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Galaxy morphology in stellar light can be described by a series of "non-parametric" or "morphometric" parameters, such as concentration-asymmetry-smoothness, Gini, $M_{20}$, and Sersic fit. These parameters can be applied to column density maps of atomic hydrogen (HI). The HI distribution is susceptible to perturbations by environmental effects, e.g. inter-galactic medium pressure and tidal interactions. Therefore, HI morphology can potentially identify galaxies undergoing ram-pressure stripping or tidal interactions. We explore three fields in the WALLABY Pilot HI survey and identify perturbed galaxies based on a k-nearest Neighbor (kNN) algorithm using an HI morphometric feature space. For training, we used labeled galaxies in the combined NGC 4808 and NGC 4636 fields with six HI morphometrics to train and test a kNN classifier. The kNN classification is proficient in classifying perturbed galaxies with all metrics -- accuracy, precision and recall -- at 70-80%. By using the kNN method to identify perturbed galaxies in the deployment field, the NGC 5044 mosaic, we find that in most regards, the scaling relations of perturbed and unperturbed galaxies have similar distribution in the scaling relations of stellar mass vs star formation rate and the Baryonic Tully-Fisher relation, but the HI and stellar mass relation flatter than of the unperturbed galaxies. Our results for NGC 5044 provide a prediction for future studies on the fraction of galaxies undergoing interaction in this catalogue and to build a training sample to classify such galaxies in the full WALLABY survey., Comment: 21 pages, 15 figures, 8 tables, accepted by PASA

We present a re-discovery of G278.94+1.35 as possibly one of the largest known Galactic supernova remnants (SNR) - that we name Diprotodon. While previously established as a Galactic SNR, Diprotodon is visible in our new EMU and GLEAM radio continuum images at an angular size of 3.33x3.23 deg, much larger than previously measured. At the previously suggested distance of 2.7 kpc, this implies a diameter of 157x152 pc. This size would qualify Diprotodon as the largest known SNR and pushes our estimates of SNR sizes to the upper limits. We investigate the environment in which the SNR is located and examine various scenarios that might explain such a large and relatively bright SNR appearance. We find that Diprotodon is most likely at a much closer distance of $\sim$1 kpc, implying its diameter is 58x56 pc and it is in the radiative evolutionary phase. We also present a new Fermi-LAT data analysis that confirms the angular extent of the SNR in gamma-rays. The origin of the high-energy emission remains somewhat puzzling, and the scenarios we explore reveal new puzzles, given this unexpected and unique observation of a seemingly evolved SNR having a hard GeV spectrum with no breaks. We explore both leptonic and hadronic scenarios, as well as the possibility that the high-energy emission arises from the leftover particle population of a historic pulsar wind nebula., Comment: 21 pages, 12 figures, 1 table. Accepted for publication in PASA

In studying compensated semimetals, the two-band model has proven extremely useful to capture electrical conductivities with intuitive parameters of densities and mobilities of electron-like and hole-like carriers, as well as to predict their magnetic field dependence. Yet, it rarely offers practical insight into magneto-thermoelectric properties. Here, we report the field dependence of thermoelectric (TE) coefficients in a highly compensated semimetal NbSb$_2$, where we find the Seebeck ($S_{xx}$) and Nernst ($S_{xy}$) coefficients increase quadratically and linearly with field, respectively. Such field dependences were predicted by the semiclassical Boltzmann transport theory and the Mott relation of the two-band system, and they are realized when two conditions are simultaneously met [1]: the multiple of cyclotron frequency ($\omega_c$) and relaxation time ($\tau$) is much larger than one, $\omega_c\tau \gg 1$ and the tangent of Hall angle ($\theta_H$) is much smaller than one, $\tan\theta_H \ll 1$. We use the relation between two carrier densities $n_e$ (electron-like) and $n_h$ (hole-like) derived from the field dependence of the TE coefficients, to refine the two-band model fittings. The compensation factor ($\frac{|\Delta n|}{n_e}$, where $\Delta n = n_e-n_h$) is found two orders of magnitude smaller than what was found in the unrestricted fitting and hence the larger saturation field scale for magnetoresistance. Within the framework of the semiclassical theory, we deduce that the thermoelectric Hall angle $\tan\theta_{\gamma} = \frac{S_{xy}}{S_{xx}}$ can be expressed $\big(\frac{|\Delta n|}{n_e}\times \omega_c\tau\big)^{-1}$. Our findings offer crucial insights not only for identifying the empirical conditions for the field-induced enhancement of the TE performance but also for engineering efficient thermoelectric devices based on semimetallic materials., Comment: 8 pages, 4 figures

