Evaluating cultural competency and patient satisfaction in an urban dermatology clinic.

Cultural competency continues to gain increased attention in medicine. Not only does it play a significant role in the delivery of health care and patient outcomes, but it also remains a major determinant of patient satisfaction. This study investigated how patients in an urban dermatology clinic rated their satisfaction with cultural competency. Compared to White patients, satisfaction scores were greater for Hispanic or Latino patients and less for Asian patients, while there was no significant difference for Black or African American patients. There were clear differences in patient satisfaction rates of various dimensions of cultural competency. A follow-up study with a larger sample size is needed for closer examination into the conclusions

Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep learning models using auxiliary hidden layers. Scalable MCMC is available for network training and inference. SDA provides a number of computational advantages over traditional algorithms, such as avoiding backtracking, local modes and can perform optimization with stochastic gradient descent (SGD) in TensorFlow. Standard deep neural networks with logit, ReLU and SVM activation functions are straightforward to implement. To illustrate our architectures and methodology, we use P\'{o}lya-Gamma logit data augmentation for a number of standard datasets. Finally, we conclude with directions for future research

X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2--6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.Comment: To appear in the 7th IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), 8 pages, 6 figures. Minor revisions, in response to reviewers' comment

Electrolyte effects on polyacrylic acid-polyvinylpyrrolidone aqueous glycol mixtures for use as de-icing fluids

Rheological and wind tunnels measurements are presented for mixtures of polymers polyacrylic acid [PAA] and polyvinylpyrrolidone [PVP] polymers dispersed in water-1,2 propylene glycol mixture to examine their use as potential aircraft de-icing fluids. PAA solutions which form the basis of de-icing fluids are known to result in undesirable gelation which may lead to undesirable and catastrophic consequences in such applications. In this study, we examine the blending of PVP with PAA blends as alternative de-icing fluid formulations that can reduce the likelihood of forming such irreversible gel deposits. Through adjustment of the electrolyte concentration, the ratio of PAA to PVP as well as the molecular weight of PVP, it is possible to achieve a required viscosity profile to that exhibited by a model de-icing fluid across a range of appropriate temperatures. Wind tunnel tests indicate that the mixtures are capable of meeting the necessary requirements for boundary layer depletion as well as having sufficient capability of retaining a stable layer required during aircraft taxiing

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc