Papers

Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence. We derive a two-parameter scaling law $R(N) = N_\text{eff}/N = 1/(1+c(N-1)N^{-\beta})$ where the regime exponent $\beta$ classifies any configuration into one of three asymptotic regimes – hard-ceiling at $1/c$ ($\beta = 0$), sublinear at $N^\beta/c$ ($0 < \beta < 1$), or linear ($\beta \ge 1$), and a mean-field theorem predicts that peer count $k$ and rounds $\tau$ during agent debate enter the dynamics only through their product $k\tau$. The law applies at two levels: answer diversity and correctness redundancy. Across 44 (model $\times$ task $\times$ condition) cells spanning peer debate, self-correction, random-noise placebo, self-consistency, three open-weight families (Qwen, Llama, Ministral) at scales from 7B to 32B with a frontier API check (Gemini), thinking models, heterogeneous teams, and sparse communication, the functional form fits every condition at $R^2 > 0.99$; only $(c, \beta)$ shifts. On free-form math, dense peer influence collapses the answer-level regime from sublinear into hard-ceiling; correctness-level fits remain hard-ceiling throughout. Three findings have practical implications. (i)~Thirty dense debating agents produce no more answer diversity than one on MMLU-Hard. (ii)~A noise placebo tracks self-correction on free-form math and at $4\times$ scale, so within homogeneous teams the gain commonly attributed to “debate” comes from re-evaluation, not peer content. (iii)~A single $N \le 5$ pilot predicts the $N=30$ structural ceiling, and within the configurations tested only architectural diversity (heterogeneous teams) lowers $c$ and escapes the hard-ceiling regime, communication-mode interventions do not.

Quasar variability, driven by multi-scale physical processing within a relativistic accretion disk, is commonly modelled with stochastic time series models. The simplest of these is the Damped Random Walk (DRW), also known as the Ornstein-Uhlenbeck (OU) process. Here, we demonstrate that, when fitting such a model to quasar light curve data, the mean of the light curve, $\mu$, should not be fixed (which is the typical approach), as this leads to overconfident inferences about the variability timescale $\tau$, with substantially underestimated uncertainties. However, the short term volatility parameter $\eta$ is typically very well constrained from short light curves. Through simulations, we compute information theoretic quantities such as the conditional entropy and the mutual information, confirming that light curves provide much more information about $\eta$ than about $\tau$. As a result, we recommend that future quasar variability studies focus on $\eta$ rather than $\tau$. To demonstrate this approach, we fit a hierarchical Bayesian regression model for $\eta$ as a function of bolometric luminosity and rest wavelength to a dataset of 570 light curves measured over decades. We perform the fit using a likelihood function that uses the light curves directly, rather than using intermediate $\eta$ values from individual light curve fits. We find that volatility decreases as a function of both bolometric luminosity and rest wavelength. The volatility also decreases more steeply with redshift than time dilation alone would suggest, pointing to an increase in intrinsic volatility as quasars evolve over cosmic time.

Cold-to-hot heat transfer in rarefied cavities is usually treated as a signature of Fourier-law failure. Here it is used to ask whether a correct anti-Fourier heat-flux field certifies the flux-side fourth-order closure state. In a two-dimensional monatomic flow, the heat-flux hierarchy observes the divergence of the composite R26-level tensor \(A_{ij}=R^{\cl}_{ij}+\Delta\delta_{ij}/3\), not the tensorial fourth-order anisotropy \(R^{\cl}_{ij}\) and scalar fourth-order excess \(\Delta\) separately. Unlike the one-dimensional shock problem, the null space is not a single algebraic direction: it is the function space of divergence-free symmetric tensor fields, including an exactly invisible out-of-plane channel \(A_{zz}\). DSMC data for argon lid-driven cavities show that the size of the anti-Fourier region is strongly regime dependent: it is suppressed when the lid speed is increased from \(100\) to \(200\,\mathrm{m\,s^{-1}}\), but enlarged when the Knudsen number is increased from \(0.05\) to \(0.10\). In all cases, the anti-Fourier channel is primarily tensorial, while scalar-excess effects remain a smaller local modulation. Hidden Airy and out-of-plane states, scaled relative to the measured RMS composite tensor, change \(R^{\cl}\) and \(\Delta\) by order-one amounts while leaving the in-plane heat-flux observable below the seed-to-seed statistical resolution, or exactly unchanged for the \(A_{zz}\) mode. These shifted states satisfy necessary scalar Cauchy and contracted fourth-order Gram-positivity checks. Thus anti-Fourier heat-flux agreement is a physical validation target, but it is not a certificate of full R26-level closure recovery.

Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Rayleigh-Taylor instability (RTI), a ubiquitous and demanding fluid instability seen from tabletop flows to supernova explosions, in which small perturbations at a density interface grow into chaotic, multiscale mixing as a lighter fluid accelerates into a heavier one. Standard ML models struggle with RTI, and despite over a century of theoretical, numerical, and experimental work, it carries an unresolved discrepancy between simulation and experiment: the late-time mixing growth rate, $\alpha$, measured in most laboratory experiments ($\sim$ 0.06-0.07), is roughly three times the value from idealized direct numerical simulations (DNS, $\sim$ 0.02). The gap's origin remains debated. These properties make RTI a stringent test for a question that matters well beyond RTI: can foundation models trained only on simulations generalise to sparse, messy, and noisy laboratory settings? We finetune Walrus, a foundation model for continuum dynamics, on three or fewer DNS realizations and recover key RTI physics over long rollouts. Applied zero-shot to sliding-barrier laboratory data, the finetuned model leaves the DNS-like regime and enters the observed growth band, having never seen a single experimental sample. These results provide independent, data-driven evidence that initial conditions play a crucial role in the longstanding sim-experiment gap in $\alpha$. The model also generalises zero-shot to stable stratification, a buoyancy regime absent from training, correctly slowing mixing-layer growth. Together, our results show that foundation models can generalise well beyond their training data, predicting laboratory behavior and unseen physical regimes, opening new ways to probe longstanding simulation-experiment gaps.

Photonic integrated circuits have emerged as a powerful platform for high speed communication, sensing, and information processing due to their large bandwidth, low latency, and inherent parallelism. However, the absence of efficient, scalable, and non-volatile memory elements remains a fundamental limitation for realizing fully programmable and adaptive photonic systems. Conventional electronic memories introduce significant energy overhead, latency, and architectural inefficiencies due to repeated optical electrical conversions. Non volatile opto electronic resistive memories or OERMs have recently emerged as a promising solution to address these challenges by integrating memory functionality directly within the photonic domain. These devices combine resistive switching mechanisms with optical readout, enabling persistent state retention, multilevel programmability, and energy efficient operation. In this review, we provide a comprehensive overview of OERMs, spanning from fundamental physical mechanisms to system level applications. We first discuss the underlying resistive switching phenomena, including filamentary conduction, interface type switching, phase change transitions, and ionic migration, with particular emphasis on their interaction with confined optical modes. We then examine key material platforms such as metal oxides, transparent conducting oxides, phase change materials, and emerging two-dimensional systems, highlighting their performance trade-offs. Furthermore, we analyse device architectures and benchmark their performance in terms of switching energy, speed, endurance, and optical modulation efficiency. The integration of OERMs into programmable photonic circuits, neuromorphic systems, and in-memory optical computing architectures is critically discussed. Finally, we outline the major challenges and future research directions toward scalable, reliable

In metrology, Fisher information is an important metric that quantifies the precision that can be achieved in a measurement. For optical measurements using coherent light it has been shown that Fisher information can be expressed simply using the scattering matrix of the system. Fisher information can be maximized over the input modes to achieve maximum information states, which produce optimally precise estimates for a parameter when the system is limited by photon noise. Here, we extend this approach to multiparameter estimation, in which case Fisher information takes the form of a matrix. We consider several scalar functions of the Fisher matrix to optimize the precision in multiple parameters at the same time. We also consider strategies for dealing with nuisance parameters, which can degrade the achievable precision of other parameters but are not of interest to measure. We corroborate our findings numerically using a scattering system of 2D coupled dipoles.

