Papers

We investigate the role of magnetic sublevels in Autler-Townes spectra of Rydberg atoms driven by radio-frequency (RF) fields with arbitrary polarization. While conventional treatments predict two symmetric sidebands from independent mJ transitions, experiments have reported additional unexplained spectral features. We show that these arise from elliptical RF polarization, which coherently couples multiple magnetic sublevels and requires a full multi-level treatment. We develop and diagonalize a Hamiltonian including all coupled mJ sublevels, predicting polarization-dependent degeneracies that produce two, three, or four resolved peaks. Using long-wavelength transitions and an anechoic environment we realize homogeneous RF fields that for the first time enable complete resolution of the mJ-dependent dressed states. We observe excellent agreement with theory as the RF ellipticity is varied. These results demonstrate that RF polarization fundamentally modifies Autler-Townes spectra and provide a consistent framework for interpreting magnetic-sublevel structure, with implications for Rydberg-based RF electrometry and polarimetry.

The prediction of aircraft icing is conventionally performed using multishot simulation frameworks that fail to predict the progressive roughening of the ice surface. To understand roughness formation, we investigate droplet impingement on clean and laser-scanned rough ice shapes using a high-fidelity computational framework based on wall-modeled large-eddy simulations and Lagrangian particle tracking. This methodology is validated against experimental data for a NACA 23012 airfoil and a NACA 64A008 swept tail, accurately predicting collection efficiency and supercooled large droplet splashing. The framework is subsequently applied to laser-scanned rime ice geometries to quantify the impact of surface roughness on local impingement distributions. The results reveal that physical roughness induces a highly nonuniform collection efficiency, with droplet impingement intensely concentrated on upstream-faces of roughness elements, creating sheltered shadow zones immediately downstream. While the spanwise-averaged collection efficiency remains remarkably similar to that of an equivalent smooth body, idealized smooth surfaces completely suppress these localized impingement peaks. Ice accretion simulations demonstrate that this localized impingement creates a self-reinforcing feedback loop, actively amplifying existing roughness features over time. These findings provide a direct physical explanation for the formation of characteristic rime ice structures and highlight the critical role of local surface topology in the accretion process.

The development of accurate exchange-correlation (XC) functionals remains a longstanding challenge in density functional theory (DFT). The vast majority of XC functionals have been hand designed by human researchers combining physical insight, exact constraints, and empirical fitting. Recent advances in large language models enable a systematic, automated alternative to this human-driven design loop. This report presents an agentic search system in which an LLM proposes structured functional-form changes guided by evolutionary history. The system attempts to improve functional performance through an iterative plan-execute-summarize loop, where improvements are measurable by optimizing functional parameters against a standard thermochemistry dataset, then evaluating performance on a held-out subset. The strongest discovered functional, SAFS26-a (Seed Agentic Functional Search 2026), improves upon the gold-standard {\omega}B97M-V baseline by ~9%. These results also surface a cautionary lesson for AI-assisted science: models powerful enough to discover genuine improvements are equally capable of exploiting unphysical shortcuts to game the benchmark; domain expertise translated into explicitly enforced constraints remains essential to keeping results scientifically grounded.

Silicon detectors typically require an insensitive area around their periphery to accommodate guard rings, which help maintain the electric field uniformity around edge pixels and isolate the high leakage current from the physical edges of the detector. Minimization of this insensitive region is desirable for applications in high-energy physics, X-ray experiments, and medical imaging. Existing active edge technology offers a solution for reduction or total elimination of the insensitive region, via a continuation of the highly doped backside up the sidewalls of the device. However, current methods for realizing this technology are complex and expensive. We propose a new technique that simplifies the fabrication of highly doped edges using side ion implantation and microwave annealing. Tests demonstrating the feasibility of this proposed process were performed on a set of sensors, and current versus bias voltage measurements probing the edge effects were performed before and after the edge implantation and annealing. To aid in interpretation of the results, TCAD simulations of the test devices were performed. Significant improvement in the edge leakage current is observed, indicating the promise of this simplified process for fabrication of active edge sensors.

