Papers

Automated features and advanced driving support systems have enabled a range of life-saving and comfort applications in modern personal vehicles. Development from mostly mechanical entities towards truly software-defined devices also means that added sensor complexity, processing power, and networking capabilities provide a platform for running even more complex algorithms and applications. As vehicles become smarter, they require even more energy to enable advanced machine learning- and artificial intelligence-based functionalities. This paper discusses the implications for sustainability and the threats to environmental ethics posed by the development of software-defined vehicles. We discuss the balance between introducing more resource-hungry software and hardware components and their effect on society and sustainability. We underscore the need for novel tools to measure, manage, and optimise energy consumption and other sustainability indicators, with design guidance and actionable recommendations.

We show that for every smooth complex cubic threefold $Y$, the fourfold $Y\times\mathbb{P}^1$ is irrational. As an application, we construct a smooth stably rational complex Fano variety $X$ which is irrational, while $X^m$ is rational for every $m\geq2$ and $\mathrm{Sym}^2 X$ is rational.

Uniform spherical arrays provide full-space coverage. However, owing to directional element patterns, full-array transmission activates many elements that contribute little to a given user, resulting in increased system overhead and inefficient power allocation. To address this issue, this letter proposes an adaptive element activation strategy for multiuser uniform spherical arrays that reduces the number of physically active elements while satisfying individual user rate requirements. The proposed strategy constructs a user-specific candidate sequence according to the angle between each element boresight and the user direction. At each iteration, it selects, among the unsatisfied users, the user-element connection that provides the largest improvement in the aggregate rate deficit. Simulation results show that fewer than half of the elements are sufficient for a single user to achieve 80% of its full-array rate, while fewer than 70% are sufficient to achieve the full-array rates of all users in multiuser scenarios. Moreover, the proposed strategy consistently requires a lower active-element ratio than the baseline methods across all tested configurations.

Feedback-based quantum algorithms recently gained attention by demonstrating how quantum computers can be used to solve optimization problems without resorting to hybrid quantum-classical architectures. Recent progress made with Feedback-based Algorithm for Quantum Optimization (FALQON) variants significantly reduced the depth of the required circuits. In this work, we merge the time-rescaled and second-order FALQON variants into a unified framework. The results show an important improvement over past modifications, reducing circuit depth and allowing a more flexible choice of time-steps, while maintaining stable solutions. This FALQON variant makes it suitable for the NISQ era.

Cryogenic computing technologies are maturing rapidly, but heat removal remains a key challenge when increasing the device density and operating power. Two-phase evaporative cooling is a promising approach to solve this issue, because it can dissipate high heat fluxes while maintaining small temperature rises. Here, we numerically investigate liquid-$\mathrm{N_2}$-filled silicon micropillar wicks as a capillary-fed thin-film evaporation concept for cryogenic electronics. The model combines Young-Laplace meniscus calculations, Hertz-Knudsen-Schrage evaporation, unit-cell heat-transfer and liquid-flow simulations, and an array-level thermal and capillary-flow model. For a representative geometry with a pillar diameter of $10\,\mu\mathrm{m}$, pitch of $24\,\mu\mathrm{m}$, and pillar height of $75\,\mu\mathrm{m}$ at an applied heat flux of $20\,\mathrm{W\,cm^{-2}}$, the predicted chip-temperature rise is approximately $2.7\,\mathrm{K}$. This is substantially below the estimated temperature rises for representative conduction cooling through an indium-interlayered copper heat sink and direct liquid-$\mathrm{N_2}$ immersion. The corresponding predicted single-fed dry-out length is approximately $2.7\,\mathrm{mm}$, equivalent to an ideal double-fed coolable width of approximately $5.3\,\mathrm{mm}$. The results indicate that local thermal performance is favorable, whereas lateral scalability is primarily constrained by capillary dry-out. An approximate capillary-viscous scaling relation provides a compact framework for comparing dry-out limits across working fluids and related wick geometries and for identifying strategies to extend capillary-fed transport.

