Net Tuition Trends in the United States

This report documents the latest trends in net tuition for American higher education. Affordability has become a topic of concern for many, but there is a lack of information on the relevant concepts of tuition, primarily published vs. net. This report seeks to shed light on this topic.While few doubt that published tuition rates (sticker price) have been increasing at alarming rates, it is often argued that "net tuition," or the out of pocket expense, for students is much lower because of financial aid. This report shows that with few exceptions, financial aid has not increased sufficiently to offset increases in published tuition. In other words, the net tuition paid by students is higher now than it was five years ago

Fast Low-Rank Matrix Learning with Nonconvex Regularization

Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows the use of power method to approximate the SVD efficiently. Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace. Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed. Extensive experiments are performed on matrix completion and robust principal component analysis. The proposed method achieves significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.Comment: Long version of conference paper appeared ICDM 201

The near wall effect of synthetic jets in a boundary layer

Copyright @ 2007 Elsevier Inc. All rights reserved.An experimental investigation to analyse the qualitative near wall effect of synthetic jets in a laminar boundary layer has been undertaken for the purpose of identifying the types of vortical structures likely to have delayed separation on a 2D circular cylinder model described in this paper. In the first instance, dye visualisation of the synthetic jet was facilitated in conjunction with a stereoscopic imaging system to provide a unique quasi three-dimensional identification of the vortical structures. Secondly, the impact of synthetic jet structures along the wall was analysed using a thermochromic liquid crystal-based convective heat transfer sensing system in which, liquid crystals change colour in response to the thermal footprints of a passing flow structure. Of the different vortical structures produced as a result of varying actuator operating and freestream conditions, the footprints of hairpin vortices and stretched vortex rings revealed a marked similarity with the oil flow pattern of a vortex pair interacting with the separation line on the cylinder hence suggesting that either of these structures was responsible in delaying separation. Conditions were established for the formation of the different synthetic jet structures in non-dimensional parameter space

Multiple-Level Power Allocation Strategy for Secondary Users in Cognitive Radio Networks

In this paper, we propose a multiple-level power allocation strategy for the secondary user (SU) in cognitive radio (CR) networks. Different from the conventional strategies, where SU either stays silent or transmit with a constant/binary power depending on the busy/idle status of the primary user (PU), the proposed strategy allows SU to choose different power levels according to a carefully designed function of the receiving energy. The way of the power level selection is optimized to maximize the achievable rate of SU under the constraints of average transmit power at SU and average interference power at PU. Simulation results demonstrate that the proposed strategy can significantly improve the performance of SU compared to the conventional strategies.Comment: 12 page

Potential Maximization and Coalition Government Formation

A model of coalition government formation is presented in which inefficient, non-minimal winning coalitions may form in Nash equilibrium. Predictions for five games are presented and tested experimentally. The experimental data support potential maximization as a refinement of Nash equilibrium. In particular, the data support the prediction that non-minimal winning coalitions occur when the distance between policy positions of the parties is small relative to the value of forming the government. These conditions hold in games 1, 3, 4 and 5, where subjects played their unique potential-maximizing strategies 91, 52, 82 and 84 percent of the time, respectively. In the remaining game (Game 2) experimental data support the prediction of a minimal winning coalition. Players A and B played their unique potential-maximizing strategies 84 and 86 percent of the time, respectively, and the predicted minimal-winning government formed 92 percent of the time (all strategy choices for player C conform with potential maximization in Game 2). In Games 1, 2, 4 and 5 over 98 percent of the observed Nash equilibrium outcomes were those predicted by potential maximization. Other solution concepts including iterated elimination of dominated strategies and strong/coalition proof Nash equilibrium are also tested.Coalition formation, Potential maximization, Nash equilibrium refinements, Experimental study, Minimal winning

Large Volume HgI2 Gamma‐Ray Spectrometers

This paper demonstrates the enhanced capability of single polarity charge sensing, the 3‐dimensional position sensing technique, developed at the University of Michigan and previously successfully demonstrated on CdZnTe detectors, to improve the spectroscopic performance of HgI2 and to extend its range for spectrometry to an unprecedented thickness of 10 mm. Energy resolutions of close to 1% FWHM at 662 keV gamma‐ray energy were obtained from individual depth locations underneath pixel anodes, and 1.4–2.0% FWHM energy resolutions from 5 out of 6 tested pixel anodes on two 10 mm thick detectors. © 2002 American Institute of PhysicsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87582/2/113_1.pd

Giovanni - The Bridge Between Data and Science

This article describes new features in the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), a user-friendly online tool that enables visualization, analysis, and assessment of NASA Earth science data sets without downloading data and software. Since the satellite era began, data collected from Earth-observing satellites have been widely used in research and applications; however, using satellite-based data sets can still be a challenge to many. To facilitate data access and evaluation, as well as scientific exploration and discovery, the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) has developed Giovanni for a wide range of users around the world. This article describes the latest capabilities of Giovanni with examples, and discusses future plans for this innovative system

Py2Cy: A Genetic Improvement Tool To Speed Up Python

Due to its ease of use and wide range of custom libraries, Python has quickly gained popularity and is used by a wide range of developers all over the world. While Python allows for fast writing of source code, the resulting programs are slow to execute when compared to programs written in other programming languages like C. One of the reasons for its slow execution time is the dynamic typing of variables. Cython is an extension to Python, which can achieve execution speed-ups by compiler optimization. One possibility for improvements is the use of static typing, which can be added to Python scripts by developers. To alleviate the need for manual effort, we create Py2Cy, a Genetic Improvement tool for automatically converting Python scripts to statically typed Cython scripts. To show the feasibility of improving runtime with Py2Cy, we optimize a Python script for generating Fibonacci numbers. The results show that Py2Cy is able to speed up the execution time by up to a factor of 18