Papers

Phase separation in network-forming materials couples mass transport to mechanical relaxation, yet how adsorbing boundaries reshape this coupling remains unclear. We compare fluid-particle-dynamics simulations resolving solvent hydrodynamics with free-draining Brownian dynamics for attractive colloids confined between parallel walls. Adsorbing boundaries do not slow coarsening uniformly but stratify the network into spatially distinct kinetic regimes. With hydrodynamics, the self-similar central network exhibits growth consistent with poroelastic relaxation, Lb~t^(1/2), whereas the adsorbed region follows an effective Lw~t^(1/3) law. In free-draining dynamics, central coarsening approaches t^(1/3), while the boundary exponent remains time dependent over the accessible times. Direction-resolved displacements reveal suppressed wall-normal motion and reduced lateral mobility near the walls. Increasing wall attraction connects adsorbed islands into an extended layer while leaving local dense-phase packing nearly unchanged. For island-like domains, we propose a testable encounter-controlled scaling framework linking boundary growth to domain geometry, size-dependent mobility, and evolving adsorbed mass. Locally self-similar central coarsening thus coexists with distinct or nonstationary boundary kinetics, precluding a common dynamic-scaling length for the central and adsorbed regions over the accessible times; in both regions, the growth law depends on whether solvent hydrodynamics is resolved.

Scientific observations are frequently distributed across locations, time periods, and institutions. Combining such observations into a continuous, differentiable field enables recovering governing physical parameters from its derivatives. This paper makes two contributions in this setting. First, the established additive structure of fixed-basis ridge-regression statistics is applied to tensor-product spline fields: each data holder computes a local Gram matrix and moment vector, and the merged solution is mathematically identical to centralized fitting, with no raw data shared and no iterative synchronization. This property is specific to the fixed-feature squared-error setting; the present derivation does not establish an analogous guarantee for general jointly trained multilayer networks. Second, a complete pipeline connects distributed observations to physical parameter inference through field reconstruction, derivative extraction, and linear regression. The pipeline is validated on four PDEs: diffusion, wave, heat-with-source, and the nonlinear viscous Burgers equation, recovering governing parameters to sub-percent accuracy in the linear cases and 5% for Burgers. In all cases, distributed merging introduces zero degradation relative to centralized fitting. Synthetic experiments validate parameter recovery; application to 41 years of NOAA sea-surface temperature data validates field reconstruction and aggregation equivalence on real spatiotemporal observations. Source code to reproduce all experiments is available at https://github.com/NAVEENMN/splinemerge.

KEMPIC-3D, a fully electromagnetic three-dimensional particle-in-cell (PIC) code, has been developed in C/C++ and systematically verified for the self-consistent simulation of kinetic plasma phenomena. The numerical framework combines a staggered Yee finite-difference time-domain solver for Maxwell's equations with a relativistic Boris particle pusher, trilinear field interpolation, and charge-conserving current deposition. Its modular architecture emphasizes algorithmic transparency and extensibility, allowing the principal numerical routines to be directly inspected and modified to incorporate additional physical models and computational capabilities. The implementation is assessed through a hierarchy of benchmark problems addressing both the individual numerical components and the self-consistent particle–field coupling. The particle pusher is verified against analytical solutions for relativistic charged-particle motion, while the electromagnetic field solver is evaluated through guided-wave propagation and grid-convergence analysis. The complete PIC algorithm is subsequently verified through plasma oscillations, global energy conservation, and discrete charge continuity, demonstrating the numerical accuracy, conservation properties, and self-consistency of the coupled formulation. The capabilities of KEMPIC-3D are further demonstrated through a representative simulation of laser-driven plasma wakefield generation in an underdense plasma. The simulations reproduce the characteristic nonlinear wakefield structure and exhibit stable numerical behavior across different particle discretizations without additional spatial filtering. Together, these results establish KEMPIC-3D as a reliable, transparent, and extensible computational framework for multidimensional kinetic plasma simulations.

