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1. Putting Rigid Bodies to Rest

   https://doi.org/10.1145/3731203  

   Baktash, Hossein; Sharp, Nicholas; Zhou, Qingnan; Jacobson, Alec; Crane, Keenan (August 2025, ACM Transactions on Graphics)

   This paper explores the analysis and design of the resting configurations of a rigid body, without the use of physical simulation. In particular, given a rigid body inR³, we identify all possible stationary points, as well as the probability that the body will stop at these points, assuming a random initial orientation and negligible momentum. The forward version of our method can hence be used to automatically orient models, to provide feedback about object stability during the design process, and to furnish plausible distributions of shape orientation for natural scene modeling. Moreover, a differentiable inverse version of our method lets us design shapes with target resting behavior, such as dice with target, nonuniform probabilities. Here we find solutions that would be nearly impossible to find using classical techniques, such as dice with additional unstable faces that provide more natural overall geometry. From a technical point of view, our key observation is that rolling equilibria can be extracted from theMorse-Smale complexof thesupport functionover the Gauss map. Our method is hence purely geometric, and does not make use of random sampling, or numerical time integration. Yet surprisingly, this purely geometric model makes extremely accurate predictions of rest behavior, which we validate both numerically, and via physical experiments. Moreover, for computing rest statistics, it is orders of magnitude faster than state of the art rigid body simulation, opening the door to inverse design—rather than just forward analysis. 

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2. Offline memory consolidation during waking rest

   https://doi.org/10.1038/s44159-022-00072-w  

   Wamsley, Erin J. (August 2022, Nature Reviews Psychology)
3. Pharmacological Inhibition of REST in Glioblastoma

   Haraguchi, M.; Li, C.; Kukreja, L.; Ezhilan, S.; Zhang, Y.J.; Kuo, J. (August 2019, 2019 BMES Conference Proceedings - REU Abstract Accepted Poster)

   Introduction: RE1-silencing transcription factor (REST) silences neuronal differentiation genes. Its overexpression in an aggressive subset of gliomas is believed to support the enhanced tumor-initiating and self-renewal capacities of glioblastoma cancer stem cells (GSCs). Therefore, REST knockdown is hypothesized to inhibit tumor growth and recurrence. Because REST, as a large protein, is difficult to target directly with small molecules, our study focuses on knocking down REST by inhibiting one of its regulatory enzymes, small C-terminal domain phosphatase 1 (SCP1). Dephosphorylation of REST by SCP1 protects the former from degradation; consequently, SCP1 inhibition with an experimental drug, T62, is expected to reduce REST protein levels. This REST knockdown is hypothesized to induce the expression of neuronal differentiation genes, thereby forcing differentiation of GSCs and making them more vulnerable to standard treatments. We begin our study by validating patient-derived GSC lines and subsequently testing the efficacy of T62 drug in these cells. Our work supports an effort to understand various molecular pathologies of GBM and its intrinsic GSCs in order to develop novel therapeutic strategies. 

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4. Random elastic space–time (REST) prediction

   https://doi.org/10.1016/j.spasta.2025.100904  

   Coloma, Nicolas; Kleiber, William (June 2025, Spatial Statistics)

   Statistical modeling and interpolation of space–time processes has gained increasing relevance over the last few years. However, real world data often exhibit characteristics that challenge conventional methods such as nonstationarity and temporal misalignment. For example, high frequency solar irradiance data are typically observed at fine temporal scales, but at sparse spatial sampling, so space–time interpolation is necessary to support solar energy studies. The nonstationarity and phase misalignment of such data challenges extant approaches. We propose random elastic space–time (REST) prediction, a novel method that addresses temporally-varying phase misalignment by combining elastic alignment and conventional kriging techniques. Moreover, uncertainty in both amplitude and phase alignment can be readily quantified in a conditional simulation framework, whereas conventional space–time methods only address am- plitude uncertainty. We illustrate our approach on a challenging solar irradiance dataset, where our method demonstrates superior predictive distributions compared to existing geostatistical and functional data analytic techniques. 

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5. Adaptive REST API Testing with Reinforcement Learning

   https://doi.org/10.1109/ASE56229.2023.00218  

   Kim, Myeongsoo; Sinha, Saurabh; Orso, Alessandro (September 2023, IEEE)