Papers

We propose a state-dependent diffusion model for strongly driven thermal-atom spectra. Starting from the trajectory-dependent internal-state evolution of individual atoms, we derive a closed spatial equation for the local density-matrix field using a velocity-moment expansion. Measurements of an $^{85}$Rb atomic-filter transmission spectrum agree well with the model up to a maximum Gaussian peak intensity of $1.27\times10^{3}$ W/cm$^2$, approaching six orders of magnitude above the $^{85}$Rb D2-line saturation intensity. Counterintuitively, the model reveals an anomalous optical-pumping pathway in which intense light transfers atoms from nominally dark states into bright states. Hyperfine Paschen–Back splitting selectively enhances this anomalous pathway while suppressing conventional optical pumping, allowing the filter to maintain approximately 97$\%$ transmission at the highest intensity studied. This work provides a framework for controlling strongly driven atomic ensembles and designing saturation-resistant atomic optical devices.

Accounts of organizational dissolution explain survivor homogenization, temporally concentrated exit, and fragmentation into cohesive subgroups in different vocabularies. Their recurrence is the fact to be explained. A finite condition structure with a minimal dynamic extension generates all three, each in its own regime, with no actor responding to another; which conclusions survive when the restrictions defining those regimes are relaxed is then determined. With two requirement masks the union class is the only mediator between the pure types and the signature with the largest closed neighbourhood, and under positive endpoint loads it is also the unique maximizer of deficient load, so the independent derivations of fragmentation and homogenization both concern that class. With more than two masks the three roles need not coincide. Forced coincidence admits an exact characterization for every number of masks: it holds if and only if the requirement-derived join lies in the realized family and separates the endpoints. The proportion of family-pair instances meeting this condition vanishes under a uniform counting measure as the number of masks increases, and, at one actor per signature, an edge density above a stated bound excludes it. A common substrate does not make the mechanisms identical: selective attrition can hold while correlated collapse fails, and a change in exit dependence can change the population outcome while every actor's marginal lifetime law is unchanged.

The influence of the diagonal Born–Oppenheimer correction (DBOC) on the electronic structure of light diatomic molecules subjected to strong magnetic fields is investigated. H$_2$, HeH$^+$, and LiH are considered, using both the Hartree–Fock (HF) approximation and configuration interaction with single and double excitations (CISD). The analysis covers magnetic field strengths up to $B_\parallel = 0.2$ a.u. ($4.7\times10^{4}$~T), relevant to astrophysical conditions near magnetic white dwarfs. Although the correction noticeably shifts total electronic energies, it varies smoothly with internuclear distance and depends only weakly on the field, so that it enters the vibrational transition frequencies as a nearly field-independent offset, shifting the fundamental interval by about $1$~cm$^{-1}$ and the higher levels by up to $15$ cm$^{-1}$. These shifts exceed the $0.1$–$2.0$ cm$^{-1}$ accuracy currently attainable for molecular lines in the atmospheres of magnetic white dwarfs. Comparison of HF and CISD results shows that electron correlation has a non-negligible effect on the DBOC, most pronounced for the strongly ionic LiH bond.

Lithium niobate on insulator (LNOI) combines the outstanding optical properties of lithium niobate (LN) with strong optical confinement, scalable fabrication and high-density integration, making it a leading platform for integrated photonic chips. Recent advances in LNOI photonics have mainly centred on passive and electro-optic components, including couplers, waveguides, microcavities and modulators, whereas efficient on-chip laser sources remain insufficiently developed, limiting the realization of fully integrated LN photonic systems. Because LN is an indirect-bandgap material, lasing on LNOI generally relies on photoluminescence from rare-earth-ion doping, yet the conversion efficiency of doped LNOI lasers has remained low. By comparing LNOI microcavity lasers with fibre lasers and waveguide amplifiers, we identify the limited number of rare-earth ions participating in stimulated emission as a key factor responsible for inefficient pump utilization. Here we demonstrate an integrated Er-doped LNOI laser that combines high-quality, highly Er-doped LN, a large-diameter wide-microring resonator, a low-loss waveguide amplifier and bidirectional pumping. This architecture enables a slope efficiency of 16.91% at 1562 nm, exceeding 10% on the LNOI platform for the first time. Our results provide a route towards high-efficiency LNOI lasers for fully integrated photonic systems.

