We report the electrochemical performance and diffusion kinetics of a newly designed NASICON type Na$_{3.3}$Mn$_{1.2}$Ti$_{0.75}$Mo$_{0.05}$(PO$_4$)$_3$/C composite material as a cathode for cost-effective sodium-ion batteries. A novel strategy of small Mo doping successfully stabilizes the sample having high Mn content in single phase rhombohedral symmerty. The high-resolution microscopy analysis reveals nanocrystallites of around $\sim$18 nm, uniformly embedded within the semi-graphitic carbon matrix, which enhances the surface electronic conductivity and effectively shortens the sodium-ion diffusion path. More importantly, we demonstrate a stable electrochemical behavior, with enhanced discharge capacity of 124 mAh/g at 0.1 C, having good reversibility and retaining 77\% of its capacity after 300 cycles, and 70\% even after 400 cycles at 2 C. The sodium-ion diffusion coefficients, estimated using both galvanostatic intermittent titration technique (GITT) and cyclic voltammetry are found to lie within the range of $10^{-9}$ to $10^{-11}$~cm$^2$/s. Additionally, the bond-valence site energy mapping predicted a sodium-ion migration energy barrier of 0.76 eV. A detailed distribution of relaxation times (DRT) analysis is used to deconvolute the electrochemical impedance spectra into distinct processes based on their characteristic relaxation times. Notably, the solid-state diffusion of sodium ions within the bulk electrode, with a relaxation time of $\sim$50 s, shows a consistent trend with the diffusion coefficients obtained from GITT and Warburg-based evaluations across the state of charge.

Recently, a new version of DC-coupled High Rate Picosecond Photodetectors (DC-HRPPDs) substantially re-designed for use at the Electron-Ion Collider (EIC) has been developed. A first batch of seven 'EIC HRPPDs' was manufactured in early 2024. These HRPPDs are DC-coupled photosensors based on Micro-Channel Plates (MCPs) that have an active area of 104 mm by 104 mm, 32 x 32 direct readout pixel array at a pitch of 3.25 mm, peak quantum efficiency in excess of 30%, exceptionally low dark count rates and timing resolution of 15-20 ps for a single photon detection. As such, these photosensors are very well suited for Ring Imaging CHerenkov (RICH) detectors that can also provide high resolution timing capability, especially in a configuration where a detected charged particle passes through the sensor window, which produces a localized flash containing a few dozens of Cherenkov photons in it.

Dual-band plasmonic nanoantennas, exhibiting two widely separated user-defined resonances, are fundamental building blocks for the investigation and optimization of plasmon-enhanced optical phenomena, including photoluminescence, Raman scattering, and various nonlinear effects such as harmonic generation or sum-frequency generation, parametric down-conversion, etc. The nanoparticle-in-slit (NPiS) or nanoparticle-in-groove (NPiG) is a recently proposed dual-band antenna with independently tunable resonances at mid-infrared and visible wavelengths. It was used to enhance the corresponding sum- and difference-frequency generation processes from optimally located molecules by an estimated $10^{13}$-fold. However, the theoretical understanding of such structures and their eigenmodes remains poor, hindering further optimization and limiting broader applications. Here, we explore a diverse range of nanocavity-like quasi-normal modes (QNMs) supported by NPiS structures, examining the contributions of both their near-field (i.e., giant photonic density of states) and far-field (i.e., spatial radiation patterns) characteristics to frequency upconversion. We identify methods for independently tuning the visible and mid-infrared resonances while conserving a good mode overlap in the near field, which is essential for efficient nonlinear processes. Moreover, through mode analysis, we unveil an experimentally unexplored fundamental resonance with greater field enhancement and much-improved mode overlap with the mid-infrared field, which could, in principle, further boost the mid-infrared upconversion efficiency by 5-fold compared to existing results. This work helps to rationalize and optimize the enhancement of nonlinear effects across a wide spectral range using a flexible and experimentally attractive nanoplasmonic platform.

Ultra-high intensity laser-plasma interactions can produce ultra-relativistic electrons via direct laser acceleration, assisted by quasi-static plasma magnetic and electric fields. These fields transversely confine electron motion and induce betatron oscillations. The net energy gain is strongly influenced by the interplay between two frequencies: the betatron frequency and the frequency of laser field oscillations experienced by the electron. Prior work has shown that energy gain is enabled by a resonance between the betatron oscillations and the oscillations of the laser field. In particular, higher-order resonances occur when the laser field completes multiple cycles during one betatron oscillation, allowing additional regimes of energy transfer beyond the fundamental (betatron) resonance. In this work, we demonstrate that such resonances become particularly effective when the laser's phase velocity is superluminal. Although the two frequencies generally evolve differently with increasing electron energy, which leads to detuning, a superluminal phase velocity introduces a non-monotonic frequency ratio with a global minimum. This minimum allows sustained frequency matching over a broad energy range, thereby enabling enhanced energy gain. As the phase velocity increases, the betatron resonance becomes ineffective due to premature frequency detuning. At the same time, higher-order resonances become increasingly effective, emerging as the dominant mechanisms for enhanced energy gain in this regime of direct laser acceleration.

Entanglement is a crucial resource for achieving quantum advantages in quantum computation, quantum sensing, and quantum communication. As shown in this Letter, entanglement is also a valuable resource for the coherent control of the large class of bimolecular chemical reactions. We introduce an entanglement-enhanced coherent control scheme, in which the initial preparation of the superposition state is divided into two steps: the first entangles the reactants, and the second is responsible for coherent control. This approach can overcome the limitations of traditional coherent control of scattering caused by non-interfering pathways, known as satellite terms. By tuning the amount of entanglement between reactants, the visibility of coherent control in chemical reactions can be modulated and optimized. Significantly, there exists an optimal amount of entanglement, which ensures complete indistinguishability of the reaction pathways, maximizing the extent of control. This entanglement-enhanced coherent control scheme is computationally illustrated using the ultracold KRb + KRb reaction, where a perfect control over the parity of the product rotational states is achieved.

The last energy-frontier lepton collider, LEP, established several limits that still hold today. A key one is the counting of three light neutrino species from the invisible decay width of the Z boson. From a collider calorimetry standpoint, the missing energy is an invitation to design an experiment to directly measure the neutrino mass. We present a new type of EM spectrometer which leverages the first adiabatic invariant in magnetic gradient drift to achieve exponentially bounded resolution in a highly compact and scalable format, enabling the PTOLEMY experiment to not only measure the neutrino mass at the tritium endpoint, but one day directly detect the Cosmic Neutrino Background. Meanwhile, the next lepton collider promises to expose the Higgs self-coupling and complete the accounting of lepton universality. We present a dual-readout, segmented crystal calorimeter for future collider detectors, combining new hardware capabilities with novel AI/ML reconstruction techniques towards realizing a detector that must definitively and unambiguously surpass its predecessors. Together, these studies confront the most pressing challenges for 21st century particle physics experiments to achieve the sensitivities needed to bridge the gap between the largest and smallest scales of reality.

This study investigates the feasibility of reconstructing the last closed flux surface (LCFS) in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current, and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of 0.04 m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to 0.03 m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in Fusion Power Plants (FPP) due to diagnostic constraints imposed by the presence of blankets and shielding.

We present a laser reaction chamber that we developed for in-situ/operando X-ray diffraction measurements at the NSLS-II 28-ID-2 XPD (X-Ray Powder Diffraction) beamline. This chamber allows for rapid and dynamic sample heating under specialized gas environments, spanning ambient conditions down to vacuum pressures. We demonstrate the capabilities of this setup through two applications: laser-driven heating in polycrystalline iron oxide and in single crystal WTe2. Our measurements reveal the ability to resolve chemical reaction kinetics over minutes with 1-s time resolution. This setup advances opportunities for in-situ/operando XRD studies in both bulk and single crystal materials.

Electrical impedance tomography (EIT) is a novel computational imaging technology. In order to improve the quality and spatial resolution of the reconstructed images, the G-CNN and HG-CNN algorithms are proposed based on a one-dimensional convolutional neural network (1D-CNN) in this paper. The input of the 1D-CNN is the reconstructed conductivity distribution obtained by the GVSPM algorithm or the H-GVSPM algorithm. The reconstructed images with higher resolution are obtained through the calculation of 1D-CNN. Finally, the Hadamard product is applied to calculate the output of the 1D-CNN. In the simulation results of the lung cross-section models, the correlation coefficients of the G-CNN algorithm and HG-CNN algorithm maximumly are 2.52 times and 2.20 times greater than the GVSPM algorithm and H-GVSPM algorithm, respectively. In the results of the simulation and experiment, the reconstructed images of the G-CNN and HG-CNN algorithms are distortion-free. In addition, the artifacts of the reconstructed images are diminished after calculations of the Hadamard product. This research provides a reference method for improving the quality of the reconstructed images so that EIT is better applied in medical detection.

By quantitatively evaluating the atomic concentrations of sputtered Sb2S3 films with different sputtering powers and Ar flows, we reveal that a sputtered Sb2S3 film becomes close to the stoichiometric composition as the sputtering power and Ar flow decrease. We characterize the optical properties of Sb2S3 and show that the lower sputtering power leads to a better figure of merit of Sb2S3 as an optical phase shifter in the near infrared (NIR) range. Based on these results, we achieve a loss per phase shift of 0.33 dB/{\pi} at a wavelength of 1.55 {\mu}m, one of the lowest losses among Sb2S3 phase shifters in the NIR range.

Two-dimensional materials, specifically transition metal dichalcogenides, for highly sensitive gas sensing are emerging as effective detection technology. Platinum ditelluride (PtTe2) is an intriguing material in the realm of field-effect transistors (FETs) due to its unique electronic properties. In this study, the CVDsynthesized PtTe2 was functionalized with Au-Pd thin film for analyte-specific (H2S) sensing for better toxic gas sensitivity and selectivity. It was concluded that PtTe2, in combination with appropriate metal or oxide decorations, had great potential for ultrasensitive and selective, real-time gas sensor applications.

