Optics
Modifications to Image Phase Alignment Super-sampling Produce up to 4.4 times Increased Image Resolution
James N. Caron
Image Phase
Alignment Super-sampling (ImPASS) is a computational method for combining displaced low-resolution images into a single high-resolution image. The general steps include measuring the relative displacements, up-sampling, aligning and combining the images, followed by a blind deconvolution. Previous ImPASS studies have shown that the resulting image resolution can significantly subceed the diffraction limit of the imaging system. Characteristics that potentially limit the processed image resolution include optical parameters, detector noise, image
alignment accuracy, or deconvolution parameters. In this report, modifications have been made to the
algorithm to improve the image
alignment accuracy and deconvolution. Applications of the modified
algorithm improved image resolution by a factor up to 1.81. Compared to the original image resolution, the modified ImPASS achieved a resolution improvement factor up to 4.41 while subceeding the diffraction limit by a factor of 2.57. This suggests that limitations imposed by the physical properties of the system have not yet been reached, and further improvement of the
algorithm is warranted.
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Fluid Dynamics
Numerical Investigation of the Effect of a Magnetic Field on the Transport of Oxygen in Air
Alexander C. Kruse, Pavlos G. Aleiferis and Andrea Giusti
The effects of magnetisation forces in a binary mixture of gases characterised by large differences in
magnetic susceptibility are studied using numerical simulations, with a focus on the differential
diffusion of the species and the role of the gradient of mixture composition on the flow field resulting from magnetically-induced forces. A quiescent binary mixture of nitrogen and oxygen, representative of air, confined between two parallel plates is considered. In all simulations, a gradient of $\mathbf{B}^2$, the square of the
magnetic flux density magnitude, uniform and directed normal to the walls is imposed. Cases characterised by different pressures, different strengths of $\nabla(\mathbf{B}^2)$, and different initial gradients of species composition are investigated, while the same initial temperature is used in all cases. Non-dimensional groups related to the examined configuration are proposed. In cases characterised by an initially uniform mixture composition, species tend to separate and accumulate at opposite walls, due to differential
magnetic forces arising from the differences in
magnetic susceptibility. For a given strength of $\nabla(\mathbf{B}^2)$, the effect of the
magnetic field on the separation of species increases with decreasing pressure.In addition to species separation, it is shown that an initial gradient in the mixture composition perpendicular to $\nabla(\mathbf{B}^2)$ induces a significant change in the velocity field, which enhances the transport of species. This effect is due to a lack of
alignment between the gradient of averaged
magnetic susceptibility and $\nabla(\mathbf{B}^2)$ and could be exploited to achieve targeted mixing using engineered
magnetic fields.
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Applied Physics · Cross-listed from q-bio.QM
Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis
Mohsen Nakhaei, Alison Pouch, Silvani Amin et al. (+8 more)
Aortic valve (AV) biomechanics play a critical role in maintaining normal cardiac function. Pathological variations, particularly in bicuspid aortic valves, alter leaflet loading, increase strain, and accelerate disease progression. Accurate patient-specific characterization of valve geometry and deformation is therefore essential for predicting disease progression and guiding durable repair. Current imaging and computational methods often fail to capture rapid valve motion and complex patient-specific features, limiting precise biomechanical assessment. To address these limitations, we developed an image registration framework coupled with the finite element method (FEM) to improve AV tracking and biomechanical evaluation. The valve geometries derived from 4D echocardiography and CT were used to simulate AV closure and generate intermediate deformation states. These FEM-generated states facilitated leaflet tracking, while image registration corrected misalignment between simulations and imaging data. In 20 patients, FEM-augmented registration improved accuracy by 40% compared with direct registration. This improvement enabled more reliable strain estimation by measuring leaflet deformation directly from imaging and reducing uncertainties associated with boundary conditions and material assumptions. Areal, Green-Lagrange, and deviatoric strains were quantified in adult trileaflet/bicuspid valves, as well as pediatric patients, revealing distinct deformation patterns across valve groups. Convergence in mean deviatoric strain between adult trileaflet and pediatric valves suggests volumetric deformation underlies age- and size-related differences in AV mechanics. The FEM-augmented registration enhances tracking and biomechanical evaluation accuracy, providing clinically relevant insights into patient-specific AV deformation to support individualized medical and intervention planning.
