Search

Differentially private selection mechanisms are fundamental building blocks for privacy-preserving data analysis. While numerous mechanisms exist for single-objective selection, many real-world applications require optimizing multiple competing objectives simultaneously. We present two novel mechanisms for differentially private multi-objective selection: PrivPareto and PrivAgg. PrivPareto uses a novel Pareto score to identify solutions near the Pareto frontier, while PrivAgg enables privacy-preserving weighted aggregation of multiple objectives. Both mechanisms support global and local sensitivity approaches, with comprehensive theoretical analysis showing how to compose sensitivities of multiple utility functions. We demonstrate the practical applicability through two real-world applications: cost-sensitive decision tree construction and multi-objective influential node selection in social networks. The experimental results showed that our local sensitivity-based approaches achieve significantly better utility compared to global sensitivity approaches across both applications and both Pareto and Aggregation approaches. Moreover, the local sensitivity-based approaches are able to perform well with typical privacy budget values $\epsilon \in [0.01, 1]$ in most experiments.

Searches for impulsive, astrophysical transients are often highly computationally demanding. A notable example is the dedispersion process required for performing blind searches for Fast Radio Bursts (FRBs) in radio telescope data. We introduce a novel approach - Efficient Summation of Arbitrary Masks (ESAM) - which efficiently computes 1-D convolution of many arbitrary 2-D masks, and can be used to carry out dedispersion over thousands of dispersion trials efficiently. Our method matches the accuracy of the traditional brute force technique in recovering the desired Signal-to-Noise ratio (S/N) while reducing computational cost by around a factor of 10. We compare its performance with existing dedispersion algorithms, such as the Fast Dispersion Measure Transform (FDMT) algorithm, and demonstrate how ESAM provides freedom to choose arbitrary masks and further optimise computational cost versus accuracy. We explore the potential applications of ESAM beyond FRB searches.

Recent advancements in large language models (LLMs) hold significant promise in improving physics education research that uses machine learning. In this study, we compare the application of various models to perform large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in students' typed lab notes through sentence-level labeling. Specifically, we use training data to fine-tune two different LLMs, BERT and LLaMA, and compare the performance of these models to both a traditional bag of words approach and a few-shot LLM (without fine-tuning).} We evaluate the models based on their resource use, performance metrics, and research outcomes when identifying skills in lab notes. We find that higher-resource models often, but not necessarily, perform better than lower-resource models. We also find that all models estimate similar trends in research outcomes, although the absolute values of the estimated measurements are not always within uncertainties of each other. We use the results to discuss relevant considerations for education researchers seeking to select a model type to use as a classifier.

Sun-like stars are well represented in the solar neighbourhood but are currently under-utilised, with many studies of chemical and kinematic evolution focusing on red giants (which can be observed further away) or turn-off stars (which have well measured ages). Recent surveys (e.g. GALAH) provide spectra for large numbers of nearby Sun-like stars, which provides an opportunity to apply our newly developed method for measuring metallicities, temperatures, and surface gravities - the EPIC algorithm - which yields improved ages via isochrone fitting. We test this on moving groups, by applying it to the large GALAH DR3 sample. This defines a sample of 72,288 solar analogue targets for which the stellar parameter measurements are most precise and reliable. These stars are used to estimate, and test the accuracy and precision of, age measurements derived with the SAMD isochrone fitting algorithm. Using these ages, we recover the age-metallicity relationships for nearby (<= 1 kpc) moving groups, traced by solar analogues, and analyse them with respect to the stellar kinematics. In particular, we found that the age-metallicity relationships of all moving groups follows a particular trend of young (age < 6 Gyr) stars having constant metallicity and older (age >= 6 Gyr) stars decreasing in metallicity with increasing age. The Hercules stream carries the highest fraction of metal-rich young stars (~ 0.1 dex) in our sample, which is consistent with a migrating population of stars from the inner Galaxy, and we discuss the possible causes of this migration in the context of our results., Comment: 13 pages, 10 figures, accepted to MNRAS

