Papers

Scattering singularities are features conventionally associated with resonant scattering bodies, which correspond to the zeros and poles of a scattering matrix, and manifest as coherent perfect absorption and lasing respectively. Here, we show that they can emerge solely from an interface, eliminating the need for any resonant body. We introduce boundary singular scattering, whereby an interface between two non-Hermitian media supports coherent perfect absorption, lasing, or both. Despite these singular responses, the two adjoining media are in parity-time (PT)-unbroken phases, with entirely real bulk spectra. This phenomenon originates from a singular boundary mode-matching condition unique to non-Hermitian systems, not from any underlying resonant modes. We demonstrate it experimentally in a programmable synthetic photonic lattice, and show coherent switching between perfect absorption and lasing by controlling only the relative phase of the incident beams. The same mechanism further generalizes from mirror-symmetric interfaces to asymmetric interfaces with dissimilar bulk dispersions. Our work establishes boundaries as a previously unexplored origin of scattering singularities, opening a resonator-free route to coherent perfect absorption, lasing, and other singular wave phenomena.

Molecular imaging has been transformed by integrating positron emission tomography (PET) or single-photon emission computed tomography (SPECT) with x-ray computed tomography (CT). However, x-ray CT in hybrid imaging is used primarily for anatomical localization and for attenuation and scatter correction of emission data. Its ability to characterize tissue composition remains underutilized. Spectral CT, including dual-energy CT (DECT) and photon-counting CT (PCCT), can provide material-specific information, such as images of iodine concentration, bone or calcium fractions, electron-density, effective atomic number and other spatially-varying material properties. These capabilities create an opportunity to bring tissue-composition information into molecular imaging. In addition, spectral CT can also reduce CT artifacts and provide more accurate corrections for emission data. In this review, we discuss the technical basis, integration pathways, and translational opportunities for combining spectral CT with molecular imaging. We review sequential and integrated PET/DECT approaches, emerging PET/PCCT concepts, PET-enabled spectral CT, and extensions to SPECT/CT. We then examine how spectral CT information may improve attenuation correction, scatter correction, tissue-fraction correction, positron range correction, reconstruction priors, and dosimetry. Clinical and translational applications include contrast-enhanced molecular imaging, vascular and perfusion imaging, quantitative bone marrow imaging, musculoskeletal imaging, and theranostics. Overall, spectral CT integration may move hybrid molecular imaging toward tissue-composition-informed molecular imaging, in which radiotracer signals are interpreted within their material context.

A novel efficient method for controlling photonic hook (PH) with an anisotropic dielectric microparticle dimer in the snowman-particle configuration is introduced and numerically justified. In contrast to existing schemes that rely on structured illumination, partial shielding, or complex morphologies, the proposed anisometric geometry utilizes a simple and easily manufacturable form composed of two partially overlapping cylinders. The finite element method was employed to study how the degree of asymmetry, spatial rotation, and optical properties of the upper particle (the head of the snowman-particle) affect the bending angle and maximum intensity of PH. For the first time, it has been demonstrated that adjusting these parameters allows for the deliberate switching between single and double PH modes, as well as controlling the direction and curvature of the hook, including the fragmentation of the focal area. This approach offers high efficiency in utilizing incident radiation, simplicity in fabrication, and broad tunability of PH characteristics. The findings pave the way for the development of compact near-field optical devices, such as optical traps, high-resolution microscopes, sensors, and tools for surface microstructuring.

Diffractive neural networks (DNNs), composed of cascaded diffractive layers, offer a promising platform for high-speed optical computing. However, accurate and efficient modeling of high-density, polarization-sensitive DNNs remains challenging. Conventional model based on thin element approximation (TEA) fails to capture electromagnetic coupling within layers, while full-wave simulations such as finite-difference time-domain (FDTD) methods are computationally expensive. Here, we propose DNNsolver, a deep learning-based electromagnetic surrogate model that learns the scattering response of diffractive layers. By predicting the scattering matrix rather than source-dependent output fields, DNNsolver decouples the incident wavefront from the model and could be applied to arbitrary input fields. A local scattering kernel and sliding-window strategy further allow efficient modeling of DNNs with arbitrary spatial sizes. For single diffractive layer under random speckle illumination, DNNsolver achieves an average mean squared error (MSE) of 0.0095 and about 10^6-fold speedup compared with FDTD simulations. DNNsolver is further applied to the optimization of a trilayer polarization-multiplexed image classifier, which exhibits better agreement with FDTD simulations than its TEA-based counterpart. Our work provides an accurate and efficient framework for the design and optimization of high-density, polarization-sensitive DNNs.