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All authors Mohsen Nakhaei, Alison Pouch, Silvani Amin, Matthew Daemer, Christian Herz, Natalie Yushkevich, Lourdes Al Ghofaily, Nimesh Desai, Joseph Bavaria, Matthew Jolley, Wensi Wu
Optics
End-to-end differentiable design of geometric waveguide displays
Xinge Yang, Zhaocheng Liu, Zhaoyu Nie et al. (+4 more)
Geometric waveguides are a promising architecture for optical see-through augmented reality displays, but their performance is severely bottlenecked by the difficulty of jointly optimizing non-sequential light transport and polarization-dependent multilayer thin-film coatings. Here we present the first end-to-end differentiable optimization framework for
geometric waveguide that couples non-sequential Monte Carlo polarization ray tracing with a differentiable transfer-
matrix thin-film solver. A differentiable Monte Carlo ray tracer avoids the exponential growth of deterministic ray splitting while enabling gradients backpropagation from eyebox metrics to design parameters. With
memory-saving strategies, we optimize more than one thousand layer-thickness parameters and billions of non-sequential ray-surface intersections on a single multi-GPU workstation. Automated layer
pruning is achieved by starting from over-parameterized stacks and driving redundant layers to zero thickness under discrete manufacturability constraints, effectively performing
topology optimization to discover optimal coating structures. On a representative design, starting from random initialization within thickness bounds, our method increases light efficiency from 4.1% to 33.5% and improves eyebox and FoV uniformity by $\sim$17$\times$ and $\sim$11$\times$, respectively. Furthermore, we jointly optimize the waveguide and an image preprocessing network to improve perceived image quality. Our framework not only enables system-level, high-dimensional coating optimization inside the waveguide, but also expands the scope of differentiable optics for next-generation optical design.
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All authors Xinge Yang, Zhaocheng Liu, Zhaoyu Nie, Qingyuan Fan, Zhimin Shi, Jim Bonar, Wolfgang Heidrich
Physics and Society
Generalization to Political Beliefs from Fine-Tuning on Sports Team Preferences
Owen Terry
Fine-tuned LLMs often exhibit unexpected behavior as a result of generalizing beyond the data they're shown. We present results in which an LLM fine-tuned to prefer either coastal sports teams or Southern sports teams adopt political beliefs that diverge significantly from those of the base model. While we hypothesized that the coastal model would become more liberal and the southern model would become more conservative, we find that their responses are usually similar to each other, without a clear-cut liberal or conservative bias. In addition to asking the models for numerical ratings of agreement with relevant political statements, we ask them to elaborate on their more radical answers, finding varying degrees of willingness to justify themselves. Further work is needed to understand the mechanisms by which fine-tuning on simple, narrow
datasets leads to seemingly unrelated changes in model behavior.
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Optics
Ultra-sensitive graphene-based electro-optic sensors for optically-multiplexed neural recording
Zabir Ahmed, Xiang Li, Kanika Sarna et al. (+3 more)
Large-scale neural recording with high spatio-temporal resolution is essential for understanding information processing in
brain, yet current neural interfaces fall far short of comprehensively capturing
brain activity due to extremely high neuronal density and limited scalability. Although recent advances have miniaturized neural probes and increased channel density, fundamental design constraints still prevent dramatic scaling of simultaneously recorded channels. To address this limitation, we introduce a novel electro-optic sensor that directly converts ultra-low-amplitude neural electrical signals into optical signals with high signal-to-noise ratio. By leveraging the ultra-high bandwidth and intrinsic multiplexing capability of light, this approach offers a scalable path toward massively parallel neural recording beyond the limits of traditional electrical interfaces. The sensor integrates an on-chip photonic microresonator with a
graphene layer, enabling direct detection of neural signals without genetically encoded optical indicators or tissue modification, making it suitable for human translation. Neural signals are locally transduced into amplified optical modulations and transmitted through on-chip waveguides, enabling interference-free recording without bulky electromagnetic shielding. Arrays of wavelength-selective sensors can be multiplexed on a single bus waveguide using wavelength-division multiplexing (WDM), greatly improving scalability while maintaining a minimal footprint to reduce tissue damage. We demonstrate detection of evoked neural signals as small as 25 $\mu$V with 3 dB SNR from mouse
brain tissue and show multiplexed recording from 10 sensors on a single waveguide. These results establish a proof-of-concept for optically multiplexed neural recording and point toward scalable, high-density neural interfaces for neurological research and clinical applications.
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All authors Zabir Ahmed, Xiang Li, Kanika Sarna, Harshvardhan Gupta, Vishal Jain, Maysamreza Chamanzar
Atomic Physics
Photodetachment energy of negative hydrogen ions
Maen Salman and Jean-Philippe Karr
We report a high-precision calculation of the photodetachment energy of the hydrogen anion \mathrm{H}^{-}. The nonrelativistic bound-state energy is obtained using an exact three-body approach, and supplemented by leading relativistic,
quantum-electrodynamic, finite-
nuclear-size, and hyperfine corrections. Our result is 6083$.$06447(68)\mathrm{cm}^{-1} for the detachment to the hydrogen ground-state hyperfine level \mathit{(F=0)}, which is 220 times more precise than the best experimental determination to date, 6082$.$99(15)\mathrm{cm}^{-1}, as reported by Lykke \mathit{et al.} Beyond their intrinsic interest, these results provide critical input for antihydrogen physics, where controlled photodetachment of \bar{\mathrm{H}}^{+} offers a path to producing ultracold antihydrogen (and its isotopes) for precision experiments. Corresponding calculations for the negative deuterium and tritium ions yield 6086$.$70676(68)\mathrm{cm}^{-1} for ^{2}\mathrm{H}^{-}(F=1/2) and 6087$.$87924(68)\mathrm{cm}^{-1} for ^{3}\mathrm{H}(F=0).
