Papers

Piezoelectric devices offer a promising magnetic-free alternative for high-frequency power electronics, enabling compact energy transfer with inherent galvanic isolation. Here we demonstrate a lithium niobate surface acoustic wave (SAW) resonator that enables isolated power transfer and addresses the challenges in existing piezoelectric devices for power applications, including operation frequencies, power delivery figure of merit (FOM), power handling, and electrical isolation. Our application-optimized 40-MHz SAW resonator, with the two electrically isolated interdigital transducers (IDTs) and acoustic Bragg mirrors, achieves a 25.7 W output power at a less than 15 {\deg}C temperature rise with only passive cooling through the back mounting side, and an isolation voltage of 2.17 kV. In DC-AC power conversion, we demonstrate a peak efficiency of 93.8 %, a power delivery FOM of 13.47 mW/V$^2$ and a peak chip power density of 81.7 W/cm$^3$. DC-DC conversion is also demonstrated by connecting a diode rectifier to the output IDT, despite efficiency degrading to 52.7 %. Our results advance SAW devices as one of the core components for next-generation very-high-frequency, miniaturized, high-density, magnetic-free, isolated power converters.

We develop a sequential ensemble MCMC scheme for Bayesian pulsar timing and noise analysis that uses data size as the tempering parameter. The method enables gradual and reliable convergence from potentially uninformed initial points by iteratively increasing the data size, thereby increasing sensitivity and progressively narrowing the target distribution until the posterior of the full dataset is reached. The initial iterations use sub-datasets that can be orders of magnitude smaller than the full dataset, substantially reducing computational cost, which is further lowered by employing approximate lower-rank models justified by the reduced sensitivity of these sub-datasets. Bayesian inference is performed using the final MCMC chain sampled from the true target distribution after applying appropriate burn-in. We demonstrate the method using the NANOGrav 12.5-year data of PSR B1855+09, showing progressive convergence to the target posterior and that most of the computational time is spent sampling the true target distribution rather than in the preceding tempered iterations.

Motivated by searches for weak primordial non-Gaussianity in the cosmic microwave background, we investigate perturbative departures from Gaussian stochastic gravitational wave background statistics using pulsar timing arrays. We consider a quadratic ansatz that shows three-point correlations at first order in the nonlinear coupling and covariance corrections at second order. We first examine the ansatz for gravitational wave helicity amplitudes. However, the timing residual bispectrum that this produces is not manifestly invariant under local polarization basis rotations for tensor and vector modes. Then, we formulate a polarization-frame-invariant phenomenological model for detector-projected amplitudes and evaluate its one-pulsar timing residual third moment with a simplified response. Our numerical benchmarks have shown that this gives a very small Gaussian-rms-normalized skewness.

Hyperedge faults remain a major challenge in circuit-level quantum error correction. Accurately exploiting hyperedge correlations often requires substantial computational resources, which complicates real-time decoding. Matching-based decoders are fast and scalable, but their pairwise graph representation limits the use of higher-order fault information. Here, we introduce Syndrome Motif Projection (SMP), a general framework for incorporating hyperedge information into matching-based decoding. The key idea is that a hyperedge fault produces a characteristic local syndrome motif. Observing such a motif provides evidence for the corresponding fault. SMP extracts this information directly from the measured syndrome with linear-complexity preprocessing while preserving the matching backend. It can therefore serve as a lightweight front end to existing scalable and real-time matching decoders. We demonstrate SMP across surface code memories, color code memories, and logical gate circuits. On Google's Willow experimental surface code data, SMP improves both standard and correlated matching and outperforms the tested belief matching baseline. For color code memories, SMP combined with Chromobius reduces logical failure probabilities by up to 77.8%. For logical gate circuits, SMP reduces the failure probability of a six-CNOT circuit by 35.5% relative to logical observable matching while achieving performance comparable to iterative belief matching. These results demonstrate that SMP provides a lightweight and transferable approach to hyperedge decoding, with a clear path toward integration into scalable real-time decoding architectures for fault-tolerant quantum computation.

