Search

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839-10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less, and can detect 10x more FRBs than the current CRAFT incoherent sum system (i.e., 0.5-2 localised FRBs per day), enabling us to better constrain the FRB emission mechanism model and use them as cosmological probes., Comment: 26 pages, 19 figures, 9 tables, Submitted for publication in PASA

We have analyzed the prompt and afterglow characteristics of the intermediate luminosity burst ``GRB 210210A". Our prompt emission analysis indicates that GRB 210210A is among the softest long GRBs detected by the Swift-BAT. The time-integrated prompt emission spectrum of GRB 210210A is aptly described by a power law function with an exponential cutoff. The spectral peak energy (E$_{p,z}$) in rest-frame and the E$_{\rm \gamma, iso}$ for this GRB marginally satisfy the 2$\sigma$ Amati correlation, a common feature observed in low/intermediate luminosity GRBs. Notably, an early bump is observed in the Swift-XRT light curve (a rare feature); the optical afterglow light curve, on the other hand, appears to follow a power law decay. However, due to the lack of sufficient early optical observations, we cannot completely rule out the possibility of an early bump in the optical light curve. For the bump observed in the early X-ray light curve, we calculated parameters such as peak time, rise time, decay time, and bulk Lorentz factor ($\Gamma_{0}$ $\sim$ 156), which perfectly satisfy the correlation between the parameters of the onset of the afterglow in GRBs. Both the optical and X-ray (including our observations) light curves exhibit a chromatic break in the late afterglow. Based on the prompt and afterglow parameters, we confirm that the intermediate luminosity GRB 210210A favors a collapsar scenario and is possibly powered by a magnetar., Comment: 17 pages, 9 figures, Revised version after addressing the referee's comments

This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://github.com/p3jitnath/climate-rl., Comment: 24 pages, 9 figures

We present a theory of false vacuum decay induced by spherical nucleation seeds. The type of seed considered has a boundary characterised by surface energy terms. The theory applies to false vacuum decay at zero and finite temperatures. Seeded nucleation may be important for enabling future false vacuum decay experiments on analogue systems using Bose Einstein Condensates (BEC). We show that our theory of seeded nucleation at finite temperature applied to a potassium BEC in two spatial dimensions agrees with numerical, real-time, simulations., Comment: 20 pages, 11 figures v2 reference added

Generative models are crucial for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalizing Flows have shown effectiveness across various modalities, and rely on Gaussian latent variables for generation. As any generated object is directly associated with a particular latent variable, we can manipulate the variables to exert control over the generation process. However, standard approaches for combining latent variables, such as spherical interpolation, only apply or work well in special cases. Moreover, current methods for obtaining low-dimensional representations of the data, important for e.g. surrogate models for search and creative applications, are network and data modality specific. In this work we show that the standard methods to combine variables do not yield intermediates following the distribution the models are trained to expect. We propose Combination of Gaussian variables (COG), a novel interpolation method that addresses this, is easy to implement yet matches or improves upon current methods. COG addresses linear combinations in general and, as we demonstrate, also supports other operations including e.g. defining subspaces of the latent space, simplifying the creation of expressive low-dimensional spaces of high-dimensional objects using generative models based on Gaussian latents.

The First Large Absorption Survey in HI (FLASH) is a large-area radio survey for neutral hydrogen in the redshift range 0.41$, and appear to be a mixture of intervening and associated systems. The overall detection rate for HI absorption lines in the Pilot Surveys (0.3 to 0.5 lines per ASKAP field) is a factor of two below the expected value. There are several possible reasons for this, but one likely factor is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper will discuss the host galaxies of the HI absorption systems identified here., Comment: 46 pages, 25 figures, 10 tables. Submitted to PASA

