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We show that there exists an algorithm that takes as input two closed, simply connected, topological 4-manifolds and decides whether or not these 4-manifolds are homeomorphic. In particular, we explain in detail how closed, simply connected, topological 4-manifolds can be naturally represented by a Kirby diagram consisting only of 2-handles. This representation is used as input for our algorithm. Along the way, we develop an algorithm to compute the Kirby-Siebenmann invariant of a closed, simply connected, topological 4-manifold from any of its Kirby diagrams and describe an algorithm that decides whether or not two intersection forms are isometric. In a slightly different direction, we discuss the decidability of the stable classification of smooth manifolds with more general fundamental groups. Here we show that there exists an algorithm that takes as input two closed, oriented, smooth 4-manifolds with fundamental groups isomorphic to a finite group with cyclic Sylow 2-subgroup, an infinite cyclic group, or a group of geometric dimension at most 3 (in the latter case we additionally assume that the universal covers of both 4-manifolds are not spin), and decides whether or not these two 4-manifolds are orientation-preserving stably diffeomorphic., Comment: 24 pages, 1 Figure

Ultraluminous X-ray sources (ULXs) were once largely believed to be powered by super-Eddington accretion onto stellar-mass black holes, although in some rare cases, ULXs also serve as potential candidates for (sub-Eddington) intermediate mass black holes. However, a total of eight ULXs have now been confirmed to be powered by neutron stars, thanks to observed pulsations, and may act as contaminants for radio/X-ray selection of intermediate mass black holes. Here we present the first comprehensive radio study of seven known neutron star ULXs using new and archival data from the Karl G. Jansky Very Large Array and the Australia Telescope Compact Array, combined with the literature. Across this sample there is only one confident radio detection, from the Galactic neutron star ULX Swift J0243.6+6124. The other six objects in our sample are extragalactic, and only one has coincident radio emission, which we conclude is most likely contamination from a background HII region. We conclude that with current facilities, neutron star ULXs do not produce significant enough radio emission to cause them to be misidentified as radio/X-ray selected intermediate mass black hole candidates. Thus, if background star formation has been properly considered, the current study indicates that a ULX with a compact radio counterpart is not likely to be a neutron star., Comment: 14 pages, 2 figures, accepted to ApJ

An $n$-vertex, $d$-regular graph can have at most $2^{n/2+o_d(n)}$ independent sets. In this paper we address what happens with this upper bound when we impose the further condition that the graph has independence number at most $\alpha$. We give upper and lower bounds that in many cases are close to each other. In particular, for each $0 < c_{\rm ind} \leq 1/2$ we exhibit a constant $k(c_{\rm ind})$ such that if $(G_n)_{n \in {\mathbb N}}$ is a sequence of graphs with $G_n$ $d$-regular on $n$ vertices and with maximum independent set size at most $\alpha$, with $d\rightarrow \infty$ and $\alpha/n \rightarrow c_{\rm ind}$ as $n \rightarrow \infty$, then $G_n$ has at most $k(c_{\rm ind})^{n+o(n)}$ independent sets, and we show that there is a sequence $(G_n)_{n \in {\mathbb N}}$ of graphs with $G_n$ $d$-regular on $n$ vertices ($d \leq n/2$) and with maximum independent set size at most $\alpha$, with $\alpha/n \rightarrow c_{\rm ind}$ as $n \rightarrow \infty$ and with $G_n$ having at least $k(c_{\rm ind})^{n+o(n)}$ independent sets. We also consider the regime $1/2 < c_{\rm ind} < 1$. Here for each $0 < c_{\rm deg} \leq 1-c_{\rm ind}$ we exhibit a constant $k(c_{\rm ind},c_{\rm deg})$ for which an analogous pair of statements can be proven, except that in each case we add the condition $d/n \rightarrow c_{\rm deg}$ as $n \rightarrow \infty$. Our upper bounds are based on graph container arguments, while our lower bounds are constructive.

