Papers

We analytically analyse the dynamics of an internally actuated particle, modelled as a compressible elastic sphere embedded with a magnetic bead at its undeformed centre, translating in a plane Poiseuille flow in the Stokes limit. The particle is constrained to translate with a prescribed velocity while remaining at an arbitrary position within the flow by applying an external point force and external point torque at its undeformed centre. The governing equations for the fluid and particle are the Stokes and Navier elasticity equations, respectively. We use the series solutions to the governing equations and the domain perturbation method to capture the deformed shape of the particle, assuming $\alpha \ll 1$. Here, $\alpha$ quantifies the elastic strain induced in the particle due to the viscous stress from the fluid. The external force and external torque are obtained until O($\alpha^2$). The particle translating along the channel length experiences an elastic-induced hydrodynamic lift as well as hydrodynamic torque both at O($\alpha$) and O($\alpha^2$). The leading-order lift depends linearly on the local shear rate and on the combined effects of slip velocity and flow curvature, where the slip velocity is defined as the particle velocity relative to the local ambient flow. The particle reaches a stable equilibrium position away from the centreline, where the net lift vanishes. We show that the direction of deformation-induced lateral migration of the internally actuated particle is qualitatively distinct from that of drops, capsules, and vesicles in the Stokes limit and from that of rigid spheres undergoing inertial migration.

Recent experiments in transparent conductive oxides (TCOs) have revealed light-induced order-unity variations in the refractive index occurring at extreme time scales, as short as a few-femtoseconds. These experimental observations remain unexplained, especially the ultrafast 10-20 femtoseconds relaxation of the index change, that cannot be explained by known phonon-mediated relaxation processes. Here, we present a simplified model followed by comprehensive simulations describing the phenomena, relying on the microscopic dynamics of electrons in TCOs under powerful ultrafast laser pulses. With this physical model, we predict and experimentally observe the unexplored regime of intraband modulation of electrons in the conduction band, leading to ultrafast oscillations of the refractive index. The observation of the oscillations validates the theory as a predictive tool, utilizing it to design experiments targeting novel effects that hinge on extreme alterations of optical properties of materials, such as photonic time-crystals and a plethora of novel extreme ultrafast phenomena.

Biomolecular condensates can undergo striking changes, such as transitioning from a liquid-like to a gel- or a solid-like aggregate due to changes in molecular interactions in response to changes in the biochemical environment. The question of how modified molecular interactions lead to such a transition in the material properties and spatial organization of condensates has not yet been elucidated. To address this question, we represent the biochemical environment as a triphasic mixture comprising a liquid-like protein-rich phase, a network-like protein-rich phase, and solvent. Owing to a change in the biochemical environment, protein molecules can reversibly switch between two conformational states. In a switched conformational state, the cross-linking domains of molecules are exposed which promote transient network formation in phase separated states. We develop a transient-network model and a continuum framework that couples phase separation, molecular switching, and dynamic cross-linking to predict condensate morphology and mechanics. The transient-network model predicts that a non-aging network behaves like a Maxwell fluid. When a network slowly ages via stabilization of cross-links, it shows Maxwell-like behavior and waiting time-dependent relaxation. However, a strongly aged network shows elastic recoil like characteristic of a Kelvin-Voigt solid. Our coupled continuum model demonstrates that the interplay of molecular switching and dynamic cross-linking in network formation shapes the spatial organization of condensate phases. In summary, this work demonstrates a mechanistic route explaining how conformational switching and molecular cross-linking regulate material properties and morphology of condensates.