We conduct direct numerical simulations to investigate the effects of dilute polymers on flow structure, statistical properties and energy transfer in 2D turbulence. As the Weissenberg number (Wi) increases, the flow undergoes a sharp but continuous crossover between a weak-elastic regime and an elastic-activated regime. In the weak-elastic regime, the flow remains close to its Newtonian counterpart and polymer stresses have weak dynamical effects. In the elastic-activated regime, elastic stresses attenuate vortical motions and promote shear-dominated filaments. Scale-by-scale energy analysis shows that polymer addition weakens both the inverse kinetic-energy cascade and the forward enstrophy cascade in the two regimes, whereas the kinetic–elastic exchange and the inter-scale redistribution of elastic energy differ between them. Kinetic-to-elastic conversion dominates over the analysed range of scales in the weak-elastic regime, whereas a partial transfer from elastic to kinetic energy emerges at small scales in the elastic-activated regime. The inter-scale transfer of elastic energy is dominated by inverse transfer over the large-scale range in the weak-elastic regime but by forward transfer over the small-scale range in the elastic-activated regime. These results link the crossover to the onset of strong elastic feedback from the polymers and the associated reorganisation of the scale-by-scale transfer pathways. Downstream-decay statistics also show the same qualitative two-regime behaviour.

A central challenge in molecular modeling is reconciling simulations with experimental observables, as force-field inaccuracies can distort equilibrium populations and long-timescale kinetics. While time-dependent restraints can improve simulated kinetics, time-dependent structural observables remain limited. Thus, most experimental data are time-averaged, informing equilibrium ensembles but not directly dynamics. Although ensemble refinement can improve thermodynamic accuracy, incorporating time-averaged observables into kinetic models remains difficult. Here, we introduce BICePs-reweighted Reversible DeepMSMs, combining Bayesian Inference of Conformational Populations (BICePs), variational learning of Markov processes, and maximum entropy (MaxEnt)/maximum caliber (MaxCal) principles to infer consistent thermodynamics and minimally perturbed kinetics. At its core is a reversible DeepMSM prior, obtained by applying an orthogonal transformation post-hoc to a pre-trained dynamics model (e.g., VAMPnet). This preserves the eigenspectrum exactly while yielding a valid transition matrix that is nonnegative, row-stochastic, and reversible. The reweighted model refines stationary populations, transition dynamics, and state-conditioned configurational landing densities. We validate the approach on a quadruple-well toy system and alanine dipeptide using backbone dihedral angles and J-coupling constants. In both systems, perturbing the observables induces predictable changes in the equilibrium ensemble and corresponding inferred kinetics. The resulting relaxation timescales and dynamical modes closely agree with analytical and MaxCal reference models. Furthermore, a diffusion-based generative model trained on MaxEnt landing densities produces physically realistic alanine dipeptide trajectories that reproduce thermodynamics and kinetics while satisfying the imposed experimental restraints.

The Plateau–Rayleigh instability of liquid jets governs a wide range of natural and industrial processes. While the classical linear stability theory accurately predicts disturbance growth rates and dominant wavelengths, it cannot quantify the amplitude and frequency spectrum of nanoscale initial surface disturbances. This critical missing information prevents accurate forecasting of breakup lengths and droplet generation. Conventional optical imaging is constrained by the diffraction limit and lacks the capability to resolve such nanoscale surface fluctuations. Here we introduce a Mie-scattering-based measurement technique to characterize nanoscale surface disturbances on liquid jets. The method employs a thin laser sheet to illuminate the jet, with scattered light captured at designated azimuthal observation angles. Based on Lorenz–Mie theory, we develop an inversion algorithm incorporating cosine similarity and cross-correlation to reconstruct jet surface disturbances from measured scattering fringes, achieving a sub-nanometer resolution for detecting diameter variations, with an overall measurement precision limited by a background noise floor of 0.18 nm RMS. Comparative analyses of forward and rainbow scattering signals further confirm that the surface disturbances become axially symmetric as the amplitude reaches a few nanometers, and thus satisfies the assumptions of the scattering model. Synchronous validations against high–speed imaging and scanning electron microscopy corroborate the quantitative accuracy of our scattering method. Applied in the near-nozzle region, the technique identifies two intrinsic signatures of initial perturbations: stochastic pulse-like fluctuations and low-frequency oscillations.