Despite the impressive advances achieved using deep learning for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in tasks such as identifying neurological disorders. For functional Magnetic Resonance Imaging (fMRI), while data may be abundantly available from healthy controls, clinical data is often scarce, especially for rare diseases, limiting the ability of models to identify clinically-relevant features. We overcome this limitation by introducing a novel representation learning strategy integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features. This approach enables generalization to challenging clinical tasks featuring scarce training data. We achieve this by leveraging self-supervised learning on the control dataset to focus on inherent features that are not limited to a particular supervised task and incorporating meta-learning to improve the generalization across domains. To explore the generalizability of the learned representations to unseen clinical applications, we apply the model to four distinct clinical datasets featuring scarce and heterogeneous data for neurological disorder classification. Results demonstrate the superiority of our representation learning strategy on diverse clinically-relevant tasks., Comment: arXiv admin note: substantial text overlap with arXiv:2312.14204

Low carrier densities in topological semimetals (TSMs) enable the exploration of novel magnetotransport in the quantum limit (QL). Reports consistent with 3D quasi-quantum Hall effect (QQHE) have repositioned TSMs as promising platforms for exploring 3D quantum Hall transport, but the lack of tunability in the Fermi has thus far limited the ability to control the QQHE signal. Here, we tune the defect concentrations in the Dirac semimetal Cd${}_3$As${}_2$ to achieve ultra-low carrier concentrations at 2 K around $2.9\times10^{16}$cm${}^{-3}$, giving way to QQHE signal at modest fields under 10 T. At low carrier densities, where QQHE is most accessible, we find that a zero resistivity state is obscured by a carrier density dependent background originating from Coulomb disorder from charged point defects. Our results highlight the interplay between QQHE and Coulomb disorder scattering, demonstrating that clear observation of QQHE in TSMs intricately depends on Fermi level. Predicted in TSMs a decade ago, we find that Coulomb disorder is an essential ingredient for understanding the magnetoresistivity for a spectrum of Fermi levels, experimentally anchoring the important roles of defects and charged disorder in TSM applications. We discuss future constraints and opportunities in exploring 3D QHE in TSMs.

In this paper, we tackle the challenging problem of delayed rewards in reinforcement learning (RL). While Proximal Policy Optimization (PPO) has emerged as a leading Policy Gradient method, its performance can degrade under delayed rewards. We introduce two key enhancements to PPO: a hybrid policy architecture that combines an offline policy (trained on expert demonstrations) with an online PPO policy, and a reward shaping mechanism using Time Window Temporal Logic (TWTL). The hybrid architecture leverages offline data throughout training while maintaining PPO's theoretical guarantees. Building on the monotonic improvement framework of Trust Region Policy Optimization (TRPO), we prove that our approach ensures improvement over both the offline policy and previous iterations, with a bounded performance gap of $(2\varsigma\gamma\alpha^2)/(1-\gamma)^2$, where $\alpha$ is the mixing parameter, $\gamma$ is the discount factor, and $\varsigma$ bounds the expected advantage. Additionally, we prove that our TWTL-based reward shaping preserves the optimal policy of the original problem. TWTL enables formal translation of temporal objectives into immediate feedback signals that guide learning. We demonstrate the effectiveness of our approach through extensive experiments on an inverted pendulum and a lunar lander environments, showing improvements in both learning speed and final performance compared to standard PPO and offline-only approaches.

Autonomous systems use independent decision-making with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE Robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them.

