Papers

Many climate change mitigation policies enjoy large majority support from the U.S. public. Yet, both Republicans and Democrats underestimate public support for climate policies, on average, with Republicans underestimating by more. Explaining this is a major puzzle in climate change politics. Homophily is one possible explanation: if citizens are selectively exposed to views reinforcing their own, then policy opponents might underestimate support more than supporters. Here, we explore how homophily could interact with social network structure to produce misperceptions of policy support, using a stochastic block model and preferential attachment model. Homophily alone can explain opponents underestimating support by more than supporters, but supporters only underestimate support when their homophily is so low that they disproportionately associate with opponents. We then expand our model to combine homophily with Bayesian rescaling, inaccurate priors, or asymmetric prominence of opposing opinions (simulating media bias). With Bayesian rescaling and inaccurate priors, homophily would still need to be highly asymmetric to produce realistic misperception patterns. Media bias combined with realistic, symmetric homophily can produce realistic misperception patterns in our model. However, empirical evidence on media bias in coverage of climate change policy is mixed. Our analyses provide theoretical foundations for advancing understanding of public opinion misperception, on climate change and other issues.

Complex-systems science provides media institutions with a rigorous framework to move from reactive reporting to anticipatory diagnosis. Critical events are understood as regime shifts emerging from the interplay between endogenous dynamics and exogenous shocks. Detecting such transitions requires identifying structured precursors, such as changes in correlations, amplification, persistence, and endogeneity, rather than relying on raw signal intensity. Recognizing dragon-king events as regime-generated outliers and incorporating non-normal transient amplification are essential, as is accounting for organizational concealment of risk. A geopolitical crisis observatory would diagnose when systems enter states of heightened susceptibility to cascading disruptions. While state actors are already developing such observatories for strategic purposes, media institutions remain largely reactive. This gap creates a strategic opportunity: leveraging open data and AI embedded within the complex-systems framework developed above, media organizations could transform journalism from reporting to diagnosis, delivering early-warning indicators, scenario-based risk maps, and transparent, data-driven narratives within a new Geopolitical Risk Intelligence Platform.

We develop a formal theory of cognitive debt: the stock of unverified reasoning obligations that accumulates when individuals use AI as a substitute rather than a complement for first-principles cognition. The model features two state variables per agent, cognitive capital and cognitive debt, and a multiplicative production technology in which cognitive capital functions as collateral that determines the return to AI adoption. We establish six propositions. Rational agents incur positive cognitive debt because the costs are deferred, partially external, and masked by short-run productivity gains. Tranquil periods lower subjective risk assessments, raise AI substitution intensity, and compound leverage, generating a cognitive Minsky moment in which subjective risk falls while true systemic fragility rises. Expected crisis losses are convex in aggregate leverage. Post-crisis, output-target pressure can produce a false-correction loop in which agents patch AI failures with more AI. The decentralised equilibrium over-adopts substitutive AI relative to the social optimum because of systemic risk, cognitive public goods, and arms-race externalities. In a two-type heterogeneous-agent economy, high-cognitive-capital agents adopt AI more intensively and may eventually erode their unaided cognitive capital below that of initially lower-skilled agents.

It was shown more than a decade ago [J. Chem. Phys. 139, 014108 (2013)] that a many-electron relativistic quantum electrodynamics (QED) Hamiltonian for high-precision electronic structure calculations can be constructed in a bottom-up fashion, by virtue of charge-conjugated contraction (CCC) of fermion operators when normal-ordering the starting unbounded relativistic Hamiltonian (second-quantized in terms of the electronic Dirac field) with respect to the filled negative-energy Dirac sea of electrons. It is shown here that the same relativistic QED Hamiltonian can also be obtained by equal average of the two relativistic Hamiltonians resulting from the normal-ordering of the starting unbounded relativistic Hamiltonians (second-quantized in terms of the electronic and positronic Dirac fields, respectively) with respect to the filled negative-energy Dirac seas of electrons and positrons, respectively, via the standard contraction of fermion operators. In essence, both procedures incorporate properly the fundamental charge-conjugation symmetry of relativistic quantum mechanics to ensure the symmetric treatment of the electronic and positronic degrees of freedom.

