Papers

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and by restricted interests and repetitive behaviors. Treatment interventions often target social-communication skills, creating a need for reliable measures of behavioral change. The Brief Observation of Social Communication Change (BOSCC) is a validated treatment-response measure based on brief play and social-communication interactions between a child and trained examiner. The BOSCC coding process is resource-intensive and requires trained experts, motivating the automation of coding in order to improve scalability and accessibility. In this work, we evaluate general-purpose large language models (LLMs) for predicting speech-related BOSCC codes from different input representations. We compare transcript, diarized-transcript, and targeted audio conditions across 163 in-house recordings. LLMs are able to perform well in assessing verbal exchange, but do not perform as well when identifying atypical speech patterns. Additionally, performance varies considerably across scoring decisions, with no consistent pattern across diagnosis groups. An audit of model predictions indicates that applying the BOSCC coding criteria and interpreting ambiguous speech evidence remain challenges.

Generating high-quality and controllable camera motion is essential for AI-assisted cinematography, video synthesis, and 3D scene understanding. We introduce TKCAM, a Text- and Keyframe-conditioned CAMera-motion synthesis framework based on generative masked modeling. We represent camera dynamics using a 12-dimensional kinematic feature comprising position, velocity, and a continuous rotation representation and discretize them into hierarchical motion tokens via a Residual Vector Quantizer (RVQ). A two-stage masked transformer architecture then learns to reconstruct and refine these tokens, utilizing explicit self- and cross-attention modules for multimodal conditioning. A central feature of our framework is sparse visual keyframe conditioning: users can provide free-form text prompts together with RGB observations at selected timestamps, which provide temporally localized visual guidance for generating coherent in-between trajectories. Furthermore, to advance evaluation standards, we curate RealEstate10K-Cap, a large-scale text-camera dataset, and establish a cross-domain benchmark with a Universal CLaTr Evaluator. Extensive experiments demonstrate that TKCAM surpasses recent state-of-the-art baselines on Fréchet distance (FID), text-motion matching scores, and retrieval metrics (R@K), while additional analyses evaluate temporal smoothness and cross-domain generalization. Code is available at https://github.com/linearalgebrayhz/TKCAM.

Accurate segmentation of thin curvilinear structures is vital for various real-world applications, from vessel analysis to road extraction. Yet their intricate geometry makes even minor pixel-wise errors enough to break the global topology, and this structural fragility turns severe domain shifts into catastrophic failures. The difficulty is most acute under cross-modality gaps, where the imaging process itself differs fundamentally between source and target. While test-time adaptation (TTA) offers a practical source-free remedy, existing methods adapt feature statistics and confidence, neither of which constrains connectivity, and thus degrade under such extreme gaps. To address this, we propose Skeleton-Guided Progressive Test-Time Adaptation (SGP-TTA). Progressive Batch Normalization (ProgBN) shifts normalization from frozen source statistics toward current target estimates under a sample-count schedule, so that the source-target balance follows the stage of adaptation rather than a fixed coefficient. Consensus Skeleton Recall (CSR) then derives a structural target from geometrically aligned multi-view predictions and updates only the BN affine parameters to preserve connected structures. Extensive experiments show that SGP-TTA consistently outperforms existing TTA methods in topological connectivity, with the largest margins under cross-modality shift. The project page is available at https://boa-jang.github.io/SGP-TTA.

The dynamic topology and heterogeneous traffic demands of multi-satellite systems present substantial challenges for beam-hopping (BH) resource management. This letter develops a topology-aware cooperative BH framework for multi-satellite systems under partial observability. A lightweight load-balancing scheme first determines the serving relationships, which are then exploited to construct an association-induced satellite-user graph. A two-phase graph neural network (GNN) extracts episode-level topology-aware structural identities for parameter-sharing agents, while a recurrent multi-agent proximal policy optimization (MAPPO) policy handles slot-level dynamics for joint beam scheduling and onboard power control. Simulation results demonstrate notable gains in energy efficiency, throughput, and fairness over the baselines.

