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The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Precision agriculture leverages data and machine learning so that farmers can monitor their crops and target interventions precisely. This enables the precision application of herbicide only to weeds, or the precision application of fertilizer only to undernourished crops, rather than to the entire field. The approach promises to maximize yields while minimizing resource use and harm to the surrounding environment. To this end, we propose a hierarchical panoptic segmentation method that simultaneously determines leaf count (as an identifier of plant growth)and locates weeds within an image. In particular, our approach aims to improve the segmentation of smaller instances like the leaves and weeds by incorporating focal loss and boundary loss. Not only does this result in competitive performance, achieving a PQ+ of 81.89 on the standard training set, but we also demonstrate we can improve leaf-counting accuracy with our method. The code is available at https://github.com/madeleinedarbyshire/HierarchicalMask2Former., Comment: Presented at the 9th Workshop for Computer Vision in Plant Phenotyping and Agriculture (CVPPA) 2024 at the European Conference of Computer Vision (ECCV) 2024. arXiv admin note: text overlap with arXiv:2310.06582

Laser-based displays are highly sought after for their superior brightness and color performance, especially in advanced applications like augmented reality (AR). However, their broader adoption has been hindered by bulky projector designs and complex optical module assemblies. Here, we introduce a new laser display architecture enabled by large-scale visible photonic integrated circuits (PICs) to address these challenges. Unlike previous projector-style laser displays, this architecture features an ultra-thin, flat-panel form factor, replacing bulky free-space illumination modules with a single, high-performance photonic chip. Centimeter-scale PIC devices, which integrate thousands of distinct optical components on-chip, are carefully tailored to achieve high display uniformity, contrast, and efficiency. We demonstrate a 2 mm-thick flat-panel laser display combining the PIC with a liquid-crystal-on-silicon (LCoS) panel, achieving 211% of the color gamut and more than 80% volume reduction compared to traditional LCoS displays. We further showcase its application in a see-through AR system. Our work represents a major advancement in the integration of nanophotonics with display technology, enabling a range of new display concepts, from high-performance immersive displays to slim-panel 3D holography.

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

Studies have underscored how, regardless of the recent breakthrough and swift advances in AI research, even state-of-the-art Large Language models (LLMs) continue to struggle when performing logical and mathematical reasoning. The results seem to suggest that LLMs still work as (highly advanced) data pattern identifiers, scoring poorly when attempting to generalise and solve reasoning problems the models have never previously seen or that are not close to samples presented in their training data. To address this compelling concern, this paper makes use of the notion of critical questions from the literature on argumentation theory, focusing in particular on Toulmin's model of argumentation. We show that employing these critical questions can improve the reasoning capabilities of LLMs. By probing the rationale behind the models' reasoning process, the LLM can assess whether some logical mistake is occurring and correct it before providing the final reply to the user prompt. The underlying idea is drawn from the gold standard of any valid argumentative procedure: the conclusion is valid if it is entailed by accepted premises. Or, to paraphrase such Aristotelian principle in a real-world approximation, characterised by incomplete information and presumptive logic, the conclusion is valid if not proved otherwise. This approach successfully steers the models' output through a reasoning pipeline, resulting in better performance against the baseline and its Chain-of-Thought (CoT) implementation. To this end, an extensive evaluation of the proposed approach on the MT-Bench Reasoning and Math tasks across a range of LLMs is provided.

We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated. Traditional machine learning approaches, which require costly retraining with each taxonomy update, become impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. However, applying them to large and dynamic taxonomies presents unique challenges as the vast number of labels can exceed LLMs' input limits. In this paper, we present novel methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. Our approach avoids retraining by leveraging zero-shot HMC for real-time label assignment. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines, and demonstrate significant improvements in both classification accuracy and cost-efficiency. By developing a tailored evaluation framework for dynamic taxonomies and publicly releasing our code, this research provides critical insights into applying LLMs for document classification, where the number of classes corresponds to the number of nodes in a large taxonomy, at an industrial scale., Comment: This paper has been accepted at COLING 2025 (Industry Track)

