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GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

In this work, we propose a Path Integral Monte Carlo (PIMC) approach based on discretized continuous degrees of freedom and rejection-free Gibbs sampling. The ground state properties of a chain of planar rotors with dipole-dipole interactions are used to illustrate the approach. Energetic and structural properties are computed and compared to exact diagonalization and Numerical Matrix Multiplication for $N \leq 3$ to assess the systematic Trotter factorization error convergence. For larger chains with up to N = 100 rotors, Density Matrix Renormalization Group (DMRG) calculations are used as a benchmark. We show that using Gibbs sampling is advantageous compared to traditional Metroplolis-Hastings rejection importance sampling. Indeed, Gibbs sampling leads to lower variance and correlation in the computed observables.

Despite being operational for only a short time, the Einstein Probe mission has already significantly advanced the study of rapid variability in the soft X-ray sky. We report the discovery of luminous and variable radio emission from the Einstein Probe fast X-ray transient EP240414a, the second such source with a radio counterpart. The radio emission at $3\,\rm{GHz}$ peaks at $\sim30$ days post explosion and with a spectral luminosity $\sim2\times10^{30}\,\rm{erg}\,\rm{s}^{-1}\,\rm{Hz}^{-1}$, similar to what is seen from long gamma-ray bursts, and distinct from other extra-galactic transients including supernovae and tidal disruption events, although we cannot completely rule out emission from engine driven stellar explosions e.g. the fast blue optical transients. An equipartition analysis of our radio data reveals that an outflow with at least a moderate bulk Lorentz factor ($\Gamma\gtrsim1.6$) with a minimum energy of $\sim10^{48}\,\rm{erg}$ is required to explain our observations. The apparent lack of reported gamma-ray counterpart to EP240414a could suggest that an off-axis or choked jet could be responsible for the radio emission, although a low luminosity gamma-ray burst may have gone undetected. Our observations are consistent with the hypothesis that a significant fraction of extragalactic fast X-ray transients are associated with the deaths of massive stars., Comment: 11 pages, 6 figures, submitted to ApJL

We have conducted a series of scattering experiments at the uranium M4 absorption edge on low-symmetry uranium compounds (U2N3 and U3O8) produced as epitaxial films. At weak and forbidden reflections, we find a resonant signal, independent of temperature, with an energy dependence resembling the imaginary part of the scattering factor. Theory, using the FDMNES code, shows that these results can be reliably reproduced assuming that they originate from aspherical 5f electron charge distributions around the U nucleus. Such effects arise from the intrinsic anisotropy of the 5f shell and from the mixing of the 5f electrons of uranium with the outer 2p electrons of the anions. The good agreement between theory and experiment includes azimuthal scattering dependencies, as well as polarization states of the scattered photons. The methodology reported here opens the way for a deeper understanding of the role of the 5f electrons in the bonding in actinide compounds., Comment: Submitted to Physical Review B

Engaging with online social media platforms is an important part of life as a public figure in modern society, enabling connection with broad audiences and providing a platform for spreading ideas. However, public figures are often disproportionate recipients of hate and abuse on these platforms, degrading public discourse. While significant research on abuse received by groups such as politicians and journalists exists, little has been done to understand the differences in the dynamics of abuse across different groups of public figures, systematically and at scale. To address this, we present analysis of a novel dataset of 45.5M tweets targeted at 4,602 UK public figures across 3 domains (members of parliament, footballers, journalists), labelled using fine-tuned transformer-based language models. We find that MPs receive more abuse in absolute terms, but that journalists are most likely to receive abuse after controlling for other factors. We show that abuse is unevenly distributed in all groups, with a small number of individuals receiving the majority of abuse, and that for some groups, abuse is more temporally uneven, being driven by specific events, particularly for footballers. We also find that a more prominent online presence and being male are indicative of higher levels of abuse across all 3 domains., Comment: 25 pages, 7 figures

Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learning attacks where minor input perturbations can significantly impact outcomes. Common countermeasures involve adversarial training and/or data or network transformation. These methods, though effective, require full access to typically proprietary classifiers and are costly for large models. Recent solutions propose purification models, which add a "purification" layer before classification, eliminating the need to modify the classifier directly. Despite their effectiveness, these methods are compute-intensive, making them unsuitable for mobile systems where resources are limited and low latency is essential. This paper introduces LightPure, a new method that enhances adversarial image purification. It improves the accuracy of existing purification methods and provides notable enhancements in speed and computational efficiency, making it suitable for mobile devices with limited resources. Our approach uses a two-step diffusion and one-shot Generative Adversarial Network (GAN) framework, prioritizing latency without compromising robustness. We propose several new techniques to achieve a reasonable balance between classification accuracy and adversarial robustness while maintaining desired latency. We design and implement a proof-of-concept on a Jetson Nano board and evaluate our method using various attack scenarios and datasets. Our results show that LightPure can outperform existing methods by up to 10x in terms of latency while achieving higher accuracy and robustness for various attack scenarios. This method offers a scalable and effective solution for real-world mobile systems.

We present radio observations of the long-duration gamma-ray burst (GRB) 221009A which has become known to the community as the Brightest Of All Time or the BOAT. Our observations span the first 475 days post-burst and three orders of magnitude in observing frequency, from 0.15 to 230GHz. By combining our new observations with those available in the literature, we have the most detailed radio data set in terms of cadence and spectral coverage of any GRB to date, which we use to explore the spectral and temporal evolution of the afterglow. By testing a series of phenomenological models, we find that three separate synchrotron components best explain the afterglow. The high temporal and spectral resolution allows us to conclude that standard analytical afterglow models are unable to explain the observed evolution of GRB 221009A. We explore where the discrepancies between the observations and the models are most significant and place our findings in the context of the most well-studied GRB radio afterglows to date. Our observations are best explained by three synchrotron emitting regions which we interpret as a forward shock, a reverse shock and an additional shock potentially from a cocoon or wider outflow. Finally, we find that our observations do not show any evidence of any late-time spectral or temporal changes that could result from a jet break but note that any lateral structure could significantly affect a jet break signature., Comment: 15 pages, 8 figures. Accepted to MNRAS

In this paper, we provide algorithmic methods for conducting exhaustive searches for periodic Golay pairs. Our methods enumerate several lengths beyond the currently known state-of-the-art available searches: we conducted exhaustive searches for periodic Golay pairs of all lengths $v \leq 72$ using our methods, while only lengths $v \leq 34$ had previously been exhaustively enumerated. Our methods are applicable to periodic complementary sequences in general. We utilize sequence compression, a method of sequence generation derived in 2013 by Djokovi\'c and Kotsireas. We also introduce and implement a new method of "multi-level" compression, where sequences are uncompressed in several steps. This method allowed us to exhaustively search all lengths $v \leq 72$ using less than 10 CPU years. For cases of complementary sequences where uncompression is not possible, we introduce some new methods of sequence generation inspired by the isomorph-free exhaustive generation algorithm of orderly generation. Finally, we pose a conjecture regarding the structure of periodic Golay pairs and prove it holds in many lengths, including all lengths $v \lt 100$. We demonstrate the usefulness of our algorithms by providing the first ever examples of periodic Golay pairs of length $v = 90$. The smallest length for which the existence of periodic Golay pairs is undecided is now $106$.

The M31 nucleus contains a supermassive black hole embedded in a massive stellar disk of apsidally-aligned eccentric orbits. It has recently been shown that this disk is slowly precessing at a rate consistent with zero. Here we demonstrate using N-body methods that apsidally-aligned eccentric disks can form with a significant (~0.5) fraction of orbits counter-rotating as the result of a gravitational wave recoil kick of merging supermassive black holes. Higher amplitude kicks map to a larger retrograde fraction in the surrounding stellar population which in turns results in slow precession. We furthermore show that disks with significant counter-rotation are more stable (that is, apsidal-alignment is most pronounced and long lasting), more eccentric, and have the highest rates of stars entering the black hole's tidal disruption radius, Comment: Accepted to MNRAS Letters

We consider a nonlocal aggregation diffusion equation incorporating repulsion modelled by nonlinear diffusion and attraction modelled by nonlocal interaction. When the attractive interaction kernel is radially symmetric and strictly increasing on its domain it is previously known that all stationary solutions are radially symmetric and decreasing up to a translation; however, this result has not been extended to accommodate attractive kernels that are non-decreasing, for instance, attractive kernels with bounded support. For the diffusion coefficient $m>1$, we show that, for attractive kernels that are radially symmetric and non-decreasing, all stationary states are radially symmetric and decreasing up to a translation on each connected subset of their support. Furthermore, for $m>2$, we prove analytically that stationary states have an upper-bound independent of the initial data, confirming previous numerical results given in the literature.

