Evaluation and Analysis of Student Academic Burden: A Global Perspective

Student academic burden exists to varying degrees throughout the world. To reach a comprehensive and unbiased understanding of the roles of academic burden, this paper explores academic burden from different viewpoints and summarizes existing assessment indexes for it. Factors correlated with student academic burden are examined and detailed evaluations on both its advantages and disadvantages are conducted. Some suggestions are proposed to better address this issue for the benefit of student healthy growth and development

Learned Image Compression with Mixed Transformer-CNN Architectures

Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, which has two challenges: 1) how to effectively fuse the two methods? 2) how to achieve higher performance with a suitable complexity? In this paper, we propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable complexity to incorporate the local modeling ability of CNN and the non-local modeling ability of transformers to improve the overall architecture of image compression models. Besides, inspired by the recent progress of entropy estimation models and attention modules, we propose a channel-wise entropy model with parameter-efficient swin-transformer-based attention (SWAtten) modules by using channel squeezing. Experimental results demonstrate our proposed method achieves state-of-the-art rate-distortion performances on three different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional Validation) compared to existing LIC methods. The code is at https://github.com/jmliu206/LIC_TCM.Comment: Accepted by CVPR2023 (Highlight

Optical rotation of heavy hole spins by non-Abelian geometrical means

A non-Abelian geometric method is proposed for rotating of heavy hole spins in a singly positive charged quantum dot in Voigt geometry. The key ingredient is the delay-dependent non-Abelian geometric phase, which is produced by the nonadiabatic transition between the two degenerate dark states. We demonstrate, by controlling the pump, the Stokes and the driving fields, that the rotations about $y$- and $z$-axes with arbitrary angles can be realized with high fidelity. Fast initialization and heavy hole spin state readout are also possible.Comment: 7 pages, 6 figure

Modeling, Configuration and Control Optimization of Power-split Hybrid Vehicles.

Hybrid electric vehicles (HEV) represent one of the most promising fuel-saving technologies in the short-term for improving fuel economy of ground vehicles. Their viability has been amply demonstrated in a few successful commercial models. Among the common configurations, the power-split (i.e., combined parallel/series) configuration offers superior design and control flexibility and achieves highest overall efficiency. Therefore, most of the full-hybrid vehicles planned for the near future from Toyota, Lexus, GM, Chrysler, and Ford are all split hybrids. In this dissertation, a model-based configuration and control optimization analysis of power-split HEV is presented. An integrated dynamic model was first developed for power-split HEV powertrain systems. From this simulation model, a math-based universal model format is generalized. It presents different designs of power-split powertrains regardless of the various connections between gear nodes and power sources. Based on this universal format, a methodology that automatically generates dynamic models is developed. It not only enables rapid generation of powertrain models, but also allows the process of automatically exploring possible configuration designs. We next introduce a design screening process and a combined configuration and control optimization strategy. In the design screening process, various design requirements including transmission efficiency, drivability, power source component sizing are utilized to evaluate possible configurations and select valid design candidates. In the combined configuration and control optimization strategy, a control design procedure based on deterministic dynamic programming (DDP) was employed to find the optimal operation of the vehicle system and achieve the performance benchmarks for different configuration candidates. The optimal design solution is then achieved by comparing these benchmarks. This methodology allows design engineers to study powertrain configurations more scientifically and efficiently. Finally, with the DDP suggesting the potential performance benchmark of the selected powertrain configuration, two alternative control strategies, stochastic dynamic programming and equivalent consumption minimization strategy, are developed to approach this performance benchmark. Both of these two control designs can be implemented in real-time and show close agreement with the DDP results in the simulation.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57675/2/jinmingl_1.pd