Continuum computational kinetic plasma models evolve the distribution function of a plasma species $f_s$ on a phase-space grid over time. In many problems of interest the distribution function has limited extent in velocity space, hence using a highly refined mesh everywhere would be costly and slow. Nonuniform velocity grids can reduce the computational cost by placing more degrees of freedom where $f_s$ is appreciable and fewer where it is not. In this work we introduce a first-of-its kind discontinuous Galerkin approach to nonuniform velocity-space discretization using mapped velocity coordinates. This new method is presented in the context of a gyrokinetic model used to study magnetized plasmas. We create discretizations of collisionless and collisional terms using mappings in a way that exactly conserves particles and energy. Numerical tests of such properties are presented, and we show that this new discretization can reproduce earlier gyrokinetic simulations using grids with up to 48 times fewer cells.

We present an integrated switch that combines plasmonic and neuromorphic technologies with a single sub-stoichiometric VO2-x nanoparticle. The presented device acts as a versatile plasmonic switch with dual thermal and electrical reconfigurability leveraging the near-room temperature phase transition of the VO2-x nanoparticles combined with the rapid phase recovery to drive the device. The change in both the optical and electrical properties of the VO2-x nanoparticle enables simultaneous optical and electrical readouts making the plasmonic device suitable as a phase change memory cell which is crucial in the convergence of computing and communication technologies. Our demonstration of reversible electrical switching, evidenced by a 6dB modulation depth and concurrent optical and electrical outputs, signifies a major stride in merging electronic and photonic functionalities within phase-change material devices. This novel strategy not only harmonizes optical communication with electronic computing but also advances the development of sophisticated integrated neuromorphic devices.

The analysis of electromagnetic fields in cylindrical waveguiding structures that contain periodic ring loading, whether for applications in charged-particle accelerators or radiation transportation, has been traditionally conducted under simplifying limits, where the structure is either single-moded at the lower-frequency limit or overmoded at high-frequency limit. These limits have often allowed us to find spectral (modal) expansions for the fields under simpler analytical and computational conditions, with ohmic effects typically being the dominant power loss mechanism in the lower limit, while diffraction effects dominate the loss in the higher limit. In this Letter, we report the observation of a transition point in the character of the main loss mechanism, where ohmic loss becomes dominant in a structure typically presumed to be dominated by diffraction loss. The results follow a formal analysis for the scattered vector fields in a highly overmoded THz waveguide. The findings bridge between the traditional theoretical descriptions for the two limits and reveal key tradeoffs that inform experiments for the transportation of THz radiation over long distances.

In this paper, we establish a numerical method for simulation of wall-bounded incompressible turbulent flows by integrating the technology of random vortex method with the core idea of Large Eddy Simulation (LES). Specifically, we utilize the filtering function in LES, interpreted as spatial averaging, along with the integral representation theorem for parabolic equations,to achieve a closure numerical scheme which may be used for calculating solutions of Navier-Stokes equations. This approach circumvents the challenge associated with handling the non-locally integrable 3-dimensional integral kernel in the random vortex method and facilitates the computation of numerical solutions for flow systems via Monte-Carlo method. Comprehensive numerical simulations are carried out for turbulent and laminar flows in full space and wall-bounded space, considering both two-dimensional and three-dimensional cases, thereby demonstrating the validity and effectiveness of the method.

Quantum correlation microscopy is an emerging technique for improving optical resolution. By taking advantage of the quantum statistics from single-photon fluorophores, more information about the emitters (including number and location) is obtained compared with classical microscopy. Although it is known that the resolution can be improved by increasing detector numbers, as well as using quantum correlation, the quantitative relationship between these two approaches is not immediately clear. Here we explore widefield quantum correlation microscopy using arrays of single-photon detectors. We explicitly compare the use of $N$ detectors used in photon counting mode vs $N/2$ detectors used to measure quantum correlations. i.e., where there are $N/2$ Hanbury Brown and Twiss systems, using the same $N$ detectors, on randomly generated two-emitter systems. We find regimes where $N/2$ Hanbury Brown and Twiss detectors provide improved localisation compared to $N$ photon counting detectors, as a function of emitter position and number of photons sampled.

We simulate the attosecond transient absorption spectroscopy (ATAS) of monolayer hexagonal boron nitride (hBN) using the time-dependent density functional theory (TDDFT) and two-band density-matrix equations within the tight-binding approximation. The simulation results from the two methods are qualitatively consistent. We focus on the fishbone structure near the energy gap at the $\textrm{M}$ point, which exhibits a temporal period equal to that of the pump laser. To gain deeper insight into this structure, we simplify the two-band model to a single-electron model located at the $\textsc{M}$ point, allowing us to derive an analytical expression that can qualitatively reproduce the numerical results. Our analytical results reveal that both the interband transition dipole moments (TDMs) and the Berry connection play important roles in the fishbone structure of the ATAS. To isolate the effect of the interband TDMs, we set the Berry connection to zero, and the corresponding results reveal that the Berry connection suppresses the intensity of the absorption spectrum. Our study may shed light on the generation mechanism of the fishbone structure of the ATAS in hBN.

Explaining empirically observed wealth and income distributions, featuring power-law tails alongside gamma or log-normal bulk shapes, challenges models that focus on either pairwise competition or individual investment mechanisms. This study proposes and analyzes a unified model that integrates pairwise competition and individual investment via an adjustable parameter, $\alpha$. Numerical simulations are conducted to analyze the model's Gini coefficient and distributional shapes using the complementary cumulative distribution function and goodness-of-fit tests. Results show that the model captures a systematic transition in the bulk distribution from gamma like (low $\alpha$) to log-normal like (high $\alpha$). Additionally, intermediate levels of mechanism mixing can reduce inequality compared with the original mechanisms. However, it is difficult to distinguish heavy tails consistent with power-laws from log-normal tails. These findings highlight the importance of considering the interaction between different economic mechanisms but suggest that accurately replicating the empirical power-law tail requires more than the simple combination investigated.

Urban areas are major sources of methane due to population needs for landfills, natural gas distribution, wastewater treatment, and residential combustion. Here we apply an inversion of TROPOMI satellite observations of atmospheric methane to quantify and attribute annual methane emissions at 12x12 km2 resolution for 12 major US urban areas in 2022. The US Environmental Protection Agency Greenhouse Gas Inventory (EPA GHGI) is used as prior estimate. Our results indicate that the GHGI underestimates methane emissions by 80% on average for the 12 urban areas, with 22%-290% underestimations in most urban areas, except Los Angeles and Cincinnati where emissions are overestimated by 32%-37%. This is corroborated by independent surface-based observations in the Northeast Corridor and Los Angeles. Landfills are the principal cause of urban emission underestimates, with downstream gas activities contributing to a lesser extent than previously found. Examination of individual landfills other than in Los Angeles shows that emissions reported by facilities with gas collection and control systems to the Greenhouse Gas Reporting Program (GHGRP) and used in the GHGI are too low by a factor of 4 when using the prevailing recovery-first reporting method. This is because GHGRP-estimated gas collection efficiencies (average 70%, range 40-87%) are much higher than inferred from our work (average 38%, range 5-90%). Los Angeles landfills have much higher collection efficiencies (average 78% in GHGRP; 85% in our work) than elsewhere in the US, suggesting that operational practices there could help inform methane mitigation in other urban areas.

As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster risks by administering interventions with optimal timing, location, and intensity. However, the optimization process is highly challenging due to the vast scale and complexity of weather phenomena, which introduces two major challenges. First, obtaining accurate gradient information for optimization is difficult. In addition, numerical weather prediction (NWP) models demand enormous computational resources, necessitating parameter optimization with minimal function evaluations. To address these challenges, this study proposes a method for designing weather interventions based on black-box optimization, which enables efficient exploration without requiring gradient information. The proposed method is evaluated in two distinct control scenarios: one-shot initial value intervention and sequential intervention based on model predictive control. Furthermore, a comparative analysis is conducted among four representative black-box optimization methods in terms of total rainfall reduction. Experimental results show that Bayesian optimization achieves higher control effectiveness than the others, particularly in high-dimensional search spaces. These findings suggest that Bayesian optimization is a highly effective approach for weather intervention computation.

Ionic transport within charged nanopores is commonly represented by resistor-capacitor transmission line circuits, where charging electrical double layers are modeled as capacitors, and the resistance to ionic current is modeled as resistors. However, these circuits fail to account for oscillations observed in experimental Nyquist plots of impedance, which are attributed ad hoc to effects such as complex porous structures or chemical reactions. Here, we show that diffusivity asymmetry between ions in confinement - overlooked in previous studies - produces Nyquist plots with two turns. Additionally, we demonstrate that ionic transport is more accurately described by magnetically coupled inductor-resistor circuits than by a simple resistor-capacitor circuit. Our results show that an impedance response of ionic transport in nanopores for arbitrary Debye lengths is better captured by two Warburg elements in parallel than a single Warburg element.

A Kappa distribution function applicable to systems comprising mixed fermions and bosons has been developed through the thermodynamic Gibbs potential utilizing the quantum versions of the Olbert kappa distributions. The generalised expressions of the partition function and the entropy have been evaluated for such mixed quantum systems. The analysis shows that boson-rich systems consistently exhibit higher entropy than fermion-rich systems. The distribution functions show heavy-tailed characteristics at low Kappa values, indicating the presence of superthermal particles. It is observed that relativistic effects lead to a significant increase in entropy.

The displacement of a more viscous fluid by a less viscous immiscible fluid in confined geometries is a fundamental problem in multiphase flows. Recent experiments have shown that such fluid-fluid displacement in micro-capillary tubes can lead to interfacial instabilities and, eventually, bubble pinch-off. A critical yet often overlooked aspect of this system is the effect of the tube's deformability on the onset of interfacial instability and bubble pinch-off. Here, we present a computational fluid-structure interaction model and an algorithm to simulate this fluid-fluid displacement problem in a soft capillary tube. We use a phase-field model for the fluids and a nonlinear hyperelastic model for the solid. Our fluid-structure interaction formulation uses a boundary-fitted approach and we use isogeometric analysis for the spatial discretization. Using this computational framework, we study the effects of inlet capillary number and tube stiffness on the control of interfacial instabilities in a soft capillary tube for both imbibition and drainage. We find that tube compliance delays or even suppresses interfacial instability and bubble pinch-off, a finding that has important implications for flow in soft porous media, bio-microfluidics, and manufacturing processes.

