Papers

The Iron CALorimeter (ICAL) detector at the India-Based Neutrino Observatory (INO) was conceived as an underground experiment designed to measure atmospheric neutrino oscillation parameters. As part of the R&D programme, a scaled prototype (mini-ICAL), 85\,ton, approximately 1/600$^{\mathrm{th}}$ the mass of the full detector, was constructed at the IICHEP Transit Campus, Madurai (altitude 150\,m; latitude 9.9372$^\circ$\,N; longitude 78.013$^\circ$\,E; geomagnetic latitude 1.44$^\circ$\,N; vertical cutoff rigidity 17\,GV) and operated between 2018 and 2022. The prototype enabled measurements of charge-dependent cosmic muon spectra in the vicinity of the geomagnetic equator and provided an important validation of detector performance, reconstruction algorithms, and simulation frameworks for the ICAL experiment. Differential fluxes of $\mu^{-}$ and $\mu^{+}$ were measured over the momentum range $\sim$\,1–5\,GeV/c. The obtained momentum spectra are systematically lower than those reported at sites with smaller geomagnetic cutoff rigidities, consistent with the suppression of low- and intermediate-rigidity primary cosmic rays at the 17\,GV cutoff. The measurements are compared with predictions from different hadronic interaction models available in CORSIKA simulations.

MANAS is a single antenna, ground-based platform built to measure the sky averaged ('global') redshifted 21-cm signal from Cosmic Dawn. The cosmological signal is expected as a ~ 100-200 mK absorption trough against a foreground of 3-4 orders of magnitude brighter. The current crop of 21-cm experiments are limited by systematics namely the sky, ionosphere, beam, and signal path effects. MANAS combines an achromatic monopole antenna, an absolutely-calibrated receiver, and in-situ beam mapping with the OVRO-LWA, and develops an end-to-end error budget that quantifies each systematic.

Nonlocal kinetic-energy density functionals (KEDFs) can encode nuclear shell structure in orbital-free density functional theory (OFDFT), but self-consistency requires accurate functional derivatives and a stable solution of the Euler equation. We construct a density-dependent-kernel KEDF whose correction is learned from Kohn-Sham (KS) reference data. Linearity in the kernel shape yields analytic Euler–Lagrange (EL) responses. The fit uses exact energy-matching equalities and soft quadratic inequality penalties for violations of prescribed tail-response and selected-path energy-rise margins, evaluated by an active-set repeated penalized least-squares iteration. We formulate the radial EL equation as the rearranged one-orbital eigenproblem. In a constant-$k_F$ $^{16}$O benchmark it converges without density mixing and agrees with imaginary-time evolution (ITE) to sub-keV energy. Adaptive-step ITE gives final species EL residuals of at most 0.07 MeV in the reported calculations, and rearranged diagonalization reaches the same stationary densities. For spherical $N=Z$ systems without spin–orbit or Coulomb terms, nucleus-specific fits for $A=16$ to $140$ reproduce shell patterns and radii. A correction trained on three nuclei transfers the shell pattern and radius, but not the absolute energy, to a previously unseen $A=140$ system.

Purpose: To assess the validity of reference region model (RRM) techniques for quantitative perfusion analysis of low temporal resolution DCE-MRI of breast lesions. Methods: Tofts model (TM), extended Tofts model (ETM), RRM, and extended RRM (ERRM) were applied to a dataset of images from ten patients with confirmed breast cancer collected before and after one cycle of chemotherapy, with known pathologic treatment response. Quantitative maps of estimated perfusion parameters were produced using each model, and changes in median perfusion parameters before and after a cycle of chemotherapy were compared across models. Relative parameters from reference region models were made absolute using the reference region and input function tail (RRIFT) technique. Tumour region voxel count before and after treatment was also recorded. Results: RRMs produced similar absolute quantitative analysis compared to AIF-driven models (TM and ETM), with scaling dicerences in Ktrans parameter estimates and disagreement in vp parameter estimates between ETM and ERRM. RRMs and AIF-driven models were equally capable of separating complete responders based on change in median Ktrans parameter, while change in tumour voxel count was not predictive of treatment response. Both RRMs were also able to fully separate complete responders using relative Ktrans parameter estimates, with no AIF information. Conclusions: RRMs show similar performance as standard models in quantitative analysis of breast lesions, with reduced reliance on the AIF . Results imply that RRMs may be a suitable alternative at low temporal resolutions to provide reliable, patient-specific quantitative analysis.

