Papers

Accurate electronic excited states are essential for photochemistry, spectroscopy and non-adiabatic molecular dynamics, but high-level calculations often scale steeply and require prior knowledge of the target state's character or symmetry. Here we show that variational excited-state optimization can be reformulated as an iterated ground-state-like problem for a low-energy subspace of the electronic Hamiltonian. Applying this variational principle to non-orthogonal Slater determinants leads to EXIDOS, an automatic method for excited state calculations controlled only by the number of states and determinants per state. EXIDOS optimizes multiple excited states simultaneously, without explicit orthogonality constraints or imposed spin and point-group symmetries. Benchmarks against FCI and state-of-the-art quantum chemistry methods show chemical accuracy for a multitude of states in N$_2$ and CO, charge-transfer states in HCl, Rydberg states in NH$_3$, double excitations and extended potential-energy curves in C$_2$, and avoided crossings and conical intersections in ethylene. These results establish EXIDOS as a low-scaling, fully variational route to chemically accurate excited states.

Data-driven closure modeling for transport partial differential equations requires training data that are accurate, affordable, diverse, and directly tailored to the target closure fields. We develop the Generalized Forcing Method (GFM), a data-generation framework for training linear transport closure models. GFM generates such data by running simulations with a zero initial condition and an extra body force that is constructed compatibly with the reduced dynamics. This framework leads to implicit GFM (iGFM), which prescribes resolved trajectories, and explicit GFM (eGFM), which constructs a basis of admissible forcings. We apply eGFM to three linear transport closure problems: homogeneous shear flows, spatially inhomogeneous flows, and homogeneous shear flows with random coefficients. The results show that eGFM can identify accurate and stable reduced models when the reduced variables and model form are consistent with the underlying closure relation.

Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialist potentials at quantum-chemical accuracy remains open. Here we show that knowledge distillation from a pretrained universal machine-learning interatomic potential (MLIP), followed by coupled-cluster fine-tuning with single and double excitations and perturbative triples [CCSD(T)], transfers not only low-cost labels but a physically meaningful prior on interaction length scales, anisotropy, and the repulsive-dispersive balance, which CCSD(T) data then sharpens to quantum-chemical accuracy. For He–benzene, fine-tuning with 30% of the CCSD(T) data outperforms direct training using the full 80%; a 60% reduction in the high-fidelity compute budget. A symmetry-adapted perturbation theory (SAPT)-informed adaptive short-range/long-range architecture further lowers the validation MAE from 0.75 1/cm to 0.49 1/cm. Across a circumarene series of polycyclic aromatic hydrocarbons (PAHs), swapping the MLIP teacher under an otherwise identical pipeline changes the coronene error by an order of magnitude while leaving the larger PAHs stable, direct evidence that distillation transfers physical structure, not labels alone. Together, these results identify the choice of pretrained teacher as a primary design axis for data-efficient quantum-chemical-accuracy potentials, alongside architecture and training protocol.

Cellulose nanocrystals (CNCs) are a promising class of materials derived from the most abundant natural polymer resource on Earth. Hydroxyl-induced polarity is a crucial advantage of CNCs, making them promising for advanced design and application. Side chain hydroxyls, hydrogen bonds, and particular crystal structures of CNCs lead to unique anisotropy. However, the nuanced anisotropy in the transverse section is not sufficiently stressed, and cannot be precisely described experimentally. Although partially covered by previous studies, a quantitative explanation of size dependency and a systematic comparison of solvent influences are still lacking. The manufacturing of cellulose materials requires a better understanding of anisotropy, size dependency, and solvent influences. In this study, the anisotropic performance of CNCs in characteristic directions and a diverse array of solvent environments was carefully inspected and compared using molecular simulations. Furthermore, a data-supported explanation for the size dependency, and transverse arrangement toughness-enhanced designs were both proposed. These systematic comparisons and unique transverse arrangements could aid future applications of cellulose.

Exceptional points (EPs) are degeneracies in the spectrum of non-Hermitian systems, where both the eigenvalues and eigenvectors coalesce. In the vicinity of an n-th order EP, the eigenvalues generally show n-th-root dependence on the system parameters, making EPs potentially promising candidates for ultra-sensitive measurements. Usually EPs are implemented in precisely fabricated nano- and microstructures. In this work, we instead show the experimental implementation of a third-order EP (EP3) using the synthetic dimension in a single-mode optical fiber, leveraging multi-frequency Brillouin scattering. We perform a multi-dimensional scan of the parameter space revealing not only an EP3 but also additional topological structures connected to it. Our work paves the way toward fabrication-free realizations of exceptional points of arbitrary order.

