Papers

X-ray fluorescence (XRF) enables element-specific, nondestructive imaging, but conventional raster scanning scales poorly with sample size, particularly for tomography, because measurements must be repeated at every projection angle and spatial position. We demonstrate direct volumetric XRF ghost tomography, which replaces point-by-point acquisition with compressive structured illumination and multiplexed fluorescence detection. Rather than reconstructing projections at each angle and then applying standard tomographic reconstruction, we recover the three-dimensional elemental distribution by solving a single inverse problem that jointly incorporates measurements from all angles. For a volume of 2.8 million voxels, we reconstruct the elemental distribution from only 400 measurements per angle, achieving a 43X reduction relative to raster scanning while maintaining spatial resolution and contrast. By exploiting sparsity directly in the volumetric domain, this approach enables scalable, multi-element XRF tomography of large and heterogeneous samples under stringent acquisition time constraints.

Left-moving (LM) supercells, characterized by anticyclonically rotating updrafts in the Northern Hemisphere, are significant due to their propensity to produce large hail. Although less common than right-moving supercells, they present notable forecasting challenges and societal impacts. However, despite these impacts, the environments of LM supercells are poorly understood compared to their right-moving counterparts. To address this gap, this research focuses on enhancing the understanding of LM supercells by examining the environmental conditions conducive to their development. A manually compiled and quality-controlled dataset of over 850 LM supercell cases across North America is used to provide a robust sample. Near-storm environments are characterized through the use of RAP/RUC inflow proximity sounding profiles. Leveraging storm properties, including mesoanticyclone strength, hail size, wind speed, and duration, we investigate whether environments can differentiate between these varying strengths and categories, thereby enhancing forecaster awareness. Results show that LMs typically form in environments supportive of right movers, with a key difference being that LMs likely only realize the shape of the hodograph above their LCLs. Lapse rates, CAPE, and LCL height are the best predictors of LM strength and hail potential. LMs with wind reports have drier boundary layer moisture, steeper 0–3 km lapse rates, larger CAPE, and higher LCL heights, leading to increased evaporational cooling. Longer-lived LMs often have weaker CAPE and stronger shear as compared to shorter-lived LMs. These results establish a unique parameter space climatology of LM supercells, thus providing essential forecasting insight and reducing the research gap for these storms.

Classical reaction dynamics suggests transport through an index-1 saddle is organized not just by flux, but by local symplectic width scales of bounded proxy neighborhoods near the bottleneck. We investigate if a related geometric effect appears in the quantum regime for highly squeezed Gaussian wavepackets. Building on de Gosson's symplectic approach, we analyze how transverse bath-mode squeezing modifies transmission across a quantum normal-form (QNF) bottleneck. To avoid the instability of propagating states with extreme phase-space eccentricity, we use the Weyl-symbol formulation of the QNF. For the quadratic saddle-center model, we derive an exact baseline transmission formula by convolving the bath's squeezed-state number distribution with the 1D Kemble transmission factor. For anharmonic truncated QNF models, we enforce strict algebraic energy conservation and evaluate exact Gaussian expectation-value diagnostics of the Weyl symbol via Wick-Isserlis moment formulas. Results reveal a pronounced squeeze-induced suppression of transmission. As the squeezed state's bath-plane geometric scale grows relative to the classical candidate width, the expected bath action grows rapidly. Consequently, effective reactive energy is strongly depleted, driving transmission into a severely suppressed regime. We interpret this as evidence of a quantum geometric suppression mechanism consistent with the classical candidate symplectic-width picture. While not yet a rigorous quantum non-squeezing theorem, this work provides a concrete framework linking squeezed-state covariance geometry, normal-form action scales, and mode-specific quantum reactivity near an index-1 saddle.

