Papers

We report experimental observations of a controlled transition from a zonal-flow (ZF) dominated regime to a coexistence regime of ZFs and streamers, and finally to a streamer-dominated state in a linear magnetized plasma column. The controlling parameter is the ion-neutral collision frequency. At low collisionality (2 x 10^-5 mbar), the plasma turbulence is dominated by coherent ZFs (600-700 Hz) that are nonlinearly driven by drift-wave fluctuations. With increasing collisionality (5 x 10^-4 mbar), the ZF growth is reduced and streamers emerge through nonlinear coupling of neighboring drift modes mediated by a mediator mode. At high collisionality (2 x 10^-3 mbar), ZFs are strongly damped and the turbulence becomes streamer-dominated. For each of these turbulent states, the corresponding edge fluctuations transition from coherent, symmetric to intermittent, asymmetric fluctuations with enhanced low-frequency content and larger spatial scales that can result in convective transport. Our results demonstrate the possibility of selective excitation of ZFs and streamers by regulating their collisional damping and establish the ion-neutral collision frequency as an effective control knob for regulating turbulent structures and edge transport in magnetized plasmas.

The Gamma Factory (GF) proposal \cite{Krasny:2015ffb} is motivated by the recognition of a largely untapped potential of the CERN accelerator complex to enable a new research programme at the intersection of particle, nuclear, atomic, fundamental, and applied physics. The central concept is to produce, accelerate, cool, and store atomic beams of highly relativistic partially stripped ions in the LHC, which would serve as an effective atomic trap. The internal degrees of freedom of these ions are then resonantly excited using laser photons. In the GF scheme, laser-cooled atomic beams serve both as high-precision probes and as low-emittance beam sources for high-luminosity LHC operation in the ion-ion collision mode. Interactions between laser pulses stored in Fabry–Perot cavities and circulating ion beams give rise to high-energy, highly collimated, and polarised secondary photon beams. Their expected intensities exceed those of existing gamma-ray sources by several orders of magnitude. These photon beams can further be used to generate unprecedented-intensity, tertiary beams of polarised electrons, positrons, muons, neutrons, radioactive ions, and flavour- or CP-tagged neutrinos. Furthermore, under a specific configuration, the same photon-driven processes may be exploited in an energy-production scheme generating the requisite plug-power for LHC operation. Together, cold relativistic atomic beams, high-intensity photon beams, and tertiary beams constitute a versatile experimental platform capable of opening a wide range of new scientific opportunities at CERN. By exploiting existing accelerator infrastructure and available state-of-the-art laser technologies, the GF offers a path to a cost-effective and timely programme capable of sustaining experimental innovation and bridging the gap between the HL-LHC era and the future FCC era.

Inferring higher-order interaction structure from observations of dynamics is a central challenge in complex systems, particularly when data are scarce, noisy, or concentrated in lower-dimensional regions of state space. We develop Bayes-THIS, a Bayesian extension of Taylor-based Hypergraph Inference using SINDy (THIS) that replaces threshold-based sparse regression with sparse Bayesian regression using automatic relevance determination. This yields adaptive, term-wise shrinkage and a posterior over inferred Taylor expansion coefficients that encode the presence or absence of interactions. Across numerical experiments, Bayes-THIS improves inference in low-data, high-noise, and ill-conditioned regimes. The Bayesian formulation also enables two capabilities absent from the original THIS framework: a posterior predictive diagnostic for assessing whether a dataset contains higher-order signal distinguishable from noise, and a posterior interval rule for pruning weakly supported hyperedges. Finally, we examine a separate, structural limitation of Taylor-based hypergraph reconstruction: higher-order interactions can produce spurious lower-order signatures that are indistinguishable from genuine lower-order edges. We find that the severity of this non-identifiability depends on cross-order support, showing that structural properties of the target hypergraph can be as important as data quality in determining what can be inferred from dynamics within this framework.

