Search

We present Bayesian Binary Search (BBS), a novel probabilistic variant of the classical binary search/bisection algorithm. BBS leverages machine learning/statistical techniques to estimate the probability density of the search space and modifies the bisection step to split based on probability density rather than the traditional midpoint, allowing for the learned distribution of the search space to guide the search algorithm. Search space density estimation can flexibly be performed using supervised probabilistic machine learning techniques (e.g., Gaussian process regression, Bayesian neural networks, quantile regression) or unsupervised learning algorithms (e.g., Gaussian mixture models, kernel density estimation (KDE), maximum likelihood estimation (MLE)). We demonstrate significant efficiency gains of using BBS on both simulated data across a variety of distributions and in a real-world binary search use case of probing channel balances in the Bitcoin Lightning Network, for which we have deployed the BBS algorithm in a production setting.

Kepler-51 is a $\lesssim 1\,\mathrm{Gyr}$-old Sun-like star hosting three transiting planets with radii $\approx 6$-$9\,R_\oplus$ and orbital periods $\approx 45$-$130\,\mathrm{days}$. Transit timing variations (TTVs) measured with past Kepler and Hubble Space Telescope (HST) observations have been successfully modeled by considering gravitational interactions between the three transiting planets, yielding low masses and low mean densities ($\lesssim 0.1\,\mathrm{g/cm^3}$) for all three planets. However, the transit time of the outermost transiting planet Kepler-51d recently measured by the James Webb Space Telescope (JWST) 10 years after the Kepler observations is significantly discrepant from the prediction made by the three-planet TTV model, which we confirmed with ground-based and follow-up HST observations. We show that the departure from the three-planet model is explained by including a fourth outer planet, Kepler-51e, in the TTV model. A wide range of masses ($\lesssim M_\mathrm{Jup}$) and orbital periods ($\lesssim 10\,\mathrm{yr}$) are possible for Kepler-51e. Nevertheless, all the coplanar solutions found from our brute-force search imply masses $\lesssim 10\,M_\oplus$ for the inner transiting planets. Thus their densities remain low, though with larger uncertainties than previously estimated. Unlike other possible solutions, the one in which Kepler-51e is around the $2:1$ mean motion resonance with Kepler-51d implies low orbital eccentricities ($\lesssim 0.05$) and comparable masses ($\sim 5\,M_\oplus$) for all four planets, as is seen in other compact multi-planet systems. This work demonstrates the importance of long-term follow-up of TTV systems for probing longer period planets in a system., Comment: 48 pages, 26 figures, accepted for publication in AJ

The fluctuation-dissipation theorem is a cornerstone result in statistical mechanics that can be used to translate the statistics of the free natural variability of a system into information on its forced response to perturbations. By combining this viewpoint on response theory with the key ingredients of Koopmanism, it is possible to deconstruct virtually any response operator into a sum of terms, each associated with a specific mode of natural variability of the system. This dramatically improves the interpretability of the resulting response formulas. We show here on three simple yet mathematically meaningful examples how to use the Extended Dynamical Mode Decomposition (EDMD) algorithm on an individual trajectory of the system to compute with high accuracy correlation functions as well as Green functions associated with acting forcings. This demonstrates the great potential of using Koopman analysis for the key problem of evaluating and testing the sensitivity of a complex system.

Ammonia is a key component of fertilizer and a potential clean fuel and hydrogen carrier. The Haber-Bosch process for ammonia synthesis consumes more than half of industrial hydrogen and contributes up to ~3% of global greenhouse gas emissions. Light-driven reactions via surface plasmon resonances offer a less energy-intensive pathway for ammonia production by altering reaction intermediates. Here, we report gold-ruthenium plasmonic bimetallic alloys for ammonia synthesis at room temperature and pressure, driven by visible light. We use colloidal synthesis to create AuRu$_x$ alloys (x=0.1, 0.2, 0.3) and disperse these nanoparticles on MgO supports for gas-phase ammonia synthesis. We observe a ~60 $\mu$mol/g/h reactivity and ~0.12% external quantum efficiency on a AuRu$_0$$_.$$_2$ sample under 100 mW/cm$^2$ visible light. In-situ diffuse reflective infrared Fourier transform spectroscopic measurements show that hydrogenation of nitrogen adsorbates is accelerated under light compared to thermocatalysis. Combining wavelength-dependent reactivity and spectroscopic findings with semi-classical electromagnetic modeling, we show plasmonic bimetallic alloys expedite ammonia synthesis by aiding hydrogenation of adsorbed nitrogen species via plasmon-mediated hot electrons. Quantum mechanical calculations reveal hydrogen-assisted N$_2$ splitting in the excited state is key to activating the reaction under ambient conditions. Therefore, light or H$_2$ alone cannot dissociate N$_2$ -- the key bottleneck to breaking N$_2$'s triple bond. Our findings are consistent with recent hypotheses on how nitrogenase enzymes catalyze ammonia production at mild conditions and provide insights for sustainable photochemical transformations., Comment: 21 pages, 4 figures, journal article submission soon

The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. This gathering brought together nearly 30 Python package developers, maintainers, and power users to engage in informal discussions about current trends in Python, with a primary focus on analysis tools and techniques in High Energy Physics (HEP). The workshop agenda encompassed a range of topics, such as defining the scope of HEP data analysis, exploring the Analysis Grand Challenge project, evaluating statistical models and serialization methods, assessing workflow management systems, examining histogramming practices, and investigating distributed processing tools like RDataFrame, Coffea, and Dask. Additionally, the workshop dedicated time to brainstorming the organization of future PyHEP.dev events, upholding the tradition of alternating between Europe and the United States as host locations. This document, prepared by the session conveners in the weeks following the workshop, serves as a summary of the key discussions, salient points, and conclusions that emerged., Comment: 16 pages, 0 figures; workshop summary