We derive an effective trapped atom–cavity model in which a constant gravitational acceleration shifts the oscillator equilibrium and changes the local standing-wave coupling, thereby encoding the acceleration in the Jaynes Cummings exchange frequencies. With the atom and motion initially in their ground states and the cavity field initially coherent, we solve the closed-system carrier dynamics exactly and derive the displaced-frame quantum Fisher information (QFI) of the joint atom-cavity state. This QFI is proportional to the square of the local coupling slope, grows quadratically with interrogation time, and scales linearly with mean photon number. At the node, phase-referenced Ramsey detection gives a sign-sensitive estimate of axial acceleration and locally saturates the joint QFI. Away from the node, photon counting and phase-optimized homodyne detection provide cavity readouts when the cavity state carries more QFI than the atomic state. At the off-node operating point studied, Lindblad simulations show that cavity loss produces a finite-time QFI optimum. Lamb Dicke and sideband-suppression conditions control the carrier approximation. In the closed-system benchmark, the carrier-model QFI agrees with the atom-cavity QFI obtained from the unexpanded model after tracing out motion.

Competitiveness in racing sports can be measured by comparing the start and finish rankings within races, as well as the start and finish rankings across races in a season. Since the importance of position changes is non-uniform and variance at the top of the ranking is more interesting than at the bottom of the ranking, we propose using weighted distances for this purpose. Therefore, two weighting schemes are applied and compared to the standard Kemeny distance to analyse the Formula One between 1950 and 2024. The evolution of competitive balance is unexpectedly robust to the weights, but competition is more intense if one focuses on the top positions. Competitive balance has been more unfavourable in the last two decades than ever before. Statistical tests uncover three structural breaks in competitive balance that are closely related to regulatory changes, highlighting the role of decision-makers in the evolution of competitiveness.

We ask whether a cored-NFW-inspired radial perturbation leaves a finite-source magnification residual after refitting the best effective spinning vacuum binary. We compute the source flux by direct image-plane integration and vary the lens scale, separation, mass ratio, two spin magnitudes, two projected spin directions, and one phase shift. After refitting, the caustics and magnification curves are almost identical and the phase shift is nearly zero. Still, a structured residual remains. At the highest resolution, the full-orbit rms residual is $1.61\times10^{-2}$ and the maximum local residual is $4.41\times10^{-2}$. Increasing the grid from $800\times522$ to $1000\times653$ changes the full-orbit rms by only $3.39\times10^{-5}$. The residual also survives nine tests with Gaussian, uniform, and tapered sources at three RMS-size factors. Thus, a finite-source residual survives strong geometric degeneracy in this effective model. We do not claim a unique dark-matter detection or a prediction for a physically scaled binary.

Parametric nonlinear solid-mechanics simulations are widely used in virtual testing, optimisation, and uncertainty analysis, but repeated finite-element simulations become costly when geometry changes. This paper presents PI-GINOT, a physics-informed neural operator that predicts finite-strain hyperelastic responses across a four-parameter family of DogBone specimens without using finite-element training data. Each specimen is described by a boundary point cloud, which is encoded into geometry features. A cross-attention decoder then predicts displacement at arbitrary points. Displacement boundary conditions are enforced exactly, while stresses are computed using automatic differentiation and a compressible Neo-Hookean plane-stress model. Training is guided by equilibrium, traction-free and symmetry conditions, deformation stability, and internal force consistency. Abaqus simulations are used only after training for validation. Across eight test geometries, PI-GINOT achieves displacement errors of 2.1%-7.1%, peak von Mises stress errors of 0.9%-13.3%, and section-force errors below 10.3%. Larger errors occur in individual stress components, especially for narrow specimens, mainly because of steep stress gradients near the gauge-to-fillet transition. These results show that PI-GINOT can provide useful geometry-dependent predictions for nonlinear solid mechanics without labelled simulation data, while also revealing where better local stress resolution is still needed.