Tracer diffusion in crowded environments is central to many biological and soft matter systems, but quantitative frameworks for linking tracer motion to environmental structure remain limited, and the co-dependence among geometric variables that facilitate or hinder tracer transport is not yet well understood. Here, we study the transport of rigid tracers in suspensions of soft particles and within living cells. Experiments reveal a transition from diffusive to confined motion as the matrix area fraction increases. The observed ensemble-level statistics, including the mean-squared displacements (MSDs), can be reproduced using a minimal simulation. Using simulation outputs, we train a parallel partial Gaussian process (PPGP) model that rapidly predicts MSDs from matrix geometric variables, including area fraction, particle size, and polydispersity. Analysis reveals that tracer transport is primarily governed by accessible pore sizes and that distinct global structures can produce indistinguishable MSDs. While MSDs do not uniquely encode system geometrical parameters, we nevertheless find correspondence between pore size distribution and the ensemble MSDs. By modeling matrix self-diffusivity, the minimal model can also phenomenologically describe MSDs of internalized tracer particles in cells. The framework enables rapid inference of structural properties in crowded environments, including transport in the intracellular environment.

Over the last two decades, network science has greatly advanced our understanding of how the collective behaviors of a complex system emerge from the interactions among its basic units. Multiplex networks, i.e. networks with many layers, whose nodes are in one-to-one correspondence, provide a more realistic description for social, biological and ecological systems where multiple types of interactions coexist. After a brief introduction on how to model the architecture of multiplex networks, we present a complete overview of the different dynamics which can unfold over these structures. We present a unified framework to describe dynamical processes such as percolation, reaction-diffusion, synchronization, epidemic spreading, social dynamics and games on multiplex networks, as well as the coupled evolution of different dynamical processes, and the coevolution of a process with the network structure. Our focus is on truly-multiplex collective behaviors, i.e., all those phenomena which cannot emerge on the corresponding aggregated networks, or when the different layers of these systems are considered in isolation. We identify three main mechanisms leading to new collective behaviors: the existence of structural correlations across layers, the presence of dynamical correlations in the processes taking place at the different layers, and the dynamical interplay of inter- and intra-layer interactions. We conclude with a summary of the main takeaways from a decade of work in the field.

Performance of deep learning models is strongly governed by architectural capacity, with width and depth as primary controls. However, in physical-science applications, models are often compared at a single fixed size or by separating accuracy and computational cost, which can be misleading since architectures exhibit different accuracy-cost scaling as width and depth vary. This issue is particularly relevant for U-Net-type encoder-decoder models, widely used for multi-scale gas, fluid, and plasma dynamics due to their ability to represent features across spatial scales. A U-Net constructs a multi-resolution representation via an encoder that progressively reduces spatial resolution, followed by a decoder that restores it for prediction. Skip connections link corresponding encoder and decoder features, preserving fine-scale information and improving optimization. In practice, U-Net width is routinely tuned, while depth is typically kept fixed (a set number of down/up-sampling stages with few convolutions per stage), limiting systematic exploration of depth for improving the accuracy-cost trade-off. We address this limitation by increasing effective depth through stacking multiple encoder-decoder "waves" in series, with skip connections both within and across waves to enable progressive cross-scale refinement. We call this architecture a Deep Wave Network (DW-Net). Training data, optimization, and schedules are kept identical across models. Instead of evaluating single configurations, we train multiple width variants of each architecture and compare accuracy vs. GPU time Pareto fronts. Across several 2D and 3D flow benchmarks, DW-Net models consistently improve the Pareto frontier over single-wave U-Nets, achieving higher accuracy at matched cost or similar accuracy at reduced cost, and reaching low-error regimes with up to 3x less training time under identical training settings.

This note examines an apparently unpublished manuscript on special relativity written by Conrad Habicht in 1914 and made available online by the ETH-Bibliothek Zürich in December 2024. To the best of my knowledge, no study of its content has yet been published. Habicht was one of Einstein's closest companions during the Bern years. Between February 1902 and mid-1904 he shared with Einstein many occasions for discussion and companionship in Bern. After leaving the city, he remained in close contact with Einstein through visits, reciprocal stays, and a substantial correspondence extending from the years immediately following 1905 to the eve of the First World War. The manuscript offers a clear and pedagogical presentation of special relativity. Its historical interest lies in the structure of the exposition and in the memory of the theory that the text preserves. Habicht does not present special relativity as an isolated creation beginning from Einstein's 1905 paper alone. He devotes considerable space to the pre-Einsteinian problem situation: the classical principle of relativity, the ether, Fizeau's experiment, Michelson–Morley, Lorentz's theory, the contraction hypothesis, local time, and the privileged system of the stationary ether. Lorentz is treated as the central figure who brought the electrodynamics of moving bodies to its most acute form before Einstein's intervention. This note provides a qualitative description of the manuscript, with particular attention to its structure, its treatment of the relation between classical mechanics and electrodynamics, and the respective roles assigned to Lorentz, Michelson–Morley, Einstein, and Minkowski.