Wave-energy relation is introduced as a new analysis tool for linear radial pulsations. Wave energy consists of kinetic energy, acoustic-potential energy, dynamical-potential energy, and entropy-potential energy. The relation describes changes in the wave energy caused by non-adiabatic processes and by leakage of wave energy. It is applied to modal analysis of adiabatic pulsations for the massive Zero Age Main Sequence models (10, 20, and 30 solar masses). The ordinary modes are well trapped in the interior cavity without any significant wave-energy leakage. On the other hand, the strange modes (S-modes) exhibit the steep increases in the eigenfunctions and kinetic energy toward the stellar surface and the significant wave-energy leakage. The kinetic energy of S-modes is no longer confined in the exterior cavity.

Of the two black holes, M87$^\ast$ and Sgr~A$^\ast$, for which event-horizon-scale imaging is currently achievable, only Sgr~A$^\ast$ at our Galactic Center exhibits sub-hour variability, making light curve observations of the inner accretion flow especially valuable for time-domain studies of the spacetime and the surrounding plasma. Observed light curves are often sampled with variable cadence, so a structure function is often used to quantify their variability properties. In this work, we assemble a heterogeneous Sgr~A$^\ast$ light curve data set from discrete observations acquired over $\sim12$ years at 230 GHz, with different instruments, durations, and cadences, and study its structure function in three frameworks. First, we consider analytic predictions for the features of structure functions of stationary random processes with Fourier spectra dictated by the Matérn covariance. We then study the structure functions resulting from lensed movies of synthetic optically thin accretion flows approximated with Matérn covariance in both space and time, and identify which features in the structure function can be attributed to the black-hole geometry. Lastly, we examine the structure functions of a library of $72$ general relativistic magnetohydrodynamic simulations. We find that, in general, magnetically arrested disk (MAD) models produce smoother structure functions with steeper slopes than standard and normal evolution (SANE) counterparts. On time scales of $5-15$ minutes, the observed structure function slope of Sgr~A$^\ast$ more closely aligns with the MAD trend, while both simulation types are generally more variable than the data on all sub-hour timescales.

In the photoreceptor compass model, a light-generated, spin-correlated radical pair undergoes singlet-triplet interconversion driven by Zeeman and anisotropic hyperfine interactions, leading to orientation-dependent reaction yields. In this study, we consider simultaneously the spin environment and the boson environment surrounding the radical-pair system and concentrate on the correlated memory effect of the spin bath. In the linear-response limit, the spin bath is mapped to an effective Gaussian environment. The system dynamics and yields under its influence together with the bosonic bath are propagated uniformly using the hierarchical equations of motion (HEOM) or equivalently the dissipaton equations of motion (DEOM) method. The HEOM/DEOM is non-Markovian and non-perturbative, exact for Gaussian environments. Corresponding results of the Markovian Lindblad master equation are also shown for comparison. Numerical demonstrations highlight the non-Markovian memory effect as a crucial ingredient for radical-pair magnetoreception.

Mixture-of-Experts (MoE) models can increase parameter capacity without proportionally increasing active computation, but it is unclear how this trade-off behaves in particle-physics transformers. We study dense and MoE Particle Transformers on 188-class JetClass-II, varying expert count, routing capacity, top-K, and auxiliary loss. We find that, when token dropping is avoided, top-1 MoE models improve over the dense baseline at nearly unchanged nominal forward compute, while further increasing the number of stored experts produces little additional accuracy gain. Activating multiple experts per token yields additional predictive improvements at higher computational cost. Routing analyses show that expert assignments become more strongly associated with particle identity and kinematics in some configurations, but this structure does not increase monotonically with classification performance. These results highlight the need to distinguish stored parameter capacity, active computation, routing capacity, and routing organization when evaluating sparse expert models for jet classification. Code and experiment configurations are available at https://github.com/kpendiyala/MPT.

This article proposes and articulates structure-driven methodology as an emerging cross-domain paradigm. In contrast to the objective-driven paradigm that has dominated inverse-problem solving for over two centuries, structure-driven methodology advocates identifying and exploiting the intrinsic structure of a problem to construct the solution method directly, rather than approaching the solution through iterative search. Drawing on examples from the Liouville-variant Goldbach conjecture, the three-dimensional Kakeya conjecture, Noether's theorem, and the author's nearly four decades of geophysical inversion practice, the article illustrates the common logic of structure-driven thinking in number theory, pure mathematics, physics, and seismic imaging. The author also systematically proposes the Structure-Driven Inversion (SDI) paradigm, comprising Mathematical Structure-Driven Inversion (MSDI) and Physical Structure-Driven Inversion (PSDI). Structure-driven methodology does not negate objective-driven methodology but complements it: objective-driven is a general search strategy, whereas structure-driven is a custom direct strategy. The article concludes that the significance of this shift lies not only in efficiency gains but also in cognitive deepening—when facing complex problems, the first question is no longer “how to solve the problem faster” but “what is the structure of the problem.”