Machine-learning interatomic potentials (MLIPs) enable nanosecond-scale atomistic simulations of inorganic semiconductor nanocrystals, but low errors on held-out configurations do not necessarily guarantee stable molecular dynamics. We benchmark five graph-neural-network MLIPs, SchNet, PaiNN, NequIP, Allegro and MACE, for dynamical stability in a chloride-passivated cadmium selenide nanocluster containing 149 atoms. The models were trained under harmonized conditions on 1,000 configurations and evaluated against 2,000 held-out configurations generated using density functional theory, considering force accuracy, computational efficiency, uncertainty and structural stability during 1 ns simulations at 300 K. Allegro produced the lowest validation force mean absolute errors, ranging from 12 to 14 meV per angstrom, whereas SchNet produced the largest, ranging from 84 to 110 meV per angstrom. This ranking did not predict dynamical robustness: NequIP remained stable for 1 ns without additional training data, whereas MACE became stable only after an ensemble-based active-learning procedure added 34 uncertainty-selected configurations. PaiNN and Allegro remained unstable after the addition of 100 and 95 configurations, respectively, and SchNet also failed to achieve stable dynamics within the tested augmentation budget. Under the benchmark hardware conditions, the machine-learning potentials required 6-66 ms per molecular-dynamics step, compared with approximately 20 s for density functional theory. These results show that held-out prediction errors alone are insufficient for selecting interatomic potentials for finite, surface-dominated nanostructures. Reliable deployment requires long-timescale dynamical testing combined with uncertainty-guided refinement, while the effectiveness and data efficiency of active learning remain strongly architecture dependent.

Dense suspensions can undergo a dramatic increase in viscosity at a critical value of the shear stress. This phenomenon, termed discontinuous shear thickening (DST), has been attributed to an increase in the fraction of particle interactions becoming frictional with increasing shear stress, and a successful mean-field theory has been developed to explain various accompanying rheological properties. On a microscopic scale, however, conventional structural analysis measures such as the grain-position pair correlation function show no significant changes with the onset of DST, though recent work has shown that similar analysis in the dual space of contact forces does lead to marked changes at this transition. Furthermore, experimental results have suggested the existence of higher-order microscopic correlations and the importance of incorporating fluctuations away from a mean-field description. To this end, we use a higher-order cluster analysis tool to study the force networks obtained from simulations of dense suspensions to construct an effective interaction potential in force space. We show that there are significant changes occurring in this potential as a function of density and stress close to DST. We discuss the implications of these observations on an emergent field theory of the DST transition.

Facial-expression classifiers convert video into multivariate time series of scores with measurement error from classifiers, videos, and recording conditions. Empirical score series cannot establish whether the score channels reflect a smaller set of latent expressive processes or whether an analysis would recover those processes. We introduce the Latent Oscillator Measurement Model (LOMM), a data-generating model that separates latent dynamics, time-varying activity, and a factor-analytic observation model. The latent processes are damped, undamped, or amplifying linear oscillators. LOMM generates bounded scores or continuous indicators. Study 1 used four-fold cross-fitting with scores from 100 MAFW videos to calibrate LOMM and evaluate generated series on held-out videos. Median plausibility and coverage were 0.970 and 0.920 for LOMM, versus 0.510 and 0.370 for a calibrated static logistic-normal generator. Study 2 tested whether Dynamic Exploratory Graph Analysis (DynEGA), static EGA, GraphicalVAR, and GIMME recovered a known dimensional structure from continuous indicators generated by LOMM. At 100 observations per clip, with failed or timed-out fits counted as incorrect, correct-dimension recovery was 0.939 for DynEGA, 0.884 for static EGA, 0.777 for GraphicalVAR, and 0.176 for GIMME. Replacing the common fixed initialization with independent stationary starts for stable dimensions and bounded independent starts for amplifying dimensions reduced recovery for DynEGA, static EGA, and GraphicalVAR. LOMM provides a controlled test of whether an analysis recovers aspecified latent structure before score patterns are interpreted psychologically.

