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Preparing quantum states is a fundamental task in various quantum algorithms. In particular, state preparation in quantum harmonic oscillators (HOs) is crucial for the creation of qudits and the implementation of high-dimensional algorithms. In this work, we develop a methodology for preparing quantum states in HOs. The HO is coupled to an auxiliary qubit to ensure that any state can be prepared in the oscillator [J. Math. Phys. 59, 072101]. By applying a sequence of square pulses to both the qubit and the HO, we drive the system from an initial state to a target state. To determine the required pulses, we use a neural network that predicts the pulse parameters needed for state preparation. Specifically, we present results for preparing qubit and qutrit states in the HO, achieving average fidelities of 99.9% and 97.0%, respectively.

The real Higgs singlet extension of the Standard Model (SM) without $Z_2$ symmetry, the RxSM, is the simplest extension of the SM that features a First Order Electroweak Phase Transition (FOEWPT) in the early universe. The FOEWPT is one of the requirements needed for electroweak baryogenesis to explain the baryon asymmetry of the universe (BAU). Thus, the RxSM is a perfect example to study features related to the FOEWPT at current and future collider experiments. The RxSM has two CP-even Higgs bosons, $h$ and $H$, with masses $m_h < m_H$, where we assume that $h$ corresponds to the Higgs boson discovered at the LHC. Our analysis is based on a benchmark plane that ensures the occurence of a strong FOEWPT, where $m_H > 2 m_h$ is found. In a first step we analyze the di-Higgs production at the (HL-)LHC, $gg \to hh$, with a focus on the impact of the trilinear Higgs couplings (THCs), $\lambda_{hhh}$ and $\lambda_{hhH}$. The interferences of the resonant $H$-exchange diagram involving $\lambda_{hhH}$ and the non-resonant diagrams result in a characteristic peak-dip (or dip-peak) structure in the $m_{hh}$ distribution. We analyze how $\lambda_{hhH}$ can be accessed, taking into account the experimental smearing and binning. We also demonstrate that the approximation used by ATLAS and CMS for the resonant di-Higgs searches may fail to capture the relevant effects and lead to erroneous results. In a second step we analyze the benchmark plane at a future high-energy $e^+e^-$ collider with $\sqrt{s} = 1000$ GeV (ILC1000). We demonstrate the potential sensitivity to $\lambda_{hhH}$ via an experimental determination at the ILC1000., Comment: 34 pages, 16 figures

Molecules provide the ultimate language in terms of which physiology and pathology must be understood. Myriads of proteins participate in elaborate networks of interactions and perform chemical activities coordinating the life of cells. To perform these often amazing tasks, proteins must move and we must think of them as dynamic ensembles of three dimensional structures formed first by folding the polypeptide chains so as to minimize the conflicts between the interactions of their constituent amino acids. It is apparent however that, even when completely folded, not all conflicting interactions have been resolved so the structure remains "locally frustrated". Over the last decades it has become clearer that this local frustration is not just a random accident but plays an essential part of the inner workings of protein molecules. We will review here the physical origins of the frustration concept and review evidence that local frustration is important for protein physiology, protein-protein recognition, catalysis and allostery. Also, we highlight examples showing how alterations in the local frustration patterns can be linked to distinct pathologies. Finally we explore the extensions of the impact of frustration in higher order levels of organization of systems including gene regulatory networks and the neural networks of the brain.

To precisely define the treatment effect of interest in a clinical trial, the ICH E9 estimand addendum describes that relevant so-called intercurrent events should be identified and strategies specified to deal with them. Handling intercurrent events with different strategies leads to different estimands. In this paper, we focus on estimands that involve addressing one intercurrent event with the treatment policy strategy and another with the hypothetical strategy. We define these estimands using potential outcomes and causal diagrams, considering the possible causal relationships between the two intercurrent events and other variables. We show that there are different causal estimand definitions and assumptions one could adopt, each having different implications for estimation, which is demonstrated in a simulation study. The different considerations are illustrated conceptually using a diabetes trial as an example.

We introduce a novel framework that integrates Hodge decomposition with Filtered Average Short-Term (FAST) functional connectivity to analyze dynamic functional connectivity (DFC) in EEG signals. This method leverages graph-based topology and simplicial analysis to explore transient connectivity patterns at multiple scales, addressing noise, sparsity, and computational efficiency. The temporal EEG data are first sparsified by keeping only the most globally important connections, instantaneous connectivity at these connections is then filtered by global long-term stable correlations. This tensor is then decomposed into three orthogonal components to study signal flows over higher-order structures such as triangle and loop structures. Our analysis of Alzheimer-related MCI patients show significant temporal differences related to higher-order interactions that a pairwise analysis on its own does not implicate. This allows us for the first time to capture higher-dimensional interactions at high temporal resolution in noisy EEG signal recordings.