Yinsen is a high-temperature-superconducting (HTS) tokamak reactor concept for off-grid applications such as maritime propulsion, remote power, and industrial energy. Rather than pursuing grid-scale power density, the design is anchored to a materials-limited fusion power density of $P_f/S_b=0.7~\mathrm{MW/m^2}$, obtained from a 35 DPA structural limit, a 20-year plant lifetime, 40% utilization, and a geometric damage-peaking correction. The resulting device has a V-4Cr-4Ti vacuum-vessel lifetime of $1040~\mathrm{MW\cdot yr}$, pointing to a minimum useful fusion power of $130~\mathrm{MW}$ and more than $25~\mathrm{MWe}$ net output. Integrated FUSE modeling refines the design into a self-consistent high-field baseline with a shaped 9.29 T, 9.67 MA plasma, while ASTRA transport analysis corroborates a broader operating window above the minimum design point. Divertor power handling is addressed with UEDGE modeling, showing that impurity-seeded detached operation is attainable with neon seeding, reducing peak heat fluxes well below $10~\mathrm{MW/m^2}$. OpenMC neutronics calculations with a double-layered WC/W$_2$B$_5$ shield show that the vacuum vessel is the lifetime-limiting solid structure, while the HTS magnets remain lifetime components: at the 130 MW baseline, total TF nuclear heating is 7.4 kW at 20 K, and the TF fast-neutron limit corresponds to roughly sixteen vacuum-vessel lifetimes. The same neutronics analysis gives $TBR\approx1.1$ with 30% $^6\mathrm{Li}$ enrichment and no dedicated neutron multiplier. Plant-level studies detail a supercritical CO$_2$ balance of plant and pulsed-power operation using a 34 kV medium-voltage backbone and local energy storage. Taken together, these results suggest that a low-power-density HTS tokamak offers a near-term path for relevant FOAK fusion reactors where many remaining challenges between $Q>1$ and economic grid operation are alleviated.

We propose a data-driven framework to estimate high-resolution (HR) velocity fields and reduced-order flow coordinates from real-time Event-Based Imaging Velocimetry (rt-EBIV). Fast event analysis first provides low-resolution (LR) velocity snapshots on a coarse grid. Offline, paired LR/HR fields are used to identify the LR-to-HR mapping and a linear dynamical model in a POD-based latent space. Online, each LR snapshot is projected onto the LR basis, the corresponding HR coordinates are estimated and temporally regularized, and the HR field is reconstructed from the retained POD modes. Three estimators are compared: a direct Kalman filter (KF), a linear stochastic estimator followed by Kalman filtering (LSE), and a variance-rescaled variant (LSE+VR). The method is tested on two turbulent flows acquired with pulsed EBIV: a submerged water jet and a channel flow over a square rib. All estimators outperform direct cubic interpolation of the LR fields, yielding more consistent HR reconstructions of instantaneous flow states, turbulent kinetic energy, spectra, reduced-order dynamics, and temporal coherence. LSE gives the lowest overall reconstruction error, while LSE+VR achieves similar errors with improved recovery of fluctuation energy and higher-order content. The direct KF is the most computationally efficient and provides the closest agreement with the HR reference in spectral analyses. Since most of the cost is associated with full-field HR reconstruction, the latent-coordinate estimation is negligible compared with LR processing. The framework allows deliberately coarse rt-EBIV processing to be combined with reduced-order refinement, extending real-time operation toward higher update rates while preserving richer and dynamically consistent HR flow representations for diagnostics and future observer-based flow-control applications.

We present the simultaneous trapping and cooling of ${}^{85}$Rb, ${}^{87}$Rb and ${}^{133}$Cs in a triple-species magneto-optical trap (MOT). This demonstration is obtained using an all-fibre compact and robust laser system based on telecom 1.5 $\mu$m and 2 $\mu$m technologies. We characterise the two-body interspecies losses in the double and triple-species MOT. We calculate the two-body interspecies trap-loss coefficient for the ${}^{85}$Rb/${}^{133}$Cs and the ${}^{87}$Rb/${}^{133}$Cs pairs, representing a variation of less than 7 % in atom number. No losses are observed between ${}^{85}$Rb and ${}^{87}$Rb in our experimental conditions. We find that interspecies interactions inside the triple-species MOT do not prevent trapping and cooling 10$^{8}$ atoms for each species, making our system suitable for future multi-species cold-atom interferometry applications.