Brain-computer interfaces (BCI) have the potential to provide transformative control in prosthetics, assistive technologies (wheelchairs), robotics, and human-computer interfaces. While Motor Imagery (MI) offers an intuitive approach to BCI control, its practical implementation is often limited by the requirement for expensive devices, extensive training data, and complex algorithms, leading to user fatigue and reduced accessibility. In this paper, we demonstrate that effective MI-BCI control of a mobile robot in real-world settings can be achieved using a fine-tuned Deep Neural Network (DNN) with a sliding window, eliminating the need for complex feature extractions for real-time robot control. The fine-tuning process optimizes the convolutional and attention layers of the DNN to adapt to each user's daily MI data streams, reducing training data by 70% and minimizing user fatigue from extended data collection. Using a low-cost (~$3k), 16-channel, non-invasive, open-source electroencephalogram (EEG) device, four users teleoperated a quadruped robot over three days. The system achieved 78% accuracy on a single-day validation dataset and maintained a 75% validation accuracy over three days without extensive retraining from day-to-day. For real-world robot command classification, we achieved an average of 62% accuracy. By providing empirical evidence that MI-BCI systems can maintain performance over multiple days with reduced training data to DNN and a low-cost EEG device, our work enhances the practicality and accessibility of BCI technology. This advancement makes BCI applications more feasible for real-world scenarios, particularly in controlling robotic systems., Comment: IEEE Telepresence 2024

We have conducted a GPU accelerated reprocessing of $\sim 87\%$ of the archival data from the High Time Resolution Universe South Low Latitude (HTRU-S LowLat) pulsar survey by implementing a pulsar search pipeline that was previously used to reprocess the Parkes Multibeam pulsar survey (PMPS). We coherently searched the full 72-min observations of the survey with an acceleration search range up to $|50|\, \rm m\,s^{-2}$, which is most sensitive to binary pulsars experiencing nearly constant acceleration during 72 minutes of their orbital period. Here we report the discovery of 71 pulsars, including 6 millisecond pulsars (MSPs) of which five are in binary systems, and seven pulsars with very high dispersion measures (DM $>800 \, \rm pc \, cm^{-3}$). These pulsar discoveries largely arose by folding candidates to a much lower spectral signal-to-noise ratio than previous surveys, and exploiting the coherence of folding over the incoherent summing of the Fourier components to discover new pulsars as well as candidate classification techniques. We show that these pulsars could be fainter and on average more distant as compared to both the previously reported 100 HTRU-S LowLat pulsars and background pulsar population in the survey region. We have assessed the effectiveness of our search method and the overall pulsar yield of the survey. We show that through this reprocessing we have achieved the expected survey goals including the predicted number of pulsars in the survey region and discuss the major causes as to why these pulsars were missed in previous processings of the survey., Comment: 18 Pages, 12 figures, 9 tables. Accepted for publication in MNRAS

We present a novel neural algorithm for performing high-quality, high-resolution, real-time novel view synthesis. From a sparse set of input RGB images or videos streams, our network both reconstructs the 3D scene and renders novel views at 1080p resolution at 30fps on an NVIDIA A100. Our feed-forward network generalizes across a wide variety of datasets and scenes and produces state-of-the-art quality for a real-time method. Our quality approaches, and in some cases surpasses, the quality of some of the top offline methods. In order to achieve these results we use a novel combination of several key concepts, and tie them together into a cohesive and effective algorithm. We build on previous works that represent the scene using semi-transparent layers and use an iterative learned render-and-refine approach to improve those layers. Instead of flat layers, our method reconstructs layered depth maps (LDMs) that efficiently represent scenes with complex depth and occlusions. The iterative update steps are embedded in a multi-scale, UNet-style architecture to perform as much compute as possible at reduced resolution. Within each update step, to better aggregate the information from multiple input views, we use a specialized Transformer-based network component. This allows the majority of the per-input image processing to be performed in the input image space, as opposed to layer space, further increasing efficiency. Finally, due to the real-time nature of our reconstruction and rendering, we dynamically create and discard the internal 3D geometry for each frame, generating the LDM for each view. Taken together, this produces a novel and effective algorithm for view synthesis. Through extensive evaluation, we demonstrate that we achieve state-of-the-art quality at real-time rates. Project page: https://quark-3d.github.io/, Comment: SIGGRAPH Asia 2024 camera ready version; project page https://quark-3d.github.io/

In distributed optimization, the communication of model updates can be a performance bottleneck. Consequently, gradient compression has been proposed as a means of increasing optimization throughput. In general, due to information loss, compression introduces a penalty on the number of iterations needed to reach a solution. In this work, we investigate how the iteration penalty depends on the interaction between compression and problem structure, in the context of non-convex stochastic optimization. We focus on linear compression schemes, where compression and decompression can be modeled as multiplication with a random matrix. We consider several distributions of matrices, among them random orthogonal matrices and matrices with random Gaussian entries. We find that in each case, the impact of compression on convergence can be quantified in terms of the norm of the Hessian of the objective, using a norm defined by the compression scheme. The analysis reveals that in certain cases, compression performance is related to low-rank structure or other spectral properties of the problem. In these cases, our bounds predict that the penalty introduced by compression is significantly reduced compared to worst-case bounds that only consider the compression level, ignoring problem data. We verify the theoretical findings on several optimization problems, including fine-tuning an image classification model., Comment: 15 pages, 3 figures

There is increasing concern about the unilateral power of the organizations involved in the development, alignment, and governance of AI. Recent pilots - such as Meta's Community Forums and Anthropic's Collective Constitutional AI - have illustrated a promising direction, where democratic processes might be used to meaningfully improve public involvement and trust in critical decisions. However, there is no standard framework for evaluating such processes. In this paper, building on insights from the theory and practice of deliberative democracy, we provide a "Democracy Levels" framework for evaluating the degree to which decisions in a given domain are made democratically. The framework can be used (i) to define milestones in a roadmap for the democratic AI, pluralistic AI, and public AI ecosystems, (ii) to guide organizations that need to increase the legitimacy of their decisions on difficult AI governance questions, and (iii) as a rubric by those aiming to evaluate AI organizations and keep them accountable., Comment: 11 pages. Accepted to the Workshop on Pluralistic Alignment at NeurIPS 2024

We present a technique for providing remote tactile feedback to the thumb and index finger via a wristband device. This enables haptics for touch and pinch interactions in mixed reality (MR) while keeping the hand entirely free. We achieve this through a novel cross-modal stimulation, which we term visually augmented electro-tactile feedback. This consists of (1) electrically stimulating the nerves that innervate the targeted fingers using our wristband device and (2) concurrently, visually augmenting the targeted finger in MR to steer the perceived sensation to the desired location. In our psychophysics study, we found that our approach provides tactile perception akin to tapping and, even from the wrist, it is capable of delivering the sensation to the targeted fingers with about 50% of sensation occurring in the thumb and about 40% of sensation occurring in the index finger. These results on localizability are unprecedented compared to electro-tactile feedback alone or any prior work for creating sensations in the hand with devices worn on the wrist/arm. Moreover, unlike conventional electro-tactile techniques, our wristband dispenses with gel electrodes. Instead, it incorporates custom-made elastomer-based dry electrodes and a stimulation waveform designed for the electrodes, ensuring the practicality of the device beyond laboratory settings. Lastly, we evaluated the haptic realism of our approach in mixed reality and elicited qualitative feedback from users. Participants preferred our approach to a baseline vibrotactile wrist-worn device., Comment: 10 pages, 14 figures, published at IEEE ISMAR 2024

We consider the negative regularity mixing properties of random volume preserving diffeomorphisms on a compact manifold without boundary. We give general criteria so that the associated random transfer operator mixes $H^{-\delta}$ observables exponentially fast in $H^{-\delta}$ (with a deterministic rate), a property that is false in the deterministic setting. The criteria apply to a wide variety of random diffeomorphisms, such as discrete-time iid random diffeomorphisms, the solution maps of suitable classes of stochastic differential equations, and to the case of advection-diffusion by solutions of the stochastic incompressible Navier-Stokes equations on $\mathbb T^2$. In the latter case, we show that the zero diffusivity passive scalar with a stochastic source possesses a unique stationary measure describing "ideal" scalar turbulence. The proof is based on techniques inspired by the use of pseudodifferential operators and anisotropic Sobolev spaces in the deterministic setting., Comment: 30 pages

The spectral form factor (SFF) is a powerful diagnostic of random matrix behavior in quantum many-body systems. We introduce a family of random circuit ensembles whose SFFs can be computed \textit{exactly}. These ensembles describe the evolution of non-interacting fermions in the presence of correlated on-site potentials drawn from the eigenvalue distribution of a circular ensemble. For disorder parameters drawn from the circular unitary ensemble (CUE), we derive an exact closed form for the SFF, valid for any choice of system size $L$ and integer time $t$. When the disorder is drawn from the circular orthogonal or symplectic ensembles (COE and CSE, respectively), we carry out the disorder averages analytically and reduce the computation of the SFF at integer times to a combinatorial problem amenable to transfer matrix methods. In each of these cases the SFF grows exponentially in time, which we argue is a signature of random matrix universality at the single-particle level. Finally, we develop matchgate circuit representations of our circuit ensembles, enabling their experimental realization in quantum simulators., Comment: This paper provides proofs of results presented in arxiv:2410.07306 in addition to previously unstated results. The reader may benefit from examining both in parallel. Comments welcome!

We consider systems of fermions evolved by non-interacting unitary circuits with correlated on-site potentials. When these potentials are drawn from the eigenvalue distribution of a circular random matrix ensemble, the single-particle sector exhibits chaotic dynamics. We study the corresponding many-body spectral statistics and show that the spectral form factor (SFF) can be computed \textit{exactly}. Due to the absence of interactions the SFF grows exponentially in time, a result which we demonstrate through simple arguments, scaling collapses, and closed-form evaluation of the SFF. We study the role of interactions by numerically analyzing a kicked Ising model and find that the SFF crosses over to a linear growth regime consistent with many-body random matrix universality. Our exact results for the SFF provide a baseline for future studies of the crossover between single-particle and many-body random matrix behavior., Comment: A companion paper titled "Exact spectral form factors of non-interacting fermions with Dyson statistics" is available at arXiv:2410.07306 and contains proofs of technical claims made here. Comments welcome!

Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of trustworthiness, such as robustness, fairness, security, privacy, factuality, and explainability, similar to those in traditional deep learning (DL) tasks. Conventional approaches for studying trustworthiness in DL tasks often fall short due to the unique characteristics of T2I DMs, e.g., the multi-modal nature. Given the challenge, recent efforts have been made to develop new methods for investigating trustworthiness in T2I DMs via various means, including falsification, enhancement, verification \& validation and assessment. However, there is a notable lack of in-depth analysis concerning those non-functional properties and means. In this survey, we provide a timely and focused review of the literature on trustworthy T2I DMs, covering a concise-structured taxonomy from the perspectives of property, means, benchmarks and applications. Our review begins with an introduction to essential preliminaries of T2I DMs, and then we summarise key definitions/metrics specific to T2I tasks and analyses the means proposed in recent literature based on these definitions/metrics. Additionally, we review benchmarks and domain applications of T2I DMs. Finally, we highlight the gaps in current research, discuss the limitations of existing methods, and propose future research directions to advance the development of trustworthy T2I DMs. Furthermore, we keep up-to-date updates in this field to track the latest developments and maintain our GitHub repository at: https://github.com/wellzline/Trustworthy_T2I_DMs, Comment: under review

We first give a cleaner and more direct approach to the derivation of the Fast model of the Kummer surface. We show how to construct efficient (N,N)-isogenies, for any odd N, both on the general Kummer surface and on the Fast model., Comment: 38 pages, 2 figures. To appear in Mathematics of Computation

We conducted controlled experimental bias audits for four versions of ChatGPT, which we asked to recommend an opening offer in salary negotiations for a new hire. We submitted 98,800 prompts to each version, systematically varying the employee's gender, university, and major, and tested prompts in voice of each side of the negotiation: the employee versus employer. We find ChatGPT as a multi-model platform is not robust and consistent enough to be trusted for such a task. We observed statistically significant salary offers when varying gender for all four models, although with smaller gaps than for other attributes tested. The largest gaps were different model versions and between the employee- vs employer-voiced prompts. We also observed substantial gaps when varying university and major, but many of the biases were not consistent across model versions. We tested for fictional and fraudulent universities and found wildly inconsistent results across cases and model versions. We make broader contributions to the AI/ML fairness literature. Our scenario and our experimental design differ from mainstream AI/ML auditing efforts in key ways. Bias audits typically test discrimination for protected classes like gender, which we contrast with testing non-protected classes of university and major. Asking for negotiation advice includes how aggressive one ought to be in a negotiation relative to known empirical salary distributions and scales, which is a deeply contextual and personalized task that has no objective ground truth to validate. These results raise concerns for the specific model versions we tested and ChatGPT as a multi-model platform in continuous development. Our epistemology does not permit us to definitively certify these models as either generally biased or unbiased on the attributes we test, but our study raises matters of concern for stakeholders to further investigate.

