Papers

Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be \(10^8\) to \(10^{10}\) times fainter than their host stars and lie at diffraction-limited angular separations, where starlight strongly obscures the companion signal. Here we present an adaptive quantum measurement method for estimating the number, positions, and brightnesses of mutually incoherent point sources in the sub-Rayleigh, ultra-high-contrast regime, operating at contrasts down to \(10^{-8}\) – five orders of magnitude beyond previous quantum imaging approaches to exoplanet detection. The method adopts a spatial-mode basis that is updated to maximize the quantum Fisher information per detected photon. Estimation is performed by maximum likelihood in log-brightness coordinates, and the source count is determined by Bayesian-information-criterion (BIC) model selection directly from photon-count statistics, without a tunable detection threshold. For point sources within sub-Rayleigh separations and with brightness ratios spanning eight orders of magnitude, the method reconstructs complete scenes with a mean success rate of \(72.5\%\). Furthermore, it is robust to misalignment, maintaining a \(71.3\%\) success rate under offsets of up to six pixels. These results demonstrate that terrestrial exoplanets can be detected below the Rayleigh limit, a regime previously inaccessible to direct imaging.

We present a variational framework in which (i) guiding-center kinetics, (ii) macroscopic force balance with gyrotropic pressure, and (iii) quasisymmetry (QS) appear as facets of a single structure. Starting from a guiding-center Vlasov–Maxwell action, constrained variations yields the guiding-center kinetic equation and Maxwell equations. Without phenomenological closure, momentum conservation yields $\mathbf J \times \mathbf{B}/c = \nabla \cdot \Pi$. This framework recovers the classical result that, for any equilibrium distribution built on the invariants of the reduced motion, the parallel projection of this balance holds identically, so that pressure anisotropy is a kinematic consequence of magnetic-moment conservation together with the variation of $B$ along field lines, rather than a modeling choice. We connect QS to an integrability condition expressed in coordinate-free form. We then derive an explicit expression for the current crossing the flux surfaces. Its flux-surface average vanishes identically in a QS field, but it vanishes pointwise only if $p_\parallel+p_\perp$ is a flux function. Combined with the parallel identity this forces $p_\parallel = p(\psi) + K(\psi)B^2$ and $p_\perp = p(\psi) - K(\psi)B^2$, consistent with the anisotropic equilibria obtained from a constrained Kruskal–Kulsrud functional, in which $(p_\parallel+p_\perp)/2$ is a flux function [Phys. Rev. E 104, 015213 (2021)]. The structure arises from the kinetic moments without a Lagrange multiplier. For such equilibria the parallel current in a QS field follows in closed form, with a single resonance at $\iota = N/M$ in place of the dense set of resonant obstructions of a generic three-dimensional field. Finally, QS is a Noether symmetry, so its breaking is measured by the non-conservation of the associated momentum. We construct a distribution-weighted residual and relate it to existing QS metrics.

We study the atom-atom correlation length in an atomic array coupled to a waveguide under the Bragg condition with delayed non-Markovian interactions caused by a finite photon propagation time. Starting from a single excited atom, the excitation partially spreads among all atoms, reaching a steady state. The remaining excitation localizes near the initially excited atom, and the atom-atom correlation length decreases as a power law with the interaction delay. This localization phenomenon reveals how the delay-induced non-Markovian behavior affects the correlation transport in waveguide QED systems.

Fluid injection into the earth's crust can induce small and frequent earthquakes in the subsurface. Predicting their sizes and temporal occurrences via statistical analysis is crucial for safe operations in unconventional oil and gas recovery, enhanced geothermal systems, and geologic carbon storage. Here we show that a simple micromechanical model of slip avalanches in slowly deforming solids predicts the slip statistics observed over drastically different spatial scales, namely meter-scale microseismic observations and nanometer- to micrometer-scale nanoindentation experiments can be described with this model. Microseismic catalogs extracted from high-pressure fluid injection operations into geological basins with various lithologies and nanoindentation experiments on shale across a wide range of temperatures and mineral compositions yield statistics consistent with model predictions. This universality across materials, temperatures, and scales is consistent with the prediction that the slip statistics result from only a few basic properties. Previously debated deviations of the statistics in layered sedimentary formations are explained by finite-size and stress-integrative effects resulting from mechanically weak bedding planes. The slip statistics therefore provide important information about the structure and scales of the bedding planes. Conversely, the basin structure can also be used to predict the probability distribution for the sizes of triggered microseismic events.

