Papers

Diffusion-based sparse-view CT (SVCT) imaging has achieved remarkable advancements in recent years, thanks to its more stable generative capability. However, recovering reliable image content and visually consistent textures is still a crucial challenge. In this paper, we present a Progressively Texture-aware Diffusion (PTD) model, a coarse-to-fine learning framework tailored for SVCT. Specifically, PTD comprises a basic reconstructive module PTD$_{\textit{rec}}$ and a conditional diffusion module PTD$_{\textit{diff}}$. PTD$_{\textit{rec}}$ first learns a deterministic mapping to recover the majority of the underlying low-frequency signals (i.e., coarse content with smoothed textures), which serves as the initial estimation to enable fidelity. Moreover, PTD$_{\textit{diff}}$ aims to reconstruct high-fidelity details for coarse prediction, which explores a dual-domain guided conditional diffusion to generate reliable and consistent textures. Extensive experiments on sparse-view CT reconstruction demonstrate that our PTD achieves superior performance in terms of structure similarity and visual appeal with only a few sampling steps, which mitigates the randomness inherent in general diffusion models and enables a better trade-off between visual quality and fidelity of high-frequency details.

Understanding what and how causal dynamical mechanisms generate collective phenomena is a central challenge in complexity science. Recent studies have focused on identifying the mechanisms underlying the synergistic interdependencies that characterise these phenomena in systems with fixed interaction structures. Yet, real-world systems displaying collective phenomena, such as brains, societies, and ecosystems, are adaptive: interactions change in time. Here, we show that heterophily is a minimal local adaptive mechanism for the emergence of self-organized synergistic interdependencies. We study a paradigmatic spin-glass-like model with co-evolving couplings to show how heterophily generates the conditions for synergy to emerge. By solving the minimal $N=3$ case analytically, we reveal the precise mechanism: heterophily weakens pairwise dependencies while inducing high-order dependencies via geometric constraints on the configurations it selects. Together, these two effects underpin synergy. Numerical simulations confirm that this mechanism persists in large systems and that it is robust under parameter heterogeneities and dynamics. We demonstrate the applicability of our results by showing how heterophily can disrupt polarization while promoting synergistic information dynamics of opinions, where individuals' opinions are better explained by group-level influences than by pairwise ones. These results offer a parsimonious route to self-organized synergistic interdependencies in information-processing systems, with potential applications in computational social science, neuroscience, and biology.

Krylov subspace methods are among the most extensively studied early fault-tolerant quantum algorithms for estimating ground-state energies of quantum systems. However, the rapid onset of ill-conditioning might make accurate energies difficult or even impossible to retrieve. In this communication, we analyse the numerical stability and statistical problems of these methods using numerical simulations both in the presence and absence of sampling noise. While in ideal numerical simulations the generalized eigenvalue problem indeed becomes unstable with increased Krylov subspace size, we find that, in realistic noisy settings, these methods do not primarily suffer from ill-conditioning. Instead, statistical fluctuations dominate and can prevent reliable solution extraction unless appropriate regularization or filtering techniques are employed. We consequently introduce two new metrics, the imaginary and unitary filters, that successfully assess the reliability of the obtained solutions without any knowledge of the true eigenspectrum.

We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic spin systems. We generalize the Behler-Parrinello (BP) ML architecture – originally developed for quantum molecular dynamics – to construct scalable and transferable ML models capable of capturing the intricate dependence of electron-mediated exchange fields on the local magnetic environment characteristic of itinerant magnets. A central ingredient of this framework is the implementation of symmetry-aware magnetic descriptors based on group-theoretical bispectrum formalisms. Leveraging these ML force fields, LLG simulations faithfully reproduce hallmark non-collinear magnetic orders – such as the $120^\circ$ and tetrahedral states – on the triangular lattice, and successfully capture the complex spin textures emerging in the mixed-phase states of a square-lattice double-exchange model under thermal quench. We further discuss a generalized potential theory that extends the BP formalism to incorporate both conservative and nonconservative electronic torques, thereby enabling ML models to learn nonequilibrium exchange fields from computationally demanding microscopic approaches such as nonequilibrium Green's-function techniques. This extension yields quantitatively accurate predictions of voltage-driven domain-wall motion and establishes a foundation for quantum-accurate, multiscale modeling of nonequilibrium spin dynamics and spintronic functionalities.