Wildfires are a major producer of fine particulate matter, impacting human health and the electrical grid. Accurately forecasting smoke impacts over long time scales incorporates fuel treatment strategies, natural fuel succession, and stochastic events like lightning strikes. However, predicting smoke for each fuel distribution with a forward simulation of a coupled fire-atmosphere model is computationally infeasible. Moreover, relatively simple fire models are tractable to run in many long-time scenarios but do not capture smoke transport. We use data-driven multilinear operators to predict a smoke concentration field from knowledge of the time since ignition for two quantities of interest: aerosol optical depth and smoke detection. Our method first computes the principal components of time-since-ignition and smoke concentration fields and then learns a map from powers of the input coefficients to the output coefficients. We apply our learned operator to smoke prediction in the Upper Rio Grande Watershed. After collecting training data, learning the approximation weights on a CPU takes less than 30 seconds, and each forward call takes less than 1 ms. On a proxy for aerosol optical depth, we obtain equal accuracy to Monte Carlo sampling with fewer than half as many coupled model calls. For smoke detection, we obtain an intersection-over-union (IoU) of 0.64 and an area under the receiver operating characteristic curve (AUC) of 0.95 on holdout data. Our method is significantly more accurate than the most similar published smoke classifier, which obtains an IoU and AUC of 0.15 and 0.61, respectively, on a 2015 bushfire in Australia.

Large Language Models (LLMs) are democratizing access to personalized tutoring; however, their effectiveness is hindered by challenges in processing multimodal content, which limits AI's potential to provide equitable, high-quality STEM support. This study evaluates LLM performance on multimodal physics problems, identifies specific failure modes through an empirical error taxonomy, and tests practical interventions designed to overcome multimodal processing limitations. We assessed three publicly available LLMs (Claude, Gemini, and ChatGPT) on multimodal physics problems from the OpenStax database and compared the results with text-only performance. An empirically derived error taxonomy was developed through pilot testing, followed by evaluation of a structured multimodal dialogue intervention. All three models achieved near-ceiling accuracy (96%) on text-only physics problems. Performance declined substantially on multimodal problems, consistent with what we term the Multimodal Interference Effect. Error analysis identified four failure modes: visual processing errors, context misinterpretation, mathematical computational errors, and hybrid errors, with visual processing errors being the most prevalent. The structured dialogue intervention corrected 82% of errors overall; visual processing errors were corrected at 100% across all models. Educators and students can implement these interventions immediately, requiring no model retraining, to improve AI tutoring reliability on image-rich STEM content, advancing equitable access to high-quality learning support.

Transformer architectures have recently surged as promising solutions for nonlinear dynamical systems, proposed as foundation models capable of zero-shot dynamics reconstruction and forecasting. Despite this success, it remains unclear whether they can truly serve as reliable digital twins of dynamical systems, i.e., whether they capture the underlying physical dynamics in distinct parameter regimes, especially in parameter regimes from which no training data is taken. For parameter-space extrapolation in nonlinear dynamical systems, reservoir computing has demonstrated broad success, as proper training can turn it into an intrinsic dynamical system capable of capturing not only the dynamical climate of the target system but more importantly, how the climate changes with parameter. Transformers, in contrast, rely on permutation-invariant attention mechanisms that can limit their ability to capture how temporal structure changes with parameter. To determine if Transformers have the capability of dynamics extrapolation, we take predicting catastrophic collapse, which occurs when a bifurcation parameter crosses a critical threshold, as a benchmark task. Models are trained on trajectories in normal parameter regimes and then tested on parameters in an unseen regime with system collapse. Our results show that Transformers, across configurations, consistently fail to capture collapse, while reservoir computing reliably predicts the transitions. This surprising finding raises questions about the generalization ability of Transformers to dynamical systems, a topic warranting future research.

Stratocumulus cloud decks exhibit bistability between patterns of high (closed cells) and low (open cells) cloud fraction. Localized transitions between these two states (pockets of open cells) have been observed but their underlying mechanism remains unclear. We model stratocumulus and their interaction with atmospheric aerosol as a data-driven and physics-informed stochastic dynamical system with time-dependent parameters. This allows us to show that pockets of open cells result from noise-induced transitions between the stratocumulus patterns. We find comparable timescales for these transitions, mesoscale self-organization into patterns and the evolution of large-scale parameters. This lack of timescale separation corresponds to an aerosol memory in cloud evolution and means that the sampling of stratocumulus states by polar-orbiting satellites lacks the encoding of process information that would be present for an asymptotic and ergodic sampling.

