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In this work, we investigate the assumptions regarding spacetime backgrounds underlying the classical double copy. We argue (contrary to the norm) that single-copy fields naturally constructed on the original curved background metric are only interpretable on a flat metric when such a well-defined limit exists, for which Kerr--Schild coordinates offer a natural choice. As an explicit example where such a distinction matters, we initiate an exploration of single-copies for the G\"odel universe. This metric lacks a (geodesic) Kerr--Schild representation yet is Petrov type-D, meaning the technology of the ``Weyl double copy" may be utilized. The Weyl derived single copy has many desirable features, including matching the defining properties of the spacetime, and being sourced by the mixed Ricci tensor just as Kerr--Schild single copies are. To compare, we propose a sourced flat-space single-copy interpretation for the G\"odel metric by leveraging its symmetries, and find that this proposal lacks the defining properties of the spacetime, and is not consistent with the flat limit of our curved-space single copy. Notably, this inconsistency does not occur in Kerr--Schild metrics. Our curved-space single copy also lead to the same electromagnetic analogue of the G\"odel universe found separately through tidal force analogies, opening a new avenue of exploration between the double copy and gravitoelectromagnetism programs., Comment: 29 pages, 2 figures

Large language models (LLMs) represent a promising, but controversial, tool in aiding scientific peer review. This study evaluates the usefulness of LLMs in a conference setting as a tool for vetting paper submissions against submission standards. We conduct an experiment at the 2024 Neural Information Processing Systems (NeurIPS) conference, where 234 papers were voluntarily submitted to an "LLM-based Checklist Assistant." This assistant validates whether papers adhere to the author checklist used by NeurIPS, which includes questions to ensure compliance with research and manuscript preparation standards. Evaluation of the assistant by NeurIPS paper authors suggests that the LLM-based assistant was generally helpful in verifying checklist completion. In post-usage surveys, over 70% of authors found the assistant useful, and 70% indicate that they would revise their papers or checklist responses based on its feedback. While causal attribution to the assistant is not definitive, qualitative evidence suggests that the LLM contributed to improving some submissions. Survey responses and analysis of re-submissions indicate that authors made substantive revisions to their submissions in response to specific feedback from the LLM. The experiment also highlights common issues with LLMs: inaccuracy (20/52) and excessive strictness (14/52) were the most frequent issues flagged by authors. We also conduct experiments to understand potential gaming of the system, which reveal that the assistant could be manipulated to enhance scores through fabricated justifications, highlighting potential vulnerabilities of automated review tools.

Myosin motors are fundamental biological actuators, powering diverse mechanical tasks in eukaryotic cells via ATP hydrolysis. Recent work revealed that myosin's velocity-dependent detachment rate can bridge actomyosin dynamics to macroscale Hill muscle predictions. However, the influence of this microscale unbinding, which we characterize by a dimensionless parameter $\alpha$, on macroscale energetic flows-such as power consumption, output and efficiency-remains elusive. Here we develop an analytical model of myosin dynamics that relates unbinding rates $\alpha$ to energetics. Our model agrees with published in-vivo muscle data and, furthermore, uncovers a performance-efficiency tradeoff governed by $\alpha$. To experimentally validate the tradeoff, we build HillBot, a robophysical model of Hill's muscle that mimics nonlinearity. Through HillBot, we decouple $\alpha$'s concurrent effect on performance and efficiency, demonstrating that nonlinearity drives efficiency. We compile 136 published measurements of $\alpha$ in muscle and myoblasts to reveal a distribution centered at $\alpha^* = 3.85 \pm 2.32$. Synthesizing data from our model and HillBot, we quantitatively show that $\alpha^*$ corresponds to a class of generalist actuators that are both relatively powerful and efficient, suggesting that the performance-efficiency tradeoff underpins the prevalence of $\alpha^*$ in nature. We leverage these insights and propose a nonlinear variable-impedance protocol to shift along a performance-efficiency axis in robotic applications., Comment: 23 pages, 6 figures. PDF of supplemental information available in zip download

The eROSITA instrument on board Spectrum-Roentgen-Gamma has completed four scans of the X-ray sky, leading to the detection of almost one million X-ray sources in eRASS1 only, including multiple new X-ray binary candidates. We report on analysis of the X-ray binary 1eRASS J085039.9-421151, using a ~55\,ks long NuSTAR observation, following its detection in each eROSITA scan. Analysis of the eROSITA and NuSTAR X-ray spectra in combination with X-shooter data of the optical counterpart provide evidence of an X-ray binary with a red supergiant (RSG) companion, confirming previous results, although we determine a cooler spectral type of M2-3, owing to the presence of TiO bands in the optical and near infrared spectra. The X-ray spectrum is well-described by an absorbed power law with a high energy cutoff typically applied for accreting high mass X-ray binaries. In addition, we detect a strong fluorescent neutral iron line with an equivalent width of ~700\,eV and an absorption edge, the latter indicating strong absorption by a partial covering component. It is unclear if the partial absorber is ionised. There is no significant evidence of a cyclotron resonant scattering feature. We do not detect any pulsations in the NuSTAR lightcurves, possibly on account of a large spin period that goes undetected due to insufficient statistics at low frequencies or potentially large absorption that causes pulsations to be smeared out. Even so, the low persistent luminosity, the spectral parameters observed (photon index, $\Gamma<1.0$), and the minuscule likelihood of detection of RSG-black hole systems, suggest that the compact object is a neutron star., Comment: 17 pages, 11 figures, accepted by A&A

Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a single camera image. This estimation guides essential driving decisions, such as braking before an obstacle or changing lanes to avoid collisions. In this paper, we explore vulnerabilities of MDE algorithms in AD systems, presenting LensAttack, a novel physical attack that strategically places optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. LensAttack encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to induce false depth perception. We first develop a mathematical model that outlines the parameters of the attack, followed by simulations and real-world evaluations to assess its efficacy on state-of-the-art MDE models. Additionally, we adopt an attack optimization method to further enhance the attack success rate by optimizing the attack focal length. To better evaluate the implications of LensAttack on AD, we conduct comprehensive end-to-end system simulations using the CARLA platform. The results reveal that LensAttack can significantly disrupt the depth estimation processes in AD systems, posing a serious threat to their reliability and safety. Finally, we discuss some potential defense methods to mitigate the effects of the proposed attack., Comment: 28 pages. arXiv admin note: substantial text overlap with arXiv:2409.17376

Magnetic Tunnel Junctions (MTJs) are of great interest for non-conventional computing applications. The Toffoli gate is a universal reversible logic gate, enabling the construction of arbitrary boolean circuits. Here, we present a proof-of-concept construction of a gadget which encodes the Toffoli gate's truth table into the ground state of coupled uniaxial nanomagnets that could form the free layers of perpendicularly magnetized MTJs. This construction has three input bits, three output bits, and one ancilla bit. We numerically simulate the seven macrospins evolving under the stochastic Landau-Lifshitz-Gilbert (s-LLG) equation. We investigate the effect of the anisotropy-to-exchange-coupling strength ratio $H_A/H_\text{ex}$ on the working of the gadget. We find that for $H_A/H_\text{ex} \lesssim 0.93$, the spins evolve to the Toffoli gate truth table configurations under LLG dynamics alone, while higher $H_A/H_\text{ex}$ ratios require thermal annealing due to suboptimal metastable states. Under our chosen annealing procedure, the s-LLG simulation with thermal annealing achieves a 100% success rate up to $H_A/H_\text{ex}\simeq3.0$. The feasibility of constructing MTJ-free-layer-based Toffoli gates highlights their potential in designing new types of MTJ-based circuits.

Long and short baseline neutrino oscillation experiments, such as DUNE, ANNIE, SBND, demand high precision in reducing systematic errors, particularly those related to neutrino-nucleus interaction cross-sections. The stroboscopic approach offers a method to capture distinct neutrino energy spectra, aiding in the separation of flux and cross-section uncertainties. This report outlines the creation of short proton bunch lengths and the transport of a narrow beam down the Booster Neutrino Beamline, essential steps for the successful implementation of the stroboscopic approach.

A crucial component of the high-contrast instrumental chain in astronomy is the wavefront sensor (WFS). A key property of this component is its sensitivities, which reflect its ability to efficiently use incoming photons to encode the phase aberrations. This paper introduces a new class of highly sensitive wavefront sensors that approach the fundamental sensitivity limits dictated by physics. Assuming a high Strehl regime, we define what linear operator is describing the ideal WFS that would achieve maximum sensitivity. We then show that there is a substantial similarity between this ideal WFS and the second-order ideal coronagraph. Leveraging the exhibited link between ideal wavefront sensing and coronagraphy, we propose a novel WFS concept based on high-performance coronagraphic architecture : the bivortex WFS. This sensor employs charge-2 vortex masks. Simulations for an ideal system demonstrate that this sensor achieves unprecedented sensitivity, even surpassing the highly sensitive Zernike WFS class (especially for low spatial frequencies), while paving the way for new high-contrast architectures integrating simultaneous sensing and coronagraphy.

Television is often seen as a site for subcultural identification and subversive fantasy, including in queer cultures. How might we measure subversion, or the degree to which the depiction of social relationship between a dyad (e.g. two characters who are colleagues) deviates from its typical representation on TV? To explore this question, we introduce the task of stereotypic relationship extraction. Built on cognitive stylistics, linguistic anthropology, and dialogue relation extraction, in this paper, we attempt to model the cognitive process of stereotyping TV characters in dialogic interactions. Given a dyad, we want to predict: what social relationship do the speakers exhibit through their words? Subversion is then characterized by the discrepancy between the distribution of the model's predictions and the ground truth labels. To demonstrate the usefulness of this task and gesture at a methodological intervention, we enclose four case studies to characterize the representation of queer relationalities in the Big Bang Theory, Frasier, and Gilmore Girls, as we explore the suspicious and reparative modes of reading with our computational methods., Comment: CHR 2024: Computational Humanities Research Conference

In this work, we survey the way in which classification is used as a sensemaking practice in cultural analytics, and assess where large language models can fit into this landscape. We identify ten tasks supported by publicly available datasets on which we empirically assess the performance of LLMs compared to traditional supervised methods, and explore the ways in which LLMs can be employed for sensemaking goals beyond mere accuracy. We find that prompt-based LLMs are competitive with traditional supervised models for established tasks, but perform less well on de novo tasks. In addition, LLMs can assist sensemaking by acting as an intermediary input to formal theory testing.