Piezoelectricity (linear coupling between mechanical deformation and electric signal) was originally observed only in solid crystals. Recently, it was discovered that liquid ferroelectric nematic liquid crystals are also piezoelectric. However, so far only their converse piezoelectric signals (applied voltage-induced mechanical deformation) were measured quantitatively. In this work, we have carried out periodic shear-induced electric current and oscillatory rheology measurements on the two archetypic ferroelectric nematic compounds, RM734 and DIO. From temperature, frequency and strain dependent results of the first and second harmonic current signals together with the results from oscillatory rheometry, we were able to quantitatively determine the shear-mode direct piezoelectric coupling constants. These values are similar for both materials and are compared to results of previous converse piezoelectric measurements. We propose a physical mechanism in which the flow alignment of ferroelectric polarization leads to the direct piezoelectric response.

Storytelling inherently revolves around characters. Using the television sitcom `Friends' as a case study, we investigate how well archetype vectors capture both individual characterization and the relational structure of a specific ensemble. Our work is based on the archetypometrics framework, which locates 2,000 fictional characters from 341 stories in a continuous space derived from 464 bipolar traits. We proceed in three stages: interpreting each character's archetypal profile against narrative evidence, projecting the ensemble onto ousiograms of the six essential dimensions, and measuring pairwise similarity with vector inner products. We show that the six characters of `Friends' occupy distinct archetypal positions that accord with their established identities, while the projections expose ensemble structure invisible in individual profiles, including the collapse of the Angel–Demon dimension, a signature of the sitcom's uniformly sympathetic cast. Based on inner products, we construct a similarity matrix that resolves three main kinds of relational structure: alignment (e.g., Phoebe–Joey), contrast (e.g., Phoebe–Ross), and orthogonality (e.g., Rachel–Ross and Monica–Chandler). The orthogonality of the romantic pairings affords a detailed view of relationships built on complementary rather than overlapping character traits. Overall, our case study suggests that for ensemble-based stories the archetypometric geometry is fully interpretable in narrative terms, from individual identities to the structure of the group's relationships.

Background: The excitation of one-neutron halo nucleus $^{15}$C from the $1/2^+$ ground state to the $5/2^+$ first excited state was measured at Argonne National Laboratory by impinging $^{15}$C on a deuterated target at $7.1 A$ MeV. This data was then analyzed in the Distorted Wave Born Approximation using a rigid rotor model for the excitation. Purpose: Being a one-neutron halo, we expect a single-particle excitation to better represent the excitation of $^{15}$C rather than a collective process. We expect the breakup of $^{15}$C to influence the reaction mechanisms because of the low one-neutron separation threshold, which is close in energy to $^{15}$C's first excited state. The goal of this work is to explore various the reaction mechanisms to reinterpret the data of Ref.[1] for the inelastic excitation of $^{15}$C. Method: We solve the scattering problem assuming a three-body model $^{14}$C$+n+d$. We use the Continuum Discretized Coupled Channel method (CDCC) and compare the results with those obtained assuming 1-step DWBA with quadrupole deformation, as done in the original experimental analysis. We also use Bayesian uncertainty quantification to estimate the uncertainties in our predictions coming from the $n$-$d$ interaction. Results: We analyze both the elastic and inelastic angular distributions for $^{15}$C(d,d')$^{15}\text{C}^*$ at $7.1 A$ MeV. Our results show that $^{15}$C breakup effects are important. Conclusions: While CDCC predicts the elastic angular distribution correctly, it is not able to fully describe the experimental inelastic angular distribution. We discuss additional effects that may be responsible for the remaining discrepancy.

