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1. Feedback Optimization of Nonlinear Strict-Feedback Systems

   https://doi.org/10.1007/s11424-025-4543-3  

   Liu, Tong; Liu, Tengfei; Jiang, Zhong-Ping (April 2025, Journal of Systems Science and Complexity)

   Feedback optimization aims at regulating the output of a dynamical system to a value that minimizes a cost function. This problem is beyond the reach of the traditional output regulation theory, because the desired value is generally unknown and the reference signal evolves according to a gradient flow using the system’s real-time output. This paper complements the output regulation theory with the nonlinear small-gain theory to address this challenge. Specifically, the authors assume that the cost function is strongly convex and the nonlinear dynamical system is in lower triangular form and is subject to parametric uncertainties and a class of external disturbances. An internal model is used to compensate for the effects of the disturbances while the cyclic small-gain theorem is invoked to address the coupling between the reference signal, the compensators, and the physical system. The proposed solution can guarantee the boundedness of the closed-loop signals and regulate the output of the system towards the desired minimizer in a global sense. Two numerical examples illustrate the effectiveness of the proposed method. 

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2. The Power of Feedback: Improving SketchTivity’s Feedback System

   Fleeman, Braeden (April 2023, Joint Proceedings of the ACM IUI Workshops 2023)

   Perspective sketching is a skill that is required for a variety of jobs including, but not limited to, architectural design, graphic design, and engineering. Sketching however, is a difficult skill to grasp for people early and can take a while to learn. Recently, there have been many intelligent tutoring systems (ITSs) designed to help improve people’s drawing skills. The feedback system for the perspective drawing lessons in SketchTivity, one such ITS, is currently limited to smoothness, speed, and accuracy of the lines. Our team plans to improve upon this feedback system so that the feedback provided to a user is now more nuanced as well as more actionable to reaffirm future learning. To evaluate our system we will conduct a user study with 40 students that involves going through several sketching lessons and then sketching a street corner in 2-D perspective. We plan to run a between-subjects user study with our participants to determine if our adjustment has any effect on the improvement of sketching skills and the usability of the application. We hope to determine that providing the user with data for their smoothness, speed, and accuracy after four sketching prompts can cause an overall improvement in the students’ scores in comparison to at the end of their sketching session. The algorithm that we created to identify a student’s potential issue we hope will be able to provide accurate, actionable feedback in most situations. The visual alterations we made to SketchTivity we expect to have a positive impact on the perspective feedback system and alter the students sketching performance. In future iterations the algorithm should be further refined and the data collected from the students sketches should be further developed to provide more data to create more actionable recommendations for improved sketching performance and retention. 

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3. Librational feedback cooling

   https://doi.org/10.1103/PhysRevA.106.023503  

   Blakemore, Charles P.; Martin, Denzal; Fieguth, Alexander; Priel, Nadav; Venugopalan, Gautam; Kawasaki, Akio; Gratta, Giorgio (August 2022, Physical Review A)
4. Feedback Communication Over the Binary Symmetric Channel with Sparse Feedback Times

   https://doi.org/10.1109/GLOBECOM52923.2024.10901602  

   Antonini, Amaael; Wesel, Richard D (December 2024, IEEE)

   Posterior matching uses variable-length encoding of the message controlled by noiseless feedback of the received symbols to achieve high rates for short average blocklengths. Traditionally, the feedback of a received symbol occurs before the next symbol is transmitted. The transmitter optimizes the next symbol transmission with full knowledge of every past received symbol. To move posterior matching closer to practical communication, this paper seeks to constrain how often feedback can be sent back to the transmitter. We focus on reducing the frequency of the feedback while still maintaining the high rates that posterior matching achieves with feedback after every symbol. As it turns out, the frequency of the feedback can be reduced significantly with no noticeable reduction in rate. 

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5. Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents

   https://doi.org/10.1007/s10803-023-06038-y  

   Riek, Nathan T.; Susam, Busra T.; Hudac, Caitlin M.; Conner, Caitlin M.; Akcakaya, Murat; Yun, Jane; White, Susan W.; Mazefsky, Carla A.; Gable, Philip A. (July 2023, Journal of Autism and Developmental Disorders)