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Upcoming surveys are likely to discover a new sample of interstellar objects (ISOs) within the Solar System, but questions remain about the origin and distribution of this population within the Galaxy. ISOs are ejected from their host systems with a range of velocities, spreading out into tidal streams - analogous to the stellar streams routinely observed from the disruption of star clusters and dwarf galaxies. We create a simulation of ISO streams orbiting in the Galaxy, deriving a simple model for their density distribution over time. We then construct a population model to predict the properties of the streams in which the Sun is currently embedded. We find that the number of streams encountered by the Sun is quite large, ~ 10^6 or more. However, the wide range of stream properties means that for reasonable future samples of ISOs observed in the Solar System, we may see ISOs from the same star ("siblings"), and we are likely to see ISOs from the same star cluster ("cousins"). We also find that ISOs are typically not traceable to their parent star, though this may be possible for ISO siblings. Any ISOs observed with a common origin will come from younger, dynamically colder streams., Comment: Submitted to AAS Journals, comments welcome

Recent developments in the understanding of optical angular momentum have resulted in many demonstrations of unusual optical phenomena, such as optical beams with orbital angular momentum and transverse spinning light. Here we detail novel contributions to spin and orbital angular momentum generated by the gradient of wavefront curvature that becomes relevant in strongly focused beams of light. While circularly polarized beams are shown to develop helicity-dependent transverse spin, a linearly polarized Gaussian beam produces longitudinal spin and orbital angular momenta in the focal region, even if lacking both of these before focusing. Analytical treatment of a nonparaxial electromagnetic field, validated with vectorial diffraction modelling, shows that the terms related to higher orders of a paraxial parameter are responsible for the appearance of non-trivial angular momenta. The obtained dependences relate these quantities to the gradient of the wavefront curvature, showing how it can be used as a novel degree of freedom for applications in optical manipulation and light-matter interactions at subwavelength scales, enabling angular momentum transfer even from a simple Gaussian beam with linear polarization.

The method of fragments was recently proposed, and its effectiveness has been empirically shown for three specialised pickup and delivery problems. We propose an enhanced fragment algorithm that for the first time, effectively solves the Pickup and Delivery Problem with Time Windows. Additionally, we describe the approach in general terms to exemplify its theoretical applicability to vehicle routing problems without pickup and delivery requirements. We then apply it to the Truck-Based Drone Delivery Routing Problem Problem with Time Windows. The algorithm uses a fragment formulation rather than a route one. The definition of a fragment is problem specific, but generally, they can be thought of as enumerable segments of routes with a particular structure. A resource expanded network is constructed from the fragments and is iteratively updated via dynamic discretization discovery. Additionally, we introduce two new concepts called formulation leveraging and column enumeration for row elimination that are crucial for solving difficult problems. These use the strong linear relaxation of the route formulation to strengthen the fragment formulation. We test our algorithm on instances of the Pickup and Delivery Problem with Time Windows and the Truck-Based Drone Delivery Routing Problem with Time Windows. Our approach is competitive with, or outperforms the state-of-the-art algorithm for both.

This paper considers gain-scheduling of QSR-dissipative subsystems using scheduling matrices. The corresponding QSR-dissipative properties of the overall matrix-gain-scheduled system, which depends on the QSR properties of the subsystems scheduled, are explicitly derived. The use of scheduling matrices is a generalization of the scalar scheduling signals used in the literature, and allows for greater design freedom when scheduling systems, such as in the case of gain-scheduled control. Furthermore, this work extends the existing gain-scheduling results to a broader class of QSR-dissipative systems. The matrix-scheduling of important special cases, such as passive, input strictly passive, output strictly passive, finite L2 gain, very strictly passive, and conic systems are presented. The proposed gain-scheduling architecture is used in the context of controlling a planar three-link robot subject to model uncertainty. A novel control synthesis technique is used to design QSR-dissipative subcontrollers that are gain-scheduled using scheduling matrices. Numerical simulation results highlight the greater design freedom of scheduling matrices, leading to improved performance., Comment: Submitted to IEEE Transactions on Automatic Control (TAC)

Laguerre-Gaussian (LG) modes are solutions of the paraxial Helmholtz equation in cylindrical coordinates and are associated with light fields carrying orbital angular momentum (OAM). It is customary to modulate such beams using phase-only vortex profiles, for example, when increasing (laddering up) or decreasing (laddering down) the OAM content of some given LG mode. However, the resulting beams have been shown to be hypergeometric-Gaussian modes, due to the changing radial amplitudes on propagation. In this work, we show that these beams in fact have the angular spectrum of elegant Laguerre-Gaussian (eLG) modes, and therefore map back to LG-type modes. Accordingly, the fields obtain new OAM and radial quantum numbers that depend on the initial OAM and additional OAM gained during modulation., Comment: 4 pages, 2 figures

With the rapid advancements in medical data acquisition and production, increasingly richer representations exist to characterize medical information. However, such large-scale data do not usually meet computing resource constraints or algorithmic complexity, and can only be processed after compression or reduction, at the potential loss of information. In this work, we consider specific Gaussian mixture models (HD-GMM), tailored to deal with high dimensional data and to limit information loss by providing component-specific lower dimensional representations. We also design an incremental algorithm to compute such representations for large data sets, overcoming hardware limitations of standard methods. Our procedure is illustrated in a magnetic resonance fingerprinting study, where it achieves a 97% dictionary compression for faster and more accurate map reconstructions.

