Papers

Quantum computing has rapidly emerged as a powerful paradigm for tackling computationally demanding problems. In particular, quantum optimization shows strong promise for hard combinatorial problems in power systems, where increasing distributed energy penetration heightens the need for intentional islanding to maintain grid reliability and resilience. However, power system islanding is an NP-hard combinatorial optimization problem that becomes computationally prohibitive for classical solvers as network size grows, motivating the use of quantum computing as a promising alternative pipeline. This study develops a resource-efficient hybrid QAOA islanding framework that brings physics-constrained power-system partitioning into the quantum optimization workflow. The framework combines coherency-informed graph reduction, physics-aware constraint modeling, and structured post-processing to efficiently convert shallow-circuit QAOA samples into high-quality feasible islanding decisions without deep circuits or large shot budgets. The proposed framework is validated on the standard IEEE benchmark systems (9-, 14-, 24-, 30-, 39-, and 57-bus), demonstrating that the hybrid workflow achieves Gurobi-optimal solution quality with a clear quantum resource advantage over vanilla QAOA, while the resulting islanding solutions satisfy all physical feasibility requirements after network separation. This study establishes QAOA-based islanding as a viable quantum approach for critical infrastructure, with structured post-processing as the key enabler of quantum resource efficiency.

We solve the inverse problem for Ising models on reduced planar graphs in a disk, i.e., recovering the edge coupling constants from the boundary spin correlations. A recursive solution to this problem was provided by Galashin–Pylyavskyy. Our solution is nonrecursive. It is based on an Ising analog of the chamber ansatz which asserts that the inverse map factors through variables on the graph that transform under the Ising Y-$\Delta$ move according to the discrete CKP equation.

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.

The ability to gather information and to act upon it is at the core of every learning agent. But what is the impact of quantum mechanics on an agent's ability to sense external inputs and to translate them into actions? Here we address the question for a prototype task of learning agency at the quantum scale: rotating a single spin based on information gathered by a single atom. We determine the ultimate performance limit for this task, revealing a fundamental tradeoff between entanglement at the sensing stage and coherence at the action stage: if the single-atom sensor is not entangled with the quantum system serving as the agent's internal memory, then the best learning strategy requires a coherent transfer of quantum information from the sensor to the system that controls the agent's actions. In contrast, if the sensor is initially entangled with the agent's memory, then the transfer of quantum information is no longer necessary. Our results indicate that the quantum properties of the sensor radically affect the optimal way to convert external stimuli into actions, revealing a link between quantum sensing and the behavior of quantum agents.

We develop quantum simulation algorithms for the Liouville equation of classical mechanics with discontinuous potential. Such discontinuities represent potential barriers at which classical particles undergo energy preserving transmission or reflection, and the resulting interface conditions must be incorporated into the numerical flux. We combine Hamiltonian-preserving schemes by Jin and Wen in Commun. Math. Sci. 3(3), 285-315 (2005) with the Schrödingerization method, which embeds the resulting nonunitary semi-discrete dynamics into a unitary Schrödinger type system in one additional auxiliary variable [arXiv:2212.14703, arXiv:2212.13969]. For one-, two-, and $n$-dimensional problems with grid aligned interfaces, we construct sparse matrix representations of the transmission and reflection fluxes using step and hat functions, derive the corresponding Hamiltonians of the Schrödingerized systems, and analyze their sparse-access query complexity. In the sparse-access oracle model, the resulting algorithms have a polynomial dependence on the inverse accuracy and avoid the exponential dependence on the phase-space dimension suffered by classical grid based Hamiltonian-preserving schemes, up to the cost of implementing the oracles and the postselection overhead. We also describe the postselected recovery of the physical solution state and the quantum readout of macroscopic observables such as density and averaged velocity through overlap estimation. Numerical experiments based on classical simulation of the Schrödingerized dynamics validate the proposed formulation and illustrate the correct transmission/reflection behavior at potential barriers.

We investigate the nonequilibrium dynamics of strong-to-weak spontaneous symmetry breaking in many-body quantum systems undergoing decoherence from thermal pure states. For generic initial pure states with volume-law entanglement entropy, we show that the system undergoes a discontinuous dynamical phase transition at a critical time. This transition is accompanied by a singularity in the entropy of the system, which saturates to its maximum value at the same critical time. Through numerical simulations of the dephasing Ising and hard-core boson models, we establish the universality of this transition across different symmetries. Our results reveal that the dynamical emergence of a decohered mixed state from a highly entangled state is not a gradual asymptotic relaxation, but rather a sharp phase transition driven by a sudden collapse of global coherence.