Advanced radiotherapy approaches such as FLASH irradiation and spatially fractionated radiotherapy (SFRT) show potential to improve the therapeutic ratio, yet their biological mechanisms and optimal delivery parameters remain uncertain. Progress requires accessible proton research platforms with flexible temporal and spatial dose delivery. We report on the adaptation of the Beam Transfer Line (BTL) of the Bern Medical Cyclotron (BMC) for radiobiology research with FLASH and proton minibeam capabilities. The BMC is optimized for the production of radionuclides for medical imaging, and is able to extract currents up to 150 $ \mathrm{\mu A}$. The 18 MeV proton beam was passively shaped using collimators, scattering foils, and extended drift space to generate irradiation fields. A dosimetric framework was implemented using an in-beam ionization chamber and radiochromic film with LET-dependent corrections. Beam uniformity and SFRT profiles with various grid spacings were evaluated at realistic target distances. The developed beamline enables stable delivery under controlled conditions in both conventional and FLASH regimes, spanning dose rates from 0.01 to 100 Gy/s. Dose uniformity within a 20 mm radius was below 8%. Film measurements confirmed the need for LET-dependent corrections and indicated that quantitative dosimetry in in-vitro setups is achievable with appropriate LET corrections. The low proton energy (15.54(12) MeV extracted into air, 8.14(28) MeV delivered to cells in flask) facilitates compact SFRT implementation with well-resolved minibeams. The adapted BMC provides a flexible and accessible platform for systematic pre-clinical proton radiobiology studies under varied dose-rate and spatial delivery conditions. This supports optimization of emerging modalities such as proton FLASH and SFRT and helps bridge accelerator technology and radiobiology.

Kappa distributions are widely used in space plasma physics to model velocity distribution functions with heavy tails. Parameter estimation in these distributions is, however, complicated by the fact that the kappa distribution does not belong to the exponential family, so it admits no sufficient statistics and direct maximum likelihood requires numerical optimization without analytically closed-form update equations. Working within the Beck-Cohen superstatistics framework, where a gamma-distributed inverse temperature \(\beta\) generates the kappa distribution upon marginalization, we treat \(\beta\) as a latent variable. This hierarchical description restores the exponential family structure that the marginal kappa distribution lacks, and yields an analytically tractable implementation of the expectation-maximization (EM) algorithm whose E-step and M-step admit closed-form expressions in terms of sufficient statistics. Applied to synthetic data drawn from the model, the algorithm converges monotonically to a stationary point of the marginal kappa log-likelihood and recovers the generating parameters consistently across the explored range of \(\kappa\). EM thus offers a tractable and transparent route to inference in superstatistical systems with local temperature fluctuations.

Large-language-model (LLM) graders promise to relieve the grading burden of upper-division STEM courses, but most deployments to date send student work to third-party APIs, violating FERPA and exposing institutions to data risk while requiring substantial assignment modification. We present $\textbf{LaTA}\ (\textit{LaTeX Teaching Assistant})$, a drop-in, open-source autograder that runs entirely on commodity on-premises hardware and assumes a LaTeX-native workflow already adopted by many engineering and physics courses. LaTA implements a four-stage pipeline (ingest, segment, grade, report) using a locally hosted open-weight chain-of-thought LLM grader (gpt-oss:120b) that compares student work to an instructor-authored reference solution and applies a YAML rubric with binary per-item scoring. We deployed LaTA in Winter~2026 in ME 373 (Mechanical Engineering Methods) at Oregon State University, grading every weekly assignment for approximately 200 students on a single Mac Studio at \$0 marginal cost per assignment and 1--3 minutes of wall-clock time per submission, enabling regrading of corrected assignments and greatly expanded TA office hour offerings. The instructor-confirmed grading-error rate held at roughly $0.02$--$0.04%$ per rubric line item across the term. Relative to the same instructor's previous traditionally-graded cohort, the LaTA-graded cohort outperformed by approximately $11%$ on the midterm exam and $8%$ on the final exam, and reported large gains in self-assessed confidence on every stated learning objective ($N = 159$ survey responses, $\Delta \geq +1.49$ Likert points, $p < 10^{-27}$ on every comparison). We release the code under AGPLv3.