Compositionality and compositional generalization--the ability to understand novel combinations of known concepts--are central characteristics of human language and are hypothesized to be essential for human cognition. In machine learning, the emergence of this property has been studied in a communication game setting, where independent agents (a sender and a receiver) converge to a shared encoding policy from a set of states to a space of discrete messages, where the receiver can correctly reconstruct the states observed by the sender using only the sender's messages. The use of communication games in generation tasks is still largely unexplored, with recent methods for compositional generation focusing mainly on the use of supervised guidance (either through class labels or text). In this work, we take the first steps to fill this gap, and we present a self-supervised generative communication game-based framework for creating compositional encodings in learned representations from pre-trained encoder-decoder models. In an Iterated Learning (IL) protocol involving a sender and a receiver, we apply alternating pressures for compression and diversity of encoded discrete messages, so that the protocol converges to an efficient but unambiguous encoding. Approximate message entropy regularization is used to favor compositional encodings. Our framework is based on rigorous justifications and proofs of defining and balancing the concepts of Efficiency, Unambiguity and Non-Holisticity in encoding. We test our method on the compositional image dataset Shapes3D, demonstrating robust performance in both reconstruction and compositionality metrics, surpassing other tested discrete message frameworks.

The goal of this work was to develop a deep network for whole-head segmentation, including clinical MRIs with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 91 MRIs with volumetric segmentation labels for a diverse set of human subjects (4 normal, 32 traumatic brain injuries, and 57 strokes). These clinical cases are characterized by extended cerebrospinal fluid (CSF) in regions normally containing the brain. Training labels were generated by manually correcting initial automated segmentations for skin/scalp, skull, CSF, gray matter, white matter, air cavity, and extracephalic air. We developed a MultiAxial network consisting of three 2D U-Net models that operate independently in sagittal, axial, and coronal planes and are then combined to produce a single 3D segmentation. The MultiAxial network achieved test-set Dice scores of 0.88 (median plus-minus 0.04). For brain tissue, it significantly outperforms existing brain segmentation methods (MultiAxial: 0.898 plus-minus 0.041, SynthSeg: 0.758 plus-minus 0.054, BrainChop: 0.757 plus-minus 0.125). The MultiAxial network gains in robustness by avoiding the need for coregistration with an atlas. It performed well in regions with abnormal anatomy and on images that have been de-identified. It enables more robust current flow modeling when incorporated into ROAST, a widely-used modeling toolbox for transcranial electric stimulation. We are releasing a state-of-the-art model for whole-head MRI segmentation, along with a dataset of 61 clinical MRIs and training labels, including non-brain structures. Together, the model and data may serve as a benchmark for future efforts.

We revisit dispersive bounds on Wilson coefficients of scalar effective field theories (EFT) coupled to gravity in various spacetime dimensions, by computing the contributions from graviton loops to the corresponding sum rules at low energies. Fixed-momentum-transfer dispersion relations are often ill-behaved due to forward singularities arising from loop-level graviton exchange, making naive positivity bounds derived from them unreliable. Instead, we perform a careful analysis using crossing-symmetric dispersion relations, and compute the one-loop corrections to the bounds on EFT coefficients. We find that including the graviton loops generically allows for negativity of Wilson coefficients by an amount suppressed by powers of Newton's constant, $G$. The exception are the few couplings that dominate over (or are degenerate with) the graviton loops at low energies. In $D=4$, we observe that assuming that the eikonal formula captures the correct forward behavior of the amplitude at all orders in $G$, and for energies of the order of the EFT cutoff, yields bounds free of logarithmic infrared divergences., Comment: 42 pages + appendices, 7 figures

We present recommendations for how to improve reproducibility in the field of condensed matter physics. This area of physics has consistently produced both fundamental insights into the functioning of matter as well as transformative inventions. Our recommendations result from a collaboration that includes researchers in academia and government laboratories, scientific journalists, legal professionals, representatives of publishers, professional societies, and other experts. The group met in person in May 2024 at a conference at the University of Pittsburgh to discuss the growing challenges related to research reproducibility in condensed matter physics. We discuss best practices and policies at all stages of the scientific process to safeguard the value condensed matter research brings to society. We look forward to comments and suggestions, especially regarding subfield-specific recommendations, and will incorporate them into the next version of the report.