Quantum Phase Estimation (QPE) is the quantum algorithmic workhorse for computing ground state energies of quantum Hamiltonians with quantum computers. Ground state energy calculation of physical systems is perhaps the most promising use case for quantum computing in terms of scientific and commercial value with a plausible path to outperformance of classical alternatives. This path, however, hinges on the availability of initial states for QPE with significant overlap with the true ground state. Using extensive (classical) numerical computations, we study whether the NISQ-era algorithm VQE (Variational Quantum Eigensolver) could be used to efficiently prepare high-overlap states of disordered fully-connected anisotropic Heisenberg spin glass quantum Hamiltonians with up to $15$ qubits. We find that (i) – consistent with widely held, but rarely numerically illustrated beliefs – VQE is generally unable to efficiently converge to the ground state for our Hamiltonians, which is a well-known issue with VQE due to a variety of factors including vanishing gradients and local minima; (ii) low energy states do not necessarily have large ground-state overlap, but there is typically a correlation between the two measures; (iii) adding more than three layers to the VQE ansatz neither improves overlap nor the energies found; and (iv) the best-found overlap scaling as a function of the Hamiltonian system size is not strongly exponentially decreasing, suggesting potential for VQE to be a heuristic state preparation algorithm for QPE.

We report composition tuning of the anomalous Nernst effect in amorphous-like Gd-Co-Pt ternary films. Pt incorporation into Gd-Co films modifies the anomalous Nernst coefficient and induces a sign reversal of S_ANE, whereas Gd incorporation into Co-Pt films suppresses S_ANE but reduces thermal conductivity. Owing to the balance between transverse thermoelectric response and thermal transport, the heat-flux sensitivity reaches approximately 0.24 {\mu}m/A. Composition maps reveal that the magnitude and heat-flux sensitivity of the anomalous Nernst effect can be systematically tuned in the Gd-Co-Pt ternary composition space. This work extends ANE material design from binary-alloy optimization to ternary-composition engineering.

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.

In differential geometry, the underlying mathematical structure behind the electroweak theory is a trivial principal $(SU(2)\times U(1))$–bundle over $\R^4$ with the Minkowski metric. The aim of this paper is to show that, using non–commutative geometry, the electroweak theory can be described using a trivial quantum principal $SU(2)$–bundle over $C^\infty_\C(\R^4)$ with the Minkowski metric.

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.

Atomistic simulations are central to materials design, but their execution involves complex, multi-step workflows that require significant human expertise. Here, we present an agent-based system embedded within the URSA (Universal Research and Scientific Agent) framework that automates the design, execution, and validation of atomistic simulations, demonstrated using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) tool. Our system autonomously selects interatomic potentials, constructs and runs simulations, and performs iterative error recovery within a closed-loop workflow. We evaluate the scientific reliability of the agent by benchmarking its outputs against LAVA, a high-throughput toolkit for LAMMPS and the Vienna Ab initio Simulation Package (VASP) calculations. Our framework reduces manual intervention and trial-and-error, thereby improving the rigor, reproducibility, and scalability of atomistic modeling.

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 $K\rightarrow0$.

AI-assisted mathematics is often evaluated on solving predefined problems. In practice, however, many important advances begin earlier, when a vague research intuition is transformed into a concrete problem, a promising route, and a theorem family worth proving. This report studies that stage through a case study that led to sign-embedding quantum algorithms for matrix equations and matrix functions, foundational primitives in quantum linear algebra and operator-output quantum algorithms. The project began with a human-originated intuition that rational approximation is especially effective for jump-type functions such as the sign function, and might therefore serve as a design principle for quantum algorithms. Rather than merely assisting after the problem was fixed, AI-assisted exploration, including workflows later integrated into the agentic AI-mathematician system AIM, played a key role in expanding this intuition into a route map, comparing candidate formulations, and converging toward sign embedding as the central framework. AIM then helped connect a known matrix-sign identity to wider classes of matrix equations and matrix functions, and drafted proof and complexity calculations. The decisive scientific judgments remained human: selecting which human-AI-expanded routes were worth pursuing, rejecting a Cayley-trapezoidal approximation when its validity required a hidden condition, and refining the Sylvester implementation from a coarse quadratic-gap query route to the final factorized and scaled analysis. The report argues that human-AI co-discovery workflows, with systems such as AIM as important components, are most valuable not as standalone theorem provers, but as research partners for problem formation, connection discovery, derivation, and skeptical review inside a human-gated research loop.

Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge. In this study, we propose a hybrid GNN–FEM framework for efficient and generalizable phase-field fracture modeling. While phase-field approaches provide a robust variational framework for simulating complex crack evolution, their high computational cost limits practical applications because they require solving coupled, nonlinear, and history-dependent systems within an incremental finite element procedure. To address this challenge, a graph neural network surrogate is integrated into the conventional staggered scheme, replacing the phase-field update at each load increment while retaining the FEM-based displacement solver to enforce mechanical equilibrium and boundary conditions. By preserving the incremental solution structure, the framework remains consistent with history-dependent fracture evolution without requiring the surrogate to approximate the full solution trajectory. This selective surrogate strategy emphasizes the identification of a physically meaningful and incrementally structured learning target, rather than relying on brute-force data generation to learn the full fracture process. The proposed framework achieves strong generalization across varying geometries, loading conditions, material properties, and discretizations through dimensionless feature design, a graph-based formulation on mesh-based domains, and a physics-informed loss derived from the governing phase-field equation. Numerical experiments demonstrate that the hybrid approach reduces computational cost while maintaining accuracy compared with conventional FEM, and exhibits robust predictive performance across diverse problem settings.

Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial. This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and reaction force data, without stress observations, plastic strain measurements, direct yield surface data, or a prescribed parametric yield function. The framework identifies the yield function as a mechanically constrained constitutive component inside elastoplastic stress integration, rather than through direct stress-space supervision. The yield function is represented by a convex neural network that enforces convexity and positive homogeneity of degree one while imposing the assumed tension-compression symmetry, and this neural yield function is trained with a differentiable stress update and a physics-informed force equilibrium loss across multiple loading cases. The proposed framework is validated using finite element (FE) benchmark studies with von Mises, Hill 1948, and Yld2000-2d yield functions, assessing yield contour agreement, displacement-noise sensitivity, identifiability through plastically active stress states, epistemic uncertainty, and polynomial-surrogate deployment. This study provides a mechanics-constrained pathway for discovering anisotropic yield functions from displacement and force data while keeping the identified component within the structure of elastoplastic stress integration.

Open-ended evolution (OEE) in artificial life is typically driven by uninterpretable, black-box neural-network complexity metrics, leaving life-like systems disconnected from physical theories of complexity. We introduce MSPD (Multi-Scale Path Divergence, denoted $D_P$), a renormalization-group-inspired scalar that quantifies how the heterogeneity of a system's local transition laws is organized across temporal and spatial scales. MSPD is defined at the population level as a functional of the realised trajectory and is computed as a windowed finite-resolution estimator, with consistency between the two stated as a proposition. The metric is an explicit formula and plays a dual role: as a gradient-free fitness function and as a post-hoc analytical lens on any simulation that exposes local transition laws. On a Flow-Lenia substrate we establish three claims. (C1) Under fixed-context replay of the exact pathwise objective, MSPD-optimized parameters score higher than matched random parameters. (C2) Along optimized trajectories, states whose local transition laws are more heterogeneous yield larger future divergence than matched, less-heterogeneous states under exact-state stochastic continuations, so the metric tracks intrinsic dynamics rather than injected noise. (C5) Higher MSPD corresponds to stronger scale-dependent frustration — larger differences between the dynamics expressed at different spatial extents — linking MSPD to the frustration criterion of biological complexity in the sense of Vanchurin et al. All three transfer to Life-like cellular automata and Particle Life++, indicating that MSPD is not specific to a single substrate. A single explicit formula thus both directs open-ended evolution and provides a principled bridge to the physics of complexity that black-box drivers do not.