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Geophysics
Statistical Analysis of the Depth-Velocity Trade-off in Reflection Seismology
Rafael da Silva Garcia and Francisco Marcio Barboza
Seismic interpretation is strongly influenced by the relationship between subsurface layer depth and velocity. Small variations in these parameters can produce almost identical responses, characterizing the depth-velocity trade-off phenomenon. This work proposes a statistical and computational approach to evaluate the extent of this effect through Monte Carlo simulations involving thousands of synthetic models. The analysis consists of generating random depth-velocity pairs, computing travel times, and calculating the root mean square error (RMSE) relative to a reference model. The results show that multiple parameter combinations yield nearly indistinguishable travel times, confirming the existence of broad ambiguity regions. Finally, a two-layer geological velocity model is presented to illustrate the relationship between structure and velocity.
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Physics Education
Profiles of Roles in the Quantum Industry
Shams El-Adawy, A.R. Piña, Benjamin M. Zwickl et al. (+1 more)
This report builds upon the Categorization of Roles in the
Quantum Industry report by providing detailed profiles for 29 distinct roles across the
quantum workforce. While the earlier report established a framework of four major role categories (hardware, software, bridging, and public facing and business) and their subcategories, the current report expands on this structural framework by characterizing what professionals in each role actually do, particularly by identifying the tasks, knowledge, skills, abilities (KSAs), and experience typically required for each role. Each role profile follows a standardized structure guided by the Occupational Information Network (O*NET) framework. By presenting a fine-grained view of day-to-day work and qualification expectations, this report serves as a practical resource for educators, students, industry professionals, and policymakers aiming to understand, educate, and support the evolving
quantum workforce.
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All authors Shams El-Adawy, A.R. Piña, Benjamin M. Zwickl, H.J. Lewandowski
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Pritthijit Nath, Sebastian Schemm, Henry Moss et al. (+3 more)
Weather and
climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that are weakly constrained and tuned offline, contributing to persistent biases that limit their ability to adapt to underlying physics. This study presents a framework that learns components of parametrisation schemes online as a function of the evolving model state using reinforcement learning (RL) and evaluates policy-driven parameter updates across idealised testbeds spanning a simple
climate bias correction (SCBC), a radiative-convective
equilibrium (RCE), and a zonal mean energy balance model (EBM) with single-
agent and
federated multi-
agent settings. Across nine RL
algorithms, Truncated
Quantile Critics (TQC), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3) achieved the highest skill and stable convergence, with performance assessed against a static baseline using area-weighted RMSE, temperature and pressure-level diagnostics. For the EBM, single-
agent RL outperformed static parameter tuning with the strongest gains in tropical and mid-latitude bands, while
federated RL on multi-
agent setups enabled specialised control and faster convergence, with a six-
agent DDPG configuration using frequent aggregation yielding the lowest area-weighted RMSE across the tropics and mid-latitudes. The learnt corrections were also physically meaningful as
agents modulated EBM radiative parameters to reduce meridional biases, adjusted RCE lapse rates to match vertical temperature errors, and stabilised heating increments to limit drift. Overall, results show that RL can learn skilful state-dependent parametrisation components in idealised settings, offering a scalable pathway for online learning within numerical models and a starting point for evaluation in weather and
climate models.
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All authors Pritthijit Nath, Sebastian Schemm, Henry Moss, Peter Haynes, Emily Shuckburgh, Mark J. Webb
Physics and Society
Information Theoretic Optimal Surveillance for Epidemic Prevalence in Networks
Ritwick Mishra, Abhijin Adiga, Madhav Marathe et al. (+3 more)
Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or nodes) to minimize objectives such as probability of outbreak detection. Such methods do not give insights into the outbreak size distribution which, despite being complex and multi-modal, is very useful in public health planning. We introduce TESTPREV, a problem of choosing a subset of nodes which maximizes the mutual information with disease prevalence, which directly provides information about the outbreak size distribution. We show that, under the independent cascade (IC) model, solutions computed by all prior disease surveillance approaches are highly sub-optimal for TESTPREV in general. We also show that TESTPREV is hard to even approximate. While this mutual information objective is computationally challenging for general networks, we show that it can be computed efficiently for various network classes. We present a greedy strategy, called GREEDYMI, that uses estimates of mutual information from cascade simulations and thus can be applied on any network and disease model. We find that GREEDYMI does better than natural baselines in terms of maximizing the mutual information as well as reducing the expected variance in outbreak size, under the IC model.