Let $G$ be a group coacting on an Artin-Schelter regular algebra $A$ homogeneously and inner-faithfully. When the identity component $A_e$ is also Artin-Schelter regular, providing a generalization of the Shephard-Todd-Chevalley Theorem, we say that $G$ is a dual reflection group for $A$. We give two examples of dual reflection groups of order 16, and study algebraic and geometric properties of three associated Artin-Schelter regular algebras of dimension four., Comment: Comments welcome

Precise measurements of neutron star observables (such as mass and radius) allow one to constrain the equations of state for supranuclear matter and develop a stronger understanding of nuclear physics. The Neutron star Interior Composition ExploreR (NICER) tracks X-ray hotspots on rotating NSs and is able to infer precise information about the compactness of the star. Gravitational waves carry information about the tidal deformability (related to the tidal Love number) of neutron stars, which has been measured by the LIGO/Virgo/KAGRA collaboration. These two observables enjoy an approximately universal property between each other that is insensitive to the equations of state (the "universal Love-C relation"). In this paper, we focus on deriving two analytic expressions for the Love-C relations that are ready-to-use and improve upon previous analytic expressions. The first model is inspired by a Newtonian polytrope, whose perturbation to the gravitational potential can be found analytically. We extend this Newtonian model to the relativistic regime by providing a quadratic fit to the gravitational potential perturbation against stellar compactness. The second model makes use of the Tolman VII model and adopts a spectral expansion with Chebyshev polynomials, which converges faster than the Taylor expansions used in previous work. We find that the first model provides a more accurate description of the Love-C relation for realistic neutron stars than the second model, and it provides the best expression among all other analytic relations studied here in terms of describing the averaged numerical Love-C relation. These new models are not only useful in practice, but they also show the power and importance of analytic modeling of neutron stars., Comment: 11 pages, 9 figures

Previous work shows evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after intrathecal injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increases at their peak after 24 hours. Its performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings indicate that using imaging data from just the first two hours post-injection for training yields tracer flow predictions comparable to those trained with additional later-stage scans. The model was further validated by comparing ventricular reflux gradings provided by neuroradiologists, and inter-rater grading among medical experts and the model showed excellent agreement. Our results demonstrate the potential of deep learning-based methods for CSF flow prediction, suggesting that fewer MRI scans could be sufficient for clinical analysis, which might significantly improve clinical efficiency, patient well-being, and lower healthcare costs., Comment: 13 pages, 7 figures

Hance and Hossenfelder recently claim that the extensive experimental confirmations of Bell's Theorem do not in fact demonstrate that nature is nonlocal, but merely that nature can be local only if the distant detector settings in a Bell-EPR setup are not statistically independent. They suggest that such interdependence could take a local-deterministic form. There is no question that in general quantum mechanics not only allows but demands that the distant detectors be entangled and thus correlated, even though it is possible to do experiments in which such correlations can be ignored. The real question is whether entanglement itself could have a deterministic explanation. We review why any such attempted deterministic underpinning for quantum statistics would run into immediate conflict with quantum indeterminacy and the noncommutativity upon which the latter is based. Some cosmological implications are briefly explored.

Tidal effects on compact objects provide profound theoretical insights into the structure of the field equations, and are wonderful probes of the equation of state of matter, the nature of black holes and of the underlying theory of gravity. The natural framework for understanding tides is a perturbative scheme. Here, we point out ambiguities in determining tidal response functions within such a framework, which may lead to bias in constraining physical parameters with gravitational-wave observations if the computed quantities are not properly linked to observables. We propose a Canonical Tidal Response Function (CTRF) definition to compare values of tides in theories beyond vacuum General Relativity in a unified manner. As an example, we provide black hole tidal response functions, including both conservative and dissipative pieces, in various theories of gravity. Tidal dissipation Love numbers for black holes are derived here for the first time in most of the non-Einsteinian theories considered in this paper., Comment: 24 pages

Given a support variety theory defined on the compact part of a monoidal triangulated category, we define an extension to the non-compact part following the blueprint of Benson--Carlson--Rickard, Benson--Iyengar--Krause, Balmer--Favi, and Stevenson. We generalize important aspects of the theory of extended support varieties to the noncommutative case, and give characterizations of when an extended support theory detects the zero object, under certain assumptions. In particular, we show that when the original support variety theory is based on a Noetherian topological space, detects the zero object, satisfies a generalized tensor product property, and comes equipped with a comparison map, then the extended support variety also detects the zero object. In the case of stable categories of finite tensor categories, this gives conditions under which the central cohomological support admits an extension that detects the zero object, confirming part of a recent conjecture made by the second author together with Nakano and Yakimov., Comment: 27 pages