In this work, we study the maximum scores that can be achieved in a team-based domino game. Specifically, we show that if the game ends because it is blocked, then the maximum score that can be obtained under this assumption is 107. -- En este trabajo estudiamos los puntajes m\'aximos que se pueden obtener en una partida, por equipos, en el juego del domin\'o. Concretamente; nosotros mostramos que, si la partida termina porque el juego esta trancado, entonces el puntaje m\'aximo que se puede obtener bajo este supuesto es de 107., Comment: in Spanish language

Hydrogen, the most abundant element in the universe, is crucial for understanding galaxy formation and evolution. The 21 cm neutral atomic hydrogen - HI spectral line maps the gas kinematics within galaxies, providing key insights into interactions, galactic structure, and star formation processes. With new radio instruments, the volume and complexity of data is increasing. To analyze and classify integrated HI spectral profiles in a efficient way, this work presents a framework that integrates Machine Learning techniques, combining unsupervised methods and CNNs. To this end, we apply our framework to a selected subsample of 318 spectral HI profiles of the CIG and 30.780 profiles from the Arecibo Legacy Fast ALFA Survey catalogue. Data pre-processing involved the Busyfit package and iterative fitting with polynomial, Gaussian, and double-Lorentzian models. Clustering methods, including K-means, spectral clustering, DBSCAN, and agglomerative clustering, were used for feature extraction and to bootstrap classification we applied K-NN, SVM, and Random Forest classifiers, optimizing accuracy with CNN. Additionally, we introduced a 2D model of the profiles to enhance classification by adding dimensionality to the data. Three 2D models were generated based on transformations and normalised versions to quantify the level of asymmetry. These methods were tested in a previous analytical classification study conducted by the Analysis of the Interstellar Medium in Isolated Galaxies group. This approach enhances classification accuracy and aims to establish a methodology that could be applied to data analysis in future surveys conducted with the Square Kilometre Array (SKA), currently under construction. All materials, code, and models have been made publicly available in an open-access repository, adhering to FAIR principles., Comment: 5 pages, 3 figures

The FitzHugh-Nagumo model, originally introduced to study neural dynamics, has since found applications across diverse fields, including cardiology and biology. However, the formation and bifurcation structure of spatially localized states in this model remain underexplored. In this work, we present a detailed bifurcation analysis of such localized structures in one spatial dimension in the FitzHugh-Nagumo model. We demonstrate that these localized states undergo a smooth transition between standard and collapsed homoclinic snaking as the system shifts from pattern-uniform to uniform-uniform bistability. Additionally, we explore the oscillatory dynamics exhibited by these states when varying the time-scale separation and diffusion coefficient. Our study leverages a combination of analytical and numerical techniques to uncover the stability and dynamic regimes of spatially localized structures, offering new insights into the mechanisms governing spatial localization in this widely used model system.

Modularity is a well-established concept for assessing community structures in various single and multi-layer networks, including those in biological and social domains. Biological networks, such as the brain, are known to exhibit group structure at a variety of scales -- local, meso, and global scale. Modularity, while useful in describing mesoscale brain organization, is limited as a metric to a global scale describing the overall strength of community structure. This approach, while valuable, overlooks important localized variations in community structure at the node level. To address this limitation, we extended modularity to individual nodes. This novel measure of nodal modularity ($nQ$) captures both meso and local scale changes in modularity. We hypothesized that $nQ$ illuminates granular changes in the brain due to diseases such as Alzheimer's disease (AD), which are known to disrupt the brain's modular structure. We explored $nQ$ in multiplex networks of a visual short-term memory binding task in fMRI and DTI data in the early stages of AD. Observed changes in $nQ$ in fMRI and DTI networks aligned with known trajectories of AD and were linked to common biomarkers of the disease, including amyloid-$\beta$ and tau. Additionally, $nQ$ clearly differentiated MCI from MCI converters showing indications that $nQ$ may be a useful diagnostic tool for characterizing disease stages. Our findings demonstrate the utility of $nQ$ as a measure of localized group structure, providing novel insights into temporal and disease related variability at the node level. Given the widespread application of modularity as a global measure, $nQ$ represents a significant advancement, providing a granular measure of network organization applicable to a wide range of disciplines., Comment: 37 pages, 11 figures, this is to be submitted to PLOS ONE for publication

The phase-amplitude framework extends the classical phase reduction method by incorporating amplitude coordinates (or isostables) to describe transient dynamics transverse to the limit cycle in a simplified form. While the full set of amplitude coordinates provides an exact description of oscillatory dynamics, it maintains the system's original dimensionality, limiting the advantages of simplification. A more effective approach reduces the dynamics to the slow attracting invariant submanifold associated with the slowest contracting direction, achieving a balance between simplification and accuracy. In this work, we present an efficient numerical method to compute the parameterization of the attracting slow submanifold of hyperbolic limit cycles and the simplified dynamics in its induced coordinates. Additionally, we compute the infinitesimal Phase and Amplitude Response Functions (iPRF and iARF, respectively) restricted to this manifold, which characterize the effects of perturbations on phase and amplitude. These results are obtained by solving an invariance equation for the slow manifold and adjoint equations for the iPRF and iARF. To solve these functional equations efficiently, we employ the Floquet normal form to solve the invariance equation and propose a novel coordinate transformation to simplify the adjoint equations. The solutions are expressed as Fourier-Taylor expansions with arbitrarily high accuracy. Our method accommodates both real and complex Floquet exponents. Finally, we discuss the numerical implementation of the method and present results from its application to a representative example.

