In complex systems, events occur at irregular intervals that inherently encode the underlying dynamics of the system. Analyzing the temporal clustering of these events reveals critical insights into the non-random patterns and the temporal evolution. Existing techniques can effectively quantify the overall clustering tendency of events using global statistical measures. However, these macroscopic approaches leave a critical gap, as they do not attempt to investigate the dynamics of individual clusters. Analyzing individual clusters is essential, as it helps comprehend the local interactions that actively drive the system dynamics, which may be obscured by global averaging, while simultaneously revealing the time scales involved. To address these limitations, we propose a complex network-based framework for analyzing clustering of events occurring at irregular intervals. The framework establishes connections using arrival times, transforming the time series into a network. Network properties are then used to quantify the clustering. Further, a community detection algorithm is used to identify individual clusters in time series. We illustrate the method by applying it to standard arrival processes, such as the Poisson process and the Markov-modulated Poisson process. To further demonstrate its scope, we apply the method to two diverse systems: the time series of droplet arrivals in turbulent flows and the R-R intervals in electrocardiogram (ECG) signals.

Rydberg-state hopping is demonstrated in a wavemeter-locked two-photon rubidium system (Rb D2 probe at 780 nm and 480 nm coupler), enabling rapid and repeatable switching between the 65S1/2 and 63D5/2 states without cavity or frequency-comb stabilization. A Fizeau-interferometer wavemeter provides the error signal for a digital feedback loop that simultaneously stabilizes the coupler and commands discrete Rydberg-state changes. The lock achieves sub MHz frequency stability and acquisition rates up to 6.5 GHz/s (0.4283 GHz engaged in 66 ms), extrapolating to ~0.93 s for a ~6 GHz 65S to 63D transition. Time resolved spectra reveal reemergent dissipative time-crystal oscillations after each hop, with distinct state dependent fundamentals and harmonics. This approach addresses the need for dynamically reconfigurable Rydberg state control for on resonant multi band field detection, while the DTC frequency reconfigurability enables adaptive, low frequency E field sensing in compact, cavity free architectures.

We introduce a method for detecting regularity loss in solutions to the three-dimensional Navier-Stokes equations using the approximation error of Sinusoidal Representation Networks (SIRENs). SIRENs use sin() activations, producing C-infinity outputs that cannot represent non-smooth features. By classical spectral approximation theory, the SIREN error is bounded by O(N^{-s}) where s is the local Sobolev regularity. At a singularity (s to 0), the error is O(1) and localizes via the Gibbs phenomenon. We decompose the velocity field into a cheap analytical baseline (advection-diffusion) and a learned residual (pressure correction), training a compact SIREN (4,867 parameters). We validate on the 3D Taylor-Green vortex, where error concentration increases from 4.9x to 13.6x as viscosity decreases from 0.01 to 0.0001, localizing to the stagnation point -- the geometry matching the singularity proven by Chen and Hou (2025) for 3D Euler. On axisymmetric equations, we reproduce blowup signatures (T* converging across resolutions) and identify a critical viscosity nu_c = 0.00582 for the regularization transition.

The number of stable macroscopic organizations in complex systems is often much smaller than the large number of microscopic degrees of freedom would suggest. Yet theoretical approaches rarely address whether general limits constrain the diversity of admissible macroscopic organizations. We develop a geometric framework in which interactions among system components define a coarse-grained interaction space endowed with a metric structure. When this space has finite intrinsic dimensionality, geometric packing constraints impose bounds on the number of mutually distinguishable collective organizations. We derive a dimension-dependent scaling law showing that the number of stable macroscopic regimes grows polynomially with exponent equal to the intrinsic dimensionality of the interaction space. This implies that increasing microscopic complexity alone does not necessarily expand the range of macroscopic organizations. Instead, diversification requires an increase in the dimensionality of effective interactions. To illustrate our approach, we analyze an interacting system in which collective regimes correspond to regions of a low-dimensional parameter space describing effective interactions. In this setting, geometric packing constrains the number of robust organizations that the system can support. Overall, we argue that dimensionality of interaction space may act as a control parameter governing a variety of collective organization across physical and biological systems.

Myopic best-response dynamics (MBRD) capture agents' bounded rationality and can generate evolutionary outcomes that differ from those produced by widely examined imitation dynamics. In this study, we apply MBRD to a three-strategy social dilemma -- the snowdrift game with an individual solution -- in which not only defection but also an individual solution that guarantees a safe, constant payoff can undermine cooperation. Monte Carlo simulations show that, on a square lattice, the evolutionary dynamics result in distinct equilibria, including the dominance of the individual solution, the coexistence of cooperators and defectors, or all-strategy coexistence. By combining simulations with a simple heuristic that approximates the transition condition between the dominance of the individual solution and the all-strategy coexistence, the analysis reveals a dual role of neighborhood size. Specifically, smaller neighborhoods can promote cooperation even when the individual solution is relatively inexpensive; however, achieving cooperation under these conditions requires greater benefits from cooperation. Notably, this hindrance to cooperation contrasts with evolutionary outcomes observed under imitation dynamics. Analysis of local strategy configurations explains the transition between the all-strategy coexistence and the coexistence of cooperators and defectors while showing that this transition is absent in a one-dimensional lattice. These observations indicate that the persistent availability of individual solutions constitutes an additional inhibiting factor of cooperation in populations of boundedly rational agents.

Optical beam steering is a key technology for free-space optical communication, sensing, and imaging. Mechanical beam steering systems suffer from limited scanning speed and bulky form factors, while existing solid-state solutions rely on pixelated synthetic aperture that requires complex fabrication and control architectures. Integrated acousto-optic beam steering (AOBS) is an emerging technology that enables continuous one-dimensional beam steering using integrated acoustic transducers and fixed-wavelength laser sources. Here, we integrate AOBS with an optical ring resonator on the same thin-film lithium niobate (TFLN) platform to significantly enhance beam steering efficiency and system functionality. The resulting device achieves a resonance-enhanced beam steering efficiency of up to 20% over a 18 degrees field of view. Moreover, by leveraging integrated electro-optic control, we dynamically lock the ring-resonator's resonance to a chirped laser frequency, enabling frequency-modulated continuous-wave (FMCW) LiDAR operation. By combining lithium niobate's piezoelectric and electro-optic properties, this work establishes a compact, efficient, and scalable beam-steering platform with co-integrated acousto-optic modulation and electro-optic control for multifunctional applications.

Phase-sensitive optical coherence tomography (PhS-OCT) enables precise, contactless measurements of temperature-dependent changes in transparent solids. In this work, we used a common-path spectral-domain OCT system to measure optical path differences (OPD) in a 1-mm-thick soda-lime glass slide immersed in a thermal bath. The OPD variation showed a strong linear correlation with temperature in the range of 20-52°C, with an experimentally determined sensitivity of 12.4 +- 1.9 nm/°C. A theoretical model incorporating the thermo-optic and thermal expansion coefficients of glass was proposed to interpret the measurements, and numerical simulations based on finite volume methods were performed to account for spatial temperature gradients in the system. The simulations showed agreement with experimental results within 5% error, validating the approach. Additionally, repeatability tests using lateral scans at constant temperature demonstrated sub-10 nm stability, supporting future extensions to spatially resolved thermal mapping. This technique provides a low-cost platform for localized temperature sensing in solid transparent materials.

In computational chemistry of molecular materials, strong static correlation effects appear when electronic states, often involving the ground state, become quasi-degenerate, as occurs, for example, in bond-breaking processes. Such situations present significant challenges for accurate theoretical treatment. In these regimes, many-body methods involving a single-determinant description, such as Hartree-Fock theory and its time-dependent extension, fail to reproduce the correct topology of the ground and excited state potential energy surfaces (e.g., near conical intersections). When strongly correlated electronic systems are further strongly coupled to a quantized radiation field within the framework of non-relativistic cavity quantum electrodynamics, an additional photonic degree of freedom introduces both new complexity and new opportunities to control. Excited cavity photons can modify bond-breaking processes and enable tunability of geometrical and spin-phase transitions, for instance, in organometallic complexes. To overcome this bottleneck, in this work, we extend the well-studied spin-flip configuration interaction singles (SF-CIS) approach to explicitly include quantized cavity photons leading to QED-SF-CIS method. We derive the spin-flip Hamiltonian and find that the double excitation subspace of the system (single with respect to electronic excitation) must be included in the configurations to properly describe singlet electronic states interacting with cavity photons. We then illustrate, through representative molecular examples, how cavity coupling can provide additional tunability in bond-breaking processes. We finally generalize this approach to include higher numbers of photonic excitations, which are required in the strong coupling regime.

Defects in two-dimensional transition metal dichalcogenides (TMDCs) broadly affect their optical and electronic properties. Directly capturing the ultrafast processes of exciton trapping and defect-bound exciton formation is crucial for understanding and advancing defect-mediated optoelectronics and quantum technologies. However, the weak transient optical absorption of defect-bound excitons has limited their experimental observation to date. Here, we report the direct observation of the ultrafast dynamics of defect-bound excitons in monolayer WS$_2$ crystals with a high density of mono-sulfur vacancies (V$_S$) and W-site defect complexes (S$_W$V$_S$) resulting from synthesis by alkali metal halide-assisted chemical vapor deposition. The dynamics of excitons bound to these defects, along with their coherent interactions with free excitons, are elucidated using ultrafast optical spectroscopy. Using above band-edge photoexcitation, we find that both free and defect-bound excitons simultaneously form within 300 fs from hot carrier relaxation. The defect-bound excitons exhibit shorter lifetimes than free excitons, leading to a population difference of the corresponding excitonic states and free exciton trapping within a 1--100 ps window. Band-edge photoexcitation of free and defect-bound exciton states reveals ultrafast interconversion within ~150 fs (comparable to our temporal resolution), indicating possible coherent coupling between these states. We further demonstrate efficient up-conversion of defect-bound excitons to free excitons with photon energies up to ~300 meV below the free exciton resonance. These findings provide insights into the ultrafast dynamics of defect-bound excitons in TMDCs and their coupling with free excitons, which are relevant to defect-engineered optoelectronic, quantum photonic, and valleytronic applications.

The non-resonant divertor (NRD) offers a promising exhaust solution for stellarators, combining topological simplicity with resilience to magnetic field perturbations. To experimentally validate the robustness of non-resonant divertors in a quasi-axisymmetric (QA) configuration, we introduce STAR_Lite, a new stellarator experiment at Hampton University. This paper details the design and analysis of the first STAR_Lite coil configuration, STAR_Lite-A. The two field-period configuration manifests an NRD through X-points with zero rotational transform, at the top and bottom of the device. The divertor legs extruding from the X-points are topologically similar to the poloidal divertors of tokamaks. To expand the experimental range, STAR_Lite-A is optimized for experimental flexibility, producing a wide range of distinct QA configurations by only varying the currents in the modular coils. The NRDs not only persist across these configurations, but numerical strike-line simulations confirm that heat exhaust remains resilient to changes in coil currents, with plasma following the divertor legs and creating a toroidal, discontinuous, strike pattern. We further examine the resilience of the NRD to magnetic perturbations caused by manufacturing errors in the modular coils. We find that quasisymmetry and the existence of X-points is well-preserved under these magnetic field changes, but the rotational transform may vary substantially and displacements of the divertor X-points may lead to one X-point having a dominant effect on edge transport. Overall, our analysis indicates a compact, modular design can likely generate a resilient NRD structure while satisfying the practical constraints of a university-scale experiment.

Resonant drive in tokamaks is routinely quantified using a variety of different metrics that target different aspects of a resonant response to an external perturbation. Two of the most direct metrics, $\Delta_{mn}$ and $b_{pen}$, are widely used but their relative behavior was previously uncharacterized. This work examines how these metrics representing the shielding current and penetrated field relate in resistive perturbed tokamak equilibria using asymptotically matched solutions with a resistive MHD inner layer model in GPEC. $b_{pen}$ scales with Lundquist number as $S^{-2/3}$ until saturation at low $S$, and $\Delta_{mn}$ remains consistent with its ideal definition but is affected by global kink structure. Both metrics are shown to yield closely similar dominant coupling modes within the same resistive model. However, the resistive physics shifts this dominant mode spectrum to lower poloidal mode numbers $m$ in a low-rotation ITER equilibrium. This alteration is predicted to be observable in experiment in the form of optimal relative phasings of resonant magnetic perturbation coils.

We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian (MAB) models from molecular dynamics (MD) trajectories for simulating nonlinear vibrational spectra of intramolecular modes of molecular liquids. By leveraging machine learning (ML) techniques to capture vibrational anharmonicity, intermolecular couplings, and bath correlation functions for each mode, sbml4md obviates empirical fitting and enables the modeling of environments with spatial and temporal heterogeneity. This work provides a set of parameters specifically tailored for the Hierarchical Equations of Motion (HEOM) framework, enabling numerically "exact" simulations of nonlinear vibrational spectra. Building upon our previous implementation for intramolecular vibrational modes [Park, Jo, and Tanimura, J. Chem. Phys. 163, 214104 (2025)], the present code enhances optimization efficiency by explicitly accounting for intermolecular vibrational contributions. This extension enables sbml4md to broaden the applicability of HEOM-based dynamical modeling by seamlessly integrating classical MD approaches, thereby providing a flexible and scalable framework for simulating both linear and nonlinear spectra under realistic conditions with minimal empirical input. The accompanying ML code, written in Python, is provided as supporting material.

We model, simulate, and analyze the intramolecular modes of liquid H2O and D2O to elucidate how energy excitation, relaxation, and vibrational dephasing interplay through anharmonic mode-mode coupling. Our analysis employs two-dimensional (2D) correlation spectra, a representative observable in nonlinear infrared vibrational spectroscopy. Accurate reproduction of these 2D spectral profiles requires not only a precise dynamical description of intramolecular vibrations but also an appropriate treatment of thermal environmental effects arising from strong interactions with surrounding molecules, which act as thermal baths. Capturing the essential features of the 2D spectra further demands a non-Markovian, non-perturbative, and nonlinear description of the interactions between intramolecular modes and their baths. To this end, we adopt a hierarchical equations of motion (HEOM) framework to compute the 2D spectra. By comparing the resulting spectra of H2O and D2O, we explore the underlying mechanisms governing their complex energy and phase relaxation dynamics.

Condensed-phase spectral line shapes encode the strength and timescale of interactions between molecules and their environments, yet these ideas are often difficult to introduce at the undergraduate level due to their reliance on formal theoretical treatments. We present a visualization-based approach that combines analytic results with numerical simulations to illustrate the physical origins of spectral line broadening in conjugated molecular systems. Using a time-dependent Hückel Hamiltonian, we derive closed-form expressions for coherent electronic motion in finite polyene chains and show how these results provide direct insight into the role of molecular orbital structure in light absorption. Environmental effects are introduced through stochastic fluctuations of the Hamiltonian matrix elements, allowing students to observe how system--environment interactions disrupt coherent motion and produce scattering-like features in electronic trajectories. Real-space animations and simulated absorption spectra provide an intuitive link between microscopic dynamics and measured line shapes. The MATLAB code provided with this work offers an accessible platform for integrating computation and visualization into undergraduate instruction while introducing key concepts in condensed-phase spectroscopy.

In this article, I advance the idea that physics plays a central role in cell differentiation and makes fundamental contributions to morphogenesis, revealing the totipotent nature of the zygote. Totipotency is a persistent mechanical memory that preserves the biomechanical records of animal morphogenesis. I examine the mechanical and biophysical pathways underlying cell differentiation in embryonic development and cancer, treating them as closely related biological and mechanical processes. Drawing inspiration from evolutionary history, I also propose a biophysical mechanism for the emergence of the animal nervous system. By linking physical principles to cellular differentiation, this review positions mechanobiology as a pillar of innovation with high-impact clinical implications for diseases such as cancer.

Electron emission from hydrogen atoms induced by antiproton impact at intermediate energies is investigated using the one-centre Basis Generator Method within a semi-classical impact-parameter framework. The formulation employs a single-centre expansion of the time-dependent Schrödinger equation with a pseudostate basis consisting of hydrogenic orbitals acted upon by powers of a Yukawa-regularized potential, providing a compact and effective representation of the electronic continuum. Ionization probabilities are obtained by projecting the time-evolved wavefunction onto Coulomb continuum states, from which energy-differential cross sections (EDCS) are extracted. Exponential piecewise functions are constructed to interpolate between the pseudostate eigenenergies, yielding smooth EDCS profiles for each partial wave. The total EDCS, reconstructed by summing over all partial-wave contributions, exhibits good agreement with results from other pseudostate-based approaches.

The mechanical response of yield-stress materials below the yield point remains a subject of debate. Two of the most widely used constitutive models for these materials offer fundamentally conflicting views: one permits plastic flow at all stress levels, the other assumes entirely recoverable viscoelasticity below yield. Using parallel superposition rheometry, we test the sub-yield behaviour of a microgel and an emulsion. When residual slip effects are properly accounted for, both fluids exhibit bounded, periodic strain responses, offering compelling evidence that they do not flow in the studied regime. Our results indicate that the sub-yield regime is underpinned by nonlinear viscoelasticity and underscore the need for improved constitutive relations that capture such effects without treating yielding as a precursor for nonlinearity.