The response of astrophysical compact objects to external tidal fields carries valuable information on the nature of these objects, on the equation of state of matter, and on the underlying gravitational theory. In this work, we highlight subtleties in describing relativistic dynamical tidal responses that arise from ambiguities in the decomposition of a perturbed metric into external tidal and induced response pieces. Observables are unambiguous. However in practice, differences arising from implicit assumptions in the definition of tidal deformabilities may lead to a bias in constraining nuclear physics or gravitational theories, if not properly tied to observational data. We propose calibration of a tidal response function for any compact objects in vacuum General Relativity. Within this framework, the dynamical tidal Love numbers of a Schwarzschild black hole in both even and odd sectors vanish at any multipole order. The calibration allows one to define dynamical tidal deformabilities of relativistic stars, such as neutron stars, as the difference from the BH values (zero) under the unified definition in a simple manner. As a straightforward extension of our framework, we compute the next-to-leading dissipative tidal response of Schwarzschild black holes for the first time., Comment: 22 pages

Gravitational waves have been shown to provide new constraints on gravitational theories beyond general relativity (GR), especially in the strong field regime. Gravitational wave signals from galactic double white dwarfs, expected to be detected by the Laser Interferometer Space Antenna (LISA), also have the potential to place stringent bounds on certain theories that give rise to relatively large deviations from GR in less compact binaries, such as through scalar radiation. Nevertheless, the orbital evolution of close double white dwarf systems is also affected by various astrophysical effects, such as stellar rotation, tidal interactions, and magnetic interactions, which add complexity to the gravity tests. In this work, we employ the parametrized post-Einsteinian model to capture the leading beyond-GR effect on the signal and estimate the measurement uncertainties using the Fisher information matrix. We then study the systematic error caused by ignoring each astrophysical effect mentioned above on the parameter estimation. Our numerical results show that, to place bounds on the non-GR effects comparable to existing bounds from pulsar observations, tight priors on the mass of the binary and long observation time are required. At this level of sensitivity, we found that systematic errors from the astrophysical effects dominate statistical errors. The most significant effects investigated here are torques from tidal synchronization and magnetic unipolar induction for sufficiently large magnetic fields ($>10^7$G). Meanwhile, even the weaker astrophysical effects from quadrupolar deformations are of a similar order of magnitude as the statistical uncertainty, and hence cannot be ignored in the waveform model. We conclude that the astrophysical effects must be carefully accounted for in the parameter estimation to test gravity with galactic double white dwarfs detected by LISA., Comment: 14 pages, 8 figures, submitted to PRD

Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a single-camera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce LensAttack, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. LensAttack encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to induce false depth perception. We begin by constructing a mathematical model of our attack, incorporating various attack parameters. Subsequently, we simulate the attack and evaluate its real-world performance in driving scenarios to demonstrate its effect on state-of-the-art MDE models. The results highlight the significant impact of LensAttack on the accuracy of depth estimation in AD systems., Comment: 26 pages, 13 figures, SecureComm 2024

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

We consider the set of solutions to $M$ random polynomial equations whose $N$ variables are restricted to the $(N-1)$-sphere. Each equation has independent Gaussian coefficients and a target value $V_0$. When solutions exist, they form a manifold. We compute the average Euler characteristic of this manifold in the limit of large $N$, and find different behavior depending on the target value $V_0$, the ratio $\alpha=M/N$, and the variances of the coefficients. We divide this behavior into five phases with different implications for the topology of the solution manifold. When $M=1$ there is a correspondence between this problem and level sets of the energy in the spherical spin glasses. We conjecture that the transition energy dividing two of the topological phases corresponds to the energy asymptotically reached by gradient descent from a random initial condition, possibly resolving an open problem in out-of-equilibrium dynamics.

A massive black hole can develop a dark-matter overdensity, and the dark matter changes the evolution of a stellar-mass compact object inspiraling around the massive black hole through the dense dark-matter environment. Specifically, dynamical friction speeds up the inspiral of the compact object and causes feedback on the dark-matter distribution. These intermediate mass-ratio inspirals with dark matter are a source of gravitational waves (GWs), and the waves can dephase significantly from an equivalent system in vacuum. Prior work has shown that this dephasing needs to be modeled to detect the GWs from these systems with LISA (the Laser Interferometer Space Antenna); it also showed that the density and distribution of dark matter can be inferred from a GW measurement. In this paper, we study whether the parametrized post-Einsteinian (ppE) framework can be used to infer the presence of dark matter in these systems. We confirm that if vacuum waveform templates are used to model the GWs from an inspiral in a dark-matter halo, then the resulting parameter estimation is biased. We then apply the ppE framework to determine whether it can reduce the parameter-estimation biases, and we find that adding one ppE phase term to a waveform template eliminates the parameter-estimation biases (statistical errors become larger than the systematic ones), but the effective post-Newtonian order in the ppE framework must be specified without uncertainties. When the post-Newtonian order has uncertainty, we find that the systematic errors on the ppE and the binary's parameters exceed the statistical errors. Thus, the simplest ppE framework would not give unbiased results for these systems, and a further extension of it, or dedicated parameter estimation with gravitational waveforms that include dark-matter effects would be needed., Comment: 16 pages, 8 figures

The Zernike wavefront sensor (ZWFS) stands out as one of the most sensitive optical systems for measuring the phase of an incoming wavefront, reaching photon efficiencies close to the fundamental limit. This quality, combined with the fact that it can easily measure phase discontinuities, has led to its widespread adoption in various wavefront control applications, both on the ground but also for future space-based instruments. Despite its advantages, the ZWFS faces a significant challenge due to its extremely limited dynamic range, making it particularly challenging for ground-based operations. To address this limitation, one approach is to use the ZWFS after a general adaptive optics (AO) system; however, even in this scenario, the dynamic range remains a concern. This paper investigates two optical configurations of the ZWFS: the conventional setup and its phase-shifted counterpart, which generates two distinct images of the telescope pupil. We assess the performance of various reconstruction techniques for both configurations, spanning from traditional linear reconstructors to gradient-descent-based methods. The evaluation encompasses simulations and experimental tests conducted on the Santa cruz Extreme Adaptive optics Lab (SEAL) bench at UCSC. Our findings demonstrate that certain innovative reconstruction techniques introduced in this study significantly enhance the dynamic range of the ZWFS, particularly when utilizing the phase-shifted version.

