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An end-to-end (E2E) visuomotor policy is typically treated as a unified whole, but recent approaches using out-of-domain (OOD) data to pretrain the visual encoder have cleanly separated the visual encoder from the network, with the remainder referred to as the policy. We propose Visual Alignment Testing, an experimental framework designed to evaluate the validity of this functional separation. Our results indicate that in E2E-trained models, visual encoders actively contribute to decision-making resulting from motor data supervision, contradicting the assumed functional separation. In contrast, OOD-pretrained models, where encoders lack this capability, experience an average performance drop of 42% in our benchmark results, compared to the state-of-the-art performance achieved by E2E policies. We believe this initial exploration of visual encoders' role can provide a first step towards guiding future pretraining methods to address their decision-making ability, such as developing task-conditioned or context-aware encoders.

The paper aims on the construction of weak solutions to equations of a model of compressible viscous fluids, being a simplification of the classical compressible Navier-Stokes system. We present a novel scheme for approximating systems that preserves structural integrity by avoiding classical regularization with $ - \varepsilon \Delta \varrho $, thus maintaining the transport character of the continuity equation. Our approach, which necessitates specific conditions on the constitutive equation, accommodates physically relevant models such as isentropic and van der Waals gases, and globally handles non-monotone pressures. From an analytical perspective, our method synthesizes techniques from Feireisl-Lions and Bresch-Jabin to demonstrate the convergence of approximate densities using compensated compactness techniques. We also apply renormalization of the continuity equations and utilize weight techniques to manage unfavorable terms.

Inherent robustness in manipulation is prevalent in biological systems and critical for robotic manipulation systems due to real-world uncertainties and disturbances. This robustness relies not only on robust control policies but also on the design characteristics of the end-effectors. This paper introduces a bi-level optimization approach to co-designing tools and control policies to achieve robust manipulation. The approach employs reinforcement learning for lower-level control policy learning and multi-task Bayesian optimization for upper-level design optimization. Diverging from prior approaches, we incorporate caging-based robustness metrics into both levels, ensuring manipulation robustness against disturbances and environmental variations. Our method is evaluated in four non-prehensile manipulation environments, demonstrating improvements in task success rate under disturbances and environment changes. A real-world experiment is also conducted to validate the framework's practical effectiveness.

The Parker Solar Probe (PSP) spacecraft has transited the inner-most regions of the zodiacal cloud and detects impacts to the spacecraft body via its electric field instrument. Multiple dust populations have been proposed to explain the PSP dust impact rates. PSP's unique orbit allows us to identify a region where the impact rates are likely dominated by $\alpha$-meteoroids, small zodiacal grains on approximately circular, bound orbits. From the distribution of voltage signals generated by dust impacts to PSP in this region, we find the cumulative mass index for grains with radii of $\sim$0.6-1.4 $\mu$m (masses of $3\times10^{-15}$ to $3\times10^{-14}$ kg) to be $\alpha = 1.1 \pm 0.3$ from 0.1-0.25 $R_\odot$. $\alpha$ increases toward the Sun, with even smaller fragments generated closer to the Sun. The derived size distribution is steeper than previously estimated, and in contrast to expectations we find most of the dust mass resides in the smallest fragments and not in large grains inside 0.15 au. As the inner-most regions of the zodiacal cloud are likely collisionally evolved, these results place new constraints how the solar system's zodiacal cloud and by extension astrophysical debris disks are partitioned in mass.

We present a novel protocol for high-fidelity qubit state preparation and measurement (SPAM) that combines standard SPAM methods with a series of in-sequence measurements to detect and remove errors. The protocol can be applied in any quantum system with a long-lived (metastable) level and a means to detect population outside of this level without coupling to it. We demonstrate the use of the protocol for three different qubit encodings in a single trapped $^{137}\mathrm{Ba}^+$ ion. For all three, we achieve the lowest reported SPAM infidelities of $7(4) \times 10^{-6}$ (optical qubit), $5(4) \times 10^{-6}$ (metastable-level qubit), and $8(4) \times 10^{-6}$ (ground-level qubit).

Magnetic impurities on superconductors present a viable platform for building advanced applications in quantum technologies. However, a controlled manipulation of their quantum states continues to pose a significant challenge, hindering the progress in the field. Here we show the manipulation of magnetic states in the radical molecule 4,5,9,10-tetrabromo-1,3,6,8-tetraazapyrene (TBTAP) on a Pb(111) superconducting surface using low-temperature scanning tunneling microscopy. Tunneling spectra reveal Yu-Shiba-Rusinov (YSR) states near the Fermi energy in isolated molecules. A quantum phase transition from singlet to doublet ground state is induced by changing the tip-molecule distance. Additionally, the presence of a second TBTAP molecule allows tuning of the YSR state position by altering the relative distance and can induce splitting of the YSR states for certain orientations. The construction of molecular chains up to pentamers shows periodic arrangements of charged and neutral molecules, with even-numbered chains forming a charged dimer structure at one end. Information can be encoded in these chains by switching the dimer position. These findings elucidate interactions between molecular assemblies and superconducting substrates, paving the way for advanced quantum-state engineering.

Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to provably ensure system safety through forward invariance. However, they often unnecessarily sacrifice performance for safety since they are purely reactive. Receding horizon control (RHC), on the other hand, consider planned trajectories to account for the future evolution of a system. This work provides a new perspective on safety-critical control by introducing Forward Invariance in Trajectory Spaces (FITS). We lift the problem of safe RHC into the trajectory space and describe the evolution of planned trajectories as a controlled dynamical system. Safety constraints defined over states can be converted into sets in the trajectory space which we render forward invariant via a CBF framework. We derive an efficient quadratic program (QP) to synthesize trajectories that provably satisfy safety constraints. Our experiments support that FITS improves the adherence to safety specifications without sacrificing performance over alternative CBF and NMPC methods., Comment: 8 pages, 4 figures

The use of solid boron hydride molecules as a fuel for proton-boron fusion was proposed by M. Kr\r{u}s and M. Londesborough at "Interaction of Inorganic Clusters, Cages, and Containers with Light" workshop in November 2021. Here we demonstrate experimentally, for first time, that the solid boron hydride, octadecaborane - \textit{anti}-B${}_{18}$H${}_{22}$, produces a relatively high yield of alpha particles of about $10^9$ per steradian using a sub-nanosecond, low-contrast laser pulse (PALS) with a typical intensity of $10^{16}$ Wcm${}^{-2}$. In contrast to previously published proton-boron studies, the boron hydrides, due to their composition containing only atoms of boron and hydrogen, represent a natural choice for future proton-boron nuclear fusion schemes., Comment: 17 pages, 6 figures

Reinforcement learning from human feedback (RLHF) is fundamentally limited by the capacity of humans to correctly evaluate model output. To improve human evaluation ability and overcome that limitation this work trains "critic" models that help humans to more accurately evaluate model-written code. These critics are themselves LLMs trained with RLHF to write natural language feedback highlighting problems in code from real-world assistant tasks. On code containing naturally occurring LLM errors model-written critiques are preferred over human critiques in 63% of cases, and human evaluation finds that models catch more bugs than human contractors paid for code review. We further confirm that our fine-tuned LLM critics can successfully identify hundreds of errors in ChatGPT training data rated as "flawless", even though the majority of those tasks are non-code tasks and thus out-of-distribution for the critic model. Critics can have limitations of their own, including hallucinated bugs that could mislead humans into making mistakes they might have otherwise avoided, but human-machine teams of critics and contractors catch similar numbers of bugs to LLM critics while hallucinating less than LLMs alone.

We propose encoding multiple qubits inside ions in existing trapped-ion quantum computers to access more qubits and to simplify circuits implementing standard algorithms. By using such `virtual' qubits, some inter-ion gates can be replaced by intra-ion gates, reducing the use of vibrational modes of the ion chain, leading to less noise. We discuss specific examples such as the Bernstein-Vazirani algorithm and random circuit sampling, using a small number of virtual qubits. Additionally, virtual qubits enable using larger number of data qubits for an error correcting code, and we consider the repetition code as an example. We also lay out practical considerations to be made when choosing states to encode virtual qubits in $^{137}\mathrm{Ba}^+$ ions, and for preparing states and performing measurements., Comment: 18+14 pages, 8+4 figures

Chronic obstructive pulmonary disease (COPD) is a serious inflammatory lung disease affecting millions of people around the world. Due to an obstructed airflow from the lungs, it also becomes manifest in patients' vocal behaviour. Of particular importance is the detection of an exacerbation episode, which marks an acute phase and often requires hospitalisation and treatment. Previous work has shown that it is possible to distinguish between a pre- and a post-treatment state using automatic analysis of read speech. In this contribution, we examine whether sustained vowels can provide a complementary lens for telling apart these two states. Using a cohort of 50 patients, we show that the inclusion of sustained vowels can improve performance to up to 79\% unweighted average recall, from a 71\% baseline using read speech. We further identify and interpret the most important acoustic features that characterise the manifestation of COPD in sustained vowels., Comment: Accepted to INTERSPEECH 2024

The term safety in robotics is often understood as a synonym for avoidance. Although this perspective has led to progress in path planning and reactive control, a generalization of this perspective is necessary to include task semantics relevant to contact-rich manipulation tasks, especially during teleoperation and to ensure the safety of learned policies. We introduce the semantics-aware distance function and a corresponding computational method based on the Kelvin Transformation. The semantics-aware distance generalizes signed distance functions by allowing the zero level set to lie inside of the object in regions where contact is allowed, effectively incorporating task semantics -- such as object affordances and user intent -- in an adaptive implicit representation of safe sets. In validation experiments we show the capability of our method to adapt to time-varying semantic information, and to perform queries in sub-microsecond, enabling applications in reinforcement learning, trajectory optimization, and motion planning., Comment: 8 pages, 6 figures, submitted to IEEE/RSJ IROS 2024