Moire superlattices formed by stacking atomically thin two-dimensional materials with a relative twist angle have emerged as a versatile platform for engineering quantum electronic, optical, and ferroic properties. Computational modelling of such systems with periodic boundary conditions requires the identification of commensurate supercells in which the moire periodicity is reproduced exactly, or within a prescribed tolerance. While several codes exist for bilayer systems, extension to three or more layers with independently chosen twist angles remains a significant challenge. Here we present MLM (Multi-Layer Moire), an open-source Python package that constructs periodic, PBC-compatible moire supercells for an arbitrary number of twisted layers with any Bravais lattice type. The package employs a solve-and-round algorithm that reduces the coincidence-site search to an $O(N^2)$ linear-algebra problem per twist angle, compared to the O(N^4) brute-force enumeration required by conventional approaches. We demonstrate the package on bilayer graphene, bilayer and trilayer MoS$_2$, bilayer SrTiO$_3$, and a PbTiO$_3$/SrTiO$_3$ oxide heterostructure, producing simulation-ready structure files for both VASP and LAMMPS. The fractional-coordinate atom-selection algorithm scales to supercells containing millions of atoms and is robust across all twist angles including very small angles below 1 degree.

We study electromagnetic perturbations and the associated quasinormal modes (QNMs) of parametrized static, spherically symmetric wormhole spacetimes, focusing on Damour-Solodukhin and braneworld geometries as well as their galactic extensions. Using the Bronnikov-Konoplya-Pappas (BKP) parametrization, we express the metric functions in terms of a compactified radial coordinate and characterize the spacetime through far-field and near-throat parameters. The far-field coefficients govern the asymptotic structure and post-Newtonian behaviour, while the near-throat continued-fraction expansion captures the strong-field geometry near the throat. We first apply the parametrization to isolated wormholes and identify its range of validity, showing that the non-analytic metric functions can limit the applicability of the BKP scheme. Moreover we have shown that such issues can indeed hamper the primary ringdown waveform. We then extend the framework to a galactic Damour-Solodukhin wormhole embedded in a Hernquist dark matter halo. Imposing observational bounds from the shadow of Sgr A$^*$, we constrain the galactic compactness and deformation parameters and obtain an observationally viable parametrized metric. Within the allowed parameter space, we compute the fundamental QNM frequencies using the transfer matrix method and analyze the corresponding time-domain ringdown signals. We find that the damping rate is more sensitive to galactic compactness, whereas the oscillation frequency remains comparatively stable. Although the spectral shifts are small within the shadow-allowed region, the framework provides a systematic link between geometric parametrization, shadow constraints, and dynamical response. Our results establish an observationally consistent parametrized description of wormhole perturbations for strong-field tests of horizonless compact objects.

The execution cost of quantum algorithms is typically quantified through asymptotic gate counts and qubit register sizes, yet these metrics do not directly capture which genuinely quantum resources, and in what amount, must be created and maintained for the computation to succeed. The systematic quantification of such information-theoretic requirements in quantum computing protocols remains an extremely challenging open problem, despite their direct role in establishing quantum advantage. To address this gap, we investigate the generation of non-stabilizerness (or magic), one of the key resources, in the paradigmatic Shor's factoring algorithm, revealing a deep connection between intrinsic quantum complexity and the computational hardness of the underlying number-theoretic problem. By developing an explicit analytic theory, we demonstrate the fundamental role of magic in the successful execution of the algorithm, and show that Shor's routine maximally exploits the quantum resource in practically relevant regimes. Our findings create a concise conceptual link between the classical algorithmic difficulty of a task and the non-stabilizer price to solve it on quantum hardware, complementing standard circuit-cost analyses with a resource-based metric that is naturally aligned with the real bottlenecks of fault-tolerant quantum computing.

Ultrafast birefringence oscillations observed in superfluid helium provide evidence for anisotropic quantum squeezing of quasiparticle pairs. The measured response is a superposition of contributions from all vibrational modes, with dominant contributions from rotons, maxons, and Pitaevskii's plateau. The nonzero initial phase follows naturally from multimode interference.