Marine heatwaves (MHWs) threaten marine ecosystems and significantly impact weather patterns. In the Arabian Sea, summer MHWs are of particular concern due to their potential impacts on the Indian summer monsoon, a lifeline for nearly a billion people. However, the drivers of these MHWs and their influence on atmospheric circulation and monsoon rainfall remain poorly understood. Using satellite observations, reanalysis datasets, and numerical model experiments, we investigate the key drivers of MHW events and assess their impacts. When SST warming trends are retained, the eastern and northern Arabian Sea emerge as MHW hotspots, showing rapid increases during 1982-2023, largely due to anthropogenic warming. On detrending the SSTs to remove the influence of anthropogenic warming on individual MHWs, we find that most MHWs are short-lived (lasting <= 20 days) and are initiated by enhanced surface shortwave radiation and reduced latent heat loss associated with the suppressed convection phase of the Boreal Summer Intraseasonal Oscillations (BSISOs). Interannual SST anomalies, including ENSO and Indian Ocean Dipole (IOD), further modulate the year-to-year MHW variability. Conversely, the warm SSTs during MHWs exert strong atmospheric feedbacks. MHWs in the eastern Arabian Sea drive cyclonic winds, intensify moisture convergence and increase the risk of extreme precipitation along the southwest coast of India. In the northern Arabian Sea, MHW-induced cyclones trigger intense rainfall over northwestern India and Pakistan, contributing to extreme events like the 2022 Pakistan floods. These findings improve our capacity to predict Arabian Sea MHWs and assess their risks, offering significant socio-economic and ecological benefits.

Background stars observed through a circumstellar disk provide valuable benchmarks for investigating the disk's extinction properties. The HD~163296 system is an excellent case study due to its large disk, the clearly visible extinction effects in JWST/NIRCam data, and the presence of numerous background sources within or around its disk. We present the measured contrasts and astrometry of sources surrounding HD~163296 from Cycle~1 JWST/NIRCam coronagraphic observations, which will serve as a useful reference for future studies of the disk's extinction characteristics.

Norrish type I reactions selectively cleave carbon-carbon bonds directly adjacent to carbonyl groups. Despite their broad use in combination with aromatic carbonyls for additive manufacturing and dental UV curing applications, the nature of the photochemically active state and its population mechanism remain insufficiently understood. Detailed mechanistic insight requires mapping of the photoexcited population flow involving internal conversion and intersystem crossing. We present a time-domain study of gas phase acetophenone as a prototypical aromatic carbonyl combining soft X-ray time-resolved near-edge X-ray absorption fine structure (TR-NEXAFS) spectroscopy at the oxygen K-edge with ab initio multiple spawning (AIMS) simulations. Exploiting the specific sensitivity of TR-NEXAFS spectroscopy to states with $n\pi^*$ character, we observe population transfer from the initially excited $^1\pi\pi^*$ state to the $^1n\pi^*$ state with a time constant of $(0.13 \pm 0.02)$ ps after an initial induction period of $(0.12 \pm 0.02)$ ps without population transfer, in quantitative agreement with the AIMS simulations. The population in the $^1n\pi^*$ state subsequently decays via intersystem crossing, likely mediated by a $^3\pi\pi^*$ state, within $(3.17 \pm 0.66)$ ps to a long-lived $^3n\pi^*$ state, which is presumed to be active towards Norrish type I chemistry.

This report describes the method used to derive fluxes of the trapped proton belt along the GPS orbit (i.e., a Medium-Earth Orbit) during 2000-2010, a period almost covering a solar cycle. This method utilizes a newly developed empirical proton radiation-belt model, with the model output scaled by GPS in-situ measurements, to generate proton fluxes that cover a wide range of energies (50keV- 6MeV) and keep temporal features as well. The new proton radiation-belt model is developed based upon CEPPAD proton measurements from the Polar mission (1996 - 2007). Comparing to the de-facto standard empirical model of AP8, this model is not only based upon a new data set representative of the proton belt during the same period covered by GPS, but can also provide statistical information of flux values such as worst cases and occurrence percentiles instead of solely the mean values. The comparison shows quite different results from the two models and suggests that the commonly accepted error factor of 2 on the AP8 flux output over-simplifies and thus underestimates variations of the proton belt. Output fluxes from this new model along the GPS orbit are further scaled by the ns41 insitu data so as to reflect the dynamic nature of protons in the outer radiation belt at geomagnetically active times. Derived daily proton fluxes along the GPS ns41 orbit, whose data files are delivered along with this report, are depicted to illustrate the trapped proton environment in the Medium-Earth Orbit. Uncertainties on those daily proton fluxes from two sources are evaluated: One is from the new proton-belt model that has error factors < ~3; the other is from the in-situ measurements and the error factors could be ~ 5.

Event-based image sensors provide microsecond temporal resolution but lack spectral discrimination, whereas diffractive spectral imagers encode wavelength information at conventional frame rates. We introduce a fluorescence microscopy architecture that fuses asynchronous event streams with diffraction-encoded CMOS measurements to decouple temporal and spectral sampling. The system achieves ~3.9 um spatial resolution over a 0.5 mm field of view, effective temporal resolution down to 100 us, and differentiates fluorophores whose emission peaks are separated by only 23 nm. By synchronizing and computationally merging both sensing modalities, we enable spectrally resolved tracking at 100,000 frames/s without scanning or filter switching.

The recent breakthroughs in laser-driving 229Th nuclear transition have created an urgent demand for coherent vacuum-ultraviolet (VUV) sources delivering high spectral brightness at the critical 148.38 nm isomer energy. However, generating sufficient photon flux to overcome the low nuclear excitation probability remains a challenge for compact setups. While resonant dispersive wave emission in gas-filled hollow-core fibers offers a promising route, standard capillaries face a fundamental trade-off: maximizing input coupling requires large core diameters, whereas efficient nonlinear VUV conversion demands the high intensities using small cores. Here, we resolve this conflict using a gas-filled tapered capillary fiber. This architecture utilizes a longitudinally decreasing core diameter to combine a large input aperture with adiabatic field concentration, thereby continuously enhancing the nonlinear interaction. Experimentally, we demonstrate a widely tunable source (135-240 nm) that achieves a twofold efficiency enhancement specifically at the 148.38 nm wavelength compared to uniform geometries. By providing a scalable route to high-flux VUV generation, this work establishes a critical tabletop tool for advancing solid-state nuclear clocks and time-resolved spectroscopy.

Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern SO(3)-equivariant neural potentials achieve high accuracy for short-range chemistry, they cannot represent the anisotropic, slowly decaying multipolar correlations governing realistic materials, while existing long-range extensions either break SO(3) equivariance or fail to maintain energy-force consistency. Here we introduce EquiEwald, a unified neural interatomic potential that embeds an Ewald-inspired reciprocal-space formulation within an irreducible SO(3)-equivariant framework. By performing equivariant message passing in reciprocal space through learned equivariant k-space filters and an equivariant inverse transform, EquiEwald captures anisotropic, tensorial long-range correlations without sacrificing physical consistency. Across periodic and aperiodic benchmarks, EquiEwald captures long-range electrostatic behavior consistent with ab initio reference data and consistently improves energy and force accuracy, data efficiency, and long-range extrapolation. These results establish EquiEwald as a physically principled paradigm for long-range-capable machine-learning interatomic potentials.

The criticality hypothesis posits that biological neural networks operate near a phase transition, yet within standard Gaussian mean-field theories this regime appears fragile and requires fine tuning. Here we show that heavy-tailed synaptic connectivity provides a robust alternative mechanism. By developing a dynamical mean-field theory for Cauchy-distributed couplings, we reduce the macroscopic dynamics to a one-dimensional gradient flow with a global Lyapunov potential. The resulting theory exhibits a continuous phase transition in which collective activity grows with the square root of the distance to criticality, and static susceptibility diverges only as the square root rather than linearly as in Gaussian mean-field theories. This structure gives rise to an emergent automatic gain control: activity-dependent noise fluctuations suppress the effective gain at high activity levels while preserving high susceptibility near the critical point. Extending this mechanism to general symmetric $\alpha$-stable inputs, we identify heavy-tailed synapses as a key microscopic origin of robust near-critical dynamics in disordered neural circuits.

Tear film rupture on the corneal surface plays a critical role in ocular health and visual comfort. Conventional theoretical approaches often idealize the cornea as a perfectly smooth surface, ignoring the surface roughness that are characteristic of healthy as well as diseased eyes. In this study, we develop a comprehensive mathematical model to investigate tear film dynamics over the corneal surface incorporating the effects of surface roughness, slip, van der Waals forces, and lipid transport at the film-air interface. The corneal surface is represented by a small-amplitude periodic modulation. Steady-state solutions obtained using asymptotics reveal nonlinear corrections to the base profile at $O(\eta^2)$, which are confirmed numerically. Linear stability analysis performed using the Floquet theory demonstrates that an increase in the amplitude of roughness destabilizes the film. Specifically, both the dominant growth rate and the most unstable wavenumber increase with the roughness amplitude. Nonlinear simulations show that surface roughness significantly accelerates tear-film rupture. The slip coefficient, amplitude of roughness of the corneal surface and the initial film profile are found to significantly influence the rupture time. Moreover, the location of the rupture is sensitive to the initial disturbance. These results highlight the crucial role of surface topography and slip in determining tear film stability. The predicted rupture times are consistent with the experimental observations. The proposed model provides a realistic and accurate prediction of tear film dynamics and rupture over the corneal surface. This study offers a new perspective on tear film instability and will help address challenges such as contact lens failure which is related to tear film behavior.

Randomness in optical systems emerges as a powerful resource for generating complex, non-deterministic light-matter interactions. In particular, random plasmonic metasurfaces harness nanoscale disorder to produce unique and irreproducible optical responses, positioning them as an ideal platform for physical unclonable function in secure optical authentication. However, realizing such random metasurfaces in a rapid, scalable, and chemical-free manner for optical PUFs remains challenging. Here, we introduce a nanosecond pulsed laser scribing method for one-step fabrication of a robust random plasmonic metasurface physical unclonable function. By delivering spatially localized, ultrafast energy bursts, this technique harnesses naturally occurring instability to generate stochastic plasmonic nanostructures in nanoseconds. The unique plasmonic metasurfaces are effectively transformed into a macroscopic, non-replicable optical fingerprint via morphology-dependent resonance at the nanoscale, enabling low-cost and fast readout. Leveraging the wavelength-selective plasmonic response, we present a multidimensional multiplexing strategy that expands the challenge response pairs space and encoding capacity by 5-fold via topography and RGB multiplexing. The resulting plasmonic keys exhibit good bit uniformity (average: 0.500), high uniqueness (inter-Hamming distance: 0.499), and large capacity (~28000 bits per PUF), with strong environmental stability and resistance to reverse nanofabrication. This work demonstrates how fast laser induced stochasticity can be rationally harnessed and engineered for optical PUFs, opening pathways toward disorder-enabled photonic devices.

This study evaluates the radiation tolerance of a 4H-SiC PIN detector under X-ray irradiation up to \SI{2}{MGy} (Si) at \SI{160}{keV}. The detector features a fully epitaxial vertical PIN structure with mesa terminations and field plates. Comprehensive pre- and post-irradiation characterization includes I-V/C-V measurements, charge collection efficiency (CCE) and timing resolution tests using $\beta$-particles ($^{90}$Sr). After \SI{2}{MGy} irradiation, the reverse leakage current remains at an ultralow level of $\sim 10^{-11}$ \si{A/cm^2} at \SI{-300}{V} with negligible degradation. C-V characteristics are basically consistent, with full depletion at \SI{~130}{V}. CCE for $\beta$-particles decreases by less than 5\%. The detector maintains good timing resolution: \SI{21}{ps} before and \SI{31}{ps} after irradiation, with jitter increasing moderately. These results demonstrate stable performance under extreme X-ray exposure, highlighting the detector's potential for radiation-hard applications in high-energy physics, space missions, and nuclear reactor monitoring.

This paper presents the design and implementation of a high-density, deterministic trigger distribution system tailored for the C-band photocathode electron gun test platform at the Southern Advanced Photon Source (SAPS). Implemented within a scalable 6U VME modular architecture, the system achieves high-density integration by consolidating a master controller, clock distribution network, and 80 heterogeneous output channels into a single chassis. This design leverages a high-performance FPGA core combined with custom backplane interconnections to establish a master-slave topology, significantly reducing the system footprint compared to stacked standalone generators. To guarantee timing determinism in high-noise environments, precise placement and timing constraints are applied to the FPGA logic, while optical isolation is employed to mitigate electromagnetic interference. Furthermore, a dual-channel SFP optical signaling architecture enables seamless expansion to 160 synchronized channels. A remote control framework based on a serial server and a virtual machine Input/Output Controller (IOC) facilitates flexible configuration. Performance tests demonstrate adjustable trigger frequencies from 1 Hz to 100 Hz, with delays and pulse widths tunable from 0 to 10 ms at a resolution of 10 ns (or the RF period). The local electrical output exhibits an ultra-low RMS jitter of 6.55 ps (60 ps peak-to-peak). For remote optical distribution, the system maintains a sub-nanosecond RMS jitter of 119.5 ps, with peak-to-peak variation confined to 1 ns due to the combined effects of transceiver optoelectronic conversion (utilizing HFBR-1414T/2412T modules) and fiber transmission. The system has been successfully commissioned and is currently in reliable routine operation, verifying the architecture as a robust, highly integrated, and cost-effective solution for compact accelerator facilities.

Access and egress trips constitute a substantial part of a train trip in minds of travellers, often being the deciding factor whether to travel by train at all. Despite a host of studies analysing individual legs within a multimodal trip chain, the full chain within a multimodal trip - including access, main and egress - has seen very limited attention. To understand the importance of all these choices, we use travel diaries from the Dutch Mobility Panel to estimate a nested logit discrete choice model. Our results suggest that as a main mode, train and bus/tram/metro (BTM) seem to be associated with an inherent disutility compared to walking, cycling or car. The in-vehicle time in train and BTM, however, seems to be perceived significantly less negatively (60% lower) than in private modes, making them comparatively more attractive for longer journeys. These results imply that, given the strong preference for walking for both access and egress, train stations should be sufficiently dense to allow most people to walk to a station. This, however, should not come at the expense of additional transfers, as they inflict substantial disutility. Operators need to find a balance between accessibility and directness. Given the strong dispreference of travelling by car to dense urban areas, these trips should be the primary target of policymakers and operators for attracting additional travellers to take the train. Future studies could further enhance our understanding of multimodal trips by including additional attributes in the data, account for respondent heterogeneity and study how individuals build their consideration set when making multimodal trips.

We present a lithographic method for fabricating free-standing microparticles directly from SUEX dry-film epoxy resist. Unlike conventional SU-8 particle fabrication, which requires patterning on solid substrates followed by sacrificial-layer release, our approach eliminates substrate use entirely and produces particles with near 100% yields. The process supports a wide design space of in-plane geometries, including high-aspect-ratio and highly complex shapes. To enable large-scale particle libraries, we integrate the method with the Nazca Python library, allowing programmatic generation of tens of thousands of parametrically defined particle designs. This combination of substrate-free fabrication and automated design provides a scalable route to custom microparticles for materials science, microfluidics, and soft-matter applications.

Drop impacts are ubiquitous in natural and industrial processes, yet the influence of drop shape on impact force remains a fundamental open question. Combining experiments with theoretical analysis, we show that drop shape plays a critical role, with impact force varying by more than an order of magnitude solely due to changes in shape. By uncovering self-similarity in time and cross-shape similarity across diverse drop profiles, we develop a universal cylinder model that accurately predicts both the magnitude and timing of the impact force. This study establishes a comprehensive framework for understanding impact forces across a wide range of drop shapes. Given the prevalence of drop impacts with varying shapes in real-world scenarios, our findings hold fundamental significance and have broad potential applications across industries such as soil erosion prevention, jet cutting, spray coating, and design of windshields and wind turbines.

Assessment of the thermo-hydraulic performance of heat exchangers using computational fluid dynamics is a challenging task. The intricate geometries of a heat exchanger require a fine discretization of the flow passage, which consequently leads to high computational costs. A streamwise periodic flow model can significantly reduce this cost, particularly for heat exchangers featuring repeating structures. This manuscript presents the streamwise-periodic turbulent source terms for flows in channels with isothermal walls, along with the implementation of the corresponding periodic flow solver in the open-source CFD-Suite, SU2. The accuracy of the implemented solver was verified by comparing its predictions against those of a full fin array simulation for the test case of offset circular fins. The results show that the streamwise periodic flow solver accurately reproduces the solutions of the full array simulation under both laminar and turbulent flow conditions.

Self-propelled liquid metal-aluminum hybrid machines represent a promising class of autonomous motion systems capable of sustained movement without external power sources. While interactions between machines and their environment inevitably occur, the fundamental question of how spatial confinement affects the motion dynamics and the controllability of speed, direction, and lifetime of such liquid metal machines (LMMs) remains underexplored. Understanding these confined dynamics is essential for practical applications. Here, we present a comprehensive investigation of the non-symmetrical fueling principle governing the direction-tuning effect in LMMs. By confining LMMs within one-dimensional semi-open channels, we thoroughly disclose their impact and turning dynamics with different end obstacles throughout their lifecycle, with particular focus on fuel region morphological evolution, overall motion, and local flow characteristics after reaction times exceeding one hour. Utilizing ultra-high-speed imaging techniques, we systematically clarify how fuel region evolution and end-obstacle interactions influence symmetry-breaking mechanisms and reciprocating dynamics. Our findings reveal complex interactions between material properties, charge transfer processes, and fluid dynamics during end-turning processes, establishing a theoretical foundation for LMM driving dynamics. Beyond the theoretical mechanisms, we further demonstrate that LMM exhibits efficient heat and mass transfer capabilities, paving the way for applications in controlled transport systems and autonomous robotics.

