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Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve familiar disease patterns in new spatial distributions. Therefore, established local anomaly detection approaches may fail to identify them as new. Here, we present a novel approach to detect the emergence of new disease phenotypes exhibiting distinct patterns of the spatial distribution of lesions. We first identify anomalies in lung CT data, and then compare their distribution in a continually acquired new patient cohorts with historic patient population observed over a long prior period. We evaluate how accumulated evidence collected in the stream of patients is able to detect the onset of an emerging disease. In a gram-matrix based representation derived from the intermediate layers of a three-dimensional convolutional neural network, newly emerging clusters indicate emerging diseases.

A recently introduced quantitative susceptibility mapping (QSM) technique, $\chi$-separation, offers the capability to separate paramagnetic ($\chi_{\text{para}}$) and diamagnetic ($\chi_{\text{dia}}$) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by $\chi$-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying $\chi$-separation at 7T presents difficulties due to the requirement of an $R_2$ map, coupled with issues such as high specific absorption rate (SAR), large $B_1$ transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T $R_2^*$ map into a 3T $R_2'$ map. Building on this development, we present a new pipeline for $\chi$-separation at 7T, enabling us to generate high-resolution $\chi$-separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scaled $R_2'$, and is validated against $\chi$-separation maps at 3T, demonstrating its accuracy. The 7T $\chi$-separation maps generated by the proposed method exhibit similar contrasts to those from 3T, while 7T high-resolution maps offer enhanced clarity and detail. Quantitative analysis confirms that the proposed method surpasses the alternative pipelines. The proposed method results well delineate the detailed brain structures associated with iron and myelin. This new pipeline holds promise for analyzing iron and myelin concentration changes in various neurodegenerative diseases through precise structural examination.

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

The development of a system vulnerability analysis tool (SVAT) for complex mission critical systems (CMCS) produced the software for operation and network attack results review (SONARR). This software builds upon the Blackboard Architecture and uses its a rule-fact logic to assess model networks to identify potential pathways that an attacker might take through them via the exploitation of vulnerabilities within the network. The SONARR objects and algorithm were developed previously; however, performance was insufficient for analyzing large networks. This paper describes and analyzes the performance of a multi-threaded SONARR algorithm and other enhancements which were developed to increase SONARR's performance and facilitate the analysis of large networks., Comment: The U.S. federal sponsor has requested that we not include funding acknowledgement for this publication

A system vulnerability analysis technique (SVAT) for the analysis of complex mission critical systems (CMCS) that cannot be taken offline or subjected to the risks posed by traditional penetration testing was previously developed. This system uses path-based analysis of vulnerabilities to identify potential threats to system security. Generalization logic building on the Blackboard Architecture's rule-fact paradigm was implemented in this system, the software for operation and network attack results review (SONARR). This paper presents an overview of additional functionality that has been added to this tool and the experimentation that was conducted to analyze their efficacy and the performance benefits of the new in-memory processing capabilities of the SONARR algorithm. The results of the performance tests and their relation to networks' architecture are discussed. The paper concludes with a discussion of avenues of future work, including the implementation of multithreading, additional analysis metrics like confidentiality, integrity, and availability, and improved heuristic development., Comment: The U.S. federal sponsor has requested that we not include funding acknowledgement for this publication

Cybersecurity software tool evaluation is difficult due to the inherently adversarial nature of the field. A penetration testing (or offensive) tool must be tested against a viable defensive adversary and a defensive tool must, similarly, be tested against a viable offensive adversary. Characterizing the tool's performance inherently depends on the quality of the adversary, which can vary from test to test. This paper proposes the use of a 'perfect' network, representing computing systems, a network and the attack pathways through it as a methodology to use for testing cybersecurity decision-making tools. This facilitates testing by providing a known and consistent standard for comparison. It also allows testing to include researcher-selected levels of error, noise and uncertainty to evaluate cybersecurity tools under these experimental conditions., Comment: The U.S. federal sponsor has requested that we not include funding acknowledgement for this publication

