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

The presence of a tumor antigen in human colonic carcinomas and their metastases was described about 25 years ago. This antigen, called carcinoembryonic antigen (CEA), is one of the first known tumor

Carcinoembryonic Antigen in Staging and Follow-Up of Patients with Solid Tumors

We propose a novel design for light field image acquisition based on compressive sensing principles By placing a randomly coded mask at the aperture of a camera, incoherent measurements of the light passing through different parts of the lens are encoded in the captured images Each captured image is a random linear combination of different angular views of a scene The encoded images are then used to recover the original light field image via a novel Bayesian reconstruction algorithm Using the principles of compressive sensing, we show that light field images with a large number of angular views can be recovered from only a few acquisitions Moreover, the proposed acquisition and recovery method provides light field images with high spatial resolution and signal-to-noise-ratio, and therefore is not affected by limitations common to existing light field camera designs We present a prototype camera design based on the proposed framework by modifying a regular digital camera Finally, we demonstrate the effectiveness of the proposed system using experimental results with both synthetic and real images

/pdf/compressive-light-field-sensing-4548skni4o.pdf

Compressive Light Field Sensing

In an image restoration problem one usually has two different kinds of information. In the first stage, one has knowledge about the structural form of the noise and local characteristics of the restoration. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on the hyperparameters, where information about those hyperparameters is included. In this work the author applies the hierarchical Bayesian approach to image restoration problems and compares it with other approaches in handling the estimation of the hyperparameters. >

On the hierarchical Bayesian approach to image restoration: applications to astronomical images

Serum concentrations of cancer antigen 125 (CA 125) were determined for 373 patients with various liver diseases: 57 with acute hepatitis, 57 with chronic hepatitis, 244 with liver cirrhosis (86 compensated and 158 decompensated), and 15 with primary liver cancer. The antigen was measured simultaneously in the serum and ascitic fluid of 46 of the patients with liver cirrhosis and sequentially in the serum and ascitic fluid of another 25 cirrhotics treated with paracentesis and (or) diuretics. Abnormal results for CA 125 were detected in sera from 4% of the patients with acute or chronic hepatitis, 60% of the patients with liver cirrhosis, and 67% of the patients with primary liver cancer. The main factor associated with abnormal serum concentrations of this antigen was the presence of ascites, with pathological CA 125 values in 94% of patients with ascites without jaundice (mean 566 +/- 528 arb. units/mL), compared with only 40% of patients with jaundice and without ascites (mean 40.1 +/- 28.5 arb. units/mL) (P less than 0.001). High concentrations of CA 125 were mainly associated with spontaneous bacterial peritonitis. The serum concentration of CA 125 decreased after treatment with paracentesis, but increased in patients treated with diuretics rather than paracentesis. The release of this antigen in liver cirrhosis appears to be independent of the liver disorder and, rather, results from peritoneal synthesis of this antigen.

Cancer antigen 125 in serum and ascitic fluid of patients with liver diseases.

In this paper we propose a novel algorithm for super resolution based on total variation prior and variational distribution approximations. We formulate the problem using a hierarchical Bayesian model where the reconstructed high resolution image and the model parameters are estimated simultaneously from the low resolution observations. The algorithm resulting from this formulation utilizes variational inference and provides approximations to the posterior distributions of the latent variables. Due to the simultaneous parameter estimation, the algorithm is fully automated so parameter tuning is not required. Experimental results show that the proposed approach outperforms some of the state-of-the-art super resolution algorithms.

/pdf/total-variation-super-resolution-using-a-variational-1498urpxmj.pdf

Rafael Molina

Papers

Carcinoembryonic Antigen in Staging and Follow-Up of Patients with Solid Tumors

Compressive Light Field Sensing

On the hierarchical Bayesian approach to image restoration: applications to astronomical images

Cancer antigen 125 in serum and ascitic fluid of patients with liver diseases.

Total variation super resolution using a variational approach