Late-stage Ca-sulfate-filled fractures are common on Mars. Notably, the Shenandoah formation in the western edge of Jezero crater preserves a variety of Ca-sulfate minerals in the fine-grained siliciclastic rocks explored by the Perseverance rover. However, the depositional environment and timing of the formation of these sulfates is unknown. To address this outstanding problem, we developed a new technique to map the crystal textures of these sulfates in situ at two stratigraphically similar locations in the Shenandoah formation, allowing us to constrain the burial depth and paleoenvironment at the time of their deposition. Our results suggest that some Ca-sulfate analyzed was formed at a burial depth greater than 80m, whereas Ca-sulfates present at another outcrop likely precipitated in a shallow-subsurface environment. These results indicate that samples collected for potential return to Earth at the two studied locations capture two different times and distinct chemical conditions in the depositional history of the Shenandoah formation providing multiple opportunities to evaluate surface and subsurface habitability.

The independent set sequence of trees has been well studied, with much effort devoted to the (still open) question of Alavi, Malde, Schwenk and Erd\H{o}s on whether the independent set sequence of any tree is unimodal. Much less attention has been given to the independent set sequence of hypertrees. Here we study one natural first question in this realm -- the unimodality of the strong independent set sequence of linear uniform hyperpaths. Via both an inductive and a combinatorial argument we obtain an explicit expression for the terms of this sequence, and we establish unimodality via an interlacing roots argument. We also suggest some further questions.

We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. This disentanglement is achieved by controlling a Neural Radiance Field's appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration. Code and additional resources can be found at https://maxfehrentz.github.io/style-ngp/., Comment: Accepted at MICCAI 2024

Contrastive pretraining can substantially increase model generalisation and downstream performance. However, the quality of the learned representations is highly dependent on the data augmentation strategy applied to generate positive pairs. Positive contrastive pairs should preserve semantic meaning while discarding unwanted variations related to the data acquisition domain. Traditional contrastive pipelines attempt to simulate domain shifts through pre-defined generic image transformations. However, these do not always mimic realistic and relevant domain variations for medical imaging such as scanner differences. To tackle this issue, we herein introduce counterfactual contrastive learning, a novel framework leveraging recent advances in causal image synthesis to create contrastive positive pairs that faithfully capture relevant domain variations. Our method, evaluated across five datasets encompassing both chest radiography and mammography data, for two established contrastive objectives (SimCLR and DINO-v2), outperforms standard contrastive learning in terms of robustness to acquisition shift. Notably, counterfactual contrastive learning achieves superior downstream performance on both in-distribution and on external datasets, especially for images acquired with scanners under-represented in the training set. Further experiments show that the proposed framework extends beyond acquisition shifts, with models trained with counterfactual contrastive learning substantially improving subgroup performance across biological sex., Comment: Code available at https://github.com/biomedia-mira/counterfactual-contrastive/

We present a long-period radio transient (GLEAM-X J0704-37) discovered to have an optical counterpart, consistent with a cool main sequence star of spectral type M3. The radio pulsations occur at the longest period yet found, 2.9 hours, and were discovered in archival low-frequency data from the Murchison Widefield Array (MWA). High time resolution observations from MeerKAT show that pulsations from the source display complex microstructure and high linear polarisation, suggesting a pulsar-like emission mechanism occurring due to strong, ordered magnetic fields. The timing residuals, measured over more than a decade, show tentative evidence of a ~6-yr modulation. The high Galactic latitude of the system and the M-dwarf star excludes the magnetar interpretation, suggesting a more likely M-dwarf / white dwarf binary scenario for this system., Comment: 11 pages, 4 figures, submitted to ApJL

A dominating set in a graph is a set of vertices with the property that every vertex in the graph is either in the set or adjacent to something in the set. The domination sequence of the graph is the sequence whose $k$th term is the number of dominating sets of size $k$. Alikhani and Peng have conjectured that the domination sequence of every graph is unimodal. Beaton and Brown verified this conjecture for paths and cycles. Here we extend this to arbitrary powers of paths and cycles., Comment: 13 pages