In this paper, we investigate the Walk on Spheres algorithm (WoS) for motion planning in robotics. WoS is a Monte Carlo method to solve the Dirichlet problem developed in the 50s by Muller and has recently been repopularized by Sawhney and Crane, who showed its applicability for geometry processing in volumetric domains. This paper provides a first study into the applicability of WoS for robot motion planning in configuration spaces, with potential fields defined as the solution of screened Poisson equations. The experiments in this paper empirically indicate the method's trivial parallelization, its dimension-independent convergence characteristic of $O(1/N)$ in the number of walks, and a validation experiment on the RR platform., Comment: 10 pages, 13 figures

We study the Equitable Connected Partition (ECP for short) problem, where we are given a graph G=(V,E) together with an integer p, and our goal is to find a partition of V into p parts such that each part induces a connected sub-graph of G and the size of each two parts differs by at most 1. On the one hand, the problem is known to be NP-hard in general and W[1]-hard with respect to the path-width, the feedback-vertex set, and the number of parts p combined. On the other hand, fixed-parameter algorithms are known for parameters the vertex-integrity and the max leaf number. As our main contribution, we resolve a long-standing open question [Enciso et al.; IWPEC '09] regarding the parameterisation by the tree-depth of the underlying graph. In particular, we show that ECP is W[1]-hard with respect to the 4-path vertex cover number, which is an even more restrictive structural parameter than the tree-depth. In addition to that, we show W[1]-hardness of the problem with respect to the feedback-edge set, the distance to disjoint paths, and NP-hardness with respect to the shrub-depth and the clique-width. On a positive note, we propose several novel fixed-parameter algorithms for various parameters that are bounded for dense graphs.

To develop robust manipulation policies, quantifying robustness is essential. Evaluating robustness in general dexterous manipulation, nonetheless, poses significant challenges due to complex hybrid dynamics, combinatorial explosion of possible contact interactions, global geometry, etc. This paper introduces ``caging in motion'', an approach for analyzing manipulation robustness through energy margins and caging-based analysis. Our method assesses manipulation robustness by measuring the energy margin to failure and extends traditional caging concepts for a global analysis of dynamic manipulation. This global analysis is facilitated by a kinodynamic planning framework that naturally integrates global geometry, contact changes, and robot compliance. We validate the effectiveness of our approach in the simulation and real-world experiments of multiple dynamic manipulation scenarios, highlighting its potential to predict manipulation success and robustness., Comment: 8 pages

Grain-grain collisions shape the 3-dimensional size and velocity distribution of the inner Zodiacal Cloud and the impact rates of dust on inner planets, yet they remain the least understood sink of zodiacal dust grains. For the first time, we combine the collisional grooming method combined with a dynamical meteoroid model of Jupiter-family comets (JFCs) that covers four orders of magnitude in particle diameter to investigate the consequences of grain-grain collisions in the inner Zodiacal Cloud. We compare this model to a suite of observational constraints from meteor radars, the Infrared Astronomical Satellite (IRAS), mass fluxes at Earth, and inner solar probes, and use it to derive the population and collisional strength parameters for the JFC dust cloud. We derive a critical specific energy of $Q^*_D=5\times10^5 \pm 4\times10^5 R_\mathrm{met}^{-0.24}$ J kg$^{-1}$ for particles from Jupiter-family comet particles, making them 2-3 orders of magnitude more resistant to collisions than previously assumed. We find that the differential power law size index $-4.2\pm0.1$ for particles generated by JFCs provides a good match to observed data. Our model provides a good match to the mass production rates derived from the Parker Solar Probe observations and their scaling with the heliocentric distance. The higher resistance to collisions of dust particles might have strong implications to models of collisions in solar and exo-solar dust clouds. The migration via Poynting-Roberson drag might be more important for denser clouds, the mass production rates of astrophysical debris disks might be overestimated, and the mass of the source populations might be underestimated. Our models and code are freely available online., Comment: 41 pages, 14 figures, 1 table - Submitted to The Planetary Science Journal (PSJ) - After 2nd review

Even though large-scale text-to-image generative models show promising performance in synthesizing high-quality images, applying these models directly to image editing remains a significant challenge. This challenge is further amplified in video editing due to the additional dimension of time. This is especially the case for editing real-world videos as it necessitates maintaining a stable structural layout across frames while executing localized edits without disrupting the existing content. In this paper, we propose RealCraft, an attention-control-based method for zero-shot real-world video editing. By swapping cross-attention for new feature injection and relaxing spatial-temporal attention of the editing object, we achieve localized shape-wise edit along with enhanced temporal consistency. Our model directly uses Stable Diffusion and operates without the need for additional information. We showcase the proposed zero-shot attention-control-based method across a range of videos, demonstrating shape-wise, time-consistent and parameter-free editing in videos of up to 64 frames.