We experimentally study cosmological particle production in a two-dimensional Bose-Einstein condensate, whose density excitations map to an analog cosmology. The expansion of spacetime is realized with tunable interactions. The particle spectrum can be understood through an analogy to quantum mechanical scattering, in which the dynamics of the spacetime metric determine the shape of the scattering potential. Hallmark scattering phenomena such as resonant forward scattering and Bragg reflection are connected to their cosmological counterparts, namely linearly expanding space and bouncing universes. We compare our findings to a theoretical description that extends beyond the acoustic approximation, which enables us to apply the model to high-momentum excitations.

Data visualization is a core part of statistical practice and is ubiquitous in many fields. Although there are numerous books on data visualization, instructors in statistics and data science may be unsure how to teach data visualization, because it is such a broad discipline. To give guidance on teaching data visualization from a statistical perspective, we make two contributions. First, we conduct a survey of data visualization courses at top colleges and universities in the United States, in order to understand the landscape of data visualization courses. We find that most courses are not taught by statistics and data science departments and do not focus on statistical topics, especially those related to inference. Instead, most courses focus on visual storytelling, aesthetic design, dashboard design, and other topics specialized for other disciplines. Second, we outline three teaching principles for incorporating statistical inference in data visualization courses, and provide several examples that demonstrate how instructors can follow these principles. The dataset from our survey allows others to explore the diversity of data visualization courses, and our teaching principles give guidance to instructors and departments who want to encourage statistical thinking via data visualization. In this way, statistics-related departments can provide a valuable perspective on data visualization that is unique to current course offerings.

The recent discovery of superconductivity in $\rm La_3Ni_2O_7$ has attracted significant attention due to its high critical temperature and analogy to cuprate oxides. The oxidation and spin states of Ni ions are among the most important local properties in this compound, extensively discussed in the context of its superconductivity. Despite their direct link to the electron filling configurations of the relevant $\rm 3d_{x^2-y^2}$ and $\rm 3d_{z^2}$ orbitals, these local electronic properties of $\rm La_3Ni_2O_7$ yet to be systematically investigated. In this work, we address this issue using x-ray absorption spectroscopy (XAS) and x-ray emission spectroscopy (XES) measurements under pressure. Comparison of Ni \textit{K}-edge XAS and $\rm K\beta$ XES with the reference spectra of $\rm NiO$ and $\rm LaNiO_3$ shows that Ni ions, with an average valence of $\sim 2.53+$, are in a low-spin ($\rm S = 1/2$) ground state under ambient conditions. High pressure XAS and XES data clearly show that the oxidation ($\sim 2.5+$) and spin ($\rm S = 1/2$) states of Ni ions remain stable across the investigated pressure (up to 30 GPa) and temperature (down to 10 K) ranges, ruling out previously proposed spin transition scenarios.

A recent report on the detection of very-high-energy gamma rays from V4641 Sagittarii (V4641 Sgr) up to ~0.8 peta-electronvolt has made it the second confirmed "PeVatron" microquasar. Here we report on the observation of V4641 Sgr with X-Ray Imaging and Spectroscopy Mission (XRISM) in September 2024. Thanks to the large field of view and low background, the CCD imager Xtend successfully detected for the first time X-ray extended emission around V4641 Sgr with a significance of > 4.5 sigma and > 10 sigma based on our imaging and spectral analysis, respectively. The spatial extent is estimated to have a radius of $7 \pm 3$ arcmin ($13 \pm 5$ pc at a distance of 6.2 kpc) assuming a Gaussian-like radial distribution, which suggests that the particle acceleration site is within ~10 pc of the microquasar. If the X-ray morphology traces the diffusion of accelerated electrons, this spatial extent can be explained by either an enhanced magnetic field (~80 uG) or a suppressed diffusion coefficient (~$10^{27}$ cm$^2$ s$^{-1}$ at 100 TeV). The integrated X-ray flux, (4-6)$\times 10^{-12}$ erg s$^{-1}$ cm$^{-2}$ (2-10 keV), would require a magnetic field strength higher than the galactic mean (> 8 uG) if the diffuse X-ray emission originates from synchrotron radiation and the gamma-ray emission is predominantly hadronic. If the X-rays are of thermal origin, the measured extension, temperature, and plasma density can be explained by a jet with a luminosity of ~$2\times 10^{39}$ erg s$^{-1}$, which is comparable to the Eddington luminosity of this system., Comment: 9 pages, 5 figures, accepted for publication in ApJL

Early childhood educators play an essential role in providing child care for families and learning and development supports for young children, yet they have long faced challenges due to low wages. Recognizing this, the District of Columbia (DC) introduced the Early Childhood Pay Equity Fund in 2022. This first-of-its-kind initiative aims to bridge the pay gap between early educators and teachers at public schools, addressing historical inequities and improving recruitment and retention efforts. This report provides an in-depth exploration of early educators' experiences with the Pay Equity Fund during its initial year of implementation. Drawing on data from surveys conducted in May 2023 and follow-up focus groups, the report offers insights into the transition from direct payments to an opt-in payment structure by employers in FY 2024. It covers educators' introduction to the Fund, their application and payment experiences, financial impacts, and perceived benefits for child care programs and the broader early childhood education field.