Unsteady thin-airfoil theory (UTAT) coupled with discrete-vortex methods has been widely employed in reduced-order aerodynamic modeling. Due to the non-uniqueness of potential-flow solutions, the Kutta condition is imposed to determine the circulation around the airfoil. Although the unsteady Kutta condition is commonly associated with the zero-loading condition at the trailing edge, its implications for the continuity of the vortex-sheet strength remain comparatively underexplored. In particular, the classical series expansion employed for the bound vorticity in unsteady thin-airfoil theory is not uniformly convergent at the trailing edge, leading to mathematical inconsistencies in the vortex-sheet and pressure distributions. In this context, the present work seeks to advance the mathematical framework of unsteady thin-airfoil theory through a physically consistent formulation of the bound vortex-sheet strength for the Wagner problem. A recurrence relation is derived for the Wagner coefficients, allowing the construction of a uniformly convergent series expansion for the bound vorticity. The proposed formulation ensures continuity between the bound and wake vortex sheets while simultaneously recovering zero pressure loading at the trailing edge, thereby providing a mathematically consistent representation of the unsteady Kutta condition. To investigate the implications of the modified framework, a discrete-vortex method based on UTAT is developed and compared with the classical formulation. The results demonstrate that the proposed approach eliminates spurious oscillatory behavior near the trailing edge and significantly improves the regularity of the computed vorticity and pressure distributions.

Hollow-core fiber (HCF) is often presented as a modestly improved transmission medium that can be inserted into networks originally designed for solid-core silica. We argue instead that recent progress – most notably the reported attenuation below 0.1 dBkm$^{-1}$, together with a broad low-loss window, reduced propagation delay, and extremely low optical nonlinearity – makes it timely to reconsider which long-standing design conventions are fundamental to optical communication and which are specific to silica fiber. By reviewing implications at the physical-layer, transceiver, and network-architecture levels, we suggest that the most durable benefits of HCF may arise not from its use as a drop-in replacement, but from cross-layer co-design. We also outline the studies and experimental demonstrations needed to determine where such advantages are genuinely achievable.

Companies developing some of the world's most powerful artificial intelligence systems are surprisingly diligent in how they map out the risks their technologies present. Yet the risk landscape that lies between emerging frontier models and their economically successful and societally beneficial deployment is becoming increasingly hard to navigate. Complicating this further, many frontier AI companies maintain more than one account of what could go wrong with their technologies. This paper documents the divergence between these accounts by comparing safety and compliance documents published by Anthropic, OpenAI, Google DeepMind and Meta between 2023 and 2026, and considers what the resulting record reveals about how these companies select the risks they manage. As these documents are timestamped and archived, they provide a valuable public record of institutional risk selection in progress. From this record the paper identifies four filters that determine which risks tend to survive in self-authored frameworks (measurability, severity, auditability and competitive cost) and introduces the "safety differential" as the gap between the risk landscape a company selects for itself, and the one regulators select for it. While acute, quantifiable risks appear across documents, less tractable risks such as harmful manipulation are articulated fluently where law compels disclosure, yet remain absent from most self-chosen frameworks. This is an exclusion that follows from how these institutions define risk. Drawing on scholarship on institutional risk selection and the framework of risk innovation, the paper shows how redefining risk as a threat to value can help explain how risks become "orphan risks," how it indicates where future blindsides may occur, and how it points to lightweight tools for de-orphaning risks that frontier AI's safety apparatuses are not currently organized to address.

We consider observables $X$ whose realizations in a sampled dataset are restricted, for example by measurement resolution, to a finite set of distinguishable categories within their possibly infinite theoretical domain. Given the probabilities of observable categories, we study the coarse-grained probability mass of families of possible datasets generated through a forward sampling process. The combinatorial construction induces an intrinsically discrete $p$-value defined directly from the sampling process rather than through additional probabilistic structure on observables. Specifying the sample means of $d+k$ arbitrary functions $g_\alpha(X)$ defines a linear family of datasets whose probability mass is obtained as a weighted sum over integer lattice points contained within the associated polyhedron. To overcome the intractable large-$N$ combinatorics, we derive via saddle-point techniques a density approximating these probability masses in the continuum limit of forward sampling within the multinomial universality class. As a demonstration, we consider conditional sampling, where $d$ structural means are fixed while $k$ means vary over admissible datasets. The information geometry emerging from the saddle-point density, together with the spherical symmetry arising at large $N$ from the intrinsic $p$-value construction, enables efficient computation of the $p$-value in the Laplace approximation via the $\chi^2_k$ distribution. The resulting statistic is given by the semi-analytic expression $2N$ times the Kullback-Leibler divergence between the information projections associated with the corresponding structural and observed linear families. These projections can be computed efficiently via standard numerical routines converging for sufficiently well-behaved sample means.

