Violation of the Maldacena-Shenker-Stanford (MSS) chaos bound has been observed in various black hole spacetimes, but its physical origin remains unclear. In particular, it is uncertain whether these violations arise from modifications of general relativity or reflect a more fundamental feature of black hole spacetimes. In this work, we systematically investigate the instability of circular geodesics across a broad class of black hole solutions in Einstein, scalar-tensor, and higher-curvature gravity. We show that the violations are governed by the relative behavior of unstable circular orbits and the horizon structure in near-extremal regimes. When the relevant orbit remains outside the horizon as the surface gravity vanishes, the instability scale persists, and the chaos bound can be violated. On the other hand, as the orbit approaches the degenerate horizon, the instability becomes suppressed by the associated divergent gravitational time dilation, ultimately leading to saturation of the bound. Motivated by these results, we propose a geometric conjecture that determines the applicability of the MSS bound directly from the photon-sphere and horizon structure of the spacetime. Our findings identify a universal geometric criterion that governs the applicability of the MSS bound in black hole spacetimes, revealing a fundamental constraint on extending the quantum chaos bound to classical gravitational settings.

Developers often struggle with maintaining GitHub Actions workflow configurations in GitHub-hosted repositories, with recent studies showing frequent execution failures. This paper empirically explores how the adoption and evolution of GitHub Actions language constructs impacts workflow reliability and maintainability. To do so, we quantitatively analyse 260K workflows from 49K GitHub repositories to understand how they are used in practice and how their usage has evolved from July 2019 to August 2025. We identify 197 language constructs available in the GitHub Actions language and map them to 14 features reflecting workflow capabilities. We observe that only a small set of constructs and features are used very frequently, and that larger and more complex workflows are associated with higher failure rates and more maintenance effort. We identify specific features that are more likely to be linked with reliability and maintainability risks. These insights can help practitioners and researchers improve their understanding and usage of the GitHub Actions language, which can help in improving and sustaining workflow automation practices.

We present updated and improved Big Bang Nucleosynthesis (BBN) constraints on heavy, long-lived beyond the Standard Model (BSM) relics $ϕ$ decaying into pairs of Standard Model particles, covering a comprehensive set of two-body decay channels. We treat the leading effects of these injections in detail, discussing the modification of the neutron-to-proton ratio from hadronic interconversions, as well as hadro- and photodisintegration of the light elements. Our analysis incorporates several important refinements with respect to earlier work. We adopt up-to-date primordial abundance measurements, including the new $^4$He determination and the latest nuclear reaction rates. The hadronic and electromagnetic injection spectra are computed using PYTHIA 8, providing a proper treatment of final-state radiation and hadronisation. We further implement an improved treatment of $p\leftrightarrow n$ interconversions, accounting for dynamical equilibrium, kaon-induced processes, and updated rates. Additionally, we make use of a refined hadrodisintegration formalism which allows us to also consider disintegration processes while BBN is still active. Together, these improvements yield updated exclusion contours on lifetime, mass, and abundance of the relic for each decay channel considered. Furthermore, we discuss the irreducible freeze-in contribution from inverse decays.

We address the problem of identifying non-Markovian quantum time evolutions of an open quantum system by only performing measurements of the system's energy. We demonstrate that violations of CP-divisibility are always witnessed by non-positive values of the energy-change Kirkwood-Dirac quasiprobability distribution associated with the system's Hamiltonian, evaluated at consecutive times. The link between non CP-divisibility and non-positivity of the system's energy-change distribution is stronger when the system-environment interactions are energy-preserving. The witness works whenever anomalous energy fluxes, due to non-Markovianity, are realized. Anomalous fluxes are also detected by the non-Markovianity measure built over the quantum mutual information between the states of the open system and of a quantum correlated reference.

