Papers

Imaging the mechanical properties of biological tissues in vivo with high spatial and temporal resolution is essential for understanding physiological function and disease progression. Optical coherence elastography (OCE) provides label-free, micrometer-scale mapping of tissue biomechanics, but its application to dynamic and volumetric measurements has been limited by slow acquisition speeds and susceptibility to motion artifacts. Here we introduce ultrafast optical coherence elastography (ultrafast OCE), a general framework for real-time volumetric biomechanical imaging in vivo. By combining synchronized multi-phase acquisition with a demodulation strategy intrinsically robust to spectral aliasing, ultrafast OCE decouples mechanical excitation from acquisition speed, enabling reconstruction of full wave fields from only three sequential B-mode images. The method achieves frame rates up to two orders of magnitude higher than conventional approaches while preserving high sensitivity over frequencies ranging from the acoustic to ultrasonic regimes. We further develop a motion-correction strategy that compensates for bulk tissue motion under physiological conditions. We validate ultrafast OCE in dynamically stretched phantoms and pulsatile arteries and demonstrate sub-second volumetric imaging of the cornea and skin in vivo. Ultrafast OCE enables real-time interrogation of tissue biomechanics across space and time, opening new opportunities for mechanobiology, cardiovascular research, and clinical diagnostics.

Since all the Standard Model (SM) parameters have been measured to precision at the percent level or smaller, one would aim to have a framework which describes baryogenesis as precise as possible, where the measured SM parameters are used as inputs. At high temperature before the electroweak symmetry breaking, the SM contains 15 approximate $U(1)$ symmetries with the associated flavor charges. To describe the evolutions of these flavor charges in a flavor-basis-covariant manner such that physical observables are flavor-basis-independent, a total of five density matrices in flavor spaces are required: three for quarks and two for leptons. Taking into account the quantum chromodynamics and electroweak sphaleron interactions together with all the Yukawa interactions, we obtain the complete flavor-covariant Boltzmann equations. By imposing chemical equilibrium in the lepton sector, we further derive a new effective quark-flavor-covariant formalism which can be used when baryogenesis occurs through number asymmetry generation in the quark sector. To verify the consistency of this complete formalism, we first apply it to leptogenesis scenarios, reproducing previous results which utilize the effective lepton-flavor-covariant formalism up to small corrections due to the quark chemical equilibrium approximations used in the latter formalism. Then, for the first time, we apply the complete formalism as well as the effective quark-flavor-covariant formalism to a cloistered baryogenesis scenario where number asymmetries are generated in the quark sector, showcasing the good agreement between the results from the two formalisms. Finally, we release the first generic baryogenesis public code BOLEH (BaryOn and Lepton charge Evolution in the Hot big bang) where all the SM interactions are taken into account.

We study the bremsstrahlung photon production from a hard jet parton induced by rescattering with a dense nuclear medium. Using the charged current interaction channel of deep inelastic scattering between an electron and a large nucleus, we derive the spectrum of medium-induced photons emitted from high-energy heavy and light quarks at the next-to-leading twist in a unified framework. Going beyond the collinear expansion approximation, we show that the photon spectrum is determined by the full momentum distribution of the gluon exchanged between the propagating quark and the medium, or equivalently, by the differential elastic scattering rate of the hard quark inside the medium. Modeling the gluon field with a static Debye screened potential reduces the photon spectrum to a dependence on the transverse momentum distribution of the exchanged gluon. This work provides a more reliable input for future phenomenological studies of quark mass effects on jet-induced photon production in relativistic heavy-ion collisions.

We present the discovery and analysis of three rotational glitches in the young pulsar PSR J1637$-$4642. The timing observations span from 19 February 2009 to 6 October 2024 (MJD 54881$-$60589) from the Murriyang radio telescope of the Parkes Observatory. The first and strongest glitch occurred around MJD 58352 with a fractional frequency change of $\Delta\nu/\nu \sim 2.7 \times 10^{-6}$, while two additional smaller glitches were detected at MJD 59443 and MJD 60445 with fractional changes of $2.2 \times 10^{-9}$ and $2.8 \times 10^{-8}$, respectively. Prior to this, the pulsar had shown no glitch activity since its discovery in the Parkes Multibeam survey. Only the first glitch exhibits detectable exponential recovery, with a decay timescale of $\sim$100 days and a small recovery fraction $\approx 0.015$, accompanied by a permanent increase in the magnitude of the spin-down rate. Modeling the post-glitch evolution of $\dot{\nu}$ within the vortex-creep framework using Bayesian inference gives a superfluid moment-of-inertia fraction $\approx 0.0187$, consistent with the inner-crust superfluid. These results reinforce the standard superfluid glitch paradigm and demonstrate that even “quiet” pulsars can still host substantial glitch activity.