Fast electrons in partially ionized plasmas lose energy through inelastic collisions with bound electrons. While the mean energy loss is well described by stopping-power theory, fluctuations associated with discrete excitation and ionization events produce energy straggling and an additional longitudinal diffusion in momentum space. We incorporate this effect into fast-electron kinetics through a derived Fokker-Planck operator whose coefficients are obtained from ab initio quantum many-body simulations. We demonstrate that neglecting inelastic energy diffusion in partially ionized D-Ar plasmas can underestimate primary runaway-electron generation by several orders of magnitude.

The combination of a Cherenkov radiator with a semi-transparent photocathode and a Micromegas based amplification stage allows PICOSEC Micromegas detectors to achieve a time resolution of better than 15ps. While tileable prototypes with 10x10 channels feature 1x1 cm^2 readout pads, finer readout granularity can be used to improve the spatial resolution. We report on the study of high readout granularity PICOSEC Micromegas prototypes which achieve around 0.5mm spatial resolution with 3.5mm large pads. No significant improvement was found when readout pad size was further reduced to 2.2mm. The timing resolution of the leading pad was found to be slightly degraded but remained better than 20ps for a medium granularity prototype. The achieved spatial resolution can enable PICOSEC Micromegas to be used as precise timing and moderate resolution tracking detector simultaneously.

Atmospheric turbulence severely limits the coupling of received optical wavefronts into single-mode fibers in satellite-to-ground free-space optical links. Spatial demultiplexing receivers address this challenge by distributing the incoming field across a bundle of single-mode fibers whose outputs are recombined coherently, relaxing the requirements on wavefront correction. In this work, we investigate the design of such receivers from two complementary angles. We first compare the power coupling statistics achieved by several modal bases and show that the spatial support of the modes matters far more than the specific choice of basis, questioning the relevance of mode-selective approaches for this application. We then present the inverse design of a compact two-plane refractive system optimized directly over an ensemble of turbulence realizations using stochastic gradient descent, with no constraint imposed on the input modal decomposition. The optimized system significantly improves over direct coupling into the fiber bundle, approaches the performance of an ideal modal projection, and remains competitive across a broad range of turbulence conditions.

We study the performance of the Particle Transformer (ParT) for jet flavor tagging using ILD full simulation events (1M jets) as well as fast simulation samples (10M and 1M jets). We perform 3-category ($b/c/d$), 6-category ($b/c/d/u/s/g$), and 11-category trainings (including quark--antiquark separation), incorporating multivariate hadron particle identification information from $dE/dx$ and time-of-flight. For $b$/$c$ tagging, we observe a factor of 5--10 improvement over previous BDT-based taggers, and we obtain reasonable performance for strange tagging and quark/antiquark separation. The 10M-jet fast simulation study indicates that further gains are possible with higher training statistics.

Controlling nonequilibrium responses in optically driven quantum materials is essential for advancing applications in energy conversion, ultrafast electronics, and quantum computation. Nonlinear optical spectroscopy serves as a powerful tool to investigate ultrafast electron and phonon dynamics in these systems; however, conventional nonlinear approaches often require intense laser pulses (> 10 GW/cm2) and typically encounter a strong background. Here, we introduce a phase-sensitive nonlinear spectroscopic technique that operates at low laser powers (~ 10 kW/cm2, pulse energies ~ 10 pJ) and enables real-time monitoring and active control of coherent phonons in a few-layer (three to five) thick 2H-MoTe2. Upon excitation with ultrashort (~ 10 fs) pump pulses, we achieve displacive excitation of coherent phonons, which periodically modulate the Kerr nonlinearity of the material, leading to cross-phase modulation (XPM) of a delayed probe pulse. This phase modulation induces spectral broadening and oscillations in the center of mass (COM) of the probe spectrum in time, enabling the detection of subtle nonlinear optical responses in a background-free manner. The nonlinear response can be selectively amplified or attenuated by adjusting the strength of the pump pulse, which controls the distribution of photoexcited carriers in the electronic bands. By combining two-color nondegenerate pump-probe measurements and time-dependent density-functional theory (TDDFT) calculations, we directly resolve the coupled nonequilibrium electronic and phonon dynamics. A dual-pump pulse scheme enables precise control of phonon oscillations, allowing selective activation or suppression of specific phonon modes and correspondingly the modulation of the Kerr nonlinearity.

We present a minimal-component bidirectional digital interconnect that enables simultaneous transmission and reception of baseband logic signals over a single coaxial cable. The circuit consists of a passive resistive hybrid providing matched line termination and directional separation, a single CMOS logic gate as driver, and a commercial LVDS receiver used as a differential comparator. No active echo cancellation, calibration, or transformer coupling is required. An analytical treatment of the hybrid network is used to determine the system parameter that maximizes the received signal amplitude. SPICE simulations predict deterministic timing errors caused by incomplete separation of transmitted and received signals. Experimental measurements confirm the predicted deterministic jitter and show good agreement with the simulation results. For typical laboratory coaxial cables up to 6 m, the measured peak-to-peak edge timing error remains below 1 ns. A bidirectional transmission experiment with randomized data at 250 MBaud demonstrates a clearly open eye diagram and confirms reliable full-duplex operation. Due to its simplicity and compatibility with existing coaxial infrastructure, the proposed approach may be useful in laboratory and detector environments where cable routing or feedthrough density is constrained.

The dynamic flow of cerebrospinal fluid (CSF) in brain ventricles exhibits flow features on several scales, both spatially and temporally. Most analysis of this complex flow and the accompanying transport has used instantaneous (Eulerian) flow variables. Such analysis makes understanding of unsteady transport challenging. Here, we analyze brain ventricular CSF flow both in a Eulerian sense and from the Lagrangian perspective -- a time-integrated view of the flow. With geometries generated from imaging data, we model CSF flow in adult human and embryonic zebrafish brain ventricles. In the human brain we model flow governed by cardiovascular pulsations, CSF secretion and motile cilia. The flow driven by cardiovascular pulsations is derived from a damped linear elastic model of brain ventricle deformations, as a result of applying displacement boundary conditions derived from experimental data. In the zebrafish brain we consider flow driven solely by motile cilia. The tissue and flow models are implemented and solved with finite element methods. We use the resulting velocity fields to compute finite-time Lyapunov exponent (FTLE) fields and use these fields to characterize Lagrangian coherent structures, which can be approximated by ridges in the FTLE fields. These coherent structures demonstrate prominent flow features in the brain ventricles congruent with findings in experimental research. In the human brain ventricles, we also investigate the role of inertia by comparing flow models governed by the Navier-Stokes and the Stokes equations. Comparisons show that solving the Stokes equations is adequate to compute integrated flow variables like stroke volumes, but that the Stokes approximation fails to resolve intricate features of flow and advective transport that are present in the solution to the Navier-Stokes equations, features that could be important to elucidating transport.

This study introduces a novel experimental configuration using OH-PLIF imaging to directly determine the stretch factor ($I_0$) in laminar premixed hydrogen flames transitioning from a quasi-stable to a thermodiffusively unstable regime. A rod-anchored V-flame is stabilised in a laminar premixed reactant flow. Near the anchoring rod, the mildly strained flame remains quasi-stable, exhibiting a smooth surface and a well-defined inclination angle ($\theta_{\mathrm{s}}$) to the main flow. This stable branch is associated with a burning rate $S_{\mathrm{s}}$. Farther downstream, the flame abruptly transitions to a regime dominated by thermodiffusive (TD) instabilities, characterised by cellular structures and a wrinkled surface. The distance between this transition and the anchor decreases with increasing equivalence ratio. This TD-unstable branch exhibits a larger mean flame-surface angle ($\theta_{\mathrm{u}}$), enabling direct determination of the flame-speed increase, $S_{\mathrm{u}}/S_{\mathrm{s}}$. It is assumed that this ratio represents the normalised flame consumption speed, $S_{\mathrm{c}}/S_{\mathrm{L}}$. Determination of $I_0$ additionally requires the increase in flame-surface area caused by the thermodiffusive instabilities. Three complementary methods are therefore used to evaluate the surface area of the TD-unstable branch ($A$) relative to a smooth reference area ($A_0$), yielding consistent trends in $A/A_0$ over the investigated equivalence-ratio range. The resulting $I_0$ values, with the main uncertainty arising from $A$, decrease monotonically with increasing equivalence ratio, from about 1.1--1.3 at $\phi=0.35$ to 0.8--0.9 at $\phi=0.40$, consistent with theoretical predictions. Additional numerical simulations in a reduced two-dimensional representation reproduce the same transition behaviour and yield qualitatively consistent results.

Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here, we develop a data-driven workflow for constructing a machine-learning interatomic potential (MLIP) tailored to gas-surface scattering dynamics, using nitric oxide (NO) scattering from highly oriented pyrolytic graphite (HOPG) as a benchmark system. Starting from an initial ab initio molecular dynamics (AIMD) dataset, local atomic environments are described by SOAP descriptors and analyzed in a reduced feature space obtained through principal component analysis. Farthest point sampling is then used to build a compact training set, and the resulting Deep Potential model is refined through a query-by-committee active-learning strategy using additional configurations extracted from molecular dynamics simulations over extended ranges of incident energies and surface temperatures. The final MLIP reproduces reference energies and forces with high fidelity and enables large-scale molecular dynamics simulations of NO scattering from graphite at a computational cost far below that of AIMD. The simulations provide detailed insight into adsorption energetics, trapping versus direct scattering probabilities, translational energy loss, angular distributions, and rotational excitation. Overall, the results reproduce the main experimental trends and demonstrate that descriptor-guided sampling combined with active learning offers an efficient and transferable strategy for constructing MLIPs for gas-surface interactions.

Rayleigh-Bénard convection under an imposed inductionless magnetic field is analysed statistically from the perspective of single-point and multi-scale energy budgets. The data is obtained from direct numerical simulations with a Rayleigh number of $10^6$, a Prandtl number of $1$ and Hartmann numbers of $0$, $20$, $40$ and $80$. Wall-parallel and wall-normal magnetic fields are considered as two separate cases. The initial analysis focuses qualitatively on the influence of the magnetic field upon the coherent structures. A central contribution of this work is the interpretation of these structural modifications through magnetohydrodynamically modified turbulent kinetic energy budgets. For example, in the wall-normal case, the thinning of the thermal plumes can be attributed to the damping of the pressure-diffusion mechanisms due to the Lorentz dissipation. In the wall-parallel configuration, Joule dissipation induces a pressure-strain redistribution mechanism that preferentially transfers kinetic energy from the wall-normal velocity component to the field-perpendicular, wall-parallel velocity component but less so to the field-parallel velocity component. This description is then extended to scale-space by considering budgets relating second- and third-order structure functions. Here, the anisotropy is accounted for by analysing directional structure functions. Despite the anisotropy, the Lorentz force appears as an isotropic sink damping intermediate and large scales of motion. The result of this is a lack of transfer between scales of motion and hence a flow with suppressed small-scale turbulence. These results establish a link between qualitative observations and long-term energy balances, providing new insight into magnetoconvective turbulence and informing future modelling and theoretical approaches to such flows.

A two-color laser-induced fluorescence (2C-LIF) technique is presented for investigating droplet impact on thin liquid films, enabling simultaneous, spatially and temporally resolved measurements of film thickness and scalar concentration. The method is applied to water droplets impacting thin liquid films over a range of Reynolds numbers, Weber numbers and dimensionless film thicknesses, providing direct access to early-time mixing processes during impact. To quantify scalar transport within the liquid film, the reconstructed concentration fields are evaluated using a coefficient-of-variaton (CV) approach, providing a quantitative measure of mixture homogeneity. This enables identification of the transition from inertia-dominated convective transport to diffusion-controlled mixing. Based on this analysis, an empirical correlation describing the evolution of CV as a function of Reynolds number and film thickness is formulated. Finally, the applicability of the 2C-LIF method is demonstrated for binary ethanol-water films, where additional transport mechanisms influence and modify the mixing dynamics.

Turbulent flows exhibit robust universal features -- including logarithmic mean velocity profiles, scale-invariant energy spectra, anisotropy constraints and strongly non-local transport -- yet a unifying dynamical principle underlying these phenomena remains elusive. We show here that turbulence can be organized around an emergent oscillatory degree of freedom governing the Reynolds stress. Starting from the exact non-local representation of the stress in terms of a propagator, we demonstrate that the spectral structure of the response contains a dominant complex-conjugate pair of poles, implying an effective oscillator coupled to the mean flow. In wall-bounded turbulence, the near-wall Airy structure selects and stabilizes this mode through non-local feedback, yielding the logarithmic velocity profile and fixing the asymptotic von Kármán constant, $\kappa \simeq 0.39$. In homogeneous turbulence, the same dynamical picture closes the inertial-range energy balance and yields the Kolmogorov constant as $C_k=2/[3(1-2^{-2/3})]\simeq 1.80$ at leading order. The resulting formulation leads to a closed tensorial set of mean-field equations in three spatial dimensions, significantly cheaper than direct numerical simulation yet rich enough to support geometry-dependent reduced dynamics interpretable as distributed networks of interacting oscillators. The associated phase field admits a geometric description connected with Berry phase, anisotropy evolution on the Lumley triangle, and an effective gauge-covariant structure of phase transport. These results suggest that turbulence is governed not by an algebraic closure, but by a dynamical and geometric organization of the mean stress.

Bayesian inference represents a principled way to incorporate Earth structure uncertainty in full-waveform moment tensor inversions, but traditional approaches generally require significant approximations that risk biasing the resulting solutions. We introduce a robust method for handling theory errors using simulation-based inference (SBI), a machine learning approach that empirically models their impact on the observations. This framework retains the rigour of Bayesian inference while avoiding restrictive assumptions about the functional form of the uncertainties. We begin by demonstrating that the common Gaussian parametrisation of theory errors breaks down under minor ($1-3 \%$) 1-D Earth model uncertainty. To address this issue, we develop two formalisms for utilising SBI to improve the quality of the moment tensor solutions: one using physics-based insights into the theory errors, and another utilising an end-to-end deep learning algorithm. We then compare the results of moment tensor inversion with the standard Gaussian approach and SBI, and demonstrate that Gaussian assumptions induce bias and significantly under-report moment tensor uncertainties. We also show that these effects are particularly problematic when inverting short period data and for shallow, isotropic events. On the other hand, SBI produces more reliable, better calibrated posteriors of the earthquake source mechanism. Finally, we successfully apply our methodology to two well studied moderate magnitude earthquakes: one from the 1997 Long Valley Caldera volcanic earthquake sequence, and the 2020 Zagreb earthquake.

Self-generated magnetic fields are commonly produced in high-power laser-plasma interactions. These fields can inhibit plasma heat-flow which makes them important in inertial fusion and controlled laboratory astrophysics experiments. In this work, we characterize the time evolution of self-generated magnetic fields using multi-view proton tomography at two timings. Tomographic reconstructions of the magnetic field show a clear transition from fields located close to the target at early time to more extended coronal fields at later time. The tomographic inversion and mesh radiography also enable a direct measurement of the magnetic-flux evolution. Comparisons with extended-MHD simulations show only moderate agreement in field structure, but good agreement in magnetic flux. This suggests that the field generation model is largely correct under these conditions, while the magnetic transport model requires additional development to reproduce the observed field structure.

Diffusion in living tissues governs essential physiological processes and is well studied within cells. Yet how extracellular molecular motion emerges from the structural complexity of tissues remains unresolved. In the brain, molecules move extensively through the extracellular space (ECS) enabling key functions, with effective diffusivities reduced by factors of 2 to 5 relative to free solution. This slowing has traditionally been captured by the phenomenological concept of tortuosity, but tortuosity does not specify the microscopic mechanisms responsible for diffusion hindrance. Here we directly visualize three dimensional extracellular diffusion in brain tissue using ultrashort single walled carbon nanotubes as nearinfrared tracers, achieving nanometric spatial precision and video rate temporal resolution. We find that motion is locally Brownian and that transport does not require scale free stochastic dynamics. Instead, hindered diffusion emerges from a geometry controlled crossover: free diffusion at short length scales gives way to constrained transport beyond a characteristic structural scale of the ECS. Thus, tortuosity arises as an emergent, scale dependent property rather than an intrinsic material constant. Beyond its biological implications, this behavior places extracellular transport within the broader physics of diffusion in disordered media. Brain tissue acts as a natural realization of geometry constrained transport phenomena observed in porous materials and random obstacle systems, linking molecular motion in living matter to the general case of structurally heterogeneous environments.