The software for operations and network attack results review (SONARR) and the autonomous penetration testing system (APTS) use facts and common properties in digital twin networks to represent real-world entities. However, in some cases fact values will change regularly, making it difficult for objects in SONARR and APTS to consistently and accurately represent their real-world counterparts. This paper proposes and evaluates the addition of verifiers, which check real-world conditions and update network facts, to SONARR. This inclusion allows SONARR to retrieve fact values from its executing environment and update its network, providing a consistent method of ensuring that the operations and, therefore, the results align with the real-world systems being assessed. Verifiers allow arbitrary scripts and dynamic arguments to be added to normal SONARR operations. This provides a layer of flexibility and consistency that results in more reliable output from the software., Comment: The U.S. federal sponsor has requested that we not include funding acknowledgement for this publication

Bayesian observer and actor models have provided normative explanations for many behavioral phenomena in perception, sensorimotor control, and other areas of cognitive science and neuroscience. They attribute behavioral variability and biases to different interpretable entities such as perceptual and motor uncertainty, prior beliefs, and behavioral costs. However, when extending these models to more complex tasks with continuous actions, solving the Bayesian decision-making problem is often analytically intractable. Moreover, inverting such models to perform inference over their parameters given behavioral data is computationally even more difficult. Therefore, researchers typically constrain their models to easily tractable components, such as Gaussian distributions or quadratic cost functions, or resort to numerical methods. To overcome these limitations, we amortize the Bayesian actor using a neural network trained on a wide range of different parameter settings in an unsupervised fashion. Using the pre-trained neural network enables performing gradient-based Bayesian inference of the Bayesian actor model's parameters. We show on synthetic data that the inferred posterior distributions are in close alignment with those obtained using analytical solutions where they exist. Where no analytical solution is available, we recover posterior distributions close to the ground truth. We then show that identifiability problems between priors and costs can arise in more complex cost functions. Finally, we apply our method to empirical data and show that it explains systematic individual differences of behavioral patterns., Comment: 19 pages, 7 figures

We present a new surrogate model for emulating the behavior of complex nonlinear dynamical systems with external stochastic excitation. The model represents the system dynamics in state space form through a sparse Kriging model. The resulting surrogate model is termed state space Kriging (S2K) model. Sparsity in the Kriging model is achieved by selecting an informative training subset from the observed time histories of the state vector and its derivative with respect to time. We propose a tailored technique for designing the training time histories of state vector and its derivative, aimed at enhancing the robustness of the S2K prediction. We validate the performance of the S2K model with various benchmarks. The results show that S2K yields accurate prediction of complex nonlinear dynamical systems under stochastic excitation with only a few training time histories of state vector.

Computer system creativity is a key step on the pathway to artificial general intelligence (AGI). It is elusive, however, due to the fact that human creativity is not fully understood and, thus, it is difficult to develop this capability in software. Large language models (LLMs) provide a facsimile of creativity and the appearance of sentience, while not actually being either creative or sentient. While LLMs have created bona fide new content, in some cases - such as with harmful hallucinations - inadvertently, their deliberate creativity is seen by some to not match that of humans. In response to this challenge, this paper proposes a technique for enhancing LLM output creativity via an iterative process of concept injection and refinement. Initial work on the development of the Creative Artificial Intelligence through Recurring Developments and Determinations (CAIRDD) system is presented and the efficacy of key system components is evaluated.

This paper presents and evaluates work on the development of an artificial intelligence (AI) anti-bullying system. The system is designed to identify coordinated bullying attacks via social media and other mechanisms, characterize them and propose remediation and response activities to them. In particular, a large language model (LLM) is used to populate an enhanced expert system-based network model of a bullying attack. This facilitates analysis and remediation activity - such as generating report messages to social media companies - determination. The system is described and the efficacy of the LLM for populating the model is analyzed herein.

Over the past 50 years, the advancements in medical and health research have radically changed the epidemiology of health conditions in neonates, children, and adolescents; and clinical research has on the whole, moved forward. However, large sections of the pediatric community remain vulnerable and underserved, by clinical research. One reason for this is the fact that most pediatric diseases are also rare diseases (i.e., they fit the EU definition of a rare condition, by affecting no more than 5 in 10,000 individuals), and indeed the majority of conditions under this umbrella heading are in fact much rarer, affecting fewer than 1 in 100,000. Rare pediatric diseases incur particular challenges, both in terms of actually conducting clinical trials but also planning trials (and indeed, stimulating the preclinical research and knowledge generation necessary to embark on clinical trials in the first place). The pediatric regulation and orphan regulation (covering rare diseases) were introduced to address the complexities in research and development of medicines specifically for children and for people living with a rare disease, respectively. The regulations have been reasonably effective, particularly in areas where adult and pediatric diseases overlap, driving the development of more pediatric medicines; however, challenges still remain, often exacerbated by the rarity of the diseases. These include issues around trial planning, the need for more innovative methodologies in smaller populations, significant delays in trial start up and recruitment, recruitment issues (due to small populations and the nature of the conditions), lack of endpoints, and scarce data. This chapter will discuss some of the major challenges in delivering trials in pediatric rare diseases while also assessing current and future solutions to address these., Comment: Ed: E Gasthuys, K Allegaert, L Dossche, M Turner, Essentials of Translational Pediatric Drug Development, 2024, Pages 483-507, ISBN 9780323884594

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor modifications, accepted for publication in Astrophysical Journal

This paper presents and evaluates a new retrieval augmented generation (RAG) and large language model (LLM)-based artificial intelligence (AI) technique: rubric enabled generative artificial intelligence (REGAI). REGAI uses rubrics, which can be created manually or automatically by the system, to enhance the performance of LLMs for evaluation purposes. REGAI improves on the performance of both classical LLMs and RAG-based LLM techniques. This paper describes REGAI, presents data regarding its performance and discusses several possible application areas for the technology.

While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-granular annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to a total of 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained models to showcase the feasibility of named-entity recognition model training. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.

Ultra-high-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 31 December 2022, with a total exposure of 135,000 km^2 sr yr. The strongest indication for an excess that we find, with a post-trial significance of 3.1{\sigma}, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable exposure, our results in those regions are in good agreement with the expectations from an isotropic distribution., Comment: submitted to ApJ

The nonlinear interaction of x-rays with matter is at the heart of understanding and controlling ultrafast molecular dynamics from an atom-specific viewpoint, providing new scientific and analytical opportunities to explore the structure and dynamics of small quantum systems. At increasingly high x-ray intensity, the sensitivity of ultrashort x-ray pulses to specific electronic states and emerging short-lived transient intermediates is of particular relevance for our understanding of fundamental multi-photon absorption processes. In this work, intense x-ray free-electron laser (XFEL) pulses at the European XFEL (EuXFEL) are combined with a gas cell and grating spectrometer for a high-intensity transmission spectroscopy study of multiphoton-induced ultrafast molecular fragmentation dynamics in CF$_4$. This approach unlocks the direct intra-pulse observation of transient fragments, including neutral atoms, by their characteristic absorption lines in the transmitted broad-band x-ray spectrum. The dynamics with and without initially producing fluorine K-shell holes are studied by tuning the central photon energy. The absorption spectra are measured at different FEL intensities to observe nonlinear effects. Transient isolated fluorine atoms and ions are spectroscopically recorded within the ultrashort pulse duration of few tens of femtoseconds. An isosbestic point that signifies the correlated transition between intact neutral CF$_4$ molecules and charged atomic fragments is observed near the fluorine K-edge. The dissociation dynamics and the multiphoton absorption-induced dynamics encoded in the spectra are theoretically interpreted. Overall, this study demonstrates the potential of high-intensity x-ray transmission spectroscopy to study ultrafast molecular dynamics with sensitivity to specific intermediate species and their electronic structure., Comment: 30 pages, with 7 figures, submitted to Phys. Rev. X

Producing an artificial general intelligence (AGI) has been an elusive goal in artificial intelligence (AI) research for some time. An AGI would have the capability, like a human, to be exposed to a new problem domain, learn about it and then use reasoning processes to make decisions. While AI techniques have been used across a wide variety of problem domains, an AGI would require an AI that could reason beyond its programming and training. This paper presents a small step towards producing an AGI. It describes a mechanism for an AI to learn about and develop reasoning pathways to make decisions in an a priori unknown domain. It combines a classical AI technique, the expert system, with a its modern adaptation - the gradient descent trained expert system (GDTES) - and utilizes generative artificial intelligence (GAI) to create a network and training data set for this system. These can be created from available sources or may draw upon knowledge incorporated in a GAI's own pre-trained model. The learning process in GDTES is used to optimize the AI's decision-making. While this approach does not meet the standards that many have defined for an AGI, it provides a somewhat similar capability, albeit one which requires a learning process before use.

Heninger, Rains and Sloane raised the question of which power series with integer coefficients can be written as the $n$th power of another power series with integer coefficients and constant term $1$. We provide necessary and sufficient conditions, as well as compare with a general integrality criterion due to Dieudonn\'e and Dwork that can be applied to this question as well., Comment: 11 pages

The advent of wearable computers enables a new source of context for AI that is embedded in egocentric sensor data. This new egocentric data comes equipped with fine-grained 3D location information and thus presents the opportunity for a novel class of spatial foundation models that are rooted in 3D space. To measure progress on what we term Egocentric Foundation Models (EFMs) we establish EFM3D, a benchmark with two core 3D egocentric perception tasks. EFM3D is the first benchmark for 3D object detection and surface regression on high quality annotated egocentric data of Project Aria. We propose Egocentric Voxel Lifting (EVL), a baseline for 3D EFMs. EVL leverages all available egocentric modalities and inherits foundational capabilities from 2D foundation models. This model, trained on a large simulated dataset, outperforms existing methods on the EFM3D benchmark.

The Pierre Auger Observatory is the most sensitive instrument to detect photons with energies above $10^{17}$ eV. It measures extensive air showers generated by ultra high energy cosmic rays using a hybrid technique that exploits the combination of a fluorescence detector with a ground array of particle detectors. The signatures of a photon-induced air shower are a larger atmospheric depth of the shower maximum ($X_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced cascades. In this work, a new analysis technique in the energy interval between 1 and 30 EeV (1 EeV = $10^{18}$ eV) has been developed by combining the fluorescence detector-based measurement of $X_{max}$ with the specific features of the surface detector signal through a parameter related to the air shower muon content, derived from the universality of the air shower development. No evidence of a statistically significant signal due to photon primaries was found using data collected in about 12 years of operation. Thus, upper bounds to the integral photon flux have been set using a detailed calculation of the detector exposure, in combination with a data-driven background estimation. The derived 95% confidence level upper limits are 0.0403, 0.01113, 0.0035, 0.0023, and 0.0021 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above 1, 2, 3, 5, and 10 EeV, respectively, leading to the most stringent upper limits on the photon flux in the EeV range. Compared with past results, the upper limits were improved by about 40% for the lowest energy threshold and by a factor 3 above 3 EeV, where no candidates were found and the expected background is negligible. The presented limits can be used to probe the assumptions on chemical composition of ultra-high energy cosmic rays and allow for the constraint of the mass and lifetime phase space of super-heavy dark matter particles., Comment: 19 pages, 22 figures

We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV=$10^{18}$ eV) using the distributions of the depth of shower maximum $X_\mathrm{max}$. The analysis relies on ${\sim}50,000$ events recorded by the Surface Detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the data set offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the Fluorescence Detector, this enables the first measurement of the evolution of the mean and the standard deviation of the $X_\mathrm{max}$ distributions up to 100 EeV. Our findings are threefold: (1.) The evolution of the mean logarithmic mass towards a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (2.) The evolution of the fluctuations of $X_\mathrm{max}$ towards a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in $X_\mathrm{max}$ at the highest energies. (3.) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum., Comment: submitted to Phys. Rev. D, 28 pages, 18 figures, 5 tables

We present measurements of the atmospheric depth of the shower maximum $X_\mathrm{max}$, inferred for the first time on an event-by-event level using the Surface Detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the $X_\mathrm{max}$ distributions up to energies of 100 EeV ($10^{20}$ eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data, we find evidence that the rate of change of the average $X_\mathrm{max}$ with the logarithm of energy features three breaks at $6.5\pm0.6~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, $11\pm 2~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, and $31\pm5~(\mathrm{stat})\pm3~(\mathrm{sys})$ EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured $X_\mathrm{max}$ distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 EeV and 100 EeV., Comment: submitted to Phys. Rev. Lett., 10 pages, 3 figures, 1 table

Since 2019, GRAVITY has provided direct observations of giant planets and brown dwarfs at separations of down to 95 mas from the host star. Some of these observations have provided the first direct confirmation of companions previously detected by indirect techniques (astrometry and radial velocities). We want to improve the observing strategy and data reduction in order to lower the inner working angle of GRAVITY in dual-field on-axis mode. We also want to determine the current limitations of the instrument when observing faint companions with separations in the 30-150 mas range. To improve the inner working angle, we propose a fiber off-pointing strategy during the observations to maximize the ratio of companion-light-to-star-light coupling in the science fiber. We also tested a lower-order model for speckles to decouple the companion light from the star light. We then evaluated the detection limits of GRAVITY using planet injection and retrieval in representative archival data. We compare our results to theoretical expectations. We validate our observing and data-reduction strategy with on-sky observations; first in the context of brown dwarf follow-up on the auxiliary telescopes with HD 984 B, and second with the first confirmation of a substellar candidate around the star Gaia DR3 2728129004119806464. With synthetic companion injection, we demonstrate that the instrument can detect companions down to a contrast of $8\times 10^{-4}$ ($\Delta \mathrm{K}= 7.7$ mag) at a separation of 35 mas, and a contrast of $3\times 10^{-5}$ ($\Delta \mathrm{K}= 11$ mag) at 100 mas from a bright primary (K<6.5), for 30 min exposure time. With its inner working angle and astrometric precision, GRAVITY has a unique reach in direct observation parameter space. This study demonstrates the promising synergies between GRAVITY and Gaia for the confirmation and characterization of substellar companions., Comment: 16 pages, 14 figures. Submitted to A&A

We prove quantitative decay estimates of macroscopic quantities generated by the solutions to linear transport equations driven by a general family of Hamiltonians. The associated particle trajectories are all trapped in a compact region of phase-space and feature a non-degenerate elliptic stagnation point. The analysis covers a large class of Hamiltonians generated by the radially symmetric compactly supported equilibria of the gravitational Vlasov-Poisson system. Working in radial symmetry, our analysis features both the 1+2-dimensional case and the harder 1+1-dimensional case, where all the particles have the same value of the modulus of angular momentum. The latter case is also of importance in both the plasma physics case and two dimensional incompressible fluid flows., Comment: 60 pages, added the decay of the macroscopic density to Theorem 1.7

We numerically analyse solutions of the spherically symmetric gravitational Vlasov-Poisson system close to compactly supported stable steady states. We observe either partially undamped oscillations or macroscopically damped solutions. We investigate for many steady states to which of these behaviours they correspond. A linear relation between the exponents of polytropic steady states and the qualitative behaviour close to them is identified. Undamped oscillations are also observed around not too concentrated King models and around all shells with a sufficiently large vacuum region. We analyse all solutions both at the non-linear and linearised level and find that the qualitative behaviours are identical at both. To relate the observed phenomena to theoretical results, we further include a comprehensive numerical study of the radial particle periods in the equilibria., Comment: 39 pages, 13 figures

A variety of forms of artificial intelligence systems have been developed. Two well-known techniques are neural networks and rule-fact expert systems. The former can be trained from presented data while the latter is typically developed by human domain experts. A combined implementation that uses gradient descent to train a rule-fact expert system has been previously proposed. A related system type, the Blackboard Architecture, adds an actualization capability to expert systems. This paper proposes and evaluates the incorporation of a defensible-style gradient descent training capability into the Blackboard Architecture. It also introduces the use of activation functions for defensible artificial intelligence systems and implements and evaluates a new best path-based training algorithm.

The detection of low-mass planets orbiting the nearest stars is a central stake of exoplanetary science, as they can be directly characterized much more easily than their distant counterparts. Here, we present the results of our long-term astrometric observations of the nearest binary M-dwarf Gliese 65 AB (GJ65), located at a distance of only 2.67 pc. We monitored the relative astrometry of the two components from 2016 to 2023 with the VLTI/GRAVITY interferometric instrument. We derived highly accurate orbital parameters for the stellar system, along with the dynamical masses of the two red dwarfs. The GRAVITY measurements exhibit a mean accuracy per epoch of 50-60 microarcseconds in 1.5h of observing time using the 1.8m Auxiliary Telescopes. The residuals of the two-body orbital fit enable us to search for the presence of companions orbiting one of the two stars (S-type orbit) through the reflex motion they imprint on the differential A-B astrometry. We detected a Neptune-mass candidate companion with an orbital period of p = 156 +/- 1 d and a mass of m = 36 +/- 7 Mearth. The best-fit orbit is within the dynamical stability region of the stellar pair. It has a low eccentricity, e = 0.1 - 0.3, and the planetary orbit plane has a moderate-to-high inclination of i > 30{\deg} with respect to the stellar pair, with further observations required to confirm these values. These observations demonstrate the capability of interferometric astrometry to reach microarcsecond accuracy in the narrow-angle regime for planet detection by reflex motion from the ground. This capability offers new perspectives and potential synergies with Gaia in the pursuit of low-mass exoplanets in the solar neighborhood., Comment: Corresponding authors: G.Bourdarot, P.Kervella, O.Pfuhl. Accepted in A&A Letters

With four companions at separations from 16 to 71 au, HR 8799 is a unique target for direct imaging, presenting an opportunity for the comparative study of exoplanets with a shared formation history. Combining new VLTI/GRAVITY observations obtained within the ExoGRAVITY program with archival data, we perform a systematic atmospheric characterisation of all four planets. We explore different levels of model flexibility to understand the temperature structure, chemistry and clouds of each planet using both petitRADTRANS atmospheric retrievals and fits to self-consistent radiative-convective equilibrium models. Using Bayesian Model Averaging to combine multiple retrievals, we find that the HR 8799 planets are highly enriched in metals, with [M/H] $\gtrsim$1, and have stellar to super-stellar C/O ratios. The C/O ratio increases with increasing separation from $0.55^{+0.12}_{-0.10}$ for d to $0.78^{+0.03}_{-0.04}$ for b, with the exception of the innermost planet which has a C/O ratio of $0.87\pm0.03$. By retrieving a quench pressure and using a disequilibrium chemistry model we derive vertical mixing strengths compatible with predictions for high-metallicity, self-luminous atmospheres. Bayesian evidence comparisons strongly favour the presence of HCN in HR 8799 c and e, as well as CH$_{4}$ in HR 8799 c, with detections at $>5\sigma$ confidence. All of the planets are cloudy, with no evidence for patchiness. The clouds of c, d and e are best fit by silicate clouds lying above a deep iron cloud layer, while the clouds of the cooler HR 8799 b are more likely composed of Na$_{2}$S. With well defined atmospheric properties, future exploration of this system is well positioned to unveil further detail in these planets, extending our understanding of the composition, structure, and formation history of these siblings., Comment: 45 pages, 25 figures, accepted by Astronomy & Astrophysics

The flux of ultra-high energy cosmic rays reaching Earth above the ankle energy (5 EeV) can be described as a mixture of nuclei injected by extragalactic sources with very hard spectra and a low rigidity cutoff. Extragalactic magnetic fields existing between the Earth and the closest sources can affect the observed CR spectrum by reducing the flux of low-rigidity particles reaching Earth. We perform a combined fit of the spectrum and distributions of depth of shower maximum measured with the Pierre Auger Observatory including the effect of this magnetic horizon in the propagation of UHECRs in the intergalactic space. We find that, within a specific range of the various experimental and phenomenological systematics, the magnetic horizon effect can be relevant for turbulent magnetic field strengths in the local neighbourhood of order $B_{\rm rms}\simeq (50-100)\,{\rm nG}\,(20\rm{Mpc}/{d_{\rm s})( 100\,\rm{kpc}/L_{\rm coh}})^{1/2}$, with $d_{\rm s}$ the typical intersource separation and $L_{\rm coh}$ the magnetic field coherence length. When this is the case, the inferred slope of the source spectrum becomes softer and can be closer to the expectations of diffusive shock acceleration, i.e., $\propto E^{-2}$. An additional cosmic-ray population with higher source density and softer spectra, presumably also extragalactic and dominating the cosmic-ray flux at EeV energies, is also required to reproduce the overall spectrum and composition results for all energies down to 0.6~EeV., Comment: Matches published version

Autonomous vehicle control is growing in availability for new vehicles and there is a potential need to retrofit older vehicles with this capability. Additionally, automotive cybersecurity has become a significant concern in recent years due to documented attacks on vehicles. As a result, researchers have been exploring reverse engineering techniques to automate vehicle control and improve vehicle security and threat analysis. In prior work, a vehicle's accelerator and brake pedal controller area network (CAN) channels were identified using reverse engineering techniques without prior knowledge of the vehicle. However, the correlation results for deceleration were lower than those for acceleration, which may be able to be improved by incorporating data from an additional telemetry device. In this paper, a method that uses IMU and GPS data to reverse-engineer a vehicle's steering wheel position CAN channels, without prior knowledge of the vehicle, is presented. Using GPS data is shown to greatly improve correlation values for deceleration, particularly for the brake pedal CAN channels. This work demonstrates the efficacy of using these data sources for automotive CAN reverse engineering. This has potential uses in automotive vehicle control and for improving vehicle security and threat analysis.