We investigate the processes of local ordering for the partisan voter model on complex networks. In this model, agents hold a binary opinion and a fixed preference that biases updates toward alignment with their preferred opinion. We first study the dynamics on uncorrelated random networks and derive a pair approximation that resolves the densities of links connecting different classes of agents. The analytical predictions are in excellent agreement with Monte Carlo simulations. In this setting, partisan bias leaves the total stationary density of links connecting nodes in different sates unchanged and at the same value as in the standard voter model, but redistributes it among different categories of links. We then consider preference-dependent networks with homophilic and heterophilic attachment to analyze the competition between the global bias mechanism and the local effect of preference-based connectivity. In this case, structural correlations qualitatively modify the stationary state. We identify different regimes of local ordering in the space of parameters measuring the strength of the preference and the strength of the homophilic attachment. Our work clarifies the distinct roles of dynamical partisan bias and structural assortativity, and provides an analytical framework to study partisan opinion dynamics beyond mean-field theory.

Quantum optimal control can produce fast and robust multi-qubit gates, but experimentally calibrating the resulting high-dimensional waveforms remains challenging because direct searches over large parameter spaces converge slowly. Building on the low-rank structure of quantum-control landscapes, we develop and benchmark a Hessian-based calibration method for optimal-control gates. The method identifies the few waveform directions that affect fidelity to leading order, with the number of directions set by the accessible leakage and coherent error channels, and optimizes only within this principal space using closed-loop experimental feedback. We apply this approach to an amplitude-robust controlled-Z gate on metastable-state 171Yb nuclear-spin qubits. Experimentally, we verify the predicted Hessian-sensitive directions and demonstrate rapid convergence of the optimization protocol. The optimized gate reaches a raw fidelity of 0.9959(2), increasing to 0.99902(7) after postselection on no detected loss, and the performance is essentially unchanged under laser-power variations of up to 20%. We further show that the same fidelity Hessian directions can correct certain Hamiltonian parameter errors. These results establish low-rank Hessian optimization as an efficient and physically motivated calibration strategy for high-dimensional optimal-control gates, which is broadly applicable to many qubit types.

Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling. From only a bulk crystal and a natural-language reaction description, eight specialized agents and 27 scientific tools predict stable facets, reconstruct working surfaces, enumerate and rank reaction pathways, locate transition states, and compute kinetics in 5-30 min on a single GPU. Two innovations address the hardest steps: UniMech finds dominant pathways for novel materials at over $10^3\times$ lower cost than exhaustive enumeration by fusing agent-guided proposals with energy-cached graph search, and a memory-augmented reinforcement loop raises barrier-calculation success from 41% to 84% across 600 catalytic surfaces. Across seven gas-solid benchmarks – stepped metals, single-atom catalysts, ordered intermetallics, vacancy-rich 2D sulfides and carbides, and a strong-metal–support-interaction (SMSI) interface – every CatDT prediction lies within 0.5-2 times experiment over four orders of magnitude. For propane dehydrogenation, CatDT independently discovers non-precious candidates rivaling the Pt-based industrial benchmark, with a proposed Ni@ZrO$_2$ SMSI overlayer reaching a simulated TOF of $1.63~\text{s}^{-1}$ at $\sim$100% selectivity. More broadly, the decisive factor for a faithful catalyst digital twin – or any multi-stage scientific simulator – is not raw LLM capability but the engineered harness around it: deterministic tools, persistent memory, and verified self-improvement that compound across models, tools, and runs.

In this paper, we present a formula for calculating the complex amplitude of the output electric field for a given input wave that impinges on a Dove prism. We use Fourier optics to decompose the input wave into plane waves, then find the output plane waves of the Dove prism as functions of the input spatial frequencies. The total output image is then obtained by integrating over all the output plane waves, resulting in a final formula in integral form. Since we conduct a wave-optical analysis for beam propagation and each incidence on Dove prism surfaces, all the physical aspects of electromagnetic waves are involved, including polarization, Fresnel losses, wave interference, phase, and intensity. The formula also explains why a rotated Dove prism rotates its input image twice its rotation angle. In addition, the formula is not limited to paraxial beams, as we find the Dove prism output as a function of the input Fourier components in general, without limiting the input spatial frequencies to small values. This generality is especially relevant for emerging applications that rely on non-paraxial beams, such as structured light generation, orbital angular momentum systems, and quantum imaging. However, since in most cases the paraxial approximation is valid and sufficient, a simplified formula is also extracted for paraxial beams. Two ray-tracing simulations are conducted to demonstrate the correctness and accuracy of our simplified formula. All the advantages mentioned make our derivation accurate, complete, comprehensive, and, to the best of our knowledge, the first to wave-optically prove the rotational feature of a Dove prism.

The effect of a pressure-dependent viscosity on the behaviour of the viscous scraper problem is investigated. In particular, it is found that the effect is qualitatively different for the classical scraper (i.e., a drag in) problem and the reverse scraper (i.e., a drag out) problem.

Small-angle scattering (SAS) is widely used in structural biology, soft matter, and colloidal science to probe molecular structures in solution. SAS rests on a single physical principle: wave interference from a distribution of scatterers, averaged over orientations. Yet the theoretical foundations of SAS are spread across the literature, often based on differing notation, definitions, and implicit assumptions. We present the theory of SAS in solution from first principles as a continuous derivation, spanning the scattering of a single electron to the observed intensity of a molecular solution and its comparison with atomistic structural models. The derivation is explicit throughout – approximations, averaging procedures, and algebraic manipulations are stated rather than assumed – and is independent of the probe (X-ray or neutron) and applicable to both rigid and flexible molecules. The framework resolves several ambiguities in the current literature, notably the role of background subtraction as a theoretical rather than a purely experimental operation and the role of boundary cross-terms in justifying that subtraction. A central result is that analytical scattering calculations and approaches based on explicit-solvent molecular dynamics, typically treated as distinct traditions, are realizations of the common theoretical framework derived here. As the precision and reproducibility of SAS data continue to increase, this unified framework provides a basis for integrating theory, simulation, and experiment in future developments of SAS.

The laser-accessible nuclear transition in the thorium-229 isotope has been identified as a promising candidate for the realization of an optical nuclear clock. Such a nuclear clock might rival or outperform current optical clocks based on electron-shell transitions in atoms or ions, is expected to be more robust against external perturbations, and provides enhanced sensitivity in clock-based tests of fundamental principles of physics. Here, we implement a thorium-229 nuclear clock by stabilizing a continuous-wave laser to the 148 nm nuclear transition with rapid feedback based on continuous absorption spectroscopy. The thorium-229 nuclei are embedded into a millimeter-sized, room temperature calcium fluoride crystal. A subharmonic of the 148 nm radiation is continuously compared to a Yb+ single-ion clock. The nuclear clock shows a simple shot-noise limited scaling of the fractional frequency instability of $3\cdot 10^{-12} \sqrt{\tau/\text{s}}$ where $\tau$ is the averaging time, approaching $10^{-15}$ instabilities over 1 day of continuous operation. Improvements of the instability by several orders of magnitude can be projected for future solid-state nuclear clocks. We use the nuclear clock to constrain models of ultralight dark matter by searching for periodic fluctuations and slow drifts in the nuclear transition energy, on time scales between 20 s and 1 day. Drawing benefit from the enhanced sensitivity of the thorium-229 transition, these constraints compete with the best atomic clocks concerning dark matter coupling to photons and go beyond previous measurements regarding coupling to the strong force and quarks.

Rotating convection in low-Prandtl-number liquid metal drives dynamo action in the Earth's outer core and is central to planetary interior dynamics. It has been proposed that flow regime transitions in rotating convection are controlled by competition between the thermal and Ekman boundary layers. However, through laboratory experiments and direct numerical simulations of rotating liquid-metal convection, we find that this mechanism breaks down in the low-Prandtl-number regime. Here we show that increasing rotation reorganises the bulk flow: the large-scale circulation is suppressed and replaced by smaller-scale structures, producing a characteristic horizontal length scale $\ell$. Transitions to the geostrophic regime are then governed by a buoyancy–Coriolis balance defined on $\ell$ rather than by the boundary-layer crossing. This scale-dependent mechanism also yields heat-transport scalings that depart from boundary-layer-based predictions in the geostrophic regime. Our results reveal a distinct route to the geostrophic regime in low-Prandtl-number rotating convection with implications for rotating liquid metal flows in planetary interiors.

