Papers

Adaptive variational quantum algorithms like ADAPT-VQE construct tailored ansätze by iteratively selecting operators from a pool using gradient-based criteria. While this avoids oversized parameter spaces, repeatedly scanning the full pool incurs a classical cost that scales linearly with pool size-a major bottleneck for systems with long-range interactions or large operator sets. Here, we reformulate adaptive operator selection as a graph-based decision problem and introduce a graph neural network (GNN) policy that predicts the next entangling operator directly from the interaction graph and state-dependent observables. Training data are generated from exact simulations of disordered long-range spin chains, using gradient magnitudes as supervision signals. The learned policy accurately reproduces the dominant structure of the greedy gradient-based selection rule, significantly outperforming heuristics based solely on interaction strength. Integrated into a variational quantum eigensolver (VQE) workflow, this GNN-VQE approach achieves energy errors close to standard ADAPT-VQE while drastically reducing full-pool gradient evaluations. To test transferability beyond spin models, we evaluate the policy on small active-space molecular benchmarks (LiH and BeH_$2$). We find the GNN is highly effective as a shortlist generator: exact rescoring over just a few GNN-proposed candidates recovers near-oracle rollout behavior while searching only a small fraction of the pool. These results demonstrate that adaptive circuit construction contains learnable structure that can be exploited to accelerate variational quantum algorithms.

An external magnetic field is applied to the classical hydrogen atom treated in classical electromagnetic theory including classical electromagnetic zero-point radiation. In an earlier article, it was shown that the average value of each of the electron's action variables appears as a discrete value, corresponding to a representation of the rotation group, because of resonance between the periodic orbit of the electron and the random classical zero-point radiation. Here it is shown that, in the presence of a magnetic field and because of resonance, the classical orbital motion of the electron is in resonance with random classical zero-point radiation only for orientations of the orbit which take integer values for the angle made with the direction of the magnetic field, but excluding the m=0 orientation where the magnetic field is parallel to the orbital plane of the electron. Classical electromagnetic explanations are given for the Stern-Gerlach result, and for the Zeeman effect.

Fluid injection into the Earth's subsurface, performed for energy extraction, waste disposal, and resource development, is known to reactivate gouge-filled faults and induce seismicity, a key hazard in modern geotechnical operations. Nevertheless, the role of injection rate in controlling fault-gouge failure remains poorly understood. Here we present both an analytical theory and coupled fluid–granular (discrete element) numerical simulations to explain this rate dependence. Assuming a pre-stressed gouge-filled fault subject to fluid injection, we derive a pore-pressure diffusion equation with a dilative sink. Its solution predicts a rate-dependent failure criterion, arising from pressure heterogeneity within the layer: slow injection allows pressure to diffuse uniformly throughout the layer, promoting uniform weakening, whereas rapid injection produces strong gradients, leaving distal regions stronger. The numerical simulations confirm the theory and reproduce experimental observations not captured by classical, uniform-pressure effective-stress theory. The framework links grain-scale physics to fault-scale failure and provides quantitative guidance for the design of injection protocols in geotechnical operations involving granular geomaterials.

Tensor networks and quantum circuits are structural objects whose meaning depends on connectivity, indices, contraction order, gate placement, measurements, and related design choices. They are often easier to reason about visually than as code, yet in Python they are frequently constructed, transformed, and checked through backend-specific objects or compact symbolic expressions. This can make structural mistakes hard to notice during development, debugging, and communication. This paper presents three complementary packages: Tensor-Network-Visualization for visual debugging and structural inspection of supported tensor-network and traced einsum workflows; Tensor-Network-Editor for visual-to-code authoring, backend code generation, JSON preservation, export, and design-level analysis; and Quantum Circuit Drawer for clear circuit rendering, inspection, and complementary comparison of circuits or documented result distributions. The packages form a visual authoring and inspection layer around existing tensor-network libraries, array-based scientific Python workflows, and quantum SDKs. They are not simulators: they do not implement new contraction algorithms, execute quantum circuits, or guarantee full semantic equivalence across arbitrary backends. Their contribution is to make structural artifacts visible, editable, inspectable, comparable, exportable, and reproducible within those ecosystems.