Individual optical addressing in chains of trapped atomic ions requires generation of many small, closely spaced beams with low cross-talk. Furthermore, implementing parallel operations necessitates phase, frequency, and amplitude control of each individual beam. Here we present a scalable method for achieving all of these capabilities using a novel integrated photonic chip coupled to a network of optical fibre components. The chip design results in very low cross-talk between neighbouring channels even at the micrometre-scale spacing by implementing a very high refractive index contrast between the channel core and cladding. Furthermore, the photonic chip manufacturing procedure is highly flexible, allowing for the creation of devices with an arbitrary number of channels as well as non-uniform channel spacing at the chip output. We present the system used to integrate the chip within our ion trap apparatus and characterise the performance of the full individual addressing setup using a single trapped ion as a light-field sensor. Our measurements showed intensity cross-talk below $10^{-3}$ across the chip, with minimum observed cross-talk as low as $O\left(10^{-5}\right)$., Comment: 14 pages, 12 figures

As model and dataset sizes continue to scale in robot learning, the need to understand how the composition and properties of a dataset affect model performance becomes increasingly urgent to ensure cost-effective data collection and model performance. In this work, we empirically investigate how physics attributes (color, friction coefficient, shape) and scene background characteristics, such as the complexity and dynamics of interactions with background objects, influence the performance of Video Transformers in predicting planar pushing trajectories. We investigate three primary questions: How do physics attributes and background scene characteristics influence model performance? What kind of changes in attributes are most detrimental to model generalization? What proportion of fine-tuning data is required to adapt models to novel scenarios? To facilitate this research, we present CloudGripper-Push-1K, a large real-world vision-based robot pushing dataset comprising 1278 hours and 460,000 videos of planar pushing interactions with objects with different physics and background attributes. We also propose Video Occlusion Transformer (VOT), a generic modular video-transformer-based trajectory prediction framework which features 3 choices of 2D-spatial encoders as the subject of our case study. The dataset and source code are available at https://cloudgripper.org., Comment: IEEE/RSJ IROS 2024

We present CloudGripper, an open source cloud robotics testbed, consisting of a scalable, space and cost-efficient design constructed as a rack of 32 small robot arm work cells. Each robot work cell is fully enclosed and features individual lighting, a low-cost custom 5 degree of freedom Cartesian robot arm with an attached parallel jaw gripper and a dual camera setup for experimentation. The system design is focused on continuous operation and features a 10 Gbit/s network connectivity allowing for high throughput remote-controlled experimentation and data collection for robotic manipulation. CloudGripper furthermore is intended to form a community testbed to study the challenges of large scale machine learning and cloud and edge-computing in the context of robotic manipulation. In this work, we describe the mechanical design of the system, its initial software stack and evaluate the repeatability of motions executed by the proposed robot arm design. A local network API throughput and latency analysis is also provided. CloudGripper-Rope-100, a dataset of more than a hundred hours of randomized rope pushing interactions and approximately 4 million camera images is collected and serves as a proof of concept demonstrating data collection capabilities. A project website with more information is available at https://cloudgripper.org., Comment: Under review at IEEE ICRA 2024

We disclose a serious deficiency of the Baym-Kadanoff construction of thermodynamically consistent conserving approximations. There are two vertices in this scheme: dynamical and conserving. The divergence of each indicates a phase instability. We show that each leads to incomplete and qualitatively different behavior at different critical points. The diagrammatically controlled dynamical vertex from the Schwinger-Dyson equation does not obey the Ward identity and cannot be continued beyond its singularity. The standardly used dynamical vertex alone cannot, hence, conclusively decide about the stability of the high-temperature phase. On the other hand, the divergence in the conserving vertex, obeying the conservation laws, does not invoke critical behavior of the spectral function and the specific heat. Moreover, the critical behavior of the conserving vertex may become spurious in low-dimensional systems. Consequently, the description of the critical behavior of correlated electrons becomes consistent and reliable only if the fluctuations of the order parameter in the conserving vertex lead to a divergence coinciding with that of the dynamical one., Comment: 2nd extended version, 13 pages, 6 figures

We present a sampling-based approach to reasoning about the caging-based manipulation of rigid and a simplified class of deformable 3D objects subject to energy constraints. Towards this end, we propose the notion of soft fixtures extending earlier work on energy-bounded caging to include a broader set of energy function constraints and settings, such as gravitational and elastic potential energy of 3D deformable objects. Previous methods focused on establishing provably correct algorithms to compute lower bounds or analytically exact estimates of escape energy for a very restricted class of known objects with low-dimensional C-spaces, such as planar polygons. We instead propose a practical sampling-based approach that is applicable in higher-dimensional C-spaces but only produces a sequence of upper-bound estimates that, however, appear to converge rapidly to actual escape energy. We present 8 simulation experiments demonstrating the applicability of our approach to various complex quasi-static manipulation scenarios. Quantitative results indicate the effectiveness of our approach in providing upper-bound estimates for escape energy in quasi-static manipulation scenarios. Two real-world experiments also show that the computed normalized escape energy estimates appear to correlate strongly with the probability of escape of an object under randomized pose perturbation., Comment: Paper submitted to ICRA 2024

