Physical Sciences and Mathematics Commons^™

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Quantum Simulation Using High-Performance Computing, Collin Beaudoin, Christian Trefftz, Zachary Kurmas

Masters Theses

Hermitian matrix multiplication is one of the most common actions that is performed on quantum matrices, for example, it is used to apply observables onto a given state vector/density matrix.

ρ→Hρ

Our goal is to create an algorithm to perform the matrix multiplication within the constraints of QuEST [1], a high-performance simulator for quantum circuits. QuEST provides a system-independent platform for implementing and simulating quantum algorithms without the need for access to quantum machines. The current implementation of QuEST supports CUDA, MPI, and OpenMP, which allows programs to run on a wide variety of systems.

Random Search Plus: A More Effective Random Search For Machine Learning Hyperparameters Optimization, Bohan Li

Masters Theses

Machine learning hyperparameter optimization has always been the key to improve model performance. There are many methods of hyperparameter optimization. The popular methods include grid search, random search, manual search, Bayesian optimization, population-based optimization, etc. Random search occupies less computations than the grid search, but at the same time there is a penalty for accuracy. However, this paper proposes a more effective random search method based on the traditional random search and hyperparameter space separation. This method is named random search plus. This thesis empirically proves that random search plus is more effective than random search. There are some case …

A Gpu Implementation Of Distance-Driven Computed Tomography, Ryan D. Wagner

Masters Theses

Computed tomography (CT) is used to produce cross-sectional images of an object via noninvasive X-ray scanning of the object. These images have a wide range of uses including threat detection in checked baggage at airports. The projection data collected by the CT scanner must be reconstructed before the image may be viewed. In comparison to filtered backprojection methods of reconstruction, iterative reconstruction algorithms have been shown to increase overall image quality by incorporating a more complete model of the underlying physics. Unfortunately, iterative algorithms are generally too slow to meet the high throughput demands of this application. It is therefore …

On The Role Of Genetic Algorithms In The Pattern Recognition Task Of Classification, Isaac Ben Sherman

Masters Theses

In this dissertation we ask, formulate an apparatus for answering, and answer the following three questions: Where do Genetic Algorithms fit in the greater scheme of pattern recognition? Given primitive mechanics, can Genetic Algorithms match or exceed the performance of theoretically-based methods? Can we build a generic universal Genetic Algorithm for classification? To answer these questions, we develop a genetic algorithm which optimizes MATLAB classifiers and a variable length genetic algorithm which does classification based entirely on boolean logic. We test these algorithms on disparate datasets rooted in cellular biology, music theory, and medicine. We then get results from these …

Efficient Simulation Of A Simple Evolutionary System, Mahendra Duwal Shrestha

Masters Theses

An infinite population model is considered for diploid evolution under the influence of crossing over and mutation. The evolution equations show how Vose’s haploid model for Genetic Algorithms extends to the diploid case, thereby making feasible simulations which otherwise would require excessive resources. This is illustrated through computations confirming the convergence of finite diploid population short-term behaviour to the behaviour predicted by the infinite diploid model. The results show the distance between finite and infinite population evolutionary trajectories can decrease in practice like the reciprocal of the square root of population size.

Under necessary and sufficient conditions (NS) concerning mutation …

Minimal-Density, Raid-6 Codes: An Approach For W = 9, Bryan Andrew Burke

Masters Theses

RAID-6 erasure codes provide vital data integrity in modern storage systems. There is a class of RAID-6 codes called “Minimal Density Codes,” which have desirable performance properties. These codes are parameterized by a “word size,” w, and constructions of these codes are known when w and w + 1 are prime numbers. However, there are obvious gaps for which there is no theory. An exhaustive search was used to fill in the important gap when w = 8, which is highly applicable to real-world systems, since it is a power of 2. This paper extends that approach to address the …

Multi-Threaded Automatic Integration Using Openmp And Cuda, Rida Assaf

Masters Theses

Problems in many areas give rise to computationally expensive integrals that beg the need of efficient techniques to solve them, e.g., in computational finance for the modeling of cash flows; for the computation of Feynman loop integrals in high energy physics; and in stochastic geometry with applications to computer graphics.

We demonstrate feasible numerical approaches in the framework of the PARINT multivariate integration package. The parallel environment is provided by the cluster of the High Performance Computational Science (HPCS) laboratory, with 22 (16- or 32-core) nodes, NVIDIA GPUs, and Intel Xeon Phi coprocessors.

Monte Carlo integration is implemented in CUDA …

A Study Of Possible Optimizations For The Task Scheduler ‘Quark’ On The Shared Memory Architecture, Vijay Gopal Joshi

Masters Theses

Multicore processors are replacing most of the single core processors nowadays.

Current trends show that there will be increasing numbers of cores on a single chip in the coming future. However, programming multicore processors remains bug prone and less productive. Thus, making use of a runtime to schedule tasks on multicore processor hides most of the complexities of parallel programming to improve productivity. QUARK is one of the runtimes available for the multicore processors. This work looks at identifying and solving performance bottlenecks for QUARK on the shared memory architecture. The problem of finding bottlenecks is divided into two parts, …

A Similarity Based Concordance Approach To Word Sense Disambiguation, Ramakrishnan B. Guru

Masters Theses

This study attempts to solve the problem of Word Sense Disambiguation using a combination of statistical, probabilistic and word matching algorithms. These algorithms consider that words and sentences have some hidden similarities and that the polysemous words in any context should be assigned to a sense after each execution of the algorithm. The algorithm was tested with sufficient sample data and the efficiency of the disambiguation performance has proven to increase significantly after the inclusion of the concordance methodology.