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

In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing data. It is also capable of estimating the contribution of each sensor towards the classification task. We use Bayesian modeling to build a GP-based classifier which combines the information provided by all sensors and approximates the posterior distribution of the GP using variational Bayesian inference. During its training phase, the algorithm estimates each sensor's weight and then uses this information to assign a label to each new sample. In the experimental section, we evaluate the classiication performance of the proposed method on both synthetic and real data and show its applicability to different scenarios.

/pdf/variational-gaussian-process-for-sensor-fusion-3nie7d6q6y.pdf

Variational Gaussian process for sensor fusion

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

Most whole-slide histological images are stained with hematoxylin and eosin dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Our model takes into account both spatial relations among image pixels and similarity to a given reference color-vector matrix. Using Variational Bayes inference, an efficient new blind color deconvolution method is proposed which provides a fully automated procedure to estimate all the unknowns in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms, using real images with known ground truth hematoxylin and eosin values, has been carried out demonstrating the superiority of the proposed approach.

Fully Automated Blind Color Deconvolution of Histopathological Images

In this paper the blind deconvolution problem is formulated using the variational framework. With its use approximations of the involved probability distributions are developed resulting in two algorithms for the estimation of the posterior distributions of the hyperparameters, the blur, and the original image. The performance of the two proposed restoration algorithms is demonstrated experimentally.

/pdf/approximations-of-posterior-distributions-in-blind-3530y72guc.pdf

Approximations of posterior distributions in blind deconvolution using variational methods

We study a model of a quantum collective spin weakly coupled to a spin-polarized Markovian environment and ﬁnd that the spectrum is divided into two regions that we name normal and exceptional Liouvillian spectral phases. In the thermodynamic limit, the exceptional spectral phase displays the unique property of being made up exclusively of second order exceptional points. As a consequence, the evolution of any initial density matrix populating this region is slowed down and cannot be described by a linear combination of exponential decays. This phase is separated from the normal one by a critical line in which the density of Liouvillian eigenvalues diverges, a phenomenon analogous to that of excited-state quantum phase transitions observed in some closed quantum systems. In the limit of no bath polarization, this criticality is transferred onto the steady state, implying a dissipative quantum phase transition and the formation of a boundary time crystal.

Rafael Molina

Papers

Variational Gaussian process for sensor fusion

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

Fully Automated Blind Color Deconvolution of Histopathological Images

Approximations of posterior distributions in blind deconvolution using variational methods

Exceptional spectral phase in a dissipative collective spin model