Papers

We introduce a family of simple $\alpha$-attractor models that can interpolate between exponential and polynomial cosmological attractors. By varying the interpolation parameter $\mu$ in these models, one can scan a wide range of values of the spectral index $n_{s}$ matching any combination of CMB and DESI data.

Trillium lattices, where magnetic ions form a chiral network of corner-sharing triangles, offer a three-dimensional magnetic framework that can host fragile classical spin-liquid states. Herein, we report on the magnetization, specific heat, electron spin resonance (ESR), and muon spin relaxation ($\mu$SR) of K$_{2}$Fe$_{2}$(MoO$_{4}$)(PO$_{4}$)$_{2}$ single crystals. Magnetization measurements reveal strong antiferromagnetic interactions coexisting with weak magnetic order at $T_{\rm N} = 5.2$~K, as evidenced by a $\lambda$-like anomaly observed in the magnetic susceptibility, a critical enhancement of the muon spin relaxation rate and the wipeout of the ESR signal as the temperature approaches $T_{\rm N}$. Above $T_{\rm N}$, two distinct developments of short-range spin correlations are identified at $T_{\rm H} = 34$~K and $T_{\rm L} = 10$~K, supported by magnetic specific heat anomalies and the temperature dependence of the ESR linewidth and $g$-factor. Upon cooling below $T_{\rm N}$, an anomaly appears at $T^{*} = 3.2$~K in thermodynamic observables and the muon spin relaxation rate, indicative of spin reorientation driven by residual interactions. Despite the presence of magnetic order, $\mu$SR experiments reveal dynamically fluctuating spins persisting even in the ordered state. Moreover, the suppression of $T_{\rm N}$ under applied magnetic fields ($\mu_{0}H \geq 2$~T) suggests that K$_{2}$Fe$_{2}$(MoO$_{4}$)(PO$_{4}$)$_{2}$ constitutes a promising candidate for exploring field-induced spin-liquid behavior in three-dimensionally coupled trillium lattices.

We investigate the phenomenology of black bounce spacetimes through a combined analytical and numerical study of relativistic observables associated with both time-like and null geodesics. Black bounce geometries provide a continuous interpolation between Schwarzschild black holes and traversable wormholes by introducing a regularization bounce parameter $\alpha$, which removes the central singularity by replacing it with a finite-radius throat. Using the Hamilton–Jacobi and Lagrangian formalisms, we derive weak-field analytical expressions for the periastron advance of massive particles and the deflection angle of light, highlighting the leading corrections induced by the bounce parameter. These results are systematically compared with numerical integrations performed with the PyGRO code. We show that increasing $\alpha$ enhances both the periastron precession and the light deflection with respect to the Schwarzschild case. Additionally, we analyze the relativistic redshift of bound orbits and identify cumulative temporal phase shifts associated with the modified geometry. For null geodesics, we also determine numerically the critical impact parameter separating scattered and captured (or transmitted through the throat) photon trajectories. Although this quantity remains identical to the Schwarzschild value in the regular black hole branch, it increases significantly in the traversable wormhole regime, providing a clear observational signature of the throat structure. Our results demonstrate that black bounce spacetimes can produce measurable deviations from standard black hole predictions, opening the possibility of constraining these geometries with current and future observations of strong gravitational fields.

We present the first numerical-relativity simulations of subsolar-mass binary strange star (SS) mergers and compare with binary neutron star (NS) mergers across equations of state, masses, and mass ratios. The self-bound nature of SSs makes them less deformed during the inspiral and keeps a sharp surface up to contact, driving strong shock heating and a large radial bounce that are far weaker in the NS. The more compact SS thus reaches a higher gravitational-wave cutoff frequency $f_\mathrm{cut}$ before contact but a lower post-merger peak frequency $f_2$. Within each class these frequencies follow quasi-universal relations with the tidal deformability, and their ratio $f_2/f_\mathrm{cut}$ cleanly separates the two classes. Both classes can eject $\sim10^{-2}\,M_\odot$ of material, neutron-rich for the NS and decompressed quark matter for the SS, a potential source of an electromagnetic counterpart whose observation could test the SS and NS hypotheses for subsolar-mass events.