We have demonstrated Laser Induced Fluorescence (LIF) in a Capacitively Coupled Plasma (CCP) discharge to directly measure the most crucial ion properties at a discharge regime suitable for a broad range of plasma research related to plasma processing and dusty plasma investigations that has been impossible for many years. The ion flow measurements in the presence and absence of dust particles show that ions move much faster directionally than expected from thermal motion, with reductions observed in the presence of dust particles. Ion temperatures are also found to exceed room temperature, contrary to a common assumption in the dusty plasma community. These findings represent a significant advancement in experimental plasma research, providing vital information to refine ion-driven process models with insights that span multiple research fields.

We present a unified theoretical framework for the mapping between diffusive and wave-like dynamics, formulated as a spectral integral operator acting on temporal fields. By introducing an analytic continuation in the complex frequency plane, we establish an explicit correspondence between thermal diffusion and a virtual wave field governed by a hyperbolic equation. This mapping is shown to define a causal, compact Fredholm operator that acts as a nonstationary low-pass filter, thereby revealing the intrinsic information loss of diffusive processes and the fundamental ill-posedness of the inverse reconstruction. Within this operator framework, we demonstrate that commonly used excitation schemes-including pulse, lock-in, chirped, and coded excitations-emerge as distinct projections onto subspaces of a single underlying transformation, corresponding to different sampling strategies of its spectral structure. This unifies previously disparate virtual wave formulations and provides a systematic interpretation of excitation design in terms of operator sampling and information encoding. The framework further generalizes to matrix-valued systems and suggests a spectral-geometric interpretation of temporal evolution across diffusive and propagative regimes.

Wave resistance is the drag generated by the wave radiation that a source moving at a fluid interface sustains. Under stochastic trajectories, the mean drag is controlled by the ensemble-averaged surface profile built from the trajectory history. We show that the result is a finite resistance below the deterministic radiation threshold and a regularization of the singular response at the minimum phase velocity of the capillary-gravity waves. We derive explicit scaling laws for drifted Brownian trajectories, including a universal high-diffusivity decay. For drifted Lévy flight, we find the mean wave resistance in closed-form, extending wave-drag theory to non-Gaussian trajectories.

Muography, commonly known as muon tomography, is a passive, non-destructive imaging technique that utilizes naturally occurring cosmic-ray muons to visualize the internal density structures of large, static, or inaccessible objects. In course of developing a small prototype muography system for material identification that relies upon the multiple Coulomb scattering of muons in matter, we explored the possible use of Thick-GEM detector as muon tracking device. It is a 5-20 fold scaled-up version of traditional GEM technology, that has become increasingly popular in recent years, owing to its mechanical robustness, cost-effective production, and excellent position sensing capabilities. A few prototypes of this detector of dimension $40\,\mathrm{mm} \times 48\,\mathrm{mm}$ with variation in other design parameters, were manufactured from a local industry. Subsequent to conditioning, detailed characterization of the detectors was performed to validate their suitability in muography applications. To identify the optimal operating region, gain variation was studied under various voltage configurations for both single and double-stage configurations. Experimental measurement of muon detection efficiency across the entire operating range yielded a maximum efficiency of 99.5% in both cases. Using a collimated Fe$^{55}$-source, the best spatial resolution was determined to be 30 $\mu$m for both single and double-stage operation.

Since its classical origin, Hamilton's principle has been formulated under an exact closure condition: admissible variations vanish at the boundaries of the variational domain. This condition removes the boundary term in the first variation of the action and yields the Euler–Lagrange equation. Although natural for isolated deterministic systems, fixed boundary admissibility is usually treated as a technical condition rather than as a physical closure hypothesis. Here we ask what follows when this hypothesis is made explicit and relaxed. We introduce variational openness as the retention of the boundary contribution in the variational balance. The retained term defines a boundary-openness density, which must be projected onto admissible variations before it becomes a dynamical source. In this formulation, the classical Euler–Lagrange equation is recovered as the exact-closure limit of an open variational balance; the source term is therefore identified with incomplete variational closure rather than with an externally imposed force. The framework is illustrated through three elementary examples: an open harmonic oscillator, a finite-compliance boundary, and a delayed oscillator with memory. These examples show how boundary openness can produce forcing, partial closure, history dependence, and non-Markovian structure while preserving standard mechanics in the closed limit. The resulting perspective suggests that Hamiltonian mechanics may be understood as the mechanics of variationally closed systems and motivates an open Hamilton–Jacobi theory in which admissibility itself becomes dynamical.

