Papers

We develop a machine learning assisted workflow for the rapid screening of permanent magnet candidates within the CaCu$_5$ structure family. We generated more than 60,000 structures through substitutions. These compositions were screened using a combination of pretrained materials models, Materials Project data, and a machine-learning model for magnetization prediction. Four criteria, namely, metallicity, negative formation energy, energy above the convex hull $\leq 50$~meV/atom, and predicted magnetization above the adopted $\geq 0.75$~T threshold reduced the initial chemical space to 439 candidates for first-principles calculations. Density functional theory calculations revealed that 159 compounds exhibit positive axial magnetic anisotropy energy, favoring [001] over the tested in-plane directions. The resulting dataset reveals chemical trends in magnetic anisotropy across the CaCu$_5$ compositional space and provides a set of candidates for further investigation. More broadly, this work demonstrates a practical strategy for combining existing machine learning models with first-principles calculations to systematically reduce large chemical spaces to tractable sets for computationally intensive materials screening.

Quantum fidelity estimation is a fundamental task in quantum information and computing to benchmark the quality of quantum states, and hence of quantum processes, on noisy quantum hardware. Point estimates of fidelity by existing methods generally lack the rigorous uncertainty quantification required for reliable preparation and verification of these tasks. While Bayesian and bootstrap frameworks are widely used for uncertainty quantification, they are sensitive to model misspecification or lack formal coverage guarantees in non-asymptotic regimes. This paper introduces a hybrid "Bayesian-informed" conformal prediction-based framework to provide distribution-free, rigorous prediction intervals for quantum state fidelity estimation which can be used, with care, to approximate confidence intervals with respect to the quantum randomness of Born's rule. The framework requires a point estimator, and we consider both classical point estimators as well as data-driven neural net estimators. We evaluate the robustness of the methodology across a diverse range of experimental challenges, including finite-sample noise, tomographic incompleteness, physical model error, and out-of-distribution data. Our results demonstrate that conformalized estimators maintain statistical validity where traditional methods fail, offering a scalable and reliable approach for the verification of modern quantum systems. The best performing variant of our methodology is based on a class-conditional conformal prediction, as this most closely approximates the conditional coverage desired for a true confidence interval.

Ceramic materials are inorganic, predominantly nonmetallic solids whose performance is governed by the interplay among atomic bonding, crystal structure, processing, microstructure, and functional properties. Their ionic and covalent bonding provides high hardness, thermal and chemical stability, wear resistance, and diverse electrical behavior, but also limits plastic deformation and increases sensitivity to flaws, porosity, grain boundaries, and residual stresses. This review examines ceramics through the processing–microstructure–property–performance relationship, emphasizing the effects of crystal chemistry, defects, grain size, porosity, and phase stability on mechanical, thermal, electrical, and biological behavior. Conventional sintering, sol–gel processing, and additive manufacturing are discussed as key routes for controlling densification, porosity, geometry, and composition. Biomedical ceramics, including alumina, zirconia, calcium phosphates, hydroxyapatite, tricalcium phosphate, and bioactive glasses, are also reviewed. Emerging topics include nanoceramics, smart ceramics, composites, multimaterial manufacturing, defect engineering, immunomodulatory bioceramics, and sustainable processing. Major challenges remain in brittle fracture, processing heterogeneity, degradation control, scale-up, and the porosity–strength trade-off. Future advances will require integrating advanced manufacturing, multiscale characterization, predictive modeling, and data-driven design.

Complex Network (CN) classification requires high-level structural characterizations that are both scale-invariant and computationally efficient. Methods based on Life-Like Network Automata (LLNA) offer an interesting way to extract network descriptors by leveraging emergent temporal patterns without requiring provided features, but their efficacy is bottlenecked by a high-cost combinatorial optimization problem: the selection of the automaton transition rule. While current literature relies on exhaustive searches that are unfeasible for large-scale applications, this work reveals that the rule space is fundamentally structured by a property we term “jaggedness”, that quantifies the resemblance of a LLNA transition function with a sawtooth shape. We demonstrate that this metric acts as a theoretical proxy for chaoticity and sensitivity – properties essential for generating discriminative dynamic behaviors among network categories. Moreover, we introduce a heuristic search strategy that uses jaggedness to guide the rule selection. Experimental results show that our approach achieves classification accuracies within 5% of the global optimum while reducing computational overhead by 90% compared to exhaustive approach. Our findings provide a novel, efficient, framework for optimizing automata-based methods for pattern recognition.