A space-based far-infrared interferometer could work synergistically with the James Webb Space Telescope (JWST) and the Atacama Large Millimeter Array (ALMA) to revolutionize our understanding of the astrophysical processes leading to the formation of habitable planets and the co-evolution of galaxies and their central supermassive black holes. Key to these advances are measurements of water in its frozen and gaseous states, observations of astronomical objects in the spectral range where most of their light is emitted, and access to critical diagnostic spectral lines, all of which point to the need for a far-infrared observatory in space. The objects of interest - circumstellar disks and distant galaxies - typically appear in the sky at sub-arcsecond scales, which rendered all but a few of them unresolvable with the successful and now-defunct 3.5-m Herschel Space Observatory, the largest far-infrared telescope flown to date. A far-infrared interferometer with maximum baseline length in the tens of meters would match the angular resolution of JWST at 10x longer wavelengths and observe water ice and water-vapor emission, which ALMA can barely do through the Earth's atmosphere. Such a facility was conceived and studied two decades ago. Here we revisit the science case for a space-based far-infrared interferometer in the era of JWST and ALMA and summarize the measurement capabilities that will enable the interferometer to achieve a set of compelling scientific objectives. Common to all the science themes we consider is a need for sub-arcsecond image resolution., Comment: 11 pages, 12 figures. To be published in Proc. SPIE Conf. 12686 "Instruments: Scientific Returns and Conceptual Designs"

The leading theory for the origin of Jupiter Trojans (JTs) assumes that JTs were captured to their orbits near the Lagrangian points of Jupiter during the early reconfiguration of the giant planets. The natural source region for the majority of JTs would then be the population of planetesimals born in a massive trans-Neptunian disk. If true, JTs represent the most accessible stable population of small Solar System bodies that formed in the outer regions of the Solar System. For this work, we compiled photometric datasets for about 1000 JTs and applied the convex inversion technique in order to assess their shapes and spin states. We obtained full solutions for $79$ JTs, and partial solutions for an additional $31$ JTs. We found that the observed distribution of the pole obliquities of JTs is broadly consistent with expectations from the streaming instability, which is the leading mechanism for the formation of planetesimals in the trans-Neptunian disk. The observed JTs' pole distribution has a slightly smaller prograde vs. retrograde asymmetry (excess of obliquities $>130^\circ$) than what is expected from the existing streaming instability simulations. However, this discrepancy can be plausibly reconciled by the effects of the post-formation collisional activity. Our numerical simulations of the post-capture spin evolution indicate that the JTs' pole distribution is not significantly affected by dynamical processes such as the eccentricity excitation in resonances, close encounters with planets, or the effects of nongravitational forces. However, a few JTs exhibit large latitude variations of the rotation pole and may even temporarily transition between prograde- and retrograde-rotating categories., Comment: Accepted for publication in A&A

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine.

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4., Comment: 100 pages; updated authors list; fixed author names and added citation

The solar system's Zodiacal Cloud is visible to the unaided eye, yet the origin of its constituent dust particles is not well understood, with a wide range of proposed divisions between sources in the asteroid belt and Jupiter Family comets. The amount of dust contributed by Oort Cloud comets is uncertain. Knowledge of the Zodiacal Cloud's structure and origins would help with NASA's aim of characterizing potentially Earth-like planets around nearby stars, since the exo-Earths must be studied against the light scattered from extrasolar analogs of our cloud. As the only example where the parent bodies can be tracked, our own cloud is critical for learning how planetary system architecture governs the interplanetary dust's distribution. Our cloud has been relatively little-studied in the near-ultraviolet, a wavelength range that is important for identifying potentially-habitable planets since it contains the broad Hartley absorption band of ozone. We show through radiative transfer modeling that our cloud's shape and size at near-UV wavelengths can be measured from Earth orbit by mapping the zodiacal light's flux and linear polarization across the sky. We quantify how well the cloud's geometric and optical properties can be retrieved from a set of simulated disk observations, using a Markov chain Monte Carlo analysis. The results demonstrate that observations with sufficient precision, covering a set of fields distributed along the ecliptic and up to the poles, can be used to determine the division between asteroidal, Jupiter Family, and Oort Cloud dust components, primarily via their differing orbital inclination distributions. We find that the observations must be repeated over a time span of several months in order to disentangle the zodiacal light from the Galactic background using the Milky Way's rotation across the sky., Comment: submitted to PASP

We consider the unsteady compressible Navier-Stokes equations in a perforated three-dimensional domain, and show that the limit system for the diameter of the holes going to zero is the same as in the perforated domain provided the perforations are small enough. The novelty of this result is the lower adiabatic exponent $\gamma>3$ instead of the known value $\gamma>6$. The proof is based on the use of two different restriction operators leading to two different types of pressure estimates. We also discuss the extension of this result for the unsteady Navier-Stokes-Fourier system as well as the optimality of the known results in arbitrary space dimension for both steady and unsteady problems., Comment: published version