Photonic reservoir computing is a promising physical machine-learning approach for time-series prediction. Quantization of the reservoir response signal is an important consideration in the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high prediction performance and low energy consumption. However, few studies have investigated the effects of the number of quantization bits and sampling frequency on both prediction performance and energy consumption. In this study, we introduce the concept of approximate reservoir computing with a semiconductor laser by quantizing the reservoir node states and output weights. We numerically evaluate the performance of a chaotic time-series prediction task and estimate the energy consumption per sample using a component-level model. By optimizing the number of quantization bits, the sampling frequency, and the injection current of the semiconductor laser, we achieve a significant reduction in the estimated energy consumption per sample while maintaining prediction performance. We also obtain model-based estimates of the throughput, energy efficiency, and energy per operation of the proposed reservoir-computing system.

Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, broad bandwidth, and spatial parallelism of light. However, the effect of the internal connection structure (network topology) on the computing performance has not been investigated for large-scale photonic reservoirs. In this study, we experimentally and numerically demonstrate photonic reservoir computing using a spatial light modulator to systematically evaluate the relationship between the network topology and the performance of reservoir computing. We introduce complex network structures such as small-world and scale-free network topologies of the internal nodes in the reservoir. We perform the memory capacity measurement and the one-step-ahead prediction task of the chaotic time series to compare the performance. We found that the small-world network exhibits the maximum memory capacity and the best prediction performance. Our numerical calculations reveal that the performance of the time-series prediction can be optimized by changing the rewiring probability of the network and the leak rate of the reservoir. We also implement photonic human brain network as a reservoir, which is designed by the connectomes of human brain activities. We found that the network topology strongly affects the performance of reservoir computing, and the small-world network structure outperforms the other configurations.

In this paper, we investigate the almost $\eta$-Ricci-Yamabe soliton as a fundamental geometric regulator for a static, spherically symmetric black hole coupled to an imperfect fluid. We have shown that the scaling parameter $\omega(r)$ is governed by thermodynamic friction along the radial vector field, and the geometric coupling with the Hawking temperature: $\alpha(r_H) S_{tt} = 2\pi T_H$ at the horizon. We also derive the Poisson equation along the gradient vector field of the soliton and prove that the flow's kinematic expansion is explicitly dependent on the fluid's equation of state $\rho = \gamma \sigma$. Diverging from traditional methodologies that assume a geometric shape function apriori, we analytically proved the geometric flow endogenously transitions the black hole geometry into a traversable wormhole throat by regularizing of temporal coordinate and satisfying spatial flare-out condition. This transition occurs when fluid enters the dark energy era at $\gamma = -1$ and violates the Null Energy Condition $\rho + \sigma < 0$, with the soliton strictly dominating the local curvature gradient $\omega^{\prime}(r_H) > f^{\prime\prime}(r_H)$, to keep the throat open. Moreover, by smoothly attenuating at spatial infinity, the soliton preserves the exact cosmological spacetime. Finally, through tensorial perturbation analysis, we demonstrate that the geometric flow introduces a localized dissipative mechanism, that the perturbation evolution reduces to damped wave equation, imposing geometric drag on the manifold.

We demonstrate that confinement of light in three dimensions is possible in photonic crystals without a complete photonic bandgap. Our approach exploits symmetry-protected quadratic degeneracies in the bulk band structure, where the photonic density of states vanishes at an isolated frequency. By introducing a point defect, we create a localized mode whose symmetry representation is incompatible with that of the surrounding bulk modes, suppressing coupling to propagating channels. The combination of vanishing density of states and a symmetry mismatch yields bound defect modes despite the absence of a spectral gap, as confirmed by time- and frequency-domain numerical simulations. This approach highlights the role of engineering the photonic environment around the defect to enable confinement, potentially providing a new route for designing optical cavities in three-dimensional photonic crystals.

