Papers

Neural operators provide fast surrogate models for PDE simulations, but standard architectures often treat geometry and discretization as secondary to field data. Physical states are usually represented as grid-channel stacks, even when different quantities naturally belong on vertices, edges, faces, cells, boundaries, or interfaces and must satisfy compatibility constraints. We propose Cellular Sheaf Neural Operators, a discretization-aware framework for structure-preserving neural PDE surrogates. The method represents PDE states on oriented cell complexes, couples local feature spaces through learned restriction maps, and uses incidence/Hodge-informed message passing to follow computational geometry. Learned update heads pass through coboundary or flux maps, allowing selected constraints to arise from cell-complex structure rather than only from loss penalties. For magnetohydrodynamics, this yields face-based magnetic-flux updates driven by edge electromotive fields and finite-volume-style fluid updates driven by learned face fluxes and cell sources. On turbulent MHD and fusion-equilibrium surrogate tasks, the method improves structure-sensitive diagnostics, including rollout behavior, divergence control, spectral error, and equilibrium-regression accuracy. These results indicate that cellular-sheaf structure is a useful inductive bias for neural PDE surrogates in constrained multiphysics systems.

We present LBFAST, a GPU-oriented lattice Boltzmann solver based on a lightweight moment-represented formulation, in which post-collision populations are reconstructed on the fly from a reduced set of moments rather than stored explicitly. This approach significantly lowers the memory footprint, enabling large three-dimensional simulations within the constraints of modern accelerator architectures, where VRAM capacity and bandwidth are critical resources. The method is assessed through standard single- and two-component benchmarks demonstrating good accuracy and stability. Extensive scaling experiments on multi-GPU systems show near-ideal weak scaling up to 512 GPUs and sustained performance across different velocity sets. The combination of reduced memory usage, competitive throughput, and stable energy efficiency makes the proposed formulation a practical route for large-scale lattice Boltzmann simulations on current and emerging HPC platforms.

Assessing structural performance under seismic hazard requires accounting for both damage accumulation and post-event recovery. In current performance-based earthquake engineering (PBEE), recovery is generally treated as a post-processing attribute, while structural performance is modeled using Poissonian exceedance assumptions that imply renewability and memorylessness. These assumptions hinder a unified treatment of reliability and resilience under repeated seismic loading. This study proposes a generalized PBEE framework in which damage and recovery are embedded directly into the system dynamics through a continuous-time Markov chain. A single generator matrix governs state-dependent transitions, providing a unified description of structural reliability and resilience while remaining compatible with standard PBEE metrics. Time-dependent failure probabilities and reliability indices are derived from the transient system dynamics, whereas resilience is quantified through the expected fraction of operational time before collapse. The framework exploits the spectral properties of the generator matrix to compute both metrics efficiently and transparently. The methodology is illustrated on a three-state example and applied to two structural archetypes: a braced frame and a base-isolated system. Results show that recovery dynamics can strongly affect long-term resilience even when conventional reliability measures exhibit limited sensitivity, emphasizing the need to explicitly account for recovery in life-cycle seismic performance assessment.