Bistable mechanical metamaterials have shown promise for mitigating the harmful consequences of impact by converting kinetic energy into stored strain energy, offering an alternative and potentially synergistic approach to conventional methods of attenuating energy transmission. In this work, we numerically study the dynamic response of a one-dimensional bistable metamaterial struck by a high speed impactor (where the impactor velocity is commensurate with the sound speed), using the peak kinetic energy experienced at midpoint of the metamaterial compared to that in an otherwise identical linear system as our performance metric. We make five key findings: 1) The bistable material can counter-intuitively perform better (to nearly 1000x better than the linear system) as the viscosity decreases (but remains finite), but only when sufficiently fine discretization has been reached (i.e. the system approaches sufficiently close to the continuum limit); 2) This discretization threshold is sharp, and depends on the viscosity present; 3) The bistable materials can also perform significantly worse than linear systems (for low discretization and viscosity or zero viscosity); 4) The dependence on discretization stems from the partition of energy into trains of solitary waves that have pulse lengths proportional to the unit cell size, where, with intersite viscosity, the solitary wave trains induce high velocity gradients and thus enhanced damping compared to linear, and low-unit-cell-number bistable, materials; and 5) When sufficiently fine discretization has been reached at low viscosities, the bistable system outperforms the linear one for a wide range of impactor conditions. The first point is particularly important, as it shows the existence of a nonlinear dynamical size effect, where, given a protective layer of some thickness...

Posterior sampling in high-dimensional spaces using generative models holds significant promise for various applications, including but not limited to inverse problems and guided generation tasks. Despite many recent developments, generating diverse posterior samples remains a challenge, as existing methods require restarting the entire generative process for each new sample, making the procedure computationally expensive. In this work, we propose efficient posterior sampling by simulating Langevin dynamics in the noise space of a pre-trained generative model. By exploiting the mapping between the noise and data spaces which can be provided by distilled flows or consistency models, our method enables seamless exploration of the posterior without the need to re-run the full sampling chain, drastically reducing computational overhead. Theoretically, we prove a guarantee for the proposed noise-space Langevin dynamics to approximate the posterior, assuming that the generative model sufficiently approximates the prior distribution. Our framework is experimentally validated on image restoration tasks involving noisy linear and nonlinear forward operators applied to LSUN-Bedroom (256 x 256) and ImageNet (64 x 64) datasets. The results demonstrate that our approach generates high-fidelity samples with enhanced semantic diversity even under a limited number of function evaluations, offering superior efficiency and performance compared to existing diffusion-based posterior sampling techniques.

Being able to accurately monitor the screen exposure of young children is important for research on phenomena linked to screen use such as childhood obesity, physical activity, and social interaction. Most existing studies rely upon self-report or manual measures from bulky wearable sensors, thus lacking efficiency and accuracy in capturing quantitative screen exposure data. In this work, we developed a novel sensor informatics framework that utilizes egocentric images from a wearable sensor, termed the screen time tracker (STT), and a vision language model (VLM). In particular, we devised a multi-view VLM that takes multiple views from egocentric image sequences and interprets screen exposure dynamically. We validated our approach by using a dataset of children's free-living activities, demonstrating significant improvement over existing methods in plain vision language models and object detection models. Results supported the promise of this monitoring approach, which could optimize behavioral research on screen exposure in children's naturalistic settings., Comment: Prepare for submission

Quasars are powered by supermassive black hole (SMBH) accretion disks, yet standard disk models are inconsistent with many quasar observations. Recently, Hopkins et al. (2024) simulated the formation of a quasar disk feeding a SMBH of mass $M=1.3\times10^7\,M_\odot$ in a host galaxy that evolved from cosmological initial conditions. The disk had surprisingly strong toroidal magnetic fields that supported it vertically from gravity and powered fast accretion. What radiation and feedback can such a system produce? To answer this, we must follow the gas to the event horizon. For this, we interpolated the accretion system onto the grid of the general-relativistic radiation magnetohydrodynamics code H-AMR and performed 3D simulations with BH spins $a=0$ and $a=0.9375$. This remapping generates spurious magnetic monopoles, which we erase using a novel divergence cleaning approach. Despite the toroidal magnetic field's dominance at large radii, vertical magnetic flux builds up at the event horizon. This causes a magnetic state transition within the inner $200$ gravitational radii of the disk, where net vertical magnetic flux begins dominating the accretion flow. This powers strong winds and, if the BH spins, relativistic jets that can spin-down the BH within $5-10\,{\rm Myrs}$. Sometimes, vertical magnetic fields of opposite sign reach the BH, causing polarity inversion events that briefly destroy the jets and, possibly, the X-ray corona. The disk powers accretion at rates $5\times$ the Eddington limit, which can double the BH mass in $5-10\,{\rm Myrs}$. When $a=0.9375$ ($a=0$), the energy in outflows and radiation equals about $60\%$ ($10\%$) and $100\%$ ($3\%$) of the accreted rest mass energy, respectively. Much of the light escapes in cool, extended $\gtrsim1300\,{\rm au}$ photospheres, consistent with quasar microlensing and the ``big blue bump'' seen in spectral energy distributions., Comment: 33 pages, 18 figures. Submitted to ApJ. Comments welcome!

The sensitivity of electron EDM experiments has been increasing at a rapid pace, and could yield indications of new physics in the coming decade. An intriguing possibility is that an EDM signal could be generated by new, electroweak-charged particles at the TeV scale that couple to the Higgs and contribute to the electron EDM at two-loop order via Barr-Zee diagrams. A high-energy muon collider could decisively search for new physics at this scale. In this work, we explore this complementarity between colliders and EDM experiments, and note that Barr-Zee diagrams from the aforementioned particles are closely related to vector-boson scattering processes at a muon collider. These loop corrections lead to kinematic features in the differential cross sections of these processes, dictated by the optical theorem. We demonstrate this connection in the context of the singlet-doublet and doublet-triplet extensions to the SM, explore the detectability of these features at a muon collider experiment, and discuss how these measurements can be used to ascertain the underlying model parameters., Comment: 22 pages + appendices, 9 figures

Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers produce vast quantities of analytic data from multiple sources and of varying types so compiling this data in an efficient matter is critical to the process. MIT Lincoln Laboratory Supercomputing Center has been utilizing the Unity 3D game engine to create a Digital Twin of our supercomputing systems for several years to perform system monitoring. Unity offers robust visualization capabilities making it ideal for creating a sophisticated representation of the computational processes. As we scale the systems to include a diversity of resources such as accelerators and the addition of more users, we need to implement new analysis tools for the monitoring system. The workloads in research continuously change, as does the capability of Unity, and this allows us to adapt our monitoring tools to scale and incorporate features enabling efficient replay of system wide events, user isolation, and machine level granularity. Our system fully takes advantage of the modern capabilities of the Unity Engine in a way that intuitively represents the real time workload performed on a supercomputer. It allows HPC system engineers to quickly diagnose usage related errors with its responsive user interface which scales efficiently with large data sets.