We study the observational features of thin-disk accretion and emission spectra for static, spherically symmetric dyonic black holes in non-linear electrodynamics (NLED). In contrast to classical Maxwell theory, where electromagnetic duality assumes equal charges, the coupling parameters in NLED introduce a new gravitational potential and modify the geometry in the strong-field regime. We also study the kinematic characteristics of particles on circular orbits, such as their angular velocity, specific energy, and specific angular momentum. It is found that modifications of NLED lead to a shrinkage of the ISCO radius, resulting in a shift of the radiation spectrum thermal maximum towards smaller radii and, consequently, an increase in the local effective temperature in comparison with the standard Schwarzschild and Reissner-Nordström solutions. Additionally, we compute null-geodesic orbits to determine the optical edge of both the photon sphere and the black hole shadow. In order to assess the observational validity of the aforementioned alternatives, we carry out a $\chi^2$ goodness-of-fit test through the angular diameter constraints at the horizon scale collected by the Event Horizon Telescope (EHT) concerning Sagittarius A* ($\text{Sgr A}^*$). We prove that both these dyonic NLED and purely magnetic models have good statistical agreement with experimental data.

Large language models (LLMs) exhibit us-vs.-them bias: A behavioral asymmetry in which prompts framed around an ingroup (“we”/“us”) receive systematically more positive continuations than matched prompts framed around an outgroup (“they”/“them”). Using Edge Attribution Patching (EAP), we localize this behavior to directed circuits in GPT-2 Small, GPT-2 Large, Llama-2 7B, and Llama-3 8B. Across models, high-attribution edges are sparse and concentrated primarily in middle-to-late layers, while ingroup- and outgroup-related pathways are only partially overlapping and reorganize under fine-tuning. Behavioral comparisons and 30-token generations show that the measured asymmetry extends beyond next-token evaluation. Targeted patching of EAP-ranked edges reduces the bias score more than equal-cardinality random-edge controls, but also substantially degrades performance on CoLA, HellaSwag, and WikiText-2, suggesting that bias-relevant pathways are functionally entangled with broader language capabilities. Overall, our findings characterize us-vs.-them bias as a sparse, context-dependent, and functionally entangled computational phenomenon whose internal organization varies across the models examined.

In this paper, we investigate the quantum behaviour near the classical singularity of a black hole. We describe the black-hole interior by the Kantowski-Sachs metric, with a string propagating in this region. This approach is motivated by the fact that, near the classical singularity, spacetime curvature reaches Planck or string scales, necessitating a description in terms of string-theoretic degrees of freedom. The string dynamics is formulated via the Polyakov action, and the combined system is canonically quantized to derive the corresponding Wheeler-DeWitt equation. Although the equation is separable in the radial and azimuthal string sectors, the remaining geometric and string degrees of freedom are nontrivially coupled. To address this, we employ a perturbative approach and obtain the wave function up to first order. At the zeroth-order, the angular part is described by the general Heun function, while the geometric part is expressed in terms of the Bessel function. The first-order wave function is governed by a source term containing the zeroth-order solution, and we obtain its explicit expression in terms of the complementary function and the particular integral. We confirm regularity of the wave function at both zeroth and first order, at the singularity as well as at the horizon. Regularity at the horizon imposes a discretization condition on one eigenvalue, while another eigenvalue is quantized due to the azimuthal periodicity of the wave function. Importantly, both zeroth- and first-order solutions satisfy the DeWitt boundary condition, implying resolution of the black hole singularity within this quantum framework.

An experimental campaign to measure double-differential cross sections (DDXS) for light-charged particles (LCPs) emitted from $^{\rm nat}$Fe was carried out in 2023 at the Neutrons For Science (NFS) facility of GANIL, France. The Medley experimental setup, specifically designed for light-ion measurements, was used for this campaign. The setup was installed in the white neutron beam line and irradiated with neutrons of energies up to 44 MeV. The Time-of-Flight (TOF) technique was used to obtain $^{\rm nat}$Fe(n,pX) double-differential cross sections for all neutron energies within the white-neutron beam's energy range. This work highlights important steps in the data-analysis procedure and reports the first results from this experimental campaign.