Residual agreement in a kinetic or moment equation does not automatically identify every higher-order closure variable entering a nonequilibrium shock. We formulate this issue as an observability problem for the fourth-order closure content of monatomic normal shocks and follow it through a hierarchy of collision models and diagnostics. The kinematic part of the result is independent of the collision operator: the one-dimensional heat-flux budget observes the projected fourth-order channel $S=R^{\cl}_{xx}+\Delta/3$, not the tensorial R26-level moment $R^{\cl}_{xx}$ separately from the scalar fourth-order excess $\Delta$. The observation map therefore has a one-dimensional null space, so a heat-flux residual can be small while the split between tensorial anisotropy and isotropic tail intensity remains wrong. A DVM-consistent scalar-excess budget supplies the missing channel and gives the two-channel reconstruction $R^{\cl}_{xx}=S-\Delta/3$ without direct $R^{\cl}_{xx}$ data. Across BGK shocks at Mach 2–5, this reduces the active-zone $R^{\cl}_{xx}$ error from about $63$–$64\%$ to $2.4$–$4.1\%$. Sparse scalar-excess interpolation is used only as an information-reduction test: a representative 24-probe operating point gives $R^{\cl}_{xx}$ errors below $4.5\%$, and below $4.7\%$ with $1\%$ probe noise. Collision-model diagnostics then separate the invariant observation channel from the model-dependent source law. Shakhov changes the heat-flux relaxation to the correct Prandtl number but is neutral in the even $|\boldsymbol c|^4$ scalar-excess source; a direct discrete Shakhov channel check recovers $S$, $\Delta$ and $R^{\cl}_{xx}$ with errors $6.4\times10^{-4}$, $2.1\times10^{-7}$ and $1.0\times10^{-3}$, respectively.

We show that persistent dynamics of a latent default-probability path can generate effective default correlation through temporal coarse-graining. In the OU–Binomial baseline, monthly defaults are conditionally independent given this latent path, but aggregating monthly default probabilities into long-horizon probabilities induces a scale-dependent effective mixing distribution for aggregated default counts. Applied to corporate default-count data, this mechanism explains long-horizon overdispersion, autocorrelation, and the emergence of effective default correlation. We then examine Davis–Lo-type contagion and Vasicek-type common-factor extensions. Direct fitting at each aggregation scale assigns increasing residual covariance shares to instantaneous dependence, but worsens the per-block expected log predictive density. In contrast, when monthly posterior latent paths are first coarse-grained and residual-dependence parameters are estimated conditional on these paths, the residual covariance contributions remain small while the predictive density improves. Thus, temporal coarse-graining provides a scale-consistent baseline that regularizes the attribution of variance and improves identifiability by suppressing the over-allocation of long-horizon fluctuations to contagion or asset-correlation parameters.

Shielding by upstream buildings is a fundamental control on urban drag, yet its influence remains poorly quantified in realistic urban environments. Here, we investigate shielding effects using building-resolved large-eddy simulations of the University of Bristol campus, comprising 110 buildings of varying height, shape and orientation. Twenty-four wind directions are considered, allowing each building to experience a wide range of upstream shielding conditions. While the total drag of the campus exhibits only moderate directional variability, the drag acting on individual buildings varies substantially. In the present case, approximately $20\%$ of buildings account for $80\%$ of the total drag, which is primarily attributed to a small number of large buildings that contribute disproportionately high drag forces. To quantify shielding, we introduce two dimensionless parameters: the upstream fetch ratio, $L_s/H_s$, and the relative height ratio, $H_s/H$, where $L_s$ is the distance to the nearest upstream obstacle, $H_s$ is the height of the upstream obstacle, and $H$ is the height of the target building. These parameters distinguish between near- and far-wake conditions and between sheltered and exposed buildings, providing a simple method to characterise shielding effects in realistic urban environments. The study provides valuable quantitative insight into drag and shielding in the Bristol campus morphology; more importantly, it establishes a general framework for analysing drag and shielding that can be applied in other complex urban environments. The results identify shielding as a primary control on building drag and motivate shielding-aware measures of effective frontal area and drag coefficient

In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL) agent is transferred to an LLM agent through in-context learning, enabling autonomous iterative planning without human intervention. While standard LLM-based planning often lacks physical intuition and struggles with convergence, the integration of DRL-derived guidance constrains the agent to a physically valid parameter space. Experimental evaluations are performed across three diverse planning scenarios: basic prostate cases, complex prostate configurations with increased organ-at-risk (OAR) constraints, and liver cases. The evaluation results demonstrate that the guided LLM agent consistently achieves optimal planning scores while significantly reducing the number of iterations compared to unguided planning. Analysis of the final TPP configurations reveals that the agent successfully learns a hierarchical priority of objectives, effectively restoring a logical "cause-and-effect" relationship between parameter tuning and dosimetric outcomes. Crucially, this prototype framework exhibits robust generalizability, maintaining high planning quality regardless of specific patient anatomy, treatment site, or initial plan quality. By bridging the specialized optimization of DRL with the adaptive reasoning of LLMs, this M2M framework establishes a scalable foundation towards generalizable autonomous treatment planning, ultimately benefiting clinical practice in realistic environments.

Liquid interfaces appear throughout nature and engineering and are typically contaminated by surface active agents (surfactants), which are characterized by a wide range of solubility. We demonstrate that solubility enhances by an order of magnitude surfactant-induced flow in air-liquid-air films, in contrast to previously studied geometries where solubility dampens the flow. The enhancement is described by a single parameter comparing the depletion length to the film thickness. Our experiments are well described by an asymptotic theory of the Navier-Stokes equations with surfactant kinetics.

We introduce an automated, agent-driven approach to the design of photonic devices. We instruct large language models (LLMs) to solve photonic design problems, given access to software tools for performance evaluation (through numerical simulations) and quantitative acceptance criteria (e.g., fabrication rules, geometric constraints, physical-consistency checks). Within this context, agents run autonomous design loops (propose, simulate, evaluate, iterate) and generate devices with state-of-the-art performance. We demonstrate this approach in two stages: First, we run it individually on four canonical problem classes in photonic chip design: a) passive components (waveguide bends, splitters, crossings, etc.); b) active devices (silicon microring modulators (MRMs)); c) radio-frequency (RF) devices (traveling-wave electrodes for a Mach-Zehnder modulator (MZM)); d) chip layout (electrical routing). Then, we combine the previous studies in one demonstration to produce a silicon photonic modulator, incorporating layout, charge transport, optical mode, and RF electrode design. The approach generalizes to any problem that combines a numerical simulator with performance criteria that an LLM can evaluate.

A static alpha eigenvalue capability was implemented in a modified version of the open-source Monte Carlo radiation transport code OpenMC and validated against Rossi-alpha measurements from 21 delayed-critical benchmark experiments and 33 subcritical configurations spanning fast, intermediate, and thermal systems with U-233, HEU, IEU, LEU, and plutonium fuels. The effective delayed neutron fraction was calculated using the k-prompt method, and the prompt neutron lifetime was calculated using the iterated fission probability method, both evaluated within the standard k-eigenvalue power iteration. The delayed-critical alpha eigenvalue was calculated from these quantities using the point kinetics equation alpha_dc = -beta_eff / ell_p. Agreement was generally within 10% for fast metal systems and within 5% for thermal solution systems. Subcritical extrapolation studies derived from the SHE-8 and STACY benchmark families show that alpha_dc remains stable as the system is driven subcritical.

Hypergraphs provide a natural framework for describing complex networked systems with higher-order, non-dyadic interactions. Due to their high dimensionality and often redundant structure, a key challenge is to develop methods that simplify hypergraph representations while preserving the essential structure of interactions. Here we present a principled, efficient, and non-parametric information-theoretic method for pruning nested and/or redundant structures in hypergraphs, enabling a minimal representation of higher-order interactions in the presence of local heterogeneity. Our approach naturally extends to weighted hypergraphs, where higher-order topology and hyperedge weights combine to identify the system's structural backbone. We validate the method on controlled synthetic hypergraphs and apply it to empirical datasets from diverse domains, demonstrating substantial sparsification without loss of core structural information.

The treatment of ischemic stroke using mechanical thrombectomy involves difficult decisions under intense time constraints. Numerical physics simulations can in theory inform operators to make better decisions regarding treatment approaches and device selection, but are too slow to do so in practice. In this thesis, we investigate if current machine learning based surrogates can accurately emulate these simulations in a step-by-step manner while making them significantly faster. To do this we train three surrogate models on two simulations that involve a simplified aspiration procedure, with varying levels of geometric complexity. Our results show that two of our models accurately predict singular simulation steps and provide substantial speedups, especially when combined with specific data augmentations. However, the models showed a lack of stability when emulating simulations with complex geometries over longer time periods. Overall, this work provides a foundation for future studies to develop stable methods that scale to realistic numerical physics simulations of mechanical thrombectomy.