The divertor in a magnetic confinement fusion reactor is an essential component for power dissipation and particle removal. The FIREFLY package for rapid evaluation of divertor designs is presented as an extension of the FLARE code for field line reconstruction from a flux tube mesh. First, divertor loads are approximated with a simplified heat transport model. Neutralized particles are then sampled from the resulting load distribution, and the EIRENE code is used to track molecules and atoms in a plasma background while accounting for dissociation, charge exchange and ionization. Particles are removed on pumping surfaces in order to estimate the exhaust efficiency for a given divertor geometry. Optimization of the divertor geometry for more efficient particle exhaust is explored by using W7-X as an example, and the sensitivity to model parameters for the plasma background in the proxy calculations is evaluated.

The manipulation of light in periodic structures is fundamental to the development of discrete photonics and provides a versatile platform for controlling light propagation in integrated and quantum photonic systems. This work reports the experimental observation of discrete one-dimensional (1D) solitons in a photonic lattice, optically induced in warm rubidium vapor. The lattice is generated by the interference of two coupling laser fields intersecting at a small angle, which creates a spatially modulated 1D refractive index. When a probe beam is focused into a single lattice site, discrete diffraction is observed. By increasing the probe intensity, discrete solitons emerge as a result of the balance between discrete diffraction and self-focusing within the nonlinear atomic medium. Experimental results are supported by numerical simulations, in which the refractive index is modeled via optical Bloch equations for a multilevel atomic system driven by the coupling and probe fields in a $\Lambda$ configuration. These results, combined with the inherent controllability of gain and loss in atomic vapors, suggest that this platform provides a versatile foundation for exploring non-Hermitian nonlinear dynamics in photonic lattices.

Observations show the multidimensional dynamics of meltwater and distribution of ice layers in the firn on the Greenland Ice Sheet. However, state-of-the-art large-scale models for firn hydrology are essentially one-dimensional, limiting their ability to explain observed datasets and failing to reduce uncertainty in surface mass balance and sea-level rise estimates. Here, we present a large-scale, multidimensional, multiphase, and thermomechanical model to simulate firn hydrology. The model is highly efficient due to a novel algorithm in which an extra equation for pressure is solved only in saturated regions. Furthermore, the model can apply spatially heterogeneous boundary conditions to the unsaturated-saturated domain and allows for the dynamic formation of fully impermeable ice layers. The numerical results show excellent comparisons against analytic solutions to one- and two-dimensional problems that involve coupled unsaturated-saturated flows, thermodynamics, and phase change. We further apply the model to investigate field data from southwest Greenland and find that lateral heterogeneities strongly influence the depth of melt percolation and ice layer formation. Improved understanding of these local, multidimensional processes will provide physics-based constraints on firn densification, reduce uncertainty in converting altimetric elevation change to mass change, and improve estimates of freshwater fluxes to the ocean under a warming climate.

Complex structures often emerge from initially homogeneous or weakly correlated states. We address the apparent tension between this ordering and entropy growth through a unified framework combining semi-microscopic phase-space dynamics, transport geometry, information theory, and coarse-grained effective modeling. The key point is that entropy depends on the level of description: a coarse-grained spatial field may become more ordered as structure forms, even while the full phase-space description becomes more complex through shell crossing, multistreaming, and the activation of velocity degrees of freedom. Using a Lagrangian–Eulerian transport map, we show how density amplification is governed by the Jacobian of the deformation and how anisotropic collapse arises from the eigenvalues of a hierarchy of deformation tensors. Long-range interaction or information flow is encoded in the displacement field, so that nonlocality enters directly through transport. We connect this geometric description to a maximum-entropy Gaussian baseline and show how nonlinear transport and nonlocal coupling generate scale coupling, higher-order correlations, and non-Gaussianity. We then formulate a Landau–Ginzburg description in which the growth of seed anisotropies is interpreted as the activation of lower effective free-energy branches, providing a coarse-grained realization of self-organization. Applied to generated cosmological fields, this framework indicates that the nonlocal tidal level becomes relevant already at moderate overdensity. Although cosmological structure formation is the main realization considered here, the framework is intended more broadly as a mesoscopic language for systems in which transport, anisotropy, nonlocality, and self-organization are central.