BN products formed on Fe–B sources under flowing N$_2$ were investigated primarily at 1250–1400~$^\circ$C over nominal B contents of 1–10~wt% to determine how rBN-related Raman character depends on growth condition and history. Spatially resolved Raman mapping across 44 growth conditions identified a restricted temperature–composition region in which the integrated rBN-related low-frequency response over 750–835~cm$^{-1}$, $A_{750-835}$, is strongly developed together with the integrated total response of the main $\sim$1367~cm$^{-1}$ BN band, $A_{1367}^{\rm{total}}$. In addition, the asymmetric high-wavenumber extension of the $\sim$1367~cm$^{-1}$ band was quantified by subtracting a symmetric fitted core and integrating the far high-wavenumber residual, $T_{\rm{high}}^{\rm{far}}$. The independently defined $A_{750-835}$ and $T_{\rm{high}}^{\rm{far}}$ responses exhibit a strong correspondence across the full dataset, supporting their use as complementary rBN-related Raman observables. In contrast, $A_{750-835}/A_{1367}^{\rm{total}}$ varies systematically among growth conditions, indicating that the relative prominence of the low-frequency response changes with temperature and composition rather than tracking the overall BN response through a fixed proportionality. Growth-duration experiments at 1350~$^\circ$C further show that this ratio also depends on high-temperature N$_2$ dwell time, establishing growth history as an additional kinetic variable. Powder X-ray diffraction on selected products independently confirms the presence of rBN-related stacking, while second-harmonic generation provides complementary symmetry-sensitive evidence. These results define a processing window favorable for strong rBN-related Raman response and introduce a quantitative framework for tracking both the low-frequency and band-asymmetry signatures associated with rBN-related BN structure.

We develop a rigorous justification for a parameter estimation algorithm which couples continuous data assimilation to generic optimization. For finite dimensional systems, we provide a rigorous justification of an asymptotic approximation of the sensitivity for the underlying modeled dynamical system, prove that the $L^2$ loss function satisfies an approximate Polyak-Lojasiewicz inequality, and use that result to justify convergence of gradient descent for the proposed algorithm. Numerical examples are provided that demonstrate the precision of the rigorous results.

Thermonuclear flame fronts in Type Ia supernovae develop hydrodynamic instabilities that generate turbulent mesoscale flow and substantial local thermodynamic variability. Recent work by \citet{Brooker+25} demonstrated systematic shortening of fuel ignition times near Rayleigh–Taylor unstable deflagration fronts, but the physical origin remained unclear. We investigate the thermodynamic evolution of fuel near the flame front using multidimensional simulations and Lagrangian tracer-particle analysis, focusing on flame-driven expansion and ignition-time variability in partially degenerate carbon–oxygen plasma. Tracer trajectories show that the most strongly preconditioned fuel parcels near the flame front, which form a minority of the fuel layer, undergo approximately pressure-constrained expansion with modest density reductions and temperature increases of $\approx 2\times10^{8}$~K to $\approx7\times10^{8}$~K ($\approx13$ to $63$ per cent above the unperturbed fuel temperature) between the low- and high-density models. Despite moderate thermodynamic perturbations, the strong temperature sensitivity of carbon ignition produces substantial shortening of ignition times. The results indicate a two-step mechanism: flame thermal expansion perturbs the fuel away from hydrostatic equilibrium, while restoration of pressure balance by weak acoustic waves redistributes internal energy from degenerate to thermal components. This raises the fuel temperature and produces localized thermodynamic preconditioning. These results suggest that unsteady thermonuclear flames can shorten ignition times ahead of the flame, influencing fuel over distances larger than the laminar flame thickness. Whether this preconditioning leads to secondary ignition or contributes to deflagration-to-detonation transition remains untested here, as nuclear burning is disabled in the analyzed fuel.

