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Behavior cloning (BC) currently stands as a dominant paradigm for learning real-world visual manipulation. However, in tasks that require locally corrective behaviors like multi-part assembly, learning robust policies purely from human demonstrations remains challenging. Reinforcement learning (RL) can mitigate these limitations by allowing policies to acquire locally corrective behaviors through task reward supervision and exploration. This paper explores the use of RL fine-tuning to improve upon BC-trained policies in precise manipulation tasks. We analyze and overcome technical challenges associated with using RL to directly train policy networks that incorporate modern architectural components like diffusion models and action chunking. We propose training residual policies on top of frozen BC-trained diffusion models using standard policy gradient methods and sparse rewards, an approach we call ResiP (Residual for Precise manipulation). Our experimental results demonstrate that this residual learning framework can significantly improve success rates beyond the base BC-trained models in high-precision assembly tasks by learning corrective actions. We also show that by combining ResiP with teacher-student distillation and visual domain randomization, our method can enable learning real-world policies for robotic assembly directly from RGB images. Find videos and code at \url{https://residual-assembly.github.io}.

Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neural networks have emerged as promising alternatives, yet the optimal strategies for their design and optimization in real-world biomedical challenges remain unclear. This study evaluates various graph representation learning models for case-control classification using high-throughput biological data from Parkinson's disease and control samples. We compare topologies derived from sample similarity networks and molecular interaction networks, including protein-protein and metabolite-metabolite interactions (PPI, MMI). Graph Convolutional Network (GCNs), Chebyshev spectral graph convolution (ChebyNet), and Graph Attention Network (GAT), are evaluated alongside advanced architectures like graph transformers, the graph U-net, and simpler models like multilayer perceptron (MLP). These models are systematically applied to transcriptomics and metabolomics data independently. Our comparative analysis highlights the benefits and limitations of various architectures in extracting patterns from omics data, paving the way for more accurate and interpretable models in biomedical research., Comment: Submitted to Machine Learning in Computational Biology 2024 as an extended abstract, 2 pages + 1 appendix

Small-angle scattering tensor tomography is a technique for studying anisotropic nanostructures of millimeter-sized samples in a volume-resolved manner. It requires the acquisition of data through repeated tomographic rotations about an axis which is subjected to a series of tilts. The tilt that can be achieved with a typical setup is geometrically constrained, which leads to limits in the set of directions from which the different parts of the reciprocal-space map can be probed. Here, we characterize the impact of this limitation on reconstructions in terms of the missing wedge problem of tomography, by treating the problem of tensor tomography as the reconstruction of a three-dimensional field of functions on the unit sphere, represented by a grid of Gaussian radial basis functions. We then devise an acquisition scheme to obtain complete data by remounting the sample, which we apply to a sample of human trabecular bone. Performing tensor tomographic reconstructions of limited data sets as well as the complete data set, we further investigate and validate the missing wedge understanding of data incompleteness by investigating reconstruction errors due to data incompleteness across both real and reciprocal space. Finally, we carry out an analysis of orientations and derived scalar quantities, to quantify the impact of this missing wedge problem on a typical tensor tomographic analysis. We conclude that the effects of data incompleteness are consistent with the predicted impact of the missing wedge problem, and that the impact on tensor tomographic analysis is appreciable but limited, especially if precautions are taken. In particular, there is only limited impact on the means and relative anisotropies of the reconstructed reciprocal space maps., Comment: Main: 32 pages, 9 figures; Supplementary: 9 pages, 7 figuers

In this paper, we prove the first existence result of weak solutions to the 3D Euler equation with initial vorticity concentrated in a circle and velocity field in $C([0,T],L^{2^-})$. The energy becomes finite and decreasing for positive times, with vorticity concentrated in a ring that thickens and moves in the direction of the symmetry axis. With our approach, there is no need to mollify the initial data or to rescale the time variable. We overcome the singularity of the initial data by applying convex integration within the appropriate time-weighted space.

Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement learning, on the other hand, can explore the environment autonomously to learn robust behaviors but may require impractical amounts of unsafe real-world data collection. To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly from small amounts of real-world data. To enable this real-to-sim-to-real pipeline, RialTo proposes an easy-to-use interface for quickly scanning and constructing digital twins of real-world environments. We also introduce a novel "inverse distillation" procedure for bringing real-world demonstrations into simulated environments for efficient fine-tuning, with minimal human intervention and engineering required. We evaluate RialTo across a variety of robotic manipulation problems in the real world, such as robustly stacking dishes on a rack, placing books on a shelf, and six other tasks. RialTo increases (over 67%) in policy robustness without requiring extensive human data collection. Project website and videos at https://real-to-sim-to-real.github.io/RialTo/, Comment: Project page: https://real-to-sim-to-real.github.io/RialTo/

3C84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of VLBI above the hitherto available maximum frequency of 86GHz. Using ultrahigh resolution VLBI observations at the highest available frequency of 228GHz, we aim to directly detect compact structures and understand the physical conditions in the compact region of 3C84. We used EHT 228GHz observations and, given the limited (u,v)-coverage, applied geometric model fitting to the data. We also employed quasi-simultaneously observed, multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. We report the detection of a highly ordered, strong magnetic field around the central, SMBH of 3C84. The brightness temperature analysis suggests that the system is in equipartition. We determined a turnover frequency of $\nu_m=(113\pm4)$GHz, a corresponding synchrotron self-absorbed magnetic field of $B_{SSA}=(2.9\pm1.6)$G, and an equipartition magnetic field of $B_{eq}=(5.2\pm0.6)$G. Three components are resolved with the highest fractional polarisation detected for this object ($m_\textrm{net}=(17.0\pm3.9)$%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228GHz. We used these findings to test models of jet formation, propagation, and Faraday rotation in 3C84. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C84. However, systematic uncertainties due to the limited (u,v)-coverage, however, cannot be ignored., Comment: 15 pages, 6 figures, published in A&A

The Event Horizon Telescope (EHT) observations carried out in 2018 April at 1.3 mm wavelengths included 9 stations in the array, comprising 7 single-dish telescopes and 2 phased arrays. The metadata package for the 2018 EHT observing campaign contains calibration tables required for the a-priori amplitude calibration of the 2018 April visibility data. This memo is the official documentation accompanying the release of the 2018 EHT metadata package, providing an overview of the contents of the package. We describe how telescope sensitivities, gain curves and other relevant parameters for each station in the EHT array were collected, processed, and validated to produce the calibration tables., Comment: 26 pages, 7 figures, EHT Memo Series 2023-L1-01

This paper studies the Cauchy problem for a helical vortex filament evolving by the 3D incompressible Navier-Stokes equations. We prove global-in-time well-posedness and smoothing of solutions with initial vorticity concentrated on a helix. We provide a local-in-time well-posedness result for vortex filaments periodic in one spatial direction, and show that solutions with helical initial data preserve this symmetry. We follow the approach of [4], where the analogue local-in-time result has been obtained for closed vortex filaments in $\mathbb{R}^3$. Next, we apply local energy weak solutions theory with a novel estimate for helical functions in non-helical domains to uniquely extend the solutions globally in time. This is the first global-in-time well-posedness result for a vortex filament without size restriction and without vanishing swirl assumptions., Comment: 29 pages. New version with minor changes to make the paper more self-contained