Scaling wide-band-gap semiconductors to the ultrathin limit offers a transformative pathway for power electronics, with gallium nitride (GaN) representing a cornerstone material in this class. However, the operational resilience and functional tunability of its two-dimensional form (g-GaN) remain underexplored. This work shifts the focus from idealized systems to the complex materials behavior under realistic conditions, investigating how the synergistic effects of point vacancy defects, strain, and external electric fields govern its electronic, magnetic, and sensing landscapes. We demonstrate that these factors are not merely perturbations but are fundamental to modulating the material response. Our first-principles calculations suggest g-GaN maintains electronic stability under intense electric fields; notably, gallium vacancies are predicted to further extend the theoretical stability limit. While in-plane tension preserves the band gap evolution under an electric field, in-plane compression facilitates low-field metallization. Using nitrogen monoxide (NO) adsorption as a prototype, we find that the interaction is defect-modulated and potentially tunable by electric fields. Analysis of adsorption energetics and diffusion barriers suggests the gallium vacancy may act as a thermodynamic trap for NO. Targeted hybrid-functional (HSE06) validation confirms the reliability of observed adsorption trends and theoretical metallization thresholds, while revealing that precise electronic-exchange treatment is critical for capturing the magnetic ground state of nitrogen vacancies. By systematically examining the geometry, energetics, band structure, density of states, magnetic response, and charge transfer, this study clarifies the interplay between defects and external electric fields, providing insights into theoretical upper bounds for property tuning and semiconductor device engineering.

Permutation-based metaheuristics are widely used for electric vehicle routing, where candidate solutions are represented as ordered sequences of customers. Such sequences, however, do not directly define feasible vehicle routes: they must be decoded by choosing where to split the permutation into routes and where to insert charging-station visits, subject to cargo capacity and battery constraints. These decisions are inherently interdependent, since each return to the depot both separates consecutive routes and restores the vehicle battery. This paper formalizes the task as the Fixed-Permutation Splitting and Charging Problem and proposes an exact forward labeling algorithm that constructs a minimum-distance feasible decoding of a fixed customer permutation using dynamic programming with dominance pruning. We further derive restricted variants representing increasingly simplified decoding strategies: first separating route splitting from charging-station insertion, and then additionally limiting each inter-customer segment to at most one charging-station visit. Computational experiments on benchmark and randomly generated instances, including comparisons with heuristic decoders from the literature, confirm that the exact decoder remains tractable in practice and reveal a clear hierarchy among decoding strategies. The most restrictive variant achieves runtimes close to those of heuristic decoders while delivering substantially higher decoding success rates and better solution quality. Less restrictive variants further improve quality and robustness at the cost of additional runtime. The exact joint decoder provides the optimal reference for each fixed permutation, clarifying the trade-offs introduced by common decoding simplifications.

We consider the non-Hermitian XY spin chain with open boundary conditions when the anisotropy parameter is extended to complex values. By analyzing the quasi-Hamiltonian matrix, we demonstrate that the free-fermion structure of the quasi-energy spectrum coincides with that of the Hermitian model and construct the corresponding biorthogonal fermionic basis away from exceptional points (EPs). We make use of an explicit Chebyshev-polynomial representation of the open-boundary eigenvectors in which the quasi-energy $\varepsilon$ is the natural spectral variable. This quasi-energy polynomial form is particularly useful at EPs, because EPs correspond to repeated roots of the same boundary polynomial, making the construction of generalized eigenvectors by $\varepsilon$-differentiation transparent. At EPs, where the quasi-Hamiltonian becomes defective, we derive the Jordan normal form and construct the associated generalized eigenvectors, which yields the correct counting of independent many-body eigenstates. We further show that EPs act as branch points in the complex anisotropy plane, leading to the characteristic permutation of eigenenergies and eigenstates upon encirclement. The branch-cut structure of the biorthogonal eigenstates provides direct evidence for the exchange of eigenstates when an EP is encircled. These results provide an analytically controlled many-body platform for studying EP physics and non-Hermitian topology beyond momentum-space descriptions.