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills., Comment: 11 pages, 8 figures, AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Compact photonic systems that offer high frequency stability and low noise are of increasing importance to applications in precision metrology, quantum computing, communication, and advanced sensing technologies. However, on-chip resonators comprised of dielectrics cannot match the frequency stability and noise characteristics of Fabry-Perot cavities, whose electromagnetic modes live almost entirely in vacuum. In this study, we present a novel strategy to interface micro-fabricated Fabry-Perot cavities with photonic integrated circuits to realize compact, high-performance integrated systems. Using this new integration approach, we demonstrate self-injection locking of an on-chip laser to a milimeter-scale vacuum-gap Fabry-Perot using a circuit interface that transforms the reflected cavity response to enable efficient feedback to the laser. This system achieves a phase noise of -97 dBc/Hz at 10 kHz offset frequency, a fractional frequency stability of 5*10-13 at 10 ms, a 150 Hz 1/pi integral linewidth, and a 35 mHz fundamental linewidth. We also present a complementary integration strategy that utilizes a vertical emission grating coupler and a back-reflection cancellation circuit to realize a fully co-integrated module that effectively redirects the reflected signals and isolates back-reflections with a 10 dB suppression ratio, readily adaptable for on-chip PDH locking. Together, these demonstrations significantly enhance the precision and functionality of RF photonic systems, paving the way for continued advancements in photonic applications.

Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We determine which of these effects leave a detectable imprint on several 2-point clustering statistics extracted from the EWSS on large scales. We generate 140 mock galaxy catalogues with the survey geometry and selection function of the EWSS and make use of the LIGER method to account for a variable number of relativistic RSD to linear order in the cosmological perturbations. We estimate different 2-point clustering statistics from the mocks and use the likelihood-ratio test to calculate the statistical significance with which the EWSS could reject the null hypothesis that certain relativistic projection effects can be neglected in the theoretical models. We find that the combined effects of lensing magnification and convergence imprint characteristic signatures on several clustering observables. Their S/N ranges between 2.5 and 6 (depending on the adopted summary statistic) for the highest-redshift galaxies in the EWSS. The corresponding feature due to the peculiar velocity of the Sun is measured with a S/N of order one or two. The $P_{\ell}(k)$ from the catalogues that include all relativistic effects reject the null hypothesis that RSD are only generated by the variation of the peculiar velocity along the line of sight with a significance of 2.9 standard deviations. As a byproduct of our study, we demonstrate that the mixing-matrix formalism to model finite-volume effects in the $P_{\ell}(k)$ can be robustly applied to surveys made of several disconnected patches. Our results indicate that relativistic RSD, the contribution from weak gravitational lensing in particular, cannot be disregarded when modelling 2-point clustering statistics extracted from the EWSS., Comment: 23 pages, 14 figures

Accretion disks around both stellar-mass and supermassive black holes are likely often warped. Whenever a disk is warped, its scale height varies with azimuth. Sufficiently strong warps cause extreme compressions of the scale height, which fluid parcels "bounce" off of twice per orbit to high latitudes. In this paper, we study the dynamics of such strong warps using two methods: (i) the nearly analytic "ring theory" of Fairbairn & Ogilvie (2021a), which we generalize to the Kerr metric; and (ii) 3D general-relativistic hydrodynamic simulations of tori ("rings") around black holes, using the H-AMR code. We initialize a ring with a warp and study the subsequent evolution on tens of orbital periods. The simulations agree excellently with the ring theory until the warp amplitude, $\psi$, reaches a critical value $\psi_{\rm c}$. When $\psi>\psi_{\rm c}$, the rings enter the bouncing regime. We analytically derive (and numerically validate) that $\psi_{\rm c}\approx (r/r_{\rm g})^{-1/2}$ in the non-Keplerian regime, where $r_{\rm g}=GM/c^2$ is the gravitational radius and $M$ is the mass of the central object. Whenever the scale height bounces, the vertical velocity becomes supersonic, which leads to a "nozzle shock" as the gas collides at the scale height minima. Nozzle shocks damp the warp within $\approx10-20$ orbits in the simulations; but, that damping is not captured by the ring theory. Nozzle shock dissipation leads to inflow timescales that are 1-2 orders of magnitude shorter than unwarped $\alpha$ disks which may result in rapid variability, such as in changing-look active galactic nuclei or in the soft state of X-ray binaries. We also propose that steady disks with strong enough warps may self-regulate to have amplitudes near $\psi_{\rm c}$., Comment: 23 pages, 9 figures. Submitted to ApJ. Comments welcome!

We investigate the absorbing state phase transition in a purely dissipative quantum reaction-diffusion model where the absorbing state itself has long range phase coherence. The model may also be viewed as a dissipative quantum state preparation procedure for the (generalized) W-state with errors. The 'error' Lindblad jump operators preserve the W-state as a dark state, but nonetheless act to decohere the system and induce the phase transition. We find cases where the preparation protocol is either fragile or robust against weak error quantum jump rates, and show that remnants of the coherence persist even after the phase transition to the decohering phase., Comment: 16 pages, 4 figures

Modern commercial games are designed for mass appeal, not for individual players, but there is a unique opportunity in video games to better fit the individual through adapting game elements. In this paper, we focus on AI Directors, systems which can dynamically modify a game, that personalize the player experience to match the player's preference. In the past, some AI Director studies have provided inconclusive results, so their effect on player experience is not clear. We take three AI Directors and directly compare them in a human subject study to test their effectiveness on quest selection. Our results show that a non-random AI Director provides a better player experience than a random AI Director.