In motor condition diagnosis, electrical current signature serves as an alternative feature to vibration-based sensor data, which is a more expensive and invasive method. Machine learning (ML) techniques have been emerging in diagnosing motor conditions using only motor phase current signals. This study converts time-series motor current signals to time-frequency 2D plots using Short-time Fourier Transform (STFT) methods. The motor current signal dataset consists of 3,750 sample points with five classes - one healthy and four synthetically-applied motor fault conditions, and with five loading conditions: 0, 25, 50, 75, and 100%. Five transformation methods are used on the dataset: non-overlap and overlap STFTs, non-overlap and overlap realigned STFTs, and synchrosqueezed STFT. Then, deep learning (DL) models based on the previous Convolutional Neural Network (CNN) architecture are trained and validated from generated plots of each method. The DL models of overlap-STFT, overlap R-STFT, non-overlap STFT, non-overlap R-STFT, and synchrosqueezed-STFT performed exceptionally with an average accuracy of 97.65, 96.03, 96.08, 96.32, and 88.27%, respectively. Four methods outperformed the previous best ML method with 93.20% accuracy, while all five outperformed previous 2D-plot-based methods with accuracy of 80.25, 74.80, and 82.80%, respectively, using the same dataset, same DL architecture, and validation steps., Comment: 4 pages, 6 images, presented at 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika)

We present deep near-infrared $K_{\rm s}$-band imaging for 35 of the 53 sources from the high-redshift ($z > 2$) radio galaxy candidate sample defined in Broderick et al. (2022). These images were obtained using the High-Acuity Widefield $K$-band Imager (HAWK-I) on the Very Large Telescope. Host galaxies are detected for 27 of the sources, with $K_{\rm s} \approx 21.6$$-$$23.0$ mag (2$''$ diameter apertures; AB). The remaining eight targets are not detected to a median $3\sigma$ depth of $K_{\rm s} \approx 23.3$ mag ($2''$ diameter apertures). We examine the radio and near-infrared flux densities of the 35 sources, comparing them to the known $z > 3$ powerful radio galaxies with 500-MHz radio luminosities $L_{500\,{\rm MHz}} > 10^{27}$ W Hz$^{-1}$. By plotting 150-MHz flux density versus $K_{\rm s}$-band flux density, we find that, similar to the sources from the literature, these new targets have large radio to near-infrared flux density ratios, but extending the distribution to fainter flux densities. Five of the eight HAWK-I deep non-detections have a median $3\sigma$ lower limit of $K_{\rm s} \gtrsim 23.8$ mag ($1.5''$ diameter apertures); these five targets, along with a further source from Broderick et al. (2022) with a deep non-detection ($K_{\rm s} \gtrsim 23.7$ mag; $3\sigma$; $2''$ diameter aperture) in the Southern H-ATLAS Regions $K_{\rm s}$-band Survey, are considered candidates to be ultra-high-redshift ($z > 5$) radio galaxies. The extreme radio to near-infrared flux density ratios ($>10^5$) for these six sources are comparable to TN J0924$-$2201, GLEAM J0856$+$0223 and TGSS J1530$+$1049, the three known powerful radio galaxies at $z > 5$. For a selection of galaxy templates with different stellar masses, we show that $z \gtrsim 4.2$ is a plausible scenario for our ultra-high-redshift candidates if the stellar mass $M_{\rm *} \gtrsim 10^{10.5}\,{\rm M}_\odot$. [abridged], Comment: 24 pages, 5 figures (one of which is a multi-page figure with 30 separate panels), 2 tables, accepted for publication in PASA

The angular correlation is a method for measuring the distribution of structure in the Universe, through the statistical properties of the angular distribution of galaxies on the sky. We measure the angular correlation of galaxies from the second data release of the GaLactic and Extragalactic All-sky Murchison Widefield Array eXtended survey (GLEAM-X) survey, a low-frequency radio survey covering declinations below +30 degrees. We find an angular distribution consistent with the LambdaCDM cosmological model assuming the best fitting cosmological parameters from Planck Collaboration et al. (2020). We fit a bias function to the discrete tracers of the underlying matter distribution, finding a bias that evolves with redshift in either a linear or exponential fashion to be a better fit to the data than a constant bias. We perform a covariance analysis to obtain an estimation of the properties of the errors, by analytic, jackknife and sample variance means. Our results are consistent with previous studies on the topic, and also the predictions of the LambdaCDM cosmological model.

