Search

Controlling hand exoskeletons for assisting impaired patients in grasping tasks is challenging because it is difficult to infer user intent. We hypothesize that majority of daily grasping tasks fall into a small set of categories or modes which can be inferred through real-time analysis of environmental geometry from 3D point clouds. This paper presents a low-cost, real-time system for semantic image labeling of household scenes with the objective to inform and assist activities of daily living. The system consists of a miniature depth camera, an inertial measurement unit and a microprocessor. It is able to achieve 85% or higher accuracy at classification of predefined modes while processing complex 3D scenes at over 30 frames per second. Within each mode it can detect and localize graspable objects. Grasping points can be correctly estimated on average within 1 cm for simple object geometries. The system has potential applications in robotic-assisted rehabilitation as well as manual task assistance.

We theoretically and computationally investigate the cooling of antihydrogen, $\bar{H}$, using optical molasses cooling. This updates the results in Ref. [1] to the current capabilities of the ALPHA experiment. Through Monte Carlo simulation, we show that $\bar{H}$s do not give the standard cooling even in an ideal optical molasses because of their small mass and large transition frequency. For optical molasses cooling in the ALPHA trap, the photons are constrained to travel in one direction only. It is only through the phase space mixing in the trap that cooling in all directions can be achieved. We explore the nontrivial role that laser intensity plays in the cooling. We also investigate the possibility for simultaneously cooling atoms in either of the trapped ground states.

The Near-Earth binary asteroid Didymos was the target of a planetary defense demonstration mission DART in September 2022. The smaller binary component, Dimorphos, was impacted by the spacecraft in order to measure momentum transfer in kinetic impacts into rubble piles. DART and associated Earth-based observation campaigns have provided a wealth of scientific data on the Didymos-Dimorphos binary. DART revealed a largely oblate and ellipsoidal shape of Dimorphos before the impact, while the post-impact observations suggest that Dimorphos now has a prolate shape. Here we add those data points to the known properties of small binary asteroids and propose new paradigms of the radiative binary YORP (BYORP) effect as well as tidal dissipation in small binaries. We find that relatively spheroidal bodies like Dimorphos made of small debris may experience a weaker and more size-dependent BYORP effect than previously thought. This could explain the observed values of period drift in several well-characterized binaries. We also propose that energy dissipation in small binaries is dominated by relatively brief episodes of large-scale movement of (likely surface) materials, rather than long-term steady-state tidal dissipation. We propose that one such episode was triggered on Dimorphos by the DART impact. Depending on the longevity of this high-dissipation regime, it is possible that Dimorphos will be more dynamically relaxed in time for the Hera mission than it was in the weeks following the impact., Comment: Accepted for PSJ

Spectral characterization of near-Earth asteroids (NEAs) has revealed a continuum of space-weathered states for the surfaces of S-complex NEAs, with Q-class NEAs, an S-complex subclass, most closely matching the un-weathered surfaces of ordinary chondrite meteorites. Dynamical calculations of the orbital evolution of S-complex NEAs revealed that Q-class NEAs tend to have close encounters with terrestrial planets, suggesting that planetary tides may play a role in refreshing NEA surfaces. However, the exact physical mechanism(s) that drive resurfacing through tidal encounters and the encounter distance at which these mechanisms are effective, has remained unclear. Through the lens of the upcoming (99942) Apophis encounter with Earth in 2029, we investigate the potential for surface mobilization through tidally-driven seismic shaking over short-timescales during encounter and subsequent surface slope evolution over longer-timescales driven by tumbling. We perform multi-scale numerical modeling and find that the 2029 encounter will induce short-term tidally-driven discrete seismic events that lead to high-frequency (>0.1 Hz) surface accelerations that reach magnitudes similar to Apophis' gravity, and that may be detectable by modern seismometers. It is still unclear if the shaking we model translates to widespread particle mobilization and/or lofting. We also find there will be a significant change in Apophis' tumbling spin state that could lead to longer-term surface refreshing in response to tumbling-induced surface slope changes. We propose that through these mechanisms, space-weathered S-class asteroid surfaces may become refreshed through the exposure of unweathered underlying material. These results will be tested by the future exploration of Apophis by NASA's OSIRIS-APEX., Comment: 39 pages, 11 figures, Accepted by The Planetary Science Journal

In our recent work, we combined dynamical simulations, meteoritic data and thermal models as well as asteroid observations to argue that the current parent body of the EL meteorites was implanted into the asteroid belt not earlier than 60 Myr after the beginning of the Solar System and that the most likely capture mechanism was the giant planet orbital instability. In the study "The link between Athor and EL meteorites does not constrain the timing of the giant planet instability" that appeared in arXiv, Izidoro and collaborators argue that the implantation of Athor into the asteroid belt does not necessarily require that the giant planet orbital instability occurred at the implantation time. Here we provide further arguments that, in the end, the giant planet instability is still the most likely dynamical process to implant asteroid Athor into the asteroid main belt between 60 and 100 Myr after the beginning of the Solar System., Comment: 4 pages, 1 figure; comment on arXiv:2404.10828