Causality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and direction of causality vary with the value of the interacting variables. In this work, we introduce a state-aware causal inference method that quantifies causality in terms of information gain about future states. The effectiveness of the proposed approach stems from two key features: its ability to characterize causal influence as a function of system state, and its capacity to distinguish between redundant and synergistic interactions. The method is validated across a range of benchmark cases in which the direction and strength of causality evolve in a prescribed manner with the state of the system. We further demonstrate the applicability of our approach in two real scenarios: the interaction between motions across scales in a turbulent boundary layer, and the Walker circulation phenomenon in tropical Pacific climate dynamics. Our results show that, without accounting for state-dependent causality as well as redundant and synergistic effects, traditional approaches to causal inference may lead to incomplete or misleading conclusions.

We present a reduced density operator for electronically open molecules by explicitly averaging over the environmental degrees of freedom of the composite Hamiltonian. Specifically, we include the particle-number non-conserving (particle-breaking) interactions responsible for the sharing of electrons between the molecule and the environment, which are neglected in standard formulations of quantum statistical mechanics. We propose an unambiguous definition of the partial trace operation in the composite fermionic Fock space based on composite states in a second quantization framework built from a common orthonormal set of orbitals. Thereby, we resolve the fermionic partial trace ambiguity. The common orbital basis is constructed by spatial localization of the full orbital space, in which the full composite Hamiltonian naturally partitions into a molecule Hamiltonian, an environment Hamiltonian, and an interaction Hamiltonian. The new reduced density operator is based on the approximation of commutativity between the subsystem Hamiltonians (i.e., molecule and environment Hamiltonians) and the interaction Hamiltonian, which we show corresponds to excluding certain electron transfer channels and neglecting electron transfer relaxation effects. The reduced density operator can be viewed as a generalization of the grand canonical density operator. We are prompted to define the generalized chemical potential, which aligns with the standard interpretation of the chemical potential, apart from the possibility of fractional rather than strictly integer electron transfer in our framework. In contrast to standard approaches, our framework enables an explicit consideration of the electron occupancy in the environment at any level of theory, irrespective of the model used to describe the molecule.

Objective: This study explores the feasibility of using living mycelium as a sustainable biosensing material for structural health monitoring in buildings and infrastructure. Methods: An electrical impedance network model was developed to characterize the signal transmission properties of mycelium. The model was coupled with the inhomogeneous wave correlation (IWC) method, commonly used in elastic wave propagation, to assess the dispersion behavior of electrical signals through mycelial networks. Experimental validation was conducted using living, dehydrated, and rehydrated mycelium samples. Results: The results demonstrate clear frequency-dependent and spatial attenuation characteristics of electrical signals in mycelium. The analysis highlights the critical role of environmental humidity in enabling effective signal transmission. Furthermore, dispersion behavior was used to assess the homogeneity of the material, revealing differences in signal behavior across hydration states. Conclusion: The combination of impedance modeling and dispersion analysis confirms that living mycelium can serve as a viable medium for transmitting and sensing electrical signals in a structurally integrated form. Its performance is significantly influenced by hydration level, reinforcing the need to control or monitor environmental conditions in practical applications. Significance: This work introduces mycelium as a novel, biodegradable material for embedded biosensing, contributing to the advancement of sustainable technologies in biomedical and structural health monitoring.

The Cahn-Hilliard (C-H) equation, as a classical diffusion-interface method of phase-field, has been extensively employed for simulating two-phase fluid dynamics. However, it suffers from a key challenge in the simulation process, specifically the volume conservation of each phase cannot be guaranteed. To address this issue, in this paper, a modified C-H equation for two-phase flow modeling is first introduced, and the basic idea of this model lies in that it combines the profile correction method with the level-set approach, and thus, it effectively improves the deficiency of the classical C-H equation in terms of volume non-conservation of each phase. Based on this modified C-H equation, we further propose an accurate interface-capturing lattice Boltzmann (LB) model. After that, we perform a range of numerical simulations, including two stationary droplets immersed in the gas phase, single vortex, Rayleigh-Plateau fluid instability, and droplet deformation under a shear flow. These simulations illustrate that the proposed LB model has superior performance in maintaining local volume conservation and accurately capturing interfaces. More importantly, compared to the LB model derived from the classical C-H equation, it not only achieves more precise volume conservation for each phase but also provides a more consistent representation of the droplet's interface morphology more consistently, especially in dealing with small droplet problems.

Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems are computationally prohibitive. To address this, we present a novel physics-informed spatio-temporal surrogate model for Rayleigh-B\'enard convection (RBC), a canonical example of convective fluid flow. Our approach combines convolutional neural networks for spatial feature extraction with an innovative recurrent architecture inspired by large language models, comprising a context builder and a sequence generator to capture temporal dynamics. Inference is penalized with respect to the governing partial differential equations to ensure physical interpretability. Given the sensitivity of turbulent convection to initial conditions, we quantify uncertainty using a conformal prediction framework. This model replicates key features of RBC dynamics while significantly reducing computational cost, offering a scalable alternative to DNS for long-term simulations.

As an essential module of optical systems, varifocal lens usually consists of multiple mechanically moving lenses along the optical axis. The recent development of metasurfaces with tunable functionalities holds the promise of miniaturizing varifocal lens. However, existing varifocal metalenses are hard to combine electrical tunability with scalable number and range of focal lengths, thus limiting the practical applications. Our previous work shows that the electrically tunable channels could be increased to 2N by cascading N polarization-dependent metasurfaces with liquid crystals (LCs). Here, we demonstrated a compact eight-channel electrically tunable varifocal metalens with three single-layer polarization-multiplexed bi-focal metalens and three LC cells. The total thickness of the device is ~6 mm, while the focal lengths could be switched among eight values within the range of 3.6 to 9.6 mm. The scheme is scalable in number and range of focal lengths and readily for further miniaturization. We believe that our proposal would open new possibilities of miniaturized imaging systems, AR/VR displays, LiDAR, etc.

We propose a novel method for measuring linear and non-linear viscoelastic properties of a liquid by the oscillatory motion of an immersed rotating body in a vessel. The shape of a rotating object is general and we tested four different types of impellers: a disk, an anchor, and two different flat bladed turbines. In deriving the expressions of complex shear moduli, two different approaches were employed: one is based on the complex viscosity and the other is on the relationship between mean shear stress and mean shear strain. Both methods yield identical expressions for complex moduli. Using the latter method, the mean shear stress was appropriately scaled with torque, and the strain magnitude was scaled with the deflection angle, enabling its application to large-strain nonlinear oscillatory tests. Aqueous polyethylene oxide (PEO) solutions, xanthan gum solution and ketchup were tested and linear viscoelastic responses of storage and loss moduli were first presented as a function of the oscillation frequency. In spite of the presence of non-rheometric and highly non-uniform flow field, comparison with the data from the conventional cone-and-plate fixture of a rheometer shows remarkably accurate measurement with at most 7% average error within the frequency range from 0.01 [rad/s] to 100 [rad/s] for all the impeller geometries. In addition, large amplitude oscillatory shear experiments were also tested and discrepancy with highly elastic fluid were discussed. The proposed method may facilitate the in-situ measurement of viscoelastic properties of a fluid within an industrial reactor/agitator as a tool for on-line monitoring.

This paper explores the potential of Large Language Models to accurately translate student written equations from the Australian Physics Olympiads into a standard format. Large Language Models were used to extract equations from student responses and convert these into a standardised format for a computer algebra system. Models with more than fourteen billion parameters were unable to complete the task in the required timeframe. No open source model was able to achieve the desired level of accuracy given resource constraints available for marking the exam. To improve the accuracy, we implement LLM-modulo and consensus frameworks and report on the results. Future work to improve performance could involve breaking the task into smaller components before parsing to the models.

When a drop of a leaky dielectric fluid is suspended in another fluid and subjected to a uniform DC electric field, it becomes polarized, leading to tangential electric stresses that drive fluid motion both inside and outside the drop. In the presence of a second drop, the dynamics of the first drop are altered due to electrohydrodynamic interactions with the second, causing the drops to translate due to dielectrophoretic forces and hydrodynamic interactions. We present a semi-analytical nonlinear three-dimensional small deformation theory for a pair of identical, widely-separated leaky dielectric drops suspended in a weakly conducting fluid. This theory is valid under conditions of large drop separation, high drop viscosity, and high surface tension, ensuring that the drops remain nearly spherical. For the first time, we develop a model within the Taylor--Melcher leaky dielectric framework that incorporates both transient charge relaxation and convection. This allows the model to capture the transition to Quincke rotation, a symmetry-breaking phenomenon in which drops begin to spontaneously rotate in sufficiently strong fields. We derive and numerically integrate coupled nonlinear ordinary differential equations for the dipole moments, shapes, and positions of the drops. Our results show good quantitative agreement with previous numerical and experimental work in the limit of zero charge relaxation and convection. We also discuss the hysteresis in the onset of Quincke rotation of isolated drops observed in experiments. Various trajectories for pairs of drops undergoing Quincke rotation are presented, along with results for fixed drops. In particular, we show that the onset of Quincke rotation for a pair of drops is qualitatively different from that for an isolated drop due to electrohydrodynamic interactions and a pair of solid spheres due to straining flows present only in drops.