Late-time ($\sim$ year) radio follow-up of optically-discovered tidal disruption events (TDEs) is increasingly resulting in detections at radio wavelengths, and there is growing evidence for this late-time radio activity to be common to the broad class of sub-relativistic TDEs. Detailed studies of some of these TDEs at radio wavelengths are also challenging the existing models for radio emission. Using all-sky multi-epoch data from the Australian Square Kilometre Array Pathfinder (ASKAP), taken as a part of the Rapid ASKAP Continuum Survey (RACS), we searched for radio counterparts to a sample of optically-discovered TDEs. We detected late-time emission at RACS frequencies (742-1032\,MHz) in five TDEs, reporting the independent discovery of radio emission from TDE AT2019ahk and extending the time baseline out to almost 3000\,days for some events. Overall, we find that at least $22^{+15}_{-11}$\% of the population of optically-discovered TDEs has detectable radio emission in the RACS survey, while also noting that the true fraction can be higher given the limited cadence (2 epochs separated by $\sim 3\,$ years) of the survey. Finally, we project that the ongoing higher-cadence ($\sim 2$\,months) ASKAP Variable and Slow Transients (VAST) survey can detect $\sim 20$ TDEs in its operational span (4\,yrs), given the current rate from optical surveys., Comment: Accepted for publication in ApJ, comments welcome

Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutational effect and stability prediction. Pre-trained to predict amino acid propensity from its surrounding 3D structure, HERMES can be fine-tuned for mutational effects using our open-source code. We present a suite of HERMES models, pre-trained with different strategies, and fine-tuned to predict the stability effect of mutations. Benchmarking against other models shows that HERMES often outperforms or matches their performance in predicting mutational effect on stability, binding, and fitness. HERMES offers versatile tools for evaluating mutational effects and can be fine-tuned for specific predictive objectives., Comment: 16 pages, 8 figures

We present the second data release for the GaLactic and Extragalactic All-sky Murchison Widefield Array eXtended (GLEAM-X) survey. This data release is an area of 12,892-deg^2 around the South Galactic Pole region covering 20h 40m <= RA <= 6h 40m, -90deg <= Dec <= +30deg. Observations were taken in 2020 using the Phase-II configuration of the Murchison Widefield Array (MWA) and covering a frequency range of 72-231MHz with twenty frequency bands. We produce a wideband source-finding mosaic over 170-231MHz with a median root mean squared noise of 1.5 (+1.5/-0.5) mJy beam^(-1). We present a catalogue of 624,866 components, including 562,302 components which are spectrally fit. This catalogue is 98% complete at 50mJy, and a reliability of 98.7% at a 5sigma level, consistent with expectations for this survey. The catalogue is made available via Vizier and the PASA datastore and accompanying mosaics for this data release are made available via AAO Data Central and SkyView., Comment: Accepted for publication in PASA; 24 pages, 15 figures

Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data. Extensive experiments performed in real ultrasound data demonstrate the effectiveness of the proposed approach, allowing for adapting to the surgeon's definition of surgical targets and outperforming non-patient-specific models, neurosurgeon experts, and high-end tracking systems. Our code is available at: \url{https://github.com/ReubenDo/MHVAE-Seg}., Comment: Early accept at MICCAI 2024 - code available at: https://github.com/ReubenDo/MHVAE-Seg