We study the conditions under which a TTF class in a module category over a ring is silting. Using the correspondence between idempotent ideals over a ring and TTF classes in the module category, we focus on finding the necessary and sufficient conditions for $R/I$ to be a silting $R$-module, and hence for the TTF class $\mathbf{Gen}(R/I)$ to be silting, where $I$ is an idempotent two-sided ideal of $R$. In our main result, we show that $R/I$ is a silting module whenever $I$ is the trace of a projective $R$-module. Furthermore, we demonstrate that the converse holds for a broad class of rings, including semiperfect rings.

Subarachnoid hemorrhage (SAH), typically due to intracranial aneurysms, demands precise imaging for effective treatment. Digital Subtraction Angiography (DSA), despite being the gold standard, broadly visualizes cerebral blood flow, potentially masking key details in areas. This study introduces an approach integrating a 3D vascular atlas with 2D DSA images to allow targeted quantitative analysis in these crucial regions, thus enhancing diagnostic accuracy during interventions. Initially, DSA data was examined to ascertain the injection site. Following this, the appropriate viewing angle was determined to align accurately with the 3D vascular atlas. Utilizing this atlas, regions corresponding to the areas indicated as perfused were selected. Concurrently, a mask representing the perfused areas was created from the DSA sequence. This mask facilitated the initial coarse alignment of the projected 3D atlas to the DSA perfused territory deformable registration techniques, ensuring a precise overlay with the DSAs perfused territories. The performance of each overlay was measured using the Structural Similarity Index Measure (SSIM). The coregistration process revealed that deformable registrations was essential to achieve precise overlays of the 3D atlas projections with the 2D DSA perfused areas. This approach enabled the extraction of targeted quantitative angiography parameters, essential for detailed vascular assessment in subarachnoid hemorrhage cases. The integration of 3D atlas registration with 2D DSA projections facilitates a more precise and targeted diagnostic process for SAH during critical interventions. This image processing strategy enhances the visualization of affected arterial territories, potentially improving the accuracy of diagnostics and supporting better informed clinical decisions at the time of intervention, Comment: 5 pages, 2 figures, to be published in SPIE Medical Imaging 2025 conference proceeds

Using molecular dynamics simulations and the ReaxFF force field, we studied a composite based on cubic (c-BN) and hexagonal (h-BN) boron nitride subjected to different temperatures to verify the possibility of a c-BN to h-BN phase transition. Our results demonstrate that the surface termination of c-BN (whether B- or N-terminated) is a crucial factor in the phase transition. The B-terminated c-BN surface presents a lower potential energy than the N-terminated one. However, compared to the potential energy of h-BN, the B- (N-) terminated c-BN surface has a lower (higher) potential energy than h-BN. As the temperature increases, the potential energy of the B-terminated c-BN surface gradually approaches that of h-BN, leading to the beginning of detachment into an h-BN layer around 700 K. With further temperature increase, free h-BN layers can form, which will modify the properties of the composite., Comment: 8 pages, 4 figures

Collisions play a crucial role in governing particle and energy transport in plasmas confined in a magnetic mirror trap. Modern gyrokinetic codes are used to model transport in magnetic mirrors, but some of these codes utilize approximate model collision operators. This study focuses on a Pastukhov-style method of images calculation of particle and energy confinement times using a Lenard-Bernstein model collision operator. Prior work on parallel particle and energy balances used a different Fokker-Planck plasma collision operator and the method needs to be extended in non-trivial ways to study the Lenard-Bernstein operator. To assess the effectiveness of our approach, we compare our results with a modern finite element solver. Our findings reveal that the particle confinement time scales like $a \exp(a^2)$ using the Lenard-Bernstein operator, in contrast to the more accurate scaling that the Coulomb collision operator would yield $a^2 \exp(a^2)$, where $a^2$ is approximately proportional to the ambipolar potential. We propose that codes modeling collisional losses in a magnetic mirrors utilizing the Lenard-Bernstein or Dougherty collision operator scale their collision frequency of any electrostatically confined species. This study illuminates the intricate role the collision operator plays in the Pastukhov-style method of images calculation of collisional confinement., Comment: 21 pages, 4 figures, 3 tables, submitted to journal of plasma physics

