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

Photographs acquired under low-light conditions require long exposure times and therefore exhibit significant blurring due to the shaking of the camera. Using shorter exposure times results in sharper images but with a very high level of noise. In this paper we address this problem and present a novel blind deconvolution algorithm for a pair of differently exposed images. We formulate the problem in a hierarchical Bayesian framework by utilizing prior knowledge on the unknown image and blur, and also on the dependency between two observed images. By incorporating a fully Bayesian analysis, the developed algorithm estimates all necessary algorithm parameters along with the unknowns, such that no user-intervention is needed. Moreover, we employ a variational Bayesian inference procedure, which allows for the statistical compensation of errors occurring at different stages of the restoration, and also provides uncertainties of the estimates. Experimental results demonstrate the high restoration performance of the proposed algorithm.

/pdf/bayesian-blind-deconvolution-from-differently-exposed-image-3i3bs730k2.pdf

Bayesian blind deconvolution from differently exposed image pairs

Juan Salvador-Sánchez,1 Luis M. Canonico,2 Ana Pérez-Rodríguez,1 Tarik P. Cysne,3 Yuriko Baba,4 Vito Clericò,1 Marc Vila,2, 5, 6 Daniel Vaquero,1 Juan Antonio Delgado-Notario,7 José M. Caridad,1 Kenji Watanabe,8 Takashi Taniguchi,9 Rafael A. Molina,10 Francisco Domínguez-Adame,4 Stephan Roche,2, 11 Enrique Diez,1 Tatiana G. Rappoport,12, 13, ∗ and Mario Amado1, † Nanotechnology Group, USAL—Nanolab, University of Salamanca Plaza de la Merced, Edificio Trilingüe, 37008, Salamanca, Spain. Catalan Institute of Nanoscience and Nanotechnology CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Spain. Instituto de Física, Universidade Federal Fluminense, 24210-346 Niterói RJ, Brazil. GISC, Departamento de Física de Materiales, Universidad Complutense, 28040 Madrid, Spain. Department of Physics, University of California Berkeley, California 94720, USA Materials Sciences Division, Lawrence Berkeley National Laboratory Berkeley, California 94720, USA CENTERA Laboratories, Institute of High Pressure Physics Polish Academy of Sciences, Warsaw 01-142, Poland. Research Center for Functional Materials, National Institute for Materials Science 1-1 Namiki, Tsukuba 305-0044, Japan. International Center for Materials Nanoarchitectonics, National Institute for Materials Science 1-1 Namiki, Tsukuba 305-0044, Japan. Instituto de Estructura de la Materia, IEM-CSIC, E-28006 Madrid, Spain. ICREA–Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain Instituto de Telecomunicações, Instituto Superior Tecnico University of Lisbon, Avenida Rovisco Pais 1, Lisboa, 1049001 Portugal Instituto de Física, Universidade Federal do Rio de Janeiro C.P. 68528, 21941-972 Rio de Janeiro RJ, Brazil (Dated: June 10, 2022)

Generation and control of non-local chiral currents in graphene superlattices by orbital Hall effect

We describe in this work how the Bayesian paradigm can be applied to a deconvolution problem in optical astronomy. The use of robust statistics in this process is also discussed.

Deconvolution in Optical Astronomy. A Bayesian Approach

Improved separation of alpha-amylase isoenzymes.

Semantic information is widely used in the deep learning literature to improve the performance of visual media processing. In this work, we propose a semantic prior based Generative Adversarial Network (GAN) model for video super-resolution. The model fully utilizes various texture styles from different semantic categories of video-frame patches, contributing to more accurate and efficient learning for the generator. Based on the GAN framework, we introduce the semantic prior by making use of the spatial feature transform during the learning process of the generator. The patch-wise semantic prior is extracted on the whole video frame by a semantic segmentation network. A hybrid loss function is designed to guide the learning performance. Experimental results show that our proposed model is advantageous in sharpening video frames, reducing noise and artifacts, and recovering realistic textures.

Rafael Molina

Papers

Bayesian blind deconvolution from differently exposed image pairs

Generation and control of non-local chiral currents in graphene superlattices by orbital Hall effect

Deconvolution in Optical Astronomy. A Bayesian Approach

Improved separation of alpha-amylase isoenzymes.

Semantic Prior Based Generative Adversarial Network for Video Super-Resolution