We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-language training data. We introduce novel evaluation datasets in several less-resourced languages, and experiment with a range of approaches including the use of machine translation; in-context learning with large language models; and various intermediate training regimes including a novel task objective, POA, that leverages paragraph-level information. Our results demonstrate significant improvements over the state of the art, with in-context learning generally giving the best performance, but with the novel POA approach giving a competitive alternative with much lower computational overhead. We also show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer, but that similarity in semantic content and structure can be equally important., Comment: The first two authors share equal contribution

Autonomous racing demands safe control of vehicles at their physical limits for extended periods of time, providing insights into advanced vehicle safety systems which increasingly rely on intervention provided by vehicle autonomy. Participation in this field carries with it a high barrier to entry. Physical platforms and their associated sensor suites require large capital outlays before any demonstrable progress can be made. Simulators allow researches to develop soft autonomous systems without purchasing a platform. However, currently available simulators lack visual and dynamic fidelity, can still be expensive to buy, lack customisation, and are difficult to use. AARK provides three packages, ACI, ACDG, and ACMPC. These packages enable research into autonomous control systems in the demanding environment of racing to bring more people into the field and improve reproducibility: ACI provides researchers with a computer vision-friendly interface to Assetto Corsa for convenient comparison and evaluation of autonomous control solutions; ACDG enables generation of depth, normal and semantic segmentation data for training computer vision models to use in perception systems; and ACMPC gives newcomers to the field a modular full-stack autonomous control solution, capable of controlling vehicles to build from. AARK aims to unify and democratise research into a field critical to providing safer roads and trusted autonomous systems., Comment: 7 pages, 5 figures

Actively participating in body movement such as dance, sports, and fitness activities is challenging for people who are blind or have low vision (BLV). Teachers primarily rely on verbal instructions and physical demonstrations with limited accessibility. Recent work shows that technology can support body movement education for BLV people. However, there is limited involvement with the BLV community and their teachers to understand their needs. By conducting a series of two surveys, 23 interviews and four focus groups, we gather the voices and perspectives of BLV people and their teachers. This provides a rich understanding of the challenges of body movement education. We identify ten major themes, four key design challenges, and propose potential solutions. We encourage the assistive technologies community to co-design potential solutions to these identified design challenges promoting the quality of life of BLV people and supporting the teachers in the provision of inclusive education.

We show that the Deep Underground Neutrino Experiment (DUNE) has the potential to make a precise measurement of the total active flux of 8B solar neutrinos via neutral-current (NC) interactions with argon. This would complement proposed precise measurements of solar-neutrino fluxes in DUNE via charged-current (CC) interactions with argon and mixed CC/NC interactions with electrons. Together, these would enable DUNE to make a SNO-like comparison of rates and thus to make the most precise measurements of $\sin^2\theta_{12}$ and $\Delta m^2_{21}$ using solar neutrinos. Realizing this potential requires dedicated but realistic efforts to improve DUNE's low-energy capabilities and separately to reduce neutrino-argon cross section uncertainties. Comparison of mixing-parameter results obtained using solar neutrinos in DUNE and reactor antineutrinos in JUNO (Jiangmen Underground Neutrino Observatory) would allow unprecedented tests of new physics., Comment: 6 pages main text (10 pages total), 7 figures. Comments are welcome

Cosmic Explorer (CE) is a next-generation ground-based gravitational-wave observatory that is being designed in the 2020s and is envisioned to begin operations in the 2030s together with the Einstein Telescope in Europe. The CE concept currently consists of two widely separated L-shaped observatories in the United States, one with 40 km-long arms and the other with 20 km-long arms. This order of magnitude increase in scale with respect to the LIGO-Virgo-KAGRA observatories will, together with technological improvements, deliver an order of magnitude greater astronomical reach, allowing access to gravitational waves from remnants of the first stars and opening a wide discovery aperture to the novel and unknown. In addition to pushing the reach of gravitational-wave astronomy, CE endeavors to approach the lifecycle of large scientific facilities in a way that prioritizes mutually beneficial relationships with local and Indigenous communities. This article describes the (scientific, cost and access, and social) criteria that will be used to identify and evaluate locations that could potentially host the CE observatories., Comment: 12 pages, 1 figure

The prompt spectra of gamma-ray bursts are known to follow broadband continuum behavior over decades in energy. GRB 221009A, given the moniker the brightest of all time (BOAT), is the brightest gamma-ray burst identified in half a century of observations, and was first identified by the Fermi Gamma-ray Burst Monitor (GBM). On behalf of the Fermi-GBM Team, Lesage et al. (2023) described the initial GBM analysis. Ravasio et al. (2024) report the identification of a spectral line in part of the prompt emission of this burst, which they describe as evolving over 80 s from $\sim$12 MeV to 6 MeV. We report a GBM Team analysis on the Ravasio Line: 1) We cannot identify an instrumental effect that could have produced this signal, and 2) our method of calculating the statistical significance of the line shows it easily exceeds the 5$\sigma$ discovery threshold. We additionally comment on the claim of the line beginning at earlier time intervals, up to 37 MeV, as reported in Zhang et al. (2024). We find that it is reasonable to utilize these measurements for characterization of the line evolution, with caution. We encourage theoretical studies exploring this newly discovered gamma-ray burst spectral feature, unless any rigorous alternative explanation unrelated to the emission from GRB 221009A is identified.