We analyse the process of convective mixing in homogeneous and isotropic porous media with dispersion. We considered a Rayleigh-Taylor instability in which the presence of a solute produces density differences driving the flow. The effect of dispersion is modelled using an anisotropic Fickian dispersion tensor (Bear, J. Geophys. Res. 1961). In addition to molecular diffusion ($D_m^*$), the solute is redistributed by an additional spreading, in longitudinal and transverse flow directions, which is quantified by the coefficients $D_l^*$ and $D_t^*$, respectively, and it is produced by the presence of the pores. The flow is controlled by three dimensionless parameters: the Rayleigh-Darcy number $Ra$, defining the relative strength of convection and diffusion, and the dispersion parameters $r=D_l^*/D_t^*$ and $\Delta=D_m^*/D_t^*$. With the aid of numerical Darcy simulations, we investigate the mixing dynamics without and with dispersion. We find that in absence of dispersion ($\Delta\to\infty$) the dynamics is self-similar and independent of $Ra$, and the flow evolves following several regimes, which we analyse. Then we analyse the effect of dispersion on the flow evolution for a fixed value of the Rayleigh-Darcy number ($Ra=10^4$). A detailed analysis of the molecular and dispersive components of the mean scalar dissipation reveals a complex interplay between flow structures and solute mixing. The proposed theoretical framework, in combination with pore-scale simulations and bead packs experiments, can be used to validate and improve current dispersion models to obtain more reliable estimates of solute transport and spreading in buoyancy-driven subsurface flows.

We investigate the intrinsic behavior of ionic liquids under shear flow, using a coarse-grained model of C4mim-PF6 as a prototypical example. The importance of long-ranged electrostatics is assessed as a function of shear rate by comparing Ewald and reaction field treatments. An appropriate comparison is achieved through the implementation of the proper Lees-Edwards boundary conditions within the ESPResSo++ simulation software. Our results demonstrate that while structural properties are relatively insensitive to the electrostatic treatment, the more accurate treatment via the Ewald approach is essential for studies of dynamics, in particular, at lower shear rates. Furthermore, we identify a critical shear rate beyond which structural and dynamical properties begin to deviate from equilibrium behavior, while remaining largely unchanged below this threshold. Finally, we demonstrate that the dynamic heterogeneity of the liquid decreases as a function of increasing shear rate, which can be primarily explained by the faster dynamics induced by the shear flow. These results hold relevance for investigations of process-dependent properties of ionic-liquid-based materials.

We show that emittance of fourth-generation 6 GeV machines such as PETRA IV is close to what is theoretically achievable due to beam intensity limitations from space charge and intra-beam scattering. Investigating these limitations, in particular their scaling with the bare lattice emittance and the beam energy, we argue that achieving further significant emittance reduction and increase in radiation brightness is only possible by increasing the beam energy. We outline the design and technological challenges on the way to such improvement.

Embedding semiconductor quantum dots into bullseye resonators has significantly advanced the development of bright telecom quantum light sources for fiber-based quantum networks. To further improve the device flexibility and stability, the bullseye approach should be combined with a pin diode structure to enable Stark tuning, deterministic charging, and enhanced coherence. In this work, we fabricate and characterize photonic structures incorporating hole gratings that efficiently support charge carrier transport while maintaining excellent optical performance. We report bright, Purcell-enhanced single-photon emission in the telecom C-band under above-band and phonon-assisted excitation. Additionally, we present electrically contacted resonators, demonstrating wide range tuneability of quantum dot transitions in the telecom O-band. These results mark significant steps toward scalable and tunable quantum light sources for real-world quantum photonic applications.

The regulation of intestinal blood flow is critical to gastrointestinal function. Imaging the intestinal mucosal micro-circulation in vivo has the potential to provide new insight into the gut physiology and pathophysiology. We aimed to determine whether ultrafast contrast enhanced ultrasound (CEUS) and super-resolution ultrasound localisation microscopy (SRUS/ULM) could be a useful tool for imaging the small intestine microcirculation in vivo non-invasively and for detecting changes in blood flow in the duodenum. Ultrafast CEUS and SRUS/ULM were used to image the small intestinal microcirculation in a cohort of 20 healthy volunteers (BMI<25). Participants were imaged while conscious and either having been fasted, or following ingestion of a liquid meal or water control, or under acute stress. For the first time we have performed ultrafast CEUS and ULM on the human small intestine, providing unprecedented resolution images of the intestinal microcirculation. We evaluated flow speed inside small vessels in healthy volunteers (2.78 +/- 0.05 mm/s, mean +/- SEM) and quantified changes in the perfusion of this microcirculation in response to nutrient ingestion. Perfusion of the microvasculature of the intestinal mucosa significantly increased post-prandially (36.2% +/- 12.2%, mean +/- SEM, p<0.05). The feasibility of 3D SRUS/ULM was also demonstrated. This study demonstrates the potential utility of ultrafast CEUS for assessing perfusion and detecting changes in blood flow in the duodenum. SRUS/ULM also proved a useful tool to image the microvascular blood flow in vivo non-invasively and to evaluate blood speed inside the microvasculature of the human small intestine.

Cities are complex systems that demand integrated approaches, with increasing attention focused on the neighborhood level. This study examines the interplay between expert-based mapping and citizen science in the Primer de Maig neighborhood of Granollers, Catalonia, Spain--an area marked by poor-quality public spaces and long-standing socio-economic challenges. Seventy-two residents were organized into 19 groups to record their pedestrian mobility while engaging in protocolized playful social actions. Their GPS identified opportunity units for meaningful public space activation. Although 56% of observed actions occurred within expert-defined units, the remaining 44% took place elsewhere. Clustering analysis of geo-located action stops revealed seven distinct clusters, highlighting overlooked areas with significant social potential. These findings underscore the complementarity of top-down and bottom-up approaches, demonstrating how citizen science and community science approaches enriches urban diagnostics by integrating subjective, community-based perspectives in public space placemaking and informing inclusive, adaptive sustainable urban transformation strategies.

The nonlocal dielectric properties of liquid water are studied in the context of {\it ab initio} molecular dynamics simulations based on density functional theory. We calculate the dielectric response from the charge structure factor of the liquid using the fluctuation-dissipation theorem. We show that the dielectric response function of {\it ab initio} simulations differs significantly from that of classical force-fields, both qualitatively and quantitatively. In particular, it exhibits a larger amplitude and a wider range of responding wave numbers. We suggest that the difference is due to the localisation of the electronic charge density inherent in classical force files and Wannier post-treatment of DFT densities. The localised charge models do not reproduce the shape of the response function even for $q$ corresponding to intermolecular distances, and could lead to a significant underestimation of the dielectric response of the liquid by a factor of 10.

Photonic lattices facilitate band structure engineering, supporting both localized and extended modes through their geometric design. However, greater control over these modes can be achieved by taking advantage of the interference effect between external drives with precisely tuned phases and photonic modes within the lattice. In this work, we build on this principle to demonstrate optical switching, directed light propagation and site-specific localization in a one-dimensional photonic lattice of coupled microresonators by resonantly driving the system with a coherent field of controlled phase. Importantly, our experimental results provide direct evidence that increased driving power acts as a tuning parameter enabling nonlinear localization at frequencies previously inaccessible in the linear regime. These findings open new avenues for controlling light propagation and localization in lattices with more elaborate band structures.

We develop a unified quantum geometric framework to understand reactive quantum dynamics. The critical roles of the quantum geometry of adiabatic electronic states in both adiabatic and non-adiabatic quantum dynamics are unveiled. A numerically exact, divergence-free topological quantum molecular dynamics method is developed through a discrete local trivialization of the projected electronic Hilbert space bundle over the nuclear configuration space. In this approach, the singular electronic quantum geometric tensor-Abelian for adiabatic dynamics and non-Abelian for non-adiabatic dynamics-is fully encoded in the global electronic overlap matrix. With numerical illustrations, it is demonstrated that atomic motion-whether adiabatic or non-adiabatic-is governed not only by the variation in electronic energies with nuclear configurations (potential energy surface) but also by the variation in electronic states (electronic quantum geometry).

This work presents a novel methodology termed Direct Diabatic States Construction (DDSC), which integrates fragment wavefunctions into an anti-symmetric wavefunction for the entire system. Using a fragment-localized state-consistent molecular orbital (FL-SC MO), this approach enables direct construction of all diabatic states at the same root. Each diabatic state is formed as a linear combination of a set of diabatic configurations. The validity and effectiveness of DDSC have been demonstrated through its application to the LiH molecule. The results show that this method is suitable for constructing both valence and Rydberg diabatic states. One of the key advantages of DDSC is its ability to directly compute diabatic couplings, which can be converted to non-adiabatic coupling (NAC) vectors along the reaction coordinate. The DDSC method efficiently builds the diabatic potential energy matrix (DPEM), especially for systems with clear fragment partitions and weak inter-fragment interactions, such as charge transfer reactions.

We present extensive \emph{ab initio} path integral Monte Carlo (PIMC) results for the dynamic properties of the finite temperature uniform electron gas (UEG) over a broad range of densities, $2\leq r_s\leq300$. We demonstrate that the direct analysis of the imaginary-time density--density correlation function (ITCF) allows for a rigorous assessment of the density and temperature dependence of the previously reported roton-type feature [T.~Dornheim, \emph{Phys.~Rev.~Lett.}~\textbf{121}, 255001 (2018)] at intermediate wavenumbers. We clearly resolve the emergence of a second roton at the second harmonic of the original feature for $r_s\gtrsim100$, which we identify as an incipient phonon dispersion. Finally, we use our highly accurate PIMC results for the ITCF as the basis for an analytic continuation to compute the dynamic structure factor, which additionally substantiates the existence of the second roton in the strongly coupled electron liquid. Our investigation further elucidates the complex interplay between quantum delocalization and Coulomb coupling in the UEG. All PIMC results are freely available online and provide valuable benchmarks for other theoretical methodologies and approximations.

Purpose: To improve the interpretability of noise amplification and apparent blurring of k-space interpolation networks, and to optimize for them in the loss function as a model-based regularizer in k-space interpolation networks. Methods: Network is subjected to noise amplification analysis through automatic differentiation of the input with respect to the input. Noise variance maps are decomposed into terms accounting for the linear and nonlinear characteristics of the network. Variance maps are derived in each iteration, allowing for runtime quality monitoring. Maximum variance (eigenpixel) and residual variance maps (pixel contamination) are introduced, which describe the network noise amplification and apparent blurring, respectively. By including the variance maps in the training, the loss function is enriched with a model-based regularizer beyond the k-space data consistency term. Accordingly, the proposed g-factor-informed RAKI (GIF-RAKI) establishes a recurrent flow of noise and apparent blurring information into the training, that drives the denoising via the trainable nonlinear activation function. Results: GIF-RAKI outperforms other RAKI implementations, supported by difference maps, and image quality metrics. Eigenpixel and pixel contamination maps provide quantitative metrics for noise amplification and apparent blurring, respectively, without the need for a gold standard reference. RAKI with tuneable Leaky ReLU is capable of adjusting its own nonlinearity automatically. Conclusion: The additional model-based loss terms allow to optimize for the trade-off between denoising and apparent blurring during RAKI training. This has the potential to eliminate the need for heuristic hyperparameter tweaking.

