Teacher-student collaborative assessment (TSCA) in integrated language classrooms

Assessing students’ productive performance is challenging in China because large class sizes inevitably lead to heavy workloads for teachers. To address this problem, a new method of assessment, teacher-student collaborative assessment (TSCA)—was proposed in 2016 to organize and balance different modes of teacher assessment, self-assessment, peer assessment, and computer-mediated assessment. The present study took one intact class as a case, aiming to explore how TSCA could be carried out efficiently and systematically in the classroom and how students perceived TSCA. Qualitative data obtained include students’ writing drafts and revision, interview, and reflective journals of the students and the teacher. Interview data indicated that the students responded to this type of assessment positively and thought they benefited greatly from the teacher’s instruction and peer discussion. This was triangulated by the students’ reflections in which all the students spoke highly of TSCA and agreed that this method was a good way to pinpoint their weaknesses and help them learn how to revise their essay better. The students reported that they formed a new perception of self-assessment and self-revision and felt that a lot was gained

Soft Actor-Critic Learning-Based Joint Computing, Pushing, and Caching Framework in MEC Networks

To support future 6G mobile applications, the mobile edge computing (MEC) network needs to be jointly optimized for computing, pushing, and caching to reduce transmission load and computation cost. To achieve this, we propose a framework based on deep reinforcement learning that enables the dynamic orchestration of these three activities for the MEC network. The framework can implicitly predict user future requests using deep networks and push or cache the appropriate content to enhance performance. To address the curse of dimensionality resulting from considering three activities collectively, we adopt the soft actor-critic reinforcement learning in continuous space and design the action quantization and correction specifically to fit the discrete optimization problem. We conduct simulations in a single-user single-server MEC network setting and demonstrate that the proposed framework effectively decreases both transmission load and computing cost under various configurations of cache size and tolerable service delay

Topological insulator: a new quantized spin Hall resistance robust to dephasing

The influence of dephasing on the quantum spin Hall effect (QSHE) is studied. In the absence of dephasing, the longitudinal resistance in a QSHE system exhibits the quantum plateaus. We find that these quantum plateaus are robust against the normal dephasing but fragile with the spin dephasing. Thus, these quantum plateaus only survive in mesoscopic samples. Moreover, the longitudinal resistance increases linearly with the sample length but is insensitive to the sample width. These characters are in excellent agreement with the recent experimental results [science {\bf 318}, 766 (2007)]. In addition, we define a new spin Hall resistance that also exhibits quantum plateaus. In particular, these plateaus are robust against any type of dephasing and therefore, survive in macroscopic samples and better reflect the topological nature of QSHE.Comment: 4 pages, 5 figure

Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.Comment: 6 page