Papers

This study investigates generalized boundary representations and transfer learning for physics-informed neural-network (PINN) estimation of spatially varying convective heat transfer coefficients (HTCs) in jet impingement cooling. Piecewise-linear (PWL) and neural-network boundary estimators are inferred from transient interior-temperature measurements and evaluated against CFD-CHT reference HTC profiles at Reynolds numbers of 5000, 10000 and 15000. A discrete Adjoint-FEM formulation with gradient regularization based on the $H^1$-seminorm provides an independent inverse-method comparison. Transfer learning is investigated under two source-target conditions: transfer between noise levels at the same Reynolds number, and transfer from a controlled Gaussian-profile problem to CFD-CHT-derived target problems at different Reynolds numbers. For the representative Re = 5000 target with 5% measurement noise, quadratic, PWL, and neural-network PINNs achieve relative HTC-profile errors of 5.08, 5.31 and 5.19%, respectively, while generalized representations reduce the error for the Re = 15000 target from 16.76% for the quadratic PINN to 14.41% and 14.46%. Transfer learning reduces target-training time by 57.86-81.71% for fixed-Reynolds-number transfer and by 50.59-84.94% for Gaussian-to-CHT transfer. Its effect on reconstruction accuracy is target dependent: fixed-profile PWL transfer remains within 0.30 percentage points of scratch training, whereas Gaussian-to-CHT transfer produces differences of 0.10-1.52 percentage points across targets and representations. These results demonstrate that generalized PINN representations can accommodate spatially varying HTC profiles without a prescribed quadratic form, while transfer learning can substantially reduce target-training cost for repeated inverse estimation, with the resulting reconstruction accuracy depending on source-target compatibility.

The environmental transport of microplastic particles has raised concerns because of their potential ecological and human-health impacts. A particular challenge for experiments on microplastic transport in soils is the fact that soil is a highly complex and non-transparent porous medium. Thus, our knowledge of transport mechanisms of microplastics in soils and related ecological implications is still limited and computational simulations offer an additional possibility to obtain important insights into the physical transport behaviour of microplastics. Yet, direct particle-resolved simulations in realistic soil structures for macroscopic sample sizes remain a computational challenge. We introduce an efficient pore-network model, minD-PNM, that predicts particle breakthrough curves in water-saturated soils using experimental X-ray micro-computed tomography ($\mu$CT) pore geometries as the only input. The model combines an approximately $t^{-3}$ pore-scale transit time distribution with a minimum-dissipation partition of multi-inlet-multi-outlet pores, and propagates these distributions through the pore network to predict breakthrough curves. We benchmark minD-PNM against lattice Boltzmann and immersed boundary simulations for small systems, and against microplastic column experiments in quartz sediments for macroscopic sample sizes. In both cases, the predicted breakthrough curves agree well with the corresponding reference data. Because it requires only geometric information, minD-PNM scales to macroscopic sample volumes offering a practical tool for contaminant-hydrology studies of particulate pollutants.

The future Electron-Ion Collider (EIC) will offer new opportunities to search for physics beyond the Standard Model. We study resonant anomaly detection using machine learning and the full particle content of an event. We consider a new neutral gauge boson and a heavy neutral lepton as illustrative signals with different electron-jet topologies. Weakly supervised learning uses this event information to enhance the sensitivity of an invariant-mass search without identifying individual signal events during training. Comparisons with high-level observables show that the electron kinematics account for an important part of the separation, while correlations among the particles provide further sensitivity. We also construct a conditional full-event generator by adapting a model pretrained on LHC jets to produce EIC background events. A scan over signal fractions demonstrates substantial signal enhancement using the learned background reference, providing a proof of concept for this approach at the EIC. Finally, we consider a fully unsupervised graph autoencoder that provides modest signal enhancement but retains sensitivity at signal fractions too small for effective weak supervision. Our results motivate full-event anomaly detection as part of the EIC physics program and identify directions for developing methods suited to its distinct kinematics.