Ideally, we would place a robot in a real-world environment and leave it there improving on its own by gathering more experience autonomously. However, algorithms for autonomous robotic learning have been challenging to realize in the real world. While this has often been attributed to the challenge of sample complexity, even sample-efficient techniques are hampered by two major challenges - the difficulty of providing well "shaped" rewards, and the difficulty of continual reset-free training. In this work, we describe a system for real-world reinforcement learning that enables agents to show continual improvement by training directly in the real world without requiring painstaking effort to hand-design reward functions or reset mechanisms. Our system leverages occasional non-expert human-in-the-loop feedback from remote users to learn informative distance functions to guide exploration while leveraging a simple self-supervised learning algorithm for goal-directed policy learning. We show that in the absence of resets, it is particularly important to account for the current "reachability" of the exploration policy when deciding which regions of the space to explore. Based on this insight, we instantiate a practical learning system - GEAR, which enables robots to simply be placed in real-world environments and left to train autonomously without interruption. The system streams robot experience to a web interface only requiring occasional asynchronous feedback from remote, crowdsourced, non-expert humans in the form of binary comparative feedback. We evaluate this system on a suite of robotic tasks in simulation and demonstrate its effectiveness at learning behaviors both in simulation and the real world. Project website https://guided-exploration-autonomous-rl.github.io/GEAR/., Comment: Project website https://guided-exploration-autonomous-rl.github.io/GEAR/

The inner hundred parsecs of the Milky Way hosts the nearest supermassive black hole, largest reservoir of dense gas, greatest stellar density, hundreds of massive main and post main sequence stars, and the highest volume density of supernovae in the Galaxy. As the nearest environment in which it is possible to simultaneously observe many of the extreme processes shaping the Universe, it is one of the most well-studied regions in astrophysics. Due to its proximity, we can study the center of our Galaxy on scales down to a few hundred AU, a hundred times better than in similar Local Group galaxies and thousands of times better than in the nearest active galaxies. The Galactic Center (GC) is therefore of outstanding astrophysical interest. However, in spite of intense observational work over the past decades, there are still fundamental things unknown about the GC. JWST has the unique capability to provide us with the necessary, game-changing data. In this White Paper, we advocate for a JWST NIRCam survey that aims at solving central questions, that we have identified as a community: i) the 3D structure and kinematics of gas and stars; ii) ancient star formation and its relation with the overall history of the Milky Way, as well as recent star formation and its implications for the overall energetics of our galaxy's nucleus; and iii) the (non-)universality of star formation and the stellar initial mass function. We advocate for a large-area, multi-epoch, multi-wavelength NIRCam survey of the inner 100\,pc of the Galaxy in the form of a Treasury GO JWST Large Program that is open to the community. We describe how this survey will derive the physical and kinematic properties of ~10,000,000 stars, how this will solve the key unknowns and provide a valuable resource for the community with long-lasting legacy value., Comment: This White Paper will be updated when required (e.g. new authors joining, editing of content). Most recent update: 24 Oct 2023

We present a multiwavelength observation of a cool core that does not appear to be associated with any galaxy, in a nearby cluster, Abell~1142. Its X-ray surface brightness peak of $\lesssim2$ keV is cooler than the ambient intracluster gas of $\gtrsim3$ keV, and is offset from its brightest cluster galaxy (BCG) by 80 kpc in projection, representing the largest known cool core -- BCG separation. This BCG-less cool core allows us to measure the metallicity of a cluster center with a much-reduced contribution from the interstellar medium (ISM) of the BCG. XMM-Newton observation reveals a prominent Fe abundance peak of $1.07^{+0.16}_{-0.15}$ Z$_{\odot}$ and an $\alpha/$Fe abundance ratio close to the solar ratio, fully consistent with those found at the centers of typical cool core clusters. This finding hints that BCGs play a limited role in enriching the cluster centers. However, the discussion remains open, given that the $\alpha/$Fe abundance ratios of the orphan cool core and the BCG ISM are not significantly different. Abell~1142 may have experienced a major merger more than 100 Myr ago, which has dissociated its cool core from the BCG. This implies that the Fe abundance peak in cool core clusters can be resilient to cluster mergers. Our recent IRAM 30-m observation did not detect any CO emission at its X-ray peak and we find no evidence for massive runaway cooling in the absence of recent AGN feedback. The lack of a galaxy may contribute to an inefficient conversion of the ionized warm gas to the cold molecular gas., Comment: 7 pages, 3 figures, MNRAS accepted