The Atlantic Meridional Overturning Circulation (AMOC) has been identified as a tipping element in the Earth system. Under the current climate change scenarios, it is urgent to develop robust methods for determining the probability of future AMOC transitions. Recent studies using an Earth System Model of Intermediate Complexity (EMIC) have revealed the importance of an AMOC edge state, located on the boundary of the attraction basin of the collapsed state, in AMOC transitions. Here, we provide a characterization of this edge state through climate networks, using instantaneous temporal correlations between geographical locations to define the network links. We apply the climate network analysis to a set of EMIC simulations with CO$_2$ forcing according to an intermediate climate change scenario (SSP2-4.5) that exhibit qualitatively different AMOC responses as a result of interaction with the edge state. We show that network measures, specifically the normalized degree centrality, reveal the presence of teleconnections across the equator as the AMOC approaches the edge state. A similar result is obtained for an Earth System Model (ESM) simulating AMOC collapse or recovery, suggesting that climate networks could be used to detect the onset of an AMOC tipping event in ESMs.

Storage-ring-based fully coherent light sources, including steady-state microbunching (SSMB), as well as compact seeded FELs driven by laser plasma accelerators, typically have relatively large intrinsic energy spreads. Extending the spectral reach of these facilities toward the X-ray regime represents a major challenge, as existing seeded schemes require rather extreme parameters to generate appreciable microbunching at high harmonics. In this Letter, we propose an echo enhanced strong focusing scheme that employs transverse-longitudinal coupling together with the beam echo effect to simultaneously resolve the energy spread bottleneck and enable efficient high-harmonic generation. This approach substantially relaxes the requirements on both the intrinsic energy spread and the transverse emittance, paving the way for soft X-ray production using relatively weak laser modulation. Based on this scheme, we further present an SSMB storage ring capable of generating kW-level average power 6.7 nm soft X-ray radiation.

The ambiguity of the active site, the empirical status of Brønsted-Evans-Polanyi relations, and the unpredictability of linear scaling relation breakdown are three symptoms of a single representational choice: treating chemical affinity as an attribute of discrete geometric sites. Here we show that all three are resolved when chemical affinity is represented as a continuous property of the interface: the covalent field. We present a framework, Covalent Field Theory (CFT), in which active sites emerge as regions where the field sustains a bias toward bond formation beyond the thermal threshold, removing the need for geometric classification. Linear scaling relations are correlation structure in the field across probe families; their breakdown is a topological bifurcation with a precise geometric signature. Brønsted-Evans-Polanyi correlations arise from the covalent field decomposition, providing a theoretical basis for what has previously been treated as an empirical regularity, demonstrated across ~120,000 candidate pathways. Applied to a high-entropy alloy nanoparticle and a partially reduced high-entropy oxide, CFT maps these properties onto surfaces of arbitrary compositional and structural complexity.

Whereas coil positioning in transcranial magnetic stimulation (TMS) to reach a specific cortical target with modern focal stimulation coils has been intensively studied, the alignment and contact of a coil with the head is often ignored. Focal figure-of-eight coils have a point on the surface, where they generate the largest induced electric field. This point should touch the head first, and the coil should be approximately tangential to the head in this point. Previous research has demonstrated the large impact if the coil does not touch the head with the right point and that many operators struggle with establishing or maintaining the correct coil-scalp alignment. This paper presents a technological support technology that can monitor the exact position of the contact point and also pressure to provide feedback to users. As the system uses exclusively components from consumer electronics, the sensor is low-cost and affordable. Through proper design, we achieved sufficient robustness so that the sensor does neither reset during TMS pulses and also not show any detectable degradation.

Cloning refers to producing identical copies of existing objects. Here, we experimentally show how to clone acoustic scattering objects. We acquire a digital twin and bring it back to life - a simple two-step process. First, we use broadband speakers to illuminate the scattering object within a closed receiver aperture. From these recorded reverberative data, we retrieve the object's scattering Green's functions using multidimensional deconvolution. In the second step, the acoustic scatterer is holographically reconstructed using the acquired scattering Green's functions. The hologram scatters any wavefield in real-time exactly like the original object would. Low-latency feedback reproduces all orders of interactions between the physical wavefield and the numerically defined hologram. This two-step process is demonstrated by cloning and modifying several rigid scatterers in a two-dimensional acoustic waveguide. Applications range from fully realistic digital scattering models to efficient metamaterial experimentation.