Synthetic gauge fields provide a powerful route to engineer photonic wave dynamics, especially in synthetic frequency dimensions formed by modulated resonator modes. While most previous realizations have focused on Abelian or lower-rank non-Abelian structures, a photonic implementation of SU(3) gauge fields in the synthetic frequency dimension has remained lacking. Here we propose a route to realize SU(3) gauge structure using three coupled ring resonators with modulation- and interference-engineered mode conversion. We formulate a tight-binding Hamiltonian with matrix-valued hopping generated by the Gell-Mann matrices and show how its evolution can be decomposed into experimentally implementable optical elements. The resulting platform supports SU(3)-structured band dispersions with color-resolved texture and wave-packet dynamics shaped by SU(3) gauge fields. Full-wave-inspired numerical modeling further confirms the expected spectral response and Zitterbewegung-like motion in SU(3) subspaces. Our results introduce SU(3) gauge fields into synthetic frequency photonics and establish a route toward higher-rank non-Abelian control of light in modulated resonator systems.

The Black Hole Universe model has advanced in the literature. It assumes that the whole mass of the Universe remains inside the Schwarzschild radius of a black hole after being subject to a spherical collapse. We present a graphical proof and show that within the realm of classical theory this is not possible if spacetime remains regular at all times during the evolving collapse/bounce. Other drawbacks are discussed. As an alternative, we propose the Star Universe Model. In this model no other ingredients (such as inflaton field and dark energy) other than the perfect fluid itself are considered, and a positive spatial curvature is naturally introduced along with a particle production rate function. We discuss new-old static solutions, show that the density of a semi-closed Universe of gravitational mass $M_{\text{g}}$ is bounded from above by $3c^6/(32 \pi G^3 M_{\text{g}}^2)$, and elaborate on the birth of the Universe and calculate its inflation rate within the Star Universe Model. A smooth graceful exit from inflationary state occurs as the evolutionary solution approaches the generalized Einstein static Universe (with uniform energy density and negative pressure) and the expansion becomes linear in cosmological time with density and (negative) total pressure proportional to the square of the inverse of the scale factor. A similar behavior for density and (positive) pressure will occur if (re)collapse takes place. The analysis results in a time variable and decreasing effective cosmological constant $8\pi G\rho(\tau)/c^2$, which remains almost constant at late times, that justifies the late accelerated expansion with a rate of particle production three times the Hubble parameter.

Hard signal-to-noise ratio (SNR) screening removes weak seismic records, but does retaining clearer waveforms improve learning enough to compensate for the discarded data? We test this question in phase picking and ambient-noise dispersion, first matching training counts and then restoring the full eligible dispersion pool at fixed compute. Phase-picking screening gave no consistent unfiltered-test benefit under fine-tuning or random initialization, including a control matched on distance and phase composition. Dispersion screening increased error at the original five-epoch endpoint but improved on an equally sized unscreened set after longer training. Retaining all 29,788 eligible fit paths instead of 9,929 randomly sampled paths reduced unfiltered-test error by 3.0% across three seeds at the same update budget. Full-available and strict-SNR training then had similar mean errors, 0.0405 and 0.0406 km s$^{-1}$, with mixed seed-paired ordering; strict training retained a 9.9% advantage on the SNR-selected test. In two-day monitoring, SNR regulated pick streams but event recovery depended on its measurement definition. High SNR did not reliably identify better training data: gains depended on the task, training schedule, and test domain, and fixed-count comparisons omitted the benefit of retaining additional valid examples.

Elastic-wave propagation connects laboratory fracture with earthquakes, raising the prospect of seismic models that reuse waveform structure across physical scales. Here we show that the phase picker PNSN and the multi-task model SeismicXM organize unseen laboratory acoustic emissions around common waveform relations. Their representations agree on 512 held-out records relative to matched random networks. Four earthquake-model families also track displaced laboratory arrivals without fitting target neural weights. Receiver-function classification is accessible to frozen pretrained features, although standardized random features remain competitive. Noise correlations test the extension to continuous propagation outputs: a nonlinear SeismicXM ensemble improves on random topology, while a period-wise median remains more accurate. Waveform interventions and synthetic controls reveal that the observed agreement depends on feature calibration and the waveforms being compared. These results establish convergent waveform organization across distinct seismic training routes and show that cross-scale arrival transfer is already accessible to compact models.