Quantum Lattice Boltzmann Methods (QLBM) have emerged as a promising candidate for quantum realizations of computational fluid dynamics solvers. However, despite intensive research into the QLBM in recent years, methods for imposing boundary conditions remain limited both in terms of efficiency and expressivity. In this work, we introduce a new method for imposing simple boundary conditions on QLBM that overcomes several limitations of current approaches. Our method forgoes the partitioning of the solid domain into segments and instead applies a single, coherent operation on the entire boundary. We show that our method requires fewer resources both asymptotically and practically for bounce-back and specular reflection boundary conditions.

Material science and engineering have benefited from the use of geometric and topological tools. A material medium can mimic effective gravitational fields while spacetime metrics serve as geometric models of physical media. Although analog models of optical, acoustic, and viscous media in curved spacetimes are well established, none have yet captured the hallmark constitutive properties of superconductors. In this work we show that the Gödel universe - an exact solution to Einstein's field equations - serves as the gravitational analog of a superconducting medium in its Meissner state.

We review the physics of many bodies in the context of general relativity. Starting from the stress energy tensor for one body, for a swarm of bodies, for a perfect fluid, we review relativistic hydrodynamics, kinetic theory, and statistical physics of $N$ identical bodies. We conclude our excursion with a thermal equivalence principle in physics.

Learned plasma transport surrogates can match short horizon states while failing over long rollouts because charge accounting, density admissibility, and Poisson compatible field reconstruction are not enforced. We study this issue in a controlled nondimensional one dimensional drift diffusion Poisson benchmark with Dirichlet electrostatic potential boundaries and zero species wall fluxes. The benchmark is a conservation and rollout test, not a complete sheath wall model. We compare Conservative FluxNet, a structure preserving flux correction model with a conservative finite volume update and positivity aware limiting, against direct next state regressors, direct variants with Poisson recomputation, charge projection, and rollout training, and a classical conservative core without learned correction. The central result is that the classical finite volume core alone achieves near roundoff rollout error, so the paper is primarily about conservative discrete structure rather than learned closure. On the headline experiment, the conservative model achieves rollout MSE $7.35\times 10^{-9}$ versus $4.23\times 10^{1}$ for the unconstrained baseline, $2.53\times 10^{1}$ with Poisson recomputation, $6.72\times 10^{1}$ with charge projection, and $2.71\times 10^{1}$ with four step rollout training. Across $64$ prespecified configurations, it wins rollout mean squared error in $60/64$ cases despite winning one step mean squared error in only $19/64$. These results show that, for this controlled benchmark and comparison class, local conservative finite volume structure is more important than one step neural regression accuracy for stable autoregressive rollout.

Hypergraphs provide a natural framework for representing neurophysiological interactions distributed across sets of sensors. A key methodological question is how hyperedges should be defined from frequency-resolved electroencephalography/magnetoencephalography (EEG/MEG) data. We demonstrate a construction strategy in which hyperedges are obtained from canonical coherence (caCOH), an extension of coherence that estimates coupling between multidimensional signal spaces. To our knowledge, this is the first work to construct hypergraphs directly from a multivariate connectivity measure specifically designed for frequency-resolved neurophysiological analysis. We propose two caCOH-based representations: a one-to-space hypergraph, where each external signal defines a hyperedge over the EEG/MEG sensor space, and a space-to-space hypergraph, where two multidimensional signal spaces are represented by a single hyperedge. We evaluate the approach in controlled simulations with known coupling frequencies and varying signal-to-noise ratio (SNR). Compared with graphs based on magnitude-squared coherence (MSC), caCOH-based hypergraphs showed statistically higher target-baseline contrasts at almost all SNR levels, indicating stronger recovery of coupling frequencies. They also recovered sensor-level spatial patterns associated with the simulated sources. In addition, one-to-space and space-to-space representations reduced 610 MSC edges per frequency to 10 and 1 hyperedges, respectively. These results establish multivariate spectral connectivity as a natural methodological basis for EEG/MEG hypergraphs.

Numerical simulations of solids undergoing dynamic fragmentation, a problem characterized by dynamic fracture and dense contacts, require accurately capturing the transition from a solid continuum to interacting fragments. We use the finite-element method with the extrinsic cohesive zone model for fracture. For contact, conventional penalty-based methods often exhibit numerical instabilities in dynamic collision-rich settings. To address this, we adapt and validate a novel semi-explicit time-integration scheme: the Nonsmooth Newmark (NSN) method for unilateral contact. Based on Nonsmooth Contact Dynamics, this formulation strongly enforces contact constraints at the velocity level. Within this scheme, bulk dynamics and fracture are integrated explicitly, while contact is integrated implicitly. Benchmark tests demonstrate that the NSN scheme achieves accuracy comparable to established nonsmooth methods and outperforms penalty-based approaches by orders of magnitude. Although it incurs a higher per-step computational cost, its enhanced stability permits significantly larger time steps, yielding overall efficiency comparable to purely explicit approaches on 1D benchmarks. We applied this framework to 1D fragmentation in both free and confined expansions. Results reveal confinement shifts the fracture energy budget from local fragment kinetic energy to larger-scale global system kinetic energy. Counterintuitively, compared to fully elastic contact, adding contact dissipation reduces fracture energy yet increases the final fragment count. This contact dissipation reduces vibration within damaged fragments, allowing cleaner stress-wave propagation and better damage localization to drive full separation. These results establish the NSN scheme as a robust tool for generating high-fidelity fragmentation statistics.

As fusion advances toward commercialization, systematic siting approaches are needed to identify locations that meet technical, economic, and infrastructural requirements, while also ensuring public acceptance and avoiding the socio-political challenges that have historically hindered fission deployment. Therefore, this study introduces a comprehensive, first-of-its-kind fusion siting framework and applies it to 85 retired (2020-2025) U.S. coal power sites as a case study. The framework evaluates 21 sub-criteria under four key attributes: State Policies, Federal Policies, Risk and Hazard Metrics, and Connectivity and Spatial Factors. Sub-attributes weights are derived using the Fuzzy Full Consistency Method with input from five fusion experts, and site rankings are determined using the Measurement Alternatives and Ranking According to COmpromise Solution method. Results indicate that federal incentives, transportation, substation, and energy prices are the most important factors for fusion siting. Sensitivity analysis reveals that landslide hazards have the greatest effect on rank stability, while fault lines is the least influential. A separate comparative assessment of the fusion deployment sites proposed by Type One Energy, Zap Energy, and Commonwealth Fusion Systems is also conducted using results from our proposed framework. This framework provides a transparent, stakeholder-inclusive decision-making tool that clarifies how sites are evaluated using weighted criteria and distinguishes inflexible policy-responsive factors, thereby enabling targeted regional and federal strategies.

We have developed a fully Bayesian survival-analysis framework that reformulates inference about system lifetimes in terms of hazard and survival functions, and extends this representation to interacting actors. Starting from J.~Richard Gott's Copernican principle, we express the scale-free prior as a baseline hazard $\lambda(t)=1/t$, thereby linking a static prior over lifetimes to the dynamic language of survival analysis. In this formulation, Bayesian updating corresponds to conditioning on survival, while the resulting posterior distribution admits a natural representation in terms of hazard and survival functions. The approach is intended for settings where data are sparse or unreliable, and where a scale-free, assumption-light baseline is preferable to heavily parameterized models. Building on this foundation, we derive general expressions for two-actor systems that characterize joint survival, conditional lifetimes, and comparative outcomes without requiring a specific parametric form of interaction. This yields a flexible and modular framework in which baseline dynamics are separated from interaction effects, allowing different mechanisms to be incorporated transparently. Thus, the primary contribution is a general hazard-based formulation of Bayesian updating and its extension to interacting systems To illustrate the framework, we consider a multiplicative resource-depletion specification in which interaction modifies the baseline hazard through cumulative engagement intensity. This example demonstrates how interaction terms can be embedded while preserving analytical tractability, including closed-form expressions under simplifying assumptions. We further provide a stylized application to an asymmetric two-actor conflict, the 2026 US/Israel–Iran hostilities, to highlight the qualitative implications of the approach.