We present the first calculation of single inclusive hadron and jet production at large transverse momentum in lepton-hadron scattering in a joint QCD+QED factorization approach. The scattering cross section is factorized into a convolution of infrared-safe hard coefficient functions with universal lepton distribution functions (LDFs) and parton distribution functions (PDFs) of the colliding lepton and hadron, respectively, together with fragmentation functions (FFs) of the observed hadron (or jet). With joint QCD+QED factorization, the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) type evolution equations for LDFs, PDFs, and FFs necessarily have evolution kernels calculated in both QCD and QED. We derive a default set of LDFs for our calculations and discuss a strategy to extract universal, non-perturbative LDFs from future data. We present our calculations for single inclusive hadron and/or jet production at the energies of Jefferson Lab and the future Electron-Ion Collider.

Modeling higher-order interactions (HOIs) in nonlinear networks with static topologies is often physically restrictive. We demonstrate that standard 3-body Kuramoto couplings are mathematically equivalent to pairwise connections modulated by latent variables of transmission channels. While standard HOI topologies emerge in the adiabatic limit of these variables, relaxing this constraint reveals that latent channel timescales dictate collective macroscopic states. Specifically, transmission inertia drives bistability for symmetric interaction tensors and anti-phase cluster synchronization for antisymmetric ones. Furthermore, dynamically induced clustering in global topologies emerges as a finite-size effect of the dynamics of the local channels. Ultimately, we show that relying exclusively on static topologies restricts interaction modeling. Integrating latent variables captures the transient inertia and fundamental asymmetry of physical networks, bridging the analytical utility of higher-order functions with the reality of the underlying transmission medium.

We report wavelength-resolved spin-wave small-angle neutron scattering (SWSANS) on the time-of-flight SANS instrument BL01 at the China Spallation Neutron Source and extend the method to pressure-cell measurements of MnSi. MnSi is used as a benchmark B20 helimagnet because its helimagnetic order and spin-wave stiffness are well characterized at ambient pressure. In a fixed magnetic field, the time-of-flight measurement provides a spectrum of neutron wavelengths. For each detector branch $s=\pm1$, the intensity profile is recentered relative to the wavelength-dependent Bragg angle $\theta_B (\lambda) = k_s \lambda / 2\pi$, and the cutoff angle $\theta_C (\lambda)$ is extracted in the local branch coordinate. The cutoff-derived spin-wave stiffness $A$ is obtained from a linear fit of $\theta_C^2$ as a function of $\lambda^2$. Ambient-pressure measurements reproduce the known stiffness scale of MnSi. Structural SANS at ambient pressure and at nominal 5 and 11 kbar verifies the magnetic state and provides an internal pressure-state check for the pressure-cell measurements. At nominal 11 kbar, within the present cutoff model, the cutoff-derived stiffness is substantially reduced, whereas the structural field scale $H_{C2}$ remains high. This contrast shows that $A$ cannot be inferred from static structural parameters alone under pressure. To our knowledge, these measurements constitute the first SWSANS implementation on a pulsed neutron source and the first SWSANS determination of spin-wave stiffness under pressure. The experiment also shows that reliable high-pressure SWSANS on a pulsed source requires high source brilliance, stable wavelength-dependent normalization, and sufficient statistics in each wavelength window.