Accelerating the electrification of thermal energy requires vapor-compression heat pumps capable of dynamic, grid-responsive operation. However, equipment engineering remains fragmented across static rating-point selection, stiff multi-phase transient simulation, and gradient-based optimal control. Here, we present an end-to-end differentiable, finite-volume vapor-compression framework implemented natively in JAX that automates machine sizing directly from stated thermal duties and unifies dynamic simulation with predictive control under a single compiled residual $\dot{y}={f}(t,{y},{u})$. Thermodynamic evaluations bypass runtime root-finding via bilinear $(p,h)$ manifolds pre-flashed from Helmholtz equations of state, enabling analytical forward-mode automatic differentiation. Mass conservation across multi-phase coils is strictly preserved by incorporating both $(\partial\rho/\partial p)_h$ and $(\partial\rho/\partial h)_p$ partial derivatives into the dynamic pressure differential equation. The sizer directly inverts compressor displacement, electronic expansion valve area, and heat-exchanger tube counts via four-point cycle synthesis and $\varepsilon$-NTU matching using the identical polytropic compressor map. Crucially, the compiled physics kernel is shared symmetrically between $L$-stable TR-BDF2 stiff integration and implicit-Euler Model Predictive Control (MPC), eliminating plant-controller surrogate mismatch. Validated against open-access experimental benchmarks without parameter fitting, the framework predicts cooling capacity with $7.37\%$ MAPE across 16 mini-split operational runs and bounds on-period cooling error within $1.19\%$–$1.62\%$ on utility-scale Hardware-in-the-Loop traces. This work provides an open-source, differentiable foundation for automated machine synthesis, dynamic grid orchestration, and gradient-based hardware-control co-design.

We present a search for flavor-changing neutral current (FCNC) interactions involving the top quark in production and decay. This analysis establishes the first global search in the top quark FCNC sector within the standard model effective field theory where all relevant operators are constrained simultaneously in a single fit to data, providing a framework for future measurements and combinations at the LHC. The analysis uses proton$-$proton collision data collected at $\sqrt{s}$ = 13 TeV by the CMS detector, corresponding to an integrated luminosity of 138 fb$^{-1}$. Signal contributions are probed in final states with two same-sign charged leptons or three leptons. Multivariate discriminants are used to separate signal from background, and their distributions enter a likelihood fit to determine 95% confidence intervals for 24 Wilson coefficients. No evidence for FCNC interactions is observed. These results provide the first simultaneous constraints on the complete operator set and place the most stringent limits to date on FCNC top quark decay branching fractions via two-quark$-$two-lepton interactions.

A search is presented for top quark-antiquark ($\mathrm{t\bar{t}}$) quasi-bound “toponium” states, near the kinematic $\mathrm{t\bar{t}}$ production threshold in final states with a single electron or muon and jets. The study uses proton-proton collision data at $\sqrt{s}$ = 13 TeV, collected by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 138 fb$^{-1}$. The analysis examines the relative velocity between the top quark and antiquark, along with two angular observables sensitive to the parity and spin of the $\mathrm{t\bar{t}}$ system. A significant excess of events is observed relative to the standard model prediction for $\mathrm{t\bar{t}}$ production calculated at next-to-next-to-leading order in perturbative quantum chromodynamics (QCD). The excess corresponds to an observed total cross section of 5.1$\pm$0.9 pb and is consistent with a simplified model of a color singlet pseudoscalar toponium motivated by nonrelativistic QCD, with a resulting observed significance of 6.1 standard deviations. The result provides an independent confirmation of the excess reported in the dilepton channel.