In traditional phase-field modeling of multiphase materials, a significant challenge arises from the non-local nature of fracture energy regularization, where interfacial toughness is inherently coupled with the properties of the surrounding bulk phases. Achieving consistency with prescribed material properties typically necessitates complex corrections and exceptionally fine local mesh refinement near the interfaces. To address this fundamental issue, we leverage the capacity of the recently proposed $\Omega^2$-model to manifest Dirac-like damage concentration and emergent displacement discontinuities, while introducing an analytical, strongly localized interfacial source term $q_{\phi}$ into the phase-field formulation. It should be emphasized that the “sharp" nature of the proposed model manifests as a naturally emergent strong discontinuity within a continuum framework, fundamentally distinguishing it from inherently discrete approaches such as cohesive element method. This allows for the independent and precise control of interface toughness in a straightforward manner. Theoretical analysis further reveals that the proposed framework can describe the cohesive failure of both bulk and interfacial regions using a unified set of parametric equations for the cohesive law, where the model parameters are directly determined by the local material properties without the need for additional corrections. The model's versatility is numerically validated through a series of benchmarks. The results confirm that the proposed model not only accurately reproduces diverse interfacial cohesive laws but also captures the intricate competition between interfacial debonding and matrix cracking. This sharp-diffusive phase-field model may provide a robust and computationally efficient tool for predicting complex fracture trajectories in sophisticated engineering materials.

We introduce new kinetic equations to describe epidemics' spread while accounting for individuals' opinions on protective behaviors. Opinion exchanges occur on a social network represented by a graphon, whose choice strongly influences the dynamics and leads to the emergence of complex nonlinear phenomena, like the creation of opinion leaders or the spontaneous formation of epidemic waves. Starting from individual-based interactions, we derive a nonlinear nonlocal Fokker-Planck model involving reaction terms and degenerate drift-diffusion operators, which depend on the underlying graphon. We establish rigorous results of convergence to equilibrium in $L^1$ space, via relative entropy estimates, and in homogeneous Sobolev spaces $\dot{H}^{-s}$, $s \in \big(\frac{1}{2}, 1\big)$, using Fourier-based techniques. We then design a structure-preserving scheme for the coupled opinion-epidemiological system, highlighting graphon effects: opinion leaders supporting protective behaviors limit disease spread, whereas influenceable individuals may shift toward opposing views, worsening epidemics. At last, we introduce a time-dependent quantity analogous to the effective reproduction number, whose oscillations are linked with the formation of epidemic waves. Notably, these waves are not induced by an explicit external forcing but they naturally emerge from the interactions between agents, depending on the connectivity level prescribed by the graphon.

High-speed quantitative phase imaging enables non-intrusive visualization of transient compressible gas flows and energetic phenomena. However, phase maps reconstructed via the transport of intensity equation (TIE) suffer from spatially correlated low-frequency artifacts introduced by the inverse Laplacian solver, which obscure meaningful flow structures such as jet plumes, shockwave fronts, and density gradients. Conventional filtering approaches fail because signal and noise occupy overlapping spatial frequency bands, and no paired ground truth exists since every frame represents a physically unique, non-repeatable flow state. We address this by developing a physics-informed synthetic training dataset where clean targets are procedurally generated using physically plausible gas flow morphologies, including compressible jet plumes, turbulent eddy fields, density fronts, periodic air pockets, and expansion fans, and passed through a forward TIE simulation followed by inverse Laplacian reconstruction to produce realistic noisy phase maps. A U-Net-based convolutional denoising network trained solely on this synthetic data is evaluated on real phase maps acquired at 25,000 fps, demonstrating zero-shot generalization to real parallel TIE recordings, with a 13,260% improvement in signal-to-background ratio and 100.8% improvement in jet-region structural sharpness across 20 evaluated frames.

Oscillating-sphere drag is a classical fluid-mechanics problem, yet no theory simultaneously accounts for confinement, rotation, viscosity and magnetic fields. We consider a conducting sphere undergoing translational oscillations inside a spherical cavity, modelling confined magnetohydrodynamic flows relevant to planet interiors and liquid-metal experiments. Planetary applications include polar (rectilinear oscillations) and equatorial (circular trajectories) Slichter modes of solid inner cores or subsurface-ocean interiors relative to outer ice shells. Existing theories are restricted to separate asymptotic regimes, including viscous drag in bounded fluids (Stokes 1851), rotation effects in inviscid cavities (Busse 1974), and magnetic coupling through oscillatory boundary layers (Buffett and Goertz 1995). We derive a unified asymptotic framework for oscillatory drag in confined spherical shells with conductivity and permeability contrasts between the inner sphere, fluid shell and outer solid. Closed-form force laws and dissipation predictions are obtained for polar and equatorial modes, with finite-rotation corrections for the polar mode. The framework accounts for viscous and magnetic pressure contributions, magnetic tension, Alfvén-wave radiation and magnetohydrodynamic Stokes-Ekman layers. Recovering classical boundary-layer theories as limiting cases, the analysis yields a local Alfvén-wave drag law for spherical shells, analogous to bounded oscillatory Stokes drag. Beyond the thin boundary-layer approximation, complementary asymptotics yield a closed-form representation of bounded Stokes flow and extend inductionless theory to spherical shells. Direct numerical simulations validate the predictions across a broad parameter range. The framework predicts oscillatory coupling, added-mass, drag and dissipation in planetary cores, subsurface oceans and liquid-metal experiments.

