Regularization and Kernelization of the Maximin Correlation Approach

Robust classification becomes challenging when each class consists of multiple subclasses. Examples include multi-font optical character recognition and automated protein function prediction. In correlation-based nearest-neighbor classification, the maximin correlation approach (MCA) provides the worst-case optimal solution by minimizing the maximum misclassification risk through an iterative procedure. Despite the optimality, the original MCA has drawbacks that have limited its wide applicability in practice. That is, the MCA tends to be sensitive to outliers, cannot effectively handle nonlinearities in datasets, and suffers from having high computational complexity. To address these limitations, we propose an improved solution, named regularized maximin correlation approach (R-MCA). We first reformulate MCA as a quadratically constrained linear programming (QCLP) problem, incorporate regularization by introducing slack variables in the primal problem of the QCLP, and derive the corresponding Lagrangian dual. The dual formulation enables us to apply the kernel trick to R-MCA so that it can better handle nonlinearities. Our experimental results demonstrate that the regularization and kernelization make the proposed R-MCA more robust and accurate for various classification tasks than the original MCA. Furthermore, when the data size or dimensionality grows, R-MCA runs substantially faster by solving either the primal or dual (whichever has a smaller variable dimension) of the QCLP.Comment: Submitted to IEEE Acces

Dissecting the Kappa Opiod Receptor System in Pain Induced Negative Affects

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor(s): Jose MOron-Concepcio

Gradient-based Inference for Networks with Output Constraints

Practitioners apply neural networks to increasingly complex problems in natural language processing, such as syntactic parsing and semantic role labeling that have rich output structures. Many such structured-prediction problems require deterministic constraints on the output values; for example, in sequence-to-sequence syntactic parsing, we require that the sequential outputs encode valid trees. While hidden units might capture such properties, the network is not always able to learn such constraints from the training data alone, and practitioners must then resort to post-processing. In this paper, we present an inference method for neural networks that enforces deterministic constraints on outputs without performing rule-based post-processing or expensive discrete search. Instead, in the spirit of gradient-based training, we enforce constraints with gradient-based inference (GBI): for each input at test-time, we nudge continuous model weights until the network's unconstrained inference procedure generates an output that satisfies the constraints. We study the efficacy of GBI on three tasks with hard constraints: semantic role labeling, syntactic parsing, and sequence transduction. In each case, the algorithm not only satisfies constraints but improves accuracy, even when the underlying network is state-of-the-art.Comment: AAAI 201

The State of Health Insurance in California: Findings from the 2005 California Health Interview Survey

Finds that eight in ten of the state's uninsured are employed, and that job-based health insurance coverage in California continues to decline

Dynamic modelling of a fractionation process for a liquid mixture using supercritical carbon dioxide

This work presents a simple dynamic modelling of a process of separation of a quaternary mixture using supercritical CO2. Thermodynamic description is accomplished using efficient available models (SRK equation of state with MHV2 mixing rules). An approximate approach was compared to the rigorous resolution of the system of algebro-differential equations, and was shown to enable a correct description of the dynamic behaviour. The modelling was compared to experiments performed on a small pilot composed of one 200-ml contactor and a cascade of three cyclonic separators. Good results were obtained for the contactor, although they were not very satisfactory for the description of the fractionation in the cyclonic separators. Even if discrepancies between experimental and calculated results may probably originate from the experimental procedure, the hydrodynamic description of the separators here is likely to be oversimplified. The cyclonic separator cannot be regarded as a simple theoretical stage (TSM), and we have proposed an alternate description (EPSM), that, although more suitable, still needs to be improved

Comparison of two dose and three dose human papillomavirus vaccine schedules: cost effectiveness analysis based on transmission model.

OBJECTIVE: To investigate the incremental cost effectiveness of two dose human papillomavirus vaccination and of additionally giving a third dose. DESIGN: Cost effectiveness study based on a transmission dynamic model of human papillomavirus vaccination. Two dose schedules for bivalent or quadrivalent human papillomavirus vaccines were assumed to provide 10, 20, or 30 years' vaccine type protection and cross protection or lifelong vaccine type protection without cross protection. Three dose schedules were assumed to give lifelong vaccine type and cross protection. SETTING: United Kingdom. POPULATION: Males and females aged 12-74 years. INTERVENTIONS: No, two, or three doses of human papillomavirus vaccine given routinely to 12 year old girls, with an initial catch-up campaign to 18 years. MAIN OUTCOME MEASURE: Costs (from the healthcare provider's perspective), health related utilities, and incremental cost effectiveness ratios. RESULTS: Giving at least two doses of vaccine seems to be highly cost effective across the entire range of scenarios considered at the quadrivalent vaccine list price of £86.50 (€109.23; $136.00) per dose. If two doses give only 10 years' protection but adding a third dose extends this to lifetime protection, then the third dose also seems to be cost effective at £86.50 per dose (median incremental cost effectiveness ratio £17,000, interquartile range £11,700-£25,800). If two doses protect for more than 20 years, then the third dose will have to be priced substantially lower (median threshold price £31, interquartile range £28-£35) to be cost effective. Results are similar for a bivalent vaccine priced at £80.50 per dose and when the same scenarios are explored by parameterising a Canadian model (HPV-ADVISE) with economic data from the United Kingdom. CONCLUSIONS: Two dose human papillomavirus vaccine schedules are likely to be the most cost effective option provided protection lasts for at least 20 years. As the precise duration of two dose schedules may not be known for decades, cohorts given two doses should be closely monitored

Master teachers as teacher leaders: evidence from Malaysia and the Philippines

The career paths of teachers in most countries lead to talented practitioners progressively reducing their classroom work to take on leadership and management responsibilities culminating in headship. Some education systems seek to keep good teachers in classrooms by offering alternative promoted posts, often described as master teachers. This article presents evidence of the role of master teacher in two underpublished Asia-Pacific contexts: Malaysia and the Philippines. Drawing on interviews with master teachers, and their principals and colleagues, the article provides a picture of the activities and role relationships of these senior practitioners. The findings show that the master teacher role largely succeeds in keeping talented and ambitious teachers in the classroom, but there is only limited evidence of a wider impact on colleagues, schools and the education system