Inferring an Allen-Cahn constitutive force from images requires more than an accurate optimizer: nonlinear observation noise alters the mean of force features, shared noise couples instruments to the response, and a temporal balance borrowed from another solver can remain biased even on clean data. We develop observation-aware, noise-adjusted moment equations that address these effects before inversion. The surface-tension-calibrated constitutive transformer (SCCT) couples the resulting moments to a positive Bernstein inverse and can adapt its prior and precision from images and moments. A conditional stability bound separates moment residuals from prior error. Synthetic tests show that correcting the estimating equation matters more than increasing regularizer flexibility; the benefit of learned adaptation is smaller and depends on the training comparison. Forces inferred from planar movies predict unseen planar and three-dimensional evolutions. A ring-cage breakup shows why diffuse-interface accuracy distinguishes forces even when their topological predictions agree, while external surface tension sets the energy scale separately.

Manipulating crystal structures towards targeted geometric properties or symmetry constraints is a longstanding challenge. Existing computational frameworks fall short: LLMs have poor spatial awareness and operate at coarse granularity in the token space, while diffusion- and gradient-based structure generation approaches generally produce complete structures de novo rather allowing for fine-grained editing of a given input under arbitrary constraints. We present LATHE, a geometric toolkit that closes this gap by expressing seven classes of crystallographic properties — bond length, bond angle, dihedral angle, coordination environment, lattice parameters, cell volume, and space group, as differentiable objectives to enable direct geometric editing of crystal structures. To demonstrate its usage in materials design, we further expose LATHE through a Model Context Protocol server and embed it in a closed-loop multi-agent system which translates hypotheses intogeometric modifications in natural language towards a given design objective. Across single-property benchmarks, LATHE attains near-perfect constraint satisfaction on all seven property types while keeping optimized structures close to local energy minima. The LATHE-equipped agent translates over 87.5% of natural-language prompts into valid executable configurations and faithfully completes them. In a band-gap inverse-design case study, the multi-agent loop reaches the target tolerance window in nine of ten independent runs at a typical cost of thirteen hypothesis-evaluation cycles. By bridging natural-language hypothesis generation and physically grounded gradient-based structural editing, this work establishes a paradigm for interpretable, closed-loop computational materials discovery that is immediately applicable to a broad class of functional material design tasks.

Cosmic birefringence, the isotropic rotation of the plane of linear polarization of photons from sources at cosmological distances, can be generated by different parity-violating mechanisms. While the net rotation angle of a single polarized source carries little information about its physical origin, sources at various redshifts $z$ can be used to distinguish between different origins. Parity-sensitive correlations of the $E$- and $B$-mode polarization fields of the cosmic microwave background measured on large and small angular scales probe sources at $z\simeq 10$ and $1100$, respectively. Thus, the detailed shape of the $EB$ power spectrum is sensitive to the redshift evolution of the birefringence source. In this paper, we calculate the $EB$ power spectrum due to a neutrino-asymmetry current, and compare it to axion dark energy and dark matter using a common Chern–Simons framework. We find that the $EB$ power spectrum due to a neutrino asymmetry is suppressed at low multipoles, $\ell\lesssim 20$, similar to the dark matter case. This can be clearly distinguished from the dark energy case, which has no such suppression. Large-scale polarization measurements, including LiteBIRD, can therefore distinguish late-time dark energy birefringence from that generated at higher redshifts by axion dark matter or neutrino asymmetry. We also find that the evolution of the neutrino density during recombination alters the shape of the $EB$ power spectrum at high multipoles. This signature can be used to distinguish between the neutrino and axion dark matter interpretations, using ground-based experiments such as the Simons Observatory.

We construct the quantized electromagnetic field and the corresponding photon propagators in a rigidly rotating cylindrical reference frame. Starting from covariant Maxwell theory, we formulate the gauge-fixed field equations and solve them in cylindrical coordinates. The vector nature of the electromagnetic field requires distinguishing orbital and intrinsic angular momentum, with the total angular momentum as the natural quantum number. We construct the two independent physical photon modes in terms of Bessel functions by solving the coupled differential equations, and provide an alternative construction based on a covariant basis of vector eigenfunctions generated by a covariantly constant tetrad. After normalizing the modes with the Maxwell inner product, we quantize the electromagnetic field and define the corresponding photon states and greater and lesser two-point functions. The physical time-ordered propagator is obtained directly from the quantized modes, with its transverse dependence encoded in polarization kernels that retain the cylindrical spatial structure and its spectral poles exhibiting the rotational energy shift. The full gauge-fixed photon propagator is constructed in two complementary ways: from the scalar Green function combined with the tensor transformation of the inertial Feynman-gauge propagator, and from the covariant vector eigensystem through its spectral decomposition and the Fock–Schwinger proper-time representation. We verify that both constructions satisfy the Green-function equation for the complete gauge-fixed Maxwell operator, providing the propagator required for covariant perturbative calculations with internal photon lines.

