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1. Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots

   Wang, Kun; Aanjaneya, Mridul; Bekris, Kostas E (September 2021, IEEE/RSJ International Conference on Intelligent Robots and Systems)

   null (Ed.)

   Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but also more difficult to accurately model and control. The sim2real gap is the main barrier to successfully transfer policies from simulation to a real robot. System identification can be applied to reduce this gap but traditional identification methods require a lot of manual tuning. Data-driven alternatives can tune dynamical models directly from data but are often data hungry, which also incorporates human effort in collecting data. This work proposes a data-driven, end-to-end differentiable simulator focused on the exciting but challenging domain of tensegrity robots. To the best of the authors’ knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable, contact, and actuation modeling. The aim is to develop a reasonably simplified, data-driven simulation, which can learn approximate dynamics with limited ground truth data. The dynamics must be accurate enough to generate policies that can be transferred back to the ground-truth system. As a first step in this direction, the current work demonstrates sim2sim transfer, where the unknown physical model of MuJoCo acts as a ground truth system. Two different tensegrity robots are used for evaluation and learning of locomotion policies, a 6-bar and a 3-bar tensegrity. The results indicate that only 0.25% of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is used for training against training the policy directly on the ground truth system. 

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2. Unpacking subjective creativity ratings: Using embeddings to explain and measure idea novelty

   Ahmed, Faez; Fuge, Mark; Hunter, Sam; Miller, Scarlett (August 2018, ASME Design Engineering Technical Conferences)

   Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets and scoring ideas on their uniqueness. However, their decisions on novelty are often subjective and difficult to explain. In this paper, we demonstrate a way to uncover human judgment of design idea similarity using two dimensional idea maps. We derive these maps by asking humans for simple similarity comparisons of the form “Is idea A more similar to idea B or to idea C?” We show that these maps give insight into the relationships between ideas and help understand the domain. We also propose that the novelty of ideas can be estimated by measuring how far items are on these maps. We demonstrate our methodology through the experimental evaluations on two datasets of colored polygons (known answer) and milk frothers (unknown answer) sketches. We show that these maps shed light on factors considered by raters in judging idea similarity. We also show how maps change when less data is available or false/noisy ratings are provided. This method provides a new direction of research into deriving ground truth novelty metrics by combining human judgments and computational methods. 

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3. The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

   https://doi.org/10.3847/1538-3881/ac5176  

   Zhao, Lily L.; Fischer, Debra A.; Ford, Eric B.; Wise, Alex; Cretignier, Michaël; Aigrain, Suzanne; Barragan, Oscar; Bedell, Megan; Buchhave, Lars A.; Camacho, João D.; et al (March 2022, The Astronomical Journal)

   Abstract Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme-precision radial-velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The Extreme-precision Spectrograph (EXPRES) Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed radial-velocity (RV) correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV rms than classic linear decorrelation, but no method is yet consistently reducing the RV rms to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved. 

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4. Observational Constraints on Direct Electron Heating in the Hot Accretion Flows in Sgr A* and M87*

   https://doi.org/10.3847/1538-4357/aca534  

   Xie, Fu-Guo; Narayan, Ramesh; Yuan, Feng (December 2022, The Astrophysical Journal)

   Abstract An important parameter in the theory of hot accretion flows around black holes is δ , which describes the fraction of “viscously” dissipated energy in the accretion flow that goes directly into heating electrons. For a given mass accretion rate, the radiative efficiency of a hot accretion flow is determined by δ . Unfortunately, the value of δ is hard to determine from first principles. The recent Event Horizon Telescope Collaboration (EHTC) results on M87* and Sgr A* provide us with a different way of constraining δ . By combining the mass accretion rates in M87* and Sgr A* estimated by the EHTC with the measured bolometric luminosities of the two sources, we derive good constraints on the radiative efficiencies of the respective accretion flows. In parallel, we use a theoretical model of hot magnetically arrested disks (MADs) to calculate the expected radiative efficiency as a function of δ (and accretion rate). By comparing the EHTC-derived radiative efficiencies with the theoretical results from MAD models, we find that Sgr A* requires δ ≳ 0.3. A similar comparison in the case of M87* gives inconclusive results as there is still a large uncertainty in the accretion rate in this source. 

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5. An integrated approach of field, weather, and satellite data for monitoring maize phenology

   https://doi.org/10.1038/s41598-021-95253-7  

   Nieto, Luciana; Schwalbert, Raí; Prasad, P. V.; Olson, Bradley J.; Ciampitti, Ignacio A. (December 2021, Scientific Reports)

   Abstract Efficient, more accurate reporting of maize ( Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain. 

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