다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

In many applications of wireless sensor networks (WSNs), the location information of users is very important. In this paper we present a localization method without on-body sensor (LWOS). The basic idea is that when a person is standing between a pair of transceivers, the human body will attenuate the received signal. From the detected attenuation of Received Signal Strength Indication (RSSI), LWOS can detect and localize people directly utilizing the wireless communication in WSNs. No additional sensor is needed and users do not need to wear a sensor node any more. A signal-shielding device is used at the transmitter side to minify the interference of RSSI variability from multi-path effects. Experiment results show a good capability of localizing a single user in an indoor environment.

https://link.springer.com/content/pdf/10.1007/11807964_50.pdf

LWOS: a localization method without on-body sensor in wireless sensor networks

Edge-preserving smoothing is a fundamental procedure for many computer vision and graphic applications. This can be achieved with either local methods or global methods. In most cases, global methods can yield superior performance over local ones. However, local methods usually run much faster than global ones. In this paper, we propose a new global method that embeds the bilateral filter in the least squares model for efficient edge-preserving smoothing. The proposed method can show comparable performance with the state-of-the-art global method. Meanwhile, since the proposed method can take advantages of the efficiency of the bilateral filter and least squares model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the bilateral filter with its variants. They can be further modified to handle more applications. We validate the effectiveness and efficiency of the proposed method through comprehensive experiments in a range of applications.

Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing.

Elliptical splats are used to represent and render the isosurface of volume data The method consists of two steps The first step is to extract points on the isosurface by looking up the case table In the second step, properties of splats are computed based on local geometry Rendering is achieved using surface splatting algorithm The obtained results show that the extraction time of isosurfaces can be reduced by a factor of three So our approach is more appropriate for interactive visualization of large medical data than the classical marching cubes (MC) technique

Elliptical splats based isosurface visualization for volume data

Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains. Meanwhile, HyperGraph Neural Network (HGNN) is currently the de-facto method for hypergraph representation learning. However, HGNN aims at single hypergraph learning and uses a pre-concatenation approach when confronting multi-modal datasets, which leads to sub-optimal exploitation of the inter-correlations of multi-modal hypergraphs. HGNN also suffers the over-smoothing issue, that is, its performance drops significantly when layers are stacked up. To resolve these issues, we propose the Residual enhanced Multi-Hypergraph Neural Network, which can not only fuse multi-modal information from each hypergraph effectively, but also circumvent the over-smoothing issue associated with HGNN. We conduct experiments on two 3D benchmarks, the NTU and the ModelNet40 datasets, and compare against multiple state-of-the-art methods. Experimental results demonstrate that both the residual hypergraph convolutions and the multi-fusion architecture can improve the performance of the base model and the combined model achieves a new state-of-the-art. Code is available at \url{this https URL}.

Residual Enhanced Multi-Hypergraph Neural Network

Estimating motion between two frames of a video sequence, up to sub-pixel accuracy, is a critical task for many image processing applications. Efficient block matching algorithms were proposed in [1, 4, 5, 6] for motion estimation up to pixel accuracy. Applying these fast block search algorithms to up-sampled and interpolated frames can produce good results but with significant increase in computations. To reduce the number of search points, and therefore the computational cost, quadratic prediction was proposed earlier [1, 2] to predict the location of minimum block matching error, and then to limit the search window to the vicinity of the predicted location. In this paper we investigate the typical behavior of block matching error surface and propose an improved higher order prediction that models the error surface more accurately, utilizing additional local image behavior. Initial experiments have proved promising results of about 50% more improvement in PSNR compared to quadratic prediction with only a marginal increase in the computational cost.

/pdf/higher-order-prediction-for-sub-pixel-motion-estimation-xseqko7zbo.pdf

Jie Yang

Papers

LWOS: a localization method without on-body sensor in wireless sensor networks

Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing.

Elliptical splats based isosurface visualization for volume data

Residual Enhanced Multi-Hypergraph Neural Network

Higher order prediction for sub-pixel motion estimation