The Event Horizon Telescope (EHT) observed in 2017 the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), at a frequency of 228.1 GHz ($\lambda$=1.3 mm). The fundamental physics tests that even a single pulsar orbiting Sgr A* would enable motivate searching for pulsars in EHT datasets. The high observing frequency means that pulsars - which typically exhibit steep emission spectra - are expected to be very faint. However, it also negates pulse scattering, an effect that could hinder pulsar detections in the Galactic Center. Additionally, magnetars or a secondary inverse Compton emission could be stronger at millimeter wavelengths than at lower frequencies. We present a search for pulsars close to Sgr A* using the data from the three most-sensitive stations in the EHT 2017 campaign: the Atacama Large Millimeter/submillimeter Array, the Large Millimeter Telescope and the IRAM 30 m Telescope. We apply three detection methods based on Fourier-domain analysis, the Fast-Folding-Algorithm and single pulse search targeting both pulsars and burst-like transient emission; using the simultaneity of the observations to confirm potential candidates. No new pulsars or significant bursts were found. Being the first pulsar search ever carried out at such high radio frequencies, we detail our analysis methods and give a detailed estimation of the sensitivity of the search. We conclude that the EHT 2017 observations are only sensitive to a small fraction ($\lesssim$2.2%) of the pulsars that may exist close to Sgr A*, motivating further searches for fainter pulsars in the region., Comment: 33 pages, 7 figures, 6 Tables. Accepted for publication in ApJ

PURPOSE: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN), a form of deep learning, are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing the initial CT scan on a CNN based algorithm. METHODS: Retrospective multicentric study of a consecutive cohort of patients with SAH between 2011-2022. Demographic, clinical and radiological variables were analyzed. Pre-processed baseline CT scan images were used as the input for training a CNN using AUCMEDI Framework. Our model's architecture leverages the DenseNet-121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Performance of the model was evaluated by statistical parameters conventionally used in studies involving artificial intelligence methods. RESULTS: Images from 219 patients were processed, 175 for training and validation of the CNN and 44 for its evaluation. 52%(115/219) of patients were female, and the median age was 58(SD=13.06) years. 18.5%(39/219) were idiopathic SAH. Mortality rate was 28.5%(63/219). The model showed good accuracy at predicting mortality in SAH patients exclusively using the images of the initial CT scan (Accuracy=74%, F1=75% and AUC=82%). CONCLUSION: Modern image processing techniques based on AI and CNN make possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients resulting in better training, development and performance on tasks which are beyond the skills of conventional clinical knowledge.

Exploration and reward specification are fundamental and intertwined challenges for reinforcement learning. Solving sequential decision-making tasks requiring expansive exploration requires either careful design of reward functions or the use of novelty-seeking exploration bonuses. Human supervisors can provide effective guidance in the loop to direct the exploration process, but prior methods to leverage this guidance require constant synchronous high-quality human feedback, which is expensive and impractical to obtain. In this work, we present a technique called Human Guided Exploration (HuGE), which uses low-quality feedback from non-expert users that may be sporadic, asynchronous, and noisy. HuGE guides exploration for reinforcement learning not only in simulation but also in the real world, all without meticulous reward specification. The key concept involves bifurcating human feedback and policy learning: human feedback steers exploration, while self-supervised learning from the exploration data yields unbiased policies. This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses. HuGE is able to learn a variety of challenging multi-stage robotic navigation and manipulation tasks in simulation using crowdsourced feedback from non-expert users. Moreover, this paradigm can be scaled to learning directly on real-world robots, using occasional, asynchronous feedback from human supervisors.