As global political tensions rise and the anticipation of additional tariffs from the United States on international trade increases, the issues of economic independence and supply chain resilience become more prominent. The importance of supply chain resilience has been further underscored by disruptions caused by the COVID-19 pandemic and the ongoing war in Ukraine. In light of these challenges, ranging from geopolitical instability to product supply uncertainties, governments are increasingly focused on adopting new trade policies. This study explores the impact of several of these policies on the global electric vehicle (EV) supply chain network, with a particular focus on their effects on country clusters and the broader structure of international trade. Specifically, we analyse three key policies: Country Plus One, Friendshoring, and Reshoring. Our findings show that Friendshoring, contrary to expectations, leads to greater globalisation by increasing the number of supply links across friendly countries, potentially raising transaction costs. The Country Plus One policy similarly enhances network density through redundant links, while the Reshoring policy creates challenges in the EV sector due to the high number of irreplaceable products. Additionally, the effects of these policies vary across industries; for instance, mining goods being less affected in Country Plus One than the Friendshoring policy.

We report high-resolution measurements of three-dimensional (3D) turbulence in a rapidly rotating fluid. By decomposing the velocity field into a vertically averaged component and a three-dimensional residual, we show that each dominates distinct frequency ranges: the quasi-2D component at low frequencies and the 3D component at higher ones. This separation is not intrinsic to the flow but strongly depends on the finite vertical span of the measurements. As the vertical scan range increases, the apparent crossover between 2D and 3D-dominated regimes shifts systematically, revealing that the commonly assumed partition is strongly shaped by measurement limits. These findings call into question the usage of the concept of pure 2D manifold, in the theoretical description of rotating turbulence and highlight the need for frameworks that account for resolution-dependent parts of the flow and the coupling between wave-like and vortex-like motions.

Time-Resolved Spectroscopy (TRS) is a powerful modality for non-invasive characterization of turbid media. However, extracting optical properties, absorption $\mu_a$ and reduced scattering $\mu_s'$, from 3D stochastic measurements remains computationally expensive for real-time applications. In this paper, we propose a data-efficient, physics-informed transfer learning strategy using a Bidirectional Long Short-Term Memory (Bi-LSTM) network. By leveraging a fast deterministic solver to establish a physical prior before fine-tuning on a restricted set of 3D Monte Carlo simulations, our model successfully bridges the analytical-to-stochastic domain gap. The proposed method eliminates the systematic bias of analytical models while maintaining a competitive error with near-instantaneous inference time.

Our objective is to study the performance and robustness of the model-free strategy for controlling the oxygen stoichiometry of a fuel cell air supply system with a proton exchange membrane. After reviewing the literature on modeling and control of this process, the model-free approach appears to be a good candidate because, on the one hand, it allows straightforward real-time adaptation to track operating points and, on the other hand, it requires a low computational burden, which is attractive for industrial applications. Numerical simulations for two scenarios (constant and variable oxygen stoichiometry) with two current profiles reveal satisfactory performance of the model-free control law. The robustness is addressed by considering significant variations in the parameters of the proton exchange membrane air supply system.

Sustainable solid-state refrigerants based on barocaloric materials are often limited by thermal hysteresis associated with supercooling effects. Here, we present imaging methods to investigate and compare thermal behaviour and transition kinetics of the barocaloric molecular crystal neopentyl glycol (NPG) with those of a lightly doped derivative, NPG$_{0.99}$PE$_{0.01}$, which incorporates 1 mol % pentaerythritol (PE). We use temperature-dependent polarised light (PL) microscopy and infrared (IR) thermography to correlate phase transition kinetics and local heat-flow with the bulk thermodynamic response obtained from calorimetry. We show that the doped system exhibits reduced supercooling and thermal hysteresis, attributed to increased microstructural disorder and an increase in the number of nucleation events. These findings provide insight into the design of low-hysteresis barocaloric materials for high-efficiency solid-state cooling applications.

Direct numerical simulations are performed to investigate the combined effects of surface roughness and wall heat transfer on spatially developing compressible turbulent boundary layers at $Ma=2.5$. The roughness consists of transverse square bars with $\lambda_x/k=8$ and $k^+ \approx 35$, under adiabatic and wall-cooling ($T_w/T_r = 0.5$) conditions. Dynamically, the conventional zero-moment method fails to yield a consistent zero-plane displacement for the present cavity-type roughness. Instead, a fitting-based optimization procedure is proposed to determine the kinematic virtual origin, which successfully restores the logarithmic behavior. Based on this displacement, Griffin–Fu–Moin (GFM) transformation outperforms the classical van Driest transformation in recovering outer-layer similarity for the velocity defect. Thermodynamically, the physical disparity between momentum form drag and the absence of a corresponding heat transfer mechanism disrupts the classical Reynolds analogy. The effective turbulent Prandtl number ($Pr_e$) deviates severely from unity within the roughness sublayer, leading to the breakdown of the classical Generalized Reynolds Analogy (GRA). To address this, a modified rough-wall GRA (rGRA) is formulated by introducing an equivalent slip-plane or reference-point boundary conditions, which accurately reconstructs the temperature-velocity relationship by bypassing the near-wall thermal heterogeneity. Finally, the refined strong Reynolds analogy (RSRA) is shown to maintain predictive accuracy for fluctuation intensities in the outer layer despite near-wall modulation by roughness and cooling.

Analyzing unsteady fluid flows often requires access to the full distribution of possible temporal states, yet conventional PDE solvers are computationally prohibitive and learned time-stepping surrogates quickly accumulate error over long rollouts. Generative models avoid compounding error by sampling states independently, but diffusion and flow-matching methods, while accurate, are limited by the cost of many evaluations over the entire mesh. We introduce scale-autoregressive modeling (SAR) for sampling flows on unstructured meshes hierarchically from coarse to fine: it first generates a low-resolution field, then refines it by progressively sampling higher resolutions conditioned on coarser predictions. This coarse-to-fine factorization improves efficiency by concentrating computation at coarser scales, where uncertainty is greatest, while requiring fewer steps at finer scales. Across unsteady-flow benchmarks of varying complexity, SAR attains substantially lower distributional error and higher per-sample accuracy than state-of-the-art diffusion models based on multi-scale GNNs, while matching or surpassing a flow-matching Transolver (a linear-time transformer) yet running 2-7x faster than this depending on the task. Overall, SAR provides a practical tool for fast and accurate estimation of statistical flow quantities (e.g., turbulent kinetic energy and two-point correlations) in real-world settings.

Many of the most interesting scientific subjects are also the hardest to study with neutrons. Metal-organic frameworks, organic superconductors, quantum magnets, pressure-tuned materials, are systems where the relevant signals are weak, the samples are tiny, or the experiments need extreme sample environments such as pressure cells and high-field cryomagnets. Existing instruments often run into practical limits before the science is exhausted. For some questions the samples are simply too small; for others, the signal is buried in background or the required measurement time becomes prohibitive. This is both a scientific opportunity and a challenge for the European neutron scattering community. We present Njord and Remora as a paired instrument concept for the European Spallation Source (ESS). The proposal focuses on two linked problems: important science cases are being limited by neutron flux and sample geometry, and the community also needs more beamtime. Njord addresses the first by pushing the available brightness into a tightly focused beam, while Remora uses the remaining spectral window to add a complementary spectrometer on the same beamport.

