Papers

White-light interferometry exploits broadband optical spectra to generate a localized interference envelope, providing a unique reference beyond the periodicity of individual fringes and underpinning numerous applications in precision metrology, ranging, and imaging. Despite the common wave nature of light and matter, a matter-wave counterpart of white-light interferometry has remained unexplored. Here we report the first realization of white-light matter-wave interferometry, extending this established optical paradigm to matter waves. Using a continuous atomic beam as a broadband matter-wave source, we synthesize velocity-dependent spectral components to generate a localized matter-wave interference envelope, introducing a new measurement observable based on envelope localization rather than conventional fringe-phase estimation. As a proof-of-principle demonstration, we implement simultaneous dual-axis rotation and acceleration sensing with a white-light Mach–Zehnder atom interferometer, revealing three defining features: envelope localization beyond phase ambiguity, a velocity-engineered measurement response, and intrinsic rejection of phase bias. We further measure the Earth's rotation with a relative error of 4.3$\%$ and achieve a long-term stability of 93 ppm relative to the Earth's rotation rate after 15,000 s of averaging. These results establish a new interferometric mechanism in which ambiguity, measurement response, and robustness can be addressed through the intrinsic design of the broadband matter-wave interference itself, extending the principles of optical white-light interferometry to atom optics and opening new opportunities for quantum sensing, precision metrology, and matter-wave metrology.

Time series analysis is a fundamental component of machine learning, especially in astrophysics and cosmology where temporal data abound. This chapter provides a pedagogical review of time series analysis techniques from a machine learning perspective. We cover the basic concepts of time series (stationarity, autocorrelation, seasonality), classical statistical models (autoregressive, moving average, ARIMA, exponential smoothing, state-space models), and modern machine learning approaches. In particular, we discuss how traditional statistical methods lay the groundwork, and then explore machine learning methods for time series, including feature-based regression, tree-based ensemble methods, hidden Markov models, Gaussian processes, and deep learning models (recurrent neural networks, convolutional networks, transformers). Throughout, we illustrate with examples drawn from multiple domains (e.g. astronomy, weather forecasting, finance) to emphasize common principles. The goal is to equip readers with both the theoretical understanding and practical context to apply machine learning techniques for time series analysis in their research.

The behavior of higher-order radiative corrections due to initial state radiation in processes of electron-positron annihilation is analyzed. Numerical results for energies of future colliders are presented. Uncertainties of the known results on these corrections are estimated. A modified scheme for simultaneous exponentiation of pure photonic and non-singlet pair corrections is presented. Matching of the exponentiated results with the existing analytic higher-order calculations is constructed. A new subtraction scheme is suggested. The results obtained are important for increasing the relative accuracy of the theoretical description of processes that will be studied in future electron-positron colliders to a level of approximately $10^{-5}$.

Imaging Atmospheric Cherenkov Telescopes (IACTs) are the leading instruments for very-high-energy (VHE) gamma-ray astronomy, covering the range from hundreds of GeV to hundreds of TeV. This chapter reviews the critical role of machine learning in reconstructing the physical properties of particles detected by IACTs. We introduce the IACT technique and its central challenge: distinguishing rare gamma-ray showers from the overwhelming cosmic-ray background. We detail the standard reconstruction pipeline, from image cleaning and Hillas parameterization to stereoscopic observation, and frame event reconstruction as a supervised learning problem encompassing particle classification and energy/direction regression. Two frontiers of innovation are then explored: the exploitation of the temporal dimension of shower images through novel timing-based features that enhance background rejection at low energies, and the application of advanced ensemble methods (gradient boosting, stacking) that surpass baseline Random Forests, notably in mitigating systematic energy bias. Finally, we discuss performance metrics and provide an outlook on next-generation approaches dominated by deep learning, including Convolutional and Graph Neural Networks.

Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes. Although population-synthesis models predict distinct planetary populations, establishing a quantitative connection between observed exoplanets and synthetic populations remains challenging. We investigate the intrinsic organisation of close-in exoplanets using physically motivated dynamical parameters and connect the resulting populations to pebble-accretion formation pathways. A two-stage Gaussian mixture model (GMM) is applied to an observed sample of close-in exoplanets, performing unsupervised probabilistic clustering in a feature space dominated by dynamical descriptors of planet-star interactions. The resulting clusters are mapped onto a pebble-accretion synthetic population within a statistically motivated three-dimensional parameter space. Formation-related quantities, including gas availability, gas fraction, and ice-rock mass ratio, are then used to interpret the mapped populations. We identify statistically supported sub-populations without imposing predefined classification boundaries, including very-massive gas giants, hot giants, warm-Jupiter-dominated systems, and lower-mass giants. The mapped synthetic populations reveal systematic differences in formation timing, gas accretion, and solid growth histories. In particular, very-massive gas giants are preferentially associated with earlier formation epochs than hot-giant and warm-Jupiter-dominated populations. These results demonstrate that physically motivated machine-learning approaches can provide a statistically robust framework for linking observed exoplanet populations to theoretical planet formation pathways.