We establish a general theoretical framework for realizing topological edge states in dimerized arrays of chiral dipolar molecules and demonstrate that molecular handedness provides a natural and tunable route to SSH-like topology in an interacting one-dimensional setting. Starting from an effective spin-$\tfrac{1}{2}$ model generated by Stark-dressed chiral molecules, we introduce bond dimerization and show that the chirality-induced Dzyaloshinskii–Moriya interaction amplifies the effective hopping amplitudes and enlarges the bulk topological gap relative to an achiral chain of equivalent dipole strength. Using self-consistent mean-field theory with periodic- and open-boundary calculations, we map out the trivial, critical, and topological regimes through bulk spectra, complex-plane winding, and boundary-localized probability densities. A central result is that the two in-gap boundary modes carry opposite molecular chirality: the left edge state localizes on a left-handed molecule and the right edge state on a right-handed molecule, a stereochemical labeling with no analogue in conventional SSH implementations. The two-leg ladder extension supports a richer four-band bulk structure and a rung-split edge sector whose robustness is characterized by a continuous sweep of the interchain coupling. All results are expressed in dimensionless units of the reference hopping scale $t_0$, making the framework directly applicable to any dipolar molecular platform – from bialkali polar molecules at MHz coupling scales to future arrays of ultracold chiral polyatomic species. These findings establish dimerized chiral molecular arrays as a controllable and chirality-addressable platform for quasi-one-dimensional topological quantum matter.

We investigate the emergence of long-time spanwise asymmetries in an unsteady wake downstream of a finite-span wing by disentangling flow asymmetries into symmetric and anti-symmetric components using global mode (structural) sensitivity and spatio-temporal harmonic resolvent analysis. The global mode sensitivity analysis shows that asymmetric modes emerge when symmetric and anti-symmetric eigenmodes appear as pairs and exhibit high levels of modal non-normality. The modal non-normality renders the eigenmodes susceptible to asymmetric disturbances, which results in phase interference between the paired symmetric and anti-symmetric modes and unfolds them into highly asymmetric modes. Such interferences further motivate the development of a spatio-temporal harmonic resolvent analysis to examine the cross-frequency phase coupling between modes of different phase velocities. We observe that the flow asymmetries are primarily driven by elliptic vortex instability and its interaction with the wake shear layers. Moreover, we show that, even with a large-amplitude departure in the base flow from the symmetric state, the asymmetric modes obtained from the asymmetric wake can be accurately reconstructed by the symmetric and anti-symmetric modes from the symmetric base flow. This important finding suggests that flow asymmetries can be understood as a superposition of symmetric and anti-symmetric structures that lie under the symmetric base flow, and their phase interference serves as a potential mechanism for the emergence of long-time flow asymmetries. We believe that the present study provides a promising path towards understanding and controlling the emergence of asymmetric flow structures over finite-span wings.

The three- and four-boson systems with a large scattering length and a short effective range in the two-body sector are studied in the framework of Short-Range Effective Field Theory (SREFT). The starting point (leading order) of the EFT is taken to be the universal unitarity limit, where the two-body sector is parameter-free and only one three-body parameter enters. In this limit, physical systems manifest discrete scale invariance. Deviations from universality arising from finite scattering-length and effective-range corrections, as well as a four-body force required by renormalization, are included perturbatively at next-to-leading order. The three-body ground state and its associated four-body ground and first-excited states are studied using the Faddeev-Yakubovsky (FY) formalism and a complementary diagrammatic approach. By employing techniques to remove contributions from deep trimers in tetramer calculations, we extend our analysis to larger cutoffs than previously accessible within the FY approach to SREFT. Our results for binding energies and radii of $^4$He three- and four-atom systems converge well to results obtained with sophisticated phenomenological potentials. These successes suggest that the physics of $^4$He atomic clusters is governed by only small deviations from discrete scale invariance.