The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management. Radiometric imaging provides per-pixel temperature estimates, a valuable improvement over non-radiometric data that requires irradiance measurements to be converted into visible images using RGB color palettes. Despite its benefits, this technology has been underutilized largely due to a lack of available data for researchers. This study addresses this gap by introducing methods for collecting and processing synchronized visual spectrum and radiometric thermal imagery using UAVs at prescribed fires. The included imagery processing pipeline drastically simplifies and partially automates each step from data collection to neural network input. Further, we present the FLAME 3 dataset, the first comprehensive collection of side-by-side visual spectrum and radiometric thermal imagery of wildland fires. Building on our previous FLAME 1 and FLAME 2 datasets, FLAME 3 includes radiometric thermal Tag Image File Format (TIFFs) and nadir thermal plots, providing a new data type and collection method. This dataset aims to spur a new generation of machine learning models utilizing radiometric thermal imagery, potentially trivializing tasks such as aerial wildfire detection, segmentation, and assessment. A single-burn subset of FLAME 3 for computer vision applications is available on Kaggle with the full 6 burn set available to readers upon request., Comment: 12 pages, 8 Figures, 8 Tables

Australia's National Science Agency conducted a six-month trial of M365 Copilot starting in January 2024 as part of an Australian Government initiative. Three hundred licenses were distributed across CSIRO using a persona-based approach to ensure diversity of roles and attributes among participants. As a scientific research organisation with a unique operational context, our objective was to study the use of M365 Copilot on enhancing productivity, efficiency, and creativity as well as overall satisfaction and ethical considerations. This paper presents the results from two surveys, conducted before and after the trial. The results showed mixed outcomes, with participants reporting improved productivity and efficiency in structured tasks such as meeting summaries and email drafting. Gaps were identified in advanced functionalities and integration, which limited Copilot's overall effectiveness. Satisfaction levels were generally positive, but user experiences varied in certain areas. Ethical concerns, particularly around data privacy, became more pronounced post-trial. These findings highlight Copilot's potential and the need for further refinement to meet users' diverse needs.

Type Ia supernovae, critical for studying cosmic expansion, arise from thermonuclear explosions of white dwarfs, but their precise progenitor pathways remain unclear. Growing evidence supports the ``double-degenerate'' scenario, where two white dwarfs interact. The absence of other companion types capable of explaining the observed Ia rate, along with observations of hyper-velocity white dwarfs interpreted as surviving companions of such systems provide compelling evidence in favor of this scenario. Upcoming millihertz gravitational wave observatories like the Laser Interferometer Space Antenna (LISA) are expected to detect thousands of double-degenerate systems, though the most compact known candidate Ia progenitors produce only marginally detectable gravitational wave signals. Here, we report observations of ATLAS J1138-5139, a binary white dwarf system with an orbital period of 28 minutes. Our analysis reveals a 1 solar mass carbon-oxygen white dwarf accreting from a helium-core white dwarf. Given its mass, the accreting carbon-oxygen white dwarf is poised to trigger a typical-luminosity Type Ia supernova within a few million years, or to evolve into a stably mass-transferring AM CVn system. ATLAS J1138-5139 provides a rare opportunity to calibrate binary evolution models by directly comparing observed orbital parameters and mass transfer rates closer to merger than any previously identified candidate Type Ia progenitor. Its compact orbit ensures detectability by LISA, demonstrating the potential of millihertz gravitational wave observatories to reveal a population of Type Ia progenitors on a Galactic scale, paving the way for multi-messenger studies offering insights into the origins of these cosmologically significant explosions., Comment: 40 pages, 7 figures, 2 tables

We report the discovery of three ultracompact binary white dwarf systems hosting accretion disks, with orbital periods of 7.95, 8.68, and 13.15 minutes. This significantly augments the population of mass-transferring binaries at the shortest periods, and provides the first evidence that accretors in ultracompacts can be dense enough to host accretion disks even below 10 minutes (where previously only direct-impact accretors were known). In the two shortest-period systems, we measured changes in the orbital periods driven by the combined effect of gravitational wave emission and mass transfer; we find $\dot{P}$ is negative in one case, and positive in the other. This is only the second system measured with a positive $\dot{P}$, and it the most compact binary known that has survived a period minimum. Using these systems as examples, we show how the measurement of $\dot{P}$ is a powerful tool in constraining the physical properties of binaries, e.g. the mass and mass-radius relation of the donor stars. We find that the chirp masses of ultracompact binaries at these periods seem to cluster around $\mathcal{M}_c \sim 0.3 M_\odot$, perhaps suggesting a common origin for these systems or a selection bias in electromagnetic discoveries. Our new systems are among the highest-amplitude known gravitational wave sources in the millihertz regime, providing exquisite opportunity for multi-messenger study with future space-based observatories such as \textit{LISA} and TianQin; we discuss how such systems provide fascinating laboratories to study the unique regime where the accretion process is mediated by gravitational waves., Comment: Accepted for publication in ApJ