The large gradients of plasma-wakefield accelerators promise to shorten accelerators and reduce their financial and environmental costs. For such accelerators, a key challenge is the transport of beams with high divergence and energy spread. Achromatic optics is a potential solution that would allow staging of plasma accelerators without beam-quality degradation. For this, a nonlinear plasma lens is being developed within the SPARTA project. As a first application of this lens, we aim to implement an achromatic spectrometer for electron bunches produced by a laser-wakefield accelerator. This will greatly improve the resolution across the typically one to tens of percent energy spread bunches and therefore help diagnosis and optimization of the plasma interaction. We report on progress in designing such an experiment.

We study the dispersion of bubble swarms rising in initially quiescent water using 3D Lagrangian tracking of deformable bubbles and tracer particles in an octagonal bubble column. First, we compare the dispersion inside bubble swarms with that for single-bubble cases and find that the horizontal mean squared displacement (MSD) in the swarm cases exhibits oscillations around the asymptotic scaling predicted for a diffusive regime. This occurs due to wake-induced bubble motion, however, the oscillatory behaviour is heavily damped compared to the single-bubble cases due to the presence of bubble-induced turbulence (BIT) and bubble-bubble interactions in the swarm. The vertical MSD in bubble swarms is nearly an order of magnitude faster than the single-bubble cases, due to the much higher vertical fluctuating bubble velocities in the swarms. We also investigate tracer dispersion in BIT and find that concerning the time to transition away from the ballistic regime, larger bubbles with a higher gas void fraction transition earlier than tracers, consistent with Mathai et al. (\textit{Phys. Rev. Lett.} 121, 054501, 2018). However, for bubble swarms with smaller bubbles and a lower gas void fraction, they transition at the same time. This differing behavior is due to the turbulence being more well-mixed for the larger bubble case, whereas for the smaller bubble case the tracer dispersion is highly dependent on the wake fluctuations generated by the oscillating motion of nearby bubbles.

Embryonic development relies on the formation of sharp, precise gene expression boundaries. In the fruit fly Drosophila melanogaster, boundary formation has been proposed to occur at a dynamical critical point. Yet, in the paradigmatic case of the hunchback (hb) gene, evidence suggests that boundary formation occurs in a bistable regime, not at the dynamical critical point. We develop a minimal model for hb expression and identify a single parameter that tunes the system from its monostable regime to its bistable regime, crossing the critical point in between. We find that boundary precision is maximized when the system is weakly bistable--near, but not at, the critical point--optimally negotiating the tradeoff between two key effects of bistability: sharpening the boundary and amplifying its noise. Incorporating the diffusion of Hb proteins into our model, we show that boundary precision is maximized simultaneously at an optimal degree of bistability and an optimal diffusion strength. Our work elucidates design principles of precise boundary formation and has general implications for pattern formation in multicellular systems.

Photo-induced dynamics of electronic processes in materials are driven by the coupling between electronic and nuclear degrees of freedom. Here we construct 1D and 2D organic-inorganic tin halides to investigate the functional role of dimensionality to exciton-phonon coupling (EPC) and exciton self-trapping. The results show that the 1D system has strong EPC leading to excitation-independent self-trapped exciton (STE) emission, while the 2D system exhibits over ten times weaker EPC resulting in free exciton emission. By performing femtosecond transient absorption experiments, we directly resolve the room-temperature vibrational wavepackets in the 1D system, some of which propagate along the STE potential energy surface. A combination of wagging and asymmetric stretching motions (~106 cm-1) in tin iodide is identified as such a mode inducing exciton self-trapping. While no room-temperature wavepackets are observed in the 2D system. These findings uncover the interplay between the dimensionality-dependent EPC and electronic/nuclear dynamics, offering constructive guidance to develop multifunctional organic-inorganic metal halides.

We introduce a time-domain generalization of the extended Koopmans' theorem within the framework of the multiconfigurational time-dependent Hartree-Fock (MCTDHF) theory. This formulation naturally yields well-defined time-dependent hole states formed by removing one electron from the multielectron system, enabling the instantaneous construction of reduced density matrices for the photofragments during MCTDHF simulations with negligible computational overhead. Leveraging this foundation, we derive the equation of motion for the time-dependent Dyson orbitals and develop a systematic approach to extract hole-resolved observables directly from the time-dependent \textit{ab initio} wavefunctions, such as channel-resolved photoelectron momentum distributions. The proposed method is universally applicable to both projection-based and flux-based schemes, offering a powerful tool for disentangling correlated electron-hole dynamics in ultrafast multichannel ionization processes.

We present a comprehensive overview of recent advances in theory and experiments on complex light propagation phenomena in nonlinear multimode fibers. On the basis of the wave turbulence theory, we derive kinetic equations describing the out-of-equilibrium process of optical thermalization toward the Rayleigh-Jeans (RJ) equilibrium distribution. Our theory explains the effect of beam self-cleaning (BSC) in graded-index (GRIN) fibers, whereby a speckled beam transforms into a bell-shaped beam at the fiber output. We theoretically explore the role of random refractive index fluctuations along the fiber, and show how these imperfections can assist the observation of BSC in a practical experimental setting. This conclusion is supported by the derivation of wave turbulence kinetic equations that account for the presence of a time-dependent disorder (random mode coupling). The kinetic theory reveals that a weak disorder accelerates the rate of RJ thermalization and condensation. On the other hand, although strong disorder is expected to suppress wave condensation, the kinetic equation reveals that an out-of-equilibrium process of condensation and RJ thermalization can still occur. The kinetic equations are validated by numerical simulations of the nonlinear Schrodinger equation. We outline a series of recent experiments, which permit to confirm the statistical mechanics approach for describing beam propagation and thermalization. For example, we highlight the demonstration of entropy growth, and point out that there are inherent limits to peak-power scaling in multimode fiber lasers. We conclude by pointing out the experimental observation that BSC is accompanied by an effect of modal phase-locking. From the one hand this explains the observed preservation of the spatial coherence of the beam, but also it points to the need of extending current descriptions in future research.

By simulating the real-time multielectron wavefunction with the multi-configurational time-dependent Hartree-Fock (MCTDHF) approach, we conduct an \textit{ab initio} study of the single-photon ionization process of a body-fixed water molecule ($\mathrm{H_2O}$) driven by attosecond pulses. To this end, we present a full-dimensional implementation of the MCTDHF method based on one-center expansions, allowing for the simulation of arbitrarily polarized lasers and multi-center polyatomic potentials. With a rigorous definition of the time-dependent hole state (TDHS) using the time-domain generalization of extended Koopmans' theorem (TD-EKT), we derive the reduced ion density matrix within the MCTDHF framework, which inherently encodes the total and channel-resolved photoionization cross sections of $\mathrm{H_2O}$. The cross sections obtained are benchmarked against existing experimental and theoretical results, validating the TDHS formalism. Furthermore, by adjusting the phase delay and intensity ratio of a pair of orthogonally polarized attosecond pulses, we explore the ultrafast control of attosecond coherence between electronic states of $\mathrm{H_2O^+}$.

Relativistic Runaway Electron Avalanches (RREA) are central to understanding a spectrum of high-energy atmospheric phenomena, including Terrestrial Gamma-ray Flashes (TGFs), Thunderstorm Ground Enhancements (TGEs), and gamma-ray glows. Despite their common physical origin, these events are often treated separately due to differences in detection methods, duration, and altitude. In this work, we present a unified conceptual and observational framework that reinterprets these radiation bursts as manifestations of the same runaway processes occurring in distinct atmospheric depths. Integrating recent results from satellite (ASIM), aircraft (ALOFT), balloon (HELEN), and ground-based (SEVAN) experiments, we demonstrate consistent spectral and temporal behavior across scales. We propose a rational revision of current terminology and challenge longstanding models that attribute TGFs to lightning leader dynamics. This study resolves key contradictions in the field, establishes new classification criteria based on physics rather than detector location, and reshapes our understanding of particle acceleration in thunderstorms.

During the extreme winter storms of 2024-2025 at Aragats, natural gamma radiation (NGR) increased by more than 1000%, with fluence reaching 2*10^7 gammas/cm^2 over 10 hours and a corresponding dose of 3.26 mSv, 120 times higher than normal background radiation for the same period. This unprecedented radiation surge was detected during dry, electrified snowstorms, exceeding levels explainable by known atmospheric mechanisms, necessitating a significant reassessment of gamma-ray sources in winter storm conditions. These results suggest similar radiation surges may occur in high-altitude and polar regions (Arctic and Antarctic), where strong winds and prolonged snowstorms are common. Understanding radiation surge conditions is essential for refining atmospheric models, improving radiation monitoring, and assessing environmental and climatic impacts in extreme weather conditions.

We investigate the indistinguishability of polaritons in optically trapped Bose Einstein condensates by implementing Hong-Ou-Mandel (HOM) interferometry and test the limitations of two-polariton interference in the coherent, limit-cycle and thermal statistical regimes. We observe that the HOM dynamics of a circularly polarized condensate follows the condensate coherence time with the characteristic HOM-dip approaching the classical limit. Linearly polarized condensates exhibit a combined effect of polariton bunching and two-polariton interference. Under elliptically polarized excitation, the temporal evolution of the spinor condensate results in the revival of the HOM-dip at the spinor Larmor precession frequency.

