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We present the implementation and results of a new model for the n-particle n-hole ($\it{np-nh}$) contribution in the NuWro event generator, grounded in the theoretical framework established by the Valencia group in 2020. For the $\it{2p2h}$ component, we introduce a novel nucleon sampling function with tunable parameters to approximate correlations in the momenta of outgoing nucleons. These parameters are calibrated by comparing our results to those of the Valencia model across a range of incoming neutrino energies. In addition, our model incorporates a distinct contribution from the $\it{3p3h}$ mechanism. We discuss the differences between the new NuWro implementation, the original Valencia model, and the previous NuWro version, focusing on the distribution of outgoing nucleon momenta. Finally, we assess the impact of the hadronic model on experimental analyses involving hadronic observables., Comment: 17 pages, 11 figures

Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the physics of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets ranging from lithium to iron. The method works even when the DNN is fine-tuned on a small dataset., Comment: 4 pages, 2 figures

Categorical covariates such as race, sex, or group are ubiquitous in regression analysis. While main-only (or ANCOVA) linear models are predominant, cat-modified linear models that include categorical-continuous or categorical-categorical interactions are increasingly important and allow heterogeneous, group-specific effects. However, with standard approaches, the addition of cat-modifiers fundamentally alters the estimates and interpretations of the main effects, often inflates their standard errors, and introduces significant concerns about group (e.g., racial) biases. We advocate an alternative parametrization and estimation scheme using abundance-based constraints (ABCs). ABCs induce a model parametrization that is both interpretable and equitable. Crucially, we show that with ABCs, the addition of cat-modifiers 1) leaves main effect estimates unchanged and 2) enhances their statistical power, under reasonable conditions. Thus, analysts can, and arguably should include cat-modifiers in linear regression models to discover potential heterogeneous effects--without compromising estimation, inference, and interpretability for the main effects. Using simulated data, we verify these invariance properties for estimation and inference and showcase the capabilities of ABCs to increase statistical power. We apply these tools to study demographic heterogeneities among the effects of social and environmental factors on STEM educational outcomes for children in North Carolina. An R package lmabc is available.

The tearing mode instability is a key process for magnetic energy conversion in magnetohydrodynamics, once anti-parallel components are allowed to reconnect, leading to the formation of magnetic islands. It has been employed to explain phenomena at different scales in nature, from galactic nuclei, to solar flares and laboratory fusion devices. In this study, we investigate the dynamics of a current sheet in the presence of a transverse magnetic field component, in the framework of viscoresistive, incompressible magnetohydrodynamics (MHD), both analytically and by means of direct numerical simulations. Firstly, we obtain analytical solution for the time-varying one-dimensional profile of an initial Harris current sheet in the presence of a transverse field. We find that the introduction of a transverse magnetic field disrupts the system's equilibrium, leading to the natural development of a neutral layer with shear flows within the current sheet, one along the antiparallel magnetic component and another along the guide field direction. Secondly, through numerical analysis, we examine the dispersion relation of the incompressible MHD equations in the context of a modified equilibrium profile due to the transverse field. Our findings indicate a rapid suppression of unstable modes of tearing instability with the width of the neutral layer, confirming the analytical predictions. These results offer new insightful understanding on the interplay between transverse magnetic fields, shear flows, and tearing mode instabilities in current sheet environments., Comment: 17 pages, 6 figures

We present an approach for modeling and imputation of nonignorable missing data. Our approach uses Bayesian data integration to combine (1) a Gaussian copula model for all study variables and missingness indicators, which allows arbitrary marginal distributions, nonignorable missingess, and other dependencies, and (2) auxiliary information in the form of marginal quantiles for some study variables. We prove that, remarkably, one only needs a small set of accurately-specified quantiles to estimate the copula correlation consistently. The remaining marginal distribution functions are inferred nonparametrically and jointly with the copula parameters using an efficient MCMC algorithm. We also characterize the (additive) nonignorable missingness mechanism implied by the copula model. Simulations confirm the effectiveness of this approach for multivariate imputation with nonignorable missing data. We apply the model to analyze associations between lead exposure and end-of-grade test scores for 170,000 North Carolina students. Lead exposure has nonignorable missingness: children with higher exposure are more likely to be measured. We elicit marginal quantiles for lead exposure using statistics provided by the Centers for Disease Control and Prevention. Multiple imputation inferences under our model support stronger, more adverse associations between lead exposure and educational outcomes relative to complete case and missing-at-random analyses., Comment: 35 pages (main text only), 8 Figures

