200438
Nanoparticle-enhanced proton computed tomography: a Monte Carlo simulation study
Proton computed tomography (pCT) has the potential to improve the accuracy of proton treatment planning, which is currently based on X-ray computed tomography (XCT). However, at this time no method exists for pCT to selectively increase the contrast of tumor tissues with respect to normal tissues. One possibility to achieve this goal is to bind gold-nanoparticles, i.e., microscopic structures in the size range of 1 nm to several hundred nanometers, to the surface of tumor cells with cell-seeking antibodies conjugated to the surface of the nanoparticles. In order to test the suitability of this approach, we used the Monte Carlo transport code GEANT4, to provide simulated histories of 200 MeV protons going through a cylindrical water phantom with embedded inhomogeneities enhanced with traces of gold. The energy loss of several million protons traversing the cylinder from 180 different angles formed the basis for reconstructing the single proton CT image representing the relative volume electron density map of the phantom using an approximate filtered back projection algorithm. Our results demonstrate that with hundred nanoparticles of 100 nm diameters per tumor cell, it may be possible to enhance the visibility of tumors in pCT.
21969531
Evaluation of an efficient compensation method for quantitative fan-beam brain SPECT reconstruction
Fan-beam collimators are designed to improve the system sensitivity and resolution for imaging small objects such as the human brain and breasts in single photon emission computed tomography (SPECT). Many reconstruction algorithms have been studied and applied to this geometry to deal with every kind of degradation factor. This work presents a new reconstruction approach for SPECT with circular orbit, which demonstrated good performance in terms of both accuracy and efficiency. The new approach compensates for degradation factors including noise, scatter, attenuation, and spatially variant detector response. Its uniform attenuation approximation strategy avoids the additional transmission scan for the brain attenuation map, hence reducing the patient radiation dose and furthermore simplifying the imaging procedure. We evaluate and compare this new approach with the well-established ordered-subset expectation-maximization iterative method, using Monte Carlo simulations. We perform quantitative analysis with regional bias-variance, receiver operating characteristics, and Hotelling trace studies for both methods. The results demonstrate that our reconstruction strategy has comparable performance with a significant reduction of computing time.
11749878
IFD: Iterative Feature and Data Clustering
In this paper, we propose a new clustering algorithm, IFD1, based on a cluster model of data coefficients D and feature coefficients F . The coefficients denote the degree (or weights) of the data and features associated with the clusters. Clustering is performed via an iterative optimization procedure to mutually reinforce the relationships between the coefficients. The mutually reinforcing optimization exploits the duality of the data and features and enable a simultaneous clustering of both data and features. We have shown the convergence property of the clustering algorithm and discussed its connections with various existential approaches. Extensive experimental results on both synthetic and real data sets show the effectiveness of IFD algorithm.
14215995
Spin detection hardware for improved management of multithreaded systems
Spinning is a synchronization mechanism commonly used in applications and operating systems. Excessive spinning, however, often indicates performance or correctness (e.g., livelock) problems. Detecting if applications and operating systems are spinning is essential for achieving high performance, especially in consolidated servers running virtual machines. Prior research has used source or binary instrumentation to detect spinning. However, these approaches place a significant burden on programmers and may even be infeasible in certain situations. In this paper, we propose efficient hardware to detect spinning in unmodified applications and operating systems. Based on this hardware, we develop 1) scheduling and power policies that adaptively manage resources for spinning threads, 2) system support that helps detect when a multithreaded program is livelocked, and 3) hardware performance counters that accurately reflect system performance. Using full-system simulation with SPEC OMP, SPLASH-2, and Wisconsin commercial workloads, we demonstrate that our mechanisms effectively improve the management of multithreaded systems
230454
Mining Logs Files for Computing System Management
With advancement in science and technology, computing systems become increasingly more difficult to monitor, manage and maintain. Traditional approaches to system management have been largely based on domain experts through a knowledge acquisition process to translate domain knowledge into operating rules and policies. This has been experienced as a cumbersome, labor intensive, and error prone process. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing systems. A popular approach to system management is based on analyzing system log files. However, several new aspects of the system log data have been less emphasized in existing analysis methods and posed several challenges. The aspects include disparate formats and relatively short text messages in data reporting, asynchronous data collection, and temporal characteristics in data representation. First, a typical computing system contains different devices with different software components, possibly from different providers. These various components have multiple ways to report events, conditions, errors and alerts. The heterogeneity and inconsistency of log formats make it difficult to automate problem determination. To perform automated analysis, we need to categorize the text messages with disparate formats into common situations. Second, text messages in the log files are relatively short with a large vocabulary size. Third, each text message usually contains a timestamp. The temporal characteristics provide additional context information of the messages and can be used to facilitate data analysis. In this paper, we apply text mining to automatically categorize the messages into a set of common categories, and propose two approaches of incorporating temporal information to improve the categorization performance
8864863
Integrating Features from Different Sources for Music Information Retrieval
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
42024829
Strong consistency of the contraction mapping method for frequency estimation
Some statistical properties in regard to the contraction mapping (CM) method are discussed. One of the requirements in this method is that the filter be parameterized to satisfy a certain fundamental property. The parameterization clearly depends on the normalized noise spectrum which theoretically has to be known or estimated a priori. If this information is available, one can first whiten the noise with a linear filter and then apply the CM method to the filtered data. In this way, the parameterization only needs to be done under the white noise assumption and filters like the AR(2) can be used by the CM method. In applications, however, prewhitening may not always be necessary. >