Rare $\alpha$-particle-induced charge clusters appear in LSST images as compact, PSF-like sources with a median FWHM of $0.\!\!^{\prime\prime}95$ and median ellipticity consistent with zero, closely resembling unresolved astrophysical point sources. These events are detected in both dark and science exposures at a rate of approximately $10^{-12}\ \mathrm{pixel}^{-1}\ \mathrm{s}^{-1}$. Their collected charge and morphology are consistent with energy deposition from $\sim$5 MeV $\alpha$-particles in silicon CCDs, and their spatial distribution across the focal plane suggests a localized material origin, plausibly associated with trace radioactive contamination in the cryostat aluminum. Despite their deceptive appearance, we demonstrate that a simple broadness statistic based on fourth-order moments cleanly separates these events from stellar PSFs, enabling efficient rejection in coadded images and real-time alert streams. Such charge clusters do not impose an intrinsic bright-end contamination floor for Rubin transient searches, as genuine fast astrophysical events would exhibit characteristically different morphological signatures.

Phononic crystals, a sequence of masses and (damped) springs, are being used more and more in practical applications, exploiting Bragg bandgaps to attenuate vibration transmission in a wide frequency range. In particular, chiral phononic crystals have demonstrated their ability to achieve low frequency bandgaps while maintaining a high static stiffness, and thus load bearing capacities. However, tuning of the bandgap frequencies is non-trivial because of their complex geometry. In this paper, viscoelastic inserts between the masses of the chain are introduced to improve the tunability of the crystal and take advantage of viscous damping. Modeling true viscoelasticity requires the implementation of frequency-dependent material properties, which is introduced in this work both for dispersion curve calculation and for harmonic force transmission simulations. As a real-world example, the intricate frequency-dependency of polyurethane is studied by examining the influence of four fractional derivative model parameters, which define the storage modulus and loss factor. The calculated dynamic force transmissibility of the phononic crystal is compared to classical, single-layer, isolation solutions. The results show that high viscous damping does not negatively affect the bandgap efficiency, which is a major advantage over resilient layer isolators where damping deteriorates the isolation properties. To validate the models, three crystals with different viscoelastic material properties in terms of stiffness and damping are manufactured and the measured force transmissibility is successfully compared to the numerical models.

Adding a polymer to a sessile water droplet not only lowers its freezing point but also suppresses the tip singularity that forms during its freezing on cold substrates. Here, we employ synchrotron X-ray and Raman imaging to elucidate the spatiotemporal mechanism underlying tip suppression in an aqueous polyvinyl alcohol (PVA) solution, a model polymer solution. As the polymer concentration increases, we observe slower propagation of the freezing front, reduced bubble entrapment, and a progressively more rounded apex across the volumes and molecular weights examined. X-ray tomography reveals that frozen PVA droplets retain low X-ray transmittance domains in their interiors and at the surface, and Raman spectral mapping confirms that these domains correspond to PVA-enriched regions, providing direct evidence of freeze-induced polymer segregation. These findings indicate that PVA is redistributed heterogeneously during water solidification rather than shifting bulk properties homogeneously, providing a spatially resolved framework for interpreting the observed tip blunting and the suppression of discrete bubble entrapment. Our work identifies freeze-induced polymer segregation as a pathway by which a dissolved polymer regulates both the external shape and the internal structure of a freezing droplet, and these findings shed light on potential applications in freezing-based processes such as freeze-casting and cryopreservation.

The GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP.

In computational fluid dynamics, Large Eddy Simulation (LES) offers a compelling balance between accuracy and computational cost by resolving large-scale flow structures while modeling unresolved subgrid scales. However, its predictive capacity is critically dependent on the choice and calibration of subgrid-scale (SGS) models, which often involve problem-dependent parameters and exhibit intricate interactions with the numerical discretization. In this work, we propose a discrete-adjoint framework to optimize SGS model parameters in the loop, leveraging automatic differentiation within a high-order Spectral Difference (SD) solver. Coarse-grained simulations of Forced Homogeneous Isotropic Turbulence (FHIT), together with filtered Direct Numerical Simulation (DNS) data, are used to optimize a limited set of parameters for classical SGS models, including the Smagorinsky model and non-linear tensor-basis formulations. For chaotic systems such as LES, the choice of objective function plays a crucial role in the stability and accuracy of the optimization. Here, we consider the spatio-temporally averaged decay of the Legendre modal coefficients as the quantity of interest for the SD scheme. The optimization is performed across different grid resolutions and polynomial orders, highlighting the impact of numerical discretization on model performance. The methodology is applied to both one-dimensional Burgers turbulence and fully three-dimensional turbulence. The trained models are subsequently assessed on out-of-sample configurations, including Decaying Homogeneous Isotropic Turbulence (DHIT) and the Taylor-Green vortex. Variations in polynomial order, grid resolution, and Reynolds number are considered to evaluate robustness and generalization. In all test cases, the optimized models demonstrate significant improvements over baseline SGS closures.

