Integrating Existing Software Toolkits into VO System

Virtual Observatory (VO) is a collection of interoperating data archives and software tools. Taking advantages of the latest information technologies, it aims to provide a data-intensively online research environment for astronomers all around the world. A large number of high-qualified astronomical software packages and libraries are powerful and easy of use, and have been widely used by astronomers for many years. Integrating those toolkits into the VO system is a necessary and important task for the VO developers. VO architecture greatly depends on Grid and Web services, consequently the general VO integration route is "Java Ready - Grid Ready - VO Ready". In the paper, we discuss the importance of VO integration for existing toolkits and discuss the possible solutions. We introduce two efforts in the field from China-VO project, "gImageMagick" and " Galactic abundance gradients statistical research under grid environment". We also discuss what additional work should be done to convert Grid service to VO service.Comment: 9 pages, 3 figures, will be published in SPIE 2004 conference proceeding

Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration.

Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., GSE2034, GSE2990, and Chanrion's datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine (p=0.03, p=0.13, and p<0.001) and Logistic Regression (p=0.006, p=0.008, and p<0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine (p=0.38, p=0.29, and p=0.047) and Logistic Regression (p=0.38, p=0.04, and p<0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making

PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe.

BackgroundAccurately assessing pain for those who cannot make self-report of pain, such as minimally responsive or severely brain-injured patients, is challenging. In this paper, we attempted to address this challenge by answering the following questions: (1) if the pain has dependency structures in electronic signals and if so, (2) how to apply this pattern in predicting the state of pain. To this end, we have been investigating and comparing the performance of several machine learning techniques.MethodsWe first adopted different strategies, in which the collected original n-dimensional numerical data were converted into binary data. Pain states are represented in binary format and bound with above binary features to construct (n + 1) -dimensional data. We then modeled the joint distribution over all variables in this data using the Restricted Boltzmann Machine (RBM).ResultsSeventy-eight pain data items were collected. Four individuals with the number of recorded labels larger than 1000 were used in the experiment. Number of avaliable data items for the four patients varied from 22 to 28. Discriminant RBM achieved better accuracy in all four experiments.ConclusionThe experimental results show that RBM models the distribution of our binary pain data well. We showed that discriminant RBM can be used in a classification task, and the initial result is advantageous over other classifiers such as support vector machine (SVM) using PCA representation and the LDA discriminant method

Ranking Medical Subject Headings using a factor graph model.

Automatically assigning MeSH (Medical Subject Headings) to articles is an active research topic. Recent work demonstrated the feasibility of improving the existing automated Medical Text Indexer (MTI) system, developed at the National Library of Medicine (NLM). Encouraged by this work, we propose a novel data-driven approach that uses semantic distances in the MeSH ontology for automated MeSH assignment. Specifically, we developed a graphical model to propagate belief through a citation network to provide robust MeSH main heading (MH) recommendation. Our preliminary results indicate that this approach can reach high Mean Average Precision (MAP) in some scenarios

An Improved PDR Localization Algorithm Based on Particle Filter

Pedestrian Dead Reckoning (PDR) helps to realize step frequency detection, step estimation and direction estimation through data collected by inertial sensors such as accelerometer, gyroscope, magnetometer, etc. The initial positioning information is used to calculate the position of pedestrians at any time, which can be applied to indoor positioning technology researching. In order to improve the position accuracy of pedestrian track estimation, this paper improves the step frequency detection, step size estimation and direction detection in PDR, and proposes a particle swarm optimization particle filter (PSO-IPF) PDR location algorithm. Using the built-in accelerometer information of the smartphone to carry out the step frequency detection, the step frequency parameter construction model is introduced to carry out the step estimation, the direction estimation is performed by the Kalman filter fusion gyroscope and the magnetometer information, and the positioning data is merged by using the particle filter. The fitness function in the particle swarm optimization process is changed in the localization algorithm to improve particle diversity and position estimation. The experimental results show that the error rate of the improved step frequency detection method is reduced by about 2.1 % compared with the traditional method. The angle accuracy of the direction estimation is about 4.12◦ higher than the traditional method. The overall positioning accuracy is improved