We prove that the sine beta point process is tail trivial for every beta >0. The key ingredient in the proof is a certain uniform entropy bound, that makes use of the DLR equations of Dereudre et al. (2021) and the bounded two-point correlation density obtained by Qu and Valko (2025) and Assiotis and Najnudel (2026), that allows us to show that tail events are translation invariant. Tail triviality then follows either from the uniqueness theorem of Assiotis (2026) or from translation mixing, which we derive from the correlation estimates of Dumaz and Malvy (2026). As consequences, we obtain the following applications: (a) extremality of sine beta in the full convex class of fixed kernel Gibbs measures without stationarity and finite-energy conditions; (b) L^2-ergodicity of the associated infinite-particle reversible diffusion for every beta>0; and (c) the almost-sure collision/noncollision dichotomy with threshold beta=1 for the diffusion.

We formulate diffractive ring focusing of paraxial beams carrying orbital angular momentum (OAM) through a finite circular aperture as a variational optimization problem. For a fixed topological charge, we determine the real-valued radial pupil that maximizes the intensity at a prescribed ring radius and propagation distance under a normalization constraint on the input field. The resulting problem admits an exact analytical solution: the optimal pupil combines the familiar Fresnel phase structure with an OAM-dependent Bessel radial modulation, and the associated eigenvalue yields a rigorous upper bound on the achievable ring intensity. We further analyze the lens-assisted configuration and derive a simple bound that allows direct comparison between diffractive and refractive vortex focusing. The theory also provides a quantitative framework for evaluating the performance of practical mask designs, including binary Fresnel-Bessel zone plates and amplitude-weighted pupils. We have experimentally tested our predictions in the optical domain by implementing the corresponding vortex beam pupils and comparing their propagation and focusing performance with the optimal solution. Our results establish the vortex-beam analogue of optimal amplitude-only focusing and identify the fundamental limits governing diffractive concentration of structured waves

We consider the SM extended by a 4th generation of vector-like leptons. This model generates strongly correlated contributions to the lepton masses, Higgs couplings and dipole moments. At leading order the Higgs coupling restricts the magnetic and electric dipole moments to an ellipse. Here we compute the one-loop corrections to the Higgs coupling and two-loop corrections to the dipole moments to obtain the correlation at next-to-leading order using the framework of SMEFT. In addition we resum the leading logarithmic corrections from SM couplings through the SMEFT renormalisation group equations. With these results we compute the RG improved correlation at NLO and discuss the implications for the muon, electron and tau correlations. Specifically, for the muon we find that the corrections contract the ellipse such that a larger part of parameter space becomes compatible with the updated SM value of the muon magnetic moment.

Several models for chirality-induced spin selectivity (CISS) in charge transfer have been proposed, but rarely compared directly with experiments. This step is essential for their validation and future progress of the field. Here we investigate a broad class of radical pair states associated with different microscopic models by computing the related electron-paramagnetic resonance spectra. This allows us to evidence peculiar signatures of different radical pair states, to study their experimental accessibility and identify conditions to verify proposed microscopic models.

Effective vacuum sectors in modified theories of gravity are often represented by anisotropic stress-energy tensors, even when the underlying source is geometric rather than material. This raises a broader question: can such effective stresses be characterized intrinsically through a covariant constitutive structure, rather than modeled phenomenologically as exotic fluids? We address this question in pseudo-complex general relativity (pcGR), where a constrained variational formulation identifies the effective vacuum source as geometric in origin but does not uniquely determine the corresponding geometric constraint sector. For the static pseudo-complex Schwarzschild solution, we show that the effective vacuum tensor separates naturally into an isotropic contribution and a purely transverse contribution associated with the intrinsic geometry of the two-dimensional subspace orthogonal to the timelike and radial directions. This decomposition leads to a constitutive formulation based on the transverse metric. Its infinitesimal deformations split uniquely into trace and traceless shear modes, with the static solution having only an isotropic transverse response. Within the class of local constitutive variables that depend algebraically on the transverse metric and whose first variation generates the trace projector with stateindependent normalization, the logarithmic transverse area strain is the minimal scalar constitutive coordinate.