Sample-based quantum diagonalization (SQD) is a hybrid quantum-classical method for electronic-structure calculations. We applied SQD to a titanium-based metallocene catalyst system for 1-hexene production, where free-energy differences must be predicted within 1 kcal/mol to accurately determine product selectivity. However, currently accessible active spaces on quantum computers are limited in size, leaving significant dynamical correlation effects outside the active space. To address this challenge, we combined SQD with Tailored Coupled Cluster (TCC) theory. In this SQD-TCC framework, static correlation is treated by SQD, while dynamical correlation is incorporated through CCSD and its perturbative triples extension, TCC(T). We calculated the relative energies of two transition states governing 1-hexene selectivity. While SQD alone did not yield converged relative energies within practically accessible active spaces, SQD-TCC and SQD-TCC(T) provided reasonably converged results. Importantly, the relative energy obtained from SQD-TCC(T) differed by more than 1 kcal/mol from the corresponding CCSD(T) result, demonstrating the significance of static correlation in this system and its impact on predicted selectivity. Although TCC(T) calculations based on CASCI are feasible for small active spaces, the present system requires substantially larger active spaces that are beyond the reach of CASCI. By using SQD, we were able to access active spaces impractical for classical CASCI calculations. These results demonstrate that both static and dynamical electron correlation are essential for a reliable description of this chemistry and highlight the importance of incorporating dynamical correlation into quantum-computing approaches targeting chemically accurate simulations.

The adverse impacts of extreme heat on human health are greater when concurrent daytime and nighttime heatwaves (CDNHWs) occur than when daytime or nighttime heatwaves occur individually, due to reduced recovery time from heat exposure. This study projects future changes in CDNHWs across East Asia under RCP2.6, RCP8.5, SSP1-2.6, and SSP5-8.5 scenarios. Daily maximum and minimum temperatures are derived from 3-hourly temperatures at 25-km resolution produced by 12 GCM-RCM chains participating in CORDEX-East Asia Phase 2. During the historical period (1981-2005), the East Asian mean CDNHW occurrence period and rate from April to September are 10.9 days and 0.9 percent, respectively. By 2071-2100, they increase to 3 weeks and 3.7 percent under RCP2.6, 2 months and 20.5 percent under RCP8.5, 2 months and 15.6 percent under SSP1-2.6, and 3 months and 45.7 percent under SSP5-8.5. CDNHW intensity and spatial extent also increase substantially. The proportion of CDNHWs lasting more than 10 days increases from 0.2 percent historically to 1.2 percent, 7.2 percent, 6.1 percent, and 17.3 percent under RCP2.6, RCP8.5, SSP1-2.6, and SSP5-8.5, respectively. Particularly large increases in CDNHW occurrence and intensity are projected over Indochina, East and West China, and India. Under continued high greenhouse gas emissions, East Asia is expected to experience unprecedented heat stress as CDNHWs become substantially more frequent and intense by the end of the 21st century.

Structured illumination microscopy (SIM) extends the optical passband, and reconstruction of detail beyond it depends on prior knowledge. Hand-designed regularizers depend on how well their structural assumptions match the specimen; learned priors can be sensitive to changes in imaging conditions and specimen structure. We introduce SIMAdapter, which pretrains a network that predicts the emitter and the point-spread function (PSF) by self-supervision on 23,237 raw SIM stacks from BioSR, BioTISR, and simulations spanning different PSFs and specimen structures, then adapts it to a single unlabeled target stack. Adaptation refines the network against a differentiable image-formation model, with the light pattern calibrated from that stack. Both stages take their supervision from the raw measurements and need no paired high-resolution reference. On two held-out synthetic domains, SIMAdapter reaches a mean emitter normalized root-mean-square error of 0.156, compared with 0.403 for Sparse-SIM. The same adaptation started from a network pretrained on BioSR alone is less accurate in both domains. In three experimental case studies, adaptation reduces flanking artifacts and yields more distinct profiles across filament pairs, mitochondrial boundaries, and calibration lines. A single pretrained network can thus be reused across SIM measurements, with each reconstruction refined against its own raw data.

