Challenges Facing Public Higher Education

Across the nation, public colleges and universities are battling the effects of what President Obama has called “an economic crisis as deep and dire as any since the days of the Great Depression.” In many states, spending on higher education has been cut. Tuition increases have been imposed, placing greater demands on financial aid resources. Hiring freezes and/or personnel cuts have been implemented. Endowment values have dropped. And at many institutions, particularly community colleges, capacities and resources are strained by growing enrollments, as more people seek to retool skills, refashion careers, or gain some measure of job security

Bayes linear analysis for ordinary differential equations

Differential equation models are used in a wide variety of scientific fields to describe the behaviour of physical systems. Commonly, solutions to given systems of differential equations are not available in closed-form; in such situations, the solution to the system is generally approximated numerically. The numerical solution obtained will be systematically different from the (unknown) true solution implicitly defined by the differential equations. Even if it were known, this true solution would be an imperfect representation of the behaviour of the real physical system that it was designed to represent. A Bayesian framework is proposed which handles all sources of numerical and structural uncertainty encountered when using ordinary differential equation (ODE) models to represent real-world processes. The model is represented graphically, and the graph proves to be useful tool, both for deriving a full prior belief specification and for inferring model components given observations of the real system. A general strategy for modelling the numerical discrepancy induced through choice of a particular solver is outlined, in which the variability of the numerical discrepancy is fixed to be proportional to the length of the solver time-step and a grid-refinement strategy is used to study its structure in detail. A Bayes linear adjustment procedure is presented, which uses a junction tree derived from the originally specified directed graphical model to propagate information efficiently between model components, lessening the computational demands associated with the inference. The proposed framework is illustrated through application to two examples: a model for the trajectory of an airborne projectile moving subject to gravity and air resistance, and a model for the coupled motion of a set of ringing bells and the tower which houses them

Detecting Mobility Patterns in Mobile Phone Data from the Ivory Coast

This paper investigates the Data for Development (D4D) challenge [3], an open challenge set by the French mobile phone company, Orange, who have provided anonymized records of their customers in the Ivory Coast. This data spans a 5 month (150 day) horizon spread across 4 different sets containing antenna-to-antenna traffic, trace data for 50,000 customers at varying spatial resolution, and social graphs for 5,000 customers. By leveraging cloud-based and open-source analytics infrastructure to (1) merge the D4D datasets with Geographic Information System (GIS) data and (2) apply data mining algorithms, this paper presents a number of techniques for detecting mobility patterns of Orange customers in the Ivory Coast. By applying a k-medoid clustering algorithm to the antenna locations and their average distance to nearby antennas, we show how the high spatial resolution mobile phone dataset reveals a number of daily mobility patterns and properties, including trends in week-day versus weekend, public holiday mobility behavior, and distributional properties of daily trip distances across each cluster. With a view towards providing tools to assist with transport infrastructure planning, we combine the high spatial resolution D4D dataset with GIS data for transport infrastructure and demonstrate an approach for detecting whether a mobile phone user is traveling on a segment of transport infrastructure. This work culminates in a preliminary cloud-based GIS tool for visualizing mobility traces

A common cardiac sodium channel variant associated with sudden infant death in African Americans, SCN5A S1103Y.

Thousands die each year from sudden infant death syndrome (SIDS). Neither the cause nor basis for varied prevalence in different populations is understood. While 2 cases have been associated with mutations in type Valpha, cardiac voltage-gated sodium channels (SCN5A), the "Back to Sleep" campaign has decreased SIDS prevalence, consistent with a role for environmental influences in disease pathogenesis. Here we studied SCN5A in African Americans. Three of 133 SIDS cases were homozygous for the variant S1103Y. Among controls, 120 of 1,056 were carriers of the heterozygous genotype, which was previously associated with increased risk for arrhythmia in adults. This suggests that infants with 2 copies of S1103Y have a 24-fold increased risk for SIDS. Variant Y1103 channels were found to operate normally under baseline conditions in vitro. As risk factors for SIDS include apnea and respiratory acidosis, Y1103 and wild-type channels were subjected to lowered intracellular pH. Only Y1103 channels gained abnormal function, demonstrating late reopenings suppressible by the drug mexiletine. The variant appeared to confer susceptibility to acidosis-induced arrhythmia, a gene-environment interaction. Overall, homozygous and rare heterozygous SCN5A missense variants were found in approximately 5% of cases. If our findings are replicated, prospective genetic testing of SIDS cases and screening with counseling for at-risk families warrant consideration