3-D Kinematics of Water Masers in the W51A Region

We report proper motion measurements of water masers in the massive-star forming region W51A and the analyses of the 3-D kinematics of the masers in three maser clusters of W51A (W51 North, Main, and South). In W~51 North, we found a clear expanding flow that has an expansion velocity of ~70 km/s and indicates deceleration. The originating point of the flow coincides within 0.1 as with a silicon-monoxide maser source near the HII region W~51d. In W51 Main, no systematic motion was found in the whole velocity range (158 km/s =< V(lsr) =< -58 km/s) although a stream motion was reported previously in a limited range of the Doppler velocity (54 km/s =< V(lsr) =< 68 kms). Multiple driving sources of outflows are thought to explain the kinematics of W51 Main. In W51 South, an expansion motion like a bipolar flow was marginally visible. Analyses based on diagonalization of the variance-covariance matrix of maser velocity vectors demonstrate that the maser kinematics in W51 North and Main are significantly tri-axially asymmetric. We estimated a distance to W51 North to be 6.1 +/- 1.3 kpc on the basis of the model fitting method adopting a radially expanding flow.Comment: 20 pages, 8 figures, 8 tables, appear in the NRO report No. 564 (ftp://ftp.nro.nao.ac.jp/nroreport/PASJ-W51.pdf) and will appear in Publ. Astron. Soc. Japan, Vol. 54, No. 5 (10/25 issue

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of local binary pattern was used for calculating feature vectors. Support vector machine (SVM) or XGBoost was trained using the feature vectors and their labels. TPE or random search was used for parameter optimization of SVM and XGBoost. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. In conclusion, XGBoost was better than SVM for classifying lung nodules. TPE was more efficient than random search for parameter optimization.Comment: 29 pages, 4 figure