We analyze the universality classes of phase transitions in a variety of nonlinear voter models. By mapping several models with symmetric absorbing states onto a canonical model introduced in previous studies, we confirm that they exhibit a Generalized Voter (GV) transition. We then propose a canonical mean-field model that extends the original formulation by incorporating a noise term that eliminates the absorbing states. This generalization gives rise to a phase diagram featuring two distinct types of phase transitions: a continuous Ising transition and a discontinuous transition we call Modified Generalized Voter (MGV). These two transition lines converge at a tricritical point. We map diverse noisy nonlinear voter models onto this extended canonical form. Using finite-size scaling techniques above and below the upper critical dimension, we show that the continuous transition of these models belongs to the Ising universality class in their respective dimensionality. We also find universal behavior at the tricitical point. Our results provide a unifying framework for classifying phase transitions in stochastic models of opinion dynamics with both nonlinearity and noise.

A magnetic dipole-dipole interaction is proposed as a quenching mechanism. The interaction rate follows $R^{-6}$ as the electric dipole-dipole interaction in F$\mathrm{\ddot{o}}$ster resonance energy transfer theory. The proposed mechanism causes a long-range resonance energy transfer, and the resonance condition is that the spins of donor and acceptor electrons both flip, and the energy level differences are the same. When organic molecules including heavy elements are dissolved in a liquid scintillator, these requirements are easier to be satisfied. The proposal in the paper adds a new approach for heavy element quenching in liquid scintillator solution.

Fabrication imperfections, spatial constraints, and prohibitive costs collectively impede the scalability of neuromorphic photonics. In this work, we introduce a large-scale, compact photonic neuron in which each microring performs modulation and weighting simultaneously. This dual functionality is realized by leveraging both the carrier effect and thermal tunability, thereby merging modulation and weighting to conserve on-chip area, enhancing tuning efficiency, and capitalizing on wavelength-division multiplexing (WDM) for scalable implementations. In addition, we investigated a range of configurations for the proposed neuron to better tailor its behavior to various computational tasks. To illustrate the adaptability of the system's tasks, we explore both spatial and temporal domains, highlighting its versatility through two representative tasks: image processing and, for the first time, financial time series analysis, which represents a promising new frontier for neuromorphic photonics. These findings underscore the considerable promise of photonic computing in addressing a breadth of real-world challenges, particularly under escalating demands for both scalability and flexibility.

The plasma photocathode has previously been proposed as a source of ultra-high-brightness electron bunches within plasma accelerators. Here, the scheme is extended by using a radially-polarized ionizing laser pulse to generate high-charge, high-brightness electron bunches with symmetric transverse emittance. Efficient start-to-end modelling of the scheme, from ionization and trapping until drive bunch depletion, enables a multi-objective Bayesian optimisation routine to be performed to understand the performance of the radially-polarized plasma photocathode, quantify the stability of the scheme, and explore the fundamental relation between the witness bunch charge and its emittance. Comparison of plasma photocathodes driven by radially- and linearly-polarized laser pulses show that the former yields higher brightness electron bunches when operating in the optimally-loaded regime.

We present an experimental imaging flow cytometer using a 1 {\mu}s temporal resolution event-based CMOS camera, with data processed by adaptive feedforward and recurrent spiking neural networks. Our study classifies PMMA particles (12, 16, 20 {\mu}m) flowing at 0.7 m/s in a microfluidic channel. Processing of experimental data highlighted that spiking recurrent networks, including LSTM and GRU models, achieved 98.4% accuracy by leveraging temporal dependencies. Additionally, adaptation mechanisms in lightweight feedforward spiking networks improved accuracy by 4.3%. This work outlines a technological roadmap for neuromorphic-assisted biomedical applications, enhancing classification performance while maintaining low latency and sparsity.

In this work, we experimentally validate the dual use of a reconfigurable photonic integrated mesh as a neuromorphic accelerator, targeting signal equalization, and as a physical unclonable function offering authentication at the hardware level. The processing node is an optical spectrum slicing self-coherent transceiver targeting the mitigation of dispersion impairments of an intensity detected QPSK signal, after 25 km of transmission at 32 Gbaud. Unavoidable fabrication related imperfections of the nodes, such as waveguide roughness, can act as fingerprints of the device, and, during neuromorphic processing, result in unique weights at the digital back-end during signal equalization. Extracted security metrics offer low false positive/negative probability for the generated responses, confirming un-clonability, whereas bit-error-ratio for the QPSK equalization task was always below the hardware forward error correction limit. The experimental results substantiate the capability of the proposed scheme to simultaneously act as an accelerator and as a security token.

We present a velocity-gauge formalism for computing nonlinear current response functions in periodic systems and apply it to the Su-Schrieffer-Heeger (SSH) model as a minimal topological testbed. By retaining the full minimal coupling Hamiltonian and avoiding the rotating wave approximation, we construct gauge-consistent expressions for the linear and third-order current susceptibilities using retarded Green's functions. Our results reveal how nonlinear optical spectra encode not only energy-level transitions but also interband phase coherence and topological winding. In the topological phase, the third-order response exhibits characteristic phase inversions and spectral asymmetries that are absent in the trivial phase. These features reflect geometric changes in the Bloch eigenstates and highlight the role of virtual pathways in shaping the nonlinear signal. Our framework offers a robust and extensible platform for modeling nonlinear light-matter interactions in topological materials beyond the dipole approximation and the standard Coulomb-gauge formulation in molecular spectroscopy.

We have designed, fabricated and tested a lens with chromatic aberration coefficient (Cc) of 1.0 mm, a 4.0 mm pole-gap and 2.0 mm bore that is wide enough to accommodate an anti-contamination system and an objective aperture. This lens extends the temporal-coherence envelope of the electron microscope beyond 2 Angstrom, using a low-cost Schottky FEG. We hope that this lens design can be used to improve all 100 keV electron microscopes designed for single-particle electron cryomicroscopy (cryoEM).

We present an application of the Portable Diagnostic Package (PDP) on the Wisconsin HTS Axisymmetric Mirror (WHAM), which integrates an optical emission spectroscopy (OES) system and an active Thomson scattering (TS) system. Due to the designed portability of our system, we realized the installation of the PDP OES and TS measurements on WHAM in $\sim$6 months. The OES system facilitates a comprehensive impurity line survey and enables flow measurements through the Doppler effect observed on impurity lines. Notably, plasma rotation profiles were successfully derived from doubly charged carbon lines. In addition, the TS system enabled the first measurements of the electron temperature in commissioning plasmas on WHAM. These successes underscore the diagnostic package's potential for advancing experimental plasma studies.

We study superconductivitiy under light irradiation based on the Floquet-Magnus expansion in the high-frequency regime. We find that, in spin-singlet superconductors with spin-orbit coupling, triplet superconductivity can be induced in the first-order perturbation for dynamical gap functions and the second-order perturbation for static gap functions. We also show that, in unitary triplet superconductors with altermagnetism, nonunitary triplet superconductivity can emerge in the firstorder perturbation for dynamical gap function and in the second-order perturbation for static gap functions. These results indicate optical generation and control of triplet superconductivity.

Diamagnetism, in which the magnetisation in a medium opposes the direction of an applied magnetic field, is a weak but familiar effect in a wide class of materials. Being weak it is also a linear response to any applied field. The problem is that the existence of diamagnetism is in direct conflict with the requirements of causality as embodied in the familiar Kramers-Kronig relations. Nature does not care about our confusion and diamagnetism exists and physics is constrained by the requirements of causality (that effect cannot precede its cause). This puzzle has received intermittent attention from time to time, with a variety of arguments made to resolve the paradox. None of these, no matter how plausible, reveal the mechanism that resolves the existence of diamagnetism without sacrificing causality. The full resolution is presented in this letter.

We investigate a Lieb lattice of quantum emitters coupled to a two-dimensional waveguide network and demonstrate that this system supports an energetically isolated flat band, enabling localization despite the presence of long-range photon-mediated couplings. We then explore the two-excitation dynamics in both the softcore and hardcore interaction regimes, which arise from the nonlinearity of the emitters. In the softcore regime, we observe interaction-induced photon transport within the flat band, mediated by the formation of bound photon pairs. In the hardcore regime, corresponding to the two-level atom limit, we instead find the emergence of metastable exciton-like dressed states involving both flat and dispersive bands. Our findings highlight how the interplay between the collective behavior of emitters and effective photon-photon interactions can provide a platform for studying highly correlated photonic states in flat-band systems.

With the Large Hadron Collider's Run 3 in progress, the 125 GeV Higgs boson couplings are being examined in greater detail, while searching for additional scalars. Multi-Higgs frameworks allow Higgs couplings to significantly deviate from Standard Model values, enabling indirect probes of extra scalars. We consider the possibility of large pseudoscalar Yukawa couplings in the softly-broken Z2xZ2' three-Higgs doublet model with CP violating coefficients. To explore the parameter space of the model, we employ a Machine Learning algorithm that significantly enhances sampling efficiency. Using it, we find new regions of parameter space and observable consequences, not found with previous techniques. This method leverages an Evolutionary Strategy to quickly converge towards valid regions with an additional Novelty Reward mechanism. We use this model as a prototype to illustrate the potential of the new techniques, applicable to any Physics Beyond the Standard Model scenario.

Wave-based platforms for novel unconventional computing approaches like neuromorphic computing require a well-defined, but adjustable flow of wave information combined with non-volatile data storage elements to implement weights which allow for training and learning. Due to their inherent nonreciprocal properties and their direct physical interaction with magnetic data storage, spin waves are ideal candidates to realize such platforms. In the present study, we show how spin-wave nonreciprocity induced by dipolar interactions of nanowaveguides with antiparallel, out-of-plane magnetization orientations can be used to create a spin-wave circulator allowing for unidirectional information transport and complex signal routing. In addition, the device can be reconfigured by a magnetic domain wall with adjustable position, which allows for a non-volatile tuning of the nonreciprocity and signal propagation. These properties are demonstrated for a spin-wave directional coupler through a combination of micromagnetic simulations and analytical modeling also showing that it functions as a waveguide crossing element, tunable power splitter, isolator, and frequency multiplexer. As magnetic material, out-of-plane magnetized Bismuth-doped Yttrium Iron Garnet has been considered. For this material, the motion of domain walls by magnonic spin transfer torque has been recently experimentally demonstrated which enables to store results from spin-wave computation. In combination with the presented concept of domain wall based reconfiguration and nonlinear spin-wave dynamics, this enables for the creation of a nano-scaled nonlinear wave computing platform with the capability for self-learning.

