다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

/pdf/a-density-based-algorithm-for-discovering-clusters-in-large-1o5u8sf38m.pdf

A density-based algorithm for discovering clusters in large spatial Databases with Noise

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In this paper we propose a space-variant kernel estimation method for effective deconvolution when combining different exposure image pairs. The proposed algorithm can be applied to images blurred by both camera and object motion in an efficient manner. The blur in the long exposure shot is mainly caused by camera shake or object motion, and the noise of the underexposed image is introduced by the gain factor applied to the sensor when the ISO is set to a high value. The main idea in this work is to incorporate a spatially-varying deblurring/denoising which is applied to image patches. The method exploits kernel estimation and error measures to choose between denoising and deblurring each patch. In addition, the proposed approach estimates all necessary parameters automatically without user supervision.

Space-variant kernel deconvolution for dual exposure problem

Bayesian methods rely on image priors that encapsulate prior image knowledge and avoid the ill-posedness of image restoration problems. In this paper a new prior based on the l1 norm of vertical and horizontal first order differences of image pixel values is introduced and its parameters are estimated . The results obtained from its application studied and compared with the ones provided by other methods in the literature.

/pdf/l1-prior-majorization-in-bayesian-image-restoration-1k8k7yckyn.pdf

L1 prior majorization in Bayesian image restoration

The aim of our study was to evaluate the clinical usefulness of the tumor markers CEA, CA 125 and PSA using the COBAS CORE system from Roche Diagnostic Systems. Our results demonstrate that determination of these markers on the COBAS CORE immunoassasy analyser provides the performance required for routine use in clinical practice. The results obtained in this clinical trial validate the correlation with disease extension, a characteristic that defines and determines the clinical utility of the tumor markers. We also conclude that learning to operate the COBAS CORE system is simple, as is management of the system through the user-friendly software.

Multicentre clinical evaluation of the COBAS CORE CEA, CA 125 II and PSA tumor marker assays

A framework for recovering high-resolution video sequences from sub-sampled and compressed observations is presented. Compression schemes that describe a video sequence through a combination of motion vectors and transform coefficients, e.g. the MPEG and ITU family of standards, are the focus of this paper. A multichannel Bayesian approach is used to incorporate both the motion vectors and transform coefficients in it. Results show a discernable improvement in resolution in the whole sequence, as compared to standard interpolation methods.

Multi-channel reconstruction of video sequences from low-resolution and compressed observations

The LIAISON immunoassay analyser was tested in a multicentre evaluation performed by 8 laboratories. The analytes evaluated were CA 15-3, CA 19-9, CA 125II, AFP, CEA, NSE and PSA. Excellent results were obtained for within-run and between-run precision with most assays showing within-run CVs 110% was obtained for some of the samples tested. None of the assays revealed a high-dose hook effect. Method comparisons were performed by using the routine method of the respective study centre. Results generally showed an acceptable agreement between the LIAISON system and the different methods of comparison. The reference ranges for all assays were found to be in accordance with data known from the literature. All assays showed similar results for serum, heparinised plasma and EDTA plasma. Additionally, two experiments were performed with only one of the analytes tested: the sample-to-sample carry-over, using the CA 19-9 assay (3.3 x 10(-6)-2.3 x 10(-5)) and the functional sensitivity for the PSA assay (0.2 ng/ml).

Rafael Molina

Papers

Space-variant kernel deconvolution for dual exposure problem

L1 prior majorization in Bayesian image restoration

Multicentre clinical evaluation of the COBAS CORE CEA, CA 125 II and PSA tumor marker assays

Multi-channel reconstruction of video sequences from low-resolution and compressed observations

External evaluation of LIAISON tumour marker assays on the fully automated chemiluminescent LIAISON immunoassay analyser.