Eliciting harmful behavior from large language models (LLMs) is an important task to ensure the proper alignment and safety of the models. Often when training LLMs, ethical guidelines are followed yet alignment failures may still be uncovered through red teaming adversarial attacks. This work frames the red-teaming problem as a Markov decision process (MDP) and uses Monte Carlo tree search to find harmful behaviors of black-box, closed-source LLMs. We optimize token-level prompt suffixes towards targeted harmful behaviors on white-box LLMs and include a naturalistic loss term, log-perplexity, to generate more natural language attacks for better interpretability. The proposed algorithm, Kov, trains on white-box LLMs to optimize the adversarial attacks and periodically evaluates responses from the black-box LLM to guide the search towards more harmful black-box behaviors. In our preliminary study, results indicate that we can jailbreak black-box models, such as GPT-3.5, in only 10 queries, yet fail on GPT-4$-$which may indicate that newer models are more robust to token-level attacks. All work to reproduce these results is open sourced (https://github.com/sisl/Kov.jl).

Microwave transversal filters, which are implemented based on the transversal filter structure in digital signal processing, offer a high reconfigurability for achieving a variety of signal processing functions without changing hardware. When implemented using microwave photonic (MWP) technologies, also known as MWP transversal filters, they provide competitive advantages over their electrical counterparts, such as low loss, large operation bandwidth, and strong immunity to electromagnetic interference. Recent advances in high performance optical microcombs provide compact and powerful multiwavelength sources for MWP transversal filters that require a larger number of wavelength channels to achieve high performance, allowing for the demonstration of a diverse range of filter functions with improved performance and new features. Here, we present a comprehensive performance analysis for microcomb based MWP spectral filters based on the transversal filter approach. First, we investigate the theoretical limitations in the filter spectral response induced by finite tap numbers. Next, we analyze the distortions in the filter spectral response resulting from experimental error sources. Finally, we assess the influence of input signals bandwidth on the filtering errors. These results provide a valuable guide for the design and optimization of microcomb-based MWP transversal filters for a variety of applications., Comment: 33 pages, 10 figures, 33 references

We present here RadioSED, a Bayesian inference framework tailored to modelling and classifying broadband radio spectral energy distributions (SEDs) using only data from publicly-released, large-area surveys. We outline the functionality of RadioSED, with its focus on broadband radio emissions which can trace kiloparsec-scale absorption within both the radio jets and the circumgalactic medium of Active Galactic Nuclei (AGN). In particular, we discuss the capability of RadioSED to advance our understanding of AGN physics and composition within youngest and most compact sources, for which high resolution imaging is often unavailable. These young radio AGN typically manifest as peaked spectrum (PS) sources which, before RadioSED, were difficult to identify owing to the large, broadband frequency coverage typically required, and yet they provide an invaluable environment for understanding AGN evolution and feedback. We discuss the implementation details of RadioSED, and we validate our approach against both synthetic and observational data. Since the surveys used are drawn from multiple epochs of observation, we also consider the output from RadioSED in the context of AGN variability. Finally, we show that RadioSED recovers the expected SED shapes for a selection of well-characterised radio sources from the literature, and we discuss avenues for further study of these and other sources using radio SED fitting as a starting point. The scalability and modularity of this framework make it an exciting tool for multiwavelength astronomers as next-generation telescopes begin several all-sky surveys. Accordingly, we make the code for RadioSED, which is written in Python, available on Github., Comment: 20 pages, 2 tables, 15 figures. Accepted for publication in MNRAS

We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated with precise control over their thicknesses and sizes. Detailed measurements are performed for the fabricated devices with different GO film thicknesses, coating lengths, and Si waveguide widths. The results show that a maximum polarization-dependent loss (PDL) of ~17 dB is achieved for the hybrid waveguides, and the hybrid MRRs achieved a maximum polarization extinction ratio (PER) of ~10 dB. We also characterize the wavelength- and power-dependent response for these polarizers. The former demonstrates a broad operation bandwidth of over ~100 nm, and the latter verifies performance improvement enabled by photo-thermal changes in GO films. By fitting the experimental results with theoretical simulations, we find that the anisotropy in the loss of GO films dominates the polarization selectivity of these devices. These results highlight the strong potential of 2D GO films for realizing high-performance polarization selective devices in Si photonic platform., Comment: 28 pages, 6 figures, 174 references