The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures; Replaced to match accepted ApJ version. New version contains small editorial changes throughout in response to referee comments, no changes to results

Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate events from 0.3 to 40 TeV with the High Energy Stereoscopic System (H.E.S.S.), covering two orders of magnitude in energy and reaching a proton rejection power of better than $10^{4}$. The measured spectrum is well described by a broken power law, with a break around 1 TeV, where the spectral index increases from $\Gamma_1 = 3.25$ $\pm$ 0.02 (stat) $\pm$ 0.2 (sys) to $\Gamma_2 = 4.49$ $\pm$ 0.04 (stat) $\pm$ 0.2 (sys). Apart from the break, the spectrum is featureless. The absence of distinct signatures at multi-TeV energies imposes constraints on the presence of nearby CRe accelerators and the local CRe propagation mechanisms., Comment: main paper: 8 pages, 4 figures, supplemental material: 12 pages, 14 figures, accepted for publication in Physical Review Letters https://journals.aps.org/prl/

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

We describe an inexpensive and accessible instructional setup which explores ion trapping with a planar linear ion trap. The planar trap is constructed using standard printed circuit board manufacturing and is designed to trap macroscopic charged particles in air. Trapping, shuttling, and splitting is demonstrated to students using these particles, visible to the naked eye. Students have control over trap voltages and can compare properties of particle motion to an analytic model of the trap using a computer vision program for particle tracking. Learning outcomes include understanding the design considerations for planar RF traps, mechanisms underpinning ion ejection, the physics of micromotion, and methods of data analysis using standard computer vision libraries., Comment: 22 pages, 5 figures

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Extremely-low-mass white dwarfs (ELM WDs) with non-degenerate companions are believed to originate from solar-type main-sequence binaries undergoing stable Roche lobe overflow mass transfer when the ELM WD progenitor is at (or just past) the termination of the main-sequence. This implies that the orbital period of the binary at the onset of the first mass transfer phase must have been $\lesssim 3-5$ d. This prediction in turn suggests that most of these binaries should have tertiary companions since $\approx 90$ per cent of solar-type main-sequence binaries in that period range are inner binaries of hierarchical triples. Until recently, only precursors of this type of binaries have been observed in the form of EL CVn binaries, which are also known for having tertiary companions. Here, we present high-angular-resolution images of TYC 6992-827-1, an ELM WD with a sub-giant (SG) companion, confirming the presence of a tertiary companion. Furthermore, we show that TYC 6992-827-1, along with its sibling TYC 8394-1331-1 (whose triple companion was detected via radial velocity variations), are in fact descendants of EL CVn binaries. Both TYC 6992-827-1 and TYC 8394-1331-1 will evolve through a common envelope phase, which depending on the ejection efficiency of the envelope, might lead to a single WD or a tight double WD binary, which would likely merge into a WD within a few Gyr due to gravitational wave emission. The former triple configuration will be reduced to a wide binary composed of a WD (the merger product) and the current tertiary companion., Comment: 8 pages, 5 figures, 1 table. Accepted for publication in MNRAS

The Helix is a visually striking and the nearest planetary nebula, yet any companions responsible for its asymmetric morphology have yet to be identified. In 2020, low-amplitude photometric variations with a periodicity of 2.8 d were reported based on Cycle 1 TESS observations. In this work, with the inclusion of two additional sectors, these periodic light curves are compared with lcurve simulations of irradiated companions in such an orbit. Based on the light curve modelling, there are two representative solutions: i) a Jupiter-sized body with 0.102 Rsol and an arbitrarily small orbital inclination i=1 deg, and ii) a 0.021 Rsol exoplanet with i approx. 25 deg, essentially aligned with the Helix Nebular inclination. Irradiated substellar companion models with equilibrium temperature 4970 K are constructed and compared with existing optical spectra and infrared photometry, where Jupiter-sized bodies can be ruled out, but companions modestly larger than Neptune are still allowed. Additionally, any spatially-unresolved companions are constrained based on the multi-wavelength, photometric spectral energy distribution of the central star. No ultracool dwarf companion earlier than around L5 is permitted within roughly 1200 au, leaving only faint white dwarfs and cold brown dwarfs as possible surviving architects of the nebular asymmetries. While a planetary survivor is a tantalizing possibility, it cannot be ruled out that the light curve modulation is stellar in nature, where any substellar companion requires confirmation and may be possible with JWST observations., Comment: 9 pages, 7 figures, 1 table, accepted to MNRAS