Hadronic vacuum polarization (hVP) enters simple atomic systems at a level that is small yet decisive for the precision spectroscopy now underway. We evaluate the hVP contributions to the Lamb shift and the hyperfine splitting (HFS) in ordinary and muonic hydrogen (H and $\mu$H) and hydrogen-like helium-3 ions ($^3$He$^+$ and $\mu^3$He$^+$), using the dispersive data-driven approach and state-of-the-art empirical parametrizations of the $R$ ratio. At the centre of the analysis is the interplay of recoil and finite-size effects: the recoil corrections that dominate the HFS in muonium (Mu), where both constituents are pointlike, are shown to be suppressed by the nuclear elastic form factors (FFs). Our results for the leading hVP contribution to the Lamb shift agree with the literature within uncertainties. Furthermore, we present a first evaluation of the subleading $O(Z^5\alpha^6)$ hVP-finite-size correction, which is by no means negligible in $\mu^3$He$^+$. Our results for the hVP contribution to the HFS deviate significantly from all previous evaluations. For the ground-state HFS, we obtain $2.153(11)~\mu$eV in $\mu$H and $-15.19(57)~\mu$eV in $\mu^3$He$^+$, as well as $0.0860(4)~$kHz and $-0.476(17)~$kHz in ordinary H and $^3$He$^+$, respectively. Notably, our result for $\mu$H differs from previous evaluations by roughly ten times the experimental precision anticipated by the upcoming CREMA and FAMU measurements.

Surface plasmon polaritons (SPPs) and magnetic polaritons (MPs) are fundamental resonance modes that are widely used to tailor the thermal radiation properties of micro/nanostructured metamaterials. Lumped circuit models (LCMs) are usually constructed empirically to describe the MP resonance conditions, and different LCMs have to be constructed for different orders of MPs, but these are difficult to be built for high-order MP modes due to the complex electromagnetic field distribution. This work proposes a new type of circuit model, distributed circuit model (DCM), to describe and predict multi-order MP resonances inside the structure based on the minimum total impedance condition. This allows both fundamental and high-order MP resonances to be predicted with a unified circuit, significantly simplifying the analysis of high-order MPs. More importantly, the DCM shares a similar and clear physical picture as the LCM for describing MPs. The MP resonance conditions for four typical structures are derived. Theoretical predictions based on DCMs are compared with and validated by rigorous numerical simulations. This study deepens the understanding and facilitates the design of MP-based thermal radiation metamaterials.

The coexistence of polar and nematic interactions, observed in a broad range of biological and synthetic active systems, gives rise to a rich phenomenology that continues to challenge our theoretical understanding of non-equilibrium collective behaviour. In this paper, we numerically investigate phase ordering and defect dynamics in a newly introduced minimal single-field model for dry nematopolar systems, where competing polar and nematic contributions enter the free energy, and activity is implemented through self-advection. At optimal balance, the system develops depolarization strings connecting half-integer defects and separating domains with opposite polarization, together with closed depolarization loops. We first characterize the elementary relaxation mechanisms of defect pairs and loops, showing that the interplay between polar and nematic alignment gives rise to non-monotonic string-mediated interactions, finite equilibrium separations and distinct loop-collapse pathways. Large-scale simulations from disordered states instead show dynamic scaling with a characteristic length growing as $\sim(t/\ln t)^{1/2}$, consistent with coarsening in systems with non-conserved order parameters and point-like defects. Upon introducing self-advection, sufficiently strong activity leads to the coexistence of positive integer and negative half-integer defects, which we term motility-induced charge symmetry breaking, and to saturation of the characteristic length scales, ultimately resulting in arrested coarsening. Overall, our results provide a simple unified framework for understanding the ordering and defect dynamics in biological and synthetic nematopolar systems.

The paper establishes extra invariant in magnetohydrodynamics (MHD) of rotating and stratified fluid layer. This invariant is conserved adiabatically, i.e. approximately over long time. The existence of the invariant is interesting by itself, as such invariants are extremely rare. However, in addition, this invariant appears to be connected to the famous dynamo phenomenon.

We study the Gelfand-Tsetlin realization of generic Verma modules for the affine Lie algebra $\widehat{\mathfrak{gl}}_n$ by viewing them as thin modules over the affine Yangian $Y(\widehat{\mathfrak{sl}}_n)$. By results of arXiv:0812.4656, these modules admit a basis indexed by periodic Gelfand-Tsetlin patterns with explicit formulas for the Yangian action, and we identify them with the evaluation modules introduced by Kodera arXiv:1806.09884. Our main result describes the specialization from generic highest weights to dominant highest weights (not necessarily integral). We call the resulting construction athinization: an irreducible $\widehat{\mathfrak{gl}}_n$-module, which is not thin as a module over the affine Kac-Moody algebra, is realized as a thin module over the larger (and ”more affine”) algebra $Y(\widehat{\mathfrak{sl}}_n)$. Combinatorially, this realization is obtained by restricting the generic periodic Gelfand-Tsetlin basis to a distinguished subset of permitted patterns. We prove that the span of these patterns carries a well-defined affine Yangian action. In particular, this construction yields explicit Gelfand-Tsetlin-type bases for admissible representations of $\widehat{\mathfrak{gl}}_n$ in the sense of Kac-Wakimoto, providing a new combinatorial realization of these modules. We compare the formulas for characters coming from this combinatorics with those for minimal models of $W$-algebras of the type $A_n$ via the principal specialization. Further, we obtain analogous results for representations of $U_q\widehat{\mathfrak{gl}}_n$ via their realization as thin modules over the quantum toroidal algebra of $\mathfrak{gl}_n$.

Single-cell molecular characterization remains a bottleneck in scalable biological analysis because of labeling requirements, limited multiplexing, and reagents that perturb physiology. Raman spectroscopy addresses these limits by providing chemically specific, label-free vibrational fingerprints, but long acquisition times and specialized instruments restrict high-throughput use. Here, we overcome this barrier by showing that spectral fingerprints can be reconstructed from brightfield microscopy using generative modeling. We introduce Pic2Spec, a framework that learns a shared latent biochemical representation linking image morphology to vibrational spectral structure, enabling virtual Raman spectroscopy without hardware. We validate Pic2Spec across mammalian and bacterial cells, generating high-fidelity spectra that reproduce measured Raman fingerprints with 98% cosine similarity and Pearson correlations of ~95%, while preserving biochemical peaks and population distributions. Beyond spectral similarity, Pic2Spec provides molecular-level resolution in bacterial systems: generated spectra discriminate mutation-driven transgenic states and predict GFP expression with accuracy approaching true Raman measurements, outperforming conventional image analysis by 20%. These findings establish Pic2Spec as a first demonstration of chemically informative virtual molecular fingerprinting from brightfield images, complementing slow, hardware-intensive spectroscopy with computational inference. By redefining microscopy as an inference-enabled molecular profiling platform, Pic2Spec democratizes label-free biochemical phenotyping and overcomes the hardware and time constraints that have confined spectroscopy to specialized laboratories. This enables high-throughput molecular analysis for clinical diagnostics, screening, and monitoring at the scale and accessibility of standard microscopy.

We present an inhomogeneous fluctuation-induced asymmetric escape event of a Feller diffusion confined to a finite interval with two competing absorbing boundaries. The dynamics correspond to an overdamped Brownian motion in a shifted harmonic potential with state-dependent diffusivity. We set two exit points (equal potential energies), equidistant from the potential minima: an extinction site near the origin and an outbreak point. The multiplicative fluctuations are suppressed near the extinction boundary, biasing trajectories toward the outbreak state. The mean exit time exhibits a non-monotonic dependence on the initial condition and the drift-to-noise strength ratio, attaining a maximum when the particle is initialized toward the extinction (low-noise) site. The outbreak possibility is more likely even if the process started with a small initial bias towards the low-noise boundary. The spatial location of the lowest coefficient of variation (CV) is nicely corroborated by the maximum exit time, which is the hallmark of a stochastic escape from an interval. The fluctuation in escape time is found to dominate its mean as a non-trivial function of the drift-to-noise strength and the initial spatial bias. The observed asymmetries in escape also shape the speed-accuracy trade-off in stochastic decision-making events.