We study the subradiant bound states (BSs) in a two-level atom array chirally coupled to a one-dimensional waveguide. We demonstrate that the chiral interaction can drive BSs to become the most subradiant two-excitation states across a wide spacing range. This phenomenon is rooted in a mechanism of chirality-mediated dispersion softening, where the BS band distortion suppresses the band curvature $|\alpha_2|$ at an extremum point. We rigorously prove that the BS decay rate follows the scaling $\Gamma \sim |\alpha_2|/N^3$, revealing that the reduction of $|\alpha_2|$ is key to suppressing emission and enhancing subradiance. We also show the existence of chiral BSs in a realistic nanofiber interface.

We study the ground-state phase diagram of Rydberg atoms in a triangular-prism optical tweezer array using the density matrix renormalization group. By tuning the detuning-to-Rabi-frequency ratio and the Rydberg blockade radius, the system realizes several density-wave phases with spontaneous breaking of translational and leg-exchange symmetries. Unlike two-leg Rydberg ladders with $\mathbb{Z}_2$ leg-exchange symmetry, the triangular prism has $\mathbb{D}_3$ symmetry, leading to a richer set of ordered phases and transitions. For blockade radius moderately larger than the lattice spacing, a phase with alternating double and single Rydberg occupancy appears at large detuning. It breaks $\mathbb{Z}_2$ translational and $\mathbb{Z}_3$ rotational symmetry while preserving a rung reflection symmetry. Upon decreasing detuning, it melts through two Berezinskii-Kosterlitz-Thouless transitions with an intermediate critical phase described by a $\mathbb{Z}_6$ clock model. At larger blockade radius, a phase with one Rydberg excitation per triangle and broken $\mathbb{D}_3$ symmetry appears through a first-order transition. When double occupation of neighboring triangles is suppressed, rung-trimerized density waves develop as detuning increases from the disordered phase. Their melting follows the same structure as in Rydberg chains and two-leg ladders: the $\mathbb{Z}_2$ case has Ising critical lines, while the $\mathbb{Z}_3$ and $\mathbb{Z}_4$ cases have chiral critical lines, with Potts and Ashkin-Teller points only on the corresponding commensurate lines. Inside the $\mathbb{Z}_2$ rung-trimerized phase, an entanglement-entropy peak signals a crossover regime with enhanced period-2 density modulation before a first-order transition into a $\mathbb{Z}_2\times\mathbb{D}_3$ phase. Floating phases with incommensurate quasi-long-range order appear between trimerized states of different periods.

Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and their domain-decomposed variants (FBPINNs) offer a mesh-free alternative but face convergence challenges when representing complex velocity fields. We present a hybrid quantum-classical FBPINN for acoustic FWI, bringing together quantum computing and classical machine learning, in which the decomposed wavefield network and the global velocity network are implemented as classical-to-quantum pipelines terminating in parameterized quantum circuits (PQCs). The PQCs are realized as differentiable JAX statevector simulators, enabling end-to-end automatic differentiation through the classical PINN, the quantum circuit, and the physics-informed loss. On a geophysical anomaly benchmark, the quantum hybrid reaches a lower L1 velocity error than the primary classical FBPINN baseline in approximately 8x fewer training iterations, despite using approximately 33% fewer trainable parameters, and it outperforms all 15 classical hyperparameter variants tested. A second benchmark (checkerboard) demonstrates the generality of the inversion pipeline, confirming that the quantum hybrid architecture can recover structured spatial variations beyond the localized anomaly benchmark. Our framework is broadly applicable to wave-based inverse problems beyond geophysics, including medical ultrasound tomography and non-destructive evaluation.