Thermal X-ray pulse profiles constrain neutron-star masses and radii through the effects of gravitational light bending and rotation on surface emission. The precision anticipated from the enhanced X-ray Timing and Polarimetry (eXTP) mission motivates more accurate analytic light-bending formulae that retain the efficiency needed for repeated model evaluations. We derive a refined analytic flux expression within the oblate–Schwarzschild (OS) approximation that combines bending, propagation-delay, and lensing approximations with a spheroidal surface. We compare its profiles with numerical Schwarzschild and second-order Hartle–Thorne ray tracing on the same spheroidal surfaces. The comparisons use a selected two-spot configuration at 300 Hz with isotropic blackbody emission and three choices for surface eccentricity: a freely specified value, the Maclaurin relation for a uniformly rotating Newtonian fluid, and a fit to rotating-neutron-star surfaces. Across these cases, the refined OS profiles have RMS flux differences of $1.76-1.98\%$ relative to the Hartle–Thorne reference, compared with $6.50-21.39\%$ for the spherical Schwarzschild-plus-Doppler (S+D) model. All differences are normalized to the phase-averaged reference flux. The corresponding differences between Schwarzschild and Hartle–Thorne ray tracing remain below $1.1\%$. These comparisons separate errors in analytic photon transfer from the effects of surface deformation and the rotating exterior spacetime. For the configurations examined, accounting for oblateness substantially improves agreement with ray tracing. The model retains the speed of an analytic calculation while making the physical origin of profile changes explicit.

Machine learning surrogates for density functional theory (DFT) have been increasingly used to reduce the cost of first-principles calculations. In this arena, predicting real-space electron densities offers a scalable and transferable initialization for self-consistent field (SCF) procedures. However, current methods face a clear dilemma. That is, grid-based architectures incur a high computational cost, while basis-set methods fail to capture the structural correlations inherent in the coefficient space. Here, we develop OrbFlow, an $\mathrm{SE}(3)$-equivariant generative model that predicts Gaussian-type orbital (GTO) coefficients via flow matching. OrbFlow retains the efficiency of a compact atom-centered basis while replacing pointwise regression with a learned probability path over the full coefficient space. It is trained through a two-phase trajectory curriculum that mitigates discretization drift during numerical integration. OrbFlow achieves state-of-the-art accuracy on QM9, reducing density error by 13.6% relative to the previous best model, and reduces error by 51% to 63% on every molecule of the MD benchmark relative to the strongest prior method sharing its basis. The predicted density also cuts SCF iterations by up to 68% with zero-shot transfer to unseen exchange-correlation functionals and recovers dipole and quadrupole moments to within a few percent of DFT references without any SCF calculation.

We study a family of aperiodic metric graphs generated by one-dimensional tilings. We prove that, generically, the spectrum of the associated Kirchhoff Laplacian has Lebesgue measure zero and, apart from a discrete set of isolated eigenvalues, is a generalized Cantor set. For a large subclass of these graphs, we further show that isolated eigenvalues are generically absent. This extends several well-known results from the setting of discrete ergodic Schrödinger operators on $\mathbb{Z}$ to a large family of metric graphs. Earlier results of this kind for metric graphs typically assume equilateral edges and exploit symmetries that reduce the analysis to a Sturm-Liouville operator on the real line. By contrast, the graph families considered here need not have equilateral edges or the symmetries needed for such a reduction and therefore require a different analytical approach.