Skill scores, which measure the relative improvement of a forecasting method over a benchmark via consistent scoring functions and proper scoring rules, are a standard tool in forecast evaluation, yet their sampling uncertainty is rarely rigorously quantified. With modern forecasting applications being increasingly multivariate and involving evaluations across multiple horizons, variables, spatial locations, and forecasting methods, standard tools like the pairwise Diebold-Mariano forecast accuracy test or pointwise confidence intervals fail to account for the multiple comparison problem, leading to inflated Type I error rates and invalid joint inference. To address the lack of a coherent, statistically rigorous framework for quantifying uncertainty across these multi-dimensional evaluation problems, we introduce simultaneous confidence bands for expected scores and skill scores. Our framework provides a versatile tool for joint inference that is applicable to any forecast type from mean and quantile to full distributional forecasts. We develop a bootstrap implementation and show that our bands are valid under multivariate extensions of the classical Diebold-Mariano assumptions. We demonstrate the practical utility of the approach in two case studies by quantifying the benefits of time-varying parameter models for macroeconomic forecasting, and by comparing data-driven and physics-based models in probabilistic weather forecasting.

Parametrically driven cavity solitons (PDCS), unlike single-pumped cavity solitons, are localized optical pulses arising from parametric processes. These cavity solitons, recently discovered in pure-Kerr media, offer great promise for nonlinear dynamics studies and metrology. Here, we present the first multimode quantum description of pure-Kerr PDCS. In the below threshold regime, we verify single- and two-mode squeezing, while above threshold we uncover novel "quantum" dispersive waves - the quantum analog of soliton Cherenkov radiation. Besides revealing these unexplored quantum properties, we show that PDCS generates up to 20 dB of squeezing, only limited by overcoupling and intrinsic losses for experimentally routine parameters. We therefore provide a pathway to observe strong multimode quantum noise reduction in these systems.

Nanogap thermophotovoltaic (TPV) devices can deliver high power densities even with the medium-temperature heat sources. As such, these devices are very promising for recovering industrial waste heat. So far, the demonstrated nanogap TPVs have shown performances far below optimal. The objective of this study is to identify the optimal designs for nanogap TPV devices targeted for industrial waste heat recovery. Optimal configurations for maximal power density, maximal efficiency, and a trade-off between the two are determined as a function of the size of the vacuum gap between the emitter and the photovoltaic (PV) cell. The effects of adding a metallic cover to the PV cell, as well as introducing an air gap between the PV cell and the reflector, are also studied through this optimization framework. Results show that the optimal device configuration is highly sensitive to the vacuum gap size. A metallic cover enhances power density for gaps below 125nm due to surface plasmon-polariton coupling, but significantly reduces efficiency due to its parasitic absorption. To realize the benefits of air gaps, ultrathin PV cells requiring mechanical support by a substrate are needed. The presence of the substrate, however, diminishes the benefits of the air gap rendering them ineffective. ITO and InAs are found as optimal materials for the emitter and PV cell, respectively, owing to tunable plasma frequency of ITO and low bandgap of InAs.

Phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) has emerged as a promising electronic structure method for correlated electronic systems. However, the quality of its predictions depends critically on the choice of trial wavefunction, and it is not obvious how to make an optimal choice especially for strongly correlated states of large systems. Mean-field wavefunctions are compelling trial wavefunction candidates as they map directly to chemical concepts and can be obtained with $O(N^4)$ cost. Yet in the strongly correlated regime one faces a symmetry dilemma and the existence of multiple nearly-degenerate solutions. In this work we investigate active space models of [2Fe-2S]$^{2+}$, mixed-valent [4Fe-4S]$^{2+}$, and [4Fe-4S]$^{4+}$ and explore the sensitivity of ph-AFQMC to the choice of unrestricted Hartree-Fock trial wavefunction. We find that chemical properties and physical symmetries, rather than the variational energy, ought to guide the choice of mean-field trial for ph-AFQMC (or reference state for coupled cluster models), and show that surprisingly accurate ground-state energies for these systems can be obtained. However, in all cases we find a rapidly vanishing overlap between the stochastic wavefunction and the UHF trial, indicating that the trials are suboptimal importance functions. By analogy to a similar situation in the stretched helium dimer cation, we show how this sampling bias pushes ph-AFQMC towards artificially negative energies, which evidently can be compensated for by the phaseless bias in certain cases.