No single AI weather model excels at all variables, pressure levels, and lead times. Rather than building yet another architecture, we reframe the forecasting problem as one of coordination. Here we present Feitian Adaptive Ensemble Weather (FTAE-Weather), a lightweight framework that learns, through deep reinforcement learning, when and where to trust each member of an open pool of pretrained forecasters. A tactical Weight-Agent reads the current atmospheric state and assigns variable- and horizon-specific fusion weights, while a strategic Evolve-Agent periodically prunes underperforming models and absorbs newly released ones. Asynchronous prediction caching keeps training cost independent of the slowest constituent model. Adding fewer than 0.01 percent extra parameters, FTAE-Weather reduces RMSE by from 17.2 percent to 78.3 percent over the best individual model in 10 atmospheric variables and outperforms conventional ensemble baselines across lead times from 72 to 360 hours. The framework thus converts a growing, fragmented inventory of specialist models into a single prediction system that strengthens as the field of AI weather forecasting releases new architectures-turning model diversity from a coordination challenge into a compounding scientific advantage.

Control signals and kinematics in batoid swimming are difficult to measure experimentally, making body-fluid interaction models essential for studying their underlying locomotion principles. To address this challenge, we developed a body-fluid interaction model of batoid-type swimming that is appropriate for both neural control study and engineering design. The body trunk is modeled as a rigid body with six degrees of freedom. The flexible pectoral fins attached to the trunk are modeled by a tensegrity structure consisting of rigid struts and elastic cables that resembles a biological musculoskeletal system. The fin is actuated by changing the tension of elastic cables distributed across the fin surface, enabling controllable and realistic deformation. Utilizing an analytical fluid force model, the body-fluid interaction model is exercised through simulation examples that respectively investigate the speed difference between tension actuation and fin kinematic waves, the effects of fin stiffness and resonance exploitation on swimming performance, and the different fin kinematics resulting from different body inertial motions.

Weak magnetic fields influence a wide range of biological processes, yet the underlying mechanisms are poorly understood. The radical pair mechanism (RPM), which involves quantum spin dynamics, is a leading hypothesis. Here we show that weak magnetic fields modulate morphological pigmentation – specifically, the number of perioptic melanophores – in Xenopus laevis tadpoles in a field-strength-dependent manner. The response is light- and eye-dependent. The observed field-strength dependence is quantitatively consistent with a radical pair model. These properties are reminiscent of the light-dependent magnetoreception that is thought to operate in migratory birds, and establish amphibian pigmentation as a tractable vertebrate system for the study of radical-pair quantum biology.

Multilayer cloud detection from active–passive observation is vital for numerical weather prediction. In this study, channel selections derived from threshold-based algorithms are embedded as feature-engineering priors into a 1D-CNN, and machine learning (ML) is used to learn latent physical relationships to simplify physical retrievals for operational deployment. The results show that the 1D-CNN achieves a multilayer-cloud probability of detection ($\mathrm{POD}{\mathrm{mul}}$) of 0.620 and a false alarm rate ($\mathrm{FAR}{\mathrm{mul}}$) of 0.240, outperforming the conventional threshold algorithm ($\mathrm{POD}{\mathrm{mul}} = 0.558$, $\mathrm{FAR}{\mathrm{mul}} = 0.369$). These results demonstrate that prior physical knowledge derived from radiative transfer theory can serve as an effective feature-engineering prior. Further experiments show that ML-revealed physical mechanisms can also enhance traditional algorithms. Replacing AGRI channel 12 (C12, centered at $10.8~\mu\mathrm{m}$) with channel 13 (C13, centered at $12.0~\mu\mathrm{m}$) increased $\mathrm{POD}{\mathrm{mul}}$ from 0.558 to 0.609 without materially affecting $\mathrm{FAR}{\mathrm{mul}}$. However, for AHI, substituting the $11.2~\mu\mathrm{m}$ channel with the $12.3~\mu\mathrm{m}$ channel yielded negligible improvement. In addition to spectral response function (SRF) mismatches, a primary contributing factor is the channels' on-orbit radiometric stability. Hence, physics-informed machine-learning methods appear promising for advancing remote-sensing AI, while sensor-specific characteristics must be considered during operational transfer.

