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

In this paper, we propose an iterative method to address the face identification problem with block occlusions. Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first fits to the errors a distribution described by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank in comparison with image size). We will show that this joint characterization is effective for describing errors with spatial continuity. Our approach is computationally efficient due to the utilization of the alternating direction method of multipliers. A special case of our fast iterative algorithm leads to the robust representation method, which is normally used to handle non-contiguous errors (e.g., pixel corruption). Extensive results on representative face databases (in constrained and unconstrained environments) document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.

Robust and Low-Rank Representation for Fast Face Identification With Occlusions

BACKGROUND

It has been proposed that a dysregulation in the balance between type T1 (IL-2, IFN-γ) and type T2 (IL-4, IL-10) cytokines may be implicated in the development of cancer.



METHODS

We determined the expression of IL-2, IL-4, IL-10, and IFN-γ in CD4 and CD8 lymphocytes by flow cytometry in 12 patients with prostate cancer and in 7 healthy subjects. In addition to the basal expression of these cytokines, their expression was also determined, following stimulation of lymphocytes with PMA (phorbol 12-mystirate 13 acetate) and ionomycin.



RESULTS

The basal expression of cytokines was scarce, while following stimulation this increased markedly. On the other hand, there was a dysregulation in the balance between T1 and T2 lymphocytes in patients with prostate cancer. To this effect, in relation to healthy subjects, we observed an increase in IL-10 expression and a decrease in IL-2 expression.



CONCLUSIONS

The disequilibrium observed in the balance between type T1 and type T2 cytokines may be implicated in the evolution of neoplastic disease. Prostate 44:271–274, 2000. © 2000 Wiley-Liss, Inc.

Analysis of type T1 and T2 cytokines in patients with prostate cancer.

We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems. As an example, a non-convex lp quasi-norm with 0 <; p <; 1 is employed as a regularization term for the image, while a simultaneous auto-regressive regularization term is selected for the blur. Nevertheless, the proposed approach is very general and it can be easily adapted to other state-of-the-art BID schemes that utilize different, application specific, image/blur regularization terms. Experimental results, obtained with simulations using blurred synthetic images and real passive millimeter-wave images, show the feasibility of the proposed method and its advantages over existing approaches.

/pdf/compressive-blind-image-deconvolution-52vf1vnfsw.pdf

Compressive Blind Image Deconvolution

SCC antigen was measured in the serum of 214 patients with benign diseases and in 251 patients with various cancers. With 2.5 micrograms/L as the upper normal limit for serum, values were positive in 2.9% of 69 healthy subjects (I), 29.0% of 214 patients with benign pathologies (II), and 41% of 217 patients with active cancer (III). The highest values in group II were for patients in renal failure (64%) or with lung diseases (40%) or head-and-neck diseases (21.2%). Specificity of SCC increased (91.1%) when we excluded patients in renal failure or with creatinine values greater than 133 mumol/L. In group III, SCC values were abnormal in 57.7% of patients with squamous cell carcinoma, but in only 9.3% of those with other histological types (P less than 0.001). In squamous cell carcinoma of the lung, cervix, or head and neck, SCC values were related to tumor stage, values being highest in patients with metastases.

SCC antigen measured in malignant and nonmalignant diseases.

This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, under the Bayesian paradigm, utilizing a variational approximation. Bayesian methods rely on image models that encapsulate prior image knowledge and avoid the ill-posedness of the image restoration problems. In this paper a new prior based on the l1 norm of vertical and horizontal first order differences of image pixel values is introduced and its parameters are estimated. The estimated HR images are compared with images provided by other HR reconstruction methods.

/pdf/bayesian-super-resolution-image-reconstruction-using-an-l1-ygzvru1v5e.pdf

Rafael Molina

Papers

Robust and Low-Rank Representation for Fast Face Identification With Occlusions

Analysis of type T1 and T2 cytokines in patients with prostate cancer.

Compressive Blind Image Deconvolution

SCC antigen measured in malignant and nonmalignant diseases.

Bayesian Super-Resolution image reconstruction using an ℓ1 prior