We show that steady-state entanglement in open quantum systems is controlled by the phase reference of a phase-sensitive reservoir. Using a covariance-matrix approach for Gaussian-preserving dynamics, we demonstrate that purely local, phase-sensitive dissipation can generate entanglement when combined with coherent coupling. The steady state exhibits a finite entangled region with an optimal squeezing strength that maximizes both the magnitude and thermal robustness of entanglement. We find that coherent coupling does not enhance entanglement monotonically, but instead regulates the conversion of local squeezing into nonlocal correlations. Importantly, the coupling dependence is controlled by the phase reference of the squeezed reservoir: phase-locked (rotating-frame) and laboratory-frame implementations yield qualitatively distinct steady states and entanglement structure. These results establish phase-sensitive reservoir engineering as a controllable route to steady-state entanglement in continuous-variable systems. Steady-state entanglement in phase-sensitive open systems depends explicitly on the reservoir phase reference and is not invariant under changes of that reference.}

We perform direct numerical simulations (DNS) and resolvent analysis of natural convection in a differentially heated cavity (DHC) at Prandtl number $Pr = 0.7$, systematically varying the aspect ratio $\Gamma$ over $0.1 \leq \Gamma \leq 60$ and Rayleigh number $Ra = 10^5$–$10^8$. Across this nearly three-decade range of $\Gamma$, the Nusselt number $Nu$ exhibits four distinct regimes driven purely by geometric confinement via altering the structure of large-scale circulation (LSC): $Nu$ rises in regime I, peaks and plateaus in regime II, falls monotonically in regime III, and reaches a minimum and plateaus again in regime IV. Resolvent analysis shows a corresponding shift in the flow's response structure across these regimes: the maximum gain drops monotonically through regime I, bottoms out in regime II, and rises sharply through regime III and IV. The dominant response mode also changes character—from a diagonal mode peaking near the adiabatic walls, to a horizontally oriented structure peaking in the bulk flow (regimes I–II), to a vertical stack of such structures in regimes III–IV. The resolvent response tracks the sensitivity of the LSC to perturbations, which is minimized in regime II and maximized in regimes III and IV. Notably, the LSC configuration with the lowest sensitivity consistently corresponds to the highest heat flux, across all $Ra$ examined. We link this sensitivity to the LSC's anisotropy, quantified by the ratio of horizontal-to-vertical velocity Reynolds numbers ($Re_u/Re_v$). Remarkably, this ratio stays nearly constant ($\approx 0.45$) at the optimal (heat-transport-maximizing) aspect ratio ($\Gamma_{\mathrm{opt}}$) across all $Ra$, and the corresponding $\Gamma_{\mathrm{opt}}$ follows the power-law scaling $\Gamma_{\mathrm{opt}} \sim Ra^{-0.17 \pm 0.01}$.

We have investigated kinematic reversibility in a cold atom system under strongly overdamped conditions. In such systems, inertia is negligible, and for noninteracting rigid particles, inverting the external force causes a perfect reversal of individual particle trajectories. We used a magneto-optical trap (MOT) as a model low Reynolds number fluid and show the kinematic reversibility survives in the presence of interparticle interactions. In our experiment, we applied controlled external forces via a linearly ramped magnetic bias field and monitored the resulting cloud dynamics. Despite the complex three-dimensional rearrangement induced by the forces, the system exhibits precise reversibility when the force is reversed, consistent with Purcell's framework for kinematic reversibility in low Reynolds number hydrodynamics. Reversibility was not universal,however– under certain MOT alignment conditions we have also observed clear deviations associated with system hysteresis. Our work shows that strongly dissipative cold atom fluids are a versatile and rich platform for exploring overdamped dynamics.

Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and the scarcity of transition state configurations in candidate pools. Active learning (AL) can mitigate these costs, but its effectiveness hinges on the acquisition rule. We investigate whether the latent space of a pretrained MLIP already contains the information necessary for effective acquisition, eliminating the need for auxiliary uncertainty heads, Bayesian training and fine-tuning, or committee ensembles. We introduce two acquisition signals derived directly from a pretrained MACE potential: a finite-width neural tangent kernel (NTK) and an activation kernel built from hidden latent space features. On reactive-chemistry benchmarks, both kernels consistently outperform fixed-descriptor baselines, committee disagreement, and random acquisition, reducing the data required to reach performance targets by an average of 38% for energy error and 28% for force error. We further show that the pretrained model induces similarity spaces that preserve chemically meaningful structure and provide more reliable residual uncertainty estimates than randomly initialised or fixed-descriptor-based kernels. Our results suggest that pretraining aligns latent-space geometry with model error, yielding a practical and sufficient acquisition signal for reactive MLIP fine-tuning.