Nonlocal quantum field theories achieve perturbative ultraviolet finiteness by inserting gauge- and diffeomorphism-covariant entire-function regulators into all kinetic terms. These operators can be viewed as nonlocal smearing maps acting on sources in the Einstein equations. In this paper I show that, when the same entire-function regulator responsible for UV-finite loops is applied to the energy-momentum tensor of a point mass, the resulting static, spherically symmetric solution develops a regular de~Sitter core in place of a curvature singularity.

The bibliography of scientific papers lists items with variable degree of relevance for the contents of the paper itself. If we could identify the sources, i.e., the works that actually inspired the paper, their citations can help us uncover the genesis of scientific projects and would be more representative of the actual importance of papers and authors than the standard citation counts, when all references are considered. Here we present an analysis of the backbone of science, i.e., the network of citations between papers and their sources. The latter are extracted from the full body of papers via Large Language Models (LLMs), which are currently very capable of correctly identifying the context in which a paper is cited. Using two different but related prompts, we find that the LLMs select only a small set of references, not taken at random, and that the resulting backbone networks are quite similar to each other with respect to their in-degree distributions, modularity, transitivity, and degree correlations. Backbone networks have higher heterogeneity in their in-degree distributions, compared to the full network, but the most cited papers are usually the same, with some important exceptions. Citation rankings among authors are also remarkably stable. We conclude that the full citation network, despite its redundancy with respect to the backbones, presents a reliable picture of the relative citation impact of papers and authors.

Highly successful drugs, such as transition-state inhibitors, were designed by mimicking the precise geometry of enzymatic transition states, a strategy that requires quantum chemistry accurate enough to specify molecular structure at sub-Å\ resolution. A central challenge during such a quantum chemistry simulation is that classical single-reference method can deviate substantially from the exact energy at strongly correlated transition states. We study the breakdown due to multireference character by studying a model system with a multireference transition state. In this work we present a proprietary PexMachina solver which reproduces the exact ground-state energy throughout the transition state scan, while single-reference methods struggle for certain geometries to achieve chemical accuracy.

Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability. In practical aerodynamic design, optimization settings such as editable regions, deformation ranges, and design-preservation constraints are typically specified manually by experienced engineers, while surrogate-based optimization may become unreliable for heterogeneous geometry databases and out-of-distribution designs. To address these challenges, we propose a knowledge-constrained shape-optimization framework that translates knowledge-based constraints and user intent into quantifiable parameters of DFFD-based deformation operators, enabling engineering-aware and controllable constrained optimization. We further develop a Mixture-of-Experts Neural Operator (MoE-NO) to improve drag prediction and trend consistency over heterogeneous aerodynamic datasets. Based on the MoE-NO encoder and Mahalanobis distance, an uncertainty-estimation strategy is introduced to detect out-of-distribution geometries and selectively trigger physics-solver feedback for local sample enrichment. Experiments on in-house MPV, SUV, and Sedan datasets show that MoE-NO achieves a test-set MAPE of $1.16\%$ and a trend-prediction accuracy of $94.34\%$, outperforming the best baseline results of $1.52\%$ and $90.34\%$, respectively. Vehicle shape-optimization experiments further yield CFD-validated drag coefficient reductions of approximately $4\%$ to $10\%$.

Variational Gaussian wave-packet dynamics is usually obtained by restricting quantum evolution to Gaussian wave packets. We instead construct finite-$N$ dynamics for a labeled bundle of classical trajectories under a constraint fixing the symplectic-area scale of its covariance. At finite $N$, the bundle itself is the dynamical state: it may be non-Gaussian, and the potential force on each trajectory is evaluated from the full potential without a local harmonic approximation. For sequences whose initial empirical distributions approach a smooth Gaussian density as $N \to \infty$, the limiting relative motion becomes linear and preserves Gaussianity. With the area scale set to $\hbar/2$, the limiting centroid and width equations coincide with those of the Gaussian time-dependent variational principle, and the limiting phase-space density coincides with the corresponding Gaussian Wigner density for matched initial data. Here $N$ counts trajectories, not orders in a Moyal or $\hbar$ expansion. This phase-space correspondence reveals two complementary constructions of the same Gaussian dynamics: a variational reduction of quantum evolution and the Gaussian large-$N$ limit of a constrained classical trajectory bundle.