This work extends to complex networks a recently described methodology (A. Benatti and L. da F Costa, Detecting Hierarchical Clusters and Estimating their Modularity Directly from Dendrograms, May 2026) for non-supervised hierarchical cluster detection and hierarchical modularity estimation. First, the edge betweenness centrality of a given complex network (or graph) is estimated, and a dendrogram is obtained from these values by using some linkage criterion (average linkage is considered in the present work). The mentioned concepts and methods can then be applied to the obtained dendrogram associated with the hierarchical structure of the nodes interrelationship, paving the way to community detection and hierarchical modularity estimation. Promising results are presented and discussed respectively to varying types of modular networks, namely fractal networks (which are intrinsically hierarchical) and prime partition networks, as well as to modular networks presenting just one (or a few) hierarchical levels.

We systematically analyze the stability of the iterative solution of the parquet equations by studying the spectrum of the Jacobian associated with the commonly used damped fixed-point iteration procedure. In this context, we provide an explicit criterion that determines when the physical fixed point of the parquet iteration becomes unstable. Importantly, we demonstrate that misleading convergence issues, observed in parquet calculations at intermediate-to-high interaction values, are not restricted to parameter regions where the two-particle irreducible vertex diverges, but can also arise in absence of vertex divergences. Hence, the misleading convergence issues of parquet-based algorithms are not directly caused by the crossings of two solutions of the (multivalued) Luttinger-Ward functional, that are associated with vertex divergences. Building on these insights, we apply the controlled stabilization strategy of Phys. Rev. Lett. 137, 016502 (2026) to the challenging case of the parquet iteration. This strategy allows the convergence to the physical solution in the instability regimes. We apply this procedure to the zero-point model and the Hubbard model in the atomic limit, where we successfully stabilize the physical solution deep in the non-perturbative regime, even across multiple divergence lines. Further, we compare our stabilization strategy to alternative fixed-point solvers that can be used to solve the parquet problem.

The Alfvén wave parametric decay instability (PDI) facilitates energy transfer, plasma heating, and turbulence generation in space, astrophysical, and fusion plasmas. Most simulation studies of Alfvén wave PDI have focused on kinetic ions under periodic boundary conditions. Here, we present fully kinetic one-dimensional simulations (perpendicular wave-vector $k_\perp=0$) of the Alfvén wave PDI at low plasma beta using absorbing boundary conditions for the waves to understand the energy partition in an open system. For $\beta=5\times 10^{-4}$ and a normalized wave amplitude $\frac{\delta B}{B_0}=0.01$, nearly 92% of the pump wave energy is transferred to the backward-propagating child Alfvén wave, and the remaining energy is partitioned between electrons ($\sim 1$-$2\%$) and ions ($\sim 6$-$7\%$). In the parameter regime considered, the ion and electron heating appears only when the PDI has sufficiently developed, and their rates are approximately twice the linear PDI growth rate, which roughly corresponds to the quadratic dependence of energy on the fluctuation amplitude. Furthermore, we find a qualitative agreement between theoretical and numerical growth rates over a range of plasma and wave parameters. This work establishes critical steps for future extension to finite $k_\perp$ waves in high dimensions, where stronger electron heating may be induced.

In 2007, Erika Bohm-Vitense published a provocative figure suggesting that the solar rotation period and activity cycle made the Sun an outlier compared to the trends observed for stars in the Mount Wilson HK survey. A decade later, after the discovery of weakened magnetic braking (WMB), an evolutionary scenario was proposed that could account for the properties of the Sun if activity cycles grow longer and weaker in the WMB regime. Recent observations of the gradual onset of WMB suggest that the efficiency of the global stellar dynamo declines by at least two orders of magnitude as the stellar Rossby number approaches a critical point slightly above the solar value. A new sample of activity cycle data from the California Legacy Survey suggests that the Sun is not an outlier, and unambiguously confirms that activity cycles grow longer and weaker on stellar evolutionary timescales.