Achieving scalable quantum computing demands high-fidelity operations capable of mitigating population leakage into non-computational states. Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful paradigm to unify quantum hardware characterization (inverse problems) and pulse engineering (direct problems), laying the foundational architecture for autonomous quantum processors. However, standard PINN frameworks face severe numerical bottlenecks, such as spectral bias, when attempting to simultaneously solve highly oscillatory multi-level dynamics and optimize continuous control fields under strict global phase constraints. In this work, we propose an enhanced PINN scheme for quantum optimal control (PINNQOC) that circumvents these limitations by incorporating Fourier feature embeddings, dynamic epoch normalization, and an informed pre-training routine. To rigorously evaluate its performance, we systematically benchmark our framework against two premier continuous control solvers: the first-order Krotov method and the second-order Projection Operator Newton Method for Trajectory Optimization (PRONTO). These techniques are applied to implement multiple quantum gates on a truncated three-level fluxonium qubit and a four-level Nitrogen-Vacancy center coupled to a Carbon-13 nuclear spin. Our advanced PINNQOC approach successfully suppresses population leakage while achieving gate fidelities exceeding 99.9$\%$, matching the efficacy of traditional solvers. Finally, we provide a comprehensive analysis of computational times, iteration efficiency, and mean leakage, highlighting the distinct trade-offs and avenues for embedding physics-guided machine learning into automated quantum hardware pipelines.

To investigate the physical mechanisms of fragmentation within the hot molecular core of the massive protocluster IRAS 17233-3606 (G351.78-0.54), we carried out a detailed analysis of continuum and lines, using the ALMA Band 3 data from the ATOMS survey and Band 6 data from the QUARKS survey. The low-resolution 3 mm data reveal a massive hot core MM1 with a mass of ~81.3 Msun, and a prominent ultracompact (UC) HII region MM2, while the high-resolution data resolve MM1 into 11 hot molecular fragments (HMFs). These HMFs exhibit hot (Trot = 100-310 K) CH3CN and CH3OH emission and high column densities (NH2 > 10^23 cm^-2), indicating their potential to form massive stars. Based on outflows, masers, HII regions, and f[CH3CN/CH3O] abundance ratios, the evolutionary sequences of the 11 HMFs are categorized as phases I to IV. The mean minimum-spanning tree (MST) separation (~1.8 x 10^3 au) of the HMFs is nearly half of the thermal Jeans length (~3.3 x 10^3 au). Together with the Q parameter Q = 0.77 and virial parameter alpha_vir = 0.84 of MM1, these results suggest an evolutionary scenario in which fragmentation is initially driven by thermal instability, followed by global gravitational contraction and growth through active accretion. Meanwhile, feedback from the B2-type zero-age main-sequence (ZAMS) star and the UC HII region significantly influence the morphology and chemical properties of MM1 and MM2. This heterogeneity highlights the role of diverse physical processes taking place in high-mass protoclusters.

Multiwavelength monitoring surveys of active galactic nuclei (AGN) have revealed correlated variability observed in the X-ray, UV, and optical bands. X-ray reverberation, arising from the absorption of X-rays illuminating the accretion disc, provides a self-consistent physical framework for interpreting these observations and imposing constraints on the geometry and energetics of accretion flows and X-ray coronae. We aim to apply the X-ray disc reverberation framework to the Seyfert 1 galaxy Fairall 9, a well-studied AGN with a clear line of sight to the accretion disc, to investigate whether this physical scenario can simultaneously account for its observed spectral and timing properties, as probed by its mean spectral energy distribution (SED), UV/optical power spectral densities (PSDs), and interband time lags. We used multiwavelength data from the 2018-2021 Swift intensive monitoring campaign to construct the mean X-ray/UV/optical SED and to compute PSDs in all bands. We first modelled the broadband average SED using KYNSED, which is a physical X-ray reverberation model assuming lamp-post geometry. The resulting best-fit parameter space was then used to model the UV/optical PSDs and further constrain the physical parameters of the system. Finally, we tested whether the observed interband time lags are consistent with the model predictions for the parameter sets that simultaneously reproduce both the SED and the PSDs. X-ray illumination of the accretion disc can explain the broadband mean SED of Fairall 9. The UV/optical variations are likely driven by the variable X-rays that illuminate the disc, and not by short-timescale disc fluctuations of unknown physical origin. X-ray disc illumination and reverberation can explain the mean energy spectrum, the UV/optical power spectra, and the wavelength-dependent time lags simultaneously for a common set of physical parameters.