Advances in large language models have raised concerns about their potential use in generating compelling election disinformation at scale. This study presents a two-part investigation into the capabilities of LLMs to automate stages of an election disinformation operation. First, we introduce DisElect, a novel evaluation dataset designed to measure LLM compliance with instructions to generate content for an election disinformation operation in localised UK context, containing 2,200 malicious prompts and 50 benign prompts. Using DisElect, we test 13 LLMs and find that most models broadly comply with these requests; we also find that the few models which refuse malicious prompts also refuse benign election-related prompts, and are more likely to refuse to generate content from a right-wing perspective. Secondly, we conduct a series of experiments (N=2,340) to assess the "humanness" of LLMs: the extent to which disinformation operation content generated by an LLM is able to pass as human-written. Our experiments suggest that almost all LLMs tested released since 2022 produce election disinformation operation content indiscernible by human evaluators over 50% of the time. Notably, we observe that multiple models achieve above-human levels of humanness. Taken together, these findings suggest that current LLMs can be used to generate high-quality content for election disinformation operations, even in hyperlocalised scenarios, at far lower costs than traditional methods, and offer researchers and policymakers an empirical benchmark for the measurement and evaluation of these capabilities in current and future models.

Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits. Our library empowers researchers and developers to interact with LLMs more effectively and enabling faster experimentation and evaluation. prompto is released with an introductory video (https://youtu.be/-eZAmlV4ypk) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto).

We present the results from an extensive follow-up campaign of the Tidal Disruption Event (TDE) ASASSN-15oi spanning $\delta t \sim 10 - 3000$ d, offering an unprecedented window into the multiwavelength properties of a TDE during its first $\approx 8$ years of evolution. ASASSN-15oi is one of the few TDEs with strong detections at X-ray, optical/UV, and radio wavelengths and featured two delayed radio flares at $\delta t \sim 180$ d and $\delta t \sim 1400$ d. Our observations at $> 1400$ d reveal an absence of thermal X-rays, a late-time variability in the non-thermal X-ray emission, and sharp declines in the non-thermal X-ray and radio emission at $\delta t \sim 2800$ d and $\sim 3000$ d, respectively. The UV emission shows no significant evolution at $>400$ d and remains above the pre-TDE level. We show that a cooling envelope model can explain the thermal emission consistently across all epochs. We also find that a scenario involving episodic ejection of material due to stream-stream collisions is conducive to explaining the first radio flare. Given the peculiar spectral and temporal evolution of the late-time emission, however, constraining the origins of the second radio flare and the non-thermal X-rays remains challenging. Our study underscores the critical role of long-term, multiwavelength follow-up., Comment: 36 pages, 13 Figures, 8 Tables. Submitted to ApJ

Fast X-ray Transients (FXTs) are a new observational class of phenomena with no clear physical origin. This is at least partially a consequence of limited multi-wavelength follow up of this class of transients in real time. Here we present deep optical ($g-$ and $i-$ band) photometry with Keck, and prompt radio observations with the VLA of FXT 210423 obtained at ${\delta t \approx 14-36}$ days since the X-ray trigger. We use these multi-band observations, combined with publicly available data sets, to constrain the presence and physical properties of on-axis and off-axis relativistic jets such as those that can be launched by neutron-star mergers and tidal disruption events, which are among the proposed theoretical scenarios of FXTs. Considering a wide range of possible redshifts $z\le3.5$, circumstellar medium (CSM) density $n={10^{-6}-10^{-1}\,\rm{cm^{-3}}}$, isotropic-equivalent jet kinetic energy $E_{k,iso}={10^{48}-10^{55}\,\rm{erg}}$, we find that we can rule out wide jets with opening angle ${\theta_{j}=15^{\circ}}$ viewed within ${10^{\circ}}$ off-axis. For more collimated jets (${\theta_{j}=3^{\circ}}$) we can only rule out on-axis (${\theta_{obs}=0^{\circ}}$) orientations. This study highlights the constraining power of prompt multi-wavelength observations of FXTs discovered in real time by current (e.g., Einstein Probe) and future facilities., Comment: 14 pages, 6 figures

