Chinese and North American Culture: a New Perspective in Linguistics Studies

We explored the two cultures in the two countries. There has been discussed on Chinese culture and North American culture. Chinese language, ceramics, architecture, music, dance, literature, martial arts, cuisine, visual arts, philosophy, business etiquette, religion, politics, and history have global influence, while its traditions and festivals are also celebrated, instilled, and practiced by people around the world. The culture of North America refers to the arts and other manifestations of human activities and achievements from the continent of North America. The American way of life or simply the American way is the unique lifestyle of the people of the United States of America. It refers to a nationalist ethos that adheres to the principle of life, liberty and the pursuit of happiness

Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended Reality

Real-time Stereo Matching is a cornerstone algorithm for many Extended Reality (XR) applications, such as indoor 3D understanding, video pass-through, and mixed-reality games. Despite significant advancements in deep stereo methods, achieving real-time depth inference with high accuracy on a low-power device remains a major challenge. One of the major difficulties is the lack of high-quality indoor video stereo training datasets captured by head-mounted VR/AR glasses. To address this issue, we introduce a novel video stereo synthetic dataset that comprises photorealistic renderings of various indoor scenes and realistic camera motion captured by a 6-DoF moving VR/AR head-mounted display (HMD). This facilitates the evaluation of existing approaches and promotes further research on indoor augmented reality scenarios. Our newly proposed dataset enables us to develop a novel framework for continuous video-rate stereo matching. As another contribution, our dataset enables us to proposed a new video-based stereo matching approach tailored for XR applications, which achieves real-time inference at an impressive 134fps on a standard desktop computer, or 30fps on a battery-powered HMD. Our key insight is that disparity and contextual information are highly correlated and redundant between consecutive stereo frames. By unrolling an iterative cost aggregation in time (i.e. in the temporal dimension), we are able to distribute and reuse the aggregated features over time. This approach leads to a substantial reduction in computation without sacrificing accuracy. We conducted extensive evaluations and comparisons and demonstrated that our method achieves superior performance compared to the current state-of-the-art, making it a strong contender for real-time stereo matching in VR/AR applications

Estimation and inference for high-dimensional nonparametric additive instrumental-variables regression

The method of instrumental variables provides a fundamental and practical tool for causal inference in many empirical studies where unmeasured confounding between the treatments and the outcome is present. Modern data such as the genetical genomics data from these studies are often high-dimensional. The high-dimensional linear instrumental-variables regression has been considered in the literature due to its simplicity albeit a true nonlinear relationship may exist. We propose a more data-driven approach by considering the nonparametric additive models between the instruments and the treatments while keeping a linear model between the treatments and the outcome so that the coefficients therein can directly bear causal interpretation. We provide a two-stage framework for estimation and inference under this more general setup. The group lasso regularization is first employed to select optimal instruments from the high-dimensional additive models, and the outcome variable is then regressed on the fitted values from the additive models to identify and estimate important treatment effects. We provide non-asymptotic analysis of the estimation error of the proposed estimator. A debiasing procedure is further employed to yield valid inference. Extensive numerical experiments show that our method can rival or outperform existing approaches in the literature. We finally analyze the mouse obesity data and discuss new findings from our method.Comment: Submitted versio

ALens: An Adaptive Domain-Oriented Abstract Writing Training Tool for Novice Researchers

The significance of novice researchers acquiring proficiency in writing abstracts has been extensively documented in the field of higher education, where they often encounter challenges in this process. Traditionally, students have been advised to enroll in writing training courses as a means to develop their abstract writing skills. Nevertheless, this approach frequently falls short in providing students with personalized and adaptable feedback on their abstract writing. To address this gap, we initially conducted a formative study to ascertain the user requirements for an abstract writing training tool. Subsequently, we proposed a domain-specific abstract writing training tool called ALens, which employs rhetorical structure parsing to identify key concepts, evaluates abstract drafts based on linguistic features, and employs visualization techniques to analyze the writing patterns of exemplary abstracts. A comparative user study involving an alternative abstract writing training tool has been conducted to demonstrate the efficacy of our approach.Comment: Accepted by HHME/CHCI 202

On the global convergence of randomized coordinate gradient descent for non-convex optimization

In this work, we analyze the global convergence property of coordinate gradient descent with random choice of coordinates and stepsizes for non-convex optimization problems. Under generic assumptions, we prove that the algorithm iterate will almost surely escape strict saddle points of the objective function. As a result, the algorithm is guaranteed to converge to local minima if all saddle points are strict. Our proof is based on viewing coordinate descent algorithm as a nonlinear random dynamical system and a quantitative finite block analysis of its linearization around saddle points