We develop a Fisher-informational reformulation of physical time in which clock time is not regarded as a fundamental ontological substance, but as an emergent calibration of causally ordered distinguishability among physical states. The operational starting point is that clocks do not measure time itself; rather, they instantiate reproducible physical processes whose distinguishable states are correlated with other events. We introduce a causal-informational parameter, denoted by Lambda_F, defined as an accumulated Fisher-geometric distance along a causally admissible trajectory in state space. In classical statistical systems, this parameter is generated by the Fisher information metric; in quantum systems, the corresponding construction is associated with quantum Fisher information, the Bures metric, and the Fubini-Study geometry of projective Hilbert space. The manuscript distinguishes model-dependent Fisher information from quantum Fisher information, clarifies the reparameterization of Schrodinger dynamics, and gives explicit examples involving a qubit clock, an exponential decay process, and a Fisher characterization of clock quality. The proposal is positioned relative to relational time, the Page-Wootters mechanism, thermal time, quantum speed-limit relations, information geometry, and the problem of time in quantum gravity. We do not claim that relational or emergent time is new. The specific contribution is the use of Fisher distinguishability as an operational precursor from which ordinary clock time can be reconstructed. In this sense, the central statement of the paper is: time is not measured by clocks; clock time is reconstructed from the Fisher distinguishability accumulated along causally ordered physical changes.

Shor's algorithm is one of the most promising applications of quantum computers. However, since $\sim 10^6$ physical qubits are believed to be required for established approaches, the algorithm will need to be distributed across many modules. In this paper, we provide a distributed compilation of Shor's algorithm on a modular atomic processor. We present an end-to-end compilation and optimization strategy that focuses on the interplay between the inter-module communication and the intra-module clock rate. With a half-million-qubit modular atomic processor with a communication rate of $10^5$ Bell pairs per second and a measurement time of 1 ms in a CPU-inspired architecture, we demonstrate that 2048-bit RSA integers can be factored in only 16% more time than a single-module architecture. Our work presents the first end-to-end analysis and simulation of large-scale integer factorization on modular atomic hardware and it provides a blueprint for the future design of other large-scale modular algorithms.

Reservoir characterization workflows increasingly rely on image-based and machine-learning/deep learning or even generative AI approaches, but openly available geological image datasets suitable for reproducible benchmarking remain limited. Here we describe a high-resolution dataset of reservoir-property image slices derived from the Groningen static geological model. The dataset contains aligned two-dimensional PNG images representing facies, porosity, permeability, and water saturation, generated from three-dimensional reservoir grids and prepared for downstream visualization, segmentation, and image-to-image translation tasks. In addition to the deposited original image corpus, we provide an archived software workflow for reproducing augmentation, mask generation, paired-image construction, and example baseline experiments. The resource is designed to support benchmarking of geological image analysis methods and the study of cross-domain relationships among reservoir properties. By separating the fixed image dataset from the reproducible processing workflow, this work provides a transparent foundation for reuse in geoscience, reservoir modeling, and machine-learning applications.

Boundary layer processes drive the air-sea exchange of momentum, heat, and moisture that powers and shapes hurricanes. The height of the boundary layer is a critical parameter in engineering and meteorological models of hurricane wind speed, turbulence intensity, and storm strength. Existing models rely on a height scale derived with the assumption of a constant eddy viscosity, a strong simplification that limits physical accuracy. This work proposes formulae for the turbulent boundary layer height in hurricanes outside the eyewall. The proposed scalings are $u_\star/\beta$ for neutral stratification, and $u_\star/\sqrt{\beta N}$ for stable stratification, where $u_\star$ is the friction velocity, $\beta$ is the absolute fluid vorticity and N is the Brunt-Vaisala frequency of the background stratification. These scalings are analogous to those used in the literature for neutrally and stably stratified turbulent atmospheric boundary layers. The formulae are backed by analytical derivation and validated against velocity profiles from large-eddy simulations and field observations. They are predictive to within 2.5% relative error on average and yield a good collapse of the simulated and observational velocity profiles away from the surface. The results further enable quantitative relationships between boundary layer height and other characteristic scales, including the height of maximum wind speed and the depth of the inflow layer. The proposed expressions offer a practical basis for interpreting observational data, informing mesoscale simulations, and specifying turbulent flow statistics in wind engineering and coastal resilience.