We present a detailed analysis of jet activity in the radio galaxy 3C348 at the center of the galaxy cluster Hercules A. We use archival Chandra data to investigate the jet-driven shock front, the radio-faint X-ray cavities, the eastern jet, and the presence of extended Inverse Compton (IC) X-ray emission from the radio lobes. We detect two pairs of shocks: one in the north-south direction at 150 kpc from the center, and another in the east-west direction at 280 kpc. These shocks have Mach numbers of $\mathcal{M} = 1.65\pm0.05$ and $\mathcal{M} = 1.9\pm0.3$, respectively. Together, they form a complete cocoon around the large radio lobes. Based on the distance of the shocks from the center, we estimate that the corresponding jet outburst is 90-150 Myr old. We confirm the presence of two radio-faint cavities within the cocoon, misaligned from the lobes, each $\sim$100 kpc wide and 40-60 Myr old. A backflow from the radio lobes might explain why the cavities are dynamically younger than the cocoon shock front. We also detect non-thermal X-ray emission from the eastern jet and from the large radio lobes. The X-ray emission from the jet is visible at 80 kpc from the AGN and can be accounted for by an IC model with a mild Doppler boosting ($\delta\sim2.7$). A synchrotron model could explain the radio-to-X-ray spectrum only for very high Lorentz factors $\gamma\geq10^{8}$ of the electrons in the jet. For the large radio lobes, we argue that the X-ray emission has an IC origin, with a 1 keV flux density of $21.7\pm1.4\text{(statistical)}\pm1.3\text{(systematic)}$ nJy. A thermal model is unlikely, as it would require unrealistically high gas temperature, density, and pressure, along with a strong depolarization of the radio lobes, which are instead highly polarized. The IC detection, combined with the synchrotron flux density, suggests a magnetic field of $12\pm3\mu$G in the lobes., Comment: Accepted for publication in Astronomy & Astrophysics

Quantum circuits with local unitaries have emerged as a rich playground for the exploration of many-body quantum dynamics of discrete-time systems. While the intrinsic locality makes them particularly suited to run on current quantum processors, the task of verification at non-trivial scales is complicated for non-integrable systems. Here, we study a special class of maximally chaotic circuits known as dual unitary circuits -- exhibiting unitarity in both space and time -- that are known to have exact analytical solutions for certain correlation functions. With advances in noise learning and the implementation of novel error mitigation methods, we show that a superconducting quantum processor with 91 qubits is able to accurately simulate these correlators. We then probe dynamics beyond exact verification, by perturbing the circuits away from the dual unitary point, and compare our results to classical approximations with tensor networks. These results cement error-mitigated digital quantum simulation on pre-fault-tolerant quantum processors as a trustworthy platform for the exploration and discovery of novel emergent quantum many-body phases., Comment: 7 + 23 pages, 3 + 17 figures, 0 + 2 tables

We present the largest Galactic neutral hydrogen HI absorption survey to date, utilizing the Australian SKA Pathfinder Telescope at an unprecedented spatial resolution of 30''. This survey, GASKAP-HI, unbiasedly targets 2,714 continuum background sources over 250 square degrees in the direction of the Magellanic Clouds, a significant increase compared to a total of 373 sources observed by previous Galactic absorption surveys across the entire Milky Way. We aim to investigate the physical properties of cold (CNM) and warm (WNM) neutral atomic gas in the Milky Way foreground, characterized by two prominent filaments at high Galactic latitudes (between $-45^{\circ}$ and $-25^{\circ}$). We detected strong HI absorption along 462 lines of sight above the 3$\sigma$ threshold, achieving an absorption detection rate of 17%. GASKAP-HI's unprecedented angular resolution allows for simultaneous absorption and emission measurements to sample almost the same gas clouds along a line of sight. A joint Gaussian decomposition is then applied to absorption-emission spectra to provide direct estimates of HI optical depths, temperatures, and column densities for the CNM and WNM components. The thermal properties of CNM components are consistent with those previously observed along a wide range of Solar neighborhood environments, indicating that cold HI properties are widely prevalent throughout the local interstellar medium. Across our region of interest, CNM accounts for ~30% of the total HI gas, with the CNM fraction increasing with column density toward the two filaments. Our analysis reveals an anti-correlation between CNM temperature and its optical depth, which implies that CNM with lower optical depth leads to a higher temperature., Comment: Largest Galactic HI Absorption Survey To Date (GASKAP-HI): Cold Atomic Gas in the Magellanic Cloud foreground using Australian SKA Pathfinder. This paper has 19 pages, 17 figures. This paper has been accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