In semilocal density functionals the exchange potential decays too rapidly outside atoms and molecules and lacks the correct $-1/r$ tail; as a consequence, functionals from PBE to modern meta-GGAs such as SCAN support no bound Rydberg-like state of an atom. We revisit the gradient-corrected exchange functional that Gill and Pople (GP93) constructed to reproduce the exact exchange potential of the hydrogen atom, solve their equation as an inverse problem, and use the resulting enhancement factor – the GP93 factor – as a tail-generating ingredient of a switched semilocal functional. The GP93 factor is exact on the hydrogen $1s$ density and, by uniform coordinate scaling, on hydrogenic $1s$ ions, where it yields the eigenvalue $-Z^2/2$ exactly (hydrogenic exactness). Its large-gradient growth has the hydrogen-exact form $O[s(\ln s)^{2/3}]$, distinct from the $s\ln s$ form of Armiento and Kümmel. The factor is combined with a kinetic-energy-density indicator so that its divergent branch acts only in one-electron-like regions; the GP93 ingredient is nonempirical, while a few switching parameters are calibrated to balance tail recovery against self-consistent-field stability. The resulting functional produces Rydberg-like series of bound virtual Kohn–Sham states in systems whose outermost shell is a one-electron-like $s$ shell – all-electron H and He, and Li, Na, and K with large-core pseudopotentials – and moves the highest-occupied eigenvalue toward the experimental ionization energy. For $p$-shell atoms (Ne, Ar) the switch closes, so the domain of applicability is fixed by design. Fixed-density tests indicate that a reduced-Laplacian switch distinguishes atomic tails from covalent bond centers, and that a density-only kinetic-energy functional renders the entire switch orbital-independent, so that no generalized Kohn–Sham solver is required.

Most MLIP benchmarks reward static accuracy while ignoring inference efficiency and hardware scalability – driving model bloat with unclear real-world value. We benchmark 23 mainstream open-source MLIPs on a low-cost NVIDIA DGX Spark (128 GB native memory, capped at 80 GB to mimic ordinary lab hardware), using a fixed 192-atom system under a unified ASE-based pipeline. We evaluate three dimensions: predictive accuracy, MD simulation throughput, and atomic scalability. Our results expose a sharp accuracy-efficiency trade-off: large SOTA models deliver only 3-5 meV/atom more accuracy than lightweight ones, but lose orders of magnitude in throughput – in the worst case, becoming only marginally faster than DFT itself. Lightweight MLIPs, by contrast, sit on the Pareto frontier and run on modest hardware. The lesson is that single-dimensional benchmarks mislead the field, and that future MLIP development should value efficiency and scalability alongside accuracy.

Despite being intrinsically athermal and strongly driven, granular materials can exhibit remarkably glass-like dynamics. Whether their rheology can be described by the same scaling concepts remains an open question. Here, we investigate the linear viscoelastic response of an air-fluidized granular bed using small-amplitude oscillatory shear over a broad range of fluidization states. We show that the frequency-dependent spectra collapse onto a single master curve when shifted by a state-dependent relaxation time, establishing a time-state superposition principle analogous to time-temperature superposition in molecular glasses. The master curve spans more than five decades in relaxation time and is quantitatively described by a Cole-Davidson relaxation spectrum. By comparison with continuous shear measurements, we identify tribocharging as the origin of history-dependent deviations from universal scaling. Our results demonstrate that fluidization primarily rescales a single structural relaxation time while preserving the underlying relaxation spectrum, establishing a direct connection between the rheology of driven granular matter and molecular glass-forming liquids.

We connect expressions for phonon phase-space distribution functions to microscopic physics of the evolution of heat waves. The role of interference effects that arise as a result of a periodic heating source typically encountered in transient thermal grating (TTG) experiments is then explored. The distribution functions are evaluated as solutions to the Peierls-Boltzmann equation (PBE) in the relaxation-time approximation (RTA). Starting from the PBE, we next develop thermal response functions. The response functions are computed using data from density-functional theory (DFT) calculations. Using this approach, it is shown how solutions to the PBE can be related to the propagation of second phonons as elementary excitations, and within this perspective the necessary conditions for the propagation and observation of second sound is elucidated. The approach developed therefore shows how PBE theory for phonon hydrodynamics and second sound can be modified to properly describe interference and phonon decoherence effects that are likely important at shorter length scales. Finally, we then discuss how many-body theory can be extended to include coupled scattering channels and hence provide a quantitative theory beyond the RTA.

