Altermagnets provide a natural platform for studying and exploiting piezomagnetism. In this paper, we introduce a class of insulating altermagnets in two dimensions (2D) referred to as Dirac quadrupole altermagnets, and show based on microscopic minimal models that the orbital piezomagnetic polarizability of such altermagnets has a topological contribution described by topological response theory. The essential low-energy electronic structure of Dirac quadrupole altermagnets can be understood from a gapless parent phase (i.e., the Dirac quadrupole semimetal), which has important implications for their response to external fields. Focusing on the strain-induced response, here we demonstrate that the topological piezomagnetic effect is a consequence of the way in which strain affects the Dirac points forming a quadrupole. We consider two microscopic models: a spinless two-band model describing a band inversion of $s$ and $d$ states, and a Lieb lattice model with collinear Néel order. The latter is a prototypical minimal model for altermagnetism in 2D and is realized in a number of recently proposed material compounds, which are discussed.

Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require ground truth data for training-data that is often unavailable in practice. To overcome this limitation, we propose a novel unsupervised training framework for HS-SISR, based on synthetic abundance data, where no high-resolution ground-truth reference is required for training. The approach begins by unmixing the hyperspectral image into endmembers and abundances. A neural network is then trained to perform abundance super-resolution using synthetic abundances only. These synthetic abundance maps are generated from a dead leaves model whose characteristics are inherited from the low-resolution image to be super-resolved and from the known point spread function (PSF) of the hyperspectral sensor. This trained network is subsequently used to enhance the spatial resolution of the original image's abundances, and the final super-resolution hyperspectral image is reconstructed by combining them with the endmembers. Experimental results demonstrate both the training value of the synthetic data and the effectiveness of the proposed method across 3 datasets, 3 scaling factors, and several evaluation metrics. The code is available at https://github.com/xinxinxu99/SISR-DL.git

Let $K$ be a number field with algebraic closure $\overline{K}$ and let $S$ be a finite set of places of $K$ containing all the archimedean places. It is known from Silverman's result that a forward orbit of a rational map $φ$ contains finitely many $S$-integers in the number field K when $φ^2$ is not a polynomial. Sookdeo stated an analogous conjecture for the backward orbits of a rational map $φ$ using a general $S$-integrality notion based on the Galois conjugates of points. He proved his conjecture for the power map $φ(z) =z^d$ for $d \geq 2$ and consequently for Chebyshev maps (J. Number Theory 131 (2011), 1229-1239). In this paper, we establish uniform bounds on the number of $S$-integral points in the backward orbits of any non-zero $β$ in $K$, relative to a non-preperiodic point $α\in \mathbb{P}^1(\overline{K})$, under the power map $φ(z) =z^d $.

Relatively little is known about the arithmetic properties of Gamma-function derivatives evaluated at arbitrary points $q\in\mathbb{Q}\setminus\mathbb{Z}_{\leq0}$. In recent work, we showed that the sequence $\left\{Γ^{\left(n\right)}\left(1\right)\right\}_{n\geq1}$ contains transcendental elements infinitely often. That result is now generalized to all sequences $\left\{Γ^{\left(n\right)}\left(q\right)\right\}_{n\geq1}$ for $q\in\tfrac{1}{2}\mathbb{Z}\setminus\mathbb{Z}_{\leq0}$. Moreover, for all such $q$ we derive a lower bound, $β\left(N\right)=\max\left\{ 0,\sqrt{N}-5/2\right\}/N$, for the density of transcendental elements $Γ^{\left(n\right)}\left(q\right)$ among $n\in\left\{1,2,\ldots,N\right\}$, where $β\left(N\right)\asymp N^{-1/2}\rightarrow0$ as $N\rightarrow\infty$. For $q\in\mathbb{Q}\setminus\tfrac{1}{2}\mathbb{Z}$, we find the somewhat weaker result that at least one of the sequences $\left\{Γ^{\left(n\right)}\left(q\right)\right\}_{n\geq1}$, $\left\{Γ^{\left(n\right)}\left(1-q\right)\right\}_{n\geq1}$ contains infinitely many transcendental elements.