Multiple scattering of light by resonant vapor is characterized by Levy-type superdiffusion with a step size distribution $P(x) \propto 1/x^{1+{\alpha}}$, with $0 < {\alpha} < 2$. The Levy parameter ${\alpha}$ was measured from $P(x)$, steady fluorescence, frequency-dependent fluorescence and time-resolved transmission, all of them in the forward direction. Here we report first measurements of this quantity from timeresolved backward fluorescence, i.e., photons that are backward diffused from light pulses exciting a hot rubidium vapor. We show experimentally that ${\alpha}$ can be extracted from this diffuse reflection, and the results are consistent with time-resolved transmission (i.e., photons that are forward diffused) and steady frequency-dependent forward fluorescence. Theoretical simulations are consistent with these results. We also show that, although we measure ${\alpha} = 1$ for both transmission and reflection, the backward photons have a non-negligible amount of single scattering events even for high density, contrary to the forward photons where multiple scattering dominates.

Osteoporosis, a major global epidemic, often goes undetected until a fracture occurs, largely due to poor access to screening using gold standard methods, such as dual-energy X-ray absorptiometry (DXA). As a potential nonionizing radiation alternative, we present a transcutaneous spatially offset Raman spectroscopy (SORS) approach combined with machine learning (ML) to recover bone spectra through overlying soft tissue and extract diagnostic information. In a human cadaveric study spanning normal, osteopenic, and osteoporotic donors, we acquired paired Raman measurements from transcutaneous fingers at multiple spatial offsets (0, 3, and 6 mm) and from the corresponding exposed finger bones. Using this paired dataset, supervised machine-learning models were trained to reconstruct exposed-bone Raman spectra from transcutaneous measurements, enabling direct recovery of bone biochemical signatures from transcutaneous tissue. The ML predicted bone spectra preserved physiologically meaningful Raman features and demonstrated statistically significant differences between normal and osteoporotic groups across four key Raman-derived metrics (p < 0.05), representing, to our knowledge, the first demonstration of transcutaneous Raman discrimination between clinically established bone-health categories in a human cadaveric study. The ML-predicted spectra further correlated with distal-radius DXA T-scores (r = 0.73, RMSECV = 1.4), approaching the performance achieved using exposed-bone measurements (r = 0.9, RMSECV = 0.8). Finally, preliminary in vivo measurements from two volunteers revealed clear bone-related transcutaneous spectral features consistent with cadaveric data, supporting translational feasibility. Together, these results establish a foundation for nonionizing radiation, transcutaneous Raman assessment of bone health using supervised spectral extraction from accessible measurement sites

The MIDNA application specific integrated circuits (ASICs) are a series of skipper-CCD readout chips fabricated in a 65 nm low-power CMOS process that implement a correlated double sampling signal processing chain based on dual-slope integrators. They are capable of working from room to cryogenic temperatures, down to 84 K. The present iteration of the ASIC has been fabricated including several design updates and the addition of an on-chip voltage reference, resulting in improved performance. This work presents the main vulnerabilities solved, the changes carried out, and the resulting performance benefits. Measurements with a skipper-CCD and the ASIC at 140 K showed that the single-electron resolution can be reached by averaging the measured charge in the analog domain using the analog pile-up technique with a readout noise as low as 0.11 erms of equivalent charge for 1200 samples. The channel-to-channel crosstalk was also characterized showing values better than -62 dB.

The impact of thermodiffusive effects on combustion noise in turbulent premixed slot jet flames is investigated using Direct Numerical Simulations. Two thermodiffusively unstable lean hydrogen-air flames are compared with a thermodiffusively stable stoichiometric methane-air flame with comparable laminar properties and same turbulence intensity. The hydrogen cases differ in bulk velocity, chosen to match either the turbulent flame brush length or the bulk velocity of the methane case. Thermodiffusive effects are found to strongly alter both the heat release rate fluctuations, which dominate the far-field acoustic radiation, and the flame surface dynamics. A theoretical framework extending the classical flamelet theory to thermodiffusively unstable flames is proposed and validated, relating the flame-generated sound to the time derivative of the flame surface area and to the stretch factor $I_0$. The analysis identifies flame stretch as a key mechanism promoting noise radiation in thermodiffusively unstable flames. Spectral analyses further show that hydrogen flames exhibit stronger low-frequency heat release rate fluctuations and reduced high-frequency content relative to the methane flame. This is shown to be related to the coupled action of turbulence and thermodiffusive instabilities, which enhance large-scale flame motions while attenuating small-scale flame annihilation events. Consequently, hydrogen flames radiate more strongly at low frequencies, near the acoustic peak, and exhibit a steeper high-frequency spectral roll-off. Finally, Spectral Proper Orthogonal Decomposition reveals that hydrogen non-equidiffusion intensifies shear layer instabilities between combustion products and ambient air. These results indicate that thermodiffusive effects influence both direct and indirect combustion noise generation mechanisms in hydrogen flames.

We investigate the mechanism of whistler-mode wave generation by shock-reflected electrons at quasi-perpendicular collisionless shocks. By employing Liouville mapping to construct the electron velocity distribution function in the shock and performing linear instability analysis, we explore whistler wave generation by the mirror-reflected electrons near the upstream edge of the shock transition layer. We find that the reflected electrons can excite two distinct instabilities with different propagation directions when both the upstream electron beta $\beta_e$ and Alfven Mach number in the de Hoffmann-Teller frame $M_A/\cos\theta_{bn}$ are sufficiently large, where $M_A$ is \Alfven Mach number and $\theta_{bn}$ is the angle between the upstream magnetic field and the shock normal. In the parameter regime of Earth's bow shock, the instability threshold condition is roughly given by $M_A/\cos\theta_{bn}\gtrsim50$. Since such shocks are super-critical with respect to the whistler critical Mach number, the generated waves cannot propagate upstream and will accumulate in the transition layer. Furthermore, we find that the pitch-angle scattering by the generated waves may trigger secondary instabilities on the same branch. We suggest that the sequence of instabilities likely happening within the shock transition layer can efficiently scatter the reflected electrons over a broad range of pitch angles. Consequently, the reflected electrons may be confined within the shock by the waves generated by themselves. The self-confinement provides the necessary ingredient of stochastic shock drift acceleration, which then offers a plausible mechanism for the electron injection into diffusive shock acceleration.

We present a computationally efficient model that describes the interaction of intense, ultrashort infrared laser pulses with transparent materials in the strong ionization regime. The model is augmented with a detailed self-consistent description of the local response due to ionization and collisional plasma dynamics. It incorporates the direct solution of Maxwell's equations without approximations and rigorous boundary conditions for the input pulse, allowing us to study the ultrafast formation of over-critical nanoscaled plasmas in dielectric materials under the influence of intense tightly focused laser pulses. We perform a scan of the parameter space, find unexpected optima regimes for different experientially relevant parameters, and explain these maxima based on spatiotemporal dynamics.

We derive analytical expressions for chemical potentials within the random phase approximation (RPA), equivalently the $GW$ energy functional evaluated using non interacting Green's functions ($G_s$). The chemical potential is obtained using two formally equivalent approaches: a direct derivative of the total energy with respect to particle number, and a functional derivative via the chain rule through $G_s$, both validated with finite difference benchmarks. We show that the functional derivative of the $GW$ correlation energy$\unicode{x2013}$i.e., the $GW$ correlation self energy$\unicode{x2013}$exhibits a discontinuity at integer particle numbers with finite jumps. This resolves the apparent inconsistency between accurate $GW$ quasiparticle energies and the large delocalization errors observed in RPA total energies, as standard $GW$ self energies neglect this nonanalytic behavior. Our results suggest that derivative discontinuities are a fundamental feature of correlation energy functionals, analogous to the known discontinuity in the exact exchange correlation energy.

This work develops a spherical-multipole expansion of Goldstein's acoustic analogy, for the prediction of tonal noise from rotating propellers. The acoustic field is expressed through spherical multipoles, which separate source integrals from the observer dependence. This decoupling leads to computational efficiency: once the multipole coefficients are computed from blade geometry and aerodynamics, the sound field at any observer location is obtained by a simple evaluation of spherical harmonics and radial propagation factors, avoiding repeated integrations for each observer point. Moreover, this enables a straightforward radiated power calculation, without resorting to far-field pressure integrals. For hovering subsonic propellers, the results show a rapid convergence of the expansion. For each harmonic, the dominant radiation is accurately captured by the first two non-zero multipoles, corresponding to the leading symmetric and antisymmetric contributions with respect to the plane of rotation. To interpret the physical content of these leading terms, two simplified descriptions of the source integral are developed. The first is a lifting-surface formulation, suited to blades at small incidence, in which the thin-airfoil approximation allows to separate lift-like loading, drag-like loading, and thickness contributions. The second is a lifting-line formulation, suited to high-aspect-ratio blades, in which the surface integral is reduced to spanwise integrals of compact sectional moments. The validity of the two formulations is assessed through comparisons of directivity, power distribution over harmonics and time-domain waveforms. The results show good accuracy in their respective regimes of validity, together with substantial computational savings.

Contrary to accepted turbulence folklore, which holds that no mathematical relation exists between the Navier-Stokes equations (NSEs) and the multifractal model (MFM) of Parisi and Frisch, we develop a theory that reconciles the MFM with Leray's weak solutions of Navier-Stokes analysis. From a combination of Euler invariant scaling and the NSEs we also derive the Paladin-Vulpiani inverse scale $L\eta_{h,pav}^{-1} = Re^{1/(1+h)}$ which acts as a mediator between the two theories. This is achieved by considering $L^{2m}$-norms of the velocity gradient to find a correspondence between $m$ and the local scaling exponent $h$ in the multifractal model. The parameter $m$ acts as if it were the sliding focus control on a telescope which allows us to zoom in and out on different structures. The range $1 \leqslant m \leqslant \infty$ is equivalent to $-2/3 \leqslant h_{min} \leqslant 1/3$, which lies precisely in the region where Bandak et al. (2022, 2024) have suggested that thermal noise makes the NSEs inadequate and generates spontaneous stochasticity. The implications of this are discussed.

We investigate how the defining statistical features of three-dimensional turbulence respond to systematic reductions of the Fourier-space triadic interaction network. Using direct numerical simulations of both fractally and homogeneously decimated Navier-Stokes dynamics, we show that progressive thinning of the set of active modes leads to a systematic suppression of intermittency and, most strikingly, to the vanishing of the mean dissipation rate in the large-Reynolds-number limit. Structure-function exponents collapse onto their dimensional values, the multifractal singularity spectrum contracts, and the analyticity width extracted from the exponential spectral tail increases monotonically with decimation-each indicating a substantial regularization of the velocity field. Together, these results provide direct evidence that anomalous dissipation in incompressible turbulence is not a generic property of the Navier-Stokes equations, but instead requires the full combinatorial richness of their triadic nonlinear interactions.

We numerically simulate the two-dimensional inertial flow with the van der Waals effect in a straight periodic channel around the Poiseuille and Couette stationary states. Even though the flow remains laminar macroscopically, we observe complex dynamics and power decay of the Fourier spectra of small fluctuations of the density, velocity divergence, vorticity and kinetic energy of the flow near their respective stationary background states. Remarkably, pinning the vorticity to its background state, and leaving only the density and velocity divergence as the variables, results in the dynamics and power decay of the Fourier spectra qualitatively similar to those of the full system. This strongly indicates that the underlying physics of the power spectra reside primarily in the density and velocity divergence variables, and are not directly related to the vorticity of the flow.

Large Eddy Simulations with flamelet-based thermochemistry are used to investigate the behaviour of a premixed hydrogen-air flame stabilised by a bluff-body. Validation against experimental data is carried out first to demonstrate the model's ability to predict both velocity field and flame structure. The capability of the model in predicting differential diffusion effects is then assessed, in particular regarding the coupling between differential diffusion, tangential strain and curvature, and their effect on mixture fraction redistribution and reaction rate variation. Results indicate that unstretched flamelet thermochemistry is capable of capturing the increase in mixture fraction caused by positive resolved strain, as well as negative variations of mixture fraction due to negative curvature. Furthermore, the model is observed to mimic the effects of negative Markstein length to a certain extent, so that positive tangential strain causes reaction rate increase. The interplay between resolved stretch and preferential diffusion is also shown to lead to a shorter flame length which is in better agreement with experimental observations as compared to simulations under unity Lewis number assumption. These findings highlight that the macroscopic effects of differential diffusion and stretch on the premixed hydrogen flame, characterised by significant strain levels, can be predicted using a flamelet-based approach and without recurring to strained flamelets database, which implies important simplifications in the combustion modelling of turbulent hydrogen-premixed flames and offers valuable insights for the design of novel combustors.

Solid-state storage architectures based on NAND or emerging memory devices (SSD), are fundamentally architected and optimized for both reliability and performance. Achieving these simultaneous goals requires co-design of memory components with firmware-architected Error Management (EM) algorithms for density- and performance-scaled memory technologies. We describe a Machine Learning (ML) for systems methodology and modeling for co-designing the EM subsystem together with the natural variance inherent to scaled silicon process of memory components underlying SSD technology. The modeling analyzes NAND memory components and EM algorithms interacting with comprehensive suite of synthetic (stress-focused and JEDEC) and emulation (YCSB and similar) workloads across Flash Translation abstraction layers, by leveraging a statistically interpretable and intuitively explainable ML algorithm. The generalizable co-design framework evaluates several thousand datacenter SSDs spanning multiple generations of memory and storage technology. Consequently, the modeling framework enables continuous, holistic, data-driven design towards generational architectural advancements. We additionally demonstrate that the framework enables Representation Learning of the EM-workload domain for enhancement of the architectural design-space across broad spectrum of workloads.

In classical dynamical systems, chaotic behavior is often associated with exponential sensitivity to initial conditions together with global phase-space structure. Translating this geometric concept to the strictly linear framework of quantum mechanics presents a conceptual puzzle. The out-of-time-ordered correlator (OTOC) is often motivated as the quantum analogue of the classical butterfly effect, but this slogan can hide important mathematical distinctions. This tutorial bridges the gap between applied mathematics and quantum information by detailing the mathematical machinery of the OTOC. We explore how classical sensitivity translates to operator non-commutativity, why standard two-point correlation functions fail to cleanly detect this sensitivity, and how the delocalization of quantum observables relates to classical notions of mixing. Crucially, we outline what the OTOC can and cannot diagnose, distinguishing between local instability and global chaos. Ultimately, we provide a precise and usable conceptual map, exploring how the Koopman-von Neumann formalism offers a framework to view classical and quantum dynamics through a shared linear perspective.

A recent paper, Phys. Rev. Research 8, 013261 (2026), claims that the polaromechanical normal-mode splitting (NMS) measured in Nat. Commun. 16, 5652 (2025) is not true based on their two results: $i$) there is no true splitting in the linear-scale spectrum; $ii$) the total or intrinsic decay rate of the cavity-magnon polariton, set by the imaginary part of the pole of the total output spectrum, remains unchanged under the coherent-perfect-absorption (CPA) condition. In this comment, we indicate that $i$) there is NMS in both the linear and logarithmic scales of our spectra in {\it a narrow frequency range} around the CPA frequency; $ii$) the total decay rate defined via the {\it pole} of the spectrum cannot characterize the vanishing {\it effective} decay rate at the CPA frequency (known as the monochromaticity of the CPA), and thus this parameter is irrelevant to the NMS measured in our experiment in {\it a narrow frequency range} around the CPA frequency. Consequently, their results above are either false or irrelevant, and thus cannot support their claim on the polaromechanical strong coupling measured in our experiment.

Replica exchange (REX) is one of the most widely used enhanced sampling methodologies, yet its efficiency is limited by the requirement for a large number of intermediate temperature replicas. Here we present Generative Replica Exchange (GREX), which integrates deep generative models into the REX framework to eliminate this temperature ladder. Drawing inspiration from reservoir replica exchange (res-REX), GREX utilizes trained normalizing flows to generate high-temperature configurations on demand and map them directly to the target distribution using the potential energy as a constraint, without requiring target-temperature training data. This approach reduces production simulations to a single replica at the target temperature while maintaining thermodynamic rigor through Metropolis exchange acceptance. We validate GREX on three benchmark systems of increasing complexity, highlighting its superior efficiency and practical applicability for molecular simulations.

In this comment, we demonstrate that the claim by Spavieri et al., asserting that Wang et al.'s interferometric experiment disproves the special theory of relativity by revealing that simultaneity must be an absolute concept independent of the observer's state of motion, is based on circular reasoning and therefore constitutes a logical fallacy.

Neural Network Variational Monte Carlo (NNVMC) has emerged as a promising paradigm for solving quantum many-body problems by combining variational Monte Carlo with expressive neural-network wave-function ansätze. Although NNVMC can achieve competitive accuracy with favorable asymptotic scaling, practical deployment remains limited by high runtime and memory cost on modern graphics processing units (GPUs). Compared with language and vision workloads, NNVMC execution is shaped by physics-specific stages, including Markov-Chain Monte Carlo sampling, wave-function construction, and derivative/Laplacian evaluation, which produce heterogeneous kernel behavior and nontrivial bottlenecks. This paper provides a workload-oriented survey and empirical GPU characterization of four representative ansätze: PauliNet, FermiNet, Psiformer, and Orbformer. Using a unified profiling protocol, we analyze model-level runtime and memory trends and kernel-level behavior through family breakdown, arithmetic intensity, roofline positioning, and hardware utilization counters. The results show that end-to-end performance is often constrained by low-intensity elementwise and data-movement kernels, while the compute/memory balance varies substantially across ansätze and stages. Based on these findings, we discuss algorithm--hardware co-design implications for scalable NNVMC systems, including phase-aware scheduling, memory-centric optimization, and heterogeneous acceleration.

