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The coset construction of two-dimensional conformal field theory (2D CFT) defines a 2D CFT by taking the quotient of two previously known chiral algebras. In this work, we use the methods of non-abelian (non-invertible) anyon condensation to describe 2D topological cosets, defined by the special case where the quotient of chiral algebras is a conformal embedding. In this case, the coset has zero central charge, and the coset theory is thus purely topological. Using non-abelian anyon condensation we describe in general the spectrum of line and local operators as well as their fusion, operator product expansion, and the action of the lines on local operators. An important application of our results is to QCD$_{2}$ with massless fermions in any representation that leads to a gapped phase, where topological cosets (conjecturally) describe the infrared fixed point. We discuss several such examples in detail. For instance, we find that the $Spin(8)_{1}/SU(3)_{3}$ and $Spin(16)_{1}/Spin(9)_{2}$ topological cosets appearing at the infrared fixed point of appropriate QCD$_{2}$ theories are described by $\mathbb{Z}_{2} \times \mathbb{Z}_{2}$ triality and $\mathbb{Z}_{2} \times \mathrm{Rep(S_{3})}$ fusion categories respectively. Additionally, using this setup, we argue that chiral $Spin(8)$ QCD$_{2}$ with massless chiral fermions in the vectorial and spinorial representations is not only gapped, but moreover trivially gapped, with a unique ground state., Comment: 55 pages, 15 figures, 7 tables

Global Categorical Symmetries are a powerful new tool for analyzing quantum field theories. This volume compiles lecture notes from the 2022 and 2023 summer schools on Global Categorical Symmetries, held at the Perimeter Institute for Theoretical Physics and at the Swiss Map Research Station in Les Diableret. Specifically, this volume collects the lectures: * An introduction to symmetries in quantum field theory, Kantaro Ohmori * Introduction to anomalies in quantum field theory, Clay C\'ordova * Symmetry Categories 101, Michele Del Zotto * Applied Cobordism Hypothesis, David Jordan * Finite symmetry in QFT, Daniel S. Freed These volumes are devoted to interested newcomers: we only assume (basic) knowledge of quantum field theory (QFT) and some relevant maths. We try to give appropriate references for non-standard materials that are not covered. Our aim in this first volume is to illustrate some of the main questions and ideas together with some of the methods and the techniques necessary to begin exploring global categorical symmetries of QFTs., Comment: Lecture notes from the Summer Schools of the Simons Collaboration on Global Categorical Symmetries

We present a novel python-based 1D sub-Neptune evolution model that emphasizes the thermal evolution and potential solidification of the rock/iron core and the structure of the radiative atmosphere. This model explores planetary structure from the molten center to nbar pressure levels. Treating the radiative atmosphere is crucial for sub-Neptunes, due to the large scale height and low gravity, which contributes up to 40\% of their observed radius, especially for low-mass, highly irradiated planets. Consequently, we generically find that lower H/He mass fractions are needed to match a given planetary radius, compared to previous work. While the presence of metal-enrichment in the H/He layers (here modeled as 50$\times$ solar) does not substantially influence the size of the convective envelope, it notably reduces the transit radius by shrinking the radiative atmospheric scale height. Sub-Neptunes cool differently from terrestrial planets, with the rock/iron core's cooling rate limited by the envelope, leading to longer solidification timescales. Complete solidification of the silicate mantle by 10 Gyr is found only for planets with very low masses ($\leq 1M_\oplus$) and small H/He envelopes ($\leq$ 0.1\%). Dynamo action in sub-Neptune iron cores persists as long as the mantle surface remains molten, often exceeding 10 Gyr, and becomes sensitive to core thermal conductivity after solidification. We examine aspects of ''boil-off,'' which sets the maximum allowed H/He mass and planetary radius for subsequent evolution. The rock/iron's cooling energy moderately decreases the post-boil-off H/He mass fraction in planets with large atmospheric scale heights only., Comment: 32 pages, 17 figures, submitted to ApJ

LLMs are frequently used tools for conversational generation. Without additional information LLMs can generate lower quality responses due to lacking relevant content and hallucinations, as well as the perception of poor emotional capability, and an inability to maintain a consistent character. Knowledge graphs are commonly used forms of external knowledge and may provide solutions to these challenges. This paper introduces three proposals, utilizing knowledge graphs to enhance LLM generation. Firstly, dynamic knowledge graph embeddings and recommendation could allow for the integration of new information and the selection of relevant knowledge for response generation. Secondly, storing entities with emotional values as additional features may provide knowledge that is better emotionally aligned with the user input. Thirdly, integrating character information through narrative bubbles would maintain character consistency, as well as introducing a structure that would readily incorporate new information., Comment: 7 pages with citations, 1 figure, accepted to the ISWC 2024 Special Session

In the past utilities relied on in-field inspections to identify asset defects. Recently, utilities have started using drone-based inspections to enhance the field-inspection process. We consider a vast repository of drone images, providing a wealth of information about asset health and potential issues. However, making the collected imagery data useful for automated defect detection requires significant manual labeling effort. We propose a novel solution that combines synthetic asset defect images with manually labeled drone images. This solution has several benefits: improves performance of defect detection, reduces the number of hours spent on manual labeling, and enables the capability to generate realistic images of rare defects where not enough real-world data is available. We employ a workflow that combines 3D modeling tools such as Maya and Unreal Engine to create photorealistic 3D models and 2D renderings of defective assets and their surroundings. These synthetic images are then integrated into our training pipeline augmenting the real data. This study implements an end-to-end Artificial Intelligence solution to detect assets and asset defects from the combined imagery repository. The unique contribution of this research lies in the application of advanced computer vision models and the generation of photorealistic 3D renderings of defective assets, aiming to transform the asset inspection process. Our asset detection model has achieved an accuracy of 92 percent, we achieved a performance lift of 67 percent when introducing approximately 2,000 synthetic images of 2k resolution. In our tests, the defect detection model achieved an accuracy of 73 percent across two batches of images. Our analysis demonstrated that synthetic data can be successfully used in place of real-world manually labeled data to train defect detection model.

The dynamical properties of the bodies in the outer Solar System hold information regarding the planets' orbital histories. In early Solar System numerical simulations, where chaos is a primary driver, it is difficult to explore parameter space in a systematic way. In such simulations, stable configurations are hard to come by, and often require special fine-tuning. In addition, it is infeasible to run suites of well-resolved, realistic simulations with massive particles to drive planetary evolution where enough particles remain to represent the transneptunian populations to robustly statistically compare with observations. To complement state of the art full N-body simulations, we develop a method to artificially control each planet's orbital elements independently from each other, which when carefully applied, can be used to test a wider suite of models. We modify two widely used publicly available N-body integrators: (1) the C code, REBOUND and (2) the FORTRAN code, Mercury. We show how the application of specific fictitious forces within numerical integrators can be used to tightly control planetary evolution to more easily explore migration and orbital excitation and damping. This tool allows us to replicate the impact a massive planetesimal disk would have on the planets, without actually including the massive planetesimals, thus decreasing the chaos and simulation runtime. We highlight an appropriate application that shows the impact of Neptune's eccentricity damping and radial outward migration on Uranus' eccentricity, Comment: submitted to ApJ

We develop a python-based state-of-the-art sub-Neptune evolution model that incorporates both the post-formation boil-off at young ages $\leq$ 1 Myr and long-lived core-powered mass loss ($\sim$ Gyrs) from interior cooling. We investigate the roles of initial H/He entropy, core luminosity, energy advection, radiative atmospheric structure, and the transition to an XUV-driven mass-loss phase, with an eye on relevant timescales for planetary mass loss and thermal evolution. With particular attention to the re-equilibration process of the H/He envelope, including the energy sources that fuel the hydrodynamic wind, and energy transport timescales, we find boil-off and core-powered escape are primarily driven by stellar bolometric radiation. We further find that both boil-off and core-powered escape are decoupled from the thermal evolution. We show that, with a boil-off phase that accounts for the initial H/He mass fraction and initial entropy, post-boil-off core-powered escape has an insignificant influence on the demographics of small planets, as it is only able to remove at most 0.1% of the H/He mass fraction. Our numerical results are directly compared to previous work on analytical core-powered mass loss modeling for individual evolutionary trajectories and populations of small planets. We examine a number of assumptions made in previous studies that cause significant differences compared to our findings. We find that boil-off, though able to completely strip the gaseous envelope from a highly irradiated ($F \geq 100 F_\oplus$) planet that has a low-mass core ($M_c \leq 4M_\oplus$), cannot by itself form a pronounced radius gap as is seen in the observed population., Comment: 41 pages, 23 figures, accepted by ApJ

A $d$-dimensional tensegrity framework $(T,p)$ is an edge-labeled geometric graph in ${\mathbb R}^d$, which consists of a graph $T=(V,B\cup C\cup S)$ and a map $p:V\to {\mathbb R}^d$. The labels determine whether an edge $uv$ of $T$ corresponds to a fixed length bar in $(T,p)$, or a cable which cannot increase in length, or a strut which cannot decrease in length. We consider minimally infinitesimally rigid $d$-dimensional tensegrity frameworks and provide tight upper bounds on the number of its edges, in terms of the number of vertices and the dimension $d$. We obtain stronger upper bounds in the case when there are no bars and the framework is in generic position. The proofs use methods from convex geometry and matroid theory. A special case of our results confirms a conjecture of Whiteley from 1987. We also give an affirmative answer to a conjecture concerning the number of edges of a graph whose three-dimensional rigidity matroid is minimally connected.

We present an approach of utilizing a multi-camera array system for capturing dynamic high-resolution videos of the human face, with improved imaging performance as compared to traditional single-camera configurations. Employing an array of 54 individual high-resolution cameras, each with its own 13 megapixel sensor (709 megapixels total), we uniquely focus each camera to a different plane across the curved surface of the human face in order to capture dynamic facial expressions. Post-processing methods then stitch together each synchronized set of 54 images into a composite video frame. Our multi-focus strategy overcomes the resolution and depth-of-field (DOF) limitations for capturing macroscopically curved surfaces such as the human face, while maintaining high lateral resolution. Specifically we demonstrate how our setup achieves a generally uniform lateral resolution of 26.75 +/- 8.8 micrometer across a composite DOF of ~43mm that covers the entire face (85 cm^2 + FOV). Compared to a single-focus configuration this is almost a 10-fold increase in effective DOF. We believe that our new approach for multi-focus camera array video sets the stage for future video capture of a variety of dynamic and macroscopically curved surfaces at microscopic resolution.

A key challenge for computationally intensive state-of-the-art Earth-system models is to distinguish global warming signals from interannual variability. Recently machine learning models have performed better than state-of-the-art numerical weather prediction models for medium-range forecasting. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with negligible drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclone genesis and intensity, and mid-latitude blocking frequency--for historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is a key step toward an accurate highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts., Comment: 24 Pages, 20 figures

Optical microscopy has long been the standard method for diagnosis in cytopathology. Whole slide scanners can image and digitize large sample areas automatically, but are slow, expensive and therefore not widely available. Clinical diagnosis of cytology specimens is especially challenging since these samples are both spread over large areas and thick, which requires 3D capture. Here, we introduce a new parallelized microscope for scanning thick specimens across extremely wide fields-of-view (54x72 mm^2) at 1.2 and 0.6 {\mu}m resolutions, accompanied by machine learning software to rapidly assess these 16 gigapixel scans. This Multi-Camera Array Scanner (MCAS) comprises 48 micro-cameras closely arranged to simultaneously image different areas. By capturing 624 megapixels per snapshot, the MCAS is significantly faster than most conventional whole slide scanners. We used this system to digitize entire cytology samples (scanning three entire slides in 3D in just several minutes) and demonstrate two machine learning techniques to assist pathologists: first, an adenocarcinoma detection model in lung specimens (0.73 recall); second, a slide-level classification model of lung smears (0.969 AUC).

We prove special cases of a general conjecture: If an invertible field theory admits a projectively topological boundary theory, then it has finite order in the abelian group of invertible field theories. One can substitute `gapped' for `projectively topological'. Our proofs use evaluations of a field theory in parametrized families., Comment: 8 pages

Solitons in two-dimensional quantum field theory exhibit patterns of degeneracies and associated selection rules on scattering amplitudes. We develop a representation theory that captures these intriguing features of solitons. This representation theory is based on an algebra we refer to as the "strip algebra", $\textrm{Str}_{\mathcal{C}}(\mathcal{M})$, which is defined in terms of the non-invertible symmetry, $\mathcal{C},$ a fusion category, and its action on boundary conditions encoded by a module category, $\mathcal{M}$. The strip algebra is a $C^*$-weak Hopf algebra, a fact which can be elegantly deduced by quantizing the three-dimensional Drinfeld center TQFT, $\mathcal{Z}(\mathcal{C}),$ on a spatial manifold with corners. These structures imply that the representation category of the strip algebra is also a unitary fusion category which we identify with a dual category $\mathcal{C}_{\mathcal{M}}^{*}.$ We present a straightforward method for analyzing these representations in terms of quiver diagrams where nodes are vacua and arrows are solitons and provide examples demonstrating how the representation theory reproduces known degeneracies and selection rules of soliton scattering. Our analysis provides the general framework for analyzing non-invertible symmetry on manifolds with boundary and applies both to the case of boundaries at infinity, relevant to particle physics, and boundaries at finite distance, relevant in conformal field theory or condensed matter systems., Comment: 59 pages, 111 figures, v2: added references, minor typos corrected, revised discussion of relation to Tube algebra

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

Large language models (LLMs) can be used to analyze cyber threat intelligence (CTI) data from cybercrime forums, which contain extensive information and key discussions about emerging cyber threats. However, to date, the level of accuracy and efficiency of LLMs for such critical tasks has yet to be thoroughly evaluated. Hence, this study assesses the performance of an LLM system built on the OpenAI GPT-3.5-turbo model [8] to extract CTI information. To do so, a random sample of more than 700 daily conversations from three cybercrime forums - XSS, Exploit_in, and RAMP - was extracted, and the LLM system was instructed to summarize the conversations and predict 10 key CTI variables, such as whether a large organization and/or a critical infrastructure is being targeted, with only simple human-language instructions. Then, two coders reviewed each conversation and evaluated whether the information extracted by the LLM was accurate. The LLM system performed well, with an average accuracy score of 96.23%, an average precision of 90% and an average recall of 88.2%. Various ways to enhance the model were uncovered, such as the need to help the LLM distinguish between stories and past events, as well as being careful with verb tenses in prompts. Nevertheless, the results of this study highlight the relevance of using LLMs for cyber threat intelligence.

This article presents the first field deployment of a multi-agent reinforcement-learning (MARL) based variable speed limit (VSL) control system on the I-24 freeway near Nashville, Tennessee. We describe how we train MARL agents in a traffic simulator and directly deploy the simulation-based policy on a 17-mile stretch of Interstate 24 with 67 VSL controllers. We use invalid action masking and several safety guards to ensure the posted speed limits satisfy the real-world constraints from the traffic management center and the Tennessee Department of Transportation. Since the time of launch of the system through April, 2024, the system has made approximately 10,000,000 decisions on 8,000,000 trips. The analysis of the controller shows that the MARL policy takes control for up to 98% of the time without intervention from safety guards. The time-space diagrams of traffic speed and control commands illustrate how the algorithm behaves during rush hour. Finally, we quantify the domain mismatch between the simulation and real-world data and demonstrate the robustness of the MARL policy to this mismatch.

We investigate the invertible and non-invertible symmetries of topological finite group gauge theories in general spacetime dimensions, where the gauge group can be Abelian or non-Abelian. We focus in particular on the 0-form symmetry. The gapped domain walls that generate these symmetries are specified by boundary conditions for the gauge fields on either side of the wall. We investigate the fusion rules of these symmetries and their action on other topological defects including the Wilson lines, magnetic fluxes, and gapped boundaries. We illustrate these constructions with various novel examples, including non-invertible electric-magnetic duality symmetry in 3+1d $\mathbb{Z}_2$ gauge theory, and non-invertible analogs of electric-magnetic duality symmetry in non-Abelian finite group gauge theories. In particular, we discover topological domain walls that obey Fibonacci fusion rules in 2+1d gauge theory with dihedral gauge group of order 8. We also generalize the Cheshire string defect to analogous defects of general codimensions and gauge groups and show that they form a closed fusion algebra., Comment: 55 pages, 20 figures. V2: fixed figures misprints and typos

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

We give a new criterion which guarantees that a free group admits a bi-ordering that is invariant under a given automorphism. As an application, we show that the fundamental group of the "magic manifold" is bi-orderable, answering a question of Kin and Rolfsen., Comment: 19 pages, 2 figures

Additively manufactured (AM) structural components with complex geometries and tailored properties at voxel-size resolution will lead to significant leap in performance in various critical engineering applications. However, at each voxel, we first need to be able to design the alloy efficiently and reliably. We demonstrate a hybrid approach combining calculation of phase diagram (CALPHAD)-based integrated computational materials engineering (ICME) with machine learning and inverse design techniques and performed a full alloy design cycle of a novel Al alloy (Al-Er-Zr-Y-Yb-Ni) for AM from virtual predictions to experimental validation. We designed this alloy to exhibit high tensile strength at room temperature through nanoscale L1$_2$-phase precipitation which stabilizes the microstructure to maintain strength after high-temperature aging. We initially exploit a fine distribution of metastable eutectic ternary phases through rapid solidification, which serve as the source for the reactive elements enabling nanoscale precipitation of a high phase fraction of the thermally stable L1$_2$ strengthening phases. The strength of the 3D-printed samples manufactured via laser powder bed fusion (LPBF) from the designed composition is comparable to that of wrought Al 7075, and after high-temperature (400$^\circ$C) aging is 50% stronger than the best benchmark printable Al alloy1. The stable strengthening strategy is applicable to a wide range of alloys and rapid solidification processes, and our hybrid ML/CALPHAD numerical framework can be used for the efficient and robust design of alloy microstructures and properties, expanding the capabilities of additive as well as traditional manufacturing.

There has been strong interest recently in the so-called Cooper pair density wave, subsequent to the proposition that such a state occurs in the hole-doped cuprate superconductors. As of now there is no convincing demonstration of such a state in the cuprate theoretical literature. We present here a brief but complete review of our theoretical and computational work on the paired-electron crystal (PEC), which has been also experimentally seen in the insulating phase proximate to superconductivity (SC) in organic charge-transfer solid (CTS) superconductors. Within our theory, SC in the CTS does indeed evolve from the PEC. A crucial requirement for the finding of the PEC is that the proper carrier density of one charge carrier per two sites is taken into consideration at the outset. Following the discussion of CTS superconductors, we briefly discuss how the theory can be extended to understand the phase diagram of the cuprate superconductors that has remained mysterious after nearly four decades of the discovery of SC in this family., Comment: To appear in special issue of Chaos

A haplotype block, or simply a block, is a chromosomal segment, DNA base sequence or string that occurs in only a few variants or types in the genomes of a population of interest, and that has an encapsulated or 'private' frequency distribution of the string types that is not shared by neighbouring blocks or regions on the same chromosome. We consider two inverse problems of genetic interest: from just the frequencies of the symbol types (4 base types, possible single-base alleles) at each position (point, base/nucleotide) along the string, infer the location of the left and right boundaries of the block (block extent), and the number and relative frequencies of the string types occurring in the block (block structure). The large majority of variable positions in human and also other (e.g., fungal) genomes appear to be biallelic, i.e., the position allows only a choice between two possible symbols. The symbols can then be encoded as 0 (major) and 1 (minor), or as $\uparrow$ and $\downarrow$ as in Ising models, so the scenario reduces to problems on Boolean strings/bitstrings and Boolean matrices. The specifying of major allele frequencies (MAF) as used in genetics fits naturally into this framework. A simple example from human chromosome 9 is presented., Comment: 7 pages, 2 figures

We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision. We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data. To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. Here, we demonstrate that some of these issues can be mitigated by recent advances in machine learning. By combining transfer learning with techniques for building machine-learned potential energy surfaces, we propose a new path forward for molecular dynamics simulations on quantum hardware. We use transfer learning to reduce the number of energy evaluations that use quantum hardware by first training models on larger, less accurate classical datasets and then refining them on smaller, more accurate quantum datasets. We demonstrate this approach by training machine learning models to predict a molecule's potential energy using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamics simulations that cannot be readily obtained directly from quantum hardware. To reduce the quantum resources needed, the model is initially trained with data derived from low-cost techniques, such as Density Functional Theory, and subsequently refined with a smaller dataset obtained from the optimization of the Unitary Coupled Cluster ansatz. We show that this approach significantly reduces the size of the quantum training dataset while capturing the high accuracies needed for quantum chemistry simulations., Comment: 1- pages, 12 figures

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

We have developed an in-situ index of refraction profile n(z) for cold polar ice, using the transit times of radio signals broadcast from an englacial transmitter to 2-5 km distant radio-frequency receivers, deployed at depths up to 200 m. For propagation through a non-uniform medium, Maxwell's equations generally admit two ray propagation solutions from a given transmitter, corresponding to a direct path (D) and a refracted or reflected path (R); the measured D vs. R timing differences (dt(D,R)) are determined by the refractive index profile. We constrain n(z) near South Pole, where the Askaryan Radio Array (ARA) neutrino observatory is located, by simulating D and R ray paths via ray tracing and comparing simulations to measured dt(D,R) values. Using previous ice density data as a proxy for n(z), we demonstrate that our data strongly favors a glaciologically-motivated three-phase densification model rather than a single exponential scale height model. Effective volume simulations for a detector of ARA station antenna depths yield a 14\% increase in neutrino sensitivity over a range of $10^{17} - 10^{21}$ eV using the three-phase model compared to a single exponential.

Monte Carlo methods have led to profound insights into the strong-coupling behaviour of lattice gauge theories and produced remarkable results such as first-principles computations of hadron masses. Despite tremendous progress over the last four decades, fundamental challenges such as the sign problem and the inability to simulate real-time dynamics remain. Neural network quantum states have emerged as an alternative method that seeks to overcome these challenges. In this work, we use gauge-invariant neural network quantum states to accurately compute the ground state of $\mathbb{Z}_N$ lattice gauge theories in $2+1$ dimensions. Using transfer learning, we study the distinct topological phases and the confinement phase transition of these theories. For $\mathbb{Z}_2$, we identify a continuous transition and compute critical exponents, finding excellent agreement with existing numerics for the expected Ising universality class. In the $\mathbb{Z}_3$ case, we observe a weakly first-order transition and identify the critical coupling. Our findings suggest that neural network quantum states are a promising method for precise studies of lattice gauge theory.