Quantum technologies increasingly require accurate modeling of their hardware components and of the non-equilibrium regimes in which they operate, where managing heat and energy flow becomes a central challenge. Thermoelectric effects, the direct conversion of a thermal gradient into electrical signals, offer one such route to this control. In this review, we present an overview of the bipolar thermoelectric effect, a recent development for thermoelectric conversion in reciprocal systems, where linear effects are forbidden by symmetry. This symmetry yields a bipolar thermoelectric signal, in which the generated voltage can exhibit both polarities at a fixed temperature gradient. This represents a non-trivial novelty relative to conventional thermoelectric effects, in which carrier dominance determines the sign of the thermoelectric signal. We summarize the underlying physical principles, showing how thermoelectricity emerges as a strong violation of detailed balance. Concrete physical conditions for obtaining bipolar thermoelectricity are then outlined, of which a tunnel junction between two superconductors with unequal energy gaps and suppressed Josephson coupling is the paradigmatic example. Afterward, we discuss the experimental observation of the effect to date and related proposals for different applications, including volatile memories and radiation detection. Finally, we briefly survey recent developments and outlooks, ranging from extensions to new platforms to a proposal for a novel quantum thermoelectric effect.

We introduce a curl-based Electric-Field Integral Equation (Curl-EFIE) for the electromagnetic scattering analysis from perfect electric conductors. The formulation is derived by enforcing a vanishing curl on the EFIE over the boundary manifold, achieved by testing the internal electric field with orthogonal tangent solenoidal disks. We demonstrate that a Method-of-Moments (MoM) discretization of the Curl-EFIE converges to a Galerkin-discretized MFIE as the testing disk dimensions vanish, yielding stable, breakdown-free impedance matrices for low frequencies and dense grids. Unlike the strongly singular kernels of the MFIE, the Curl-EFIE utilizes weakly singular kernels, significantly simplifying source integral evaluations. As a first-kind integral equation, it bypasses the MFIE's Gram matrix requirement, facilitating the analysis of non-matching triangulations. Furthermore, a linear combination of the Curl-EFIE and the conventional EFIE provides an interior-resonance-free formulation analogous to the Combined Field Integral Equation (CFIE). Finally, the Curl-EFIE performs particularly well at capturing the scattering behavior of sharp- edged and cornered geometries.

Standard quorum-sensing models in active matter exhibit collective phenomena such as motility-induced phase separation. Here, we show that incorporating competing sensing ranges– a minimal ingredient inspired by microbial communication –qualitatively changes this behavior, replacing macroscopic phase separation with self-organized microphases characterized by an emergent finite length scale. Starting from the microscopic dynamics, we derive a coarse-grained field theory whose coefficients are explicitly related to the moments of the microscopic sensing function. This mapping enables a direct comparison between particle-based simulations and continuum theory, allowing the characteristic modulation and correlation lengths to be predicted directly from the microscopic interaction parameters. Two-dimensional numerical simulations confirm these predictions and reveal a transition from macrophase separation to finite-wavelength density modulations as the competition between sensing scales increases. For stronger competing interactions, the system develops a peculiar cluster phase with an interstitial percolating network, which is captured by a higher-order gradient expansion. Our results identify competing quorum-sensing interactions as a simple microscopic mechanism for generating tunable active microphases.

The cooling of neutrons stars, which are composed of ultra-high-density nuclear matter, strongly depends on the equation of state because the dominant cooling sources are neutrinos produced in their interiors. Here, we investigate the cooling properties of neutron stars, focusing on effective nucleon masses, whose impacts have been recently examined in supernova explosion and proto-neutron star cooling. The uncertainties of effective masses originate from the difference in the attractive interaction of scalar meson and the definition of the Dirac and Landau masses. At first, we employ so-called Walecka models that allow these two masses to be introduced within a unified framework and evaluate the resulting cooling curves. Based on the Walecka model, we compute the emissivity of the neutrons and the direct Urca process and their cooling curves. We demonstrate that the Dirac and Landau masses affect cooling of massive neutron stars, although most previous cooling simulations do not distinguish them. We also perform cooling simulations with two realistic equation of states based on relativistic mean-field (RMF) theory, whose differences arise from properties of only effective masses among characteristic parameters. These results are broadly consistent with those obtained from the Walecka models, indicating that the treatment of effective nucleon masses may be an important source of uncertainty in neutron-star cooling.