Recent results have established dramatic advantages in learning properties of quantum states when a quantum computer is available to process or jointly measure multiple copies of the unknown quantum state. Learning tasks can be accomplished with exponentially fewer copies of the state when compared to optimized classical learning strategies that are restricted to measuring one copy of the state at a time. While these results were established in abstract settings and for artificial learning tasks, they motivate the application of quantum computers to imaging and sensing of weak electromagnetic fields since these settings are ultimately concerned with the learning of unknown quantum states. In this work we apply these new results in quantum learning to the problem of learning Gaussian states of the electromagnetic field, which are germane since they describe most fields used in imaging and sensing. In order to connect with quantum learning theory, we consider the transduction of an $n$-mode Gaussian state into a register of qubits on a quantum computer followed by optimized measurements on these qubits to extract the parameters defining the original Gaussian state. We rigorously bound the number of copies of the Gaussian state required to achieve worst-case additive error in parameter estimates. The scaling of this bound with $n$ is exponentially better than naïve strategies for characterizing Gaussian states and matches recently derived bounds for characterization of Gaussian states using continuous-variable (CV) classical shadows. In addition, our bound has a polynomially better dependence on the energy of the multimode Gaussian state compared to the CV shadows protocol.

Conventional approaches to simulating quantum many-body dynamics produce a single trajectory: if the Hamiltonian or the initial state is changed, the computation must be re-performed. Recent efforts toward foundation models have begun to address this limitation, yet existing methods transfer across either Hamiltonians or initial states, but not both. In this work, we introduce the Universal Neural Propagator (UNP), a single, unified model that learns the functional mapping from driving protocols to time-evolution propagators. Trained in an entirely self-supervised way, a single UNP model predicts dynamics across a function space of driving protocols and an exponentially large Hilbert space of initial states simultaneously. We benchmark on a two-dimensional driven Ising model and demonstrate the UNP's accuracy and transferability across product and entangled initial states, as well as for both in- and out-of-distribution driving protocols. The UNP remains accurate at system sizes beyond exact diagonalization, and can be efficiently fine-tuned across all initial states using observable data. By shifting the object of learning from quantum states to operators, this work opens a route toward transferable simulation of driven quantum matter.

We investigate the possibility that idealized quantum state-reduction processes may produce a local violation of charge conservation. If this occurs, the corresponding electromagnetic fields cannot be consistently described within Maxwell electrodynamics, and a natural alternative is provided by Aharonov-Bohm electrodynamics, which reduces to Maxwell theory when local charge conservation holds, but remains compatible with non-conserved sources. Within this framework we first analyze how state reduction may generate non-conserved local currents, including statistically compensated cases and biased tunnelling configurations with persistent average current. We then study the interaction of gauge waves with fermionic and bosonic quantum systems, the latter being described by a modified Schrödinger equation previously proposed for boson matter. As an application, we discuss the interaction of gauge waves with superconductors and show that they can effectively shield such waves. Finally, we present experimental proposals based on inverse-biased diodes and estimate the expected detector response.

Electro-optic modulation is central to classical optical communications and emerging quantum technologies. High-confinement optomechanical crystal modulators enable microwave-optical transduction through strong optomechanical interactions and offer a promising interface between superconducting qubits and optical fibers. However, their performance is limited by thermal noise from optical absorption. Release-free optomechanical crystals provide improved thermal anchoring but have not yet been integrated into a microwave-optical transducer. Here, we demonstrate a release-free electro-optomechanical transducer combining strong optomechanical interactions in silicon with the efficient piezoelectricity of lithium niobate via micro-transfer printing. We observe electro- and optomechanical coupling rates compatible with quantum-level operation when co-integrated with a superconducting microwave circuit. This advance moves release-free electro-optomechanical devices toward practical microwave-optical interfaces.