The negatively charged boron vacancy center in hexagonal boron nitride is a premier candidate for quantum sensing, yet its performance is critically limited by longitudinal spin-lattice relaxation time ($T_1$). A microscopic understanding of spin relaxation in the high magnetic field regime remains elusive, as the relevant Zeeman transitions lie far below the optical phonon energies typically invoked to describe the relaxation process. Here, we apply an ab initio acoustic mode spin-phonon relaxation theory to this problem and quantitatively reproduce the experimental magnetic field and temperature dependence of $T_1$ without empirical fitting parameters. We demonstrate that the relaxation dynamics are driven by a direct one-phonon emission and absorption process resonant with the Zeeman splitting. Furthermore, we identify the out-of-plane flexural phonon branch which is unique to two-dimensional hosts, as the primary source of decoherence, creating a distinct low-energy spectral function that facilitates spin relaxation. Our results provide a microscopic interpretation of the experimentally observed non-monotonic field and temperature dependence in two-dimensional quantum defect centers.

The Stark effect in autoionizing high-$n$ Rydberg states decouples the Rydberg electron from the ion core through $\ell$ mixing with core-nonpenetrating high-$\ell$ states. The Rydberg states become long-lived, which is ideal for precision spectroscopy, and their structures reflect the fine and hyperfine structures of the ion-core levels. We report on precision measurements, in weak electric fields, of the fine and hyperfine structures of two distinct categories of high autoionizing molecular Rydberg-Stark states differing by the nature of the ion-core angular momentum: Rydberg states of para-H$_2$ (total nuclear spin $I=0$) with a rotationally excited ($N^+=2$) H$_2^+$ ion core and Rydberg states of ortho-D$_2$ ($I=2$) with a rotationless ($N^+=0$) ion core. The spectra reveal striking differences which are interpreted as arising from the dominance of anisotropic charge-quadrupole interactions between the rotating quadrupolar ion core and the Rydberg electron in para-H$_2$ and the absence of such interactions in rotationless ortho-D$_2$ Rydberg states. In ortho-D$_2$, the dominant interaction, the magnetic Fermi-contact hyperfine interaction in the ion core, does not significantly affect the motion of the Rydberg electron. By analyzing these spectra based on a treatment combining multichannel quantum-defect theory and matrix diagonalization, we derive new experimental values of the hyperfine coupling constant $b_F$ = 139.84(5) MHz of D$_2^+ (v^+=1, N^+=0)$, the spin-rotation coupling constant $c_e$ = 39.62(11) MHz of H$_2^+ (v^+=1, N^+=2)$ and the fundamental vibrational interval of ortho-D$_2^+$ (47279980.8(1.9) MHz). The approach followed here in the study of molecular Rydberg-Stark states is general and broadly applicable to measurements of the fine and hyperfine structures of molecular cations.

Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A major difficulty arises from the time reparameterization invariance of time series data, which complicates the extraction of meaningful temporal features. In this work, we address the problem of time series classification by exploring the application of quantum computation techniques. We propose a hybrid quantum-classical architecture that integrates recent advances in quantum neural networks with the mathematical framework of path signatures, mitigating the impact of time reparametrization invariance. The architecture employs feature layers that compute a signature kernel between pairs of input paths, consisting of a reference path and a target path for classification, using either classical or quantum variational linear solvers (VQLS). These feature layers are followed by a Quantum Convolutional Neural Network (QCNN) to perform downstream learning tasks. We evaluate several realizations of the proposed architecture, differing in QCNN configurations, on a binary classification task involving time series representations of handwritten digits. Our experiments demonstrate the potential advantages of implementing path signature kernel layers within quantum circuits and provide an analysis of the computational limitations associated with the VQLS component.

Existing RLC circuit model leads to inaccurate predictions of the quality factor (Q-factor) of magnetic polariton (MP) resonances under imperfect absorption conditions due to the omission of radiation loss. Moreover, the lumped-parameter nature of RLC models also limits their applicability for predicting higher-order MP modes. In this letter, we propose a distributed circuit model (DCM) for predicting the Q-factor of MP resonances, which overcomes these limitations. By introducing a radiation resistance to characterize radiation loss and establishing a mapping between distributed and lumped parameters, we derive a unified analytical expression for the Q-factor of arbitrary-order MPs. Validation via rigorous coupled-wave analysis (RCWA) demonstrates that this model can accurately predict the Q-factors of MPs of all orders in various metal-insulator-metal (MIM) structures. This work provides a simple yet effective tool for designing metamaterial emitters/absorbers and advances the understanding of MP loss mechanisms.