Deep Learning algorithms, such as those used in Reinforcement Learning, often require large quantities of data to train effectively. In most cases, the availability of data is not a significant issue. However, for some contexts, such as in autonomous cyber defence, we require data efficient methods. Recently, Quantum Machine Learning and Boltzmann Machines have been proposed as solutions to this challenge. In this work we build upon the pre-existing work to extend the use of Deep Boltzmann Machines to the cutting edge algorithm Proximal Policy Optimisation in a Reinforcement Learning cyber defence environment. We show that this approach, when solved using a D-WAVE quantum annealer, can lead to a two-fold increase in data efficiency. We therefore expect it to be used by the machine learning and quantum communities who are hoping to capitalise on data-efficient Reinforcement Learning methods.

Target skyrmions (TSks) are topological spin textures where the out-of-plane component of the magnetization twists an integer number of $m$-$\pi$ rotations. Based on a magnetic multilayer stack in the form of $n\times$[CoFeB/MgO/Ta], engineered to host topological spin textures via dipole and DMI energies, we have successfully stabilized 1$\pi$, 2$\pi$ and 3$\pi$ target skyrmions by tuning material properties and thermal excitations close to room temperature. The nucleated textures, imaged via Kerr and Magnetic Force Microscopies, are stable at zero magnetic field and robust within a range of temperatures (tens of Kelvin) close to room temperature (RT = 292 K) and over long time scales (months). Under applied field (mT), the TSks collapse into the central skyrmion core, which resists against higher magnetic fields ($\approx$ 2 $\times$ TSk annihilation field), as the core is topologically protected. Micromagnetic simulations support our experimental findings, showing no TSk nucleation at 0 K, but a $\approx$ 30 $\%$ probability at 300 K for the experimental sample parameters. Our work provides a simple method to tailor spin textures in continuous films, enabling free movement in 2D space, creating a platform transferable to technological applications where the dynamics of the topological textures can be exploited beyond geometrical confinements.

Quasiperiodic oscillations (QPOs) have been recently discovered in the short gamma-ray bursts (GRBs) 910711 and 931101B. Their frequencies are consistent with those of the radial and quadrupolar oscillations of binary neutron star merger remnants, as obtained in numerical relativity simulations. These simulations reveal quasiuniversal relations between the remnant oscillation frequencies and the tidal coupling constant of the binaries. Under the assumption that the observed QPOs are due to these postmerger oscillations, we use the frequency-tide relations in a Bayesian framework to infer the source redshift, as well as the chirp mass and the binary tidal deformability of the binary neutron star progenitors for GRBs 910711 and 931101B. We further use this inference to estimate bounds on the mass-radius relation for neutron stars. By combining the estimates from the two GRBs, we find a 68\% credible range $R_{1.4}=12.48^{+0.41}_{-0.41}$~km for the radius of a neutron star with mass $M=1.4$~M$_\odot$, which is one of the tightest bounds to date.

Cosmic demographics -- the statistical study of populations of astrophysical objects -- has long relied on *multivariate statistics*, providing methods for analyzing data comprising fixed-length vectors of properties of objects, as might be compiled in a tabular astronomical catalog (say, with sky coordinates, and brightness measurements in a fixed number of spectral passbands). But beginning with the emergence of automated digital sky surveys, ca. ~2000, astronomers began producing large collections of data with more complex structure: light curves (brightness time series) and spectra (brightness vs. wavelength). These comprise what statisticians call *functional data* -- measurements of populations of functions. Upcoming automated sky surveys will soon provide astronomers with a flood of functional data. New methods are needed to accurately and optimally analyze large ensembles of light curves and spectra, accumulating information both along and across measured functions. Functional data analysis (FDA) provides tools for statistical modeling of functional data. Astronomical data presents several challenges for FDA methodology, e.g., sparse, irregular, and asynchronous sampling, and heteroscedastic measurement error. Bayesian FDA uses hierarchical Bayesian models for function populations, and is well suited to addressing these challenges. We provide an overview of astronomical functional data, and of some key Bayesian FDA modeling approaches, including functional mixed effects models, and stochastic process models. We briefly describe a Bayesian FDA framework combining FDA and machine learning methods to build low-dimensional parametric models for galaxy spectra., Comment: 9 pages, 2 figures; for the Proceedings of the 43rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Quantum money is the first invention in quantum information science, promising advantages over classical money by simultaneously achieving unforgeability, user privacy, and instant validation. However, standard quantum money relies on quantum memories and long-distance quantum communication, which are technologically extremely challenging. Quantum "S-money" tokens eliminate these technological requirements while preserving unforgeability, user privacy, and instant validation. Here, we report the first full experimental demonstration of quantum S-tokens, proven secure despite errors, losses and experimental imperfections. The heralded single-photon source with a high system efficiency of 88.24% protects against arbitrary multi-photon attacks arising from losses in the quantum token generation. Following short-range quantum communication, the token is stored, transacted, and verified using classical bits. We demonstrate a transaction time advantage over intra-city 2.77 km and inter-city 60.54 km optical fibre networks, compared with optimal classical cross-checking schemes. Our implementation demonstrates the practicality of quantum S-tokens for applications requiring high security, privacy and minimal transaction times, like financial trading and network control. It is also the first demonstration of a quantitative quantum time advantage in relativistic cryptography, showing the enhanced cryptographic power of simultaneously considering quantum and relativistic physics., Comment: 74 pages, 6 figures

