Research on the Motivation and Attitude of College students' Physical Education in Taiwan

College students' physical education plays an important role in physical activity and cultivates the concept of independent health management. At present, what kind of learning attitude do Taiwan college students face in physical education? What motivation does the student influence the attitude of the physical education? What is the relevance? All of the above are the purpose of this study. The research method adopts the questionnaire survey method, and the survey data adopts descriptive statistical analysis, independent sample t test, single factor variance analysis, LSD post hoc comparison method, and typical correlation analysis. Research results: 1. The different background variables of Taiwanese college students are that the main motivation factor of physical education is to obtain good health fitness for "physical health". 2. Taiwanese college students have different background variables. They all think that the "cognitive learning" of physical education is the main factor of attitude, that is, the knowledge about health care and sports skills. 3. There is a positive correlation between learning motivation and learning attitude (ρ=.90). Learning motivation is one of the important factors affecting learning attitude. Research conclusions: 1. The factors of Taiwanese male and female college students' motivation for learning in physical education are mainly based on "physical health". 2. Freshmen have higher motivations and learning attitudes in physical education than second-grade to fourth-grade. 3. Taiwan female college students average 1 or 2 times per week, male college students have the most athletes 2 to 3 times per week, more than 90% of college students like sports. 4. There is a positive correlation between learning motivation and learning attitude, indicating that the stronger the attribute of learning motivation "physical health", the higher the student's learning attitude. 5. Satisfying students' motivation for learning helps students to learn positively. 6. Another important task of the college physical education class is to prepare students for future lifelong sports

Thermodynamics of f(R)f(R) Gravity with Disformal Transformation

We study thermodynamics in $f(R)$ gravity with the disformal transformation. The transformation applied to the matter Lagrangian has the form of \g_{\m\n} = A(\phi,X)g_{\m\n} + B(\phi,X)\pa_\m\f\pa_\n\f with the assumption of the Minkowski matter metric \g_{\m\n} = \e_{\m\n}, where $\phi$ is the disformal scalar and $X$ is the corresponding kinetic term of $\phi$. We verify the generalized first and second laws of thermodynamics in this disformal type of $f(R)$ gravity in the Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) universe. In addition, we show that the Hubble parameter contains the disformally induced terms, which define the effectively varying equations of state for matter.Comment: 23 pages, no figure, published version in Entropy 21, 172 (2019

Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling

Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages. However, dense retrievers typically require a substantial amount of paired data, which poses even greater challenges in multilingual scenarios. This paper introduces UMR, an Unsupervised Multilingual dense Retriever trained without any paired data. Our approach leverages the sequence likelihood estimation capabilities of multilingual language models to acquire pseudo labels for training dense retrievers. We propose a two-stage framework which iteratively improves the performance of multilingual dense retrievers. Experimental results on two benchmark datasets show that UMR outperforms supervised baselines, showcasing the potential of training multilingual retrievers without paired data, thereby enhancing their practicality. Our source code, data, and models are publicly available at https://github.com/MiuLab/UMRComment: Accepted to Findings of EACL 202

CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation

Conversational search provides a natural interface for information retrieval (IR). Recent approaches have demonstrated promising results in applying dense retrieval to conversational IR. However, training dense retrievers requires large amounts of in-domain paired data. This hinders the development of conversational dense retrievers, as abundant in-domain conversations are expensive to collect. In this paper, we propose CONVERSER, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. Specifically, we utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus. Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable performance to fully-supervised models, demonstrating the effectiveness of our proposed framework in few-shot conversational dense retrieval. All source code and generated datasets are available at https://github.com/MiuLab/CONVERSERComment: Accepted to SIGDIAL 202

Using Virtual Instrument in Teaching Automatic Measurement Technology Course

The use of an automatic measurement technology is highly important in current industries. The technology has been sued in various applications such as environment monitoring, quality control of production line, and medical disease analysis. Automatic measurement technology requires programming, facilities integration, control application, function innovation, and maintenance technology. Developing suitable teaching equipment that can satisfy the demand of industry-orientation Automatic Measurement Technology Course (AMTC) is a challenge. In this study, a virtual instrument is introduced to solve the problem. LabVIEW, which is utilized to design virtual instruments, provides powerful functions for instrument control and measurement. Therefore, in this proposed AMTC, anbsp LabVIEW-based virtual instrument system is established as teaching equipment for undergraduate students in colleges of engineering or technology

A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges