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Particle discretizations of partial differential equations are advantageous for high-dimensional kinetic models in phase space due to their better scalability than continuum approaches with respect to dimension. Complex processes collectively referred to as \textit{particle noise} hamper long-time simulations with particle methods. One approach to address this problem is particle mesh adaptivity or remapping, known as \textit{particle resampling}. This paper introduces a resampling method that projects particles to and from a (finite element) function space. The method is simple; using standard sparse linear algebra and finite element techniques, it can adapt to almost any set of new particle locations and preserves all moments up to the order of polynomial represented exactly by the continuum function space. This work is motivated by the Vlasov-Maxwell-Landau model of magnetized plasmas with up to six dimensions, $3X$ in physical space and $3V$ in velocity space, and is developed in the context of a $1X$ + $1V$ Vlasov-Poisson model of Landau damping with logically regular particle and continuum phase space grids. The evaluation codes are publicly available, along with the data and reproducibility artifacts, and developed in the PETSc numerical library.

The KOTO II experiment is proposed to measure the branching ratio of the decay $K_L\to\pi^0\nu\bar{\nu}$ at J-PARC. With a beamline to extract long-lived neutral kaons at 5 degrees from a production target, the single event sensitivity of the decay is $8.5\times 10^{-13}$, which is much smaller than the Standard Model prediction $3\times 10^{-11}$. This allows searches for new physics beyond the Standard Model and the first discovery of the decay with a significance exceeding $5\sigma$. As the only experiment proposed in the world dedicated to rare kaon decays, KOTO II will be indispensable in the quest for a complete understanding of flavor dynamics in the quark sector. Moreover, by combining efforts from the kaon community worldwide, we plan to develop the KOTO II detector further and expand the physics reach of the experiment to include measurements of the branching ratio of the $K_L\to\pi^0\ell^+\ell^-$ decays, studies of other $K_L$ decays, and searches for dark photons, axions, and axion-like particles. KOTO II will therefore obtain a comprehensive understanding of $K_L$ decays, providing further constraints on new physics scenarios with existing $K^+$ results., Comment: Submitted to the J-PARC PAC. arXiv admin note: substantial text overlap with arXiv:2110.04462

Turbulent heat and momentum transfer processes due to thermal convection cover many scales and are of great importance for several natural and technical flows. One consequence is that a fully resolved three-dimensional analysis of these turbulent transfers at high Rayleigh numbers, which includes the boundary layers, is possible only using supercomputers. The visualization of these dynamics poses an additional hurdle since the thermal and viscous boundary layers in thermal convection fluctuate strongly. In order to track these fluctuations continuously, data must be tapped at high frequency for visualization, which is difficult to achieve using conventional methods. This paper makes two main contributions in this context. First, it discusses the simulations of turbulent Rayleigh-B\'enard convection up to Rayleigh numbers of $Ra=10^{12}$ computed with NekRS on GPUs. The largest simulation was run on 840 nodes with 3360 GPU on the JUWELS Booster supercomputer. Secondly, an in-situ workflow using ASCENT is presented, which was successfully used to visualize the high-frequency turbulent fluctuations., Comment: 16 pages, 15 figures, 6 tables

We investigate the pressure-driven structural and electronic evolution of PdSe\(_2\) using powder X-ray diffraction, Raman spectroscopy, and first-principles calculations. Beyond 2.3 GPa, suppression of the Jahn-Teller distortion induces in-plane lattice expansion and metallization. Around 4.8 GPa, the interlayer \(d_{z^2}-\pi^*\) orbital hybridization drives the dimensional crossover, facilitating the transformation from the 2D distorted to a 3D undistorted pyrite phase. Above 9 GPa, a novel phase emerges, characterized by octahedral distortions in the $d$ orbitals of Pd. Structural analysis suggests the presence of marcasite (\(Pnnm\)) or arsenopyrite (\(P2_1/c\)) phase with orthorhombic and monoclinic configurations, respectively. Furthermore, the observed phonon anomaly and electronic structure modifications, including the emergence of flat bands in the high-pressure phases, elucidate the fundamental mechanisms underlying the previously reported exotic superconductivity with an enhanced critical temperature. These results highlight the pivotal role of dimensional crossover and structural transitions in tuning the electronic properties of puckered materials, providing pathways for novel functionalities.

We study the piecewise constant bandit problem where the expected reward is a piecewise constant function with one change point (discontinuity) across the action space $[0,1]$ and the learner's aim is to locate the change point. Under the assumption of a fixed exploration budget, we provide the first non-asymptotic analysis of policies designed to locate abrupt changes in the mean reward function under bandit feedback. We study the problem under a large and small budget regime, and for both settings establish lower bounds on the error probability and provide algorithms with near matching upper bounds. Interestingly, our results show a separation in the complexity of the two regimes. We then propose a regime adaptive algorithm which is near optimal for both small and large budgets simultaneously. We complement our theoretical analysis with experimental results in simulated environments to support our findings., Comment: 44 pages, 7 figures

Seeking to unravel the enigmatic Verwey transition and its interplay with magnetism, we have conducted comprehensive measurements on the temperature-dependent electrical resistivity and magnetic moment of stoichiometric and doped-magnetite single crystals at temperatures reaching 1000 K. These investigations have allowed us to identify the Curie temperature, $T_C$, and other characteristic temperatures of the electrical resistivity. Remarkably, we have identified correlations between these temperatures and the Verwey temperature, $T_V$, indicating that the electrical transport properties and the mechanism of the Verwey transition are closely related to the magnetic properties.

We provide guidance on multiple imputation of missing at random treatments in observational studies. Specifically, analysts should account for both covariates and outcomes, i.e., not just use propensity scores, when imputing the missing treatments. To do so, we develop outcome-assisted multiple imputation of missing treatments: the analyst fits a regression for the outcome on the treatment indicator and covariates, which is used to sharpen the predictive probabilities for missing treatments under an estimated propensity score model. We derive an expression for the bias of the inverse probability weighted estimator for the average treatment effect under multiple imputation of missing treatments, and we show theoretically that this bias can be made small by using outcome-assisted multiple imputation. Simulations demonstrate empirically that outcome-assisted multiple imputation can offer better inferential properties than using the treatment assignment model alone. We illustrate the procedure in an analysis of data from the National Longitudinal Survey of Youth.

Given the hyperinflation that most of the Latin American countries suffered in the 90 and their decision towards adopting dollarization and in most cases keeping their own currency, this paper analyzes the effectiveness of dollarization as a protective mechanism against economic disruptions in Latin American countries. It assesses the context that led Latin American dollarized countries to dollarize and analyzes CPI, GDP, and the poverty rates pre, during, and postpandemic in Latin American countries, considering those that are dollarized and those that are not, and evaluating its relation to the US. Interviews were carried out with experts in the field. It assesses the advantages and disadvantages of dollarization regarding global crises. The data was compared and analyzed to check if there were patterns that support the paper objective which is that dollarization might serve as a protective mechanism against economic disruption. It was found that dollarization protects the economy against inflation, however, it does not fully protect the economy when considering economic performance and poverty. In conclusion, this research concludes that dollarization does not completely serve as a protective mechanism against economic disruptions nonetheless, it found that a bigger role is played by domestic policies and government action., Comment: 45 pages, 8 figures

Substance use disorders (SUDs) are a serious public health concern in the United States. Alcohol and cannabis are two of the most widely used substances. For adolescent/youth users of alcohol or cannabis, we propose a joint Bayesian learning model to predict their risks of developing alcohol use disorder (AUD) and cannabis use disorder (CUD) in adulthood based on their personal risk factors. The model is trained on nationally representative longitudinal data from Add Health (n = 12503). It consists of sub-models that predict the two SUDs for three groups of users-those who use alcohol only, cannabis only, and both substances - based on shared as well as unique risk factors. The model comprises of ten predictors. We externally validate the model on two independent datasets. The areas under the receiver operating characteristic curves for AUD and CUD, respectively, are: (a) 0.719 and 0.690 based on 5-fold cross-validation, (b) 0.748 and 0.710 based on validation dataset 1, and (c) 0.650 and 0.750 based on validation dataset 2. A simulation study shows that the proposed joint modeling approach generally performs better than separate univariate modeling of the corresponding dependent outcomes in terms of predictive accuracy. Our model may help in identifying adolescent substance users at high risk of developing SUD in adulthood, who can then be helped with appropriate intervention.

For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of stochastic dynamics and sensor uncertainty. In this paper, we propose a distributed, multi-agent receding-horizon feedback motion planning approach using Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC) that is able to reason about both model and measurement uncertainty to achieve robust multi-agent formation control while navigating cluttered obstacle fields and avoiding inter-robot collisions. Our approach relies not only on the underlying PAC-NMPC algorithm but also on a terminal cost-function derived from gyroscopic obstacle avoidance. Through numerical simulation, we show that our distributed approach performs on par with a centralized formulation, that it offers improved performance in the case of significant measurement noise, and that it can scale to more complex dynamical systems., Comment: 10 pages, 12 figures

Scenario-based testing is currently the dominant simulation-based validation approach for ADS. Its effective application raises two interrelated issues. The first is the choice of the method used to generate scenarios, based on various criteria such as risk, degree of autonomy, degree of coverage and representativeness, and complexity. The other is the choice of the evaluation method for estimating the safety and performance of the system under test. This work extends a study of the critical configuration testing (CCTest) approach we have already applied to four open modular autopilots. This approach differs from general scenario-based approaches in that it uses only realistic, potentially safe critical scenarios. It enables an accurate assessment of the ability to drive safely in critical situations for which feasible safety policies exist. Any incident observed in the simulation involves the failure of a tested autopilot. The contribution of this paper is twofold. First, we apply the critical configuration testing approach to four end-to-end open autopilots, Transfuser, InterFuser, MILE and LMDriver, and compare their test results with those of the four modular open autopilots previously tested with the same approach implemented in the Carla simulation environment. This comparison identifies both differences and similarities in the failures of the two autopilot types in critical situations. Secondly, we compare the evaluations of the four autopilots carried out in the Carla Leaderboard with our results obtained by testing critical configurations. This comparison reveals significant discrepancies, reflecting differences in test case generation criteria and risk assessment methods. It underlines the need to work towards the development of objective assessment methods combining qualitative and quantitative criteria.

We explore the evolving relationship between galaxies and their dark matter halos from $z \sim 0.1$ to $z \sim 12$ using mass-limited angular clustering measurements in the 0.54 deg$^2$ of the COSMOS-Web survey. This study provides the first measurements of the mass-limited two-point correlation function at $z \ge 10$ and a consistent analysis spanning 13.4 Gyr of cosmic history, setting new benchmarks for future simulations and models. Using a halo occupation distribution (HOD) framework, we derive characteristic halo masses and the stellar-to-halo mass relationship (SHMR) across redshifts and stellar mass bins. Our results first indicate that HOD models fit data at $z \ge 2.5$ best when incorporating a non-linear scale-dependent halo bias, boosting clustering at non-linear scales (r = 10-100 kpc). We find that galaxies at z > 10.5 with $\log(M_\star / M_\odot) \ge 8.85$ are hosted by halos with $M_{\rm h} \sim 10^{10.5}\,M_\odot$, achieving a star formation efficiency (SFE) $M_\star / (f_b M_{\rm h}) $ up to 1 dex higher than at $z \le 1$. The high galaxy bias at $z \ge 8$ suggests that these galaxies reside in massive halos with intrinsic high SFE. Our SHMR evolves significantly with redshift, starting high at $z \ge 10.5$, decreasing until $z \sim 2 - 3$, then increasing again until the present. Current simulations fail to reproduce both massive high-$z$ galaxies and this evolution, while semi-empirical models linking SFE to halo mass, accretion rates, and redshift align with our findings. We propose that $z > 8$ galaxies experience bursty star formation without significant feedback altering their growth, driving the rapid growth of massive galaxies observed by JWST. Over time, increasing feedback efficiency and exponential halo growth suppress star formation. At $z \sim 2 - 3$ and after, halo growth slows down while star formation continues, supported by gas reservoirs in halos., Comment: Submitted to Astronomy & Astrophysics

We investigated the temporal distribution of goals in soccer using event-level data from 3,433 matches across 21 leagues and competitions. Contrary to the prevailing notion of randomness, we found that the probability of a goal being scored is higher as matches progress, and we observed fewer-than-expected goals in the early minutes of each half. Further analysis of the time between subsequent goals shows an exponential decay, indicating that most goals naturally cluster closer together in time. By splitting this distribution by the team that scores the next goal, we observe bursty goal-scoring dynamics, wherein the same team is more likely to score again shortly after its previous goal. These findings highlight the importance of match context (whether driven by fatigue, tactical adaptations, or psychological momentum) in shaping when teams are able to score. Moreover, the results open avenues for extending data-driven methods for identifying high-impact moments in a match and refining strategic decision-making in soccer's evolving analytical landscape.

The evolution of cognition is frequently discussed as the evolution of cognitive abilities or the evolution of some neuronal structures in the brain. However, since such traits or abilities are often highly complex, understanding their evolution requires explaining how they could have gradually evolved through selection acting on heritable variations in simpler cognitive mechanisms. With this in mind, making use of a previously proposed theory, here we show how the evolution of cognitive abilities can be captured by the fine-tuning of basic learning mechanisms and, in particular, chunking mechanisms. We use the term chunking broadly for all types of non-elemental learning, claiming that the process by which elements are combined into chunks and associated with other chunks, or elements, is critical for what the brain can do, and that it must be fine-tuned to ecological conditions. We discuss the relevance of this approach to studies in animal cognition, using examples from animal foraging and decision-making, problem solving, and cognitive flexibility. Finally, we explain how even the apparent human-animal gap in sequence learning ability can be explained in terms of different fine-tunings of a similar chunking process.

An explicit near-optimal quantum algorithm is proposed for modeling dissipative initial-value problems. This method, based on the Linear Combination of Hamiltonian Simulations (LCHS), approximates a target nonunitary operator as a weighted sum of Hamiltonian evolutions, thereby emulating a dissipative problem by mixing various time scales. We propose an efficient encoding of this algorithm into a quantum circuit based on a simple coordinate transformation that turns the dependence on the summation index into a trigonometric function and significantly simplifies block-encoding. The resulting circuit has high success probability and scales logarithmically with the number of terms in the LCHS sum and linearly with time. We verify the quantum circuit and its scaling by simulating it on a digital emulator of fault-tolerant quantum computers and, as a test problem, solve the advection-diffusion equation. The proposed algorithm can be used for modeling a wide class of nonunitary initial-value problems including the Liouville equation and linear embeddings of nonlinear systems.

Authenticated data structures allow untrusted third parties to carry out operations which produce proofs that can be used to verify an operation's output. Such data structures are challenging to develop and implement correctly. This paper gives a formal proof of security and correctness for a library that generates authenticated versions of data structures automatically. The proof is based on a new relational separation logic for reasoning about programs that use collision-resistant cryptographic hash functions. This logic provides a basis for constructing two semantic models of a type system, which are used to justify how the library makes use of type abstraction to enforce security and correctness. Using these models, we also prove the correctness of several optimizations to the library and then show how optimized, hand-written implementations of authenticated data structures can be soundly linked with automatically generated code. All of the results in this paper have been mechanized in the Coq proof assistant using the Iris framework.

Context. The DESI Legacy Imaging Survey DR9, with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study Baryon Acoustic Oscillations (BAO) in the region covered by the ongoing DESI spectroscopic survey. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6 to 0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAO in DR9 data. We then use template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis revealed the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck's observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we also find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements., Comment: 20 pages, 14 figures, 3 tables, accepted for publication in Astronomy and Astrophysics

Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching. We introduce FoundationStereo, a foundation model for stereo depth estimation designed to achieve strong zero-shot generalization. To this end, we first construct a large-scale (1M stereo pairs) synthetic training dataset featuring large diversity and high photorealism, followed by an automatic self-curation pipeline to remove ambiguous samples. We then design a number of network architecture components to enhance scalability, including a side-tuning feature backbone that adapts rich monocular priors from vision foundation models to mitigate the sim-to-real gap, and long-range context reasoning for effective cost volume filtering. Together, these components lead to strong robustness and accuracy across domains, establishing a new standard in zero-shot stereo depth estimation. Project page: https://nvlabs.github.io/FoundationStereo/

The rapid advancements in quantum computing present significant threats to existing encryption standards and internet security. Simultaneously, the advent of Web 3.0 marks a transformative era in internet history, emphasizing enhanced data security, decentralization, and user ownership. This white paper introduces the W3ID, an abbreviation of Web3 standard meeting universal digital ID, which is a Universal Digital Identity (UDI) model designed to meet Web3 standards while addressing vulnerabilities posed by quantum computing. W3ID innovatively generates secure Digital Object Identifiers (DOIs) tailored for the decentralized Web 3.0 ecosystem. Additionally, W3ID employs a dual-key system for secure authentication, enhancing both public and private verification mechanisms. To further enhance encryption strength and authentication integrity in the quantum computing era, W3ID incorporates an advanced security mechanism. By requiring quadruple application of SHA-256, with consecutive matches for validation, the system expands the number of possibilities to 256^4, which is approximately 4.3 billion times the current SHA-256 capacity. This dramatic increase in computational complexity ensures that even advanced quantum computing systems would face significant challenges in executing brute-force attacks. W3ID redefines digital identity standards for Web 3.0 and the quantum computing era, setting a new benchmark for security, scalability, and decentralization in the global digital twin ecosystem.

We present the discovery of 11 new transiting brown dwarfs and low-mass M-dwarfs from NASA's TESS mission: TOI-2844, TOI-3122, TOI-3577, TOI-3755, TOI-4462, TOI-4635, TOI-4737, TOI-4759, TOI-5240, TOI-5467, and TOI-5882. They consist of 5 brown dwarf companions and 6 very low mass stellar companions ranging in mass from $25 M_{\rm J}$ to $128 M_{\rm J}$. We used a combination of photometric time-series, spectroscopic, and high resolution imaging follow-up as a part of the TESS Follow-up Observing Program (TFOP) in order to characterize each system. With over 50 transiting brown dwarfs confirmed, we now have a large enough sample to directly test different formation and evolutionary scenarios. We provide a renewed perspective on the transiting brown dwarf desert and its role in differentiating between planetary and stellar formation mechanisms. Our analysis of the eccentricity distribution for the transiting brown dwarf sample does not support previous claims of a transition between planetary and stellar formation at $\sim42$ $M_{\rm J}$. We also contribute a first look into the metallicity distribution of transiting companions in the range $7 - 150$ $M_{\rm J}$, showing that this too does not support a $\sim42$ $M_{\rm J}$ transition. Finally, we also detect a significant lithium absorption feature in one of the brown dwarf hosts (TOI-5882) but determine that the host star is likely old based on rotation, kinematic, and photometric measurements. We therefore claim that TOI-5882 may be a candidate for planetary engulfment., Comment: Submitted, 32 pages, 16 figures

Approximating the ground state of many-body systems is a key computational bottleneck underlying important applications in physics and chemistry. It has long been viewed as a promising application for quantum computers. The most widely known quantum algorithm for ground state approximation, quantum phase estimation, is out of reach of current quantum processors due to its high circuit-depths. Quantum diagonalization algorithms based on subspaces represent alternatives to phase estimation, which are feasible for pre-fault-tolerant and early-fault-tolerant quantum computers. Here, we introduce a quantum diagonalization algorithm which combines two key ideas on quantum subspaces: a classical diagonalization based on quantum samples, and subspaces constructed with quantum Krylov states. We prove that our algorithm converges in polynomial time under the working assumptions of Krylov quantum diagonalization and sparseness of the ground state. We then show numerical investigations of lattice Hamiltonians, which indicate that our method can outperform existing Krylov quantum diagonalization in the presence of shot noise, making our approach well-suited for near-term quantum devices. Finally, we carry out the largest ground-state quantum simulation of the single-impurity Anderson model on a system with $41$ bath sites, using $85$ qubits and up to $6 \cdot 10^3$ two-qubit gates on a Heron quantum processor, showing excellent agreement with density matrix renormalization group calculations., Comment: 22 pages, 6 figures

We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitations of the standard two-pipeline approach by unifying arc scoring and labeling into a single network, reducing scalability issues caused by the information bottleneck and lack of parameter sharing. Additionally, our architecture overcomes limited arc interactions with transformer layers to efficiently simulate higher-order dependencies. Experiments on PTB and UD show that our model outperforms state-of-the-art parsers in both accuracy and efficiency.

Goldstone modes acquire a frequency gap in the presence of perturbations that break the underlying continuous symmetry. Here, we study the response of a spin-based Goldstone mode to strain and magnetic field in the broken helix, a multi-$\textbf{Q}$ phase of EuIn$_2$As$_2$. Optical polarimetry with spatial and temporal resolution allows us to access information about both the structure and frequency of optically excited spin-wave modes under different strain conditions. We observe nearly uniform spin precession characteristic of a Goldstone mode only when magnetic field dominates over strain. In this regime, the frequency depends linearly on the applied field. A symmetry analysis for predicting the mode frequency near zero field demonstrates that the observed scaling is of the lowest allowed order. This work thus demonstrates the connections between magnetic symmetries and the frequency dependence of the Goldstone mode in an external field, and illustrates the power of our technique for studying the dynamics of complex magnets., Comment: 5 pages, 3 figures

PAX (antiProtonic Atom X-ray spectroscopy) is a new experiment with the aim to test strong-field quantum electrodynamics (QED) effects by performing high-precision x-ray spectroscopy of antiprotonic atoms. By utilizing advanced microcalorimeter detection techniques and a low-energy antiproton beam provided by the ELENA ring at CERN, gaseous targets will be used for the creation of antiprotonic atoms, and the measurement of transitions between circular Rydberg states will be conducted with up to two orders of magnitude improved accuracy over previous studies using high-purity germanium detectors. Our approach eliminates the longstanding issue of nuclear uncertainties that have hindered prior studies using highly charged ions, thus enabling direct and purely QED-focused measurements. By precisely probing atomic systems with electric fields up to two orders of magnitude above the Schwinger limit, PAX will test vacuum polarization and second-order QED corrections, opening new frontiers in fundamental physics and uncovering potential pathways to physics beyond the Standard Model.

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is addressed through sparse, spike-based signaling, minimizing energy consumption and latency. In contrast, modern AI workloads, which keep scaling ever larger across distributed compute systems, are increasingly constrained by bandwidth limitations, creating bottlenecks that hinder scalability and energy efficiency. Inspired by the brain's efficient communication strategies, we propose SNAP, a hybrid neural network architecture combining spiking neural networks (SNNs) and artificial neural networks (ANNs) to address these challenges. SNAP integrates SNNs at bandwidth-constrained regions, such as chip boundaries, where spike-based encoding reduces data transfer overhead. Within each chip, dense ANN computations are maintained to preserve high throughput, accuracy, and robustness. Historically, SNNs have faced difficulties scaling up, with limitations in task-specific performance and reliance on specialized hardware to exploit sparsity. SNAP overcomes these barriers through an algorithm-architecture co-design leveraging learnable sparsity for die-to-die communication while limiting spiking layers to specific network partitions. This composable design integrates spike-based and non-spiking pathways, making it adaptable to diverse deep learning workloads. Our evaluations on language processing and computer vision tasks demonstrate up to 5.3x energy efficiency improvements and 15.2x reductions in inference latency, outperforming both traditional SNNs and non-spiking models. We find that as model resources scale, SNAP's improvement margins grow. By addressing the critical bottleneck of inter-chip communication, SNAP offers a scalable, biologically inspired pathway to more efficient AI systems.

We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity. We give specific, parameter-dependent bounds for two particular quantum reservoir classes. We analyse how the generalisation bounds scale with growing numbers of qubits. Applying our results to classes with polynomial readout functions, we find that the risk bounds converge in the number of training samples. The explicit dependence on the quantum reservoir and readout parameters in our bounds can be used to control the generalisation error to a certain extent. It should be noted that the bounds scale exponentially with the number of qubits $n$. The upper bounds on the Rademacher complexity can be applied to other reservoir classes that fulfill a few hypotheses on the quantum dynamics and the readout function.

We consider when there is absolute or unconditional convergence of series of various types of stochastic processes. These processes include differences of averages in ergodic theory and harmonic analysis, like the classical Cesaro average in ergodic theory and Lebesgue derivatives in harmonic analysis.

Introduction: Substance use disorders (SUDs) have emerged as a pressing public health crisis in the United States, with adolescent substance use often leading to SUDs in adulthood. Effective strategies are needed to prevent this progression. To help in filling this need, we develop a novel and the first-ever absolute risk prediction model for cannabis use disorder (CUD) for adolescent or young adult cannabis users. Methods: We train a Bayesian machine learning model that provides a personalized CUD absolute risk for adolescent or young adult cannabis users using data from the National Longitudinal Study of Adolescent to Adult Health. Model performance is assessed using 5-fold cross-validation (CV) with area under the curve (AUC) and ratio of the expected to observed number of cases (E/O). External validation of the final model is conducted using two independent datasets. Results: The proposed model has five risk factors: biological sex, delinquency, and scores on personality traits of conscientiousness, neuroticism, and openness. For predicting CUD risk within five years of first cannabis use, AUC and E/O, computed via 5-fold CV, were 0.68 and 0.95, respectively. For the same type of prediction in external validation, AUC values were 0.64 and 0.75, with E/O values of 0.98 and 1, indicating good discrimination and calibration performances of the model. Discussion and Conclusion: The proposed model is the first absolute risk prediction model for an SUD. It can aid clinicians in identifying adolescent/youth substance users at a high risk of developing CUD in future for clinically appropriate interventions.

We introduce inferential methods for prediction based on functional random effects in generalized functional mixed effects models. This is similar to the inference for random effects in generalized linear mixed effects models (GLMMs), but for functional instead of scalar outcomes. The method combines: (1) local GLMMs to extract initial estimators of the functional random components on the linear predictor scale; (2) structural functional principal components analysis (SFPCA) for dimension reduction; and (3) global Bayesian multilevel model conditional on the eigenfunctions for inference on the functional random effects. Extensive simulations demonstrate excellent coverage properties of credible intervals for the functional random effects in a variety of scenarios and for different data sizes. To our knowledge, this is the first time such simulations are conducted and reported, likely because prediction inference was not viewed as a priority and existing methods are too slow to calculate coverage. Methods are implemented in a reproducible R package and demonstrated using the NHANES 2011-2014 accelerometry data.

Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains' geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo's approach., Comment: 19 pages, 1 figure

Motivated by emerging urban applications in commercial, public sector, and humanitarian logistics, we revisit continuous $p$-hub location problems in which several facilities must be located in a continuous space such that the expected minimum Manhattan travel distance from a random service provider to a random customer through exactly one hub facility is minimized. In this paper, we begin by deriving closed-form results for a one-dimensional case and two-dimensional cases with up to two hubs. Subsequently, a simulation-based approximation method is proposed for more complex two-dimensional scenarios with more than two hubs. Moreover, an extended problem with multiple service providers is analyzed to reflect real-life service settings. Finally, we apply our model and approximation method using publicly available data as a case study to optimize the deployment of public-access automated external defibrillators in Virginia Beach.

The large apertures of the upcoming generation of Giant Segmented Mirror Telescopes will enable unprecedented angular resolutions that scale as $\propto$ $\lambda$/D and higher sensitivities that scale as $D^4$ for point sources corrected by adaptive optics. However, all will have pupil segmentation caused by mechanical struts holding up the secondary mirror [European Extremely Large Telescope and Thirty Meter Telescope] or intrinsically, by design, as in the Giant Magellan Telescope. These gaps will be separated by more than a typical atmospheric coherence length (Fried Parameter). The pupil fragmentation at scales larger than the typical atmospheric coherence length, combined with wavefront sensors with weak or ambiguous sensitivity to differential piston, can introduce differential piston areas of the wavefront known as "petal modes". Commonly used wavefront sensors, such as a pyramid WFS, also struggle with phase wrapping caused by >$\lambda$/2 differential piston WFE. We have developed the holographic dispersed fringe sensor, a single pupil-plane optic that employs holography to interfere the dispersed light from each segment onto different spatial locations in the focal plane to sense and correct differential piston between the segments. This allows for a very high and linear dynamic piston sensing range of approximately $\pm$10 $\mu$m. We have begun the initial attempts at phasing a segmented pupil utilizing the HDFS on the High Contrast Adaptive optics phasing Testbed and the Extreme Magellan Adaptive Optics instrument (MagAO-X) at the University of Arizona. Additionally, we have demonstrated use of the HDFS as a differential piston sensor on-sky for the first time. We were able to phase each segment to within $\pm\lambda$/11.3 residual piston WFE ($\lambda$ = 800 nm) of a reference segment and achieved ~50 nm RMS residual piston WFE across the aperture in poor seeing conditions., Comment: 34 pages, 26 figures

There is increased interest in time-dependent (non-autonomous) Hamiltonians, stemming in part from the active field of Floquet quantum materials. Despite this, dispersive time-decay bounds, which reflect energy transport in such systems, have received little attention. We study the dynamics of non-autonomous, time-periodically forced, Dirac Hamiltonians: $i\partial_t\alpha =D(t)\alpha$, where $D(t)=i\sigma_3\partial_x+ \nu(t)$ is time-periodic but not spatially localized. For the special case $\nu(t)=m\sigma_1$, which models a relativistic particle of constant mass $m$, one has a dispersive decay bound: $\|\alpha(t,x)\|_{L^\infty_x}\lesssim t^{-\frac12}$. Previous analyses of Schr\"odinger Hamiltonians suggest that this decay bound persists for small, spatially-localized and time-periodic $\nu(t)$. However, we show that this is not necessarily the case if $\nu(t)$ is not spatially localized. Specifically, we study two non-autonomous Dirac models whose time-evolution (and monodromy operator) is constructed via Fourier analysis. In a rotating mass model, the dispersive decay bound is of the same type as for the constant mass model. However, in a model with a periodically alternating sign of the mass, the results are quite different. By stationary-phase analysis of the associated Fourier representation, we display initial data for which the $L^\infty_x$ time-decay rate are considerably slower: $\mathcal{O}(t^{-1/3})$ or even $\mathcal{O}(t^{-1/5})$ as $t\to\infty$.

Gamma-ray bursts detected at high energies provide valuable insights into the emission mechanisms behind these still puzzling enigmatic events. In this study, we focus on GRB 090510, which is an unusual short GRB exhibiting plateau emission observed by the Fermi-LAT. Using the general relativistic magnetohydrodynamic code (HARM), we aim to infer the key properties of this GRB, such as the jet opening angle, the energetics, the Lorentz Gamma factor, the jet structure and its variability, and the progenitor parameters of the compact binary system. We explored both the 2D and 3D models and estimated the variability timescales. Our findings show that the predicted jet opening angle is within $88\%$ of the observed upper limit from observations, and the energetics are in general agreement with observed values when accounting for the evolution of jet opening angle with redshift. This work establishes the foundation for ongoing exploration, which will further align the theoretical model simulations with observational data., Comment: accepted in the Proceedings of the MarcellGrossmann

Changing-Look AGN (CLAGN) Mkn 590 recently underwent a sudden \lq re-ignition\rq, marked by substantial increases in optical/UV and X-ray continuum flux since last year. \textit{Swift}-XRT observations revealed the re-emergence of a soft X-ray excess (SXE) as the source transitioned from a low-flux state in July 2023 to a significantly higher flux state in October 2024. This evolution was in response to an order-of-magnitude increase in extreme-UV (EUV) continuum emission, detected by \textit{Swift}-UVOT. Follow-up optical spectra from the Las Cumbres Observatory confirmed the presence of dynamically broadened Balmer lines, He II emission, and the emergence of the Fe II complex. As the Eddington fraction increased by 10\% over the last 15 months, we found clear evidence of formation of a warm corona, strongly linked to the cold accretion disc underneath. A global amplification of ionizing radiation after approximately 11 years is consistent with propagating heating fronts in inflated accretion discs. Based on our multi-wavelength study on recent data, we propose that Mkn 590 is currently becoming a Seyfert-1, similar to 1990s., Comment: 7 pages, 4 figures; Submitted to MNRAS Letters

The stochastic gravitational wave background (SGWB) can be observed in the nanohertz band using a pulsar timing array (PTA). Here a computationally efficient state-space framework is developed for analysing SGWB data, in which the stochastic gravitational wave strain at Earth is tracked with a non-linear Kalman filter and separated simultaneously from intrinsic, achromatic pulsar spin wandering. The filter is combined with a nested sampler to estimate the parameters of the model, and to calculate a Bayes factor for selecting between models with and without a SGWB. The procedure extends previous state-space formulations of PTA data analysis applied to individually resolvable binary black hole sources. The performance of the new algorithm is tested on synthetic data from the first International PTA Mock Data Challenge. It is shown that the algorithm distinguishes a SGWB from pure noise for $A_{\rm gw} \geq 3 \times 10^{-14}$, where $A_{\rm gw}$ denotes the standard normalization factor for a power spectral density with power-law exponent $-13/3$. Additional, systematic validation tests are also performed with synthetic data generated independently by adjusting the injected parameters to cover astrophysically plausible ranges. Full posterior distributions are recovered and tested for accuracy. The state-space procedure is memory-light and evaluates the likelihood for a standard-sized PTA dataset in $\lesssim 10^{-1}$ s without optimization on a standard central processing unit., Comment: 10 pages, 4 figures + appendices. Accepted for publication in MNRAS; v2 corrected references

Erbium (Er) ions are the gain medium of choice for fiber-based amplifiers and lasers, offering a long excited-state lifetime, slow gain relaxation, low amplification nonlinearity and noise, and temperature stability compared to semiconductor-based platforms. Recent advances in ultra-low-loss silicon nitride (Si$_3$N$_4$) photonic integrated circuits, combined with ion implantation, have enabled the realization of high-power on-chip Er amplifiers and lasers with performance comparable to fiber-based counterparts, supporting compact photonic systems. Yet, these results are limited by the high (2 MeV) implantation beam energy required for tightly confined Si$_3$N$_4$ waveguides (700 nm height), preventing volume manufacturing of Er-doped photonic integrated circuits. Here, we overcome these limitations and demonstrate the first fully wafer-scale, foundry-compatible Er-doped photonic integrated circuit-based tunable lasers. Using 200 nm-thick Si$_3$N$_4$ waveguides, we reduce the ion beam energy requirement to below 500 keV, enabling efficient wafer-scale implantation with an industrial 300 mm ion implanter. We demonstrate a laser wavelength tuning range of 91 nm, covering nearly the entire optical C- and L-bands, with fiber-coupled output power reaching 36 mW and an intrinsic linewidth of 95 Hz. The temperature-insensitive properties of erbium ions allowed stable laser operation up to 125$^{\circ}$C and lasing with less than 15 MHz drift for over 6 hours at room temperature using a remote fiber pump. The fully scalable, low-cost fabrication of Er-doped waveguide lasers opens the door for widespread adoption in coherent communications, LiDAR, microwave photonics, optical frequency synthesis, and free-space communications.

We investigate the systematic uncertainties and potential biases arising from the inclusion of large-$x$ corrections to proton and deuteron DIS data in global QCD analyses. Using the CTEQ-JLab framework, we examine various approaches to implementing higher-twist corrections in nucleon structure functions and off-shell PDF modifications in deuteron targets. We analyze how these components interact and influence the determination of the $d$-quark PDF and the neutron structure function at large $x$. We find that it is very important to consider isospin-dependent higher-twist corrections in order to minimize implementation biases in the extracted quantities.

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green's function approaches, quantum and ab initio simulations.

We present a comprehensive analysis of the optical and infrared (IR) properties of high-mass X-ray binary (HMXB) IC 10 X-2, classified as a super-giant HMXB and super-fast X-ray transient (SFXT) by previous work. Our analysis of regular (daily and weekly) observations by both the Zwicky Transient Factory and Las Cumbres Observatory over a ~5 year period indicates both periodic flares and variations in the apparent magnitude and color with a $\sim26.5$~d period -- likely the orbital period of this binary system. The periodic flaring suggests the stellar companion is a Be star, with flares resulting from increased accretion onto the neutron star when it enters the stellar decretion disk. The periodic variations in the optical/IR brightness and color likely result from orbital variations in the Hydrogen column density along the line of sight or a transient accretion disk around the neutron star. Lastly, the numerous, short duration, episodes where IC 10 X-2 is significantly ``redder'' or ``bluer'' than normal likely result from from clumps within this system -- which can accrete onto the neutron star (causing IC 10 X-2 to appear bluer), or pass through the line of sight (causing IC 10 X-2 to appear redder). These results substantially increase our understanding of the evolution of this source, a significant source of ionizing photons in its host galaxy IC 10, a low mass, metal-poor starburst galaxy similar in many respect to those thought to be common in the early Universe., Comment: 21 pages, 12 figures, accepted for publication in ApJ

Multivariate anomaly detection finds its importance in diverse applications. Despite the existence of many detectors to solve this problem, one cannot simply define why an obtained anomaly inferred by the detector is anomalous. This reasoning is required for network operators to understand the root cause of the anomaly and the remedial action that should be taken to counteract its occurrence. Existing solutions in explainable AI may give cues to features that influence an anomaly, but they do not formulate generalizable rules that can be assessed by a domain expert. Furthermore, not all outliers are anomalous in a business sense. There is an unfulfilled need for a system that can interpret anomalies predicted by a multivariate anomaly detector and map these patterns to actionable rules. This paper aims to fulfill this need by proposing a semi-autonomous anomaly rule miner. The proposed method is applicable to both discrete and time series data and is tailored for radio access network (RAN) anomaly detection use cases. The proposed method is demonstrated in this paper with time series RAN data.

We present a realizability-preserving numerical method for solving a spectral two-moment model to simulate the transport of massless, neutral particles interacting with a steady background material moving with relativistic velocities. The model is obtained as the special relativistic limit of a four-momentum-conservative general relativistic two-moment model. Using a maximum-entropy closure, we solve for the Eulerian-frame energy and momentum. The proposed numerical method is designed to preserve moment realizability, which corresponds to moments defined by a nonnegative phase-space density. The realizability-preserving method is achieved with the following key components: (i) a discontinuous Galerkin (DG) phase-space discretization with specially constructed numerical fluxes in the spatial and energy dimensions; (ii) a strong stability-preserving implicit-explicit (IMEX) time-integration method; (iii) a realizability-preserving conserved to primitive moment solver; (iv) a realizability-preserving implicit collision solver; and (v) a realizability-enforcing limiter. Component (iii) is necessitated by the closure procedure, which closes higher order moments nonlinearly in terms of primitive moments. The nonlinear conserved to primitive and the implicit collision solves are formulated as fixed-point problems, which are solved with custom iterative solvers designed to preserve the realizability of each iterate. With a series of numerical tests, we demonstrate the accuracy and robustness of this DG-IMEX method., Comment: 39 pages, 19 figures

We present the results of the Distant Giants Survey, a three-year radial velocity (RV) campaign to search for wide-separation giant planets orbiting Sun-like stars known to host an inner transiting planet. We defined a distant giant to have $a$ = 1--10 AU and $M_{p} \sin i = 70-4000$ \mearth~ = 0.2-12.5 \mj, and required transiting planets to have $a<1$ AU and $R_{p} = 1-4$ \rearth. We assembled our sample of 47 stars using a single selection function, and observed each star at monthly intervals to obtain $\approx$30 RV observations per target. The final catalog includes a total of twelve distant companions: four giant planets detected during our survey, two previously known giant planets, and six objects of uncertain disposition identified through RV/astrometric accelerations. Statistically, half of the uncertain objects are planets and the remainder are stars/brown dwarfs. We calculated target-by-target completeness maps to account for missed planets. We found evidence for a moderate enhancement of distant giants (DG) in the presence of close-in small planets (CS), P(DG|CS) = $30^{+14}_{-12}\%$, over the field rate of P(DG) = $16^{+2}_{-2}\%$. No enhancement is disfavored ($p \sim$ 8%). In contrast to a previous study, we found no evidence that stellar metallicity enhances P(DG|CS). We found evidence that distant giant companions are preferentially found in systems with multiple transiting planets and have lower eccentricities than randomly selected giant planets. This points toward dynamically cool formation pathways for the giants that do not disturb the inner systems., Comment: 32 pages, 20 figures, 4 tables. Comments welcome

The next-generation Very Large Array (ngVLA) is intended to be the premier centimeter-wavelength facility for astronomy and astrophysics, building on the substantial scientific legacies of the Karl G. Jansky Very Large Array (VLA) and the Very Long Baseline Array (VLBA). The ngVLA would open a new window on the Universe through ultra-sensitive imaging of thermal line and continuum emission to milliarcsecond resolution, while delivering unprecedented broad-band continuum imaging and polarimetry of non-thermal emission. The ngVLA would provide a critical electromagnetic complement to a suite of particle detectors and gravitational-wave observatories, as well as space- and ground-based telescopes operating from infrared to gamma-ray wavelengths, hence enabling multi-messenger and multi-band astronomy and astrophysics. Current construction plans call for the ngVLA to leverage some of the physical infrastructure of both the VLA and the VLBA, potentially drawing on overlapping personnel and information infrastructure. Multiple options can be envisioned for a VLA+VLBA to ngVLA transition. In order to assess risks and benefits of possible transition plans, the ngVLA project established the VLA+VLBA to ngVLA Transition Advisory Group (TAG). The primary deliverable from the TAG is a ``VLA+VLBA to ngVLA Transition Option Concepts'' report (this report) that includes a prioritized list of transition options., Comment: This report reflects an initial set of recommendations by the Transition Advisory Group for the ngVLA Project and is distributed for the purposes of obtaining community comment. Modification of this report in response to community comment is expected. Please submit your feedback at ngvla-transition-feedback@listmgr.nrao.edu

The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from the STARS program in integrating reinforcement learning-based multi-satellite control, run time assurance approaches, and flexible human-autonomy teaming interfaces, into a new integrated testing environment for collaborative autonomous satellite systems. The primary results describe analysis of the reinforcement learning multi-satellite control and run time assurance algorithms. These algorithms are integrated into a prototype human-autonomy interface using best practices from human-autonomy trust literature, however detailed analysis of the effectiveness is left to future work. References are provided with additional detailed results of individual experiments.

The task of text-to-image generation has encountered significant challenges when applied to literary works, especially poetry. Poems are a distinct form of literature, with meanings that frequently transcend beyond the literal words. To address this shortcoming, we propose a PoemToPixel framework designed to generate images that visually represent the inherent meanings of poems. Our approach incorporates the concept of prompt tuning in our image generation framework to ensure that the resulting images closely align with the poetic content. In addition, we propose the PoeKey algorithm, which extracts three key elements in the form of emotions, visual elements, and themes from poems to form instructions which are subsequently provided to a diffusion model for generating corresponding images. Furthermore, to expand the diversity of the poetry dataset across different genres and ages, we introduce MiniPo, a novel multimodal dataset comprising 1001 children's poems and images. Leveraging this dataset alongside PoemSum, we conducted both quantitative and qualitative evaluations of image generation using our PoemToPixel framework. This paper demonstrates the effectiveness of our approach and offers a fresh perspective on generating images from literary sources.

We performed a detailed spectroscopic analysis of three extremely metal-poor RR Lyrae stars, exploring uncharted territories at these low metallicities for this class of stars. Using high-resolution spectra acquired with HARPS-N at TNG, UVES at VLT, and PEPSI at LBT, and employing Non-Local Thermodynamic Equilibrium (NLTE) spectral synthesis calculations, we provide abundance measurements for Fe, Al, Mg, Ca, Ti, Mn, and Sr. Our findings indicate that the stars have metallicities of [Fe/H] = -3.40 \pm 0.05, -3.28 \pm 0.02, and -2.77 \pm 0.05 for HD 331986, DO Hya, and BPS CS 30317-056, respectively. Additionally, we derived their kinematic and dynamical properties to gain insights into their origins. Interestingly, the kinematics of one star (HD 331986) is consistent with the Galactic disc, while the others exhibit Galactic halo kinematics, albeit with distinct chemical signatures. We compared the [Al/Fe] and [Mg/Mn] ratios of the current targets with recent literature estimates to determine whether these stars were either accreted or formed in situ, finding that the adopted chemical diagnostics are ineffective at low metallicities ([Fe/H] $\lesssim -$1.5). Finally, the established horizontal branch evolutionary models, indicating that these stars arrive at hotter temperatures on the Zero-Age Horizontal Branch (ZAHB) and then transition into RR Lyrae stars as they evolve, fully support the existence of such low-metallicity RR Lyrae stars. As a consequence, we can anticipate detecting more of them when larger samples of spectra become available from upcoming extensive observational campaigns., Comment: Accepted for publication in Astronomy & Astrophysics; 13 pages. Appendices D-E available on Zenodo

Transition states are a critical bottleneck in chemical transformations. Significant efforts have been made to develop algorithms that efficiently locate transition states on potential energy surfaces. However, the computational cost of ab-initio potential energy surface evaluation limits the size of chemical systems that can routinely studied. In this work, we develop and fine-tune a graph neural network potential energy function suitable for describing organic chemical reactions and use it to rapidly identify transition state guess structures. We successfully refine guess structures and locate a transition state in each test system considered and reduce the average number of ab-initio calculations by 47% though use of the graph neural network potential energy function. Our results show that modern machine learning models have reached levels of reliability whereby they can be used to accelerate routine computational chemistry tasks., Comment: 9 pages, 4 figures, 3 tables

Quantum computing provides a novel approach to addressing conventionally intractable issues in large-scale optimization. Space logistics missions require the efficient routing of payloads, spacecraft, and resources across complex networks, often resulting in an exponential growth of the solution space that classical methods cannot efficiently solve. This paper leverages entropy quantum computing to model and solve the space logistics problem as a time-dependent multicommodity network flow, enabling the exploration of large solution spaces. The findings highlight quantum computing's potential to address complex aerospace logistics, demonstrating its suitability for complex interplanetary mission planning.