We report the results of an observational campaign using the Effelsberg 100-m telescope of the pulsars J1746$-$2849, J1746$-$2850, J1746$-$2856 and J1745$-$2912 located in the Central Molecular Zone (CMZ) close to the Galactic centre in order to study rotation measure (RM) variations. We report for the first time the RM value of PSR J1746$-$2850 to be $-12234 \pm 181$ rad m$^{-2}$. This pulsar shows significant variations of RM of $300-400$ rad m$^{-2}$ over the course of months to years that suggest a strongly magnetized environment. The structure function analysis of the RM of PSR J1746$-$2850 revealed a steep power-law index of $1.87_{-0.3}^{+0.4}$ comparable to the value expected for isotropic turbulence. This pulsar also showed large dispersion measure (DM) variation of $\sim 50$ pc cm$^{-3}$ in an event lasting a few months where the RM increased by $\sim 200$ rad m$^{-2}$. The large difference in RM between PSR J1746$-$2849 and PSR J1746$-$2850 despite the small angular separation reveals the presence of a magnetic field of at least 70 $\mu$G in the CMZ and can explain the lack of polarization in the radio images of the region. These results contribute to our understanding of the magnetic field in the CMZ and show similarities between the RM behaviours of these pulsars and some fast radio bursts (FRBs)., Comment: Accepted for publication on Monthly Notices of the Royal Astronomical Society, 13 pages, 7 figures

BlackHoleCam is a project funded by a European Research Council Synergy Grant to build a complete astrophysical description of nearby supermassive black holes by using a combination of radio imaging, pulsar observations, stellar astrometry and general relativistic magneto-hydrodynamic models. BlackHoleCam scientists are active partners of the Event Horizon Telescope Consortium. In this talk I will discuss the use of pulsars orbiting Sagittarius A* for tests of General Relativity, the current difficulties in detecting such sources, recent results from the Galactic Centre magnetar PSR J1745-2900 and how BlackHoleCam aims to search for undiscovered pulsars in the Galactic Centre., Comment: 4 pages, 3 figures, Proceedings of The Fifteenth Marcel Grossmann Meeting on General Relativity, University of Rome La Sapienza, Italy, 1 - 7 July 2018

The nearby radio galaxy M87 is a prime target for studying black hole accretion and jet formation^{1,2}. Event Horizon Telescope observations of M87 in 2017, at a wavelength of 1.3 mm, revealed a ring-like structure, which was interpreted as gravitationally lensed emission around a central black hole^3. Here we report images of M87 obtained in 2018, at a wavelength of 3.5 mm, showing that the compact radio core is spatially resolved. High-resolution imaging shows a ring-like structure of 8.4_{-1.1}^{+0.5} Schwarzschild radii in diameter, approximately 50% larger than that seen at 1.3 mm. The outer edge at 3.5 mm is also larger than that at 1.3 mm. This larger and thicker ring indicates a substantial contribution from the accretion flow with absorption effects in addition to the gravitationally lensed ring-like emission. The images show that the edge-brightened jet connects to the accretion flow of the black hole. Close to the black hole, the emission profile of the jet-launching region is wider than the expected profile of a black-hole-driven jet, suggesting the possible presence of a wind associated with the accretion flow., Comment: 50 pages, 18 figures, 3 tables, author's version of the paper published in Nature

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U , and V respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties., Comment: Accepted for publication in ApJ

We report measurements of the gravitationally lensed secondary image -- the first in an infinite series of so-called "photon rings" -- around the supermassive black hole M87* via simultaneous modeling and imaging of the 2017 Event Horizon Telescope (EHT) observations. The inferred ring size remains constant across the seven days of the 2017 EHT observing campaign and is consistent with theoretical expectations, providing clear evidence that such measurements probe spacetime and a striking confirmation of the models underlying the first set of EHT results. The residual diffuse emission evolves on timescales comparable to one week. We are able to detect with high significance a southwestern extension consistent with that expected from the base of a jet that is rapidly rotating in the clockwise direction. This result adds further support to the identification of the jet in M87* with a black hole spin-driven outflow, launched via the Blandford-Znajek process. We present three revised estimates for the mass of M87* based on identifying the modeled thin ring component with the bright ringlike features seen in simulated images, one of which is only weakly sensitive to the astrophysics of the emission region. All three estimates agree with each other and previously reported values. Our strongest mass constraint combines information from both the ring and the diffuse emission region, which together imply a mass-to-distance ratio of $4.20^{+0.12}_{-0.06}~\mu{\rm as}$ and a corresponding black hole mass of $(7.13\pm0.39)\times10^9M_\odot$, where the error on the latter is now dominated by the systematic uncertainty arising from the uncertain distance to M87*.