Variational Monte Carlo (VMC) is a powerful and fast-growing method for optimizing and evolving parameterized many-body wave functions, especially with modern neural-network quantum states. In practice, however, the stochastic estimators that form the backbone of the method can become unstable or biased due to the presence of nodes, a ubiquitous feature of quantum wave functions. In the continuum, this results in heavy-tailed estimators with potentially divergent variances, while in discrete Hilbert spaces the sampling distribution can miss parts of the support needed to form unbiased estimators. These statistical pathologies lead to unreliable optimization trajectories in stochastic reconfiguration or incorrect variational dynamics in time-dependent Variational Monte Carlo (t-VMC), and severely limit the power of the numerical simulations. We introduce blurred sampling to address these difficulties. The method has a number of rigorous properties that make it well-behaved, effective and efficient. Additionally it is a post-processing approach that can be used without modifying the underlying sampler and incurs only minimal overhead. We demonstrate its effectiveness on several representative examples where standard sampling approaches are known to fail, and apply it to large-scale problems in spin dynamics. This work establishes a broadly applicable framework for robust VMC and t-VMC calculations.

The intricate relations between elements in natural and human-made systems sustain the complex processes that shape our world, forming multiscale networks of interactions. These networks can be represented as graphs composed of nodes connected by links and, regardless of their domain, they share a set of fundamental structural properties. The family of network models in hyperbolic space constitutes one of the most advanced frameworks accounting for such properties, including sparsity, the small-world property, heterogeneity and hierarchical organization, high clustering, and scale invariance under network renormalization transformations. These geometric models also exhibit other intriguing phenomena, such as an anomalous, temperature-dependent phase transition between a geometric and a non-geometric phase. In simple graph representations, where network links are unweighted, the model can be derived within a statistical-mechanics framework by maximizing the Gibbs entropy of the graph ensemble subject to constraints imposed by observations, with links effectively behaving as fermionic particles. In this topical review, I revisit these derivations previously scattered across different sources and complement them, in order to properly contextualize and consolidate hyperbolic random graphs within the broad framework of the maximum-entropy principle in the statistical mechanics of complex networks. The approach presented here represents the least-biased prediction of the fundamental set of core network properties and establishes a principled framework for analyzing network structure, offering new perspectives and powerful analytical tools for both theoretical and empirical studies.

In this paper we discuss a protocol for charging a two-level quantum battery using a bipartite charger composed of two quantum harmonic oscillators. As one of its features, it allows us to fully charge the battery and is universally optimal in the regime of a single excitation added as energy input. We also make use of a selective interaction to extend the protocol for a different class of quantum states and show that, in this case, the presence of quantum coherence can be harnessed as energetic resource to charge multiple similar batteries. Among these, we also explore symmetries of the derived effective dynamics to quickly discuss how the same protocol can be adapted to the task of \textit{active state resetting}, a task which is particularly useful in the quantum computation area.

We investigate dissipative phase transitions (DPTs) in a parametrically amplified open quantum Rabi model (QRM) with both single- and two-photon decay. In the classical oscillator limit, four composite phases emerge, arising from the possible normal or superradiant regimes across the upper and lower spin branches. A mean-field analysis reveals an ``inverted" regime where superradiance emerges only at sufficiently low spin-boson coupling. This regime features first- and second-order DPTs separated by a tricritical point, while two-photon dissipation preserves the stability of the superradiant phase. Utilizing an adiabatic approach and the semi-classical Langevin formalism, we further study the steady-state structure beyond the mean-field level. We show that the tricriticality stems from the intrinsic nonlinearity of QRM, unveiled by the interplay of coherent and dissipative two-photon processes. The universality classes of the DPTs are identified, with the corresponding critical and finite-size scaling exponents derived and a scaling ansatz proposed to describe the critical behavior.

High-dimensional photonic entanglement holds significant promise for advancing quantum communication, computation, and metrology. For example, large-alphabet quantum communication protocols are known to benefit from enhanced noise resilience and information capacity via multi-bit time-bin encoding. Yet, characterizing high-dimensional entangled states is challenging, as full state tomography becomes prohibitively costly and often requires unrealizable measurements. Here, we demonstrate a scan-free method to characterize high-dimensional entanglement in the time-frequency domain. Our reconstruction achieves a record $5.70\pm0.07$ ebits and a fidelity of $65.4\pm0.4\%$ with the maximally entangled state of local dimension $1021$, certifying the presence of $668$-dimensional entanglement. We further prove the attainability of a secure key rate of $15.6$ kB/s in a composable finite-size, entanglement-based protocol, and show that in continuous operation, the setup can quickly approach asymptotic key rates. Using commercial telecom components and state-of-the-art low-jitter single-photon detectors, our scalable architecture offers a practical path towards high-rate, noise-resilient quantum communication testbeds.

In computational fluid dynamics (CFD), the numerical integration of the Navier-Stokes equations is frequently constrained by the Poisson equation to determine the pressure. Discretization of this equation often results in the need to solve a system of linear algebraic equations. This step typically represents the primary computational bottleneck. Quantum linear system algorithms such as Harrow-Hassidim-Lloyd (HHL) offer the potential for exponential speedups for solving sparse linear systems, such as those that arise from the discretized Poisson equation. In this work, we successfully couple HHL to a discretized formulation of the incompressible Navier-Stokes equations and demonstrate both accurate lid-driven cavity flow simulations as a fully integrated benchmark problem, and accurate flow of the Taylor-Green vortex. To address the readout limitation, we utilize a recent novel quantum state tomography (QST) approach based on Chebyshev polynomials, which enables approximate statevector extraction without full state reconstruction. Together, these results clarify the algorithmic structure required for quantum CFD, explicitly confront the measurement bottleneck, and establish benchmark problems for future quantum fluid simulations. We implement the solver using IBM's Qiskit framework and validate the hybrid quantum-classical simulation against standard classical numerical methods. Our results demonstrate that the hybrid solver successfully captures the global vortex dynamics of the lid-driven cavity problem and the Taylor-Green vortex, offering a robust pathway for integrating quantum subroutines into more practical higher-Reynolds number CFD workflows.

We present Active Learning for Accelerated Bayesian Inference (\texttt{alabi}): an open-source Python package for performing Bayesian inference with computationally expensive models. Given a forward model and observational data to construct a likelihood and priors, \texttt{alabi}\ uses a Gaussian Process (GP) surrogate model trained to predict posterior probability as a function of input parameters, and employs active learning to iteratively improve GP predictive performance in high-likelihood regions where the GP is most uncertain. \texttt{alabi}\ provides a uniform interface for using Markov chain Monte Carlo (MCMC) with different packages, including the affine-invariant sampler \texttt{emcee}, and nested samplers \texttt{dynesty}, \texttt{multinest}, and \texttt{ultranest}. This approach facilitates accurate estimation of the desired posterior distribution, while reducing the number of computationally expensive model evaluations required by factors of thousands. We demonstrate the performance of \texttt{alabi}\ on a variety of test cases, including where inference is challenging due to complex posterior structure or high dimensionality. We show that \texttt{alabi}\ offers a substantial improvement for likelihood functions with evaluation times $\gtrsim 1$\,s, speeding up MCMC computations by a factor of $10-1000\times$ when tested on problems with up to 64 dimensions.

Graph colorings have been of interest to mathematicians for a long time, but relatively recently, social scientists have also found them to be interesting tools for studying group behavior. In the last 20 years, scientists have begun to study how coloring problems can be solved by groups of individuals on a graph, which has led to new insights into network structure, group dynamics, and individual human behavior. Despite this newfound utility, the exact nature of these distributed coloring problems is not well-understood, and established mathematical tools like the chromatic polynomial miss the unique challenges that arise in these social problem-solving situations with limited information. In this paper, we provide a new framework for understanding these distributed problems by defining a new kind of graph coloring with particular relevance to consensus formation on networks, in which all vertices are trying to agree on a common color. These strict gridlock colorings represent roadblocks to consensus where the group will not reach a uniform coloring using natural update processes. We describe a recurrence relation that provides an algorithm for counting these gridlocked colorings, which establishes a mathematical measure of how much a given graph hinders consensus in a group.

Photonic quantum computing has gained significant interest in recent years due to its potential for scaling to large numbers of qubits. A critical requirement for fault-tolerant quantum computation is the reliable generation of non-Gaussian quantum states, typically achieved using Gaussian operations and photon-number-resolving detectors. However, the probabilistic nature of quantum measurement typically results in low success rates for state preparation. Conventionally, these circuits are optimized to herald a single specific target outcome, thereby disregarding the potential utility of alternative measurement patterns generated by the same physical setup. In this work, we propose and demonstrate a multi-outcome optimization strategy that increases the overall acceptance probability by allowing a single circuit to produce useful quantum states across several measurement patterns. To evaluate this approach, we apply the framework to the generation of Gottesman-Kitaev-Preskill core states, Schrodinger cat states, binomial codes, and cubic phase states using both two-mode and three-mode Gaussian circuits. We demonstrate that the success probability can be enhanced through two distinct mechanisms: first, by simultaneously targeting a diverse set of useful resource states, and second, by aggregating degenerate outcomes to maximize the production rate of a single target state.

Multiple pairs of bronze pieces were joined along a common seam and then exposed to Nb vapor via sputter deposition during heating at $\sim$715 $^\circ$C to form a diffusion bond between the pieces. Polishing and alignment of the pieces created smooth surfaces normal to the Nb flux with seams perpendicular to the surface (i.e. parallel to the Nb flux). Conversion of Nb to Nb$_3$Sn took place simultaneously with diffusion bonding, resulting in Nb$_3$Sn thin films that coated bronze surfaces and spanned seams with uniform thickness. Characterization of superconducting properties via magneto-optical imaging suggests that supercurrent flows freely across the seam in several examples when cooled to 9 K and shielding or trapping low magnetic field. Modification of the process to coat the pieces with Nb prior to diffusion bonding and Nb$_3$Sn formation resulted in varying degrees of seam coverage by the resultant Nb$_3$Sn films. The pre-coating method did not produce any example with quality comparable to the examples obtained by the hot bronze approach. This work could enable new approaches to joining Nb$_3$Sn materials in magnet conductor and RF cavity applications.

A quantum mirror is a device whose optical response, that is, transmission and reflection, can be controlled by a single qubit. Here, we propose the use of quantum mirrors as nodes in quantum networks. Propagating coherent states mediate the interaction between the control qubits of each quantum mirror. This allows implementing quantum teleportation, quantum state transfer, and entanglement swapping with success probability and average fidelity exponentially approaching unity as the average photon number increases. Furthermore, we show that quantum teleportation exhibits robustness against known sources of error, such as optical path phase difference, photon loss, and reduced quantum mirror reflectivity, presenting a promising alternative towards long-distance quantum communication.

Wavelet-based grid adaptation methods use multiresolution analysis for error estimation, offering a mathematically rigorous approach to adaptive grid refinement when solving Partial Differential Equations (PDEs). However, applying these methods to PDE discretizations with immersed geometries is challenging, as standard interpolating wavelet transforms lose consistency near non-grid-aligned boundary intersections. To address this, we propose a high-order interpolating wavelet transform adaptation strategy compatible with sharp immersed boundary and interface discretizations. The approach performs consistent high-order wavelet transforms on narrow intervals using a 1D polynomial extrapolation technique. To maintain high order, the technique incorporates boundary values and derivatives, which are evaluated from multivariate interpolating polynomials similar to those used in high order immersed finite difference discretizations. Consequently, the proposed approach maintains the wavelet order on any arbitrary smooth multidimensional domain, including near concave geometry sections. This approach enables grid adaptation in complex domains while robustly bounding the numerical error via a manually set refinement threshold. The algorithm's performance is validated on both static and dynamic problems, including the Navier-Stokes equations with moving boundaries and temporally adapting grid resolutions. The results demonstrate that the proposed method enables effective grid adaptation, establishing a robust, predictable relationship between a user-defined refinement threshold and the overall solution error, even for problems with complex, moving boundaries.

The generation of a trapping potential for dark-state polaritons in a two-dimensional electromagnetically induced transparency system is theoretically studied. We show that such a trap can arise from a spatially inhomogeneous effective mass of the dark-state polariton. Because this mass inhomogeneity can be engineered by tuning the parameters of the control fields, the motion, spatial profile, and coherent behavior of bound dark-state polaritons can be tailored accordingly. Our results enable spatial controls of optical information and provide a possible route toward realizing Bose-Einstein condensation of dark-state polaritons in a trapping potential.

We study a continuous-time dynamical system of nodes diffusively coupled over a hierarchical network to examine the efficiency and performance tradeoffs that organizations, teams, and command and control units face while achieving coordination and sharing information across layers. Specifically, after defining a network structure that captures real-world features of hierarchical organizations, we use linear systems theory and perturbation theory to characterize the rate of convergence to a consensus state, and how effectively information can propagate through the network, depending on the breadth of the organization and the strength of inter-layer communication. Interestingly, our analytical insights highlight a fundamental performance tradeoff. Namely, networks that favor fast coordination will have decreased ability to share information that is generated in the lower layers of the organization and is to be passed up the hierarchy. Numerical results validate and extend our theoretical results.

We report a comparative study on transmon qubit control using (i) conventional attenuated coaxial microwave line and (ii) an optical control system using modulated laser light delivered over telecommunications optical fiber to a photodiode located at the 1K stage of a dilution cryostat. During each experiment, we performed repeated measurements of the energy relaxation and coherence times of a transmon qubit using one of the control signal delivery methods. Each measurement run spanned 20 hours of measurement time and from these datasets we observe no measurable effect on coherence of the qubit compared to random coherence fluctuations. Our results open up the possibility of large scale integration of the optical qubit control system.

Aerodynamic drag reduction on highways through vehicle platooning is a well-known concept, but it has not yet seen systematic uptake, arguably because of significant technological and legislative obstacles. As a low-tech entry point to real multi-vehicle platooning, "Windbreaking-as-a-Service" (WaaS) was introduced recently. Here we use a QUBO formulation to study classical metaheuristics such as simulated annealing and tabu search, together with emerging quantum heuristics including quantum annealing and variants of the Quantum Approximate Optimization Algorithm (QAOA). These heuristic solvers do not guarantee optimality, but they traverse the same higher-order landscape using polynomial memory. They can also be parallelized aggressively, and efficient classical post-processing can be used in hybrid workflows to return only valid schedules. This paper therefore positions QUBO as a common language that allows heterogeneous classical, quantum, and hybrid solvers to address the optimization of highway platooning.

Quantum computing experiments and testbeds with large qubit counts have until recently been a privilege afforded only to large companies or quantum technologies where scaling to hundreds or thousands of qubits does not require a substantial increase in quantum control hardware (neutral atoms, trapped ions, or spin defects). Superconducting and spin qubit testbeds critically depend on scaling their control systems beyond what a single electronics board can provide. Multi-board control systems combining RF, fast DC control, bias, and readout require precise synchronization and communication across many hardware and firmware components. To address this, we present XCOM, a network that synchronizes QICK boards and the absolute clocks governing quantum program execution to within 100 ps, free of drift and loss of lock. XCOM also provides deterministic, all-to-all simultaneous data communication with latency below 185 ns. Like QICK itself, XCOM is compatible with a broad range of qubit technologies and is designed to scale to large systems.

Classical reverse diffusion is generated by changing the drift at fixed noise. We show that the quantum version of this principle obeys an exact law with a sharp phase boundary. For Gaussian pure-loss dynamics -- the canonical model of continuous-variable decoherence in optical attenuation channels, squeezed-light interferometric sensing, and superconducting bosonic architectures -- complete positivity, the requirement that the dynamics remain physical even for systems entangled with an ancilla, creates an exact phase boundary at which the minimum reverse cost vanishes, fixes the reverse-noise budget on both sides, and makes pure nonclassical targets dynamically singular. The minimum reverse cost vanishes exactly at a critical squeezing-to-thermal ratio and is strictly positive away from it, with a sharp asymmetry: below the boundary, standard reverse prescriptions such as the fixed-diffusion Bayes reverse remain feasible at mild cost; above it, these prescriptions become infeasible, the covariance-aligned generator remains CP-feasible and uniquely optimal, and the cost can be severe. The optimal reverse noise is locked to the state's own fluctuation geometry and simultaneously minimizes the geometric, metrological, and thermodynamic price of reversal. For multimode trajectories, the exact cost is additive in a canonical set of mode-resolved data, and a globally continuous protocol attains this optimum on every mixed-state interval. If a pure nonclassical endpoint is included, the same pointwise law holds for every $t>0$, but the optimum diverges as $2/t$: exact reversal of a pure quantum state is dynamically unattainable. These results establish an exact law of quantum reversibility in the canonical pure-loss setting and provide a sharp benchmark for broader theories of quantum reverse diffusion.

Topological Bloch oscillations are a hallmark of quantum transport phenomenon in which wavepackets undergo oscillatory motion driven by the interplay between an external force and topological edge states and serve as a powerful dynamical probe for the geometric properties of topological bands. Spin-orbit coupling (SOC) has also emerged as a crucial ingredient for manipulating quantum states in materials, with the corresponding gauge fields arising from the Rashba and Dresselhaus interactions. In this work, we investigate the propagation of spinor wavepackets in a honeycomb Zeeman lattice governed by the Gross-Pitaevskii equation. By tuning the relative strengths of Rashba and Dresselhaus SOC, we engineer a non-Abelian gauge field that drives anomalous topological Bloch oscillations (ATBOs). Unlike conventional topological Bloch oscillation (TBOs), these ATBOs exhibit asymmetric motion, including a freezing effect in one half of the oscillation cycle, which can be tuned by the SOC parameters and external forces. Our findings establish SOC-based non-Abelian gauge fields as a powerful mechanism controlling topological quantum dynamics, with implications for spintronic devices and quantum data processing.