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839-10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less, and can detect 10x more FRBs than the current CRAFT incoherent sum system (i.e., 0.5-2 localised FRBs per day), enabling us to better constrain he models for FRBs and use them as cosmological probes., Comment: 26 pages, 19 figures, 9 tables, Accepted for publication in PASA

Reinforcement learning (RL) agents are costly to train and fragile to environmental changes. They often perform poorly when there are many changing tasks, prohibiting their widespread deployment in the real world. Many Lifelong RL agent designs have been proposed to mitigate issues such as catastrophic forgetting or demonstrate positive characteristics like forward transfer when change occurs. However, no prior work has established whether the impact on agent performance can be predicted from the change itself. Understanding this relationship will help agents proactively mitigate a change's impact for improved learning performance. We propose Change-Induced Regret Proxy (CHIRP) metrics to link change to agent performance drops and use two environments to demonstrate a CHIRP's utility in lifelong learning. A simple CHIRP-based agent achieved $48\%$ higher performance than the next best method in one benchmark and attained the best success rates in 8 of 10 tasks in a second benchmark which proved difficult for existing lifelong RL agents., Comment: 7 pages, 9 figures

The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realised as an artificial neural network, to reconstruct multiple attractors simultaneously using the same set of trained weights. However there are many additional phenomena that arise when training a RC to reconstruct more than one attractor. Previous studies have found that, in certain cases, if the RC fails to reconstruct a coexistence of attractors then it exhibits a form of metastability whereby, without any external input, the state of the RC switches between different modes of behaviour that resemble properties of the attractors it failed to reconstruct. In this paper we explore the origins of these switching dynamics in a paradigmatic setting via the `seeing double' problem., Comment: Preprint submitted to Frontiers in Network Physiology

We examine the energy distribution of the fast radio burst (FRB) population using a well-defined sample of 63 FRBs from the ASKAP radio telescope, 28 of which are localised to a host galaxy. We apply the luminosity-volume ($V/V_{\mathrm{max}}$) test to examine the distribution of these transient sources, accounting for cosmological and instrumental effects, and determine the energy distribution for the sampled population over the redshift range $0.01 \lesssim z \lesssim 1.02$. We find the distribution between $10^{23}$ and $10^{26}$J Hz$^{-1}$ to be consistent with both a pure power-law with differential slope $\gamma=-1.96 \pm 0.15$, and a Schechter function with $\gamma = -1.82 \pm 0.12$ and downturn energy $E_{\rm max} \sim 6.3 \cdot 10^{25}$J Hz$^{-1}$. We identify systematic effects which currently limit our ability to probe the luminosity function outside this range and give a prescription for their treatment. Finally, we find that with the current dataset, we are unable to distinguish between the evolutionary and spectral models considered in this work., Comment: 23 pages, 10 figures, submitted to PASA

The robotics research field lacks formalized definitions and frameworks for evaluating advanced capabilities including generalizability (the ability for robots to perform tasks under varied contexts) and reproducibility (the performance of a reproduced robot capability in different labs under the same experimental conditions). This paper presents an initial conceptual framework, MIRRER, that unites the concepts of performance evaluation, benchmarking, and reproduced/replicated experimentation in order to facilitate comparable robotics research. Several open issues with the application of the framework are also presented., Comment: IEEE International Conference on Robotics and Automation (ICRA) 2024 Workshop on Ontologies and Standards for Robotics and Automation (WOSRA), Yokohama, Japan, May 2024