Background: Existing MRI LLM benchmarks rely mainly on review-book multiple-choice questions, where top proprietary models already score highly, limiting discrimination. No systematic benchmark has evaluated vendor-specific scanner operational knowledge central to research MRI practice. Purpose: We developed MRI-Eval, a tiered benchmark for relative model comparison on MRI physics and GE scanner operations knowledge using primary multiple-choice questions (MCQ), with stem-only and primed diagnostic conditions as complementary analyses. Methods: MRI-Eval includes 1365 scored items across nine categories and three difficulty tiers from textbooks, GE scanner manuals, programming course materials, and expert-generated questions. Five model families were evaluated (GPT-5.4, Claude Opus 4.6, Claude Sonnet 4.6, Gemini 2.5 Pro, Llama 3.3 70B). MCQ was primary; stem-only removed options and used an independent LLM judge; primed stem-only tested responses to incorrect user claims. Results: Overall MCQ accuracy was 93.2% to 97.1%. GE scanner operations was the lowest category for every model (88.2% to 94.6%). In stem-only, frontier-model accuracy fell to 58.4% to 61.1%, and Llama 3.3 70B fell to 37.1%; GE scanner operations stem-only accuracy was 13.8% to 29.8%. Conclusion: High MCQ performance can mask weak free-text recall, especially for vendor-specific operational knowledge. MRI-Eval is most informative as a relative comparison benchmark rather than an absolute competency measure and supports caution in using raw LLM outputs for GE-specific protocol guidance.

The Parallel Minority Game (PMG) refers to a set of Minority Games (MG), played in parallel, where each agent only has two choices to pick from, but each choice can host agents of many kind i.e., their other alternative can be from any other choices. While the pay-off function remains the same as that in the MG – agents picking the less crowded of their two choices win positive pay-off – the optimization of resource allocation is significantly harder in the PMG. While a global optimization demands a uniform population in all choices, a local optimization attempts to balance the population in the two choices for a given agent. In the MG these two objectives coincides, but generally in the PMG these are competing. We study several non-dictated, stochastic strategies and compare their efficiencies in attaining the local and global optimization objectives. Counterintuitively, a strategy with partial information of populations perform the best in terms of population fluctuation and overall payoff maximization.

We present three-dimensional Dedalus simulations of Rayleigh-Bénard convection with a blackbody-radiating free upper surface, subject to a low-amplitude oscillatory forcing that mimics tidal perturbations in convective envelopes of stars and planets. The forcing period is 10-100 times shorter than the convective timescale, $t_{\rm conv}$. Using a Reynolds decomposition of the velocity field averaged over one oscillation period, in which the tidal oscillations naturally constitute the fluctuating field and convection the mean flow, we elucidate the kinetic energy exchange between the two. Provided the oscillatory Reynolds number exceeds a modest threshold, we find that the oscillations systematically transfer kinetic energy to the mean flow at a volume-averaged rate $D_R \sim u'^2 t_{\rm conv}^{-1}$, where $u'$ is the rms fluctuation velocity. This reflects strong, order-unity correlations between the fluctuation velocities and the mean flow. These arise because the oscillatory forcing displaces fluid elements that are then redirected by buoyancy and incompressibility in the same manner as the mean flow. The transfer is dominated by correlations involving vertical velocity fluctuations and vertical gradients of the mean flow. The resulting energy transfer rate is consistent, within the equilibrium-tide framework, with the observed tidal circularisation of solar-type binaries and with the orbital evolution of moons of Jupiter and Saturn. This validates the formalism proposed by Terquem (2021) for the dissipation of fast tides, a longstanding problem. Replacing the free surface with a rigid upper boundary significantly and artificially modifies the correlations.

In this work, we calculate the hyperfine-structure constants of the $^{45}$Sc$^{+}$ ion using a relativistic hybrid approach that combines configuration-interaction and coupled-cluster singles-and-doubles methods. Magnetic-dipole and electric-quadrupole hyperfine-structure constants are determined for the states arising from the $3d4s$, $3d^{2}$, $4s^{2}$, $4s4p$, $3d4p$, $3d5s$, $3d4d$, and $3d5p$ configurations. For most of these states, our magnetic-dipole hyperfine-structure constants agree well with available experimental data and represent a substantial improvement over previous theoretical results. By combining our calculated electric-field gradients with the measured electric-quadrupole hyperfine-structure constants for the $^{3}F_{2,3,4}$, $^{3}P_{1,2}$, and $^{1}G_{4}$ states within the $3d^{2}$ configuration, we derive a nuclear quadrupole moment $Q=-0.222(5)$ b, which is fully consistent with the value recently obtained from molecular data ( J. P. Dognon and P. Pyykkö, Phys. Chem. Chem. Phys. 27, 20453 (2025).).