Satellite megaconstellations in low Earth orbit interfere with ground-based optical astronomy, but the scale of that interference has been hard to measure. Here we report an uncued photometric survey from a mid-northern latitude site in late 2024 which recorded some 25,000 tracks of 5,118 individual satellites to a limiting visual magnitude of +9.0. The observed population is systematically brighter than the altitude-adjusted limit of order seventh magnitude recommended by the International Astronomical Union Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (IAU CPS). Starlink satellites exceeded that limit on roughly a quarter of all geometrically possible passes, rocket bodies on about a fifth, and the emerging Guowang/Hulianwang constellation on more than half. OneWeb was the most compliant major operator (exceeding the IAU CPS limit on only about one in twenty possible passes), which shows that effective brightness mitigation is achievable with current technology. Satellite visibility is concentrated into the two hours after sunset and before sunrise and into limited regions of the sky. From the measured plane-of-sky satellite flux we predict that of order one in three 30 s twilight exposures at the Vera C. Rubin Observatory will carry a satellite trail.

Machine-learning interatomic potentials have become indispensable tools in atomistic simulations. The most popular models rely heavily on physical priors, such as locality, smoothness and symmetry, that are assumed to improve the accuracy and transferability of the trained models. However, recent evidence suggests that, in the data-rich regime, unconstrained models that learn symmetry from the data can achieve very competitive accuracy and computational efficiency, without sacrificing stability and generalization power. Many equivariant symmetric architectures historically rely on the assumption that the interatomic potential can be approximated well by a convergentcluster expansion, i.e., a decomposition in terms associated to atomic pairs, triplets, quadruplets, and higher-order tuples. We describe an architecture that incorporates locality, smoothness, and symmetry priors, but that – thanks to a computationally efficient tensor product formulation – does not assume low-order truncation of the body-ordered series.

We prove an exact identity, as convergent logarithmic germs at large Euclidean momentum, between the equal-mass two-dimensional $(n-1)$-loop sunset Feynman integral, with $n\geq4$, and the genus-zero one-point descendant invariants of the local Calabi–Yau $n$-fold $X_n=\operatorname{Tot}(K_{F_n})$, where $F_n\subset(\mathbb P^1)^{n}$ is a smooth $(1,\ldots,1)$ hypersurface. Writing $s$ for the momentum variable and $y=-1/s$, the identity reads \[ I^{(n-1)}_{\circleddash}(s)=(-1)^{n-1}yA\,\Bigl[nR^{n-1}-\theta_Q{\mathcal D}_0^\Delta\,R^{n-2} +\sum_{j=0}^{n-3}c_jR^j\Bigr], \] where $R=\log y+O(y)$ is the local mirror coordinate, $A=\theta_yR$, $Q=e^R$, ${\mathcal D}_0^\Delta(Q)$ is the primary one-point potential, and each $c_j$ has the form \[ c_j=c_j^{(0)}+\sum_{a=0}^{n-2}\bigl(c_{j,a}\,{\mathcal D}_a^\Delta(Q) +c'_{j,a}\,\theta_Q{\mathcal D}_a^\Delta(Q)\bigr) \] with the coefficients in $\mathbb Q[\zeta(3),\zeta(5),\ldots]$ the odd zeta values coming from the Gamma class. The descendant series ${\mathcal D}_a^\Delta(Q)$ are given explicitly by the ambient hypergeometric series and the inverse mirror map. We also prove global univalence of the principal mirror map, with explicit upper and lower bounds on the $Q$-Taylor radii, extract the full primary evaluation divisor and its integral cover transform, and reduce the ambient endpoint constants to a finite universal Gamma-class part and $\lfloor(n-1)/2\rfloor$ real parameters.

