Physical Sciences and Mathematics Commons^™

Kaczmarz Algorithm With Soft Constraints For User Interface Layout, Noreen Jamil, Deanna Needell, Johannes Muller, Christof Lutteroth, Gerald Weber

CMC Faculty Publications and Research

The Kaczmarz method is an iterative method for solving large systems of equations that projects iterates orthogonally onto the solution space of each equation. In contrast to direct methods such as Gaussian elimination or QR-factorization, this algorithm is efficient for problems with sparse matrices, as they appear in constraint-based user interface (UI) layout specifications. However, the Kaczmarz method as described in the literature has its limitations: it considers only equality constraints and does not support soft constraints, which makes it inapplicable to the UI layout problem.

In this paper we extend the Kaczmarz method for solving specifications containing soft constraints, …

Signal Space Cosamp For Sparse Recovery With Redundant Dictionaries, Mark A. Davenport, Deanna Needell, Michael B. Wakin

CMC Faculty Publications and Research

Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis. In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary. Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well conditioned, but these approaches fail to address the case of a truly …

Using Correlated Subset Structure For Compressive Sensing Recovery, Atul Divekar, Deanna Needell

CMC Faculty Publications and Research

Compressive sensing is a methodology for the reconstruction of sparse or compressible signals using far fewer samples than required by the Nyquist criterion. However, many of the results in compressive sensing concern random sampling matrices such as Gaussian and Bernoulli matrices. In common physically feasible signal acquisition and reconstruction scenarios such as super-resolution of images, the sensing matrix has a non-random structure with highly correlated columns. Here we present a compressive sensing recovery algorithm that exploits this correlation structure. We provide algorithmic justification as well as empirical comparisons.

Broad Vision: The Art & Science Of Looking, Heather Barnett, John R. A. Smith

The STEAM Journal

Undergraduate students and academic staff from diverse disciplines in the arts and sciences investigated questions of mediated vision through a year-long interdisciplinary research project at the University of Westminster, London, United Kingdom. The Broad Vision project explored the perception and interpretation of microscopic worlds, and investigated the benefits and challenges of working across disciplinary divides in a university setting. This article describes the three-phase model for interdisciplinary learning and research developed through the project, providing a valuable case study for inquiry based art/science education.

Stable Image Reconstruction Using Total Variation Minimization, Deanna Needell, Rachel Ward

CMC Faculty Publications and Research

This article presents near-optimal guarantees for stable and robust image recovery from undersampled noisy measurements using total variation minimization. In particular, we show that from O(s log(N)) nonadaptive linear measurements, an image can be reconstructed to within the best s-term approximation of its gradient up to a logarithmic factor, and this factor can be removed by taking slightly more measurements. Along the way, we prove a strengthened Sobolev inequality for functions lying in the null space of suitably incoherent matrices.

Virtual Machine Workloads: The Case For New Nas Benchmarks, Vasily Tarasov, Dean Hildebrand, Geoffrey H. Kuenning, Erez Zadok

All HMC Faculty Publications and Research

Network Attached Storage (NAS) and Virtual Machines (VMs) are widely used in data centers thanks to their manageability, scalability, and ability to consolidate resources. But the shift from physical to virtual clients drastically changes the I/O workloads to seen on NAS servers, due to guest file system encapsulation in virtual disk images and the multiplexing of request streams from different VMs. Unfortunately, current NAS workload generators and benchmarks produce workloads typical to physical machines.

This paper makes two contributions. First, we studied the extent to which virtualization is changing existing NAS workloads. We observed significant changes, including the disappearance of …

Exploring The Baccalaureate Origin Of Domestic Ph.D. Students In Computing Fields, Susanne Hambrusch, Ran Libeskind-Hadas, Fen Zhao, David Rabson, Amy Csizmar Dalal, Ed Fox, Charles Isbell, Valerie Taylor

All HMC Faculty Publications and Research

Increasing the number of US students entering graduate school and receiving a Ph.D. in computer science is a goal as well as a challenge for many US Ph.D. granting institutions. Although the total computer science Ph.D. production in the U.S. has doubled between 2000 and 2010 (Figure 1), the fraction of domestic students receiving a Ph.D. from U.S. graduate programs has been below 50% since 2003 (Figure 2).

The goal of the Pipeline Project of CRA-E (PiPE) is to better understand the pipeline of US citizens and Permanent Residents (henceforth termed domestic students ) who apply, matriculate, and graduate from …

A Machine Learning Approach To Diagnosis Of Parkinson’S Disease, Sumaiya F. Hashmi

CMC Senior Theses

I will investigate applications of machine learning algorithms to medical data, adaptations of differences in data collection, and the use of ensemble techniques.

Focusing on the binary classification problem of Parkinson’s Disease (PD) diagnosis, I will apply machine learning algorithms to a primary dataset consisting of voice recordings from healthy and PD subjects. Specifically, I will use Artificial Neural Networks, Support Vector Machines, and an Ensemble Learning algorithm to reproduce results from [MS12] and [GM09].

Next, I will adapt a secondary regression dataset of PD recordings and combine it with the primary binary classification dataset, testing various techniques to consolidate …

School Choice As A One-Sided Matching Problem: Cardinal Utilities And Optimization, Sinan Aksoy, Alexander Adam Azzam, Chaya Coppersmith, Julie Glass, Gizem Karaali, Xueying Zhao, Xinjing Zhu

Pomona Faculty Publications and Research

The school choice problem concerns the design and implementation of matching mechanisms that produce school assignments for students within a given public school district. Previously considered criteria for evaluating proposed mechanisms such as stability, strategyproofness and Pareto efficiency do not always translate into desirable student assignments. In this note, we explore a class of one-sided, cardinal utility maximizing matching mechanisms focused exclusively on student preferences. We adapt a well-known combinatorial optimization technique (the Hungarian algorithm) as the kernel of this class of matching mechanisms. We find that, while such mechanisms can be adapted to meet desirable criteria not met by …