Background:Flatbed scanners introduce a lateral response artifact (LRA) perpendicular to lamp motion. Because LRA varies with position, pixel value (PV), color channel, and film model, it may violate the channel-common perturbation assumption of multichannel dosimetry. Purpose:To characterize PV-domain LRA in EBT4 and EBT-XD films, compare Lewis and Full corrections, and evaluate their effects on single-, double-, and triple-channel dosimetry. Methods:Seven films were scanned at seven lateral positions using a cyclic-shift design. For each position and RGB channel, center-equivalent response was modeled as PVc = A + B * PV. Lewis correction used two films with linear interpolation of A and B; Full correction used all center-local pairs with PCHIP interpolation of PVc - PV. Performance was assessed by portrait-landscape profile consistency and dose accuracy. Results: LRA was affine in PV, asymmetric about scanner center, and dependent on film model and channel. Both corrections reduced profile differences. Mean absolute profile differences across MU levels ranged from 1.4%-5.3% for EBT4 and 2.4%-6.8% for EBT-XD. The methods performed similarly for EBT4, while Full correction reduced discrepancies for several EBT-XD double- and triple-channel methods. Residual blue-channel errors limited blue, red-blue, and triple-channel performance. At 500 MU and above, red showed the best dose agreement; at 100 MU, both corrections increased deviations for several single-channel estimates. Full correction produced smaller deviations than Lewis in several EBT-XD conditions. Conclusions:LRA should be corrected in the raw-PV domain before dose reconstruction. Full correction better preserved profile consistency and dose accuracy, particularly for EBT-XD. Because improved profile consistency did not always improve central-dose agreement, both should be evaluated separately in commissioning.

Kerr photonic integrated circuit optical parametric oscillators (PIC OPOs) provide a promising platform for coherent visible-light generation, offering broad wavelength accessibility and low frequency noise for applications in quantum information processing, precision metrology, spectroscopy, and sensing. However, achieving large fiber-coupled output powers and high conversion efficiency remains challenging, impacting the practical deployment of these sources. Here we demonstrate optical injection locking of a commercially available visible Fabry-Pérot (FP) laser diode to a PIC OPO, realizing a hybrid laser that combines the complementary strengths of nonlinear frequency conversion with those of semiconductor laser technologies. Using 25 $\mu$W of injected PIC OPO signal, we generate coherent visible-light at 635 nm with a fiber-coupled output power of $\approx$ 40 mW and side-mode suppression ratio $\gtrsim$ 36.3 dB. The injection-locked FP laser faithfully inherits the low frequency noise characteristics of the PIC OPO signal, achieving a frequency noise floor of 714 Hz$^2$/Hz. We further demonstrate $\gtrsim1$ GHz wide locking bandwidths that enable stable injection locking without active PIC OPO stabilization, together with coarse wavelength tunability over 6 nm while preserving both high output power and spectral purity. Our results establish optical injection locking with nonlinear light sources as a compelling option for coherent, high-power, and wavelength-agile integrated coherent visible sources.

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

Reliable out-of-distribution generalization remains a central challenge in machine learning, particularly when models are deployed in data-scarce high-fidelity domains. Existing approaches often rely on statistical distribution alignment, or invariance assumptions that can fail when domain shifts reflect changes in underlying mechanisms. Inspired by a longstanding paradigm in physics that understands complex systems by starting from tractable idealizations and progressively incorporating additional mechanisms and levels of fidelity, we propose physics-transfer (PT) learning, a physics-constrained learning theory for zero-shot generalization along a ladder of complexity. PT constructs chains of anchor domains (CADs) that connect low-cost source domains to complex targets and introduces computable feasibility and reliability indicators for assessing transfer before target-domain labels are observed. Under physical-consistency and stable-representation assumptions, we derive an operational upper bound on target risk in terms of source empirical risk, the PT indicators, and finite-sample errors, and establish convergence as adjacent complexity gaps shrink and data coverage improves. We evaluate PT on excited-state energy prediction in quantum systems and dynamical-transition prediction in classical systems using neural networks trained without target-domain labels. Across substantial distribution shifts, PT yields accurate high-fidelity predictions, while the indicators identify incorrect physical mappings before target-domain prediction fails. These results provide a model-agnostic framework for designing, diagnosing, and controlling generalization in data-scarce learning problems.