We present a traceable instructional module that maps documented learner difficulties in quantum superposition and measurement to explicit learning goals, classroom activities, and assessment evidence. Using the general two-level Hamiltonian as the organizing physics framework, the module implements this mapping through a five-activity classroom sequence and grading rubric targeting four recurrent difficulties: interpreting superposition as physical splitting, confusing a quantum state with its representation or measurement context, mixing theoretical probabilities with finite-sample frequencies, and using inconsistent notation across states, amplitudes, probabilities, and measurement outcomes. The instructional design is organized through a reversible mapping from documented barriers to learning goals, activity responses, and assessment evidence, allowing each assessment item to be traced back to the specific difficulty it is intended to address. The module uses the standard general two-level Hermitian Hamiltonian, represented by a \(2\times2\) matrix, as the physical framework connecting its eigenstates, computational-basis amplitudes, Born probabilities, and sampled outcomes. The contribution is a challenge-targeted instructional design that organizes standard quantum mechanics into a traceable classroom sequence linking documented learner difficulties to learning goals, activities, assessment criteria, and implementation guidance. The mathematical structure uses standard Hamiltonian and measurement conventions, supporting direct incorporation into an undergraduate quantum-mechanics course.

We present Suêtes, a fully differentiable, non-hydrostatic, limited-area atmospheric dynamical core implemented in JAX. Suêtes includes both a semi-implicit semi-Lagrangian scheme and a Split-Explicit Runge-Kutta scheme with acoustic substepping, enabling applications from coarse regional simulations to convection-permitting dynamics. Its terrain-following geometry, spatial discretization, time integration, initial conditions, lateral boundary treatment, and included physical parameterizations are all compatible with reverse-mode automatic differentiation. A single reverse-mode pass can therefore compute sensitivities of forecast diagnostics to selected initial and boundary fields, physical parameters, topography, and computational geometry. We provide a series of numerical experiments—including a tracer-source inverse problem, a sensitivity analysis of a three-dimensional squall-line, adversarial perturbation construction an ERA5-driven downslope hurricane force wind event, topography optimization, and terrain-following coordinate optimization—demonstrating the versatility of Suêtes as a framework for gradient-based diagnosis and model development at regional and convection-permitting scale.

Deep-learning (DL) segmentation enables cardiac substructure (CS) delineation for advanced cardiac-sparing radiation therapy (RT). Patients presenting for RT may have implantable devices (cardioverter-defibrillators (ICDs)) and metal artifacts. Stereotactic body RT (SBRT) is used to treat ventricular tachycardia (VT), a population frequently with ICDs. We apply a DL model for RT patients with ICDs or undergoing VT-SBRT, enabling segmentation and CS-level dose analysis. CT simulation (CT-SIM) of 19 patients with ICDs and received VT-SBRT were evaluated. A validated segmentation model was retrained using a highly-curated CT-SIM cohort, supplemented with ICD images for training and validation, with a hold-out ICD test set (n=10), to predict 20 CS. Additional CT-SIM datasets included cancer-RT patients with (n=9) and without (n=10) ICDs to evaluate generalizability. The retrained ICD-supplemented model was compared against a non-ICD model using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and Wilcoxon signed-rank test (p<0.05). Dosimetric evaluation was performed for VT-SBRT CS. For VT-SBRT, the ICD-supplemented model achieved average DSC/HD95 of 0.71(0.19)/10.7(22.5)mm respectively, outperforming (p<0.05) the non-ICD model. On CT-SIM with ICDs, average DSC/HD95 were 0.70(0.22)/7.9(5.1)mm respectively, outperforming (p<0.05) the non-ICD model. On CT-SIM without ICDs, the average DSC was 0.74(0.17) (Non-ICD, DSC, 0.75(0.16), p<0.05) and average HD95 was 5.5(3.1)mm (Non-ICD, HD95, 5.3(2.8mm)). For VT-SBRT, CS dose depended on target location, with higher relative dose delivered to the ventricles, tricuspid valve, atrioventricular node, and left coronary arteries than other CS. ICD-supplemented training improved CS segmentation despite metal artifacts without meaningfully degrading non-ICD performance, supporting segmentation and CS-level dosimetry in ICD populations.