The neutron shielding properties of high-density concrete and magnetite aggregates were evaluated using both experimental measurements and Monte Carlo simulations. Because these materials are commonly used in medical accelerator facilities, it is essential to characterize their behavior under neutron radiation to ensure adequate shielding performance. Our experimental results show good agreement with the Monte Carlo calculations, confirming the reliability of the simulation approach. The attenuated neutron doses for various shielding thicknesses were determined for each aggregate type based on simulation and then compared as dose ratios. The findings indicate that magnetite provides superior neutron shielding, exhibiting a shorter attenuation length than conventional concrete for the same barrier thickness. The neutron attenuation characteristics of both concrete and magnetite were studied for typical neutron spectra encountered in clinical proton-therapy accelerators, including treatment rooms, primary, secondary barriers, and mazes. These results can support the optimization of radiation-shielding designs in medical and research facilities.

The anisotropic diffusion of electrons with various rigidity and the anomalous conductivity of a collisionless plasma in the presence of Weibel-generated quasi-static turbulent and uniform external magnetic fields are examined. Using an original code based on the Boris algorithm, the electron diffusion coefficients and the longitudinal, transverse, and Hall mobility factors are determined for a representative set of plasma parameters. It is shown that these values and their anisotropy depend strongly on the electron temperature, external magnetic field, average level of magnetic turbulence, and its spectrum. The physical origin and expected limits of such dependencies are indicated. Applications of the results are discussed in the case of coronal plasma, where the anomalous resistivity prevails over the collisional one and can be responsible for the redistribution of large-scale currents in magnetic loops and small-scale currents in the regions of reconnection of magnetic-field lines.

The power spectrum (PS) of the density field in supersonic turbulence is a fundamental quantity that characterizes the statistical properties of the structures formed in compressible flows. It is also widely used to estimate the Mach number in the interstellar medium from simulation-derived relations. In this paper, we provide a first quantitative explanation for the evolution of the slope of the PS of the density field with the Mach number in homogeneous isotropic isothermal turbulence using a time-invariant quantity derived by Chandrasekhar (1951). For simulated turbulent flows, the model reproduces the measured slopes for different widths of the inertial range and density variances very well. Our model also provides a comprehensive interpretation of the characteristic slopes of the PS of the density field measured in the interstellar medium. Based on these results, we stress that the Mach number cannot be reliably deduced from the slope of the PS of the density field. In closing, we discuss a resolution criterion that must be fulfilled to correctly simulate a turbulent flow with a given density PS slope.

Reservoir geomodeling is central to subsurface characterization, but it remains challenging because conditioning data are sparse, geological heterogeneity is strong, and conventional geostatistical workflows often struggle to capture nonlinear relationships between facies and petrophysical properties. This study evaluates the robustness and transferability of Pix2Geomodel on a different and more complex reservoir dataset with reduced vertical support. The new case includes a heterogeneous reservoir-quality classification and only 54 retained layers, providing a stricter test of whether Pix2Pix-based image-to-image translation can preserve facies-property relationships under constrained data conditions. Facies, porosity, permeability, and clay volume (VCL) were extracted from a reference reservoir model, exported as aligned two-dimensional slices, augmented using consistent geometric transformations, and assembled into paired image datasets. Six bidirectional tasks were evaluated: facies to porosity, facies to permeability, facies to VCL, porosity to facies, permeability to facies, and VCL to facies. The Pix2Pix model, consisting of a U-Net generator and PatchGAN discriminator, was evaluated using image-based metrics, visual comparison, and variogram-based spatial-continuity validation. Results show that the model preserves the dominant geological architecture and main spatial-continuity trends. Facies to porosity achieved the highest pixel accuracy and frequency-weighted intersection over union of 0.9326 and 0.8807, while VCL to facies achieved the highest mean pixel accuracy and mean intersection over union of 0.8506 and 0.7049. These findings show that Pix2Geomodel can transfer beyond its original case study as a practical framework for rapid bidirectional facies-property translation in complex reservoir modeling.