Non-Gaussian statistics are a challenge for data assimilation. Linear methods oversimplify the problem, yet fully nonlinear methods are often too expensive to use in practice. The best solution usually lies between these extremes. Triangular measure transport offers a flexible framework for nonlinear data assimilation. Its success, however, depends on how the map is parametrized. Too much flexibility leads to overfitting; too little misses important structure. To address this balance, we develop an adaptation algorithm that selects a parsimonious parametrization automatically. Our method uses P-spline basis functions and an information criterion as a continuous measure of model complexity. This formulation enables gradient descent and allows efficient, fine-scale adaptation in high-dimensional settings. The resulting algorithm requires no hyperparameter tuning. It adjusts the transport map to the appropriate level of complexity based on the system statistics and ensemble size. We demonstrate its performance in nonlinear, non-Gaussian problems, including a high-dimensional distributed groundwater model.

Statistical mechanics reveals that the properties of a macroscopic physical system emerge as an average over an ensemble of statistically independent microscopic subsystems, each occupying a specific microstate. In some models of quantum systems, these microstates are the wavefunction states of individual quantum this http URL physical principles that govern the distribution of a wavefunction ensemble, even under conditions of thermal equilibrium, are not well established. For instance, the canonical Boltzmann distribution cannot be applied to wavefunctions because they lack a definite energy. In this manuscript, we present a maximum entropy principle for the quantum wavefunction ensemble at thermal equilibrium, the so-called Scrooge ensemble. We highlight that a constraint on the energy expectation value, or even the shape of the associated eigenstate distribution, fails to yield a valid equilibrium state. We find that in addition to these constraints, one must also constrain the measurement entropy to be equal to the Rényi divergence of the ensemble with respect to the Gibbs state, indicating that the Rényi divergence may have uninvestigated physical importance to thermal equilibrium in quantum systems.

We investigate the persistence and regularity of spectral submanifolds (SSMs) in high-dimensional parametric dynamical systems undergoing a Hopf bifurcation. By analyzing how resonances in the linearized spectrum near bifurcation points limit the existence and smoothness of SSMs, a phenomenon that has been mostly overlooked, we show that low-order Taylor coefficients of the SSM expansion and the associated reduced dynamics persist smoothly through the bifurcation. This analysis generalizes to any local bifurcation and provides a clear estimate of the parameter ranges over which a parametric SSM model can be justified, thus illustrating how globally the model can be extended despite the presence of resonances near criticality. We demonstrate these findings on multiple examples, including a data-driven SSM approach to the lid-driven cavity flow. For that problem, we construct a parametric SSM-reduced model that accurately captures the full transition to periodic dynamics and the critical Reynolds number. These results provide a mathematical foundation for robust data- and equation-driven model reduction of fluid flows across bifurcations, enabling an accurate prediction of nonlinear dynamics across critical parameter regimes.

Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities remains compelling, quantum algorithm development must adapt to the evolving constraints of the emerging quantum hardware in order to accomplish any advantage for the computational chemistry practice. At the same time, the continuous advancement of classical wavefunction-theory methods narrows the window for a broad quantum advantage. Here, we explore potential benefits of quantum computation from the broader perspective of utility-scale applications. We argue that quantum algorithms need not only enable accurate calculations for a few challenging, that is strongly correlated, molecular structures, that might be hard to describe with traditional methods. Instead, they must also support the practical integration of quantum-accelerated computations into high-throughput pipelines for routine calculations on arbitrary molecules, ultimately delivering a tangible value to society.

Models of interacting quantum spins are used in many areas of physics ranging from the study of magnetism and strongly correlated materials to quantum sensing. In this work, we study coherent many-body dynamics of interacting spin models realized using polar molecules trapped in rearrangeable optical tweezer arrays. Specifically, we encode quantum spins in long-lived rotational states and use the electric dipolar interaction between molecules, together with Floquet Hamiltonian engineering, to realize $1/r^3$ XXZ and XYZ models. We microscopically probe several types of coherent dynamics in these models, including quantum walks of single spin excitations, the emergence of magnon bound states, and coherent creation and annihilation of magnon pairs. Our results establish molecular tweezer arrays as a new quantum simulation platform for interacting quantum spin models.

The paper describes a numerical method for solving acoustic multibody scattering problems in two and three dimensions. The idea is to compute a highly accurate approximation to the scattering operator for each body through a local computation, and then use these scattering matrices to form a global linear system. The resulting coefficient matrix is relatively well-conditioned, even for problems involving a very large number of scatterers. The linear system is amenable to iterative solvers, and can readily be accelerated via fast algorithms for the matrix-vector multiplication such as the fast multipole method. The key point of the work is that the local scattering matrices can be constructed using potentially ill-conditioned techniques such as the method of fundamental solutions (MFS), while still maintaining scalability and numerical stability of the global solver. The resulting algorithm is simple, as the MFS is far simpler to implement than alternative techniques based on discretizing boundary integral equations using Nyström or Galerkin.

Dynamic metasurface antennas (DMAs) are an emerging hybrid-MIMO technology distinguished by an ultrathin form factor, low cost, and low power consumption. In real-world DMA prototypes, mutual coupling (MC) between meta-elements is generally non-negligible; some architectures even deliberately exploit strong MC to enhance wave-domain flexibility. In this paper, we address channel estimation (CE) for DMAs with known MC by formulating it as a tensor-decomposition problem. We develop a generalized block Tucker alternating least squares (BTALS) algorithm, together with specialized variants for cases with known direct and/or feed channel. We also develop a reciprocity-aware bilinear factorization method for the case with known direct channel. We experimentally validate our algorithms using an 18 GHz DMA prototype whose 7 feeds and 96 meta-elements are strongly coupled via a chaotic cavity. Our general BTALS algorithm reaches an accuracy of 43.1 dB, only 0.3 dB below the upper bound imposed by experimental noise. All proposed algorithms generally outperform the prior-art reference scheme thanks to the superior noise rejection enabled by the tensor-based framework. We further study the minimum number of required measurements as a function of the number of feeds and demonstrate the importance of MC awareness by comparison with an MC-unaware benchmark. Finally, we apply BTALS to a second setup enabling the prediction of the DMA's full dual-polarization 3D radiation diagram. We also measure the latter for DMA configurations optimized for channel-gain enhancement based on the estimated channels. Altogether, our work establishes the practical relevance of MC-aware tensor methods; beyond DMAs, it applies to all wireless systems with wave-domain programmability enabled by tunable lumped elements.

We present a fast and memory-efficient algorithm for transient, space-time-domain, and elastodynamic boundary-integral analysis. Associated data-sparse approximations and operations are named fast domain partitioning hierarchical matrices (FDP=H-matrices). The fast domain partitioning method (the FDPM) solves a known problem of hierarchical matrices (H-matrices) in compressing discretized elastodynamic kernel functions. A novel set of plane-wave approximations then unites the FDPM and H-matrices in an accurate analytic manner. Memory usage is $\mathcal O(N \log N)$ and computation time $\mathcal O(NM \log N)$ in our algorithm throughout one run with $N$ boundary elements and $M$ time steps. The amount of associated cost reduction is remarkable, as the memory usage and computational time have been originally $\mathcal O(N^2M)$ and $\mathcal O(N^2M^2)$, respectively, to run the orthodox time-marching implementation. Numerical experiments indicate that FDP=H-matrices achieve $\mathcal O(NM/\log N)$ times smaller memory and computation time while ensuring the accuracy of the analyses.

An attractive way to model nuclear quantum effects is to describe select nuclei quantum mechanically at the same level as the electrons. This non-Born-Oppenheimer (non-BO) method is known by many names including the nuclear-electronic orbital (NEO) and the multicomponent method. Two basis sets are typically used for such calculations: a nuclear basis set and an electronic basis set. In this work, we investigate the convergence of non-BO proton affinities (PAs) with respect to the protonic and electronic basis sets. PAs are a sensitive measure of the proton and electron densities. We demonstrate that most protonic basis sets are sufficient for non-BO density-functional calculations of PAs, resulting in convergence to within 0.1 kcal/mol of the complete protonic basis set limit. This indicates that the truncation error is dominated by the electronic basis, and that smaller protonic basis sets could be developed. We show that non-BO calculations should use uncontracted electronic basis sets on the quantum protons. The contraction coefficients in typical electronic basis sets have been derived for point nuclear charge distributions, and uncontracting the electronic basis set on the quantized proton leads to significantly faster convergence to the electronic basis set limit. Uncontraction leads to results of one $\zeta$-level higher quality with negligible additional computational cost in multiple diffuse basis set families: Jensen's polarization consistent aug-pc-X basis sets, Dunning's correlation-consistent aug-cc-pVXZ basis sets, as well as the Karlsruhe def2-XZPD basis sets. In specific, the aug-pc-3 electronic basis set already affords PAs converged beyond 0.1 kcal/mol when uncontracted on the quantum proton.

We propose schemes to produce highly nuclear-spin polarized small molecules in an intense and cold molecular beam via microwave or infrared rotational excitation, followed by hyperfine-induced quantum beats. Repumping schemes can be used to achieve polarization above $90\%$ in cases where single-pumping schemes are insufficient. We discuss the possibility of high production rates which allow applications including nuclear-magnetic-resonance signal enhancement, and spin-polarized nuclear fusion, where polarized nuclei are known to enhance D-T and D-$^3$He fusion cross sections by $50\%$.

We present a new careful and comprehensive analysis the observations of the satellites of Jupiter from the Sidereus Nuncius that extends and complements previous similar studies. Each observation is compared to the predictions obtained using a modern sky simulator, verifying and trying to understand them individually. The work considers both the information that can be extracted from the sketches and the angular measurements reported by Galilei. Angular measurements allow assessing the absolute accuracy in relation to modern ephemerides. We evaluate the performances of the telescope in terms of separation power of close-by satellites and the inefficiency in the detection connected to the proximity to the disk. A sinusoidal fit of the data, allows measuring the relative major semi-axes of the satellites' orbits and their periods with a statistical precision of 2-4\% and 0.1-0.3\% respectively. The posterior fit error is used to estimate the measurements precision. We show that with this data one can infer in a convincing way the third law of Kepler for the Jupiter system. The 1:2:4 orbital resonance between the periods of Io and Europa/Ganymede can be determined with \% precision. In order to obtain these results it is important to separate the four datasets. This operation was an extremely difficult task for Galilei. Nevertheless we show how some indication on the periods emerge from the using the modern Lomb-Scargle technique on the full data-set. We briefly extend the use of the simulator to verify the accuracy in the seven observations of the Moon and the performance in reproducing the Pleiades, the Orion belt, the Orion head and the Beehive cluster. Finally we present images obtained with a replica of the telescope that highlights the challenges of these observations thus confirming the excellence underlying this amazing set of early scientific data.

This study presents a surrogate model designed to predict the Nusselt number distribution in an enclosed impinging jet arrays, where each jet function independently and where jets can be transformed from inlets to outlets, leading to a vast number of possible flow arrangements. While computational fluid dynamics (CFD) simulations can model heat transfer with high fidelity, their cost prohibits real-time application such as model-based temperature control. To address this, we generate a CNN-based surrogate model that can predict the Nusselt distribution in real time. We train it with data from implicit large eddy computational fluid dynamics simulations (Re < 2,000). We train two distinct models, one for a five by one array of jets (83 simulations) and one for a three by three array of jets (100 simulations). We introduce a method to extrapolate predictions to higher Reynolds numbers (Re < 10,000) using a correlation-based scaling. The surrogate models achieve high accuracy, with a normalized mean average error below 2% on validation data for the five by one surrogate model and 0.6% for the three by three surrogate model. Experimental validation confirms the model's predictive capabilities. This work provides a foundation for model-based control strategies in advanced thermal management applications.

We demonstrate significant tunability of the plasma frequency in a wire medium by mechanically deforming a lattice of parallel metallic wires arranged at the nodes of a honeycomb structure. Numerical simulations predict up to 78% tunability and a proof-of-concept experiment confirms 64%, surpassing previously reported values for tunable wire media.

Purpose: The clinical feasibility and translation of many advanced quantitative MRI (qMRI) techniques are inhibited by their restriction to 'research mode', due to resource-intensive, offline parameter estimation. This work aimed to achieve 'clinical mode' qMRI, by real-time, inline parameter estimation with a trained neural network (NN) fully integrated into a vendor's image reconstruction environment, therefore facilitating and encouraging clinical adoption of advanced qMRI techniques. Methods: The Siemens Image Calculation Environment (ICE) pipeline was customised to deploy trained NNs for advanced diffusion MRI parameter estimation with Open Neural Network Exchange (ONNX) Runtime. Two fully-connected NNs were trained offline with data synthesised with the neurite orientation dispersion and density imaging (NODDI) model, using either conventionally estimated (NNMLE) or ground truth (NNGT) parameters as training labels. The strategy was demonstrated online in two healthy volunteers (one rescanned) and evaluated offline with synthetic data, testing two diffusion protocols. Results: NNs were successfully integrated and deployed natively in ICE, performing inline, whole-brain, in vivo NODDI parameter estimation in <10 seconds. The proposed workflow was reproducible across protocols, volunteers and rescans. DICOM parametric maps were exported from the scanner for further analyses. Comparisons between NNMLE and NNGT suggested NNMLE parameter estimates to be more consistent with conventional fitting, a finding supported by offline evaluations. Conclusion: Real-time, inline parameter estimation with the proposed generalisable framework resolves a key practical barrier to the potential clinical uptake of advanced qMRI methods, enabling their efficient integration into clinical workflows. Next steps include incorporation of pre-processing methods and evaluation in pathology.

Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available. To emulate this encounter, in this study, we assume it is unknown how to solve the imaging problem of Computed Tomography (CT). An unsupervised deep learning approach is introduced, that leverages the inherent similarities between deep neural network training, deep image prior (DIP) and unrolled optimization schemes. We demonstrate the feasibility of reconstructing images from measurement data by pure network inference, without relying on ground-truth images in the training process or additional gradient steps for unseen samples. Our method is evaluated on the two-dimensional 2DeteCT dataset, showcasing superior performance in terms of mean squared error (MSE) and structural similarity index (SSIM) compared to traditional filtered backprojection (FBP) and maximum likelihood (ML) reconstruction techniques as well as similar performance compared to a supervised DL reconstruction. Additionally, our approach significantly reduces reconstruction time, making it a promising alternative for real-time medical imaging applications. Future work will focus on extending this methodology for adaptability of the projection geometry and other use-cases in medical imaging.

Band structure analysis is central to understanding wave propagation in periodic media; however, it becomes challenging in open systems owing to energy leakage. Photonic crystal (PhC) slabs exemplify such systems, featuring periodicity in the $x$-$y$ plane and finite extent in the $z$-direction, and supporting diverse guided-mode resonances whose interactions give rise to phenomena such as bound states in the continuum (BICs), exceptional points (EPs), and circular polarisation states. Although numerical simulations can reveal these effects, effective non-Hermitian Hamiltonians are often employed to elucidate the underlying physical mechanisms. This approach, however, relies on manually selected resonant modes and may suffer from basis incompleteness. Here, a systematic first-principles approach is presented to derive the complex band structure. The minimal channels in the scattering matrix, either open or closed, are determined by the number of propagating bulk Bloch waves. The interactions between these waves fully reveal the complex band structure. For instance, two Bloch waves predict the leading-order imaginary frequency $\omega''$ and identify accidental BICs, each associated with a dual Fabry--Pérot mode, whereas three waves reveal robust Friedrich--Wintgen and symmetry-protected BICs together with the associated linewidth behaviours. Orthogonally polarised waves are further incorporated to characterise far-field polarisation and EPs. When extended to a two-dimensional periodic structure, this framework accurately predicts $\omega''$, encompasses all known BICs, and tracks their evolution with system parameters. Overall, this first-principles approach provides a unified foundation for studying complex band structure and facilitates the exploration of light confinement in periodic media.

This work presents a fast, uncertainty-aware sequential data assimilation framework for estimating key aerodynamic states (e.g., instantaneous vorticity fields and aerodynamic loads) during severe gust encounters, where vortex-gust interactions strongly affect the flow dynamics. The framework comprises an ensemble Kalman filter (EnKF) designed to detect and reconstruct nearly impulsive flow disturbances with a wide range of strengths and orientations introduced at arbitrary times. The forecast and measurement update stages of the EnKF are composed of learned operators in a low-dimensional latent space obtained via a physics-augmented autoencoder. The forecast operator propagates undisturbed baseline dynamics but cannot predict random gust-induced deviations. The analysis stage therefore frequently assimilates surface pressure measurements to detect disturbance signals and initiate deviations from the nominal trajectory. The methodology is trained and tested on flowfield snapshots from high-fidelity simulations of two-dimensional airfoil-gust encounters and corresponding sparse pressure data. Because assimilation occurs entirely in the latent space, updates are computationally efficient and aerodynamic states can be continuously estimated from streaming pressure measurements. The latent state remains physically interpretable via decoding to the original high-dimensional flow. Eigenvalue decomposition of state and observation Gramians reveals the dominant correction directions required to capture the disturbance and quantifies how sensors inform state corrections during gust interaction. The framework also accounts for sensor failure: sensor-dropout experiments show that the EnKF adaptively reweights neighboring sensors to compensate for lost information, preserving estimation quality under degraded sensing.