Neutral hydrogen (HI) bubbles and shells are common in the interstellar medium (ISM). Studying their properties provides insight into the characteristics of the local ISM as well as the galaxy in which the bubbles reside. We report the detection of magnetic fields associated with superbubbles in the nearby irregular galaxy, the Small Magellanic Cloud (SMC). Using the Polarisation Sky Survey of the Universe's Magnetism (POSSUM) pilot survey, we obtain a high-density grid ($\approx 25 \,\rm sources\,deg^{-2}$) of Faraday rotation measure (RM) from polarized sources behind the SMC. This provides a sufficiently large number of RM measurements to study the magnetic properties of three of the largest HI shells previously identified in the SMC. The RM profiles as a function of distance from the shell centre show characteristic patterns at angular scales comparable to the shell size. We demonstrate that this can be explained by magneto-hydrodynamic simulation models of bubbles expanding in magnetised environments. From the observations, we estimate the line-of-sight magnetic field strength at the edges of the shells is enhanced by $\sim1\,\rm \mu G$ with respect to their centres. This is an order of magnitude larger than the field strength in the ambient medium ($\sim 0.1\,\rm \mu G$) estimated based on the expansion velocity of the shells. This paper highlights the power of densely mapped RM grids in studying the magnetic properties of galactic substructures beyond the Milky Way., Comment: 15 pages, 7 figures, Accepted for publication in MNRAS

This work develops a zero-shot mechanism, Comp-LTL, for an agent to satisfy a Linear Temporal Logic (LTL) specification given existing task primitives trained via reinforcement learning (RL). Autonomous robots often need to satisfy spatial and temporal goals that are unknown until run time. Prior work focuses on learning policies for executing a task specified using LTL, but they incorporate the specification into the learning process. Any change to the specification requires retraining the policy, either via fine-tuning or from scratch. We present a more flexible approach -- to learn a set of composable task primitive policies that can be used to satisfy arbitrary LTL specifications without retraining or fine-tuning. Task primitives can be learned offline using RL and combined using Boolean composition at deployment. This work focuses on creating and pruning a transition system (TS) representation of the environment in order to solve for deterministic, non-ambiguous, and feasible solutions to LTL specifications given an environment and a set of task primitive policies. We show that our pruned TS is deterministic, contains no unrealizable transitions, and is sound. We verify our approach via simulation and compare it to other state of the art approaches, showing that Comp-LTL is safer and more adaptable., Comment: 16 pages, 11 figures. Updated to reflect additional results

We utilize the Quark-Novae (QN) model for Fast Radio Bursts (FRBs; Ouyed et al. 2021; arXiv:2005.09793) to evaluate its performance in reproducing the distribution and statistical properties of key observations. These include frequency, duration, fluence, dispersion measure (DM), and other relevant features such as repetition, periodic activity window, and the sad trombone effect. In our model, FRBs are attributed to coherent synchrotron emission (CSE) originating from collisionless QN chunks that traverse ionized media both within and outside their host galaxies. By considering burst repetition from a single chunk and accounting for the intrinsic DM of the chunks, we find agreement between our model and the observed properties of FRBs. This agreement enhances our confidence in the model's effectiveness for interpreting FRB observations. Our model generates testable predictions, allowing for future experiments and observations to validate and further refine our understanding of FRBs., Comment: 22 journal pages, 15 figures, 2 tables, accepted for publication in MNRAS

We present initial results from the Polarisation Sky Survey of the Universe's Magnetism (POSSUM), analysing 22,817 Faraday Rotation Measures (RMs) with median uncertainties of 1.2 rad m^-2 across 1,520 square degrees to study magnetised gas associated with 55 nearby galaxy groups (z less than 0.025) with halo masses between 10^12.5 and 10^14.0 M_sun. We identify two distinct gas phases: the Intragroup Medium (IGrM) within 0-2 splashback radii and the Warm-Hot Intergalactic Medium (WHIM) extending from 2 to 7 splashback radii. These phases enhance the standard deviation of residual (i.e., Galactic foreground RM-subtracted) RMs by 6.9 +/- 1.8 rad m^-2 and 4.2 +/- 1.2 rad m^-2, respectively. Estimated magnetic field strengths are several microGauss within the IGrM and 0.1-1 microGauss in the WHIM. We estimate the plasma beta in both phases and show that magnetic pressure might be more dynamically important than in the ICM of more massive clusters or sparse cosmic web filaments. Our findings indicate that "missing baryons" in the WHIM likely extend beyond the gravitational radii of group-mass halos to Mpc scales, consistent with large-scale, outflow-driven "magnetised bubbles" seen in cosmological simulations. We demonstrate that RM grids are an effective method for detecting magnetised thermal gas at galaxy group interfaces and within the cosmic web. This approach complements X-ray and Sunyaev-Zel'dovich effect methods, and when combined with Fast Radio Burst Dispersion Measures, data from the full POSSUM survey, comprising approximately a million RMs, will allow direct magnetic field measurements to further our understanding of baryon circulation in these environments and the magnetised universe., Comment: 15 pages, 7 figures, 3 tables. Accepted for publication in MNRAS

