Papers

Solidification is expected to suppress fingering by removing mobile liquid. Here we report an unexpected re-emergence of fingering in impacting molten-tin drops: cooling first weakens peripheral fingers, but stronger undercooling restores them. At weak undercooling, fingers originate from a decelerating liquid rim and solidification reduces the effective acceleration that drives Rayleigh–Taylor-like amplification. At strong undercooling, solidification structures develop while liquid is still spreading, creating lateral mobility contrasts that redirect the residual flow. Comparison between the kinematically estimated solidification time and the experimentally measured fingering-onset and growth-termination times reveals a reversal in temporal ordering across the temperature range. A physics-based reduced-order model combines solidification-modified rim deceleration with mobility-gradient growth, captures the characteristic onset dynamics and accounts for the final finger length within the same physical framework.These findings establish solidification as an active means of regulating interfacial flow and morphology, with broad implications spanning metal-based additive manufacturing and debris mitigation in semiconducting industry.

This paper proposes and analyzes a compact fluorescence-photometer lab-on-a-chip that is simpler than a fluorescence spectrometer, and that does not require spectrum reconstruction by a computer. An on-chip AlGaN VCSEL lasing at 280 nm, or at 310 nm, pumps an on-chip sample in the form of liquid or solid or tissue, thereby exciting signature fluorescence in the 300 to 540 nm wavelength range. This emission signal is detected and read out by an on-chip UV-enhanced Silicon photodiode. The 2 cm x 5 cm chip consists of C-plane (0001) Sapphire, and no lenses, prisms, or mirrors are utilized in the photometer. The chip is opto-electronic and features co-packaged electronics using tiny electronic chiplets that are bonded to the sapphire. These consist of an LD driver and transimpedance amplifier for the UV photonics, together with ASICs, ADC, microcontroller, micro-battery and RF-wireless or Ethernet chiplets. The mission of this self-contained cost-effective chip is sensing targeted medical-chemical-biological (MCB) agents in a local or network setting. Eight significant MCB applications are detailed here.

The structure of high-entropy alloys, which include 5 or more components in equimolar proportions, is proposed to be described in the mathematical language of Latin squares. This model is fundamentally discrete and allows for a quantitative description of ideal mixing, in which any given alloy element is guaranteed not to have an identical element as its nearest neighbor. For the two-dimensional case, the model possesses four axes of translational symmetry, which makes it possible to tile the plane with a single Latin square and, based on the properties of this square, describe the properties of macroscopic material samples. The model makes it possible to make quantitative estimates of the suitability of a particular atomic structure (configuration) for implementation in a perfectly mixed high-entropy alloy with maximum mixing entropy. A condition has been obtained for acceptable fluctuations in the concentration of components at which the thermodynamic stability of the alloy is maintained. The possibility of identifying superstructures in high-entropy alloys based on group theory, as applied to the description and construction of Latin squares, is discussed. Based on the proposed model, the probability of forming a multicomponent alloy with ideal mixing is estimated. For a five-component alloy, this probability is no more than 2.6*10^{-10}.

Tidally-locked planets express a range of climate regimes distinct from those found in our solar system. Their atmospheres are often dominated by planetary-wide overturning circulations and super- rotating jets. In this paper we explore the hydrological cycle of such planets and how it might depend on the total inventory of condensate. To this end we use a set of idealised GCM simulations to probe the response of the hydrological cycle to the surface moisture budget in their atmospheres. We find that for low moisture inventories the climate quickly develops a barren day side and a moist night side where the moisture is trapped, as might be expected. For higher moisture inventories the drying of the day side is significantly delayed by the development of a substellar lake just east of the substellar point, which is sustained by the presence of a local thunderstorm-like convective system that persists even after the lake disappears. The timescales for the lake to disappear are millennia, suggesting that even a weak transfer of moisture through surface processes could maintain a moist day side. We also find that adjusting the threshold at which the evaporative rate becomes constrained by the remaining surface liquid depth modifies the longevity of the lake and surrounding day side conditions. These findings have implications for understanding a diverse range of planetary climates and habitability, given the distribution of moisture and presence of a substellar lake.