Quantum technologies are increasingly recognized as a strategic priority for economic competitiveness, national security, and technological innovation in the United States. As quantum systems transition from research prototypes to deployable technologies, attention has shifted toward the preparedness of the quantum workforce, particularly the alignment between higher education and industry skill needs. While prior research has examined individual aspects of quantum education or workforce demand, few studies integrate systematic curriculum analysis with documented industry expectations. This study addresses that gap by analyzing primary U.S. masters programs in quantum science and technology, focusing on curriculum structure and skill development. Using a structured coding framework, course offerings were mapped across six quantum-relevant skill categories and aggregated to produce program-level skill profiles. These profiles were then compared with industry-identified competencies reported in recent workforce studies. The findings reveal strong emphasis on quantum theory across programs, alongside substantial variability in technical skills, applied learning opportunities, and professional development components. The results highlight areas of alignment as well as persistent gaps related to workforce readiness, cross-disciplinary integration, and emerging technological demands. This study provides a scalable framework for evaluating quantum education programs and offers evidence-based insights for curriculum design, workforce policy, and the continued development of the U.S. quantum ecosystem.

Advanced vision systems require retinomorphic, energy-efficient spike-based preprocessing of dynamic visual scenes. Here, we demonstrate multiple retinal preprocessing functionalities by leveraging a Hodgkin-Huxley-based optical spiking neuron (OSHN) that incorporates a two-dimensional anti-ambipolar phototransistor operated in the subthreshold regime to minimize power consumption. OSHN exhibits wavelength- and intensity-sensitive spike encoding with energy consumption per spike of 0.9 pJ under dark, 2 pJ at 480 nm (mid wavelength, M), and 24.5 pJ at 800 nm (long wavelength, L). The low (biological)-to-high spiking rate (0 - 2 kHz) with substantially faster response times (4.2 $\mu$s - 1.25 ms) than the human retina (30 ms - 60 ms), reveal OSHN's fast decision-making capability. OSHN facilitates concurrent spectral-spatial processing by emulating retinal antagonistic center-surround receptive fields (CSRFs) at a single wavelength (480 nm or 800 nm) with varying intensities, visual adaptation (at 480 nm) to prevent system saturation, and L-M cone opponency in midget ganglion cells. Finally, a CSRF-augmented spiking neural network (SNN) has been developed for camouflaged object detection, achieving 4.4%, 10.4%, and 28.4% improvements in accuracy over conventional SNN on FMNIST, COD10K, and synthetic camouflaged datasets, outperforming existing photoactive spiking architectures while enabling event-driven intelligent edge vision systems.

High precision variational calculations for helium in Hylleraas coordinates are used to obtain a combination of quantum defect expansions for the nonrelativistic energy and $1/n$ expansions for the relativistic and quantum electrodynamic (QED) corrections. The extrapolations based on direct calculations for the singlet and triplet $P$-states up to principal quantum number $n = 35$ provide ionization energies of the $1snp\;^1P_1$ and $^3P_c$ (centroid) states up to $n=102$ with accuracies better than $\pm$1 kHz. The calculated ionization energies are combined with 28 measured transition frequencies to obtain values for the ionization energy of the $1s2s\;^3S_1$ state. The final result of 1152 842 742.705(16) MHz differs from theory by $0.474\pm 0.052$ MHz, and provides a strong confirmation of the 9$\sigma$ disagreement between theory and experiment obtained previously by quantum defect extrapolation of experimental data to the series limit. An analysis of the quantum defect method is presented, and second-order mass polarization (recoil) terms are identified that vary as $1/n^2$ in lowest order. The nonrelativistic part provides a theoretical justification for the effective reduced-mass Rydberg $R_M^{(+)}$ based on the phenomenological model of a Rydberg electron scattering from a He$^+$ core. The Ritz expansion for the nonrelativistic energy is verified to an unprecedented 20-figure accuracy.

Since 1939, when Wigner published his proposal to define particles via symmetries expressed as algebraic groups, we have seen a long stream of attempts to formulate an ontology of matter based on mathematics. It has become apparent that such attempts must fail, and more particularly that we cannot derive an ontology of matter from the Standard Model on the basis of quantum field theory. We briefly recapitulate the reasons for this and demonstrate how the philosophy of physics has become scholasticised through a series of vain attempts to obtain a coherent ontology of matter. Here we propose an alternative approach in the form of an ontology of the science of physics. It draws on a framework developed by Husserl which admits not only real material entities but also the idealities which physicists make use of in their thinking. We specify the upper level of this ontology, which addresses the way in which physicists use mathematics when conducting their enquiries. This ontology provides a new perspective concerning the dilemmas of modern physics, including the measurement problem.

A classic theorem of Weyl (1921) states that a Weyl metric – a natural generalisation of a pseudo-Riemannian metric – is uniquely determined by its conformal and projective structures (i.e. by its conformal structure and its set of unparametrised geodesics). An equivalent formulation of Weyl's result is that a torsion-free linear connection compatible with a pseudo-Riemannian conformal structure is uniquely determined by its projective structure. We discuss analogous results for suitably defined notions of conformal structure for Galilei and Carroll geometry, i.e. for spacetime geometries arising as the `non-relativistic' and `ultra-relativistic' limits of Lorentzian geometry.

The event-by-event higher-order cumulants of conserved quantities such as net-baryon, net-electric charge, and net-strangeness in heavy-ion collisions have been extensively utilized in experimental searches for the QCD critical point, notably in the RHIC-STAR experiment. In this study, we conduct a systematic analysis of higher-order cumulants of proton number distributions in Au+Au collisions at center-of-mass energies of $\sqrt{s_{\rm NN}} = 3.2$, $3.5$, $3.9$, and $4.5$ GeV using the JAM model. We calculate cumulants, factorial cumulants, and their ratios using a novel method, Centrality-Independent Genuine Cumulant Analysis fRamework (CIGAR), which effectively eliminates initial volume fluctuations. We comprehensively compare the CIGAR method with the traditional Centrality Bin Width Correction (CBWC) method. In addition, the effect of spectators on cumulant is systematically investigated. Our results provide a dynamic non-critical baseline in the high-baryon-density regime which is crucial for QCD critical point searches in heavy-ion collisions.

Plasma-material interaction (PMI) at lithium conditioned plasma facing components (PFCs) depends not only on lithium inventory, but also on coating freshness, chemical state, and spatial coverage. We use LTX-$\beta$ measurements and lithium evaporator development to translate these constraints into a design basis for the NSTX-U flash lithium evaporator (f-LITER). During an LTX-$\beta$ run day with evaporations at the beginning and midpoint of the run day, discharges immediately after each evaporation exhibited higher plasma current, lower line averaged density, and shorter effective particle confinement times than later discharges with identical programmed fueling and coil currents, consistent with stronger particle pumping by the freshly conditioned wall. The subsequent relaxation is interpreted in the context of published in vacuo x-ray photoelectron spectroscopy observations of Li$_2$O growth and molecular dynamics calculations showing that deuterium reflection and retention depend on oxide thickness. To refresh this evolving interface while limiting contamination, a low thermal mass evaporator was coupled to an in vacuo liquid lithium dropper and tested on LTX-$\beta$ and in a dedicated test chamber. Quartz crystal microbalance measurements gave a 401 nm deposition in 17.4 min, equivalent to 100 nm in approximately 4.8 min of active evaporation. A qualitative temperature programmed desorption comparison showed weaker impurity desorption from a coating deposited by the evaporator loaded using the liquid dropper than from one deposited by the evaporator loaded with solid lithium. These results establish the PMI requirements for an NSTX-U f-LITER architecture based on the LTX-$\beta$ low thermal mass evaporators. The system is intended to make lithium delivery a reproducible PMI actuator for controlling recycling, impurity uptake, and PFC conditioning in double null NSTX-U operation.