Learned surrogates for expensive inner solver blocks are a widely pursued route to faster multiphysics simulation. We report a controlled, end-to-end negative result and identify three structural barriers, none of them a deficiency of the network we trained. The testbed is the implicit Newton solve coupling energy-dependent neutrino radiation to matter in a general-relativistic radiation-hydrodynamics code, its most expensive physics routine per call. First, per-call cost and share of runtime are different quantities, and only the second bounds acceleration. An exclusive self-time profile puts the target block at 16.9% of critical-rank wall clock, capping any surrogate at ~1.2x by Amdahl's law. A surrogate 5.8x cheaper per call merely ties the solver, and the configuration stable enough to run without fallback reaches only parity. Second, offline accuracy cannot rank surrogates for deployment: across fourteen networks the pooled Spearman error-versus-survival correlation (rho = +0.73) is a between-family confound that vanishes under control (rho = -0.04). Third, a correct out-of-distribution gate cannot accelerate a loop that leaves its training distribution. We give the break-even deferral fraction in closed form: because the visited states sit 73x off the data manifold, the gate defers 96.8 to 99.7% of cells, almost invariant to surrogate quality. Including its own cost, the gated loop is a 0.94 to 0.96x slowdown. We further separate stability from fidelity: a never-crashing gated run accumulates a linear -19.9% density bias over 6000 steps. The error is a directed, ballistically accumulating bias, not the variance-driven divergence the autoregressive literature targets.

Monocrystalline yttrium iron garnet (YIG) is a key material for magneto optics and quantum magnonics owing to its high optical transparency, large magneto-optical (MO) effects at room temperature, and exceptionally-long spin coherence. While rich MO phenomena have been demonstrated in the microwave regime, extending these concepts to technologically important telecommunication wavelengths remains challenging due to the difficulty of fabricating high-quality YIG nanostructures. Here, we demonstrate photonic crystal (PhC) defect nanocavities based on monocrystalline Bi-substitudted YIG by developing a YIG-on-insulator platform and high-precision YIG nanopatterning. The fabricated nanocavities exhibit cavity resonances around lambda = 1500 nm with Q factors up to 1,800 and a mode volume V of 1.1(lambda/n)^3, corresponding to Q/V reaching around 10^3. The measured Q factor is primarily governed by intentionally introduced lattice modulations for out-of-plane light coupling, suggesting that further optimization of the cavity geometry and measurement configuration could yield an order-of-magnitude improvement of the experimental Q factor. YIG-based PhC nanocavities provide a platform for strongly confined light-magnetism interactions in the optical regime, opening pathways toward downsized nonreciprocal photonic devices and enhanced photon-magnon coupling.

We demonstrate a new lower bound on the sensitivity of $\phi$-OTDR by estimating phase using Kalman filtering. Numerical analysis shows a $\sim$35-dB reduction in the minimum detectable SNR, compared to when using conventional phase estimation.

Real-time quantum-electrodynamical density-functional theory (QEDFT) for an open device requires local electron and field histories to determine their sources. For occupation-dependent interactions and a gapped finite-band photon continuum, we prove a causal one-to-one map on the forward image over a nonzero time interval. Three source-reconstruction benchmarks test the electronic limit, the photon-continuum limit, and a coupled joint inverse against a closed-form continuum endpoint. This provides the density–source foundation for real-time open-system QEDFT.

Training large language models is costly. How low a loss the same compute can ultimately reach depends on how each step's gradient is converted into a weight update; the rule that performs this conversion is the optimizer. From SGD and AdamW to the recent Muon, effective update rules have mostly been shaped by engineering intuition and then selected on benchmarks. Muon semi-orthogonalizes the momentum matrix before applying the update and has kept breaking records on public training benchmarks; yet why the semi-orthogonalized direction works, and over how long a history the momentum should average, are two questions at present answered mainly by experience. Here we treat the weight matrix during training as a responsive medium with memory and build a physical model for it, in which both questions find answers: the semi-orthogonalized direction is the maximally dissipative response under an output-side safety budget, which explains why it works; momentum is the internal stress accumulated by the medium; how long it should average is set by the relaxation of this stress, and a real medium relaxes on more than one timescale, the simplest form being one fast and one slow. On this basis we propose the Bi-Maxwell optimizer. The framework further yields a testable consequence: gradient directions change fast early in training and more slowly later, so the optimal memory length should grow with training stage; step-by-step measurements of a proxy for it by a read-only probe across 8 independent training trajectories are consistent with this consequence. Replacing the memory kernel alone, from a single timescale to two, brings training to the target loss in noticeably fewer steps on a public large-language-model optimizer benchmark.