Functionalities of ferroelectric materials are governed by the spatial organization and coupling of polarization, strain, lattice rotation, and structural order accessible via atomically resolved scanning transmission electron microscopy (STEM) images. Quantitative interpretation of atomic-resolution STEM data has conventionally relied on locating atomic columns and converting their fitted coordinates into local structural descriptors. Here, we develop a field-based approach in which atomic-resolution images are represented by spatially varying latent Bragg fields, whose amplitudes and phases provide continuous maps of crystalline order, lattice displacement, strain, rotation, and mode-specific residual structure. The observed atomically resolved images are decoded from the latent fields. We apply this framework to image series of Sm-substituted BiFeO3 spanning 0-20% Sm and crossing the composition-driven boundary between the R3c ferroelectric phase and the orthorhombic, nonpolar Pnma phase. Conventional atom-resolved parameterization is used as an independent validation, showing that reconstructed Bragg amplitude tracks local atomic-column intensity and that field-derived shear reproduces unit-cell angular distortions obtained from atom fitting. The combined analysis reveals a systematic evolution from extended ferroelectric domains at low Sm concentration, through the appearance and growth of localized regions with period-doubled Pnma order at intermediate compositions, to a connected Pnma-dominated state at high Sm content. The period-doubled order is accompanied by enhanced shear and lattice rotation and by progressive reorganization of the ferroelectric domain structure. These results establish latent-field reconstruction as a physically interpretable complement to atom finding and provide a unified framework for resolving composition-driven phase evolution in ferroic materials.

Galaxy morphology is shaped by both assembly history and baryonic processes, but their relative roles remain uncertain. We use the VariableTNG (VTNG) simulation suite to investigate how variations in baryonic feedback regulate galaxy morphology while keeping the initial conditions fixed. VTNG consists of cosmological magnetohydrodynamic simulations performed with the moving-mesh code AREPO, varying eight parameters governing stellar and AGN feedback. At $z=1$, we characterize morphology using $\kappa_{\rm co}$, $v/\sigma$, and the axis ratio $c/a$. We find substantial morphological diversity across feedback models, with the dominant mechanism strongly dependent on stellar mass. For $M_\ast \lesssim 10^{11}\,{\rm M_\odot}$, morphology is primarily controlled by the supernova temperature $T_{\rm SN}$: larger $T_{\rm SN}$ delays early star formation, promotes a denser and more rotationally supported gas reservoir, and favours subsequent disc growth through in-situ star formation. At higher masses, morphology becomes increasingly sensitive to AGN feedback, particularly the quasar-mode coupling efficiency $\epsilon_{\rm f,high}$. Higher $\epsilon_{\rm f,high}$ suppresses early black hole growth, thereby weakening subsequent radio-mode feedback associated with gas depletion and loss of rotational support. The resulting morphology–mass relation is non-monotonic, with maximum rotational support at intermediate stellar masses. Our results demonstrate that baryonic feedback regulates galaxy morphology through distinct mass-dependent pathways, with stellar feedback dominating at lower masses and AGN self-regulation becoming increasingly important at the massive end.

Objects rolling upward on diverging inclined rails exhibit a counterintuitive behavior that has intrigued physicists and students alike. Among the most well-known is the double-cone paradox, where a cone appears to roll uphill due to the geometry of the rails. In this paper, we extend this idea to more general rigid bodies, including spheres and ellipsoids. We examine the physical conditions under which this motion occurs, and we derive the necessary geometric and energetic constraints using analytical mechanics. We then validate our theoretical models through carefully designed experiments.

We construct a semistrict higher Chern–Simons (HCS) gauge theory in $2n+2$ dimensions associated with balanced 2-term $L_\infty$-algebras. Starting from the homotopy Maurer–Cartan theory, we first introduce 2-term $L_\infty$-algebra gauge theory, and show that there is a four-dimensional HCS construction. Then we extend invariant bilinear pairings to invariant multilinear forms of the appropriate degree, and define a $(2n+3)$-dimensional higher Pontryagin–Chern form, which is closed and invariant under infinitesimal gauge transformations. Its transgression yields an explicit $(2n+2)$-dimensional HCS form. We further establish a higher Chern–Weil theorem that generates higher transgression forms, and prove that the HCS theory is a distinguished instance of the higher transgression gauge theory. Finally, we apply the extended Cartan homotopy formula in this semistrict setting, and show that it is a common origin of both the higher Chern–Weil theorem and the associated triangle equation.