We investigate the realisability of the Casson-Sullivan invariant for homeomorphisms of smooth $4$-manifolds, which is the obstruction to a homeomorphism being stably pseudo-isotopic to a diffeomorphism, valued in the third cohomology of the source manifold with $\mathbb{Z}/2$-coefficients. We prove that for all orientable pairs of homeomorphic, smooth $4$-manifolds this invariant can be realised fully after stabilising with a single $S^2\times S^2$. As an application, we obtain that topologically isotopic surfaces in a smooth, simply-connected $4$-manifold become smoothly isotopic after sufficient external stabilisations. We further demonstrate cases where this invariant can be realised fully without stabilisation for self-homeomorphisms, which includes for manifolds with finite cyclic fundamental group. This method allows us to produce many examples of homeomorphisms which are not stably pseudo-isotopic to any diffeomorphism but are homotopic to the identity. Finally, we reinterpret these results in terms of finding examples of smooth structures on $4$-manifolds which are diffeomorphic but not stably pseudo-isotopic., Comment: 40 pages, 1 figure. Comments welcome!

In this work, we consider the imaginary time evolution of matrix product states. We present a novel quantum-inspired classical method that, when combined with time evolving block decimation (TEBD), is able to potentially speed-up the convergence to a ground state compared to TEBD alone. Our method, referred to as boosted imaginary time evolution, relies on the use of reflections to boost to lower energy states. Interleaving TEBD steps with boosts reduces the total number of TEBD steps and potentially the computational cost required to imaginary time evolve a matrix product state to a ground state. We give the mathematical details of the method followed by an algorithmic implementation and finally some results for a simple test case.

We present a multi-wavelength analysis, from the radio to the X-ray band, of the redshift $z=6.44$ VIK J2318$-$31 radio-loud (RL) quasi stellar object (QSO), one of the most distant currently known in this class. The work is based on newly obtained (uGMRT, ATCA, Chandra) as well as archival (GNIRS and X-Shooter) dedicated observations that have not been published yet. Based on the observed X-ray and radio emission, its relativistic jets are likely young and misaligned from our line of sight. Moreover, we can confirm, with simultaneous observations, the presence of a turnover in the radio spectrum at $\nu_{\rm peak} \sim 650$ MHz which is unlikely to be associated with self-synchrotron absorption. From the NIR spectrum we derived the mass of the central black hole, M$_{\rm BH}=8.1^{+6.8}_{-5.6} \times 10^8 {\rm M_{\odot}}$, and the Eddington ratio, $\lambda_{\rm EDD} = 0.8^{+0.8}_{-0.6}$, using broad emission lines as well as an accretion disc model fit to the continuum emission. Given the high accretion rate, the presence of a $\sim$8$\times$10$^8$ M$_\odot$ black hole at $z=6.44$ can be explained by a seed black hole ($\sim$10$^{4}$ M$_\odot$) that formed at $z\sim25$, assuming a radiative efficiency $\eta_{\rm d}\sim0.1$. However, by assuming $\eta_{\rm d}\sim0.3$, as expected for jetted systems, the mass observed would challenge current theoretical models of black hole formation., Comment: Accepted for publication on A&A the 22nd April 2024

Modern wide field radio surveys typically detect millions of objects. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm that projects a many-dimensional dataset onto a two- or three-dimensional lattice of neurons. This dimensionality reduction allows the user to visualize common features of the data better and develop algorithms for classifying objects that are not otherwise possible with large datasets. To this aim, we use the PINK implementation of a SOM. PINK incorporates rotation and flipping invariance so that the SOM algorithm may be applied to astronomical images. In this cookbook we provide instructions for working with PINK, including preprocessing the input images, training the model, and offering lessons learned through experimentation. The problem of imbalanced classes can be improved by careful selection of the training sample and increasing the number of neurons in the SOM (chosen by the user). Because PINK is not scale-invariant, structure can be smeared in the neurons. This can also be improved by increasing the number of neurons in the SOM. We also introduce pyink, a Python package used to read and write PINK binary files, assist in common preprocessing operations, perform standard analyses, visualize the SOM and preprocessed images, and create image-based annotations using a graphical interface. A tutorial is also provided to guide the user through the entire process. We present an application of PINK to VLA Sky Survey (VLASS) images. We demonstrate that the PINK is generally able to group VLASS sources with similar morphology together. We use the results of PINK to estimate the probability that a given source in the VLASS QuickLook Catalogue is actually due to sidelobe contamination., Comment: Accepted for publication in Astronomy and Computing