What a guided beam carries after passing through an all-optically switched magnetic film has not been computed, although such switching has been demonstrated on a silicon nitride waveguide with electrical readout. We close the chain for an integrated Si$_3$N$_4$/GdFeCo element, an 8 nm film on a 60 $\mu$m waveguide, coupling a three-dimensional dispersive finite-difference time-domain solver to absorbed optical work, a four-temperature model, and stochastic two-sublattice Landau-Lifshitz-Bloch dynamics with a fluctuation-dissipation-consistent Langevin field, the magnetization feeding back into the gyrotropic permittivity that a second guided shot reads. Transmission, reflection and absorption are magnetization-blind, closing to 0.974-0.975 and shifting by at most 0.02 between saturated, demagnetized and multidomain films, because magnetization enters the dielectric tensor only through an antisymmetric element on which absorbed power depends at second order; the odd-in-magnetization polarimetry separates them by an order of magnitude in Faraday rotation and two in Kerr. The guided geometry levies a cost free-space measurement does not: a $-11.77^\circ$ geometric pedestal on the reflected channel, exceeding the $8.24^\circ$ rotation it hides. Referencing against a demagnetized film returns it to $0.063^\circ$. The transmitted mode carries a second signature in its lobe balance, needing no polarimetry. Calibrated pulses toggle 0.859 of the film from either saturated state across a $2.4\times$ fluence window, and the shortfall from unity is photonic rather than magnetic: the switched fraction is the complementary cumulative distribution of the guided-mode absorption map at the single-cell threshold, holding to $8\times10^{-4}$ at five full-wave anchors. From exactly zero magnetization, branch selection passes to the Langevin field alone, at occupancy $0.502 \pm 0.004$ over 16 seeds.

Odd viscosity, the nondissipative part of the viscous response of a time-reversal-broken fluid, is notoriously difficult to measure precisely because it does no work. Here we show that parity-time ($\mathscr{PT}$) symmetry, familiar from non-Hermitian optics, converts this elusiveness into a measurement principle. The odd Navier-Stokes equations, that include the nonlinear inertial terms, are $\mathscr{PT}$-symmetric, follow from a Lagrangian, and linearize to a Schrödinger equation in which the odd viscosity plays the role of Planck's constant; potential vorticity obeys a generalized Ertel conservation law. A probe trapped in an odd liquid realizes a pair of oscillators coupled by odd friction, and supplying balanced loss and gain drives a twofold $\mathscr{PT}$ transition whose exceptional point and Rabi sidebands locate the odd viscosity with square-root-enhanced sensitivity. Upon quantization the spectrum is of Fock-Darwin form, and the dissipative pair exhibits a Liouvillian exceptional point separating linear from exponential heating. These results furnish mechanical, stochastic, and spectroscopic protocols for measuring odd transport coefficients in classical and quantum fluids.

Accurate traveltime computation for the eikonal equation is essential in seismic applications such as tomography and migration. The fast sweeping method (FSM) is widely used because of its unconditional stability and computational efficiency. We develop a 3D fast sweeping solver for vertical transverse isotropic (VTI) media that combines multiplicative factorization with a six-tetrahedron pyramidal stencil. The factorization removes the point-source singularity, while the stencil improves local accuracy. In the unfactored formulation, the six-tetrahedron finite-difference scheme requires solving only quadratic equations. After factorization, however, the local update for the perturbation factor becomes quartic, and the update systems on oblique stencil faces are substantially more complicated than those on non-oblique faces. To resolve these difficulties, we solve the update equation using Ferrari's method with a robust root-selection strategy and derive complete update formulas for all oblique-face configurations. For horizontally constrained faces, the characteristic constraint reduces to a linear relation by exploiting the structure of the VTI Hamiltonian, so the local system still reduces to a quartic equation. For mixed horizontal-vertical constrained faces, we design a bisection-based iterative solver. Numerical examples show that the proposed method effectively suppresses source-related errors and improves traveltime accuracy.