AI alignment relies on annotator judgments, yet annotation pipelines often treat annotators as interchangeable, obscuring how their social position shapes annotation. We introduce reflexive annotating as a probe that invites crowd workers to reflect on how their positionality informs subjective annotation judgments in a language model alignment context. Through a qualitative study with crowd workers (N=30) and follow-up interviews (N=5), we examine how our probe shapes annotators' behaviour, experience, and the situated metadata it elicits. We find that reflexive annotating captures epistemic metadata beyond static demographics by eliciting intersectional reasoning, surfacing positional humility, and nudging viewpoint change. Crucially, we also denote tensions between reflexive engagement and affective demands such as emotional exposure. We discuss the implications of our work for richer value elicitation and alignment practices that treat annotator judgments as situated and selectively integrate positional metadata.

Context. Solar activity, dominated by the 11-year cyclic evolution, has been observed directly since 1610. Before that, indirect cosmogenic proxy data are used to reconstruct it over millennia. Recently, the precision of radiocarbon measurements has improved sufficiently to allow reconstructing solar activity over millennia. Aims. The first detailed reconstruction of solar activity, represented by annual sunspot numbers, is presented for 1-969 CE. Methods. The reconstruction of sunspot numbers from D14C was performed using a physics-based method involving several steps: using a carbon-cycle box model, the 14C production rate, corrected for the geomagnetic shielding, was computed from the measured data; The open solar magnetic flux was computed using a model of the heliospheric cosmic-ray modulation; Sunspot numbers were calculated using a model of the evolution of the Sun's magnetic field. The Markov Chain Monte Carlo approach was used to account for different sources of uncertainty. Results. Annual sunspot numbers were reconstructed for the first millennium CE. This period includes one extreme solar event of 774 CE and one Grand solar minimum of 650-730 CE. We could identify 91 solar cycles, of which 26 were well-defined, while 24 and 41 were reasonably and poorly defined, respectively. The mean cycle length was 10.6 years, but the lengths of individual cycles vary between 8 and 15 years. The existence of empirical Waldmeier's relations remains inconclusive. No significant periodicities were found beyond the 11-year cycle. Conclusions. This work fills the gap in the solar cycle statistics between the previously reconstructed first millennium BCE and the second millennium CE, providing vital constraints for the solar dynamo and irradiance models. A consistent 3-millennium-long reconstruction of sunspot numbers, based on a composite multi-proxy cosmogenic record, is pending.

Executing Spiking Neural Networks (SNNs) on neuromorphic hardware poses the problem of mapping neurons to cores. SNNs operate by propagating spikes between neurons that form a graph through synapses. Neuromorphic hardware mimics them through a network-on-chip, transmitting spikes, and a mesh of cores, each managing several neurons. Its operational cost is tied to spike movement and active cores. A mapping comprises two tasks: partitioning the SNN's graph to fit inside cores and placement of each partition on the hardware mesh. Both are NP-hard problems, and as SNNs and hardware scale towards billions of neurons, they become increasingly difficult to tackle effectively. In this work, we propose to raise the abstraction of SNNs from graphs to hypergraphs, redesigning mapping techniques accordingly. The resulting model faithfully captures the replication of spikes inside cores by exposing the notion of hyperedge co-membership between neurons. We further show that the overlap and locality of hyperedges strongly correlate with high-quality mappings, making these properties instrumental in devising mapping algorithms. By exploiting them directly, grouping neurons through shared hyperedges, communication traffic and hardware resource usage can be reduced be yond what just contracting individual connections attains. To substantiate this insight, we consider several partitioning and placement algorithms, some newly devised, others adapted from literature, and compare them over progressively larger and bio-plausible SNNs. Our results show that hypergraph based techniques can achieve better mappings than the state-of-the-art at several execution time regimes. Based on these observations, we identify a promising selection of algorithms to achieve effective mappings at any scale.

As AI systems become embedded in everyday practice, value misalignment has emerged as a pressing concern. Yet, dominant alignment approaches remain model centric, treating users as passive recipients of prespecified values rather than as epistemic agents who encounter and respond to misalignment during interactions. Drawing on situated perspectives, we frame alignment as an interactional practice co-constructed during human AI interaction. We investigate how users understand and wish to contribute to this process through a participatory workshop that combines misalignment diaries with generative design activities. We surface how misalignments materialise in practice and how users envision acting on them, grounded in the context of researchers using Large Language Models as research assistants. Our findings show that misalignments are experienced less as abstract ethical violations than as unexpected responses, and task or social breakdowns. Participants articulated roles ranging from adjusting and interpreting model behaviour to deliberate non-engagement as an alignment strategy. We conclude with implications for designing systems that support alignment as an ongoing, situated, and shared practice.

We show that a non-universal Polish group can induce a complete orbit equivalence relation, which answers a question of Sabok from \cite{OPENPROBLEMS}.