In the Nice model of solar system formation, Uranus and Neptune undergo an orbital upheaval, sweeping through a planetesimal disk. The region of the disk from which material is accreted by the ice giants during this phase of their evolution has not previously been identified. We perform direct N-body orbital simulations of the four giant planets to determine the amount and origin of solid accretion during this orbital upheaval. We find that the ice giants undergo an extreme bombardment event, with collision rates as much as ~3 per hour assuming km-sized planetesimals, increasing the total planet mass by up to ~0.35%. In all cases, the initially outermost ice giant experiences the largest total enhancement. We determine that for some plausible planetesimal properties, the resulting atmospheric enrichment could potentially produce sufficient latent heat to alter the planetary cooling timescale according to existing models. Our findings suggest that substantial accretion during this phase of planetary evolution may have been sufficient to impact the atmospheric composition and thermal evolution of the ice giants, motivating future work on the fate of deposited solid material., Comment: 19 pages, 9 figures; accepted to AJ

Planet-planetesimal interactions cause a planet to migrate, manifesting as a random walk in semi-major axis. In models for Neptune's migration involving a gravitational upheaval, this planetesimal-driven migration is a side-effect of the dynamical friction required to damp Neptune's orbital eccentricitiy. This migration is noisy, potentially causing Trans Neptunian Objects (TNOs) in mean motion resonance to be lost. With Nbody simulations, we validate a previously-derived analytic model for resonance retention and determine unknown coefficients. We identify the impact of random-walk (noisy) migration on resonance retention for resonances up to fourth order lying between 39 au and 75 au. Using a population estimate for the weak 7:3 resonance from the well-characterized Outer Solar System Origins Survey (OSSOS), we rule out two cases: (1) a planetesimal disk distributed between 13.3 and 39.9 au with $\gtrsim$ 30 Earth masses in today's size distribution and $T_{\rm mig} \gtrsim$ 40Myr and (2) a top-heavy size distribution with $\gtrsim$ 2000 Pluto-sized TNOs and $T_{\rm mig} \gtrsim$ 10Myr, where $T_{\rm mig}$ is Neptune's migration timescale. We find that low-eccentricity TNOs in the heavily populated 5:2 resonance are easily lost due to noisy migration. Improved observations of the low-eccentricity region of the 5:2 resonance and of weak mean motion resonances with Rubin Observatory's Legacy Survey of Space and Time (LSST) will provide better population estimates, allowing for comparison with our model's retention fractions and providing strong evidence for or against Neptune's random interactions with planetesimals., Comment: 17 pages, 11 figures; accepted to MNRAS

This article investigates how AI-generated content can disrupt central revenue streams of the creative industries, in particular the collection of dividends from intellectual property (IP) rights. It reviews the IP and copyright questions related to the input and output of generative AI systems. A systematic method is proposed to assess whether AI-generated outputs, especially images, infringe previous copyrights, using a similarity metric (CLIP) between images against historical copyright rulings. An examination (economic and technical feasibility) of previously proposed compensation frameworks reveals their financial implications for creatives and IP holders. Lastly, we propose a novel IP framework for compensation of artists and IP holders based on their published "licensed AIs" as a new medium and asset from which to collect AI royalties., Comment: 7 pages, 2 figures, submitted to AAAI

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

Recent state and national achievement exam results, as well as academic progress reports, have underscored how the COVID-19 pandemic and related school closures had a large, negative impact on students' reading and math development. While a great deal is known about the test-based academic impacts of the pandemic such as test scores that provide valuable evidence, there are a range of non-test measures like attendance, engagement in school, student retention, course failure rates, degree completion, and enrollment in subsequent levels of education that also yield important insights about students' educational well-being. These measures matter in their own right and in how they affect longer-term outcomes like employment, earnings, and adult well-being. This consensus panel report reviewed the best available evidence to understand how the pandemic affected non-tested academic areas, and it offers recommendations for shaping a policy response.