A new model for dark matter is put forward which consists of uniform droplets of Bose Einstein condensate. In this model, structure forms rapidly, shortly after the hot big bang plasma de-ionises. The model also produces modifications to the expansion rate before droplet formation that affect the measurement of cosmological parameters from Cosmic Microwave Background data. The model could contribute to explaining why observations at high redshift see anomalously high structure formation and predict low values for the Hubble constant., Comment: 9 pages, no figures

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

We have developed a new all-optical method to directly measure the pressure-volume-temperature (PVT) equation of state (EOS) of fluids and transparent solids in the diamond anvil high pressure cell by measuring the volume of the sample chamber. Our method combines confocal microscopy and white light interference with a new analysis method which exploits the mutual dependence of sample density and refractive index: Experimentally, the refractive index determines the measured sample chamber thickness (and therefore the measured sample volume/density), yet the sample density is by far the dominant factor in determining the variation in refractive index with pressure. Our analysis method allows us to obtain a set of values for the density and refractive index which are mutually consistent, and agree with the experimental data within error. We have conducted proof-of-concept experiments on a variety of samples (H$_{2}$O, CH$_{4}$, C$_{2}$H$_{6}$, C$_{3}$H$_{8}$, KCl and NaCl) at ambient temperature, and at high temperatures up to just above 500 K. Our proof-of-concept data demonstrate that our method is able to reproduce known fluid and solid EOS within error. Furthermore, we demonstrate that our method allows us to directly and routinely measure the PVT EOS of simple fluids at GPa pressures up to, at least, 514 K (the highest temperature reached in our study). A reasonable estimation of the known sources of error in our volume determinations indicates that the error is currently $\pm$ 2.7% at high temperature, and that it is feasible to reduce it to ca. $\pm$ 1% in future work., Comment: Accepted for publication in Physics of Fluids

Optical polarizers are essential components for the selection and manipulation of light polarization states in optical systems. Over the past decade, the rapid advancement of photonic technologies and devices has led to the development of a range of novel optical polarizers, opening avenues for many breakthroughs and expanding applications across diverse fields. Particularly, two dimensional (2D) materials, known for their atomic thin film structures and unique optical properties, have become attractive for implementing optical polarizers with high performance and new features that were not achievable before. This paper reviews recent progress in 2D material based optical polarizers. First, an overview of key properties of various 2D materials for realizing optical polarizers is provided. Next, the state of the art optical polarizers based on 2D materials, which are categorized into spatial light devices, fiber devices, and integrated waveguide devices, are reviewed and compared. Finally, we discuss the current challenges of this field as well as the exciting opportunities for future technological advances., Comment: 45 pages, 7 figures, 277 references

We present a detailed model atmosphere analysis of massive white dwarfs with $M > 0.9~M_\odot$ and $T_{\rm eff}\geq11,000$ K in the Montreal White Dwarf Database 100 pc sample and the Pan-STARRS footprint. We obtained follow-up optical spectroscopy of 109 objects with no previous spectral classification in the literature. Our spectroscopic follow-up is now complete for all 204 objects in the sample. We find 118 normal DA white dwarfs, including 45 massive DAs near the ZZ Ceti instability strip. There are no normal massive DBs: the six DBs in the sample are strongly magnetic and/or rapidly rotating. There are 20 massive DQ white dwarfs in our sample, and all are found in the crystallization sequence. In addition, 66 targets are magnetic (32% of the sample). We use magnetic white dwarf atmosphere models to constrain the field strength and geometry using offset dipole models. We also use magnetism, kinematics, and rotation measurements to constrain the fraction of merger remnant candidates among this population. The merger fraction of this sample increases from 25% for 0.9-$1~M_{\odot}$ white dwarfs to 49% for 1.2-$1.3~M_{\odot}$. However, this fraction is as high as $78_{-7}^{+4}$% for 1.1-$1.2~M_{\odot}$ white dwarfs. Previous works have demonstrated that 5-9% of high-mass white dwarfs stop cooling for $\sim8$ Gyr due to the $^{22}$Ne distillation process, which leads to an overdensity of Q-branch stars in the solar neighborhood. We demonstrate that the over-abundance of the merger remnant candidates in our sample is likely due to the same process., Comment: 34 pages, 16 figures, accepted for publication in ApJ