In the BCS theory of superconductivity the amplitude (Higgs) mode is degenerate with the pair-breaking threshold: its frequency coincides with $2\Delta$, so it is an edge singularity rather than a true collective mode. This is why after a sudden weak perturbation at $t=0$ its oscillations decay algebraically as $1/\sqrt{t}$. We show that the retardation effects due to the phonon dynamics in the strong-coupling theory lift this degeneracy. The Higgs mode detaches from the pair-breaking edge $2\Delta_g$, set by the spectral gap, and becomes a sharp subgap excitation. We determine its frequency from the longitudinal pair susceptibility evaluated directly on the real frequency axis, using the Eilenberger equations for Migdal–Eliashberg theory with the Einstein phonon spectrum. The response of the mass renormalization supplies at least two fifths of the displacement, and possibly all of it: with it switched off, the binding falls below the resolution of the calculation. Its consequences, however, are significant because a percent-level shift transfers about a quarter of the spectral weight of the amplitude channel out of the two-quasiparticle continuum into a discrete energy level. What experiment sees is therefore not the displacement itself but its consequence: amplitude oscillations that persist instead of decaying as $1/\sqrt{t}$.

Carbon ion radiotherapy (CIRT) provides greater biological effectiveness than conventional photon therapy because of its high linear energy transfer and its ability to induce complex DNA damage. Mayo Clinic Florida is developing the first CIRT facility in the United States, using a synchrotron-based system with dose-driven continuous scanning (DDCS). Although DDCS improves delivery efficiency by keeping the beam on during spot transitions, it also creates the challenge of accurately modeling the dose delivered during beam motion (move dose), which is not fully addressed in current treatment planning systems. In this work, we present a method for accurate simulation of move dose to support Monte Carlo (MC) dose calculations. The method models realistic beam trajectories, including the characteristic hockey stick motion caused by different scanning speeds in the X and Y directions, and accounts for time-dependent beam intensity with linear ramp-up behavior. The beam path is divided into small segments, each represented by a move source with analytically determined position, intensity, and monitor units. Beam intensity fluctuations are also included using a stochastic model based on measurements. Validation using TOPAS simulations for a 430 MeV/u carbon ion beam in a water phantom showed accurate modeling of move dose and the resulting dose distributions. This method provides a practical framework for MC-based dose calculation in clinical CIRT.

We determine the equal-mass two-dimensional three- and four-loop sunset Feynman integrals $I^{(3)}_\circleddash$ and $I^{(4)}_\circleddash$ at large Euclidean momentum in terms of the genus-zero Gromov–Witten theory of the local Calabi–Yau fourfold and fivefold $X_L=\operatorname{Tot}(K_{F_L})$, where $F_L\subset(\mathbb P^1)^{L+1}$, $L=3,4$, is a smooth hypersurface of multidegree $(1,\ldots,1)$. At two loops the sunset integral is governed by the prepotential of a local Calabi–Yau threefold. For $L\geq3$ the moduli spaces of unmarked stable maps have positive virtual dimension and there is no prepotential, therefore the Feynman integral is governed instead by the one-point potential $\mathcal F_{\gamma_L}$ with insertion $\gamma_L=H^{L-1}/(L-1)!$, by its gravitational descendants, and by $\zeta(3)$ boundary terms coming from the Gamma class. At both loop orders the coefficient of $R^{L-1}$ in the Frobenius expansion in the mirror coordinate $R$ is $-\theta_Q\mathcal F_{\gamma_L}$ with the Novikov variable $Q=\exp(R)$. At three loops the whole Feynman integral is an explicit expression in $\mathcal F_{\gamma_3}$ alone, because genus-zero recursion determines all descendant series from it; at four loops two diagonal series suffice, and the $\zeta(3)$ term is itself corrected by $\theta_Q\mathcal F_{\gamma_4}$. At three loops we also derive the analytic normalization independently, from the Picard–Fuchs equation and the polar part of a Mellin transform, prove integrality of the multiple-cover transforms for arbitrary integral insertions together with an explicit quantum-$K$ formula, and compute the exact period connection polynomials. Finally we give endpoint values along the Euclidean axis, bounds on the Taylor radii in the Novikov variable $Q$, a computer-assisted proof of the four-loop endpoint nonanalyticity, and a comparison of the normalizations at two, three and four loops.

