In this paper we establish improved Sobolev inequalities on the quaternionic sphere under higher-order moment vanishing conditions with respect to the measure \(|u|^{p^*}\,dξ\). As an application, we give a new proof of the existence of extremals for the sharp Sobolev embedding \[ S^{1,2}(S^{4n+3}) \hookrightarrow L^{2^*}(S^{4n+3}). \]

We discuss complex Farey graphs for the Euclidean imaginary quadratic number fields $\mathbb Q(\sqrt{-d})$, $d\in\{1, 2, 3, 7, 11\}$. We study hyperbolic versions of A. Schmidt's Farey polygons living in $3$-dimensional hyperbolic space $\mathbb{H}^3$. Using these Farey polygons we recover tessellations of the hyperbolic plane $\mathbb{H}^2$ that are defined by the action of the Hecke groups $H_4$ and $H_6$ and have been studied earlier by I. Short and M. Walker. Moreover, hyperbolic Farey polygons allow us to define polyhedra that induce Farey tessellations of $\mathbb{H}^3$ by the action of certain Bianchi groups. Using complex Farey graphs we consider geodesic complex continued fraction expansions. Our method provides a different and more general approach as the one from the discussion by M. Hockman.

Let $C$ be a complex irreducible plane curve that is not the vanishing locus of a modular polynomial. We show that $C$ contains finitely many real algebraic curves whose projection on each coordinate axis is a union of special geodesics.

Randomly-assembled dynamical systems are theoretically predicted to be unstable upon crossing a critical threshold of complexity, as first shown by May. Yet, empirical complex systems exhibit remarkable stability, indicating the presence of additional mechanisms playing a stabilizing role. The relation between complexity and stability is typically assessed by assuming fixed interactions, whereas real systems often evolve in intrinsically time-dependent states. To understand how this affects stability, we linearize a general non-autonomous dynamics around a reference operating state and model the resulting parameters as stochastic processes, which represent the minimal extension of static random interactions to time-varying ones. We derive exact stability bounds that generalize complexity-stability theory to dynamically varying systems. Notably, we find that temporal variability allows systems to remain stable even when their instantaneous Jacobian would predict instability. We compare our results against a non-linear neural network model, where our theory applies exactly, and the generalized Lotka-Volterra equations, where we numerically find that time-varying interactions systematically postpone the onset of replica-symmetry breaking. Overall, our results indicate that temporal variability systematically improves stability, demonstrating a general mechanism by which complex systems can violate classical complexity-stability bounds.

We study fairness in decision-making when the data may encode systematic bias. Existing approaches typically impose fairness constraints while predicting the observed decision, which may itself be unfair. We propose a novel framework for characterising and addressing fairness issues by introducing the notion of desert decision, a latent variable representing the decision an individual rightfully deserves based on their actions, efforts, or abilities. This formulation shifts the prediction target from the potentially biased observed decision to the desert decision. We advocate achieving fair decision-making by predicting the desert decision and assessing unfairness by the discrepancy between desert and observed decisions. We establish nonparametric identification results under causally interpretable assumptions on the fairness of the desert decision and the unfairness mechanism of the observed decision. For estimation, we develop a sieve maximum likelihood estimator for the desert decision rule and an influence-function-based estimator for the degree of unfairness. Sensitivity analysis procedures are further proposed to assess robustness to violations of identifying assumptions. Our framework connects fairness with measurement error models, aligning predictive accuracy with fairness relative to an appropriate target, and providing a structural approach to modelling the unfairness mechanism.

Molybdenum disulfide (MoS2) is a promising 2D transition metal dichalcogenide (TMD) for optoelectronics and quantum technology applications, but faces challenges in scalable synthesis and defect engineering. Oxygen-assisted chemical vapor deposition (O-CVD), which introduces in-situ oxygen during growth, shows excellent potential in resolving both issues at once. Although co-flowing oxygen shows improvement in growth, the underlying mechanistic role of oxygen remains unclear. In this work, a combination of oxygen dosing experiments, density functional theory (DFT) calculations, computational fluid dynamics (CFD) simulations, and ab initio molecular dynamics (AIMD) simulations, uncover the dual role of oxygen in O-CVD. Firstly, AIMD reveals that oxygen increases MoO3 sublimation and enhances Mo3O9 supply. Concomitantly, DFT reveals that sulphur oxides, due to their bulkier nature than pure S2, limit the formation of reactive MoS6 intermediates. Subsequently, by experimentally varying the oxygen flow-interval, flow-rate, and flow-time, and correlating them with CFD simulations, we decouple oxygen's roles in source-poisoning prevention (i.e. MoO3 evaporation) and growth regulation. We find that maintaining a low sulphur-to-oxygen (S:O2) ratio at the MoO3 boat and substrate during nucleation, and a high S:O2 ratio at the substrate during growth is the key to obtaining large-area high-quality monolayer MoS2, confirmed by our optical measurements. Based on our understanding, we present a kinetic phase diagram for MoS2 synthesis, which will enable controlled oxygen dosing as a tuning parameter for scalable, defect-controlled monolayer MoS2 synthesis.