Upper-tropospheric water vapor and cloud ice associated with tropical deep convection are central to water-vapor and high-cloud feedbacks. We investigate a warm-pool transition in tropical convective moistening using 20 years of Aura Microwave Limb Sounder (MLS) Version 5 H2O and cloud-ice observations (August 2004-December 2024), together with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission (June 2006-June 2023). The combined day-plus-night CALIPSO product (CALIPSO-A) provides an independent ice-cloud constraint; the H2O column shown on the CALIPSO grid is derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), not from CALIOP. We combine objective sea surface temperature (SST) breakpoint fitting, cross-sensor ice water path (IWP) comparisons, Clouds and the Earth's Radiant Energy System (CERES) longwave, shortwave, and net cloud radiative effect (CRE), and Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation diagnostics from the Global Precipitation Measurement (GPM) mission. MLS and CALIPSO-A show cloud-ice maxima over deep-convective warm-pool regions and identify warm-regime SST breakpoints near 299-300 K. Normalized MLS and CALIPSO-A IWP vary coherently in the western Pacific and tropical mean. CERES indicates that stronger longwave CRE in the warm regime is partly offset by stronger shortwave cooling, while GPM IMERG confirms that MLS IWP increases with convective rain rate. These results show that convective moistening and cloud-ice production across the tropical warm-pool transition are persistent cross-sensor features of the tropical atmosphere.

The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealing ocean features at finer scales. Nevertheless, the two-dimensional observations of KaRIn altimeter of SWOT suffer from instrumental errors. This noise degradation is altering the high frequencies of SWOT signal, and the small to sub-mesoscale dynamics of interest for some oceanographers. For this reason, Tréboutte et al. (2023) have developed a convolutional neural network (CNN) based on U-Net architecture to separate the noise from the physical signals contained in the SSH. Their approach has demonstrated great potential on simulated SWOT measurements, and Dibarboure et al. (2024) report a positive influence on actual flight data from SWOT. However, degraded denoising performance, and occasional negative side-effects have been observed in atypical conditions (e.g. very high surface waves, internal tides solitons). In this study, we illustrate some of these limitations and we present an improved approach of the CNN-based denoising: we modified the training procedure to obtain a more robust version of the algorithm, to avoid biases and artifacts in the denoised SSH. This paper also presents a more complete validation process with a robust and standardized evaluation benchmark: these metrics could be of interest to assess other SWOT filtering and denoising algorithms.

We present an operator based derivation of Bloch's theorem. In this approach, we first construct an explicit operator form of the Hamiltonian by writing the potential part in terms of an infinite sum of the momentum translation operators. Next, we prove that it commutes with the position translation operator corresponding to a lattice translation, using the exponential reordering identity. We then build on Merzbacher's treatment of simultaneous diagonalization of commuting operators to reproduce Kittel's central equation and also to derive a new formula for the simultaneous eigenket corresponding to a specific energy eigenvalue. Moreover, through the Born rule, we provide a precise account of the position and momentum probability distributions for Bloch states, and we examine the concept of crystal momentum. Finally, we apply our findings to the Kronig Penney model, where we numerically depict the band structure and illustrate the delocalized nature of the single electron.

The quality of a detector design is ultimately determined by the quality of the inference it enables, that is, by the accuracy with which the quantities of interest are reconstructed from the raw detector response. For complex detectors, the inference is performed by machine learning models, and the relation between the design and the attainable inference performance is, in general, non-trivial. In this work, we consider the optimization of the inference performance with respect to the detector design. The conventional approach prescribes retraining the inference model at every candidate design, thus, treating the evaluations as independent tasks and discarding the shared structure of the optimal inference algorithms at different designs. We propose the meta-learned objective estimate (MLOE): instead of solving the inference problem anew at every candidate design, a single meta-inference model, conditioned on the design and trained continually along the optimization path, is shared across all of them. We test MLOE on three families of optimization problems, the last of which comprises two design spaces of the Spectrometer Straw Tracker of the Search for Hidden Particles (SHiP) experiment; under matched budgets of simulation calls, the meta-inference model evaluates a candidate design using fewer simulation calls than the baseline strategies and holds the better rank over the convergence curve in all examined cases.