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

We report the ASKAP discovery of a new Galactic supernova remnant (SNR) candidate G308.73+1.38, which we name Raspberry. This new SNR candidate has an angular size of 20.7 arcmin $\times$ 16.7 arcmin, and we measure a total integrated flux of 407$\pm$50 mJy. We estimate Raspberry's most likely diameter of 10$-$30 pc which would place it at a distance of 3$-$5 kpc, on the near side of the Milky Way's Scutum$-$Centaurus Arm. We also find a Stokes$-$V point source close to the centre of Raspberry with a $\sim$5$\sigma$ significance. This point source may be the remaining compact source, a neutron star, or possibly a pulsar, formed during the initial supernova event., Comment: 3 pages, 1 figure

The Polarisation Sky Survey of the Universe's Magnetism (POSSUM) will conduct a sensitive $\sim$1 GHz radio polarization survey covering 20 000 square degrees of the Southern sky with the Australian Square Kilometre Array Pathfinder (ASKAP). In anticipation of the full survey, we analyze pilot observations of low-band (800-1087 MHz), mid-band (1316-1439 MHz), and combined-band observations for an extragalactic field and a Galactic-plane field (low-band only). Using the POSSUM processing pipeline, we produce prototype RM catalogs that are filtered to construct prototype RM grids. We assess typical RM grid densities and RM uncertainties and their dependence on frequency, bandwidth, and Galactic latitude. We present a median filter method for separating foreground diffuse emission from background components, and find that after application of the filter, 99.5% of measured RMs of simulated sources are within 3$\sigma$ of their true RM, with a typical loss of polarized intensity of 5% $\pm$ 5%. We find RM grid densities of 35.1, 30.6, 37.2, and 13.5 RMs per square degree and median uncertainties on RM measurements of 1.55, 12.82, 1.06, and 1.89 rad m$^{-2}$ for the median-filtered low-band, mid-band, combined-band, and Galactic observations, respectively. We estimate that the full POSSUM survey will produce an RM catalog of $\sim$775 000 RMs with median-filtered low-band observations and $\sim$877 000 RMs with median-filtered combined-band observations. We construct a structure function from the Galactic RM catalog, which shows a break at $0.7^{\circ}$, corresponding to a physical scale of 12-24 pc for the nearest spiral arm., Comment: 51 pages, 19 figures, 5 tables

Conveying human goals to autonomous systems (AS) occurs both when the system is being designed and when it is being operated. The design-step conveyance is typically mediated by robotics and AI engineers, who must appropriately capture end-user requirements and concepts of operations, while the operation-step conveyance is mediated by the design, interfaces, and behavior of the AI. However, communication can be difficult during both these periods because of mismatches in the expectations and expertise of the end-user and the roboticist, necessitating more design cycles to resolve. We examine some of the barriers in communicating system design requirements, and develop an augmentation for applied cognitive task analysis (ACTA) methods, that we call robot task analysis (RTA), pertaining specifically to the development of autonomous systems. Further, we introduce a top-down view of an underexplored area of friction between requirements communication -- implied human expectations -- utilizing a collection of work primarily from experimental psychology and social sciences. We show how such expectations can be used in conjunction with task-specific expectations and the system design process for AS to improve design team communication, alleviate barriers to user rejection, and reduce the number of design cycles., Comment: Presented at the End-User Development for Human-Robot Interaction (EUD4HRI) workshop at HRI 2024

Micro-electro-mechanical system (MEMS) microphones (mics) with two acoustic ports are currently receiving considerable interest, with the promise of achieving higher directional sensitivity compared to traditional one-port architectures. However, measuring pressure differences in two-port microphones typically commands sensing elements that are softer than in one-port mics, and are therefore presumably more prone to interference from external vibration. Here we derive a universal expression for microphone sensitivity to vibration and we experimentally demonstrate its validity for several emerging two-port microphone technologies. We also perform vibration measurements on a one-port mic, thus providing a one-stop direct comparison between one-port and two-port sensing approaches. We find that the acoustically-referred vibration sensitivity of two-port MEMS mics, in units of measured acoustic pressure per external acceleration (i.e., Pascals per g), does not depend on the sensing element stiffness nor on its natural frequency. We also show that this vibration sensitivity in two-port mics is inversely proportional to frequency as opposed to the frequency independent behavior observed in one-port mics. This is confirmed experimentally for several types of microphone packages., Comment: 8 pages, 14 figures