We demonstrate a nonlinear-optics approach to precise measurement of photon arrival time, by translating the temporal information of single photons to a wavelength distribution of frequency conversion followed by de-multiplexed detection. It uses a multi-color, pulse-delayed pump laser to drive multiplexed frequency conversion, transducing photons to various frequency channels according to their arrival time. By photon detection in each channel, the measurement resolution and accuracy can reach picosecond level, much lower than the detectors' naive resolution and significantly beating the shot-noise limited direct detection. Distinct to any method relying on repeated, multiple sampling, our approach supports event-ready operations, capable of detecting randomly arriving single photons with no dead window. It is thus particularly suitable for practical applications of ranging, sensing, and communications in the dynamic, photon-starving environment.

In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial complexity of interaction architectures, ambiguity of causal attribution under limited interventions, and state-dependent dynamics that confound structural inference. Each obstacle is structural in origin and calls for a structural solution. We address these challenges by adopting a reductionist approach, introducing Fundamental Dynamical Units (FDUs): signed three-node interaction patterns as composable primitives that convert the interaction hypothesis space into a finite, constructive, and tractable representation. We show that local interaction structure determines the perturbation conditions required to disentangle direct from relayed influence, making intervention design a structural consequence of the FDU representation. We embed FDU-regularized structural inference within a physics-informed neural ordinary differential equation (ODE) whose governing-equation constraint transforms structural hypotheses into verifiable dynamical predictions, enabling joint recovery of interaction structure and perturbation-resolved trajectories. Validated on synthetic benchmarks with known ground truth, the framework supports structural commitment, expressed through FDU primitives, motif-prescribed intervention design, and physics-informed learning, as a principled basis for mechanistically interpretable inference in networked dynamical systems.

This work presents a physics-informed neural network (PINN) for modeling a transient two-dimensional electrochemical flow reactor with diffusion, migration, convection, and nonlinear anodic Butler–Volmer kinetics. The model is trained without labeled concentration data by embedding the governing transport equation and all initial and boundary conditions into a composite loss function. Spatial and temporal coordinates together with anodic overpotential, temperature, inlet concentration, maximum flow velocity, and diffusivity are used as inputs, allowing the network to predict concentration fields over a broad operating domain. Validation against finite-difference-based solutions shows strong agreement for representative transient and near-steady cases, with a mean relative space–time error of $(9.99 \pm 0.65)\times10^{-3}$ (sub-percent level) across the conditioning domain. PINN inference is faster than a traditional finite difference solver by a factor of $5.34$, thus reducing runtime by $81.3\%$. Generalization tests further show that the surrogate remains robust under in-domain boundary-focused sampling and controlled extrapolation, although anodic overpotential is the most challenging parameter due to its exponential effect on interfacial kinetics. The results indicate that physics-informed neural networks can serve as accurate and efficient parametric surrogates for electrochemical transport problems and provide a foundation for low-computational-cost digital-twin modeling of electrochemical flow reactors.

Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms. Chemical transport models (CTMs) provide gridded forecasts but retain local biases, whereas graph neural networks (GNNs) track monitoring sites well at short lead times but do not produce gridded outputs. Here we present OmniPM-Net, a Convolutional Conditional Neural Process (ConvCNP)-based fusion model that reconciles these two forecast types within a shared spatial representation. A terrain-aware Gaussian set convolution lifts irregular GNN station forecasts onto a regular grid, where a multi-scale Spatial Source Attention (SSA) module blends them with Copernicus Atmosphere Monitoring Service (CAMS) forecasts; a shared omni-query readout then decodes this representation into consistent PM10 predictions at either stations or grid cells over a 108 h horizon. Evaluated across 1,618 air-quality monitoring stations throughout China over the full year of 2024, OmniPM-Net matches the station-level accuracy of the stronger GNN baseline (mean absolute error 21.14 versus 22.00 ug/m3) and reduces the CAMS mean absolute error by 30%, while simultaneously delivering the gridded fields that the discrete GNN cannot. Its clearest gains are in the high-concentration tail, where the 90th-percentile MAE falls by 9% relative to the GNN and 25% relative to CAMS, and during dust episodes, where it improves categorical detection skill while tracking the evolving spatial trajectory.

We investigate whether a minimal effective pressure in the dark matter sector can modify nonlinear halo structure while preserving the successful large-scale predictions of the $\Lambda$ cold dark matter ($\Lambda$CDM) model. We consider a barotropic relation $P=K\rho^{\gamma}$ with $\gamma=3/2$, interpreted as an effective coarse-grained closure of the Jeans hierarchy in virialized regions. In this framework, dark matter remains effectively pressureless at cosmological densities while developing a finite effective sound speed inside collapsed halos. For $\gamma=3/2$, equilibrium halo configurations correspond to the $n=2$ Lane–Emden solution, producing finite-radius density profiles with quadratic central flattening. When embedded within the empirical concentration–mass relation of $\Lambda$CDM halos, the resulting core scale exhibits only weak mass dependence across dwarf-to-galaxy mass ranges. For parameter values yielding kiloparsec-scale cores, the background expansion history and linear growth of density perturbations remain observationally indistinguishable from $\Lambda$CDM, while the present-day Jeans scale remains confined to sub-megaparsec lengths. We compute projected surface-density and weak-lensing convergence profiles for the model. Relative to mass-matched Navarro–Frenk–White halos, the model predicts a moderate suppression of the central lensing amplitude, while the convergence power spectrum is modified only at sufficiently high multipoles. The model introduces a single additional parameter controlling nonlinear pressure support and continuously reduces to collision-free cold dark matter in the limit $K\rightarrow0$.

The Wigner representation for quantum mechanics of particles is generalized to Bose fields. The standard Hilbert space quantization becomes, via the Weyl transform, a quantization method that consists of adding a Gaussian zeropoint field distribution to the vacuum. I comment on the possible advantages of the method in order to study quantum fields in curved spaces. I study a unified formulation of non-relativistic quantum electrodynamics in the Weyl-Wigner formalism, in terms of (classical-like) c-numbers.

We propose using Transonic Flow Focusing (TFF) to produce 2D and 0D nanomaterials. This technique focuses liquid suspensions into high-speed micrometer-scale jets, combining extremely high shear and elongational stresses in a confined, contact-free zone. For the Graphene Nanoplatelets suspensions and TFF operating conditions investigated here, the process promoted exfoliation without added surfactants or oxidative chemistry. Both graphene monolayer flakes ($\sim 300-400$ nm in lateral size) and monolayer graphene nanofragments with lateral sizes compatible with quantum dots ($\sim 10-15$ nm) were obtained in a single TFF step using isopropanol and pure water. Our theoretical analysis reveals that, during microsecond residence times at the meniscus-jet transition, shear and extensional stresses of the order of $10^6$ s$^{-1}$ act on the suspended particles, yielding viscous power densities of the order of $10^{10}$ $\mathrm{W/m^{3}}$. High-resolution transmission electron microscopy and atomic force microscopy show that the monolayer fraction exceeded 99% for isopropanol and 92.9% for water. These results suggest that TFF can combine solvent versatility with a high monolayer fraction in a purely mechanical top-down process.

We compute the reflectance R and transmittance T of a plane-parallel slab of optical thickness tau for Henyey-Greenstein (HG) scattering with asymmetry parameter g, single-scattering albedo a, and incidence angle theta_0. The method is a Monte-Carlo-free first-passage transfer operator on the depth-direction state (z, mu): exact in formulation, with no physical approximation beyond the transport model, and evaluated by a numerically convergent discretization. A free flight in depth at fixed direction cosine is followed by the azimuthally averaged HG angular redistribution. Confining the operator to (0, tau) with absorbing boundaries yields, in a single evaluation, the order-resolved reflection and transmission laws P_R(n) and P_T(n), from which R and T follow at every albedo through the weighting sum_n P(n) a^n, together with the emergent angular distributions R(mu), T(mu) at no extra cost. Reflectance obeys the factorization R_tau = sum_n P_inf(n) S(n, tau) a^n. Beyond serving as a forward model, the central result is structural: the finite slab recovers the half-space first-return law P_inf(n) order by order as tau -> infinity, placing slab reflectance and the half-space return statistics in one framework. The operator reproduces full three-dimensional Monte Carlo in both channels to <= 1.6 x 10^-3 (absolute) across g in [0, 0.95], tau in [0.5, 16], albedo a in [0.5, 1], and normal-to-oblique incidence, with energy conservation R + T = 1 recovered to < 10^-4 at a = 1.

We propose a multiscale probabilistic reconstruction framework for hypersonic Couette flow, where near-wall states are inferred from limited top-wall observations using conditional diffusion model. The boundary layer is divided into overlapping wall-normal subdomains, and a single height- and Mach-conditioned Elucidating Diffusion Model (EDM) is trained jointly for M=6,7,8 to sample velocity, density, pressure, and temperature fields conditioned on a top-wall boundary slice. A soft overlap inpainting strategy assembles subdomain predictions into full-volume reconstructions while maintaining inter-subdomain continuity and small-scale variability. To improve the spectral fidelity of the generated fields, we introduce a novel bounded binned spectral power (BSP) loss that preserves high-wavenumber content while remaining numerically stable across the diffusion noise schedule. Validation against direct numerical simulation data shows that the model recovers instantaneous structures, spectra, statistical profiles, correlations, and wall quantities across all training Mach numbers, while providing spatially structured uncertainty estimates. The reconstructed Mach-conditioned profiles also collapse under the Trettel-Larsson transformation, indicating consistency with compressibility scaling. These results establish the domain decomposed conditional diffusion model with a bounded binned spectral loss as an effective probabilistic surrogate for near-wall reconstruction in hypersonic wall-bounded turbulence.

We propose a novel measurement of $\beta$ decays using low-temperature solid-state detector technologies. The $\beta$ startpoint, where the $\beta$ kinetic energy is zero, offers a unique probe of weak nuclear physics that has not yet been exploited experimentally. We describe how this technique enables searches for heavy sterile neutrinos in the keV–MeV energy range and show that, with current technologies, sensitivities to sterile neutrino coupling to electrons can be achieved that exceed the current best constraints. The spectrum produced by the recoiling daughter ion also offers a calibration of the nuclear recoil response, addressing key assumptions in sub-GeV dark matter searches. We outline a potential experimental scheme using neutron activation to produce $^{32}$P $\textit{in situ}$ in a silicon substrate, discuss projected sensitivities to sterile neutrinos, and evaluate the prospects for nuclear recoil calibrations.

Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiting their use for systems governed by long-range interactions and electrical response. Previously, we introduced Latent Ewald Summation (LES), which learns latent atomic charges and long-range electrostatics from density functional theory (DFT) energy and force labels alone. Here, we use LES to extract electrostatics that are latent in foundation models: energies and forces predicted by a teacher model are used to train a lightweight LES-augmented student MLIP, with optional fine-tuning on additional DFT data. The resulting models reduce computational cost while providing access to Born effective charge tensors, and infrared spectra. We benchmark student models distilled from a broad set of foundation MLIPs, including UMA, MACE, Orb, eSEN, GemNet-OC, PET, and EquiformerV2-based models, against experimental infrared spectra for liquid water, concentrated hydrochloric acid, and the anatase TiO2(101)-water interface. Across these systems, electrostatic response can be extracted from most foundation MLIPs. The benchmark further shows that the underlying DFT level and dataset used to train the teacher model play a larger role than architecture in determining electrostatic and spectroscopic accuracy. For the TiO2-water interface, fine-tuning with a modest amount of higher-level DFT data improves structural and infrared predictions. LES-based distillation therefore provides a practical route for converting foundation MLIPs into efficient, electrically responsive models, while also testing the physical fidelity encoded in foundation models.

Microgravity environments provide unique opportunities for ultracold-atom experiments by enabling long interrogation times and reduced acceleration-induced dynamics. However, their realization has largely been restricted to specialized facilities such as drop towers, sounding rockets, and space-based laboratories. Here we realize synthetic microgravity for quantum degenerate gases using optically engineered force landscapes that compensate Earth's gravity to the milli-g level while maintaining continuous confinement of the atomic ensemble. These force landscapes are generated by dynamically painted optical dipole potentials and calibrated in situ through Bloch oscillations in a vertical optical lattice, enabling precise control of the residual acceleration. We use this capability to demonstrate matter-wave beam splitting with arm separations of several hundred microns. We further implement a Bloch-band atom interferometer in which interaction-induced dephasing is strongly suppressed through controlled three-dimensional expansion in the synthetic microgravity potential. This reduction of mean-field effects restores near-$\sqrt{N}$ scaling of interferometric sensitivity for large quantum degenerate ensembles. Our results establish a versatile platform for realizing synthetic microgravity with trapped quantum gases in terrestrial laboratories, bringing the advantages of microgravity experiments to continuously operating systems and opening new opportunities for quantum sensing, matter-wave interferometry, and precision measurements.

The Ring Electron Cooler (REC) is being developed for the Electron-Ion Collider to provide cooling at the top proton energy of 275GeV to reduce emittance growth from intra-beam scattering (IBS) and other effects. Cooling electrons circulate in a 150 MeV storage ring equipped with strong damping wigglers. While these wigglers counteract emittance degradation driven by intra-beam scattering and proton-electron beam-beam scattering, they introduce significant nonlinear dynamics which can affect electron beam lifetime. In this paper we present the nonlinear optimization of the REC, including optimized focusing wiggler fields, chromatic correction, misalignments and orbit correction and space-charge studies. A novel sextupole-like wiggler field configuration is shown to substantially reduce chromatic aberrations compared with quadrupole-like focusing. Transverse and longitudinal dynamic apertures exceeding 5$\sigma$ are achieved in the presence of realistic alignment and BPM errors. Space-charge simulations for a Gaussian beam using the Bassetti-Erskine model show manageable tune spread and limited equilibrium emittance growth. These results demonstrate the feasibility of REC operation under the required cooling parameters.

Grain boundary stabilization by light interstitials is central to the performance of Ni-based superalloys, yet the thermodynamic mechanisms governing their interactions with substitutional chemistry remain poorly resolved. Here, we use hybrid Monte Carlo molecular dynamics simulations to quantify how boron and carbon modify the thermodynamic, structural, and chemical ordering of grain boundaries in Ni–Cr alloys. By analyzing interfacial state variables, site-resolved segregation spectra, local chemical ordering, and structural evolution, we show that boron and carbon stabilize grain boundaries through complementary pathways. Carbon drives saturation-controlled stabilization by recruiting Cr and conditioning boundary chemistry, while suppressing temperature-driven structural transformations of the boundary. In contrast, boron stabilizes grain boundaries through a selective mechanism that lowers the interfacial grand potential via localized ordering while permitting gradual structural evolution. These effects arise from coupled interactions between interstitial segregation and Cr redistribution, which together regulate site accessibility, chemical competition, and the range of accessible interfacial states. This work provides a thermodynamic framework for grain boundary engineering and suggests design principles for leveraging interstitial-substitutional interactions in alloys.

How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning. Here, we leverage data released through the Machine Intelligence from Cortical Networks (MICrONS) program–a functional connectomics resource spanning multiple areas of mouse visual cortex, in which dense calcium imaging is co-registered with high-resolution electron microscopy reconstruction from the same animal–to build biologically grounded recurrent neural networks. Using neuronal spatial coordinates, anatomical connectivity, and function-derived relationships from nearly 12,000 coregistered excitatory neurons, we initialize recurrent weights and impose communication-aware spatial constraints during learning. Across three cognitive decision-making tasks, networks constrained by cortical structure and function consistently outperform baseline and partially constrained models. Functional weight initialization provides the largest gain, while real spatial embedding yields robust additional improvements across conditions. These biologically grounded networks also develop low-entropy, modular, and small-world organization, and retain strong performance even when recurrence is restricted to positive weights. Together, our results show that the machinery of cortex–its geometry, wiring, and functional structure–can be harnessed as a powerful inductive basis for building recurrent networks that learn more effectively while converging toward key organizational principles of biological computation.

Purpose: The dependence of the long-time (tortuosity) limit of the extra-cellular diffusivity on the intra-cellular volume fraction is of fundamental importance for microstructure modeling. While such dependencies have been explored for the white matter, the tortuosity limit in gray matter is unknown due to complex cell composition and geometry. Here we rationalize and validate numerically the analytical relation between the extra-cellular diffusivity and intra-cellular fractions of cell bodies (somas) and neurites. Methods: The tortuosity relation for extra-cellular diffusivity qualitatively follows from effective medium theory, coarse-grained by diffusion outside somas (spheres) and neurites (cylinders), respectively. This problem is equivalent to finding the overall conductivity in a medium of grains in a matrix, with methodology dating back to the 19th century. We extend the effective medium methodology to populations of impermeable spheres and randomly oriented cylinders with various volume fractions, yielding closed-form expressions corroborated by Monte Carlo simulations. Results: We establish the power-law scaling of the extra-cellular diffusivity with the volume fractions of the extra-soma and extra-neurite spaces. We further evaluate the proposed framework using simulations in realistic tissue geometries. Conclusion: Theory and simulations relate extra-cellular tortuosity to soma and neurite fractions, potentially offering a diffusion MRI protocol design optimized for in vivo assessment of soma size and soma/neurite fractions within clinical scan times. Such in vivo measurements can be used to study development, aging and neurodegenerative disorders.

Superradiance is an extensive cooperative spontaneous emission phenomenon, exhibited by some atomic collective-spin states. However, distinct initial states differ in their decay dynamics. Dicke states $|j,m\rangle$ with distinct numbers of excitations $n=m+j$, driven by vacuum fluctuations, have their peak emission intensity shifted in time. Rotating Dicke states relative to the decay axis introduces an interesting parity structure that affects the pulse profile and photon correlations. Squeezed-bath prepared states undergo a squeezing-controlled crossover to the rotated Dicke states, making the emission character dependent on the amount of squeezing transferred from light to the atomic state. For semiclassical states with a macroscopic dipole moment, like the atomic coherent state, the emission intensity depends on their polarization. We present detailed results on the superradiant dynamics of a representative selection of states expanded in Dicke states to highlight the initial state as an independent dynamical control parameter. For large-$N$, we are able to predict fairly accurately the pulse profile in each case using the mean-field approximation, an approach based on the Fokker–Planck equation. We also present comparative results on the intensity correlation function, quantify the coherent and incoherent contributions of the emission, and contrast between small and large ensembles.

The turbulence driving parameter ($b \equiv \sigma_{\rho/\langle \rho \rangle}/\mathcal{M}$; the ratio of the density to velocity fluctuations) is widely used to infer the dominant mode of energy injection in interstellar turbulence. Numerical simulations of turbulence using Fourier Space Driving (FSD) establish a mapping from $b\approx 1/3$ for purely solenoidal to $b\approx 1$ for purely compressive driving. We test the robustness of this calibration by comparing FSD against Point Source Driving (PSD), which stochastically injects radial momentum at random locations mimicking supernovae. Using isothermal hydrodynamic simulations in a periodic box with AthenaK, we run a suite of carefully curated simulations to match Mach numbers between the two driving methods and compare morphology, probability density functions, and power spectra of density and velocity. Despite injecting purely compressive motions, the PSD models yield $b=0.33$ to $0.49$, values that the FSD calibration would associate with more solenoidal driving. With mass-weighted mean Mach number, excluding high-velocity bubble interiors, $b_M=0.74$ to $0.79$ still does not recover the expected $b\approx 1$ for volume-filling, purely compressive driving. More broadly, the PSD models show density and velocity statistics closer to solenoidal and compressive FSD models, respectively, and exhibit unique features, including non-Gaussian velocity tails and a positive density-Mach number correlation at high densities. Within the FSD framework itself, varying the forcing correlation time changes $b$ by a factor of more than 3 for compressive driving. These results demonstrate that $b$ is degenerate with both the spatial locality and the temporal correlation of the driving, limiting its utility as a standalone diagnostic of the energy injection mode.

Wavefront synthesis is a central objective in many applications of programmable metasurfaces (PMs), ranging from electromagnetic holography and computational imaging to massive backscatter communications. Yet, fundamental limits on the ability of a given real-world PM prototype to synthesize a desired output wavefront remain largely unknown. Here, we derive prototype-aware and electromagnetically consistent bounds on target-wavefront synthesis in reconfigurable MIMO wave systems whose programmability stems from tunable lumped elements. Our approach combines multiport network theory (MNT), experimentally estimated proxy MNT parameters, and semidefinite relaxation. We account for relevant practical aspects of typical real-world PMs, such as mutual coupling, binary programmability, and lossy tunable loads. We derive bounds on strength-agnostic wavefront-synthesis fidelity, shape-agnostic target-mode strength, and the strength–fidelity Pareto frontier using two complementary threshold sweeps. We evaluate these bounds for four experimental MIMO systems whose transfer functions are parametrized by a reconfigurable intelligent surface (RIS), involving up to 100 1-bit-programmable elements and radio environments ranging from rich scattering to free space. Our bounds yield practical insights such as the identification of unattainable performance regions and the close-to-optimality certification of certain optimization outcomes. Comparisons with feasible discrete-optimization benchmarks show that the bounds can often be closely approached in practice, indicating tightness. While demonstrated with a RIS prototype, our methodology applies broadly to lumped-element-reconfigurable wave systems, including dynamic metasurface antennas. Altogether, this work contributes to the development of a prototype-aware electromagnetic information theory for reconfigurable wave systems.

Spectral encoding enables single-shot measurements of ultrafast transients by mapping temporal information onto the spectrum of a chirped probe. This encoding allows dynamics to be recorded that are beyond the response limits of conventional electronic detectors. However, because the measurements record only spectral intensity, the phase of encoded signals is lost, and dispersion in the detection process introduces waveform distortions that complicate reconstruction and quantitative interpretation of spectra. In single-shot terahertz time-domain spectroscopy (THz-TDS), these distortions manifest as a tradeoff between temporal resolution and the measurement window of signals and can produce spectral null frequencies that limit the recoverable THz bandwidth. To address this challenge, a Bayesian inversion framework is developed to recover the underlying waveform from the squared spectral observable by inferring the THz field, the modulation coefficient, and a low-dimensional empirical parameterization of the probe spectrum jointly, while a Gaussian process prior regularizes the waveform. The framework is validated using single-shot THz-TDS experiments spanning two probe spectral profiles and three chirp conditions with $\alpha$ ranging from 14.5 to 40 ps$^{-2}$. Across all cases, the inversion reconstructs both the time-domain waveform and spectral null frequency structure within the credible interval of a delay-line reference measurement. These results establish a pathway to eliminate penalties that are associated with the detection process in spectral encoding methods without adding additional optics or alignment complexity.

In this study, we analyze the choice of local unitary cluster Jastrow (LUCJ) ansatz initialization and sensitivity of the sample-based quantum diagonalization (SQD) algorithm within the quantum-centric supercomputing (QCSC) framework. We examine six initialization strategies, including those based on coupled-cluster singles and doubles (CCSD), Møller-Plesset second-order perturbation theory (MP2), data-driven coupled-cluster (DDCC), and trivial (zeroes and random) initializations, across twelve molecular systems and three basis sets (STO-3G, cc-pVDZ, and aug-cc-pVDZ). We find that while the mean absolute percentage errors (MAPEs) between the alternative and CCSD-initialized t2-amplitudes span many orders of magnitude, the resulting SQD energies are largely insensitive to this variation. In particular, most initializations recover energies within chemical accuracy (+/-1.6 mEh) of the CCSD reference, with convergence improving as the basis set size increases. Notably, random initialization achieves performance competitive with CCSD across all basis sets, while zeroes initialization, despite having smaller deviations from CCSD, yields the worst energy agreement. Our results highlight that the proximity to the CCSD initialization is not a reliable predictor of the quality of electronic energies. These findings establish that configuration recovery within SQD, rather than circuit initialization, is the dominant factor governing energy accuracy, and suggest that computationally cheaper initialization strategies are viable alternatives to CCSD for QCSC workflows

We introduce a Mori–Zwanzig graph neural network (MZ–GNN) framework for learning reduced-order Lagrangian dynamics of tracer particles in homogeneous isotropic turbulence. The model represents particle acceleration as a finite-memory expansion over present and delayed particle-neighborhood graphs, with each memory contribution parameterized by an equivariant message-passing graph neural network. By construction, the architecture respects the relevant physical symmetries of the problem, including permutation equivariance, Galilean invariance, and equivariance under rotations and reflections. Trained on direct numerical simulation data, the model is rolled out autoregressively and evaluated on observables that are not imposed during training. We show that memory is essential for recovering the intermittent, heavy-tailed acceleration statistics, and that the learned dynamics accurately reproduce single-particle dispersion, pair-dispersion statistics, and four-particle tetrad geometry. Our results establish a physically structured, scalable route to data-driven multi-particle simulation of turbulent transport, and a template for learning reduced dynamics of correlated, symmetry-rich particle systems.

Capturing stylized facts and their evolution is essential for understanding the impact of climate change on complex environmental systems. In this work, we investigate the spectral properties of correlation matrices constructed from sea surface temperature data, employing tools from random matrix theory to identify signatures of global warming and long-term climate variability. By constructing yearly ensembles of correlation matrices, we analyze the evolution of both the eigenvalue density and the statistical behavior of the largest eigenvalue. Our results reveal significant departures from the universal behavior expected for random correlations. In particular, the empirical spectra systematically deviate from the Marchenko–Pastur law, indicating the presence of strong collective correlations in ocean temperature dynamics. Moreover, we find that the average largest eigenvalue exhibits a pronounced increase over time, closely following the rise in global mean ocean temperature. The distribution of the largest eigenvalue is found to be approximately Gaussian rather than Tracy–Widom, suggesting that the system lies outside the standard universal regime associated with weakly correlated Wishart ensembles. Together with conventional statistical indicators, these spectral signatures consistently reflect the progressive modification of the ocean-temperature correlation structure. Our findings demonstrate that spectral observables derived from correlation matrices, particularly the largest eigenvalue and its fluctuations, provide a sensitive framework for characterizing climate dynamics and detecting emerging signatures of long-term climate change.

High-purity germanium gamma spectra often require time-consuming analyses from subject matter experts. Photopeaks within these spectra are carefully fitted and numerical methods are employed to assist with nuclide identification (NID) and quantification. Amending the list of nuclides identified by analysis software can be nontrivial. When many samples need to be analyzed, it is therefore challenging to make timely and correct decisions. Supervised machine-learning-based NID can serve as an expert-informed, automated tool to improve the initial set of radionuclides suggested to an analyst and more effectively drive subsequent quantification. To that end, we implemented machine learning models that map photopeaks carefully fitted by analysts to NID results for experimental spectra containing various isotopic combinations drawn from a set of 65 isotopes. The best model achieved an F1 score of 0.97, markedly surpassing the F1 score of 0.84 achieved by traditional software when compared using a nuclide library comprising the same 65 isotopes assessed by the models. Finally, we illustrated the most important input features for model predictions using Shapley Additive Explanations. These explanations revealed that the models use physically relevant photopeaks when making predictions for the isotopes in our nuclide library.

Breaking reciprocity in thermal radiation opens opportunities for energy harvesting, sensing, and thermal management. Traditional nonreciprocal radiative semiconductor devices need external magnetic field. In this work, we predict a series of magnetic topological materials for magnetic-field-free nonreciprocal thermal radiation in the infrared regime, by combining first-principles calculations with Maxwell electrodynamics. We find strong and broadband nonreciprocity in magnetic Weyl semimetals (e.g., Co$_3$Sn$_2$S$_2$), outperforming the conventional semiconductor such as InAs. Furthermore, we propose universal material design recipes: strong nonreciprocity requires a large anomalous Hall response relative to the optical loss, whereas the broadband response favors large optical loss and small dielectric dispersion. Our work establishes a predictive materials-discovery framework and quantitative design rules for next-generation magnet-free nonreciprocal thermal devices.

Anomaly detection (AD) has recently emerged as an exciting alternative to conventional search strategies in high energy physics using artificial intelligence (AI) and machine learning (ML). The integration of these techniques into trigger systems is even more recent, but represents a crucial step in expanding the coverage of LHC triggers. In this paper, we explore the direct comparison, as well as combination, of two compression techniques for variational autoencoder (VAE) AD trigger algorithms: utilizing only latent-space derived variables and therefore requiring only half of the VAE that we call "chopping" and applying knowledge distillation (KD) to distill the VAE into a student architecture that we call "distillation." We demonstrate the feasibility of deploying such techniques on an FPGA within the resource and latency constraints of an LHC trigger environment and further find that a combination of the two leads to the smallest models that maintain, and in some cases, improve, performance with respect to the original VAE architecture.