In this paper we present EgoLifter, a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects. The system is specifically designed for egocentric data where scenes contain hundreds of objects captured from natural (non-scanning) motion. EgoLifter adopts 3D Gaussians as the underlying representation of 3D scenes and objects and uses segmentation masks from the Segment Anything Model (SAM) as weak supervision to learn flexible and promptable definitions of object instances free of any specific object taxonomy. To handle the challenge of dynamic objects in ego-centric videos, we design a transient prediction module that learns to filter out dynamic objects in the 3D reconstruction. The result is a fully automatic pipeline that is able to reconstruct 3D object instances as collections of 3D Gaussians that collectively compose the entire scene. We created a new benchmark on the Aria Digital Twin dataset that quantitatively demonstrates its state-of-the-art performance in open-world 3D segmentation from natural egocentric input. We run EgoLifter on various egocentric activity datasets which shows the promise of the method for 3D egocentric perception at scale., Comment: ECCV 2024 camera ready version. Project page: https://egolifter.github.io/

We present a full general relativistic analytic solution for a radiation-pressure supported equilibrium fluid torus orbiting a rotating neutron star (NS). Previously developed analytical methods are thoroughly applied in the Hartle-Thorne geometry, including the effects of both the NS's angular momentum and quadrupole moment. The structure, size and shape of the torus are explored, focusing especially on the critically thick solution - the cusp tori. For the astrophysically relevant range of NS parameters, we examine how our findings differ from those obtained for the Schwarzschild spacetime. The solutions for rotating stars display signatures of the interplay between relativistic and Newtonian effects where the impact of NS angular momentum and quadrupole moment are almost counterbalanced at the given radius. Nevertheless, the spacetime parameters still strongly influence the size of tori, which can be shown in a coordinate-independent way. Finally, we discuss the importance of the size of the central NS, determining whether or not the surrounding torus may exist. We provide a set of tools in a Wolfram Mathematica code, which poses a basis allowing for a further investigation of the impact of the NSs' superdense matter equation of state on the spectral and temporal behaviour of accretion tori., Comment: 7 pages, 5 figures, Accepted for publication in A&A. Extended discussion on the quadrupole moment effect

The four characteristic oscillation frequencies of accretion flows are, in addition to the Keplerian orbital frequency, often discussed in the context of the time variability of the black hole and neutron star (NS) low-mass X-ray binaries (LMXBs). These are namely the frequencies of the axisymmetric radial and vertical epicyclic oscillations, and the frequencies of non-axisymmetric oscillations corresponding to the periastron (radial) and Lense-Thirring (vertical) precessions. In this context, we investigate the effect of the quadrupole moment of a slowly rotating NS and provide complete formulae for calculating these oscillation and precession frequencies, as well as their convenient approximations. Simple formulae corresponding to the geodesic limit of a slender torus (and test particle motion) and the limit of a marginally overflowing torus (torus exhibiting a critical cusp) are presented, and furthermore, more general approximate formulae are included to allow calculations for arbitrarily thick tori. We provide the Wolfram Mathematica code used for our calculations together with C++ and PYTHON codes for calculations of the frequencies. Our formulae can be used for various calculations describing the astrophysical signatures of the NSs' superdense matter equation of state. For instance, we demonstrate that, even for a given fixed number of free parameters, a model accounting for fluid flow precession better matches the frequencies of twin-peak quasiperiodic oscillations observed in NS LMXBs than a model using geodesic precession., Comment: 20 pages, 15 figures, accepted for publication in A&A. Extended discussion on the influence of the fluid polytropic index and the precession frequencies