Magnetically intercalated transition metal dichalcogenides are emerging as a rich platform for exploring exotic quantum states in van der Waals magnets. Among them, CoxTaS2 has attracted intense interest following the recent discovery of a distinctive 3Q magnetic ground state and a pronounced topological Hall effect below a critical doping of x=1/3, both intimately tied to cobalt concentration. To date, direct signatures of this enigmatic 3Q magnetic order in the electronic structure remain elusive. Here we report a comprehensive doping dependent angle resolved photoemission spectroscopy study that unveils these long-sought fingerprints. Our data reveal an unexpected "inverse Mexican hat" dispersion along the K-M-K direction, accompanied by two van Hove singularities. These features are consistent with theoretical predictions for a 3Q magnetic order near three-quarters band filling on a cobalt triangular lattice. These results provide evidence of 3Q magnetic order in the electronic structure, establishing TMD van der Waals magnets as tunable materials to explore the interplay between magnetism and topology.

Extending community detection from pairwise networks to hypergraphs introduces fundamental theoretical challenges. Hypergraphs exhibit structural heterogeneity with no direct graph analogue: hyperedges of varying orders can connect nodes across communities in diverse configurations, introducing new trade-offs in defining and detecting community structure. We address these challenges by developing a unified framework for community detection in non-uniform hypergraphs under the Hypergraph Stochastic Block Model. We introduce a general signal-to-noise ratio that enables a quantitative analysis of trade-offs unique to higher-order networks, such as which hypergedges we choose to split across communities and how we choose to split them. Building on this framework, we derive a Bethe Hessian operator for non-uniform hypergraphs that provides efficient spectral clustering with principled model selection. We characterize the resulting spectral detectability threshold and compare it to belief propagation limits, showing the methods coincide for uniform hypergraphs but diverge in non-uniform settings. Synthetic experiments confirm our analytical predictions and reveal systematic biases toward preserving higher-order and balanced-shape hyperedges. Application to empirical data demonstrates the practical relevance of these higher-order detectability trade-offs in real-world systems.

We study the observable $v_0(p_T)$, which quantifies the relative change of $p_T$ spectra induced by event-by-event density fluctuations in the medium created in heavy-ion collisions. This quantity provides a direct measure of radial flow and serves as a probe of collectivity, complementing anisotropic flow coefficients. Using hydrodynamic model calculations, we predict the behavior of $v_0(p_T)$ and show that the scaled quantity $v_0(p_T)/v_0$ exhibits very little dependence on centrality and transport coefficients. We further find that the apparent influence of transport coefficients$-$particularly bulk viscosity$-$ on $v_0(p_T)$ largely originates from modifications of the event-averaged mean transverse momentum, $\langle p_T \rangle$. By expressing $v_0(p_T)/v_0$ as a function of $p_T/\langle p_T \rangle$, the genuine sensitivity of $v_0(p_T)$ to transport coefficients can be isolated. Moreover, since $v_0(p_T)$ is the $p_T$-differential measure of event-by-event $[p_T]$ fluctuations, it naturally explains the observed $p_T$-cut dependence of $σ_{p_T}$ measured by ATLAS collaboration.

Motivated by the analogy between many-body localization (MBL) and single-particle Anderson localization on hierarchical graphs, we study localization on the Husimi tree, a generalization of the Bethe lattice with a finite density of local loops of arbitrary but finite length. The exact solution of the model provides a transparent and quantitative framework to systematically inspect the effect of loops on localization. Our analysis indicates that local loops enhance resonant processes, thereby reducing the critical disorder with increasing their number and size. At the same time, loops promote local hybridization, leading to an increase in the spatial extent of localized eigenstates. These effects reconcile key discrepancies between MBL phenomenology and its single-particle Anderson analog. These results show that local loops are a crucial structural ingredient for realistic single-particle analogies to many-body Hilbert spaces.

Recently, Jiang et al. \cite{JIANG2025102522} obtained several classes of Permutation Polynomial of the form $x+γ\operatorname{Tr}_q^{q^2}(h(x))$ over finite fields $\mathbb{F}_{q^2},q=2^n$. In this paper, we find the compositional inverse of six classes of permutation polynomials of this form.