This article examines public exposure to and perceptions of deepfakes based on insights from a nationally representative survey of 1403 UK adults. The survey is one of the first of its kind since recent improvements in deepfake technology and widespread adoption of political deepfakes. The findings reveal three key insights. First, on average, 15% of people report exposure to harmful deepfakes, including deepfake pornography, deepfake frauds/scams and other potentially harmful deepfakes such as those that spread health/religious misinformation/propaganda. In terms of common targets, exposure to deepfakes featuring celebrities was 50.2%, whereas those featuring politicians was 34.1%. And 5.7% of respondents recall exposure to a selection of high profile political deepfakes in the UK. Second, while exposure to harmful deepfakes was relatively low, awareness of and fears about deepfakes were high (and women were significantly more likely to report experiencing such fears than men). As with fears, general concerns about the spread of deepfakes were also high; 90.4% of the respondents were either very concerned or somewhat concerned about this issue. Most respondents (at least 91.8%) were concerned that deepfakes could add to online child sexual abuse material, increase distrust in information and manipulate public opinion. Third, while awareness about deepfakes was high, usage of deepfake tools was relatively low (8%). Most respondents were not confident about their detection abilities and were trustful of audiovisual content online. Our work highlights how the problem of deepfakes has become embedded in public consciousness in just a few years; it also highlights the need for media literacy programmes and other policy interventions to address the spread of harmful deepfakes.

Cryptographic hash functions play a crucial role in ensuring data security, generating fixed-length hashes from variable-length inputs. The hash function SHA-256 is trusted for data security due to its resilience after over twenty years of intense scrutiny. One of its critical properties is collision resistance, meaning that it is infeasible to find two different inputs with the same hash. Currently, the best SHA-256 collision attacks use differential cryptanalysis to find collisions in simplified versions of SHA-256 that are reduced to have fewer steps, making it feasible to find collisions. In this paper, we use a satisfiability (SAT) solver as a tool to search for step-reduced SHA-256 collisions, and dynamically guide the solver with the aid of a computer algebra system (CAS) used to detect inconsistencies and deduce information that the solver would otherwise not detect on its own. Our hybrid SAT + CAS solver significantly outperformed a pure SAT approach, enabling us to find collisions in step-reduced SHA-256 with significantly more steps. Using SAT + CAS, we find a 38-step collision of SHA-256 with a modified initialization vector -- something first found by a highly sophisticated search tool of Mendel, Nad, and Schl\"affer. Conversely, a pure SAT approach could find collisions for no more than 28 steps. However, our work only uses the SAT solver CaDiCaL and its programmatic interface IPASIR-UP., Comment: To appear in the 2024 Proceedings of the International Workshop on Satisfiability Checking and Symbolic Computation

The difficulty of factoring large integers into primes is the basis for cryptosystems such as RSA. Due to the widespread popularity of RSA, there have been many proposed attacks on the factorization problem such as side-channel attacks where some bits of the prime factors are available. When enough bits of the prime factors are known, two methods that are effective at solving the factorization problem are satisfiability (SAT) solvers and Coppersmith's method. The SAT approach reduces the factorization problem to a Boolean satisfiability problem, while Coppersmith's approach uses lattice basis reduction. Both methods have their advantages, but they also have their limitations: Coppersmith's method does not apply when the known bit positions are randomized, while SAT-based methods can take advantage of known bits in arbitrary locations, but have no knowledge of the algebraic structure exploited by Coppersmith's method. In this paper we describe a new hybrid SAT and computer algebra approach to efficiently solve random leaked-bit factorization problems. Specifically, Coppersmith's method is invoked by a SAT solver to determine whether a partial bit assignment can be extended to a complete assignment. Our hybrid implementation solves random leaked-bit factorization problems significantly faster than either a pure SAT or pure computer algebra approach., Comment: To appear in the 2024 Proceedings of the International Symposium on Symbolic and Algebraic Computation

Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 U.S. political issues from 24 language models spanning several orders of magnitude in size. We then deploy these messages in a large-scale randomized survey experiment (N = 25,982) to estimate the persuasive capability of each model. Our findings are twofold. First, we find evidence of a log scaling law: model persuasiveness is characterized by sharply diminishing returns, such that current frontier models are barely more persuasive than models smaller in size by an order of magnitude or more. Second, mere task completion (coherence, staying on topic) appears to account for larger models' persuasive advantage. These findings suggest that further scaling model size will not much increase the persuasiveness of static LLM-generated messages., Comment: 16 pages, 4 figures

We generalize the recent results on radial projections by Orponen, Shmerkin, Wang using two different methods. In particular, we show that given $X,Y\subset \mathbb{R}^n$ Borel sets and $X\neq \emptyset$. If $\dim Y \in (k,k+1]$ for some $k\in \{1,\dots, n-1\}$, then \[ \sup_{x\in X} \dim \pi_x(Y\setminus \{x\}) \geq \min \{\dim X + \dim Y - k, k\}. \] Our results give a new approach to solving a conjecture of Lund-Pham-Thu in all dimensions and for all ranges of $\dim Y$. The first of our two methods for proving the above theorem is shorter, utilizing a result of the first author and Gan. Our second method, though longer, follows the original methodology of Orponen--Shmerkin--Wang, and requires a higher dimensional incidence estimate and a dual Furstenberg-set estimate for lines. These new estimates may be of independent interest., Comment: 17 pages

We prove a version of the Erd\H{o}s--Beck Theorem from discrete geometry for fractal sets in all dimensions. More precisely, let $X\subset \mathbb{R}^n$ Borel and $k \in [0, n-1]$ be an integer. Let $\dim (X \setminus H) = \dim X$ for every $k$-dimensional hyperplane $H \in \mathcal{A}(n,k)$, and let $\mathcal L(X)$ be the set of lines that contain at least two distinct points of $X$. Then, a recent result of Ren shows $$ \dim \mathcal{L}(X) \geq \min \{2 \dim X, 2k\}. $$ If we instead have that $X$ is not a subset of any $k$-plane, and $$ 0<\inf_{H \in \mathcal{A}(n,k)} \dim (X \setminus H) = t < \dim X, $$ we instead obtain the bound $$ \dim \mathcal{L}(X) \geq \dim X + t. $$ We then strengthen this lower bound by introducing the notion of the "trapping number" of a set, $T(X)$, and obtain \[ \dim \mathcal L(X) \geq \max\{\dim X + t, \min\{2\dim X, 2(T(X)-1)\}\}, \] as consequence of our main result and of Ren's result in $\mathbb{R}^n$. Finally, we introduce a conjectured equality for the dimension of the line set $\mathcal{L}(X)$, which would in particular imply our results if proven to be true., Comment: 15 pages, 3 figures

Since the increasing popularity of large language model (LLM) backend systems, it is common and necessary to deploy stable serverless serving of LLM on multi-GPU clusters with autoscaling. However, there exist challenges because the diversity and co-location of applications in multi-GPU clusters will lead to low service quality and GPU utilization. To address them, we build ENOVA, a deployment, monitoring and autoscaling service towards serverless LLM serving. ENOVA deconstructs the execution process of LLM service comprehensively, based on which ENOVA designs a configuration recommendation module for automatic deployment on any GPU clusters and a performance detection module for autoscaling. On top of them, ENOVA implements a deployment execution engine for multi-GPU cluster scheduling. The experiment results show that ENOVA significantly outperforms other state-of-the-art methods and is suitable for wide deployment in large online systems.

In the high-stakes world of baseball, every nuance of a pitcher's mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven pitch statistics. Experimental validation demonstrates that PitcherNet achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification, reduced joint position error by 1.8mm and superior analytics compared to baseline methods. By enabling performance-critical kinematic analysis from broadcast video, PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics, forever transforming the game., Comment: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW'24)

We show that the $n$'th digit of the base-$b$ representation of the golden ratio is a finite-state function of the Zeckendorf representation of $b^n$, and hence can be computed by a finite automaton. Similar results can be proven for any quadratic irrational. We use a satisfiability (SAT) solver to prove, in some cases, that the automata we construct are minimal.