The main asteroid belt (MAB) is known to be primarily composed of objects from two distinct taxonomic classes, generically defined here as S- and C-complex. The former probably originated from the inner solar system (interior to Jupiter's orbit), while the latter probably from the outer solar system. Following this definition, (4) Vesta, a V-type residing in the inner MAB (a < 2.5 au), is the sole D > 500 km object akin to S-complex that potentially formed in-situ. This provides a useful constraint on the number of D > 500 km bodies that could have formed, or grown, within the primordial MAB. In this work we numerically simulate the accretion of objects in the MAB region during the time when gas in the protoplanetary disk still existed, while assuming different MAB primordial masses. We then accounted for the depletion of that population happening after gas disk dispersal. In our analysis, we subdivided the MAB into five sub-regions and showed that the depletion factor varies throughout the MAB. This results in uneven radial- and size-dependent depletion of the MAB. We show that the MAB primordial mass has to be $\lesssim$ 2.14$\times$10$^{-3}$ Earth masses. Larger primordial masses would lead to the accretion of tens-to-thousands of S-complex objects with D > 500 km in the MAB. Such large objects would survive depletion even in the outer sub-regions (a > 2.5 au), thus being inconsistent with observations. Our results also indicate that S-complex objects with D > 200-300 km, including (4) Vesta, are likely to be terrestrial planetesimals implanted into the MAB rather than formed in-situ., Comment: Accepted for publication in PSJ, 23 pages, 7 figures, 4 tables

Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to provide reasons as to why certain parameters are proposed to be evaluated. This is particularly relevant in human-in-the-loop applications of BO, such as in robotics. We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values.They quantify each parameter's contribution to BO's acquisition function. Exploiting the linearity of Shapley values, we are further able to identify how strongly each parameter drives BO's exploration and exploitation for additive acquisition functions like the confidence bound. We also show that ShapleyBO can disentangle the contributions to exploration into those that explore aleatoric and epistemic uncertainty. Moreover, our method gives rise to a ShapleyBO-assisted human machine interface (HMI), allowing users to interfere with BO in case proposals do not align with human reasoning. We demonstrate this HMI's benefits for the use case of personalizing wearable robotic devices (assistive back exosuits) by human-in-the-loop BO. Results suggest human-BO teams with access to ShapleyBO can achieve lower regret than teams without., Comment: Preprint. Copyright by the authors. 19 pages, 24 figures

Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial.

Coordinate exchange (CEXCH) is a popular algorithm for generating exact optimal experimental designs. The authors of CEXCH advocated for a highly greedy implementation - one that exchanges and optimizes single element coordinates of the design matrix. We revisit the effect of greediness on CEXCHs efficacy for generating highly efficient designs. We implement the single-element CEXCH (most greedy), a design-row (medium greedy) optimization exchange, and particle swarm optimization (PSO; least greedy) on 21 exact response surface design scenarios, under the $D$- and $I-$criterion, which have well-known optimal designs that have been reproduced by several researchers. We found essentially no difference in performance of the most greedy CEXCH and the medium greedy CEXCH. PSO did exhibit better efficacy for generating $D$-optimal designs, and for most $I$-optimal designs than CEXCH, but not to a strong degree under our parametrization. This work suggests that further investigation of the greediness dimension and its effect on CEXCH efficacy on a wider suite of models and criterion is warranted.

Numerical modeling has long suggested that gravitationally-bound (or so-called rubble-pile) near-Earth asteroids (NEAs) can be destroyed by tidal forces during close and slow encounters with terrestrial planets. However, tidal disruptions of NEAs have never been directly observed nor have they been directly attributed to any families of NEAs. Here we show population-level evidence for the tidal disruption of NEAs during close encounters with the Earth and Venus. Debiased model distributions of NEA orbits and absolute magnitudes based on observations by the Catalina Sky Survey during 2005--2012 underpredict the number of NEAs with perihelion distances coinciding with the semimajor axes of Venus and the Earth. A detailed analysis of the orbital distributions of the excess NEAs shows that their characteristics agree with the prediction for tidal disruptions, and they cannot be explained by observational selection effects or orbital dynamics. Accounting for tidal disruptions in evolutionary models of the NEA population partly bridges the gap between the predicted rate of impacts by asteroids with diameters of tens of meters and observed statistics of fireballs in the same size range., Comment: 8 pages, 5 figures, accepted for publication in ApJL