We show that for any $R,\epsilon >0$ and any prime $p\geq 2$, there exists an infinite family of $p$-ary quantum low-density parity-check (QLDPC) codes, rate $R$, checks of weight $O_\epsilon(1)$, and normalized distance at least $\delta_{\mathrm{GV}}(p,R)-\epsilon$. Here, $\delta_{\mathrm{GV}}(p,R)$ denotes the quantum Gilbert-Varshamov (GV) bound for $p$-ary stabilizer codes. In fact, we construct an ensemble of such QLDPC codes, such that a random code from this ensemble is close to the GV bound with high probability. Moreover, this ensemble matches the performance of random stabilizer codes on several quantum channels. Specifically, it approaches the quantum capacity of the erasure channel and the hashing bound for memoryless Pauli channels, including the depolarizing channel. A significant challenge in working with QLDPC codes is that they are necessarily degenerate, i.e., contain many low-weight stabilizers. A key contribution of our work is a construction of QLDPC codes with quantitative control on their degeneracy. These codes are obtained by combining known constructions of asymptotically good QLDPC codes with the expander-based distance amplification procedure of Alon, Edmonds, and Luby [FOCS'95]. Our random ensemble is constructed by starting with these low-degeneracy QLDPC codes near the quantum Singleton bound and concatenating each coordinate with a random inner code. This can be viewed as a quantum analogue of Thommesen's construction, and as an LDPC version of a result of Ouyang, with an appropriately designed outer code.

The low-lying $1/2^+$ state of $^9$Be has been interpreted either as a near-threshold resonance or as an $^8$Be+$n$ virtual state. Because both pictures can produce a pronounced threshold enhancement, its nature is difficult to determine from energy-domain observables alone. We investigate the low-lying $1/2^+$ state through its real-time decay dynamics and compare its behavior with that of the well-established $5/2^+$ resonance. A wave packet produced by electric-dipole excitation of the $^9$Be ground state is propagated in an $\alpha+\alpha+n$ three-body model. We analyze the survival probability, the fit-window dependence of the effective decay width, and time-dependent densities in different Jacobi-coordinate representations. The $1/2^+$ survival dynamics are further compared with an $s$-wave effective-range description motivated by the dominant $^8$Be+$n$ component found in our previous three-body studies. The $5/2^+$ channel shows approximately exponential late-time decay, with an effective width of $0.264$ MeV, close to the complex-scaled pole width of $0.285$ MeV. In contrast, the $1/2^+$ effective width decreases as the fitting interval is shifted to later times, and no stationary decay width is obtained. Its late-time survival probability is well described by the effective-range spectral distribution. The density evolution develops an $^8$Be+$n$-like asymptotic configuration while retaining a localized $p_{3/2}$ $\alpha n$ component suggestive of a $^5$He+$\alpha$-like configuration in the internal region. The $1/2^+$ decay differs qualitatively from resonance-like exponential decay and is consistent with near-threshold virtual-state dynamics. The distinct internal and asymptotic configurations revealed by the time-dependent densities provide complementary structural information beyond that contained in a single decay width.

Zero-determinant (ZD) strategies are a class of strategies in repeated games, which unilaterally control payoffs. It has been shown that several ZD strategies promote cooperation in social dilemma games. It has been widely believed that the performance of ZD strategies is expressed as “unilaterally enforce linear relations between payoffs”. However, in order to interpret properties of ZD strategies in repeated games without discounting, previous studies assumed that the limit of the Cesaro average of the probability distribution of the action profile exists. Here, we explain the performance of ZD strategies without this assumption, which leads to a stronger result than previous ones.

We show that adaptive Bayesian estimation in fluorescence state detection reduces to a comparison of elapsed time with two arithmetic time sequences fixed by the bright and dark count rates and confidence parameters $\epsilon_d$ and $\epsilon_b$. No probabilities are computed in real time and the resulting implementation, which we term sequential deadline detection, is provably optimal in mean detection time. The analysis permits closed-form expressions for detection-time distributions, mean detection times, error rates of both channels accounting for miscalibrated rates, decay between the states during detection, and afterpulsing in single photon counting modules. The predictions are validated by Monte Carlo simulation and demonstrated by fluorescence detection with Ba$^+$

Removing an input-scaling module changes both a classifier and the perturbations reaching its encoder. A robustness difference can therefore reflect the comparison rule as well as the module. We demonstrate this problem in a four-qubit quantum-attention detector on generated power-grid trajectories. A learned scaling module appears beneficial at a fixed physical attack budget, but matching an upper bound on perturbations at the encoder reverses the ordering. Neither comparison alone establishes a robustness benefit caused by the module. The initial test also perturbs clean examples into attacked examples while retaining their original labels; tests restricted to already attacked examples do not establish a benefit. Replacing a trained model's input scales disrupts detection. Retraining its linear classification layer restores the detection rate, but changes individual predictions, leaving the comparison descriptive rather than causal. Two further design checks explain why the input quantum Fisher information regularizer cannot train this model's query parameters, and why removing confidence bounds does not establish a larger certified radius. The evidence is limited to ten seeds, exact simulation, synthetic data, and a restricted set of attacks; classical baselines achieve better clean prediction. The practical lesson is to specify which perturbation budget is fixed, check that attacks preserve labels and interventions preserve predictions, and distinguish exploratory controls from confirmatory evidence.