The Igbo language is facing a risk of becoming endangered, as indicated by a 2025 UNESCO study. This highlights the need to develop language technologies for Igbo to foster communication, learning and preservation. To create robust, impactful, and widely adopted language technologies for Igbo, it is essential to incorporate the multi-dialectal nature of the language. The primary obstacle in achieving dialectal-aware language technologies is the lack of comprehensive dialectal datasets. In response, we present the IgboAPI dataset, a multi-dialectal Igbo-English dictionary dataset, developed with the aim of enhancing the representation of Igbo dialects. Furthermore, we illustrate the practicality of the IgboAPI dataset through two distinct studies: one focusing on Igbo semantic lexicon and the other on machine translation. In the semantic lexicon project, we successfully establish an initial Igbo semantic lexicon for the Igbo semantic tagger, while in the machine translation study, we demonstrate that by finetuning existing machine translation systems using the IgboAPI dataset, we significantly improve their ability to handle dialectal variations in sentences., Comment: Accepted to the LREC-COLING 2024 conference

Although the principal physical behaviour of a material is inherently connected to its fundamental crystal structure, the behaviours observed in the real-world are often driven by the microstructure, which for many polycrystalline materials, equates to the size and shape of the constituent crystal grains. Here we highlight a cutting edge synthesis route to the controlled engineering of grain structures in thin films and the simplification of associated 3-dimensional problems to less complex 2D ones. This has been applied to the actinide ceramic, uranium dioxide, to replicate structures typical in nuclear fission fuel pellets, in order to investigate the oxidation and subsequent transformation of cubic UO$_{2}$ to orthorhombic U$_{3}$O$_{8}$. This article shows how this synthesis approach could be utilised to investigate a range of phenomena, affected by grain morphology, and highlights some unusual results in the oxidation behaviour of UO$_{2}$, regarding the phase transition to U$_{3}$O$_{8}$., Comment: 21 pages, 3 figures

The study of transient and variable events, including novae, active galactic nuclei, and black hole binaries, has historically been a fruitful path for elucidating the evolutionary mechanisms of our universe. The study of such events in the millimeter and submillimeter is, however, still in its infancy. Submillimeter observations probe a variety of materials, such as optically thick dust, which are hard to study in other wavelengths. Submillimeter observations are sensitive to a number of emission mechanisms, from the aforementioned cold dust, to hot free-free emission, and synchrotron emission from energetic particles. Study of these phenomena has been hampered by a lack of prompt, high sensitivity submillimeter follow-up, as well as by a lack of high-sky-coverage submillimeter surveys. In this paper, we describe how the proposed Atacama Large Aperture Submillimeter Telescope (AtLAST) could fill in these gaps in our understanding of the transient universe. We discuss a number of science cases that would benefit from AtLAST observations, and detail how AtLAST is uniquely suited to contributing to them. In particular, AtLAST's large field of view will enable serendipitous detections of transient events, while its anticipated ability to get on source quickly and observe simultaneously in multiple bands make it also ideally suited for transient follow-up. We make theoretical predictions for the instrumental and observatory properties required to significantly contribute to these science cases, and compare them to the projected AtLAST capabilities. Finally, we consider the unique ways in which transient science cases constrain the observational strategies of AtLAST, and make prescriptions for how AtLAST should observe in order to maximize its transient science output without impinging on other science cases., Comment: 19 pages, 5 figures

Fast X-ray Transients (FXTs) are extragalactic bursts of soft X-rays first identified >10 years ago. Since then, nearly 40 events have been discovered, although almost all of these have been recovered from archival Chandra and XMM-Newton data. To date, optical sky surveys and follow-up searches have not revealed any multi-wavelength counterparts. The Einstein Probe, launched in January 2024, has started surveying the sky in the soft X-ray regime (0.5-4 keV) and will rapidly increase the sample of FXTs discovered in real time. Here, we report the first discovery of both an optical and radio counterpart to a distant FXT, the fourth source publicly released by the Einstein Probe. We discovered a fast-fading optical transient within the 3 arcmin localisation radius of EP240315a with the all-sky optical survey ATLAS, and our follow-up Gemini spectrum provides a redshift, z=4.859+/-0.002. Furthermore, we uncovered a radio counterpart in the S-band (3.0 GHz) with the MeerKAT radio interferometer. The optical (rest-frame UV) and radio luminosities indicate the FXT most likely originates from either a long gamma-ray burst or a relativistic tidal disruption event. This may be a fortuitous early mission detection by the Einstein Probe or may signpost a mode of discovery for high-redshift, high-energy transients through soft X-ray surveys, combined with locating multi-wavelength counterparts., Comment: Updated to match version accepted for publication in ApJL (17 pages, 4 figures, 2 tables)