Structured light carrying angular momentum, such as spin angular momentum (SAM) and orbital angular momentum (OAM), has been at the core of new science and applications, driving the need for compact on-chip sources. While many static on-chip solutions have been demonstrated, as well as on-chip sources of free-space modes, no architecture that is fully reconfigurable in all angular momentum states and all on-chip has so far been possible. Here we report the first all-on-chip structured light generator for the creation of both scalar and vectorial angular momentum beams, facilitated through a silicon-on-insulator (SOI) chip with a silica mode multiplexer (silica chip). We selectively stimulate six linearly-polarized (LP) modes of the silica multimode bus waveguide, precisely controlling the modal powers and phases with the SOI chip. This allows us to tailor arbitrary superpositions of the mode set thus synthesizing common cylindrical vector vortex beams as well as OAM beams of controlled spin and topological charge. Our compact structured light generator exhibits high switching speed and operates across the telecom band, paving the way for applications such as optical communication and integrated quantum technologies.

Maxwell's demon (MD) has proven an instructive vehicle by which to explore the relationship between information theory and thermodynamics, fueling the possibility of information driven machines. A long standing debate has been the concern of entropy violation, now resolved by the introduction of a quantum MD, but this theoretical suggestion has proven experimentally challenging. Here, we use classical vectorially structured light that is non-separable in spin and orbital angular momentum to emulate a quantum MD experiment. Our classically entangled light fields have all the salient properties necessary of their quantum counterparts but without the experimental complexity of controlling quantum entangled states. We use our experiment to show that the demon's entropy increases during the process while the system's entropy decreases, so that the total entropy is conserved through an exchange of information, confirming the theoretical prediction. We show that our MD is able to extract useful work from the system in the form of orbital angular momentum, opening a path to information driven optical spanners for the mechanical rotation of objects with light. Our synthetic dimensions of angular momentum can easily be extrapolated to other degrees of freedom, for scalable and robust implementations of MDs at both the classical and quantum realms, enlightening the role of a structured light MD and its capability to control and measure information., Comment: 14 pages, 6 Figures

The topology of a pure state of two entangled photons is leveraged to provide a discretization of quantum information. Since discrete signals are inherently more resilient to the effects of perturbations, this discrete class of entanglement observables may offer an advantage against noise. Establishing this is the primary objective of this paper. We develop a noise model that exploits the specific form of such topological wave functions - an entangled state of two photons with one in an orbital angular momentum state and the other in a polarization state. We show that noise affecting both photons can be recast as a position-dependent perturbation affecting only the photon in the polarization state. This approach allows us to utilize both the language and concepts used in studying noisy qubits, as well as recent advances in quantum polarimetry. By adding noise to a finite-dimensional Hilbert space of polarization states, we can describe the noise using quantum operations expressed through appropriate Krauss operators, whose structure is determined by quantum polarimetry. For non-depolarizing noise, we provide an argument based on homotopic maps that demonstrates the topology's resilience to noise. For depolarizing noise, numerical studies using the quantum channel description show that the discrete entanglement signal remains completely resilient. Finally, we identify sources of local noise that can destabilize the topology. This foundational work establishes a framework for understanding how topology enhances the resilience of quantum information, directly impacting the distribution of information through entanglement in noisy environments, such as quantum computers and quantum networks., Comment: 24 pages, 15 figures

Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges, such as poor sample efficiency, generalization, and the need for well-designed reward functions to guide learning effectively. On the other hand, controllers based on complete system dynamics do not require data. This paper addresses the intermediate situation where there is not enough model information for complete controller design, but there is enough to suggest that a model-free approach is not the best approach either. By carefully decoupling known and unknown information about the system dynamics, we obtain an embedded controller guided by our partial model and thus improve the learning efficiency of an RL-enhanced approach. A modular design allows us to deploy mainstream RL algorithms to refine the policy. Simulation results show that our method significantly improves sample efficiency compared with standard RL methods on continuous control tasks, and also offers enhanced performance over traditional control approaches. Experiments on a real ground vehicle also validate the performance of our method, including generalization and robustness., Comment: Accepted to IROS 2024

The formation details of globular clusters (GCs) are still poorly understood due to their old ages and the lack of detailed observations of their formation. A large variety of models for the formation and evolution of GCs have been created to improve our understanding of their origins, based on GC properties observed at z=0. We present the first side-by-side comparison of six current GC formation models with respect to their predictions for the GC ages and formation redshifts in Milky Way (MW)-like galaxies. We find that all the models are capable of forming most of the surviving GCs at more than 10 Gyr ago, in general agreement with the observation that most GCs are old. However, the measured MW GC ages are still systematically older than those predicted in the galaxies of four of the models. Investigating the variation of modelled GC age distributions for general MW-mass galaxies, we find that some of the models predict that a significant fraction of MW-mass galaxies would entirely lack a GC population older than 10 Gyr, whereas others predict that all MW-mass galaxies have a significant fraction of old GCs. This will have to be further tested in upcoming surveys, as systems without old GCs in that mass range are currently not known. Finally, we show that the models predict different formation redshifts for the oldest surviving GCs, highlighting that models currently disagree about whether the recently observed young star clusters at high redshifts could be the progenitors of today's GCs., Comment: 14 pages, 6 figures, submitted to MNRAS

In Deep Reinforcement Learning models trained using gradient-based techniques, the choice of optimizer and its learning rate are crucial to achieving good performance: higher learning rates can prevent the model from learning effectively, while lower ones might slow convergence. Additionally, due to the non-stationarity of the objective function, the best-performing learning rate can change over the training steps. To adapt the learning rate, a standard technique consists of using decay schedulers. However, these schedulers assume that the model is progressively approaching convergence, which may not always be true, leading to delayed or premature adjustments. In this work, we propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta-learning approach that selects the learning rate based on the agent's performance during training. LRRL is based on a multi-armed bandit algorithm, where each arm represents a different learning rate, and the bandit feedback is provided by the cumulative returns of the RL policy to update the arms' probability distribution. Our empirical results demonstrate that LRRL can substantially improve the performance of deep RL algorithms.

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-based reward shaping (PBRS), has not been applicable to many IM methods, as they are often complex, trainable functions themselves, and therefore dependent on a wider set of variables than the traditional reward functions that PBRS was developed for. We present an extension to PBRS that we prove preserves the set of optimal policies under a more general set of functions than has been previously proven. We also present {\em Potential-Based Intrinsic Motivation} (PBIM) and {\em Generalized Reward Matching} (GRM), methods for converting IM rewards into a potential-based form that are useable without altering the set of optimal policies. Testing in the MiniGrid DoorKey and Cliff Walking environments, we demonstrate that PBIM and GRM successfully prevent the agent from converging to a suboptimal policy and can speed up training. Additionally, we prove that GRM is sufficiently general as to encompass all potential-based reward shaping functions. This paper expands on previous work introducing the PBIM method, and provides an extension to the more general method of GRM, as well as additional proofs, experimental results, and discussion., Comment: To be submit to joint AIJ-JAIR special track for award-winning papers. arXiv admin note: substantial text overlap with arXiv:2402.07411

Bayesian Optimal Experimental Design (BOED) is a powerful tool to reduce the cost of running a sequence of experiments. When based on the Expected Information Gain (EIG), design optimization corresponds to the maximization of some intractable expected {\it contrast} between prior and posterior distributions. Scaling this maximization to high dimensional and complex settings has been an issue due to BOED inherent computational complexity. In this work, we introduce an {\it expected posterior} distribution with cost-effective sampling properties and provide a tractable access to the EIG contrast maximization via a new EIG gradient expression. Diffusion-based samplers are used to compute the dynamics of the expected posterior and ideas from bi-level optimization are leveraged to derive an efficient joint sampling-optimization loop, without resorting to lower bound approximations of the EIG. The resulting efficiency gain allows to extend BOED to the well-tested generative capabilities of diffusion models. By incorporating generative models into the BOED framework, we expand its scope and its use in scenarios that were previously impractical. Numerical experiments and comparison with state-of-the-art methods show the potential of the approach.

High-resolution imaging and strong gravitational lensing of high-redshift galaxies have enabled the detection of compact sources with properties similar to nearby massive star clusters. Often found to be very young, these sources may be globular clusters detected in their earliest stages. In this work, we compare predictions of high-redshift ($z \sim 1$--$10$) star cluster properties from the E-MOSAICS simulation of galaxy and star cluster formation with those of the star cluster candidates in strongly lensed galaxies from James Webb (JWST) and Hubble Space Telescope (HST) imaging. We select galaxies in the simulation that match the luminosities of the majority of lensed galaxies with star cluster candidates observed with JWST. We find that the luminosities, ages and masses of the brightest star cluster candidates in the high-redshift galaxies are consistent with the E-MOSAICS model. In particular, the brightest cluster ages are in excellent agreement. The results suggest that star clusters in both low- and high-redshift galaxies may form via common mechanisms. However, the brightest clusters in the lensed galaxies tend to be $\approx 1$--$1.5$ mag brighter and $\approx 0.5$ dex more massive than the median E-MOSAICS predictions. We discuss the large number of effects that could explain the discrepancy, including simulation and observational limitations, stellar population models, cluster detection biases and nuclear star clusters. Understanding these limitations would enable stronger tests of globular cluster formation models., Comment: 17 pages, 9 figures, 1 table. Submitted to MNRAS

This paper proposes a robust nonlinear observer synthesis method for a population of systems modelled using the Koopman operator. The Koopman operator allows nonlinear systems to be rewritten as infinite-dimensional linear systems. A finite-dimensional approximation of the Koopman operator can be identified directly from data, yielding an approximately linear model of a nonlinear system. The proposed observer synthesis method is made possible by this linearity that in turn allows uncertainty within a population of Koopman models to be quantified in the frequency domain. Using this uncertainty model, linear robust control techniques are used to synthesize robust nonlinear Koopman observers. A population of several dozen motor drives is used to experimentally demonstrate the proposed method. Manufacturing variation is characterized in the frequency domain, and a robust Koopman observer is synthesized using mixed $\mathcal{H}_2$-$\mathcal{H}_\infty$ optimal control., Comment: 16 pages, 15 figures

We present the first comprehensive census of the satellite population around a Large Magellanic Cloud (LMC) stellar-mass galaxy, as part of the Magellanic Analog Dwarf Companions and Stellar Halos (MADCASH) survey. We have surveyed NGC 2403 (D=3.0 Mpc) with the Subaru/Hyper Suprime-Cam imager out to a projected radius of 90 kpc (with partial coverage extending out to ~110 kpc, or ~80% of the virial radius of NGC 2403), resolving stars in the uppermost ~2.5 mags of its red giant branch. By looking for stellar overdensities in the red giant branch spatial density map, we identify 149 satellite candidates, of which only the previously discovered MADCASH J074238+65201-dw is a bona fide dwarf, together with the more massive and disrupting satellite DDO 44. We carefully assess the completeness of our search via injection of artificial dwarf galaxies into the images, finding that we are reliably sensitive to candidates down to M_V ~ -7.5 mag (and somewhat sensitive to even fainter satellites). A comparison of the satellite luminosity function of NGC 2403 down to this magnitude limit to theoretical expectations shows overall good agreement. This is the first of a full sample of 11 Magellanic Cloud-mass host galaxies we will analyze, creating a statistical sample that will provide the first quantitative constraints on hierarchical models of galaxy formation around low-mass hosts., Comment: 20 pages, 17 figures; accepted for publication in the Astrophysical Journal; figures 11-17 at reduced resolution

A regular-graph design is a block design for which a pair $\{a,b\}$ of distinct points occurs in $\lambda+1$ or $\lambda$ blocks depending on whether $\{a,b\}$ is or is not an edge of a given $\delta$-regular graph. Our paper describes a specific construction for regular-graph designs with $\lambda = 1$ and block size $\delta + 1$. We show that for $\delta \in \{2,3\}$, certain necessary conditions for the existence of such a design with $n$ points are sufficient, with two exceptions in each case and two possible exceptions when $\delta = 3$. We also construct designs of orders 105 and 117 for connected 4-regular graphs., Comment: 18 pages. The paper will be submitted to a journal. Some basic definitions have been lifted from other papers by the same authors

We present closed-form expressions for marginalizing and conditioning Gaussians onto linear manifolds, and demonstrate how to apply these expressions to smooth nonlinear manifolds through linearization. Although marginalization and conditioning onto axis-aligned manifolds are well-established procedures, doing so onto non-axis-aligned manifolds is not as well understood. We demonstrate the utility of our expressions through three applications: 1) approximation of the projected normal distribution, where the quality of our linearized approximation increases as problem nonlinearity decreases; 2) covariance extraction in Koopman SLAM, where our covariances are shown to be consistent on a real-world dataset; and 3) covariance extraction in constrained GTSAM, where our covariances are shown to be consistent in simulation., Comment: Submitted to IEEE ICRA 2025

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially-resolved simulations of the turbulent, multiphase, star-forming ISM have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the Pressure-Regulated Feedback-Modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the "native" simulation outputs with post-processed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step towards implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations., Comment: 33 pages, 12 figures, Accepted for publication in ApJ. This is a Learning the Universe Publication. All codes and data used to produce this work can be found at the following $\href{https://github.com/sultan-hassan/tng50-post-processing-prfm}{GitHub \,Link.}$

We present Hubble Space Telescope ACS/WFC and WFC3/UVIS imaging for a sample of 50 low surface brightness (LSB) galaxies in the $\sim$10$^{15}$ M$_{\odot}$ Perseus cluster, which were originally identified in ground-based imaging. We measure the structural properties of these galaxies and estimate the total number of globular clusters (GCs) they host. Around half of our sample galaxies meet the strict definition of an ultra-diffuse galaxy (UDG), while the others are UDG-like but are either somewhat more compact or slightly brighter. A small number of galaxies reveal systems with many tens of GCs, rivalling some of the richest GC systems known around UDGs in the Coma cluster. We find the sizes of rich GC systems, in terms of their half-number radii, extending to $\sim$1.2 times the half-light radii of their host galaxy on average. The mean colours of the GC systems are the same, within the uncertainties, as those of their host galaxy stars. This suggests that GCs and galaxy field stars may have formed at the same epoch from the same enriched gas. It may also indicate a significant contribution from disrupted GCs to the stellar component of the host galaxy as might be expected in the 'failed galaxy' formation scenario for UDGs., Comment: 16 pages, 11 figures, 2 tables, accepted for publication in MNRAS

The Koopman operator framework can be used to identify a data-driven model of a nonlinear system. Unfortunately, when the data is corrupted by noise, the identified model can be biased. Additionally, depending on the choice of lifting functions, the identified model can be unstable, even when the underlying system is asymptotically stable. This paper presents an approach to reduce the bias in an approximate Koopman model, and simultaneously ensure asymptotic stability, when using noisy data. Additionally, the proposed data-driven modeling approach is applicable to systems with inputs, such as a known forcing function or a control input. Specifically, bias is reduced by using a total least-squares, modified to accommodate inputs in addition to lifted inputs. To enforce asymptotic stability of the approximate Koopman model, linear matrix inequality constraints are augmented to the identification problem. The performance of the proposed method is then compared to the well-known extended dynamic mode decomposition method and to the newly introduced forward-backward extended dynamic mode decomposition method using a simulated Duffing oscillator dataset and experimental soft robot arm dataset., Comment: 18 pages, 6 figures

This paper considers gain-scheduling of very strictly passive (VSP) subcontrollers using scheduling matrices. The use of scheduling matrices, over scalar scheduling signals, realizes greater design freedom, which in turn can improve closed-loop performance. The form and properties of the scheduling matrices such that the overall gain-scheduled controller is VSP are explicitly discussed. The proposed gain-scheduled VSP controller is used to control a rigid two-link robot subject to model uncertainty where robust input-output stability is assured via the passivity theorem. Numerical simulation results highlight the greater design freedom, resulting in improved performance, when scheduling matrices are used over scalar scheduled signals., Comment: To be published in IEEE Conference on Control Technology and Applications (CCTA) 2024

The recent discovery that linearly polarized light with a helical wavefront can exhibit vortex dichroism (also referred to as helical dichroism) has opened up new horizons in chiroptical spectroscopy with structured chiral light. Recent experiments have now pushed optical activity with vortex beams into the regime of nonlinear optics. Here we present the theory of two-photon absorption (TPA) of focused optical vortices by chiral molecules: nonlinear vortex dichroism (NVD). We discover that highly distinct features arise in the case of TPA with focused vortex beams, including the ability to probe chiral molecular structure not accessible to current methods and that the differential rate of TPA is significantly influenced by the orientation of the state of linear polarization. This work provides strong evidence that combining nonlinear optical activity with structured light provides new and improved routes to studying molecular chirality.

Fully non-linear, plane-symmetric exact solutions of the Einstein equations describing the scattering of gravitational and electromagnetic waves have existed for many years. For these closed-form solutions to be found, idealized wave profiles such as the Dirac delta and Heaviside theta functions must be assumed. Although pathological in that future curvature singularities generically occur, these exact solutions give useful insights into the non-linear features of the scattering process. Only a limited number of exact solutions exist and this leaves many other physically-motivated scattering situations without a non-linear description. The aim of this paper is to shed light on these unexplored cases. This is achieved through numerical solutions of the Friedrich-Nagy initial boundary value problem for the Einstein equations coupled to the source-free Maxwell equations in plane symmetry. Interesting non-linear scattering effects are presented for a variety of electromagnetic and gravitational wave profiles not currently described by an exact solution. Implications for electromagnetic wave observations are investigated through analyzing the time-delay and frequency shift imparted on the electromagnetic wave through the non-linear scattering with a gravitational wave of a given strain. Simple arguments supported by the numerical solutions suggest dramatic effects on the radiation may be observable., Comment: 40 pages, 22 figures

Quantum double models provide a natural framework for realising anyons by manipulating a lattice of qudits, which can be directly encoded in quantum simulators. In this work, we consider a lattice of $d=6$ qudits that give rise to $\mathbf{D}(\mathbf{S}_3)$ non-Abelian anyons. We present a method that demonstrates the non-commutativity of the braiding and fusion evolutions solely by utilising the operators that create and measure anyons. Furthermore, we provide a dense coding scheme where only two qudits are sufficient to determine the anyonic braiding and fusion matrices. The minimal resource requirement of our approach offers a viable blueprint for demonstrating the non-Abelian nature of anyons in various quantum platforms, including optical, atomic, and solid-state systems, where higher-spin states can be effectively encoded. This work represents a foundational step towards the realisation of non-Abelian quantum error-correcting codes., Comment: 19 pages, 9 figures

The CO(1-0) line has been carefully calibrated as a tracer of molecular gas mass. However, recent studies often favor higher J transitions of the CO molecule which are brighter and accessible for redshift ranges where CO(1-0) is not. These lines are not perfect analogues for CO(1-0), owing to their more stringent excitation conditions, and must be calibrated for use as molecular gas tracers. Here we introduce the Arizona Molecular ISM Survey with the SMT (AMISS), a multi-CO line survey of z~0 galaxies conducted to calibrate the CO(2-1) and CO(3-2) lines. The final survey includes CO(2-1) spectra of 176 galaxies and CO(3-2) spectra for a subset of 45. We supplement these with archival CO(1-0) spectra from xCOLD GASS for all sources and additional CO(1-0) observations with the Kitt Peak 12m Telescope. Targets were selected to be representative of the galaxy population in the stellar mass range of $10^9$ to $10^{11.5}$ M$_\odot$. Our project emphasized careful characterization of statistical and systematic uncertainties to enable studies of trends in CO line ratios. We show that optical and CO disk sizes are on average equal, for both the CO(1-0) and CO(2-1) line. We measure the distribution of CO line luminosity ratios, finding medians (16th-84th percentile) of 0.71 (0.51-0.96) for the CO(2-1)-to-CO(1-0) ratio, 0.39 (0.24-0.53) for the CO(3-2)-to-CO(1-0) ratio, and 0.53 (0.41-0.74) for the CO(3-2)-to-CO(2-1) ratio. A companion paper presents our study of CO(2-1)'s applicability as a molecular gas mass tracer and search for trends in the CO(2-1)-to-CO(1-0) ratio. Our catalog of CO line luminosities will be publicly available with the published version of this article., Comment: Resubmitted to ApJ after incorporating reviewer feedback

In this paper, we analyse the light distribution in the Hydra I cluster of galaxies to explore their low surface brightness features, measure the intra-cluster light, and address the assembly history of the cluster. For this purpose, we used deep wide-field g- and r-band images obtained with the VST as part of the VEGAS project. The VST mosaic covers ~0.4 times the virial radius around the core of the cluster, which enabled us to map the light distribution down to faint surface brightness levels of mu_g ~ 28 mag/arcsec^2. In this region of the cluster, 44 cluster members are brighter than m_B<16 mag, and the region includes more than 300 dwarf galaxies. Similar to the projected distribution of all cluster members (bright galaxies and dwarfs), we find that the bulk of the galaxy light is concentrated in the cluster core, which also emits in the X-rays, and there are two overdensities: in the north (N) and south-east (SE) with respect to the cluster core. We present the analysis of the light distribution of all the bright cluster members. After removing foreground stars and other objects, we measured the diffuse intra-cluster light and compared its distribution with that of the globular clusters and dwarf galaxies in the cluster. We find that most of the diffuse light low surface brightness features, and signs of possible gravitational interaction between galaxies reside in the core and in the group in the N, while ram-pressure stripping is frequently found to affect galaxies within the SE group. All these features confirm that the mass assembly in this cluster is still ongoing. By combining the projected phase-space with these observed properties, we trace the different stages of the assembly history. We also address the main formation channels for the intra-cluster light detected in the cluster, which contributes ~ 12% to the total luminosity of the cluster., Comment: 18 pages, 13 figures, accepted for publication in A&A

Bayesian estimation is a vital tool in robotics as it allows systems to update the belief of the robot state using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing non-parametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. In this paper, we introduce a novel approach to nonparametric Bayesian filtering to cope with multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks., Comment: Preprint under review. Code available at https://github.com/montrealrobotics/harmonic-filter. Webpage and additional videos at https://montrealrobotics.ca/hef/

Volumetry is one of the principal downstream applications of 3D medical image segmentation, for example, to detect abnormal tissue growth or for surgery planning. Conformal Prediction is a promising framework for uncertainty quantification, providing calibrated predictive intervals associated with automatic volume measurements. However, this methodology is based on the hypothesis that calibration and test samples are exchangeable, an assumption that is in practice often violated in medical image applications. A weighted formulation of Conformal Prediction can be framed to mitigate this issue, but its empirical investigation in the medical domain is still lacking. A potential reason is that it relies on the estimation of the density ratio between the calibration and test distributions, which is likely to be intractable in scenarios involving high-dimensional data. To circumvent this, we propose an efficient approach for density ratio estimation relying on the compressed latent representations generated by the segmentation model. Our experiments demonstrate the efficiency of our approach to reduce the coverage error in the presence of covariate shifts, in both synthetic and real-world settings. Our implementation is available at https://github.com/benolmbrt/wcp_miccai, Comment: Early accepted at MICCAI 2024

Progress in machine understanding of sign languages has been slow and hampered by limited data. In this paper, we present FSboard, an American Sign Language fingerspelling dataset situated in a mobile text entry use case, collected from 147 paid and consenting Deaf signers using Pixel 4A selfie cameras in a variety of environments. Fingerspelling recognition is an incomplete solution that is only one small part of sign language translation, but it could provide some immediate benefit to Deaf/Hard of Hearing signers as more broadly capable technology develops. At >3 million characters in length and >250 hours in duration, FSboard is the largest fingerspelling recognition dataset to date by a factor of >10x. As a simple baseline, we finetune 30 Hz MediaPipe Holistic landmark inputs into ByT5-Small and achieve 11.1% Character Error Rate (CER) on a test set with unique phrases and signers. This quality degrades gracefully when decreasing frame rate and excluding face/body landmarks: plausible optimizations to help models run on device in real time., Comment: Access FSboard at https://www.kaggle.com/datasets/googleai/fsboard

Fixed-target proton-beam experiments produce a multitude of charged pions that rescatter in the beam dump. These charged pion scattering events can be an additional irreducible source of exotic particles which couple to photons or hadrons. We analyze the sensitivity of the DUNE Near Detector complex to millicharged particles (MCPs) and heavy axion-like particles (ALPs) with low-energy couplings to gluons. Using the framework of chiral perturbation theory, we demonstrate regimes of parameter space where the charged pion production channel dominates over previously-considered production mechanisms for both MCPs and ALPs, thereby improving the sensitivity of DUNE to these new particles compared to previous studies., Comment: 11+1 pages, 11 figures. v2: corrected implementation of momentum transfer cutoff, conclusions unchanged

Non-charge based logic in single-hole spin of semiconductor quantum dots (QDs) can be controlled by anisotropic gate potentials providing a notion for making next generation solid-state quantum devices. In this study, we investigate the isotropic and anisotropic behavior of phonon mediated spin relaxation of heavy-hole spin hot spots in QDs. For the electron spin in isotropic QDs, hot spots are known to be always present due to the Rashba spin-orbit coupling. But for heavy holes in isotropic dots, we show that the occurrences of spin hot spots are sensitive to the bulk g-factor. The hot spot for Rashba coupling in InAs and GaSb dots arises because these materials possess negative bulk g-factor, while that for the Dresselhaus coupling in GaAs and InSb dots is found due to their positive bulk g-factor. For anisotropic QDs, on the other hand, the spin hot spot is universally present due to their broken in-plane rotational symmetry. Further, the increasing electric field, that strengthens the Rashba coupling, is shown to cover a wide range of magnetic field by the hot spots. Results demonstrate that the magnetic field, choice of dot materials and size anisotropy can act as effective control parameters which can be experimentally used to design the device for detecting the phonon mediated heavy-hole spin-relaxation behavior of III-V semiconductor QDs., Comment: 15 pages, 11 figures

The Tully-Fisher Relation (TFR) is a well-known empirical relationship between the luminosity of a spiral galaxy and its circular velocity, allowing us to estimate redshift independent distances. Here we use high signal-to-noise HI 21-cm integrated spectra from the second pilot data release (PDR2, 180 deg2) of the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY). In order to prepare for the full WALLABY survey, we have investigated the TFR in phase 2 of the pilot survey with a further three fields. The data were obtained with wide-field Phased Array Feeds on the Australian Square Kilometre Array Pathfinder (ASKAP) and have an angular resolution of 30 arcsec and a velocity resolution of ~4 km/s. Galaxy luminosities have been measured from the Wide-field Infrared Survey Explorer (WISE), and optical galaxy inclinations from the Dark Energy Camera Legacy Survey. We present TFRs for wavelengths from 0.8-3.4{\mu}m. We examine sources of galaxy inclination data and investigate magnitudes from the DECam Local Volume Exploration Survey (DELVE) and DENIS catalogues and the 4HS target catalogue based on the VISTA Hemisphere Survey (VHS). We consider the baryonic TFR. These are all of interest for TFR using the full WALLABY survey of 200,000 galaxies. We demonstrate that WALLABY TFR distances can take their place among state of the art studies of the local velocity field., Comment: to appear in MNRAS. One figure removed

Autonomous driving has experienced remarkable progress, bolstered by innovations in computational hardware and sophisticated deep learning methodologies. The foundation of these advancements rests on the availability and quality of datasets, which are crucial for the development and refinement of dependable and versatile autonomous driving algorithms. While numerous datasets have been developed to support the evolution of autonomous driving perception technologies, few offer the diversity required to thoroughly test and enhance system robustness under varied weather conditions. Many public datasets lack the comprehensive coverage of challenging weather scenarios and detailed, high-resolution data, which are critical for training and validating advanced autonomous-driving perception models. In this paper, we introduce PLT-D3; a Dynamic-weather Driving Dataset, designed specifically to enhance autonomous driving systems' adaptability to diverse weather conditions. PLT-D3 provides high-fidelity stereo depth and scene flow ground truth data generated using Unreal Engine 5. In particular, this dataset includes synchronized high-resolution stereo image sequences that replicate a wide array of dynamic weather scenarios including rain, snow, fog, and diverse lighting conditions, offering an unprecedented level of realism in simulation-based testing. The primary aim of PLT-D3 is to address the scarcity of comprehensive training and testing resources that can simulate real-world weather variations. Benchmarks have been established for several critical autonomous driving tasks using PLT-D3, such as depth estimation, optical flow and scene-flow to measure and enhance the performance of state-of-the-art models.

Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated in the transverse spatial degree of freedom to realise an adaptable scheme for learning unitary operations. Our approach borrows from concepts in variational quantum computing, where a search or optimisation problem is mapped onto the task of finding a minimum ground state energy for a given energy/goal function. We achieve this by a pseudo-random walk procedure over the parameter space of the unitary operation, implemented with optical matrix-vector multiplication enacted on arrays of Gaussian modes by exploiting the partial Fourier transforming capabilities of a cylindrical lens in the transverse degree of freedom for the measurement. We outline the concept theoretically, and experimentally demonstrate that we are able to learn optical unitary matrices for dimensions d = 2, 4, 8 and 16 with average fidelities of >90%. Our work advances high dimensional information processing and can be adapted to both process and quantum state tomography of unknown states and channels.

Nine years of mesospheric wind data from two meteor radars at 52{\deg}N latitude were analyzed to investigate planetary waves (PWs) and tides by estimating their zonal wavenumber through longitudinal phase differences. Our results reveal that PW normal modes (NMs) primarily drive multi-day oscillations, showing seasonal variability and statistical associations with Sudden Stratospheric Warming (SSW) events. Specifically, a significant 6-day NM emerges in April, followed by predominant 4- and 2-day NMs until June, with peaks of 2-, 4-, and 6-day NMs spanning July to October. Furthermore, our study provides the first observational verification of frequency and zonal wavenumber of over ten secondary waves from nonlinear interactions among planetary-scale waves. One notable finding is the prevalence of non-migrating components in winter 24-hour and summer 8-hour tides, attributed to these nonlinear interactions. Our findings underscore the diverse nonlinear dynamics of planetary-scale waves, triggering a variety of periodic oscillations.

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process, reducing uncertainty and enhancing reproducibility. This article contributes to developing a framework for automated evaluation techniques testing, emphasizing automated fitting methods that do not require the user to provide any prior information. This approach simplifies the process and reduces the manual effort needed in the initial evaluation stage. It highlights the capability of the framework to validate and optimize subroutines, targeting the performance analysis and optimization of the fitting procedure using high-fidelity synthetic data (labeled experimental data) and the concept of a fully controlled computational experiment. An error metric is introduced to provide a clear and intuitive measure of the fitting quality by quantifying the accuracy and performance across the specified energy. This metric sets a scale for comparison and optimization of routines or hyperparameter selection, improving the entire evaluation process methodology and increasing reproducibility and objectivity., Comment: 39 pages, 12 figures; As a follow-up to arXiv:2303.09698 and arXiv:2402.14122

As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enabling H mapping, including local hydrogen concentration analyses at specific microstructural features, is essential for understanding the multiple ways that H affect the properties of materials, including for instance embrittlement mechanisms and their synergies, but also spatial mapping and quantification of hydrogen isotopes is essential to accurately predict tritium inventory of future fusion power plants, ensuring their safe and efficient operation for example. Atom probe tomography (APT) has the intrinsic capabilities for detecting hydrogen (H), and deuterium (D), and in principle the capacity for performing quantitative mapping of H within a material's microstructure. Yet the accuracy and precision of H analysis by APT remain affected by the influence of residual hydrogen from the ultra-high vacuum chamber that can obscure the signal of H from within the material, along with a complex field evaporation behavior. The present article reports the essence of discussions at a focused workshop held at the Max-Planck Institute for Sustainable Materials in April 2024. The workshop was organized to pave the way to establishing best practices in reporting APT data for the analysis of H. We first summarize the key aspects of the intricacies of H analysis by APT and propose a path for better reporting of the relevant data to support interpretation of APT-based H analysis in materials.