Text emotions are extracted using natural language processing technique on a substantial corpus of analyst reports on the Chinese stock market. Subsequently, the text-based analyst sentiment indices are constructed. It is observed that both optimistic and pessimistic sentiments represent short-range memory. Optimistic and pessimistic sentiments are correlated with volatility positively and negatively, respectively. The analysis of transfer entropy reveals that past pessimistic sentiment affects future volatility. Further, we model the driving effect of analyst sentiment on volatility using a GARCH model. The results show that pessimistic sentiment is an explanatory factor for volatility, while optimistic sentiment is not.

Ferromagnets in free space are predicted to exhibit pure Larmor precession at near-zero magnetic fields and provide exceptional sensitivity for magnetometry and gyroscopy. Notably, pure Larmor precession has not been observed in a macroscopic ferromagnetic particle, despite its fundamental importance and potential for probing relativistic effects and dark-matter interactions. Realizing such dynamics requires true free fall to eliminate clamping losses and trap-induced systematics. A central challenge is designing a tunable trap that is weak enough to permit near-free evolution yet robust enough to withstand the disturbances of launch and release. Here, we propose and demonstrate a novel master proportional-integral-differential magnetic trap (MPIDMT) combining a PID-controlled coil system with a master control coil system. Implemented in the third-generation drop tower - Einstein-Elevator, during the microgravity phase the system stably levitates a ferromagnetic particle against shock accelerations up to 1.5 g and resolves its motion in both a low-field (0.4 g) configuration and in pure free fall. These results represent a key step toward free-fall ferromagnetic magnetometry, the long-sought direct observation of macroscopic Larmor precession, and future space-based experiments.

We demonstrate that nonlinear computing can be achieved with a single linear diffractive surface under coherent illumination. We introduce a compact encoder-decoder co-localization (E+D) architecture in which an input-dependent dynamic encoder and a static optimized decoder are integrated within the same phase-only diffractive plane. Following free-space propagation, coherent interference between the encoder and decoder fields, combined with intensity detection, generates programmable nonlinear input-output mappings without requiring nonlinear optical materials or multiple diffractive layers. We prove that the proposed E+D optical processor is a universal approximator for arbitrary real-valued band-limited nonlinear functions and identify the physical factors governing its approximation fidelity, including the decoder degrees-of-freedom, detector aperture, and axial propagation distance. Crucially, we demonstrate that introducing a trained, frozen phase bias to the encoder region systematically enhances functional expressivity, providing robustness against coarse phase quantization on spatial light modulators. Using this framework, we accurately synthesize diverse nonlinear functions, including commonly used neural network activation functions and complex-valued nonlinear functions. Finally, we experimentally validate the proposed approach using a visible-light optical set-up trained through in situ learning, demonstrating the parallel approximation of 9 nonlinear functions in a single optical forward pass. By collapsing nonlinear optical computation into a single diffractive surface, the E+D architecture substantially reduces hardware and alignment complexity while preserving powerful function-approximation capabilities, providing a compact and scalable framework for analog information processing.

We investigate how wave-mixing (WM)-induced symmetry breaking leads to giant third-order polarization rotation of a weak probe field in a Rydberg electromagnetically induced transparency (EIT) medium. A far-detuned counterpropagating WM field is adiabatically eliminated and retained solely as a Raman dressing of the lower Zeeman manifold. In this reduced description, the weak static magnetic field defines the two circular propagation channels, while the WM-induced Raman coherence breaks the symmetry between these channels, without acting as a gain channel or an independent nonlinear source. The weak-probe response is calculated using a reduced density-matrix expansion for van der Waals (vdW) correlations and self-consistent Maxwell-Bloch propagation, with the nonlinear rotation extracted by subtracting the linear propagation background. Including WM dressing increases the extracted third-order rotation from 1.06 degrees to 25.70 degrees, an enhancement of more than 24 times, for the parameters considered. The response is nonmonotonic in WM strength and can even reverse sign, revealing that the WM field controls the propagation channels through symmetry breaking rather than merely amplifying the probe. Eigenchannel diagnostics further indicate that this giant rotation requires coherent excitation of both WM-dressed propagation channels, which in turn depends on three factors: Raman-induced asymmetry, the EIT-supported Rydberg pathway, and vdW nonlocality. These results demonstrate a symmetry-breaking-controlled mechanism for Rydberg magneto-optics, with applications to weak-light polarimetry and all-optical polarization control.

An exploration is presented of the start-up and linear stability of pressure-driven channel flow of an elasto-viscoplastic fluid described by Saramito's constitutive law. Streamwise uniform base states are non-unique, depending on the initial stress configuration, and develop discontinuities in the normal stresses and shear rate at the yield surfaces over infinite times. Such stress discontinuities can be eliminated by introducing a sufficient extensional pre-stress; true plugs bordered by stress jumps then become replaced by marginally yielded, plug-like flow, or pseudo-plugs. To examine the stability of all of these state, the linear initial-value problem is solved along with the evolving base states. Because this analysis is performed for finite times, the base states remain continuous and there is no need to perturb any stress discontinuities. Armed with the insights provided, stability is then analyzed as a normal-mode problem for the final states, building in perturbations to the stress discontinuities via certain jump conditions across any yield surfaces. Regardless of whether the base flows contain true plugs or pseudo-plugs, the base states are found to be linearly unstable at zero Reynolds number. The most unstable perturbations possess the highest streamwise wavenumbers and become spatially localized to the regions where stresses lie close to the yield stress.

A recent pedagogical paper by Strauch concretely demonstrated how interaction-mediated entanglement can suppress fringe visibility in a one-dimensional model of the electron double-slit experiment. Here we extend that framework to model actual experimental data from electron biprism interferometry. Kerker et al. showed that the macroscopic QED model of Scheel and Buhmann successfully describes their measured results. We show that this Scheel-Buhmann model can be recovered from Strauch's simplified framework by employing a Markov approximation and including thermal effects. The resulting decoherence rate is expressed in terms of mode-resolved scattering probabilities familiar from electron energy-loss spectroscopy (EELS), directly relating EELS to decoherence. The thermal dependence is significant in its own right, as recent theoretical work suggests that visibility reduction could serve as a non-invasive thermal probe for nanoscale systems. This progression from a toy model, to a quantitative account of real data, to a measurement application offers a case study in how simplified models can be made experimentally relevant.

We consider the task of distinguishing between a single weak incoherent optical point source and two weak incoherent optical point sources located symmetrically about the first source. $\theta$ is the separation between the two point sources scaled to the Point Spread Function (PSF) width in the image plane. Using an ideal focal plane array of intensity detectors (ideal direct imaging), we quantify the performance using the Bhattacharyya distance and find the scaling of its leading order term in terms of $\theta$ in the sub-Rayleigh regime. A suite of previous analyses of this problem lacked a comprehensive analysis for when the amplitude spread function (ASF) of the imaging system has zeros and reported a scaling that we find to be incorrect. We complete this analysis by explicitly calculating the leading order term of the Bhattacharyya distance for ideal direct imaging with any ASF, for small $\theta$ and show the difference in scaling based on the presence or absence of zeros in the ASF. This is similar to the ASF dependent performance in the task of estimating the separation between the two point sources and the task of detecting a change to an object. We then apply our results to the specific example of a Gaussian and a Sinc ASF and show good agreement with numerical calculations. Our results allow the accurate comparison of other measurement schemes with ideal direct imaging, and to the quantum limit.

We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows. Taking as a baseline the results of training agents directly targeting wall-shear stress and opposition control, three SHAP-guided approaches are compared. In the first, the reward is computed from SHAP attributions of a U-net predicting the future velocity field; in the second, from SHAP attributions of a U-net predicting the skin-friction coefficient; in the third, from a combination of SHAP attributions of two U-nets predicting the skin-friction coefficient and the wall pressure fluctuations, respectively. The combined SHAP strategy based on skin-friction coefficient and wall-pressure fluctuations achieves the best overall performance, achieving a DR of 34.44% and a NES of 34.01% with only 0.43% normalized input power. Relative to opposition control, drag reduction and net energy saving increase by 49.41% and 48.52%, respectively. Compared with the direct wall-shear-stress baseline, the proposed strategy simultaneously improves performance while reducing the normalized actuation cost from 5.90% to 0.43%. Analysis of the results reveals that the energetically efficient policy is consistent with pressure-gated actuation, activating predominantly at near-zero wall pressure, and operates on a temporal timescale comparable to the lifetime of the near-wall turbulent structures.