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.

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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

Surveys the field of super resolution (SR) processing for compressed video The introduction of motion vectors, compression noise, and additional redundancies within the image sequence makes this problem fertile ground for novel processing methods In conducting this survey, though, we develop and present all techniques within the Bayesian framework This adds consistency to the presentation and facilitates comparison between the different methods The article is organized as follows We define the acquisition system utilized by the surveyed procedures Then we formulate the HR problem within the Bayesian framework and survey models for the acquisition and compression systems This requires consideration of both the motion vectors and transform coefficients within the compressed bit stream We survey models for the original HR image intensities and displacement values We discuss solutions for the SR problem and provide examples of several approaches

High-resolution images from low-resolution compressed video

ProGRP: a new biomarker for small cell lung cancer.

Super-resolution algorithms recover high-frequency information from a sequence of low-resolution observations. In this paper, we consider the impact of video compression on the super-resolution task. Hybrid motion-compensation and transform coding schemes are the focus, as these methods provide observations of the underlying displacement values as well as a variable noise process. We utilize the Bayesian framework to incorporate this information and fuse the super-resolution and post-processing problems. A tractable solution is defined, and relationships between algorithm parameters and information in the compressed bitstream are established. The association between resolution recovery and compression ratio is also explored. Simulations illustrate the performance of the procedure with both synthetic and nonsynthetic sequences.

Bayesian resolution enhancement of compressed video

In this paper, we review recent results on audiovisual (AV) fusion. We also discuss some of the challenges and report on approaches to address them. One important issue in AV fusion is how the modalities interact and influence each other. This review will address this question in the context of AV speech processing, and especially speech recognition, where one of the issues is that the modalities both interact but also sometimes appear to desynchronize from each other. An additional issue that sometimes arises is that one of the modalities may be missing at test time, although it is available at training time; for example, it may be possible to collect AV training data while only having access to audio at test time. We will review approaches to address this issue from the area of multiview learning, where the goal is to learn a model or representation for each of the modalities separately while taking advantage of the rich multimodal training data. In addition to multiview learning, we also discuss the recent application of deep learning (DL) toward AV fusion. We finally draw conclusions and offer our assessment of the future in the area of AV fusion.

Audiovisual Fusion: Challenges and New Approaches

To evaluate the utility of CEA and CA 15.3 for early diagnosis of recurrence, serial serum determinations of both antigens were performed in 1023 patients (follow-up: 1–10 years, mean 6.2 years) with primary breast cancer (CA 15.3 in 533 cases) and no evidence of residual disease (NED) after radical treatment (radical mastectomy or simple mastectomy and radiotherapy). 246 patients developed metastases during follow-up. Results: CEA and CA 15.3 were elevated (> 10 ng/ml or > 60 U/ml, respectively) prior to diagnosis in 40% (98/246) and 41% (37/91) of the patients with recurrence, with a lead time of 4.9 ± 2.2 and 4.2 ± 2.3 months, respectively. When patients with locoregional recurrences were excluded, sensitivity improved to 46% (CEA) and 54% (CA 15.3), and to 64% with both tumor markers (CEA and/or CA 15.3). Higher levels of both CEA and CA 15.3 at diagnosis of recurrence, higher sensitivity in early diagnosis of relapse, and a higher lead time were found in ER+ (CEA) or PgR+ patients (CA 15.3) than in those that were negative for these receptors in the primary tumor (p < 0.001). Specificity of the tumor markers was 99% for both CEA (777 NED patients) and for CA 15.3 (444 NED patients), respectively. In conclusion, CEA and CA 15.3 are useful tools for early diagnosis of metastases, mainly in those patients with ER+ or PR+ tumors.

Rafael Molina

Papers

High-resolution images from low-resolution compressed video

ProGRP: a new biomarker for small cell lung cancer.

Bayesian resolution enhancement of compressed video

Audiovisual Fusion: Challenges and New Approaches

Use of serial carcinoembryonic antigen and CA 15.3 assays in detecting relapses in breast cancer patients.