We analyze the geometry and kinematics of substructures within molecular clouds identified in an unbiased catalog from the MWISP survey. These substructures are defined as spatially connected regions enclosed by the 20% peak-integrated-intensity contour of each cloud. After applying selection criteria on voxel size and excluding structures truncated by map boundaries, we construct a sample and quantify their projected morphology using the projected scale ratio $R=\Delta b/(\Delta l\cdot\cos b)$. This ratio essentially measures $\tan\theta$ where $\theta$ is the plane-of-sky angle of an elongated filament relative to the Galactic plane. The resulting sample exhibits a median $R=0.96$, indicating a slight but systematic preference for elongation along Galactic longitude. This tendency becomes more pronounced at larger spatial scales. We further investigate the relative orientations among the structural major axes, velocity-gradient directions, and plane-of-sky magnetic-field orientations derived from Planck data for a subsample of well-defined structures. We find that, for cloud structures within our sample, with physical scale $\sim 0.3$ to $\sim 30$ pc, velocity gradients tend to be perpendicular to the major axes, while magnetic-field are generally aligned parallel to them. This scale range differs from those typically probed in studies of dense cores ($\sim 0.05$ pc) and GMC-scale structures ($\gtrsim$ 10 to 100 pc), which have reported scale-dependent variations in relative orientations. In addition, the alignment between velocity gradients and magnetic fields shows a gradual weakening with increasing physical scale. These results suggest that the observed anisotropy of molecular cloud substructures may arise from a combination of large-scale Galactic dynamics, anisotropic gas motions, and magnetic fields, with the relative importance of these effects varying with scale.

In this paper, we investigate energy extraction via the Comisso-Asenjo (CA) magnetic reconnection process near a coupled $\mathcal{N}=2,\,U(1)^2$ gauged supergravity Black Hole (BH). Our study focuses on the combined impact of the independent parameter set $p_i\in(N_g,g,v,e)$ with the spin parameter $a$ on the extracted energy ($\epsilon_{\pm}$), efficiency ($\eta$), and extracted power ($\mathcal{P}_{CA}$), aiming to identify optimal combinations where energy can be extracted with higher efficiency in certain cases at lower spin $(a\sim0.39)$ than the Kerr extremal case $(a\sim1)$. Using the spacetime parameters, we explore various cases leading to distinct spacetimes and provide an extended comparison with the Kerr Black Hole (KBH). We also examine the influence of the orientation angle ($\xi$) and magnetization parameter ($\sigma_0$) on both efficiency and extracted power. Investigating low $[\,\forall p_i<0.2 \land N_g<0.08\,]$, mid $[\,\exists p_i\ge0.5 \land N_g\in(0.08,0.15)\,]$, high $[\,\exists p_i>0.7 \land N_g\in(0.16,0.23)\,]$, and mixed $[\,\forall p_i\in(0,1) \land N_g\in(0,0.23)\,]$ parameter combinations, we explore only extremal cases for all spacetime parameters and demonstrate that the extremal Kerr efficiency limit ($\eta>1.495$) can be exceeded. The statistical Kendall's Tau approach allows us to identify the key independent parameters acting as boosters or dampers in the energy extraction process and to visualize the relationship between $(N_g,g,v,e)$ and the physical outputs $(a_{\rm ext},r_E,r_{\rm ergo},\epsilon_{\pm},\eta,\mathcal{P}_{CA},R_{\eta},R_{\mathcal{P}})$. Furthermore, we show that the observable Lundquist number $S_{\rm obs}$ in rotating BH spacetimes acquires an observer-dependent angular dependence through the lapse function $(\alpha)$. This leads to deviations from the standard Sweet-Parker scaling when expressed in terms of observable quantities.

We study the dynamics of quantum correlations in the hydrogen hyperfine spin system subject to Markovian phase noise. Treating the electron and proton spin degrees of freedom as an open two-qubit system governed by an isotropic hyperfine Hamiltonian and local dephasing, we obtain the exact time-dependent density matrix and derive analytical expressions for the full X-state family. We compute concurrence($C$), trace-distance measurement-induced nonlocality (Trace MIN–$\mathcal{N}_1$), and average steering coherence (ASC) in closed form and establish their strict ordering $ C(t)\leq \mathcal{N}_1(t)\leq \mathrm{ASC}(t) $ at all times. Entanglement is identified as the most fragile resource, undergoing sudden death at a finite time. Trace MIN exhibits dephasing-immune freezing for states with nonzero population imbalance, while ASC is the most robust quantity, persisting longest in every scenario studied.We additionally demonstrate that the dephased thermal hyperfine state serves as a resource for quantum teleportation, deriving a closed-form expression for the average fidelity and establishing that the teleportation advantage window coincides exactly with the entanglement survival interval, $\mathcal{F}_A > 2/3 \Longleftrightarrow \mathcal{C} > 0$, for the full X-state family with maximally mixed marginals. We identify four distinct dynamical regimes and map all three correlation measures onto directly measurable Pauli spin correlators, enabling experimental reconstruction of the full hierarchy without full state tomography.