Binary black holes are the most abundant source of gravitational-wave observations. Gravitational-wave observatories in the next decade will require tremendous increases in the accuracy of numerical waveforms modeling binary black holes, compared to today's state of the art. One approach to achieving the required accuracy is using spectral-type methods that scale to many processors. Using the SpECTRE numerical-relativity code, we present the first simulations of a binary black hole inspiral, merger, and ringdown using discontinuous Galerkin methods. The efficiency of discontinuous Galerkin methods allows us to evolve the binary through ~18 orbits at reasonable computational cost. We then use SpECTRE's Cauchy Characteristic Evolution (CCE) code to extract the gravitational waves at future null infinity. The open-source nature of SpECTRE means this is the first time a spectral-type method for simulating binary black hole evolutions is available to the entire numerical-relativity community., Comment: 32 pages, 12 figures, 28 ancillary input files

We present the first detailed study of an 8-channel $2\times2$ mm$^{2}$ WSi superconducting microwire single photon detector (SMSPD) array exposed to 120 GeV proton beam and 8 GeV electron and pion beam at the Fermilab Test Beam Facility. The SMSPD detection efficiency was measured for the first time for protons, electrons, and pions, enabled by the use of a silicon tracking telescope that provided precise spatial resolution of 30 $\mu$m for 120 GeV protons and 130 $\mu$m for 8 GeV electrons and pions. The result demonstrated consistent detection efficiency across pixels and at different bias currents. Time resolution of 1.15 ns was measured for the first time for SMSPD with proton, electron, and pions, enabled by the use of an MCP-PMT which provided a ps-level reference time stamp. The results presented is the first step towards developing SMSPD array systems optimized for high energy particle detection and identification for future accelerator-based experiments.

Rest-frame far-ultraviolet (FUV) observations from JWST are revolutionizing our understanding of the high-z galaxies that drove reionization and the mechanisms by which they accomplished it. To fully interpret these observations, we must be able to diagnose how properties of the interstellar medium (ISM; e.g., column density, covering fraction, outflow velocity) directly relate to the absorption features produced. Using the high-S/N and high-resolution FUV spectra of 45 nearby star-forming galaxies from CLASSY, we present the largest uniform, simultaneous characterization of neutral and low-ionization state (LIS) interstellar UV absorption lines (OI, SiII, SII, CII, AlII) across a wide range of galaxy properties. We also present 21-cm HI observations for 35 galaxies, multiple of which are gas-poor or non-detected, possibly indicating the onset of a post-starburst phase. We find that our simultaneous 1-component Voigt profile fits are capable of accurately modeling the LIS absorption for ~75% of galaxies, mitigating challenges associated with saturation, infilling, and degeneracies. While the most massive galaxies require additional components, our 1-component fits return average properties of the absorbing gas and follow the scaling relations described by a single gas cloud. We explore connections between LIS absorption and direct tracers of the neutral ISM (OI, Ly-alpha, HI 21-cm), finding that CII most closely traces the neutral gas trends while other ions exhibit weaker correlations. Given the challenges with directly observing HI at higher-z, we demonstrate that LIS absorption can be a powerful means to study the neutral ISM and present empirical relationships for predicting neutral gas properties., Comment: 25 pages with 16 figures and 2 tables. Long appendix with figure sets and tables. Accepted to ApJ

Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While several strategies have been proposed to deal with the detrimental effects of noise, these come at the cost of an extra shot budget. Given that shots are a precious resource for sensing -- as infinite measurements could lead to infinite precision -- care must be taken to truly guarantee that any shot not being used for sensing is actually leading to some metrological improvement. In this work, we study whether investing shots in error-mitigation, inference techniques, or combinations thereof, can improve the sensitivity of a noisy quantum sensor on a (shot) budget. We present a detailed bias-variance error analysis for various sensing protocols. Our results show that the costs of zero-noise extrapolation techniques outweigh their benefits. We also find that pre-characterizing a quantum sensor via inference techniques leads to the best performance, under the assumption that the sensor is sufficiently stable., Comment: 17+18 pages, 8+2 figures, 1+1 tables

PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise levels in PET imaging, which can significantly hinder the accurate interpretation and quantitative analysis of the scans. With advances in deep learning techniques, diffusion model-based PET denoising techniques have shown remarkable performance improvement. However, these models often face limitations when applied to volumetric data. Additionally, many existing diffusion models do not adequately consider the unique characteristics of PET imaging, such as its 3D volumetric nature, leading to the potential loss of anatomic consistency. Our Conditional Score-based Residual Diffusion (CSRD) model addresses these issues by incorporating a refined score function and 3D patch-wise training strategy, optimizing the model for efficient volumetric PET denoising. The CSRD model significantly lowers computational demands and expedites the denoising process. By effectively integrating volumetric data from PET and MRI scans, the CSRD model maintains spatial coherence and anatomical detail. Lastly, we demonstrate that the CSRD model achieves superior denoising performance in both qualitative and quantitative evaluations while maintaining image details and outperforms existing state-of-the-art methods., Comment: Accepted to MICCAI 2024

The "IO Wall" problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage between workflow stages. One way to avoid this problem in scientific workflows is to stream data directly from producers to consumers and avoiding storage entirely. However, the manner in which this is accomplished is key to both performance and usability. This paper presents the Sustainable Staging Transport, an approach which allows direct streaming between traditional file writers and readers with few application changes. SST is an ADIOS "engine", accessible via standard ADIOS APIs, and because ADIOS allows engines to be chosen at run-time, many existing file-oriented ADIOS workflows can utilize SST for direct application-to-application communication without any source code changes. This paper describes the design of SST and presents performance results from various applications that use SST, for feeding model training with simulation data with substantially higher bandwidth than the theoretical limits of Frontier's file system, for strong coupling of separately developed applications for multiphysics multiscale simulation, or for in situ analysis and visualization of data to complete all data processing shortly after the simulation finishes.

We present large-eddy-simulation (LES) modeling approaches for the simulation of atmospheric boundary layer turbulence that are of direct relevance to wind energy production. In this paper, we study a GABLS benchmark problem using high-order spectral element code Nek5000/RS and a block-structured second-order finite-volume code AMR-Wind which are supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) and ExaWind projects, respectively, targeting application simulations on various acceleration-device based exascale computing platforms. As for Nek5000/RS we demonstrate our newly developed subgrid-scale (SGS) models based on mean-field eddy viscosity (MFEV), high-pass filter (HPF), and Smagorinsky (SMG) with traction boundary conditions. For the traction boundary conditions, a novel analytical approach is presented that solves for the surface friction velocity and surface kinematic temperature flux. For AMR-Wind, standard SMG is used and discussed in detail the traction boundary conditions for convergence. We provide low-order statistics, convergence and turbulent structure analysis. Verification and convergence studies were performed for both codes at various resolutions and it was found that Nek5000/RS demonstrate convergence with resolution for all ABL bulk parameters, including boundary layer and low level jet (LLJ) height. Extensive comparisons are presented with simulation data from the literature., Comment: 35 pages, 24 figures, 1 table