The present state of cosmology is facing a crisis where there is a fundamental disagreement in measurements of the Hubble constant ($H_{0}$), with significant tension between the early and late universe methods. Type Ia supernovae (SNe Ia) are important to measuring $H_{0}$ through the astronomical distance ladder. However, there remains potential to better standardise SN Ia light curves by using known dependencies on host galaxy properties after the standard light curve width and colour corrections have been applied to the peak SN Ia luminosities. To explore this, we use the 5-year photometrically identified SNe Ia sample obtained by the Dark Energy Survey, along with host galaxy spectra obtained by the Australian Dark Energy Survey. Using host galaxy spectroscopy, we find a significant trend with the equivalent width (EW) of the [OII] $\lambda\lambda$ 3727, 29 doublet, a proxy for specific star formation rate, and Hubble residuals. We find that the correlation with [OII] EW is a powerful alternative to the commonly used mass step after initial light curve corrections. Applying this [OII] EW correction to 20 SNe Ia in calibrator galaxies observed with WiFeS, we examined the impact on SN Ia absolute magnitudes and $H_{0}$. Our [OII] EW corrections result in $H_{0}$ values ranging between 73.04 to 73.51 $\mathrm{km} \mathrm{s}^{-1} \mathrm{Mpc}^{-1}$, with a combined statistical and systematic uncertainty of $\sim$1.31 $\mathrm{km} \mathrm{s}^{-1} \mathrm{Mpc}^{-1}$. However, even with this additional correction, the impact of host galaxy properties in standardising SNe Ia appears limited in reducing the current tension ($\sim$5$\sigma$) with the CMB result for $H_{0}$., Comment: 16 pages, 13 figures. Accepted for publication in MNRAS

We present the first numerical implementation of the massive SMOM (mSMOM) renormalization scheme and use it to calculate the charm quark mass. Based on ensembles with three flavours of dynamical domain wall fermions with lattice spacings in the range 0.11 -- 0.08 fm, we demonstrate that the mass scale which defines the mSMOM scheme can be chosen such that the extrapolation has significantly smaller discretisation effects than the SMOM scheme. Converting our results to the $\overline{\mathrm{MS}}$ scheme we obtain $\overline{m}_c(3\,\mathrm{GeV}) = 1.008(13)\,\mathrm{GeV}$ and $\overline{m}_c(\overline{m}_c) = 1.292(12)\,\mathrm{GeV}$., Comment: 14 pages, 10 figures, 10 tables

Long-period radio transients are an emerging class of extreme astrophysical events of which only three are known. These objects emit highly polarised, coherent pulses of typically a few tens of seconds duration and minutes to hour-long periods. While magnetic white dwarfs and magnetars, either isolated or in binary systems, have been invoked to explain these objects, a consensus has not emerged. Here we report on the discovery of ASKAP J193505.1+214841.0 (henceforth ASKAPJ1935+2148) with a period of 53.8 minutes exhibiting three distinct emission states - a bright pulse state with highly linearly polarised pulses with widths of 10-50 seconds; a weak pulse state which is about 26 times fainter than the bright state with highly circularly polarised pulses of widths of approximately 370 milliseconds; and a quiescent or quenched state with no pulses. The first two states have been observed to progressively evolve over the course of 8 months with the quenched state interspersed between them suggesting physical changes in the region producing the emission. A constraint on the radius of the source for the observed period rules out a magnetic white dwarf origin. Unlike other long-period sources, ASKAPJ1935+2148 is the first to exhibit drastic variations in emission modes reminiscent of neutron stars. However, its radio properties challenge our current understanding of neutron star emission and evolution., Comment: Published in Nature Astronomy

The observation of neutron stars enables the otherwise impossible study of fundamental physical processes. The timing of binary radio pulsars is particularly powerful, as it enables precise characterization of their (three-dimensional) positions and orbits. PSR~J0437$-$4715 is an important millisecond pulsar for timing array experiments and is also a primary target for the Neutron Star Interior Composition Explorer (NICER). The main aim of the NICER mission is to constrain the neutron star equation of state by inferring the compactness ($M_p/R$) of the star. Direct measurements of the mass $M_p$ from pulsar timing therefore substantially improve constraints on the radius $R$ and the equation of state. Here we use observations spanning 26 years from Murriyang, the 64-m Parkes radio telescope, to improve the timing model for this pulsar. Among the new precise measurements are the pulsar mass $M_p=1.418\pm 0.044$ $M_{\odot}$, distance $D=156.96 \pm 0.11$ pc, and orbital inclination angle $i=137.506 \pm 0.016^\circ$, which can be used to inform the X-ray pulse profile models inferred from NICER observations. We demonstrate that these results are consistent between multiple data sets from the Parkes Pulsar Timing Array (PPTA), each modeled with different noise assumptions. Using the longest available PPTA data set, we measure an apparent second derivative of the pulsar spin frequency and discuss how this can be explained either by kinematic effects due to the proper motion and radial velocity of the pulsar or excess low-frequency noise such as a gravitational-wave background., Comment: 13 pages, 3 figures, accepted for publication in Astrophysical Journal Letters