We developed a new explicit cloud microphysical model, based on direct numerical simulation (DNS) with Lagrangian particle tracking. The model employs a vertically-elongated quasi-1D computational domain extending from the ground to the cloud top to explicitly capture the vertical structure of clouds. This allows us to simulate the all warm-cloud microphysical processes, including activation, condensation growth, collision-coalescence growth, and sedimentation. A homogeneous isotropic turbulence field is incorporated into this domain to explicitly resolve the turbulent wind fluctuations. Cloud microphysics simulations with and without turbulent wind fluctuations were performed to clarify the impact of turbulence on droplet growth. We obtained new insights into the altitude- and time-dependent microphysical statistics, which cannot be obtained through conventional DNS researches for a cubic box domain with periodic boundaries. The comparison have shown that turbulence promoted the collision-coalescence growth of droplets. During the early developing stage, where the updraft was present, turbulence promoted the collisions between droplets with similar sizes (autoconversions) in the middle layer of the cloud. In later stage, relatively large droplets produced by autoconversions actively collected smaller droplets (accretions) in the middle and lower layers. The onset of precipitation at the ground occurred earlier and the first raindrop at the ground was larger in turbulence case than that in non-turbulence case.

The precise location of the liquid-vapor critical point in aluminum has remained elusive for decades, with reported critical temperatures spanning nearly 4000 K. Here we resolve this long-standing uncertainty by combining deep potential molecular dynamics with large-scale simulations trained on high-fidelity electronic-structure data. We benchmark multiple exchange-correlation functionals against experimental liquid densities and identify PBEsol as providing the most consistent description. Using complementary approaches – spinodal analysis of the equation of state and direct coexistence simulations with Gaussian mixture phase identification – we converge on a critical temperature of 6531-6576 $^\circ$K, a critical density of $0.637$ g/cm$^{3}$, and a critical pressure of $1.6$ kbar. The precision of these values, with temperature uncertainties of $\sim$50 K, represents a marked improvement over previous estimates. Our framework establishes a transferable strategy for predicting critical phenomena in metals, with implications for laser ablation, shock compression, and planetary modeling under extreme conditions.

The propagation of the quantum states of light in dispersive and anisotropic media is a fundamental problem in quantum optics. We present a unified theoretical framework for the propagation of the quantum states of light in voltage-controlled nematic liquid crystals, incorporating both material dispersion and electrically tunable birefringence. By treating photons as finite-bandwidth wave packets, we derive analytical expressions for group velocoity, temporal walk-off, and phase evolution of orthogonally polarized modes. The results demonstrate that nematic liquid crystals can serve as electrically tunable quantum photonic devices capable of manipulating photon arrival times, polarization correlations, and temporal indistinguishability of entangled photon pairs. These results show the direct relevance to quantum communication and photonic quantum information processing.

Using nonequilibrium and equilibrium molecular dynamics simulations, we investigate heat conduction in a momentum-conserving mesoscopic fluid modeled by multiparticle collision dynamics. Across quasi-two-dimensional (q-2D) to three-dimensional (3D) systems, we identify three distinct transport regimes: (i) a ballistic regime, where thermal conductivity scales linearly with system size ($\kappa \sim L$) and the total heat current autocorrelation function $C(t)$ remains constant; (ii)~a kinetic regime, characterized by size-independent $\kappa$ and exponentially decaying $C(t)$, demonstrating that normal heat conduction dominated by kinetic effects is far more ubiquitous than previously observed in 1D systems; and (iii)~a hydrodynamic regime, where the q-2D system exhibits logarithmically divergent conductivity ($ \kappa \sim \ln L $ ) with $ C(t) \sim t^{-1} $ , while the 3D system displays finite $ \kappa $ and $ C(t) \sim t^{-3/2} $. Our results, observed in the hydrodynamic regime, quantitatively validate the scaling predictions for heat transport and reveal a clear dimensional crossover – from 2D-like anomalous transport to 3D Fourier behavior. These results lay a foundation for understanding thermal transport in q-2D to 3D systems and have practical implications for the design of micro- and nanoscale thermal devices.