In the third APOKASC catalog, we present data for the complete sample of 15,808 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismology. We used ten independent asteroseismic analysis techniques and anchor our system on fundamental radii derived from Gaia $L$ and spectroscopic $T_{\rm eff}$. We provide evolutionary state, asteroseismic surface gravity, mass, radius, age, and the spectroscopic and asteroseismic measurements used to derive them for 12,418 stars. This includes 10,036 exceptionally precise measurements, with median fractional uncertainties in \nmax, \dnu, mass, radius and age of 0.6\%, 0.6\%, 3.8\%, 1.8\%, and 11.1\% respectively. We provide more limited data for 1,624 additional stars which either have lower quality data or are outside of our primary calibration domain. Using lower red giant branch (RGB) stars, we find a median age for the chemical thick disk of $9.14 \pm 0.05 ({\rm ran}) \pm 0.9 ({\rm sys})$ Gyr with an age dispersion of 1.1 Gyr, consistent with our error model. We calibrate our red clump (RC) mass loss to derive an age consistent with the lower RGB and provide asymptotic GB and RGB ages for luminous stars. We also find a sharp upper age boundary in the chemical thin disk. We find that scaling relations are precise and accurate on the lower RGB and RC, but they become more model dependent for more luminous giants and break down at the tip of the RGB. We recommend the usage of multiple methods, calibration to a fundamental scale, and the usage of stellar models to interpret frequency spacings., Comment: 43 pages, 25 figures, submitted ApJSupp. Comments welcome. Data tables available on request from pinsonneault.1@osu.edu

Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to different factors, including climate change, habitat loss, and pesticide use. Thus, developing alternative pollination methods is essential for sustainable crop production. This paper introduces a robotic system for precision pollination in apples, which are not self-pollinating and require precise delivery of pollen to the stigmatic surfaces of the flowers. The proposed robotic system consists of a machine vision system to identify target flowers and a mechatronic system with a 6-DOF UR5e robotic manipulator and an electrostatic sprayer. Field trials of this system in 'Honeycrisp' and 'Fuji' apple orchards have shown promising results, with the ability to pollinate flower clusters at an average spray cycle time of 6.5 seconds. The robotic pollination system has achieved encouraging fruit set and quality, comparable to naturally pollinated fruits in terms of color, weight, diameter, firmness, soluble solids, and starch content. However, the results for fruit set and quality varied between different apple cultivars and pollen concentrations. This study demonstrates the potential for a robotic artificial pollination system to be an efficient and sustainable method for commercial apple production. Further research is needed to refine the system and assess its suitability across diverse orchard environments and apple cultivars.

This paper presents opt-in camera, a concept of privacy-preserving camera systems capable of recording only specific individuals in a crowd who explicitly consent to be recorded. Our system utilizes a mobile wireless communication tag attached to personal belongings as proof of opt-in and as a means of localizing tag carriers in video footage. Specifically, the on-ground positions of the wireless tag are first tracked over time using the unscented Kalman filter (UKF). The tag trajectory is then matched against visual tracking results for pedestrians found in videos to identify the tag carrier. Technically, we devise a dedicated trajectory matching technique based on constrained linear optimization, as well as a novel calibration technique that handles wireless tag-camera calibration and hyperparameter tuning for the UKF, which mitigates the non-line-of-sight (NLoS) issue in wireless localization. We realize the proposed opt-in camera system using ultra-wideband (UWB) devices and an off-the-shelf webcam installed in the environment. Experimental results demonstrate that our system can perform opt-in recording of individuals in near real-time at 10 fps, with reliable identification accuracy for a crowd of 8-23 people in a confined space., Comment: 7 pages, 6 figures, submitted to international conference on robotics and automation (ICRA) 2025

It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction $\alpha$ of stake owned by an adversary, the maximum fraction $f(\alpha)$ of rounds that a strategic adversary can propose. We further implement our methodology and compute $f(\cdot)$ for all $\alpha$. For example, we conclude that an optimal strategic participant with $5\%$ of the stake can propose a $5.048\%$ fraction of rounds, $10\%$ of the stake can propose a $10.19\%$ fraction of rounds, and $20\%$ of the stake can propose a $20.68\%$ fraction of rounds., Comment: 28 pages, 7 figures, full version of the AFT 2024 paper

In this paper, we propose a novel and generalizable zero-shot knowledge transfer framework that distills expert sports navigation strategies from web videos into robotic systems with adversarial constraints and out-of-distribution image trajectories. Our pipeline enables diffusion-based imitation learning by reconstructing the full 3D task space from multiple partial views, warping it into 2D image space, closing the planning loop within this 2D space, and transfer constrained motion of interest back to task space. Additionally, we demonstrate that the learned policy can serve as a local planner in conjunction with position control. We apply this framework in the wheelchair tennis navigation problem to guide the wheelchair into the ball-hitting region. Our pipeline achieves a navigation success rate of 97.67% in reaching real-world recorded tennis ball trajectories with a physical robot wheelchair, and achieve a success rate of 68.49% in a real-world, real-time experiment on a full-sized tennis court., Comment: This manuscript has been submitted to 2025 IEEE International Conference on Robotics & Automation (ICRA)

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents with significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on K-edge spectra database covering eight 3d transition metals (Ti-Cu). Our framework is built upon three distinct strategies. First, we use M3GNet to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving up to order-of-magnitude improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multi-task model across elements before fine-tuning for element-specific predictions. This cascaded approach after element-wise fine-turning yields models that outperform element-specific models by up to 31\%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a much higher computational cost. This approach improves prediction accuracy by up to 24\% over models trained on the target fidelity alone. Our approach is extendable to XAS prediction for a broader range of elements and offers a generalizable transfer learning framework to enhance other deep-learning models in materials science., Comment: Main manuscript: 21 pages, 11 figures. Supplemental material (12 pages, 6 figures) available as a separate file in arXiv ancillary files (additional downloadable files)