We report a detailed temperature-dependent Raman spectroscopy and Co $K$-edge extended x-ray absorption fine structure (EXAFS) study of the insulating antiferromagnetic double perovskite SrLaCoNbO$_6$. Interestingly, pronounced changes in the phonon frequencies, linewidths, and intensities are observed around 60~K, well above the long-range antiferromagnetic ordering temperature, $T_{\rm N} \approx 15$~K. These anomalies indicate a modification of the lattice dynamics that may involve local structural distortions, short-range magnetic correlations, or an interplay between them. Additional changes in both high- and low-energy Raman modes are detected near 160–180~K via variations in phonon intensities and linewidths, together with changes in the Fano asymmetry of selected low-energy modes. The asymmetric line shapes of these modes are well described phenomenologically by Fano profiles and indicate coupling of the discrete phonons to an underlying continuum, whose possible microscopic origins are discussed. Moreover, the EXAFS analysis reveals correlated changes in bond distances and Debye–Waller factors around the Co ions near 60~K and 160~K, indicating subtle local structural distortions at temperatures corresponding to the Raman anomalies. The combined Raman and EXAFS results therefore demonstrate a close correlation between anomalous lattice dynamics and local structural evolution in SrLaCoNbO$_6$.

Polarization measurements of a classical 1550-nm signal are collected and analyzed on 15-km hybrid aerial-inground fiber connections over 11 months. The spectral area and spectral moments of mHz-resolution Fast-Fourier-Transform (FFT) of these measurements are extracted, and related to temperature, humidity, wind speed, and time of day. Spectral area correlations show a strong diurnal structure: daytime maxima align with temperatures/wind speed peaks and humidity dips, with lower levels during the night. These diurnal patterns also show seasonality, with higher mean and variance in summer than winter. A random forest regressor is used to estimate FFT features from environmental measurements, informed by a theoretical model

Nonlinear least-squares objectives form the foundation of scientific machine-learning tasks, yet even curvature-aware optimizers remain geometrically inconsistent at finite step sizes: Levenberg-Marquardt (LM) derives its direction from local Riemannian metrics but realizes it as a straight parameter update. Here we introduce RNC-LM, which carries the LM direction along a locally constructed curved trajectory in Riemann normal coordinates. A recursive reformulation of the geodesic equation generates arbitrary finite-order corrections while reusing the same damped Gauss-Newton matrix factorization, and curve length is controlled separately from damping. On a reaction-diffusion physics-informed neural network benchmark, RNC-LM reduces relative $L^2$ errors below $8\times10^{-3}$, whereas L-BFGS, LM and LM with geodesic acceleration remain near one. On a large-scale machine-learning potential fitting task with 985,160 configurations, fourth-order RNC-LM reaches a fixed training-error target with a $34\times$ wall-clock speedup over LM. These results establish finite-step geometric realization as a distinct optimizer design principle.

We establish a localization principle for odd-dimensional supergravity theories containing Chern–Simons interactions. Our construction relies on combining an equivariant extension of the gauge-invariant part of the action with an equivariant completion of the Chern–Simons anomaly form ${\Phi}^{(\text{anom})}$ to form a relative equivariant cohomology class. This leads to a universal fixed point formula for the on-shell action of supersymmetric solutions that is expressed in terms of ${\Phi}^{(\text{anom})}$ and an equivariant Euler class. We illustrate the formalism in $D=11$ and $D=5$ supergravity, with applications including the supersymmetric Casimir energy and superconformal index of M5-branes, as well as black ring and black lens solutions.