The LUX-ZEPLIN (LZ) experiment aims to detect rare interactions between dark matter particles and xenon. Although the detector is designed to be the most sensitive to GeV/$c^2$--TeV/$c^2$ Weakly Interacting Massive Particles (WIMPs), it is also capable of measuring low-energy ionization signals down to a single electron that may be produced by scatters of sub-GeV/$c^2$ dark matter. The major challenge in exploiting this sensitivity is to understand and suppress the ionization background in the few-electron regime. We report a characterization of the delayed electron backgrounds following energy depositions in the LZ detector under different detector conditions. In addition, we quantify the probability for photons to be emitted in coincidence with electron emission from the high voltage grids. We then demonstrate that spontaneous grid electron emission can be identified and rejected with a high efficiency using a coincident photon tag, which provides a tool to improve the sensitivity of future dark matter searches.

Convectively Coupled Equatorial Waves (CCEWs) dominate atmospheric variability on timescales of 2--30 days in the Tropics, bringing episodes of widespread heavy precipitation. This study compares the representation of CCEWs and their connection to upscale energy transfer in two Met Office Unified Model simulations of the full tropical channel with identical km-scale resolution for the DYAMOND Summer period. The principal difference between the simulations is that one parametrizes convection (GAL9), while the other (RAL3) is convection permitting. The GAL9 convection scheme acts to remove vertical instability without explicitly representing the resolved-scale circulation associated with convective plumes. We present the first quantitative diagnosis of interscale energy transfer and its relation to CCEWs. This diagnosis is important because upscale energy transfer between convection and large-scale waves may influence accurate simulation of tropical weather systems. The average upper-tropospheric upscale transfer simulated by RAL3 is approximately 50% higher than GAL9. CCEWs are more coherent in RAL3, with an average phase-speed variability 80% higher than observations, compared with 166% higher in GAL. RAL3 also simulates greater upscale energy transfer within waves than GAL9 with a stronger correlation between the interscale energy transfer and equatorial wave winds. Kelvin and Rossby waves are associated with upscale energy transfer from scales 4-8 times smaller than their wavelength, related to active deep convection within a particular sector of the wave phase. Our findings show that the explicit representation of convection has a significant impact on the simulation of upscale energy transfer, and is very likely to be a significant factor in the faithful simulation of convective coupling within CCEWs.

When a hole is introduced into an elastic material, it will usually act to reduce the overall mechanical stiffness. A general ambition is to investigate whether a stiff shell around the hole can act to maintain the overall mechanical properties. We consider this effect from a macroscopic continuum perspective down to atomistic scales. For this purpose, we focus on the basic continuum example situation of an isotropic, homogeneous, linearly elastic material loaded uniformly under compressive plane strain for low concentrations of holes. As we demonstrate, the thickness of the shell can be adjusted in a way to maintain the overall stiffness of the system. We derive a corresponding mathematical expression for the thickness of the shell that conceals the hole. Thus, one can work with given materials to mask the presence of the holes simply by adjusting the thickness of the surrounding shells, with no need to change the materials. Our predictions from linear elasticity continuum theory are extended to atomistic levels using molecular dynamics simulations of a model Lennard-Jones solid. These extensions attest the robustness of our predictions down to atomistic scales. Thus, they open a straightforward possibility to adjust the strategy of mechanical cloaking via atomistic manipulations. From both perspectives, the underlying concept is important in the context of light-weight construction.

We present a single-shot near-field technique, where the near-field scan is performed on a single sample without repeating measurements or averaging over multiple samples, to reconstruct the isofrequency surfaces of metamaterials in the microwave regime. In our approach, we excite resonant modes using a fixed source in a resonator composed of the material under test and map the in-plane field distribution with a movable probe. Applying a fast Fourier transform (FFT) to the measured field reveals the sample's in-plane dispersion. By extending this analysis over multiple frequencies and comparing the results with Fabry-Pérot resonances, we retrieve the full three-dimensional dispersion relation. When we apply the method to a double non-connected wire metamaterial, it accurately captures the low-frequency hyperbolic isofrequency surface, providing both a precise experimental tool and conceptual insight into spatially dispersive metamaterials.

TADF emitter performance depends on both thermodynamic and kinetic factors. We analyze 747 experimentally known TADF molecules computationally to extract quantitative design guidelines. Using a validated xTB-based workflow, we examine how architecture, geometry, and electronic structure affect photophysical properties. Among architectures, D-A-D frameworks achieve the smallest \deltaest. A favorable torsional angle of $50°-90°$ balances small $\Delta E_{\text{ST}}$ with the spin--orbit coupling needed for reverse intersystem crossing. Clustering separates high-performance candidates and highlights multi-resonance emitters for blue emission. From these results, we identify 127 candidates with predicted $\Delta E_{\text{ST}} < 0.1 eV$ and oscillator strength $f > 0.1$. These HTVS-derived design guidelines and candidates can guide future TADF emitter development.

A hybrid dual-readout calorimeter concept, comprising both electromagnetic and hadronic sections, has recently been proposed to meet the performance requirements of experiments at future e$^{+}$e$^{-}$ colliders. The front compartment consists of a homogeneous electromagnetic calorimeter made of high-density crystals, each coupled to a pair of Silicon Photomultipliers (SiPMs) providing the simultaneous readout of scintillation and Cherenkov light. To efficiently detect Cherenkov photons in the presence of dominant scintillation signals, an optical filter is placed in front of one of the two SiPMs to suppress photons in the wavelength region corresponding to that of scintillation emission. In this study, PWO, BGO, and BSO crystals with different dimensions were tested to measure their scintillation light yield and decay time, as well as their transmission and emission spectra. A set of $\sim 100~\rm \mu m$-thick optical filters was also characterized by measuring their transmittance curves. The experimental results were used to model and estimate the expected filter performance in attenuating scintillation light for the various crystals. The performance of each filter was experimentally validated by measuring the crystal light output with and without the filter using a $^{22}$Na radioactive source and a LYSO:Ce crystal, confirming the accuracy of the calculations. The results show that interference filters are unsuitable for this application because their transmittance strongly depends on the photon incidence angle. Conversely, two absorptive long-pass filters with cutoff wavelengths around 590 nm were found to block more than 99% of the scintillation light from PWO crystals, satisfying the calorimeter specifications.

We derived and implemented the calculation of the one-body reduced density matrix for Aufbau suppressed coupled cluster theory, from which excited state natural orbitals and one-body properties, like atomic populations and dipole moments, are obtained. We utilized the natural orbitals to refine the ASCC solution for simple valence and Rydberg systems, exploring the process of repeatedly solving the ASCC equations in successive natural orbital bases to achieve independence from the starting molecular orbitals. For dipole moments in small molecules where high-level comparison data is available, we find that the accuracy of ASCC essentially matches that of linear response and equation-of-motion coupled cluster as long as care is taken to preserve the response's perturbative completeness.

The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limited by poor transferability to out-of-distribution samples. Here, we systematically evaluate different MLIP architectures with long-range corrections across diverse chemical spaces and show that such schemes are essential, not only for improving in-distribution performance but, more importantly, for enabling significant gains in transferability to unseen regions of chemical space. To enable a more rigorous benchmarking, we introduce biased train-test splitting strategies, which explicitly test the model performance in significantly different regions of chemical space. Together, our findings highlight the importance of long-range modeling for achieving generalizable MLIPs and provide a framework for diagnosing systematic failures across chemical space. Although we demonstrate our methodology on metal-organic frameworks, it is broadly applicable to other materials, offering insights into the design of more robust and transferable MLIPs.

The solution of potential-driven steady-state flow in large networks is required in various engineering applications, such as transport of natural gas or water through pipeline networks. The resultant system of nonlinear equations depends on the network topology, and its solution grows more challenging as the network size increases. We present an algorithm that utilizes a given partition of a network into tractable sizes to compute a global solution for the full nonlinear system through local solution of smaller subsystems induced by the partitions. When the partitions are induced by interconnects or transfer points corresponding to networks owned by different operators, the method ensures data is shared solely at the interconnects, leaving network operators free to solve the network flow system corresponding to their own domain in any manner of their choosing. The proposed method is shown to be connected to the Schur complement and the method's viability demonstrated on some challenging test cases.

Understanding fluid flow through porous media with complex geometries is essential for improving the design and operation of packed-bed reactors. Most existing studies focus on spherical packings, having as a consequence that accurate models for irregular interstitial geometries are scarce. In this study, we numerically investigated the flow through a set of packed-bed geometries consisting of square bars stacked on top of each other and arranged in disk-shaped modules. Rotation of each module allows the generation of a variety of geometrical configurations at Reynolds numbers of up to 200 (based on the bar size). Simulations were carried out using the open-source solver OpenFOAM. Selected cases (e.g., $\alpha = 30^\circ$, $\mathrm{Re}_\mathrm{p} = 100, 200$) were compared against Particle Image Velocimetry measurements. Results reveal that, based on the relative rotation angle, the realized geometries can be classified as channel-like ($\alpha \leq 10^\circ$) and lattice-like ($\alpha \geq 15^\circ$), fundamentally altering the friction factor. Furthermore, the maximum friction factor obtained in the creeping regime occurred at $\alpha = 25^\circ$, whereas in the inertial regime, this occurred at $\alpha = 60^\circ$. The module-equivalent diameter, based on the angle-dependent wetted surface area, collapses the friction factor onto the Ergun correlation and yields good permeability predictions for the lattice-like geometries.

Recently, numerical examples of stable soap bubble clusters with multiple torus bubbles have been presented. The geometry of these clusters is based on the Platonic solids whose vertices have valence $3$ (in order to fulfill Plateau's laws): the tetrahedron, the cube, the dodecahedron. The clusters respectively contain a bubble of genus $3, 5, 11$. The construction is quite generic and can be used with any convex polyhedron. If stable, the cluster obtained using a polyhedron with $n$ faces has $3n+2$ bubbles and one of these bubbles has genus $n-1$. We propose here to show that is it possible to get stable soap bubble clusters with multiple torus bubbles using a geometry based on prisms and Archimedean solids as well.

In South Korea, age-disaggregated tuberculosis (TB) data at the district level are not publicly available due to privacy constraints, limiting fine-scale analyses of healthcare accessibility. To address this limitation, we present a high-resolution, district-level dataset on tuberculosis (TB) fatalities and hospital accessibility in South Korea, covering the years 2014 to 2022 across 228 districts. The dataset is constructed using a reconstruction method that infers age-disaggregated TB cases and fatalities at the district level by integrating province-level age-specific statistics with district-level spatial and demographic data, enabling analyses that account for both spatial heterogeneity and age structure. Building on an existing hospital allocation framework, we extend the objective function to an age-weighted formulation and apply it to the reconstructed dataset to minimize TB fatalities under different age-weighting schemes. We demonstrate that incorporating age structure can give rise to distinct optimized hospital allocation patterns, even when the total number of minimized fatalities is similar, revealing trade-offs between efficiency and demographic targeting. In addition, the dataset supports temporal analyses of TB burden, hospital availability, and demographic variation over time, and provides a testbed for spatial epidemiology and optimization studies that require high-resolution demographic and healthcare data.

Next generation quantum technologies will need to rely on efficient transduction between electrical, optical, and mechanical quantum degrees of freedom to generate large-scale entanglement over large distances. The performance of such transducers is fundamentally limited by the cryogenic properties of the underlying materials. Here, we demonstrate that engineering strain in ferroelectric thin-film strontium titanate ($\mathrm{SrTiO_3}$) not only results in an exceptionally large Pockels coefficient, but also in a robust linear piezoelectric response at cryogenic temperatures, surpassing previous thin-film benchmarks. We measure piezoelectric tensor elements of $d_{15} = 151.8 \pm 1.5$ pm/V and $d_{33} = 54.8 \pm 4$ pm/V, and an effective photoelastic coefficient of $p_{\mathrm{eff}}$ = 0.56 at 5~K. Utilizing these enhanced properties, we demonstrate the first $\mathrm{SrTiO_3}$-on-oxide acousto-optic modulator with a voltage-length product ($V_{\pi}L$) of $0.874 \pm 0.084$ V cm, outperforming state-of-the-art unreleased modulators that typically feature a $V_{\pi}L$ of a few V cm. Our results establish thin-film $\mathrm{SrTiO_3}$ as a promising material system for integrated quantum photonics operating at cryogenic temperatures.

Simulations and observations of the low-latitude magnetosphere-magnetosheath boundary layer indicate that the Kelvin-Helmholtz instability (KHI) drives vortex structures that enhance plasma mixing and magnetic reconnection, influencing transport and particle acceleration. We investigate the spatial localization, species dependence, and physical mechanisms of plasma mixing driven by the nonlinear evolution of the KHI. We perform high-resolution two-dimensional Particle-In-Cell simulations using a finite-Larmor-radius shear-flow initial configuration. Plasma mixing is quantified using particle labeling, a complementary density-based mixing tracer, and diagnostics of magnetic reconnection. Mixing across the shear layer is present but localized, occurring mainly in narrow interface regions and plasma structures. Ions mix more effectively than electrons, which remain largely frozen to field lines. Enhanced mixing spatially and temporally correlates with localized magnetic reconnection within and between KH vortices. Cross-boundary transport driven by the kinetic KHI remains intrinsically localized and is mediated by vortex advection and magnetic reconnection. Electron mixing is strongly constrained, indicating that kinetic-scale transport across collisionless shear layers remains limited.

The dielectric properties of proteins, particularly in their hydrated state, have been extensively studied. Numerous theoretical and experimental investigations have reported values of both the permittivity and the intrinsic dipole moments of specific proteins under well-defined hydration conditions. Since even approximate estimates of these properties are relevant from both fundamental and applied perspectives, we propose a easy-to-use method to calculate the relative permittivity that can be readily integrated into proteotronics workflows. To validate the proposed approach, we compare the results with those obtained using a classical macroscopic method. The outcomes are consistent and contribute further insight into this long-debated issue.

This paper investigates the potential of near-field (NF) indoor communications in the FR2 frequency bands using fully dielectric structures to generate orbital angular momentum (OAM) waves. All-dielectric platforms based on distributions of T-shaped unit cells are employed for this purpose. The unit cell design is based on a circuital approach and analytical formulations, where phase shifts necessary for OAM generation are achieved by varying the dielectric-to-air ratio within the structure. Based on this unit cell, a set of transmitarrays (TAs) are designed to produce specific OAM modes. These TAs are fabricated in-house using stereolithographic 3D printing and experimentally tested. The tests evaluate two key features of OAM beams: the orthogonality of distinct vortex modes, as characterized by their electric field distributions, and their object-avoidance capability, enabled by the central null characteristic of the wavefront. In addition, a field-test within an indoor environment is conducted emulating a real wireless system. A bit error rate lower than 10\textsuperscript{$-$6} is observed for solidary modes in Tx and Rx, whereas orthogonal modes produces an increment in 4 order of magnitude. The obtained results reveals that the prototype is suitable for short-range scenarios, enabling techniques such as OAM-multiplexation or physical-layer security thanks to the effective orthogonality beteween modes.

High-brightness X-ray Free Electron Lasers (FELs) produce spatially and temporally coherent pulses on attosecond to femtosecond timescales, providing a transformative tool for discovery across biology, chemistry, physics, and materials science. This paper proposes a compact accelerator that enables a high-repetition-rate (MHz) 1 nm soft X-ray FEL with a footprint of less than 100 meters. Such an FEL is suitable for installation within research institution settings where space is limited. The accelerator leverages a multi-turn recirculating linear accelerator that integrates state-of-theart superconducting accelerator technology with recent advances in diffraction-limited storage rings. We present the conceptual layout and analyze the impact of two most challenging factors for such a compact accelerator, incoherent and coherent synchrotron radiation. We have systematically studied both effects for different multi-bend achromat lattices and electron beam peak currents. For a peak current of 60 Ampere before final compression and using 11-bending magnets, the horizontal emittance growth after the 90-degree arc can be kept below 10%, demonstrating that these effects are not limiting factors for achieving high-quality electron beams. Such a compact X-ray FEL facility would substantially reduce both construction and operational costs, greatly expanding access to these powerful research tools. Furthermore, this concept provides a potential upgrade path to generating hard X-ray radiation by incorporating high accelerating gradient structures to further accelerate a portion of the MHz electron beam.

Future generations of gravitational wave detectors require increased sensitivity, for which the availability of large mirrors with high reflectivity and low mechanical loss is essential. Current amorphous multilayer mirror designs present constraining limitations in terms of thermal noise. These mirrors require a large number of thin film layers to achieve near-perfect reflectivity. However, the thermal noise generated by this type of stack increases with the number of layers used. Reducing thermal noise is therefore very challenging and highlights the need for new technical solutions that can address this specific issue. Here, we provide insights into the expected performance of mirrors that combine a resonant metasurface with a multilayer stack. The suggested mirror design ensures the high reflectivity required for interferometric gravitational wave detectors, while using fewer layers of properly selected materials. It allows to reduce the total thickness of the material with the poorest thermal-noise performance, namely TiO2:Ta2O5, by a factor of more than 3, making it a promising option for potentially reducing thermal noise as well.