This is an introduction to Hall algebras from the perspective of $2$-Segal spaces or decomposition spaces, as introduced by Dyckerhoff and Kapranov and G\'{a}lvez-Carrillo, Kock and Tonks, respectively. We explain how linearizations of the $2$-Segal space arising as the Waldhausen $\mathcal{S}_{\bullet}$-construction of a proto-exact category recover various previously known Hall algebras. We use the $2$-Segal perspective to study functoriality of the Hall algebra construction and explain how relative variants of $2$-Segal spaces lead naturally to representations of Hall algebras., Comment: 32 pages

In this paper we solve mixed-integer linear programs (MILPs) via distributed asynchronous saddle point computation. This work is motivated by the MILPs being able to model problems in multi-agent autonomy, such as task assignment problems and trajectory planning with collision avoidance constraints in multi-robot systems. To solve a MILP, we relax it with a linear program approximation. We first show that if the linear program relaxation satisfies Slater's condition, then relaxing the problem, solving it, and rounding the relaxed solution produces a point that is guaranteed to satisfy the constraints of the original MILP. Next, we form a Lagrangian saddle point problem that is equivalent to the linear program relaxation, and then we regularize the Lagrangian in both the primal and dual spaces. Doing so gives a regularized Lagrangian that is strongly convex-strongly concave. We then develop a parallelized algorithm to compute saddle points of the regularized Lagrangian, and we show that it is tolerant to asynchrony in the computations and communications of primal and dual variables. Suboptimality bounds and convergence rates are presented for convergence to a saddle point. The suboptimality bound accounts for (i) the error induced by regularizing the Lagrangian and (ii) the suboptimality gap between the solution to the original MILP and the solution to its relaxed form. Simulation results illustrate these theoretical developments in practice, and show that relaxation and regularization combined typically have only a mild impact on the suboptimality of the solution obtained., Comment: 22 pages, 4 figures. arXiv admin note: text overlap with arXiv:2211.11842

Missing data, the data value that is not recorded for a variable, occurs in almost all statistical analyses and may be caused by many reasons, such as lack of collection or a lack of documentation. Researchers need to adequately deal with this issue to provide a valid analysis. The visualization of missing values plays an important role in supporting the investigation and understanding of the missing data patterns. While some techniques and tools for visualization of missing values are available, it is still a challenge to select the right visualization that will fulfil the user requirements for visualizing missing data. This paper provides an overview and state-of-the-art report (STAR) of research literature focusing on missing values visualization. To the best of our knowledge, this is the first survey paper with a focus on missing data visualization. The goal of this paper is to encourage visualization researchers to increase their involvement with Missing data visualization.

Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deployment. In particular, it is important to certify that a system obeys hard constraints on unsafe behavior in settings when it is unacceptable to design a tradeoff between performance and safety via tuning the policy (i.e. soft constraints). This leads to the question, how does enforcing hard constraints impact the performance (meaning safely completing tasks) of an IL policy? To answer this question, this paper builds a reachability-based safety filter to enforce hard constraints on IL, which we call Reachability-Aided Imitation Learning (RAIL). Through evaluations with state-of-the-art IL policies in mobile robots and manipulation tasks, we make two key findings. First, the highest-performing policies are sometimes only so because they frequently violate constraints, and significantly lose performance under hard constraints. Second, surprisingly, hard constraints on the lower-performing policies can occasionally increase their ability to perform tasks safely. Finally, hardware evaluation confirms the method can operate in real time., Comment: * denotes equal contribution

Valvular heart disease accounts for up to 20% of cardiac surgery in the United States. Computer simulation of "virtual interventions" may inform optimal valve repair for a given patient prior to intervention. However, there is a paucity of methods to noninvasively determine in vivo mechanical properties of valve tissue from clinically acquired 3D images, limiting the accuracy of computer prediction and translational potential of in silico valve repairs. Here, we propose ADEPT, A noninvasive method for Determining Elastic Properties of valve Tissue, to overcome this methodological gap. Our framework combines image registration and physics-informed neural networks (PINNs) to estimate material properties of valve tissue from 3D echocardiograms (3DE). The PINN model was validated on a series of benchmarks before being applied to the 3DE of the tricuspid valve in a child with congenital heart disease. Our approach yielded accurate material parameter estimations in the examples accompanying this work.

We present cosmological parameter forecasts for the Euclid 6x2pt statistics, which include the galaxy clustering and weak lensing main probes together with previously neglected cross-covariance and cross-correlation signals between imaging/photometric and spectroscopic data. The aim is understanding the impact of such terms on the Euclid performance. We produce 6x2pt cosmological forecasts, considering two different techniques: the so-called harmonic and hybrid approaches, respectively. In the first, we treat all the different Euclid probes in the same way, i.e. we consider only angular 2pt-statistics for spectroscopic and photometric clustering, as well as for weak lensing, analysing all their possible cross-covariances and cross-correlations in the spherical harmonic domain. In the second, we do not account for negligible cross-covariances between the 3D and 2D data, but consider the combination of their cross-correlation with the auto-correlation signals. We find that both cross-covariances and cross-correlation signals, have a negligible impact on the cosmological parameter constraints and, therefore, on the Euclid performance. In the case of the hybrid approach, we attribute this result to the effect of the cross-correlation between weak lensing and photometric data, which is dominant with respect to other cross-correlation signals. In the case of the 2D harmonic approach, we attribute this result to two main theoretical limitations of the 2D projected statistics implemented in this work according to the analysis of official Euclid forecasts: the high shot noise and the limited redshift range of the spectroscopic sample, together with the loss of radial information from subleading terms such as redshift-space distortions and lensing magnification. Our analysis suggests that 2D and 3D Euclid data can be safely treated as independent, with a great saving in computational resources., Comment: 32 pages, 20 figures. Comments are welcome