We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private information beyond training data reconstruction. Motivated by the low reconstruction quality of existing methods, we propose a novel gradient inversion attack that applies a denoising diffusion model as a strong image prior in order to enhance recovery in the large batch setting. Unlike traditional attacks, which aim to reconstruct individual samples and suffer at large batch and image sizes, our approach instead aims to recover a representative image that captures the sensitive shared semantic information corresponding to the underlying user. Our experiments with face images demonstrate the ability of our methods to recover realistic facial images along with private user attributes., Comment: Presented at the International Workshop on Federated Learning in the Age of Foundation Models in conjunction with NeurIPS 2023

In distributed learning settings, models are iteratively updated with shared gradients computed from potentially sensitive user data. While previous work has studied various privacy risks of sharing gradients, our paper aims to provide a systematic approach to analyze private information leakage from gradients. We present a unified game-based framework that encompasses a broad range of attacks including attribute, property, distributional, and user disclosures. We investigate how different uncertainties of the adversary affect their inferential power via extensive experiments on five datasets across various data modalities. Our results demonstrate the inefficacy of solely relying on data aggregation to achieve privacy against inference attacks in distributed learning. We further evaluate five types of defenses, namely, gradient pruning, signed gradient descent, adversarial perturbations, variational information bottleneck, and differential privacy, under both static and adaptive adversary settings. We provide an information-theoretic view for analyzing the effectiveness of these defenses against inference from gradients. Finally, we introduce a method for auditing attribute inference privacy, improving the empirical estimation of worst-case privacy through crafting adversarial canary records.

UW Coronae Borealis (UW CrB) is a low mass X-ray binary that shows both Type 1 X-ray and optical bursts, which typically last for 20 s. The system has a binary period of close to 2 hours and is thought to have a relatively high inclination due to the presence of an eclipse in the optical light curve. There is also evidence that an asymmetric disc is present in the system, which precesses every 5.5 days based on changes in the depth of the eclipse. In this paper, we present optical photometry and spectroscopy of UW CrB taken over 2 years. We update the orbital ephemeris using observed optical eclipses and refine the orbital period to 110.97680(1) min. A total of 17 new optical bursts are presented, with 10 of these bursts being resolved temporally. The average $e$-folding time of $19\pm3$s for the bursts is consistent with the previously found value. Optical bursts are observed during a previously identified gap in orbital phase centred on $\phi=0.967$, meaning the reprocessing site is not eclipsed as previously thought. Finally, we find that the apparent P-Cygni profiles present in some of the atomic lines in the optical spectra are due to transient absorption., Comment: 14 pages, 17 figures, submitted to OJAp

The cadmium zinc TElluride Radiation Imager, or TERI, is an instrument to space qualify large-volume $4 \times 4 \times 1.5 \ \mathrm{cm}^3$ pixelated CdZnTe (CZT) detector technology. The CZT's anode is composed of a $22 \times 22$ array of pixels while the cathode is planar. TERI will contain four of those crystals with each pixel having an energy range of $40 \ \mathrm{keV}$ up to $3 \ \mathrm{MeV}$ with a resolution of $1.3 \%$ full-width-at-half maximum at $662 \ \mathrm{keV}$ all while operating in room temperature. As the detectors are 3D position sensitive, TERI can Compton image events. TERI is fitted with a coded-aperture mask which permits imaging low energy photons in the photoelectric regime. TERI's primary mission is to space-qualify large-volume CZT and measure its degradation due to radiation damage in a space environment. Its secondary mission includes detecting and localizing astrophysical gamma-ray transients. TERI is manifested on DoD's STP-H10 mission for launch to the International Space Station in early 2025.