We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an excess over that expected from star formation alone. Combining data in the Galaxy and Mass Assembly (GAMA) G23 region from GAMA, Evolutionary Map of the Universe (EMU) early science observations, and Wide-field Infrared Survey Explorer (WISE), we compare this technique with a selection of different AGN diagnostics to explore the similarities and differences in AGN classification. We find that diagnostics based on optical and near-infrared criteria (the standard BPT diagram, the WISE colour criterion, and the mass-excitation, or MEx diagram) tend to favour detection of AGN in high mass, high luminosity systems, while the ``ProSpect'' SED fitting tool can identify AGN efficiently in low mass systems. We investigate an explanation for this result in the context of proportionally lower mass black holes in lower mass galaxies compared to higher mass galaxies and differing proportions of emission from AGN and star formation dominating the light at optical and infrared wavelengths as a function of galaxy stellar mass. We conclude that SED-derived AGN classification is an efficient approach to identify low mass hosts with low radio luminosity AGN., Comment: 16 pages, 12 figures, 5 tables. Accepted for publication in PASA

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ability to leverage large-scale unlabelled data to learn robust representations could help improve the performance of numerous inference tasks on biosignals. Given the inherent domain differences between multimedia modalities and biosignals, the established objectives for self-supervised learning may not translate well to this domain. Hence, there is an unmet need to adapt these methods to biosignal analysis. In this work we propose a self-supervised model for EEG, which provides robust performance and remarkable parameter efficiency by using state space-based deep learning architecture. We also propose a novel knowledge-guided pre-training objective that accounts for the idiosyncrasies of the EEG signal. The results indicate improved embedding representation learning and downstream performance compared to prior works on exemplary tasks. Also, the proposed objective significantly reduces the amount of pre-training data required to obtain performance equivalent to prior works., Comment: 6 Pages, 5 figures, Submitted to EMBC 2024

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero., Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648

We introduce the concept of finite $\gamma$-scaled quadratic variation along a sequence of partitions for paths on a given interval. This concept, with historical roots in the study of Gaussian processes by Gladyshev (1961) and Klein \& Gin\'e (1975), includes the fractional Brownian motion (fBM) with Hurst index $H$, which has finite $1-2H$-scaled quadratic variation. We show that a path that is controlled by a path with finite $\gamma$-scaled quadratic variation in the sense of M. Gubinelli inherits this property, and the corresponding scaled quadratic variation satisfies an It\^o-isometry type formula. Moreover, we prove quantitative error bounds that establish a relationship between the convergence rates of the scaled quadratic variation of the controlled path and that of the controlling path. Additionally, we introduce a consistent estimator for the parameter $\gamma$ based on a single sample path, complete with quantitative error bounds. We apply these results to the parameter estimation for fractional diffusions. Our findings specify convergence rates for the estimation of both the Hurst index and the parameters in the noise vector fields. The paper concludes with numerical experiments that substantiate our theoretical findings.

Graph-structured data is ubiquitous throughout natural and social sciences, and Graph Neural Networks (GNNs) have recently been shown to be effective at solving prediction and inference problems on graph data. In this paper, we propose and demonstrate that GNNs can be applied to solve Combinatorial Optimization (CO) problems. CO concerns optimizing a function over a discrete solution space that is often intractably large. To learn to solve CO problems, we formulate the optimization process as a sequential decision making problem, where the return is related to how close the candidate solution is to optimality. We use a GNN to learn a policy to iteratively build increasingly promising candidate solutions. We present preliminary evidence that GNNs trained through Q-Learning can solve CO problems with performance approaching state-of-the-art heuristic-based solvers, using only a fraction of the parameters and training time.