Inverse problems are predominantly solved within the optimization-driven paradigm, which formulates the problem as objective-function minimization and approaches the solution by iterative search. Though universal, it suffers from limitations in efficiency, interpretability, and multi-parameter decoupling. This paper proposes a new paradigm—Structure-Driven Inversion (SDI). Instead of iterative search, SDI identifies and exploits the intrinsic structure of the problem to construct a solution method. It has two types of structure—mathematical structure and physical structure—and two corresponding driving inversion types: Mathematical Structure-Driven Inversion (MSDI) and Physical Structure-Driven Inversion (PSDI). The current representative method of MSDI is Mathematical Structure-Driven Pseudo-Inverse Inversion (MSDPII), which constructs a pseudo-inverse in the spectral domain via unitary diagonalization. The current representative methods of PSDI are Physical Structure-Driven Waveform Inversion Imaging (PSDWII), which performs inversion through virtual-source projection, and Physical Structure-Driven Back-Propagation (PSDBP), which implements three structural projections for deep neural networks via physical-system analogy. SDI is not a rejection but a complement and extension of Optimization-Driven Inversion (ODI). To the best of the author's knowledge, no existing study has systematically presented structure-driven inversion as an independent paradigm; this paper aims to fill that gap.

Magnetic MXenes are promising candidates for future spintronics due to their intriguing properties. Nevertheless, the topological properties of magnon bands and superexchange mechanism remain relatively underexplored. In this work, we have calculated the spin wave dispersions for MXene Fe$_2$C using the linear spin wave method, with the exchange coupling parameters obtained from first-principles calculations. Owing to the staggered stacking of two Fe sub-lattices, the interlayer exchange coupling lifts the degeneracy of the ferromagnetic magnon modes. A Dirac point is identified in the magnon bands at the $K$ point. The topological properties of magnon bands, including Berry curvature, valley Chern number and edge states, are further computed by means of a two-band model. We also derive an effective Hamiltonian to explain the magnonic topology, which is protected by the $C_{3v}$ rotational symmetry. To understand the microscopic origin of the exchange couplings that govern the magnon bands, we then construct a refined model integrating perturbation theory and the tight-binding approach. The ferromagnetic superexchange coupling is mediated by the virtual hopping between the d orbitals of two distinct Fe sites via a pair of orthogonal p orbitals at the bridging C site. In detail, the intralayer exchange coupling is governed by the in-plane $p_x$ and $p_y$ orbitals, while the out-of-plane $p_z$ orbital is responsible for the interlayer exchange coupling. Our results establish a direct link between the orbital-resolved anisotropic superexchange and topological properties of the magnon bands in magnetic MXene Fe$_2$C, providing theoretical guidance for future spintronics applications.

Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of 3D generative models for larger and more complex molecules, restrict access to unexplored chemistry. Here, we introduce QALPA ("Quantum-Aware Learning for Property-space Augmentation"), a property-guided generative framework that combines an E(3)-equivariant diffusion model with active learning and efficient quantum-mechanical (QM) methods to iteratively explore targeted QM property manifolds. By coupling generation with physics-based evaluation, QALPA improves molecular sampling and model reliability in sparsely populated regions of chemical space. Our results show that training on complementary QM datasets spanning both small (QM7-X) and large (Aquamarine) drug-like compounds enables accurate molecular generation across a broad size range, improving transferability beyond the training distribution for complex property manifolds involving both extensive and intensive properties. As a proof of concept, QALPA coupled with the machine learning-augmented tight-binding method EquiDTB efficiently augments alloQM, a QM dataset introduced in this work, comprising 6,253 conformers of allosteric drug molecules, by populating sparse regions of the property landscape defined by the many-body dispersion energy and HOMO-LUMO energy gap. These results demonstrate that the integration of generative AI with efficient ML/QM methods offers a practical pathway toward augmenting sparse QM datasets and sustainably expanding the exploration of chemical space for molecular discovery.

Reduced-order modeling of transport-dominated systems remains challenging because moving fronts and shocks are poorly represented by low-dimensional linear subspaces. We develop a structure-informed data-driven reduced-order model(ROM) for scalar hyperbolic conservation laws based on the kinetic defect formulation. This formulation separates the nonlinear dynamics into known characteristic transport and a kinetic entropy defect localized on the shock manifold. We exploit this structure by first removing the known transport from the solution snapshots. We then extract and register the remaining defect-driven dynamics in a shock-attached coordinate system. Separate ROMs are used to evolve the shock geometry and the registered defect-driven source. During prediction, the predicted shock geometry is used to inverse-register the learned defect-driven source, which advances the kinetic state and recovers the physical solution. Numerical examples in one and two spatial dimensions demonstrate accurate reconstruction and prediction of nonlinear transport with shocks, including evolution beyond the training interval, while accurately capturing the mass and entropy-dissipation behavior of the reference solution.