Precise mass constraints are vital for the characterisation of brown dwarfs and exoplanets. Here we present how the combination of data obtained by Gaia and GRAVITY can help enlarge the sample of substellar companions with measured dynamical masses. We show how the Non-Single-Star (NSS) two-body orbit catalogue contained in Gaia DR3 can be used to inform high-angular-resolution follow-up observations with GRAVITY. Applying the method presented in this work to eight Gaia candidate systems, we detect all eight predicted companions, seven of which were previously unknown and five are of a substellar nature. Among the sample is Gaia DR3 2728129004119806464 B, which - detected at an angular separation of (34.01 $\pm$ 0.15) mas from the host - is the closest substellar companion ever imaged. This translates to a semi-major axis of (0.938 $\pm$ 0.023) AU. WT 766 B, detected at a greater angular separation, was confirmed to be on an orbit exhibiting an even smaller semi-major axis of (0.676 $\pm$ 0.008) AU. The GRAVITY data were then used to break the host-companion mass degeneracy inherent to the Gaia NSS orbit solutions as well as to constrain the orbital solutions of the respective target systems. Knowledge of the companion masses enabled us to further characterise them in terms of their ages, effective temperatures, and radii via the application of evolutionary models. The inferred ages exhibit a distinct bias towards values younger than what is to be expected based on the literature. The results serve as an independent validation of the orbital solutions published in the NSS two-body orbit catalogue and show that the combination of astrometric survey missions and high-angular-resolution direct imaging holds great promise for efficiently increasing the sample of directly imaged companions in the future, especially in the light of Gaia's upcoming DR4 and the advent of GRAVITY+., Comment: Accepted for publication in Astronomy and Astrophysics

Global variance-based reliability sensitivity indices arise from a variance decomposition of the indicator function describing the failure event. The first-order indices reflect the main effect of each variable on the variance of the failure event and can be used for variable prioritization; the total-effect indices represent the total effect of each variable, including its interaction with other variables, and can be used for variable fixing. This contribution derives expressions for the variance-based reliability indices of systems with multiple failure modes that are based on the first-order reliability method (FORM). The derived expressions are a function of the FORM results and, hence, do not require additional expensive model evaluations. They do involve the evaluation of multinormal integrals, for which effective solutions are available. We demonstrate that the derived expressions enable an accurate estimation of variance-based reliability sensitivities for general system problems to which FORM is applicable.

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.

Major efforts in recent years have been directed towards understanding molecular transport in polymeric membranes, in particular reverse osmosis and nanofiltration membranes. Transition-state theory is an increasingly common approach to explore mechanisms of transmembrane permeation with molecular details, but most applications treat all free energy barriers to transport within the membrane as equal. This assumption neglects the inherent structural and chemical heterogeneity in polymeric membranes. In this work, we expand the transition-state theory framework to include distributions of membrane free energy barriers. We show that the highest free energy barriers along the most permeable paths, rather than typical paths, provide the largest contributions to the experimentally-observed effective free energy barrier. We show that even moderate, random heterogeneity in molecular barriers will significantly impact how we interpret the mechanisms of transport through membranes. Simplified interpretations of experimentally measured barriers can lead to incorrect assumptions about the underlying mechanisms governing transport and miss the mechanisms most relevant to the overall permeability., Comment: 46 pages, 12 figures, 2 tables, uses elsarticle.cls and elsarticle-num.bst

Dual-field interferometric observations with VLTI/GRAVITY sometimes require the use of a "binary calibrator", a binary star whose individual components remain unresolved by the interferometer, with a separation between 400 and 2000 mas for observations with the Units Telescopes (UTs), or 1200 to 3000 mas for the Auxiliary Telescopes (ATs). The separation vector also needs to be predictable to within 10 mas for proper pointing of the instrument. Up until now, no list of properly vetted calibrators was available for dual-field observations with VLTI/GRAVITY on the UTs. Our objective is to compile such a list, and make it available to the community. We identify a list of candidates from the Washington Double Star (WDS) catalogue, all with appropriate separations and brightness, scattered over the Southern sky. We observe them as part of a dedicated calibration programme, and determine whether these objects are true binaries (excluding higher multiplicities resolved interferometrically but unseen by imaging), and extract measurements of the separation vectors. We combine these new measurements with those available in the WDS to determine updated orbital parameters for all our vetted calibrators. We compile a list of 13 vetted binary calibrators for observations with VLTI/GRAVITY on the UTs, and provide orbital estimates and astrometric predictions for each of them. We show that our list guarantees that there are always at least two binary calibrators at airmass < 2 in the sky over the Paranal observatory, at any point in time. Any Principal Investigator wishing to use the dual-field mode of VLTI/GRAVITY with the UTs can now refer to this list to select an appropriate calibrator. We encourage the use of "whereistheplanet" to predict the astrometry of these calibrators, which seamlessly integrates with "p2Gravity" for VLTI/GRAVITY dual-field observing material preparation., Comment: 16 pages, 6 figures. Submitted to A&A