Binary populations encode information about the physical conditions of star formation and the subsequent evolution of stellar populations. Adopting a statistical method based on multi-epoch line-of-sight velocity variations, we constrain the binary fraction, $f_b$, of field main-sequence turn-off stars in the Milky Way using data from the second data release of the DESI Milky Way Survey. $f_b$ is found to primarily increase with lower [Fe/H]. At $[\mathrm{Fe}/\mathrm{H}]\gtrsim -1.3$, $f_b$ shows a modest increase with increasing [Mg/Fe], whereas no clear trend is detected at lower [Fe/H]. The apparent variations of $f_b$ with angular momentum and Galactic height are largely explained by their underlying [Fe/H] distributions, while the remaining variations broadly follow differences in [Mg/Fe]. Within overlapping regions of the [Fe/H]–[Mg/Fe] plane, the thin disk, thick disk, stellar halo and GSE populations exhibit no significant differences in their binary fractions. Thus, our measurements indicate that the variation in $f_b$ is dominated by chemical abundance, which could be explained by the metallicity-dependent gas cooling efficiency and fragmentation. The weaker [Mg/Fe] dependence at fixed [Fe/H] might be associated with the formation conditions of stars, with higher [Mg/Fe] stars generally forming earlier under a more gas-rich and turbulent environment.

We establish a quantum super Schur–Weyl duality for the mirabolic Hecke algebra \(H_n^{\mathrm{mir}}(q)\) with \(q\) indeterminate and determine the corresponding bimodule decomposition. The centralizer of the mirabolic action is shown as a direct sum of homogeneous quantum Schur superalgebras. We describe the tensor-space annihilator and the resulting faithful quotient. When nonzero, the annihilator is generated by an explicit spectral idempotent attached to the smallest rectangle excluded by the hook condition. From the quantum super Schur–Weyl duality, we derive a super Frobenius formula for mirabolic Hecke algebras. This formula then yields a Murnaghan–Nakayama rule and Regev-type formulas for the irreducible characters. We also construct a super mirabolic RSK bijection from words in a super alphabet with an additional even letter to hook semistandard insertion tableaux together with recording pairs that distinguish the positions of the additional letter. This correspondence yields a Roichman formula for irreducible characters of \(H_n^{\mathrm{mir}}(q)\). Finally, we establish a parameter-inversion isomorphism between \(H_n^{\mathrm{mir}}(q^{-1})\) and the \(q\)-rook monoid algebra \(R_n(q)\) and use it to transport the duality, annihilators, and character formulas in both directions. The transported tensor decomposition provides a representation-theoretic interpretation of hook and two-row character sums for \(R_n(q)\).

We establish a uniform small cancellation theorem for non-elementary torsion-free hyperbolic groups: given any prescribed small cancellation constant, finitely many fixed words suffice to produce a small cancellation element from every pair of noncommuting elements. As an application, we prove that the first-order theory of every non-elementary torsion-free hyperbolic group does not admit a model companion.

Speaker verification (SV) performance degrades under language mismatch due to the entanglement of speaker identity with language-specific acoustic cues. To address this problem, we leverage large-scale self-supervised pre-trained models (PTMs) to learn language-agnostic speaker representations. We utilize PTMs as robust front-end feature extractors, capitalizing on their rich acoustic and linguistic knowledge acquired from vast, diverse audio data. These generalized features are then used to train a downstream speaker embedding network, effectively disentangling speaker identity from language-specific characteristics. We validate our approach on the TidyVoice2026 benchmark, which benchmarks SV under language mismatch. Our proposed system (team T02) achieves equal error rates (EERs) of 2.21% on tv26_eval-A and 2.99% on tv26_eval-U.