A circumsolar dust ring has been recently discovered close to the orbit of Mercury. There are currently no hypotheses for the origin of this ring in the literature, so we explore four different origin scenarios here: the dust originated from (1) the sporadic meteoroid complex that comprises the major portion of the Zodiacal Cloud, (2) recent asteroidal/cometary activity, (3) hypothetical dust-generating bodies locked in mean-motion resonances beyond Mercury, and (4) bodies co-orbiting with Mercury. We find that only scenario (4) reproduces the observed structure and location of Mercury's dust ring. However, the lifetimes of Mercury's co-orbitals (<20 Ma) preclude the primordial origin of the co-orbiting source population due to dynamical instability and meteoroid bombardment, demanding a recent event feeding the observed dust ring. We find that an impact on Mercury can eject debris into the co-orbital resonance. We estimate the ages of six candidate impacts that formed craters larger than 40 km in diameter using high-resolution spacecraft data from MESSENGER and find two craters with estimated surface ages younger than 50 Ma. We find that the amount of mass transported from Mercury's surface into the co-orbital resonance from these two impacts is several orders of magnitude smaller than what is needed to explain the magnitude of Mercury's ring inferred from remote sensing. Therefore we suggest that numerous younger, smaller impacts collectively contributed to the origin of the ring. We conclude that the recent impact hypothesis for the origin of Mercury's dust ring is a viable scenario, whose validity can be constrained by future inner solar system missions., Comment: 31 pages, 10 figures, 3 tables, Accepted to the Planetary Science Journal

Launched on 12 Aug. 2018, NASA's Parker Solar Probe had completed 13 of its scheduled 24 orbits around the Sun by Nov. 2022. The mission's primary science goal is to determine the structure and dynamics of the Sun's coronal magnetic field, understand how the solar corona and wind are heated and accelerated, and determine what processes accelerate energetic particles. Parker Solar Probe returned a treasure trove of science data that far exceeded quality, significance, and quantity expectations, leading to a significant number of discoveries reported in nearly 700 peer-reviewed publications. The first four years of the 7-year primary mission duration have been mostly during solar minimum conditions with few major solar events. Starting with orbit 8 (i.e., 28 Apr. 2021), Parker flew through the magnetically dominated corona, i.e., sub-Alfv\'enic solar wind, which is one of the mission's primary objectives. In this paper, we present an overview of the scientific advances made mainly during the first four years of the Parker Solar Probe mission, which go well beyond the three science objectives that are: (1) Trace the flow of energy that heats and accelerates the solar corona and solar wind; (2) Determine the structure and dynamics of the plasma and magnetic fields at the sources of the solar wind; and (3) Explore mechanisms that accelerate and transport energetic particles., Comment: 157 pages, 65 figures

We present an exactly solvable effective model of a double quantum dot coupled to superconducting leads. This model is a generalization of the well-known superconducting atomic limit approximation of the paradigmatic superconducting impurity Anderson model. However, in contrast to the standard atomic limit and other effective models, it gives quantitatively correct predictions for the quantum phase transition boundaries, subgap bound states as well as Josephson supercurrent in a broad range of parameters including experimentally relevant regimes. The model allows fast and reliable parameter scans important for the preparation and analysis of experiments which are otherwise inaccessible by more precise but computational heavy methods such as quantum Monte Carlo or the numerical renormalization group. The scans also allowed us to identify and investigate new previously unnoticed phase diagram regimes. We provide a thorough analysis of the strengths and limitations of the effective model and benchmark its predictions against numerical renormalization group results., Comment: 17 pages, 15 figures

We introduce a non-parametric density estimator deemed Radial Voronoi Density Estimator (RVDE). RVDE is grounded in the geometry of Voronoi tessellations and as such benefits from local geometric adaptiveness and broad convergence properties. Due to its radial definition RVDE is continuous and computable in linear time with respect to the dataset size. This amends for the main shortcomings of previously studied VDEs, which are highly discontinuous and computationally expensive. We provide a theoretical study of the modes of RVDE as well as an empirical investigation of its performance on high-dimensional data. Results show that RVDE outperforms other non-parametric density estimators, including recently introduced VDEs., Comment: 13 pages

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can handle data from single-dish, aperture-synthesis, and Very Long Baseline Interferometery (VLBI) telescopes. One of its core functionalities is to support the calibration and imaging pipelines for ALMA, VLA, VLA Sky Survey (VLASS), and the Nobeyama 45m telescope. This paper presents a high-level overview of the basic structure of the CASA software, as well as procedures for calibrating and imaging astronomical radio data in CASA. CASA is being developed by an international consortium of scientists and software engineers based at the National Radio Astronomical Observatory (NRAO), the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), and the Joint Institute for VLBI European Research Infrastructure Consortium (JIV-ERIC), under the guidance of NRAO., Comment: Accepted for publication in PASP (20 pages, 4 figures). Joint publication with CASA-VLBI paper