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data., Comment: 17 pages, 10 figures, 30 supplementary pages

We present the first detection of pulsations from a neutron star in the submillimeter range. The source is the magnetar XTE J1810-197, observed with the James Clerk Maxwell Telescope (JCMT) on 2020 February 27, 2020 July 9 and 2021 May 15. XTE J1810-197 is detected at 353 GHz ($\lambda=0.85\,$mm) in the three epochs, but not detected in the simultaneously-observed band at 666 GHz ($\lambda=0.45\,$mm). We measure an averaged flux density at 353 GHz of 6.7$\pm$1.0, 4.0$\pm$0.6, and 1.3$\pm$0.3 mJy and set 3$\sigma$ flux density upper limits at 666 GHz of 11.3, 4.7 and 4.3 mJy, at each of the three observing epochs, respectively. Combining close-in-time observations with the Effelsberg 100m and IRAM 30m telescopes covering non-contiguously from 6 to 225 GHz (5.0 cm$>\lambda>$1.33 mm), we investigate the spectral shape and frequency range of a potential spectral turn-up predicted by some pulsar radio emission models. The results demonstrate that the beamed radio emission from neutron stars can extend into the submillimeter regime, but are inconclusive on the existence and location of a potential spectral turn-up within the covered frequency range. The observed properties of the submillimeter emission resemble those of the longer wavelengths, and support a coherent mechanism for the production of pulsations at 353 GHz., Comment: 14 pages, 2 tables, 5 figures. Accepted for publication in ApJ Letters

Very-long-baseline interferometry (VLBI) observations of active galactic nuclei at millimeter wavelengths have the power to reveal the launching and initial collimation region of extragalactic radio jets, down to $10-100$ gravitational radii ($r_g=GM/c^2$) scales in nearby sources. Centaurus A is the closest radio-loud source to Earth. It bridges the gap in mass and accretion rate between the supermassive black holes (SMBHs) in Messier 87 and our galactic center. A large southern declination of $-43^{\circ}$ has however prevented VLBI imaging of Centaurus A below ${\lambda}1$cm thus far. Here, we show the millimeter VLBI image of the source, which we obtained with the Event Horizon Telescope at $228$GHz. Compared to previous observations, we image Centaurus A's jet at a tenfold higher frequency and sixteen times sharper resolution and thereby probe sub-lightday structures. We reveal a highly-collimated, asymmetrically edge-brightened jet as well as the fainter counterjet. We find that Centaurus A's source structure resembles the jet in Messier 87 on ${\sim}500r_g$ scales remarkably well. Furthermore, we identify the location of Centaurus A's SMBH with respect to its resolved jet core at ${\lambda}1.3$mm and conclude that the source's event horizon shadow should be visible at THz frequencies. This location further supports the universal scale invariance of black holes over a wide range of masses., Comment: 27 pages, 9 figures. This is a post-peer-review, pre-copyedit version of an article published in Nature Astronomy. The final authenticated version is available online at: https://doi.org/10.1038/s41550-021-01417-w