Achieving resilient and sustainable cities requires scalable approaches to decarbonising residential buildings, which account for about 20% of UK greenhouse gas emissions and 25% of energy-related emissions in the European Union. Energy Performance Certificates (EPCs) support regulation and retrofit planning, but their reliance on on-site inspections limits timely city-scale assessment. This study introduces a gated multimodal model to predict Standard Assessment Procedure (SAP) energy efficiency and Environmental Impact (EI) scores by integrating EPC tabular variables, assessor-written free text, and Geographic Information System (GIS)-derived spatial features describing footprint geometry, height, area, and orientation. Sample-wise gating learns property-specific modality weights, while an auxiliary band classification head stabilises training. In a Westminster, London case study, the model predicts SAP and EI scores with MAEs of 4.03 and 4.76 points and R2 values of 0.757 and 0.748, respectively, achieving a mean MAE of 4.39. Ablation results show that full multimodal fusion outperforms unimodal and bimodal baselines for both score prediction and band-level classification. Interpretability analyses provide decision-relevant evidence: gating weights indicate strong reliance on assessor text; SHAP highlights main fuel, built form, and construction age band; text occlusion prioritises roof and wall fields; and spatial attribution is dominated by height and footprint area, with sensitivity to footprint shape. The validated framework is further applied to retrofit scenarios for wall insulation, roof insulation, and window glazing upgrades, indicating projected improvements in SAP, EI, annual energy cost, and equivalent CO2 emissions. Overall, the framework provides scalable property-level evidence for retrofit screening, intervention prioritisation, and net-zero housing transitions.

We consider the stabilization of Vlasov–Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an ideal controller would use and what experiments can actually observe: while optimal policy may rely on the full phase-space state, practical feedback is typically limited to sparse macroscopic diagnostics. We therefore study imitation learning methods that distill a fully observed expert policy into controllers operating only on macroscopic measurements. We show the stability guarantees of the learned policy, where the error floor depends on the minimal behavior cloning loss achievable under the observation constraints. We further characterize this minimal loss in terms of a notion of entropy that quantifies the complexity of the initial distribution. Our results demonstrates the theoretical feasibility of learning stabilizing feedback policies for kinetic plasma dynamics from macroscopic observations, and exhibits the adaptivity of the learning approach to low-complexity structures. Through extensive numerical experiments, we validate our theory and show that the learned policies can stabilize the system using only macroscopic observations, within a significantly longer time horizon than non-adaptive baseline controllers.

Self-injection-locked (SIL) narrow-linewidth lasers based on high-Q microresonators are promising sources for frequency-modulated continuous-wave (FMCW) LiDAR, but the SIL mechanism as well as its key characteristics such as the frequency sweep range and the noise performance are often determined by uncontrolled backscattering in the resonator. Here, we investigate a tunable SIL laser based on a corrugated photonic-crystal (PhC) microresonator in which the feedback strength is set by design. Numerical and experimental results show that stronger SIL feedback expands the sweep range accessible through resonator modulation while also impacting the phase-noise and linewidth during sweeping, revealing a trade-off between frequency tunability and noise performance. Using CMOS-compatible microheater tuning (sub-1 V driving voltage), we demonstrate linearized up- and down-chirps with 224 THz/s over approximately 3 GHz and, in a proof-of-concept ranging experiment, measure a 10 m fiber length with a standard deviation below 3 mm. These results establish PhC microresonators with engineered SIL feedback as robust, compact, CMOS-compatible LiDAR engines.