The paper reviews the theoretical and experimental progress achieved in the last 25 years in understanding the novel physics of artificial atoms and molecules as arising from the formation of Wigner molecules (WMs) of localized (to a stronger or lesser extent) fermionic or bosonic particles, which are finite quantum analogs of the more familiar bulk Wigner crystal. The term artificial atoms, as used here, encompasses a broad range of recently fabricated quantum nanodevices and experimental apparatuses consisting of a finite number of mutually repelling confined particles, including two-dimensional semiconductor and moiré transition metal dichalcogenide quantum dots, as well as trapped ultracold neutral atoms or ions. These nano-sized or micro-sized artificial devices and apparatuses (in single well or multi-well of variable-shape arrangements) hold a great promise for technological applications in the field of quantum information and quantum computers, as well as for advances in fundamental many-body physics. Prominent quantum effects of Wigner molecularization are the strong quenching of the spectral energy gaps, the appearance of rovibational spectra (in analogy with natural molecules), entanglement, and pinning due to an external perturbation. In high magnetic fields or at rapid rotation, WMs provide an alternative theory to the fractional quantum Hall effect. The physics of Wigner molecules is shown to derive from the solutions of the many-body Schrödinger equation (MBSE) in the regime of strong interparticle correlations arising from the dominance of the potential over the kinetic energy, or from a high magnetic field, as well as from a rapid rotation... (continues in the paper).

Recent demonstrations of unidirectional imaging using diffractive multilayers have brought a concept akin to optical isolation to imaging, yet the fundamental limits of such asymmetry have remained unknown. Here we develop a general theory of asymmetric imaging in linear, passive, reciprocal optical systems and derive a fundamental bound on the achievable unidirectionality. Reciprocity requires the forward and reverse transmission operators to have identical singular values, a wave-optical analogue of étendue equality, with a simple consequence: perfect asymmetry is attainable only for sets of $M \leq N/2$ orthogonal object patterns, where $N$ is the number of available object/image modes; for $M > N/2$, the average power asymmetry is bounded by $(N-M)/M$. Guided by this result, we present two complementary realizations: refractive imaging systems formed by bulk lenses and stops that attain the half-occupation limit, and a 1 cm-diameter all-silicon metasurface doublet that exhibits asymmetric focusing at 4 $\mu$m while fully complying with Lorentz reciprocity. We fabricate the doublet by direct laser writing and experimentally demonstrate its focusing asymmetry. By establishing mechanism-independent limits, our results provide a benchmark and design principle for asymmetric imaging across platforms, with implications for defense, surveillance and secure communications.

Hardware accelerators based on physical dynamical systems offer an attractive route toward energy-efficient reinforcement learning applications. However, their scalability is challenging because it requires many statistically independent entropy sources. Here, we introduce a quasi-analog decision-making architecture based on asynchronous Boolean networks (or lattices) implemented on a clockless reconfigurable chip. Each node in the network consists of a single logic element that acts as an autonomous entropy source. This architecture gives rise to distributed Boolean chaos, in which a spatially coupled network generates parallel streams of chaotic Boolean transitions with very low statistical dependence between nodes. We experimentally demonstrate parallel decision-making on a 1024-armed bandit problem, which is beyond the scale of previous hardware implementations, while significantly improving power-law scaling performance. Separately, we scale the proposed entropy source to 5120 parallel channels, yielding an aggregate sample generation rate of 2.14 TS/s. Our solution is implemented on a commercial reconfigurable CMOS chip and offers high integration density and ease of programmability. Our results pave the way for using distributed Boolean chaos as a valuable hardware substrate for large-scale reinforcement learning and for the development of fully integrated, high-throughput decision-making accelerators.

Accurate prediction of post-fire snow conditions does not necessarily establish how wildfire changed those conditions. We developed an explainable geospatial artificial intelligence framework combining multisource Earth observations, meteorological information, and matched before-after comparisons to examine seasonal snow persistence following the 2020 Creek Fire in California's Sierra Nevada. Harmonized Landsat Sentinel-2 observations were used to estimate the fraction of clear-sky observations containing snow during October-July for eleven water years, 2016-2026, on a 500 m analysis grid. We matched 3,778 snow-zone cells inside the fire perimeter to comparable unburned controls using terrain and pre-fire snow conditions; absolute standardized mean differences after matching were no greater than 0.047. HLS persistence agreed closely with MODIS, with a mean annual spatial correlation of 0.947. The landscape-average before-after control-impact contrast was +0.0017, with a year-level 95% confidence interval of -0.023 to +0.027. A stronger response emerged in the highest burn-severity class, where observed persistence increased by 0.026 relative to matched controls, equivalent to 2.6 percentage points. Under 5 km spatial cross-validation, XGBoost predicted raw post-fire persistence with an out-of-fold coefficient of determination of 0.811, whereas predictive skill for the fire-adjusted anomaly reached 0.046. Elevation and temperature together accounted for 65.7% of mean absolute model attribution. These findings distinguish a severity-associated optical snow response from the terrain-climate relationships that dominate predictive skill. Combining matched comparisons with explainable GeoAI provides a practical framework for forest monitoring that separates accurate environmental mapping from inference about disturbance effects.