Two-dimensional materials that exhibit optically active spin and valley degrees of freedom represent one of the most fascinating – and potentially most technologically useful – platforms for the ultrafast interaction of light and matter. Here we show, via the example of Cr$_2$SO, that two dimensional altermagnets host valley states controllable by femtosecond laser light: linearly polarized light pulses excite charge at one of two inequivalent valleys, with which valley charge is excited at determined by the polarization vector direction. This underpins a rich spin and valley physics including: (i) valleytronics $-$ the generation of nearly 100$\%$ spin polarized valley currents, as well as (ii) a "ghost Hall" effect $-$ the ultrafast creation of states in which spin and charge currents are orthogonal without invoking Hall physics. Our findings establish 2d altermagents as a platform providing a new route for the control of spin- and charge currents at ultrafast times.

Solid-state platforms are particularly attractive for quantum optics because they facilitate on-chip integration and are compatible with established semiconductor and photonic technologies. However, a major challenge in solid-state quantum optics is the fabrication of arrays of identical emitters, such as quantum dots. In this work, we propose moire superlattices as a novel solid-state platform for manipulating light at the single-photon level. Moire superlattices form arrays of artificial-atom states characterized by nearly identical optical transition energies, tunable spacing, and highly adjustable electronic structures. They naturally operate as atomically thin, scalable, periodic emitters, making them ideal for quantum applications. Additionally, the extensive materials database of moire superlattices offers spectral coverage spanning a broad range of optical wavelengths.

Cooperation is a key driver of human social progress. Studies of the evolution of cooperation typically assume a deterministic outcome for social interactions. But in real-world social interactions, interaction outcomes are often subject to stochastic perturbations arising from open environments. Individuals may show different attitudes towards such uncertainty, some are risk-seeking, while others tend to be risk-averse. Here we investigate how risk preference towards uncertain payoffs affects the evolution of cooperation on social networks, where uncertainty originates from random punishment of defectors initiated by cooperators. We provide an analytical treatment of how the distribution of risk preference among individuals alters the threshold required for cooperation. We find that, at the population level, risk-averse behavior promotes or even rescues cooperation. At the node level, variation in risk preference has a significant impact when it occurs on nodes with high degree centrality. When nodes have the same degree centrality, the nodes with lower betweenness centrality exhibit a stronger effect on strategy evolution. Our analysis reveals how risk preference, together with spatial structure, jointly shapes and potentially reverses the evolutionary dynamics of cooperation.

Since their inception in 1984, the European Framework Programmes (FPs) have funded collaborative R&D to promote excellence, cohesion, and competitiveness in a growing European Union. However, their integrative impact and the evolution of the research landscape alongside its collaborative structures remain insufficiently understood. In this longitudinal study, we leverage CORDIS data from all nine FPs to reconstruct the evolution of country-level collaboration networks over time. We observe an increasing equity in project participation between FP1 and FP6, although newly included countries systematically tend to be marginal when first joining the programmes. However, we find that the collaborative nature of EU projects progressively integrates marginal countries in the network, even if this integration is still in progress. We also trace the evolution in time of research topics using semantic embeddings of project descriptions, identifying 117 topics grouped into 16 macro-topics. By computing the minimum spanning tree length of project embeddings within yearly time windows, we quantify how European research progressively explores a wider knowledge space. A comparison with a null model with points randomly distributed in the semantic space indicates that this exploration is more focused than a uniform coverage. Moreover, it appears uneven, with few topics mostly attracting industry and others academia. Our findings suggest that, while European funding promotes international cooperation, it has not yet fully resolved core-periphery asymmetries, and European research remains concentrated along established trajectories rather than broadly exploratory, with implications for future programme design and the excellence-cohesion debate.

This work reports back-end-of-line (BEOL) compatible, thin-film transistors (TFTs) with sub-10 nm tungsten-doped tin oxide (TWO) channels deposited by atomic layer deposition (ALD) at 150 $^\circ$C. TFTs with undoped SnO$_{\mathrm{x}}$, undoped WO$_{\mathrm{x}}$, and W-doped SnO$_{\mathrm{x}}$ channels with W concentrations of 5% and 10% were investigated. TFT with 10% W doping exhibited the best electrostatic control and overall device performance. Post-fabrication O$_{\mathrm{2}}$ annealing at 300 $^\circ$C for 5 minutes significantly enhanced device characteristics, reducing the subthreshold swing (SS) by nearly 2$\times$, increasing the I$_{\mathrm{on}}$/I$_{\mathrm{off}}$ ratio from $10^7$ to $10^9$, decreasing hysteresis by nearly 3$\times$ and positive bias stress-induced threshold shift by over 2$\times$ to a low value of 93 mV at a stress field of 4 MV/cm. Kinetic Monte Carlo simulations using Ginestra$^{\mathrm{TM}}$ support the experimental observations and attribute the bias instability to charge trapping in the gate dielectric and at the interface. This work demonstrates low-temperature ALD-grown TWO TFTs as a promising indium-free platform for BEOL and monolithic 3D integration.

Large Language Models are increasingly deployed as interacting agents in settings such as online platforms, recommendation systems, and multi-agent applications. Understanding the collective behaviors that emerge from their interactions is therefore increasingly crucial, especially as these behaviors may shape public opinion and contribute to polarization. In this work, we investigate how network structure and group composition shape the evolution of opinions in populations of LLM agents engaged in multi-round debates. We generate networks with controlled levels of homophily and varying group sizes, and perform ten independent runs per configuration. The results show that LLM agents exhibit patterns that are highly sensitive to network structure, relative group sizes, and to the model itself. We highlight that even within LLM populations, low homophily facilitates convergence by increasing opportunities for cross-group interaction, whereas higher homophily limits cross-group exposure and can preserve distinct opinion states, consistent with previous research. We further show that the choice of LLM substantially affects the resulting dynamics, with different models exhibiting different patterns of opinion updating under comparable network conditions. At the individual level, providing agents with information about their local neighborhood further modulates opinion transitions, revealing substantial differences between models in their sensitivity to local social context. Overall, our findings show that the collective dynamics of LLM populations arise from interactions among model-specific behavior, network structure, population composition, and local social information.

Nature-inspired hyperuniform disorder offers a promising route to broadband light trapping in ultrathin perovskite solar cells by avoiding narrowband, illumination-sensitive responses commonly associated with periodic nanophotonic textures. Here, we introduce a nature-inspired ingenious hyperuniform nanohole architecture integrated into the front glass of a planar MAPbI$_3$ perovskite solar cell, serving as a junction-preserving strategy to enhance optical absorption and photovoltaic performance. In comparison with planar and periodic textures, the hyperuniform architecture redistributed incident light across a broader spectrum of in-plane momentum states, strengthened near-interface electromagnetic fields, and improved long-wavelength coupling into the absorber, thereby increasing the effective optical path length without altering the electronically active interfaces. To quantify these effects, we employed a coupled three-dimensional multiphysics framework that integrates finite-difference time-domain (FDTD) optical simulations with drift-diffusion electrical modeling. The optimized design exhibited broadband absorption enhancement, weak polarization dependence, and strong angular tolerance, while suppressing interference-driven spectral oscillations and reducing sensitivity to patterned-layer thickness. Relative to the planar structure, the hyperuniform architecture increased the short-circuit current density from 21.57 to 23.92 mAcm$^{-2}$ and improved the power conversion efficiency from 21.03% to 23.62%, while maintaining $\mathrm{V_{oc}}$ at 1.13 V and preserving a high fill factor of 87.66%. In addition to statistical pattern-invariant performance, stochastic radius-variation analysis indicated a positive enhancement in photocurrent and under fabrication-relevant dimensional disorder.