A planar chiral plasmonic nanoantenna exhibiting an excitation-chirality-dependent hot spot in a nanogap is numerically investigated. Additionally, the underlying design principles are examined, providing a broadly applicable framework for engineering chiral nanoantennas through controlled geometrical or modal asymmetry. The hot spot can be turned on and off by changing the handedness of the exciting circularly polarized light (CPL). This effect stems from the rationally designed interference of plasmonic modes excited by the linearly polarized orthogonal components of CPL. The hot spot exhibits maximal near-field dissymmetry factor (about -2) at a wavelength of 842 nm. The intensity at the hot spot can also be continuously modulated by varying the excitation ellipticity and handedness, approaching a modulation depth of 100%. These attributes enable chirality- and ellipticity-dependent switching and dynamic modulation of the plasmonic near field. Moreover, placing a quantum emitter in the gap generates almost perfectly circularly polarized emission, offering a simple yet effective avenue to realize nanoscale circularly polarized single-photon sources.

Using Hartree-Fock mean-field approach, we study the role of on-site ($U$) and extended ($V$) Hubbard interactions on the existence and evolution of edge modes in a half-filled Su-Schrieffer-Heeger (SSH) chain. We analyze the energy spectrum, local probability amplitudes, and site-resolved charge and spin density profiles across topological, critical, and trivial hopping regimes. We find that the localization of bound states is controlled by the ratio $2V/U$, with edge spin-density-wave modes for $U>2V$ and mid-chain charge-density-wave domain walls for $U<2V$, independent of band topology. These results establish the correlation-driven origin of localized states in finite one-dimensional chains.

Snow slab avalanches are among the most dangerous hazards in mountain regions. Recent numerical, field, and laboratory studies have renewed interest in shear-failure interpretations of avalanche release, particularly in relation to dynamic crack propagation and supershear fracture. Yet most analytical models either idealize the weak layer as perfectly brittle or neglect its pre-peak elasticity, although post-peak dissipation and compliance both control stress redistribution and critical length. Here, we derive an analytical solution for shear-failure propagation beneath an elastic snow slab with finite linear softening. Building on the weak-spot model of Gaume et al. (2013), failure is described by a fully softened residual core, a fracture process zone, and an intact elastic region. The solution recovers the classical brittle length as softening vanishes, distinguishes the residual crack length from the total affected length, and links weak-spot and fracture-energy descriptions through the softening law. Depth-averaged Material Point Method simulations confirm the analytical stress and displacement profiles and the predicted characteristic lengths. We then extend the same compliant-softening framework to collapse-driven anticrack propagation. A simplified Timoshenko anticrack analogue shows that slab bending and transverse shear deformation amplify normal stress at the weak-layer front and introduce a bending-controlled softening length with an approximately fourth-root dependence on softening displacement, supported by three-dimensional MPM simulations. Finally, a mixed-mode Timoshenko formulation couples weak-layer compression, slope-parallel shear, and slab rotation. A compact sharp-front model and a fully coupled finite-softening model reproduce the observed slope-angle dependence of critical cut length using realistic elastic and failure-envelope parameters.

State-of-the-art Earth system models (ESMs) cannot explicitly resolve many small-scale atmospheric processes such as atmospheric gravity waves, and thus must represent, or parameterise, their effects on the resolved state. Machine learning (ML) has the potential to improve these parameterisations. In our study, we train neural networks (NNs) on ERA5 reanalysis data to predict momentum fluxes of orographic gravity waves as a function of the state variables at the resolution of a coarse ESM. Employing a full year of data, we extract inertia-gravity waves using the software MODES, which applies linear theory for wave filtering, and train ML models on data coarse-grained to the ESM's target resolution. We consider four different cases: the full spectrum of inertia-gravity waves resolved in ERA5, or just the part of the spectrum that is subgrid-scale in the target ESM, both over all land or just over mountainous terrain. Our NNs successfully predict momentum fluxes, with a global coefficient of determination ($R^2$) ranging from 0.72 to 0.56, depending on the case, when evaluated offline with data from another year. An analysis of our models using SHAP values, an explainable AI technique, suggests that the networks learned physically meaningful relationships. In addition, we give a comparison with the physics-based parameterisation scheme by Lott and Miller. This work forms the basis for the development of operational ML-based parameterisations to improve the representation of gravity waves and their effects in climate models.