A symmetric quartic potential is a physics model with expansive applications, ranging from broadband energy harvesters, quantum tunneling in molecules and the early universe, to torque-free spacecraft rotation. Its rich dynamics have been traditionally classified into regimes and expressed as disjointed time-domain solutions. Here we build a taxonomy for this broad class of motions and discover that their regimes exhibit a symmetrical spectral structure: they share a fundamental clock, obey parity selection, and dissolve into the separatrix through a discrete-to-continuum transition. Applied to the famous Dzhanibekov effect where a rotating body periodically undergoes rapid 180-degree flips in its attitude, the taxonomy reveals its spectral anatomy. The three principal-axis rotations share a common clock while occupying distinct parity channels, with stable-axis branches exchanging DC bias across the separatrix. We discuss a case study where the three spectral pillars: clock, parity, and continuum, survive the Wick rotation from real-time into imaginary-time kinematics.

Purpose: To compare detuning performance and evaluate the power requirements of optical detuning methods, and to demonstrate the feasibility of an optically detuned four-channel receive array. Methods: Four optical detuning methods were compared in simulations, bench tests, and phantom measurements at 3T against conventional galvanic detuning. Passive detuning was also tested as an additional wireless detuning option. Optical power requirements for the detuning networks were investigated, and a flexible, optically detuned 4-channel shielded-loop resonator (SLR) array was constructed and tested in vivo. Results: A photodiode-PIN diode combination exhibited the highest unloaded Q (68.6) and Q ratio (1.9), with detuning performance and signal-to-noise ratio comparable to that of galvanic detuning at an optical power of 10 mW. Using this detuning strategy, in vivo images of the knee and brain were successfully acquired with a 4-channel flexible array. Conclusion: Optical detuning is a practical alternative to conventional galvanic detuning in flexible SLR arrays. With advances in optical signal and power transmission, optimizing optical detuning while meeting manageable power requirements is an important step toward fully optical receive-coil arrays. This study provides a baseline for the total optical power required for active detuning in such optical coil systems.

Plasma-based mineral-processing routes, such as hydrogen plasma smelting reduction (HPSR), which converts iron-ore fines directly to liquid metal in a single scalable step are commonly modeled by treating the arc as a spatially uniform heat source. Yet the reduction chemistry is governed by the strongly non-uniform conditions at the plasma-melt interface, which spatially averaged diagnostics cannot resolve. Here we spatially and temporally resolve the arc of a transferred arc HPSR reactor using multi-species optical emission spectroscopy (OES), in which neutral and ionic argon (Ar I, Ar II), hydrogen Balmer, and neutral iron (Fe I) emissions serve as intrinsic spatial filters set by their differing ionization thresholds. Combined with infrared thermography of the melt surface and an LTE thermal-plasma model validated against the benchmark free-burning argon arc, the measurements reveal a strongly stratified, non-isothermal discharge: an argon-defined core (>10,000 K), a partially recombined Balmer envelope (7,000-10,000 K), and an Fe I-traced interfacial boundary layer (3,000-4,000 K) directly above a melt surface at ~1,900-2,300 K. Across this steep thermal drop, positive hydrogen ions recombine before reaching the surface, so the reductant flux delivered to the oxide is overwhelmingly neutral; atomic hydrogen (H) and vibrationally excited molecular hydrogen H2(v), rather than the energetic ions often assumed. The measured electron density and excitation temperature bound the interfacial ionization. These findings redefine the boundary conditions for kinetic modeling of plasma-based ore reduction and establish a spatially resolved multi-species diagnostic framework transferable across plasma mineral-processing systems.

Biological and physical systems ranging from Fermi surfaces and skeletal structures to reaction–diffusion patterns and cosmological models may be viewed as binary mixtures in which a smooth interface separates two complementary phases. While the interface is often directly observable, the topology of one of the phases may remain hidden. To study such systems, we introduce tubular tilings, a geometric framework for discretizing binary mixtures on smooth manifolds of arbitrary dimension and topology. We prove that tubular tilings satisfy global Euler balance laws relating the topology of the ambient manifold, the discretized phases, and their interfaces. These balance laws provide a practical inference principle: topological information about a hidden phase can be recovered from the observable phase and the geometry of the separating interface. We further show that, in dimensions $d>2$, tubular tilings form a subclass of soft tilings, the recently discovered class of corner-free tessellations. Applications to Fermi surfaces and cosmological shell decompositions illustrate how the theory can be used to extract otherwise inaccessible topological information about complex geometric structures.