Pair creation is a fundamental prediction of quantum field theory in curved spacetimes. While classical aspects of this phenomenon have been observed, the experimental confirmation of its quantum origin remains elusive. In this article, we quantify the entanglement produced by pair creation in two dimensional Bose-Einstein Condensate (BEC) analogues of expanding universes and examine the impact of various experimental factors, including decoherence from thermal noise and losses. Our analysis evaluates the feasibility of detecting entanglement in these systems and identifies optimal experimental configurations for achieving this goal. Focusing on the experimental setup detailed in \cite{Viermann:2022wgw}, we demonstrate that entanglement can be observed in these BEC analogues at a significance level of $\sim 2\sigma$ with current capabilities, and at $\gtrsim 3.3\sigma$ with modest improvements. Achieving this would provide unequivocal evidence of the quantum nature of pair creation and validate one of the most iconic predictions of quantum field theory in curved spacetimes., Comment: 21 pages, 10 Figures, comments are welcome and encouraged

We revisit the calculation of classical observables from causal response functions, following up on recent work by Caron-Huot at al. [JHEP 01 (2024) 139]. We derive a formula to compute asymptotic in-in observables from a particular soft limit of five-point amputated response functions. Using such formula, we re-derive the formulas by Kosower, Maybee and O'Connell (KMOC) for the linear impulse and radiated linear momentum of particles undergoing scattering, and we present an unambiguous calculation of the radiated angular momentum at leading order. Then, we explore the consequences of manifestly causal Feynman rules in the calculation of classical observables by employing the causal (Keldysh) basis in the in-in formalism. We compute the linear impulse, radiated waveform and its variance at leading and/or next-to-leading order in the causal basis, and find that all terms singular in the $\hbar \to 0$ limit cancel manifestly at the integrand level. We also find that the calculations simplify considerably and classical properties such as factorization of six-point amplitudes are more transparent in the causal basis., Comment: 25+3 pages, many figures

The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives worldwide are developing innovative solutions and setting the direction for the key features of these networks. Some common emerging themes are the tight integration of AI, the convergence of multiple access technologies and sustainable operation, aiming to meet stringent performance and societal requirements. To that end, we are introducing REASON - Realising Enabling Architectures and Solutions for Open Networks. The REASON project aims to address technical challenges in future network deployments, such as E2E service orchestration, sustainability, security and trust management, and policy management, utilising AI-native principles, considering multiple access technologies and cloud-native solutions. This paper presents REASON's architecture and the identified requirements for future networks. The architecture is meticulously designed for modularity, interoperability, scalability, simplified troubleshooting, flexibility, and enhanced security, taking into consideration current and future standardisation efforts, and the ease of implementation and training. It is structured into four horizontal layers: Physical Infrastructure, Network Service, Knowledge, and End-User Application, complemented by two vertical layers: Management and Orchestration, and E2E Security. This layered approach ensures a robust, adaptable framework to support the diverse and evolving requirements of 6G networks, fostering innovation and facilitating seamless integration of advanced technologies., Comment: Accepted for publication at IEEE Access

Conventional computer vision models rely on very deep, feedforward networks processing whole images and trained offline with extensive labeled data. In contrast, biological vision relies on comparatively shallow, recurrent networks that analyze sequences of fixated image patches, learning continuously in real-time without explicit supervision. This work introduces a vision network inspired by these biological principles. Specifically, it leverages a joint embedding predictive architecture incorporating recurrent gated circuits. The network learns by predicting the representation of the next image patch (fixation) based on the sequence of past fixations, a form of self-supervised learning. We show mathematical and empirically that the training algorithm avoids the problem of representational collapse. We also introduce \emph{Recurrent-Forward Propagation}, a learning algorithm that avoids biologically unrealistic backpropagation through time or memory-inefficient real-time recurrent learning. We show mathematically that the algorithm implements exact gradient descent for a large class of recurrent architectures, and confirm empirically that it learns efficiently. This paper focuses on these theoretical innovations and leaves empirical evaluation of performance in downstream tasks, and analysis of representational similarity with biological vision for future work.

The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) project has discovered the most repeating fast radio burst (FRB) sources of any telescope. However, most of the physical conclusions derived from this sample are based on data with a time resolution of $\sim$1 ms. In this work, we present for the first time a morphological analysis of the raw voltage data for 118 bursts from 32 of CHIME/FRB's repeating sources. We do not find any significant correlations amongst fluence, dispersion measure (DM), burst rate, and burst duration. Performing the first large-scale morphological comparison at timescales down to microseconds between our repeating sources and 125 non-repeating FRBs, we find that repeaters are narrower in frequency and broader in duration than non-repeaters, supporting previous findings. However, we find that the duration-normalized sub-burst widths of the two populations are consistent, possibly suggesting a shared physical emission mechanism. Additionally, we find that the spectral fluences of the two are consistent. When combined with the larger bandwidths and previously found larger DMs of non-repeaters, this suggests that non-repeaters may have higher intrinsic specific energies than repeating FRBs. We do not find any consistent increase or decrease in the DM ($\lessapprox 1$ pc cm$^{-3}$ yr$^{-1}$) and scattering timescales ($\lessapprox 2$ ms yr$^{-1}$) of our sources over $\sim2-4$ year periods., Comment: 29 pages, 17 figures, 4 tables; Submitted to ApJ