High-quality speech coding at low bitrates is crucial for bandwidth-constrained applications, yet remains challenging due to the severe loss of quality-critical information in highly compressed representations. To overcome this challenge, we propose CFMDCTCodec, a low-bitrate neural speech codec that operates entirely in the modified discrete cosine transform (MDCT) domain. CFMDCTCodec integrates a lightweight encoder-quantizer-decoder-style MDCT-spectral codec with a noise-prior-aware, conditional-flow-matching (CFM)-based MDCT-spectral enhancer. Within this framework, the codec serves as a base module that compactly discretizes the MDCT spectrum extracted from speech and produces an initial coarse reconstruction, while the enhancer further restores fine-grained spectral details. The enhancer improves the decoded MDCT spectrum by integrating a conditional MDCT velocity-field filter with an ordinary differential equation (ODE) solver, under the guidance of an MDCT-derived magnitude-adaptive noise prior, aiming to emphasize perceptually significant high-energy regions while stabilizing low-energy and silent regions. Finally, the enhanced MDCT spectrum is reconstructed into the decoded speech using the inverse MDCT. When optimizing CFMDCTCodec, we adopt a unified non-adversarial training strategy that jointly combines reconstruction, quantization and CFM objectives. Both objective and subjective evaluations show that CFMDCTCodec outperforms competitive baselines in low-bitrate regimes, e.g., 0.65 kbps, while approaching the perceptual quality of large-scale codecs with significantly fewer parameters and computations.

Drawing on well-known results from the theory of canonical extensions and the theory of categories enriched over a quantale, we define canonical extensions of quantale-enriched categories and establish their basic properties.

High-fidelity numerical simulations of compressible flow past a rapidly rotating cylinder are used to investigate the evolution of aerodynamic loads and flow instability over a wide range of Reynolds numbers (Re = 1000 to 6000). The study reveals a transition from periodic vortex shedding to complex multi-mode oscillatory states, with a critical bifurcation identified near Re = 5650. Spectral analysis of lift and drag signals shows the emergence and interaction of multiple dominant frequencies, accompanied by amplitude modulation and nonlinear mode coupling in the post-bifurcation regime. To model these highly nonlinear dependencies, data-driven approaches are systematically explored using a database of 101 high-fidelity simulations (1 million core hours). Polynomial regression provides baseline fits but fails to capture localized fluctuations near bifurcation. Bayesian regression frameworks employing B-spline and Gaussian radial basis functions improve flexibility and uncertainty quantification, with spline-based models demonstrating superior performance in capturing piecewise nonlinear trends. Artificial neural networks (ANNs) are then developed as high-capacity surrogate models, achieving excellent predictive accuracy for maximum lift coefficient and instability onset time, while maintaining reasonable fidelity for the more challenging drag coefficient. Beyond regression, the ANN is further evaluated as a generative model to reconstruct flow behavior at unseen Re. A hierarchical refinement strategy is introduced, and results show that when trained on high-fidelity data, ANN-based models can serve as efficient and reliable surrogates for complex fluid dynamics problems.

Context. Direct imaging enables the characterisation of substellar companions on wide orbits. These objects provide a testbed for our formation theories; therefore, it is important to obtain accurate physical parameters for them. One of these objects is YSES 1 b. Aims. Our objective is to improve the spectral energy distribution (SED) modelling of YSES 1 b and determine the bulk and atmospheric parameters. Methods. We obtained observations in the r', i', and z' bands using MagAO-X on the 6.5 metre Magellan Clay telescope at Las Campanas Observatory. We combined this data with archival VLT/SPHERE and VLT/NACO data and used a forward modelling approach to estimate the physical parameters. We tested models both without and with a circumplanetary disc (CPD) model. We represented the CPD by including a dust extinction model and a blackbody radiation component. Using the derived bolometric luminosity, we estimated the mass of YSES 1 b by fitting evolutionary models. Results. Including the CPD model provides a significantly better fit to the photometric data, yielding an object that is considerably warmer (2854+110-94 K vs 1727+172-127 K) and smaller (1.58+0.06-0.07 RJ vs 3.0+0.2-0.7 RJ) than previous estimates. The newly determined radius suggests that the addition of dust extinction could resolve the large radius anomaly identified previously. Depending on the age of the system, the estimated mass increases from 14+-3 MJ (17 Myr) to either 25.7+4.1-3.6 (17 Myr) or 41.6+3.6-3.4 MJ (27 Myr). Conclusions. Dust extinction and blackbody radiation from a CPD can substantially change the estimated physical parameters of an object. For YSES 1 b, this moves it into the brown dwarf regime.

In this article we establish a large deviation principle for the empirical measures of a simple spatially inhomogeneous random walk on $\overline{\mathbb{Z}}$, the two-point compactification of $\mathbb{Z}$. The classical Donsker--Varadhan framework does not apply, since the random-walk kernel and the topology of $\overline{\mathbb{Z}}$ fall outside its standard assumptions. In certain regimes, the resulting rate function is non-convex on its effective domain. We also derive a large deviation principle for empirical means of observables $f:\mathbb{Z} \to \mathbb{R}^d$ admitting limits at $\pm\infty$. This result is optimal in the sense that in general, no large deviation principle holds for the larger class of bounded continuous functions on $\mathbb{Z}$.

Regev and Stephens-Davidowitz conjectured that the Gaussian mass $Θ_Λ(t) = \sum_{x \in Λ} e^{-t\lVert x\rVert^2}$ of any integral lattice $Λ\subset \mathbb{R}^n$ is bounded above by $Θ_{\mathbb{Z}^n}(t)$. For $n\ge 4$, we prove a saturation theorem for the natural scalar Poisson-summation certificates of this conjecture: any such certificate that is sharp at $\mathbb{Z}^n$ must interpolate the Gaussian, and have vanishing Fourier transform, at every nonzero point of integer squared norm. Applied to the lattice $E_8 \oplus \mathbb{Z}^{n-8}$, this rigidity is incompatible with the strict theta-series gap $Θ_{\mathbb{Z}^8}(t) - Θ_{E_8}(t) = θ_2(it/π)^4\,θ_4(it/π)^4 > 0$. Consequently, in dimensions $n \ge 8$, no scalar Poisson certificate can attain the sharp $\mathbb{Z}^n$ Gaussian mass bound. The same argument rules out the corresponding scalar certificate strategy for the stable-lattice formulation of the conjecture, and extends to orbit-constant graded families $Λ\mapsto h_Λ$; near-sharp sequences are similarly excluded under a uniform summability hypothesis.

Sampling is a fundamental algorithmic task in wide-ranging applications across multiple disciplines such as scientific computing, statistics and machine learning. In this paper, an efficient stochastic Runge-Kutta scheme is proposed to accelerate the Schrödinger-Föllmer sampler, designed for sampling from complex and high-dimensional multimodal distributions. The resulting stochastic Runge-Kutta Schrödinger-Föllmer sampler (SRKSFS) is proved to achieve a convergence rate of order $\mathcal{O} ( h^{3/2} |\ln h|)$ in the $L^2$-Wasserstein distance, considerably improving the order $\mathcal{O}(h)$ of the existing Euler type sampler. Obtaining the enhanced convergence rate is, however, not trivial, by noting that the drift of the diffusion process is not differentiable but only $\frac{1}{2}$-Hölder continuity with respect to the time variable. To address the difficulty, we rely on delicate error estimates to overcome the singularity due to time derivatives of the drift, at the expense of the logarithmic factor. Furthermore, the framework is extended to data-driven Schrödinger-Föllmer generation with empirical measures, enabling data-driven sampling without known density. A variety of numerical experiments are reported to validate the effectiveness of the proposed sampling algorithms.

Understanding coherent optical responses of topological surface states (TSSs) requires disentangling excitation pathways from the electronic band structure. Here, using angle-resolved two-photon photoemission spectroscopy, we identify two distinct excitation pathways in the TSSs of Bi$_2$Te$_3$: an off-resonant transition via virtual states and a resonant transition via unoccupied intermediate states. A pronounced modulation of the spectral response is observed, revealing a competition between the two coherent pathways. This competition is tunable via temperature-induced shifts of the chemical potential, which selectively modify the resonant channel. These results provide microscopic insight into the optical excitation mechanisms of TSSs and highlight the potential for controlling their optical responses, relevant for future spintronic devices.

Incentive-based load curtailment unlocks critical demand-side flexibility but is hindered by the limited knowledge of private user parameters and the inherent nonsmoothness of responses due to physical device constraints. We address this via a constrained bilevel optimization framework and propose the Bi-ZOL (Bilevel Zeroth-Order Learning) algorithm. Unlike conventional black-box methods, Bi-ZOL exploits the bilevel structure to decompose the hypergradient, integrating the exact analytical information of the SO's objective with a zeroth-order estimate of the unknown response sensitivity. This structural decomposition-based learning method mathematically smoothes the nonsmooth response landscape and reduces hypergradient estimation error. We provide theoretical convergence guarantees to an approximate stationary point and demonstrate through simulations that Bi-ZOL achieves near-optimal performance.

We theoretically investigate the properties of phonon angular momentum in the superconducting state, using fulleride compounds in an external magnetic field as a model system. The electron orbital angular momentum injected by an external field is transferred to the phonon subsystem via electron--phonon coupling. We show that this field-induced phonon angular momentum is significantly enhanced and undergoes a sign reversal upon entering the superconducting state. In the normal state, the dominant energy scale governing the response function is the electronic bandwidth $D$. In the superconducting state, the phonon energy scale $ω_1$ enters the denominator, leading to an enhancement of order $D/ω_1$. The observed sign change in the response can be explained by the competition between Fermi surface and Fermi volume contributions.

Podcasts have become an increasingly popular medium for knowledge sharing within the software engineering (SE) community, offering insights into industry developments and the perspectives of professionals with different backgrounds. As this medium grows, it presents a potentially valuable resource not only for practitioners but also for researchers seeking to understand the evolving field. However, little is known about the actual content of SE podcasts or how they are perceived and used by researchers. This study systematically explores the SE podcast landscape, analyzing its content and surveying researchers to assess how podcasts can serve as a meaningful resource for advancing empirical software engineering research.

We study an opinion dynamics model in which $n$ agents hold directed trust or distrust opinions about one another, represented as a matrix $M \in \{0,1\}^{n \times n}$ in which 1 represents trust and 0 represents distrust. A gossip event $(a, z, y)$ causes agent $z$ to adopt agent $a$'s opinion of $y$, provided that $z$ trusts $a$. We characterize the absorbing states of this process, i.e. the states in which no further opinion change can take place: we find that they are the states in which agents are partitioned into isolated factions, each faction containing a subset of core members who share mutual trust, while the remaining peripheral members trust all core members but receive no trust in return. This structure establishes a bijection between absorbing states on $[n]$ and pairs consisting of a set partition $π$ of $[n]$ together with a choice of non-empty subset of each faction of $π$. The number of such absorbing states is therefore given by OEIS A143405, with exponential generating function $\exp(\exp(x) \cdot (\exp(x) - 1))$. In addition, up to isomorphism, the count equals the number of plane partitions of $n$, given by OEIS A000219, recovering MacMahon's classical product formula $\prod_{k \geq 1} 1/(1 - x^k)^k$. Exhaustive computation for $n \leq 7$ confirms both counts.

YARA rules are widely shared across threat intelligence communities to enable collective defence against malware. This practice implicitly assumes that removing metadata (e.g., author fields) sufficiently protects the identity of contributing organisations. To assess the validity of this assumption, we systematically evaluate how much can be inferred from YARA rule text alone. Specifically, using a corpus of 23,305 rules from three major public repositories, we train independent classifiers along four stylometric fingerprint dimensions: individual author, source repository, malware family, and temporal drift, using three complementary methods: lexical n-grams (Burrows' Delta), syntactic AST features (Caliskan-Islam), and fine-tuned CodeBERT. Our results demonstrate that repository origin is almost perfectly recoverable (up to 99% accuracy), individual authors can be re-identified well above chance (76%), and malware family classification reaches 95%. Comparing the same repository attribution task across full-history and time-restricted subsets reveals a 9-18% accuracy gap, providing preliminary evidence of temporal drift in repository fingerprints.To further disentangle content from style, we conduct per-malware family author attribution experiments. Even when the malware family is the same for all samples considered, authors can still be re-identified for five of seven tested families (mean accuracy 74.6%). These findings constitute the first systematic demonstration that YARA rule sharing is a measurable OPSEC attack surface, and that metadata removal alone does not mitigate it.

In this work, we introduce a real-time capable algorithm for considering monotonicity assumptions for recursive Gaussian Process regression (RGP). Therefore, we present how to efficiently calculate the RGP gradients online. Then, we utilize an extended Kalman filter and pseudo-measurements in combination with a ReLU pseudo-measurement function to enforce soft inequality constraints. This work builds upon a previously published conference paper with the same goal and a similar fundamental approach. Opposite to our previous work, however, we now use an exact covariance calculation for the RGP gradients. Furthermore, we also present a real-time optimized version of this algorithm with less simplifications compared to the previously published version. These and several other algorithmic innovations lead to an algorithm with greatly improved numerical robustness. The algorithm is validated and compared to its previously published version for a 2D numerical example. The paper is concluded with a successful experimental validation of the developed algorithm for the monotonicity-preserving learning of pneumatic valve characteristics for the control of a pneumatic system, leveraging a partial input - output linearization.

The low-temperature fate of the spin-liquid regime in the classical kagome Heisenberg antiferromagnet has been debated for over three decades. Using an expansion in the number of spin components, we show that, contrary to earlier Monte Carlo simulations, the spin liquid terminates at a weak first-order phase transition into the $\sqrt{3}\times\sqrt{3}$ phase which ordered moment saturates at zero temperature. Adding second-neighbor interactions, this transition belongs to a line of first-order phase transitions that ends at a critical point. For comparison, the pyrochlore antiferromagnet remains disordered at all temperatures.

We prove several new results on the combinatorial structures of the unit spheres of the norms induced by Thurston's metric on the tangent and cotangent spaces of the Teichm{ü}ller space of a closed surface of negative Euler characteristic. These results include a formula for the dimension of every face of a unit sphere in the tangent space in terms of an invariant of the chain-recurrent lamination representing the face. We then prove that the combinatorial structure of such a unit sphere is independent of the underlying point in Teichm{ü}ller space. Provided the genus of the surface is $\ge$ 2, we show that there is a natural isomorphism between the extended mapping class group of the surface and the group of combinatorial automorphisms of such a unit sphere. In the case of genus 2, we obtain a natural epimorphism between the two groups whose kernel is the class of the hyperelliptic involution. Regarding the unit spheres of Thurston's metric in the cotangent spaces, we obtain a formula describing the codimensions of faces of such a sphere in terms of corresponding projective measured laminations. We then give a necessary and sufficient condition for a face to be exposed, and of a face to correspond to a projectively weighted multi-curve. Some of the results obtained answer open questions.

We study both diffuse and sharp liquid-vapor interfaces. The equilibrium equation of fluids is derived by using the principle of virtual work in a domain including the interfaces. For diffuse interfaces, the surface tension coefficient depends on the density profile across the interface. For sharp interfaces, the liquid-vapor layer is mathematically represented by a geometric surface and its specific energy is a Dirac delta function at the surface. We compare the both approaches and find relations between the surface tension coefficient and parameters of the models.

Understanding the asymptotic behavior of the number of Galois orbits of newforms in $S_k(Γ_0(N), Ψ)$ as the weight increases is a central problem motivated by Maeda's conjecture. For trivial nebentypus, prior work of Dieulefait, Pacetti, and Tsaknias established a lower bound for the number of non-CM Galois orbits using local inertial types and Atkin-Lehner signs as invariants. We extend this framework to newforms with non-trivial quadratic nebentypus. On the local side, the quadratic nebentypus imposes strict central character constraints, and we explicitly determine the number of Galois orbits of admissible local inertial types. We then establish the Galois equivariance of Atkin-Li pseudo-eigenvalues, which serves as a second global invariant when taken up to a natural equivalence relation. Using existence results for newforms with prescribed local invariants, we obtain a lower bound for the number of non-CM Galois orbits for sufficiently large weights (with conditions on $N$) by counting compatible pairs of these invariants. Finally, computations in small weights reveal a strict inequality in the quadratic nebentypus setting, indicating that certain local equivalences are not realized globally by Galois conjugation over the coefficient field of the modular form.

Due to a result by Andreotti and Frankel \cite{andreotti1959}, it can be seen that the complement of a complex projective curve has the homotopy type of a $2$-dimensional CW complex. However, no general method has been given to compute explicitly this complex. Here we give a explicit construction of a $2$- dimensional $Δ$-complex that is a strong deformation retract of the complement of a Fermat curve of degree $d$ in the complex projective space. The retraction is performed in several steps, using the branched cover structure of the Fermat curves over the degree $1$ case.

We performed parallel measurements of heat effects and shear modulus relaxation for glassy Te$_{75}$Ge$_{15}$Ga$_{10}$ taken as a representative of practically important non-metallic glasses with covalent bonding. It is shown that the heat effects occurring upon heating are quantitatively linked to the shear moduli in the glassy and crystalline states and their temperature derivatives as implied by Eq.(1), which was originally derived for metallic glasses. This relationship provides a good description of exo- and endothermal reactions using shear modulus relaxation data as an input. This is the first application of this approach to a non-metallic glass with directional interatomic bonding. The obtained results suggest that relaxation phenomena are governed by elastic dipoles -- atomic configurations with the symmetry lower than that of surrounding matrix.

We investigate charm-quark dynamics in asymmetric Cu+Au collisions at top RHIC energy using a Langevin approach embedded in a realistic hydrodynamic background. The intrinsic asymmetry of the colliding nuclei leads to a spatially lopsided initial energy-density profile, which generates a dipolar flow structure in the transverse plane even at midrapidity. As charm quarks propagate through this medium, they acquire a finite directed flow, $v_1$. We find that the $p_T$-integrated heavy-quark $v_1$ is approximately an order of magnitude larger than that of charged hadrons. In addition, the $p_T$-differential $v_1$ exhibits strong sensitivity to the initial spatial distribution of heavy quarks, emphasizing the importance of pre-equilibrium dynamics in determining final-state anisotropies. Beyond geometric effects, $v_1$ also provides direct sensitivity to medium interactions through the temperature-dependent drag coefficient. Its pronounced dependence on this transport input indicates that precision measurements of heavy-flavor directed flow could place meaningful constraints on heavy-quark transport coefficients, thereby improving Langevin-based descriptions and predictive power for heavy-flavor observables in heavy-ion collisions.

The transition between turbulent and zonal flow dominated states is investigated by varying the ion temperature gradient in nonlinear gyrokinetic simulations. Independent gradient scans reveal three distinct regimes: a turbulent regime at high gradients, a zonal flow dominated regime with strongly reduced heat transport at low gradients, and an intermediate regime characterized by intermittent switching between these two states. These different regimes can be classified using an order parameter, defined as the fraction of the zonal to total free energy in the system. To assess the memory effects, the temperature gradient is first lowered gradually from high values that result in fully developed turbulence, to lower values in the Dimits shift region that form strong zonal flows, and then is slowly increased back. Once the zonal flows form, and efficiently suppress turbulence, they can persist at higher gradients, leading to an asymmetric response implying a hysteresis loop. It is observed that, in the zonal flow dominated state, the free energy is mostly condensated in the largest radial scale, with a steep slope of the $k_x$ spectrum, while in the turbulent state, it exhibits a wider spectrum with two distinct slopes.

Recent high-resolution observations have revealed filamentary accretion flows (``streamers'') in protoplanetary disks older than 1 Myr, suggesting that late-stage interstellar gas infall (late infall) may affect disk evolution and stellar accretion. In Lupus, observations report a positive correlation between ambient gas density and stellar accretion rate. However, it remains unclear whether infall can truly boost stellar accretion, because incoming gas may instead be lost through photoevaporation or magnetically driven disk winds, or remain trapped in the outer disk. We perform one-dimensional long-term ($\sim$1--10 Myr) disk evolution simulations. We first treat late infall as a mass source and then include the effective torque arising from the angular-momentum difference between the infalling gas and Keplerian disk gas. We find that even if substantial gas reaches the outer disk ($\sim 10^{2}$ au), much of it is eventually lost through photoevaporation. Sustained stellar accretion therefore requires efficient inward gas delivery by mechanisms that locally remove angular momentum. Without an effective infall torque, strong viscosity can provide this transport, but it also drives outward angular-momentum transport and excessive disk spreading, inconsistent with the compact disk sizes observed in Lupus. In contrast, MHD disk winds can remove angular momentum without significantly expanding the disk, allowing late infall to sustain stellar accretion while keeping disks compact. Thus, if the Lupus accretion--density correlation is caused by late infall without an effective infall torque, efficient angular-momentum removal by MHD disk winds is required. By contrast, when the effective torque is included, the angular-momentum mismatch itself can promote inward gas transport and enhance stellar accretion, even without strong MHD disk winds.