A Brief Analysis on the Common Mistakes Made by American Students in Learning Chinese as A Second Language

From a practical perspective, this article roughly listed and briefly analyzed the common mistakes made by American students in Learning Chinese as a second language due to the interference from their native English language. Through the presentation and analysis, it is expected that teaching Chinese as a second language to native English speakers should receive adequate attention, and more research in this filed is highly recommended for a continuing development in teaching Chinese as a second language

A Discussion on the Approach of “Connections” to Chinese Character Studies in Teaching Chinese as a Foreign Language

Based upon over ten years of his Chinese character teaching experience, the author introduced the approach of “connections” to the Chinese character studies in teaching Chinese as a foreign language. The application of “connections” would make Chinese character more interesting, feasible and effective, thus potentially improving students’ passion for and confidence in Chinese language and cultural studies

A Brief Analysis on the Common Mistakes Made by American Students in Learning Chinese as A Second Language

From a practical perspective, this article roughly listed and briefly analyzed the common mistakes made by American students in Learning Chinese as a second language due to the interference from their native English language. Through the presentation and analysis, it is expected that teaching Chinese as a second language to native English speakers should receive adequate attention, and more research in this filed is highly recommended for a continuing development in teaching Chinese as a second language

EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.Comment: 9 pages, 5 figures, 1 table. Accompanying video: https://youtu.be/eMHZBSoq0sE. Dataset: https://daniilidis-group.github.io/mvsec/, Robotics: Science and Systems 201

An interpretable approach for social network formation among heterogeneous agents

Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an “endowment vector” that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology.King Abdulaziz City of Science and Technology (Saudia Arabia)MIT Trust Data Consortiu