[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

The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC)  

Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC  

Endorsed by the European Stroke Organisation (ESO)

/pdf/2016-esc-guidelines-for-the-management-of-atrial-ba0601yb9m.pdf

2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS

/pdf/2016-esc-guidelines-for-the-management-of-atrial-36b2cidm1x.pdf

2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS: The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC). Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Endorsed by the European Stroke Organisation (ESO)

Supplementary Table 9, column 'Edoxaban', row 'eGFR category', '95 mL/min' (page 15). The cell should be coloured green instead of yellow. It should also read "60 mg"instead of "60 mg (use with caution in 'supranormal' renal function)."In the above-indicated cell, a footnote has also been added to state: "Edoxaban should be used in patients with high creatinine clearance only after a careful evaluation of the individual thromboembolic and bleeding risk."Supplementary Table 9, column 'Edoxaban', row 'Dose reduction in selected patients' (page 16). The cell should read "Edoxaban 60 mg reduced to 30 mg once daily if any of the following: creatinine clearance 15-50 mL/min, body weight <60 kg, concomitant use of dronedarone, erythromycin, ciclosporine or ketokonazole"instead of "Edoxaban 60 mg reduced to 30 mg once daily, and edoxaban 30 mg reduced to 15mg once daily, if any of the following: creatinine clearance of 30-50 mL/min, body weight <60 kg, concomitant us of verapamil or quinidine or dronedarone."

/pdf/2020-esc-guidelines-for-the-diagnosis-and-management-of-38dpic1xu8.pdf

2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS).

This paper introduces an efficient motion planning method for on-road driving of the autonomous vehicles, which is based on the rapidly exploring random tree (RRT) algorithm. RRT is an incremental sampling-based algorithm and is widely used to solve the planning problem of mobile robots. However, due to the meandering path, the inaccurate terminal state, and the slow exploration, it is often inefficient in many applications such as autonomous vehicles. To address these issues and considering the realistic context of on-road autonomous driving, we propose a fast RRT algorithm that introduces a rule-template set based on the traffic scenes and an aggressive extension strategy of search tree. Both improvements lead to a faster and more accurate RRT toward the goal state compared with the basic RRT algorithm. Meanwhile, a model-based prediction postprocess approach is adopted, by which the generated trajectory can be further smoothed and a feasible control sequence for the vehicle would be obtained. Furthermore, in the environments with dynamic obstacles, an integrated approach of the fast RRT algorithm and the configuration-time space can be used to improve the quality of the planned trajectory and the replanning. A large number of experimental results illustrate that our method is fast and efficient in solving planning queries of on-road autonomous driving and demonstrate its superior performances over previous approaches.

Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

Probability iterative closest point algorithm for m-D point set registration with noise

Feature concatenation multi-view subspace clustering

Background
Current guidelines recommend beta-blocker as the first-line preventive treatment of atrial fibrillation (AF) after cardiac surgery; if beta-blocker therapy is contraindicated, then amiodarone is recommended. There is still lack of strong evidence of directly comparing the efficacy of amiodarone and beta-blocker in preventing postoperative AF (POAF).

Aim
This meta-analysis was to determine whether amiodarione and beta-blocker are equally effective and safe, or one is superior in preventing POAF.

Methods
We searched the Medline, Web of Science, Cochrane Library databases and clinical trial databases for related articles published from January 1990 to October 2011. The primary outcome was development of AF after cardiac surgery. We used random-effects model when there was significant heterogeneity between trials and fixed-effects method when heterogeneity was negligible. Moreover, subgroup and sensitivity analyses were also performed.

Results
We identified totally six trials, which involved 1033 patients. The amiodarone group did not significantly differ from the beta-blocker group in AF occurrence (risk ratio 0.77, 95% confidence interval 0.55 to 1.06, P = 0.11) or the length of hospital stay (weighted mean difference −0.05 day, 95% confidence interval −0.64 to 0.54, P = 0.86). Subgroup analysis stratified by different beta-blockers revealed that amiodarone significantly improved POAF as compared with propranolol. In addition, there was no difference in adverse events after operation.

Conclusion
These data indicate that the occurrence of AF and length of hospital stay after surgery are similar in the amiodarone and beta-blocker groups.

Meta-analysis of amiodarone versus beta-blocker as a prophylactic therapy against atrial fibrillation following cardiac surgery

This paper presents an central difference Kalman filter (CDKF) based Simultaneous Localization and Mapping (SLAM) algorithm, which is an alternative to the classical extended Kalman filter based SLAM solution (EKF-SLAM). EKF-SLAM suffers from two important problems, which are the calculation of Jacobians and the linear approximations to the nonlinear models. They can lead the filter to be inconsistent. To overcome the serious drawbacks of the previous frameworks, Sterling's polynomial interpolation method is employed to approximate nonlinear models. Combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem. The proposed approach improves the filter consistency and state estimation accuracy. Both simulated experiments and bench mark data set are used to demonstrating the superiority of the proposed algorithm.

Jihua Zhu

Papers

Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving

Probability iterative closest point algorithm for m-D point set registration with noise

Feature concatenation multi-view subspace clustering

Meta-analysis of amiodarone versus beta-blocker as a prophylactic therapy against atrial fibrillation following cardiac surgery

A SLAM algorithm based on the central difference Kalman filter