Synthetic gauge fields enable nontrivial light control but remain largely restricted to compact groups such as U(1) and SU(2). Here, we study paraxial light propagation under a noncompact SU(1,1) gauge field realized in a non-Hermitian medium. Under this field, light evolves pseudo-unitarily and conserves pseudo-intensity despite material gain or loss. The gauge field drives Lorentzian precession of the optical pseudospin on a Bloch hyperboloid, producing rotations, boosts, and exceptional-point critical dynamics in an internal Minkowski space. A non-Abelian Lorentz force converts these internal dynamics into real-space oscillatory, hyperbolic, and cubic pseudo-intensity centroid trajectories, even in a uniform medium, while light of opposite pseudo-intensity can drift oppositely-a non-Hermitian spin-Hall-like effect. Our results connect non-Hermitian wave physics with noncompact gauge dynamics and reveal hidden conserved quantities in non-Hermitian light transport.

NJOY21 is part of a modernisation effort for NJOY2016, but mostly consists of C++ wrappers. Agentic coding represents a unique opporunity for NJOY modernisation in Rust, but agents require human expert supervision, which may be in short supply. Moreover, expert human review is the bottleneck in agentic software development as human resources are limited in this context. This work describes a methodology for how a human manager of AI agents can reasonably translate and refactor scientific software with limited domain expertise using principles borrowed from nuclear quality assurance documents. Moreover, it presents a methodology for when to allocate limited human resources for reviewing the agentically generated code. This methodology centres on agentic V&V and documentation, and it is demonstrated via construction of Monte Carlo web demos for the Rust fork of NJOY an Outram Monte Carlo (OutramMC), Rust ports of NJOY2016 and OpenMC, in Outram Park. In this work, an integrated pipeline for the Rust port of NJOY and OutramMC was demonstrated in web demos, and code to code verification and validation was performed with ICSBEP benchmarks for Godiva, HST009 and LCT008. ACE files produced with Outram Park's NJOY port were identical for more than 30 ACE files. These web demos also served as part of agentic documentation, which prepared the codebase for human review.

The principal proof of concept revolves around an argon-hydrogen plasma reactor that melts, reduces, atomizes and quenches critical raw material in one step, with premium spherical powder as the deliverable output and control of the composition chemistry. Each usage of the reactor is monitored through thermocouples and pressure sensors, which provide a daily data source of the real-world experiments. The reactor is modeled through COMSOL Multiphysics, which represents the core solver used to provide multiphysics simulations. The usage of COMSOL is complemented with Artificial Intelligence (AI) models, to enable seamless data assimilation and optimization. This paper presents the COMSOL twin of the reaction chamber and converging-diverging nozzle, together with a custom phase-change particle-tracing layer validated on Ti-6Al-4V (Ti64). Moreover, we highlight how the synergy between COMSOL simulations and AI-based digital surrogates can be leveraged to build self-consistent optimization loops geared toward (i) fully autonomous live control of the reactor and (ii) optimization of the process.

Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the conditioning mechanism to the trained model. Classifier guidance avoids this coupling in continuous domains by steering a frozen model with a classifier's gradient, but discrete graph diffusion samples discrete edge states, so gradients cannot propagate through the sampled graph. We introduce ProtoGuide, a post-hoc, backbone-agnostic framework that recovers an analogous mechanism. At each reverse step the denoiser's per-edge output is relaxed into a differentiable soft adjacency, embedded by a frozen Siamese graph neural network, and scored against a target-class prototype and its nearest competitor; the resulting per-edge gradient, damped by a cosine schedule, is injected back into the denoiser output. All components stay frozen, so guidance is retargeted by supplying a different prototype. On five classes of real-world networks and two architecturally different backbones, EDGE and DiGress, ProtoGuide raises macro classification accuracy from 50.7% to 73.5% and from 73.6% to 83.8%, and outperforms DiGress's built-in conditional training under our configuration. Gains are largest where the unguided models are weakest, and are not uniform across classes. Per-graph coverage remains high in most settings, while distributional effects are class-dependent. A Best-of-N selection baseline matches this accuracy given enough oversampling, but at a substantial cost in graph diversity. An independently initialized classifier, a directionality test, and a few-shot analysis support target-directed steering and robustness to very small support sets.