Helium incorporation during magnetron sputtering can strongly affect thin film growth and lead to the formation of gas/solid nanocomposites and nanoporous structures. Here, molecular dynamics (MD) simulations are used to investigate the trapping mechanism of helium atoms (He) and the subsequent formation of He bubbles during the sputter deposition of aluminum (Al) and titanium (Ti) thin films. The simulations account for the kinetic energy distributions of sputtered metal atoms and backscattered gas species. Results indicate that He is efficiently incorporated into the growing films, leading to the formation of high-pressure, dense He bubbles, with a much higher He-to-metal atomic ratio in Ti ($\sim$ 60%) than in Al ($\sim$ 12%). More importantly, the simulations reveal a clear energy dependence of He trapping in Ti: background He atoms at thermal energies are essentially not incorporated, whereas efficient trapping starts at an incident energy of approximately 20 eV and increases up to a saturation regime near 100 eV. Simulated thermal desorption spectroscopy (TDS) further reveals distinct release mechanisms in the two investigated metals: He desorption from Al occurs at higher temperatures and is preceded by the removal of surface Al atoms, whereas He and Ti atom desorption starts continuously around 950 K, leading to a porous structure. Comparative simulations with argon (Ar) demonstrate negligible trapping even at comparable kinetic energy or momentum, showing that neither parameter alone accounts for the strong He incorporation observed in Ti. The above results are in very good agreement with what is reported in literature for low-kinetic energy He ion implantation in metal surfaces, suggesting similar insertion mechanisms. They clarify the atomistic conditions governing noble gas trapping during film growth and provide a basis for controlling He incorporation and porosity in films synthesized by magnetron sputtering.

The effect of mechanical stresses and deformations upon the behavior and properties of cells and tissues is a topic of paramount interest in the field of biophysics. Its understanding is essential for deepening our comprehension of cellular responses to environmental stimuli, with implications in fields ranging from embryogenesis to tumor development. For boosting the understanding in this field, it is important to develop devices for the application of simple deformations in order to probe the cellular response to the basic mechanical transformations. While a number of stretching devices have been developed, in-plane shearing deformation has been little investigated. Here, a new device is introduced that allows the application of homogeneous in-plane shear upon a cell monolayer while imaging the sample with a commercial optical microscope. A description of the faced experimental challenges is reported and a full characterization is presented. It is demonstrated the capability of the device to apply reproducible, controlled and homogeneous shear of both single cells and confluent cellular monolayers.

When investigating the performance of supercapacitors (SCs), it was found that they offer high-power density and long life but are limited in capacity and energy density. Water-in-salt electrolytes, i.e., 21___m lithium bis(trifluoromethanesulfonyl) imide (LiTFSI), offer a solution by expanding the electrochemical window compared to a diluted aqueous environment. While significant research has been dedicated to improving these electrolytes, the impact of carbon properties on energy storage remains relatively underexplored. This study investigated the influence of carbon structures, defects, and surface functional groups on supercapacitive performance in LiTFSI electrolytes. For this purpose, a series of carbon materials, including carbon nano-onions and porous conductive carbon black (ENSACO), were synthesized and systematically modified by oxidative and reductive thermal treatments. Our results indicated that higher specific surface area and active surface area improve the ion adsorption and capacitance, while specific oxygen functionalities, such as carboxylic and anhydride groups, were found to significantly enhance capacitance. Furthermore, crystallinity and graphitic characteristics play a role in capacitance, with smaller graphene domains and greater disorder promoting ion storage. The study demonstrates a direct correlation between carbon microstructure and electrochemical behavior, providing insights into the rational design of carbon electrodes in high-performance SCs.