As a rapidly expanding family of two dimensional (2D) materials, MXenes have recently gained considerable attention due to their appealing properties. Here, by developing a solution based coating method that enables transfer free and layer by layer film coating, we investigate the layer dependent nonlinear optical absorption of Ti3C2Tx films, an important member of the MXene family. By using the Zscan technique, we characterize the nonlinear absorption of the prepared MXene films consisting of different numbers of monolayers. The results show that there is a strong and layer dependent nonlinear absorption behavior, transitioning from revisable saturable absorption (RSA) to saturable absorption (SA) as the layer number increases from 5 to 30. Notably, the nonlinear absorption coefficient beta varies significantly within this range, changing from 7.13 x 100 cm/GW to -2.69 100 cm/GW. We also characterize the power dependent nonlinear absorption of the MXene films at various incident laser intensities, and a decreasing trend in beta is observed for increasing laser intensity. These results reveal the intriguing layer dependent nonlinear optical properties of 2D MXene films, highlighting their versatility and potential for implementing high-performance nonlinear photonic devices., Comment: 27 pages, 6 figures, 149 references

We state a conjecture, local Langlands in families, connecting the centre of the category of smooth representations on $\mathbb{Z}[\sqrt{q}^{-1}]$-modules of a quasi-split $p$-adic group $\mathrm{G}$ (where $q$ is the cardinality of the residue field of the underlying local field), the ring of global functions on the stack of Langlands parameters for $\mathrm{G}$ over $\mathbb{Z}[\sqrt{q}^{-1}]$, and the endomorphisms of a Gelfand-Graev representation for $\mathrm{G}$. For a class of classical $p$-adic groups (symplectic, unitary, or split odd special orthogonal groups), we prove this conjecture after inverting an integer depending only on $\mathrm{G}$. Along the way, we show that the local Langlands correspondence for classical $p$-adic groups (1) preserves integrality of $\ell$-adic representations; (2) satisfies an "extended" (generic) packet conjecture; (3) is compatible with parabolic induction up to semisimplification (generalizing a result of Moussaoui), hence induces a semisimple local Langlands correspondence; and (4) the semisimple correspondence is compatible with automorphisms of $\mathbb{C}$ fixing $\sqrt{q}$., Comment: 57 pages

Amid growing concerns over AI's societal risks--ranging from civilizational collapse to misinformation and systemic bias--this study explores the perceptions of AI experts and the general US registered voters on the likelihood and impact of 18 specific AI risks, alongside their policy preferences for managing these risks. While both groups favor international oversight over national or corporate governance, our survey reveals a discrepancy: voters perceive AI risks as both more likely and more impactful than experts, and also advocate for slower AI development. Specifically, our findings indicate that policy interventions may best assuage collective concerns if they attempt to more carefully balance mitigation efforts across all classes of societal-scale risks, effectively nullifying the near-vs-long-term debate over AI risks. More broadly, our results will serve not only to enable more substantive policy discussions for preventing and mitigating AI risks, but also to underscore the challenge of consensus building for effective policy implementation., Comment: 9 pages, 18 page supplementary materials

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermo-optic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications., Comment: 41 pages, 8 figures, 169 references

On-chip integration of two-dimensional (2D) materials with unique structures and distinctive properties endow integrated devices with new functionalities and improved performance. With a high flexibility in modifying its properties and a strong compatibility with various integrated platforms, graphene oxide (GO) becomes an attractive 2D material for implementing functional hybrid integrated devices. Here, we demonstrate novel functionalities that go beyond the capabilities of conventional photonic integrated circuits, by harnessing the photo-thermal effects in 2D GO films integrated onto them. These include all-optical control and switching, optical power limiting, and non-reciprocal light transmission. The 2D layered GO films are on-chip integrated with precise control of their thicknesses and sizes. Benefitting from the broadband response of 2D GO films, all the three functionalities feature a very wide operational bandwidth. By fitting the experimental results with theory, we also extract the changes in material properties induced by the photo-thermal effects, which reveal interesting insights about 2D GO films. These results highlight the versatility of 2D GO films in implementing functional integrated photonic devices for a range of applications., Comment: 34 pages, 6 figures, 162 references