Exceptional points provide a mechanism for producing strong spectral sensitivity in non-Hermitian wave systems. For subwavelength resonators with highly dispersive material parameters, however, the resonance problem is not a linear eigenvalue problem in the frequency. In this paper, we use the Lippmann-Schwinger formulation and the associated volume-potential reduction to study exceptional points in dispersive resonator arrays. The reduction leads to a finite-dimensional nonlinear matrix pencil whose entries encode both the local material response and the volume-mediated interaction between the resonators. We characterize exceptional points as defective characteristic values of this pencil and derive explicit conditions for dimers and higher-order systems. We show that passive damping and detuning can generate exceptional points without imposed gain, and we formulate an inverse-design condition for placing such degeneracies at prescribed frequencies. We also prove the corresponding Puiseux splitting laws and identify how material dispersion enters the leading sensitivity prefactor. Numerical experiments, including halide-perovskite-inspired dimers and trimers, illustrate the theory and show that the exceptional-point mechanism persists for explicit highly dispersive passive material laws.

Spatial correlation functions of density fluctuations are measured in the Tokamak à Configuration Variable (TCV) using a dual-channel Doppler backscattering (DBS) diagnostic. In certain cases, the spatial correlation function exhibits two characteristic length scales. By analogy with nonlinear reduced simulations, the presence of two correlation lengths may be indicative of avalanche-like transport. The correlation functions obtained from DBS are compared with those from short-pulse reflectometry measurements and show reasonable agreement. Both short- and long-range correlations are measured in the same plasma geometry for different heating powers. Short-scale correlation lengths are found to be on the order of 3-5 Larmor radii, while large-scale correlations extend over approximately 5-15 Larmor radii. The correlations are found to decrease towards the very edge of electron cyclotron heated discharges, coinciding with a narrow Er well.

We present a new version of the classic specific heat of water experiment that emphasizes scaling analysis and data collapse as tools for model construction in contrast to the usual approach of using a previously-established formula. The activity combines structured experimental and analytical guidance with instructor-mediated questioning; the corresponding thermodynamic theory is deliberately postponed. Students collect temperature-time data under various conditions, producing multiple data sets that initially appear unrelated. Through successive rescalings, they reduce the dimensionality of the variable space and achieve data collapse onto a single master curve, from which they formulate an empirical model relating energy input, mass, and temperature change. That the effect of the calorimeter cannot be eliminated in the analysis leads to a non-identifiability of the subsystem contributions. The conventional thermodynamic description is introduced a posteriori as a boundary-setting framework for interpreting the empirical model and clarifying its applicability. The central contribution of this work is to use data-driven modeling both to construct models and to reveal their limitations.

Spin-orbit interaction (SOI) of light manifests as the generation of a spin-dependent vortex beam and the spin-Hall effect when a spin-polarized beam interacts with an optical interface at normal and oblique incidence, respectively. The weak SOI limits its widespread use for quantifying physical quantities in various applications. We demonstrate a significantly enhanced SOI effect, approaching the strong-coupling regime, by retroreflecting a nonparaxial focused beam of light at a multilayer dielectric mirror. The large difference in angle-of-incidence-dependent Fresnel reflection coefficients (r-TE, r-TM ) introduced by the mirror simultaneously enhances the spin-orbit-converted vortex-beam amplitude and the spin-Hall effect in the reflected beam. Near-field superposition of the orthogonal polarization components in the reflected beam yields a spatially nonuniform beam field with C-, V-, and L- polarization singularities. These topological features are mapped and are used to demonstrate the transverse and 3D spin-Hall effect in the focal region. The superposition of enhanced optical near-fields and the lossless broadband nature of the mirror reflectivity are expected to enable a variety of polarization topologies and to tailor their features via designed mirror reflectivities.

We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodynamic behaviour in the human aorta. The model incorporates a time-resolved simulation of pulsatile blood flow over a two-minute interval, enabling the extraction of pressure and velocity fields with high temporal fidelity. The mechanical stress exerted on the aortic wall was quantified through Laplace's law, with temporal averaging applied to derive representative stress distributions. This approach circumvents the computational overhead associated with conventional computational fluid dynamics (CFD) methods by eliminating mesh generation and exploiting the automatic differentiation capabilities inherent to neural networks. The proposed methodology demonstrates that PINNs can serve as an efficient and accurate alternative for modelling complex vascular flow phenomena, offering significant advantages in scalability and computational cost reduction while maintaining physical consistency.

The development of compact and intense $\gamma$-ray sources in the MeV energy range remains a significant frontier in radiation physics, with profound implications for nuclear physics, medicine and applied science. In this work, we present a comprehensive numerical investigation of the photon emission probability and brilliance of a Crystalline Undulator (CU) based on a periodically bent Si(110) crystal. Our approach integrates realistic deformation profiles obtained via Finite Element Method simulations of a realistic sample, where bending is induced by patterned Si$_3$N$_4$ surface stressors. Relativistic molecular dynamics simulations, performed using the MBN Explorer software package, consider a 10 GeV positron beam consistent with the foreseen FACET-II facility specifications. Our results reveal a distinct undulator radiation peak in the 1.6-2.1 MeV range, well-separated from the broader channeling radiation background. We show that for an optimal aperture angle of $1/2\gamma$, the source reaches a maximum peak brilliance of about $5\times10^{22}$~photons/s/mm$^2$/mrad$^2$/0.1%~BW. This performance is highly competitive with large-scale Gamma-Beam Systems and exceeds that of Inverse-Compton Scattering sources, confirming the potential of crystalline undulators as high-brilliance, compact light sources for the MeV domain.

Analytical Solov'ev equilibria with freely prescribed plasma boundaries can be constructed by enforcing the boundary condition in a least-squares sense on a polynomial basis of homogeneous solutions. Within this approach, a systematic scan of boundary shape is carried out well beyond the convex regime: non-convex star polygons of arbitrary symmetry order and concavity are examined alongside conventional convex tokamak cross-sections, using a quantitative boundary-fidelity criterion to delimit the range of shapes the polynomial basis can represent. For standard convex shapes, poloidal beta is insensitive to shaping due to the Solov'ev current profile, while polygon boundaries raise it monotonically with the number of sides, driven by the flat sides compressing flux surfaces toward the axis. For the Shafranov shift, odd-fold boundaries admit a critical concavity below which the geometric centre of the boundary overtakes the magnetic axis, reversing the sign of $\Delta/a$; no such reversal occurs for even-fold boundaries, and the effect has no analogue among convex shapes.

A projector-based formulation of force constants in crystalline materials provides a systematic framework for constructing force-constant bases and determining force constants from force–displacement datasets while rigorously satisfying crystal symmetry, permutation symmetry, and translational invariance. In this work, we develop an efficient eigenvalue solver for projection matrices and integrate it into the projector-based framework. The proposed method substantially reduces the computational cost and enables practical calculations for large-scale systems, low-symmetry crystals, and higher-order force constants that are difficult to treat using conventional approaches. Applications to self-consistent phonon calculations for assessing the grain-boundary excess free energy, lattice thermal conductivity calculations in complex compounds, and fourth-order force-constant estimations demonstrate the efficiency and robustness of the proposed framework for large and complex materials systems.

We develop a self-consistent phasor-particle-in-cell algorithm to capture bidirectional inductive and capacitive feedback between an inductively coupled plasma and a distributed radio-frequency coil circuit. The method couples a two-dimensional axisymmetric particle-in-cell model with Monte Carlo collisions to a network that resolves the current and potential of each coil turn. Precomputed unit-current Helmholtz solutions provide the vacuum impedance matrix, while projection of the plasma-only field supplies the series back electromotive force. An established charge-based electrostatic coupling is extended to individual turns: surface charges obtained from a discrete Gauss law consistent with the Poisson solver yield shunt displacement currents through their fundamental harmonics. Both responses are accumulated over a complete radio-frequency period to update the relaxed circuit state once per period. Vacuum tests showed differences of -1.58 percent in single-solenoid inductance and -4.38 percent in mutual inductance relative to analytical references. A particle-free five-node manufactured benchmark with both coupling channels active yielded a scaled complex-state error of 1.90 x 10^-5 against the continuous reference and approximately second-order spatial convergence. Application to an unshielded argon reference cell reached statistically stationary states at coil-port powers of 60 and 100 W. Net capacitive heating accounted for 29.35 percent and 20.07 percent, respectively, of the combined inductive and capacitive plasma power. Independently accumulated field-side and circuit-side powers, including conductor loss, agreed within approximately 0.5 percent. These results support turn-resolved bidirectional field-circuit coupling for axisymmetric plasma simulations using a fundamental-frequency circuit representation.

We investigate the generation and characterization of higher-order harmonics in the probe-laser absorption arising from nonlinear interactions in an electromagnetically induced transparency (EIT) ladder system involving Rydberg states and driven by two microwave fields in the heterodyne configuration. We characterize the amplitude and phase of the generated harmonics as a function of the relative frequency and phase of the applied microwave fields. The phase of the $n^{\mathrm{th}}$ harmonic follows the relation $\phi_n=n\phi$, demonstrating phase multiplication and suggesting that higher-order harmonics may offer an enhanced phase response for phase-sensitive measurements. We further characterize the harmonic amplitudes and bandwidths and find that the measured bandwidths are significantly larger than the intrinsic Rydberg-state linewidth, consistent with power broadening under the experimental conditions. The experimental observations are supported by density-matrix calculations, which reproduce the key features of the measured harmonic response.