These notes summarize the content of a one-week course given by Luis Vega on the binormal flow model applied to describe the dynamics of three-dimensional vortex filaments in incompressible fluids following the Euler equations. This topic touches connections between the binormal flow, the Schrödinger map and the 1d cubic nonlinear Schrödinger equation, providing a rich casuistry linked to the physical and numerical phenomenology. This course was part of the $2^{\text{nd}}$ Advanced Summer School held at the Institut d'Etudes Scientifiques de Cargèse (IESC), at Cargèse (Corsica), from $20^{\text{th}}$ to $30^{\text{th}}$ June 2023. These notes have been written and completed by two of its attendees, Elena Danesi and Pablo Merino.

Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic strategies for new-physics searches at the Large Hadron Collider. Searching for rare events in learned low-dimensional representations removes the need to commit to hand-crafted observables a priori, but sacrifices the immediate physical interpretation those observables provide. For anomaly detection to enter a discovery pipeline, flagged events must therefore be diagnosed locally, both to distinguish potential new physics from mismodeling or detector effects and to identify observables for targeted follow-up searches. We evaluate supervised and self-supervised neural embedding strategies on COLLIDE-2V, a 750-million-event dataset simulated under realistic High-Luminosity LHC conditions, using the reconstruction error of an autoencoder trained in the learned latent space as the anomaly score. Across the configurations and benchmark signals considered, supervised objectives, which exploit simulation process labels during training, generally outperform their self-supervised counterparts and, in their best configurations, improve on an autoencoder trained directly on kinematic features, raising the signal efficiency at 10% background acceptance on all held-out processes. We further introduce a local diagnostic pipeline that fits a Gaussian Mixture Model to the SM latent space, profiles each component through high-level observables without process labels, localizes the flagged events, and ranks observables by their local discriminating power. The ranking recovers the defining kinematics of the held-out processes, while correlating the anomaly score with the same observables quantifies how closely the score tracks them, revealing cases where the most discriminating observable is not the one the score follows most closely.

Self-consistent numerical simulations of free boundary instabilities, such as resistive wall modes (RWMs) and external kink modes, require special attention given their relevance on the path towards magnetic fusion based nuclear reactors. In this work, a new implementation and a verification benchmark are presented for the nonlinear three-dimensional (3D) full magneto-hydrodynamics (MHD) code SpeCyl, which self-consistently combines a free flow 3D treatment for the edge plasma velocity with double resistive wall thin shell magnetic boundary conditions, capable of capturing the essential features of common setups in experimental devices. Two coaxial resistive shells surround the plasma. Vacuum conditions are assumed between and outside the resistive shells, whose relative positions and time constants can be varied on purpose. The implementation is extensively verified against Shafranov's analytic theory, valid for a circular cross section large aspect ratio tokamak and assuming a flat current profile. The correctness of newly implemented Boundary Conditions (BCs) is confirmed by the good agreement obtained for the linear growth rates of (m=2,n=1) RWM and external kink, over a wide range of wall positions, resistive shell time constants, and for different initial reference equilibria. The (m=2,n=1) RWM growth rates are also benchmarked against previous different RWMs modelling and successful validation studies for RFX-mod tokamak plasmas. The new BCs make SpeCyl a flexible and robust tool to investigate free boundary MHD modes in large aspect ratio magnetic confinement configurations, such as those achieved with the RFX-mod machine and expected in RFX-mod2.

We report a soft-aperture Kerr-lens mode-locked Tm:Y2O3 ceramic laser stabilized by suppressing wavelength competition under high population-inversion conditions. The laser delivers an average output power of 0.5 W with a pulse duration of 112 fs at a repetition rate of 90 MHz and a central wavelength of 2.08 um. Further optimization for shorter pulses yields a pulse duration of 107 fs, through with a reduced output power of 0.22 W. These results demonstrate that spectral-control is an effective approach for achieving stable mode-locking in such laser with strong spectral competition.