We prove that some pairs of binary words of every sufficiently large prescribed length $n$ require $\Omega(\log n\log\log n)$ states to separate by a deterministic finite automaton whose letters act as permutations. We obtain this bound by constructing distinct equal-length positive binary words that form an identity of $\mathfrak{S}_k$ of length $\exp(O(k/\log k))$. This improves the previous lower bound $(3/2-o(1))\log n$ of Bulatov, Karpova, Shur, and Startsev (arXiv:1609.03199). The proof combines a positive-word construction with a cover of permutation orders. Consecutive segments of prime powers reduce the covering problem to enumerating sets of positive integers with bounded sum. The required estimates use the central binomial coefficient and the classical asymptotic formula for partitions into distinct parts. An appendix gives sharper constants and lower-order terms using a greedy cover and a quantitative prime number theorem.

We show that, for stabilisers codes, there always exists a logical subspace requiring $\tilde{\Omega}(k^2)$ ancillary qubits to be measured with a sparse surgery ancilla. For CSS codes the hard subspace can be taken to be generated by all-X, or all-Z operators. As a result, for codes with large $k$, constant overhead ancillas can only realise a small fraction of possible measurements.

Visual tracking across the air-water interface is challenged by splashes, bubbles, refraction, reflections, and abrupt appearance changes that can temporarily invalidate observations. This setting poses two coupled difficulties: first, for evaluation, image-only annotation cannot reliably describe the target's physical location during visual blindness; second, for online tracking, corrupted observations can contaminate motion estimates and appearance templates. We address the first difficulty with a construction pipeline that synchronizes camera frames with motion-capture poses, projects known target geometry, corrects underwater projection with a medium-gated residual, and subjects the annotations to manual review. This yields an evaluation-only cross-medium test set of 22,346 frames. We further introduce a Cross-Medium Recovery Policy (CMRP) centered on confidence-triggered template selection. It supplies MixFormerV2 with the fixed initial template, a window-best pre-trigger template, and a trigger-frame Kalman-guided image crop, together with their associated weights, without retraining the visual backbone. In the accuracy evaluation, CMRP achieves 49.90 Macro Success AUC, 2.95 points above MixFormerV2 Official. On selected cross-medium transition and occlusion-recovery intervals, CMRP increases MixFormerV2 tracking coverage from 47.91% to 50.43% relative to Official updating, while mean loss-to-recovery latency over successfully recovered videos decreases from 55.3 to 49.3 frames.

Radioactive nuclei in neutron-star merger ejecta carry information about both their abundance and their production history. Using the $^{225}\mathrm{Ra}$-$^{225}\mathrm{Ac}$ sequence, we establish exact conditions linking a surviving isotope partition to the time available for parent decay. An Ac fraction of 0.1 produced solely through Ra decay requires at least 2.456 days, providing a direct consistency test for proposed source states. We show that subsequent activity retains additional production memory: two histories with the same mean birth time can produce different response crossings. This distinction is captured analytically and separates mean chronology from the influence of the production-time distribution. A controlled radiation calculation then shows how deposition and photon escape transform the nuclear contrast into a common light-curve pivot for fixed ejecta properties. The results connect nuclear production chronology to late-time merger emission and provide benchmarks for nucleosynthesis and radiation-transport calculations.

Conventional resource theories quantify utility against an idealized free set $\mathcal{F}$. In practice, however, realizable states are confined to a specific supply environment $\mathcal{E}$ shaped by experimental and noise constraints. We propose a framework that evaluates the relative utility of arbitrary states with respect to $\mathcal{E}$, using two reference representations: the set of states reachable by free operations (defining the attainable boundary) and the set obtained via numerical optimization (defining operational equivalence). Two complementary methodologies are established. First, by assessing each state under its individually optimized strategy, we provide a strategy-dependent benchmark that quantifies achievable performance. Second, by deriving the optimal strategy for each state separately, we obtain a computable lower bound, yielding a strategy-independent measure of relative superiority that is independent of specific setups. To realize these methodologies numerically, we introduce relative measures based on the generalized robustness and the relative entropy. We then establish, theoretically, a quantitative hierarchical relation between the strategy-dependent benchmark and the strategy-independent lower bound. In a thermodynamic setting, the minimum work cost for synthesizing a target state is lower-bounded by the free-energy difference, which reflects only absolute utility. Applying our framework, we identify this bound as a strategy-independent quantification. Through two scenarios based respectively on information-theoretic distance and thermodynamic distance, we derive upper bounds on the minimum work cost for recovering entanglement in a noisy environment. We show that, at moderate and low temperatures in the low-noise regime, the thermodynamic-distance-based upper bound is tighter than the information-theoretic one.