Linear radio-frequency quadrupole (RFQ) traps are crucial for ion and phase-space manipulation across diverse physics platforms, including quantum information processing, precision atomic spectroscopy, and high-resolution mass or laser spectrometry. We present the design, electrostatic field optimization, and performance characterization of a scalable, multi-layer printed circuit board (PCB) linear RFQ trap. By utilizing a PCB-based "stacked-electrode" geometry to generate high-quality quadrupolar fields, this architecture suppresses higher-order multipole field components by up to an order of magnitude compared to planar "flat surface-electrode" designs and allows for reclaiming up to 60% of the radial pseudopotential-well depth of an ideal hyperbolic quadrupole. Following a thorough optimisation and characterization using both simulations and experiments, we demonstrate its suitability as a cooler buncher, showing rapid helium buffer-gas cooling with time constants between 34 +/- 3 us and 412 +/- 23 us, and achieving a highly compressed longitudinal phase-space emittance of only 58 +/- 4 eV*ns. The low beam emittance provides flexible control of the extracted bunch properties: weak extraction fields yield energy spreads down to 2.5 +/- 0.4 eV, whereas strong fields produce ultra-narrow temporal widths down to 2.6 +/- 0.3 ns. The results establish our PCB-RFQ platform as a versatile, scalable, and cost-effective alternative to traditionally machined rod-based RFQ assemblies for advanced ion-trapping, beam-preparation, or quantum applications.

Several researchers have reported an atypical inversion of the lowest singlet ($S_{1}$) and triplet ($T_{1}$) excited states in nitrogen-substituted planar hydrocarbons. Contrary to Hund's rule of maximum multiplicity, poly-aza derivatives of phenalenyl, such as cyclazine and heptazine, have long been known to possess a negative singlet-triplet gap, $\Delta E_{ST}=E(S_{1})-E(T_{1})$. Using the well-known Pariser-Parr-Pople (PPP) model, a semi-empirical, wave-function-based Hamiltonian for correlated $\pi$-electrons, we investigate the singlet-triplet gap in ten N-substituted phenalenyl motifs. Two schemes of long-range Coulomb interactions are considered, and key parameters, including the on-site orbital energy, Hubbard U, and hopping integrals, are systematically tuned to benchmark the model for reliably estimating negative singlet-triplet gaps. Full configuration-interaction (FCI) method is employed to incorporate electron-correlation effects. Ground-state and $T_{1}$ absorption spectra are subsequently calculated using the optimized PPP parameters that best reproduce the available experimental data. For validation, first-principles TDDFT calculations using various exchange–correlation functionals are also performed. Conventional adiabatic TDDFT fails to reproduce the inverted ($S_{1}-T_{1}$) ordering, indicating that the inclusion of the correlation driven multiconfigurational effects are essential for describing it. We find that the singlet TDDFT ground-state absorption spectra reproduce several qualitative features of the PPP-FCI spectra, although the TDDFT excitation energies are systematically higher. Detailed analysis of the ground-state and $T_{1}$ absorption spectra reveals significant configuration mixing, providing a comprehensive characterization of the optical response of the N-substituted phenalenyl derivatives studied.

Weak, non-covalent interactions such as electrostatics, induction, and dispersion are commonly treated as distinct physical mechanisms, often using separate models and approximations. Here, we present a unified quantum-electrodynamical framework in which all weak interactions between neutral or charged subsystems emerge from a single interaction Hamiltonian and are expressed entirely in terms of electromagnetic Green functions. Starting from macroscopic QED in arbitrary linear environments, we derive compact master formulas that generate permanent electrostatic, mixed induction, and fully induced (dispersion) interactions on equal footing. Monopoles, dipoles, and quadrupoles are treated consistently within the same multipole expansion, leading to a common structural form in which all interaction channels are obtained from contractions of multipole moments and response functions with Green tensors. Environmental effects enter universally through dressed Green functions. The standard non-retarded free-space limits, including Keesom, Debye, and London/van der Waals interactions, are recovered as a consistency check of the formalism. Beyond this validation, the formulation shows that once the general expressions are established, different physical scenarios, such as interfaces, confinement, or polarisable continua, can be treated by direct substitution of the corresponding Green function, without reformulating the interaction for each case. This elevates Green functions to a universal organising principle for weak interactions in complex environments.