AstroForge seeks to mine platinum group metals (PGM) from asteroids. Asteroid reserves appear to be unlimited, and at current market price the gross margin of asteroid mining would be very high. It is natural to ask: when AstroForge successfully demonstrates economic space mining of PGM, will they cause the PGM market to collapse? We answer the question with a non-steady system dynamics model of the PGM market. We find that the market price for PGM will eventually drop towards the much lower cost of asteroid mining, but only after the entire supply has shifted off-world. In the meanwhile, huge fortunes will be made. And everybody on Earth will benefit from new applications of lower-price PGM.

We demonstrate how the unified framework for binary-choice dynamics can be used to study the role of annealed and quenched disorders in homogeneous and heterogeneous systems. The framework defines the structure of interactions between agents without imposing their functional forms. Such a high level of generality allows us to connect many different models across disciplines and find universal rules that apply to all of them. Within this framework, agents update their states under the influence of two competing mechanisms chosen according to individual preferences. We review the literature to classify existing models as homogeneous or heterogeneous based on their preference distribution, and we discuss the role of annealed (changing) and quenched (fixed) disorders in modeling these preferences. Using the framework, we derive a constraint on the transition rates. When a model meets this condition, three major things happen: annealed and quenched dynamics become equivalent, any heterogeneous system can be mapped into a homogeneous one, and oscillations cannot emerge. We illustrate these consequences using models from statistical physics, opinion dynamics, and disease spreading. Finally, we discuss the framework limitations and its potential further developments.

Drying of nanowire-laden elongated droplets is a ubiquitous process in printed electronics fabrication, where the resulting deposition pattern critically determines device performance by controlling nanowire alignment, connectivity, and percolating charge-transport pathways. However, the physical understanding of evaporation-driven deposition is still largely derived from studies of spherical droplets on homogeneous substrates. This gap limits the ability to predict and control deposit morphology in realistic printing scenarios. Here, we use mesoscale lattice Boltzmann simulations to investigate the drying of nanowire-laden elongated droplets on wettability-patterned substrates, focusing on the effects of droplet geometry, nanowire interactions, and nanowire length. The elongated droplet geometry is found to intrinsically induce distinct axial and transverse inhomogeneities in the final deposit. Increasing the effective attraction between nanowires, which mimics changes in surface chemistry or solvent conditions, can improve electrical connectivity but also promotes clustering and local ordering, reducing structural uniformity. In contrast, increasing nanowire length yields a dual benefit by improving long-range connectivity while simultaneously enhancing deposit homogeneity. Our findings provide design guidance for balancing electrical transport and structural uniformity in evaporation-driven printed electronics.

We demonstrate two Doppler-free (DF) excitation schemes for coherent manipulation of the ${}^1\!S_0 - {}^3\!P_0$ clock transition in $^{88}$Sr that achieve microsecond-scale excitation times. The first approach uses three-photon excitation with distinct phase-coherent spectral components to couple the ground and clock states while canceling the first-order Doppler shift. The second approach is a sequential protocol that combines a single-photon excitation with a two-photon Raman excitation, reducing coherent clock transition manipulation times to below a microsecond. With both methods, we perform high-contrast Ramsey spectroscopy on thermal ensembles of $3 \times 10^{6}$ atoms in free space. We observe three orders of magnitude suppression of Doppler dephasing compared to single-photon excitation, relaxing the need for tight confinement or ultra-low temperatures. These broadly applicable techniques enable fast, coherent manipulation of narrow-line transitions, with implications for optical atomic clocks, matter-wave interferometers, quantum-enhanced metrology, and quantum information processing.

The low-frequency dielectric response of liquid water is commonly described by a dominant Debye relaxation together with additional faster contributions whose microscopic origin remains debated. Here we show that the dielectric function of water between 0.14 and 1.21 THz can be represented by a collective Debye relaxation plus a Drude-Smith term constrained to the zero-dc-conductivity limit. The Drude-Smith spectral weight increases upon heating pure H2O from 20 C to 50 C and decreases upon isotopic substitution (D2O at 20 C vs. H2O at 20 C). Molecular dynamics simulations including nuclear quantum effects show correlated changes in the population of water molecules with unequal numbers of donated and accepted hydrogen-bonds. Ab-initio-based spectra calculations further indicate that the ~0.1-1 THz response contains both nuclear-motion and explicit electronic-polarisation/charge-redistribution contributions. We therefore interpret the excess low-frequency THz response as a localised, mixed nuclear-electronic dielectric response correlated with transient donor-acceptor imbalance in the hydrogen-bond network.