Can a regulator promise both a risk ceiling and a development deadline when safety productivity is unknown? A ceiling below the final model's unprotected hazard requires a minimum stock of safety knowledge, so both promises hold only if weak research can be ruled out. Learning first and then replaying development, with spare compute in safety, comes close to that minimum. In a calibration with only state risk, constant safety yield, and full knowledge transfer, it needs only 3.3 percent more productivity than the necessary bound. Customer services pay for the guarantee. Rules that fix compute allocation fix dates, not risk.

We prove that almost all finite groups are $2$-groups of nilpotency class two, when groups of order at most $x$ are counted up to isomorphism and $x\to\infty$. More precisely, uniformly for $2^m\leq x<2^{m+1}$, all but an $O(2^{-cm^2})$ proportion have order $2^m$ and class two, for some absolute constant $c>0$. For each fixed prime $p$, we give a formula for the number of groups of order $p^m$, with relative error $O(p^{-m/3})$ as $m\to\infty$. This refines the classical logarithmic estimates for the number of $p$-groups. For every fixed prime $p$ and sufficiently large $m$, we also prove that all but a $p^{-m^2/300}$ proportion of groups of order $p^m$ have central elementary abelian Frattini subgroup. For fixed $p$, the distribution of the minimum number of generators among groups of order $p^m$ is asymptotically supported on one or two adjacent values, with explicit probabilities depending on $m$ modulo three.

Ultralight bosons are well-motivated candidates for physics beyond the Standard Model and may constitute part or all of the dark matter. Black-hole superradiance provides a powerful means of probing these particles through their gravitational interaction with rotating black holes. This mechanism has motivated a growing body of constraints from electromagnetic measurements of black-hole spins and, more recently, gravitational-wave observations. Gravitational-wave probes draw on several distinct signatures: the spins of merging binary black holes, gravitational radiation emitted by boson clouds, and potential imprints of cloud-induced changes to binary dynamics. The interpretation of these probes depends on method-specific astrophysical and theoretical assumptions, including black hole formation, natal spin, age, accretion history, population priors, and boson self-interactions. In this paper, we review the principal electromagnetic and gravitational-wave constraints derived from black-hole superradiance, with particular emphasis on their underlying assumptions and systematic uncertainties. The distinct observational signatures and systematic uncertainties of these approaches offer complementary perspectives on the viability of ultralight-boson models. In particular, their overlapping coverage around boson masses of $10^{-13}$-$10^{-12}$ eV makes this region particularly informative for comparing constraints, with attention to the assumptions underlying each result.

Predicting how a patient's vital signs respond to an intervention is a central question in intensive care. World models can do so by learning dynamics as a function of prior actions. However, such models tend to be brittle outside of training data. Clinicians choose drug dosages based on the patient's state, so the association a model learns between dose and outcome runs opposite to the drug's effect, generalizing poorly to out-of-distribution (OOD) dosing practices. Yet, pharmacological information provides the directional effects of different drugs, which are invariant to OOD shifts. To resolve the OOD sensitivity of current ICU world models, we introduce PHYSIO WORLD, which isolates an intervention's effect by evaluating a forward pass twice: once under recorded doses, and once with those doses set to zero. The difference is projected onto pharmacologically admissible effect directions stated by 70 rules over 28 drugs, leaving the magnitude to be learned from data. Across five distribution-shift parameters over cohorts from three intensive-care databases, and against forecasters, counterfactual models, and invariance objectives, PHYSIO WORLD attains the lowest OOD RMSE in every setting, improving on the strongest external baseline by 8-12%, while matching the in-distribution error of its backbone. Reversing the pharmacological directions degrades accuracy below the backbone, and data-mined directions recover almost no gain, indicating the improvement derives from the content of the pharmacological knowledge. The construction may extend to other settings where the sign of an effect is known in advance and its magnitude is confounded by treatment assignment.