We presents an approach for early cycle classification of lithium-ion batteries into high and low-performing categories, coupled with the prediction of their remaining useful life (RUL) using a linear lasso technique. Traditional methods often rely on extensive cycling and the measurement of a large number of electrochemical impedance spectroscopy (EIS) frequencies to assess battery performance, which can be time and resource consuming. In this study, we propose a methodology that leverages specific EIS frequencies to achieve accurate classification and RUL prediction within the first few cycles of battery operation. Notably, given only the 20 kHz impedance response, our support vector machine (SVM) model classifies batteries with 100\% accuracy. Additionally, our findings reveal that battery performance classification is frequency agnostic within the high frequency ($<20$ kHz) to low-frequency (32 mHz) range. Our model also demonstrates accurate RUL predictions with $R^2>0.96$ based on the out of phase impedance response at a single high (20 kHz) and a single mid-frequency (8.8 Hz), in conjunction with temperature data. This research underscores the significance of the mid-frequency impedance response as merely one among several crucial features in determining battery performance, thereby broadening the understanding of factors influencing battery behavior., Comment: 10 pages, 4 figures

We describe Na0.67Mn0.625Fe0.25Co0.125O2 (NMFCO), a P2-type sodium-ion battery cathode. Our composition, with significantly less Co than in an earlier study, shows discharge capacity close to 190 mAhg-1 and specific energy density exceeding 500 mWhg-1 in the 1.5 to 4.3 V range. The material also shows an improved structural stability over similar materials. Such changes, between the pristine phase (P63/mmc, P63 (OP4), or orthorhombic Cmcm) and the so-called Z phase, are endemic to other P2-type cathodes such as Na0.67Mn0.65Fe0.35O2 (NMFO). We propose two equivalent circuit models of impedance spectroscopy to understand electrochemical processes in our cells with a sodium metal anode. Our equivalent circuit modeling, combined with an analysis of the initial galvanostatic slope, reveals a significant reduction in the polarization and interfacial charge-transfer resistance at the solid electrolyte interface. We reveal that the combined effects of crystal structure stability, lower internal resistance, relatively high specific energy density, and improved battery health make this low-cobalt P2-type cathode composition a very promising candidate for new sodium-ion batteries., Comment: 49 pages, 9 figures

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

Context. B supergiants (BSGs) represent an important connection between the main sequence and more extreme evolutionary stages of massive stars. Additionally, lying toward the cool end of the hot star regime, determining their wind properties is crucial to constrain the evolution and feedback of massive stars as, for instance, they might manifest the bi-stability jump phenomenon. Aims. We undertake a detailed analysis of a representative sample of 18 Small Magellanic Cloud (SMC) BSGs within the ULLYSES and XShootU datasets. Our UV and optical analysis spans BSGs from B0 to B8 - covering the bi-stability jump region. We aim to evaluate their evolutionary status and verify what their wind properties say about the bi-stability jump in a low-metallicity environment. Methods. We used the CMFGEN to model the spectra and photometry (from UV to infrared) of our sample. We compare our results with different evolutionary models, with previous determinations in the literature of OB stars, and with diverging mass-loss recipes at the bi-stability jump. Additionally, we provide the first BSG models in the SMC including X-rays. Results. (i) Within a single-stellar evolution framework, the evolutionary status of early BSGs seem less clear than that of late BSGs, which agree with H-shell burning models. (ii) UV analysis shows evidence that BSGs contain X-rays in their atmospheres, for which we provide constraints. In general, we find higher X-ray luminosity (close to the standard log(L_X/L) ~ -7) for early BSGs. For cooler BSGs, lower values are preferred, log(L_X/L) ~ -8.5. (iii) The obtained mass-loss rates suggest neither a jump nor a monotonic decrease with temperature. Instead, a rather constant trend is observed, which is at odds with the increase found for Galactic BSGs. (iv) The wind velocity behavior with temperature shows a sharp drop at ~19 kK, similar to what is observed for Galactic BSGs., Comment: 33 pages (23+10)+(22 at Zenodo), 34 figures (21+13)+(21 at Zenodo), 7 tables (3+4)+(1 at Zenodo), accepted for publication

