L'enseignement du droit en Chine et ses perspectives d'avenir

Ce texte dresse un tableau de l'enseignement du droit en Chine après quinze ans de développement accéléré. Il décrit les trois niveaux deformation offerts dans les établissements d'enseignement supérieur (formation spécialisée, formation universitaire, études avancées), et expose le contenu des programmes de sciences juridiques. Il signale également le développement notable de la formation juridique parascolaire, sous ses diverses formes. Les auteurs exposent ensuite les conditions générales dans lesquelles va se poursuivre le développement de l'enseignement du droit, et notamment celles qui découlent des réformes économiques. Ils précisent les orientations prévisibles de l'enseignement vers la formation de juristes polyvalents d'un haut niveau de compétence. Ils décrivent les programmes pilotes et les nouveaux programmes d'études avancées mis en place dans divers établissements, ainsi que le développement de programmes interdisciplinaires combinant droit et économie, droit et technologie, ou droit et gestion du commerce international.This paper presents an overview of the teaching of law in China after fifteen years of accelerated development. It describes the three levels of training provided in institutions of higher learning (specialized training, university training, advanced studies), and specifies the content of legal sciences programs. It also emphasizes the significant development of paralegal training in its various forms. The authors go on to explain the general conditions under which the teaching of law will continue, namely those conditions arising from economic reforms. They examine foreseeable orientations of teaching in the training of highly qualified and versatile legal practitioners. They describe pilot programs and new advanced study programs implemented in various institutions, as well as the development of interdisciplinary programs combining law and economics, law and technology, or law and international trade management

A Riemannian ADMM

We consider a class of Riemannian optimization problems where the objective is the sum of a smooth function and a nonsmooth function, considered in the ambient space. This class of problems finds important applications in machine learning and statistics such as the sparse principal component analysis, sparse spectral clustering, and orthogonal dictionary learning. We propose a Riemannian alternating direction method of multipliers (ADMM) to solve this class of problems. Our algorithm adopts easily computable steps in each iteration. The iteration complexity of the proposed algorithm for obtaining an $\epsilon$-stationary point is analyzed under mild assumptions. To the best of our knowledge, this is the first Riemannian ADMM with provable convergence guarantee for solving Riemannian optimization problem with nonsmooth objective. Numerical experiments are conducted to demonstrate the advantage of the proposed method

Setting the Structural Reform of Supply-Side as the Focus and Shifting the Economic Development Model

To focus on SRSS during the 13th Five-Year Plan period (2016-2020) is a necessary response to the changes of the international economic environment and to China’s goal of achieving the new normal in economic development. The main reason why the focus shifts from changing EDM to reforming the supply-side structure lies in the fact that structural reform boosts the transformation of EDM, and only by accomplishing the former will the latter be realized. Today SRSS is faced with challenges like different ideologies, insufficient conditions and backward regulations. It should be led by the Five Major Development Concepts proposed by President Xi Jinping, “Innovation, coordination, greenness, openness and sharing,” and the “supply-side” and “demand-side” must be simultaneously propelled

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Time series analysis is widely used in extensive areas. Recently, to reduce labeling expenses and benefit various tasks, self-supervised pre-training has attracted immense interest. One mainstream paradigm is masked modeling, which successfully pre-trains deep models by learning to reconstruct the masked content based on the unmasked part. However, since the semantic information of time series is mainly contained in temporal variations, the standard way of randomly masking a portion of time points will seriously ruin vital temporal variations of time series, making the reconstruction task too difficult to guide representation learning. We thus present SimMTM, a Simple pre-training framework for Masked Time-series Modeling. By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series. SimMTM further learns to uncover the local structure of the manifold, which is helpful for masked modeling. Experimentally, SimMTM achieves state-of-the-art fine-tuning performance compared to the most advanced time series pre-training methods in two canonical time series analysis tasks: forecasting and classification, covering both in- and cross-domain settings