253 research outputs found

    Conversions between barycentric, RKFUN, and Newton representations of rational interpolants

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    We derive explicit formulas for converting between rational interpolants in barycentric, rational Krylov (RKFUN), and Newton form. We show applications of these conversions when working with rational approximants produced by the AAA algorithm [Y. Nakatsukasa, O. S\`ete, L. N. Trefethen, arXiv preprint 1612.00337, 2016] within the Rational Krylov Toolbox and for the solution of nonlinear eigenvalue problems

    Is Critical Reading Indispensible to College English for General Purpose in China?

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    This paper aims to depict the current situation of application of critical reading skills and strategies among the undergraduates in reading books in English in a local university in the coastal city in China. The findings turn out that critical thinking strategies are used neither automatically nor frequently among the undergraduates. The findings are complemented by data collections from classroom observations, interviews, questionnaire, reading comprehension tests and notes taken in class. In order to have a better understanding of the reasons underlying the infrequent use of critical reading skills and strategies, a qualitative study was conducted in 12 volunteers. This study also reveals that college English for general purpose has, to some extent, contributed to improvement of students’ use of critical thinking skills in reading. It is worth pointing out that the role of College English for General Purpose is not merely for imparting use of English to students but also taking on a vital role of cultivating students’ critical thinking and enhancing the application of critical thinking skills and strategies in reading. This paper has implications for college English teachers in their teaching practice for desirability of enhancing critical thinking in college students. Explicit and systematic teaching of critical thinking strategies is surely encouraged, complete with critical writing in the process of teaching College English for the General Purpose.

    Chief Introduction for the Door of Metro made in China

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    AbstractThis paper describes the types, uses and structure of the door of Metro made in China, making an introduction of the advantages and disadvantages on different structural forms passenger compartment door

    SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion

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    The Central Pattern Generator (CPG) is adept at generating rhythmic gait patterns characterized by consistent timing and adequate foot clearance. Yet, its open-loop configuration often compromises the system's control performance in response to environmental variations. On the other hand, Reinforcement Learning (RL), celebrated for its model-free properties, has gained significant traction in robotics due to its inherent adaptability and robustness. However, initiating traditional RL approaches from the ground up presents computational challenges and a heightened risk of converging to suboptimal local minima. In this paper, we propose an innovative quadruped locomotion framework, SYNLOCO, by synthesizing CPG and RL that can ingeniously integrate the strengths of both methods, enabling the development of a locomotion controller that is both stable and natural. Furthermore, we introduce a set of performance-driven reward metrics that augment the learning of locomotion control. To optimize the learning trajectory of SYNLOCO, a two-phased training strategy is presented. Our empirical evaluation, conducted on a Unitree GO1 robot under varied conditions--including distinct velocities, terrains, and payload capacities--showcases SYNLOCO's ability to produce consistent and clear-footed gaits across diverse scenarios. The developed controller exhibits resilience against substantial parameter variations, underscoring its potential for robust real-world applications.Comment: 7 Page

    Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen

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    The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github.io/expertise-style-transfer.Comment: 11 pages, 6 figures; To appear in ACL 202
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