Topological photonic lattices are commonly designed using short-range Hamiltonians, yet realistic nanophotonic structures are governed by geometry- and wavelength-dependent long-range electromagnetic interactions. Here we introduce a geometry-calibrated quantum equilibrium-propagation framework for inference and inverse design in finite nonlocal plasmonic Su-Schrieffer-Heeger lattices. A two-qubit equilibrium sensor is trained in effective-coupling space to distinguish boundary-localized from trivial finite-lattice responses. Because labels inherited from the nearest-neighbor SSH model become unreliable in the presence of nonlocal hopping, each sample is independently relabeled using nonlocal winding numbers and a finite-gap criterion. On this physics-verified evaluation set, the sensor achieves 99.8% sensitivity and 98.1% specificity. A physics-gated robustness score then ranks verified configurations by boundary response, response contrast, gap stability and compatibility with the selected nonlocality regime. Full-wave extinction spectra of isolated and paired gold nanoparticles establish a geometry-to-coupling calibration linking particle size and separation to wavelength-resolved pair couplings. Projecting this calibration onto the verified coupling landscape identifies a finite plasmonic geometry supporting spectrally distinct corner- and edge-dominated responses at 546 and 642 nm, with sector-to-bulk intensity contrasts of $2.4 \times 10^{4}$ and $1.5 \times 10^{3}$, respectively. The framework links realistic electromagnetic geometry to nonlocal topological design, moving beyond nearest-neighbor design rules with physics-verified, geometry-resolved inference.

Machine-learning interatomic potentials enable accurate and efficient atomistic simulations, yet their reliability for out-of-distribution configurations far beyond the training domain remains a significant challenge. Here, we introduce a semiparametric interatomic potential based on a generalization of the Abell–Tersoff bond-order potential, incorporating a chemically informed functional form and explicit high-order many-body correlations to address this challenge. The model is trained and evaluated on various datasets of silicon, carbon, water, and small molecules, achieving interpolation accuracy comparable to existing MLIP models while exhibiting improved extrapolation to unseen configurations, including those at high pressures and temperatures. These results provide insights into the design of specific inductive biases for reliable extrapolation in interatomic potentials. inductive biases that can be used to control extrapolation in machine-learning interatomic potentials.

Fock-space variational Monte Carlo (FS-VMC) evaluates variational quantities by sampling discrete many-body configurations. In molecular applications, concentrated Born distributions can hinder Markov-chain mixing, while Monte Carlo estimators can also exhibit large variance. We introduce importance-reweighted FS-VMC (IR-FS-VMC), which combines Metropolis–Hastings sampling, an evaluation kernel, and self-normalized importance sampling to estimate the Born-distribution energy, gradient, and stochastic reconfiguration (SR) matrix. Controlled comparisons on equilibrium H2O and Fe2S2 show that the acceptance rate and local-energy variance can respond differently to the sampling and estimation procedure. A single production protocol yields accurate variational energies for H2O dissociation, 36-site hydrogen lattices, and Fe2S2 and Fe4S4 active spaces without system-specific sampling parameters.

We investigate the effects of horizontal oscillations on the drafting–kissing–tumbling (DKT) dynamics of two monodisperse spherical particles settling under gravity in a viscous fluid. Applying particle-resolved direct numerical simulations, we systematically vary the oscillation frequency and amplitude to assess their impact on the behavior of individual particles, their mutual interaction, and the orientation of the particle arrangement. The results demonstrate that the oscillatory effects on DKT become significant only when the particle Reynolds number $Re_p$, defined as the ratio of oscillation-induced inertial to viscous forces, exceeds unity. In this regime, oscillations alter the temporal characteristics of the DKT process, with moderate amplitudes tending to prolong and larger amplitudes to reduce the kissing phase. Moreover, oscillations affect particle reorientation. At low $Re_p$, the particles maintain their initial orientation throughout the interaction, whereas an increasing $Re_p$ promotes a preferential alignment perpendicular to the direction of oscillation. We explain these findings by analyzing the oscillation-induced pressure fields surrounding the individual particles, which develop increasingly pronounced lateral anisotropy with increasing $Re_p$. The corresponding lateral hydrodynamic forcing likewise becomes increasingly anisotropic, providing a consistent physical basis for the observed modification of particle interactions and reorientation. These findings provide a physical framework for understanding how horizontal oscillations govern binary particle–particle interactions and orientation during gravitational settling.