We investigate the impact of turbulence on magnetic reconnection through high-resolution 3D MHD simulations, covering Lundquist numbers from $S=10^3$ to $10^6$. Our simulations introduce small-scale perturbations injected into the system until $t=0.1\, t_A$. Even after the initial perturbation ceases, turbulence grows and persists, leading to sustained high reconnection rates of $V_\text{rec}/V_A \sim 0.03-0.08$, which surpass those generated solely by resistive tearing modes (plasmoids) observed in 2D and 3D PIC/MHD simulations by factors of 5 to 6. Our findings reveal that the achieved reconnection rates align with observations in solar phenomena and those reported in 3D MHD global simulations of accretion flows and relativistic jets. Notably, our simulations exhibit a steady-state reconnection rate coinciding with the full development of turbulence, showcasing the robustness and persistence of the reconnection in a turbulent environment. We establish the independence of the reconnection rate from the Lundquist number, consistent with the theory of turbulent reconnection proposed by Lazarian and Vishniac. Our results demonstrate a mild dependence of $V_\text{rec}$ on the plasma-$\beta$ parameter, the ratio between the thermal and magnetic pressure, illustrating a decrease from $0.036$ to $0.028$ as $\beta$ increases from $2$ to $64$ for simulations with $S=10^5$. Finally, we explore the influence of the magnetic Prandtl number ($\text{Pr}_m=\nu/\eta$) on the reconnection rate and find this influence to be negligible during the turbulent regime across the range tested, from $\text{Pr}_m=1$ to $60$. These findings carry significant implications for research into reconnection and particle acceleration within realistic magnetized space and astrophysical environments., Comment: 22 pages, 21 figures

Functional data analysis, which models data as realizations of random functions over a continuum, has emerged as a useful tool for time series data. Often, the goal is to infer the dynamic connections (or time-varying conditional dependencies) among multiple functions or time series. For this task, we propose a dynamic and Bayesian functional graphical model. Our modeling approach prioritizes the careful definition of an appropriate graph to identify both time-invariant and time-varying connectivity patterns. We introduce a novel block-structured sparsity prior paired with a finite basis expansion, which together yield effective shrinkage and graph selection with efficient computations via a Gibbs sampling algorithm. Crucially, the model includes (one or more) graph changepoints, which are learned jointly with all model parameters and incorporate graph dynamics. Simulation studies demonstrate excellent graph selection capabilities, with significant improvements over competing methods. We apply the proposed approach to study of dynamic connectivity patterns of sea surface temperatures in the Pacific Ocean and discovers meaningful edges., Comment: Revised for Computational Statistics and Data Analysis

Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Connectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g., branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks., Comment: CVPR 2024 (Highlight)

Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version

While progress has been made in the domain of video-language understanding, current state-of-the-art algorithms are still limited in their ability to understand videos at high levels of abstraction, such as news-oriented videos. Alternatively, humans easily amalgamate information from video and language to infer information beyond what is visually observable in the pixels. An example of this is watching a news story, where the context of the event can play as big of a role in understanding the story as the event itself. Towards a solution for designing this ability in algorithms, we present a large-scale analysis on an in-house dataset collected by the Reuters News Agency, called Reuters Video-Language News (ReutersViLNews) dataset which focuses on high-level video-language understanding with an emphasis on long-form news. The ReutersViLNews Dataset consists of long-form news videos collected and labeled by news industry professionals over several years and contains prominent news reporting from around the world. Each video involves a single story and contains action shots of the actual event, interviews with people associated with the event, footage from nearby areas, and more. ReutersViLNews dataset contains videos from seven subject categories: disaster, finance, entertainment, health, politics, sports, and miscellaneous with annotations from high-level to low-level, title caption, visual video description, high-level story description, keywords, and location. We first present an analysis of the dataset statistics of ReutersViLNews compared to previous datasets. Then we benchmark state-of-the-art approaches for four different video-language tasks. The results suggest that news-oriented videos are a substantial challenge for current video-language understanding algorithms and we conclude by providing future directions in designing approaches to solve the ReutersViLNews dataset.

This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively, video models deal with the added temporal dimension, increasing complexity and posing challenges in identifying dynamic concepts over time. In this work, we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end, we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts, and ranking their importance to the output of a model. The resulting concepts are highly interpretable, revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations, we discover that some of these mechanism are universal in video transformers. Finally, we show that VTCD can be used for fine-grained action recognition and video object segmentation., Comment: CVPR 2024 (Highlight)

Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the bootstrap approach and the Monte Carlo dropout approach. In our analysis, the $\chi^2$ defines the loss function, including point-to-point and normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of $7$%. To test these models, we compare their predictions to test datasets excluded from the training process and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and theoretical calculations. We also perform a comparison to a dataset lying beyond the covered kinematic region, and find that the bootstrap approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm., Comment: 12 pages, 9 figures; fits and full list of plots are available from repository: https://github.com/bekowal/CarbonElectronNeuralNetwork