MnBi$_2$Se$_4$, MnSb$_2$Se$_4$, and MnSb$_2$Te$4$ are layered magnetic quantum materials of interest for spintronic and topological electronic applications, where control of magnetic order at the atomic scale is essential. These materials are particularly sensitive to strain. We investigate their strain-dependent magnetism in the monolayer limit using first-principles calculations across the full two-dimensional strain space spanned by independent variations of the two in-plane strain components. Our results show that the Mn-projected local moments depend primarily on the volumetric strain, whereas the magnetic ground state, exchange interactions, magnetocrystalline anisotropy, and ordering temperature show pronounced directional and composition-dependent responses. We also observe strain-induced competition between the phases, with MnBi$_2$Se$_4$, and MnSb$_2$Se$_4$ monolayers exhibiting a more diverse range of competing magnetic states, while MnSb$_2$Te$_4$ monolayer remains ferromagnetic over a larger portion of strain space. MnSb$_2$Te$_4$ also exhibits a substantially larger out-of-plane magnetocrystalline anisotropy, while showing a weaker dependence on strain than the Se-based materials. The calculated magnetic ordering temperature maps further highlight the role of two-dimensional strain to tune thermal magnetic stability in these materials. These results establish magnetoelastic trends across this family of Mn-based chalcogenide monolayers.

We describe the design and characterization of a neutron scatter camera specifically developed for the study of neutron fields (flux, energy, direction) in experimental areas of spallation facilities, where multiple neutron sources can simultaneously contribute. A combination of hardware and logic triggers provides the isotropic response required for this application. Neutron source locations are accurately reconstructed on images from a built-in 360$^\circ$ panoramic camera. The full understanding of neutron fields that the device provides can be used for site selection and abatement of neutron backgrounds affecting sensitive instruments such as coherent elastic neutrino-nucleus scattering (CE$\nu$NS) detectors to be located at the upcoming European Spallation Source.

Purpose: Adaptive proton therapy (APT) relies on routine offline CT simulations for updated anatomy. We present a modality-agnostic regularized Brownian bridge (rBBrg) framework to deterministically predict stopping power ratio (SPR) maps from CBCTs or MRIs. Methods: rBBrg consisted of (1) generation mapping where a Brownian bridge predicted SPR from input and (2) reconstruction mapping where a cGAN reconstructed input from predicted SPR. Class embedding was adopted for modality conditioning. Efficient, deterministic prediction was achieved via one-step sampling. Unimodality rBBrg and ResNet were implemented for comparison. Matched-pair planning CT-CBCT (n=37) and CT-MRI (n=21) from head-and-neck cancer patients were evaluated. A calibration phantom was scanned to establish Hounsfield look-up table for real SPR. Predicted SPR maps were evaluated via mean absolute error (MAE), peak-signal-to-noise ratio and structural similarity against ground truth with demonstration of dosimetric performance. Results: For CBCT-SPR synthesis, most metrics were comparable across models (p>=0.05). Modality-agnostic rBBrg outperformed ResNet in MAEbone (0.059 vs 0.067, difference=11%, p<0.05) and showed lower MAEexternal than ResNet (0.038 vs 0.040, p=0.05). Modality-agnostic and unimodality rBBrg were comparable (p>0.05). Qualitatively, both rBBrg better preserved anatomical fidelity than ResNet. For MRI-SPR synthesis, performance was overall comparable across models (p>0.05). Modality-agnostic rBBrg obtained MAEexternal=0.057. Dose calculation comparisons between real and CBCT-predicted SPR were higher in modality-agnostic rBBrg predictions compared to ResNet with gamma pass rate=98.8%/99.3% vs 94.5%/96.9% at 2 mm/2%, respectively. Conclusion: A novel modality-agnostic rBBrg model was developed to generate high fidelity, deterministic SPR from CBCT or MRI to support efficient and flexible APT workflow.