Protecting information in systems that have more than two basis states (qudits) not only offers a promising route for reducing the number of individual quantum locations that must be protected, while more accurately reflecting the structure of realistic quantum hardware, but also has some possibly enticing foundational strengths. While work in the past has largely focused on protecting information in quantum devices with locations that are some consistent local structure, this work considers coding-theoretic constraints on devices constructed from locations which may vary in their local structures -- these are mixed-register quantum devices. In this work we provide some general results for mixed-register Pauli operators, then identify some stabilizer encoded information forms that are forbidden. Building on these insights, we construct coding-theoretically optimal mixed-register stabilizer codes from sets of codes defined on coprime local-dimensions. The construction of such codes results in codes with logical subspaces that do not directly correspond to any of the constituent local-dimensions.

The conjectured three generic local bulk statistics amongst all non-Hermitian random matrix symmetry classes have recently been extended to three generic local edge statistics. We study analytically and numerically complex spacing ratios and nearest-neighbour (NN) spacing distributions that characterise such local statistics. We choose the three simplest representatives of these universality classes, given by the Gaussian ensembles of complex Ginibre, complex symmetric and complex self-dual matrices, denoted by class A, AI$^†$ and AII$^†$. In the first part, we analytically study the complex spacing ratio in class A, at finite matrix size $N$. Introducing a conditional point process, we simplify existing expressions and show why an uncontrolled approximation introduced earlier converges well in the large-$N$ limit in the bulk. When specifying to the elliptic Ginibre ensemble, we present a parameter-dependent $N=3$ surmise for the complex spacing ratio, interpolating to that of the Gaussian unitary ensemble (GUE), where such a surmise is very accurate. In the second numerical part, we compare complex spacing ratios, its moments, and NN spacing distributions for all three ensembles with that of uncorrelated points, the two-dimensional (2D) Poisson process, both in the bulk and at the edge. The varying degree of repulsion within these different edge universality classes can be well understood in terms of an effective 2D Coulomb gas description, at different values of inverse temperature $β$. We find indications that the complex spacing ratio does not fully unfold the local statistics at the edge. Finally we verify that for small argument, in all three symmetry classes the NN spacing distributions in the bulk and at the edge are consistent with the universal cubic repulsion.

The first observation of the doubly charmed baryon $\itΞ_{cc}^+$ is reported through its decay to the $\itΛ_c^+ K^-π^+$ final state, with a statistical significance exceeding seven standard deviations. The observation is made using proton-proton collision data collected in 2024 with the LHCb Run 3 detector at a center-of-mass energy of 13.6 TeV, corresponding to a total integrated luminosity of $6.9\,\mathrm{fb}^{-1}$. The $\itΞ_{cc}^+$ mass is measured to be $3619.97 \pm 0.83 \pm 0.26 \,^{+1.90}_{-1.30}\,\mathrm{MeV}/c^2$, where the first uncertainty is statistical, the second is systematic, and the third is due to the unknown $\itΞ_{cc}^+$ lifetime, which is assumed to lie in the range 15-160 fs with a baseline value of 45 fs. The difference between the masses of the $\itΞ_{cc}^+$ and $\itΞ_{cc}^{++}$ baryons is determined to be $-1.77 \pm 0.84 \pm 0.15 \,^{+1.90}_{-1.30}\,\mathrm{MeV}/c^2$. This is the first observation of a new particle made with the LHCb Run 3 detector.