Reduced order modeling provides an efficient alternative to high fidelity simulations for the rapid prediction of fluid flows in many query settings. Among non intrusive approaches, Operator Inference (OpInf) enables the identification of reduced dynamical operators directly from data while preserving a prescribed mathematical structure. The present work extends physics structured OpInf to the construction of parametric Reduced Order Models for transient incompressible flows governed by the Navier-Stokes equations. The velocity and pressure fields are represented using separate global reduced bases, and their reduced operators are identified directly from temporal and parametric POD coefficients. The identification procedure is formulated as a sequence of uncoupled least squares problems, leading to an efficient offline stage while avoiding the explicit projection of the governing equations. The approach is first evaluated on a lid driven cavity flow in a periodic regime to assess temporal extrapolation and prediction at previously unseen parameter values. Its applicability is then demonstrated for transient flow through a porous medium with a square obstacle, for which the nonlinear terms are not directly amenable to a standard Galerkin projection. The results demonstrate that the approach can accurately reproduce both velocity and pressure fields while providing computationally efficient predictions for unseen parameter values.

Micro- and nanostructures fabricated by focused ion beam (FIB) milling invariably deviate from input designs due to systematic and stochastic effects such as ablation-rate fluctuations, and redeposition. As such, nominally identical iterations of a given process yield a distribution of non-identical fabrication outcomes, leading to variations in the physical (optical, mechanical) properties of fabricated structures. Existing models of the milling process typically predict a single deterministic fabrication result, neglecting this variability. Here we present a conditional diffusion model that learns the statistical distribution of process outcomes conditioned on input design, enabling it to predict a realistic distribution of possible output sample structures. These predictions accurately reproduce edge morphology, linewidth variations, and defect probabilities observed in fabricated samples, enabling statistical analysis of milling performance and the identification of dominant structural and process parameter-dependent deformation modes. The work establishes a probabilistic modelling framework for fabrication variability analysis and fabrication-aware design optimization.

Efficient simulations of grain growth are essential for investigating the process-structure-property relationships in polycrystalline materials. We introduce a two-dimensional von Neumann-Mullins-based graph grain growth simulation (VN-GraphGG), which couples the grain-scale kinetics with microstructure reconstruction. The polycrystalline microstructure is represented as a graph with grain and junction nodes. The grain-area evolution on the grain graph is governed by the von Neumann–Mullins (VN) law, while a graph neural network is trained to reconstruct the microstructure from the graph. This separation allows kinetic coefficients to be varied without retraining the neural reconstruction model. Topological changes during grain growth such as disappearance of grains and changes in neighbor relations are handled straightforwardly through reconstruction and postprocessing. Simulations demonstrate accurate predictions of VN kinetics and coarsening statistics, and significant speedup over the phase-field simulations. The proposed method provides a foundation for efficient grain growth simulation based on grain-scale kinetics, opening the way to incorporate a broader range of physical mechanisms. An application example is presented for abnormal grain growth, in which kinetic heterogeneity significantly influences the grain microstructure evolution.

The Filoche–Mayboroda (FM) localization landscape predicts where the spatially localized modes of a self-adjoint elliptic operator reside without diagonalizing it, but the construction fails for the complex, non-normal Green's matrix G of an open scattering array of electric dipoles. We propose three real-valued, positive landscapes built from the Feinberg–Zee hermitization of G: a regularised Gramian inverse (Gram), the diagonal Schur block that carries the Metz–Neri–Rogers spectral density (SY), and the full hermitized inverse contracted against a polarization-isotropic source pair (Herm). The three form a progression from spectrum-blind to fully hermitisation-faithful, agree in the limit $z\to 0$, and diverge where it matters: at nonzero spectral shift SY resolves modes that Gram collapses, and Herm resolves modes that SY collapses. We demonstrate the progression on random and deterministic aperiodic scattering arrays with a wide range of structural correlations, extend to the vectorial case of transverse electric polarization (TE), and provide rigorous justifications along with mathematical proofs and a complete numerical recipe in the appendices and in the supplementary material.

