Country background report for Korea

노트 : OECD Review on Evaluation and Assessment Frameworks for Improving School Outcome

On The Source and Five Characters of Romeo and Juliet

In composing Romeo and Juliet, Shakespeare did not have to invent its basic story, because it was common dramatic practice that the Elizabethan dramatists drew upon known histories, legends and stories for the plot material of their plays. The story on which Romeo and Juliet is formed was originally written by Luigi da Porto of Italy. His tale of the noble lovers of Verona, Giulietta and Romeo, published in 1530, mainly agrees with Shakespeare's play in its plot, except a notable variant that Giulietta awakes and exchanges words with Romeo before his death of the poison, and then she dies by drawing her breath and holding it long. This tale was so popular in Italy that it was soon retold both in prose and verse by many writers, and later dramatized in Italy, Spain and France. In 1554 Matteo Bandello of Italy published his novella, Romeo e Giulietta, based on da Porto

Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-off between fairness and the maximum welfare achievable by the optimal policy. We evaluate the methods in a simulation study and illustrate them in a real-world case study

Shakespeare's Treatment of the Source in Coriolanus

In the Elizabethan Age, plays, "often put together by writers hired to revise and patch the work of others, were scarcely regarded as literature" ,1l and playwrights had no need to be original in the plots of their plays. Likewise, Shakespeare did not demonstrate his originality in the invention of novel plots. He found the subject-matter of his plays in various sources-familiar stories, historical chronicles, biographies, or plays written by his predecessors. As a matter of fact, each of all his plays can be traced to at least one definite source, with only one exception, Love's Labour's Lost, the plot of which is thought to have been invented by the dramatist himself, though it contains many contemporary topical allusions. A comparison of anyone of Shakespeare's plays with its source is "a sound instinct and a natural and fruitful approach"2J to the study of his dramaturgy. Certainly it may be one of the most effective ways of understanding not only the play itself, but the essentials of his unequalled dramatic art. Coriolanus is a good example of his resourcefulness in transforming the lifeless dull story of the original into a higher artistic form. Shakespeare found the source of Coriolanus chiefly in Plutarch's Parallel Lives, in which the lives and careers of celebrated Greeks and Romans were described in pairs-e. g. Alexander and Caesar, Dion and Brutus, Demetrius and Antony, etc.-and comparisons between them were given

Counterfactual Mean-variance Optimization

We study a new class of estimands in causal inference, which are the solutions to a stochastic nonlinear optimization problem that in general cannot be obtained in closed form. The optimization problem describes the counterfactual state of a system after an intervention, and the solutions represent the optimal decisions in that counterfactual state. In particular, we develop a counterfactual mean-variance optimization approach, which can be used for optimal allocation of resources after an intervention. We propose a doubly-robust nonparametric estimator for the optimal solution of the counterfactual mean-variance program. We go on to analyze rates of convergence and provide a closed-form expression for the asymptotic distribution of our estimator. Our analysis shows that the proposed estimator is robust against nuisance model misspecification, and can attain fast $\sqrt{n}$ rates with tractable inference even when using nonparametric methods. This result is applicable to general nonlinear optimization problems subject to linear constraints whose coefficients are unknown and must be estimated. In this way, our findings contribute to the literature in optimization as well as causal inference. We further discuss the problem of calibrating our counterfactual covariance estimator to improve the finite-sample properties of our proposed optimal solution estimators. Finally, we evaluate our methods via simulation, and apply them to problems in healthcare policy and portfolio construction

Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models

We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we ``carve'' the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety

PLLay: Efficient Topological Layer based on Persistence Landscapes

29 pages, 7 figuresInternational audienceWe propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure. In this work, we show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of PLLay is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide a novel adaptation for the DTM function-based filtration, and show that the proposed layer is robust against noise and outliers through a stability analysis. We demonstrate the effectiveness of our approach by classification experiments on various datasets