Single-photon emitters (SPEs) are central to quantum communication, computing, and metrology, yet their development remains constrained by trade-offs in purity, indistinguishability, and tunability. This review presents a mechanism-based classification of SPEs, offering a physics-oriented framework to clarify the performance limitations of conventional sources, including quantum emitters and nonlinear optical processes. Particular attention is given to hybrid organic-inorganic perovskite quantum dots (HOIP QDs), which provide size- and composition-tunable emission with narrow linewidths and room-temperature operation. Through comparative analysis of physical mechanisms and performance metrics, we show how HOIP QDs may address key limitations of established SPE platforms. Recognizing the constraints of current deterministic sources, we introduce a performance framework to guide the development of scalable SPEs, and examine the theoretical potential of bright squeezed vacuum (BSV) states, discussing how BSV mechanisms could serve as a promising avenue for multiplexable, high-purity photon generation beyond conventional heralded schemes. The review concludes by outlining future directions for integrating HOIP- and BSV-based concepts into scalable quantum photonic architectures.

We introduce Bitcoin-IPC, a software stack and protocol that scales Bitcoin towards helping it become the universal Medium of Exchange (MoE) by enabling the permissionless creation of fully programmable Proof-of-Stake (PoS) Layer-2 chains, called subnets, whose stake is denominated in L1 BTC. Bitcoin-IPC subnets rely on Bitcoin L1 for the communication of critical information, settlement, and security. Our design, inspired by SWIFT messaging and embedded within Bitcoin's SegWit mechanism, enables seamless value transfer across L2 subnets, routed through Bitcoin L1. Uniquely, this mechanism reduces the virtual-byte cost per transaction (vB per tx) by up to 23x, compared to transacting natively on Bitcoin L1, effectively increasing monetary transaction throughput from 7 tps to over 160 tps, without requiring any modifications to Bitcoin L1.

We study a simple problem of allocating common-value goods. The designer seeks to allocate the goods to as many unit-demand agents as possible without monetary transfers, while agents, who possess partial private information about the goods, are willing to receive them only when the goods are of high value. Mechanisms screen each agent's private information using the information of other agents, and in doing so shape what agents learn from other agents about the value of the goods. The optimal mechanism can be summarized by two parameters: one adjusts the allocation probability, while the other governs the amount of learning induced by allocation. Although the designer prefers to allocate the goods, the optimal mechanism excludes some agents and, as a result, may withhold allocation even when all agents would be willing to receive them. The optimal mechanism has the same structure even when payments are available, but it may not exclude any agent and may involve strictly positive payments that are decreasing in allocation.

We show that the semiclassical limit of thermodynamic Bethe Ansatz equations naturally reconstructs the algebro-geometric spectra of finite-gap periodic potentials. This correspondence is illustrated using the traveling-wave (snoidal) solution of the defocusing modified Korteweg--de Vries equation. In this framework, the Bethe-root distribution of the associated quantum field theory yields an Abelian differential of the second kind on the elliptic Riemann surface specified by the spectral endpoints, a structure central to the algebro-geometric theory of solitons. The semiclassical parameter is identified with the large-rank limit of the internal symmetry group ($O(2N)$) of the underlying quantum field theory (the Gross-Neveu model with a chemical potential). Our analysis indicates that the analytic structure of the spectrum is dictated solely by the Dynkin diagram ($D_N$) and its large-rank limit ($D_\infty$), independently of the particular integrable model used to realize it.

We survey recent results and current challenges concerning the growth rate inequality for sphere endomorphisms, and present a number of open problems and conjectures arising in this context.

Explicit channel state information (CSI) feedback in IEEE~802.11 conveys \emph{transmit beamforming directions} by reporting quantized Givens rotation and phase angles that parametrize the right-singular subspace of the channel matrix. Because these angles encode fine-grained spatial signatures of the propagation environment, recent work have shown that plaintext CSI feedback can inadvertently reveal user activity, identity, and location to passive eavesdroppers. In this work, we introduce a standards-compatible \emph{differentially private (DP) quantization mechanism} that replaces deterministic angular quantization with an $\varepsilon$-DP stochastic quantizer applied directly to the Givens parameters of the transmit beamforming matrix. The mechanism preserves the 802.11 feedback structure, admits closed-form sensitivity bounds for the angular representation, and enables principled privacy calibration. Numerical simulations demonstrate strong privacy guarantees with minimal degradation in beamforming performance.

