A Case Study of Choices of the Host University and Decisions to Stay or Leave the U.S. upon Graduation of Chinese Adult and Traditional Students

Chinese students are the largest group among all the international students. Many factors motivate them to study in the U.S. and their decision to stay or leave the U.S. after graduation. However, limited research investigates these aspects by differentiating students into non-traditional students and traditional student groups. As a result, this study conducted individual interviews to examine: 1) factors that influence Chinese students’ (non-traditional students vs. traditional students) choices of the host college or university in the U.S.; and 2) their decisions to stay or leave the U.S. after graduation. Eleven Chinese students participated in this study, including seven female students and four male students. Their ages range from 23 to 30 years old with the mean of 25.8. They are from majors across STEM and non-STEM fields. Among them, four had working experience who are considered as non-traditional students, while the rest are traditional students. Findings show that Chinese non-traditional students consider financial aid availability most when choosing the college abroad, while traditional students focused on the ranks of academic programs or colleges. In addition, most Chinese non-traditional students preferred to return to their home country. However, most traditional students would choose to remain in the U.S. after graduation. Meanwhile, most students desired to have some working experience in the U.S. It is hoped that this study would lead to a greater awareness of Chinese international students and enlighten higher education professionals and administrators with practical ideas to build a better campus environment and climate to serve this growing population

A Study on the Implementation of Teaching Mathematical Modeling Classes Based on Mathematical Learning Objectives

Mathematical modeling literacy is one of the six core literacies in high school mathematics and occupies an important place in the objectives of the high school mathematics curriculum. “The core element of mathematical modeling literacy is to abstract mathematically from real problems, express them in mathematical language, and construct models to solve them with mathematical methods. The STEAM education concept has received much attention in the education field because of its effective integration of “science, technology, engineering, art and mathematics” in the practice of education and teaching, which not only makes up for the shortage of traditional teaching in knowledge inquiry, but also helps to improve the problems of traditional classrooms. This paper explores the development of mathematical modeling literacy in high school by drawing on the STEAM education concept, and examines the effectiveness of the integration of the two through specific teaching cases

Skybridge: 3-D Integrated Circuit Technology Alternative to CMOS

Continuous scaling of CMOS has been the major catalyst in miniaturization of integrated circuits (ICs) and crucial for global socio-economic progress. However, scaling to sub-20nm technologies is proving to be challenging as MOSFETs are reaching their fundamental limits and interconnection bottleneck is dominating IC operational power and performance. Migrating to 3-D, as a way to advance scaling, has eluded us due to inherent customization and manufacturing requirements in CMOS that are incompatible with 3-D organization. Partial attempts with die-die and layer-layer stacking have their own limitations. We propose a 3-D IC fabric technology, Skybridge[TM], which offers paradigm shift in technology scaling as well as design. We co-architect Skybridge's core aspects, from device to circuit style, connectivity, thermal management, and manufacturing pathway in a 3-D fabric-centric manner, building on a uniform 3-D template. Our extensive bottom-up simulations, accounting for detailed material system structures, manufacturing process, device, and circuit parasitics, carried through for several designs including a designed microprocessor, reveal a 30-60x density, 3.5x performance per watt benefits, and 10X reduction in interconnect lengths vs. scaled 16-nm CMOS. Fabric-level heat extraction features are shown to successfully manage IC thermal profiles in 3-D. Skybridge can provide continuous scaling of integrated circuits beyond CMOS in the 21st century.Comment: 53 Page

Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance

Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.Comment: Published in AAAI 202