We investigate the energetic and thermodynamic performance of an interacting two-qubit system serving as both a quantum battery and a quantum Otto heat engine. The working medium is described by an anisotropic Heisenberg Hamiltonian supplemented by a dipolar interaction, a symmetric spin–orbit interaction, and an external magnetic field. Within a unified microscopic framework, we first analyze a coherent unitary charging protocol and characterize the resulting energy-storage performance through the ergotropy, anti-ergotropy, charging power, storage capacity, and $\ell_1$-norm of quantum coherence. We investigate the effects of the dipolar interaction, temperature, and magnetic field on these quantities. We then employ the same working medium in a quantum Otto cycle and study the absorbed and released heat, net work, and thermodynamic efficiency as functions of the magnetic-field modulation, dipolar interaction, and temperature bias. A direct comparison between the two protocols reveals a pronounced contrast in their response to the dipolar interaction. In the investigated parameter regime, increasing the dipolar interaction substantially enhances the maximum ergotropy and storage capacity of the quantum battery, whereas the maximum work extracted per Otto cycle decreases. The Otto efficiency exhibits a nonmonotonic dependence on the dipolar interaction while remaining below the Carnot bound. These results demonstrate that an enhancement of quantum energy-storage capability does not necessarily imply an enhancement of heat-to-work conversion. Our findings highlight the complementary nature of quantum batteries and quantum heat engines and show how microscopic spin interactions can be used to control different forms of quantum energy conversion within the same physical platform.

Machine-learning force fields provide a promising route for accelerating molecular simulation by replacing expensive quantum-chemical calculations with differentiable models of molecular energies and atomic forces. However, learning accurate and energy-conserving forces remains challenging, especially when the model must capture both global energy trends and local potential-energy gradients from limited data. In this work, we propose a Hybrid Quantum SchNet architecture that integrates variational quantum circuit modules into the continuous-filter SchNet framework. Quantum modules are inserted into the filter generator, atom-wise update, and readout transformations, allowing quantum-enhanced feature mappings to contribute to distance-dependent interactions and atomic energy prediction while preserving the energy-gradient formulation of forces. The model is evaluated on eight MD17 molecular systems using 1000 training configurations per molecule. Compared with energy-only training, joint energy–force supervision substantially improves both energy and force prediction accuracy. Compared with energy-only training, joint energy–force supervision substantially improves both energy and force prediction accuracy. Averaged over the benchmark, the energy MAE decreases from 2.567 to 0.593 kcal mol$^{-1}$, while the force MAE decreases from 16.340 to 1.540 kcal mol$^{-1}$ Å$^{-1}$. Ablation experiments on ethanol further show that the performance of the hybrid model depends on the balance between quantum circuit width, circuit depth, and optimization stability. These results demonstrate that variational quantum circuits can be incorporated into neural force-field architectures and trained end-to-end to improve molecular energy and force prediction.

Identifying single photon emitters from large, inhomogeneous candidate populations is key to realizing many quantum applications. This requires measuring the emitters second order autocorrelation function, whose statistical reliability is fundamentally limited by acquisition time. Machine learning classifiers can accelerate identification from sparse data, but their performance relative to physics-based inference has not been systematically examined. Here, we introduce sequential Bayesian inference for single photon emitter classification and benchmark it against Levenberg-Marquardt fitting and a feedforward neural network. We use synthetic training and test data calibrated against real Hanbury Brown-Twiss measurements from hexagonal boron nitride emitters, enabling evaluation against an exactly known ground-truth emitter number under realistic noise and background conditions. All three approaches achieve high, near-perfect accuracy with sufficient integration time, but differ greatly in convergence rate and robustness under sparse photon statistics. The neural network is most robust at short integration times. The Bayesian classifier reaches near-perfect accuracy fastest, while retaining full physical interpretability. Levenberg-Marquardt fitting remains a valuable, fully interpretable method, achieving the highest recall despite being the slowest to converge. These results lead to several key conclusions. No single method dominates across all performance metrics. Relying on any one metric alone can give a misleading picture of classifier performance, particularly under sparse photon statistics. Physics-based and data-driven methods are complementary rather than competing approaches. Together, these findings provide practical guidance for selecting and combining classification strategies for scalable single-photon-source screening and other quantum-emitter characterization tasks.

Resolving extended incoherent objects below the diffraction limit poses an application-rich imaging challenge whose solution may enable a new generation of observational instruments and capabilities. In this work, we invoke a practical model for general imaging by approximating an arbitrary extended incoherent object as a finite grid of thermal point emitters parameterized by their brightnesses. We derive the quantum Fisher information matrix (QFIM) for simultaneous brightness estimation and show that the symmetric logarithmic derivatives weakly commute, indicating that the Helstrom bound furnishes the ultimate quantum limit on the estimation error for incoherent imaging. Furthermore, for deeply sub-diffraction scenes, we find numerical evidence of a gap between the Nagaoka-Hayashi (NH) bound and the Helstrom bound. This gap reveals that separable measurements, though more experimentally accessible, are insufficient to reach the quantum limit, and points to the prospective advantage of joint measurements acting on multiple state copies. Additionally, we show that spatial mode-demultiplexing (SPADE) often saturates the NH bound solidifying its status as a near-optimal separable measurement strategy that significantly outperforms direct imaging. Finally, we articulate two joint detection receivers implemented with bona fide quantum resources that asymptotically achieve the Helstrom bound.