GQ Lup B is one of the few substellar companions with a detected cicumplanetary disk, or CPD. Observations of the CPD suggest the presence of a cavity, possibly formed by an exo-satellite. Using the Keck Planet Imager and Characterizer (KPIC), a high contrast imaging suite that feeds a high resolution spectrograph (1.9-2.5 microns, R$\sim$35,000), we present the first dedicated radial velocity (RV) observations around a high-contrast, directly imaged substellar companion, GQ Lup B, to search for exo-satellites. Over 11 epochs, we find a best and median RV error of 400-1000 m/s, most likely limited by systematic fringing in the spectra due to transmissive optics within KPIC. With this RV precision, KPIC is sensitive to exomoons 0.6-2.8% the mass of GQ Lup B ($\sim 30 M_{\text{Jup}}$) at separations between the Roche limit and $65 R_{\text{Jup}}$, or the extent of the cavity inferred within the CPD detected around GQ Lup B. Using simulations of HISPEC, a high resolution infrared spectrograph planned to debut at W.M. Keck Observatory in 2026, we estimate future exomoon sensitivity to increase by over an order of magnitude, providing sensitivity to less massive satellites potentially formed within the CPD itself. Additionally, we run simulations to estimate the amount of material that different masses of satellites could clear in a CPD to create the observed cavity. We find satellite-to-planet mass ratios of $q > 2 \times 10^{-4}$ can create observable cavities and report a maximum cavity size of $\sim 51 \, R_{\text{Jup}}$ carved from a satellite., Comment: 15 pages, 5 figures

The Keck Planet Imager and Characterizer (KPIC) combines high contrast imaging with high resolution spectroscopy (R$\sim$35,000 in K band) to study directly imaged exoplanets and brown dwarfs in unprecedented detail. KPIC aims to spectrally characterize substellar companions through measurements of planetary radial velocities, spins, and atmospheric composition. Currently, the dominant source of systematic noise for KPIC is fringing, or oscillations in the spectrum as a function of wavelength. The fringing signal can dominate residuals by up to 10% of the continuum for high S/N exposures, preventing accurate wavelength calibration, retrieval of atmospheric parameters, and detection of planets with flux ratios less than 1% of the host star. To combat contamination from fringing, we first identify its three unique sources and adopt a physically informed model of Fabry-P\'{e}rot cavities to apply to post-processed data. We find this strategy can effectively model the fringing in observations of A0V/F0V stars, reducing the residual systematics caused by fringing by a factor of 2. Next, we wedge two of the transmissive optics internal to KPIC to eliminate two sources of fringing and confirm the third source as the entrance window to the spectrograph. Finally, we apply our previous model of the Fabry-P\'{e}rot cavity to new data taken with the wedged optics to reduce the amplitude of the residuals by a factor of 10., Comment: 13 pages, 6 figures

A crucial role of container shipping is maximizing container uptake onto vessels, optimizing the efficiency of a fundamental part of the global supply chain. In practice, liner shipping companies include block stowage patterns that ensure that containers in above and below deck partitions of bays have the same destination. Despite preventing restows, increasing free space, and benefits for crane makespan and hydrostatics, this practical planning requirement is rarely included in stowage optimization models. In our paper, we introduce a novel 0-1 IP model that searches in the space of valid paired block stowage patterns, named template planning, which ensures sufficient vessel capacity and limits to crane makespan, trim, and bending moment. Our results show that template planning outperforms traditional allocation planning concerning optimality and runtime efficiency while preserving a sufficiently accurate representation of master planning constraints and objectives., Comment: Preprint accepted for 15th International Conference on Computational Logistics

Spin-Hall nano-oscillators (SHNO) are nanoscale spintronic devices that generate high-frequency (GHz) microwave signals useful for various applications such as neuromorphic computing and creating Ising systems. Recent research demonstrated that hybrid SHNOs consisting of a ferromagnetic metal (permalloy) and lithium aluminum ferrite (LAFO), a ferrimagnetic insulator, thin films have advantages in having lower auto-oscillation threshold currents ($I_{\text{th}}$) and generating larger microwave output power, making this hybrid structure an attractive candidate for spintronic applications. It is essential to understand how the tunable material properties of LAFO, e.g., its thickness, perpendicular magnetic anisotropy ($K_u$), and saturation magnetization ($M_s$), affect magnetic dynamics in hybrid SHNOs. We investigate the change in $I_{\text{th}}$ and the output power of the device as the LAFO parameters vary. We find the $I_{\text{th}}$ does not depend strongly on these parameters, but the output power has a highly nonlinear dependence on $M_s$ and $K_u$. We further investigate the nature of the excited spin-wave modes as a function of $K_u$ and determine a critical value of $K_u$ above which propagating spin-waves are excited. Our simulation results provide a roadmap for designing hybrid SHNOs to achieve targeted spin excitation characteristics.