Quantum states with conserved total spins, or spin eigenfunctions, are important for studying quantum chemistry and quantum manybody physics problems. A typical class of spin eigenfunctions are Dicke states, which attain maximal spins. While we already have many efficient quantum algorithms to prepare Dicke states, it is not yet clear if we could do so for arbitrary spin eigenfunctions deterministically. Generalizing Bärtschi and Eidenbenz's elegant algorithms for Dicke state preparation, we successfully prepare arbitrary spin eigenfunctions characterized by branching paths and binary spin trees. As a byproduct, we also develop the corresponding classical algorithms to reconstruct all these spin states.

The collision operator for the Boltzmann equation is a nonlinear nonlocal operator. When lifted in the extended 2-particle space, it is viewed as the projection of a collisional linear operator. In this paper, we propose a particle method that samples the post-collision relative velocity directly from the Green's function of this operator (the transition probability of the generated time-continuous Markov chain). The normalizing flow amortized sampling is then proposed to reduce the sampling complexity. The resulted method takes $O(N)$ each time where $N$ is the particle number, and conserves momentum and energy exactly. This method does not require the boundedness of the kernel and, more importantly, it allows learning the Green's function directly from the scattering data without selecting the kernel in a specified family.

Parnassus is a framework for fast detector simulation and reconstruction, directly mapping truth-level particles onto reconstructed objects. Such models can be built from deep generative networks trained on paired samples, which are fit automatically to a target detector, or from parametric prescriptions of the kind used by Delphes, which are constructed by hand. We remove this asymmetry by making the parametric models fully differentiable so that their parameters can be fit to a target sample by gradient descent. We demonstrate closure by fitting a parametric model to samples from a known configuration of itself, recovering the generating parameters and characterizing the degeneracies among them, and we present a first fit to CMS full simulation. The resulting models are interpretable, inexpensive, and run in the standard Parnassus pipeline which is fully Python based and GPU enabled.

Determining the binding mechanisms between polymer dots and proteins is important for developing novel nanotechnologies for biomedicine and bioimaging. In this work, we use all-atom molecular dynamics simulations to determine the binding affinity of Pdots with ovalbumin protein at pH = 7 and 1. The selected Pdots are mixtures of Poly[(9,9-dioctylfluorenyl-2,7-diyl)-alt-co-(1,4-benzo-(2,1',3)-thiadiazole)] (PFBT) and poly(styrene/maleic anhydride) (PSMA) with varying composition. At pH = 7, the Pdots have a net negative charge due to the COO- functional groups on the PFBT, and the protein also has a net negative charge. At pH = 1, the Pdots are charge neutral with PFBT containing only COOH functional groups, and the protein also has a net positive charge. We sample the initial position of the protein by varying its initial position through all 6 orientations of a cube. For each orientation, we pull the protein towards the PFBT region of the Pdot. We compare the Coulombic and Lennard-Jones interaction energies for the 6 different interacting faces and two selected pH values. We find that the LJ interaction energies are similar among all 12 of these cases. The measured Coulombic interaction energies suggest that pH = 1 has better binding affinity than pH = 7. The potentials of mean force (PMF) along the pulling coordinates differ with pH. The PMFs from 2 of the 6 initial configurations at pH = 1 are negative whereas none of them are negative at pH = 7, confirming the preferred binding affinity when pH = 1. One of the faces at pH = 1 has the lowest PMF of about -30 kcal/mol, which is much lower than about 6 kcal/mol seen for the lowest case at pH = 7. Comparison of protein residue charge distributions at pH = 7 and 1 further shows that the electrostatic interaction is critical to the binding affinity, and negatively charged residues reduce at pH = 7 does not bind to negatively charged Pdot.

