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Academic Commons Search Resultsen-usDosimetric Optimization Method for CyberKnife Robotic RadioSurgery System Using a Memetic Algorithm
https://academiccommons.columbia.edu/catalog/ac:gmsbcc2fsm
Clancey, Owen10.7916/D854315RThu, 26 Oct 2017 18:32:58 +0000The CyberKnife Robotic RadioSurgery System is a robot controlled 6 MV linear accelerator based radiation delivery system with the linear accelerator attached to a six-axis robotic manipulator. Summation of all radiation beams creates a three-dimensional dose distribution within a patient. Each beam's direction, weight, and collimator size affect its contribution to the dose distribution. Hence, the CyberKnife treatment planning problem is to select a set of beams that produce a desired dose distribution.
With a dose-based objective function and user-supplied weighted, dose-volume goals, a memetic algorithm is used to solve the CyberKnife treatment planning problem. Before optimization begins, two thousand radiation beams are generated, and for each beam, dose-deposition coefficients are calculated for all optimization points within the target(s) and critical structures. Then, the memetic algorithm optimizes beam weights using global and local operators and problem-specific knowledge within an evolutionary computation framework. Concurrently, beams are pared down to emphasize promising regions of the solution space and to generate clinically deliverable treatment plans.
Algorithmic analysis is two-fold: parameter analysis and comparison to MultiPlan, the only commercially available CyberKnife treatment planning software. Parameter analysis optimizes and justifies parametric choices given hardware, optimization time, and treatment time constraints analogous to clinical limitations. Thereafter, MultiPlan and the memetic algorithm generate ten treatment plans and are evaluated based upon dose-volume histograms, target dose homogeneity, target dose conformality, dosimetric success rates, total beam-on time or MU, and total number of beams. Analysis shows the memetic algorithm is equivalent or superior for all metrics, and given that MultiPlan is the only available CyberKnife treatment planning software, the memetic algorithm is a state-of-the-art CyberKnife dosimetric optimization method.Physics, Computer science, Memetics, Biomedical engineering, Radiation dosimetryApplied Physics and Applied MathematicsThesesAn Efficient Spectral Dynamical Core for Distributed Memory Computers
https://academiccommons.columbia.edu/catalog/ac:166989
Rivier, L.; Loft, R.; Polvani, Lorenzo M.10.7916/D8XD1BTTMon, 04 Nov 2013 21:36:58 +0000The practical question of whether the classical spectral transform method, widely used in atmospheric modeling, can be efficiently implemented on inexpensive commodity clusters is addressed. Typically, such clusters have limited cache and memory sizes. To demonstrate that these limitations can be overcome, the authors have built a spherical general circulation model dynamical core, called BOB (“Built on Beowulf”), which can solve either the shallow water equations or the atmospheric primitive equations in pressure coordinates.
That BOB is targeted for computing at high resolution on modestly sized and priced commodity clusters is reflected in four areas of its design. First, the associated Legendre polynomials (ALPs) are computed “on the fly” using a stable and accurate recursion relation. Second, an identity is employed that eliminates the storage of the derivatives of the ALPs. Both of these algorithmic choices reduce the memory footprint and memory bandwidth requirements of the spectral transform. Third, a cache-blocked and unrolled Legendre transform achieves a high performance level that resists deterioration as resolution is increased. Finally, the parallel implementation of BOB is transposition-based, employing load-balanced, one-dimensional decompositions in both latitude and wavenumber.
A number of standard tests is used to compare BOB's performance to two well-known codes—the Parallel Spectral Transform Shallow Water Model (PSTSWM) and the dynamical core of NCAR's Community Climate Model CCM3. Compared to PSTSWM, BOB shows better timing results, particularly at the higher resolutions where cache effects become important. BOB also shows better performance in its comparison with CCM3's dynamical core. With 16 processors, at a triangular spectral truncation of T85, it is roughly five times faster when computing the solution to the standard Held–Suarez test case, which involves 18 levels in the vertical. BOB also shows a significantly smaller memory footprint in these comparison tests.Atmosphere, Computer science, System theorylmp3Applied Physics and Applied Mathematics, Earth and Environmental SciencesArticlesGPU-based, Microsecond Latency, Hecto-Channel MIMO Feedback Control of Magnetically Confined Plasmas
https://academiccommons.columbia.edu/catalog/ac:155493
Rath, Nikolaus10.7916/D8M90GRCMon, 14 Jan 2013 20:39:32 +0000Feedback control has become a crucial tool in the research on magnetic confinement of plasmas for achieving controlled nuclear fusion. This thesis presents a novel plasma feedback control system that, for the first time, employs a Graphics Processing Unit (GPU) for microsecond-latency, real-time control computations. This novel application area for GPU computing is opened up by a new system architecture that is optimized for low-latency computations on less than kilobyte sized data samples as they occur in typical plasma control algorithms. In contrast to traditional GPU computing approaches that target complex, high-throughput computations with massive amounts of data, the architecture presented in this thesis uses the GPU as the primary processing unit rather than as an auxiliary of the CPU, and data is transferred from A-D/D-A converters directly into GPU memory using peer-to-peer PCI Express transfers. The described design has been implemented in a new, GPU-based control system for the High-Beta Tokamak -- Extended Pulse (HBT-EP) device. The system is built from commodity hardware and uses an NVIDIA GeForce GPU and D-TACQ A-D/D-A converters providing a total of 96 