We implement a variant of the quantum pigeonhole paradox thought experiment to study whether classical counting principles survive in the quantum domain. We observe strong measurements significantly violate the pigeonhole principle (that among three pigeons in two holes, at least one pair must be in the same hole) and the sum rule (that the number of pigeon pairs in the same hole is the sum of the number of pairs across each of the holes) in an ensemble that is pre and postselected into particular separable states. To investigate whether measurement disturbance is a viable explanation for these counter-intuitive phenomena, we employ the first ever variable-strength measurement of a non-local observable. As we decrease the measurement strength, we find the violation of the sum rule decreases, yet the pigeonhole principle remains violated. In the weak limit, the sum rule is restored due to the cancellation between two weak values with equal and opposite imaginary parts. We observe the same kind of cancellation at higher measurement strengths, thus raising the question: do strong measurements have imaginary parts?, Comment: 8 pages, 4 figures

This review is focused on tests of Einstein's theory of general relativity with gravitational waves that are detectable by ground-based interferometers and pulsar-timing experiments. Einstein's theory has been greatly constrained in the quasi-linear, quasi-stationary regime, where gravity is weak and velocities are small. Gravitational waves are allowing us to probe a complimentary, yet previously unexplored regime: the non-linear and dynamical \emph{extreme gravity regime}. Such a regime is, for example, applicable to compact binaries coalescing, where characteristic velocities can reach fifty percent the speed of light and gravitational fields are large and dynamical. This review begins with the theoretical basis and the predicted gravitational-wave observables of modified gravity theories. The review continues with a brief description of the detectors, including both gravitational-wave interferometers and pulsar-timing arrays, leading to a discussion of the data analysis formalism that is applicable for such tests. The review then discusses gravitational-wave tests using compact binary systems, and ends with a description of the first gravitational wave observations by advanced LIGO, the stochastic gravitational wave background observations by pulsar timing arrays, and the tests that can be performed with them.., Comment: Update to Living Rev.Rel. 16 (2013) 9, now with twice the content and three times the number of references! arXiv admin note: substantial text overlap with arXiv:1304.3473

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Young, self-luminous super-Jovian companions discovered by direct imaging provide a challenging test of planet formation and evolution theories. By spectroscopically characterizing the atmospheric compositions of these super-Jupiters, we can constrain their formation histories. Here we present studies of the recently discovered HIP 99770 b, a 16 MJup high-contrast companion on a 17 au orbit, using the fiber-fed high-resolution spectrograph KPIC (R~35,000) on the Keck II telescope. Our K-band observations led to detections of H2O and CO in the atmosphere of HIP 99770 b. We carried out free retrieval analyses using petitRADTRANS to measure its chemical abundances, including the metallicity and C/O ratio, projected rotation velocity (vsini), and radial velocity (RV). We found that the companion's atmosphere has C/O=0.55(-0.04/+0.06) and [M/H]=0.26(-0.23/+0.24) (1{\sigma} confidence intervals), values consistent with those of the Sun and with a companion formation via gravitational instability or core accretion. The projected rotation velocity < 7.8 km/s is small relative to other directly imaged companions with similar masses and ages. This may imply a near pole-on orientation or effective magnetic braking by a circumplanetary disk. In addition, we added the companion-to-primary relative RV measurement to the orbital fitting and obtained updated constraints on orbital parameters. Detailed characterization of super-Jovian companions within 20 au like HIP 99770 b is critical for understanding the formation histories of this population., Comment: 18 pages, 5 figures, accepted to AJ

We consider a PDE-constrained optimization problem of tracking type with parabolic state equation. The solution to the problem is characterized by the Karush-Kuhn-Tucker (KKT) system, which we formulate using a strong variational formulation of the state equation and a super weak formulation of the adjoined state equation. This allows us to propose a preconditioner that is robust both in the regularization and the diffusion parameter. In order to discretize the problem, we use Isogeometric Analysis since it allows the construction of sufficiently smooth basis functions effortlessly. To realize the preconditioner, one has to solve a problem over the whole space time cylinder that is elliptic with respect to certain non-standard norms. Using a fast diagonalization approach in time, we reformulate the problem as a collection of elliptic problems in space only. These problems are not only smaller, but our approach also allows to solve them in a time-parallel way. We show the efficiency of the preconditioner by rigorous analysis and illustrate it with numerical experiments., Comment: This work was supported by the Research Council of Norway through the grants 300305 and 301013. This work was supported by the Austrian Science Fund (FWF): 10.55776/P33956