A ball spinning about a vertical axis is pushed sideways by the Magnus force, so its ground track curves. We show that when the lift and drag coefficients are constant, the horizontal part of this problem is exactly solvable in elementary functions. The step that makes this work is to write the horizontal velocity as a complex number and to use path length rather than time as the independent variable. Both aerodynamic forces are quadratic in speed, so the speed cancels and what remains is a linear equation with constant coefficients in which gravity does not appear. Three exact results follow from it. The heading turns at a fixed rate per unit path length, so a half turn always costs the same distance whatever the launch conditions and whatever the drag. The horizontal speed decays exponentially in the turn angle at a rate fixed by the drag-to-lift ratio, which makes the horizontal hodograph a logarithmic spiral. The radius of curvature of the ground track equals the lift length times the cosine of the flight-path angle. The whole flight problem also collapses to one scalar differential equation controlled by two dimensionless numbers. We use this to settle a concrete question. Two identical balls thrown in opposite horizontal directions with opposite spin can meet head-on when drag is absent, on a one-parameter family we give in closed form, and they fail to meet once drag acts. Every analytic claim is checked against machine-precision numerics, and the code and data are openly available.

Microwave cavity haloscopes search for dark-matter axions through the weak electromagnetic field generated by axion-photon conversion in a static magnetic field. Realistic synthetic signals are essential for end-to-end calibration of the receiver, validation of the analysis pipeline, measurement of signal recovery efficiency, and blind-injection studies. Here, we present a general method for producing stochastic synthetic axion waveforms by shaping Gaussian white noise in the frequency domain. The target axion power spectral density is imposed directly on the Fourier coefficients, and arbitrarily long continuous time streams are generated efficiently using an overlap-save implementation. The method reproduces both the ensemble-averaged spectral line shape and the time-domain fluctuations expected for a classical axion field, while providing direct control of the total injected power. Because the spectral model enters only through a replaceable transfer function, the routine can accommodate the Standard Halo Model as well as nonstandard velocity distributions and narrow substructure.

In the history of physics, the name scaffolding has been given to whatever served to raise a theory and is afterwards removed because, it is said, it is no longer needed. This article documents, from the originals, what it cost Maxwell's theory when that method was applied to it: what was removed from it, who did it, with what argument, and how over the years it had to be recovered because it was understood that it was needed after all. It also records what Maxwell himself missed in Ampère, what he calls the traces of the scaffolding: the template with which Ampère himself had given shape to his work.

Capability and efficiency are two key dimensions of reasoning in large language models (LLMs). Capability refers to the ability to solve a given problem correctly, whereas efficiency refers to the ability to do so with limited resources. When LLMs use Chain-of-Thought (CoT) reasoning to solve problems of controlled hardness, both the number of problems solved correctly and the number of tokens required to reach a correct answer depend on problem hardness and model size. However, how these factors jointly shape capability and efficiency remains poorly understood. Here, we use hierarchical Bayesian models to evaluate the capability and efficiency of LLMs from the DeepSeek-R1-Distill model family across four classes of arithmetic and algorithmic reasoning problems. At a fixed model size, the probability of correctly solving an instance decays approximately exponentially with instance size, our proxy for problem hardness. The decay scale grows sublinearly with model size, indicating that larger models are more capable, but that capability gains diminish with scale. Output length grows as a power law with instance size, which serves as a proxy for difficulty. However, the parameters of this power law do not vary systematically with model size, suggesting that larger models do not become more efficient. Together, these findings reveal potential limitations of naive scaling as a strategy for developing more capable AI systems: capability improves with diminishing returns, while efficiency shows little to no improvement.

In the pursuit of steady-state fusion energy, Advanced Tokamak regimes rely on Internal Transport Barriers (ITBs) with reversed magnetic shear. While Resonant Magnetic Perturbations (RMPs) are commonly applied to control Edge Localized Modes (ELMs) by intentionally making the plasma edge chaotic, the RMPs can also affect the reversed-shear cores. To address this compatibility, we propose a new, adjustable analytical model for plasma current that explicitly incorporates the core, ITB, and edge pedestal components. By applying this current formulation to a Hamiltonian map, we compare how easily magnetic chaos reaches the core in ITB-driven reversed-shear (Non-Twist) plasmas. We find that the localized ITB current creates "twin" inner resonances and pushes outer magnetic island chains closer to the edge. These results highlight a critical operational trade-off: while the Non-Twist topology makes ELM control easier at lower coil currents, it severely shrinks the safety margin against a complete, RMP-induced loss of global confinement.