A number of nearby dwarf galaxies have globular cluster (GC) candidates that require spectroscopic confirmation. Here we present Keck telescope spectra for 15 known GCs and GC candidates that may be associated with a host dwarf galaxy, and an additional 3 GCs in the halo of M31 that are candidates for accretion from a now disrupted dwarf galaxy. We confirm 6 star clusters (of intermediate-to-old age) to be associated with NGC~247. The vast bulk of its GC system remains to be studied spectroscopically. We also confirm the GC candidates in F8D1 and DDO190, finding both to be young star clusters. The 3 M31 halo GCs all have radial velocities consistent with M31, are old and very metal-poor. Their ages and metallicities are consistent with accretion from a low mass satellite galaxy. Finally, three objects are found to be background galaxies -- two are projected near NGC~247 and one (candidate GCC7) is near the IKN dwarf. The IKN dwarf thus has only 5 confirmed GCs but still a remarkable specific frequency of 124., Comment: 7 pages, 5 figures, accepted for publication in PASA

Empirical evidence shows stock returns are often heavy-tailed rather than normally distributed. The $\kappa$-generalised distribution, originated in the context of statistical physics by Kaniadakis, is characterised by the $\kappa$-exponential function that is asymptotically exponential for small values and asymptotically power law for large values. This proves to be a useful property and makes it a good candidate distribution for many types of quantities. In this paper we focus on fitting historic daily stock returns for the FTSE 100 and the top 100 Nasdaq stocks. Using a Monte-Carlo goodness of fit test there is evidence that the $\kappa$-generalised distribution is a good fit for a significant proportion of the 200 stock returns analysed.

In order to help facilitate the future study of ultra-diffuse galaxies (UDGs) we compile a catalogue of their spectroscopic properties. Using it, we investigate some of the biases inherent in the current UDG sample that have been targeted for spectroscopy. In comparison to a larger sample of UDGs studied via their spectral energy distributions (SED), current spectroscopic targets are intrinsically brighter, have higher stellar mass, are larger, more globular cluster-rich, older, and have a wider spread in their metallicities. In particular, many spectroscopically studied UDGs have a significant fraction of their stellar mass contained within their globular cluster (GC) system. We also search for correlations between parameters in the catalogue. Of note is a correlation between alpha element abundance and metallicity as may be expected for a `failed galaxy' scenario. However, the expected correlations of metallicity with age are not found and it is unclear if this is evidence against a `failed galaxy' scenario or simply due to the low number statistics and the presence of outliers. Finally, we attempt to segment our catalogue into different classes using a machine learning K-means method. We find that the clustering is very weak and that it is currently not warranted to split the catalogue into multiple, distinct sub-populations. Our catalogue is available online and we aim to maintain it beyond the publication of this work., Comment: 14 pages, 5 figures and 1 appendix. Accepted for Publication in MNRAS The full catalogue is available here -> https://github.com/gannonjs/Published_Data/tree/main/UDG_Spectroscopic_Data Please contact JSG if you wish to contribute to the catalogue

We present spatially resolved Keck Cosmic Web Imager stellar spectroscopy of the Virgo cluster dwarf galaxies VCC 9 and VCC 1448. These galaxies have similar stellar masses and large half-light radii but very different globular cluster (GC) system richness ($\sim$25 vs. $\sim$99 GCs). Using the KCWI data, we spectroscopically confirm 10 GCs associated with VCC 1448 and one GC associated with VCC 9. We make two measurements of dynamical mass for VCC 1448 based on the stellar and GC velocities respectively. VCC 1448's mass measurements suggest that it resides in a halo in better agreement with the expectation of the stellar mass -- halo mass relationship than the expectation from its large GC counts. For VCC 9, the dynamical mass we measure agrees with the expected halo mass from both relationships. We compare VCC 1448 and VCC 9 to the GC-rich galaxy Dragonfly 44 ($\sim74$ GCs), which is similar in size but has $\sim 1$ dex less stellar mass than either Virgo galaxy. In dynamical mass -- GC number space, Dragonfly 44 and VCC 1448 exhibit richer GC systems given their dynamical mass than that of VCC 9 and other `normal' galaxies. We also place the galaxies in kinematics -- ellipticity space finding evidence of an anticorrelation between rotational support and the fraction of a galaxy's stellar mass in its GC system. i.e., VCC 9 is more rotationally supported than VCC 1448, which is more rotationally supported than Dragonfly 44. This trend may be expected if a galaxy's GC content depends on its natal gas properties at formation., Comment: 17 pages, 12 figures + 1 in the Appendix, Accepted for publication in MNRAS