In the present study, we explore the existence and characteristics of a rotating vibrational mode in a discrete buckled ring that manifests under transverse harmonic forcing. The study is conducted through mathematical analysis complemented by numerical simulations. The ring is composed of uniformly distributed concentrated masses connected by linear and torsional springs and supported by an elastic foundation. The buckled state of interest results from homogeneous in-plane compression and is characterized by vibrational modes that have a rotating nature because of the rotational symmetry of the system. The resulting vibrational mode, called a rotational soft mode, is characterized by large amplitude rotations of the ring deformation while keeping its angular momentum zero. It is shown that rotational soft modes can be activated via transverse harmonic forcing through parametric resonance, which is possible only when nonlinearities are introduced to the foundation. In the absence of nonlinear behavior, transverse vibrations of the ring cannot lead to excitation of the rotational mode. Analysis of a constrained kinematics model shows that linear stiffness of the foundation leads to decoupling of the rotational mode from transverse vibrations. When the foundation is considered piecewise linear, with different stiffness values in compression and in extension, it is shown that three different behaviors are possible for the dynamic response of the forced ring: i) no rotation, ii) a limit cycle within a limited finite rotation angle range, iii) induced rotation between subsequent equilibrium angles. The present study contributes to the understanding of the activation and control of soft modes in buckled structures and may lead to actuation applications in soft robotics, as well as advanced manipulation strategies of molecular systems in the context of nanomechanics.

Analytical studies of zonal-flow (ZF) excitation by fluctuating fields, such as drift-wave turbulence and Alfvén eigenmodes (AEs) driven by energetic particles (EPs) in tokamaks, are usually carried out via intricate gyrokinetic calculations and simulations whose results are notoriously difficult to interpret. Here, we report a transparent result that is also not limited to any particular modes or frequency range. Using oscillation-center theory that captures both ponderomotive forces and quasilinear diffusion, we derive a compact formula for the ZF drive produced by any fluctuating field in terms of the field's canonical momentum and the dissipation power density. For ZFs generated by drift waves, our model subsumes the local relation between the zonal velocity and the drift-wave energy density that was previously derived ad hoc. The simplicity and generality of our result also opens a path toward optimization of ZF excitation with external waves and bridges the physics of AE-EP interactions with that of poloidal flows driven by radiofrequency waves in fusion plasmas.

We report the design and experimental realization of a low-loss dielectric metasurface mirror operating at 1064 nm, the primary wavelength of interferometric gravitational-wave detectors. The mirror consists of a single layer of titanium dioxide nanopillars on a glass substrate, achieving high reflectivity via guided-mode resonance. The fabricated mirror has a peak reflectance of 99.10% with a total optical loss of 0.26%, representing, to our knowledge, the highest reflectance and lowest optical loss experimentally demonstrated in a substrate-supported purely amorphous dielectric metasurface mirror operating in the visible to near-infrared. This single-layer metasurface is projected to reduce coating Brownian noise by a factor of 3-5 relative to conventional dielectric Bragg mirrors at comparable reflectivity, which would improve the performance of precision optical cavities currently limited by this noise. These results indicate the potential of dielectric metasurfaces as a platform for next-generation precision measurement systems.

We have developed a computer code that models the three dimensional nature of a sodium plasma created by laser resonance saturation. This was accomplished by taking account of the temporal and spatial distortion suffered by the laser pulse as it propagates through a nonuniform sodium atom distribution. We have also undertaken the first measurements of the radial and axial electron density and temperature profiles arising in such a plasma and have been able to demonstrate that our computer code predictions are in reasonable agreement with these results. Absorption of the laser pulse is found to be quite significant for even a few centimeters of $10^{16}$~cm$^{-3}$ sodium atom density and that as a result quite low temperature plasmas can be produced for modest energy laser pulses.