We present the design and assembly of a cavity microscope for quantum simulations with ultracold atoms. The system integrates a high-finesse optical cavity with a pair of high-numerical aperture lenses sharing a common optical axis, enabling simultaneous operation with light close-to-atomic resonance. The system keeps the advantages of a rigid, single-block structure holding the lenses and cavity together, and improves over existing designs by using most of the solid angle left free by the cavity mode for imaging and atomic manipulation purposes. The cavity has a length of $19.786$mm, a finesse of $2.35\times 10^4$ and operates $214\mu\text{m}$ away from the concentric limit, deep in the strong coupling regime. The two lenses offer a numerical aperture of $0.52$ each and maximal optical access in all directions transverse to the cavity axis, compatible with applications in quantum-gas microscopes, micro-tweezer arrays or few-fermions systems, as well as future cavity-assisted quantum simulation protocols demanding sub-cavity-mode control of the atom-cavity coupling.

We propose a hybrid quantum-classical algorithm for ground state energy (GSE) estimation that remains robust to highly noisy data and exhibits low sensitivity to hyperparameter tuning. Our approach -- Fourier Denoising Observable Dynamic Mode Decomposition (FDODMD) -- combines Fourier-based denoising thresholding to suppress spurious noise modes with observable dynamic mode decomposition (ODMD), a quantum-classical signal subspace method. By applying ODMD to an ensemble of denoised time-domain trajectories, FDODMD reliably estimates the system's eigenfrequencies. We also provide an error analysis of FDODMD. Numerical experiments on molecular systems demonstrate that FDODMD achieves convergence in high-noise regimes inaccessible to baseline methods under a limited quantum computational budget, while accelerating spectral estimation in intermediate-noise regimes. Importantly, this performance gain is entirely classical, requiring no additional quantum overhead and significantly reducing overall quantum resource demands.

While phonons and electrons are well-established heat carriers in solids, photons are typically associated only with radiative transfer between surfaces. Yet for over 70 years, theorists have speculated that thermal photons could also conduct heat within dense, opaque materials -- an idea that has remained unproven and unquantified. Here, we resolve this longstanding question by developing a first-principles framework that reveals and quantifies the internal radiative contribution to thermal conductivity in solids. By analyzing 15 crystalline materials, we uncover photon mean free paths (MFPs) ranging from $\sim$100$\mu$m to over 1cm, with some materials exhibiting surprisingly large radiative thermal conductivity ($\kappa_{\text{rad}}$). Contrary to common assumptions, we show that $\kappa_{\text{rad}}$ can scale steeply with temperature (from $T^{1}$ to $T^{4}$), even as MFPs decrease (from $T^{-0.3}$ to $T^{-3}$). We also discover a robust link between photon MFP and phonon linewidths, revealing an unexpected interplay between radiative and phononic heat transport. Crucially, we establish a general formalism to calculate $\kappa_{\text{rad}}$ across arbitrary sample thicknesses and surface emissivities -- bridging ballistic and diffusive regimes. Our findings overturn long-held assumptions, uncover a missing channel of heat conduction, and provide a powerful new tool for thermal management in extreme environments.

Lattice Boltzmann method (LBM) is particularly well-suited for implementation on quantum circuits owing to its simple algebraic operations and natural parallelism. However, most quantum LBMs fix $\tau$ = 1 to avoid nonlinear collision, which restricts the simulation to a fixed mesh size for a given Reynolds number. To preserve the simplicity of setting $\tau$ = 1 while enhancing flexibility, we propose a quantum lattice kinetic scheme (LKS) by introducing a constant parameter $A$ into the equilibrium distribution function (EDF), enabling independent adjustment of the fluid's viscosity. This modification removes the constraint on mesh size, making it possible to simulate flows with arbitrary Reynolds numbers. The Chapman-Enskog analysis confirms the modified EDF still recovers the Navier-Stokes equations without compromising collision accuracy. We evaluate the method on 2D and 3D Taylor-Green vortex and lid-driven cavity flows, demonstrating that quantum LKS attains the same accuracy and convergence order as classical LKS. The first application of quantum LBM to 3D incompressible flows represents a significant step forward in large-scale fluid dynamics simulation.

This study investigates the intricate relationship between the formation mechanisms and luminescent properties of lignin-derived carbon quantum dots (LG-CQDs) synthesized from spruce biomass by hydrothermal treatment. A comprehensive understanding of LG-CQD structure and its photoluminescence requires insights into the native architecture of lignin and the distribution of its acidolysis-derived fragments. Research showed how these lignin-derived units interact with dopant molecules in three different approaches during synthesis, contributing to core and surface structures that govern the optical behavior. Our findings reveal a clear correlation between structural features and luminescent properties, emphasizing the role of surface chemistry in tuning emission characteristics. These insights provide a foundation for the rational design of LG-CQDs with tailored luminescent properties, advancing their potential applications in sustainable optoelectronics, sensing, and bioimaging.

We present a fully digital approach for simulating single-mode squeezed states on a superconducting quantum processor using an enhanced bosonic encoding strategy. By mapping up to 2^{n} photonic Fock states onto n qubits, our framework leverages Gray-code-based encodings to reduce gate overhead compared to conventional one-hot or binary mappings. We further optimize resource usage by restricting the simulation on Fock states with even number of photons only, effectively doubling the range of photon numbers that can be represented for a given number of qubits. To overcome noise and finite coherence in current hardware, we employ a variational quantum simulation protocol, which adapts shallow, parameterized circuits through iterative optimization. Implemented on the Zuchongzhi-2 superconducting platform, our method demonstrates squeezed-state dynamics across a parameter sweep from vacuum state preparation (r=0) to squeezing levels exceeding the Fock space truncation limit (r>1.63). Experimental results, corroborated by quantum state tomography and Wigner-function analysis, confirm high-fidelity state preparation and demonstrate the potential of Gray-code-inspired techniques for realizing continuous-variable physics on near-term, qubit-based quantum processors.

We demonstrate how center-of-mass (COM) motion influences polymer segment fluctuations. Cancellation of internal forces, together with spatially uncorrelated external noise, generally yields COM diffusivity scaling as $1/s$ with segment length $s$, regardless of fractal dimension, viscoelasticity, or activity. This introduces distinct dynamic scaling corrections to two-point fluctuations and quenched-induced tangential correlations, validated by theory, simulations, and chromatin imaging data. In the latter, the extracted dynamic exponent reveals topological constraints, thereby resolving the discrepancy between chromatin's crumpled structure and its Rouse-like dynamics.

This study presents a comprehensive methodology for determining the thermal conductivity (TC) of materials with high reliability. The methodology addresses issues such as surface topographical variations and substrate interference by combining Scanning Thermal Microscopy (SThM) with machine learning (ML) models and normalization techniques. Micro- and nanostructural variations in thin films exacerbate measurement inconsistencies, reducing repeatability and reliability. These interconnected challenges highlight the need for a novel, flexible, and adaptive methodology that can comprehensively address the complexities of thin film characterization while maintaining accuracy and efficiency. In this approach, sample surface was divided into fine spatial grids for localized thermal and topographical measurements. A substrate-thickness factor (C factor) was introduced to account for thickness and substrate effects on thin film TC, and high-performance Random Forest regression was used to predict TC across a broad range of materials. The models were trained on a dataset of 2,352 measurements that covered a wide range of material properties and then validated with an additional 980 measurements. They achieved high predictive accuracy, with a $R^2$ of 0.97886 during training and 0.96630 during testing. This approach addresses instrumental limitations and integrates experimental techniques with computational modeling, providing a scalable framework for a wide range of material science applications.

Near-term quantum devices require wavefunction ans\"atze that are expressive while also of shallow circuit depth in order to both accurately and efficiently simulate molecular electronic structure. While unitary coupled cluster (e.g., UCCSD) has become a standard, the high gate count associated with the implementation of this limits its feasibility on noisy intermediate-scale quantum (NISQ) hardware. K-fold unitary cluster Jastrow (uCJ) ans\"atze mitigate this challenge by providing $O(kN^2)$ circuit scaling and favorable linear depth circuit implementation. Previous work has focused on the real orbital-rotation (Re-uCJ) variant of uCJ, which allows an exact (Trotter-free) implementation. Here we extend and generalize the $k$-fold uCJ framework by introducing two new variants, Im-uCJ and g-uCJ, which incorporate imaginary and fully complex orbital rotation operators, respectively. Similar to Re-uCJ, both of the new variants achieve quadratic gate-count scaling. Our results focus on the simplest $k=1$ model, and show that the uCJ models frequently maintain energy errors within chemical accuracy. Both g-uCJ and Im-uCJ are more expressive in terms of capturing electron correlation and are also more accurate than the earlier Re-uCJ ansatz. We further show that Im-uCJ and g-uCJ circuits can also be implemented exactly, without any Trotter decomposition. Numerical tests using $k=1$ on $H_2$, $H_3^+$, $Be_2$, $C_2H_4$, $C_2H_6$ and $C_6H_6$ in various basis sets confirm the practical feasibility of these shallow Jastrow-based ans\"atze for applications on near-term quantum hardware.

Development of energy storage technologies that can exhibit higher energy densities, better safety, and lower supply-chain constraints than the current state-of-the-art Li-ion batteries (LIBs) is crucial for our transition into sustainable energy use. In this context, Mg batteries (MBs) offer a promising pathway to design energy storage systems with superior volumetric energy densities than LIBs but require the development of positive electrodes (cathodes) exhibiting high energy and power densities. Notably, amorphous materials that lack long range order can exhibit `flatter' potential energy surfaces than crystalline frameworks, possibly resulting in faster Mg$^{2+}$ motion. Here, we use a combination of ab initio molecular dynamics (AIMD), and machine learned interatomic potential (MLIP) based calculations to explore amorphous V$_2$O$_5$ as a potential cathode for MBs. Using an AIMD-generated dataset, we train and validate moment tensor potentials that can accurately model amorphous (Mg)V$_2$O$_5$ Due to the amorphization of V$_2$O$_5$, we observe a 10-14% drop in the average Mg intercalation voltage $-$ but the voltage remains higher than sulfide Mg cathodes. Importantly, we find a $\sim$seven (five) orders of magnitude higher Mg$^{2+}$ diffusivity in amorphous MgV$_2$O$_5$ than its crystalline version (thiospinel-Mg$_x$Ti$_2$S$_4$), which is directly attributable to the amorphization of the structure. Also, we note the Mg$^{2+}$ motion in the amorphous structure is significantly cross-correlated at low temperatures, with the correlation decreasing with increase in temperature. Thus, our work highlights the potential of amorphous V$_2$O$_5$ as a cathode that can exhibit both high energy and power densities, resulting in the practical deployment of MBs.