The dynamics of buoyant droplets rising freely in a large body of an immiscible liquid is investigated numerically for a moderate drop-to-fluid viscosity ratio $\mu^\ast$. We focus on toluene droplets rising in clean water, for which $\mu^\ast=0.62$, and vary the radius over $0.5\,\text{mm}\leq R\leq3.0\,\text{mm}$. Direct numerical simulations are performed in imposed axisymmetric and fully three-dimensional configurations. As $R$ increases, the system displays a rich sequence of rise regimes. Starting from steady vertical rise with an axisymmetric disturbance flow, it first undergoes an internal flow instability associated with an azimuthal mode $m=2$, leading to a biplanar-symmetric wake and reduced terminal speed. This state is followed by a steady oblique regime, in which the $m=1$ mode also becomes unstable and coexists with the $m=2$ mode. At larger radii, the path becomes nearly vertical again before the flow enters an $m=2$ rotating-wave regime, where the wake drifts azimuthally at an approximately constant angular velocity. For still larger droplets, persistent shape oscillations and vortex shedding lead to fully three-dimensional chaotic paths. Simulations initialised from finite-amplitude asymmetric states further reveal several multistable size ranges, in which distinct terminal states coexist depending on the initial condition. Taken together, these findings show that the path instability of moderate-viscosity-ratio droplets differs fundamentally from that of bubbles and solid particles: in most regimes encountered here, axisymmetry breaking is initiated within the droplet, highlighting the central role of the internal flow instability in shaping the subsequent wake structure, rise speed and droplet dynamics.

V-doped [Ca$_{2}$Nb$_{3-x}$V$_{x}$O$_{10}$]$^{-}$ (x = 0, 0.15, 0.3, 0.6, 0.75) nanosheets were produced by solid state reaction followed by protonation and exfoliation by tetrabutylammonium ions. 2D nanosheets have improved photocatalytic activity due to their small thickness that reduces electron-hole recombination, larger surface area that increases photocatalytic sites, and decreased band gap from added V. V-doping has caused the wide band gap of 3.54 eV of undoped nanosheets to decrease to 2.60-2.88 eV range. 10 vol% methanol was used as a hole scavenger in the hydrogen evolution reactions, and 3 wt% Pt was deposited on nanosheets as a co-catalyst. Under a full-spectrum Xe light, [Ca$_{2}$Nb$_{2.7}$V$_{0.3}$O$_{10}$]$^{-}$ has produced 4.7 times the H$_{2}$ yield as undoped nanosheets with a 11.3 mmol/g/h production rate, [Ca$_{2}$Nb$_{2.7}$V$_{0.3}$O$_{10}$]$^{-}$ with Pt co-catalyst has produced 2.9 times the H$_{2}$ yield as undoped Pt-loaded nanosheets.

While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables. Standard deep learning optimization often yields overly smooth forecasts, attenuating extreme events and spatial textures. We propose PredHydro-Net, a physics-guided dual-decoding framework that mitigates this smoothing. To resolve multi-variable optimization conflicts, it employs a decoupled architecture where macroscopic thermodynamic and dynamic fields unidirectionally modulate hydrometeor generation. By integrating wavelet-based frequency decoupling, spectral amplitude matching, and adversarial training, the model achieves a favorable trade-off between quantitative accuracy and spatial fidelity. In a 72-h global evaluation, PredHydro-Net outperforms both spatiotemporal deep learning baselines (Earthformer and PredRNNv2) and the operational Global Forecast System (GFS) in extreme-event detection and spectral representation. Furthermore, it demonstrates strong climatological consistency with Global Precipitation Measurement (GPM) satellite retrievals. The model reasonably reproduces the three-dimensional cloud structures in extreme weather events, such as Hurricane Ian. Feature attribution confirms its dependence on physical precursors such as relative humidity and wind convergence, offering a robust, physics-informed approach to long-tailed atmospheric prediction.

I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, machine learning, and biology, for example, but may also apply to physics and other forms of engineering. I describe how Bayesian probability and information theoretic optimization, including Active Inference, may be incorporated with promise semantics – as well as how Promise Theory supplements solutions, helping to avoid probability's pitfalls, which include non-local coordination, calibrating, and normalizing probabilistic computations. The role of boundary conditions in constraining allowed states and selecting decision thresholds is a form of promise, and agent alignment provides a scalable definition of intent. Autonomous agents may congeal into swarms with superagent characteristics by trying to minimize their information, despite uncertainty that works to maximize it. The use of Promise Theory involves some research challenges as well as stylistic preferences.