Transporting droplets along cylindrical surfaces is an emerging area of research with a wide range of practical applications such as fog collection and filtration. Recent experimental studies by Feng et al. (2024) have revealed a wealth of new droplet coalescence dynamics on a pre-wetted cylindrical fibre, illustrating the need for more advanced theory. In this paper, we study both the early-stage and late-stage dynamics of droplet coalescence on a sloped cylindrical fibre. In the early stage, when the minimum thickness of the liquid bridge connecting two droplets is small, we propose a lubrication-based model to analyze the capillary-driven self-similar dynamics of the liquid bridge. Our theory predicts the bridge growth rate and demonstrates that asymmetry in the adjoining contact angles contributes to the migration of the merged droplet, showing good agreement with experimental observations. In the late stage, as droplet merging progresses, inertia effects become significant and lead to rapid mass transfer. To capture this regime, we develop a weighted residual model for liquid films flowing on a sloped pre-wetted cylinder, incorporating key physical effects such as gravity, low-to-moderate inertia, surface tension, and intermolecular forces. This model explains the experimentally observed directional self-propelled transport during droplet coalescence, where the larger droplet migrates toward the smaller one. Numerical simulations based on this model further support these findings, showing good agreement between the predicted coalescence-induced droplet migration and experimental observations.

Distributed quantum computing (DQC) has been proposed as a way to scale quantum algorithms for practical applications beyond monolithic quantum processor architectures. Among these applications, quantum chemistry is widely regarded as one of the most promising use cases for quantum computing. In this work, we estimate the distributed-resource requirements of unitary coupled-cluster (UCC) ansätze for quantum chemistry, focusing on unitary coupled-cluster singles and doubles (UCCSD), unitary pair coupled-cluster doubles (UpCCD), and unitary pair coupled-cluster with generalized singles and doubles (UpCCGSD) circuits for hydrogen chains. We focus on a teleportation-based approach to DQC, quantitatively comparing a naive distribution method to the output of the TeleSABRE algorithm. For both approaches, we estimate the cost of handling nonlocal two-qubit gates across a fixed midpoint or quarter-point partition, reporting Bell-pair/classical-communication costs in teleportation. Across Jordan-Wigner and Bravyi-Kitaev, we find that UpCCD with spin-blocked Jordan-Wigner ordering gives the most favorable scaling, while UCCSD incurs substantially larger distributed-resource requirements.

We consider an infinite array of graphene disks in a square unit cell, where every disk of the quadrumer is excited in the dipole regime. Hybridization of plasmonic eigenmodes belonging to different IRREPs of the point group of symmetry C4v is discussed. It is shown that the hybridization of the pair of 1D IRREPs modes A1 and B2 as well as the pair A2 and B1 can exist in this symmetry. For the cell period p0 and the quadrumer period P, the relation P/p0 = 2 defines polarization independence of these pairs of modes. The discussed modes are dark ones and require for their excitation breaking the point symmetry of the quadrumer and, for breaking their degeneracy, i.e. separation in the two pairs, a modification of the discrete translation symmetry, i.e. change the parameter P/p0. The separated modes can be excited by normally incident plane wave with orthogonal polarizations.

Photosystem II (PSII) contains a diverse array of pigment-protein subunits with specialized roles in the antenna network. We investigate the functional significance of this subunit diversity in two structurally similar yet functionally distinct PSII subunits: minor light-harvesting complex CP29 and major light-harvesting complex LHCII. We combine two-dimensional electronic-vibrational spectroscopy with lifetime density analysis and structure-based kinetic modeling to assign spectral features to exciton states, providing molecular detail to probed spectral features. We show how the distinct pigment compositions of CP29 and Lhcb1, a monomeric subunit of the major LHCII trimer, reshape excitonic connectivity and energy transfer pathways in the complexes. Compared to Lhcb1, energy transfer pathways in CP29 are more spatially connected and reversible, suggesting CP29 evolved to distribute excitation between the core and periphery of the PSII antenna. We find that small structural modifications from a common framework tune CP29 and Lhcb1 to complementary roles in the PSII supercomplex.

Our recent research on Langevin dynamics with self-harmonic drift and classical spins has revealed a strong connection to “Stochastic Localization," a concept attracting attention in the fields of mathematical geometry and data science. Roughly speaking, the former can be viewed as a version of the latter in which the $n$-dimensional Euclidean space - the domain of the probability distribution function appearing in the latter - is “compactified” to the surface of an $n$-dimensional sphere. By comparing the two frameworks, we argue that this compactification is a consequence of requiring the drift to be autonomous. The relationship between the two approaches may expand the scope of Stochastic Localization.