Precise measurement of the beam sigma matrix is essential for matching the optics in transport lines and ensuring reliable accelerator operation. In this work, we present a method for measuring and reconstructing the non-temporal five-dimensional beam sigma matrix using quadrupole scans performed in a dispersive transport region. The proposed approach enables characterization of the beam moments using only quadrupoles and beam transverse profile diagnostics, without requiring longitudinal diagnostics or a dedicated beamline section. To achieve robust and computationally efficient reconstruction, we formulate the problem within a differentiable simulation framework, allowing direct gradient-based optimization of the initial beam covariance matrix. We demonstrate the method experimentally in the Booster-to-Storage-ring (BTS) transport line at the Advanced Photon Source (APS), where it produces consistent reconstructions of the beam sigma matrix from measurements. We further show that the framework is flexible with respect to the number and placement of diagnostic screens, making it applicable to a broad range of existing transport-line configurations. These results establish the proposed 5D beam sigma matrix reconstruction method as a practical and broadly deployable approach for fast, efficient beam characterization during accelerator operation.

This work presents a compact RF MEMS switching platform for automated one-port SOL calibration of microwave lines. The system is based on a Menlo Micro MM5230 SP4T RF MEMS switch integrated on a grounded coplanar waveguide board and driven by a dedicated MM101-based high-voltage control circuit. The board design is presented, and the repeatability of the calibrated reflection coefficient is assessed through the covariance matrix evaluated from 100 repeated SOL calibration measurements. The platform is tested from 100~kHz to 20~GHz with a Rohde & Schwarz ZNB43 VNA and the performance compared with an electromechanical microwave switch. The RF MEMS fixture shows lower dispersion of the calibrated reflection coefficient and improved temporal stability, demonstrating its suitability for repeatable automated microwave calibration in measurement environments where compactness, low mass, thermal constraints, and reduced power dissipation are key requirements.

All published models of ultraviolet (UV) inactivation of airborne viruses in saliva droplets have neglected UV light scattering. To the best of our knowledge, this work presents the first Computational Fluid Dynamics-Discrete Dipole Approximation (CFD-DDA) platform for investigating the physical mechanisms governing UV disinfection of virus-laden airborne saliva droplets. The DDA solver predicts UV light scattering by both spherical and irregularly shaped saliva droplets, while the CFD solver predicts droplet evaporation and transport in airflow. By coupling the DDA and CFD solvers, we demonstrate that infected saliva droplets, whether spherical or irregularly shaped due to evaporation, experience highly non-uniform UV light scattering that significantly affects virus inactivation and cannot be neglected. This phenomenon has not previously been investigated within a fully three-dimensional framework. The coupled Euler-Lagrange CFD-DDA model further quantifies the effects of (i) the initial droplet size distribution and concentration, (ii) airflow rate, and (iii) droplet interactions with the surrounding airflow and bounding walls on the total number of surviving coronavirus copies $N_s$, assuming a virion diameter of 100 nm, an air temperature of 21 $^{\circ}$C, and a relative humidity of 65%. Based on the DDA results, a new virus inactivation model, referred to as the Dbouk-Yurkin law, is proposed. This model extends the classical Chick-Watson law by explicitly accounting for UV light scattering in both spherical and non-spherical airborne saliva droplets. The proposed three-dimensional CFD-DDA platform provides a powerful framework for improving the understanding of UV-based airborne virus disinfection and for optimizing the design and performance of UV air purification systems.

Density functional theory (DFT) often is the method of choice for simulating the electronic properties of extended solids and surfaces from first principles due to a favorable compromise between accuracy and computational cost. In the field of heterogeneous catalysis, DFT is indispensable for deriving mechanistic insights and understanding trends in surface reactivity. The accuracy of DFT for surface reaction energetics depends strongly on the exchange-correlation (XC) approximation. We show here that optimization of such XC functionals for surface binding energies can lead to a worse description of surface reaction barriers, unless important physical exact conditions are fulfilled.