The quantum-technology revolution is reshaping computing, sensing, and communication. In magnetometry, recent advances leverage precise control of spin qubits and color centers in solid-state crystals for mesoscopic-scale sensing. Yet at very low temperatures, superconducting sensing technology remains unrivaled because of its non-invasiveness and higher sensitivity. Here we describe a class of superconducting sensors that offers low loss and quantum non-demolition measurement characteristics. We designed and fabricated a superconducting flux-tunable resonator (tRes) in a superconducting chip foundry and matured it to a level that combines the speed of an inductor-capacitor circuit with the flux sensitivity of a superconducting quantum interference device (SQUID) to perform magnetometry at milli-kelvin temperature to investigate targets. We introduce its fundamental functionality readily at MHz magnetic sampling rate, showcase two measurement modalities, and investigate three circuits with gradually increasing complexity to extract target-specific information. The combination of high sensitivity and fast readout characteristics make tRes an attractive and versatile magnetometer.

Pairwise interactions among general nonlinear oscillators can be reduced, via phase reduction, to a Kuramoto-type phase coupling $\sin(- \theta_j+\theta_k )$. For higher-order interactions, multiple phase couplings exist – such as $\sin(-2\theta_j+\theta_k+\theta_l )$ and $\sin(-\theta_j+2\theta_k-\theta_l)$. Since different nonpairwise coupling functions produce qualitatively different dynamics, it is important to understand which phase couplings should be included in coupled phase oscillator models. In this Letter, we establish selection rules for higher-order phase coupling functions. These selection rules, which can be applied without the need of explicit phase reduction, are solely based on the symmetry of the isolated oscillator velocity field and the $n$-body interaction functions. As phase reduction established the mechanistic basis for the Kuramoto model, our results provide a theoretical link between physical systems and higher-order phase models.

This study investigates the combined effects of anisotropy and auxetic mesh geometry on the performance of skin graft expansion. Finite element models of auxetic slit-based geometries were developed and subjected to 25 percent tensile strain. Skin was modelled using an anisotropic constitutive formulation. Langer's line orientations were varied relative to the load direction. Results showed anisotropy strongly influenced expansion behaviour. The effect was observed to be complex and highly dependent on mesh type. Anisotropy was observed to enhance or inhibit the auxetic expansion behaviour. In all mesh types studied, the expansion performance is lowest when Langer's lines align with the transverse direction. Greatest expansion was typically observed when Langer's lines were close to the loading direction. Isotropic models overpredicted stress relative to the anisotropic models. These findings support the use of auxetic structures for skin mesh expansion applications and show that anisotropy is an important factor in both deformation and stress prediction.

A reduced-order Particle-in-Cell method is introduced for kinetic simulation of otherwise axisymmetric cylindrical plasmas that exhibit azimuthal instabilities. The method spatially decomposes all field quantities into a small number of azimuthal Fourier modes $m=0,...,N_m,\quad N_m \ll N_\theta$, reducing the costly three-dimensional field solve $\mathcal{O}(N_zN_rN_\theta)$ to a family of decoupled independent two-dimensional problems $\mathcal{O}((N_m+1)N_zN_r)$ on the meridional plane - one per mode - while particles continue to move in full three-dimensional space. Fields are reconstructed at particle positions by coherent superposition of these modal contributions, preserving complete azimuthal variation at a fraction of the cost of a conventional three-dimensional simulation. For initial validation, the method is tested against two benchmark problems with restricted axial dimension: The diocotron instability of a hollow electron annulus across three geometrically distinct configurations, recovering linear growth rates and eigenpotential structures within 7% of closed-form analytic predictions, and reproducing the non-linear vortex dynamics characteristic of the instability saturation; and a further benchmark against the community-standard Landmark Penning discharge problem recovers the rotating-spoke frequency, radial plasma profiles, and modal energy hierarchy in quantitative agreement with long-time reference simulations, at approximately 640 CPU-hours - a factor of 46 speed-up compared to the median benchmark cost. The approach represents a step toward addressing the important gap between computationally prohibitive full three-dimensional kinetic simulation and the physically limited reduced-dimensionality models on which predictive modelling of anomalous transport in magnetised plasma devices currently relies.

In this paper, we consider methods that allow for memory-efficient storage of high-dimensional distributions and retain certain key features thereof, specifically in a kinetic theory context. We propose an extension to the entropic quadrature method that allows for enforcing sparsity, and propose a new low-rank decomposition approach that ensures preservation of moment information. The methods are applied to several model kinetic distributions, as well as to distributions obtained from high-resolution kinetic simulations of the Vlasov–Maxwell system.