How can human researchers use artificial intelligence (AI) to make progress on difficult mechanics problems while retaining control of physical significance, mathematical rigor, and validation? We explore this opportunity through a quantitative stability problem rooted in the Eshelby conjecture. For a uniform dilatational eigenstrain in a homogeneous isotropic solid, we establish a nonlinear two-sided relation between whole-interior strain heterogeneity and a geometry-only distance from the ellipsoid family, with half-order Sobolev scaling that captures morphological length scale. We then develop a complementary finite-shape theory: ellipsoidal subtraction yields an exact residual-field budget from which systematically convergent upper and lower bounds are obtained for a prescribed geometry. Applied to protruded, indented, and necked inclusions, the bounds agree with independent displacement-based finite-element calculations. The study also documents a human-led, AI-assisted research process in which researchers select consequential problems, shape targets, audit arguments, require independent checks, and interpret mechanics, while AI broadens analytical exploration and supports computation. This experience also led us to develop Open Problems in Mechanics, a versioned community-facing map that makes selected mechanics questions, their known/open boundaries, and criteria for progress explicit.

A particle beam with constant intensity that propagates into a plasma triggers a bump-on-tail instability. It has been demonstrated that beam modulation can be used to significantly reduce this instability. However, finding the optimal beam profiles that accomplish this is a complex optimization problem that suffers from an extremely rugged optimization landscape and a relatively high-dimensional parameter space. In this paper, we evaluate a number of optimization algorithms and find that population based methods (in particular, CMA-ES and Differential Evolution) perform best. In particular, we investigate beam profiles where the beam is either completely turned ON or OFF. This configuration is easier to accomplish experimentally, but leads to a higher-dimensional and thus more challenging optimization problem. We obtain ON/OFF beam profiles that reduce the instability by more than 80% and thus perform significantly better than what has been reported in the literature.

Surface-enhanced Raman scattering (SERS) is typically achieved by depositing the analyte on a metal or semiconductor substrate. We propose a bottom-up approach, enabling SERS by depositing a metal on the surface of the object being studied. A Mie-theory analysis of electric-field enhancement in dielectric core-shell particles is presented. A correlative approach combining SERS, atomic force microscopy, and scanning electron microscopy is employed to model the conditions required for sensitive SERS detection and spectral analysis of polystyrene microspheres (PMSs) as models of globular and vesicle-like objects. Based on these findings, we propose a SERS-active system that provides strong signal enhancement (up to four orders of magnitude for a single globular object) while maintaining spectral fidelity, where PMSs act both as model analytes and as integral components of a metal-dielectric resonator.

Short-term aftershock incompleteness biases Gutenberg-Richter b-value estimates because many small earthquakes remain undetected immediately after large events. Two strategies address this problem. Earthquake replenishment reconstructs missing events to restore a more complete catalog, whereas robust positive-difference estimators infer the b-value directly from incomplete observations. Whether these approaches provide equivalent information on the magnitude distribution remains open. We address this issue using synthetic aftershock sequences generated with the Epidemic-Type Aftershock Sequence (ETAS) model and degraded through a time- and magnitude-dependent incompleteness model. Incomplete catalogs are replenished using AutoBETA, and b is estimated using the standard maximum-likelihood, b-more-positive, and b-more-incomplete estimators. Replenishment reconstructs the overall structure of the missing-event population and substantially improves conventional maximum-likelihood estimates, although small residual biases remain. By contrast, both positive-difference estimators recover the imposed b-value directly from incomplete synthetic catalogs. Applications to the Ridgecrest, Landers, and Hector Mine sequences show that b-more-incomplete is less sensitive to replenishment than b-more-positive. Ten independent replenishment seeds indicate that seedto-seed variability is small relative to differences among estimators. Replenishment and robust b-value estimation therefore address different aspects of catalog incompleteness: replenishment provides a more complete aftershock representation, whereas robust estimators are preferable when accurate b-value estimation is the primary objective.