We have developed a non-isothermal analytical model for the impedance of the cathode catalyst layer (CCL) in a PEM fuel cell. In-phase harmonic perturbations to the current density and temperature reduce the impedance and the static polarisation resistivity of the CCL due to lowering proton transport losses. A special selection of the current and temperature perturbation amplitudes allows for complete elimination of these losses.

On-chip coherent visible and near-infrared (NIR) light generation has broad applications in metrology, bio-sensing, and quantum information. High-Q microresonators are ideal candidates for generating light across such broad wavelength ranges via efficient second- ($\chi^{(2)}$) and third-order ($\chi^{(3)}$) nonlinear optical processes. However, harnessing these diverse nonlinearities simultaneously in a single microresonator remains elusive yet highly attractive both fundamentally and technologically. Here, we demonstrate coherent light generation from the ultraviolet to NIR in a silicon nitride microresonator pumped by a single continuous-wave telecom laser. This broad frequency generation arises from the interplay of $\chi^{(2)}$ and $\chi^{(3)}$ nonlinear processes. A cascade of nonlinear processes, including harmonic generation and optical parametric oscillation (OPO), is initiated by the photoinduced second harmonic generation enabled by all-optical poling. The dynamic reconfigurability of this $\chi^{(2)}$ nonlinearity enables access to different transverse spatial modes at the second harmonic, enabling highly tunable OPO processes triggered by hybrid modal phase matching conditions and yielding milliwatt-level NIR light. This work sheds new insights into the fundamental physics of cooperative nonlinear multimode interactions in resonant systems and provides a versatile approach for reconfigurable OPOs, highlighting their potential to generate light at wavelengths beyond the reach of photonic integrated lasers.

The simulation hypothesis has recently excited renewed interest in the physics and philosophy communities. However, the hypothesis specifically concerns {\textit{computers}} that simulate physical universes. So to formally investigate the hypothesis, we need to understand it in terms of computer science (CS) theory. In addition we need a formal way to couple CS theory with physics. Here I couple those fields by using the physical Church-Turing thesis. This allow me to exploit Kleene's second recursion, to prove that not only is it possible for {us} to be a simulation being run on a computer, but that we might be in a simulation being run a computer \emph{by us}. In such a ``self-simulation'', there would be two identical instances of us, both equally ``real''. I then use Rice's theorem to derive impossibility results concerning simulation and self-simulation; derive implications for (self-)simulation if we are being simulated in a program using fully homomorphic encryption; and briefly investigate the graphical structure of universes simulating other universes which contain computers running their own simulations. I end by describing some of the possible avenues for future research. While motivated in terms of the simulation hypothesis, the results in this paper are direct consequences of the Church-Turing thesis. So they apply far more broadly than the simulation hypothesis.

Short-term precipitation nowcasting is essential for flood management, transportation, energy system operations, and emergency response. However, many existing models fail to fully exploit the extensive atmospheric information available, relying primarily on precipitation data alone. This study examines whether integrating multi variable weather-station measurements with radar can enhance nowcasting skill and introduces two complementary architectures that integrate multi variable station data with radar images. The SmaAt-fUsion model extends the SmaAt-UNet framework by incorporating weather station data through a convolutional layer, integrating it into the bottleneck of the network; The SmaAt-Krige-GNet model combines precipitation maps with weather station data processed using Kriging, a geo-statistical interpolation method, to generate variable-specific maps. These maps are then utilized in a dual-encoder architecture based on SmaAt-GNet, allowing multi-level data integration. Experimental evaluations were conducted using four years (2016--2019) of weather station and precipitation radar data from the Netherlands. Results demonstrate that SmaAt-Krige-GNet outperforms the standard SmaAt-UNet, which relies solely on precipitation radar data, in low precipitation scenarios, while SmaAt-fUsion surpasses SmaAt-UNet in both low and high precipitation scenarios. This highlights the potential of incorporating discrete weather station data to enhance the performance of deep learning-based weather nowcasting models.

Security analyses in quantum key distribution (QKD) and other adversarial quantum tasks often assume perfect device models. However, real-world implementations often deviate from these models. Thus, it is important to develop security proofs that account for such deviations from ideality. In this work, we extend the idea of squashing maps to develop a general framework for analysing imperfect threshold detectors, treating uncharacterised device parameters such as dark counts and detection efficiencies as adversarially controlled within some ranges. This approach enables a rigorous worst-case analysis with exactly characterised devices, ensuring security proofs remain valid under realistic conditions. Our results strengthen the connection between theoretical security and practical implementations by introducing a flexible framework for integrating detector imperfections into adversarial quantum protocols.

Crystal structures can be viewed as assemblies of space-filling polyhedra, which play a critical role in determining material properties such as ionic conductivity and dielectric constant. However, most conventional crystal structure prediction methods rely on random structure generation and do not explicitly incorporate polyhedral tiling, limiting their efficiency and interpretability. In this highlight, we introduced a novel crystal structure generation method based on discrete geometric analysis of polyhedral information. The geometry and topology of space-filling polyhedra are encoded as a dual periodic graph, and the corresponding crystal structure is obtained via the standard realization of this graph. We demonstrate the effectiveness of our approach by reconstructing face-centered cubic (FCC), hexagonal close-packed (HCP), and body-centered cubic (BCC) structures from their dual periodic graphs. This method offers a new pathway for systematically generating crystal structures based on target polyhedra, potentially accelerating the discovery of novel materials for applications in electronics, energy storage, and beyond.

We present a method to suppress crosstalk from implementing controlled-Z gates via local addressing in neutral atom quantum computers. In these systems, a fraction of the laser light that is applied locally to implement gates typically leaks to other atoms. We analyze the resulting crosstalk in a setup of two gate atoms and one neighboring third atom. We then perturbatively derive a spin-echo-inspired gate protocol that suppresses the leading order of the amplitude error, which dominates the crosstalk. Numerical simulations demonstrate that our gate protocol improves the fidelity by two orders of magnitude across a broad range of experimentally relevant parameters. To further reduce the infidelity, we develop a circuit to cancel remaining phase errors. Our results pave the way for using local addressing for high-fidelity quantum gates on Rydberg-based quantum computers.

Transfer learning (TL) is used to extrapolate the physics information encoded in a Generative Adversarial Network (GAN) trained on synthetic neutrino-carbon inclusive scattering data to related processes such as neutrino-argon and antineutrino-carbon interactions. We investigate how much of the underlying lepton-nucleus dynamics is shared across different targets and processes. We also assess the effectiveness of TL when training data is obtained from a different neutrino-nucleus interaction model. Our results show that TL not only reproduces key features of lepton kinematics, including the quasielastic and $\Delta$-resonance peaks, but also significantly outperforms generative models trained from scratch. Using data sets of 10,000 and 100,000 events, we find that TL maintains high accuracy even with limited statistics. Our findings demonstrate that TL provides a well-motivated and efficient framework for modeling (anti)neutrino-nucleus interactions and for constructing next-generation neutrino-scattering event generators, particularly valuable when experimental data are sparse.

We present benchmarking results for single-qubit gates implemented on a neutral atom quantum processor using Direct Randomized Benchmarking (DRB) and Gate Set Tomography (GST). The DRB protocol involves preparing stabilizer states, applying $m$ layers of native single-qubit gates, and measuring in the computational basis, providing an efficient error characterization under a stochastic Pauli noise model. GST enables the full, self-consistent reconstruction of quantum processes, including gates, input states, and measurements. Both protocols provide robust to state preparation and measurement (SPAM) errors estimations of gate performance, offering complementary perspectives on quantum gate fidelity. For single-qubit gates, DRB yields an average fidelity of $99.963 \%$. The protocol was further applied to a 25-qubit array under global single-qubit control. GST results are consistent with those obtained via DRB. We also introduce a gauge optimization procedure for GST that brings the reconstructed gates, input states, and measurements into a canonical frame, enabling meaningful fidelity comparisons while preserving physical constraints. These constraints of the operators -- such as complete positivity and trace preservation -- are enforced by performing the optimization over the Stiefel manifold. The combined analysis supports the use of complementary benchmarking techniques for characterizing scalable quantum architectures.

A modern chiral potential incorporating the three-body force is adopted to investigate bulk properties, spectra, and nuclear responses of closed-(sub)shell nuclei throughout the nuclear chart within a particle-hole (p-h) renormalized random-phase approximation (RRPA) scheme using a Hartree- Fock (HF) single-particle basis. Our analysis shows that all instabilities induced by the quasiboson approximation (QBA) underlying RPA are removed and an overall better consistency with the experiments is achieved for all observables of the investigated nuclei. The residual discrepancies point out the need of going beyond the p-h space.

The decomposition of arbitrary unitary transformations into sequences of simpler, physically realizable operations is a foundational problem in quantum information science, quantum control, and linear optics. We establish a 1D Quantum Field Theory model for justifying the universality of a broad class of such factorizations. We consider parametrizations of the form $U = D_1 V_1 D_2 V_2 \cdots V_{M-1}D_M$, where $\{D_j\}$ are programmable diagonal unitary matrices and $\{V_j\}$ are fixed mixing matrices. By leveraging concepts like the anomalies of our effective model, we establish universality criteria given the set of mixer matrices. This approach yields a rigorous proof grounded in physics for the conditions required for the parametrization to cover the entire group of special unitary matrices. This framework provides a unified method to verify the universality of various proposed architectures and clarifies the nature of the ``generic'' mixers required for such constructions. We also provide a deterministic algorithm for verifying this genericity condition and a geometry-aware optimization method for finding the parameters of a decomposition.

Within a fixed hyperangular channel $s>0$ of a harmonically trapped system, the $1/R^2$ perturbation is absorbed exactly into a shift of the channel parameter, $s\to s_\eta$, so the single-channel model remains a harmonic oscillator with a shifted inverse-square term: radial gaps stay at $2\hbar\omega$ exactly and no monopole spectral weight appears at forbidden frequencies at any order. The first-order cancellation is also proved independently by a compact algebraic argument in which the ket and bra contributions cancel pairwise; this is the main new result. Substituting single-channel quantities into the established $m_1/m_{-1}$ sum-rule bound yields $Q^{-1}$ scaling of the sum-rule estimate ($Q\equiv 2q+s+1$, $q$ the radial quantum number) with an explicit coefficient; its finite-temperature average has a low-$T$ plateau and a $1/T$ high-$T$ tail. All results hold for any real $s>0$. The Laguerre polynomial identities extend formally to three dimensions, but exact 3D results show $q$-dependent contact corrections along $SO(2,1)$ ladders, so the physical interpretation there requires a separate derivation.

We present a path-integral Monte Carlo estimator for calculating the dipole polarizability of interacting Coulomb plasma in the long-wavelength limit, i.e., the optical region. We present comprehensive details and method validation studies for our approach based on both collective and one-particle dipole autocorrelation functions in the imaginary time. The simulation of thermal equilibrium in imaginary time has exact Coulomb interactions and Boltzmann quantum statistics. For reference, we use analytically continued Drude model as the long-wavelength limit of the Lindhard response. Our collective response shows perfect match to the analytical reference. The one-particle response is used in systematic studies of physical and numerical parameters, and to discuss the phenomenological Drude scattering model.

Ultrafast imaging of photon statistics in two dimensions is a powerful tool for probing non-equilibrium and transient optical phenomena, yet it remains experimentally challenging due to the simultaneous need for high temporal resolution and statistical fidelity. In this work, we demonstrate spatially resolved single-shot measurements of photon number distributions using difference-frequency generation (DFG) in a nonlinear BBO crystal. We show that our platform can discriminate between coherent and thermal photon statistics across two spatial dimensions with picosecond resolution. At the same time, we find that the retrieved distributions deviate from the ideal ones, a consequence of vacuum contamination and the multimodal response of the amplifier. To explain this, we develop a temporal mode decomposition framework that captures the essential physics of signal amplification and fluorescence, and quantitatively reproduces the experimental findings. This establishes a robust approach for measuring two-dimensional photon statistics while clarifying the fundamental factors that limit the fidelity of such measurements.

In this paper, we present a first quantitative test of detected light signals produced in a pulsed neutron source run in a small vertical drift LArTPC at the CERN neutrino platform ColdBox test facility. The ColdBox cryostat, detectors, neutron sources, and particle interactions are modeled and simulated using Fluka. A good agreement is found in the detected number of photoelectrons, with values below 650 photoelectrons in both data and simulation, for all four X-ARAPUCA photodetectors on the cathode in the LArTPC. A time constant is also fitted from the neutron-beam-off light signal spectrum and found consistent between data and MC. Several important systematic effects are discussed and serve as guides for future runs at larger LArTPCs.

The temporal degree-of-freedom of light is incredibly powerful for modern quantum technologies, enabling large-scale quantum computing architectures and record key-rates in quantum key distribution. However, the generalized measurement of large and complex quantum superpositions of the time-of-arrival of a photon remains a unique experimental challenge. Conventional methods based on unbalanced Franson-type interferometers scale poorly with dimension, requiring multiple cascaded devices and active phase stabilization. In addition, these are limited by construction to a restricted set of phase-only superposition measurements. Here we show how the coupling of spatial and temporal information inside a single multi-mode fiber can be harnessed to program completely generalized measurements for high-dimensional superpositions of photonic time-bin. Using the multi-spectral transmission matrix of the fiber, we find special sets of spatial modes that experience distinct dispersive delays through the fiber. By exciting coherent superpositions of these spatial modes, we engineer the equivalent of large, unbalanced multi-mode interferometers inside the fiber and use them to perform high-quality measurements of arbitrary time-bin superpositions in up to dimension 11. The single fiber functions as a scalable, common-path interferometer for time-bin qudits that significantly eases the experimental overheads of standard approaches based on unbalanced Franson-type interferometers, serving as an essential tool for quantum technologies that harness the temporal properties of light.

Learning surrogate models for time-dependent PDEs requires balancing expressivity, stability, and computational efficiency. While highly expressive generative models achieve strong short-term accuracy, they rely on autoregressive sampling procedures that are computationally expensive and prone to error accumulation over long horizons. We propose a continuous-time Koopman autoencoder in which latent dynamics are governed by a parameter-conditioned linear generator. This formulation enables exact latent evolution via matrix exponentiation, allowing predictions at arbitrary temporal resolutions without autoregressive rollouts. We evaluate our method on challenging fluid dynamics benchmarks and compare against autoregressive neural operators and diffusion-based models. We evaluate our method on challenging fluid dynamics benchmarks against autoregressive neural operators and diffusion-based models. Our results demonstrate that imposing a continuous-time linear structure in the latent space yields a highly favorable trade-off: it achieves massive computational efficiency and extreme long-horizon stability while remaining competitive in short-term generative accuracy.

We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean state forecasting in a two-layer quasi-geostrophic (QG) system. By projecting nonlinear dynamics into a latent space governed by a linear ordinary differential equation, the model enforces structured and interpretable temporal evolution while enabling temporally resolution-invariant forecasting via a matrix exponential formulation. Across 2083-day rollouts, CT-KAE exhibits bounded error growth and stable large-scale statistics, in contrast to autoregressive Transformer baselines which exhibit gradual error amplification and energy drift over long rollouts. While fine-scale turbulent structures are partially dissipated, bulk energy spectra, enstrophy evolution, and autocorrelation structure remain consistent over long horizons. The model achieves orders-of-magnitude faster inference compared to the numerical solver, suggesting that continuous-time Koopman surrogates offer a promising backbone for efficient and stable physical-machine learning climate models.

Spontaneous parametric downconversion (SPDC) has been considered as a reliable source of high- dimensional entangled states in orbital angular momentum (OAM) basis. In real-world experiments, the spatial walk-off of the pump often degrades the fidelity of the generated quantum state. Since the walk-off effect breaks the rotational symmetry of the system, the conservation of total OAM is violated. Although the compensation of walk-off effects has become a well-established experimental technique, a systematic modal analysis of the spatial walk-off effect is still incomplete for SPDC. Here, we quantitatively analyze the violation of OAM conservation due to the pump walk-off effect in SPDC processes. We have derived a scaling law of the total OAM distribution with respect to the pump walk-off angle. We have also explored the feasibility of using the spatial walk-off as a mechanism to engineer the generated quantum state. Our study has provided guidelines for the generation of OAM-entangled state under realistic experimental conditions.

Phononic Crystals provide a versatile platform for controlling phonons in applications such as waveguiding, filtering, and sensing. To minimize dissipation, cavity resonators are often embedded within the bandgap of phononic crystals and integrated with suitable transduction techniques. Here, we demonstrate one-dimensional (1D) phononic transmission using electrostatic transduction, enabling the realization of high-quality mechanical oscillators. Using a double-ended tuning fork resonator embedded in a 1D phononic crystal, we observe degenerate flexural modes (in-phase and out-phase) exhibiting enhanced and comparable quality factors within the same device due to mode degeneracy. The in-phase mode, whose frequency lies inside the phononic bandgap, shows an approximately two-fold increase in quality factor compared to an anchored resonator, while this enhancement diminishes for the out-phase mode (frequency outside the bandgap) at temperatures where thermoelastic dissipation is negligible. This approach offers a promising route toward low-loss, encapsulated phononic devices for sensing and signal processing applications.