Quantum mechanics is justifiably held to be the most successful physical theory ever created; yet no universally accepted interpretation of its mathematical formalism exists after a century of debate. We examine the specific claim that classical physics stands as the low-action limit of quantum mechanics, arguing that the deep interpretive difficulties exposed by the quantum formalism, the measurement problem, the subject-object divide, and the nature of scientific representation, were already latent in classical physics, constituting what we call a blind spot of objectivity. Taking quantum theory as an invitation to re-examine the implicit metaphysical commitments bundled with 'classical physics', we explore two key issues-the objective/subjective split and the nature of theoretical representation-and argue that quantum mechanics does not represent a break with classical physics per se but rather brings previously unacknowledged philosophical assumptions into the limelight. We discuss the London-Bauer interpretation and QBism as exemplars of interpretations that embrace this broader reconceptualisation.

Acousto-electric (AE) interactions enable electrical control of acoustic propagation through piezoelectric media. Bringing AE control onto integrated photonic platforms provides a powerful on-chip control mechanism to reconfigure both the acoustic propagation through piezoelectric media. Bringing AE control onto integrated photonic platforms provides a powerful on-chip control mechanism to reconfigure both the acoustic delay line response and the effective photon-phonon interaction by electrically tuning the phonon propagation. Here we report a mode-selective AE modulation effect in a scalable aluminum nitride on silicon-on-insulator (AlN-on-SOI) platform. An applied DC field is seen to modulate the phonon dissipation via changes in carrier concentration in a mode-selective fashion, producing up to 20 dB forward transmission suppression of non-fundamental modes within an acoustic delay line while maintaining the fundamental Rayleigh mode, a phenomenon not captured by conventional AE treatments. To probe these dynamics, we integrate an optical waveguide along the acoustic delay line, providing a broadband, non-destructive interface between the acoustic and optical domains. Leveraging both this optical interface and the underlying mode selectivity, we demonstrate proof-of-concept multi-domain transduction and Rayleigh-mode filtering on this hybrid platform, outlining a scalable path toward electrically reconfigurable mode engineering and microwave-photonic functionality.

We derive a partially linearized version of the mapping approach to surface hopping (MASH) that provides a new method for simulating nonadiabatic dynamics in molecular systems. As in the original MASH formalism, trajectories travel on adiabatic surfaces with hops determined by the dynamics of a spin vector on the Bloch sphere. However, because the new approach is based on the partially linearized density matrix (PLDM), it has two spin vectors, which may be in the same or different hemispheres of the Bloch sphere. The former scenario describes an electronic population moving on a single adiabatic surface, whereas the latter describes a coherence moving on the average surface. We show that this new method, called MASH-PLDM, leads to improved results over the original MASH dynamics for a variety of scattering problems and spin-boson models, especially in cases where coherences play an important role. A further benefit of the new methodology is that it can treat multi-time correlation functions such as those required for nonlinear spectroscopy. We demonstrate this capability with a proof-of-principle simulation of time-resolved pump-probe spectroscopy.

Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is an extreme dense-sparse problem in which 3D seismic provides volumetric but indirect measurements and well logs provide high-resolution 1D measurements at sparse borehole locations. We introduce a physically grounded cross-modal masked autoencoder (CM-MAE) for seismic-to-well representation learning. The model jointly tokenizes seismic volumes and well-log depth patches, embeds both modalities in continuous physical coordinates using four-axis rotary position embeddings, and reconstructs masked targets with a cross-modal decoder. Sparse Mixture-of-Experts layers provide modality-specific capacity while dense attention allows information exchange between modalities. Pretraining uses 23 seismic volumes covering approximately 178,000 square km across U.S. offshore and onshore basins, together with approximately 92,000 wells. Matched-mask ablations show strongly asymmetric information flow. Seismic context improves held-out well-log reconstruction by 9.73%, while well-log context improves seismic reconstruction by 0.65%. We evaluate seismic-only pseudo-log predictions against independent interpreter-drawn salt-geobody masks to determine whether they contain geologic signal. Across offshore U.S. surveys, compressional-slowness-derived salt scores reach an AUROC of 0.910. In onshore basins, predicted compressional slowness preserves formation-scale structure and partial relative organization in an unseen survey despite calibration drift. CM-MAE learns useful seismic-to-well representations under extreme modality asymmetry, although absolute pseudo-log calibration remains survey-dependent.