Recurrent small-scale eruptions are fascinating phenomena in the solar atmosphere. However, their underlying physical mechanisms remain unclear. On 2021 May 23, five recurrent jetlets (J1-J5) were observed continuously ejecting from a satellite spot located at the north edge of AR 12824. Using high-resolution, multi-wavelength data from NVST, SDO, and IRIS, we investigate the physical characteristics of these jetlets and their relationship with the satellite spot. The widths of these jetlets range from 1300 to 2900 km, their lifetimes range span 3 to 10 minutes, and their projection speeds vary from 152.8 to 406.0 km s$^{-1}$. During the eruptions, the satellite spot moved northwest at a low speed of 376 $\pm$ 12 m s$^{-1}$. Its area gradually decreased due to magnetic cancellation with surrounding positive magnetic field, resulting in an average cancellation rate of 1.3$\times$10$^{18}$ Mx hr$^{-1}$. Dark lanes that separated from the satellite spot and small pores were observed to move toward nearby these features or dark lanes with opposite polarities, eventually disappearing during the magnetic cancellation process. J4 was driven by an eruption of a micro-filament. Spectral observations revealed a redshift on the right side of J4 and a blueshift on the left side of its base, suggesting a counterclockwise rotation. The horizontal magnetic field of the satellite spot consistently exhibited a vortex structure throughout its evolution until it vanished. The nonlinear force-free field extrapolation confirms that the satellite spot serves as one footpoint of a mini-flux rope. These observations reveal that these jetlets might result from three-dimensional null-point magnetic reconnection, initiated by the continuous eruption of a mini-flux-rope or multiple mini-flux-ropes, driven by sustained magnetic cancellation.

The goal of this talk is to give an overview of the current status of the development of the Einstein Telescope and Cosmic Explorer ground based gravitational wave (GW) detectors and of their foreseen scientific goals. These detectors will be up to a factor 8 more sensitive across the band covered by current detectors, namely LIGO, Virgo and KAGRA, and will extend the accessible frequency band towards the low frequency regime, i.e., below 10 Hz. These improvements will not only enhance the number and quality of GW observations, but will also enable researchers to have access to sources and physical processes which are out of reach for current detectors and explore the possibility of detecting previously unknown GW sources. The improvement in sensitivity in the low frequency regime will also increase the observation time of compact binary coalescence events, strengthening the collaboration with electromagnetic observatories for multimessenger observations of binary neutron star events. In fact, current detectors proved that joint observations of GW events with electromagnetic observatories are not only possible, but they can also give us unprecedented insights on the underlying physics of astrophysical processes.

Quantum mechanics poses significant challenges for audio-visual representation, particularly concerning quantum entanglement. Sonification -- the auditory representation of data -- offers a promising complementary approach. This paper investigates sonification techniques applied to dynamical entanglement generation in many-qubit systems with the help of phase space methods and entanglement measure. We study dynamics of entanglement generation in many-qubit system in dynamical protocol governed by two models: the one-axis twisting model, and a quantum kicked-rotor exhibiting both regular and quantum chaotic behavior. We present a procedure of entanglement dynamics sonification, allowing mapping the phase-space representation of a many-qubit quantum state and von Neuman entanglement entropy to sound. Results demonstrate how sonification enhances perception of dynamic entanglement offering intuitive and artistic insight into quantum correlations behaviors.

Biomolecular condensates are essential for cellular organization and result from phase separation in systems far from thermodynamic equilibrium. Among various models, chemically active droplets play a significant role, consisting of proteins that switch between attractive and repulsive states via nonequilibrium chemical reactions. While field-based simulations have provided insights into their behavior, these coarse-grained approaches fail to capture molecular-scale effects, particularly in crowded cellular environments. Macromolecular crowding, a key feature of intracellular organization, strongly influences molecular transport within condensates, yet its quantitative impact remains underexplored. This study investigates the interplay between chemically active droplets and crowders by using particle-based models, that provide molecular insight, and a field-based model, that complements this picture. Surprisingly, crowding reduces droplet size while expanding the overall dense phase volume, challenging equilibrium-based expectations. This effect arises from the interplay between depletion interactions, diffusion hindrance, and nonequilibrium particle fluxes. Our findings provide a step towards a more comprehensive understanding of chemically active droplets in complex, realistic cellular environments.

These notes present a review of the status of quantum computing with arrays of neutral atom qubits, an approach which has demonstrated remarkable progress in the last few years. Scaling digital quantum computing to qubit counts and control fidelities that will enable solving outstanding scientific questions, and provide commercial value, is an outstanding challenge, not least because of the requirement of connecting and entangling distant qubits. Long-range Rydberg gates and physical motion outfit atomic qubit arrays with tools for establishing connectivity. These tools operate on different timescales and with distinct levels of parallelization. We analyze several prototypical architectures from the perspective of achieving fast connectivity for circuits with large scale entanglement, as well as fast cycle times for measurement based quantum error correcting codes. Extending Rydberg interactions to multiple atomic species has emerged as a promising route to achieving this latter requirement.

Partial Differential Equations (PDEs) describe phenomena ranging from turbulence and epidemics to quantum mechanics and financial markets. Despite recent advances in computational science, solving such PDEs for real-world applications remains prohibitively expensive because of the necessity of resolving a broad range of spatiotemporal scales. In turn, practitioners often rely on coarse-grained approximations of the original PDEs, trading off accuracy for reduced computational resources. To mitigate the loss of detail inherent in such approximations, closure models are employed to represent unresolved spatiotemporal interactions. We present a framework for developing closure models for PDEs using synthetic data acquired through the method of manufactured solutions. These data are used in conjunction with reinforcement learning to provide closures for coarse-grained PDEs. We illustrate the efficacy of our method using the one-dimensional and two-dimensional Burgers' equations and the two-dimensional advection equation. Moreover, we demonstrate that closure models trained for inhomogeneous PDEs can be effectively generalized to homogeneous PDEs. The results demonstrate the potential for developing accurate and computationally efficient closure models for systems with scarce data.

Recently-developed time series foundation models for scientific machine learning exhibit emergent abilities to predict physical systems. These abilities include zero-shot forecasting, in which a model forecasts future states of a system given only a short trajectory as context. Here, we show that foundation models applied to physical systems can give accurate predictions, but that they fail to develop meaningful representations of the underlying physics. Instead, foundation models often forecast by context parroting, a simple zero-shot forecasting strategy that copies directly from the context. As a result, a naive direct context parroting model scores higher than state-of-the-art time-series foundation models on predicting a diverse range of dynamical systems, at a tiny fraction of the computational cost. We draw a parallel between context parroting and induction heads, which explains why large language models trained on text can be repurposed for time series forecasting. Our dynamical systems perspective also ties the scaling between forecast accuracy and context length to the fractal dimension of the attractor, providing insight into the previously observed in-context neural scaling laws. Context parroting thus serves as a simple but tough-to-beat baseline for future time-series foundation models and can help identify in-context learning strategies beyond parroting.

We show that any $N$-dimensional unitary matrix can be realized using a finite sequence of concatenated identical multiport beamsplitters. Our construction is based on a Lie group theorem and is explicitly demonstrated for the two- and three-dimensional cases. We further establish that the widely used Clements decomposition naturally arises as a special case of this general framework. As an application, we present a reconfigurable linear optical circuit that implements a three-dimensional unitary emerging in the unambiguous discrimination of two nonorthogonal qubit states.

The electromagnetic field of standing-wave or ring cavities induces a spatially modulated, infinite-range interaction between atoms in an ultracold Fermi gas, with a single wavelength comparable to the Fermi length. This interaction has no analog in other systems of itinerant particles and has so far been studied only in the regime where it is attractive at zero distance. Here, we fully solve the problem of competing instabilities of the Fermi surface induced by single-wavelength interactions. We find that while the density-wave (superradiant) instability dominates on the attractive side, it is absent for repulsive interactions, where the competition is instead won by non-superradiant superfluid phases at low temperatures, with Fermion pairs forming at both vanishing and finite center-of-mass momentum. Moreover, even in the absence of such symmetry-breaking instabilities, we find the Fermi surface to be always nontrivially deformed from an isotropic shape. We estimate this full phenomenology to be within reach of dedicated state-of-the-art experimental setups.

This study critically examines the performance of physics-informed machine learning (PIML) approaches for traffic flow modeling, defining the failure of a PIML model as the scenario where it underperforms both its purely data-driven and purely physics-based counterparts. We analyze the loss landscape by perturbing trained models along the principal eigenvectors of the Hessian matrix and evaluating corresponding loss values. Our results suggest that physics residuals in PIML do not inherently hinder optimization, contrary to a commonly assumed failure cause. Instead, successful parameter updates require both ML and physics gradients to form acute angles with the quasi-true gradient and lie within a conical region. Given inaccuracies in both the physics models and the training data, satisfying this condition is often difficult. Experiments reveal that physical residuals can degrade the performance of LWR- and ARZ-based PIML models, especially under highly physics-driven settings. Moreover, sparse sampling and the use of temporally averaged traffic data can produce misleadingly small physics residuals that fail to capture actual physical dynamics, contributing to model failure. We also identify the Courant-Friedrichs-Lewy (CFL) condition as a key indicator of dataset suitability for PIML, where successful applications consistently adhere to this criterion. Lastly, we observe that higher-order models like ARZ tend to have larger error lower bounds than lower-order models like LWR, which is consistent with the experimental findings of existing studies.