Structured light in space and time has become a powerful playground in which to explore the fundamental physics of wave systems, while simultaneously introducing new exotic forms of light, from spatio-temporal vortices to toroidal pulses of light. Yet their creation remains restricted by complex optical systems while directly observing their evolution in arbitrary channels remains elusive, complicated by the interplay of dispersion and diffraction and exacerbated by the lack of suitable detection tools. Here we create topological space-time beams in a single step by a resonant response of a symmetry-broken metasurface, mixing spatial, temporal and polarisation degrees of freedom in a single microwave field. Our realisation in the microwave regime allows us to directly observe their dynamics in arbitrary channels, from free-space to complex random media, showing the preservation of topology while the foundational degrees of freedom are scrambled. We use our control to show how to unscramble the space-time properties for the first crosstalk-free transmission of space-time waves. Our work advances the physics of space-time waves, introduces a new toolkit for their creation and control, and offers an exciting roadmap to their exploitation in real-world scenarios, e.g., for robust communications through noisy channels.

Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate that the polymer linked nanoparticle networks can potentially realize information processing using heat current. Using high-throughput molecular dynamics (MD) simulations and the trust region Bayesian optimization (TuRBO) methods, we tune the plasticity of polymer linkers and the temperatures of nanoparticles to optimize the performance of the PNNP machines, which is similar to tune the weights and bias in ANNs. After 5 rounds of high-throughput MD simulations, we show that the PNNP machines have improved in performance. We also use a testing data set to verify the heat flow outputs from the top 5 PNNP machines in each round.

Dual-comb spectroscopy (DCS) enables high-speed, high-resolution spectroscopy by down-converting optical spectra to the radio-frequency (RF) domain through the interference of two mutually detuned frequency combs. Accurately resolving narrow molecular absorption features requires closely spaced comb lines generated by large laser cavities with long round-trip times, thereby hindering the miniaturization of high-resolution spectrometers. Here, we circumvent this limitation by demonstrating a swept-source dual-comb spectrometer using two unidirectional racetrack semiconductor lasers integrated on the same chip. The traveling-wave frequency combs are generated by strong RF injection at the laser cavity round-trip frequency of 14.3~GHz and are continuously tuned in frequency by varying the laser drive currents. This enables continuous spectral sampling over a 32~cm$^{-1}$ range centered near 8~\textmu m, with an effective sampling interval of approximately 80~MHz. The outputs of both combs are coupled into the same monolithically integrated light coupler, providing automatic collinear alignment before the combined beam interrogates the sample. We benchmark the spectrometer against an external-cavity tunable laser and validate its performance using HITRAN simulations for 1.25% nitrous oxide. Finally, the unidirectional comb architecture suppresses the detrimental effects of optical feedback, yielding comparable residuals even under deliberately introduced strong feedback. More broadly, the benefits of miniaturization extend beyond reduced footprint: chip-scale integration enables new forms of electrical and optical control that can fundamentally change how dual-comb spectrometers are operated.

We propose a novel, highly efficient method for isolating the isomeric state $^{26\text{m}}\text{Al}$ from an overwhelming ground-state $^{26\text{g}}\text{Al}$ background (isomeric ratio $\sim 20:1$) using optical pumping through a 2.0 m flight zone in collinear laser spectroscopy (CLS). To investigate the underlying optical pumping (OP) dynamics, we developed a comprehensive rate equation framework. While the transition pathways can be intuitively conceptualized via a primary 7-manifold scheme, our numerical simulation solves the full 47-level rate equations by explicitly accounting for all degenerate Zeeman sublevels ($m_F$) to rigorously incorporate polarization selection rules and Clebsch-Gordan coefficients. When the continuous acceleration voltage matches the resonance conditions of the $^{26\text{g}}\text{Al}$ hyperfine transitions, the ground-state atoms undergo a 100% efficient transition into uncoupled dark states within the 2.0 m flight zone. Consequently, background fluorescence from the ground state is completely suppressed in the detection chamber, whereas $^{26\text{m}}\text{Al}$ atoms utilize a closed cycling structure to survive the flight zone, yielding a high-intensity, background-free resonance peak.