Graph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studies to explore their representational capability based on the graph isomorphism task. These works inherently assume a countable node feature representation, potentially limiting their applicability. Interestingly, only a few study GNNs with uncountable node feature representation. In the paper, a novel perspective on the representational capability of GNNs is investigated across all levels$\unicode{x2014}$node-level, neighborhood-level, and graph-level$\unicode{x2014}$when the space of node feature representation is uncountable. More specifically, the strict injective and metric requirements are softly relaxed by employing a pseudometric distance on the space of input to create a soft-injective function such that distinct inputs may produce similar outputs if and only if the pseudometric deems the inputs to be sufficiently similar on some representation. As a consequence, a simple and computationally efficient soft-isomorphic relational graph convolution network (SIR-GCN) that emphasizes the contextualized transformation of neighborhood feature representations via anisotropic and dynamic message functions is proposed. A mathematical discussion on the relationship between SIR-GCN and widely used GNNs is then laid out to put the contribution into context, establishing SIR-GCN as a generalization of classical GNN methodologies. Experiments on synthetic and benchmark datasets then demonstrate the relative superiority of SIR-GCN, outperforming comparable models in node and graph property prediction tasks.

Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings. However, it is sensitive to the choice of augmentation pipeline. Positive pairs should preserve semantic information while destroying domain-specific information. Standard augmentation pipelines emulate domain-specific changes with pre-defined photometric transformations, but what if we could simulate realistic domain changes instead? In this work, we show how to utilise recent progress in counterfactual image generation to this effect. We propose CF-SimCLR, a counterfactual contrastive learning approach which leverages approximate counterfactual inference for positive pair creation. Comprehensive evaluation across five datasets, on chest radiography and mammography, demonstrates that CF-SimCLR substantially improves robustness to acquisition shift with higher downstream performance on both in- and out-of-distribution data, particularly for domains which are under-represented during training., Comment: Accepted for publication at the MICCAI 2024 Data Engineering in Medical Imaging workshop. Code available at https://github.com/biomedia-mira/counterfactual-contrastive. Extended version of this work available at arXiv:2409.10365

For integers $r,t\geq2$ and $n\geq1$ let $f_r(t,n)$ be the minimum, over all factorizations of the complete $r$-uniform hypergraph of order $n$ into $t$ factors $H_1,\dots,H_t$, of $\sum_{i=1}^tc(H_i)$ where $c(H_i)$ is the number of maximal cliques in $H_i$. It is known that $f_2(2,n)=n+1$; in fact, if $G$ is a graph of order $n$, then $c(G)+c(\overline G)\geq n+1$ with equality iff $\omega(G)+\alpha(G)=n+1$ where $\omega$ is the clique number and $\alpha$ the independence number. In this paper we investigate $f_r(t,n)$ when $r>2$ or $t>2$. We also characterize graphs $G$ of order $n$ with $c(G)+c(\overline G)=n+2$., Comment: 22 pages