One aspect of complexity for quantum many-body systems resides in their ability to exhibit entanglement, uniquely quantum correlations central to quantum computation. To quantitatively explore the diminishing effect that quantum noise has on entanglement resources, we focus on the negativity witness, an entanglement measure based on local time reversal transformations that provides a perfect witness in simple physical contexts. Beyond calculation of the negativity, we analyze the associated eigenvalue spectra of partially transposed density matrices for qudit-qudit bipartitions, finding that higher negativity rank provides greater(reduced) robustness to targeted(depolarizing) noise. In particular, we extend the maximum number of separable states that can be arbitrarily mixed into an entangled pure ensemble while retaining negativity to be exactly equal to the original negativity rank. Furthermore, we report how understanding the role of the negativity rank with respect to the completely depolarizing channel allows direct calculation of (1) certain noise boundaries for the Absolutely Positive Partial Transpose (APPT) subspace beyond which no unitary operator is capable of generating negativity and (2) the maximum qutrit-qutrit APPT purity. Beyond proving tight bounds and contributing to the ongoing search for APPT entanglement, these results expand our understanding of the nuanced relationship between computation with entanglement and practical environments for operating quantum devices.

We study the properties of matrix coupled oscillators in ring networks and show that the system supports several families of synchronized clustered solutions. One of these families is characterized by consecutive oscillators belonging to distinct clusters, called alternate ordering, with two classes of solutions. The first consists of one or two static clusters, that lock on phases that depend on the coupling parameters, despite the non-zero natural oscillation frequency. The second one is comprised of clusters that rotate rigidly. The number of clusters depends on the neighbor connectivity. We also investigated sequential ordered clusters and showed that the constraints imposed by the interface oscillators imposes additional restrictions to equilibrium configurations. Although some of these solutions are stable, their basins of attraction depend on model parameters: when the system is initialized with random phases, the oscillators tend to organize themselves into dynamical clusters whose interfaces move along the ring. The oscillators within each cluster remain close to one of the stable phase configurations, while particles are exchanged at the interfaces. When interfaces moving in opposite directions collide, the corresponding clusters annihilate, until a state with interfaces moving in a common direction is reached.

Supermoire lattices in large-angle twisted bilayers have recently been shown to emerge from periodic arrangements of commensurate stacking motifs, yet the microscopic structural mechanism governing their formation has remained unresolved. Here, we uncover the microscopic origin of supermoire order as a hierarchical lattice relaxation process. Combining force-field simulations with experimental validation via super-resolution, layer-resolved multislice electron ptychography, our results identify interlayer Se-Se repulsion at the picometer scale as the driving force, giving rise to coupled short-range (about 1 nm) moire relaxation and coexisting long-range (about 10 nm) supermoire modulation. These findings demonstrate that even picometer-scale lattice relaxation remains critical in large-angle moire systems and must be explicitly considered in understanding their emergent electronic and quantum phenomena.

An explorative study of Type Ia Supernovae (SNe Ia) light curves is presented. $\Delta m_{t}(X)$ decline-rate metrics are used to evaluate alternative characterisations of light curve diversity and the Luminosity-Width Relation (LWR). Using a sample of 362 SNe Ia from the Carnegie Supernova Project (CSP-I and CSP-II), Gaussian Process regression is used to fit $B$- and $g$-band light curves and measure decline-rates across multiple post-maximum timescales. The LWR is calibrated using a clean subsample of SNe Ia with host-independent distance moduli and Fitzpatrick (1999) reddening corrections. Decline-rates $\Delta m_{10}(B)$ and $\Delta m_{10}(g)$ are found to capture the LWR effectively, while mitigating the severe degeneracy that affects longer timescales for fast-declining ($\Delta m_{15}(B) \geq 1.6$ mag) events. Continuous, piecewise linear models are presented that seamlessly anchor the transition between normal and fast-declining regimes. The decline-rate $\Delta m_{10}(X)$ closely approximates the results of the more complex colour-stretch parameters, requiring less post-maximum temporal coverage than $\Delta m_{15}(B)$ or $s_{BV}$, and offers an even greater sensitivity for slow decliners. $\Delta m_{10}(B)$ provides a precise and practical tool for large-scale photometric surveys and real-time observer classification.photometric surveys and real-time observer classification.

