31684732
Achieving high availability and performance computing with an HA-OSCAR cluster
High availability (HA) computing has long gained much attention in enterprise and mission critical systems. HA goals are to maximize the uptime, thus undoubtedly complementing high-performance computing (HPC) objectives. HA-OSCAR is a project that aims to improve HA in commercial-off-the-shelf (COTS)-based HPC environments. In this paper, we introduce a multi-head HPC cluster architecture. Server redundancy is an initial key aspect aiming toward downtime reduction. Two HA-OSCAR types, active-standby and active-active, are studied. We evaluate system dependability for given two models. Stochastic Reward Nets (SRN) are used to model the system availability. We describe our SRN modeling using Stochastic Petri Net Package, and compute several interesting results that characterize HA-OSCAR availability.
13929087
Availability prediction and modeling of high mobility OSCAR cluster
Since the initial introduction of open source cluster application resources (OSCAR), this software package has been a well-accepted choice for building high performance computing systems. As it continues to be applied to mission-critical environments, high availability (HA) features therefore are needed to be included in OSCAR cluster. In this paper, we provide a HA solution for OSCAR cluster. As a widely used technique in HA solutions, component redundancy is adopted to improve the system availability. Based on the proposed architecture, we develop a detailed failure-repair model for predicting the availability of HA OSCAR cluster. stochastic reward nets (SRN) are used to model the behavior of the system. We specify our SRN model to stochastic Petri net package, describe and compute several interesting output measures that characterize availability features of HA OSCAR cluster.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.
19118237
A machine learning approach for the curation of biomedical literature: KDD Cup 2002 (task 1)
In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naïve Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources.
811735
Classifying Market States with WARS
In this paper, a new indicator - WARS (Weighted Accumulated Reconstruction Series) at classifying the state of financial market, either trending state or mean-reverting state, was presented. Originated from the computation of Entropy, this new indicator was found to be able to reflect the market behavior accurately and easily. The algorithm of generating WARS and its meaning related to Entropy were introduced and some comparison results between WARS and the Daily Profit Curve were listed. As a new indicator, WARS also can be used to build a trading system - to provide buy, sell and hold signals. Through the application on S&P 500 index, it was verified to be effective and was a promising indicator.
14826038
A Spatio-Temporal Modeling Method for Shape Representation
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected three dimensional (3D) objects. Because SPHARM based 3D models can derive functional information analysis and classify different pathological symptoms, it has been used in many applications in biomedical image computing. There is an urgent requirement for efficient spatio-temporal shape modeling to represent the dynamic anatomical structures in many applications (e.g., medical image analysis, geospatial information systems). In this paper we propose a novel real spherical harmonics based spatio-temporal shape modeling method to efficiently and flexibly represent the shapes sequence of anatomical structures in medical images. Our method works well on the simply connected 3D objects and the effectiveness of our approach is demonstrated through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for spatio-temporal shape modeling efficiency is proposed and the comparison results showed the good performance of our method.
2536263
Multi-scale Voxel-Based Morphometry Via Weighted Spherical Harmonic Representation
Although the voxel-based morphometry (VBM) has been widely used in quantifying the amount of gray matter of the human brain, the optimal amount of registration that should be used in VBM has not been addressed. In this paper, we present a novel multi-scale VBM using the weighted spherical harmonic (SPHARM) representation to address the issue. The weighted-SPHARM provides the explicit smooth functional representation of a true unknown cortical boundary. Based on this new representation, the gray matter tissue density is constructed using the Euclidean distance map from a voxel to the estimated smooth cortical boundary. The methodology is applied in localizing abnormal cortical regions in a group of autistic subjects.