We review completely monotone (CM) and Stieltjes functions, which are classes of functions obeying an infinite hierarchy of positivity constraints. While these are classical concepts in analysis, such properties have recently been shown to arise in many fundamental building blocks and observables of quantum field theory (QFT), including scalar Feynman integrals in the Euclidean region and Coulomb branch amplitudes in $\mathcal{N}=4$ SYM. After reviewing their mathematical structure, we discuss the physical and geometric origins of these properties, ranging from unitarity and analyticity in scattering amplitudes to the structure of parametric representations of Feynman integrals. We then survey a number of applications, including constraints on the analytic S-matrix, implications for numerical bootstrap methods, and connections to positive geometry, where we present evidence for a close relation between these functions and geometric volume interpretations. These notes are based on an extended series of lectures delivered at the \emph{Positive Geometry in Scattering Amplitudes and Cosmological Correlators} workshop, held at the International Centre for Theoretical Sciences (ICTS), Bengaluru, in February 2025.

Test flakiness is a common problem in industry, which hinders the reliability of automated build and testing workflows. Most existing research on test flakiness has primarily focused on unit and small-scale integration tests. In contrast, flakiness in system-level testing such as REST APIs are comparatively under-explored. A large body of literature has been dedicated to the topic of fuzzing REST APIs, whereas relatively little attention has been paid to detecting and possibly mitigating negative effects of flakiness in this context. To fill this major gap, in this paper, we study the flakiness of tests generated by one of the popularly applied REST API fuzzer in the literature, namely EvoMaster, conduct empirical studies with a corpus of 36 REST APIs to understand flakiness of REST APIs. Based on the results of the empirical studies, we categorize and analyze flakiness sources by inspecting near 3000 failing tests. Based on the understanding, we propose FlakyCatch to detect and mitigate flakiness in REST APIs and empirically evaluate its performance. Results show that FlakyCatch is effective in detecting and handling flakiness in tests generated by white-box and black-box fuzzers.

In this paper, we study symmetry-improved fractional Sobolev embeddings on closed Riemannian manifolds under the action of compact isometry groups. We prove that \(G\)-invariant fractional Sobolev spaces embed into higher \(L^p\) spaces, with corresponding compactness results depending on the minimal orbit dimension. We also investigate the associated optimal constants in the improved critical inequality and in the standard critical inequality under finite-orbit symmetry.

With the development of PMUs in power systems, the response-based real-time emergency control becomes a promising way to prevent power outages when power systems are subjected to large disturbances. The first step in the emergency control is to start up accurately and fast when needed. To this end, this paper proposes a well-qualified start-up scheme for the power system real-time emergency control. Three key technologies are proposed to ensure the effectiveness of the scheme. They are an instability index, a Critical Machines (CMs) identification algorithm and a two-layer Single Machine Infinite Bus (SMIB) equivalence framework. The concave-convex area based instability index shows good accuracy and high reliability, which is used to identify the transient instability of the system. The CMs identification algorithm can track the changes of CMs and form the proper SMIB system at each moment. The new two-layer SMIB equivalence framework, compared with conventional ones, can significantly reduce the communication burden and improve the computation efficiency. The simulations in two test power systems show that the scheme can identify the transient instability accurately and fast to restore the system to stability after the emergency control. Besides, the proposed method is robust to measurement errors, which enhances its practicality.

In this paper, we study the tracking controllability of a 1D parabolic type equation. Notably, with controls acting on the boundary, we seek to approximately control the solution of the equation on specific points of the domain. We prove that acting on one boundary point, we control the solution on one target point, whereas acting on two boundary points, we can control the solution on up to two target points. In order to do so, when the target is fixed, we study the controllability by minimizing the corresponding problem with duality results. Afterwards, we study the controllability on moving points by applying a transformation that takes the problem to a fixed target. Lastly, we also solve some of these control problems numerically and compute approximations of the solutions and the desired targets, which validates our theoretical methodology.

Energy-sharing UAV-UGV systems extend the endurance of Uncrewed Aerial Vehicles (UAVs) by leveraging Uncrewed Ground Vehicles (UGVs) as mobile charging stations, enabling persistent autonomy in infrastructure-sparse environments. Trajectory optimization for these systems is often challenging due to UGVs' terrain access constraints and the discrete nature of task scheduling. We propose a smooth nonlinear program model for the joint trajectory optimization for these systems. Unlike existing models, the proposed model allows smooth parameterization of UGVs' terrain access constraints and supports partial UAV recharging. Further, it introduces a smooth approximation of disjunctive constraints that eliminates the need for computationally expensive integer programming and enables efficient solutions via nonlinear programming algorithms. We demonstrate the proposed model on a one-UAV-one-UGV system with multiple task locations. Compared with mixed-integer nonlinear programs, this model reduces the computation time by orders of magnitude.

We formulate shifted affine iquantum groups of arbitrary quasi-split ADE types via Drinfeld presentations. We construct GKLO-type representations of shifted affine iquantum groups via algebras of difference operators, which allow us to construct truncated shifted affine iquantum groups. This provides a q-deformation of truncated shifted iYangians in our prior work arising as a quantization of affine Grassmannian islices.

We propose two novel data-driven dynamic mode decomposition (DMD)-type methods, the Crank--Nicolson DMD and the semi-implicit DMD, to predict the highly oscillatory dynamics of the semiclassical Schrödinger equations efficiently and accurately. Unlike many existing DMD-type methods which directly models the dynamics of the wave function, our approach is based on learning the Schrödinger operator while explicitly incorporating mass and energy conservation laws. This approach ensures physical fidelity and endows the resulting methods with built-in model order reduction capabilities, without the necessity for additional dimensionality-reduction preprocessing. An analysis of training and prediction errors are given for theoretical guarantees. Extensive numerical experiments demonstrate the noise robustness, computational efficiency, and transferability to other equations of the proposed methods.

Solving optimal control problems for transport-dominated partial differential equations (PDEs) can become computationally expensive, especially when dealing with high-dimensional systems. To overcome this challenge, we focus on developing and deriving reduced-order models that can replace the full PDE system in solving the optimal control problem. Specifically, we explore the use of the shifted proper orthogonal decomposition (POD) as a reduced-order model, which is particularly effective for capturing low-dimensional representations of high-fidelity transport-dominated phenomena. In this work, a reduced-order model is constructed first, followed by the optimization of the reduced system. We consider a 1D linear advection equation problem and prove existence and uniqueness of solutions for the reduced-order model as well as the existence of an optimal control. Moreover, we compare the computational performance of the shifted POD method against the standard POD.

We present a computationally efficient relativistic formulation of the equation-of-motion coupled-cluster method for the double electron attachment problem. In this work, the exact two-component Hamiltonian within the atomic mean-field approximation is employed, yielding results that are in close agreement with the corresponding four-component calculations. However, canonical DEA-EOM-CCSD calculations become prohibitively expensive for heavy elements and large basis sets due to the substantial memory requirements associated with complex 3p1h excitation manifold. To address this limitation, we introduce a state-specific frozen natural spinor basis that significantly reduces the virtual space through two controllable truncation thresholds. Furthermore, the use of Cholesky decomposition for the two-electron integrals provides an additional reduction in computational cost and memory. The performance of the proposed approach is demonstrated through calculations of double ionization potentials and excitation energies for group-12 and group-14 heavy elements. Vertical excitation energies for heavy chalcogen dimers are also presented. In addition, a range of diatomic spectroscopic constants is evaluated for group-13 halides.

Additional active power control (AAPC) of wind turbines (WTs) is essential to improve the transient frequency stability of low-inertia power systems. Most of the existing research has focused on imitating the frequency response of the synchronous generator (SG), known as virtual inertia control (VIC), but are such control laws optimal for the power systems? Inspired by this question, this paper proposes an optimal AAPC of WTs to maximize the frequency nadir post a major power deficit. By decoupling the WT response and the frequency dynamics, the optimal frequency trajectory is solved based on the trajectory model, and its universality is strictly proven. Then the optimal AAPC of WTs is constructed reversely based on the average system frequency (ASF) model with the optimal frequency trajectory as the desired control results. The proposed method can significantly improve the system frequency nadir. Meanwhile, the event insensitivity makes it can be deployed based on the on-line rolling update under a hypothetic disturbance, avoiding the heavy post-event computational burden. Finally, simulation results in a two-machine power system and the IEEE 39 bus power system verify the effectiveness of the optimal AAPC of WTs.

We study the asymptotic behavior of small solutions to the Vlasov--Klein--Gordon system in high dimensions. The standard argument of Glassey and Strauss \cite{GS87} for studying small solutions to the Vlasov--Maxwell system does not apply to the Vlasov--Klein--Gordon system due to the massiveness of the Klein--Gordon field. In this paper we use the vector field method and consider solutions in dimensions $ n \geq 4 $ with the hyperboloidal foliation of the Minkowski spacetime to obtain the asymptotic properties for the Vlasov--Klein--Gordon system.

Pulgon-tools is an open-source software package providing building blocks for the analysis and modeling of quasi-one-dimensional (quasi-1D) periodic systems based on line-group theory. While mature libraries exist for space-group detection in three-dimensional crystals, an automated and structure-based identification of line groups has so far been lacking. We present software that integrates four complementary components within a consistent line-group framework: (i) structure generation, (ii) symmetry detection, (iii) irreducible representations (irreps) and character table and (iv) harmonic interatomic force constants (IFCs) correction. This paper introduces the general code structure and several examples that illustrate some relevant applications of the program.

The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the strict computation and storage limits of blockchain systems. We propose a novel concept to decentralize the AI training process using blockchain technology and Multi-task Peer Prediction. By leveraging smart contracts and cryptocurrencies to incentivize contributions to the training process, we aim to harness the mutual benefits of AI and blockchain. We discuss the advantages and limitations of our design.

The emission of photon from an individual atom encodes the phase of its initialized quantum state. Using single-shot heterodyne detection, we measure the phase distribution of the emission from a superconducting transmon qubit in an open waveguide configuration and track its evolution over time. We demonstrate that the presence of a quantum superposition is encoded in the phase statistics of the emission and remains resolvable despite a high noise level. These phase statistics serve as a quantitative probe of the qubit coherence. The decay of the emission envelope with increasing integration time reveals the energy relaxation rate of the emitted wavepacket, while phase distribution broadening tracks pure dephasing processes. We thereby establish a direct link between the decoherence dynamics of an open quantum system and the statistical properties of its radiated field.

Optical cavities enable strong, long-range, light-matter interactions that can drive collective ordering phenomena, such as superradiant self-organization in ultracold atomic gases. Extending these ideas to solid-state electron systems could enable continuous-wave optical control of electronic order, but is impeded by the mismatch between optical wavelengths and electronic length scales. Here, we propose a platform for realizing superradiant charge density waves (sCDWs) in doped, driven transition-metal dichalcogenides coupled to an optical cavity. A nanoscale grating generates electric fields at large in-plane optical momenta, allowing cavity photons to couple efficiently to electronic density fluctuations through exciton-polaron processes. Using a linear-stability analysis, we determine the threshold for superradiant ordering and map out the driven phase diagram. We show that tuning the grating periodicity to match the enhanced electronic density fluctuations - such as those near Wigner crystallization - substantially lowers the required pump intensity. Our results establish a novel route toward cavity-controlled electronic order in quantum materials.

AI agents are rapidly expanding in both capability and population: they now write code, operate computers across platforms, manage cloud infrastructure, and make purchasing decisions, while open-source frameworks such as OpenClaw are putting personal agents in the hands of millions and embodied agents are spreading across smartphones, vehicles, and robots. As the internet prepares to host billions of such entities, it is shifting toward what we call Open Agentic Web, a decentralized digital ecosystem in which agents from different users, organizations, and runtimes can discover one another, negotiate task boundaries, and delegate work across open technical and social surfaces at scale. Yet most of today's agents remain isolated tools or closed-ecosystem orchestrators rather than socially integrated participants in open networks. We argue that the next generation of agents must become Agentic Citizens, defined by three requirements: Agentic-Web-Native Collaboration, participation in open collaboration networks rather than only closed internal orchestration; Agent Identity and Personhood, continuity as a social entity rather than a resettable function call; and Lifelong Evolution, improvement across task performance, communication, and collaboration over time. We present Synergy, a general-purpose agent architecture and runtime harness for persistent, collaborative, and evolving agents on Open Agentic Web, grounding collaboration in session-native orchestration, repository-backed workspaces, and social communication; identity in typed memory, notes, agenda, skills, and persistent social relationships; and evolution in an experience-centered learning mechanism that proactively recalls rewarded trajectories at inference time.

The exchange bias (EB) in ferromagnetic/antiferromagnetic (FM/AFM) bilayer systems causes a shift of the magnetic hysteresis curve after field cooling through the Néel temperature of the AFM. In some cases, this shift is accompanied by an asymmetry between ascending and descending branches. In the past, this asymmetric magnetization reversal has been studied in different bilayer systems, including polycrystalline Co/CoO thin films. Here we investigate the asymmetric magnetization reversal in an epitaxial fcc-Co(001)/CoO(001) thin film grown on MgO(001) by molecular beam epitaxy at varying temperatures up to room temperature after field cooling along the easy and the hard axes. Room temperature measurements of the longitudinal and transverse magneto-optic Kerr effect show different magnetization reversal processes via stable intermediate states for different angles between external magnetic field and magnetic easy axes. Once the sample is cooled below the blocking temperature, a pronounced asymmetric magnetization reversal can be observed. We show that in contrast to polycrystalline bilayers, the loop asymmetry stays constant after multiple training cycles and that the magnitude of the asymmetry is directly correlated with the magnitude of the EB.

We introduce a novel class of graphical models, termed profile graphical models, that represent, within a single graph, how an external factor influences the dependence structure of a multivariate set of variables. This class is quite general and includes multiple graphs and chain graphs as special cases. Profile graphical models capture the conditional distributions of a multivariate random vector given different levels of a risk factor, and learn how the conditional independence structure among variables may vary across these risk profiles; we formally define this family of models and establish their corresponding Markov properties. We derive key structural and probabilistic properties that underpin a more powerful inferential framework than existing approaches, underscoring that our contribution extends beyond a novel graphical representation.Furthermore, we show that the resulting profile undirected graphical models are independence-compatible with two-block LWF chain graph models.We then develop a Bayesian approach for Gaussian undirected profile graphical models based on continuous spike-and-slab priors to learn shared sparsity structures across different levels of the risk factor. We also design a fast EM algorithm for efficient inference. Inferential properties are explored through simulation studies, including the comparison with competing methods. The practical utility of this class of models is demonstrated through the analysis of protein network data from various subtypes of acute myeloid leukemia. Our results show a more parsimonious network and greater patient heterogeneity than its competitors, highlighting its enhanced ability to capture subject-specific differences.

To every $ω$-categorical structure $M$ one can associate two spaces of symmetries which determine the structure up to first-order bi-interpretability: the topological group $\mathrm{Aut}(M)$ of its automorphisms and the topological monoid $\mathrm{EEmb}(M)$ of its elementary embeddings, both equipped with the topology of pointwise convergence $τ_{\mathrm{pw}}$. We investigate the relation of $τ_{\mathrm{pw}}$ to other topologies on these spaces: in particular, when $τ_{\mathrm{pw}}$ is minimal, i.e.~does not admit any strictly coarser Hausdorff semigroup topology. A common method to prove minimality of $τ_{\mathrm{pw}}$ on $\mathrm{EEmb}(M)$ is to show that it coincides with the algebraically defined semigroup Zariski topology $τ_{\mathrm{Z}}$. We show that $τ_{\mathrm{pw}}$ differs from $τ_{\mathrm{Z}}$ on $\mathrm{EEmb}(M)$ whenever $\mathrm{Aut}(M)$ has non-trivial centre. We then provide general conditions on the behaviour of algebraic closure on $M$ that imply minimality of $τ_{\mathrm{pw}}$. These condition cover, for example, countable vector spaces and projective spaces over finite fields. Turning to $\mathrm{Aut}(M)$, we describe the minimal $T_1$ semigroup topologies on the automorphism groups of model-theoretically simple one-based $ω$-categorical structures with weak elimination of imaginaries. We conclude by proving that the metric pointwise topology $τ_{\mathrm{mpw}}$ is minimal, equals $τ_{\mathrm{Z}}$, and is strictly coarser than $τ_{\mathrm{pw}}$, on $\mathrm{EEmb}(M)$ for the real and the rational Urysohn space and sphere.

We investigate quasinormal modes, grey-body factors, and absorption cross-sections of a massive scalar field in regular black-hole spacetimes supported by the Einasto density profile. The analysis is performed for $\tilde n=1/2$, $1$, and $5$, where the scalar mass $μ$ is treated as an effective parameter induced by an external environment. Quasinormal frequencies are computed with high-order WKB expansions and Padé resummation, and are cross-checked by time-domain evolution. We show that increasing the effective mass and varying the Einasto parameters can strongly suppress the damping rate, leading to long-lived modes and clear quasi-resonant behavior. Grey-body factors obtained from direct WKB transmission and from the QNM-based correspondence agree well in the considered regimes, while their differences remain controlled. Using the transmission coefficients, we derive partial and total absorption cross-sections and demonstrate the expected transition from low-frequency suppression to efficient high-frequency absorption. Our results show that regularity of the core together with environmental parameters leaves a noticeable imprint on both ringdown and scattering observables. Within this setup, the magnetic field acts as the physical agent that controls the effective mass scale and therefore governs how close the system can approach the quasi-resonant regime.