Radiation reaction describes the effective force experienced by an accelerated charge due to radiation emission. Quantum effects dominate charge dynamics and radiation production[1][2] for charges accelerated by fields with strengths approaching the Schwinger field, $\mathbf{E_{sch}=}$\textbf{\SI[detect-weight]{1.3e18}{\volt\per\metre}[3]. Such fields exist in extreme astrophysical environments such as pulsar magnetospheres[4], may be accessed by high-power laser systems[5-7], dense particle beams interacting with plasma[8], crystals[9], and at the interaction point of next generation particle colliders[10]. Classical radiation reaction theories do not limit the frequency of radiation emitted by accelerating charges and omit stochastic effects inherent in photon emission[11], thus demanding a quantum treatment. Two quantum radiation reaction models, the quantum-continuous[12] and quantum-stochastic[13] models, correct the former issue, while only the quantum-stochastic model incorporates stochasticity[12]. Such models are of fundamental importance, providing insight into the effect of the electron self-force on its dynamics in electromagnetic fields. The difficulty of accessing conditions where quantum effects dominate inhibited previous efforts to observe quantum radiation reaction in charged particle dynamics with high significance. We report the first direct, high significance $(>5{\sigma})$ observation of strong-field radiation reaction on charged particles. Furthermore, we obtain strong evidence favouring the quantum radiation reaction models, which perform equivalently, over the classical model. Robust model comparison was facilitated by a novel Bayesian framework which inferred collision parameters. This framework has widespread utility for experiments where parameters governing lepton-laser collisions cannot be directly measured, including those using conventional accelerators., Comment: 31 pages, 14 figures

Randomized smoothing is a defensive technique to achieve enhanced robustness against adversarial examples which are small input perturbations that degrade the performance of neural network models. Conventional randomized smoothing adds random noise with a fixed noise level for every input sample to smooth out adversarial perturbations. This paper proposes a new variational framework that uses a per-sample noise level suitable for each input by introducing a noise level selector. Our experimental results demonstrate enhancement of empirical robustness against adversarial attacks. We also provide and analyze the certified robustness for our sample-wise smoothing method., Comment: 20 pages, under preparation

Acoustic Soft Tactile (AST) skin is a novel sensing technology which derives tactile information from the modulation of acoustic waves travelling through the skin's embedded acoustic channels. A generalisable data-driven calibration model maps the acoustic modulations to the corresponding tactile information in the form of contact forces with their contact locations and contact geometries. AST skin technology has been highlighted for its easy customisation. As a case study, this paper discusses the possibility of using AST skin on a custom-built robotic end effector finger for strawberry handling. The paper delves into the design, prototyping, and calibration method to sensorise the end effector finger with AST skin. A real-time force-controlled gripping experiment is conducted with the sensorised finger to handle strawberries by their peduncle. The finger could successfully grip the strawberry peduncle by maintaining a preset force of 2 N with a maximum Mean Absolute Error (MAE) of 0.31 N over multiple peduncle diameters and strawberry weight classes. Moreover, this study sets confidence in the usability of AST skin in generating real-time tactile feedback for robot manipulation tasks., Comment: TAROS 2024

PSR B1259-63 is a gamma-ray binary system that hosts a pulsar in an eccentric orbit, with a 3.4 year period, around an O9.5Ve star. At orbital phases close to periastron passages, the system radiates bright and variable non-thermal emission. We report on an extensive VHE observation campaign conducted with the High Energy Stereoscopic System, comprised of ~100 hours of data taken from $t_p-24$ days to $t_p+127$ days around the system's 2021 periastron passage. We also present the timing and spectral analyses of the source. The VHE light curve in 2021 is consistent with the stacked light curve of all previous observations. Within the light curve, we report a VHE maximum at times coincident with the third X-ray peak first detected in the 2021 X-ray light curve. In the light curve -- although sparsely sampled in this time period -- we see no VHE enhancement during the second disc crossing. In addition, we see no correspondence to the 2021 GeV flare in the VHE light curve. The VHE spectrum obtained from the analysis of the 2021 dataset is best described by a power law of spectral index $\Gamma = 2.65 \pm 0.04_{\text{stat}}$ $\pm 0.04_{\text{sys}}$, a value consistent with the previous H.E.S.S. observations of the source. We report spectral variability with a difference of $\Delta \Gamma = 0.56 ~\pm~ 0.18_{\text{stat}}$ $~\pm~0.10_{\text{sys}}$ at 95% c.l., between sub-periods of the 2021 dataset. We also find a linear correlation between contemporaneous flux values of X-ray and TeV datasets, detected mainly after $t_p+25$ days, suggesting a change in the available energy for non-thermal radiation processes. We detect no significant correlation between GeV and TeV flux points, within the uncertainties of the measurements, from $\sim t_p-23$ days to $\sim t_p+126$ days. This suggests that the GeV and TeV emission originate from different electron populations., Comment: accepted to A&A

