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  1. Free, publicly-accessible full text available October 24, 2024
  2. Free, publicly-accessible full text available June 5, 2024
  3. This study analyzed 281 lesson plans collected from the producers’ websites of 12 educational physical computing and robotics (ePCR) devices. We extracted and coded five variables from each lesson. They were ePCR functionality, coding skills, computational thinking skills, math knowledge, and activity design. First, a two-step cluster analysis was administered to find how three ePCR-related knowledge: ePCR functionality, coding skills, and computational thinking skills, were integrated to teach students ePCR technology in middle-grade math lessons. Results showed three types of lesson plans, including lessons to use basic ePCR functionality to teach students lower-level CT skills, lessons to teach students basic to intermediate coding skills, and lessons to use the technology at the advanced level. Next, we applied the Technological Pedagogical Content Knowledge (TPACK) framework and conducted a second two-step cluster analysis to identify how the technology (ePCR technology), content (math knowledge), and pedagogy (activity design) were integrated into those lesson plans. Results suggested ten clusters of lesson plans with distinct features. We summarized those ten lesson clusters into five categories: 1) ePCR technology lessons, 2) transdisciplinary problem-based learning lessons, 3) technology-assisted lessons, 4) lessons without real-world connections, and 5) lessons integrating middle-grade math learning into ePCR projects. Implications for educators and researchers were discussed at the end of the article. 
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  4. When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make them faster, much more energy efficient, and accurate. Despite having excellent properties, the memristor-based DNNs are yet to be commercially available because of Stuck-At-Fault (SAF) defects. A Mapping Transformation (MT) method is proposed in this paper to mitigate Stuck-at-Fault (SAF) defects from memristor-based DNNs. First, the weight distribution for the VGG8 model with the CIFAR10 dataset is presented and analyzed. Then, the MT method is used for recovering inference accuracies at 0.1% to 50% SAFs with two typical cases, SA1 (Stuck-At-One): SA0 (Stuck-At-Zero) = 5:1 and 1:5, respectively. The experiment results show that the MT method can recover DNNs to their original inference accuracies (90%) when the ratio of SAFs is smaller than 2.5%. Moreover, even when the SAF is in the extreme condition of 50%, it is still highly efficient to recover the inference accuracy to 80% and 21%. What is more, the MT method acts as a regulator to avoid energy and latency overhead generated by SAFs. Finally, the immunity of the MT Method against non-linearity is investigated, and we conclude that the MT method can benefit accuracy, energy, and latency even with high non-linearity LTP = 4 and LTD = −4. 
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  5. Recently, the Resistive Random Access Memory (RRAM) has been paid more attention for edge computing applications in both academia and industry, because it offers power efficiency and low latency to perform the complex analog in-situ matrix-vector multiplication – the most fundamental operation of Deep Neural Networks (DNNs). But the Stuck at Fault (SAF) defect makes the RRAM unreliable for the practical implementation. A differential mapping method (DMM) is proposed in this paper to improve reliability by mitigate SAF defects from RRAM-based DNNs. Firstly, the weight distribution for the VGG8 model with the CIFAR10 dataset is presented and analyzed. Then the DMM is used for recovering the inference accuracies at 0.1% to 50% SAFs. The experiment results show that the DMM can recover DNNs to their original inference accuracies (90%), when the ratio of SAFs is smaller than 7.5%. And even when the SAF is in the extreme condition 50%, it is still highly efficient to recover the inference accuracy to 80%. What is more, the DMM is a highly reliable regulator to avoid power and timing overhead generated by SAFs. 
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  6. Funded by the NSF Division of Computer and Network Systems, this grant establishes a new Research Experiences for Teachers (RET) Site at the University of South Alabama (USA). In the summer of 2021, eight middle school and high school teachers from two local public-school districts spent six weeks engaged with research activities on biologically-inspired computing systems. They worked on discovery-based research projects and obtained transdisciplinary research experience on biologically-inspired computing systems spanning application (cancer detection), algorithm (Spiking Neural Networks), architecture and circuit (synaptic memory design), and device (memristor). The USA faculty mentors, curriculum development specialist from school districts, Instructional Coach from Science/Mathematics faculty at USA coached participants as they designed standards-compliant curriculum modules and conducted professional development activities. The implementation details of the summer program and the evaluation results are presented in this paper. 
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  7. null (Ed.)