Can noisier threshold selection produce a group with higher mean true value? We answer this question by varying the scale of a fixed assessment error. Holding the selection rate fixed, less noise yields a first-order stochastically dominant distribution of selected values. At a fixed threshold, however, more noise can raise the selected mean even as selection loss, which weights selection mistakes by the distance of true value from the threshold, increases. Under joint Gaussianity, we characterize when the selected mean rises or falls with noise. In treatment selection, noise alone can generate subgroup differences in participants' average gains despite identical population gain distributions.

We investigate CP violation in the decays of a GeV-scale dark scalar boson into mesons, muons and photons. Both scalar and pseudoscalar couplings to the SM fermions are considered to study how the CP structure of the dark scalar boson can be probed through its decays. The scalar and pseudoscalar interactions predominantly induce decays into two-meson and three-meson final states, respectively. We propose the simultaneous observation of these two classes of decay modes as a probe of CP violation in the dark scalar sector. The expected signal rates at the SHiP and FASER2 experiments are evaluated based on the dark scalar production from heavy-meson decays. We find that the ratio of three-meson to two-meson decay events at SHiP has the potential to probe a wide range of the CP-violating parameter space for dark scalar masses around the GeV scale. We also show that the two-muon and di-photon channels can provide complementary information for the CP violation search.

The rapid growth of scientific publishing has strained peer review, particularly in machine learning, raising concerns about declining review quality and increasing reviewer workload. Large language models (LLMs) have been proposed as automated review assistants, yet their evaluation has focused largely on imitating human-written reviews rather than supporting the core functions of peer review. Here, we introduce a verification-centric perspective on LLM-assisted peer review, emphasizing error detection as a critical and resource-intensive task. We present a scalable benchmark that evaluates review systems' ability to identify logical contradictions, constructed through synthetic insertion of errors into conference papers, yielding unambiguous evaluation targets and enabling systematic comparison. We further propose a Multi-Layered Review (MLR) framework that prioritizes detailed manuscript comprehension before review generation, aligning more closely with human reviewing practices while improving token efficiency. Across evaluations, our approach demonstrates strong alignment with human review scores, achieves high error detection performance, and provides complementary perspectives on reviewer focus. These improvements can be attributed to both the choice of the underlying LLM and the design of our system. At the same time, we corroborate persistent vulnerabilities to adversarial manipulation, underscoring the need for robustness in automated review systems. Our findings highlight the importance of rigorous, error-focused evaluation to guide responsible deployment of LLM-based tools in peer review and other critical scientific workflows.

Purpose: Cervical cancer is one of the leading causes of mortality worldwide. Deep learning has shown promising performance in medical image classification. The influence of image preprocessing algorithms on classification performance remains insufficiently investigated in the literature. This research aims to evaluate the impact of image preprocessing algorithms on the performance of CNNs for Pap smear image classification. Methods: Three CNN architectures (ResNet-34, MobileNet-V2, and DenseNet-121) were trained and evaluated using the SIPaKMeD dataset. Two preprocessing algorithms were applied: the PMD filter for noise reduction and CLAHE for contrast enhancement. The model performance was assessed using a confusion matrix. Results: Preprocessing improved the classification performance of all models. CLAHE significantly increased the accuracy of ResNet-34 from 76.73% to 84.16% and DenseNet-121 from 76.73% to 84.16%. The PMD filter yielded limited improvement and slightly reduced the MobileNet-V2 performance. Novelty: This research provides a systematic comparison of contrast enhancement and noise reduction techniques across CNN architectures. This research demonstrates that contrast enhancement is more effective than noise reduction in improving CNN performance. The research provides new pipelines for improving cervical cancer classification.