How should one design efficient chemically open optical cavities for molecular strong coupling? Addressing this question is important for the development of soft-cavity platforms for dynamically tunable light–matter interactions, where direct access to confined electromagnetic modes is essential. Conventional cavity figures of merit such as $Q/\sqrt{V}$ and cooperativity successfully describe spectral confinement and dissipation but do not fully capture the role of linewidth asymmetry between cavity and molecular degrees of freedom. Here, we systematically investigate strong coupling between TDBC dye molecules and whispering gallery modes of polystyrene microspheres by varying the microsphere radius over a broad range. To quantify the robustness of strong coupling, we define the parameter $\chi = \frac{g}{\max(\kappa,\gamma)}$, where $g$ is the coupling strength, while $\kappa$ and $\gamma$ denote the cavity and molecular linewidths, respectively. Although the coupling strength decreases monotonically with increasing cavity size due to mode-volume scaling, we find that $\chi$ exhibits a pronounced maximum near the condition $\kappa \approx \gamma$. This observation suggests that linewidth matching is not merely a criterion for improved spectral visibility, but reflects a dissipation-matching condition that optimizes the robustness of coherent light–matter exchange in soft-cavities. Our results provide an alternative framework for designing morphology-dependent cavities for molecular strong coupling.

Gravitational waves from extreme-mass-ratio inspirals (EMRI) provide a direct probe of the strong-field geometry of black holes. Motivated by this, we study the motion of test particles and the resulting gravitational wave emission in the spacetime of a charged black hole inspired by loop quantum gravity (LQG), where the classical singularity is replaced by a smooth transition surface arising from the LQG polymerization, in which its radius is set by the LQG area gap condition. As a result, the polymerization parameter $\delta_b$ is uniquely determined by the mass $M$ and charge parameter $Q$, so that all cases examined in this work contain LQG correction. By constructing the effective potential, the innermost stable circular orbit (ISCO) and the marginally bound orbit (MBO) are determined. Periodic orbits are classified using the Levin-Perez-Giz zoom-whirl taxonomy, showing how the orbit topology shapes the waveform, so that each closed trajectory is labeled by the triple integer $(z, w, v)$ and located through the rational frequency ratio $q = \omega_\phi/\omega_r - 1$. Within the quadrupole approximation, the gravitational waveforms for an EMRIs are estimated, and the resulting polarizations are obtained in the time-domain and frequency-domain. The resulting polarizations in the time-domain exhibit a zoom-whirl morphology, with the waveform amplitude and phase dependent on the LQG parameter. The characteristic strain peaks in the millihertz band for all values of the charge parameter $Q$, and they exceed the projected sensitivities of LISA, Taiji, and TianQin, suggesting that future observations could place meaningful constraints on the LQG polymerization parameter in the strong-field regime.

We perform a Bayesian inference analysis of an inflationary model based on an inverse-tangent potential, incorporating reheating dynamics in both constant and dynamical equation-of-state (DEOS) frameworks. Using Planck and ACT constraints on the scalar spectral index, we find preferred values $\kappa\simeq0.5-0.6$ and $N_k\simeq40-60$, leading to reheating temperatures $T_{RH}\sim10^{10}-10^{14}$ GeV and reheating durations $N_{RH}\sim3-36$ e-folds. Reheating weighted $H_0$ posteriors shift the Planck inference towards the ACT preferred region through the intrinsic $n_s-H_0$ degeneracy of the CMB likelihood. In the DEOS framework, reheating with a constant decay rate yields $N_{RH}\simeq4-8$ e-folds and $T_{RH}\simeq10^{13}$ GeV, while a dynamical decay rate produces a strong dependence on the Yukawa coupling $y$, with $N_{RH}$ varying from $\mathcal{O}(30)$ to $\mathcal{O}(1)$ e-folds and the reheating temperature spanning $\sim10^{-2}-10^{14}$ GeV. Imposing inflation-reheating consistency significantly restricts the viable parameter space to a narrow region around $n_s\simeq0.9720-0.9725$ and $r\simeq0.026-0.060$, demonstrating that reheating dynamics provide a nontrivial bridge between early-universe inflation and late-time cosmological parameter inference.