Distinguishing trivial from topological superconducting phase remains a central and contested experimental challenge, since most existing probes infer the bulk phase transition indirectly, from boundary signatures that can be sensitive to local physics. Here, we propose an alternative that probes the bulk topological phase and is based on supercurrent response. We study a one-dimensional Rashba spin-orbit-coupled superconducting nanowire with an applied magnetic field that opens a topological gap. We show that for strong spin-orbit coupling, the superfluid stiffness, and consequently the supercurrent, is suppressed by a factor of two inside the topological phase, producing a non-monotonic signature of the bulk phase transition as a function of chemical potential. We further use a low-energy helical model to understand this suppression analytically. Our results show that the bulk topological phase transition can be detected through kinetic inductance measurements.

Estimating the symbolic or analytical form of probability density functions (PDFs) from observed samples is a fundamental challenge in statistical and computational modelling. This process is critical for deriving interpretable and generalizable relationships characterizing the underlying phenomenon. Traditionally, this estimation depends strongly on domain expertise and prior field-specific knowledge, with experts selecting appropriate functional forms or parametric families based on empirical evidence and theoretical understanding. The coefficients of these forms are then typically determined through parameter estimation. In this paper, we develop a framework for estimating symbolic expressions of unnormalized distributions from observed samples using domain-specific prior knowledge, such as the range of interactions and a predefined set of primitive functions. We integrate deep generative models with symbolic regression (SR), incorporating inductive biases, such as factorizing large distributions, to keep the problem tractable. The deep generative models we examine include likelihood-based models, viz., flow models, and score-based models. Experiments show the effectiveness of the proposed framework for estimating density functions for multivariate toy distributions as well as lattices from computational physics, namely, XY model and $\phi^4$ theory. When applied to the renormalization problem in $\phi^4$ theory, the proposed framework estimates compact symbolic approximations of the hamiltonian function at different scales directly from samples, yielding expressions that may be challenging to derive using traditional perturbative or analytic approaches in nonperturbative settings.

We construct high-accuracy semi-analytic mode functions for scalar cosmological perturbations in mLQC-I within the hybrid approach to study ultraviolet regularity and gravitational particle creation. The evolution comprises an asymptotic pre-de Sitter branch, a pre-inflationary region containing the quantum bounce, and inflation. In the intermediate region, we construct the complete third-order uniform asymptotic solution and introduce adaptive defect correction. The third-order approximation achieves sub-percent complex-mode accuracy for representative higher-wavenumber modes; defect-corrected solutions agree with numerical evolution at the 0.00001% level over the tested low-wavenumber range. Retaining amplitudes and phases provides a controlled basis for future precision calculations of primordial power spectra and non-Gaussian correlations. We compare the de Sitter vacuum selected in the remote contracting branch with a fourth-order adiabatic state imposed at the bounce. Finite-wavenumber matching and the strict ultraviolet limit require different asymptotic treatments: extrapolating the leading connection to infinite wavenumber yields a spurious constant negative-frequency coefficient and an apparent ultraviolet divergence. Instead, the large-wavenumber de Sitter mode agrees with the fourth-order adiabatic expansion through the order required for energy-momentum tensor renormalization, and smooth exact evolution preserves this ultraviolet structure. Finite and intermediate modes can nevertheless undergo nonadiabatic Bogoliubov mixing and particle creation. Finite-mode particle production thus coexists with ultraviolet regularity. The ultraviolet argument also extends to the dressed-metric approach in its corresponding large-wavenumber regime, while quantitative mode functions must be recalculated using its effective mass.

A compressible fluid column between two thin elastic plates carrying plane Poiseuille flow supports discrete structural-acoustic wall modes. This paper analyses their coupled dispersion and reports a flow-induced instability. Because a no-slip profile has zero velocity at the wall, the convective (Ingard-Myers) term in the compliant-wall boundary condition vanishes identically, so the plate-admittance conditions stay Mach-independent and a Fredholm solvability argument yields a closed-form $O(M)$ correction to every coupled wavenumber, valid for any smooth no-slip shear profile. Above the critical Mach number $M_c=\Omega/\xi$ at which an interior critical layer first appears, the two structural branches behave oppositely. The antisymmetric mode is not absorbed by the critical layer, a null protected by an exact mid-plane pressure node rather than by weak coupling. The symmetric mode, which carries no such node, is convectively unstable. Its growth rate, fixed both by a spatial calculation anchored to the dissipative wall-Stokes term and by a convention-independent temporal one, strengthens towards the inviscid limit, identifying an inviscid critical-layer instability rather than a viscous artefact. A Briggs-Bers analysis classifies it as convective. Its neutral curve follows $M_c(\Omega)$, and its growth rate is proportional to the wall compliance, vanishing for rigid walls, so it is distinct from the Tollmien-Schlichting and Kelvin-Helmholtz mechanisms. Three independent numerical schemes agree to one part in $10^9$, and the closed-form correction matches numerical differentiation across three shear profiles.

