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

Segmentation of histopathological images is an essential task for cancer diagnosis and prognosis in computational pathology. Unfortunately, Machine Learning (ML) techniques need large labeled datasets to train accurate segmentation algorithms that generalize well. A possible solution to alleviate this burden is crowdsourcing, which distributes the effort of labeling among a group of (non-expert) individuals. The bias and noise from less experienced annotators may hamper the performance of machine learning techniques. So far, crowdsourcing approaches in ML leveraging these noisy labels achieve promising results in classification. However, crowdsourcing segmentation is still a challenge in histopathological images. This paper presents a novel crowdsourcing approach to the segmentation of Triple Negative Breast Cancer images. Our method is based on the UNet architecture incorporating a pre-trained ResNet-34 as a backbone. The noisy behavior of the annotators is modeled with a coupled network. Our methodology is validated on a real-world dataset annotated by medical students, where five classes are distinguished. The results show that our method with crowd labels achieves a high level of accuracy in segmentation (DICE: 0.7578), outperforming the well-known STAPLE (DICE: 0.7039) and close to the segmentation model using expert labels (DICE: 0.7723). In conclusion, the initial results of our work suggest that crowdsourcing is a feasible approach to segmentation in histopathological images https://github.com/wizmik12/CRowd_Seg . 

Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students

Impaired ex vivo production of TNF-α by whole blood cells in the presence of PSA

Fast and accurate algorithms are essential for the efficient search and retrieval of the huge amount of video data that is generated for different purposes and applications every day. The interesting properties of sparse representation and the new sampling theory named Compressive Sensing (CS) constitute the core of the new approach to video representation and retrieval we are presenting in this paper to deal with the search of noisy video clips with also possibly missing frames. Once the representation (where sparsity is expected) has been chosen and the observations have been taken, the proposed approach utilizes Bayesian modeling and inference to tackle the retrieval problem. In order to speed up the inference process the use of Principal Components Analysis (PCA) to provide an alternative representation of the frames is analyzed. Experimental results validate the proposed approach to the retrieval of video clips with missing frames as well as its robustness against noise.

/pdf/retrieval-of-video-clips-with-missing-frames-using-sparse-2ta2cj9w00.pdf

Retrieval of video clips with missing frames using sparse Bayesian reconstruction

With the increase of popularity of high and ultra high definition displays, the need to improve the quality of content already obtained at much lower resolutions has grown. Since current video super-resolution methods are trained with a single degradation model (usually bicubic downsampling), they are not robust to mismatch between training and testing degradation models, in which case their performance deteriorates. In this work we propose a new Convolutional Neural Network for video super resolution which is robust to multiple degradation models and uses the pseudo-inverse image formation model as part of the network architecture during training. The experimental validation shows that our approach outperforms current state of the art methods.

Multiple-Degradation Video Super-Resolution with Direct Inversion of the Low-Resolution Formation Model

We experimentally study the infinite-size limit of the Dicke model of quantum optics with a parity-breaking deformation strength that couples the system to an external bosonic reservoir. We focus on the dynamical consequences of such symmetry-breaking, which makes the classical phase space asymmetric with non-equivalent energy wells. We present an experimental implementation of the classical version of the deformed Dicke model using a state-of-the-art bi-parametric electronic platform. Our platform constitutes a playground for studying representative phenomena of the deformed Dicke model in electrical circuits with the possibility of externally controlling parameters and initial conditions. In particular, we investigate the dynamics of the ground state, various phase transitions, and the asymmetry of the energy wells as a function of the coupling strength $\gamma$ and the deformation strength $\alpha$ in the resonant case. Additionally, to characterize the various behavior regimes, we present a two-dimensional phase diagram as a function of the two intrinsic system parameters. The onset of chaos is also analyzed experimentally. Our findings provide a clear connection between theoretical predictions and experimental observations, demonstrating the usefulness of our bi-parametric electronic setup.

Rafael Molina

Papers

Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students

Impaired ex vivo production of TNF-α by whole blood cells in the presence of PSA

Retrieval of video clips with missing frames using sparse Bayesian reconstruction

Multiple-Degradation Video Super-Resolution with Direct Inversion of the Low-Resolution Formation Model

Experimental observation of phase transitions of a deformed Dicke model using a reconfigurable, biparametric electronic platform