The black-hole images obtained with the Event Horizon Telescope (EHT) are expected to be variable at the dynamical timescale near their horizons. For the black hole at the center of the M87 galaxy, this timescale (5-61 days) is comparable to the 6-day extent of the 2017 EHT observations. Closure phases along baseline triangles are robust interferometric observables that are sensitive to the expected structural changes of the images but are free of station-based atmospheric and instrumental errors. We explored the day-to-day variability in closure phase measurements on all six linearly independent non-trivial baseline triangles that can be formed from the 2017 observations. We showed that three triangles exhibit very low day-to-day variability, with a dispersion of $\sim3-5^\circ$. The only triangles that exhibit substantially higher variability ($\sim90-180^\circ$) are the ones with baselines that cross visibility amplitude minima on the $u-v$ plane, as expected from theoretical modeling. We used two sets of General Relativistic magnetohydrodynamic simulations to explore the dependence of the predicted variability on various black-hole and accretion-flow parameters. We found that changing the magnetic field configuration, electron temperature model, or black-hole spin has a marginal effect on the model consistency with the observed level of variability. On the other hand, the most discriminating image characteristic of models is the fractional width of the bright ring of emission. Models that best reproduce the observed small level of variability are characterized by thin ring-like images with structures dominated by gravitational lensing effects and thus least affected by turbulence in the accreting plasmas., Comment: Accepted for Publication in ApJ

There have been several successful deep learning models that perform audio super-resolution. Many of these approaches involve using preprocessed feature extraction which requires a lot of domain-specific signal processing knowledge to implement. Convolutional Neural Networks (CNNs) improved upon this framework by automatically learning filters. An example of a convolutional approach is AudioUNet, which takes inspiration from novel methods of upsampling images. Our paper compares the pre-upsampling AudioUNet to a new generative model that upsamples the signal before using deep learning to transform it into a more believable signal. Based on the EDSR network for image super-resolution, the newly proposed model outperforms UNet with a 20% increase in log spectral distance and a mean opinion score of 4.06 compared to 3.82 for the two times upsampling case. AudioEDSR also has 87% fewer parameters than AudioUNet. How incorporating AudioUNet into a Wasserstein GAN (with gradient penalty) (WGAN-GP) structure can affect training is also explored. Finally the effects artifacting has on the current state of the art is analysed and solutions to this problem are proposed. The methods used in this paper have broad applications to telephony, audio recognition and audio generation tasks.

The high stellar density in the central parsecs around the Galactic Centre makes it a seemingly favourable environment for finding relativistic binary pulsars. These include pulsars orbiting other neutron stars, stellar-mass black holes or the central supermassive black hole, Sagittarius A*. Here we present multi-epoch pulsar searches of the Galactic Centre at four observing frequencies, (4.85, 8.35, 14.6 18.95) GHz, using the Effelsberg 100-m radio telescope. Observations were conducted one year prior to the discovery of, and during monitoring observations of, the Galactic Centre magnetar PSR J1745-2900. Our data analysis features acceleration searches on progressively shorter time series to maintain sensitivity to relativistic binary pulsars. The multi-epoch observations increase the likelihood of discovering transient or nulling pulsars, or ensure orbital phases are observed at which acceleration search methods work optimally. In ~147 h of separate observations, no previously undiscovered pulsars have been detected. Through calibration observations, we conclude this might be due to insufficient instantaneous sensitivity; caused by the intense continuum emission from the Galactic Centre, its large distance and, at higher frequencies, the aggregate effect of steep pulsar spectral indices and atmospheric contributions to the system temperature. Additionally we find that for millisecond pulsars in wide circular orbits ~<800 d around Sagittarius A*, linear acceleration effects cannot be corrected in deep observations (9 h) with existing software tools. Pulsar searches of the Galactic Centre with the next generation of radio telescopes - such as MeerKat, ngVLA and SKA1-mid - will have improved chances of uncovering this elusive population., Comment: 17 pages, 12 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Society

Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. Through ablation experiments, we prove that the combination of multiple biological modalities (i.e multi-omics) leads to powerful embeddings and better link prediction performances. Our evaluation shows that the integration of gene methylation profiles and RNA-sequencing data significantly improves the link prediction performance. Overall, the combination of RNA-sequencing and gene methylation data leads to a link prediction accuracy of 71% on GGI networks. By harnessing graph representation learning on multi-omics data, our work brings novel insights to the current literature on multi-omics integration in bioinformatics., Comment: This paper was accepted at the 2021 ICML Workshop on Computational Biology