Accurate and efficient surrogate models for aerodynamic surface pressure fields are essential for accelerating aircraft design and analysis, yet deterministic regressors trained with pointwise losses often smooth sharp nonlinear features. This work presents a conditional denoising diffusion probabilistic model for predicting surface pressure distributions on the NASA Common Research Model wing under varying conditions of Mach number, angle of attack, and four control surface deflections. The framework operates on unstructured surface data through a principal component representation used as a non-truncated, reversible linear reparameterization of the pressure field, enabling a fully connected architecture. A signal-aware training objective is derived by propagating a reconstruction loss through the diffusion process, yielding a timestep-dependent weighting that improves fidelity in regions with strong pressure gradients. The stochastic sampling process is analyzed through repeated conditional generations, and two diagnostic metrics are introduced, the Local Reliability Index and Global Reliability Index, to relate sampling-induced spread to reconstruction error. Relative to the considered deterministic baselines, the proposed formulation reduces mean absolute error and improves the reconstruction of suction peaks, shock structures, and control surface discontinuities. The sampling-induced spread exhibits strong correspondence with surrogate error, supporting its interpretation as a qualitative reliability indicator rather than calibrated uncertainty quantification.

We present a combined experimental and theoretical study of enhanced third-harmonic generation (THG) in silicon nitride metasurfaces. These structures exhibit strong resonant nonlinear responses, enabling up to two orders of magnitude enhancement in THG compared to a flat silicon nitride etalon, driven by strong electromagnetic field localization. We investigate two polarization-selective metasurface geometries supporting transverse electric (TE) and transverse magnetic (TM) resonances, implemented in fully planar architecture. When driven by ultrafast near-infrared laser pulses, these resonances confine optical energy at the nanoscale, enabling efficient frequency up-conversion from the visible to the ultraviolet (UV) and deep-UV spectral regions. Through spectral mapping of the nonlinear response under both TE and TM excitation, we quantify field confinement, extract the effective nonlinear enhancement, and characterize the spectral dependence of the third-harmonic generation efficiency. This two-dimensional periodic platform provides a flexible design toolbox for engineering polarization-dependent UV and deep-UV light sources. More broadly, our results demonstrate that silicon nitride, a CMOS-compatible dielectric, can support efficient nonlinear up-conversion deep into the UV. This finding shows that access to short-wavelength nonlinear photonics does not require complex materials or architectures, but can instead be achieved using widely available dielectrics through careful structural design.

The pore-surface and surface-surface correlation functions are structural characteristics that play an important role in theoretical materials science and in small-angle scattering theory. Exact analytical expressions for the surface correlation functions are available only for very few models, and we here derive such expressions for the general class of dead-leaves models. Within these models, a two-phase pore/solid structure is created by sequentially and randomly filling space with pore-like or solid-like grains that overlap any pre-existing structure, in the same way as dead leaves fall on the ground. The obtained mathematical expressions are valid for any grain shape, in arbitrary dimension. The results are illustrated with monodispersed spherical grains,as well as with a dead-leaves realization of a Debye random medium. In the latter case, the size distribution of the grains is designed to produce a structure having exponential two-point correlation function. Compared to Debye random media obtained by numerical reconstruction, the dead-leaves structure has almost identical surface-surface correlation function, but distinctly different pore-surface correlation function. As a byproduct of our analysis, we also submit a general expression for the pore-surface and surface-surface correlation functions of the Boolean model, valid for arbitrary grains.

Rotating space tethers have long been proposed as momentum-exchange devices capable of transporting payloads between orbital regimes without continuous propellant expenditure, offering a potential alternative to conventional propulsion for transfers from low Earth orbit to higher orbits. In this work, we numerically investigate a system of multiple rotating tethers distributed across different orbital radii and coupled through intermediate transfer platforms (elliptical nodes) moving along Keplerian trajectories. We identify families of dynamically consistent configurations in which neighboring tethers achieve near-phase synchronization, enabling coordinated payload exchange without impulsive maneuvers. Based on these results, we introduce the concept of a Space-Clock Elevator: a modular orbital transport architecture in which payloads are transferred sequentially between synchronized rotating tethers via intermediate elliptical nodes. Numerical experiments demonstrate that such synchronized tether networks can support outward payload transport while maintaining bounded tether tension and dynamically stable orbital motion.