We present a comprehensive study on the longitudinal magneto-thermal transport in a paramagnetic effective spin-1/2 magnetic insulator CsYbSe$_2$, by introducing a minimal model requiring only Zeeman splitting and magnetoelastic coupling. We use it to argue that hybridized excitations -- formed from acoustic phonons and localized spin-flip-excitations across the Zeeman gap of the crystal electric field ground doublet -- are responsible for a non-monotonic field dependence of longitudinal thermal conductivity. Beyond highlighting a starring role for phonons, our results raise the prospect of universal magneto-thermal transport phenomena in paramagnetic insulators that originate from simple features shared across many systems., Comment: 8 pages, 5 figures

Despite the impressive advancements achieved using deep-learning for functional brain activity analysis, the heterogeneity of functional patterns and scarcity of imaging data still pose challenges in tasks such as prediction of future onset of Post-Traumatic Epilepsy (PTE) from data acquired shortly after traumatic brain injury (TBI). Foundation models pre-trained on separate large-scale datasets can improve the performance from scarce and heterogeneous datasets. For functional Magnetic Resonance Imaging (fMRI), while data may be abundantly available from healthy controls, clinical data is often scarce, limiting the ability of foundation models to identify clinically-relevant features. We overcome this limitation by introducing a novel training strategy for our foundation model by integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features. In this way we enable generalization to other downstream clinical tasks, in our case prediction of PTE. To achieve this, we perform self-supervised training on the control dataset to focus on inherent features that are not limited to a particular supervised task while applying meta-learning, which strongly improves the model's generalizability using bi-level optimization. Through experiments on neurological disorder classification tasks, we demonstrate that the proposed strategy significantly improves task performance on small-scale clinical datasets. To explore the generalizability of the foundation model in downstream applications, we then apply the model to an unseen TBI dataset for prediction of PTE using zero-shot learning. Results further demonstrated the enhanced generalizability of our foundation model.

We demonstrate the importance of radio selection in probing heavily obscured galaxy populations. We combine Evolutionary Map of the Universe (EMU) Early Science data in the Galaxy and Mass Assembly (GAMA) G23 field with the GAMA data, providing optical photometry and spectral line measurements, together with Wide-field Infrared Survey Explorer (WISE) infrared (IR) photometry, providing IR luminosities and colours. We investigate the degree of obscuration in star forming galaxies, based on the Balmer decrement (BD), and explore how this trend varies, over a redshift range of 0

The structure and dynamics of the star-forming disk of the Small Magellanic Cloud (SMC) have long confounded us. The SMC is widely used as a prototype for galactic physics at low metallicity, and yet we fundamentally lack an understanding of the structure of its interstellar medium (ISM). In this work, we present a new model for the SMC by comparing the kinematics of young, massive stars with the structure of the ISM traced by high-resolution observations of neutral atomic hydrogen (HI) from the Galactic Australian Square Kilometer Array Pathfinder survey (GASKAP-HI). Specifically, we identify thousands of young, massive stars with precise radial velocity constraints from the Gaia and APOGEE surveys and match these stars to the ISM structures in which they likely formed. By comparing the average dust extinction towards these stars, we find evidence that the SMC is composed of two structures with distinct stellar and gaseous chemical compositions. We construct a simple model that successfully reproduces the observations and shows that the ISM of the SMC is arranged into two, superimposed, star-forming systems with similar gas mass separated by ~5 kpc along the line of sight., Comment: ApJ accepted. 20 pages, 18 figures

We report the discovery of a bow-shock pulsar wind nebula (PWN), named Potoroo, and the detection of a young pulsar J1638-4713 that powers the nebula. We present a radio continuum study of the PWN based on 20-cm observations obtained from the Australian Square Kilometre Array Pathfinder (ASKAP) and MeerKAT. PSR J1638-4713 was identified using Parkes radio telescope observations at frequencies above 3 GHz. The pulsar has the second-highest dispersion measure of all known radio pulsars (1553 pc/cm^3), a spin period of 65.74 ms and a spin-down luminosity of 6.1x10^36 erg/s. The PWN has a cometary morphology and one of the greatest projected lengths among all the observed pulsar radio tails, measuring over 21 pc for an assumed distance of 10 kpc. The remarkably long tail and atypically steep radio spectral index are attributed to the interplay of a supernova reverse shock and the PWN. The originating supernova remnant is not known so far. We estimated the pulsar kick velocity to be in the range of 1000-2000 km/s for ages between 23 and 10 kyr. The X-ray counterpart found in Chandra data, CXOU J163802.6-471358, shows the same tail morphology as the radio source but is shorter by a factor of 10. The peak of the X-ray emission is offset from the peak of the radio total intensity (Stokes I) emission by approximately 4.7", but coincides well with circularly polarised (Stokes V) emission. No infrared counterpart was found., Comment: 16 pages, 8 figures, 4 tables; Accepted for publication in PASA on 18 Jan 2024