Offline multi-task reinforcement learning (Offline MTRL) heavily depends on the quality and distribution of pre-collected data. However, existing methods mainly focus on algorithmic optimization, with less emphasis on data-level improvements to enhance learning ability and generalization performance. This paper, from a data perspective, reveals three key bottlenecks that limit Offline MTRL performance:(i) ineffective utilization of prompts length under diverse task complexities, and (ii) semantic irrelevance of randomly sampled prompt segments, (iii) misleading supervision induced by fragmented and discontinuous trajectories. To address these challenges, we propose DaCe-DT, a robust offline MTRL framework designed to be insensitive to heterogeneous task complexities and data quality, featuring length-gated prompt masking (LGPM), retrieval-augmented prompt construction (RAPC), and value-adaptive return calibration (VARC). Together, these mechanisms enable DaCe-DT to deliver data-centric prompt adaptation and trajectory refinement, resulting in robust multi-task generalization and stable policy learning amid heterogeneous offline data and tasks. Experimental results on Meta-World show that DaCe-DT consistently outperforms state-of-the-art methods, achieving an average improvement of 11.73% on optimal datasets and an improvement of 13.34% on suboptimal datasets, demonstrating its effectiveness in learning stably from imperfect data and improving overall multi-task performance.

Consider a $d$-sphere $\mathbb{S}^d$ and denote by $\Gamma(T\mathbb{S}^d)$ the set of all vector fields on $\mathbb{S}^d$. We study the Navier-Stokes equations (NSE): $$ \partial_t u + \nabla_u u + grad \pi = \mathbf{\Delta} u + \operatorname{div} f(\cdot, t);\, \operatorname{div} u=0,$$ for the vector field $u(\cdot, t)\in \Gamma(T\mathbb{S}^d)$, where $\mathbf{\Delta}$ denotes the Ebin-Marsden Laplace operator defined by $\mathbf{\Delta} u= \operatorname{div} (\nabla u + \nabla u^t)^{\sharp}$, and $\operatorname{div} f(\cdot, t)$ is the periodic external force. We investigate the Navier-Stokes equations in the framework of $L^p$-spaces over vector fields on $\mathbb{S}^d$ and prove the existence and uniqueness of a periodic solution to such equations. Moreover, exploiting the distribution of eingenvalues of Ebin-Marsden Laplace operator we show the existence of an inertial manifold for solutions around that solution.

Denote by $\mathrm{Kl}(a,n)$ the normalized Kloosterman sum modulo $n$. In this paper, we study the moments of $|\mathrm{Kl}(1,n)|$ on average over $n\leqslant X$, and show that \begin{align*} \sum_{n\leqslant X}|\mathrm{Kl}(1,n)|\asymp X(\log X)^{\frac{8}{3\pi}-1}. \end{align*} The lower bound is proven by considering a subfamily of moduli which factorize in a nice way such that vertical Sato–Tate distributions of Kloosterman sums (as variants of Katz) apply. The upper bound is based on a new density version of upper bound sieves, combining with averages of divisor functions in arithmetic progressions and a similar vertical Sato–Tate distribution.

The kernel bandit problem consists of sequentially optimizing an unknown function with noisy feedback, where the function has bounded norm in a given Reproducing Kernel Hilbert Space (RKHS). A central quantity in the regret analysis of kernel bandits is the maximum information gain $\gamma_T$. In particular, the best existing upper bounds scale as $\sqrt{T\gamma_T}$ up to log factors, and nearly-matching lower bounds have been derived for specific kernels such as squared exponential and Matérn. However, lower bounds for general kernels are lacking, thus making it unclear in what generality the upper bounds are near-optimal. In this paper, we establish a general $\Omega(\sqrt{T\gamma_T/\log T})$ minimax regret lower bound for non-constant continuous kernels on compact domains, establishing near-optimality (within log factors) in a very general sense. We show that the log factor appearing in this bound is unavoidable in general, but that it can be removed under certain conditions. Among other things, our findings imply that the minimax-optimal scaling is exactly $\Theta(\sqrt{T\gamma_T})$ (i.e., within constant factors) for the Matérn-$\nu$ kernel with $\nu \in (0,2)$, $\gamma$-exponential kernel with $\gamma \in (0,2)$, and certain piecewise-polynomial kernels.