Online harms, such as hate speech, trolling and self-harm promotion, continue to be widespread. While some work suggests women are disproportionately affected, other studies find mixed evidence for gender differences in experiences with content of this kind. Using a nationally representative survey of UK adults (N=1992), we examine exposure to a variety of harms, fears surrounding being targeted, the psychological impact of online experiences, the use of safety tools to protect against harm, and comfort with various forms of online participation across men and women. We find that while men and women see harmful content online to a roughly similar extent, women are more at risk than men of being targeted by harms including online misogyny, cyberstalking and cyberflashing. Women are significantly more fearful of being targeted by harms overall, and report greater negative psychological impact as a result of particular experiences. Perhaps in an attempt to mitigate risk, women report higher use of a range of safety tools and less comfort with several forms of online participation, with just 23% of women comfortable expressing political views online compared to 40% of men. We also find direct associations between fears surrounding harms and comfort with online behaviours. For example, fear of being trolled significantly decreases comfort expressing opinions, and fear of being targeted by misogyny significantly decreases comfort sharing photos. Our results are important because with much public discourse happening online, we must ensure all members of society feel safe and able to participate in online spaces.

The Mid-Infrared Instrument (MIRI) aboard the James Webb Space Telescope (JWST) provides the observatory with a huge advance in mid-infrared imaging and spectroscopy covering the wavelength range of 5 to 28 microns. This paper describes the performance and characteristics of the MIRI imager as understood during observatory commissioning activities, and through its first year of science operations. We discuss the measurements and results of the imager's point spread function, flux calibration, background, distortion and flat fields as well as results pertaining to best observing practices for MIRI imaging, and discuss known imaging artefacts that may be seen during or after data processing. Overall, we show that the MIRI imager has met or exceeded all its pre-flight requirements, and we expect it to make a significant contribution to mid-infrared science for the astronomy community for years to come.

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.

Unique player identification is a fundamental module in vision-driven sports analytics. Identifying players from broadcast videos can aid with various downstream tasks such as player assessment, in-game analysis, and broadcast production. However, automatic detection of jersey numbers using deep features is challenging primarily due to: a) motion blur, b) low resolution video feed, and c) occlusions. With their recent success in various vision tasks, masked autoencoders (MAEs) have emerged as a superior alternative to conventional feature extractors. However, most MAEs simply zero-out image patches either randomly or focus on where to mask rather than how to mask. Motivated by human vision, we devise a novel domain-guided masking policy for MAEs termed d-MAE to facilitate robust feature extraction in the presence of motion blur for player identification. We further introduce a new spatio-temporal network leveraging our novel d-MAE for unique player identification. We conduct experiments on three large-scale sports datasets, including a curated baseball dataset, the SoccerNet dataset, and an in-house ice hockey dataset. We preprocess the datasets using an upgraded keyframe identification (KfID) module by focusing on frames containing jersey numbers. Additionally, we propose a keyframe-fusion technique to augment keyframes, preserving spatial and temporal context. Our spatio-temporal network showcases significant improvements, surpassing the current state-of-the-art by 8.58%, 4.29%, and 1.20% in the test set accuracies, respectively. Rigorous ablations highlight the effectiveness of our domain-guided masking approach and the refined KfID module, resulting in performance enhancements of 1.48% and 1.84% respectively, compared to original architectures., Comment: Submitted to 21st International Conference on Robots and Vision (CRV'24), Guelph, Ontario, Canada

Precise Human Mesh Recovery (HMR) with in-the-wild data is a formidable challenge and is often hindered by depth ambiguities and reduced precision. Existing works resort to either pose priors or multi-modal data such as multi-view or point cloud information, though their methods often overlook the valuable scene-depth information inherently present in a single image. Moreover, achieving robust HMR for out-of-distribution (OOD) data is exceedingly challenging due to inherent variations in pose, shape and depth. Consequently, understanding the underlying distribution becomes a vital subproblem in modeling human forms. Motivated by the need for unambiguous and robust human modeling, we introduce Distribution and depth-aware human mesh recovery (D2A-HMR), an end-to-end transformer architecture meticulously designed to minimize the disparity between distributions and incorporate scene-depth leveraging prior depth information. Our approach demonstrates superior performance in handling OOD data in certain scenarios while consistently achieving competitive results against state-of-the-art HMR methods on controlled datasets., Comment: Submitted to 21st International Conference on Robots and Vision (CRV'24), Guelph, Ontario, Canada