Urban policies are often evaluated assuming their effects decay with geographic distance. However, cities are systems of human movement, and policy effects propagate through individual mobility rather than spatial proximity alone. This study demonstrates that neglecting mobility can systematically distort estimates of how urban policies affect activity. Using large-scale mobility data, we estimate the causal effects of three transportation interventions in Boston and New York City using both conventional geographic and mobility-based definitions of exposure. Geographic designs detect no effects, or even negative ones. When exposure is defined through mobility networks, the same interventions reveal substantial behavioral change. Boston's free-fare program increases overall activity by approximately 16%, with more pronounced effects among lower-income communities. Open Streets programs significantly increase visits to nearby businesses, particularly food establishments. For congestion pricing, geographic approaches suggest a decline in activity in Manhattan, while a mobility-based perspective indicates no net reduction but a shift away from shopping and grocery destinations. Analysis across 138 metropolitan areas in eight countries further reveals that geographic and mobility-network distances diverge locally in every city, indicating that this discrepancy is a general characteristic of urban mobility. Collectively, these findings show that urban policies operate not only where they are implemented but where and how people move, so their impact must be measured through mobility rather than through space.

Quantum Fourier sampling may help audit the spectral learnability of delay-based physical unclonable functions (PUFs). We ask whether that promise survives access matching, a strong classical comparator, and oracle synthesis. Three gates structure the evaluation. Structure: low degree is not small support at reachable challenge lengths; for 4-XOR at $n=14$, degree $\le d_f(0.1)$ admits $91\%$ of all $2^n$ characters and the median $90\%$-mass set spans a third of the spectrum. Algorithmics: constructing the phase oracle logically implies classical membership access, making Kushilevitz–Mansour the correct baseline; across 45 tasks it exhausts each finite domain, and no 4-XOR ideal-sampling case reaches $90\%$ mass within $2^n$ calls. A quantum-kernel diagnostic appears more favorable, with geometric difference rising to $2.151$ at $N=512$ challenges, but it correlates $0.991$ with $1/\sqrt{\lambda_{\min}(K_C)}$ for the classical Gram matrix $K_C$, and the 4-XOR label-complexity ratio does not exceed a balance-preserving permutation null ($p=0.930$). Trace-normalized geometric difference can therefore grow through classical ill-conditioning alone, without task-label alignment. Implementation: a simulator-validated fixed-point phase oracle based on the quantum Fourier transform admits an $18.9\%$ routed-depth reduction, yet the least certified precisions have estimated durations of $1.18$–$1.55\times$ the median dephasing time $T_2$ of the mapped qubits on a static backend snapshot, without hardware execution. We find no end-to-end advantage in the evaluated regime, although ideal sampling does use fewer coherent calls on the thresholded task. The contribution is the Three-Gate Quantum Audit Protocol: a reproducible procedure separating an ideal query advantage from a realizable security benefit. This is not a claim about deployed silicon and not an impossibility result.

In conventional Landau theory, simultaneous continuous onset of superconductivity and the breaking of additional symmetries generally requires a multidimensional order parameter or fine tuning. We show that symmetry fractionalization provides a different route. In a Kondo lattice with a $\mathbb Z_2$ spin liquid, the Kondo fields inherit the projective symmetry action of the spinons. When the projective time-reversal action on these fields squares to $-1$, the projective symmetry algebra forbids a condensate that preserves time-reversal symmetry and enforces twofold degeneracy of the critical modes. Because Kondo condensation in a $\mathbb Z_2$ spin liquid also breaks electromagnetic $U(1)$ symmetry, superconductivity and time-reversal symmetry breaking can emerge at a single continuous transition, even if crystal symmetry admits only one-dimensional irreducible representations. We demonstrate this mechanism in an anisotropic Kondo-Kitaev model, where superconductivity and the breaking of time-reversal and mirror symmetries emerge simultaneously.