The stochastic block model is a paradigmatic model of networks with community structure. Yet percolation in the model has been studied primarily in cases with a fixed block structure, even though in real networks, the community structure may evolve as the network grows. Here we study percolation in sequences of stochastic block models in which the numbers and sizes of communities, as well as the intra- and intercommunity connection probabilities may all change with the network size. We analyze five such sequences using two methods: linearized self-consistent equations for the locally tree-like models and a branching process at the community scale for the models with nonvanishing clustering. We find that the critical average degree is not generally equal to $1$, even in locally tree-like sequences, because the transition depends on how connections are distributed across the evolving community structure. We also show that the community-scale branching process accurately predicts the transition when intercommunity connections are sufficiently sparse, even in the presence of nonvanishing clustering, while a geometric stochastic block model sequence demonstrates the limitations of this method when correlations between intercommunity connections cannot be neglected. These results extend percolation studies in the stochastic block model to more realistic scenarios with evolving community structure, and may provide new methods to derive the upper and lower bounds for the percolation threshold in geometric long-range percolation.

Recent indoor experiments on small-scale hovering rotors have reported mid-frequency quasi-tonal peaks of uncertain origin in the residual (aperiodic) far-field sound spectrum, even without wake recirculation. This study investigates their physical origin using hybrid Reynolds-averaged Navier-Stokes/large-eddy simulations coupled with a Ffowcs Williams-Hawkings acoustic analogy, applied to a four-bladed ideally twisted rotor in hover. The high-resolution simulation reproduces the measured higher-harmonic blade-passage-frequency tones, confirming that they arise from the rotor flow itself and not from the enclosed-chamber test environment. Flow-field analysis attributes the tones to blade secondary vortex interaction (BSVI): the outboard blade sections, between approximately r/R = 0.88 and 0.96, are repeatedly impinged upon by coherent, S-shaped secondary vortex worms formed by entrainment of the wake shear layer into the primary tip vortices of preceding blades. These aperiodic secondary vortices produce blade-to-blade correlated loading, yielding a quasi-tonal signature distinct from both purely periodic blade-vortex interaction and mostly stochastic blade-wake interaction noise. Spectral proper orthogonal decomposition of the zero-mean upwash confirms that the mid-frequency (3-10 kHz) content is dominated by a single, low-rank, spatially coherent mode associated with the braids, whereas a lower-frequency mode near 1 kHz is linked to the primary tip vortex core. BSVI is thus identified as a distinct and previously uncharacterized source of aperiodic tonal noise in hovering rotors, with direct implications for the acoustic design of small- to medium-scale rotorcraft and electric vertical take-off and landing propulsors.

Large-scale simulations of correlated electron systems require repeated evaluations of the electronic forces and transition energies driving collective dynamics. Machine-learning surrogates alleviate this bottleneck, but incorporating symmetry often involves carefully designed descriptors or specialized network architectures. We show that ordinary multilayer perceptrons equipped with finite-group averaging provide exactly equivariant surrogates directly from microscopic configurations. The construction separates symmetry enforcement from the internal network architecture and applies to both discrete and continuous lattice degrees of freedom. In the Falicov–Kimball model, it predicts directional hopping free-energy differences; in the Holstein model, its invariant limit generates conservative lattice forces. Benchmarks against exact diagonalization establish microscopic accuracy and agreement of dynamical correlations, while large-scale simulations recover multiscale charge ordering and charge-density-wave coarsening. These results demonstrate an accessible, reusable route to symmetry-preserving many-body dynamics for systems with finite lattice point groups.