The topological confinement is a new mechanism that allows the transmission of cutoff orbital angular momentum (OAM) modes with negligible loss in ring-core fibers (RCFs) and provides a natural immunity against mode coupling. We investigate the influence of fiber design parameters and wavelength on the characteristics of topologically confined modes (TCMs) in step index ring-core fibers (SI-RCFs), and propose a type of graded index ring-core fibers (GI-RCF) with better characteristics. Furthermore, as TCMs occurs in structures with high refractive index difference and are often accompanied by relatively high scattering loss, we fabricate a type of low-loss SI-RCF and observe the stable existence of 24 low-loss TCMs in total. Subsequently, we use an analytical model to estimate the maximum signal-to-noise (SNR) and spectral efficiency (SE) of the fiber, demonstrating its strong capacity advantages.

Secondary droplets produced by interactions between falling fluid drops and a liquid pool play a significant role in engineering applications and geophysical processes in nature. This study uses direct numerical simulations to investigate the dynamics of secondary droplets generated by raindrop-liquid pool interactions. The raindrop parameters feature a realistic speed of 7 m/s, effective diameters of 1-4 mm, and surface tension values ranging from $25\%$ to twice the typical air-water interface value. The numerical configurations include both a single raindrop and two raindrops separated by distances between two and four times the raindrop diameter. The secondary droplet size distribution, $N_d$, is found to scale with the droplet radius, $r_s$, as $N_d(r_s)\propto r_s^{-5/2}$, with additional dependencies on surface tension and raindrop diameter. When normalized according to this new scaling law, the droplet size distribution obtained from simulations with different parameter values collapses onto a single curve. Analysis of the impact morphology reveals distinct stages of raindrop interactions and identifies the formation and breakup of a central liquid film. Spatial and temporal analyses of the secondary droplets show that raindrop interaction can influence both the percentage of droplets captured by the cavity and the duration over which they re-merge with the pool. These behaviors arise from the combined effects of differences in the birth times of secondary droplets of various sizes and aerodynamic forcing associated with the cavity airflow.

Mie-void metasurfaces have so far been developed mainly in reflection, where subwavelength voids embedded in high-index media support localized resonances and spectrally selective optical responses. Yet, many optical systems could benefit from integrating such optical elements operating in transmission mode. Motivated by this great need, we hereby introduce Mie-void metasurfaces operating in transmission. To allow for their operation in the visible range, our Mie-voids are implemented using the silicon-rich nitride (SRN) platform. We show that this transition from reflection to transmission is not a simple change in geometry: placing the voids in a finite film on a substrate introduces slab-guided and Fabry-Perot-like contributions that hybridize with the underlying Mie-void response. Rigorous coupled-wave analysis shows that the dominant spectral transformation occurs when the semi-infinite host is replaced by a finite SRN film, while the substrate acts mainly as a secondary perturbation. Thickness-dependent dispersion maps reveal an avoided crossing between interacting modes, supporting the interpretation of a hybrid transmission regime and identifying film thickness as a clean parameter for tracking the evolution of the coupled modal structure. Experimentally, we realize transmission-mode structural colors by varying the void depth and observe good agreement between measured and simulated spectra and chromaticity coordinates. By spatially programming the void depth, we further demonstrate transmitted-light patterns and image encoding within a single metasurface architecture. These results establish transmission-mode Mie-void metasurfaces as a viable inverse-dielectric platform operating in transmission, with plethora of potential important applications such as transmissive spectral filtering, optical encoding, and display-oriented photonic elements, to name a few.