Microwave electric-field measurements with Rydberg atoms require accurate Rydberg-transition electric-dipole matrix elements. We calculate these matrix elements for Rb and Cs using a Dirac-Fock plus core-polarization method, which core-valence exchange is treated explicitly and core-valence correlation is represented by a cutoff core-polarization potential. A smooth cutoff-radius model fitted to quantum-defect energies reproduces the target energies with MHz-level residuals for states up to $n=90$. Comparisons with model-potential matrix elements and internal length-velocity consistency show that the $s$-$p$ transitions are generally the most robust, whereas the $p$-$d$ and $d$-$f$ transitions show stronger atom- and branch-dependent sensitivity to short-range modeling. The largest relative differences arise from radial-integral cancellation, where short-range phase changes are amplified by the small final matrix element. These results provide a practical reliability assessment for Rydberg-transition electric-dipole matrix elements used in field-sensing applications.

A recent experimental study reports the spontaneous formation of a three-dimensional woven ferroelectric domain fabric in bulk KTN:Li during sufficiently slow cooling through the ferroelectric transition. The present work develops a nondimensional phase-field framework to examine a physically plausible route by which such a state can emerge. A large-area two-dimensional model first resolves the cooling-rate-dependent formation of a frustrated crossing precursor by combining first-order ferroelectric thermodynamics, electrostatic and elastic interactions, compositional modulation, and effective charge screening. Slow cooling produces a persistent population of charge-associated crossings, whereas the corresponding fast-cooling pathway does not. A three-dimensional extension then incorporates cubic gradient anisotropy, flexoelectric coupling, and strain-gradient regularization to examine whether the planar precursor can develop a genuine woven topology. Within the explored nondimensional parameter regime, slow cooling produces a directly resolved geometrical separation and exchange of depth ordering between the intersecting wall families, whereas the corresponding fast-cooling pathway does not. This distinction is reproduced across independent realisations and remains robust to timestep refinement and changes in computational domain size. The simulations suggest that slow cooling provides a kinetic window for topological selection and that global charge relaxation can coexist with strong local charge concentration at surviving crossings. The model is not calibrated to material-specific KTN:Li coefficients and does not reproduce the experimentally observed low-temperature disappearance of the woven state.

The stability of markets hosting leveraged exchange-traded products is governed not by any single product's loop gain but by the spectral radius of a loop-gain matrix, and scalar per-product monitoring underestimates system feedback by construction. Recent work measures the self-reinforcement of a leveraged fund's daily close rebalancing through a scalar loop gain and treats cross-asset spillovers as bias. We model complexes on correlated underlyings as a coupled feedback system with matrix gain L and show that scalar monitoring has two blind spots: (i) cycle amplification, since rho(L) >= max_i l_ii for nonnegative coupling, strict under two-way coupling; and (ii) transmitted displacement, which arises already under one-way coupling and is invisible to the receiver's own gain. We give a reduced-form estimator of L requiring only prices and public fund assets – no signed order flow – via cross-asset overnight reversals, reporting its measurement-convention sensitivity explicitly. In simulation the spectral radius is recovered with RMSE 0.005 at T=250, a lead-lag confounder yields a 2% false-alarm rate, and in a calibrated blind-spot configuration the scalar monitor reports "safe" and the matrix monitor "unsafe" on 100% of paths. In the 2026 Korean single-stock LETF episode we detect transmission from the SK Hynix complex into Samsung Electronics' closing price (DiD z=-2.82; exact randomization p=0.0055 against 182 control pairs), scaling with the sender's rebalancing capital; conservatively, about 41% of Samsung's closing displacement variance is imported – invisible to its own "moderate" gain of 0.24. The same estimator returns nulls for the U.S. MSTR-Bitcoin-Coinbase complex, whose capital is comparable but whose closing venue is far deeper. Monitoring should be organized around the (complex x venue) matrix, not around products.

We investigate the duty-cycle-dependent atomic response under full-depth intensity pulse-width modulation in Bell–Bloom optical pumping. A time-domain Bloch model explicitly resolves the pump-on/off spin dynamics, yielding piecewise analytical transient solutions and a periodic steady-state description without cycle averaging or harmonic truncation. An extended free-induction-decay method independently determines the effective dark and pump-on transverse-relaxation rates, $W_0$ and $W$. The predicted lock-in responses agree well with experimental results. We find that for each Larmor frequency $\omega_0$, the slope is maximized at a finite pump rate that increases with $\omega_0$. These results provide a practical framework for sensitivity-oriented optimization of the duty cycle and pump rate in PWM-driven atomic magnetometers.

Everettian quantum mechanics is often said to lack coherent probability because every possible measurement result occurs. If all of them exist globally, what is probability supposed to be a probability of? This paper defends an axiomatic answer: normalized Born measure is postulated as objective probability over the records borne by an observer's future continuations. Collapse interpretations likewise postulate the Born probabilities, but instead assign them to possible global outcomes. Neither theory derives the probabilities from its ontology. The contribution is an explicit record-level probability model that separates probabilities for future local records from certainty about their global existence. I use this model to answer objections to the axiomatic approach and argue that neither global selection nor a further theory of personal persistence is required. At the level of recorded results, the Everettian law supports the same predictions and empirical inferences as the collapse law, despite the theories' different ontologies.

This work presents a real-time disruption prediction and mitigation system developed for high-current operations in the EXL-50U Spherical Torus. By leveraging Reflective Memory (RFM) technology, the system establishes a low-latency real-time data path, creating a fully integrated pipeline that synchronizes multi-channel diagnostic acquisition, online preprocessing, real-time inference, and Massive Gas Injection (MGI) triggering. At its core, a lightweight prediction model based on a Temporal Convolutional Network (TCN) with a channel attention mechanism extracts disruption precursor features while adaptively weighting the importance of different diagnostic channels. {Tested across discharges \#14036–\#14790, the system achieves a true positive rate of 82.4% and a false positive rate of 16.5%, with end-to-end latency below $1~\mathrm{ms}$ in online operation.} Mitigation experiments further show that the MGI system can supply the required gas inventory and trigger a rapid post-injection plasma response, supporting the operational requirements of EXL-50U and providing engineering guidance for future devices such as EHL-2. These results confirm the engineering feasibility of integrated real-time disruption control on EXL-50U, offering a robust basis for future research in higher-parameter fusion devices.

Cooperation is ubiquitous in both natural and human societies, yet its evolutionary basis remains a major challenge. A long-standing puzzle is whether having more information leads to better decision-making and thus a higher level of cooperation. To address this question, we adopt a recently developed reinforcement learning framework in which individuals learn through trial and error to maximize cumulative rewards - a paradigm that has successfully explained diverse emergent patterns in human behaviors. Specifically, we equip a structured population with the Q-learning algorithm and systematically vary the size of the interactive neighborhood, which serves as a proxy for perceived information. Interestingly, we observe a non-monotonic relationship between cooperation prevalence and neighborhood size in both two-dimensional square lattices and Barabasi-Albert scale-free networks. This inverted U-shaped dependence reveals that an optimal amount of information exists, yielding the highest level of cooperation. Mechanistic analyses show that a moderate neighborhood size enables individuals to strike an optimal balance between information sufficiency and decision-making tractability. This balance allows them to detect reciprocal opportunities while avoiding the deterioration of decision quality due to information overload. Our findings challenge everyday intuition, suggesting that a proper amount of information - not more - is optimal for the emergence of cooperation.