Molecular hydrides have attracted relatively less attention in the search for high Tc superconductors because their hydrogen quasi-molecular units tend to be electronically inactive for superconductivity. In contrast, hydrogen rich compounds under high pressure have been widely considered strong candidates for achieving room-temperature superconductivity. However, their dependence on extreme pressure conditions significantly constrains their practical applicability. This work investigates hydrogen-rich superconducting materials that may be stable under ambient pressure conditions. Motivated by recent studies on the MgZrH2n family, a LiMgZr2H12 structure with Pmmm symmetry was designed. The mechanical, thermodynamic, and dynamical stability of the compound, together with its electronic and optical properties, were systematically investigated using first-principles calculations. Li doping in LiMgZr2H12 significantly increases the hydrogen derived contribution near the Fermi level (EF) and strengthens the electron-phonon coupling constant ({\lambda}) compared with MgZrH6. LiMgZr2H12 exhibits a critical temperature of 72.76 K at ambient pressure, which is further enhanced by applying pressure. At 10 GPa the critical temperature increases to 77.3 K. Elastic property analysis shows that the material remains mechanically stable over the pressure range studied (0-10 GPa). It also behaves like a ductile material suitable for current carrying applications. The material has a high machinability index, which is much higher than that of stainless steels. In addition, LiMgZr2H12 exhibits a gravimetric hydrogen storage capacity of 5.36 wt%, indicating its potential as a promising candidate for hybrid hydrogen storage technologies. This work offers a new direction for designing high-Tc hydrides at ambient conditions.

Motivated by the observed $KD^{(*)}$ molecular candidates $D_{s0}(2317)$ and $D_{s1}(2460)$, their bottom–strange counterparts, $K\bar{B}^{(*)}$ molecular states, are naturally expected, although not yet experimentally established. This discrepancy may reflect sizable heavy-quark flavor symmetry breaking, which introduces significant model uncertainties. Current studies of heavy-quark flavor symmetry breaking effects still exhibit strong parameter dependence, and further experimental input is required to constrain these effects, in particular regarding possible additional $K^{(*)}D^{(*)}$ and $K^{(*)}\bar{B}^{(*)}$ molecular states. In this work, we examine whether additional $K^{*}D^{(*)}$ molecular states can be identified among the observed $D_s$ resonances. Within the Gaussian expansion method, we solve the Schrödinger equation using $\sigma$, $\rho$, $\omega$, $\pi$, and $\eta$ exchange potentials, systematically including $S$-wave and higher partial waves. We find that $D_{s1}(2700)$ can be interpreted as a pure $P$-wave $DK^{*}$ molecule, while $D_{s1}(2860)$ and $D_{s3}(2860)$ are well described as $D^{*}K^{*}$ molecular states dominated by the $^{1}P_{1}$ and $^{5}P_{3}$ components, respectively. We also predict additional molecular states with various $J^{P}$ quantum numbers. These results provide a new description of the charmed-strange spectrum, and once confirmed will provide additional input data for studies of heavy-quark flavor symmetry breaking effects.

We are developing a compact three-element Wide Field Corrector (WFC) with spherical and aspherical lenses for the 2.34 m Vainu Bappu Telescope (VBT) to enhance its field coverage for imaging and spectroscopic applications. The design consists of three optical elements, with at least one spherical lens movable to serve as an Atmospheric Dispersion Corrector (ADC), while the aspherical elements remain fixed to maintain optical stability. We are currently testing two design configurations, one with two spherical lenses and one aspherical lens, and another with two aspherical lenses and one spherical lens. The ADC is designed to correct atmospheric dispersion for zenith angles ranging from 0 degree to 60 degree. The system is optimized to operate over a wavelength range of 0.4 {\mu}m to 0.9 {\mu}m, targeting an effective field of view of about 0.5 degree. Considering the limited mechanical space available at the VBT prime focus, the design emphasizes compactness, ease of alignment, and manufacturability. The system achieves a mean D80 better than 0.3 arcsec and 0.23 arcsec for Design 1 and Design 2, respectively, at zenith, and maintains a mean D80 within 0.57 arcsec and 0.45 arcsec up to a zenith angle of 60 degree after atmospheric dispersion correction. Atmospheric dispersion at higher zenith angles (up to 60 degrees) is corrected using a movable lens element, enabling the system to preserve high image quality across the field.

We explicitly compute the axial anomaly in dense matter and show that it takes the same form as in vacuum, even in the massless limit. This result follows from a subtle cancellation in the anomalous Ward identity between the medium-induced contributions to the divergence of the axial current and to the pseudoscalar density. We then revisit the chiral magnetic effect in a fermionic medium coupled to an axial chemical potential in an external magnetic field. We show that, in the clean limit, the medium supports a persistent, conserved anomalous current carried by fermions. The current is determined by the axial chemical potential and suppressed by the Fermi velocity, in agreement with anomalous axial-current correlation functions. Finally, we discuss applications to axion physics, where axion dark matter acts as an effective axial chemical potential, and estimate how helicity relaxation by impurity scattering limits the induced current in realistic conductors.