Accurate decomposition of methanol maser spectra is essential for understanding high-mass star-forming regions, especially in complex blended spectra where small differences alter physical interpretation. Conventional Gaussian fitting often fails to capture non-Gaussian structure and lacks uncertainty quantification. We develop a Bayesian spectral decomposition framework using Gaussian, Lorentzian, and Voigt profiles with Markov Chain Monte Carlo sampling, enabling model comparison and uncertainty estimation. Applied to the 6.7\,GHz methanol maser G339.884$-$1.259 observed with the Ghana Radio Astronomy Observatory, our method reveals seven velocity-coherent components. The Voigt model is statistically preferred, yielding the lowest AIC and BIC ($\approx 1.98 \times 10^{4}$ and $1.99 \times 10^{4}$), the smallest RMSE ($\approx 11.1$ Jy), and the highest $R^{2}$ (0.985). Purely Gaussian or Lorentzian models leave systematic residuals. Elevated reduced $\chi^{2}_{\nu}$ values indicate unresolved substructure and non-ideal noise. Bayesian inference provides a robust framework for maser spectral analysis, extendable to other molecular lines and combinable with high-resolution interferometry.

We investigate the spin-interaction energy spectrum of spin turbulence in a driven spinor Bose-Einstein condensate. Continuous spin driving of a spin-1 condensate produces a nonequilibrium steady state with spatially fluctuating magnetization. We observe a power-law scaling consistent with the $-7/3$ exponent predicted for spin-wave turbulence, which persists across our full range of drive strengths despite substantial changes in the spectral anisotropy. After switching off the drive, we track the free-decay evolution and find evidence consistent with a direct cascade of spin-interaction energy toward higher wavenumbers. These results establish an energy-spectral hallmark of spin turbulence and enable quantitative studies of cascade dynamics in spinor superfluids.

In 2005 and 2006, we began to consider the feasibility of long-pulse (LP) and continuous-wave (CW) operation for the E-XFEL. The operation modes considered were assumed to be complementary to the short-pulse operation (SP), with ~1 ms RF pulses and a 10 Hz repetition rate, which at that time had already been chosen and presented in the TDR [1] of the E-XFEL facility. This operation mode originated from the previously proposed linear collider TESLA project [2]. We initiated several R&D programmes in 2005 and 2006 to enable operation modes with a duty factor significantly higher than that of the short-pulse mode, which is still approximately 1%. In this report, we briefly present the initiated R&D programmes and their results, with particular emphasis on the results of the E- XFEL cryomodule tests, which led to minor modifications of the design and subsequently to the implementation of these cryomodules in large-scale X-ray FEL facilities.

Layered halide perovskites host exceptionally strong excitons, whose optical signatures are usually interpreted as absorptive resonances on a smooth dielectric background. Strong excitons, however, can also reshape the dielectric response itself and drive the real permittivity negative, opening a reflective band: the visible, excitonic analogue of an infrared Reststrahlen band. Whether bare (PEA)$_2$PbI$_4$ slabs reach this regime has remained unclear. Here we show that low-temperature transmission of (PEA)$_2$PbI$_4$ slabs, driven by the intralayer-exciton manifold, evolves with increasing thickness from an excitonic dip into a broad near-zero-transmission interval with compressed Fabry-Pérot-like fringes. Transfer-matrix analysis with an effective Lorentz-oscillator dielectric response reproduces this crossover, reconstructs a finite negative-Re($\varepsilon$) window, and implies near-ultrastrong exciton-photon coupling. Calculated field maps show suppressed in-plane field penetration within this interval and a driven longitudinal response near the high-energy ($\varepsilon=0$) edge. These results identify (PEA)$_2$PbI$_4$ slabs as a cavity-free visible-frequency excitonic Reststrahlen material.