We present the results of an ultra-deep radio continuum survey, containing $\sim480$ hours of observations, of the Galactic globular cluster 47 Tucanae with the Australia Telescope Compact Array. This comprehensive coverage of the cluster allows us to reach RMS noise levels of 1.19 $\mu Jy~\textrm{beam}^{-1}$ at 5.5 GHz, 940 $nJy~\textrm{beam}^{-1}$ at 9 GHz, and 790 $nJy~\textrm{beam}^{-1}$ in a stacked 7.25 GHz image. This is the deepest radio image of a globular cluster, and the deepest image ever made with the Australia Telescope Compact Array. We identify ATCA J002405.702-720452.361, a faint ($6.3\pm1.2$ $\mu Jy$ at 5.5 GHz, $5.4\pm0.9$ $\mu Jy$ at 9 GHz), flat-spectrum ($\alpha=-0.31\pm0.54$) radio source that is positionally coincident with the cluster centre and potentially associated with a faint X-ray source. No convincing optical counterpart was identified. We use radio, X-ray, optical, and UV data to show that explanations involving a background active galactic nucleus, a chromospherically active binary, or a binary involving a white dwarf are unlikely. The most plausible explanations are that the source is an undiscovered millisecond pulsar or a weakly accreting black hole. If the X-ray source is associated with the radio source, the fundamental plane of black hole activity suggests a black hole mass of $\sim54-6000$ M$_{\odot}$, indicating an intermediate-mass black hole or a heavy stellar-mass black hole., Comment: ApJ in press, 25 pages, 10 figures

We present an initial analysis of Radio Frequency Interference (RFI) flagging statistics from archived Australian SKA Pathfinder (ASKAP) observations for the 'Survey and Monitoring of ASKAP's RFI environment and Trends' (SMART) project. The survey component covers ASKAP's full 700 MHz to 1800 MHz frequency range, including bands not typically used due to severe RFI. In addition to this dedicated survey, we routinely archive and analyse flagging statistics for all scientific observations to monitor the observatory's RFI environment in near real-time. We use the telescope itself as a very sensitive RFI monitor and directly assess the fraction of scientific observations impacted by RFI. To this end, flag tables are now automatically ingested and aggregated as part of routine ASKAP operations for all science observations, as a function of frequency and time. The data presented in this paper come from processing all archived data for several ASKAP Survey Science Projects (SSPs). We found that the average amount of flagging due to RFI across the routinely-used 'clean' continuum science bands is 3%. The 'clean' mid band from 1293 MHz to 1437 MHz (excluding the 144 MHz below 1293 MHz impacted by radionavigation-satellites which is discarded before processing) is the least affected by RFI, followed by the 'clean' low band from 742 MHz to 1085 MHz. ASKAP SSPs lose most of their data to the mobile service in the low band, aeronautical service in the mid band and satellite navigation service in the 1510 MHz to 1797 MHz high band. We also show that for some of these services, the percentage of discarded data has been increasing year-on-year. SMART provides a unique opportunity to study ASKAP's changing RFI environment and informing the implementation of a suite of RFI mitigation techniques., Comment: accepted for publication in PASA. 14 pages, 11 Figures, 4 Tables

Coronal mass ejections (CMEs) are large-scale eruptions with a typical radial size at 1 au of 0.21 au but their angular width in interplanetary space is still mostly unknown, especially for the magnetic ejecta (ME) part of the CME. We take advantage of STEREO-A angular separation of 20$^\circ$-60$^\circ$ from the Sun-Earth line from October 2020 to August 2022, and perform a two-part study to constrain the angular width of MEs in the ecliptic plane: a) we study all CMEs that are observed remotely to propagate between the Sun-STEREO-A and the Sun-Earth lines and determine how many impact one or both spacecraft in situ, and b) we investigate all in situ measurements at STEREO-A or at L1 of CMEs during the same time period to quantify how many are measured by the two spacecraft. A key finding is that, out of 21 CMEs propagating within 30$^\circ$ of either spacecraft, only four impacted both spacecraft and none provided clean magnetic cloud-like signatures at both spacecraft. Combining the two approaches, we conclude that the typical angular width of a ME at 1 au is $\sim$ 20$^\circ$-30$^\circ$, or 2-3 times less than often assumed and consistent with a 2:1 elliptical cross-section of an ellipsoidal ME. We discuss the consequences of this finding for future multi-spacecraft mission designs and for the coherence of CMEs., Comment: 21 pages, revision submitted to ApJ