input and 64 output channels. The system is able to run with sampling periods down to 4 μs and latencies down to 8 μs. The GPU provides a total processing power of 1.5 x 10^12 floating point operations per second. To illustrate the performance and versatility of both the general architecture and concrete implementation, a new control algorithm has been developed. The algorithm is designed for the control of multiple rotating magnetic perturbations in situations where the plasma equilibrium is not known exactly and features an adaptive system model: instead of requiring the rotation frequencies and growth rates embedded in the system model to be set a priori, the adaptive algorithm derives these parameters from the evolution of the perturbation amplitudes themselves. This results in non-linear control computations with high computational demands, but is handled easily by the GPU based system. Both digital processing latency and an arbitrary multi-pole response of amplifiers and control coils is fully taken into account for the generation of control signals. To separate sensor signals into perturbed and equilibrium components without knowledge of the equilibrium fields, a new separation method based on biorthogonal decomposition is introduced and used to derive a filter that performs the separation in real-time. The control algorithm has been implemented and tested on the new, GPU-based feedback control system of the HBT-EP tokamak. In this instance, the algorithm was set up to control four rotating n=1 perturbations at different poloidal angles. The perturbations were treated as coupled in frequency but independent in amplitude and phase, so that the system effectively controls a helical n=1 perturbation with unknown poloidal spectrum. Depending on the plasma's edge safety factor and rotation frequency, the control system is shown to be able to suppress the amplitude of the dominant 8 kHz mode by up to 60% or amplify the saturated amplitude by a factor of up to two. Intermediate feedback phases combine suppression and amplification with a speed up or slow down of the mode rotation frequency. Increasing feedback gain results in the excitation of an additional, slowly rotating 1.4 kHz mode without further effects on the 8 kHz mode. The feedback performance is found to exceed previous results obtained with an FPGA- and Kalman-filter based control system without requiring any tuning of system model parameters. Experimental results are compared with simulations based on a combination of the Boozer surface current model and the Fitzpatrick-Aydemir model. Within the subset of phenomena that can be represented by the model as well as determined experimentally, qualitative agreement is found.Plasma (Ionized gases), Physics, Computer sciencenr2303Applied Physics and Applied MathematicsThesesLarge Scale Machine Learning in Biology
https://academiccommons.columbia.edu/catalog/ac:138449
Raj, Anil10.7916/D82N5863Fri, 09 Sep 2011 15:22:11 +0000Rapid technological advances during the last two decades have led to a data-driven revolution in biology opening up a plethora of opportunities to infer informative patterns that could lead to deeper biological understanding. Large volumes of data provided by such technologies, however, are not analyzable using hypothesis-driven significance tests and other cornerstones of orthodox statistics. We present powerful tools in machine learning and statistical inference for extracting biologically informative patterns and clinically predictive models using this data. Motivated by an existing graph partitioning framework, we first derive relationships between optimizing the regularized min-cut cost function used in spectral clustering and the relevance information as defined in the Information Bottleneck method. For fast-mixing graphs, we show that the regularized min-cut cost functions introduced by Shi and Malik over a decade ago can be well approximated as the rate of loss of predictive information about the location of random walkers on the graph. For graphs drawn from a generative model designed to describe community structure, the optimal information-theoretic partition and the optimal min-cut partition are shown to be the same with high probability. Next, we formulate the problem of identifying emerging viral pathogens and characterizing their transmission in terms of learning linear models that can predict the host of a virus using its sequence information. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using multi-class Adaboost, a powerful discriminative machine learning algorithm. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome. We then extend this learning algorithm to the problem of predicting disease risk in humans using single nucleotide polymorphisms (SNP) -- single-base pair variations -- in their entire genome. While genome-wide association studies usually aim to infer individual SNPs that are strongly associated with disease, we use popular supervised learning algorithms to infer sufficiently complex tree-structured models, built from single-SNP decision rules, that are both highly predictive (for clinical goals) and facilitate biological interpretation (for basic science goals). In addition to high prediction accuracies, the models identify 'hotspots' in the genome that contain putative causal variants for the disease and also suggest combinatorial interactions that are relevant for the disease. Finally, motivated by the insufficiency of quantifying biological interpretability in terms of model sparsity, we propose a hierarchical Bayesian model that infers hidden structured relationships between features while simultaneously regularizing the classification model using the inferred group structure. The appropriate hidden structure maximizes the log-probability of the observed data, thus regularizing a classifier while increasing its predictive accuracy. We conclude by describing different extensions of this model that can be applied to various biological problems, specifically those described in this thesis, and enumerate promising directions for future research.Mathematics, Bioinformatics, Computer sciencear2384Applied Physics and Applied MathematicsTheses