We present two complementary methods to calculate the Andreev bound state energies of a single-level quantum dot connected to superconducting leads described by the superconducting impurity Anderson model. The first method, which is based on a mapping to a low-energy model, can be utilized to extract the Andreev bound state energies from finite-temperature, imaginary-time quantum Monte Carlo data without the necessity of any analytic continuation technique. The second method maps the full model on an exactly solvable superconducting atomic limit with renormalized parameters. As such, it represents a fast and reliable method for a quick scan of the parameter space. We demonstrate that after adding a simple band correction this method can provide predictions for measurable quantities, including the Josephson current, that are in a solid quantitative agreement with precise results obtained by the numerical renormalization group and quantum Monte Carlo., Comment: 16 pages, 7 figures

The halfspace depth is a well studied tool of nonparametric statistics in multivariate spaces, naturally inducing a multivariate generalisation of quantiles. The halfspace depth of a point with respect to a measure is defined as the infimum mass of closed halfspaces that contain the given point. In general, a closed halfspace that attains that infimum does not have to exist. We introduce a flag halfspace - an intermediary between a closed halfspace and its interior. We demonstrate that the halfspace depth can be equivalently formulated also in terms of flag halfspaces, and that there always exists a flag halfspace whose boundary passes through any given point $x$, and has mass exactly equal to the halfspace depth of $x$. Flag halfspaces allow us to derive theoretical results regarding the halfspace depth without the need to differentiate absolutely continuous measures from measures containing atoms, as was frequently done previously. The notion of flag halfspaces is used to state results on the dimensionality of the halfspace median set for random samples. We prove that under mild conditions, the dimension of the sample halfspace median set of $d$-variate data cannot be $d-1$, and that for $d=2$ the sample halfspace median set must be either a two-dimensional convex polygon, or a data point. The latter result guarantees that the computational algorithm for the sample halfspace median form the R package TukeyRegion is exact also in the case when the median set is less-than-full-dimensional in dimension $d=2$.

We are conducting a survey using twilight time on the Dark Energy Camera with the Blanco 4m telescope in Chile to look for objects interior to Earth's and Venus' orbits. To date we have discovered two rare Atira/Apohele asteroids, 2021 LJ4 and 2021 PH27, which have orbits completely interior to Earth's orbit. We also discovered one new Apollo type Near Earth Object (NEO) that crosses Earth's orbit, 2022 AP7. Two of the discoveries likely have diameters greater than 1 km. 2022 AP7 is likely the largest Potentially Hazardous Asteroid (PHA) discovered in about eight years. To date we have covered 624 square degrees of sky near to and interior to the orbit of Venus. The average images go to 21.3 mags in the r-band, with the best images near 22nd mag. Our new discovery 2021 PH27 has the smallest semi-major axis known for an asteroid, 0.4617 au, and the largest general relativistic effects (53 arcseconds/century) known for any body in the Solar System. The survey has detected about 15 percent of all known Atira NEOs. We put strong constraints on any stable population of Venus co-orbital resonance objects existing, as well as the Atira and Vatira asteroid classes. These interior asteroid populations are important to complete the census of asteroids near Earth, including some of the most likely Earth impactors that cannot easily be discovered in other surveys. Comparing the actual population of asteroids found interior to Earth and Venus with those predicted to exist by extrapolating from the known population exterior to Earth is important to better understand the origin, composition and structure of the NEO population., Comment: In Press at The Astronomical Journal

We investigate the existence of weak solutions to a certain system of partial differential equations, modelling the behaviour of a compressible non-Newtonian fluid for small Reynolds number. We construct the weak solutions despite the lack of the $L^\infty$ estimate on the divergence of the velocity field. The result was obtained by combining the regularity theory for singular operators with a certain logarithmic integral inequality for $BMO$ functions, which allowed us to adjust the method from (Feireisl et al., 2015) to more relaxed conditions on the velocity.

More than two years after its outbreak, the COVID-19 pandemic continues to plague medical systems around the world, putting a strain on scarce resources, and claiming human lives. From the very beginning, various AI-based COVID-19 detection and monitoring tools have been pursued in an attempt to stem the tide of infections through timely diagnosis. In particular, computer audition has been suggested as a non-invasive, cost-efficient, and eco-friendly alternative for detecting COVID-19 infections through vocal sounds. However, like all AI methods, also computer audition is heavily dependent on the quantity and quality of available data, and large-scale COVID-19 sound datasets are difficult to acquire -- amongst other reasons -- due to the sensitive nature of such data. To that end, we introduce the COVYT dataset -- a novel COVID-19 dataset collected from public sources containing more than 8 hours of speech from 65 speakers. As compared to other existing COVID-19 sound datasets, the unique feature of the COVYT dataset is that it comprises both COVID-19 positive and negative samples from all 65 speakers. We analyse the acoustic manifestation of COVID-19 on the basis of these perfectly speaker characteristic balanced `in-the-wild' data using interpretable audio descriptors, and investigate several classification scenarios that shed light into proper partitioning strategies for a fair speech-based COVID-19 detection.

