salaam. Anthropometric and blood pressure measurements were taken using standard procedures. Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Child obesity was defined as BMI at or above 95th percentile for age and sex. Socio-demographic characteristics of children were determined using a structured questionnaire. Logistic regression was used to determine association between independent variables with obesity among primary school children in Dar es Salaam. A total of 446 children were included in the analysis. The mean age of the participants was 11.1±2.0 years and 53.1% were girls. The mean BMI, SBP and DBP were 16.6±4.0 kg/m2, 103.9±10.3mmHg and 65.6±8.2mmHg respectively. The overall prevalence of child obesity was 5.2% and was higher among girls (6.3%) compared to boys (3.8%). Obese children had significantly higher mean values for age (p=0.042), systolic and diastolic blood pressures (all p<0.001). Most obese children were from households with fewer children (p=0.019) and residing in urban areas (p=0.002). Controlling for other variables, age above 10 years (AOR=3.3, 95% CI=1.5-7.2), female sex (AOR=2.6, 95% CI=1.4-4.9), urban residence (AOR=2.5, 95% CI=1.2-5.3) and having money to spend at school (AOR=2.6, 95% CI=1.4-4.8) were significantly associated with child obesity. The prevalence of childhood obesity in this population was found to be low. However, children from urban schools and girls were proportionately more obese compared to their counterparts. Primary preventive measures for childhood obesity should start early in childhood and address socioeconomic factors of parents contributing to childhood obesity

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure