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

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

On robust estimation of the location parameter

Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free methods (e.g., genetic algorithms or evolution strategies) help mitigate these issues, poor initialization and local optima are still concerns in highly nonconvex spaces. This paper presents a method for policy optimization based on Monte-Carlo tree search and gradient-free optimization. Our method, called Monte-Carlo tree search for policy optimization (MCTSPO), provides a better exploration-exploitation trade-off through the use of the upper confidence bound heuristic. We demonstrate improved performance on reinforcement learning tasks with deceptive or sparse reward functions compared to popular gradient-based and deep genetic algorithm baselines.

/pdf/monte-carlo-tree-search-for-policy-optimization-j12px3x1lr.pdf

Monte Carlo Tree Search for Policy Optimization

Currently, during a commercial space launch, the Federal Aviation Administration prohibits air traffic within a large column of airspace around the launch trajectory. The prohibited airspa...

Efficient Aircraft Rerouting During Commercial Space Launches

System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear systems. We empirically show that the certainty-equivalent approximation to expectation-maximization can be a reliable and scalable approach for high-dimensional deterministic systems, which are common in robotics. We formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation. The algorithm is tested on a simulated system of coupled Lorenz attractors, demonstrating its ability to identify high-dimensional systems that can be intractable for particle-based approaches. Our approach is also used to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, a model is learned that predicts the acceleration of the helicopter better than state-of-the-art approaches. The codebase for this work is available at this https URL.

/pdf/scalable-identification-of-partially-observed-systems-with-1r370k8frc.pdf

Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Mean-variance portfolio optimization problems often involve separable nonconvex terms, including penalties on capital gains, integer share constraints, and minimum position and trade sizes. We propose a heuristic algorithm for this problem based on the alternating direction method of multipliers (ADMM). This method allows for solve times in tens to hundreds of milliseconds with around 1000 securities and 100 risk factors. We also obtain a bound on the achievable performance. Our heuristic and bound are both derived from similar results for other optimization problems with a separable objective and affine equality constraints. We discuss a concrete implementation in the case where the separable terms in the objective are piecewise-quadratic, and we demonstrate their effectiveness empirically in realistic tax-aware portfolio construction problems.

Portfolio Construction as Linearly Constrained Separable Optimization

Effective evacuation efforts can save lives during natural disasters. Uncertainty makes planning optimal evacuation routes difficult. Most current approaches use open-loop deterministic linear programming and integer programming. Robust programming variants have also been proposed. In this paper, we frame the evacuation route planning problem as a Markov decision process (MDP). We solve the MDP approximately using deterministic mixed-integer programs (MIPs) solved in a closed-loop fashion. We benchmark this policy against the optimal MDP policy where tractable. We also solve deterministic integer programs in an open-loop fashion to compare against our closed-loop MIP solutions. Closed-loop integer programming techniques are shown to obtain up to 90% of the performance of the optimal MDP policy, and can outperform open-loop approaches by as much as 52%. Performance is measured in terms of number of lives saved.

Mykel J. Kochenderfer

Papers

Monte Carlo Tree Search for Policy Optimization

Efficient Aircraft Rerouting During Commercial Space Launches

Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Portfolio Construction as Linearly Constrained Separable Optimization

Closed-Loop Planning for Disaster Evacuation with Stochastic Arrivals