Predictive modelling of agglomeration in spray drying and particle capture in aerosol scavenging requires a fundamental understanding of droplet-particle collisions. The study complements prior work by investigating mid-air collisions between free micron-sized spherical droplets and particles with a size ratio of three. Particle wettability and density are varied to elucidate the mechanisms governing collision outcomes and the role of collision offset. Results show that particle density determines whether a particle is engulfed by the droplet or remains at the droplet interface during capture, while high wettability suppresses particle separation even in glancing collisions. A modified effective Weber number incorporating particle density and wettability is proposed to map collision outcomes. To assess its robustness, the present data are combined with literature results in a unified regime map. The regime boundaries separating collision outcomes collapse when the size ratio and Ohnesorge number are held constant. However, at a given collision offset, variations in size ratio and Ohnesorge number alter the critical effective Weber number for particle separation through changes in collision geometry and viscous resistance.

In this contribution I will review recent developments in the antenna subtraction method for higher-order calculations in QCD. In particular, I will illustrate the definition and applications of generalised antenna functions for final-state radiation at NNLO and present the first N$^3$LO differential calculation performed entirely with antenna subtraction for jet production at electron-positron colliders. Finally, I will discuss how the extension of generalised antenna functions at N$^3$LO will allow to tackle more complicated processes at this perturbative order.

Phasor measurement units (PMUs) are widely used for sub-synchronous oscillation monitoring, yet the effect of windowed discrete Fourier transform (DFT)-based phasor estimation on oscillation observability is not fully characterized. This letter derives the complete complex-valued frequency response of the windowed DFT phasor estimator under both magnitude and phase modulation. The analysis shows that the estimation window introduces both frequency-dependent magnitude attenuation and phase shift to oscillation components, governed by the complex gain. A simple recovery method is also proposed to restore the true oscillation amplitude and phase from PMU data using the analytically known complex gain. The results are validated through time-domain simulations and provide guidance for industry practitioners on interpreting PMU-based oscillation measurements and selecting appropriate window lengths.

Interacting rotating spiral waves have been observed in complex systems, such as cardiac fibrillation, cognitive processing in the brain cortex and oscillating chemical reactions, during dynamical regimes that are still poorly understood. We present the equivalent of Newton's gravitational attraction law for spiral waves on planar reaction-diffusion systems. The spiral waves' phases and positions determine their regions of influence, separated by collision interfaces. At the collision interfaces, wave front deflections cause spiral drift that pushes the interfaces forward. As a result, the spiral wave drift velocity is proportional to the total force exerted on on it, which can be determined by a boundary integral over its region of influence. The proportionality factor between force and response is akin to the `mass' of the spiral. However, this spiral mass depends on the region of influence of the spiral and thus also varies over time. The forces between spiral wave pairs are not directed along the line connecting their centers, violating Newton's law of action and reaction. Our solution to the N-body interaction problem for spirals in extended excitable media encompasses both pairwise interactions and spiral wave drift in bounded domains, with application to cardiac fibrillation.

Total knee arthroplasty (TKA) and total hip arthroplasty (THA) improve symptoms in end-stage osteoarthritis, yet long-term objective characterization of perioperative physical activity trajectories remains limited. We conducted a longitudinal observational study within the All of Us Research Program dataset, linking electronic health records with continuous Fitbit-derived step count data over a four-year perioperative window (two years before and two years after arthroplasty). Piecewise linear mixed-effects models characterized preoperative declines and postoperative recovery trajectories, and time-to-recovery was evaluated using Kaplan-Meier curves and Cox proportional hazards models under remote and immediate preoperative physical activity baseline definitions. Among 238 participants (147 TKA; 91 THA), both procedures exhibited progressive preoperative decline with distinct procedure-specific patterns and staged postoperative recovery: rapid improvement during weeks 1-6, decelerating gains through weeks 7-19/20, and subsequent stabilization through week 104. Recovery to remote and immediate baselines differed in timing (median 22 vs 13 weeks) and associated predictors. Higher immediate preoperative activity was associated with greater likelihood of recovery to habitual activity levels, underscoring the relevance of preoperative functional reserve and surgical timing. These findings demonstrate the potential of long-term wearable monitoring to refine assessment of functional outcomes, guide recovery expectations, and support perioperative management.

Accurate overland runoff and infiltration predictions are critical for effective water resources management, in particular for urban flood management. However, the inherent uncertainty in rainfall patterns, soil properties, and initial conditions makes reliable flood forecasting a challenging task. This paper presents a framework for quantifying the impact of these uncertainties on hydrologic and hydrodynamic simulations via a state space approach based on a differential algebraic equation (DAE) formulation that couples surface and subsurface constraints with the governing dynamics. Under this formulation, the complex interactions between overland flow and infiltration dynamics are captured in realtime. To account for uncertainty in inputs and parameters, the proposed framework quantifies and propagates these uncertainties through the DAE model formulation under partial measurements. The effectiveness of the approach is demonstrated through a series of numerical experiments on synthetic and real world catchments, highlighting its ability to provide probabilistic estimates of watershed state conditions while accounting for uncertainty. An important aspect of the proposed methods is that they are distribution-agnostic, i.e., they only require covariances of uncertainty and not specific types of distributions. The proposed framework is further validated against Monte Carlo (MC) ensemble simulations while providing probabilistic state estimates for measured and unmeasured watershed states under partial gauging.

The Variational Quantum Algorithms (VQAs) are hybrid quantum-classical algorithms and they can be used in the Nosiy Intermadiate Scale Quantum (NISQ) devises. The Variational Quantum Eigensolver (VQE) was suggested as a first VQA. VQE is based on the variational method of quantum mechanics and it is used to find the ground state energy of a quantum system. In this study, VQE is used for the analysis of NMR spectra for the AB and AB2 spin systems. The frequencies and the spin coupling values are obtained from the sample spectra for these spin systems. Then the Hamiltonians are written in terms of pauli spin operators and transformed into a suitable forms for quantum computer. By employing VQE the ground state energies are obtained for the related spin systems. They are found to be in good agreement with the results obtained from the known variation method.

The motion of compressible, inviscid fluid under the constant pressure on a rotating sphere is studied. The hodograph equations for the corresponding Euler equation are presented. They provide us with the class of solutions of the Euler equation parameterized by two arbitrary functions of two variables. Several particular explicit solutions are given. The blow-up curves, on which the derivatives of velocitiy blows up, are described. The limiting cases of slowly and rapidly rotating sphere are considered. The equation describing the deformations of elliptic functions modulus is presented.

The generalized Honeymoon Oberwolfach Problem (HOP) asks whether it is possible to seat $2n$ participants consisting of $n$ newlywed couples at a conference with $s$ tables of size $2$ and $t$ ''round'' tables of sizes $2m_1, 2m_2, \ldots, 2m_t$, where $n = s + \sum_{i=1}^{t} m_i $ with all $m_i \geq 2$, over several nights so that each participant sits next to their spouse every time and next to each other participant exactly once. We denote this problem by $\mathrm{HOP}(2^{\langle s \rangle}, 2m_1, \ldots, 2m_t)$. This paper is the first of two papers investigating the generalized HOP. While the second paper will deal with the generalized HOP with a single round table (i.e. table of size at least $4$), the present work develops solutions for the generalized HOP with multiple round tables. In particular, we present solutions to certain cases with two round tables, showing that a solution to $\mathrm{HOP}(2^{\langle s \rangle}, 2m_1, 2m_2)$ exists when $n \equiv 1 \pmod{(2m_1 + 2m_2)}$ or $n \equiv m_1 + m_2 \pmod{(2m_1 + 2m_2)}$. We also develop solutions for cases with small round tables, showing that $\mathrm{HOP}(2^{\langle s \rangle}, 2m_1, \dots, 2m_t)$ has a solution whenever $m = m_1 + \dots + m_t \leq 10$, $n = s + m$ is odd, and $n(n - 1) \equiv 0 \pmod{2m}$.

We investigate more closely the class of generalized b-weakly compact operators on locally convex-solid Riesz spaces and we provide new sequential and operator characterizations in relation with the subject. We introduce explicitly the so-called KR-spaces in order to study the factorization problem for generalized b-weakly compact operators by analogy with the well-known factorization of b-weakly compact operators through KB-spaces.

A unified framework is developed for determining whether a gravitational-wave (GW) background behaves as a classical field or as a genuinely quantum environment. Unified here means that both descriptions originate from the same tidal coupling derived from geodesic deviation, which yields an identical quadratic interaction Hamiltonian for the detector; the only distinction lies in whether the GW degrees of freedom are modeled as classical phase-randomized coherent states or as quantized graviton modes. Within this common framework, the reduced dynamics of a quantum harmonic oscillator exhibit a sharp structural contrast: a quantized graviton bath preserves coherence within the lowest phonon-number manifold, forming a protected sector at leading order, whereas a classical stochastic GW field inevitably induces decoherence even inside this subspace. This difference provides an operational criterion for diagnosing the classical or quantum nature of gravitational waves using mesoscopic optomechanical systems. Our results establish decoherence structure - not merely its magnitude - as a sensitive probe of gravitational quantumness and delineate the experimental regimes under which such tests may become feasible.

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as in tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator PDE surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa-Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

Modern computing is shifting from homogeneous CPU-centric systems to heterogeneous systems with closely integrated CPUs and GPUs. While the CPU software stack has benefited from decades of memory safety hardening, the GPU software stack remains dangerously immature. This discrepancy presents a critical ethical challenge: the world's most advanced AI and scientific workloads are increasingly deployed on vulnerable hardware components. In this paper, we study the key challenges of ensuring memory safety on heterogeneous systems. We show that, while the number of exploitable bugs in heterogeneous systems rises every year, current mitigation methods often rely on unfaithful translations, i.e., converting GPU programs to run on CPUs for testing, which fails to capture the architectural differences between CPUs and GPUs. We argue that the faithfulness of the program behavior is at the core of secure and reliable heterogeneous systems design. To ensure faithfulness, we discuss several design considerations of a GPU-native fuzzing pipeline for CUDA programs.

The increasing intensity and frequency of wildfires are causing significant economic and societal impacts on communities through direct effects on the built environment, particularly critical infrastructure. Electrical systems can both initiate wild-fires (grid-to-fire) and be damaged by wildfire exposure (fire-to-grid). Therefore, resilient electric systems can both limit ignitions and be hardened such that they are more robust to fire demands. Researchers have investigated wildfire mitigation strategies using traditional transmission and distribution electrical test-system models. However, these test cases may not accurately represent realistic electrical system configurations or fuel landscapes, nor capture community impacts, particularly the social and economic effects of mitigation strategies. A wildfire-aware modeling framework enables researchers to develop test cases that benchmark resilience and mitigation strategies while reducing reliance on overly simplistic assumptions about wildfire effects on electrical systems and communities. This study proposes a modeling framework for wildfire-electrical system research by analyzing recent literature and identifying key dimensions as well as gaps within these dimensions. In particular, the framework considers how fire in the wildland-urban interface propagates in space and time, how hazard-infrastructure interactions (e.g., wind and fire) cause system- and component-level damage, and how wildfire-related power outages affect communities.

With evidence for a nanohertz gravitational-wave background now established by Pulsar Timing Arrays, the search focuses on identifying individual supermassive black hole binaries. We show that these binaries produce a distinct spatial correlation pattern across the array, acting as a deterministic analogue to the stochastic Hellings and Downs curve. We derive a closed analytic expression for this single-source overlap reduction function, $Υ_{ab}$, factorizing the signal into a source-dependent amplitude and a purely geometric fingerprint. Using simulated datasets, we demonstrate that this fingerprint breaks the degeneracy between an individual binary and a stochastic background. Including these cross-correlations yields Bayes factors of $ 144$ favoring the continuous-wave model over a stochastic-background model and $\sim 80$ favoring the continuous-wave model over an uncorrelated red-noise model. Furthermore, these new cross-correlations improve sky localization by a factor of $11\times$ over an uncorrelated search. Finally, while coherent matched filtering offers higher theoretical sensitivity, we argue that a cross-correlation-based search for individual binaries provides a robust alternative that hedges against the possibility of overfitting to noise fluctuations by focusing on the evidence for the correlations. The geometric fingerprints we present here rely on stable spatial correlations rather than phase coherence to identify the first nanohertz gravitational-wave sources.

We consider the task of estimating the trace of a matrix function, ${\rm tr}(f({\bf A}))$, of a large symmetric positive semi-definite matrix ${\bf A}$. This problem arises in multiple applications, including kernel methods and inverse problems. A key challenge across existing trace estimation methods is the need for matrix-vector products (matvecs) with $f({\bf A})$, which can be very expensive. In this article, we introduce a novel trace estimator, FlexTrace, an exchangeable, single-pass method that estimates ${\rm tr}(f({\bf A}))$ solely using matvecs with ${\bf A}$. We consider the case where $f$ is an operator monotone matrix function with $f(0)=0$, which includes functions such as $\log(1+x)$ and $x^{1/2}$, and derive probabilistic bounds showcasing the theoretical advantages of FlexTrace. Numerical experiments across synthetic examples and application domains demonstrate that FlexTrace provides substantially more accurate estimates of the trace of $f({\bf A})$ compared to existing methods.

The K-dwarf TOI-4504 hosts two giant planets in 2:1 mean-motion resonance, with orbital periods of 41.3 days (planet d) and 82.8 days (planet c). They exhibit among the largest known absolute transit-timing variations, with respective peak-to-node amplitudes up to 5 and 3 days. Newer TESS data show that the previously non-transiting planet d has now precessed into transiting, and we derive updated system parameters with significant discrepancies with the discovery paper. The revised parameters place planets d and c deep in the resonance and close to or in the fully-relaxed limit-cycle state, with the resonant and secular modes interfering nonlinearly to induce non-zero relaxed free eccentricities which precess at the same rate as the forced eccentricities and the longitude of conjunctions, in turn enabling precise measurement of the full eccentricities and apsidal angles. We discuss the predictions of linear theory and how it can be used to understand the true state of the system revealed by N-body integrations, and more generally why it is that the posteriors of systems more compact than 2:1 tend to suffer from significant eccentricity degeneracy. We show that the extraordinary dynamical states of the giant pairs orbiting TOI-4504 and the M-dwarf GJ 876 are remarkably similar, in spite of the significant difference in their host-star masses, and discuss the implications for damping timescales during the relatively gentle formation process of Type II migration.

Activation foils are used to independently measure the time integrated neutron yield and total fusion energy produced in both inertial and magnetic confinement fusion, making them crucial in the neutron diagnostic suite. The activated foils must be remotely transported from the neutron source to the detector inside of a small capsule, which can impact both the foil irradiation and the associated activation measurement. The aim of this paper is to evaluate the performance of various activation foils and to characterize the effects of different capsule materials to inform the design choices for future systems, such as the SPARC tokamak. Through a combination of FISPACT simulations and irradiation experiments with a deuterium-tritium neutron generator, we tested several different material choices for foils, capsules, and gamma-ray spectrometers. Aluminum and copper foils are found to be suitable for a multi-foil irradiation configuration. The use of 3D-printed thermoplastic capsules reduces the number of measured decay-photon counts, yet the reduction is smaller than the associated measurement uncertainty. Finally, lanthanum-based detectors are shown to be viable alternatives to the standard high-purity germanium spectrometer, although with poorer energy resolution.

The pyrochlore vanadates are compelling candidates for next-generation dissipationless devices. Lu2V2O7 and Y2V2O7 are ferromagnetic insulators (Tc ~ 70 K) that are believed to exhibit the magnon Hall effect and are expected to host topological magnons. Their completely dissipationless magnon edge states could be harnessed to realize low-power information transport in spintronic or magnonic devices. As a crucial step in the realization of devices, we synthesize the first thin films of pyrochlore Y2V2O7 on isostructural Y2Ti2O7 substrates and explore the evolution of their magnetic properties down to the ultrathin limit. All films are insulating ferromagnets with transition temperatures of up to the bulk value (Tc ~ 68 K) that decrease with thickness according to finite-size effects. Our films also exhibit a change in anisotropy from in-plane to out-of-plane easy axis coincident with the development of partial strain relaxation and nonzero magnetic hysteresis in an applied field. This evolution demonstrates the impact of strain on magnetic anisotropy and paves the way to tunable magnon topology.

We investigate the effect of dilute Ir substitution on the magnetism of the trimer-based ruthenate Ba$_4$Ru$_3$O$_{10}$ using neutron diffraction, magnetic susceptibility measurements, atomistic simulations, and first-principles calculations. Neutron diffraction shows that Ir doping preserves the zigzag antiferromagnetic structure and the ordered-moment magnitude of the parent compound, in which the moments reside exclusively on the two outer Ru(2) sites of each $\rm Ru_3O_{12}$ trimer, while the central Ru(1) site remains nonmagnetic. The Néel temperature is reduced from $\approx\!105$ K to 84.0(1) K upon 8% Ir substitution, while magnetic susceptibility reveals a pronounced low-temperature Curie-like upturn, indicating the coexistence of paramagnetic spins with long-range antiferromagnetic order. Density-functional calculations shows that Ir preferentially occupies the central Ru(1) site, where its extended $5d$ orbitals disrupt the Ru-Ru molecular-orbital network and intra/inter-trimer exchange pathways. Atomistic simulations incorporating this paramagnetic dilution reproduce the suppressed ordering temperature and the coexistence of ordered and paramagnetic components.

An iterative method is presented for reconstructing the height-temperature profile of the solar atmosphere above a sunspot using multi-frequency spectro-polarimetric microwave observations. It is assumed that the emission is formed predominantly under gyroresonance conditions at harmonics of the electron gyrofrequency, and that the contribution at each frequency is associated with a layer of optical depth of order unity. The frequency-height correspondence is determined from extrapolation of the photospheric magnetic field into the corona. The reconstruction of the profile is reduced to solving an overdetermined system of linear equations with regularization, ensuring noise stability and physical smoothness of the solution. The method is tested on synthetic data for a dipole sunspot model and applied to observations of active region NOAA 11312 obtained with the RATAN-600 radio telescope. The derived temperature profiles are consistent with contemporary models of active regions and reproduce the observed spectra in the 3-18~GHz range with an accuracy of a few percent.

Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce $μ$TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by combining a Gaussian multiple Coulomb scattering model with Bayesian updating, then maps scattering density through voxel wise M-values for core integrity verification. We evaluate a representative hexagonal core containing 61 fuel flakes with embedded control drums and shutdown rods, using both idealized 5 GeV muons and zenith-angle-dependent 0-60 GeV cosmic-ray spectra. A single missing fuel flake is detected with $3\times 10^{6}$ muons at 50 mm voxel resolution. Incorporating per-muon momentum further increases detectability by up to 149.85% for laser-driven sources and 105.11% for cosmic-ray sources relative to momentum-agnostic reconstruction. The approach remains robust under practical detector limits, with only an 8.88% reduction in detectability for 10 mm spatial resolution and 10% energy resolution. Compared with PoCA, $μ$TRec delivers 326.13% to 392.14% higher detectability at equal muon counts, enabling faster defect identification.

The partial pivoted Cholesky approximation accurately represents matrices that are close to being low-rank. Meanwhile, the Vecchia approximation accurately represents matrices with inverse Cholesky factors that are close to being sparse. What happens if a partial Cholesky approximation is combined with a Vecchia approximation of the residual? This paper shows how the sum is exactly a Vecchia approximation of the original matrix with an augmented sparsity pattern. Thus, Vecchia approximations subsume a class of existing matrix approximations and have broad applicability.

We investigate the impact of an isotropic antiferromagnetic Heisenberg perturbation on the toric code, focusing on the resulting quantum phase transition and the nature of the phase that emerges beyond topological order. Using neural-network quantum states (NQS), we compute ground states over a wide range of Heisenberg couplings while fully respecting the exact symmetries of the model. In the weak-coupling regime, the numerical results are in excellent agreement with an effective low-energy description derived from a Schrieffer-Wolff (SW) transformation, providing analytic control over the perturbative breakdown of topological order. We show that the Heisenberg perturbation only renormalizes local operators at low orders, whereas mixing between topological sectors occurs only at a perturbative order proportional to the system size. At intermediate values of the Heisenberg interaction, the topological phase breaks down. We estimate the critical point through a combination of the fidelity susceptibility and the logarithmic susceptibility of non-contractible Wilson loops for various system sizes. Furthermore, we utilize the topological entanglement entropy to provide a comprehensive characterization of the phase transition. Beyond the transition, an antiferromagnetic $\pm X/\pm Z$ Néel phase emerges, characterized by a fourfold-degenerate symmetry-broken manifold, which is explicitly probed using staggered-magnetization-based diagnostics. Our results show how local two-spin interactions, which naturally arise in realistic implementations of the toric code, drive the breakdown of topological order. Moreover, we establish the SW approach as a systematic framework for analyzing such perturbations in combination with variational many-body methods.

This paper examines vertex colorings of graphs with constraints on the distribution of colors in vertex neighborhoods. We introduce color 2-switches and color degree matrices. The color degree matrix of a $k$-colored graph is an analog of the degree sequence, while a color 2-switch provides a way to transform a $k$-colored graph to another such graph while maintaining the color of each vertex and the multiset of colors in each vertex neighborhood. We prove that two $k$-colored graphs have the same color degree matrix if and only if one can be obtained from the other by a sequence of color 2-switches. In related work, we generalize neighborhood balanced colorings by allowing for $k$ colors (instead of two) and more flexibility on the number of vertices of each color in a neighborhood. We introduce three classes of $k$-colored, $λ$-balanced graphs, in which any two color classes in a vertex neighborhood differ in size by at most $λ$. These classes are distinguished by whether the balancing condition is imposed on the open neighborhood $N(v)$, the closed neighborhood $N[v]$, or allowed to vary by vertex. For each class, the minimum $λ$ for which a graph admits a balanced coloring defines its $λ$-balance number. We prove general results about these classes and their $λ$-balance numbers. For $k = 2$, we introduce a fourth class, parity balanced graphs, in which the number of vertices of each color are equal in open neighborhoods for even-degree vertices and in closed neighborhoods for odd-degree vertices. Additionally, we focus on the important case where $k=2$ and $λ\le 1$ and introduce the technique of red-blue removals. We provide separating examples between these four classes and prove balance number results for paths, cycles, wheels, trees, caterpillars, and complete multipartite graphs, and a counting result for caterpillars.

Conflicts of interest often arise between data sources and their users regarding how the users' information needs should be interpreted by the data source. For example, an online product search might be biased towards presenting certain products higher than in its list of results to improve its revenue, which may not follow the user's desired ranking expressed in their query. The research community has proposed schemes for data systems to implement to ensure unbiased results. However, data systems and services usually have little or no incentive to implement these measures, e.g., these biases often increase their profits. In this paper, we propose a novel formal framework for querying in settings where the data source has incentives to return biased answers intentionally due to the conflict of interest between the user and the data source. We propose efficient algorithms to detect whether it is possible for users to extract relevant information from biased data sources. We propose methods to detect biased information in the results of a query efficiently. We also propose algorithms to reformulate input queries to increase the amount of relevant information in the returned results over biased data sources. Using experiments on real-world datasets, we show that our algorithms are efficient and return relevant information over large data.

One of the most important classes of even $Δ$-matroids arises from orientable ribbon graphs, which play a role analogous to that of graphic matroids in matroid theory. Motivated by a natural correspondence between strong $Δ$-matroids and even $Δ$-matroids due to Geelen and Murota, we characterize the class of strong $Δ$-matroids that correspond to orientable ribbon-graphic $Δ$-matroids. These are precisely the $Δ$-matroids associated with what we call pseudo-orientable ribbon graphs. Moreover, we present a geometric construction that transforms a pseudo-orientable ribbon graph into an orientable ribbon graph, thereby realizing this correspondence. As consequences, we obtain the Matrix--Quasi-tree Theorem, the Hurwitz stability of quasi-tree generating polynomials, and a log-concavity result for the sequence counting quasi-trees of size $2i-1$ or $2i$ for pseudo-orientable ribbon graphs. To establish the log-concavity, we generalize Stanley's log-concavity theorem for regular matroids to regular $Δ$-matroids. Finally, we exhibit an infinite family of non-pseudo-orientable ribbon graphs that fail to satisfy the Matrix--Quasi-tree theorem and Hurwitz stability.

We propose a simple scheme for the dissipative generation of entangled states of multiple emitters coupled to a waveguide. Our approach exploits collective interactions arising from the formation of subradiant and superradiant excited states, combined with the quantum Zeno effect. We show that, starting from an arbitrary initial state, the system deterministically evolves toward a W-type entangled steady state, with an infidelity that scales inversely with the cooperativity. The protocol is scalable to an arbitrary number of emitters. We further analyze the impact of additional experimental imperfections and present a detailed implementation based on trapped $^{133}$Cs atoms.

The ocean is filled with phytoplankton that contribute as much photosynthesis as all land plants combined, making them vital to the carbon cycle and climate system. Recent advances in flow cytometry allow oceanographers to measure the optical traits of individual cells along research cruise tracks, generating single-cell resolution microbial data. In marine microbial ecology, detecting locations of abrupt changes in the environmental response of cytometric plankton distributions is an important task. This manuscript proposes a latent space Gaussian mixture-of-experts model, facilitating change point detection in replicated and clustered phytoplankton observations. Change points are identified through shifts in prior means of low-dimensional representations, with piecewise-constant structure enforced by a group-fused LASSO penalty. The optimization problem is then solved via Alternating Direction Method of Multipliers. Applied to flow cytometry data, the proposed method identifies a scientifically important change point that aligns with a transition zone between two marine provinces.

This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth conditions and material properties, consistently producing high-quality films of complex materials like LaVO$_3$ remains difficult due to the highly non-equilibrium nature of PLD and the defects and competing phases that accumulate during growth. Here, we use an active learning framework based on Gaussian process Bayesian optimization that incorporates measured bulk and surface lattice properties along with impurity phase information to efficiently map the multidimensional growth space of LaVO$_3$ by PLD. By tuning the relative weighting of these properties, the model identifies an optimized region where phase-pure films of LaVO$_3$ exhibit two-dimensional surfaces, near-ideal lattice parameters, and minimal sub-band gap optical absorption. The trained model reveals clear competition among different defect formation mechanisms that are connected to unseen parameters like supersaturation and surface mobility, thus giving insight into the highly non-equilibrium process of PLD growth. Together, this demonstrates that property-guided machine learning can accelerate materials optimization while providing a new way to address fundamental growth mechanisms in PLD that enable understanding and utilization of quantum phenomena found in complex oxides.