The stretching and pinch-off of a liquid bridge is a simple way to probe when a suspension of particles stops behaving as a continuum. In this study, we consider density-matched suspensions of rigid nylon fibers with aspect ratios (length over diameter) ranging from 2 to 84, and volume fractions $\phi$ spanning the dilute to dense regimes. High-speed imaging of pendant-drop breakup reveals three successive regimes, as previously observed for spherical particles: an equivalent-fluid regime at early times, a dislocation regime corresponding to the separation of the rods, and a final regime controlled by the interstitial liquid once the neck is devoid of rods. The thresholds between these regimes follow the previously proposed scaling for spherical particles, in which the rod length, rather than the rod diameter, is used as the relevant discrete scale. In the equivalent-fluid regime, pinch-off also leads to an effective extensional viscosity that increases with both volume fraction and aspect ratio. This viscosity is not equal to the shear viscosity measured in a parallel-plate rheometer, but both sets of data are well described by Mills' law using a critical volume fraction $\phi_c$. Finally, the critical volume fraction $\phi_c$ decreases monotonically with the aspect ratio and is well captured by an empirical law. These results show that pinch-off is a sensitive probe of continuum breakdown in anisotropic suspensions and that, for rigid rods, the rod length controls the onset of heterogeneous thinning.

Tunable bound states in the continuum (BICs) in photonic crystal slabs are highly sensitive to substrate-induced mirror-symmetry breaking and typically degrade into finite-$Q$ quasi-BICs in realistic integrated platforms. Here we show that such degradation can be deterministically reversed. Using temporal coupled-mode theory and full-wave simulations, we demonstrate that the radiation channel opened by the substrate can be exactly cancelled by introducing a second, independent odd-parity perturbation inside the slab. This dual-asymmetry strategy restores the singularity of the radiation matrix and thereby recovers a tunable BIC in a substrate-supported photonic crystal slab. The recovered state regains both the polarization vortex and the characteristic $Q\propto \Delta k^{-2}$ scaling. The recovery points further follow a linear relation in the two-asymmetry parameter space, revealing a simple mode-dependent compensation law. The same mechanism also restores merging-BIC configurations, showing that it applies not only to isolated tunable BICs but also to higher-order topological resonance states built from them. Our results establish a practical route for preserving tunable topological resonances in substrate-supported nanophotonic systems.

Ground-state electronic structure calculations using Kohn-Sham density functional theory (KS-DFT) offer an unprecedented balance between efficiency and accuracy, now paradigmatic to the fields of quantum chemistry and condensed matter physics. KS-DFT can be extended to model electronic excitations through a density mapping onto a non-interacting ensemble state in which, unlike in thermal theories, the weights assigned to the excited states vary independently. Thanks to its numerous appeals, like the adequate treatment of multiple excitations for which the widely-used time-dependent extension of DFT struggles, ensemble DFT (eDFT) has lately become a vibrant area of research. Recently, an enlarged type of ensemble, referred to as N-centered (Nc) ensemble, has been introduced to describe within the same unified formalism both neutral and charged electronic excitations. This perspective paper provides a detailed exposition of exact Nc-eDFT, with a comprehensive review of its formal developments. To cut practical computational tools out of the exact theory, three original strategies are presented, complementing existing approaches. The first one, related to the design of ensemble density-functional approximations, consists in recycling regular ground-state functionals by dressing them with a weight-dependent scaling function deduced from exact properties of eDFT. We then explore quasi-degenerate formulations of ensemble density-functional perturbation theory, suggesting alternative definitions for the ensemble Hartree, exchange, and correlation energies, individually, and paving the way toward robust orbital-dependent eDFAs. Finally, we revisit and generalize the concept of quantum bath for an ensemble of non-interacting states, laying the foundations of an in-principle exact (in the sense of lattice eDFT) quantum embedding theory of excited states.