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1. Progress on the University of Hawaii 2.2-meter adaptive secondary mirror

   https://doi.org/10.1117/12.2629046  

   Chun, Mark R.; Ryan, Alan; Zhang, Ruihan; Kuiper, Stefan; Ackaert, Gilles; Baranec, Christoph; Baeten, M. J.; Bos, Arjo; Bowens-Rubin, Rachel; Dekker, Bert; et al (August 2022, Proc. SPIE 12185, Adaptive Optics Systems VIII)

   Schmidt, Dirk; Schreiber, Laura; Vernet, Elise (Ed.)

   We report on progress at the University of Hawaii on the integration and testing setups for the adaptive secondary mirror (ASM) for the University of Hawaii 2.2-meter telescope on Maunakea, Hawaii. We report on the development of the handling fixtures and alignment tools we will use along with progress on the optical metrology tools we will use for the lab and on-sky testing of the system. 

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2. Teacher Impact on Student Learning Using LC-DLM Implementations in the Classroom

   Curtis, H.; Gartner, J.; Dahlke, K.; Adesope, O.; Dutta, P.; Van Wie, B.; Thiessen, D. (August 2022, ASEE annual conference proceedings)

   Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an F- test using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for hands- on, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students. 

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3. Teaching Fluid Mechanics and Heat Transfer in Hands-on and Virtual Settings with Low Cost Desktop Learning Modules

   Reynolds, O. M. (October 2022, International journal of engineering education)

   Although there is extensive literature documenting hands-on learning experiences in engineering classrooms, there is a lack of consensus regarding how student learning during these activities compares to learning during online video demonstrations. Further, little work has been done to directly compare student learning for similarly-designed hands-on learning experiences focused on different engineering subjects. As the use of hands-on activities in engineering continues to grow and expand to non-traditional virtual applications, understanding how to optimize student learning during these activities is critical. To address this, we collected conceptual assessment data from 763 students at 15 four-year institutions. The students completed activities with one of two highly visual low-cost desktop learning modules (LCDLMs), one focused on fluid mechanics and the other on heat transfer principles, using two different implementation formats: either hands-on or video demonstration. To examine the effect of implementation format and of the learning tool used, learning gains on conceptual assessments were compared for virtual and hands-on implementations of fluid mechanics and heat transfer LCDLMs. Results showed that learning gains were positive and similar for hands-on and video demonstrations for both modules assessed, suggesting both implementation formats support an impactful student learning experience. However, a significant difference was observed in effectiveness based on the type of LCDLM used. Score increases of 31.2% and 24% were recorded on our post-activity assessment for hands-on and virtual implementations of the fluid mechanics LCDLM compared to pre-activity assessment scores, respectively, while smaller 8.2% and 9.2% increases were observed for hands-on and virtual implementations of the heat transfer LCDLM. In this paper, we consider existing literature to ascertain the reasons for similar effectiveness of hands-on and video demonstrations and for the differing effectiveness of the fluid mechanics and heat transfer LCDLMs. We discuss the practical implications of our findings with respect to designing hands-on or video demonstration activities. 

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4. Teacher Impact on Student Learning Using LC-DLM Implementations in the Classroom

   Curtis, Heidi; Gartner, Jacqueline; Dutta, Prashanta; Adesope, Olusola; Van Wie, Bernard; Watson, Carah (January 2022, ASEE annual conference)

   Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an Ftest using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for handson, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students. 

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5. MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

   Yuan, Geng; Ma, Xiaolong; Niu, Wei; Li, Zhengang; Kong, Zhenglun; Liu, Ning; Gong, Yifan; Zhan, Zheng; He, Chaoyang; Jin, Qing; et al (January 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))

   Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory-Economic Sparse Training (MEST) framework targeting for accurate and fast execution on edge devices. The proposed MEST framework consists of enhancements by Elastic Mutation (EM) and Soft Memory Bound (&S) that ensure superior accuracy at high sparsity ratios. Different from the existing works for sparse training, this current work reveals the importance of sparsity schemes on the performance of sparse training in terms of accuracy as well as training speed on real edge devices. On top of that, the paper proposes to employ data efficiency for further acceleration of sparse training. Our results suggest that unforgettable examples can be identified in-situ even during the dynamic exploration of sparsity masks in the sparse training process, and therefore can be removed for further training speedup on edge devices. Comparing with state-of-the-art (SOTA) works on accuracy, our MEST increases Top-1 accuracy significantly on ImageNet when using the same unstructured sparsity scheme. Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works. A reviewer strongly against our work based on his false assumptions and misunderstandings. On top of the previous submission, we employ data efficiency for further acceleration of sparse training. And we explore the impact of model sparsity, sparsity schemes, and sparse training algorithms on the number of removable training examples. Our codes are publicly available at: https://github.com/boone891214/MEST. 

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