Light-matter interaction in time-varying metasurfaces brings about phenomena that transcend the limits of static systems. Temporal modulation enables energy exchange between light and matter, and electromagnetic fields are coupled in both momentum and frequency, with implications across a broad range of photonic applications. Nevertheless, a precise description of such systems necessitates a theory that captures the intertwined effects of spatiotemporal variations while accounting for dispersion in a causal manner, as realistic optical materials exhibit frequency-dependent response and finite temporal memory. Here, a self-contained Floquet-Bloch theory is developed to capture the response of dispersive, time-varying metasurfaces, respecting causality through physically consistent constitutive relations. The theory treats spatial periodicity, nonadiabatic temporal modulation, and material dispersion within a unified formalism, providing access not only to the metasurface's scattering response but also to its inherent modal structure. The formulation is validated against full-wave simulations. As illustrative examples, it is first applied to asymmetric Floquet harmonic generation in an excitonic time-varying metasurface, where excitonic dispersion enables selective harmonic enhancement. It is then used to investigate metasurface-based photonic time crystals, revealing how modal dispersion governs momentum-bandgap formation and dynamics. This work establishes a comprehensive platform for understanding dispersive, time-varying metasurfaces and their underlying physical mechanisms.

Here, magneto-optical magnetometry measurements on Py/NiO bilayers reveal a pronounced magnetic-field-induced wavelength shift, demonstrating strong magneto-optical coupling in this antiferromagnetic system. A systematic and monotonic spectral shift of up to ~ 400 nm is observed as the applied magnetic field increases, saturating at higher fields. Quantitative analysis shows that the associated magneto-optical energy variation is on the order of 107 eV, comparable to the magnon energy scale in NiO. Owing to the large NiO thickness, the observed effect originates from magnons intrinsic to the antiferromagnetic NiO layer rather than from spin currents injected by the Py underlayer. These results provide direct experimental evidence for magnetic-field control of antiferromagnetic magnon energies via magneto-optical interactions, establishing Py/NiO bilayers as a promising platform for optically probing and manipulating antiferromagnetic spin dynamics.

We report the first experimental observation of resonantly enhanced propagating phonon transport induced by magnon pumping in a ferromagnetic/piezoelectric bilayer. Surface acoustic waves (SAWs) generated in a 128$^{\circ}$ Y-cut LiNbO$_3$ delay-line device resonantly excite magnetization dynamics in an adjacent Co film through magnetoelastic coupling. The enhancement of the transmitted acoustic signal occurs exclusively when the SAW frequency satisfies the ferromagnetic resonance condition predicted by the Kittel dispersion, providing a direct experimental fingerprint of resonant magnon-phonon coupling. A systematic comparison between the Co/LiNbO$_3$ bilayer and the bare LiNbO$_3$ substrate demonstrates that the observed transmission enhancement originates solely from the dynamic interaction between propagating phonons and coherent magnetization precession. Furthermore, measurements performed at different SAW harmonics reveal that only the harmonic satisfying the FMR condition produces a measurable enhancement, confirming the frequency-selective nature of the phenomenon. These findings establish an efficient mechanism for transferring energy from magnons to propagating phonons and provide a new strategy for actively controlling coherent acoustic transport in hybrid spintronic, straintronic, and quantum phononic platforms.

Due to progressing miniaturization, material fatigue is expected to gain importance on microscopic scales. Nevertheless, most non-destructive testing techniques are optimized for macroscopic scales. Therefore, new approaches applicable to miniaturized samples need to be explored. High spatial resolution imaging of a material's magnetic stray field enables sensitive detection of microstructural changes due to the local interplay of magnetic properties, strain, and defects. Up to now, this approach has rarely been used for materials testing since high-resolution magnetic sensing is traditionally challenging. Novel quantum sensing techniques offer the potential to close this gap. One of the most prominent quantum sensors, the nitrogen vacancy center in diamond, is investigated for non-destructive testing applications within the scope of this work. An experimental system based on a widefield sensing approach was developed to image magnetic stray field distributions within seconds to minutes over areas up to 1 x 1 $mm^2$. Magnetic sensitivities below 10 $\mu$T/$\sqrt{\text{Hz}}$ and a spatial resolution of 1-2 $\mu$m were achieved. The technique offers high mechanical stability, which is crucial for non-destructive testing applications. Comparison with magneto-optical Kerr effect measurements showed that the magnetic field maps are strongly correlated with the material's surface domains while containing additional information from deeper inside the sample. Characteristic changes in the magnetic stray field distribution were detected in an electrical steel sample after cyclic loading. A potential marker for early fatigue damage was deduced from the splitting gradient distribution, while 2D Fourier transform analysis provided additional insight.