How global tropical cyclone frequency will respond to climate warming remains uncertain, with projections spanning both increases and decreases. Here we drive an environment-dependent probabilistic tropical cyclone model with 14 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under intermediate- and high-emissions scenarios. Initially weak global changes evolve into a clearer late-century decline, driven by a strengthening Southern Hemisphere decline and a diminishing Northern Hemisphere increase, and stronger under higher emissions. Counterfactual decomposition shows that this stronger decline is not explained by individual environmental predictors, whose contributions largely offset, but by negative nonlinear interactions among absolute vorticity, vertical wind shear, relative humidity and potential intensity. Across 28 model-scenario projections the frequency trend scales linearly with global warming; conditioning on 2 degrees C narrows the 95% confidence interval from [-20.6%, +9.9%] to [-8.2%, -2.1%]. These results reconcile divergent projections through a common warming-dependent decline.

Many important chemical systems, from radiation-driven processes to condensed-phase photochemistry, involve reaction mechanisms that are so complex it is a challenge to characterize them experimentally or predict them from chemical intuition. Existing computational approaches to mechanism discovery typically assess pathway importance through thermodynamic favorability alone, which does not provide time-dependent kinetics or inform how to model spatial inhomogeneities. Here we describe an integrated workflow that discovers complex reaction mechanisms without prescribing them and connects molecular-scale reactivity to spatiotemporal observables. The workflow combines high-throughput DFT, automated reaction network construction with chemical plausibility filtering, stochastic pathway sampling to identify reactions which are likely to occur, and spatially resolved reaction-diffusion kinetics simulations with explicit tracking of species in space and time. To demonstrate the workflow on a system of high complexity, we apply it to radiolytic chemistry in an extreme ultraviolet (EUV) organic polymer thin film photoresist, where a single 92 eV photon initiates cascades of radical ions, fragments, and low-energy electrons across a nanoscale radiolytic spur. Starting from over 3,300 species and millions of candidate reactions, the workflow identifies the most likely reaction pathways and produces spatiotemporal maps that resolve product formation on femtosecond-to-nanosecond timescales across a 15.5-nm domain. The simulations predict products detected experimentally and reveal that the identity of the initially photoionized species profoundly shapes the downstream product distribution through multi-step pathways governing the balance between deprotection and crosslinking reactions. The methodology is broadly applicable to complex condensed-phase reactive systems.

We investigate how unsupervised machine-learning methods can characterize the active-absorbing phase transition in the contact process in one and two spatial dimensions. Our analysis focuses on principal component analysis (PCA) and variational autoencoders (VAEs), and we show that direct applications of these methods encounter a central difficulty: contact process configurations are intrinsically positive-definite. In the PCA case, standard centering procedures fail to isolate the order parameter in the leading component. To address this, we augment the data with sign-reversed configurations, creating a balanced dataset with zero mean while preserving the density information required to describe the transition. This preprocessing allows the dominant principal component to recover the order parameter and reproduce the expected finite-size scaling near criticality. For VAEs, we find a related limitation: a fixed Gaussian prior does not provide sufficient latent regularization across the full range of control parameters and system sizes for positive-definite data. We overcome this by adopting a heteroscedastic Gaussian prior that adapts to the control parameter, leading to a substantial improvement in the finite-size scaling of reconstruction-based observables near the critical threshold. Taken together, these results highlight a key lesson: the success of machine learning in phase-transition problems does not rely on algorithmic complexity alone, but on tailoring the representation to respect the physical structure of the system, including its intrinsic constraints and symmetries.

A variety of problems at the frontier of nonlinear optics involve strongly multimode interactions. Simulating these dynamics is often computationally challenging due to the complexity of evaluating a large number of nonlinear overlaps. This is especially acute in ultrafast Kerr effect physics, where simulations in a modal representation have a generic complexity scaling quartically with the number of modes. This has limited the largest number of modes retainable in simulations in practice to a few tens of modes (often fewer), even when run on modern GPUs. Here, I show how by taking advantage of parallel factor decompositions of the nonlinear overlap tensor, a far larger number of modes can be accommodated. Realistic values for generic index profiles without special symmetries or separability can be in the thousands, and with special symmetries, far larger. I show that simulations with $>$1,000 modes can be done on modern hardware, illustrating this with the example of a multimode soliton propagating over a meter of GRIN fiber. I further show that the improved scaling immediately makes it practical to consider statistical effects, illustrating with the example of noise amplification in soliton fission. These results, implemented in public code with examples, should enable access to simulating the most demanding regimes of multimode nonlinear optics. Compression of the nonlinear interaction tensor may provide a route for more efficient simulation of nonlinear wave dynamics across a variety of other systems including acoustics, water waves, phononics, magnonics, and Bose condensates.