This work uses twin 22-year free-running simulations of the Gulf of Mexico hydrodynamics performed with the HYCOM, one considering only ocean dynamics and the other incorporating atmospheric forcing, to study the behavior of the Yucatan Channel transport (YCT), the Loop Current (LC), the Loop Current Eddies (LCEs), their relationships, and the atmospheric forcing effect on them in short (daily) and long (monthly) time scales. A more comprehensive description of the LC intrusion and LCE separations was obtained by considering the upper eastern or western YCT (whose magnitudes are determined by the longitudinal displacements of the Yucatan Current's core), a perspective not evident when considering the upper total YCT; specifically, the eastern YCT provides the most meaningful description of the studied processes. Atmospheric forcing mainly affects the extended stage of the LC by creating a higher dispersion in the YCT and LC circulation values in comparison when considering only ocean dynamics. For the long-term analysis, standardized indexes that integrate the daily values of the eastern YCT and LC circulation in time were used; their temporal propagation and persistence (the changes of their characteristics from short to long time scales) were studied. Intrinsic ocean dynamics produces a persistent YCT and LC intrusion behavior and consistent LCE separation patterns from daily to 5-month scales. The atmospheric forcing effects are more emphasized on the LC intrusion and LCE separations than on the YCT: the YCT persistence is maintained but not that of the LC intrusion. An increased occurrence of LCE separations with low or moderate LC intrusion is expected due to climate change. Using the standardized indexes of the LC metrics to construct a predictive model of the LC intrusion and LCE separations using only current and past LC information is proposed for future research., Comment: 14 pages, 5 figures

We provide eigenvalue asymptotics for a Dirac-type operator on $\mathbb Z^n$, $n\geq 2$, perturbed by multiplication operators that decay as $|\mu|^{-\gamma}$ with $\gamma

In high-energy physics (HEP), both the exclusion and discovery of new theories depend not only on the acquisition of high-quality experimental data but also on the rigorous application of statistical methods. These methods provide probabilistic criteria (such as p-values) to compare experimental data with theoretical models, aiming to describe the data as accurately as possible. Hypothesis testing plays a central role in this process, as it enables comparisons between established theories and potential new explanations for the observed data. This report reviews key statistical methods currently employed in particle physics, using synthetic data and numerical comparisons to illustrate the concepts in a clear and accessible way. Our results highlight the practical significance of these statistical tools in enhancing the experimental sensitivity and model exclusion capabilities in HEP. All numerical results are estimated using Python and RooFit, a high-level statistical modeling package used by the ATLAS and CMS collaborations at CERN to model and report results from experimental data., Comment: 28 pages, 25 figures. Comments and suggestions are welcome

This study investigates the impact of morphological typology on tokenization and language modeling performance. We focus on languages with synthetic and analytical morphological structures and examine their productivity when tokenized using the byte-pair encoding (BPE) algorithm. We compare the performance of models trained with similar amounts of data in different languages. Our experiments reveal that languages with synthetic features exhibit greater subword regularity and productivity with BPE tokenization and achieve better results in language modeling tasks. We also observe that the typological continuum from linguistic theory is reflected in several experiments. These findings suggest a correlation between morphological typology and BPE tokenization efficiency., Comment: 15 pages, 6 figures

The rapid growth of end-user AI applications, such as computer vision and generative AI, has led to immense data and processing demands often exceeding user devices' capabilities. Edge AI addresses this by offloading computation to the network edge, crucial for future services in 6G networks. However, it faces challenges such as limited resources during simultaneous offloads and the unrealistic assumption of homogeneous system architecture. To address these, we propose a research roadmap focused on profiling AI models, capturing data about model types, hyperparameters, and underlying hardware to predict resource utilisation and task completion time. Initial experiments with over 3,000 runs show promise in optimising resource allocation and enhancing Edge AI performance.