We investigate the quantum dynamics of a single ion confined in a radio-frequency (RF) Paul trap in the presence of weak octupole-induced anharmonicity, as can occur in realistic ion-trap potential. The explicitly time-dependent quadrupole potential is treated in a reference-oscillator Fock basis, allowing the effects of the RF drive and the quartic correction to be resolved directly at the level of motional-state couplings. While the quadratic RF interaction generates $\Delta n=\pm2$ transitions and Gaussian squeezing, the quartic contribution opens additional $\Delta n=\pm4$ coupling pathways. Starting from the reference-oscillator vacuum, we show that the octupole interaction broadens the even-parity Fock-state distribution and enhances the phonon-number fluctuations more strongly than the mean occupation. At the same time, the RF-induced sub-vacuum quadrature squeezing remains robust, although its magnitude and orientation are modified by the quartic interaction. The number statistics and quadrature fluctuations therefore respond differently to the same nonlinear perturbation, reflecting the distinct roles of Fock-space redistribution and phase coherence. The resulting dynamics provide a quantum-mechanical characterization of weak octupole corrections beyond the ideal quadrupole approximation and clarify how realistic anharmonicities modify nonclassical trapped-ion motion. Importantly, the motional nonclassicality remains robust against the weak octupole perturbation, as evidenced by persistent sub-vacuum quadrature squeezing and Klyshko-inequality violation. These results characterize how realistic anharmonic corrections reshape trapped-ion motion beyond the ideal quadrupole approximation.

Lagrangian Neural Networks (LNNs) learn mechanical systems from trajectory data by parameterizing a scalar Lagrangian and deriving the dynamics through the Euler-Lagrange equations, biasing the model toward motion that respects the variational structure of mechanics. The interactions studied so far have been pairwise-additive, leaving open whether LNNs can recover an irreducible many-body interaction from motion alone. We address this using the Axilrod-Teller triple-dipole interaction, the leading three-body dispersion term, whose dependence on the collective geometry of three particles cannot be represented by any sum of pairwise terms. We prescribe the kinetic energy exactly and learn only the potential, as a function of interparticle distances and angles. Combined with a signal-aware sampling strategy that concentrates training data where the three-body signal is strongest while preserving broad coverage of configuration space, this recovers the interaction from accelerations alone. The learned potential reproduces the radial scaling, angular dependence, and characteristic sign reversal of the Axilrod-Teller interaction, and generates stable trajectories. Embedding it within a pairwise Lennard-Jones potential lets us examine its observability: the three-body term is recovered once its dynamical signature exceeds the residual modeling error, which it does cleanly at amplified coupling but not at the coupling of a real noble gas, bracketing the transition between the two. Finally, symbolic regression on the trained potential, with no assumed functional form, returns the Axilrod-Teller expression with angular numerator, radial law, and coupling constant intact. An LNN with domain-specific structure can thus recover an irreducible three-body interaction from trajectories as a closed-form law, pointing toward learning interaction laws whose structure is not known a priori.

Recent numerical simulations have shown that black-hole magnetospheres evolve rapidly toward alignment between the magnetic axis and spin axis but the physical origin of this process has remained unclear. We develop an analytic theory of this alignment within resistive relativistic magnetohydrodynamics. Taking the slow-spin Blandford-Znajek monopole solution as the exterior magnetic field, we reduce the induction equation to a relaxation equation for the current-sheet inclination angle. Our analysis shows that alignment results from second-order spin effects in the Blandford-Znajek solution combined with explicit resistive dissipation. We test this prediction using resistive-GRMHD simulations at several spins and resistivities and find a regime in which the measured late-time secular decay agrees quantitatively with the analytic prediction.

Simulating acid-base equilibria from first principles is a tremendous challenge, as it requires modeling bond-breaking events in a complex hydrogen-bonding environment. The quantum mechanical behavior of the protons is often important, complicating simulations even further. We show that by combining state-of-the-art machine-learning potentials, accelerated sampling of the dissociation process, and an efficient estimation of nuclear quantum corrections it is possible to reliably compute the dissociation constants of different species in water. We demonstrate the transferability of this workflow, applying it successfully to the autoionization of water and to the entire ladder of the aqueous dissociation of phosphoric acid. The definition of the dissociation constant, the choice of order parameters, sampling artifacts and finite-size effects, as well as quantum nuclear effects all play a non-trivial role in determining the outcome of the calculation, so that cutting corners is likely to introduce biases that can only be compensated by fortuitous cancellations of errors.