Energetic plasma is everywhere in the Universe. The terrestrial Magnetospheric System is a key case where direct measures of plasma energization and energy transport can be made in situ at high resolution. Despite the large amount of available observations, we still do not fully understand how plasma energization and energy transport work. Key physical processes driving much plasma energization and energy transport occur where plasma on fluid scales couple to the smaller ion kinetic scales. These scales (1 RE) are strongly related to the larger mesoscales (several RE) at which large-scale plasma energization and energy transport structures form. All these scales and processes need to be resolved experimentally, however existing multi-point in situ observations do not have a sufficient number of measurement points. New multiscale observations simultaneously covering scales from mesoscales to ion kinetic scales are needed. The implementation of these observations requires a strong international collaboration in the coming years between the major space agencies. The Plasma Observatory is a mission concept tailored to resolve scale coupling in plasma energization and energy transport at fluid and ion scales. It targets the two ESA-led Medium Mission themes Magnetospheric Systems and Plasma Cross-scale Coupling of the ESA Voyage 2050 report and is currently under evaluation as a candidate for the ESA M7 mission. MagCon (Magnetospheric Constellation) is a mission concept being studied by NASA aiming at studying the flow of mass, momentum, and energy through the Earth magnetosphere at mesoscales. Coordination between Plasma Observatory and MagCon missions would allow us for the first time to simultaneously cover from mesoscales to ion kinetic scales leading to a paradigm shift in the understanding of the Earth Magnetospheric System., Comment: A White Paper submitted for the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033

Accurately modeling protein 3D structure is essential for the design of functional proteins. An important sub-task of structure modeling is protein side-chain packing: predicting the conformation of side-chains (rotamers) given the protein's backbone structure and amino-acid sequence. Conventional approaches for this task rely on expensive sampling procedures over hand-crafted energy functions and rotamer libraries. Recently, several deep learning methods have been developed to tackle the problem in a data-driven way, albeit with vastly different formulations (from image-to-image translation to directly predicting atomic coordinates). Here, we frame the problem as a joint regression over the side-chains' true degrees of freedom: the dihedral $\chi$ angles. We carefully study possible objective functions for this task, while accounting for the underlying symmetries of the task. We propose Holographic Packer (H-Packer), a novel two-stage algorithm for side-chain packing built on top of two light-weight rotationally equivariant neural networks. We evaluate our method on CASP13 and CASP14 targets. H-Packer is computationally efficient and shows favorable performance against conventional physics-based algorithms and is competitive against alternative deep learning solutions., Comment: Accepted as a conference paper at MLCB 2023. 8 pages main body, 20 pages with appendix. 10 figures

We construct the first examples of non-smoothable self-homeomorphisms of smooth $4$-manifolds with boundary that fix the boundary and act trivially on homology. As a corollary, we construct self-diffeomorphisms of $4$-manifolds with boundary that fix the boundary and act trivially on homology but cannot be isotoped to any self-diffeomorphism supported in a collar of the boundary and, in particular, are not isotopic to any generalised Dehn twist., Comment: Improved exposition and pictures

In situ measurements from spacecraft typically provide a time series at a single location through coronal mass ejections (CMEs) and they have been one of the main methods to investigate CMEs. CME properties derived from these in situ measurements are affected by temporal changes that occur as the CME passes over the spacecraft, such as radial expansion and ageing, as well as spatial variations within a CME. This study uses multi-spacecraft measurements of the same CME at close separations to investigate both the spatial variability (how different a CME profile is when probed by two spacecraft close to each other) and the so-called ageing effect (the effect of the time evolution on in situ properties). We compile a database of 19 events from the past four decades measured by two spacecraft with a radial separation <0.2 au and an angular separation <10{\deg}. We find that the average magnetic field strength measured by the two spacecraft differs by 18% of the typical average value, which highlights non-negligible spatial or temporal variations. For one particular event, measurements taken by the two spacecraft allow us to quantify and significantly reduce the ageing effect to estimate the asymmetry of the magnetic field strength profile. This study reveals that single-spacecraft time series near 1 au can be strongly affected by ageing and that correcting for self-similar expansion does not capture the whole ageing effect., Comment: Accepted for publication at ApJ. 18 pages, 6 figures, 2 tables