During the evolution of a binary black hole, energy and angular momentum are exchanged between the orbital motion and the individual black holes through horizon absorption, modifying both the binary dynamics and the black hole masses and spins. This leaves an imprint on the emitted gravitational waves that may be relevant for the accurate modeling of signals observed by current and future detectors, while also offering a probe of the nature of compact objects. In this work, we derive, for the first time and at leading order in the post-Newtonian expansion, the effect of horizon absorption in binary black hole inspirals with both orbital eccentricity and spin-induced precession, and we incorporate these corrections into the pyEFPEHM waveform model. We then quantify their impact through analytical estimates of the orbital dephasing, waveform mismatches, and Bayesian parameter-estimation studies. The effect is largest for systems with large spin components (anti-)aligned with the orbital angular momentum ($|\vec{\chi}_i \cdot \hat{l}| \sim 1$), highly unequal mass ratios ($q=m_2/m_1 \ll 1$), and long inspirals spanning a wide frequency range ($\log(f_\mathrm{max}/f_\mathrm{min}) \gg 1$). For such systems, neglecting horizon absorption biases the recovered binary parameters at moderate signal-to-noise ratios. In quasi-circular binaries these biases largely absorb the effect, rendering it difficult to detect. In eccentric binaries, however, the richer signal morphology breaks this degeneracy, making horizon absorption potentially measurable in high signal-to-noise-ratio events.

Artificial intelligence is driving intense interest in alternative computing hardware capable of neural information processing beyond conventional charge-based electronics. Among emerging approaches, wave-based computing promises highly parallel and energy-efficient operation, but scalable physical neural hardware has remained elusive because wave systems generally lack cascadable nonlinear neurons with signal regeneration and phase-robust operation. Here we demonstrate integrated magnonic neural circuits based on nonlinear threshold neurons realized in nanoscale yttrium iron garnet waveguides. The neurons perform weighted summation of multiple spin-wave inputs, while a pump-controlled nonlinear activation defines continuously tunable firing thresholds. Owing to deeply nonlinear spin-wave dynamics, the activated neurons emit self-normalized outputs whose intensities are largely independent of the input amplitudes, while nonlinear phase self-adjustment suppresses sensitivity to the relative input phases, enabling deterministic neuron-to-neuron cascading without external signal restoration. We experimentally realize programmable threshold neurons, reconfigurable weighted classification and deterministic cascading between sequential neuronal stages, and further demonstrate reconfigurable physical pattern recognition in a seven-neuron integrated magnonic circuit through experimental classification of the binary letter patterns 'HUST'. These results establish nonlinear magnons as a scalable platform for integrated neural hardware and position nonlinear wave dynamics as a general paradigm for physical neuromorphic computing.

Manipulating phonon band structures through wave interference in artificial periodic nanostructures is of fundamental interest and forms the foundation of phonon band engineering. Yet, direct observation of thermal phonon wave interference has remained elusive despite half a century of extensive research. Here, utilizing inelastic x-ray scattering, we directly observed momentum-resolved coherently modulated thermal phonon bands and lifetimes in a short-period GaAs/AlAs superlattice at 300 K. The modulation persists at even 500 K, demonstrating robust coherent thermal phonon transport at high temperatures. Our measurements, combined with ab initio lattice-dynamics calculations, further show that thermal phonon interference expands the three-phonon scattering phase space and strengthens anharmonic effects, highlighting the interplay between phonon band modulation and anharmonicity. Our observations establish nanostructuring as a versatile route for engineering thermal phonon bands and lifetimes at high temperatures across diverse applications.

Latent diffusion and flow matching have emerged as leading approaches for synthetic turbulence generation, yet they systematically under-represent dissipation-range amplitudes. We introduce a latent flow matching framework with a spectrally regularized compression stage that directly targets this failure mode. On a 256^2 DNS dataset at Re_f \approx 2250, replacing an MSE-trained VAE with a zone-weighted log-spectral objective raises deep-dissipation retained spectral power from 25% to 94% in reconstruction and from 20% to 79% in unconditional generation. The improved latent representation also yields a substantially better sampling cost-fidelity tradeoff: the MSE-trained latent space imposes a fundamental quality ceiling near DD bias -0.70 that no integrator or step-count can overcome, while the spectrally regularized latent space reaches DD bias -0.117 at just 20 function evaluations. Mechanistically, encoder-decoder swap experiments show that the improvement is driven primarily by encoder-induced latent reorganization rather than decoder capacity, while a support-amplitude decomposition reveals that MSE-trained models behave as conservative suppression models, minimizing pointwise error by attenuating intermittent high-wavenumber structure. Both pipelines recover the second-order structure function and the correct sign of S_3, indicating the correct cascade direction without explicit supervision. A small residual gap in the magnitude of S_3 suggests that phase-coherent triadic organization remains a complementary axis to amplitude fidelity for future generative turbulence models.

