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1. Inverse Optimal Control with Sit-to-Stand Data

   Danforth, S; Holmes, P; Vasudevan, R (January 2019, Dynamic Walking)

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2. Workers Standing Times with Use of Sit-Stand Desks and Social Influences: An Investigation of Two Months of Office Workers’ Sit-Stand Desks Utilization

   https://doi.org/10.1177/21695067231192637  

   Duffie, Holden; Barbieri, Dechristian Franca; Fuller, Patrick; Lin, Jia-Hua; Cha, Jackie S (September 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The use of sit-stand desks (SSD) has shown positive results in reducing sitting time for office workers. Different factors can help with the use of the table, such as participatory ergonomics and an SSD reminder system. However, little is known about the influence of social factors at work, such as the influence of peers or supervisors using the device. The aim of this study was to investigate the use of SSDs and how the location and proximity of office workers affects SSD utilization over the course of two months. One-hundred eighty workers with SSDs participated in this study, and their desk locations and utilization were documented. It was observed that office workers spent on average (standard deviation) 7.1% (20.8%) of their time standing at their desks each day. Among these users, 12 participants were categorized as high SSD users, which spent more than two hours a day standing on average. It was found that during the study period, participants located next to high SSD utilizers did not change the SSD usage by increasing or decreasing their time spent standing. The results from the study suggest that the relative locations and proximity of workers do not significantly influence users to increase SSD use, and high users do not impact the SSD use of other users. These results showed that SSD use by peers or supervisors is not a strong social factor in improving the worker’s behavior of using the table and that other combined strategies are necessary. 

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3. Carbonyl Desaturation: Where Does Catalysis Stand?

   https://doi.org/10.1021/acscatal.0c04712  

   Gnaim, Samer; Vantourout, Julien C.; Serpier, Fabien; Echeverria, Pierre-Georges; Baran, Phil. S. (January 2021, ACS Catalysis)

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4. United We Stand: Accelerating Privacy-Preserving Neural Inference by Conjunctive Optimization with Interleaved Nexus

   https://doi.org/10.1609/aaai.v38i15.29620  

   Zhang, Qiao; Xiang, Tao; Xin, Chunsheng; Wu, Hongyi (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Privacy-preserving Machine Learning as a Service (MLaaS) enables the powerful cloud server to run its well-trained neural model upon the input from resource-limited client, with both of server's model parameters and client's input data protected. While computation efficiency is critical for the practical implementation of privacy-preserving MLaaS and it is inspiring to witness recent advances towards efficiency improvement, there still exists a significant performance gap to real-world applications. In general, state-of-the-art frameworks perform function-wise efficiency optimization based on specific cryptographic primitives. Although it is logical, such independent optimization for each function makes noticeable amount of expensive operations unremovable and misses the opportunity to further accelerate the performance by jointly considering privacy-preserving computation among adjacent functions. As such, we propose COIN: Conjunctive Optimization with Interleaved Nexus, which remodels mainstream computation for each function to conjunctive counterpart for composite function, with a series of united optimization strategies. Specifically, COIN jointly computes a pair of consecutive nonlinear-linear functions in the neural model by reconstructing the intermediates throughout the whole procedure, which not only eliminates the most expensive crypto operations without invoking extra encryption enabler, but also makes the online crypto complexity independent of filter size. Experimentally, COIN demonstrates 11.2x to 29.6x speedup over various function dimensions from modern networks, and 6.4x to 12x speedup on the total computation time when applied in networks with model input from small-scale CIFAR10 to large-scale ImageNet. 

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5. Unified Distributed Control of Stand-Alone DC Microgrids

   https://doi.org/10.1109/TSG.2017.2757498  

   Wang, Zhaojian; Liu, Feng; Chen, Ying; Low, Steven H.; Mei, Shengwei (January 2019, IEEE Transactions on Smart Grid)