108 research outputs found
An Assessment-Informed Collaborative Initiative: Curriculum Mapping for PhD Program Improvement
Faculty and doctoral students of the Department of Urban and Regional Planning (DURP) initiated a review of their PhD program requirements and curriculum. This poster highlights the usefulness of faculty-student collaboration for program assessment to enable program improvement. The assessment coordinator organized a colloquium in which PhD candidates presented their dissertation research. Faculty evaluated the presentations based on the PhD program student learning outcomes (SLOs). Written comments from faculty evaluators were then coded as strengths and weaknesses corresponding to each SLO component to generate a preliminary qualitative assessment. At a subsequent meeting, faculty and doctoral students reviewed the preliminary qualitative assessment, PhD curriculum, core course syllabi, and PhD guidelines. They identified shortcomings in the existing curriculum, mapped degree requirements and program SLOs, and proposed revisions to better align the curriculum with program SLOs. They also outlined next steps toward improving the curriculum such as increasing the number of core course credits and providing more teaching opportunities for PhD candidates through summer school and online courses
Black-box optimization on hyper-rectangle using Recursive Modified Pattern Search and application to ROC-based Classification Problem
In Statistics, multi-modal and non-smooth likelihood (or, objective function)
maximization problems often arise with known upper and lower bound of the
parameters. A novel derivative-free global optimization technique is developed
to optimize any black-box function on a hyper-rectangular euclidean space. In
literature, pattern search technique has been shown to be a powerful tool for
blackbox optimization. The proposed algorithm follows the principle of pattern
search technique where new updated solution is obtained from the current
solution making movements (within the constrained sample space) along the
coordinates. Before making a jump from the current solution point to a new
solution point, objective function is evaluated in several neighborhood points
around the current solution and the best solution point is chosen based on the
objective function values at those points. Parallel threading can be used to
make the algorithm more scalable. Performance of the proposed method is
evaluated based on optimization of upto 5000 dimensional multi-modal benchmark
functions. The proposed algorithm is shown to perform upto 40 and 368 times
faster compared to Genetic Algorithm (GA) and Simulated Annealing (SA)
respectively. The proposed method is used to estimate the optimal biomarker
combination from Alzheimer data by maximizing the empirical estimates of area
under ROC curve
Topology of Quantum Grey Soliton in Multi-Component Inhomogeneous Bose-Einstein Condensates
We study the dispersion mechanism of Lieb mode excitations of both single and
multi component ultra-cold atomic Bose gas, subject to a harmonic confinement
through chirp management. It is shown that in some parameter domain, the
hole-like excitations lead to the soliton's negative mass regime, arising due
to the coupling between chirp momentum and Kohn mode. In low momenta region the
trap considerably affects the dispersion of the grey soliton, which opens a new
window to observe Lieb-mode excitations. Further, we extend our analysis to
binary condensate, which yields usual shape compatible grey-bright soliton
pairs. The inter-species interaction induces a shift in the Lieb-mode
excitations, where the pair can form a bound state. We emphasize that the
present model provides an opportunity to study such excitations in the low
momenta regime, as well as the formation of bound state in binary condensate.Comment: 9 pages, 7 figure
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