Resolving the instantaneous, position-dependent isotropic magnetic exchange coupling $J_{ij}$ across a dynamically deforming crystal lattice requires a computational approach that simultaneously handles structural forces and magnetic interactions at length scales inaccessible to first-principles methods. Here we introduce DSpinGNN, a bifurcated machine-learning architecture comprising an $E(3)$-equivariant graph neural network (E-GNN) for classical Langevin structural dynamics and a physics-informed $\Delta$-MLP that maps instantaneous local Cr-I-Cr bond geometry to isotropic exchange couplings, with the Goodenough-Kanamori superexchange relationship embedded as an analytical inductive bias. Trained on 345 DFT+U configurations of monolayer CrI$_3$ and evaluated on a strictly withheld 61-configuration test set, DSpinGNN simultaneously achieves an energy MAE of $1.1$ meV/atom, a force MAE of $6.5$ meV/Å, and an exchange coupling MAE of $0.18$ meV ($R^2 = 0.91$). Deployed at 400$\times$ scale in a 3,200-atom supercell under a collinear Ising-constrained adiabatic approximation at $5$ K, the model maps the local exchange response to a propagating biaxial strain wave. Wave reflection at periodic boundaries generates transient constructive interference regions where local compressive strain exceeds the DFT-established FM-to-AFM threshold, producing spatially heterogeneous exchange coupling textures that damp as the wave dissipates. Quantitative analysis yields a domain wall width of $\xi = 1.7 \pm 0.3$~nm and a constructive-interference oscillation period of $\tau = 0.27$~ps – mesoscopic observables inaccessible to direct DFT and constituting testable predictions for cryogenic magnetic force microscopy. DSpinGNN provides a reproducible, transferable framework for mesoscale exchange mapping in strain-driven 2D magnetic materials.

The position operator plays a central role in condensed-matter observables such as velocity, orbital moment, and electric polarization. In solid-state physics, the evaluation of operators incorporating the position operator has not reached a consensus, as observed in the operator-level discrepancy between the local circulation of Wannier functions and the self-rotation of wave packets. Here, to achieve a consistent evaluation of such operators, we propose three rules for evaluating operators involving the position operator in the Bloch representation. The rules are devised to satisfy physical conditions: independence from the choice of unit cell, preservation of Hermitian conjugacy for the product of operators, and recovery of the correct intraband velocity. We further address the gauge dependence of the position operator and introduce a scheme termed gauge filtration, which systematically removes gauge-dependent contributions from the operators containing the position operator. This methodology ensures that the quantities obtained from the operator evaluation correspond to observable physical phenomena. By applying our framework, we reconcile the results concerning the self-rotation of the wave packet and the local circulation of the Wannier function. We expect our proposal to establish a consistent framework for evaluating operators involving the position operator.

Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid deep-learning parameterized Probabilistic Cellular Automata (CA) framework implemented in JAX. Our approach employs a Multi-Scale Convolutional Neural Network to dynamically generate spatially varying parameters that govern fire-spread probability, wind alignment, and slope influence. This hybrid design captures complex, nonlinear environmental interactions while preserving the physical interpretability of the underlying three-state CA. The JAX implementation enables hardware acceleration and gradient-based parameter calibration. Evaluated on six large-scale wildfires in the western United States, the model maintains IoU > 0.6 over 72-hour forecast horizons after a 10-day data assimilation window during which the model is fitted incrementally to observed perimeters; the resulting forecast is a conditional projection of fire growth under the suppression regime already ncoded in those observations.

Standard dot-product self-attention computes, in a single layer, only pairwise (order-2) interactions between tokens; representing a generic order-$k$ interaction is known to require either super-quadratic resources in one layer or composition across depth. We introduce Quantum Higher-Order Attention (QHA), a shallow, hardware-realizable quantum attention head that, via data re-uploading and an all-to-all non-Clifford entangler, synthesizes order-$k$ token interactions inside the circuit and exposes them through a local single-qubit read-out. We prove (i) an expressivity separation: any single standard self-attention layer with embedding dimension $m$, $H$ heads and $p$-bit precision satisfying $mHp=o(N/\log\log N)$ cannot represent the order-$k$ correlation family that one QHA head represents with circuit depth $O(\log k)$ ($O(k)$ two-qubit gates); and (ii) a trainability guarantee for its local-design instantiation: with a local read-out and $O(\log n)$ depth the gradient variance is $\Omega(1/\mathrm{poly}(n))$ (no barren plateau), which we confirm empirically – while being explicit that the more expressive all-to-all instantiation we benchmark is trained empirically and shows exponentially decaying gradients. Empirically, at a $6.5\times$ smaller parameter budget, QHA generalizes hidden-subset parity of every order $k\le6$ from disjoint inputs, whereas the larger classical attention head collapses past order~2; consistent with theory, the size of the advantage tracks the target's Fourier degree - largest for parity and shrinking when low-order structure is present. As an application, QHA serves as a compact high-order interaction detector across three domains - genetic epistasis, learning-parity-with-noise, and graph triangle detection - reaching the noise ceiling at the smallest parameter budget where field-standard linear methods fail.