AIM. This study examines the PLATO mission sensitivity to small transiting planets (radii between 0.5 and 2.0 R$_\oplus$) using Autoregressive Planet Search (ARPS) methodology based on simulated lightcurves. Methods. Several thousand simulations of PLATO-like lightcurves from magnetically active stars expected from the PLATO Long Observation Phase South 2 (LOPS2) field with injected transiting planets smaller than 2.0 R$_\oplus$ radius are examined. The lightcurves are analyzed with ARPS that uses autoregressive modeling for filtering of nonplanetary signals and the Transit Comb Filter periodogram for planetary transit detection. False alarms of nonexistent periodic signals are reduced by thresholds on two ARPS parameters, peak periodogram power, and transit duration. Results. When applied to PLATO's simulated first three months observations of bright stars (P1 and P2 samples), at least 50% of Earth-size planets are recovered for periods shorter than 40 d. In two years of LOPS2 observation, 25% are recovered for periods less than 240 d. Methodological implications on detrending, periodograms, False Alarms, vetting, and catalog evaluation are discussed. The utility of a positive predicted value heat map after detection and vetting is demonstrated. Conclusions: While the ARPS planet detection procedure is not entirely well-suited for PLATO analysis, this study provides methodological insight into the sensitivity and reliability of small transiting planet detection.

We study the normal-mode stability of a flat interface between compressible Euler flow in the air and incompressible Euler flow in the water, including gravity and surface tension. The base state is quiescent water beneath a hydrostatic, constant-entropy shear flow $U(z)$ with $U(0)=0$, so the atmosphere is neutrally stratified. For a small ratio $\varepsilon$ of the air density at the interface to the water density, and for uniformly subsonic wave-relative flow, we establish a compressible analog of the critical-layer instability of Miles, extending the rigorous incompressible theory of Bühler, Shatah, Walsh, and Zeng. For a real phase speed $c_r$, the singular coefficient at a regular critical level $U(z_c)=c_r$ has a residue proportional to $Q=U''+gU'/a^2=\bar\rho_0(U'/\bar\rho_0)'$, with gravitational acceleration $g$, sound speed $a$, and normalized base-state air density $\bar\rho_0$. Thus the gradient of specific vorticity replaces the curvature $U''$ of the incompressible theory. We prove a limiting absorption principle at regular, non-resonant phase speeds and a residue formula for the imaginary part of the boundary value $Z_{\mathrm a}^+(k,c_r)$ of the air-side pressure response at wavenumber $k>0$. Let $c_k>0$ be the phase speed of the free gravity-capillary wave. If $\mathrm{Im}\,Z_{\mathrm a}^+(k,c_k)>0$, then, for sufficiently small $\varepsilon$, the dispersion relation has exactly one unstable root $c_\varepsilon$ in a neighborhood of $c_k$ that does not depend on $\varepsilon$. A sufficient condition is $Q\le0$ at every critical level and $Q<0$ at the highest one. As the Mach number $M\to0$, $Z_{\mathrm a}^+$ converges to its incompressible counterpart at rate $O(M^2)$. Under the corresponding incompressible sign condition, the unstable root satisfies $|c_\varepsilon(M)-c_\varepsilon(0)|\le C\varepsilon M^2$ with $C$ independent of $\varepsilon$ and $M$.

Quantum Fisher information adds over independent probes, so a single parameter is best measured probe by probe. We show that Gaussian measurements, the linear optics and homodyne detection of optical and microwave experiments, break this rule: two independent modes are better measured together. The uncertainty principle leaves a linear detector half of phase space, and for two modes the choice of half is a resource. Bell homodyne, a balanced beam splitter followed by two homodyne detectors, beats the best separate readout when the modes differ in quadrature width and in how the width responds to the parameter. Heterodyne detection splits a mode on a beam splitter to read both quadratures and pays one unit of vacuum noise for the empty port. Bell homodyne fills that port with the second mode, so the noise turns into signal. We prove that the gain stays below $(\sqrt2-1)^2=17.157\%$ for every parameter carried by widths. For thermal modes at one temperature, the canonical case, a gain was conjectured impossible. We prove that any frequency difference opens a temperature window and that the gain peaks at $12.699\%$ at frequency ratio $3.318$, where Bell homodyne is the optimal Gaussian measurement. Standard hardware reaches the gain: two coupled resonators with two homodyne detectors, or a phase-preserving amplifier whose idler band is fed by the same thermal source.