We prove that the universal local point processes of random matrix theory are characterized by their loop equation hierarchies. More precisely, for every rational $\beta>0$, the $\mathrm{Sine}_{\beta}$ point process is the unique solution of the bulk loop equation hierarchy, and the $\mathrm{Airy}_{\beta}$ point process is the unique solution of the edge loop equation hierarchy. These uniqueness results provide a direct route to universality: it suffices to verify the corresponding approximate loop equations for the ensemble. In many models, these equations follow from local laws and integration by parts.

Multi-stage cryogenic readout chains with a wide bandwidth and added noise within a few quanta of the quantum limit are frequently constructed using traveling-wave parametric amplifiers (TWPAs) as the first stage, and a semiconductor amplifier as the second stage. Unfortunately, for highly-scaled superconducting detector arrays, or quantum information systems, and space-based observatories, the power dissipation of the semiconductor amplifier becomes problematic from the perspective of available cryogenic cooling power near 3 K. Here we demonstrate a readout chain based on a two-stage kinetic inductance TWPA (KTWPA). This quantum-amplifier-based-readout-chain (QARC) provides sufficient gain that a cryogenic semiconductor follow-on amplifier can be eliminated without degradation of the system noise. Due to the ultra-low dissipation of KTWPAs and their higher output-power-to-total-power ratio, the QARC dissipates approximately three orders of magnitude less power than readout chains containing semiconductor amplifiers while adding noise of less than 2 quanta over a 1 GHz bandwidth. In addition, by leveraging the high power handling of kinetic inductance technology, the QARC maintains an input compression point of -93 dBm, which exceeds that of many contemporary Josephson-junction-based parametric amplifiers.

We prove the existence of time-periodic solutions of the two-dimensional incompressible Euler equations bifurcating from a translating vortex pair. The reference configuration consists of two symmetric vortex patches of equal strength and opposite sign traveling at constant speed. In a regime of large separation between the vortices, the dynamics may be viewed as a small perturbation of an integrable system. Working in a co-moving frame and using the contour dynamics formulation, we reduce the problem to a nonlinear transport equation for the vortex boundaries. The linearized operator exhibits degeneracies associated with symmetries and transport effects. By combining a Lyapunov-Schmidt reduction, Nash-Moser scheme, and spectral analysis with sharp asymptotic expansions of the eigenvalues in order to overcome the degeneracy, we construct families of non-rigid, time-periodic vortex patch solutions for a large Cantor set of parameters. The analysis reveals that translating dipoles possess a surprisingly rich nearby dynamics, far beyond the classical rigid paradigm usually associated with vortex patch motion. More generally, the approach developed in this work is flexible and robust, and is expected to extend to a broader class of nonlocal PDEs from Fluid Mechanics with degenerate behaviours.

Background: Graph neural networks improve computational prediction of polypharmacy side effects, but standard binary cross-entropy training allocates equal capacity to well-classified and difficult examples, potentially missing clinically significant interactions. We evaluated whether an asymmetric focal objective could improve multi-relational drug-drug interaction (DDI) prediction by emphasizing difficult positive interactions. Methods: ClinicalFocal loss was integrated into a relation-aware graph convolutional network using molecular fingerprints, physicochemical descriptors, and learned embeddings. The model was evaluated on TWOSIDES using five-fold cross-validation with identical experimental conditions (architecture, features, data partitions, hyperparameters, and random seeds) for ClinicalFocal loss and binary cross-entropy baseline. Results: ClinicalFocal loss increased accuracy from 0.699 to 0.892 (+19.3 percentage points) and F1 score from 0.700 to 0.894 (+19.4 percentage points). AUROC increased from 0.766 to 0.914, and AUCPR increased from 0.714 to 0.860. The false-negative rate decreased from 29.8% to 9.1%, while specificity increased from 69.6% to 87.5%. Overall classification error decreased from 30.1% to 10.8%, corresponding to a 64.1% relative reduction. Improvements were consistent across all five folds. Conclusions: Asymmetric focal optimization improved classification and ranking performance while achieving 90.9% recall for observed interaction triples, without modifying the underlying architecture. Loss-function design is a direct, tunable lever for improving graph-based DDI prediction.