The imaging atmospheric Cherenkov technique provides potentially the highest angular resolution achievable in astronomy at energies above the X-ray waveband. High-resolution measurements provide the key to progress on many of the major questions in high-energy astrophysics, including the sites of particle acceleration to PeV energies. The potential of the next-generation CTA observatory in this regard can be realised with the help of improved algorithms for the reconstruction of the air-shower direction and energy. Hybrid methods combining likelihood-fitting techniques with neural networks represent a particularly promising approach and have recently been applied to the reconstruction of astrophysical neutrinos. Here, we present the FreePACT algorithm, a hybrid reconstruction method for IACTs. In this, making use of the neural ratio estimation technique from the field of likelihood-free inference, the analytical likelihood used in traditional image likelihood fitting is replaced by a neural network that approximates the charge probability density function for each pixel in the camera. The performance of this algorithm is demonstrated using simulations of the planned CTA southern array. For this setup, FreePACT provides significant performance improvements over analytical likelihood techniques, with improvements in angular and energy resolution of 25% or more over a wide energy range and an angular resolution as low as 40 arcseconds at energies above 50 TeV for observations at 20 degrees zenith angle. It also yields more accurate estimations of the uncertainties on the reconstructed parameters and speeds up the reconstruction compared to analytical likelihood techniques while showing the same stability with respect to changes in the observation conditions. Therefore, the FreePACT method is a promising upgrade over the current state-of-the-art likelihood event reconstruction techniques., Comment: 16 pages, 15 figures; Accepted for publication in Astroparticle Physics

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Interferometric experiments designed to detect the highly redshifted 21-cm signal from neutral hydrogen are producing increasingly stringent constraints on the 21-cm power spectrum, but some k-modes remain systematics-dominated. Mutual coupling is a major systematic that must be overcome in order to detect the 21-cm signal, and simulations that reproduce effects seen in the data can guide strategies for mitigating mutual coupling. In this paper, we analyse 12 nights of data from the Hydrogen Epoch of Reionization Array and compare the data against simulations that include a computationally efficient and physically motivated semi-analytic treatment of mutual coupling. We find that simulated coupling features qualitatively agree with coupling features in the data; however, coupling features in the data are brighter than the simulated features, indicating the presence of additional coupling mechanisms not captured by our model. We explore the use of fringe-rate filters as mutual coupling mitigation tools and use our simulations to investigate the effects of mutual coupling on a simulated cosmological 21-cm power spectrum in a "worst case" scenario where the foregrounds are particularly bright. We find that mutual coupling contaminates a large portion of the "EoR Window", and the contamination is several orders-of-magnitude larger than our simulated cosmic signal across a wide range of cosmological Fourier modes. While our fiducial fringe-rate filtering strategy reduces mutual coupling by roughly a factor of 100 in power, a non-negligible amount of coupling cannot be excised with fringe-rate filters, so more sophisticated mitigation strategies are required., Comment: 19 pages, 12 figures, submitted to MNRAS

The recent developments of Diffusion Models (DMs) enable generation of astonishingly high-quality synthetic samples. Recent work showed that the synthetic samples generated by the diffusion model, which is pre-trained on public data and fully fine-tuned with differential privacy on private data, can train a downstream classifier, while achieving a good privacy-utility tradeoff. However, fully fine-tuning such large diffusion models with DP-SGD can be very resource-demanding in terms of memory usage and computation. In this work, we investigate Parameter-Efficient Fine-Tuning (PEFT) of diffusion models using Low-Dimensional Adaptation (LoDA) with Differential Privacy. We evaluate the proposed method with the MNIST and CIFAR-10 datasets and demonstrate that such efficient fine-tuning can also generate useful synthetic samples for training downstream classifiers, with guaranteed privacy protection of fine-tuning data. Our source code will be made available on GitHub.