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All authors Ritwick Mishra, Abhijin Adiga, Madhav Marathe, S. S. Ravi, Ravi Tandon, Anil Vullikanti
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
A Comprehensive Computational Framework for Materials Design, Ab Initio Modeling, and Molecular Docking
Md Rakibul Karim Akanda and Michael P. Richard
To facilitate rational molecular and materials design, this research proposes an integrated computational framework that combines
stochastic simulation, ab initio
quantum chemistry, and molecular docking. The suggested workflow allows systematic investigation of structural stability, binding affinity, and electronic properties across biological and materials science domains by utilizing complementary tools like Avogadro for molecular construction and visualization, AutoDock for docking and interaction analysis, and ORCA for high-level electronic structure computations. Uncertainty, configurational sampling, and optimization in high-dimensional chemical spaces are addressed by combining Monte Carlo-based and annealing-inspired techniques. The work shows how materials science ideas such as
polymer design, thin films, crystalline
lattices, and bioelectronic systems can be applied to drug development. On-device, open-source computational methods are viable, scalable, and economical, as demonstrated by comparative platform analysis. All things considered, the findings highlight the need of an integrated, repeatable computational pipeline for speeding up de novo molecule assembly and materials architecture while lowering experimental risk and expense.
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Computational Physics · Cross-listed from cs.LG
Robust Physics Discovery from Highly Corrupted Data: A PINN Framework Applied to the Nonlinear Schrödinger Equation
Pietro de Oliveira Esteves
We demonstrate a deep learning framework capable of recovering physical parameters from the Nonlinear Schrodinger Equation (NLSE) under severe noise conditions. By integrating Physics-Informed Neural Networks (PINNs) with automatic differentiation, we achieve reconstruction of the nonlinear coefficient beta with less than 0.2 percent relative error using only 500 sparse, randomly sampled data points corrupted by 20 percent additive Gaussian noise, a regime where traditional finite difference methods typically fail due to noise amplification in numerical derivatives. We validate the method's generalization capabilities across different physical regimes (beta between 0.5 and 2.0) and varying data availability (between 100 and 1000 training points), demonstrating consistent sub-1 percent accuracy. Statistical analysis over multiple independent runs confirms robustness (standard deviation less than 0.15 percent for beta equals 1.0). The complete pipeline executes in approximately 80 minutes on modest cloud GPU resources (NVIDIA Tesla T4), making the approach accessible for widespread adoption. Our results indicate that physics-based regularization acts as an effective filter against high measurement uncertainty, positioning PINNs as a viable alternative to traditional optimization methods for inverse problems in spatiotemporal dynamics where experimental data is scarce and noisy. All code is made publicly available to facilitate reproducibility.
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Chemical Physics
SAP-X2C: Optimally-Simple Two-Component Relativistic Hamiltonian With Size-Intensive Picture Change
Kshitijkumar A. Surjuse and Edward F. Valeev
We present a simple relativistic exact 2-component (X2C)
Hamiltonian that models two-electron picture-change effects using Lehtola's superposition of atomic potentials (SAP) [S. Lehtola, J. Chem. Theory Comput. 15, 1593-1604 (2019)]. The SAP-X2C approach keeps the low-cost and technical simplicity of the popular 1-electron X2C (1eX2C) predecessor, but is significantly more accurate and has a well-defined thermodynamic limit, making it applicable to extended systems (such as large molecules and periodic
crystals). The assessment of the SAP-X2C-based Hartree-Fock total and spinor energies,
spin-orbit splittings,
equilibrium bond distances, and harmonic vibrational frequencies suggests that SAP-X2C is similar to the more complex atomic mean-field (AMF) X2C counterparts in its ability to approximate the 4-component Dirac-Hartree-Fock reference.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Stochastic Path Compression for Spectral Tensor Networks on Cyclic Graphs
Ryan T. Grimm and Joel D. Eaves
We develop a new approach to compress cyclic tensor networks called
stochastic path compression (SPC) that uses an iterative importance sampling procedure to target edges with large bond-dimensions. Closed random walks in SPC form compression pathways that spatially localize large bond-dimensions in the tensor network. Analogous to the phase separation of two immiscible
liquids, SPC separates the graph of bond-dimensions into spatially distinct high and low density regions. When combined with our integral decimation
algorithm, SPC facilitates the accurate compression of cyclic tensor networks with continuous degrees of freedom. To
benchmark and illustrate the methods, we compute the absolute thermodynamics of $q$-state clock models on two-dimensional square
lattices and an XY model on a Watts-Strogatz graph, which is a small-world network with random connectivity between
spins.
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Space Physics
Properties of Magnetic Switchbacks in the Near-Sun Solar Wind
Samuel T. Badman, Naïs Fargette, Lorenzo Matteini et al. (+24 more)
Magnetic switchbacks are fluctuations in the
solar wind in which the interplanetary
magnetic field sharply deflects away from its background direction so as to create folds in
magnetic field lines while remaining of roughly constant magnitude. The
magnetic field and velocity fluctuations are extremely well correlated in a way corresponding to Alfvénic fluctuations propagating away from the Sun. For a background field which is nearly radial this causes an outwardly propagating jet to form. Switchbacks and their characteristic velocity jets have recently been observed to be nearly ubiquitous by Parker
Solar Probe with in situ measurements in the inner heliosphere within 0.3 AU. Their prevalence, substantial energy content, and potentially fundamental role in the dynamics of the outer corona and
solar wind motivate the significant research efforts into their understanding. Here we review the in situ measurements of these structures (primarily by Parker
Solar Probe). We discuss how they are identified and measured, and present an overview of the primary observational properties of these structures, both in terms of individual switchbacks and their collective arrangement into “patches”. We identify both properties for which there is a strong consensus and those that have limited or qualified support and require further investigation. We identify and collate several open questions and recommendations for future studies.
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All authors Samuel T. Badman, Naïs Fargette, Lorenzo Matteini, Oleksiy V. Agapitov, Mojtaba Akhavan-Tafti, Stuart D. Bale, Srijan Bharati Das, Nina Bizien, Trevor A. Bowen, Thierry Dudok de Wit, Clara Froment, Timothy Horbury, Jia Huang, Vamsee Krishna Jagarlamudi, Andrea Larosa, Maria S. Madjarska, Olga Panasenco, Etienne Pariat, Nour E. Raouafi, Alexis P. Rouillard, David Ruffolo, Nikos Sioulas, Shirsh Lata Soni, Luca Sorriso-Valvo, Gabriel Ho Hin Suen, Marco Velli, Jaye Verniero
Optics · Cross-listed from quant-ph
Below-shot-noise capacity in phase estimation using nonlinear interferometers
Cristofero Oglialoro, Gerard J. Machado, Felix Farsch et al. (+7 more)
Over the past decade, several schemes for imaging and sensing based on nonlinear interferometers have been proposed and demonstrated experimentally. These interferometers exhibit two main advantages. First, they enable probing a sample at a chosen wavelength while detecting light at a different wavelength with high efficiency (bicolor
quantum imaging and sensing with undetected light). Second, they can show
quantum-enhanced sensitivities below the shot-noise limit, potentially reaching Heisenberg-limited precision in parameter estimation. Here, we compare three
quantum-imaging configurations using only easily accessible intensity-based measurements for phase estimation: a Yurke-type SU(1,1) interferometer, a Mandel-type induced-coherence interferometer, and a hybrid scheme that continuously interpolates between them. While an ideal Yurke interferometer can exhibit Heisenberg scaling, this advantage is known to be fragile under realistic detection constraints and in the presence of loss. We demonstrate that differential intensity detection in the Mandel interferometer provides the highest and most robust phase sensitivity among the considered schemes, reaching but not surpassing the shot-noise limit, even in the presence of loss. Intensity measurements in a Yurke-type configuration can achieve genuine sub-shot-noise sensitivity under balanced losses and moderate gain; however, their performance degrades in realistic high-gain regimes. Consequently, in this regime, the Mandel configuration with differential detection outperforms the Yurke-type setup and constitutes the most robust approach for phase estimation.
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All authors Cristofero Oglialoro, Gerard J. Machado, Felix Farsch, Daniel F. Urrego, Alejandra A. Padilla, Raj B. Patel, Ian A. Walmsley, Markus Gräfe, Juan P. Torres, Enno Giese
Optics
Backscatter Communication Through Disordered Media Enabled by a Programmable Leaky Cavity
Clément Ferise, Pierre Granier, Antton Goïcoechea et al. (+3 more)
Retrieving information through disordered scattering systems remains a major challenge in wireless communication and sensing due to the severe distortion and attenuation caused by multiple scattering. Here, we demonstrate robust focusing through random media using a leaky cavity made programmable by reconfigurable metasurfaces. We show that the leaky cavity leverages backscattering from the disordered medium to enhance the degree of control over the transmitted field. We achieve an enhancement in focused intensity by an order of magnitude compared to a horn
antenna. We further demonstrate robust communication through the medium using low-power amplitude-modulated signals, and finally implement a backscatter communication scheme in which the same cavity illuminates and decodes data from a passive reflectivity-modulating target embedded within the disorder. Our results open new possibilities for reliable communication and sensing in complex environments.
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All authors Clément Ferise, Pierre Granier, Antton Goïcoechea, François Sarrazin, Philippe Besnier, Matthieu Davy
Fluid Dynamics
Microbubble surface instabilities in a strain stiffening viscoelastic material
Sawyer Remillard, Bachir A. Abeid, Timothy L. Hall et al. (+5 more)
Understanding the dynamics of instabilities along fluid-solid interfaces is critical for the efficacy of focused ultrasound therapy tools (e.g., histotripsy) and microcavitation rheometry techniques. Non-uniform pressure fields generated by either ultrasound or a focused
laser can cause non-spherical microcavitation bubbles. Previous perturbation amplitude
evolution models in viscoelastic materials either assume pure radial deformation or have inconsistent kinematic fields between the fluid and solid contributions. We derive a kinematically-consistent theoretical model for the
evolution of surface perturbations. The model captures the non-linear kinematics of a strain-stiffening viscoelastic material surrounding a non-spherical bubble. The model is validated for (i) small, approximately linear radial oscillations and (ii) large inertial oscillations using
laser-induced microcavitation experiments in a soft hydrogel. For the former, the bubble is allowed to reach mechanical
equilibrium, and then surface perturbations are excited using ultrasound forcing. For the latter, the microbubble forms small bubble surface perturbations at its maximum radius that grow during collapse. The model's dominant surface perturbation mode scales linearly with
equilibrium radius and matches experiments. Similarly, the model's perturbation amplitude
evolution sufficiently constrains the rheometry problem and is experimentally validated.
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All authors Sawyer Remillard, Bachir A. Abeid, Timothy L. Hall, Jonathan R. Sukovich, Jacob Baker, Jin Yang, Jonathan B. Estrada, Mauro Rodriguez Jr
Computational Physics · Cross-listed from cond-mat.str-el
Point-Group Equivariant Neural Networks for Local Dynamics in Lattice Systems
Yunhao Fan and Gia-Wei Chern
We develop point-group equivariant neural networks for learning local mappings used in dynamical simulations of
lattice systems. The same network acts on a finite neighborhood around each site, respecting
lattice translations through shared parameters. The local configuration is decomposed into features that transform according to irreducible representations of the point group, and hidden features retain this organization throughout the network. Equivariant linear maps and
symmetry-allowed tensor products then learn nonlinear spatial correlations while preserving the required transformation laws. This approach avoids constructing a fixed set of hand-crafted invariant descriptors, whose selection requires choices about which local correlations to retain. It also allows the complexity of the learned mapping to grow without enlarging its spatial cutoff. We demonstrate the construction by learning the forces governing adiabatic dynamics in the square-
lattice Holstein model. Comparisons with exact diagonalization test the accuracy of the predicted forces and the
evolution of spatial correlations. Force evaluation scales linearly with system size, allowing the trained model to simulate charge-density-wave ordering on large
lattices. These simulations reproduce anomalously slow coarsening of the charge-density-wave domains, illustrating the use of the learned local mapping for collective dynamics.
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Computational Physics
A constrained-transport embedded boundary method for compressible resistive magnetohydrodynamics
Samuel W. Jones, Colin P. McNally and Meritt Reynolds
Motivated by the increased interest in pulsed-power magneto-inertial fusion devices in recent years, we present a method for implementing an arbitrarily shaped embedded boundary on a Cartesian mesh while solving the equations of compressible resistive magnetohydrodynamics. The method is built around a finite volume formulation of the equations in which a Riemann solver is used to compute fluxes on the faces between grid
cells, and a face-centered constrained transport formulation of the induction equation. The small time step problem associated with the cut
cells is avoided by always computing fluxes on the faces and edges of the Cartesian mesh. We extend the method to model a moving interface between two materials with different properties using a ghost-fluid approach, and show some preliminary results including shock-wave-driven and magnetically-driven dynamical compressions of magnetohydrostatic
equilibria. We present a thorough verification of the method and show that it converges at second order in the absence of discontinuities, and at first order with a discontinuity in material properties.
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Physics and Society
Bridging the Linear-Quadratic Gap: A Quantum-Classical Hybrid Approach to Robust Supply Chain Design
Rudraksh Sharma, Ravi Katukam and Arjun Nagulapally
The design of supply chain networks in densely populated urban logistics systems faces a timely dilemma: the traditional optimisation approaches are effective to maximise the level of demand perfusion, but they are limited to embracing large expenses in overlapping the facilities and cannibalisation in the
market. When tested on a high-fidelity digital twin of the Delhi NCR road network of thirty candidate sites, we establish that Classical Greedy
algorithms using the theoretical maximum demand of (473 units) lack any theoretical overlap penalty, but incur a prohibitive overlap penalty (5.08). Here, in comparison, the
Quantum-Inspired solution only losses 3.2% of demand (450 compared to 465 units relative to the optimal solution), but the solution preserves 21.8% less operational overlap risk (3.26 compared to 4.17), which can be viewed as a 35.8% improvement compared to the Greedy solution. Geospatial analysis shows that it can be attributed to a shift in strategies: This, in contrast to Classical approaches, which focus on locating facilities in the high-density central areas (North/Central Delhi), the
quantum-inspired solver autonomously chooses the diversified
topology of the North-south network, penetrating into the underserved periphery growth
markets. This is a spatially balanced arrangement which is congruent to the polycentric structure of modern time megacities, and displays better stability to
volatility in demand. We have shown that
quantum-inspired optimisation methods can close the so-called Linear-Quadratic Gap phenomenon, i.e. the systematic inability of greedy methods to capture the actual quadratic interactions between facilities, and offer a way of computing the pathway to operationally robust and risk-optimised supply chain networks in dense urban conditions.
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Applied Physics · Cross-listed from quant-ph
Surface Optimization of Superconducting Aluminum Resonators for Robust Quantum Device Fabrication
Simon J. K. Lang, Ignaz Eisele, Alwin Maiwald et al. (+8 more)
Aluminum (Al) remains the central material for superconducting
qubits, and considerable effort has been devoted to optimizing its deposition and patterning for
quantum devices. However, post-processing strategies focused on
oxide removal of niobium (Nb) and tantalum (Ta) -based resonators using buffered
oxide etch (BOE), which can not be used for Al. This challenge becomes particularly relevant for industry-scale fabrication with multi-chip bonding, where delays between sample preparation and cooldown require surface treatments that preserve low dielectric loss during extended exposure to ambient conditions. In this work, we investigate surface modification approaches for Al resonators subjected to a 24-hour delay prior to cryogenic measurement. Passivation using self-limiting oxygen and fluorine chemistries was evaluated utilizing different
plasma processes. Remote oxygen
plasma treatment reduced dielectric losses, in contrast to direct oxygen
plasma. A fluorine-based
plasma process was developed that passivated the Al surface for subsequent BOE treatment. However, the fluorine content in the surface resulted in higher loss, identifying fluorine as an unsuitable passivation material for Al resonators. Above all, selective
oxide removal using HF (hydrogen fluoride) vapor and phosphoric acid yielded median dielectric losses as low as $\tilde{\delta}_\mathrm{LP} = 5.7 \times 10^{-7}$ ($Q_\mathrm{LP} \approx 1.7\,\mathrm{M}$) with $\tilde{\delta}_\mathrm{TLS} = 3.6 \times 10^{-7}$ ($Q_\mathrm{TLS} \approx 2.8\,\mathrm{M}$) in the single
photon regime. Selective
oxide removal provides a promising pathway for robust Al-based
qubit fabrication, as it preserves low dielectric losses for a 24-hour delay before cooldown.
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All authors Simon J. K. Lang, Ignaz Eisele, Alwin Maiwald, Emir Music, Luis Schwarzenbach, Carla Moran-Guizan, Johannes Weber, Daniela Zahn, Thomas Mayer, Rui N. Pereira, Christoph Kutter
Physics and Society
Multi-Dimensional Opinion Formation
Hanna Bartel, Martin Burger and Marie-Therese Wolfram
In this paper we propose and investigate a multi-dimensional opinion dynamics model where people are characterised by both opinions and importance weights across these opinions. Opinion changes occur through binary interactions, with a novel coupling mechanism: the change in one topic depends on the weighted similarity across the full opinion vector. We state the kinetic equation for this process and derive its mean-field partial differential equation to describe the overall dynamics. Analytical computations and numerical simulations confirm that this model exhibits a variety of qualitatively distinct stationary states, and we demonstrate that the final opinion structures are critically determined by the people's opinion weights.
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Optics
1.1 kW, 100 Hz room-temperature diode-pumped nanosecond laser by water immersion cooling
Xinxing Lei, Suyang Wang, Zichao Wang et al. (+3 more)
We report a room-temperature diode-pumped solid-state
laser by water immersion cooling, which delivers a pulse energy of 11 J at the repetition rate of 100 Hz and the pulse duration of 7 ns, while the beam quality factor is 2.6 times the diffraction limit. To the best of our knowledge, this represents the highest performance achieved for room-temperature nanosecond
lasers operating above 100 Hz, which demonstrates the great potentials of room-temperature immersion-cooled nanosecond active mirror
lasers.
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All authors Xinxing Lei, Suyang Wang, Zichao Wang, Lei Huang, Qiang Liu, Xing Fu
Chemical Physics
Solvent Effects on Triplet Yields in BODIPY-Based Photosensitizers
Leonardo Coello Escalante, Thomas P. Fay and David T. Limmer
We employ molecular dynamics simulations and
quantum rate theories to elucidate the complex condensed-phase dynamics underpinning triplet-state formation in organic photosensitizers. Using models informed by first-principles calculations complete with a molecular representation of solvents of different polarities, we elucidate the interplay of the internal and environmental interactions underlying triplet yield. We find that triplet yields depend sensitively on the dielectric stabilization of the charge transfer intermediate that facilitates a transition into the triplet
manifold. Our results illustrate the importance of molecularly detailed models in understanding the excited-state internal charge-transfer dynamics of photochemically-relevant organic molecules.
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Optics
Visible octave frequency combs in silicon nitride nanophotonic waveguides driven by Ti:sapphire lasers
Abdullah Alabbadi, Ewan Allan, Hanna Ostapenko et al. (+3 more)
Nonlinear nanophotonic waveguides have opened a route to compact frequency combs for precision metrology, spectroscopy and astronomy, yet broadband comb access to the visible remains challenging on CMOS-compatible platforms. Silicon nitride is widely accessible and low loss into the visible, but most demonstrations rely on telecom pumping and thick stress-managed films, where the large spectral gap to the visible dispersive wave raises the
soliton order and power required for efficient conversion. Here we show that pumping closer to the visible provides a complementary route. Starting from crack-free 400 nm SiN films, we implement dispersion-engineering with air-clad nanophotonic waveguides whose enhanced
geometric dispersion opens an anomalous-dispersion window across the Ti-sapphire tuning range. Femtosecond Ti-sapphire pulses then drive octave-spanning combs from the visible to the near-infrared, with the visible edge and overall bandwidth lithographically tuned by the waveguide width at pulse energies of only tens of picojoules. The air-clad geometry also produces strong polarization-dependent dispersion, enabling switching between all-normal and
soliton-dominated broadening in the same device, and support octave-spanning combs at 1 GHz repetition rates directly driven by a compact diode-pumped Ti-sapphire oscillator. These results position air-clad SiN nanophotonic waveguides as an efficient interface between emerging short-wavelength integrated gain platforms and fully integrated visible frequency-comb engines.
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All authors Abdullah Alabbadi, Ewan Allan, Hanna Ostapenko, Pablo Castro-Marin, Derryck T. Reid, Pascal Del'Haye
Optics · Cross-listed from cond-mat.mtrl-sci
Magnetoluminescence of ZnMnSe/BeMnTe heterostructures with type-II band alignment at millikelvin temperatures
Dennis Kudlacik, Linda Kersting, Nataliia E. Kopteva et al. (+3 more)
The magneto-optical properties of a Zn$_{0.99}$Mn$_{0.01}$Se/Be$_{0.93}$Mn$_{0.07} $Te diluted
magnetic semiconductor heterostructure with type-II band
alignment are investigated at cryogenic temperatures down to 16 mK. The temperature of the Mn
spin system, which at the lowest possible
laser power reaches 270 mK, is evaluated from the
giant Zeeman splitting of the direct
exciton in the Zn$_{0.99}$Mn$_{0.01}$Se layers subject to an external
magnetic field. The degree of circular polarization of the direct and indirect optical transitions, induced by the
magnetic field, is a sensitive indicator for the
laser heating of the Mn
spin system. Evidence of
spin glass formation in the Mn
spin system of the Be$_{0.93}$Mn$_{0.07}$Te layers with the critical temperature of $T_{SG}=400$ mK is found.
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All authors Dennis Kudlacik, Linda Kersting, Nataliia E. Kopteva, Mladen Kotur, Dmitri R. Yakovlev, Manfred Bayer
Physics and Society
Convergence criteria for self-consistent measures in bipartite networks
János Török, Takashi Shimada, Fumiko Ogushi et al. (+3 more)
Many quantities that characterize network elements are defined in an explicit form and calculated directly from the network structure; examples of include several centrality measures like degree, closeness, or betweenness. However, there are also implicitly defined quantitative measures, which are usually calculated iteratively, in a self-consistent manner, like PageRank or countries' fitness / products' complexity relations. The iteration
algorithms involve calculations over the entire network; therefore, their convergence properties depend on the structure of the network. Here, we focus on investigating self-consistently defined quantities in bipartite networks of two sets of nodes where the quantities in one set are determined by the quantities in the other set and vice versa. We derive an explicit convergence criterion for iterations of these quantities and describe two different approaches to improve the convergence properties. In the first one, we identify "problematic nodes" that can be removed or merged while in the second one, we introduce a regularization scheme and show how to estimate the regularization parameter.
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All authors János Török, Takashi Shimada, Fumiko Ogushi, Kata Tunyogi, János Kertész, Kimmo Kaski
Fluid Dynamics
Modelling of pressure drop in periodic square-bar packed beds
Hakan Demir, Wojciech Sadowski and Francesca di Mare
Understanding fluid flow through porous media with complex geometries is essential for improving the design and operation of packed-bed reactors. Most existing studies focus on spherical packings, having as a consequence that accurate models for irregular interstitial geometries are scarce. In this study, we numerically investigated the flow through a set of packed-bed geometries consisting of square bars stacked on top of each other and arranged in disk-shaped modules. Rotation of each module allows the generation of a variety of geometrical configurations at Reynolds numbers of up to 200 (based on the bar size). Simulations were carried out using the open-source solver OpenFOAM. Selected cases (e.g., $\alpha = 30^\circ$, $\mathrm{Re}_\mathrm{p} = 100, 200$) were compared against Particle Image Velocimetry measurements. Results reveal that, based on the relative rotation angle, the realized geometries can be classified as channel-like ($\alpha \leq 10^\circ$) and
lattice-like ($\alpha \geq 15^\circ$), fundamentally altering the friction factor. Furthermore, the maximum friction factor obtained in the creeping regime occurred at $\alpha = 25^\circ$, whereas in the inertial regime, this occurred at $\alpha = 60^\circ$. The module-equivalent diameter, based on the angle-dependent wetted surface area, collapses the friction factor onto the Ergun correlation and yields good permeability predictions for the
lattice-like geometries.
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