Exploring How Tourism Majors’ Perceived Professional Competence Influences Their Choice of Tourism Careers in China

With the rapid development of tourism in China, various economic sectors such as agriculture, sports, food and beverages, cultural heritage, and outdoor adventure have become integrated into the tourism industry. China\u27s tourism industry has changed and these changes now require tourism practitioners to adapt. Chinese universities must also adapt their tourism curriculum and educational practices to reflect changes in the tourism sector. Research suggests that university training programs should increase their emphasis on developing students’ professional competency and expand the range of competencies they address in their curriculum. At the same time, tourism enterprises in China are unable to recruit enough competent employees, resulting in a shortage of qualified workers. To improve the professional competence of tourism students in China, tourism education departments must respond to the needs of, and changes in, the tourism industry. The purpose of this two-phase, mixed-method exploratory design study is to identify the professional competencies that tourism experts in China believe tourism students must acquire, and examine the relationship between these competencies, tourism students’ perceptions of professional competence, and their intent to pursue a career in the tourism sector. The present study began with basic qualitative research in the form of interviews with Chinese tourism experts in China to identify the professional competencies that Chinese tourism students need. During the second stage of research, these results were incorporated into a written questionnaire that was distributed to approximately 800 tourism majors in China. Through the analysis of survey data, we examined the relationship between student demographics, their perceived professional competence, and their intent to pursue a career in the tourism sector. The study results indicate that the causal relationship between students\u27 perceived professional competence and students\u27 intention for a career in tourism is valid. These findings provide theoretical support for improving tourism students\u27 perceived professional competency. The results also suggest strategies to increase the percentage of tourism students who will choose to work in the tourism sector upon graduation

SINCO: A Novel structural regularizer for image compression using implicit neural representations

Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model with fewer weights than the number of image pixels to map the coordinates of the image to corresponding pixel values. While traditional training approaches for INRs are based on enforcing pixel-wise image consistency, we propose to further improve image quality by using a new structural regularizer. We present structural regularization for INR compression (SINCO) as a novel INR method for image compression. SINCO imposes structural consistency of the compressed images to the groundtruth by using a segmentation network to penalize the discrepancy of segmentation masks predicted from compressed images. We validate SINCO on brain MRI images by showing that it can achieve better performance than some recent INR methods

A Plug-and-Play Image Registration Network

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approach comes with a limitation that it exclusively relies on a pre-trained CNN without explicitly enforcing fidelity between the registered image and the reference. We present plug-and-play image registration network (PIRATE) as a new DIR method that addresses this issue by integrating an explicit data-fidelity penalty and a CNN prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs" it into an iterative method as a regularizer. We additionally present PIRATE+ that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ). PIRATE+ interprets the fixed-point iteration of PIRATE as a network with effectively infinite layers and then trains the resulting network end-to-end, enabling it to learn more task-specific information and boosting its performance. Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR

Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model

Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and deep equilibrium models (DEQ) are two DMBA frameworks that have received significant attention. The key difference between the two is that the image prior in DEQ is trained by using a specific measurement model, while that in PnP is trained as a general image denoiser. This difference is behind a common assumption that PnP is more robust to changes in the measurement models compared to DEQ. This paper investigates the robustness of DEQ priors to changes in the measurement models. Our results on two imaging inverse problems suggest that DEQ priors trained under mismatched measurement models outperform image denoisers