We introduce a nonconservative nonlinear Schrödinger equation (NLSE) which includes additional nonlinear terms accounting for the pseudo-stimulated-Raman-scattering (pseudo-SRS) effect and cubic loss. An essentially novel feature of the model is the linear anti-diffusion gain, which leads to the obvious blow-up in the infinite or spatially periodic system. We consider the absorptive boundary conditions, which suppress the blow-up driven by effective convective instability in the setup. The NSLE model is derived, in the extended adiabatic approximation, from the underlying Zakharov system, which includes the viscosity term acting on its low-frequency component and the anti-diffusion gain in the high-frequency (HF) one. The latter term appears if the expansion, with respect to the deviation of the wavenumber, is performed not around the minimum-loss point (as in the usual situation), but around a maximum-loss point. Dynamics of moving solitons in the model is addressed by means of analytical and numerical methods. The sech approximation for the soliton predicts it as a stable fixed point (FP) of the system of balance equations for the HF norm and wave momentum, derived in the moving reference frame. The FP attraction basin is identified too. The predictions are corroborated by direct simulations of the full NSLE with the absorptive boundary conditions.

Ultrafast laser-assisted field emission from metallic nanotips is a promising route for generating coherent electron pulses. However, existing theoretical models often neglect many-body strong correlation effects in the emitter's electronic structure and its response to the optical field. In this work, we propose a theoretical framework based on the non-equilibrium Green's function formalism to simulate the out-of-equilibrium electron dynamics across a strongly driven matter-vacuum interface, in a one-dimensional model. Treating the tip-vacuum coupling perturbatively, we find that the interplay between strong-field emission and multi-photon emission regimes can be described in terms of a filtering of the emitter's local density of occupied states through field-dressed vacuum propagators. By including electron-electron interactions via the second-Born approximation, we show that correlation-induced modifications in the electronic structure are directly transferred to the far-field spectrum. Finally, by self-consistently solving the Dyson equation at the interface, we uncover a dynamic back-action regime driven by the optical field, characterized by a periodic polarization of the emitter, which reshapes the far-field spectrum. This shows that the non-equilibrium Green's functions approach provides a versatile framework for exploring and analyzing field emission of correlated electrons.

Currently available methods for processing atmospheric wave disturbances are often based on integral wavelet transform techniques, which efficiently estimate spectral data but are fundamentally limited by the uncertainty principle. To address this limitation, this work proposes a novel morphological approach based on partitioning spatial disturbance profiles into separate, well-defined wave segments. By focusing exclusively on the local waveform morphology of a central half-wave and its two adjacent out-of-phase counterparts, and taking the modified Morlet wavelet as a primary example of a model wave packet, we successfully reconstruct the complete set of local wave parameters directly from the amplitudes and positions of these three dominant half-waves. Consequently, the theory demonstrates good agreement with observations, confirming the high accuracy of the proposed waveform morphology approach in reconstructing the localized structure of atmospheric disturbances. When applied to evaluating the phase shift between different wave field components, the method reveals that this shift is a weighted combination of the classical monochromatic phase difference and a morphological one determined by the internal symmetry of the underlying wave packets. Although the classical factor yields the dominant contribution, the symmetry-related factor provides a moderate correction to it. The applicability and accuracy of the approach are verified using observational data gathered over several orbits of the low-Earth-orbit satellite Dynamic Explorer 2 as it passed over Earth's polar caps. The obtained results confirm that the observed disturbances correspond to medium-scale acoustic-gravity waves propagating at a small angle to the horizontal plane, demonstrating the broader methodological potential of the proposed technique for analyzing a wide range of wave phenomena.