Assessing cyber risk in complex IT infrastructures poses significant challenges due to the dynamic, interconnected nature of digital systems. Traditional methods often fall short, relying on static and largely qualitative models that do not scale with system complexity and fail to capture systemic interdependencies. In this work, we introduce a novel quantitative approach to cyber risk assessment based on Quadratic Unconstrained Binary Optimization (QUBO), a formulation compatible with both classical computing and quantum annealing. We demonstrate the capabilities of our approach using a realistic 255-nodes layered infrastructure, showing how risk spreads in non-trivial patterns that are difficult to identify through visual inspection alone. To assess scalability, we further conduct extensive experiments on networks up to 1000 nodes comparing classical, quantum, and hybrid classical-quantum workflows. Our results reveal that although quantum annealing produces solutions comparable to classical heuristics, its potential advantages are significantly hindered by the embedding overhead required to map the densely connected cyber-risk QUBO onto the limited connectivity of current quantum hardware. By contrast, hybrid quantum-classical solvers avoid this bottleneck and therefore emerge as a promising option, combining competitive scaling with an improved ability to explore the solution space and identify more stable risk configurations. Overall, this work delivers two main advances. First, we present a rigorous, tunable, and generalizable mathematical model for cyber risk that can be adapted to diverse infrastructures and domains through flexible parameterization. Second, we provide the first comparative study of classical, quantum, and hybrid approaches for cyber risk scoring at scale, highlighting the emerging potential of hybrid quantum-classical methods for large-scale infrastructures.

Simulation-based inference (SBI) with neural posterior estimation (NPE) provides rapid X-ray spectral fitting in both Gaussian and Poisson regimes by learning approximate parameter posteriors from simulations. We investigate auto-encoders for compressing high-resolution X-ray spectra, motivated by newAthena X-ray Integral Field Unit (X-IFU), and use likelihood-based importance sampling to refine NPE outputs. Our auto-encoder maps spectra to a low-dimensional latent space and is trained with a custom loss equal to the Cash statistic (C-stat) between simulated and reconstructed spectra. A neural density estimator is then trained on the latent representations. Both models are trained in multiple rounds: at each round, new simulations are drawn from a truncated proposal concentrated around the observation, improving efficiency as the proposal contracts. After NPE convergence, we apply likelihood-based importance sampling to correct the learned posterior. To assess information retention, we train a diagnostic network that predicts the original spectral parameters from the latent space, and we also train a network to learn the likelihood directly to accelerate importance sampling. On X-IFU-like simulations, the auto-encoder and multi-round NPE outperforms PCA and hand-crafted spectral summaries in accuracy and robustness. After importance sampling, the resulting posteriors are statistically indistinguishable from those obtained with nested sampling. On a standard laptop, the full pipeline (simulation, compression, inference, correction) delivers 10x speedups. We further demonstrate the approach on XRISM/Resolve and on lower-resolution NICER and XMM-Newton EPIC-pn data, confirming applicability across instruments and resolutions. Overall, NPE on compressed spectra paired with likelihood-based importance sampling offers an exact yet efficient alternative for Bayesian X-ray spectral fitting.

In this short note, we prove that every closed, oriented, connected 3-manifold arises as Dehn surgery along a braid positive link.

Online government services are increasingly regarded as critical national infrastructure. Because these services directly influence public trust, any disruption can have significant societal and political consequences. Yet their supporting infrastructures remain vulnerable to outages from natural disasters, geopolitical tensions, and targeted attacks. Central to their operation is the authoritative Domain Name System (DNS) infrastructure, the single source of truth that maps government domain names to service endpoints. While indispensable, this infrastructure also represents a potential and critical point of system failure. In this paper, we introduce a comprehensive assessment framework with purpose-designed mechanisms to systematically evaluate the operational resilience of authoritative DNS infrastructure supporting government services. Complementing prior studies on website hosting, recursive resolution, and DNS record integrity, our work provides a holistic view of authoritative DNS operation. Our first contribution develops a multi-sourced data schema that characterizes a (government) domain's authoritative DNS infrastructure across four hierarchical levels: physical hosting infrastructure, server functionality, name servers, and individual hosting instances. Using data collected from six representative countries, our second contribution identifies resilience attributes at their finest applicable hierarchy across three operational phases: infrastructure placement, service configuration, and DNS record dispatch. Our method assigns numerical scores to each attribute and aggregates them algorithmically to enable consistent and cross-domain comparisons. We apply our method to government domains in the six countries, highlighting their strengths and weaknesses in authoritative DNS resilience and pinpointing operational practices that require improvement.

We study CR-manifolds of arbitrary CR codimension, mainly focusing on Levi and contact-nondegeneracy and depth. We investigate these and other invariants in the locally homogeneous case, developing a comprehensive theory which establishes correspondences with related properties of the associated CR-algebras and, in the parabolic case, with the combinatorics of their cross-marked painted root diagrams.

We present disperon QED, a method to deal with data input in loop processes in Monte Carlo codes. It relies on dispersion relations, automated tools such as OpenLoops, effective field theory methods and a threshold subtraction. We motivate this method and apply it to the process $ee\toππ$ in McMule to deal with hadronic vacuum polarisation insertions in two-loop contributions as well as the vector form factor of the pion within the form-factor scalar QED approximation. The generality of this method for more complicated processes is emphasised.

In this work, we analyse the effect of adding Gaussian white noise to the slow variable of a slow--fast system passing through a saddle--node (or fold) bifurcation. This problem is mainly motivated by applications to non-equilibrium energy sinks. While the effect of adding noise to the fast variable, which is important for noise-induced tipping, has been previously analysed in detail, the case where the slow variable is perturbed by noise has not been considered before. Our main result is that the noise increases the slow variable on average. We compute the effect of the noise, to lowest order, on the expectation and variance of the slow variable after the bifurcation. The contribution of the noise can be explicitly expressed in terms of Airy functions. We also provide numerical simulations, which show that the expansion to lowest order matches the observations for fairly large values of the noise intensity.

Secondary anisotropies in the cosmic microwave background (CMB) contain information that can be used to test both cosmological models and models of galaxy formation. Starting from lightcone-based HEALPix maps and catalogues, we present a new set of mock CMB maps constructed in a self-consistent manner from the FLAMINGO suite of cosmological hydrodynamical simulations, including CMB lensing, thermal and kinetic Sunyaev-Zeldovich effects, cosmic infrared background, radio point source and anisotropic screening maps. We show that these simulations reproduce a wide range of observational constraints. We also compare our simulations with previous predictions based on dark matter-only simulations which generally model the secondary anisotropies independently from one another, concluding that our hydrodynamical simulation mocks perform at least as well as previous mocks in matching the observations whilst retaining self-consistency in the predictions of the different components. Using the model variations in FLAMINGO, we further explore how the signals depend on cosmology and feedback modelling, and we predict cross-correlations between some of the signals that differ significantly from those in previous mocks. The mock CMB maps should provide a valuable resource for exploring correlations between different secondary anisotropies and other large-scale structure tracers, and can be applied to forecasts for upcoming surveys.

Accurate and efficient surrogate modeling is essential for modern computational science, and there are a staggering number of emulation methods to choose from. With new methods being developed all the time, comparing the relative strengths and weaknesses of different methods remains a challenge due to inconsistent benchmarking practices and (sometimes) limited reproducibility and transparency. In this work, we present a large-scale, fully reproducible comparison of $29$ distinct emulators across $60$ canonical test functions and $40$ real emulation datasets. To facilitate rigorous, apples-to-apples comparisons, we introduce the R package \texttt{duqling}, which streamlines reproducible simulation studies using a consistent, simple syntax, and automatic internal scaling of inputs. This framework allows researchers to compare emulators in a unified environment and makes it possible to replicate or extend previous studies with minimal effort, even across different publications. Our results provide detailed empirical insight into the strengths and weaknesses of state-of-the-art emulators and offer guidance for both method developers and practitioners selecting a surrogate for new data. We discuss best practices for emulator comparison and highlight how \texttt{duqling} can accelerate research in emulator design and application.

Observations of core-collapse supernovae suggest that some massive stars undergo intense mass loss shortly before explosion, but the underlying mechanisms remain unknown. Here we report evidence of giant outbursts of clumpy material from a massive star in the final decades before explosion. Photometric, spectroscopic, and polarimetric data of SN~2024qiw reveal a bumpy light curve, a broad H$α$ profile, and variable polarization, all consistent with interaction between SN ejecta and clumpy circumstellar material, implying a mass-loss rate of $\gtrsim 10^{-2}$ M$_\odot$ yr$^{-1}$. Taken together, the most likely explanation is multiple major eruptions, similar to those of Luminous Blue Variables (LBVs), but occurring shortly before explosion. This challenges standard stellar evolution theory by requiring either that LBVs explode terminally, or that other evolutionary phases produce eruptive episodes. In spite of very high pre-SN mass loss, the resulting SN is of Type~II, rather than Type IIn, highlighting diverse and previously unrecognized late-stage mass-loss processes.