Decay energy spectrometry (DES) is a novel radiometric technique for high-precision analysis of nuclear materials. DES employs the unique thermal detection physics of cryogenic microcalorimeters with ultra-high energy resolution and 100$\%$ detection efficiency to accomplish high precision decay energy measurements. Low-activity nuclear samples of 1 Bq or less, and without chemical separation, are used to provide elemental and isotopic compositions in a single measurement. Isotopic ratio precisions of 1 ppm - 1,000 ppm (isotope dependent), which is close to that of the mass spectrometry, have been demonstrated in 12-hour DES measurements of ~5 Bq samples of certified reference materials of uranium (U) and plutonium (Pu). DES has very different systematic biases and uncertainties, as well as different sensitivities to nuclides, compared to mass-spectrometry techniques. Therefore, the accuracy and confidence of nuclear material assays can be improved by combining this new technique with existing mass-spectrometry techniques. Commercial-level DES techniques and equipment are being developed for the implementation of DES at the Nuclear Material Laboratory (NML) of International Atomic Energy Agency (IAEA) to provide complementary measurements to the existing technologies. The paper describes details of DES measurement methods, as well as DES precision and accuracy to U and Pu standard sources to discuss its capability in analysis of nuclear safeguards samples., Comment: This was submitted to 2022 IAEA symposium on nuclear safeguards (https://www.iaea.org/events/sg-2022), and posted at https://media.superevent.com/documents/20221027/668fdac0ee8d895ec6bcf293b1c42e6a/id-145.pdf

Recent years witnessed significant performance advancements in deep-learning-driven natural language models, with a strong focus on the development and release of Large Language Models (LLMs). These improvements resulted in better quality AI-generated output but rely on resource-expensive training and upgrading of models. Although different studies have proposed a range of techniques to enhance LLMs without retraining, none have considered computational argumentation as an option. This is a missed opportunity since computational argumentation is an intuitive mechanism that formally captures agents' interactions and the information conflict that may arise during such interplays, and so it seems well-suited for boosting the reasoning and conversational abilities of LLMs in a seamless manner. In this paper, we present a pipeline (MQArgEng) and preliminary study to evaluate the effect of introducing computational argumentation semantics on the performance of LLMs. Our experiment's goal was to provide a proof-of-concept and a feasibility analysis in order to foster (or deter) future research towards a fully-fledged argumentation engine plugin for LLMs. Exploratory results using the MT-Bench indicate that MQArgEng provides a moderate performance gain in most of the examined topical categories and, as such, show promise and warrant further research.

Carbon quantum dots have become attractive in various applications, such as drug delivery, biological sensing, photocatalysis, and solar cells. Among these, pH sensing via luminescence lifetime measurements of surface-functionalised carbon dots is one application currently investigated for their long lifetime and autonomous operation. In this manuscript, we explore the theoretical connection between excitation lifetimes and the pH value of the surrounding liquid via the protonation and deprotonation of functional groups. Example calculations applied to m-phenylenediamine, phloroglucinol and tethered disperse blue 1 are shown by applying a separation approach treating the electronic wavefunction of functional groups separately from the internal electronic structure of the (large) carbon dot. The bulk of the carbon dot is treated as an environment characterised by its optical spectrum that shifts the transition rates of the functional group. A simple relationship between pH, pKa and mixed fluorescence lifetime is derived from transition rates of the protonated and deprotonated states. pH sensitivity improves when the difference in transition rates is greatest between protonated and deprotonated species, with the greatest sensitivity found where the pKa is close to the pH region of interest. The introduced model can directly be extended to consider multicomponent liquids and multiple protonation states.

As the burden of herbicide resistance grows and the environmental repercussions of excessive herbicide use become clear, new ways of managing weed populations are needed. This is particularly true for cereal crops, like wheat and barley, that are staple food crops and occupy a globally significant portion of agricultural land. Even small improvements in weed management practices across these major food crops worldwide would yield considerable benefits for both the environment and global food security. Blackgrass is a major grass weed which causes particular problems in cereal crops in north-west Europe, a major cereal production area, because it has high levels of of herbicide resistance and is well adapted to agronomic practice in this region. With the use of machine vision and multispectral imaging, we investigate the effectiveness of state-of-the-art methods to identify blackgrass in wheat and barley crops. As part of this work, we provide a large dataset with which we evaluate several key aspects of blackgrass weed recognition. Firstly, we determine the performance of different CNN and transformer-based architectures on images from unseen fields. Secondly, we demonstrate the role that different spectral bands have on the performance of weed classification. Lastly, we evaluate the role of dataset size in classification performance for each of the models trialled. We find that even with a fairly modest quantity of training data an accuracy of almost 90% can be achieved on images from unseen fields., Comment: 19 pages, 6 figures