The physical origin of foreshocks remains one of the major unresolved problems in statistical seismology. In bottom-up (BU) triggering models, foreshocks are interpreted as ordinary earthquakes that stochastically trigger a larger event through cascading interactions, as in the Epidemic-Type Aftershock Sequence (ETAS) model. In contrast, top-down (TD) models interpret foreshocks as manifestations of a preparatory process preceding the mainshock. This ambiguity has important implications not only for earthquake forecasting, but also for declustering itself, since the inferred number of foreshocks may depend on the assumed physical mechanism governing seismicity. Here we investigate the detectability of foreshocks using synthetic catalogues representing two limiting physical scenarios: purely bottom-up triggering and mixed preparatory-triggering dynamics. These cases are implemented through the ETAS and ETAFS models, respectively. We compare the classical nearest-neighbor (NN) declustering metric with a reverse nearest-neighbor metric (rNN) introduced here. While the standard NN metric scales the correlation metric to the magnitude of the earlier event, rNN scales it with the magnitude of the later event. We show that rNN is mathematically equivalent to applying the standard nearest-neighbor metric to the time-reversed catalog, establishing a direct symmetry between the two approaches. Our simulations reveal [...]

Chiral anomaly in topological materials manifests as negative longitudinal magnetoresistance, yet the tunability and evolution of this effect remain elusive. Here we show that doped tellurium serves as a versatile platform to realize a giant negative longitudinal magnetoresistance that is highly tunable via the Fermi level and magnetic field. By moving the Fermi level towards the Weyl point, we observe a pronounced enhancement of negative magnetoresistance, which arises from chiral anomaly and robustly persists up to ambient temperature. Beyond the quantum limit, the system exhibits positive linear magnetoconductance, serving as a compelling transport signature of the chiral anomaly in the lowest Landau level. Notably, in the extreme quantum limit at cryogenic temperatures, the magnetoresistance undergoes a dramatic reversal from negative to positive. This reversal points to a potential field-induced phase transition into a collective electronic state driven by enhanced electron-electron interactions. Our findings provide valuable insights into the unconventional chiral transport and emergent correlated quantum states under extreme magnetic fields.

The resolution of spectroscopic measurements is fundamentally limited by the convolution between a system's intrinsic response and the instrument response function, creating an ill-posed inverse problem. While entangled photons can theoretically bypass these limits, low photon flux has hindered their experimental realization. Here, we propose quantum cross-correlation spectroscopy (QCCS), utilizing macroscopic twin beams (signal and idler beams) to physically circumvent this convolution limit in coherent anti-Stokes Raman scattering. Employing the signal beam as a broadband probe, QCCS achieves all-optical deconvolution through a correlation-enhanced sum-frequency generation channel between the convoluted anti-Stokes and idler beams. By subtracting the uncorrelated background using a 500-femtosecond temporal window, we successfully retrieve narrow Raman bands (8 cm^{-1} and 15 cm^{-1}) completely obscured by a 75 cm^{-1} probe bandwidth without numerical deconvolution or prior knowledge. Our results establish macroscopic quantum correlations as a robust resource for high-fidelity spectroscopy, paving the way for ultrashort-pulse-free operando studies under natural, incoherent excitation conditions.

Neural operators that learn physical laws from data should generalize beyond their training distribution and expose the learned law in closed form. The Kolmogorov-Arnold neural operator achieves both for position-dependent dynamics by learning a dense pseudo-differential symbol over all positions and frequencies, at a cost that grows with the square of the resolution and with closed forms that must be read out by hand. This dense symbol is unnecessary: a finite-order variable-coefficient differential operator has a symbol whose separation rank is bounded by the number of its coefficient functions, independently of the resolution, and the symbols that this network learns on its own benchmarks have rank two. We introduce the Kolmogorov-Arnold flow (KAFlow), which learns the generator of a physical operator as a low-rank symbol built from univariate Kolmogorov-Arnold functions, fixes the gauge of these functions so that every term has a unique place, integrates the dynamics with exact exponential integrators, and distills its closed form automatically. On the synthetic benchmarks of the Kolmogorov-Arnold neural operator, KAFlow lowers the out-of-distribution loss by 3.4 to 11 times and recovers every coefficient to within 10^-4 without symbolic templates, reaching the precision floor of the data. On long-horizon quantum dynamics, it lowers the state infidelity by up to 6 orders of magnitude, and by 30 times when trained only on position and momentum measurements. KAFlow also learns a variable-order fractional operator 15 times more accurately than existing operator networks and discovers nonlocal, fractional and non-polynomial dispersion relations on which sparse regression fails.