Fermionic gradient flow in combination with the short-flow-time expansion provides a computational method where the renormalisation of hadronic matrix elements on the lattice can be simplified to address e.g. the issue that operators with different mass dimension can mix. We demonstrate our gradient flow renormalisation procedure by determining matrix elements of four-quark operators describing neutral meson mixing or meson lifetimes. While meson mixing calculations are well-established on the lattice and serve to validate our procedure, a lattice calculation of matrix elements for heavy meson lifetimes is still outstanding. Preliminary results for mesons formed of a charm and strange quark are presented., Comment: 11 pages, 7 figures, 1 table, Contribution to the 41st International Symposium on Lattice Field Theory, July 28th - August 3rd 2024, Liverpool, UK

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links between weather and climate modelling, this raises the question whether machine learning models could also revolutionize climate science, for example by informing mitigation and adaptation to climate change or to generate larger ensembles for more robust uncertainty estimates. Here, we show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states corresponding to pre-industrial, present-day, and future 2.9K warmer climates. This indicates that the dynamics shaping the weather on short timescales may not differ fundamentally in a changing climate. It also demonstrates out-of-distribution generalization capabilities of the machine learning models that are a critical prerequisite for climate applications. Nonetheless, two of the models show a global-mean cold bias in the forecasts for the future warmer climate state, i.e. they drift towards the colder present-day climate they have been trained for. A similar result is obtained for the pre-industrial case where two out of three models show a warming. We discuss possible remedies for these biases and analyze their spatial distribution, revealing complex warming and cooling patterns that are partly related to missing ocean-sea ice and land surface information in the training data. Despite these current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science and transform established approaches by complementing conventional physics-based models., Comment: 14 pages, 4 figures

Excitons are fundamental excitations that govern the optical properties of semiconductors. Interacting excitons can lead to various emergent phases of matter and large nonlinear optical responses. In most semiconductors, excitons interact via exchange interaction or phase space filling. Correlated materials that host excitons coupled to other degrees of freedom offer hitherto unexplored pathways for controlling these interactions. Here, we demonstrate magnon-mediated excitonic interactions in CrSBr, an antiferromagnetic semiconductor. This interaction manifests as the dependence of exciton energy on exciton density via a magnonic adjustment of the spin canting angle. Our study demonstrates the emergence of quasiparticle-mediated interactions in correlated quantum materials, leading to large nonlinear optical responses and potential device concepts such as magnon-mediated quantum transducers., Comment: 33 pages, 14 figures

The terms self-sovereign identity (SSI) and decentralised identity are often used interchangeably, which results in increasing ambiguity when solutions are being investigated and compared. This article aims to provide a clear distinction between the two concepts in relation to the revised Regulation as Regards establishing the European Digital Identity Framework (eIDAS 2.0) by providing a systematisation of knowledge of technological developments that led up to implementation of eIDAS 2.0. Applying an inductive exploratory approach, relevant literature was selected iteratively in waves over a nine months time frame and covers literature between 2005 and 2024. The review found that the decentralised identity sector emerged adjacent to the OpenID Connect (OIDC) paradigm of Open Authentication, whereas SSI denotes the sector's shift towards blockchain-based solutions. In this study, it is shown that the interchangeable use of SSI and decentralised identity coincides with novel protocols over OIDC. While the first part of this paper distinguishes OIDC from decentralised identity, the second part addresses the incompatibility between OIDC under eIDAS 2.0 and Web3. The paper closes by suggesting further research for establishing a digital identity bridge for connecting applications on public-permissionless ledgers with data originating from eIDAS 2.0 and being presented using OIDC., Comment: Conference paper

The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations; across sensory modalities, this capacity is aided by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities. To function optimally, its plastic network requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations. Input is delivered to a column of duplicated spiking principal neurons via heterogeneous synaptic weights; this regularizes layer utilization, yoking total activity to the network's operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics, optimizing resource utilization by balancing activity regularization and information retention.

This paper presents AquaMILR+, an untethered limbless robot designed for agile navigation in complex aquatic environments. The robot features a bilateral actuation mechanism that models musculoskeletal actuation in many anguilliform swimming organisms which propagates a moving wave from head to tail allowing open fluid undulatory swimming. This actuation mechanism employs mechanical intelligence, enhancing the robot's maneuverability when interacting with obstacles. AquaMILR+ also includes a compact depth control system inspired by the swim bladder and lung structures of eels and sea snakes. The mechanism, driven by a syringe and telescoping leadscrew, enables depth and pitch control-capabilities that are difficult for most anguilliform swimming robots to achieve. Additional structures, such as fins and a tail, further improve stability and propulsion efficiency. Our tests in both open water and indoor 2D and 3D heterogeneous aquatic environments highlight AquaMILR+'s capabilities and suggest a promising system for complex underwater tasks such as search and rescue and deep-sea exploration.

There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhouse of large-scale non-parametric knowledge, however existing techniques do not directly transfer to the embodied domain, which is multimodal, data is highly correlated, and perception requires abstraction. To address these challenges, we introduce Embodied-RAG, a framework that enhances the foundational model of an embodied agent with a non-parametric memory system capable of autonomously constructing hierarchical knowledge for both navigation and language generation. Embodied-RAG handles a full range of spatial and semantic resolutions across diverse environments and query types, whether for a specific object or a holistic description of ambiance. At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail. This hierarchical organization allows the system to efficiently generate context-sensitive outputs across different robotic platforms. We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 200 explanation and navigation queries across 19 environments, highlighting its promise for general-purpose non-parametric system for embodied agents., Comment: Web: https://quanting-xie.github.io/Embodied-RAG-web/

Humans naturally obtain intuition about the interactions between and the stability of rigid objects by observing and interacting with the world. It is this intuition that governs the way in which we regularly configure objects in our environment, allowing us to build complex structures from simple, everyday objects. Robotic agents, on the other hand, traditionally require an explicit model of the world that includes the detailed geometry of each object and an analytical model of the environment dynamics, which are difficult to scale and preclude generalization. Instead, robots would benefit from an awareness of intuitive physics that enables them to similarly reason over the stable interaction of objects in their environment. Towards that goal, we propose StackGen, a diffusion model that generates diverse stable configurations of building blocks matching a target silhouette. To demonstrate the capability of the method, we evaluate it in a simulated environment and deploy it in the real setting using a robotic arm to assemble structures generated by the model.