Multidimensional Gaussian Process (multi-GP) regression is widely used to disentangle stellar and planetary signals in radial velocities (RVs) by jointly modelling ancillary activity indicators. However, identifying the combination of indicators that best constrains the stellar signal in the RVs is non-trivial, as classical model comparison methods are not directly applicable when multi-GPs involve different time series combinations. In this work, we present an information criterion to compare multi-GP models based on their ability to explain the RV component, $\mathrm{MGIC}_{\rm rv}$. This metric combines the conditional RV likelihood with an effective parameter count that accounts for the regularisation imposed by the multi-GP model on the RV component. We demonstrate that $\mathrm{MGIC}_{\rm rv}$ provides a quantitative and robust framework for multi-GP model comparison, identifying the activity indicators that most effectively constrain the RV signal. Although developed in the context of RV analysis, the proposed criterion is general and applicable to multi-GP problems in which the inference focuses on a specific observable.

We report ab initio relativistic calculations on the complex open shell Sn$^{12+}$ highly charged ion, a prototypical plasma ion relevant for extreme ultraviolet (EUV) nanolithography. Previous calculations of EUV emissivity in tin plasmas consistently generate a spectrum in which the region of peak emissivity is blueshifted relative to experiment. By optimising our numerical methods to take full advantage of modern, high-performance CPU architectures, we are able to completely saturate the configuration interaction within the $n=4$ shell. Coupled with a thorough treatment of core-valence correlation we resolve the blueshift, finding a surprisingly large influence of highly excited states on the spectrum that is dominated by multiply excited states.

We investigate the settling dynamics of rotating objects in a yield stress fluid by combining controlled experiments with numerical simulations. Experiments were conducted using cylinders and spheres of varying surface roughness, rotated within a Helmholtz coil and immersed in a Carbopol based yield stress fluid. Complementary numerical simulations employed a viscoplastic Herschel Bulkley model to capture the coupled effects of sedimentation and rotation. To parameterize the problem, we define rotation rate to characterize rotation and the Bi to characterize sedimentation. Measurements of the drag coefficient show a strong dependence on both surface roughness and rotation rate. Flow visualization reveals that enhanced rotation generates a plastic deformation zone in the orthogonal plane and promotes wall slip, while at a stagnation point flow develops in the wake, gradually weakening and disappearing as rotation increases. In addition, the plastic drag coefficient decreases with increasing Bi and approaches an asymptotic plateau at high Bi. Numerical simulations reproduce the general scaling of drag with and but consistently underpredict experimental values, likely due to wall slip and nonlinear effects such as the stagnation point flow not present in the model. The onset of sedimentation (yield limit) was also measured and found to increase with increasing rotation and to depend on surface roughness. Finally, simulations highlight scaling relations for drag coefficient, providing new insight into the interplay of sedimentation, rotation, and viscoplastic rheology.

Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization taps coefficients, which jointly affect quantization noise, nonlinear distortion, and bandwidth-induced inter-symbol interference (ISI). Conventional grid search-based optimization is computationally prohibitive and impractical for optical communication. In this work, we propose a reinforcement-learning (RL)-enabled DA-RoF fronthaul agent architecture, capable of autonomously learning optimal transmitter parameters from end-to-end signal-to-noise ratio (SNR) feedback without a differentiable channel model. Experimental results demonstrate that the trained agent steadily improves SNR through sequential decision making and outperforms baseline, achieving ~2.7-dB SNR improvement for 1- to 4-order DA-RoF transmission, reaching final SNR of 35.8 dB, 42.9 dB, 53.8 dB, and 63.2 dB and supporting 1024-, 4096-, 16384-, 65536-quadrature amplitude modulation (QAM) format, respectively. These results validate that the proposed RL-enabled framework provides online, scalable, and hardware-efficient parameter optimization for DA-RoF fronthaul systems, paving the way toward high-order modulation format and intelligent next-generation radio access networks.