The hyperfine structure that the nitrogen-vacancy (NV) center in diamond has with its nitrogen nucleus is of immense importance for quantum sensing and quantum computing. Current methods of calculating the NV hyperfine energy eigenvalues and eigenstates with respect to an axial magnetic field - i.e., calculating the NV Breit-Rabi formula - resort to numerical methods and analytic approximations. Here we provide an exact, closed-form expression to the NV hyperfine energy eigenvalues and eigenstates for both the nitrogen-14 and -15 cases.

Accurately resolving the near-field flow generated by many swimming bacteria while retaining computational efficiency remains challenging. Stokeslet-based models with many force points can capture detailed near-field hydrodynamics but are computationally expensive. Other approaches include far-field and low-order models that use fewer points but sacrifice near-field accuracy. We introduce a reduced-order framework based on the method of regularized Stokeslets that preserves important near-field flow features while using substantially fewer force points. The method replaces the force distribution of a high-fidelity model with forces on a sparse set of points whose strengths are determined by constrained least-squares calibration to the high-fidelity velocity field of a single bacterium. The calibrated forces yield an approximation that preserves the dominant reference-flow structure while satisfying the force-free and torque-free conditions of self-propelled swimming. Principal component analysis is then used to represent the phase-dependent variation of the calibrated forces using only a few dominant modes. By fitting the corresponding modal coefficients as continuous functions of flagellar phase, the reduced-order forces can be approximated at any phase of the flagellar cycle. The framework is applied in both free space and near a no-slip planar boundary, where it preserves the dominant flow structures and captures the wall-induced redirection of the surrounding fluid. The optimized cell-body regularization parameter depends weakly on wall distance, and the remaining velocity discrepancy is concentrated primarily near the cell body. By substantially reducing the number of force points, the reduced-order model lowers both the computational cost and memory requirements of velocity-field evaluation, making large-domain simulations of multi-swimmer flow fields more practical.

Data-driven materials development requires the collection of large amounts of high-quality materials data. Full autonomy of materials experiments is anticipated, but its technical hurdles are high and its adoption remains limited. In this study, we constructed a simple, easy-to-deploy automated experimentation platform that focuses not on full autonomy but on the reliable automation and quantitative recording of experimental processes. Specifically, commercially available instruments such as robot arms, electric pipettes, web cameras, and an electronic balance are combined, components such as fixtures are fabricated with a 3D printer, and the instruments are operated by control code generated by an AI agent based on a large language model. As a demonstration, we applied the platform to a two-solution mixing experimental system and synthesized ZIF-8, a metal-organic framework. A white suspension phase was observed in the product, and X-ray diffraction measurements confirmed that it was ZIF-8. We also found that its particle size distribution depends strongly on the solution dispensing speed of the electric pipette, which is a parameter that is difficult to control or record in manual operation. This dependence was reproduced in repeated runs, confirming the repeatability of the automated synthesis. This result is a good example showing that the control and recording of process parameters that are rarely quantified in manual work can govern the quality of materials data. All control code, CAD models, and documentation are made publicly available to encourage the spread of laboratory-scale automation of experiments.

We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework for measuring how individual concepts influence the organization of scientific knowledge, providing a quantitative framework for detecting scientific revolutions. The observable is defined by the geometric perturbation induced when removing documents associated with a candidate concept from the embedding space before and after its historical emergence. Statistical validation is performed using five historical case studies spanning physics, mathematics, and machine learning: special relativity, Gödel's incompleteness theorems, the Higgs mechanism, deep learning, and the attention mechanism underlying transformer architectures. Across the historical case studies, the framework identifies measurable geometric signatures associated with conceptual reorganization, while the validation studies expose important limitations arising from document assignment and sparse historical data. These results establish embedding geometry as a medium for quantifying conceptual reorganization, providing a new approach for studying how scientific fields restructure over time.