We report the discovery of the repeating fast radio burst source FRB 20240209A using the CHIME/FRB telescope. We have detected 22 bursts from this repeater between February and July 2024, six of which were also recorded at the Outrigger station KKO. The 66-km long CHIME-KKO baseline can provide single-pulse FRB localizations along one dimension with $2^{\prime\prime}$ accuracy. The high declination of $\sim$86 degrees for this repeater allowed its detection with a rotating range of baseline vectors, enabling the combined localization region size to be constrained to $1^{\prime\prime}\times2^{\prime\prime}$. We present deep Gemini observations that, combined with the FRB localization, enabled a robust association of FRB 20240209A to the outskirts of a luminous galaxy (P(O|x) = 0.99; $L \approx 5.3 \times 10^{10}\,L_{\odot}$). FRB 20240209A has a projected physical offset of $40 \pm 5$ kpc from the center of its host galaxy, making it the FRB with the largest host galaxy offset to date. When normalized by the host galaxy size, the offset of FRB 20240209A is comparable to that of FRB 20200120E, the only FRB source known to originate in a globular cluster. We consider several explanations for the large offset, including a progenitor that was kicked from the host galaxy or in situ formation in a low-luminosity satellite galaxy of the putative host, but find the most plausible scenario to be a globular cluster origin. This, coupled with the quiescent, elliptical nature of the host as demonstrated in our companion paper, provide strong evidence for a delayed formation channel for the progenitor of the FRB source., Comment: Submitted to AAS Journals

The discovery and localization of FRB20240209A by the Canadian Hydrogen Intensity Mapping Fast Radio Burst (CHIME/FRB) experiment marks the first repeating FRB localized with the CHIME/FRB Outriggers and adds to the small sample of repeating FRBs with associated host galaxies. Here we present Keck and Gemini observations of the host that reveal a redshift $z=0.1384\pm0.0004$. We perform stellar population modeling to jointly fit the optical through mid-infrared data of the host and infer a median stellar mass log$(M_*/{\rm M_{\odot}})=11.34\pm0.01$ and a mass-weighted stellar population age $\sim11$Gyr, corresponding to the most massive and oldest FRB host discovered to date. Coupled with a star formation rate $<0.36\,{\rm M_{\odot}\ yr^{-1}}$, the specific star formation rate $<10^{-11.8}\rm\ yr^{-1}$ classifies the host as quiescent. Through surface brightness profile modeling, we determine an elliptical galaxy morphology, marking the host as the first confirmed elliptical FRB host. The discovery of a quiescent early-type host galaxy within a transient class predominantly characterized by late-type star-forming hosts is reminiscent of short-duration gamma-ray bursts, Type Ia supernovae, and ultraluminous X-ray sources. Based on these shared host demographics, coupled with a large offset as demonstrated in our companion paper, we conclude that preferred progenitors for FRB20240209A include magnetars formed through merging binary neutron stars/white dwarfs or the accretion-induced collapse of a white dwarf, or a luminous X-ray binary. Together with FRB20200120E localized to a globular cluster in M81, our findings provide strong evidence that some fraction of FRBs may arise from a process distinct from the core collapse of massive stars., Comment: 15 pages, 8 figures; Submitted to AAS Journals

Particle production in de Sitter spacetime arises from the exponential expansion of space, rendering the Bunch-Davies vacuum perceived as a particle-containing state by late-time observers. For states defined as eigenstates of both momentum and the Hamiltonian, the Bunch-Davies vacuum exhibits a constant particle density per physical momentum. We explore particle production beyond this baseline, focusing on deviations from exact de Sitter conditions and non-gravitational interactions, such as slow-roll inflation or interactions arising from the coupling of inflation to other fields. Using Bogoliubov transformations, we calculate the number density of energy/momentum eigenstates. In single-field inflation, this density captures the observed spectral index of the primordial power spectrum, while in two-field models, it reflects the non-gravitational coupling driving background trajectory turning. We present analytical results applicable to various scenarios involving particle production from non-adiabatic processes during inflation., Comment: 35 pp. + refs

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures. The second version includes an additional citation and adds four previously missing authors

Fast radio bursts (FRBs) are unique probes of extragalactic ionized baryonic structure as each signal, through its burst properties, holds information about the ionized matter it encounters along its sightline. FRB 20200723B is a burst with a scattering timescale of $\tau_\mathrm{400\,MHz} >$1 second at 400 MHz and a dispersion measure of DM $\sim$ 244 pc cm$^{-3}$. Observed across the entire CHIME/FRB frequency band, it is the single-component burst with the largest scattering timescale yet observed by CHIME/FRB. The combination of its high scattering timescale and relatively low dispersion measure present an uncommon opportunity to use FRB 20200723B to explore the properties of the cosmic web it traversed. With an $\sim$arcminute-scale localization region, we find the most likely host galaxy is NGC 4602 (with PATH probability $P(O|x)=0.985$), which resides $\sim$30 Mpc away within a sheet filamentary structure on the outskirts of the Virgo Cluster. We place an upper limit on the average free electron density of this filamentary structure of $\langle n_e \rangle < 4.6^{+9.6}_{-2.0} \times 10^{-5}$ cm$^{-3}$, broadly consistent with expectations from cosmological simulations. We investigate whether the source of scattering lies within the same galaxy as the FRB, or at a farther distance from an intervening structure along the line of sight. Comparing with Milky Way pulsar observations, we suggest the scattering may originate from within the host galaxy of FRB 20200723B., Comment: 20 pages, 6 figures, submitted. Comments welcome!

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures