Papers

Solute mixing plays a pivotal role in a broad spectrum of chemical and biological processes across natural and engineered porous media. However, current understanding of mixing dynamics remains largely constrained to steady flows in fully or partially water-saturated environments. Multiphase flow systems are generally unsteady, with moving fluid interfaces and flow paths that change in time. Despite the widespread occurrence of dynamic multiphase flows, their impacts on solute mixing are largely unknown. Here, we use experiments and numerical simulations to investigate the effect of dynamic two-phase flow on the stretching and folding of fluid elements, a fundamental mechanism driving solute mixing and reactions in porous media. We find that dynamic two-phase flows induce chaotic mixing, characterized by exponential stretching of fluid elements, leading to strongly enhanced mixing compared to steady single phase flows. By extensive numerical multiphase flow simulations, we establish dynamic steady states where we reliably measure the mean fluid stretching rate as a function of flow rate. We show that stretching is maximized at an optimum flow rate which balances fluid shear deformation against the frequency of flow reorientation by the intermittent motion of the fluid interface. The findings are rationalized by a mechanistic model linking basic multiphase flow characteristics to the stretching rate, opening new perspectives to understand and control mixing and reactions in a wide range of multiphase flow systems.

Phase-sensitive pump-probe hyperspectral imaging is a precise technique for absolute two-beam measurements of the optical Kerr coefficient ($n_2$). The irradiance profile is characterized and background effects are rejected by rastering the pump beam across the probe beam to yield a complex-valued hyperspectral image of the pump-induced nonlinear response. Information about the temporal irradiance profile is carried in the spectral response. The technique is demonstrated by measuring $n_2$ of a fused silica sample near 1~$\mu$m wavelength and benchmarked against a measurement using Z-scan [Sheik-Bahae et al., IEEE J. Quantum Electron. 26, 760–769 (1990)], the most widely used single-beam technique. The two measurements are consistent when the two-beam grating effect from the Raman contribution to the nonlinearity is considered. Uncertainty contributions are described in detail and the outlook is discussed for improvements in precision.

We present a scalable method for geolocalizing buried fiber-optic cables using Distributed Acoustic Sensing (DAS) and traffic-induced quasi-static seismic signals. Assuming access to one end of the fiber, the method fuses DAS measurements with vehicle trajectories obtained from either video tracking or vehicle-mounted GPS. The fiber geometry is estimated by minimizing the mismatch between the measured and physics-based synthetic strain-rate maps. The framework combines a matched-filter initialization with neural-network-based trajectory optimization, enabling robust convergence under realistic noise and trajectory-uncertainty conditions. Simulation and field experiments demonstrate sub-meter localization accuracy, often on the order of tens of centimeters, and strong agreement with manual calibration by tap-testing. This approach provides a practical tool for mapping poorly documented underground fiber infrastructure and for supporting urban sensing applications.

We develop a general theory of null-constrained parameter estimation in a time-reversed Young (TRY) interferometer, where measurement is performed through programmable source-basis encoding with a fixed detector. We address the fundamental question of how to design source patterns that enforce a true metrological null – vanishing nominal response at the operating point – while preserving finite first-order sensitivity to the parameter. Under a general shot-noise-limited channel model, we show that the optimal null-constrained receiver is obtained by projecting the derivative response onto the subspace orthogonal to the nominal background in the inverse-noise metric. This yields a constructive solution in which the optimal source-basis code is given by the inverse-noise-weighted derivative response with its background-parallel component removed. We further derive an exact and universal information-retention law: the locally accessible Fisher information is reduced by a factor $1-\chi^2$, where $\chi$ quantifies the inverse-noise overlap between the nominal and derivative response vectors. This result establishes a precise geometric interpretation of the cost of null enforcement. Numerical examples demonstrate the null-coded TRY receivers can retain nearly the full local information and can be accurately implemented using binary and positive-only source patterns. These findings identify source-basis null engineering as a distinct and practically viable modality for derivative-mode sensing, with implications for superresolution metrology and programmable optical measurement architectures.

The Cavallo multiplier [http://archive.org/details/b28771035_0003] is an electrostatic inductance machine that can generate low-noise high voltages electrically isolated from its voltage input, making it ideally suited for precision experiments. Its in-situ production makes it especially useful in cryogenic experiments, where the use of traditional feedthroughs is challenging due to thermal, electrical, magnetic, and physical size considerations. One such experiment is a cryogenic measurement of the neutron electric dipole moment (nEDM) [arXiv:1908.09937,arXiv:2512.14975], which requires several hundred kilovolts on a measurement cell electrode in 0.4 K liquid helium (LHe). A Cavallo multiplier can generate this voltage by stepping up a smaller input (e.g., 50 kV) from a feedthrough. We designed Cavallo electrodes using finite element analysis to provide high voltage gain and low probability of electrical breakdown in the experimental apparatus. The final geometry achieves a gain of 18, providing a target of 650 kV with peak electric fields of 116 kV/cm distributed over small areas to limit breakdown risk.

Solid-state high-harmonic spectroscopy allows the study of strongly driven ultrafast electron dynamics. Microscopically, high harmonics are generated by strong-laser-field acceleration of electron-hole pairs through the lattice. At finite temperatures, atomic-scale structural fluctuations are ubiquitous and are expected to influence the electron-hole trajectories. Yet, the effect of thermal lattice fluctuations on solid-state high-harmonic generation (HHG) has not been quantified. Here, we demonstrate a profound sensitivity of HHG to thermal lattice fluctuations, by characterizing the temperature dependence of HHG in Re6Se8Cl2, a superatomic semiconductor. As the sample temperature is decreased, the high-harmonic yield exhibits a slow increase, followed by an abrupt increase below 50 K, consistent with the temperature at which lattice vibrations are strongly suppressed. Our calculations show that thermal lattice fluctuations both weaken the harmonic response from individual distorted configurations and induce phase dispersion across the ensemble, leading to a pronounced suppression of the coherently emitted harmonics. We show that this effect can be interpreted in terms of an effective electronic dephasing time that varies with temperature. Our results are relevant to dephasing in broad strong-field phenomena, including lightwave electronics and Floquet engineering. The wide tunability of superatomic crystals further enables materials-controlled strong-field physics.

Sea ice is a complex system, and observations have shown that ice segments (i.e., floes) have a wide range of sizes, with a floe size distribution that follows a power law. However, a theory for the power law and its exponent have remained elusive. Here, floe-resolving numerical simulations are investigated with a discrete element model, in order to gain further information by gathering statistics of fracture and welding events. Then, based on the insights from the floe-resolving simulations, a stochastic fragmentation-coagulation theory is proposed. Exact solutions are found with a power law. The power-law exponent can take a variety of values, and it depends on the fracture and welding rates. Such behavior is reminiscent of seasonal changes in the power-law exponent, which have been reported in past analyses of observational data.

Fusion plasmas are highly sensitive to external magnetic perturbations which result in complex responses near a region known as the resonant layer. Past analytic descriptions of this phenomena used boundary layer theory in a simplified system assuming low plasma beta to predict the onset of instabilities. Here, we present a novel extension of the analytic theory utilizing nested boundary layers to capture the physics of ion parallel flow at the plasma resonant layer. This new prediction supports previous numerical results and suggests ion shielding against magnetic disruptions in parameter regimes relevant to future device operation.

Unmanned aerial vehicles (UAVs) operating in confined, cluttered environments face significant performance degradation due to nonlinear, time-varying unmodeled dynamics-such as ground/ceiling effects and wake recirculation-that are unaccounted for in traditional controllers. While learning based compensators (e.g., MLPs, TCNs, LSTMs) struggle with historical data dependency, vanishing gradients, and prohibitive computational costs, this work pioneers the integration of a deep photonic reservoir computer (PRC) with feedforward control to overcome these limitations. Harnessing semiconductor laser dynamics and optical feedback, our hardware implemented deep PRC architecture achieves intrinsic temporal memory without explicit historical inputs, while reducing training time from hours to milliseconds and slashing inference latency to nanoseconds. Reliable high-performance CFD simulations capturing proximity-induced flows demonstrate that deep PRC delivers residual-force prediction accuracy comparable to or exceeding TCN/MLP baselines, while training only a linear readout layer via ridge regression. By injecting these predictions into a nonlinear feedback PID controller via a feedforward channel, the framework significantly enhances closed-loop tracking stability in confined spaces. Essentially, this work establishes the first deep PRC-based lightweight, ultrafast solution for real-time UAV dynamic compensation, with promising extensibility to unseen scenarios with more complex fluid environments.

Registration between preoperative CT and intraoperative laparoscopic video plays a crucial role in augmented reality (AR) guidance for minimally invasive surgery. Learning-based methods have recently achieved registration errors comparable to optimization-based approaches while offering faster inference. However, many supervised methods produce coarse alignments that rely on additional optimization-based refinement, thereby increasing inference time. We present a discrete-action reinforcement learning (RL) framework that formulates CT-to-video registration as a sequential decision-making process. A shared feature encoder, warm-started from a supervised pose estimation network to provide stable geometric features and faster convergence, extracts representations from CT renderings and laparoscopic frames, while an RL policy head learns to choose rigid transformations along six degrees of freedom and to decide when to stop the iteration. Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence. The proposed RL-based formulation enables automated, efficient iterative registration without manually tuned step sizes or stopping criteria. This discrete framework provides a practical foundation for future continuous-action and deformable registration models in surgical AR applications.

Objectives: Existing voxel-based dose converters transform hypofractionated dose distributions into biologically effective dose (BED) or equivalent dose in 2 Gy fractions (EQD2), but they are not reliably applicable to organ-at-risk (OAR) dose constraints, particularly in low-dose regions, which may lead to dose misinterpretation. This study develops and demonstrates a voxel-based method to convert hypofractionated dose distributions into 2 Gy-equivalent OAR constraints. Methods: To2GyConstraints converter (www.healthy-innovations.com) was applied to a prostate cancer case. The method uses the Linear Quadratic (LQ) model for doses per fraction less than or equal to 7.5 Gy and the Linear Quadratic Linear (LQ-L) model for higher doses. For voxel fraction doses below a threshold defined as the mean between the prescribed hypofractionated fraction dose and 2 Gy, an equivalent number of fractions is calculated. The method then applies an EQDx-type conversion, rather than EQD2, using this calculated fraction number to better reproduce normofractionated dose behavior. Results: For doses above the defined threshold, unlike BED, the To2GyConstraints model produced results consistent with EQD2 and provided clinically realistic dose values comparable to standard dosimetric constraints, thereby offering a clearer demonstration of the radiobiological benefits of hypofractionation in prostate cancer. For doses below the threshold, unlike EQD2, the To2GyConstraints model showed behavior consistent with BED, yielding higher dose estimates when converted to a normofractionation scheme. Conclusions: To2GyConstraints converter shows promising results for radiobiological interpretation of hypofractionation. Further multicenter validation is required. Advances in knowledge: A voxel-based method enabling application of normofractionation OAR constraints to hypofractionated dosimetry after conversion.

Concentration regimes in polymer and polyelectrolyte solutions can be identified by scaling laws for the relation between specific zero-shear viscosity and concentration. Recently, we have shown that the same is true for the infinite-shear viscosity plateau. The shear-thinning range is usually accessed by focusing on the viscosity functions for the respective concentration regime. For salt-free aqueous xanthan solutions, we find power-law dependencies of the specific viscosity on concentration throughout the entire shear-rate range. We distinguish six different concentration regimes. Apart from those already known for the zero-shear viscosity of polyelectrolyte solutions, i.e. dilute, semidilute unentangled, semidilute entangled and neutral semidilute entangled, we identify a linear regime for low shear rates at high concentrations, where the solution gels and a regime at both, higher concentrations and higher shear rates. Within some regimes, the power-law exponents change smoothly with shear rate, particularly, when deviating from the zero-shear viscosity plateau before the power-law of the viscosity function. Some regimes merge as their power-law exponents approach each other. The fact that the regimes extend smoothly from the zero-shear regime into finite shear rates, i.e. away from thermodynamic equilibrium, shows that indicators such as critical concentrations remain valid at finite shear rates. This motivates us to interpret the data in the light of existing scaling laws and current knowledge about shear-rate dependent interaction mechanisms in polyelectrolyte solutions, particularly in xanthan solutions. It allows to follow the shift of relevant interaction mechanisms with shear rate. We think that the consideration of scaling laws under shear can be particularly helpful for identifying, for instance, thresholds for shear-induced disentanglement or disaggregation.

Sampling lattice field theories near criticality is severely hindered by critical slowing down, which makes standard Markov chain methods increasingly inefficient at large lattice volumes. We introduce a multiscale generative sampler, inspired by renormalization-group ideas, that models the Boltzmann distribution through a coarse-to-fine hierarchy across length scales. At each level, a conditional Gaussian mixture model captures the main local dependence of newly introduced variables on the already-sampled coarse field, while a masked continuous normalizing flow refines the remaining conditional structure. Coarse levels encode the dominant long-wavelength modes, and finer levels progressively add short-distance fluctuations. In addition, because the architecture preserves coarse fields exactly during refinement, it provides exact restriction maps at no additional computational cost and directly enables asymptotically unbiased Multilevel Monte Carlo (MLMC) variance reduction. For the two-dimensional scalar $\phi^4$ theory near criticality, the method achieves integrated autocorrelation times orders of magnitude smaller than Hybrid Monte Carlo (HMC) on large volumes, maintains high importance-sampling efficiency relative to other generative baselines, and reproduces asymptotically unbiased physical observables in statistical agreement with long HMC simulations.

The physical origin of the pseudogap in cuprate superconductors, whether a precursor to superconductivity or a distinct intertwined order, remains an open question. Although hydrostatic pressure provides a clean tuning parameter, probing the relevant energy scales under compression has remained experimentally elusive. Here, we employ ultrafast optical spectroscopy to track the non-equilibrium quasiparticle dynamics of slightly underdoped Bi$_2$Sr$_2$CaCu$_2$O$_{8+{\delta}}$ up to 37 GPa. By disentangling the pseudogap and superconducting relaxation channels in the time domain, we uncover a contrasting pressure evolution: despite an increase in carrier density, the pseudogap thermal onset (T$^*$) rises monotonically, whereas its energy gap ($\Delta_{PG}$) is continuously suppressed. Conversely, the critical temperature (T$_C$) and the superconducting gap ($\Delta_{SC}$) exhibit a synchronized dome-like trajectory. The effective coupling ratio 2$\Delta_{SC}$/k$_B$T$_C$ collapses abruptly from ~10 toward the weak-coupling d-wave limit, signifying a crossover from a phase-fluctuating two-dimensional regime to a globally coherent three-dimensional state. Upon compression to 37 GPa, the superconducting condensate is completely quenched into an insulating-like state, yet the pseudogap persists. The extended phase diagram reveals an unambiguous decoupling between the pseudogap and superconductivity, imposing stringent constraints on microscopic theories of high-temperature superconductivity.

In this work, we investigate the thermoacoustic generation and propagation of spherical waves in a viscous fluid induced by a laser-heated spherical particle. Periodic laser excitation gives rise to two coupled mechanisms of acoustic emission. Heat transfer from the particle to the surrounding fluid produces periodic compressions and rarefactions, giving rise to the thermophone effect, while periodic thermal expansion of the solid particle modulates its radius and launches acoustic waves through a piston-like action, known as the mechanophone effect. The thermophone contribution dominates at low frequencies, whereas the mechanophone mechanism becomes more relevant at higher frequencies, with the crossover governed by the interfacial thermal resistance at the solid-fluid boundary. We investigate the effect of nanoparticle embedding fluid viscosity on acoustic wave propagation. Viscous dissipation has a significant impact on attenuation and substantially alters the acoustic penetration depth, thereby affecting the effectiveness of the signal transmission. Viscous damping plays a key role in the mechanophone effect, where hypersonic frequency waves are generated, notably by photoacoustic excitation with picosecond and subpicosecond laser pulses. We develop a theoretical model based on the coupled conservation equations of mass, momentum, and energy in both phases, explicitly accounting for thermal diffusion and viscous losses. The reciprocal coupling between thermal and acoustic fields is fully described, allowing us to quantify how frequency and fluid viscosity jointly control the penetration length of the generated acoustic waves in realistic media. Finally, we discuss the implications for theranostics, highlighting how ensembles of laser-activated particles embedded in biological tissue may be optimized for diagnostic and therapeutic applications.

The increasing demand for multi-wavelength optical sources to support dense wavelength-division multiplexing (DWDM) channels has driven the development of compact and reconfigurable multi-wavelength fiber lasers. Here, we demonstrate a continuously tunable and deterministically switchable multi-wavelength erbium-doped fiber laser based on nonlinear polarization rotation (NPR) in a compact all-fiber ring cavity. By controlling the intracavity birefringence, NPR acts as a reconfigurable comb filter that enables flexible wavelength selection without modifying the cavity architecture. The laser supports stable spectral states ranging from single- to seven-wavelength mode-locking, enabling reversible wavelength switching and activation/suppression of individual channels. The selectable spectral states can be mapped to binary bit operations, where each wavelength channel represents a controllable logical state. The behavior arises from the interplay between NPR-induced birefringent comb filtering and nonlinear phase modulation, providing a simple and compact platform for reconfigurable multi-channel ultrafast sources for DWDM and photonic signal processing.

Collective subradiant states in waveguide QED are highly sensitive to disorder, limiting their scalability and robustness. We propose a deterministic approach to engineering atom arrays based on a Minkowski sum construction, generating quasi-disordered structures with built-in correlations. This leads to mode-selective radiative coupling: interactions between dark modes are parametrically suppressed, while bright modes can hybridize. We study the stability of these subradiant and bound-state-like modes against moderate positional disorder. Our work provides a route to robust, analytically controllable subradiance through engineered quasi-disorder, with direct relevance to atom-waveguide and circuit QED experiments.

We present a comprehensive investigation of He I line profile calculations used in the spectroscopic analyses of DB white dwarfs. Our study includes an in-depth photometric and spectroscopic analysis of all DB white dwarfs in the Data Release 17 of the Sloan Digital Sky Survey, examining the effects of frequency sampling, Doppler broadening, line dissolution, broadening by neutral particles, and 3D hydrodynamical corrections on our results. More importantly, we compare the outcomes obtained from the semi-analytical He I Stark profiles commonly used in DB white dwarf spectroscopic analyses with our recent calculations of Stark-broadened profiles derived from computer simulations.

In chemically reactive environments, the mechanical integrity of geomaterials is fundamentally compromised by solid matrix dissolution. In this study, we propose a fully coupled chemo-mechanical phase-field framework to capture the dynamic interplay between mineral dissolution and fracture propagation. A key feature of the proposed model is the dynamic coupling of local mass removal to the fracture length scale, while also incorporating the damage-accelerated reaction-diffusion processes. Our results capture the development of an enlarged fracture process zone driven by chemical mass removal. This chemically induced widening blunts the sharp crack tip, alleviating the near-tip stress concentrations and causing a pronounced degradation in material stiffness before failure. Furthermore, we reveal a distinct ductilization effect, characterized by a more gradual accumulation of damage and a delayed onset of macroscopic failure. We show that the transition between brittle and ductile failure modes is dictated by the competing timescales of chemical degradation and mechanical deformation. Highly acidic environments enhance matrix dissolution and promote ductile fracture, whereas rapid mechanical loading limits chemical interaction and preserves brittle failure mode.

Purpose: Recent proton-only investigations of radiation-induced contrast enhancements (RICE) in brain tumor patients indicated variability in proton relative biological effectiveness (RBE) and increased radiosensitivity of the periventricular region (PVR). Because RBE is defined relative to a reference radiation, these studies required assumptions on the photon dose-response relationship. This study aimed to validate proton RBE variability and PVR radiosensitivity using predictive modeling of RICE in a combined photon-proton cohort. Methods and Materials: Predictive models for RICE detected on follow-up magnetic resonance imaging were developed in 152 intracranial tumor patients treated with photons or protons. Logistic regression was applied at the voxel level to model spatial occurrence and at the patient level to model incidence. A clinical RBE model was derived from voxel-wise comparisons of estimated risk between photon and proton irradiation. Results: In total, 128 RICE of various grades occurred in 64 patients. Voxel-level modeling validated absorbed dose (D), D multiplied by dose-averaged linear energy transfer (LETd) for proton therapy, and PVR as independent predictors of RICE. The model implied a variable proton RBE described by RBE=1+m$\cdot$LETd, with m=0.10 $\mu$m/keV. At the patient level, the PVR D2ml based on this RBE achieved the highest predictive performance. Conclusions: RICE was spatially associated with dose and PVR proximity across photon and proton therapy, with an additional LET-dependent component after proton therapy. The cross-modality framework validates proton RBE variability against an observed photon reference rather than predefined reference assumptions, and supports PVR radiosensitivity as modality-independent. Accounting for variable proton RBE and the PVR as an organ at risk may improve risk assessment and mitigation of radiation-induced side effects.

Magnetic point-group analysis classifies altermagnets but returns only a binary symmetry verdict, leaving spin-splitting energy (SSE) inaccessible without spin-polarized density functional theory (DFT). This binary ceiling is not fundamental. Sublattice symmetry breaking is promoted here to a continuous, DFT-free scalar – the Motif Symmetry-Breaking Index (MSBI) – that quantifies $\mathcal{PT}$-symmetry breaking between antiparallel magnetic motifs directly from crystal coordinates. SHAP analysis of an XGBoost surrogate trained on 3,851 DFT-labeled binary structures identifies three dominant descriptors: MSBI (symmetry-breaking axis), motif packing fraction MPF (superexchange axis), and the $p/d$ electron ratio (covalency axis), each mapping onto a directly tunable experimental handle. A controlled VO–CrSb comparison within the same P$6_3$/mmc host lattice demonstrates that composition alone boosts SSE sevenfold. Bayesian optimization over this three-axis space, followed by independent DFT validation, recovers $\alpha$-NiS (SSE $= 0.823$\,eV) as cross-validation against an independent symmetry-based prediction and identifies three previously unrecognized high-SSE candidates – square-planar FeS (1.297\,eV), octahedral CoS (1.103\,eV), and FeAs (1.089\,eV) – all matching or exceeding CrSb. Square-planar Fe–S is proposed as a transferable coordination motif for giant altermagnetic spin splitting, advancing altermagnet design from symmetry classification to continuous quantitative optimization.

This manuscript presents PoreDiT, a novel generative model designed for high-efficiency digital rock reconstruction at gigavoxel scales. Addressing the significant challenges in digital rock physics (DRP), particularly the trade-off between resolution and field-of-view (FOV), and the computational bottlenecks associated with traditional deep learning architectures, PoreDiT leverages a three-dimensional (3D) Swin Transformer to break through these limitations. By directly predicting the binary probability field of pore spaces instead of grayscale intensities, the model preserves key topological features critical for pore-scale fluid flow and transport simulations. This approach enhances computational efficiency, enabling the generation of ultra-large-scale ($1024^3$ voxels) digital rock samples on consumer-grade hardware. Furthermore, PoreDiT achieves physical fidelity comparable to previous state-of-the-art methods, including accurate porosity, pore-scale permeability, and Euler characteristics. The model's ability to scale efficiently opens new avenues for large-domain hydrodynamic simulations and provides practical solutions for researchers in pore-scale fluid mechanics, reservoir characterization, and carbon sequestration.

Band gap engineering of oxide semiconductors through doping is critical for photocatalysis and optoelectronics, yet the combinatorial space of dopant elements, substitution sites, and co-doping combinations far exceeds typical density functional theory (DFT) budgets. We screen doped candidates across five oxide hosts (ZnO, TiO2, SrTiO3, SnO2, MgO), culminating in a 529-candidate ZnO co-doping campaign, and identify Cu-containing co-doped ZnO systems as consistently achieving visible-light-range band gaps (1.0-1.8 eV), with Y2Cu2 co-doped ZnO as the optimal candidate (1.84 eV). A three-tier validation funnel (PBE, PBE+U, ionic relaxation) reveals that no single level of theory suffices: V-doped ZnO shifts from near-metallic to wide-gap upon Hubbard U correction, while Cu-doped SrTiO3 enters the visible-light window only after correcting for d-electron localization. To make this screening tractable, we introduce a multi-fidelity screening strategy that replaces 81% of DFT evaluations with computationally inexpensive surrogate predictions, reducing a 529-candidate closed-loop Quantum ESPRESSO campaign from an estimated 440 to 62 CPU-hours while finding the global optimum in 100% of 50 independent trials (p = 5.0e-8 versus random screening, Wilcoxon signed-rank). Cross-host analysis of the dopant-host interaction matrix reveals that dopant performance is governed by just two latent chemical dimensions, enabling prediction of rankings in unseen hosts. All 583 DFT calculations, screening code, and stability proofs are released as an open benchmark.

The fermion sign problem remains the primary obstacle in simulating the thermodynamic properties of various fermionic systems. In this work, we present a sign-blocking method to mitigate the numerical instability inherent in the sign problem. In the sign-blocking method, the Monte Carlo importance sampling remains identical to traditional methods; instead, the sign-blocking method is applied during the post-processing of signed samples. Given the significant progress in simulating the 2D Fermi-Hubbard model over the past decade, a wealth of energy benchmarks is available for comparison. Consequently, we use the 2D Fermi-Hubbard model as a benchmark to validate the sign-blocking method. Surprisingly, our results align exceptionally well with existing state-of-the-art benchmarks, even in regimes previously considered challenging. The physical mechanism of the sign-blocking method lies in uncovering the correlation between energy and sign factors through data blocking, thereby successfully inferring the fermionic system's energy. Our findings suggest that the sign-blocking method holds promise for complex quantum systems, particularly when combined with appropriate simulation techniques such as auxiliary-field formalisms that trace out the fermionic degrees of freedom.

Bound states in the continuum (BICs) are spatially localized eigenmodes that remain perfectly confined even though their energies reside within a continuum of radiating modes. BICs were predicted in 1929, but their experimental realization awaited more than 8 decades. Following their experimental observation, BICs were explored in a variety of wave systems, and found to exhibit a plethora of fundamental features such nontrivial topology and extremely high Q-factor. Recently, with foundational advances in the new field of electromagnetic waves in time-varying media, BICs were predicted to exist in the time domain, with their wavenumber embedded in a continuum of unbound momentum modes. Here, we present the first experimental realization of the time-domain Bound States in the Continuum. We use a transmission-line network with a time-modulated wave-impedance and show that a sinusoidal wave launched into the network naturally evolves into a time-domain BIC with a well-defined peak and decaying-oscillating tails. We show that the time-domain BIC is anti-symmetric despite the symmetric nature of the modulation. These experiments pave the way for exploring new phenomena in the fields of BICs and time-varying wave-systems in nonconservative regimes where time-translation symmetry is broken.

Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies. Space-based imaging spectroscopy is an emerging tool for identifying emissions globally, but existing approaches largely rely on manual plume identification. Here, we present the Methane Analysis and Plume Localization with EMIT (MAPL-EMIT) model, an end-to-end vision transformer framework that leverages the complete radiance spectrum from the Earth Surface Mineral Dust Source Investigation (EMIT) instrument to jointly retrieve methane enhancements across all pixels within a scene. This approach brings together spectral information with spatial context to significantly lower detection limits. MAPL-EMIT simultaneously supports enhancement quantification, plume delineation, and source localization, even for overlapping plumes. The model was trained on 3.6 million physics-based synthetic plumes injected into global EMIT radiance data. Synthetic evaluation confirms the model's ability to identify plumes with high recall and precision and to capture weaker plumes relative to existing matched-filter approaches. On real-world benchmarks, MAPL-EMIT captures 84% of known hand-annotated NASA-L2B plume complexes across a test set of 1084 EMIT granules, while capturing roughly 1.5 times as many plausible plumes compared to human analysts. Further verification against coincident airborne data, top-emitting landfills, and controlled release experiments confirms the model's ability to identify previously uncaptured sources. By incorporating model-generated metrics such as spectral fit scores and estimated noise levels, the framework can limit false-positives. Overall, MAPL-EMIT enables high-throughput implementation on the full EMIT data catalog, shifting methane monitoring to a rapid, scalable paradigm for global plume mapping at the facility scale.