We study the effect of electric current and magnetic field on the dynamics of chirality in a helimagnet with a strong easy-plane anisotropy. Using a continuum theory, derived for a quasi-one-dimensional frustrated ferromagnet close to the Lifshitz point, we show that a domain wall connecting domains with opposite chiralities can be driven by the current via the dissipative (non-adiabatic) component of the spin-transfer torque. Further, it is demonstrated that the adiabatic part of the torque in the presence of a magnetic field breaks the symmetry of the effective magnetic potential energy with respect to the chirality. We show that the leading symmetry-breaking term arises in the third order in the magnetization gradient, and derive the conditions of the chirality switching. Our conclusions are supported by numerical spin-lattice simulations.

Scalable reconfigurable photonic integrated circuits require low-loss, high-speed optical control without continuous holding power. Yet widely used thermo-optic tuning and continuously biased electro-optic tuning consume static power and introduce thermal crosstalk or bias drift. Here we demonstrate a monolithic non-volatile photonics platform on lithium tantalate-on-insulator (LTOI). In congruent x-cut lithium tantalate, the switched ferroelectric-domain configuration is retained after the write field is removed. On the same LTOI platform, we demonstrate a waveguide propagation loss of approximately 0.05-0.06 dB/cm and multilevel non-volatile phase tuning in separate devices. The programmed states remain distinguishable through $10^{6}$ write cycles. Weighted segmented electrodes resolve 137 phase positions across a $\pi$ range, corresponding to an analogue phase-setting resolution of approximately $0.007\pi$. We further combine non-volatile phase control with high-speed electro-optic modulation to achieve zero-static-power bias control of a >110 GHz modulator and a 59.3 dB extinction ratio after non-volatile trimming. At the system level, an image-edge-detection chip achieves a measured energy efficiency of 3.48 TOPS/W. These results establish LTOI as an integrated photonics platform that combines persistent optical reconfigurability with low-loss routing and high-speed electro-optic modulation.

This paper develops a unified kernel-based method for computing exact matrix elements of composite quantum operations by inserting resolutions of the identity after individual operators or between successive factors of an operator chain. A single insertion gives an exact scalar identity, and nested insertions between the factors give multiple integral identities for chains of closed operators under explicit Bochner-integrability conditions. The procedure is carried through on polynomial and analytic functions of the ladder operators, on one- and two-mode Gaussian unitaries, and on three composite operations: a Kerr phase followed by squeezing or by a cubic phase gate, a two-mode squeezer followed by a beam splitter, and a heralded cat-state circuit with two beam splitters. At rational Kerr phases the Kerr-squeezed amplitudes reduce to finite sums of squeezed coherent-state amplitudes weighted by quadratic Gauss sums, with an explicit error bound at the remaining phases; the two-mode network gives a bivariate Hermite polynomial; and the heralded circuit gives closed forms for the amplitudes, the heralding probabilities and their extension to an arbitrary passive network fed by one squeezed source. The examples are set against the reach of four established techniques: operator ordering, the Berezin symbol calculus, Lie disentangling and the symplectic formalism. Each technique reaches a subset of the examples directly, while the insertion procedure reaches all of them under the same hypotheses. A comparison with direct Fock summation identifies the regime in which the finite Kerr sums are efficient, and the choice of resolution at each boundary of a chain follows a selection rule set by the action of the adjacent factor. Each closed form agrees with an independent numerical computation.