The Short-Baseline Neutrino program in Fermilab aims to resolve the nature of the low-energy excess events observed in LSND and MiniBooNE, and analyze with unprecedented precision neutrino interactions with argon. These studies require reliable estimate of neutrino cross sections, in particular for charged current quasielastic scattering (CCQE). Here, we report updates of the NuWro Monte Carlo generator that, most notably, bring the state-of-the-art spectral functions to model the ground state properties of the argon nucleus, and improve the accuracy of the cross sections at low energies by accounting for the effects of the nuclear Coulomb potential. We discuss these developments in the context of electron and neutrino interactions, by comparing updated NuWro predictions to experimental data from Jefferson Laboratory Hall A and MicroBooNE. The MicroBooNE CCQE data are described with the $\chi^2$ per degree of freedom of 0.7, compared with 1.0 in the local Fermi gas model. The largest improvement is observed for the angular distributions of the produced protons, where the $\chi^2$ reduces nearly by half. Being obtained using the axial form factor parametrization from MINERvA, our results indicate a~consistency between the CCQE measurements in MINERvA and MicroBooNE., Comment: 9 pages, 4 figures, matches the published version

Quantile regression is a powerful tool for inferring how covariates affect specific percentiles of the response distribution. Existing methods either estimate conditional quantiles separately for each quantile of interest or estimate the entire conditional distribution using semi- or non-parametric models. The former often produce inadequate models for real data and do not share information across quantiles, while the latter are characterized by complex and constrained models that can be difficult to interpret and computationally inefficient. Neither approach is well-suited for quantile-specific subset selection. Instead, we pose the fundamental problems of linear quantile estimation, uncertainty quantification, and subset selection from a Bayesian decision analysis perspective. For any Bayesian regression model -- including, but not limited to existing Bayesian quantile regression models -- we derive optimal point estimates, interpretable uncertainty quantification, and scalable subset selection techniques for all model-based conditional quantiles. Our approach introduces a quantile-focused squared error loss that enables efficient, closed-form computing and maintains a close relationship with Wasserstein-based density estimation. In an extensive simulation study, our methods demonstrate substantial gains in quantile estimation accuracy, inference, and variable selection over frequentist and Bayesian competitors. We use these tools to identify and quantify the heterogeneous impacts of multiple social stressors and environmental exposures on educational outcomes across the full spectrum of low-, medium-, and high-achieving students in North Carolina.

The development of X-ray scintillators with ultrahigh light yields and ultrafast response times is a long sought-after goal. In this work, we theoretically predict and experimentally demonstrate a fundamental mechanism that pushes the frontiers of ultrafast X-ray scintillator performance: the use of nanoscale-confined surface plasmon polariton modes to tailor the scintillator response time via the Purcell effect. By incorporating nanoplasmonic materials in scintillator devices, this work predicts over 10-fold enhancement in decay rate and 38% reduction in time resolution even with only a simple planar design. We experimentally demonstrate the nanoplasmonic Purcell effect using perovskite scintillators, enhancing the light yield by over 120% to 88 $\pm$ 11 ph/keV, and the decay rate by over 60% to 2.0 $\pm$ 0.2 ns for the average decay time, and 0.7 $\pm$ 0.1 ns for the ultrafast decay component, in good agreement with the predictions of our theoretical framework. We perform proof-of-concept X-ray imaging experiments using nanoplasmonic scintillators, demonstrating 182% enhancement in the modulation transfer function at 4 line pairs per millimeter spatial frequency. This work highlights the enormous potential of nanoplasmonics in optimizing ultrafast scintillator devices for applications including time-of-flight X-ray imaging and photon-counting computed tomography., Comment: 34 pages, 3 figures

Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with $\tau_{\chi}>10^{27}$s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario., Comment: 93 pages (including author list, appendix and references), 143 figures. Submitted to JCAP

We employ a statistical approach to investigate the influence of axial asymmetry on the nuclear level density and entropy along the fission pathways of a superheavy nucleus, explicitly focusing on the $^{296}$Lv isotope. These pathways are determined within multidimensional deformation spaces. Our analysis reveals a significant impact of triaxiality on entropy. Additionally, suppressing shell effects can alter the fission scenario depending on the available excitation energy. We derive the deformation-dependent level density parameter, which plays a crucial role in estimating the survival probability of a superheavy nucleus. Furthermore, we utilize a set of master equations to obtain the time-dependent fission probabilities and calculate the ratio of decay probabilities for both axial and triaxial paths., Comment: submitted to PRC