We present a computational platform for modeling chemical reactions in complex molecular environments, focused on ligand-protein binding in drug discovery. The platform implements our new quantum-in-quantum-in-classical (QM/QM/MM) multiscale embedding model that integrates molecular dynamics with a quantum-information-enhanced density matrix embedding theory and quantum chemistry solvers, including explicit solvent. Quantum-information metrics are utilized to generate entanglement-consistent orbitals, enabling a high-accuracy description of strongly correlated regions. The framework supports multiple computational backends, including multi-CPU, NVIDIA multi-GPU architectures, and quantum hardware (IQM, IonQ, IBM) integrated under CUDA-Q, and is designed for compatibility with future fault-tolerant quantum systems. The new platform's capabilities are demonstrated by modeling covalent docking of zanubrutinib to Bruton's tyrosine kinase via a Michael addition mechanism, computing the full reaction energy profiles and energy barriers at a reduced computational cost relative to existing methods. As a 2nd-generation anticancer agent, zanubrutinib serves as a proof of concept for covalent inhibitor discovery. Accurate first-principles reaction barrier estimations provided by our method can contribute to reducing false positive and negative rates in drug discovery pipelines. Scalability is validated through benchmarks on GPU clusters, cloud-based CPU infrastructures. We demonstrate integration with quantum devices (up to 20 qubits), alongside resource estimates for fault-tolerant quantum computing, indicating potential speedups of up to 20x. Beyond single reactions, the platform supports the construction of reaction networks in chemical metric space, facilitating ligand screening and systematic exploration of reactive pathways.

Time is the odd dimension out: Unlike space, it follows the arrow of time, forbidding back-reflections and requiring momentum yet not energy conservation. Tailored temporal variations manipulate momentum bands and engineer waves in time. We show that momentum bands exhibit unique topology, hidden when conventionally considering energy bands: Complex momentum bands may wind, mandating topological localization at time interfaces. We observe this effect in photonic quantum walks and study it under disorder. Remarkably, unlike any known topological phenomenon, the topology is immune against arbitrarily strong disorder. Only exotic conditions through extreme spatiotemporally random non-Hermiticity can destroy it. Our findings uncover a disorder-immune type of topological physics, inviting explorations of complex momentum or energy-momentum topology with potential applications like ultrarobust lasing, temporal pulse shaping or amplification.

The ubiquitous $3/4$ metabolic scaling exponent, known as Kleiber's law, has long been attributed to the minimization of viscous dissipation within fractal transport networks. In this paper, we invert this standard narrative, demonstrating that Kleiber's law is fundamentally a signature of pulsatile wave physics rather than steady-state geometry. By coupling local branching optimization to global allometry, we derive the exact generalized metabolic exponent $\beta = d\alpha/(2d+\alpha)$, which strictly maps local transport microphysics to global organismal scaling. We show that dynamic wave-impedance matching in the proximal vasculature uniquely enforces $\beta = 3/4$ in three dimensions. This bound is dynamically protected: no static optimization of a viscous network can reproduce it. Consequently, we analytically predict the critical body mass for the wave-to-viscous transition, successfully explaining the empirical shift to steeper allometric scaling ($\beta \approx 0.9$) in small mammals and invertebrates with no free parameters. Furthermore, we demonstrate that the classical West–Brown–Enquist (WBE) derivation is structurally divergent under its own geometric assumptions, failing at the required proximal-dominance limit. Our framework is validated across nine biological systems spanning five phyla – including vertebrate vasculature, insect tracheae, plant xylem, and sponge canals – accurately predicting empirical branching exponents from independent biophysical measurements. Ultimately, we establish a general allometric equation of state that organizes diverse biological networks into discrete universality classes, generating falsifiable predictions across clades from shrews to flatworms.

Optical imaging of quantum emitters is essential for a wide range of quantum applications. Conventional confocal imaging relies on point-by-point raster scanning, which is inherently time-consuming and photon-inefficient, particularly for sparse emitter distributions and photon-limited samples. Here, we demonstrate a compressive sensing-based imaging approach, where spatially structured wide-field excitation replaces raster scanning, enabling reconstruction of sparse emitters. In our implementation, random binary patterns are used to acquire compressive measurements, from which the spatial fluorescence distribution is reconstructed using a GPSR-BB algorithm. We experimentally demonstrate this approach using nitrogen-vacancy (NV) centers in diamond as a representative platform, with high-fidelity image reconstruction achieved using only approximately $20\%$ of the measurements required for conventional raster scanning. In addition to intensity reconstruction, we extend this framework to reconstruct spatial maps of the second-order correlation function $g^{(2)}(0)$ from compressive measurements. This enables identification of single-photon emitters through antibunching signatures using significantly reduced data.