Essential-state models are central for quantum control and technology in broad regimes of light-matter interaction. The canonical effective Hamiltonian is obtained equivalently from adiabatic elimination, the Markov approximation, and the pole approximation. These approximations are known to break down at high intensities, significantly limiting their applicability to moderate light-matter interaction. We show how this limitation can be addressed by applying quasi-degenerate Rayleigh-Schrödinger perturbation theory (QD-RSPT). We reconcile QD-RSPT with adiabatic elimination and propose a quasi-degenerate extension of adiabatic elimination that is robust when the detuning of the essential states is non-negligible. The accuracy of QD-RSPT is demonstrated in both the low- and high-frequency regime, showing excellent agreement with Floquet calculations at high intensities. The crucial corrections to adiabatic elimination make the eigenvectors of the effective Hamiltonian non-orthogonal. Physically, this allows us to account for the asymmetric strength with which different essential states couple to the non-essential states. We expect that our systematic approach to effective Hamiltonians from QD-RSPT will constitute a new state of the art in intense light-matter interaction and quantum optics with novel forms of strong coupling and quantum control phenomena being conceivable.

Intestinal villi undergo pendular-wave motility – an active, propagating tissue motion driven by underlying longitudinal muscles. This motility drives irreversible, counter-wave fluid pumping, akin to the antiplectic metachrony of ciliary carpets, and generates a viscous mixing boundary layer above the villi tips, whose height is controlled by flow inertia. Using a simplified 2D model of the rat duodenum, we quantify the system's viscous energy dissipation and axial pumping efficiency. In contrast to the classical Stokes' second problem, we show that the fluid volume dominating energy dissipation is dictated by the intervillous geometry, remaining insensitive to the dynamically varying viscous mixing boundary layer height. The computed pumping efficiency is orders of magnitude lower than that of canonical peristalsis for equivalent flux pumping. We thus infer that bulk fluid pumping is not the primary biophysical function of propagating pendular-wave motility; instead, we postulate that its main role is to shear the mucus barrier layer over the villi-lined mucosa. Comparing the strain rate in the barrier region with canonical peristaltic reference values for a villi-free wall strongly supports our hypothesis. Finally, for biomimetic microfluidic applications, geometric optimization reveals that pumping efficiency scales quadratically with the channel-to-villi height ratio in Stokes flow, whereas in the inertial regime, dynamic flux confinement renders this geometric optimization strategy redundant.

We review the problem of description of the dynamics of driven-disspipative spinor polariton condensates, focusing on the terms corresponding to the coupling between a macroscopic wavefunction of the condensdate and incoherent excitonic reservoir created by a non-resonant pump. We demonstrate that the existing version of the theory breaks down in case, when reservoir has non-zero components of the Stokes vector corresponding to in-plane linear polarization. The polarization invariant theory of reservoir to condensate coupling is formulated with use of the spin density matrix formalism.

We present the design of a high-energy polarized gamma-ray facility based on Compton backscattering (CBS) of laser pulses off the FCC-ee full-energy booster beams in the Z, WW, ZH and $t\bar{t}$ modes. Saturating the safe value of the kinematic parameter $\kappa=4.35$ fixes the laser wavelength in each mode and yields backscattered photons up to $\omega_{\max}=148$ GeV. The conversion point operates on the booster cycle structure: fully parasitically on the 0.1 s top-up flat-tops ($f_{\rm CBS}=10^{-8}$ per bunch crossing, cumulative electron loss $3\times10^{-6}$ per cycle) or, as the baseline, in dedicated extended-flat-top fills inside the idle windows between top-up cycles ($f_{\rm CBS}=10^{-6}$, beam loss below 1% per cycle). The collider luminosity is unaffected in both scenarios, and the operational laser pulse energies lie in the sub-millijoule to few-millijoule range. Photon selection is performed event-by-event with a pair spectrometer on the high-energy Compton edge; for the unpolarized booster beam the Compton polarization transfer limits the achievable band-averaged circular polarization, and the selection is set to $|\langle S_2\rangle|=0.90$. We project the sensitivity to the polarized gluon distribution $\Delta g(x)$ via open-charm photoproduction $\gamma p\to c\bar{c}X$ on an NH$_3$ dynamically polarized target, including NLO QCD corrections via K-factors and propagating polarized-PDF uncertainties through the 100 Monte Carlo replicas of NNPDFpol2.0. The projected total precision is $\delta(\Delta g/g)_{\rm tot}\simeq 1.8$-$3.0\times10^{-2}$, a factor of 4-7 below the total uncertainty of the most precise existing direct measurement (HERMES), at four values of $\langle x\rangle$ in the medium-$x$ region $0.07\le x\le 0.19$. The facility would set the dominant constraint on $\Delta g$ in this region, complementary to the low-$x$ reach of the Electron-Ion Collider.