The pace of evolution depends on how rapidly new phenotypes arise. We show that neutral Wright-Fisher evolution exhibits anomalous first passage despite diffusive mutations. The mean time for the first individual to reach a prescribed phenotypic distance scales approximately as $(\sigma^2)^{-3/2}$ with mutation variance $\sigma^2$. Two crossovers bound this regime, with inverse-variance scaling on either side. Combining coalescent theory with Kardar-Parisi-Zhang (KPZ) fluctuations at the dilute population edge, we develop an edge-KPZ theory of all three regimes. The anomaly persists under weak selection.

Turbulence is synonymous with inertia dominated fluid motion, entailing high velocities and large spatial scales. Conversely, in complex fluids, elastic material properties can promote and sustain turbulent-like motions even at arbitrarily small inertia. The common route to this purely elastic state of turbulence, however, strictly occurs only in specific flows with curved streamlines, thus excluding canonical cases such as pipe flow. Investigating dilute polymer solutions in pipe experiments across an unprecedented elasticity and viscosity range, we trace an instability, previously assumed to require finite inertia, to Reynolds numbers four orders of magnitude below the theoretically predicted minimum level. At sufficiently high polymer concentrations and large shear rates, an abrupt drop in the transition threshold confirms that the vanishing inertia limit has been reached, and consequently that the fluctuating motions observed are of purely elastic origin.

We develop a two-dimensional inviscid model for extensible membranes with continuous vortex shedding from both edges, coupled to a weak spectral Galerkin discretization of the membrane equation that permits direct computation at zero bending rigidity. Viewing the pressure jump as a flux for bound vorticity explains how membranes respond to concentrated fluid loading. For fixed-fixed membranes, compliance draws the body toward the leading-edge vortex, delaying its detachment and enhancing lift primarily during start-up; after periodic shedding develops, compliance mainly amplifies the force oscillations rather than mean lift. Vortex detachment can unload heavy membranes into compression, producing bending-regularized wrinkles. For $R_2>0$, the membrane equation remains well-posed independently of the sign of the tension, provided the membrane remains an immersion. Releasing the trailing edge creates an intrinsic degeneracy: the tension vanishes at the free endpoint, making the $R_2=0$ problem formally underdetermined, while the weak formulation selects its bounded-energy branch. This vanishing-tension endpoint traps pressure disturbances and drives rapid tip-snapping; the dominant frequency and effective wave number approach their zero-rigidity limits with an inverse-logarithmic correction arising from a singular Bessel branch. Finally, decreasing the stretching modulus destabilizes periodic flutter, and the repeated fluid-driven departure and elastic return suggest a homoclinic tangle underlying the transition from periodic to chaotic flutter.

A spatial photonic Ising machine (SPIM) solves large-scale combinatorial optimization problems by computing the Ising Hamiltonian through an optical Fourier transform. However, the ground-state search relies on the annealing process, in which spins are optimized stochastically and sequentially. We propose a ground-state search based on phase retrieval (PR), in which all spins are updated simultaneously and deterministically. By imposing an amplitude constraint in the Fourier plane, the modulated phase distribution corresponding to a spin configuration is guided toward the optimal solution. We numerically demonstrate that the proposed scheme reaches the ground state of rank-one Ising Hamiltonians with $10^4$ spins in a single iteration for all trials. Moreover, a radial rearrangement of the amplitude and the suitable design of the target pattern relaxed the search stagnation and promoted the optimization of spin configurations. The collective and deterministic spin update by phase retrieval provides a fast ground-state search for large-scale combinatorial optimization.

Purpose: To characterise and harmonise multileaf collimator (MLC) gap calibrations across four beam-matched TrueBeams and assess a common planning-system model. Methods: Four Millennium 120 TrueBeams were studied: TS1 (installed 2016), TS5 (2021), TS6 and TS7 (2024). Gap calibration was assessed by RayStation sweeping-gap measurement, feeler gauge, the EPID-based Stakitt Fence test, and a leaf-resolved Machine Performance Check (MPC). TS1 and TS5 were adjusted in service mode, TS5 also for MLC centreline offset. Ten clinical VMAT plans, and six acquired earlier on an unadjusted reference machine, were measured with an ArcCHECK 1220 against doses computed at 0.053 and 0.065 cm x-offsets in RayStation. Results: The 2024 machines had wider baseline gaps. All four methods registered the TS1 and TS5 changes in the same direction and within 0.060 mm of predictions. After adjustment TS1 and TS5 lay within the TS6/TS7 sweeping-gap and Stakitt ranges but read wider on the feeler gauge. A conventional picket-fence test on TS5 registered no change. Raising the RayStation x-offset parameter increased mean local 3%/2 mm pass rates from 92.3% to 94.8% on TS1 and 93.2% to 95.6% on TS5; all 24 plan-criterion pairs improved on the unadjusted machine. Conclusions: Mechanical, EPID, and sweeping gap MLC gap measurements responded consistently to the MLC adjustment, moving TS1 and TS5 towards the local reference range. Every global 3%/2 mm ArcCHECK measurement exceeded the TG-218 95% limit.

Our understanding of how geographical and social segregation interact remains limited, as relatively few studies investigate them jointly, and existing approaches often lack a framework distinguishing geographical, social, and total segregation. Additionally, large-scale individually resolved geo-social network data are rarely publicly available. We address both. Conceptually, we develop a unified framework that measures segregation in geosocial networks by comparing network models to appropriate null models and recovers the Theil index, dissimilarity index, and network modularity as special cases. Empirically, we turn to privacy-preserving aggregated relational data (ARD): we combine the Facebook Social Connectedness Index for the US with US Census and Pew data, and introduce an ARD-compatible joint geosocial intervening-opportunities model to infer link probabilities between region–group cell pairs. Applying our segregation framework, we find that social segregation predominates over geographical segregation, with notable separation for White–Black, college-degree–no-degree, and high-income–low/middle-income across both segregation types. We find increasing social homophily with geographical distance and group-specific geographical connectivity patterns, suggesting that geographical segregation may affect cross-group connectivity not only directly but also by amplifying social segregation.

The hulusi is a wind instrument that was invented in Yunnan Province, China, and has become tremendously popular in recent years. It consists of a mouthpiece, a gourd, and three bamboo tubes, all with free reeds made of copper. The main bamboo tube in the middle has seven finger holes. In this instrument, the pipe length, not the free reed's eigenfrequency, determines the instrument's pitch, unlike, for example, with the Western accordion or the blues harp. In this study, a machine learning model implemented in the COMSAR framework (https://github.com/ifsm) was used to investigate the timbre characteristics of the hulusi to cluster different instruments and pitches. The measured hulusi pitches C, B, A, G, and F were analyzed according to seven psychoacoustic features, among which only the spectral centroid, sharpness, and fractal correlation dimension are shown to form pitch clusters. These timbre features were used to train Kohonen self-organizing maps (SOMs) for clustering. Brightness and sharpness analysis revealed that the highest pitches were less bright and less sharp than mid- and low-range pitches were. Furthermore, the fractal correlation dimension, which mainly determines the chaoticity of the initial transients, was the best-clustering timbre feature for the hulusi, with the highest pitches showing the least chaoticity. This result is supported by defining a cluster quality index for the SOMs.

Kalshi's multivariate-event architecture produces market objects on demand from exact selected legs. Across a registered seven-day interval, 190 independently validated temporal shards yield 7,611,594 unique REST MVE market tickers after excluding 5,777 boundary-overlap observations; the population was created at an average rate of 1.087 million objects per day, with strong hourly burstiness. The hierarchy is sharply compressed relative to the market-object count: 5,262,526 exact event keys, three collection keys, 83,701 selected-leg primitives, and 66,344,938 selected-leg occurrences. The largest collection accounts for 7,217,085 objects (94.82 percent), with an effective collection count of 1.11; the effective primitive count is approximately 720. REST and WebSocket are distinct observation surfaces: 1,487,330 created-notification tickers intersect the REST population in only 276,177 exact tickers. All three observed collection keys have current endpoint confirmation without establishing historical collection or rule versions. The exact structural signature yields 7,611,594 unique signatures with zero collision groups. At retrieval, 2,692,787 objects (35.38 percent) have positive cumulative volume or open interest; all observed 24-hour-volume values are zero and trade-count fields are unavailable. This is current-snapshot activity eligibility, not historical trading. The central result is that a very large on-demand market-object population is generated by a concentrated collection layer and reused primitive vocabulary; economic breadth must be measured at several hierarchical levels rather than by ticker count alone. Exact public endpoint evidence is available for 7,357,576 MVE objects, whereas only 126,806 have an exact determination-to-endpoint pair: terminal-state coverage and lifecycle-path coverage are distinct statistical objects.

The ensemble smoother with multiple data assimilation (ES-MDA) is an algorithmic framework for the ensemble-based solution of inverse problems in reservoir engineering (and beyond). ES-MDA gradually transitions a prior ensemble to a posterior ensemble. The details of how this transition, or "multiple data assimilation," is implemented defines the accuracy and computational cost of ES-MDA. We show that many popular, adaptive variants of ES-MDA can be understood within a simple three-step framework: inflation proposal, pre-analysis revision, and post-analysis revision. The three steps interact to resolve a trade-off between accuracy (many assimilations with small updates) and efficiency (few assimilations with large updates), inherent to ES-MDA. We then present a new adaptive and accuracy-aware method, ES-MDA-A2, that combines large updates with a "catch-up" mechanism that decreases the update size allowing for additional assimilations if the accuracy is low. ES-MDA-A2 requires only two inputs: a targeted accuracy and a maximum number of data assimilations. We test existing and new ES-MDA variants in systematic numerical experiments with a toy model, two electromagnetic inversions with field data, and a subsurface flow reservoir simulation. We find that ES-MDA-A2 resolves the accuracy-efficiency trade-off differently from existing methods, leading to accurate inversions at a reasonable computational cost in all experiments.

We present an implicit time integration capability for high-order curvilinear finite element Lagrangian hydrodynamics. Starting from an existing explicit formulation, the implicit treatment builds directly on the original discretization and operator structure and does not alter the underlying spatial formulation or physics model. We demonstrate our implementation using the Laghos miniapp, which is built on the MFEM finite element library. To support gradient-based nonlinear solution methods, we compute Jacobian actions automatically using MFEM's $\partial$FEM interface together with Enzyme-based automatic differentiation, and apply the resulting Jacobian in a matrix-free or fully-assembled manner within a Newton-Krylov solver. To ensure robust and differentiable nonlinear solves in the presence of shocks, we introduce a smooth artificial viscosity treatment based on smooth approximations of non-differentiable pointwise operations. The differentiable artificial viscosity presently lacks a limiter to ensure high-order scaling away from shocks, but is sufficient for illustrating the benefits of implicit Lagrangian hydrodynamics. The behavior and performance of the implicit method are demonstrated on several standard benchmark problems. We verify high-order convergence on the smooth Taylor-Green vortex in the absence of artificial viscosity, show correct strong-shock behavior on the Sedov blast problem, and obtain significant improvements in accuracy-per-time-to-solution on the Triple Point problem where explicit stability constraints become increasingly severe for high-order discretizations.

Jets in stratified flows are ubiquitous in geophysics. A fundamental question is whether the instabilities and coherent structures generated by the flow interact with the physical boundaries and, if so, how. To this end, a simple canonical model flow is employed: a parallel Bickley jet confined between walls in a uniformly stratified fluid. We first show that short wavelength Kelvin-Helmholtz instability can generate asymmetry in the mean flow. Although the fluctuations decay rapidly away from the jet, the asymmetric mean flow does not. We then examine long-wavelength coherent structures that can emit internal gravity waves, which decay slowly towards the walls or, in some cases, radiate away from the jet. These waves transport momentum and distort the mean flow, reducing the jet centreline velocity significantly. When radiation occurs, whether the instability persists at high Reynolds numbers depends strongly on the boundary conditions imposed at the walls. Moreover, we found an instability and associated nonlinear coherent structures that persist at Richardson numbers above the Miles-Howard criterion. The existence of this viscous mode does not rely on the presence of the walls.

Laboratory automation can improve experimental throughput, accessibility, and reproducibility, but many robotic laboratory systems remain difficult to reconfigure. This challenge is especially pronounced in free-space optics, where experiments are built from heterogeneous components, require precise alignment, and are frequently rearranged as experimental goals change. In this work, we present the Programmable Infrastructure for Cloud Optics (PICO), a robotic cloud-laboratory architecture designed to make reconfigurable optical experiments easier to program, operate, and reproduce. PICO provides a common domain-specific abstraction and software layer through which experimental configurations and actions can be controlled across different user interfaces. This enables the same physical laboratory to support remote interactive use, scripted experiments, autonomous routines, and features such as version control. We implement PICO on a robotic free-space optics platform and demonstrate it through an experimental case study.

We present a highly parallelizable, matrix-free, real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory. In particular, we avoid the explicit construction and eigen-decomposition of the response function matrix and use Lanczos quadrature to evaluate the trace of a matrix function on a real-space grid. We also show it is possible to exploit the spatial smoothness of the RPA correlation energy density in real-space to reduce computational cost. Specifically, we compute the energy density on a coarse grid, then reconstruct an approximation to the full, fine grid via interpolation. We implement this formulation within the SPARC electronic structure package and demonstrate its convergence, accuracy, agreement with planewave results, and scaling. Interpolation can enable an $8\times$ reduction in the computational prefactor. Given the embarrassingly parallel nature of the proposed method, we achieve near-ideal speedups and near-cubic scaling, thus allowing us to compute the RPA correlation energy for a silicon system with 256 valence electrons at chemical accuracy in less than 30 minutes on 4,096 CPU cores.

Beam monitoring detectors play an important role in experimental control and radiological protection by enabling the real-time characterization of particle and photon beams. They provide information on beam intensity, position, and scattering, helping to ensure measurement accuracy, protect sensitive detectors, and quantify stray radiation in the surrounding environment. In this work, a Geant4 simulation was developed to investigate the performance of an X-ray beam monitoring detector (BMD) composed of six hexagonal BC404 plastic scintillators arranged to provide high granularity, using a reflector with a realistic reflectivity of 93%. Under the simulation conditions considered in this study, the detector exhibited an intrinsic time resolution of 100.8 $\pm$ 2.0~ps. The results indicated that only about 10% of the incident photons interacted with the detector, suggesting that the proposed design could be used for beam characterization while minimally perturbing the primary beam. In addition, the X-ray multiplicity decreased with distance according to an inverse-square dependence. A backscatter study performed using a water phantom showed that approximately 6% of the incident beam was detected as scattered radiation. These results suggest that the proposed detector may be a useful tool for X-ray beam characterization and for studies related to scatter radiation and radiological protection. Further experimental validation is required to confirm these findings.

X-ray spectroscopy techniques probe chemical states in systems with atom-site specificity; however, the site-selective characterization of local bond environments in novel research materials often relies on a combination of reference spectra, multiple complementary spectroscopic techniques and electronic structure calculations. Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. Here I show that the wealth of information encoded in the Auger spectrum offers the opportunity to train convolutional neural networks for site-selective functional group classification in organic molecules directly from the Auger spectrum lineshape. Furthermore, the inclusion of the core-electron binding energy as an additional modality improves the classification, either by augmenting the input fitted intensities with the binding energy or by conditioning the classification on the binding energy via feature-wise linear modulation layers. The latter approach was previously developed for visual reasoning in image classification, and the present results show that this is the more robust approach for including the binding energy. This work demonstrates the potential for new data-driven characterization capabilities in spectroscopic techniques that are information-rich but difficult to interpret.

In thin-film lithium niobate Mach-Zehnder modulators, half-wave voltage and bandwidth are tied to the same design parameters; hence, improving one usually costs the other. Several methods have been pursued in the past to alleviate this tradeoff. Specifically, the bias voltage can be reduced through non-centered placement of optical waveguides with respect to electrodes, slow-wave optical gratings, and high-permittivity cladding layers. Bandwidth can be enhanced through implementing periodic segmented electrodes and exploiting low-permittivity bottom cladding and substrate materials. The objective of this work is to study the augmentative impact of these methods. We pursue a rigorous modeling and optimization approach to achieve this objective, estimate the impact of each augmentation, and evaluate the ultimate performance of TFLN MZMs based on these methods. It is shown that a co-designed structure that incorporates all these enhancing methods can attain a drive voltage of 0.84 V and a modulation bandwidth of almost 220 GHz.