Weak magnetic fields have long been reported to either enhance or suppress transmembrane ionic currents, yet the physical origin of these apparently contradictory responses remains unresolved. Here, we develop a mesoscopic equilibrium framework showing that weak magnetic fields primarily modify the equilibrium occupation probabilities of metastable transport states, thereby altering the resulting nonequilibrium ionic current. Rather than acting directly on microscopic ionic trajectories, magnetic fields regulate transport through the statistical redistribution of conducting states. This mechanism naturally explains both magnetic enhancement and suppression within a unified theoretical framework and predicts a universal criterion for magnetic sign reversal governed by a single equilibrium covariance. The theory further identifies experimentally testable signatures, including characteristic magnetic-field dependence and state-dependent transport modulation, providing a quantitative framework for interpreting weak-field magnetic effects in biological ionic transport.

We study the chiral dynamics of an intruder immersed in a nonequilibrium bath in two complementary limits: the dilute kinetic regime and the dense hydrodynamic regime. In the dilute limit, starting from a Boltzmann-Lorentz description, we derive an effective Langevin equation whose coefficients are given explicitly by geometry-dependent boundary integrals. This formulation separates the effects of intruder-shape chirality from those of chiral intruder-bath interactions. We find that the chiral interactions generate an odd response and a torque, whereas the chirality of the intruder leads to a ratchet effect. We also show that fluctuation-dissipation-like relations exist and that certain symmetry-allowed couplings vanish in the dilute regime. In the dense regime, we argue that intruder dynamics are governed primarily by bath hydrodynamics and torque-density-driven edge currents not captured by the previous framework. These currents can generate both an antisymmetric drag and a curvature-induced torque, leading to an antisymmetric response when inertia is accounted for. Taken together, these results provide a step toward understanding the mechanisms governing the chiral dynamics of an intruder in a nonequilibrium bath across different scales.

We introduce and simulate an analytically tractable model for an intruder of arbitrary shape in a nonequilibrium bath, with chirality originating from the bath, the intruder, or their coupling. In the dilute regime, a Langevin description derived from a Boltzmann-Lorentz equation shows how intruder geometry governs ratchet effects and odd response. In the dense regime, the dynamics of the intruder are instead governed by the hydrodynamic modes of the bath and edge currents, which are described by a Stokes equation including a chiral torque density. Our results link shape to chiral transport and show that odd response arises from distinct mechanisms in the dilute and dense limits.

We investigate the influence of radiation pressure on the collective dynamics of a degenerate electron–positron plasma interacting with an intense radiation flux. Assuming the Fermi energy substantially exceeds the thermal energy, we apply general covariant hydrodynamic equations to demonstrate that in a sufficiently dense plasma, radiation pressure outpaces the increase of relativistic inertia, yielding an enhanced terminal bulk Lorentz factor. Furthermore, this process is accompanied by dynamic plasma cooling.

Multi-ionic materials pose a distinct representational challenge in machine learning-driven materials design. Different from single-molecule or composition-based materials, their properties arise from how charged building blocks aggregate into specific assemblies. Here, we show how pretrained machine-learned interatomic potentials (MLIPs) can bypass full crystal-structure prediction and support pre-synthesis screening from stoichiometric ionic clusters using multi-ionic integrated explosives (MIXs) as a synthesis-facing example. This strategy combines a stoichiometric ionic-cluster representation, which represents each candidate material by a non-periodic, stoichiometry-preserved formula-unit cluster, with multi-task fine-tuning (MT-FT), which adapts a pretrained atomistic backbone while retaining the energy–force objective as physical regularization for the sparse detonation-velocity labels. With the pretrained backbone regularized by MT-FT, this surrogate provides a cross-validated screen across only 25 structurally curated perovskite-type energetic materials (PEMs) with experimentally derived Kamlet–Jacobs (K–J) detonation velocities. Representation probes show that the learned descriptors implicitly retain site-aware ionic organization, density information, and coarse packing compatibility, implying why non-periodic clusters can remain predictive before full crystal structures are known. The surrogate extends known PEMs chemistry to three newly synthesized ABX$_4$ materials with both unseen ABX$_4$ stoichiometry and an unseen ethylenediammonium B-site cation, yielding three-point concordance with K–J reference velocities and a mean absolute error (MAE) of 92~m$\cdot$s$^{-1}$ without retraining. Together, these results establish stoichiometry-preserved cluster learning as a synthesis-facing screening strategy for data-scarce multi-ionic materials.