In quantum mechanics, the wave function encodes all information about a particle through its probability density and phase. While stationary states are characterized by conserved quantum numbers such as energy and, in central potentials, angular momentum, non-stationary superpositions - particularly in anisotropic or time-dependent fields - generally lack equally universal descriptors of their spatiotemporal dynamics. Here we introduce quantum hodographs: the trajectories traced in time by the expectation value of observable vector quantities. For a free electron in a superposition of three plane waves, all hodographs of the probability current lie on a universal cubic surface with conical singularities. Rational frequency (energy) difference ratios produce non-contractible loops with well-defined winding numbers. In anisotropic harmonic oscillators the Ehrenfest trajectories - hodographs of the expectation value of particle radius-vector - form three-dimensional Lissajous knots, echoing the classical Thomson vortex-atom model. Externally driven quantum systems allow controllable initiation of knotted hodographs. We propose an optical modulation spectroscopy scheme for reconstructing these topological features in trapped ions and single-electron systems. The topological indices of the loops and knots are robust to parameter variations, offering a new tool for characterizing complex quantum dynamics.

Cloaking and holography – usually treated as distinct problems – are two limits of a single operation that we call acoustic disguising, realized here using immersive boundary conditions on a closed surface. Driving the boundary with homogeneous Green's functions suppresses any incident field inside the enclosed volume and cloaks unknown objects broadband; driving it with scattering Green's functions synthesizes a holographic scatterer indistinguishable from a target for arbitrary illuminations. Combining the two, using heterogeneous Green's functions, replaces the scattering signature of one object with that of another, transforming its acoustic identity. We demonstrate the framework in three-dimensional FDTD simulations driven by impulsive Green's functions, complemented by data-driven Green's-function retrieval, establishing a direct route to real-time 3D acoustic cloaking, holography, cloning, and disguising.

Deep learning based approaches have shown great promise in cross-device disruption prediction for tokamaks, however, the robustness of these models heavily relies on massive amounts of training data. For the upcoming ITER, to ensure the safety of the first plasma and subsequent operations, experimental data should be entirely unavailable initially, and disruptive discharges should be strictly avoided thereafter. This extreme data scarcity inherently conflicts with the data-intensive nature of deep learning algorithms. To address this challenge, we utilize synthetic diagnostic signals from the target device to supplement the experimental data from existing devices for the zero-shot disruption prediction on a new device. The detailed implementation pipeline of this scheme is presented. For experimental validation, a predictive model trained on data from the EAST tokamak is deployed for a zero-shot cross-device experiment on the J-TEXT tokamak. A synthetic diagnostic framework, configured with the diagnostic parameters of the target device, is developed to process NIMROD magnetohydrodynamic (MHD) simulation data based on the target device's magnetic configuration, thereby achieving effective data augmentation. Ultimately, the results demonstrate that by integrating the target device's synthetic diagnostic data with Fourier Domain Adaptation, the zero-shot accurate early warning rate of the model on 1,596 J-TEXT discharges is improved from 50% to 57%, while exhibiting enhanced predictive robustness.

Neutral-atom arrays combine scalable qubit registers, long coherence times, flexible optical control, and strong Rydberg-mediated entangling interactions, making them a promising platform for quantum information processing. However, physical error rates remain a challenge, and fault-tolerant quantum error correction (QEC) requires repeated mid-circuit measurement and reset of ancilla qubits without disturbing nearby data qubits. This requirement introduces significant control and architectural overhead, making qubit encoding an important architectural decision. Here, we propose a dual metastable-state qubit encoding for $^{171}\mathrm{Yb}$ atoms that utilizes two independent qubit subspaces in the $(6s6p)\,{}^3\mathrm{P}_0$ and $(6s6p)\,{}^3\mathrm{P}_2$ manifolds. The ${}^3\mathrm{P}_0$ manifold provides a long-coherence nuclear-spin (NS) qubit suitable for storage and arithmetic operations, while the ${}^3\mathrm{P}_2$ manifold provides a hyperfine-spin (HF) qubit, with $\Delta_{\mathrm{HF}} = 2\pi \times 6.7~\mathrm{GHz}$, that enables fast Raman operations and direct state-selective imaging. Coherent shelving between the two metastable manifolds connects the qubit subspaces, allowing operations to be assigned to spectrally distinct processor zones. We simulate single-qubit and two-qubit gate fidelities in ${}^3\mathrm{P}_2$, as well as coherent shelving between the HF and NS qubit subspaces. We incorporate these physical-level estimates into an architectural resource estimation and logical-level simulation. Our approach integrates mid-circuit measurements and fast qubit operations within a single-species platform, providing a versatile framework for future fault-tolerant quantum computing with neutral-atom qubits.

While neural networks excel in autonomous control, their black-box nature makes control decisions difficult to interpret and diagnose in dynamic fluids. Here, we show how self-evolving scientific agents can design explicit, neural-network-free white-box controllers by iteratively interpreting simulation evidence, accumulating control knowledge and refining controller code. We demonstrate this approach on an underactuated two-joint swimmer navigating unsteady flows via joint angular accelerations. Starting from a target-blind propulsive controller, the agent gradually constructs key mechanisms, including travelling-wave propulsion, body-frame guidance, phase-selective steering, redirect bursts and adaptive relief. The resulting controllers reach targets and generalize across changes in target position, wake geometry, cylinder count, and inflow speed without revision. Moreover, 2D control priors transfer successfully to accelerate 3D adaptation. Our work demonstrates that self-evolving agents can autonomously design physically reasoned and generalizable white-box fluid control, showing a promising paradigm beyond traditional reinforcement learning and black-box neural network control.

Finite-strain hyperelasticity on curved embedded domains poses a geometric challenge for lattice Boltzmann methods. After streaming across an embedded material surface, the missing population is recovered at the physical cut-link point, where the lattice direction, surface normal, and tangential deformation directions are generally distinct. We develop a total-Lagrangian vectorial lattice Boltzmann method that resolves this geometric mismatch for two- and three-dimensional hyperelastic dynamics. The continuum equations are written as a conservative first-order system for material velocity and deformation gradient. Vector-valued populations are chosen so that their moments recover the state and the material-coordinate Piola fluxes, giving D2Q4\(\times\)6 and D3Q6\(\times\)12 schemes from one \(D\)-dimensional construction. Curved boundaries are embedded by a level set and closed link by link through opposite-population moment identities, cut-link interpolation, and local geometric information at the boundary point. The reconstruction is coupled to a compatibility projection that keeps the recovered displacement aligned with the evolved deformation gradient on embedded active-node graphs. The resulting method extends the previous grid-aligned two-dimensional formulation to curved domains and three-dimensional lattices while retaining explicit collide-stream updates on Cartesian grids. Benchmarks in two and three dimensions show agreement with exact finite-strain fields, nonlinear radial boundary-value problems, and finite-element references.

We discuss the early history of quasiperiodic ~27-day recurrent geomagnetic activity starting with Maunder (1904, 1905), Chree (1913) and Bartels (1932, 1934), and the Bartels term "M-regions". We show the iconic "interaction region (IR)" schematic of Belcher and Davis (1971) and the further development of Smith and Wolfe (1976) and the term "corotating interaction region (CIR)". We quote the Jian et al. (2006) definition of a "stream interaction region (SIR)". We disagree with Jian et al. (2006) on the use of the term (SIR) to indicate "transient and possibly localized stream interactions" with "poor recurrence" (Gosling et al., 2001). We feel that this description is too vague for use in scientific studies. We suggest, instead identifying the specific known interplanetary phenomena: interplanetary coronal mass ejection (ICME) sheaths, ICMEs (loops, magnetic clouds, filaments), CIRs, high-speed streams (HSSs) and slow streams. All of these various interplanetary phenomena have different solar and interplanetary origins and different plasma and magnetic field properties. The different interplanetary phenomena have been shown to have different geomagnetic effectivenesses. In keeping with this theme of naming specific interplanetary phenomenon, we introduce the term "Super CIR (SCIR)", which describes a CIR associated with magnetic reconnection at the edge of a solar coronal hole with an embedded coronal jet. SCIRs are a new form of a "transient event" and can be identified by exceptionally strong internal magnetic fields and bounded by both forward and reverse shocks. The SCIR on 6-7 April 2000 caused an exceptionally strong SYM-H = -319 nT superstorm, a first detected/reported event of its kind.

Accurate oceanic forecasting is critical for climate monitoring and disaster early warning. However, ocean spatiotemporal forecasting encounters the double challenges of modeling complex dynamical systems and ensuring computational efficiency. We present Koopman Fourier Time-Differentiable (KFTD) Network, a time continuous twostage paradigm that decouples interpolation from prediction to achieve efficient and scalable spatiotemporal modeling. We map complex nonlinear dynamics into the Koopman linear space and exploit Fourier analysis to enable continuous time interpolation at arbitrary sub-steps. A lightweight residual network consumes the high fidelity intermediate states to yield the final forecast. Unlike diffusion models, KFTD eliminates multi step noise sampling and directly evolves the system in continuous time, yielding a 4 computational speedup. We further introduce a DPP Loss that supports arbitrary PDE constraints in an endtoend manner, breaking the physical consistency bottleneck of pure data-driven approaches. Empirical results on four ocean datasets confirm that our continuous time framework reduces MSE by an average of 5.6% (up to 12.7% for SST) and improves efficiency over MCVD by 76.25%.

This work advances epidemic control beyond traditional mass vaccination models by integrating population heterogeneity, network structure, and machine-learning-based decision policies. Using the Email-Eu-core contact network, we compare classical centrality-driven vaccination strategies with graph neural network (GNN) and reinforcement learning (RL) approaches. Across 30 stochastic simulations, classical heuristics, including degree, betweenness, and layer-based vaccination, exhibit similar performance, reflecting the network's dense connectivity and modest community structure. In contrast, the GNN-based strategy substantially reduces peak infection, final epidemic size, and time to peak, demonstrating its ability to identify structurally critical nodes that classical metrics overlook. These results show that learning-based vaccination policies can significantly outperform traditional heuristics by exploiting higher-order relational patterns in real-world networks, offering a powerful framework for targeted epidemic intervention.

Technological societies are separated in time, not just space – that is the lesson of the Drake equation. Might the best way to seek them be to find technosignatures that persist long after their creators? I present work I and my collaborators have done on the idea of passive technosignatures, requiring no upkeep from an active society. These range from microscopic to galactic in scale, including specular reflections from shiny artifacts in the Solar System, lens flares from X-ray binaries, and the survivability of Dyson swarms. I discuss prospects for detecting these technosignatures. In the end, what we may be left with are the end products of collisional cascades: dust.

Solving Bethe-Salpeter equation (BSE) for the two-particle Green's function is the most widely used approach for taking into account the particle-hole (exciton) interaction in electronic excitation in the context of the many-body theory based on Green's function. In BSE calculations, the static approximation to the screened Coulomb interaction kernel is commonly employed. However, when the excitonic character is significant as typically indicated by a large exciton binding energy, dynamical screening effects become non-negligible, rendering the static approximation questionable. Because of the large computational cost due to the dense Brillouin zone integration necessary for convergence, solving the dynamical BSE for extended systems remains a significant challenge, especially when combined with GW calculation for the calculation of quasi-particle energies. In this work, we formulate the plane-wave based effective dielectric function method [Zhang, et al., Phys. Rev. B 107, 235205 (2023)] for the dynamical BSE calculation using atom-centered orbitals as basis functions. We implement this approach in our recently developed all-electron numerical atom-centered orbital (NAO) implementation of BSE@GW [Zhou, et. al. J. Chem. Theory Comput. 21, 291 (2025)] for extended systems. We validate our all-electron NAO-based implementation of the dynamical BSE method, and we then discuss its realistic application to molecular crystal of naphthalene by performing the dynamical BSE@G0W0 calculation.

Stochastic nonlinear dynamics underlie many models in engineering and computational physics, yet accurate high-dimensional simulation remains challenging. We present a quantum algorithm for a broad class of $N$-dimensional stochastic differential equations with dissipation and quadratic drift. The algorithm applies to strongly nonlinear systems with all-to-all interactions, thereby extending the scope of previously known quantum algorithms that were limited to weak nonlinearity and sparse systems. For norm-preserving drifts, a condition satisfied by key fluid dynamics discretizations, our method approximates expectation values of low-order correlation functions with rigorous error bounds at a cost polynomial in $\log{(N)}$ and linear in the evolution time. Our main technical advance is a subroutine for simulating an auxiliary system of $N$ interacting quantum harmonic oscillators with cost polylogarithmic in $N$. Finally, we formulate turbulence models, including Navier-Stokes and damped Euler equations, within this framework, opening a route to quantum simulation of strongly nonlinear SDEs governing turbulence and nonlinear wave dynamics.