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

Monofunctional inducers (MIs) enhance phase 2 enzymes such as nicotinamide-adenine-dinucleotide-phosphate [NAD(P)H] quinone oxidoreductase (NQO1) without modifying oxidation enzymes. The induction of these protective enzymes appears to be mediated by genetic regulatory elements in their promoter regions known as the antioxidant response element (ARE). The aim of this study was to identify, through an in vitro study, which of the 30 fruits and vegetables commonly consumed in Catalonia, Spain, contain MIs of NQO1. We assayed the capacity of extracts of these fruits and vegetables to induce NQO1 [by more than 1.5-fold: ratio of induction (cells treated/control) >1.5, 8-mg/ml dose] in two murine hepatoma cell lines: Hepa 1c1c7 and BPrC1, a modified cell line that possesses a nonfunctional aryl hydrocarbon receptor nuclear translocator system and is thus nonresponsive to bifunctional inducers. We also used a third cell line, papiloma (PE) murine keratinocytes, a stably transfected cell line with an ARE-luc+ plasmid (AREPE cell line) for verifying induction through the ARE with a simple luminescence screening assay. Broccoli (Hepa 1c1c7, ratio=5.5; BPrC1, ratio=2.3), calcot (Allium cepa L.) (Hepa 1c1c7, ratio=4.7; BPrC1, ratio=.5), green onion (Hepa 1c1c7, ratio=4.6; BPrC1, ratio=2), green cabbage (Hepa 1c1c7, ratio=3.6; BPrC1, ratio=2.7), purple cabbage (Hepa 1c1c7, ratio=3.4; BPrC1, ratio=2), and black cabbage (Hepa 1c1c7, ratio=3; BPrC1, ratio=3) were active NQO1 inducers in both murine hepatoma cell lines. Extracts from broccoli (ratio=3.5), calcot (ratio=4.8), cauliflower (ratio=4.2), cabbage (ratio=2.2), green onion (ratio=3.2), green cabbage (ratio=3.6), black cabbage (ratio=4.5), and purple cabbage (ratio=3.7) were confirmed to contain MIs in the AREPE cell line. These results are very similar to those described for vegetables consumed in the United States, with the exception of calcot, which is common in Catalonia but is not grown or consumed widely in the United States.

Induction of NAD(P)H quinone oxidoreductase by vegetables widely consumed in Catalonia, Spain.

In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ?1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.

/pdf/super-resolution-of-multispectral-images-using-l-1-image-1gxkhx84g0.pdf

Super Resolution of Multispectral Images using l 1 Image Models and Interband Correlations

A new super resolution Bayesian method for pansharpening multispectral images which incorporates sensor characteristics to model the observation process of both the panchromatic and the multispectral images is presented in this paper. The quality of the pansharpened images is assessed both qualitatively and quantitatively. Preliminary results are very promising: the method succeeded in preserving the spectral information while increasing the spatial resolution of the Landsat ETM+ multispectral image. Further research will focus on extending the method to fuse imagery acquired by other sensors.

/pdf/a-new-super-resolution-bayesian-method-for-pansharpening-6yc1eccbog.pdf

A new super resolution Bayesian method for pansharpening Landsat ETM+ imagery

https://pure.hud.ac.uk/ws/files/12535345/Villena_et_al_Image_super_resolution_for_outdoor_digital_forensics_ComputersInIndustry_Feb2018.pdf

Image super-resolution for outdoor digital forensics. Usability and legal aspects

SPECT (single photon emission computed tomography) is used in nuclear medicine to determine the distribution of a radioactive isotope within a patient from tomographic views or projection data. These images are severely degraded due to the presence of noise and several physical factors like attenuation and scattering. We use, within the Bayesian framework, a compound Gauss Markov random field (CGMRF) as prior model to reconstruct such images. In order to find the maximum a posteriori (MAP) estimate we propose a new iterative method, which is stochastic for the line process and deterministic for the reconstruction. The proposed method is tested and compared with other reconstruction methods on both synthetic and real SPECT images.

https://decsai.ugr.es/vip/files/conferences/01icassp.pdf

Rafael Molina

Papers

Induction of NAD(P)H quinone oxidoreductase by vegetables widely consumed in Catalonia, Spain.

Super Resolution of Multispectral Images using l 1 Image Models and Interband Correlations

A new super resolution Bayesian method for pansharpening Landsat ETM+ imagery

Image super-resolution for outdoor digital forensics. Usability and legal aspects

SPECT image reconstruction using compound models