The SpinQuest experiment at Fermilab employs a dynamically polarized solid ammonia target to probe the spin structure of the proton, requiring stable, optimized microwave-driven Dynamic Nuclear Polarization (DNP) under high radiation conditions. We present the design, operation, and automation of a 140 GHz microwave system based on an extended interaction oscillator (EIO), integrated with real-time polarization feedback from a continuous-wave NMR system and cryogenic diagnostics. The system enables fine frequency control through motorized cavity tuning and is operated remotely to mitigate radiation exposure. To continuously optimize target polarization, we develop an automation framework supported by a Monte Carlo (digital twin) of the DNP process. The simulation incorporates rate-equation dynamics, frequency-dependent steady-state behavior, dose-induced frequency drift, beam-induced depolarization, and realistic NMR noise. This framework is used to design and benchmark control strategies, including a heuristic feedback algorithm, reinforcement learning (RL), and unsupervised RL approaches. These methods enable autonomous frequency tuning, improve ramp-up efficiency, and maintain near-optimal polarization under evolving conditions. We also demonstrate integration of EIO power-supply control into the feedback loop via anode voltage modulation, providing an additional degree of freedom for simultaneous control of microwave frequency and RF power. This combined control of cavity tuning and anode voltage allows the system to avoid frequency-dependent power nonuniformities and to better match broad Larmor distributions in irradiated targets. The results establish a scalable framework for AI-driven control of complex microwave systems in polarized-target experiments, with implications for future spin-physics measurements and other cryogenic, high-field applications.

We present 2D and 3D Particle-in-Cell simulations of driven collisionless magnetic reconnection triggered by the compression and merger of two Lundquist-type force-free flux tubes in a strongly magnetized pair plasma, with a focus on magnetic energy dissipation and particle acceleration. We show that 3D effects systematically delay the onset of reconnection in comparison with equivalent 2D runs, an effect further enhanced by a strong guide field, due to reduced linear growth rates and phase decoherence of oblique modes. Increasing the external drive accelerates both tearing and drift-kink instabilities, while a strong guide field suppresses coherent drift-kink activity and has a comparatively mild impact on tearing. Despite these differences in early-time dynamics, all simulations enter a fast-merging phase characterized by a normalized reconnection rate 0.08–0.10, coinciding with a transient reduction of the guide-to-reconnecting field ratio inside the current sheet. The high-energy cutoff of accelerated particles converges to a common asymptotic value, gamma_cut/sigma_in ~ 50, with only a weak dependence on the driving strength. This behavior is consistent with an electric-field-limited acceleration process, in which the maximum energy is set by the reconnection electric field and the duration of the energization phase. The resulting nonthermal particle spectra are similar across all runs, with power-law indices p ~ 1.6–2.0.

We investigate the interaction between wall-bounded turbulence and defect-embedded phononic subsurface (D-Psub) using a weakly coupled fluid–structure framework, in which the flow and structure are advanced sequentially without sub-iterations. The D-Psub subsurface is modeled as a dynamic wall with a resonance introduced via a localized structural defect, driven by spatially averaged wall-pressure fluctuations from a turbulent channel flow. This configuration enables a controlled study of how a narrow-band structural response interacts with the broadband forcing of near-wall turbulence. Despite broadband turbulent forcing, the D-Psub exhibits a narrow-band response that modifies near-wall dynamics, with representative cases showing suppression of velocity fluctuations, increased coherence of streamwise streaks, and a measurable reduction in turbulent drag. Crucially, the coupled system displays behavior that cannot be replicated by prescribed wall motion: the dominant oscillation frequency shifts away from the designed resonance due to fluid–structure interaction. Additionally, the phase between panels is shown to be governed by the convection of turbulent structures. These results reveal a mechanism by which phononic subsurfaces filter and reorganize turbulent energy through frequency-selective coupling, distinct from conventional compliant or actively forced walls. The findings provide a physical basis for designing passive resonant surfaces that exploit turbulence-structure coupling for flow control.

We investigate whether ideas from symplectic topology, in particular Gromov's non-squeezing theorem and symplectic capacity, can provide useful geometric insight into classical reaction dynamics near an index-1 saddle. Using Poincaré-Birkhoff normal form theory, we describe the phase-space structures that organize transport through the transition-state region, including dividing surfaces, normally hyperbolic invariant manifolds (NHIMs), and the associated bath-action geometry. For quadratic saddle-center and saddle-center-center models, the normal-form geometry identifies natural bath-action area scales associated with the reactive bottleneck. For anharmonic systems (Eckart-Morse and Eckart-Morse-Morse), we formulate corresponding candidate symplectic width scales – based on transverse bath actions – using high-order normal forms for bounded local neighborhoods associated with the reaction bottleneck near the saddle. We then present two numerical illustrations: the backward propagation of a locally coupled phase-space ball to examine linear non-squeezing behavior, and a bath-localized ensemble calculation in an anharmonic normal-form model. These computations are consistent with the idea that heavily biasing the initial phase-space distribution of an ensemble toward the high-action boundaries of the bath modes can induce a severe finite-time dynamical delay, influencing reactivity in ways not captured by total phase-space volume or flux alone. The results suggest a new geometric perspective on mode selectivity and reaction bottlenecks, while highlighting open mathematical questions concerning the precise relation between these candidate width scales and genuine symplectic capacities of suitably defined reactive neighborhoods.

We present an analytical study for predicting the finger width of the Saffman-Taylor finger in a tapered Hele-Shaw cell. We consider a rectilinear geometry with a constant depth gradient and apply analytical techniques of singular perturbation analysis and WKB approximation to derive an expression for the finger selection mechanism for such tapered Hele-Shaw cells with small depth gradients. We establish \[ \Lambda - \frac{1}{2} \sim f(\alpha) Ca_m^{2/3} \quad \mbox{as} \quad Ca_m \rightarrow 0, \;\;\; \mbox{and} \;\;\; \lvert \alpha \rvert \ll 1.\] Here, $\Lambda$ is the dimensionless finger width, $Ca_m$ denotes the modified Capillary parameter, and $f(\alpha)$ is a linear function of the gap gradient $\alpha$, such that $f(\alpha = 0) = 1$ recovering the results of parallel Hele-Shaw cell (Hong and Langer \cite{hong1986analytic}, Combescot et al. \cite{Combescot1986}, Shraiman \cite{shraiman1986velocity}). Our findings indicate that the Hele-Shaw cell gap gradient plays a crucial role in determining $\Lambda$, allowing for control over fingering instabilities such that the single-finger steady state can be stabilised or destabilised depending on the sign of the gradient, compared to the standard Hele-Shaw cell. The theoretical estimates reveal excellent agreement with experimental finger-width data and predictions from linear stability analyses.

Existing hologram super-resolution (HSR) methods primarily focus on angle-of-view expansion. Adapting them for volumetric spatial up-sampling introduces severe quadratic depth distortion, degrading 3D focal accuracy. We propose CV-HoloSR, a complex-valued HSR framework specifically designed to preserve physically consistent linear depth scaling during volume up-sampling. Built upon a Complex-Valued Residual Dense Network (CV-RDN) and optimized with a novel depth-aware perceptual reconstruction loss, our model effectively suppresses over-smoothing to recover sharp, high-frequency interference patterns. To support this, we introduce a comprehensive large-depth-range dataset with resolutions up to 4K. Furthermore, to overcome the inherent depth bias of pre-trained encoders when scaling to massive target volumes, we integrate a parameter-efficient fine-tuning strategy utilizing complex-valued Low-Rank Adaptation (LoRA). Extensive numerical and physical optical experiments demonstrate our method's superiority. CV-HoloSR achieves a 32% improvement in perceptual realism (LPIPS of 0.2001) over state-of-the-art baselines. Additionally, our tailored LoRA strategy requires merely 200 samples, reducing training time by over 75% (from 22.5 to 5.2 hours) while successfully adapting the pre-trained backbone to unseen depth ranges and novel display configurations.

Scenario pathways (e.g. for the energy transition) often use a single trajectory or a band. That is not sufficient when one needs to understand why outcomes differ and under what stress or uncertainty they arise. Doing so requires tracking disequilibrium along pathways, comparing runs across "worlds" or storylines, and surfacing outcomes that are unlikely under a central view but plausible when how factors interact is uncertain. Cross-Impact Balance (CIB) is a well-established method for generating pathways. This paper extends CIB to formalise and implement these dimensions in pathway runs, and defines four run types that respectively emphasise one-off shocks, extremes under alternative regimes, influence-structure uncertainty that widens over time, and exogenous shocks as a baseline for comparison. The approach is applied to a socio-technical decarbonisation pathway for illustration. Together, the extensions support stress-testing, comparison across storyline or regime assumptions, and exploration of rare or surprising futures, and help analysts distinguish results that are stable across those assumptions from those that depend on structural uncertainty about the influence table.

The sensitivity of experiments searching for the electron electric dipole moment (eEDM) using the symmetric top molecules can be greatly enhanced by laser cooling. A detailed understanding of the Zeeman structure of the eEDM-sensitive levels is crucial for controlling systematic effects. We have developed a method for calculating the $g$-factors of $K$-doublet levels in symmetric top molecules and applied it to RaOCH$_3$. The electric-field-dependent $g$-factors of the first excited rotational level of RaOCH$_3$ are calculated. $K$-doublet levels with a small difference in $g$-factors are identified, and the main contributions to this difference are determined.

We present the CET Omega framework, a causal-informational extension of standard cosmology that predicts a universal doubly logarithmic correction to the radiation energy density in the early Universe. This correction arises naturally from scale-invariant spectral sectors with logarithmically-running infrared scales and represents a low-energy manifestation of the full CET Omega theory. We derive the doubly logarithmic form from two complementary perspectives – spectral integration and renormalization group flow – and perform a full Markov Chain Monte Carlo analysis jointly varying six LambdaCDM parameters and alpha_log, using Planck 2018 TT, TE, EE + lowE likelihoods and BBN constraints. The result, alpha_log = -0.008 +/- 0.006 (68% C.L.), is consistent with zero. We identify the expected N_eff degeneracies with H0 and n_s, establish the first observational bound |alpha_log| <= 0.006, and demonstrate that future CMB-S4 measurements can probe |alpha_log| ~ 10^{-3}.

Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property prediction paradigm into property-to-compound generation. Chemists now have access to a rich AI toolbox for organic chemistry, including drug discovery. However, the application of these methods to inorganic compounds remains limited by the challenges posed by their intrinsic nature. This Review analyzes how these challenges have been addressed, considering widely diverse systems ranging from molecules to crystals, including transition metal complexes and microporous materials. The analysis focuses on how generative AI methods have evolved towards data-representation-model pipelines that address the full complexity of inorganic compounds, including their chemical composition, geometry, symmetry, and electronic structure. Future directions, like benchmark standardization and the development of synthesizability metrics, are also discussed.

During the voyages that led him to discover the new continent bearing his name, Amerigo Vespucci made interesting astronomical observations of the southern sky. In the past, his data have been interpreted with criteria that do not follow Vespucci's indications, resulting in identifications that are not credible or even leading to the assertion that the data themselves are incomprehensible. However, it is possible to construct a coherent picture of all the information, arriving at an identification that is in some cases very probable, in other cases almost certain, of the stars described by Vespucci. Analysis of documents shows that he made good-quality measurements, but his incomplete knowledge of ancient texts prevented him from distinguishing the new stars from the already known ones, giving rise to a period of confusion in 16th century celestial cartography.

According to various no-go results in the foundations of quantum mechanics, for any system associated to a Hilbert space of dimension higher than two, it is not possible to assign definite truth values to all propositions pertaining to the system without generating a Kochen-Specker contradiction. In this respect, the Bub-Clifton uniqueness theorem is utilized for arguing that truth-value definiteness is consistently restored with respect to a determinate sublattice of propositions defined by the state of the quantum system concerned and a particular observable to be measured. On this basis, a perspectivist/contextual account of truth valuation in the quantum domain is produced that satisfies Tarski's criterion of material adequacy for a theory of truth. In light of the latter, perspectivist truth conforms to perspective or context-bound correspondence of a de re nature, designating locally an objectively existing state of affairs. Such an account derives by virtue of the microphysical nature of physical reality in displaying a context-dependence of facts; thus, it essentially opposes a non-perspectival, metaphysically fixed point of reference, or a panoptical standpoint from which to state all facts of nature.