Hyperbolic geometry hosts a plethora of exotic quantum phenomena ranging from holographic duality to novel topological phases. However, investigating these effects experimentally remains challenging due to the Euclidean constraints of natural crystalline materials. While recent synthetic lattices have achieved static realizations, they often lack the dynamical tunability required to explore the full geometric landscape. Here, we overcome these limitations by implementing a quantum simulation of hyperbolic geometry within the parameter space of a superconducting quantum circuit. We construct a Hyperbolically Driven Quantum System (HDQS) by mapping the hyperbolic properties onto the dynamically controllable Hamiltonian parameters. By probing the systems' response under geometric quantum driving, we elucidate how the intrinsic geometry of the parameter space dictates observable physical quantities. Furthermore, we extend the theoretical framework to realize ensemble simulation. This generalization enables us to harness the fast coherent control of superconducting qubits to capture essential geometric characteristics, establishing a versatile platform for exploring quantum dynamics on hyperbolic manifolds.

Building on recent ignition-access work of Delgado-Aparicio, Ono, and Menard, we show that even a single-use, gram-scale quantity of spin-polarized fuel (SPF) is useful for increasing fusion power and gain in magnetic confinement fusion machines such as tokamaks and stellarators. While fueling a fusion power plant continuously with SPF requires $\sim$kilograms per day, far beyond present capabilities, a single-use short pulse of SPF allows a fusion plasma to cross into a high-gain regime, and stay there, even after switching back to regular unpolarized fuel. With continuous polarized fueling, polarization also makes high-gain plasmas easier to control: a resonant wave that depolarizes the fuel lowers the reactivity quickly, on a much faster timescale than transport. Grams of polarized fuel can therefore improve plasma performance and open a path to scaling SPF sources and usage, from small first experiments to continuous fueling.

We show that the fast surgery framework of Baspin-Berent-Cohen can be instantiated using at most $\tilde{O}(n(k + d))$ ancilla qubits, improving on the previously achievable $O(n\cdot k \cdot d)$ overhead. This result also establishes that fast surgery can be performed at a cost comparable to the $\tilde{O}(nk)$ overhead required for regular surgery. We build the surgery ancilla by first starting with a dense, but expanding system; and then applying expansion-preserving sparsification methods from gauging and code embeddings.

The Whitehead problem, which asks whether every abelian group $A$ satisfying $\mathrm{Ext}^1(A,\mathbb{Z}) = 0$ is free, is independent of ZFC. However, this problem has an analogue in condensed mathematics that can be answered affirmatively. In this note, we give a new proof that an abelian group $ A $ of size $\kappa$ is free if and only if $\underline{\mathrm{Ext}}^1(\underline{A}, \underline{\mathbb{Z}})(S_\kappa) = 0$, where $ S_\kappa $ is the Stone space of the Boolean completion of $\operatorname{Add}(\omega,\kappa)$.

We give a convergent explicit representation of the Ricci-flat Kähler metrics supplied by the Calabi–Yau theorem in a one-parameter family of classes on a fixed Kummer K3 surface. An explicit radial background has normalized Monge–Ampère residual \(O(a^4)\). Its scalar Green operator \(G_a\) is represented by periodic Ewald kernels, separated radial kernels, finite-dimensional Schur complements, and convergent Neumann series. The coefficients are defined by \(U_{a,1}=-G_af_a\) and \(U_{a,n}=G_a\sum_{j=1}^{n-1}Q_a(U_{a,j},U_{a,n-j})\), where \(Q_a\) is the polarized quadratic Monge–Ampère term. A Green estimate of order \(a^{-1}\) gives a first correction of order \(a^3\) and a convergence parameter of order \(a^2\). A Catalan majorant proves absolute convergence on the entire smooth surface for every sufficiently small fixed \(a\). The sum defines a positive form solving the volume equation; comparison identifies the sum with the smooth normalized Calabi–Yau potential. We also record sufficient analytic conditions for the same recursion on collapsing elliptic K3 surfaces.