Ptychography overcomes the limits of lenses by co-designing optics and computation. Yet prevailing implementations reconstruct on a pixel grid, where weakly-constrained modes drift and recovery demands redundant data. Here we introduce Gaussian-splatting ptychography, representing object and probe as Gaussian primitives. Relocation concentrates primitives where structure is dense, and overlapping primitives couple neighbouring pixels to suppress mode drift. The scheme unexpectedly restores the low-frequency phase that conventional approaches lose, enabling uniform phase transfer across spatial frequencies. The probe is represented and updated in its pupil plane from a random start. In Fourier ptychography, the pupil-plane model recovers severe aberrations where pixel-grid solvers fail. In conventional optical, X-ray and electron ptychography, the pupil-plane primitives also recover the real-space probes with no model of beam-forming optics. The representation cuts memory up to 14-fold and recovers specimens from fewer acquisitions. At electron wavelengths, it resolves atomic structure at tens of electrons per square angstrom.

The multimode fiber is among the thinnest imaging conduits available, carrying hundreds to thousands of spatial modes through a cross-section comparable to a human hair, but its endoscopic capabilities are currently limited by the sensitivity of the transmission matrix to the fiber's deformed state. Proximal-only recovery of the fiber's transmission matrix is an appealing approach for enabling general use multimode fiber endoscopy, and within the last decade, machine learning techniques have been applied to both single-ended and double-ended transmission matrix recovery tasks. We present a new approach to this interdisciplinary problem and show that neural networks can generalize to recover transmission matrices of an arbitrarily deformed graded-index multimode fiber from proximal measurements alone.

The generalized source integral equation (GSIE)approach is extended to enhance the compressibility of moment matrices for essentially-convex homogeneous penetrable scatterers. In the proposed indirect surface integral equation (SIE) formulation, the scattered field representations use a single distribution of generalized sources that radiate only weakly into the scatterer. A separate distribution of conventional sources is used for representing the interior fields. The wave interactions between surface subdomains via the generalized kernel are of a reduced effective dimensionality. The corresponding exterior integral operator moment matrix blocks exhibit enhanced rank deficiency. For lossy scatterers, this secures the low-rank (LR) compressibility of hierarchical matrix structures for the full system. For scatterers in a much slower background medium, significant savings with very delayed asymptotic scaling of the costs can be achieved. The GSIE is formulated for the transverse magnetic (TM) scattering problem. It is validated for representative examples and its superior compressibility is demonstrated.

In many physics and space experiments the direction of a magnetic field must be known to one milliradian or better. We have developed a new type of magnetic compass, based on rotation, in which a spinning Hall probe (HP) produces an alternating signal proportional to the component of the magnetic field transverse to the axis of rotation. Aligning the rotation axis so that this signal vanishes gives the direction of the field. The device requires no calibration and is insensitive to electronics drift. A spinning-HP compass was built and used in a recent experiment at Jefferson Lab, where it determined the direction of the 25~G holding field of a polarized $^3$He target to about 1~mrad; the direction of the rotation axis was transferred to the laboratory frame with a laser and a flat mirror attached to the rotor. A second implementation of the rotation approach, a spinning field concentrator (SFC) with a stationary Hall probe, was also built. With 10 seconds of averaging the SFC compass resolves a transverse field of 18~$\mu$G, corresponding to a directional resolution of 70~$\mu$rad in a field of 0.25 G, close to the Earth's field.

Parnassus is a fast detector-simulation and reconstruction framework that maps truth-level particles directly to reconstructed particles. In this work, we investigate cross-detector transfer learning by adapting a Parnassus model for the ALEPH detector to the SLD detector. ALEPH and SLD have similar detector responses, as they were both targeting hadronic $Z$-pole $e^+e^-$ events, while differing substantially in the underlying detector technology, reconstruction, and archived data representation. We initialize the SLD particle model with weights learned on ALEPH, fine-tune it on SLD, and compare it with the same architecture trained directly on SLD. The ALEPH-initialized model gives substantially improved particle- and jet-level agreement with the SLD reference, including a factor of 5.2 improvement in the charged-particle angular response and an improvement in the jet angular resolution from $1.73$ to $1.15$ times the SLD reference. The results show that detector-response information learned on ALEPH can be reused when modeling SLD and motivate reusable pretrained Parnassus models for legacy $e^+e^-$ detectors, which is especially critical for experiments without access to the original software pipeline. With this paper, we also release an AI-ready version of simulated SLD $e^+e^-$ events.