We investigate the relaxation dynamics and steady state of a qubit interacting with a coherent environment through an energy-conserving flip-flop interaction within the Markovian repeated-interaction framework. We derive an exact analytical solution for the evolving and stationary state, nonperturbative in the environmental coherence. We show that environmental coherence provides a resource for dissipative state preparation by expanding the set of accessible steady states. In particular, coherent ancillas can increase the system ground- state population beyond that of the bath, corresponding to an effective temperature below the bath temperature, and enhance its steady-state purity, with these effects controlled by the interplay between the system energy scale and the system-ancilla coupling. Nevertheless, we prove that the purity of the system can never exceed the purity of the ancilla. We further analyze the thermodynamics of the protocol and show that environmental coherence can modify and even reverse the natural direction of heat transfer, giving rise to anomalous heat flow. Remarkably, for fixed interaction parameters, the direction of the coherence-induced heat transfer is governed by the relative phase between the system and ancilla coherence, whereas their magnitudes determine the amount of heat exchanged. Our results establish environmental coherence as a resource for controlling steady-state preparation and energy transfer in repeated-interaction dynamics.

Asymptotic Safety provides a promising framework for the quantization of Scalar-Tensor theories as non-perturbatively renormalizable quantum field theories. Over the past years, growing evidence has accumulated for the existence of non-trivial renormalization group fixed points in a variety of Scalar-Tensor models. In this work, we investigate the robustness of these fixed points under significant modifications of the computational setup. In particular, we employ a gauge condition that explicitly depends on the dynamical scalar field, going beyond a mere gauge-parameter deformation of the standard linear covariant gauges commonly used in the literature. Exploring different field parameterizations, we identify fixed points belonging to the Gaussian Matter class in four spacetime dimensions that are consistent with previous studies. The real part of the critical exponents associated with the scalar-field mass and the non-minimal scalar-curvature coupling turn out to have very small absolute value. We provide a comprehensive exploration of how the anomalous dimension of the scalar field and field parameterization impact those critical exponents showing that fixed-point candidates with two or four relevant directions emerge.

Seismic data augmentation is critical for training robust deep learning (DL) models for fault detection and characterization, yet conventional approaches remain inadequate. Geometric transforms, e.g., flips or rotations, and noise injection fail to capture realistic seismic textures, while physics-based forward modeling is computationally expensive and often poorly aligned with the statistical characteristics of field data. We present a denoising diffusion probabilistic model (DDPM) framework that learns the high-dimensional distribution of seismic images directly from field data via iterative noise injection and learned denoising. We develop two variants: an unconditional model (U-DDPM) that generates realistic post-stack seismic patches and a fault-conditioned model (FC-DDPM) that produces diverse seismic realizations that honor user-specified fault geometries encoded as binary masks. Three open-source field datasets with differing amplitude scales and processing histories are jointly standardized for training, demonstrating cross-survey generalization. Generated images from U-DDPM reproduce the dominant textural patterns, amplitude distributions, and reflector continuity observed in real seismic data. FC-DDPM maintains structural fidelity to imposed fault masks while introducing realistic variability in surrounding seismic textures, enabling one-to-many realizations from a single structural template. Frequency-wavenumber spectral analysis confirms that synthetic samples preserve the spatiotemporal bandwidth and dip content of the training distribution. The proposed framework provides a practical, controllable augmentation mechanism for expanding limited fault-labeled training sets without computationally expensive forward modeling or survey-specific parameter tuning, directly addressing a key bottleneck in DL-based seismic interpretation.