We study a mass-asymmetric one-dimensional electron-hole biwire system at zero temperature using the diffusion quantum Monte Carlo method. We characterize electron-hole pairing and long-distance pair coherence through the pair correlation function and the projected two-body density matrix over a broad range of carrier densities $r_{\rm s}$ and interwire separations $d$. We identify distinct correlation regimes ranging from a weakly correlated two-component electron-hole plasma to a quasi-condensate excitonic regime and a strongly correlated Wigner-like regime. To understand long-distance pair coherence, we analyze the algebraic decay of the projected two-body density matrix and extract the corresponding power-law exponent $\alpha$ which is less than 2. Strong electron-hole Coulomb correlations allow quasi-excitonic coherence to persist to high densities, down to effective $r_{\rm s} = 0.3$ a.u.\ in addition to low densities. For a representative parameter set, we further find that mass asymmetry suppresses long-distance pair coherence while enhancing short-range electron-hole pairing. Our results provide a microscopic characterization of competing pairing and spatial ordering correlations in mass-asymmetric one-dimensional electron-hole systems.

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 revisit the variational principle for relativistic perfect fluids in a manifestly covariant formulation based on differential forms, with particular attention to the boundary data required for a well-posed action principle. For timelike flows, the formulation reproduces the standard formulation of Schutz and Brown. We then examine the extension obtained by replacing proper time normalization of the velocity with a nullity constraint, with the he Bi{\v c}ák-Kucha{\v r} null dust action emerging as a special case. Requiring the complete action to be invariant under local rescalings of the null flow vector imposes the condition that the energy density depend only on the entropy per particle. For this class of null flows, the conventional equilibrium condition, i.e., that the energy density is convex, fails at non-zero temperatures, therefore limiting the interpretation of the fluid as an ordinary thermodynamic system.

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.

Near-infrared (NIR) variability studies of active galactic nuclei (AGNs) are still limited, as long-term multiepoch monitoring in the NIR is observationally challenging. The depth, wavelength coverage, and 14-year temporal baseline of UltraVISTA make it one of the few surveys capable of providing a detailed characterization of AGN variability in this regime. We aim to quantify the NIR variability of known AGNs in the COSMOS field and to investigate the physical origin of variability in type 2 AGNs. In particular, we examine how NIR variability can help clarify the discrepancies between optical and X-ray classifications. Using the 14-year multiepoch UltraVISTA DR6 dataset in the YJHKs bands, we constructed calibrated NIR light curves and quantified their variability through a set of metrics. AGN-like stochastic variability was identified by modeling the light curves with a damped random walk (DRW) process. We find that about 7-17% of the 533 type 2 AGNs are variable in the NIR, with variability fractions increasing toward Ks, where the dusty torus dominates the emission. Based on the wavelength dependence of the DRW variability amplitude, we classify variable type 2 AGNs into disk-dominated, torus-dominated, and highly obscured groups. About one third of the X-ray unobscured (XR I) type 2 AGNs are variable in the NIR, consistent with misclassified weak type 1 or true type 2 AGNs. On the other hand, 21.4% (30/140) of the X-ray obscured (XR II) type 2 AGNs show detectable variability in the NIR, most of them only in H or Ks, consistent with obscuration of the bluer (accretion disk) bands. Type 2 AGNs without X-ray counterparts (165) show the smallest fraction (3.6%) of variable objects. NIR variability provides an effective and independent diagnostic for confirming optical classifications and for identifying weak or misclassified type 1 AGNs in deep extragalactic surveys.

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.

Optical quantum memories operating directly at telecom wavelengths are a key enabling technology for long-distance quantum networks, yet on-demand storage onto long-lived ground-state spins in this spectral region has remained elusive due to the challenge of coherently transferring optical excitations to hyperfine spin states. Here we demonstrate spin-wave storage in $^{167}$Er$^{3+}$:Y$_2$SiO$_5$ at 0.8 K and 1.1 T, establishing the core operational primitive required for on-demand telecom quantum memories. Using classical optical control pulses, we coherently transfer collective optical excitations to erbium hyperfine states with transfer efficiency exceeding 12%, enabling on-demand retrieval. We measure a hyperfine population lifetime of 25 s and demonstrate spin-wave storage for up to 25 $\mu$s. By identifying hyperfine inhomogeneous broadening as the dominant present limitation, our measurements define a clear pathway toward second-scale storage through improved spectral tailoring and dynamical decoupling. The results highlight the application of erbium-based solid-state memories for scalable fiber-compatible quantum repeater architectures.