Ion pickup at the outer planets' active moons is a fundamental plasma process in which newly ionized particles from moon exospheres interact with the ambient corotating plasma and are accelerated to match the background flow. Spacecraft observations have revealed intense electromagnetic wave activity commonly attributed to this pickup process. Here we investigate ion pickup using hybrid-kinetic simulations in which ions are treated kinetically while electrons are modeled as a massless fluid. In the moon's rest frame, ambient ions initially stream perpendicular to the background magnetic field at the corotation velocity, creating a nongyrotropic velocity distribution with two ion populations clustered at opposite gyrophases. Within a few ion gyroperiods, this configuration simultaneously excites transverse magnetic perturbations associated with electromagnetic ion cyclotron waves and compressional perturbations associated with mirror-mode and ion Bernstein waves, reaching amplitudes of several percent of the background field strength. Using field-particle correlation analysis, we quantify the energy transfer between waves and particles and demonstrate how these perturbations scatter ions in velocity space, efficiently incorporating newly created ions into the background plasma and leading to isotropization in both gyrophase and pitch angle. These results provide a kinetic framework for understanding pickup-driven wave-particle interactions and offer guidance for interpreting in situ measurements at active moons throughout the outer solar system.

We propose a novel holographic recording technique to improve the recording speed for holographic data storage (HDS). In this technique, holograms are recorded by scanning a digital micromirror device (DMD) that displays a data page with a focused, power density-increased line beam, while synchronously shifting the recording medium and a spherical reference wave. This approach eliminates the stop-and-go motion of driving mechanisms, enabling rapid, continuous, multiplexed recording. Although the recorded localized holograms retain only partial information about the data page, the entire page can be reconstructed simultaneously using the spherical reference wave. Experimental demonstrations achieved a bit error rate (bER) of less than 10% at a 5 ms exposure time and stable multiplexed recording at 150 Hz. The proposed technique represents a significant step toward the realization of practical HDS systems.

The analysis of income and wealth inequality is often constrained by the lack of reliable data. In this work, we introduce a proxy-based approach in which sports performance data are used to mimic economic distributions. In particular, the total runs scored by a batter in a single T20 season are treated as an analogue of annual income, while the cumulative runs scored across all seasons are taken to represent individual wealth. Using run distributions from the Indian Premier League (IPL), we explore how inequality evolves and estimate its upper limits in the absence of policy intervention. Our findings indicate that inequality derived from seasonal runs closely resembles the highest levels of income inequality observed in real-world data. In addition, the distribution of cumulative runs evolves over time and gradually approaches a limiting value consistent with global patterns of wealth inequality, in line with the Pareto principle. Overall, this study shows that proxy systems can capture essential features of economic inequality and offer a useful way to understand its inherent limits.

The non-Hermitian skin effect is nonreciprocity-induced localization phenomena in which a macroscopic number of eigenstates accumulate anomalously at the boundary, accompanied by the extreme sensitivity to boundary conditions. Here, we develop a geometric characterization of the non-Hermitian skin effect. We demonstrate that the localization length scale associated with the skin effect is encoded in the quantum metric defined solely from right eigenstates, but not in the biorthogonal quantum metric. Moreover, we show that the quantum metrics can exhibit the power-law divergences at gapless points that depend on the different boundary conditions. We also reveal that cusps of the generalized Brillouin zone in non-Bloch band theory are signaled by discontinuities in the quantum metrics. We illustrate these behavior using prototypical non-Hermitian models, such as the Hatano-Nelson model and the non-Hermitian, nonreciprocal Su-Schrieffer-Heeger model.