We present an approach for the inclusion of non-spherical constituents in high-resolution N-body discrete element method (DEM) simulations. We use aggregates composed of bonded spheres to model non-spherical components. Though the method may be applied more generally, we detail our implementation in the existing N-body code pkdgrav. It has long been acknowledged that non-spherical grains confer additional shear strength and resistance to flow when compared with spheres. As a result, we expect that rubble-pile asteroids will also exhibit these properties and may behave differently than comparable rubble piles composed of idealized spheres. Since spherical particles avoid some significant technical challenges, most DEM gravity codes have used only spherical particles, or have been confined to relatively low resolutions. We also discuss the work that has gone into improving performance with non-spherical grains, building on pkdgrav's existing leading-edge computational efficiency among DEM gravity codes. This allows for the addition of non-spherical shapes while maintaining the efficiencies afforded by pkdgrav's tree implementation and parallelization. As a test, we simulated the gravitational collapse of 25,000 non-spherical bodies in parallel. In this case, the efficiency improvements allowed for an increase in speed by nearly a factor of three when compared with the naive implementation. Without these enhancements, large runs with non-spherical components would remain prohibitively expensive. Finally, we present the results of several small-scale tests: spinup due to the YORP effect, tidal encounters, and the Brazil-nut Effect. In all cases, we find that the inclusion of non-spherical constituents has a measurable impact on simulation outcomes., Comment: 35 pages, 8 figures

Near-Earth asteroids Ryugu and Bennu, were visited, characterised, and sampled by the Hayabusa2 and OSIRIS-REx missions: remote sensing data and sample return analysis showed that both asteroids have primitive, hydrated and organic-rich compositions. The dark families of the inner main belt (IMB) that belong to the spectroscopic C-complex have been claimed to be the sources of both Ryugu and Bennu. Hence, there has been large effort to characterise them. Here we used the Gaia Data Release 3 (DR3) asteroid reflectance spectra to investigate the 11 known IMB C-complex families (Chaldaea, Chimaera, Clarissa, Erigone, Eulalia, Klio, Polana, Primordial, Sulamitis, Svea, Tamara). For each family, we extracted the family members that have known geometric visible albedo values and Gaia DR3 data and we created an average reflectance spectrum per family between 370 and 950 nm. The average DR3 reflectance spectra of each family were compared with the previous literature data and to Bennu's and Ryugu's spectra. We found that DR3 reflectance spectra of the IMB C-complex families are in general consistent with previous findings with the only exception of the Svea family. We also showed that the Polana and the Eulalia families can be distinguished in the wavelength region 370 - 500 nm. Among all the IMB C-complex families, we determined that the average reflectance spectra of the Eulalia and Polana families are the most similar to those of Bennu and Ryugu, respectively. In particular, Eulalia family's average spectrum is a good match to Bennu's in the wavelength range 450 - 800 nm, while beyond 800 nm the spectrum of Bennu is bluer than that of Eulalia. Moreover, the spectrum of the Polana family has the smallest discrepancy against the spectrum of Ryugu, although this match is formally unsatisfactory (reduced chi^2 ~ 1.9)., Comment: Accepted for publication in Astronomy and Astrophysics

Many advanced, image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but their ability to recognize growth stages, often with stark differences in appearance, is uncertain. Amaranthus palmeri (Palmer amaranth) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production, exhibiting highly variable plant morphology both across growth stages over a growing season, as well as between plants at a given growth stage due to high genetic diversity. In this paper, we investigate eight-class growth stage recognition of A. palmeri in cotton as a challenging model for You Only Look Once (YOLO) architectures. We compare 26 different architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 on an eight-class growth stage dataset of A. palmeri. The highest mAP@[0.5:0.95] for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar growth stages. With all growth stages grouped as a single class, performance increased, with a maximum mean average precision (mAP@[0.5:0.95]) of 67.05% achieved by v7-Original. Single class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures., Comment: 27 pages, 10 figures, 5 tables

Modern reinforcement learning systems produce many high-quality policies throughout the learning process. However, to choose which policy to actually deploy in the real world, they must be tested under an intractable number of environmental conditions. We introduce RPOSST, an algorithm to select a small set of test cases from a larger pool based on a relatively small number of sample evaluations. RPOSST treats the test case selection problem as a two-player game and optimizes a solution with provable $k$-of-$N$ robustness, bounding the error relative to a test that used all the test cases in the pool. Empirical results demonstrate that RPOSST finds a small set of test cases that identify high quality policies in a toy one-shot game, poker datasets, and a high-fidelity racing simulator., Comment: 26 pages, 13 figures. To appear in Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023)