After ten years of operation at the LHC, the planar pixel sensors of the innermost barrel layer of the ATLAS Pixel detector have accumulated an average bulk damage fluence in excess of $2\times10^{15}$ 1 MeV-neutrons equivalent/cm$^2$. The macroscopic effects of this radiation are an increase of the sensor leakage current, a loss of charge collection efficiency and an increase of the depletion voltage. Using regular bias voltage scans performed at the beginning and end of each data taking campaign the evolution of the pixel cluster charge and bulk depletion is studied as a function of particle fluence. Results are interpreted with the modelling provided by standalone TCAD and ATLAS Monte Carlo simulation including radiation damage effects. The dependence of the collected charge and the depletion voltage with integrated luminosity are studied through the full period of operation.

Gliomas constitute one of the most aggressive and heterogeneous forms of brain tumors, posing major challenges for understanding their biology and developing effective treatments. Animal models enable the collection of rich longitudinal datasets describing tumor dynamics, which can be integrated within mathematical models to elucidate the biological mechanisms governing tumor growth. While most formulations rely on reaction-diffusion systems with limited insight on tissue deformation and fluid transport, we propose a magnetic resonance imaging (MRI)-informed, poroelastic model to describe C6 glioma growth in rats. We use data from animals (n=4) that were imaged five times after intracranial injection of cancer cells. Each MRI dataset includes (i) anatomical T1-weighted data for brain and tumor segmentation and to assign mechanical properties; (ii) diffusion-weighted MRI, which enables estimation of the fraction of each voxel that is tumor; and (iii) dynamic contrast-enhanced MRI, which informs permeability as well as vascular and liquid fraction maps. Using finite-element simulations, model calibration for each rat uses the Levenberg-Marquardt method informed by the first three MRI datasets. Tumor forecasts are validated by assessing model-data agreement on the remaining two MRI datasets. Our results show relative tumor volume errors between 0.94 percent and 11.27 percent at calibration, and prediction errors between 4.73 percent and 36.03 percent. Additionally, Dice scores ranged from 0.80 to 0.93 during calibration, and from 0.75 to 0.93 during validation. Thus, our results suggest that our poromechanical model can describe C6 glioma growth. This study provides a first step toward a patient-specific, multiscale model of the spatiotemporal poromechanics underlying glioma progression and therapeutic response.

Regionalization aims to partition a spatial domain into contiguous regions that share similar characteristics, enabling more effective spatial analysis, policy making, and resource management. Existing approaches for spatial regionalization typically rely on static spatial snapshots rather than evolving time series. Meanwhile, most time series clustering methods ignore spatial structure or enforce spatial continuity through ad hoc regularization, constraining the number of inferred regions a priori either explicitly or implicitly. Utilizing the minimum description length principle from information theory, here we propose an efficient and fully nonparametric framework for the regionalization of spatial time series. Our method jointly infers a spatial partition along with a set of representative time series archetypes ("drivers") that best compress a spatiotemporal dataset, with a runtime log-linear in the number of time series. We demonstrate that this method can accurately recover planted regional structure and drivers in synthetic time series, and can extract meaningful structural regularities in large-scale empirical air quality and vegetation index records. Our method provides a principled and scalable framework for spatially contiguous partitioning, allowing interpretable temporal patterns and homogeneous regions to emerge directly from the data itself.

We study how the topological properties of a one-dimensional staggered lattice, loaded into states with orbital angular momentum $l=1$, can be controlled simply by tuning the relative angle between sites. The original system under consideration can be depicted as a Creutz ladder model when unwrapping the different state circulations in a synthetic dimension. Depending on the hopping strengths of the chain, different topological regimes may be accessed by changing the ladder angle, as determined by the value of the winding number of the chain. We analytically and numerically explore the different available regimes, and determine the number of topologically protected edge states that exist in each case. We also study the emergence of band inversion across topological transitions and show that it agrees with the winding number calculations, thus serving as an additional topological marker. Then, we propose a realistic experimental implementation in a photonic waveguide system, where the topological transition manifests as a sudden change of the behavior of the propagation of light in the system.