Power law scaling relations between size and output are central to quantitative theories of cities, organisms, and other complex systems. Competing theories predict scaling exponents that differ by small fractions, but there is no existing theory for verifying whether a given dataset can even distinguish exponents at the required resolution to address such discrepancies. Here we derive a resolution limit for scaling exponents, giving the smallest exponent difference that any method of analysis can detect. We find that the Hurst exponents governing the evolution of systems' sizes and deviations from the scaling law determine how long a record of growing systems must be before it can separate competing scaling theories. Empirical results suggest that many available data panels are insufficient for reliable scaling model selection.

Debate adjudication requires tracking how arguments develop through interaction, yet existing datasets rarely combine fine-grained debate transcripts with professional judgments collected during real competitions under a shared rubric. We introduce a dataset and benchmark for evaluating large language models' understanding of competitive Chinese-language debate at the match, stage, and speaker levels. We organized 182 matches and recruited 120 professional judges, with each match independently adjudicated by three judges using a predefined rubric. After excluding matches with incomplete records, the dataset contains 148 matches, 2,698 stages, and 20,542 exchange units, with manually verified transcripts and segmentation. It preserves original stage scores, match votes, best-debater ballots, and adjudication rationales. We define three tasks: winner-tendency prediction, stage-score prediction, and best-debater prediction. Zero-shot evaluation of multiple large language models yields a highest winner-prediction accuracy of 66.2%, a highest Pearson correlation of 0.250 between model stage scores and mean human ratings, and a highest best-debater prediction accuracy of 56.8%. The dataset and benchmark provide a testbed for studying large language models' understanding of interactive argumentation and their agreement with professional judges.

Recent work applies human psychometric questionnaires to large language models to elicit moral and value profiles, but it is not clear whether these instruments measure anything stable in models or whether the resulting profiles can be moved toward a target human population. We administer the Norwegian Moral Foundations Questionnaire (MFQ-30) to six open-weight LLMs and compare their foundation profiles to a sample of N = 1,282 Norwegian respondents. We test two steering interventions, prompt-level persona steering and activation-level ActAdd. Half the models engage with the questionnaire under our attention check. The other half default to flat or central-tendency outputs that look near-human on average without tracking item content. A neutral Nordic-respondent persona, written without any distributional information from the human sample, brings the engaging models 44-77% closer to the Norwegian mean in Mahalanobis $d^2$. One-pair ActAdd at a fixed mid-layer flattens the foundation profile rather than steering individual foundations. For at least one model the same persona that shifts the profile also induces engagement that was absent at baseline, a concrete instance of the cognitive phantoms that Peereboom et al. (2025) warn about.

Purpose: Very-high-energy gamma-ray astronomy is an important window to study the extreme astrophysical processes in the universe, and how to effectively distinguish between gamma photons and background signals (mainly protons) is a key technical challenge to realize very-high-energy gamma-ray detection. Spaceborne calorimeters record secondary particle spatial distributions, achieving a background rejection power of 10000-100000 near 1 TeV. Ground-based imaging Cherenkov telescopes measure Cherenkov photon angular distributions, yet they only reach a rejection factor of about 10 in this energy range, leaving substantial room for optimization. Methods: We use CORSIKA and sim_telarray to simulate H.E.S.S. detector responses. Photon emission positions are reconstructed from Cherenkov shower images. Inspired by analysis techniques for space-borne calorimeter, we propose new observables derived from the transverse distribution of Cherenkov photon emission positions to separate gamma-ray signals from proton backgrounds. We employ a likelihood-ratio method to combine single-telescope variables across multiple telescopes and compare its performance with conventional Hillas-based analysis. Results: The results show that observables built from the transverse distribution of Cherenkov photon emission positions are less sensitive to Cherenkov image size and outperform the Hillas width parameter for single-telescope gamma-proton discrimination. Furthermore, the likelihood ratio combining width yields substantially better performance than the conventional mean reduced scaled width: the improvement reaches roughly one order of magnitude near 1 TeV, and this enhancement remains above 50% for energies above 10 TeV.