Time series on the unit n-sphere arise in directional statistics, compositional data analysis, and many scientific fields. There are few models for such data, and the ones that exist suffer from several limitations: they are often computationally challenging to fit, many of them apply only to the circular case of n=2, and they are usually based on families of distributions that are not flexible enough to capture the complexities observed in real data. Furthermore, there is little work on Bayesian methods for spherical time series. To address these shortcomings, we propose a state space model based on the projected normal distribution that can be applied to spherical time series of arbitrary dimension. We describe how to perform fully Bayesian offline inference for this model using a simple and efficient Gibbs sampling algorithm, and we develop a Rao-Blackwellized particle filter to perform online inference for streaming data. In analyses of wind direction and energy market time series, we show that the proposed model outperforms competitors in terms of point, set, and density forecasting., Comment: 28 pages, 8 figures

The theoretical prediction that magnetic reconnection spontaneously drives turbulence has been supported by magnetohydrodynamic (MHD) and kinetic simulations. While reconnection with externally driven turbulence is accepted as an effective mechanism for particle acceleration, the acceleration during the reconnection with self-driven turbulence is studied for the first time in this work. By using high-resolution 3D MHD simulations of reconnection with self-generated turbulence, we inject test particles into the reconnection layer to study their acceleration process. We find that the energy gain of the particles takes place when they bounce back and forth between converging turbulent magnetic fields. The particles can be efficiently accelerated in self-driven turbulent reconnection with the energy increase by about 3 orders of magnitude in the range of the box size. The acceleration proceeds when the particle gyroradii become larger than the thickness of the reconnection layer. We find that the acceleration in the direction perpendicular to the local magnetic field dominates over that in the parallel direction. The energy spectrum of accelerated particles is time-dependent with a slope that evolves toward -2.5. Our findings can have important implications for particle acceleration in high-energy astrophysical environments., Comment: 13 pages, 5 Figures. Accepted for publication in Journal of High Energy Astrophysics

We performed a search for three-quasiparticle high-$K$ isomer candidates in odd-even Md - Rg nuclei by considering the lowest lying 1$\pi$2$\nu$ and 3$\pi$ excitations. Our approach involves calculating the energies of different nuclear configurations using a microscopic-macroscopic model with the Woods-Saxon potential. We explore three pairing scenarios: blocking, quasi-particle BCS method, and particle number projection formalism. The optimal deformations for both ground-states and high-$K$ configurations are determined through a four-dimensional energy minimization process. By analyzing the obtained excitation energies, we discuss the most promising candidates for high-$K$ isomers and compare them, where possible, with existing experimental data. We also discuss a possible isomer $\alpha$-decay hindrance using calculated $Q_{\alpha}$-hindrances., Comment: Submitted to PRC

The probabilities of $xn$-, $pxn$-, and $\alpha xn$-evaporation channels in excited superheavy nuclei were evaluated using the Monte Carlo method. The calculations utilized microscopically determined nuclear level densities and were compared with results obtained from the phenomenological Jackson formula. Effective temperatures derived from the microscopic approach were incorporated into the Jackson formula for different evaporation channels at low and moderate excitation energies. Additionally, an analytical formula was introduced to estimate the average kinetic energy of emitted particles in multi-step processes., Comment: 10 pages, 3 figures

The elastic and inelastic neutral current $\nu$ ($\overline{\nu}$) scattering off the polarized nucleon is discussed. The inelastic scattering concerns the single-pion production process. We show that the spin asymmetries' measurement can help to distinguish between neutrino and antineutrino neutral current scattering processes. The spin asymmetries also encode information about a type of target. Eventually, detailed studies of the inelastic spin asymmetries can improve understanding of the resonant-nonresonant pion production mechanism., Comment: 10 pages, 5 figures, one table

Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bayesian inference with these models only provide either scalable computing or accurate approximations to the posterior distribution, but not both. We introduce a new MCMC sampling strategy for highly efficient and fully Bayesian regression with longitudinal functional data. Using a novel blocking structure paired with an orthogonalized basis reparametrization, our algorithm jointly samples the fixed effects regression functions together with all subject- and replicate-specific random effects functions. Crucially, the joint sampler optimizes sampling efficiency for these key parameters while preserving computational scalability. Perhaps surprisingly, our new MCMC sampling algorithm even surpasses state-of-the-art algorithms for frequentist estimation and variational Bayes approximations for functional mixed models -- while also providing accurate posterior uncertainty quantification -- and is orders of magnitude faster than existing Gibbs samplers. Simulation studies show improved point estimation and interval coverage in nearly all simulation settings over competing approaches. We apply our method to a large physical activity dataset to study how various demographic and health factors associate with intraday activity.

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This paper introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN.