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

SCC antigen (Ag) is a tumor-associated Ag (TAA) obtained from squamous cell carcinoma of the uterine cervix. This study reports the evaluation of this TAA in patients with head and neck malignant diseases and its possible prognostic value. Serum samples from 28 patients with benign head and neck diseases from 399 patients with cancer were obtained prior to treatment. SCC Ag serum levels were determined by radioimmunoassay using 2.5 ng/ml as the upper limit of normality. Elevated SCC Ag serum levels were found in 14% of 28 patients with benign diseases, in 29% of 217 patients with primary tumors, in 48% of 46 patients with recurrence (43% in locoregional, 64% in metastases) and in 4% of 136 patients with no evidence of disease. In patients with primary tumors, SCC Ag serum levels were related to nodal involvement and tumor location with significantly higher levels in node-positive patients (p = 0.001) and in tumors located in the nasopharynx and piriform sinus (p = 0.02). Presurgical SCC Ag serum levels in patients with primary tumors had prognostic value with shorter disease-free survival in those patients with abnormal values of this TAA (p < 0.001), in both, node-negative and node-positive patients (p < 0.01). Multivariate analyses showed that SCC Ag is a significant independent predictor of disease-free survival even when other prognostic factors are considered. In conclusion, pretreatment SCC Ag serum levels are an independent prognostic indicator in patients with head and neck malignancies.

Prognostic significance of SCC antigen in the serum of patients with head and neck cancer.

Using machine learning to detect and localize concealed objects in passive millimeter-wave images

A method for simultaneously estimating the high-resolution frames and the corresponding motion field from a compressed low-resolution video sequence is presented. The algorithm incorporates knowledge of the spatio-temporal correlation between low and high-resolution images to estimate the original high-resolution sequence from the degraded low-resolution observation. Information from the encoder is also exploited, including the transmitted motion vectors, quantization tables, coding modes and quantizer scale factors. Simulations illustrate an improvement in the peak signal-to-noise ratio when compared with traditional interpolation techniques and are corroborated with visual results.

Bayesian high-resolution reconstruction of low-resolution compressed video

In a prospective study, CA 125 and CA 19.9 serum levels were measured in 229 patients with ovarian cancer [121 with active disease, 108 in complete remission (CR)], and in 20 patients with other malig

A prospective study of tumor markers CA 125 and CA 19.9 in patients with epithelial ovarian carcinomas.

Passive millimeter wave images (PMMWIs) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render these tasks challenging. In this paper, we discuss a deep learning approach to this detection/localization problem. The effect of the nonstationary acquisition noise on different architectures is analyzed and discussed. A comparison with shallow architectures is also presented. The achieved detection accuracy defines a new state of the art in object detection on PMMWIs. The low computational training and testing costs of the solution allow its use in real-time applications.

Rafael Molina

Papers

Prognostic significance of SCC antigen in the serum of patients with head and neck cancer.

Using machine learning to detect and localize concealed objects in passive millimeter-wave images

Bayesian high-resolution reconstruction of low-resolution compressed video

A prospective study of tumor markers CA 125 and CA 19.9 in patients with epithelial ovarian carcinomas.

Deep CNNs for Object Detection Using Passive Millimeter Sensors