We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hamiltonian, and outputs an autoregressive sequence of qubit measurement probabilities. Its performance is studied in the vicinity of a quantum phase transition in Rydberg atoms in a square lattice array. We explore the ability of the architecture to generalize, by producing groundstate measurements for Hamiltonian parameters not seen in the training set. We focus on examples of physical observables obtained from inference on three different models, trained in fixed compute time on a single NVIDIA A100 GPU. These can act as benchmarks for the scaling of larger RydbergGPT models in the future. Finally, we provide RydbergGPT open source, to aid in the development of foundation models based off of a wide variety of quantum computer interactions and data sets in the future., Comment: 9 pages, 4 figures

The Scintillating Bubble Chamber (SBC) collaboration purchased 32 Hamamatsu VUV4 silicon photomultipliers (SiPMs) for use in SBC-LAr10, a bubble chamber containing 10~kg of liquid argon. A dark-count characterization technique, which avoids the use of a single-photon source, was used at two temperatures to measure the VUV4 SiPMs breakdown voltage ($V_{\text{BD}}$), the SiPM gain ($g_{\text{SiPM}}$), the rate of change of $g_{\text{SiPM}}$ with respect to voltage ($m$), the dark count rate (DCR), and the probability of a correlated avalanche (P$_{\text{CA}}$) as well as the temperature coefficients of these parameters. A Peltier-based chilled vacuum chamber was developed at Queen's University to cool down the Quads to $233.15\pm0.2$~K and $255.15\pm0.2$~K with average stability of $\pm20$~mK. An analysis framework was developed to estimate $V_{\text{BD}}$ to tens of mV precision and DCR close to Poissonian error. The temperature dependence of $V_{\text{BD}}$ was found to be $56\pm2$~mV~K$^{-1}$, and $m$ on average across all Quads was found to be $(459\pm3(\rm{stat.})\pm23(\rm{sys.}))\times 10^{3}~e^-$~PE$^{-1}$~V$^{-1}$. The average DCR temperature coefficient was estimated to be $0.099\pm0.008$~K$^{-1}$ corresponding to a reduction factor of 7 for every 20~K drop in temperature. The average temperature dependence of P$_{\text{CA}}$ was estimated to be $4000\pm1000$~ppm~K$^{-1}$. P$_{\text{CA}}$ estimated from the average across all SiPMs is a better estimator than the P$_{\text{CA}}$ calculated from individual SiPMs, for all of the other parameters, the opposite is true. All the estimated parameters were measured to the precision required for SBC-LAr10, and the Quads will be used in conditions to optimize the signal-to-noise ratio., Comment: 25 pages, 19 figures

Multi-scale systems often exhibit stochastic and deterministic dynamics. Capturing these aspects in a compartmental model is challenging. Notably, low occupancy compartments exhibit stochastic dynamics and high occupancy compartments exhibit deterministic dynamics. Failing to account for stochasticity in small populations can produce 'atto-foxes', e.g. in the Lotka-Volterra ordinary differential equation (ODE) model. This limitation becomes problematic when studying extinction of species or the clearance of infection, but it can be resolved by using discrete stochastic models e.g. continuous time Markov chains (CTMCs). Unfortunately, simulating CTMCs is infeasible for most realistic populations. We develop a novel mathematical framework, to couple continuous ODEs and discrete CTMCs: 'Jump-Switch-Flow' (JSF). In this framework populations can reach extinct states ("absorbing states"), thereby resolving atto-fox-type problems. JSF has the desired behaviours of exact CTMC simulation, but is substantially faster than existing alternatives. JSF's utility for simulation-based inference, particularly multi-scale problems, is demonstrated by several case-studies. In a simulation study, we demonstrate how JSF can enable a more nuanced analysis of the efficacy of public health interventions. We also carry out a novel analysis of longitudinal within-host data from SARS-CoV-2 infections to quantify the timing of viral clearance. JSF offers a novel approach to compartmental model development and simulation.

Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications. This motivates the use of statistical techniques to provide accurate and calibrated probabilistic predictions at a lower cost compared to numerical simulations. Wind represents a particularly challenging variable to model due to its high spatial and temporal variability. This paper presents a novel approach that integrates Gaussian processes (GPs) and neural networks to model surface wind gusts, leveraging multiple data sources, including numerical weather prediction (NWP) models, digital elevation models (DEM), and in-situ measurements. Results demonstrate the added value of modeling the multivariate covariance structure of the variable of interest, as opposed to only applying a univariate probabilistic regression approach. Modeling the covariance enables the optimal integration of observed measurements from ground stations, which is shown to reduce the continuous ranked probability score compared to the baseline. Moreover, it allows the direct generation of realistic fields that are also marginally calibrated, aided by scalable techniques such as Random Fourier Features (RFF) and pathwise conditioning. We discuss the effect of different modeling choices, as well as different degrees of approximation, and present our results for a case study., Comment: 18 pages, 10 figures. Submitted to AMS AI4ES journal on May 17th, 2024

With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus allow their usage for virtual testing of driving functions. Especially in research and development areas related to the safety impacts of the "open world", there is a significant shortage of real-world data to parametrize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated vehicles will meet in mixed traffic. This paper presents the intermediate results of the German AVEAS research project (www.aveas.org) which aims at developing methods and metrics for the harmonized, systematic, and scalable acquisition of real-world data for virtual verification and validation of advanced driver assistance systems and automated driving, and establishing an online database following the FAIR principles., Comment: 6 pages, 5 figures, 2 tables

With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions., Comment: 8 pages, 5 figures

To plan safely in uncertain environments, agents must balance utility with safety constraints. Safe planning problems can be modeled as a chance-constrained partially observable Markov decision process (CC-POMDP) and solutions often use expensive rollouts or heuristics to estimate the optimal value and action-selection policy. This work introduces the ConstrainedZero policy iteration algorithm that solves CC-POMDPs in belief space by learning neural network approximations of the optimal value and policy with an additional network head that estimates the failure probability given a belief. This failure probability guides safe action selection during online Monte Carlo tree search (MCTS). To avoid overemphasizing search based on the failure estimates, we introduce $\Delta$-MCTS, which uses adaptive conformal inference to update the failure threshold during planning. The approach is tested on a safety-critical POMDP benchmark, an aircraft collision avoidance system, and the sustainability problem of safe CO$_2$ storage. Results show that by separating safety constraints from the objective we can achieve a target level of safety without optimizing the balance between rewards and costs., Comment: In Proceedings of the 2024 International Joint Conference on Artificial Intelligence (IJCAI)

Travel to academic conferences -- where international flights are the norm -- is responsible for a sizeable fraction of the greenhouse gas (GHG) emissions associated with academic work. In order to provide a benchmark for comparison with other fields, as well as for future reduction strategies and assessments, we estimate the CO2-equivalent emissions for conference travel in the field of astronomy for the prepandemic year 2019. The GHG emission of the international astronomical community's 362 conferences and schools in 2019 amounted to 42,500 tCO2e, assuming a radiative-forcing index factor of 1.95 for air travel. This equates to an average of 1.0 $\pm$ 0.6 tCO2e per participant per meeting. The total travel distance adds up to roughly 1.5 Astronomical Units, that is, 1.5 times the distance between the Earth and the Sun. We present scenarios for the reduction of this value, for instance with virtual conferencing or hub models, while still prioritizing the benefits conferences bring to the scientific community., Comment: Supplementary material is available at PNAS Nexus online: https://academic.oup.com/pnasnexus/article/3/5/pgae143/7659884

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.