Quartz tuning forks, functioning as electrically driven piezoelectric resonators, have long served as exceptionally stable and widely adopted timing references in diverse domains of research and industry. Yet, experimentally measured electrical resonance spectra often exhibit resonance evolutions that remain unexplained within existing theoretical descriptions. Here we develop a unified continuum electromechanical modal framework that integrates piezoelectric electrodynamics, variational structural dynamics, and symmetry-selected electromechanical observability. Our theory shows quantitative agreement with experimental results, demonstrating that electrical observability emerges not from the underlying mechanical eigenmodes alone. The resulting framework unifies conventional coupled-oscillator, equivalent-circuit, and continuum descriptions within a single first-principles theory and provides a rigorous basis for precision electromechanical characterization.

Curved beams, that is, beams that are able to propagate on nonlinear trajectories, are often envisioned as ideal candidates for blockage avoidance in future wireless connectivity. Owing to this unique feature, they are considered as ideal beams for bending around and behind corners to reach users beyond the line-of-sight (LoS), thus offering unprecedented connectivity. In this work, we explain the various mechanisms of beam propagation beyond the LoS, and we demonstrate that beam bending behind corners results from an interplay between wavefront engineering and edge diffraction, with distinct characteristics that depend on the extent of blockage and the beam formation efficiency. We identify three distinct regimes of operation, namely the unblocked, the partially blocked, and the fully blocked regime, and we show that beam bending through wavefront engineering dominates in the unblocked and partially blocked regimes, while edge diffraction dominates in the fully blocked regime; as a result, curved beams cannot really bend behind the corner, unless there is some LoS between the user and the transmitter. Based on our findings, we compare curved beams with focused beams, and we demonstrate that they perform similarly in the partially blocked regime, while focused beams outperform curved beams in the unblocked and fully blocked regimes.

Transition metal complexes are central to catalysis, drug design, and materials science, with relevant properties strongly sensitive to their three-dimensional geometry. However, the electronic diversity and unconventional bonding environments of transition metal complexes pose a major challenge for accurate structure generation. In this work, we introduce TMCgen, a manifold diffusion machine learning model that efficiently and accurately generates geometries of transition metal complexes. By formulating the diffusion process over the metal-ligand coordination angles, combined with torsional and rotational diffusion of the ligands, TMCgen focuses on the key geometric degrees of freedom of transition metal complexes. TMCgen shows strong performance in generating accurate coordination environments on a diverse set of experimentally derived bioinorganic and organometallic complexes while requiring only few inference steps, enabling efficient generation. Our results demonstrate the potential of manifold-based generative modeling for data-efficient geometry generation, paving the way for property-conditioned design of transition metal complexes.

Sliding ferroelectricity in van der Waals (vdW) layered systems has emerged as a promising route toward non-volatile nanoscale devices, where interlayer displacement in non-centrosymmetric bilayers generates an out-of-plane polarization. In particular, 3R-stacked bilayer transition metal dichalcogenides (TMDs) grown via chemical vapor deposition (CVD) have been shown to host such polarization due to broken inversion symmetry. However, a detailed investigation of the 2D ferroelectric (FE) properties of CVD-grown 2D films, particularly the role of intrinsic disorder, such as structural defects and domain structure, remains poorly understood. Here, we investigate the FE switching characteristics of CVD-grown 3R-stacked WSe2 using a graphene-based ferroelectric field-effect transistor (graphene-FE-FET) architecture, where graphene serves as a highly sensitive probe of induced charge modulation due to polarization switching of FEs. We show that the growth-induced structural disorder significantly impacts polarization switching, while multi-domain kinetics governs the evolution of the FE response. These findings provide important insights into the design and optimization of FE devices based on vdW materials.