It is well established that in time-dependent quantum systems, integrability preserving time-dependent interaction strengths are identical to the renormalization group (RG) trajectories of the corresponding static model when time `$t$' in the driven model is identified with the logarithm of the cutoff `$\log\Lambda$' of the static model. We refer to this integrability preserving driving as the RG protocol. In this work we extend the class of time-dependent integrable models to include non-Hermitian quantum models with time-dependent interaction strengths. Using the recently formulated generalized Bethe ansatz framework [P. R. Pasnoori, Phys. Rev. B 112, L060409 (2025)], we show that the constraints imposed by integrability are more general: The interaction strengths of the static model that flow in the RG follow the respective RG trajectories in the corresponding time-dependent model as described above. In addition, the interaction strengths of the static model that are RG invariant can either be constant or have a specific time-dependence in the corresponding time-dependent model which is constrained by integrability. Thus we establish that in the context of time-dependent non-Hermitian systems, the set of integrability preserving time-dependent strengths is larger than the set corresponding to the RG protocol.

While recent surveys have resolved hundreds of nearby protostellar discs, numerical simulations assuming ideal magnetohydrodynamics (MHD) have historically struggled to achieve disc formation due to efficient angular momentum removal by magnetic torques. Non-ideal MHD effects, relevant at the low ionization fractions typical of molecular clouds, have been shown to reduce the effectiveness of magnetic braking and promote disc formation. In this work, we present the results from a suite of calculations following the gravitational collapse of 50 $M_{\odot}$ turbulent molecular cloud cores down to the formation and evolution of stellar systems and protostellar discs. We use the radiation-MHD code GIZMO including non-ideal MHD (Ohmic resistivity, ambipolar diffusion, and the Hall effect) and the STARFORGE numerical framework for modeling star formation and stellar feedback. We compare the effects of assuming ideal vs. non-ideal MHD and including sub-grid protostellar jet feedback on disc formation and evolution. Discs form in all of our models but are least massive in the model with ideal MHD and sub-grid jet feedback. Apart from the ideal MHD$+$jets model, we do not observe any significant differences in disc properties between the ideal and non-ideal MHD models; however, ideal MHD discs are embedded in smaller rotating envelopes. Disc sizes are in general agreement with those of observed discs. Jet feedback increases core fragmentation and reduces final stellar masses. Our results suggest that magnetic braking does not efficiently suppress disc formation, regardless of whether ideal or non-ideal MHD is assumed, under the dynamical conditions in which multiple stellar systems form.

Powerful explosions may undergo sustained energy injection as a central engine launches a wind into the surrounding gas, generating a forward and a reverse shock separated by a contact discontinuity. During the adiabatic phase, the dynamics depend strongly on the wind-to-ambient density ratio $f \equiv \rho_{\rm w} / \rho_{\rm a}$. For $f << 1$, the reverse shock lies well inside the contact discontinuity, and the mechanical energy deposited by the wind is retained in a radially extended, approximately isobaric shocked-wind region whose pressure drives the swept-up ambient shell. For $f \gg 1$, the reverse shock remains close to the contact, and the expansion is governed by the ram-pressure interaction between the freely expanding wind and the swept-up ambient gas. We use analytic scalings and one-dimensional shock-capturing hydrodynamic simulations to determine how outflows in these two limits evolve in ambient density profiles $\rho_{\rm a} \propto r^{-n}$, where $2 \leq n \leq 3$, and whether their shock structures accelerate or coast at constant velocity. For $n > 2$, initially underdense outflows produce accelerating forward shocks whose radii evolve as $R_{\rm s} \propto t^{3/(5-n)}$. Because $\rho_{\rm w} \propto r^{-2}$, f increases with radius, causing the reverse-shocked wind region to contract relative to the contact position as the forward shock transitions toward constant-velocity expansion. This occurs when $f \sim$ a few at $t_{\rm dec} \propto f_0^{1/(2-n)}$, where $f_0$ is the initial wind-to-ambient density ratio. By contrast, outflows initialized with $f_0 \gg 1$ do not develop an extended accelerating phase and remain approximately coasting throughout their adiabatic evolution. We discuss applications to tidal disruption event outflows and luminous fast blue optical transients, whose environments are often inferred to have steep density profiles with $n > 2$.