We introduce an algorithm for active function approximation based on nearest neighbor regression. Our Active Nearest Neighbor Regressor (ANNR) relies on the Voronoi-Delaunay framework from computational geometry to subdivide the space into cells with constant estimated function value and select novel query points in a way that takes the geometry of the function graph into account. We consider the recent state-of-the-art active function approximator called DEFER, which is based on incremental rectangular partitioning of the space, as the main baseline. The ANNR addresses a number of limitations that arise from the space subdivision strategy used in DEFER. We provide a computationally efficient implementation of our method, as well as theoretical halting guarantees. Empirical results show that ANNR outperforms the baseline for both closed-form functions and real-world examples, such as gravitational wave parameter inference and exploration of the latent space of a generative model., Comment: Accepted at the International Conference on Machine Learning (ICML) 2022

The Voronoi Density Estimator (VDE) is an established density estimation technique that adapts to the local geometry of data. However, its applicability has been so far limited to problems in two and three dimensions. This is because Voronoi cells rapidly increase in complexity as dimensions grow, making the necessary explicit computations infeasible. We define a variant of the VDE deemed Compactified Voronoi Density Estimator (CVDE), suitable for higher dimensions. We propose computationally efficient algorithms for numerical approximation of the CVDE and formally prove convergence of the estimated density to the original one. We implement and empirically validate the CVDE through a comparison with the Kernel Density Estimator (KDE). Our results indicate that the CVDE outperforms the KDE on sound and image data., Comment: Accepted at the Conference on Uncertainty in Artificial Intelligence (UAI) 2022. This version contains erratas of the published material

Object detection and semantic segmentation with the 3D lidar point cloud data require expensive annotation. We propose a data augmentation method that takes advantage of already annotated data multiple times. We propose an augmentation framework that reuses real data, automatically finds suitable placements in the scene to be augmented, and handles occlusions explicitly. Due to the usage of the real data, the scan points of newly inserted objects in augmentation sustain the physical characteristics of the lidar, such as intensity and raydrop. The pipeline proves competitive in training top-performing models for 3D object detection and semantic segmentation. The new augmentation provides a significant performance gain in rare and essential classes, notably 6.65% average precision gain for "Hard" pedestrian class in KITTI object detection or 2.14 mean IoU gain in the SemanticKITTI segmentation challenge over the state of the art., Comment: Submitted on 15th June 2022 to IEEE RA-L journal

Radar and spacecraft observations show the permanently shadowed regions around Mercury's North Pole to contain water ice and complex organic material. One possible source of this material are impacts by interplanetary dust particles (IDPs), asteroids, and comets. We have performed numerical simulations of the dynamical evolution of asteroids and comets over the few Myr and checked for their impacts with Mercury. We use the N-body integrator RMVS/Swifter to propagate the Sun and the eight planets from their current positions. We add comets and asteroids to the simulations as massless test particles, based on their current orbital distributions. Asteroid impactors are assigned a probability of being water-rich (C-class) based on the measured distribution of taxonomic types. For comets, we assume a constant water fraction. For IDPs, we use a dynamical meteoroid model to compute the dust flux on Mercury. Relative to previous work on asteroid and comet impacts (Moses et al. 1999), we leverage 20 years of progress in minor body surveys. Immediate post-impact ejection of impactor material into outer space is taken into account as is the migration efficiency of water across Mercury's surface to the polar cold traps. We find that asteroids deliver $\sim 1 \times 10^{3}$ kg/yr of water to Mercury, comets deliver $\sim 1 \times 10^{3}$ kg/yr and IDPs deliver $\sim 16 \times 10^{3}$ kg/yr within a factor of several. Over a timescale of $\sim 1$ Gyr, this is enough to deliver the minimum amount of water required by the radar and MESSENGER observations. While other sources of water on Mercury are not ruled out by our analysis, we show that they are not required to explain the currently available observational lower limits., Comment: 38 pages, 10 figures, published in Icarus

Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13$^{th}$ SDG is a call for action to combat climate change for a better world. In this work, we provide an overview of areas in which audio intelligence -- a powerful but in this context so far hardly considered technology -- can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects. Earth and water are concerned with the early detection of environmental changes and, thus, with the protection of humans and animals, as well as the monitoring of land and aquatic organisms. Aerial audio is used to monitor and obtain information about bird and insect populations. Furthermore, acoustic measures can deliver relevant information for the monitoring and forecasting of weather and other meteorological phenomena. The fourth considered element is fire. Due to the burning of fossil fuels, the resulting increase in CO$_2$ emissions and the associated rise in temperature, fire is used as a symbol for man-made climate change and in this context includes the monitoring of noise pollution, machines, as well as the early detection of wildfires. In all these areas, computer audition can help counteract climate change. Aether then corresponds to the technology itself that makes this possible. This work explores these areas and discusses potential applications, while positioning computer audition in relation to methodological alternatives.