WIMP scattering from 40Ar is investigated using a self-consistent Skyrme Hartree-Fock-Bogoliubov (HFB) approach. Nuclear form factors relevant to dark matter direct detection are calculated from the resulting one-body density matrix elements and compared with shell-model predictions. Good agreement is found for the spin-independent response, while significant differences are observed for the spin-orbit response due to variations in single-particle occupancies. The effects of particle-number projection are shown to be small for 40Ar. These results demonstrate the sensitivity of certain dark matter response channels to the underlying nuclear structure model and establish a framework for extending mean-field calculations to nuclei beyond the reach of large-scale shell-model studies.

The appeal of adiabatic quantum computing lies in its intrinsic robustness against various technical imperfections, making it attractive for many quantum information applications. However, it faces a fundamental challenge: accelerating the adiabatic operations while preserving adiabaticity within the qubit coherence time. In this article, we propose an electromagnetically induced transparency-based adiabatic CNOT gate protocol which harnesses atomic hyperfine intermediate states (HISs) to speed up the adiabatic evolution. The HISs, naturally-existed in two-photon transitions, often need to be suppressed due to their significant decay errors. In contrast, this paper introduces a novel method that utilizes appropriately chosen HISs not only to enhance the adiabaticity in STAY pathway but also to accelerate the population transfer in TRANSFER pathway. Through pulse optimization, we achieve adiabatic gate fidelities exceeding 0.9991 within 0.3903 {\mu}s in realistic Cs atomic setups. To demonstrate the generality of protocol we further assess the impact of decays from multiple HIS and extend our model to arbitrary number of states, providing a practical route toward fast and robust adiabatic quantum gates in Rydberg-atom platforms.

Mangroves are increasingly promoted as nature-based solutions for coastal protection, yet many existing models neglect the vertical variation of vegetation biomass, leading to oversimplified representations of root-flow interactions. In this study, we introduce a generalised parametrisation of the mangrove vegetation profile and derive a wave attenuation model that explicitly accounts for the mangrove root characteristics. The model reveals that wave attenuation is species specific and exhibits a non-monotonic dependence on wave frequency. We further quantify the sensitivity of wave attenuation efficiency to sea-level rise and show that rising sea levels do not necessarily reduce attenuation capacity. In particular, when the water depth is less than approximately 1.6 times the mean root height, wave attenuation may instead be enhanced under sea-level rise. Future changes in attenuation efficiency under different climate scenarios are also quantified. OpenFOAM simulations are further used to calibrate drag coefficients as a function of the root submergence ratio. These results highlight the critical role of vertical root structure in governing wave attenuation and provide a framework for assessing the long-term resilience of mangrove-based coastal protection under climate change.

Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conventional simulators that support contemporary verification and validation. In this work, we construct data-driven reduced-order models that serve as structure-preserving, real-time surrogates. Remarkably, the exterior calculus that imposes physical conservation structure also exposes topological structure that we use to build a Gaussian process (GP) representation of uncertainty in state-flux relationships, ultimately yielding a Dirichlet-to-Neumann map for quantities of interest with closed-form expressions for posterior uncertainty. We specifically propose structure-preserving $H(\mathrm{div})$–$L^2$ subspaces of conventional Raviart–Thomas and $dgP_0$ elements prescribed by a lightweight transformer. Reduced-order dynamics consistent with this subspace are learned by posing a conservation law in which a GP describes the fluxes between volumes. This work hinges on a novel interface between mixed FEM spaces and GP regression; when training is posed as the optimal recovery problem (ORP), the resulting GP regression can be written as an optimization problem with equality constraints that impose a conservation structure, amenable to a fast Schur-complement training strategy. The trained model can then be solved in real time with closed-form estimators for boundary fluxes driven by prescribed Dirichlet data. The paper includes RKHS posterior error bounds for linear functionals to support uncertainty quantification, as well as numerical experiments demonstrating the accuracy of the posterior distribution as a surrogate for error estimation.