Additive friction stir deposition (AFSD) is attractive for naval and aerospace sustainment because it enables high rate, solid state near net shape fabrication and repair of large metallic components. However, full part scale thermomechanical simulations remain computationally demanding because of the moving heat source, progressive material deposition, and evolving geometry. This work presents a physics-based and geometry-aware reduced order framework for rapid prediction of temperature and residual stress fields in part scale AFSD builds. The transient temperature field is represented using a tensor product quadratic B spline basis on a fixed Cartesian background grid, while arbitrary part geometries are embedded using a signed distance function (SDF) and a smooth material indicator. Residual stresses are recovered through an inherent strain formulation in which an offline thermo elasto plastic representative volume element (RVE) simulation supplies a calibrated eigenstrain field that is spatially scaled using the part scale peak temperature history. The thermal formulation is evaluated against published numerical and experimental AFSD data and two sample multilayer builds are simulated as demonstrations. For a four layer thin-wall build, the thermal solution requires approximately 22 s compared to 806 s for the finite element model, while the complete thermo-mechanical framework requires approximately 86.4 s compared to 2,406 s. For a larger 20 layer hollow cylindrical build, the reduced order thermal simulations requires 0.148 to 0.152 h compared with 1.8 h for the finite element model, corresponding to thermal speed-ups of approximately 11.8 to 12.2 times. The complete thermomechanical framework requires 0.346 h compared to 5.4 h for the full finite element analysis, providing an overall speed-up of approximately 15.6 times.

Transcranial focused ultrasound is limited by the skull, whose thickness, density and curvature aberrate and attenuate the beam in a position- and target-dependent way, making the choice of where to couple a transducer for a given deep target a central planning problem. This work shows that a single full-wave time-reversal solve resolves it. A virtual point source at the target radiates outward through a CT skull model, and by acoustic reciprocity the time-reversed field gives the transmit coupling of every point on the skull surface at once (a per-patch skull transparency map) and, in the same pass, the per-element signals of any array. For a phased array, time reversal conjugates the skull aberration, so the optimal placement maximizes the delivered energy, a surface integral over the aperture; a single element instead follows a coherent score mixing delivered energy with phase coherence, and the two optimizers select different windows. Placement, aperture size and orientation are then optimized by searching this single recorded map in seconds, with no per-candidate wave solve. The map is computed on a dry-skull micro-CT model for the left dentate nucleus with a 1 MHz heterogeneous Fullwave 2 solver at 6.16 points per wavelength with bone attenuation. A 120 degree occipital array refocuses within 0.25 mm of the dentate at 20.7 Pa per 1 Pa of per-element drive, and a 64 mm spherical transducer seated in the delivered-energy-optimal suboccipital window (27 mm from the target) refocuses at 7.9x gain; the same procedure focuses the thalamus at 15.2x and the dorsal anterior cingulate at 11.4x. The aberration correction raises the on-target peak 7.5x in pressure (56x in intensity) over geometric focusing, which mis-steers the focus by 4.3 mm. The same field measures the aberration's few-millimeter coherence length, which sets the required placement and target-localization accuracy.

Simulating dispersive shock waves (DSWs) using physics-informed neural networks (PINNs) presents significant numerical difficulties. In particular, the square-root initial condition exhibits singularity near the origin, making it difficult to fit accurately. This initial fitting error is amplified through nonlinear evolution and gradually accumulates during extrapolation, imposing high demands on the long-term extrapolation capability of numerical methods. Accordingly, this paper proposes an improved physics-guided multistage neural network (IPgMSNN) framework. IPgMSNN is built upon the original PgMSNN framework, with its core innovation focused on the third-stage training strategy. Specifically, online fine-tuning and a weighted target mechanism are introduced, which effectively suppress error accumulation during long-term extrapolation. Numerical experiments systematically evaluate IPgMSNN from three perspectives: forward problem solving, model stability, and parameter inversion. Experimental results demonstrate that IPgMSNN effectively captures high-frequency wavefront details, maintains long-term extrapolation stability, and achieves high-precision parameter inversion, providing an efficient solution for deep learning-based simulation of dispersive nonlinear systems.

We determine exactly the range of the modular Hamiltonian of an interval in a chiral conformal field theory over states of bounded energy. For an interval of length $R$ in a theory of central charge $c$, and states of mean energy at most $E$, the supremum of the modular energy is $M_{\max} = \frac{\pi R}{2}E + \frac{c}{6} - \frac{\pi c^{2}}{288\,RE} + O((RE)^{-2})$, and neither constant can be improved. The infimum is logarithmic, $M_{\min} \simeq -\frac{c}{12}[\ln(24\pi RE/c)-1]$, so the range is linear above and logarithmic below, the two meeting as $RE \to 0$; it is strictly wider above, by more than $\frac{2}{5}\pi RE$ at every energy, which reduces after a Stieltjes substitution to the positivity of a variance. Both bounds are attained, the one-parameter family of weights proving them being the Legendre structure of the answer. The two ingredients separate cleanly. The coefficient $\pi R/2$ is Möbius representation theory alone: the complementary weight is a translate of the special conformal generator, hence positive, so for the full modular Hamiltonian the bound needs no additive constant. The constant $c/6$ is precisely the price of truncating that weight at the endpoints, supplied by the quantum energy inequality of Fewster and Hollands; the saturating state places its negative energy exactly there. The constant is accordingly additive over entangling points, being $nc/6$ for $n$ disjoint intervals of equal length. Composing with positivity of relative entropy gives a sharp Bekenstein bound, $\Delta S_I \leq \frac{\pi R}{2}E + \frac{c}{6}$, recovering the original form of that inequality from the modular form that by itself imposes no constraint.

The integration of distributed energy resources into power networks is accelerating. The resulting variability narrows operating margins, so a disturbance can cascade into a wide-area blackout. Controlled islanding arrests that propagation by splitting a compromised grid into self-sustaining islands that keep coherent generators together. Exact classical solutions become intractable as the bus count and island number grow. Gate-based quantum optimization provides a different route through this combinatorial space, although its reach is limited when one circuit carries every bus assignment, since qubit count and depth then follow grid size. In this study, a round-synchronous distributed quantum computing framework is developed for coherent controlled islanding under a fixed per-circuit qubit budget. Every round derives all regional subproblems from one frozen grid-wide snapshot, dispatches them to independent quantum backends at the same time, and merges the returned candidates classically into one globally evaluated update. Circuits executed in parallel therefore keep a constant size as the grid grows, and a round costs the slowest region rather than the sum of all of them. Benchmarking spans IEEE systems from 9 to 300 buses on simulation and on quantum processors of different architectures. The framework attains optimal and operationally feasible partitions on every platform under noise, even where compilation cost differs by nearly an order of magnitude. Bounding width in this way places grids beyond the reach of monolithic circuits within range of present devices and establishes a multi-backend baseline for quantum computing in large-scale power-system optimization.

High-temperature vacuum annealing near a metal's melting point drives controlled grain growth, but it normally requires an expensive turn-key vacuum induction furnace. We retrofit a bare commercial radio frequency (RF) induction generator with computer power control through LabVIEW, dual-wavelength optical temperature feedback, and a high-vacuum quartz-tube chamber with inert-gas backfill. A machined graphite crucible doubles as a susceptor, an RF-absorbing element that heats the specimen it encloses. The only requirement is a monotonic analog power-control input, so the retrofit transfers between generators. Our reference build uses a 6 kW solid-state generator. Surprisingly, nickel annealed in this system produced high-quality electron backscatter diffraction (EBSD) patterns with zero specimen preparation. Samples went directly from the furnace to the electron microscope without grinding, polishing, or etching. The EBSD maps indexed hundreds of equiaxed grains delineated by deep thermal grooves. A modified sample assembly extends the furnace to ceramics that do not couple to the RF field. It coarsened yttria-stabilized zirconia (YSZ) grains in 45 min at 2500{\deg}, compared with 228h at 1600{\deg} in a conventional box furnace. For a fixed configuration, the power-temperature calibration is linear ($R^2$ = 0.991) from 1200 to 1400{\deg}. Eight 12h nickel anneals at 1200{\deg} reproduced their soak temperature to 1201.2 $\pm$ 1.3{\deg}, and 40h soaks at 1325{\deg} remained stable. Complete design files, a bill of materials, control software, and data are openly available. This gives laboratories an affordable route to near-melting-point annealing with direct anneal-to-EBSD characterization.

This paper describes the operating principle, circuit implementation, and mathematical basis of a logarithmic analog-to-digital converter (ADC) that exploits the natural exponential decay of a damped LC tank oscillator. An input charge pulse excites a tuned LC resonator into free oscillation. A multi-stage high-speed amplifier chain buffers the decaying waveform, and a fast comparator counts the number of oscillation cycles that exceed a programmable threshold. Because the tank envelope decays exponentially, the cycle count is proportional to the natural logarithm of the input charge, yielding logarithmic compression over a wide dynamic range without explicit logarithmic circuitry. The upgraded unit incorporates a modern MCU with USB, Ethernet interfaces and SD card for collected data storage. The architecture is well suited to applications requiring large dynamic range in a compact, low-power implementation.

Verification protocols provide cryptographic guarantees that the outcome of a delegated quantum computation is correct, a key requirement for scalable and trustworthy quantum computing. A central assumption underlying these protocols is secret independence: the noise affecting state preparation must not depend on the classical secrets that specify the prepared states. In earlier protocols this assumption was enforced by the physical separation of client and server; in the recent on-chip setting it becomes a property of the hardware itself, and one that high-fidelity devices do not automatically satisfy. We first make the requirement quantitative, deriving a bound that shows how residual secret dependence limits the size of any verifiable computation within this framework. Motivated by this, we introduce a calibration framework that enforces secret independence through pulse-level quantum control, producing state preparations that are simultaneously high-fidelity and secret-independent. We assess its performance on a nitrogen-vacancy centre system via emulation, showing that it reduces the secret dependence of the preparations by more than an order of magnitude compared to standard optimal control, and that it is robust against detuning and drive-amplitude errors commonly encountered in experiment. The framework operates entirely at the calibration stage and is platform-agnostic, offering a practical pathway towards verifiable quantum computing at scale.

Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such tools, ecological marine provinces, "eco-provinces", ecologically meaningful regions in the global ocean can be used. We use unsupervised machine learning (ML) to identify eco-provinces with corresponding uncertainty measures based on output of a global simulation of phytoplankton functional types. Our work aims to create a proof of concept to predict eco-provinces based on satellite ocean color data. To do so, we develop a hierarchy of explainable dense ensemble networks to infer how well the eco-provinces can be detected from modeled ocean color fields. Key results include that the delineated eco-provinces are both ecologically meaningful and can be inferred with high skill. However, no straightforward relationship was found where adding more input data to the network consistently improves inference skill, and there is an intricate tradeoff between inputs and prediction fidelity. Our work is a case for optimism and a cautionary tale of needing uncertainty quantification and careful validation of prediction fidelity.

We report cryogenic characterization and compact modeling of GlobalFoundries (GF) 12LP regular-voltage-threshold (RVT) n- and p-FinFETs, measured from 292 K to 10 mK. Both devices retain sub-Kelvin gate control without carrier freeze-out, with the n-type device exhibiting an intrinsic sub-threshold slope (SS) floor of 24.8 mV/dec. To bypass high on-chip series routing resistance that confounds peak-gm extraction, we adopt a fixed-current (3.26 uA/um) methodology. These low-current metrics inform a continuous 1.36 K to 292 K compact model using dynamic parameter injection, preserving room-temperature process development kit (PDK) qualification.

We examine the viability of Big Bang Nucleosynthesis (BBN) constraints in $f(R,L_m)$ gravity, where the gravitational Lagrangian is an arbitrary function of the the Ricci scalar $R$ and matter Lagrangian density $L_m$. We derive stringent bounds on the underlying model parameters of four different $f(R,L_m)$ gravity models by examining both the primordial abundances of helium-4 ($^4\text{He}$) and the fractional variations in the neutron–proton freeze out temperature. Our results reveal that $f(R,L_m)$ gravity remains viable under the BBN constraints. With the freeze-out condition offering the most dominat constraint on the parameter space, whereas the bounds from $^4\text{He}$ abundance act as a separate abundance level consistency checks on the modified expansion rate. In summary, the presence of these feasible parameter regions demonstrates that $f(R,L_m)$ gravity models can successfully accommodate primordial element abundances without disrupting standard early Universe dynamics.

A quantum $(r,\rho)$-locally recoverable code ($(r,\rho)$-qLRC) is a quantum code in which every qudit can be recovered from at most $r+\rho-1$ other qudits, even after $\rho-1$ additional erasures inside the recovery set. The bounds currently known for this class, namely the Singleton-like and the GG Singleton-like bounds, are alphabet independent and are therefore loose for small-to-moderate qudit dimensions. In this letter, we derive three alphabet-dependent upper bounds for pure $(r,\rho)$-qLRCs obtained through the Hermitian CSS construction: a Griesmer-like, a Plotkin-like, and a sphere-packing-like bound. We further establish the asymptotic hierarchy among these bounds and identify the relative-distance regions in which each of them yields the tightest rate constraint.