Many biological objects possess bilateral symmetry about a midline or midplane, up to a ``noise'' term. This paper uses landmark-based methods to measure departures from bilateral symmetry, especially for the two-group problem where one group is more asymmetric than the other. In this paper, we formulate our work in the framework of size-and-shape analysis including registration via rigid body motion. Our starting point is a vector of elementary asymmetry features defined at the individual landmark coordinates for each object. We introduce two approaches for testing. In the first, the elementary features are combined into a scalar composite asymmetry measure for each object. Then standard univariate tests can be used to compare the two groups. In the second approach, a univariate test statistic is constructed for each elementary feature. The maximum of these statistics lead to an overall test statistic to compare the two groups and we then provide a technique to extract the important features from the landmark data. Our methodology is illustrated on a pre-registered smile dataset collected to assess the success of cleft lip surgery on human subjects. The asymmetry in a group of cleft lip subjects is compared to a group of normal subjects, and statistically significant differences have been found by univariate tests in the first approach. Further, our feature extraction method leads to an anatomically plausible set of landmarks for medical applications., Comment: 20 pages

We extend our previous demonstration of the first on-sky primary mirror segment closed-loop control on Keck using a vector-Zernike wavefront sensor (vZWFS), which improved the Strehl ratio on the NIRC2 science camera by up to 10 percentage points. Segment co-phasing errors contribute to Keck contrast limits and will be necessary to correct for the segmented Extremely Large Telescopes and future space missions. The goal of the post-AO vZWFS on Keck is to monitor and correct segment co-phasing errors in parallel with science observations. The ZWFS is ideal for measuring phase discontinuities and is one of the most sensitive WFSs, but has limited dynamic range. The Keck vZWFS consists of a metasurface mask imposing two different phase shifts to orthogonal polarizations, split into two pupil images, extending its dynamic range. We report on the vZWFS closed-loop co-phasing performance and early work towards understanding the interactions between the AO system and segment phasing. We discuss a comparison of the AO performance when co-phasing by aligning segment edges, as is currently done at Keck, compared with aligning to the average phase over the segments, as is done by the vZWFS., Comment: Proceedings of SPIE, 13097-61, 8 pages, 3 figures, 3 tables

With the release of large-scale motion datasets with textual annotations, the task of establishing a robust latent space for language and 3D human motion has recently witnessed a surge of interest. Methods have been proposed to convert human motion and texts into features to achieve accurate correspondence between them. Despite these efforts to align language and motion representations, we claim that the temporal element is often overlooked, especially for compound actions, resulting in chronological inaccuracies. To shed light on the temporal alignment in motion-language latent spaces, we propose Chronologically Accurate Retrieval (CAR) to evaluate the chronological understanding of the models. We decompose textual descriptions into events, and prepare negative text samples by shuffling the order of events in compound action descriptions. We then design a simple task for motion-language models to retrieve the more likely text from the ground truth and its chronologically shuffled version. CAR reveals many cases where current motion-language models fail to distinguish the event chronology of human motion, despite their impressive performance in terms of conventional evaluation metrics. To achieve better temporal alignment between text and motion, we further propose to use these texts with shuffled sequence of events as negative samples during training to reinforce the motion-language models. We conduct experiments on text-motion retrieval and text-to-motion generation using the reinforced motion-language models, which demonstrate improved performance over conventional approaches, indicating the necessity to consider temporal elements in motion-language alignment., Comment: To appear at ECCV 2024. Project page: https://kfworks.com/CAR-WP/

This work proposes a methodology for determining the maximum dependency length of a recurrent neural network (RNN), and then studies the effects of architectural changes, including the number and neuron count of layers, on the maximum dependency lengths of traditional RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) models., Comment: 13 pages, 12 figures

Highly-multiplexed, robotic, fiber-fed spectroscopic surveys are observing tens of millions of stars and galaxies. For many systems, accurate positioning relies on imaging the fibers in the focal plane and feeding that information back to the robotic positioners to correct their positions. Inhomogeneities and turbulence in the air between the focal plane and the imaging camera can affect the measured positions of fibers, limiting the accuracy with which fibers can be placed on targets. For the Dark Energy Spectroscopic Instrument, we dramatically reduced the effect of turbulence on measurements of positioner locations in the focal plane by taking advantage of stationary positioners and the correlation function of the turbulence. We were able to reduce positioning errors from 7.3 microns to 3.5 microns, speeding the survey by 1.6% under typical conditions., Comment: 11 pages, 5 figures

The textbook algorithm for single-source shortest paths with real-valued edge weights runs in $O(m n)$ time on a graph with $m$ edges and $n$ vertices. A recent breakthrough algorithm by Fineman [Fin24] takes $\tilde O(m n^{8/9})$ randomized time. We present an $\tilde O(m n^{4/5})$ randomized time algorithm building on ideas from [Fin24].

Optimization seeks extremal points in a function. When there are superextensively many optima, optimization algorithms are liable to get stuck. Under these conditions, generic algorithms tend to find marginal optima, which have many nearly flat directions. In a companion paper, we introduce a technique to count marginal optima in random landscapes. Here, we use the statistics of marginal optima calculated using this technique to produce generic bounds on optimization, based on the simple principle that algorithms will overwhelmingly tend to get stuck only where marginal optima are found. We demonstrate the idea on a simple non-Gaussian problem of maximizing the sum of squared random functions on a compact space. Numeric experiments using both gradient descent and generalized approximate message passing algorithms fall inside the expected bounds.