On a charged interface, broken inversion symmetry permits a large field-linear Pockels response through $\chi^{(2)}(\omega;\omega,0)$; at the water/ITO interface $|r_{13}|$ has been reported to reach the $10^{2}\,\mathrm{pm/V}$ order. The coexisting third-order DC Kerr term $\chi^{(3)}(\omega;\omega,0,0)$ – small in bulk water ($|\chi^{(3)}_{\mathrm{bulk}}|\sim5.5\times10^{-21}\,\mathrm{m^2/V^2}$) – had not been jointly parameterized with the Pockels term along the fundamental-frequency ($\omega$) electro-optic path. Superimposing an AC modulation and a DC bias in 0.1\,M NaCl mixes the $\chi^{(3)}$ contribution with the 1f response through the cross-term $2 s_{1133}\,E_{\mathrm{DC}}$, so that the water refractive-index modulation $\Delta n_{\mathrm{water}}$ varies linearly with $V_{\mathrm{WE}}$; a model-assisted linear fit then determines both terms from a single AC$+$DC sweep. At $V_{\mathrm{WE}}=0\,\mathrm{V}$ vs Ag/AgCl, $|r_{13}|=(1.18 \pm 0.06_{\mathrm{PZC}})\times10^{2}\,\mathrm{pm/V}$ and, under $\eta_{\mathrm{DC}}=1$, the thickness-normalized DC Kerr coefficient $|s_{1133}/d_{\mathrm{EDL}}|=33.0 \pm 5.6\,\mathrm{pm/V^{2}}$. Across physically reasonable $d_{\mathrm{EDL}}$ ($0.6$–$1.6\,\mathrm{nm}$), the interfacial DC Kerr susceptibility reaches $|\chi^{(3),\mathrm{int}}_{1133}| \approx (2\text{--}5.5)\times10^{-20}\,\mathrm{m^2/V^2}$, several-fold above the visible-range bulk-water value. This response is a property of the specific interface, tunable through the choice of electrode, electrolyte, and solvent rather than intrinsic to bulk water. Amid renewed interest in the Kerr response of water (including recent THz-band optical Kerr studies), the method directly probes this DC Kerr term along the $\omega$ path and complements SHG/SFG (the $2\omega$ path).

Quantum cloud platforms are poised to deliver powerful computing capabilities, but users have no direct means to verify which physical device executes their workload. This lack of transparency enables hardware substitution attacks, where a malicious adversary could redirect a job to a substituted or inferior processor. We present a general authentication framework that addresses this problem by constructing multi-dimensional quantum fingerprints from raw measurement data. Without any curve fitting, we directly concatenate the raw statistics of complementary experiments into a high-dimensional feature vector that preserves subtle device-specific information. A Mahalanobis nearest-neighbor classifier achieves 100% benign authentication accuracy on three superconducting processors over a three-week chronological split. The classifier naturally yields an authentication confidence $C_{\mathrm{claimed}}$ which reveals device-specific safety margins and motivates per-device alert thresholds. We assess the framework's robustness under two distinct scenarios. Under additive isotropic Gaussian noise, $C_{\mathrm{claimed}}$ decays predictably at a rate explained by inverse covariance traces, enabling an early warning mechanism. Against white-box adversarial perturbations, the same confidence threshold detects $L_2$ targeted attacks with near-perfect success and reveals device-dependent empirical thresholds for $L_\infty$ attacks, while untargeted and sparse attacks are ineffective. The proposed framework thus unifies fingerprint extraction, drift-resilient authentication, proactive health monitoring, and adversarial defense, offering a practical step toward trustworthy quantum cloud computing.

The coupling theory of quasinormal modes (QNMs) for a coupled system of generally lossy and dispersive optical nanoresonators has been established in a rigorous manner based on the first principle of Maxwell's equations [Phys. Rev. B 102, 045430 (2020)], and can achieve superior computational efficiency and physical intuitiveness compared with full-wave numerical methods if a small set of modes can achieve a high accuracy. The QNMs suffer from an exponential divergence of far field and can form a complete basis inside but not outside the resonator. In the QNM coupling theory (QCT), it is required that the QNMs of each resonator form a complete basis in expanding the scattered field both inside and outside the resonator, which can be achieved by using regularized QNMs (RQNMs). However, a strict numerical demonstration of the impact of the mode completeness of RQNMs on the accuracy of QCT by using a virtually complete basis of RQNMs is still absent. In this paper, we will provide such a numerical demonstration along with an improvement of the QCT and some theoretical demonstrations on a rigorous incorporation of RQNMs into the QCT. The RQNMs are obtained by introducing an equivalent surface current (ESC) encircling the resonator (called ESC-RQNMs) or the perfectly matched layer (PML) surrounding the computational domain (called PML-RQNMs). The numerical example is selected as two one-dimensional resonators of slabs in the extreme coupling case of direct contact, for which a virtually complete basis of RQNMs can be solved either analytically (for ESC-RQNMs) or numerically (for PML-RQNMs). The results show that by using a virtually complete basis of RQNMs, the QCT can achieve a high accuracy in predicting both the source-free eigenmodes and the source-excited scattered field of the coupled system, which is not true if using the incomplete basis of not-regularized QNMs (i.e., physical QNMs).