A new semi-analytical model for electron scattering in foils is presented valid for thin foils and electron-beam kinetic energies up to roughly $100$ keV and from 20 keV at a dimensionless foil thickness of $20$, and higher energies for thinner foils. To perform calculations, an approximate solution is constructed by Hankel transforms of an adapted Bethe transport equation for electron scattering. The Wentzel differential cross section is used in the Bethe transport Eq. and resulting integrals are solved analytically or approximated. These approximate solutions of the transport Eq.'s were compared to the Goudsmit-Saunderson solutions in gold, for thin samples with dimensionless thicknesses $\lambda=5,10,20$. We also compare to the Kawrakow \cite{Kawr} model, which uses the small angle approximation. We show that in the regime of $20$ to $100$ keV beam energies, our model performs better than Kawrakow's model and this is opposite above about 100 keV. Exactly, some angularly distributed beams and the influence of energy loss of the beam can be simulated with our model.

We present TERRA-NG, a portable, GPU-accelerated, matrix-free mantle-convection code. A single Kokkos C++ implementation runs at scale on NVIDIA, AMD, and Intel GPU supercomputers. TERRA-NG has a deliberately narrow design: built on a radially extruded mesh of spherical wedges, tailored to the spherical shell geometry, which enables domain-specific optimizations like single quadrature-point integral-evaluations, radial coordinate storage compression and radial shared-memory tiling. The corresponding low-order $W_1$-iso-$W_2/W_1$ wedge-based Stokes–energy discretisation is verified against the Zhong et al.(2008) spherical-shell convection benchmark suite. We showcase TERRA-NG through strong- and weak-scaling on the JUWELS Booster (NVIDIA A100), MareNostrum 5 (NVIDIA H100), LUMI-G (AMD MI250X), Hunter (AMD MI300A APU), and SuperMUC-NG Phase 2 (Intel PVC) supercomputers. Coupled mantle convection simulations at $\sim\!11$ km and $\sim\!5.6$ km radial spacing ($\sim 2.8$ B and $\sim 22$ B DoFs) can be run routinely on standard node partitions of all considered systems. Global $\sim\!1$ km-per-gridpoint mantle convection ($\sim 1.4$ T DoFs) is feasible on an extreme-scale allocation, and a sub-km hero-run at $\sim\!0.7$ km grid spacing scaling up to $\sim 11,000$ GPUs of LUMI-G ($\sim 11$ T DoFs) shows the potential of the code on future, larger machines.

The rapid development of artificial intelligence is often discussed primarily as a technological breakthrough. Such an approach is insufficient because AI is also transforming the allocation of capital, energy, natural resources, labour, and political power. Investment in generative AI and computing infrastructure is increasing rapidly and is increasingly concentrated among a small number of corporations and countries. At the same time, the expansion of data centres creates new electricity and water demands, potentially producing local infrastructure bottlenecks and distributive conflicts. The AI value chain is dependent on geographically concentrated supplies of advanced semiconductors, manufacturing equipment, cloud services, energy, and specialised knowledge. Control over these bottlenecks may give states and corporations structural power that does not derive directly from military superiority. The environmental effects of AI are similarly ambivalent: AI systems consume energy, water, materials, and computing hardware, but may also improve energy efficiency, climate modelling, renewable-energy integration, and environmental monitoring. Finally, AI-enabled automation, including intelligent robotics, may reduce employment in some occupations while augmenting labour and creating new tasks in others. The social outcome of this transformation will therefore depend not only on the technology itself but also on competition policy, infrastructure planning, environmental regulation, social protection, collective bargaining, and the distribution of productivity gains.

Pfaffian quantum Monte Carlo extends auxiliary field methods to anomalous fermionic Hamiltonians, opening access to pairing systems and Hubbard–Stratonovich decompositions that are unavailable in determinant formulations. We show that this flexibility can be combined with the global updates of Hybrid Monte Carlo (HMC), leading to an algorithm scaling as $\mathcal{O}(N_\tau L^3)$, similar to its determinant quantum Monte Carlo (DQMC) counterpart without pseudofermions. The central observation is that the molecular dynamics force depends only on the magnitude of the Pfaffian weight, which admits a determinant representation and leads to a simplified force expression. This construction also yields unequal-time Majorana correlators at essentially no additional cost. We demonstrate the method for the interacting Kitaev chain using the imaginary and symmetric Majorana hopping channel. The resulting noncommuting auxiliary-field bond operators are treated through an even-odd decomposition. Their block structure permits an analytic reduction to a sparse effective interaction matrix of bandwidth two and allows explicit force expressions. Results agree with exact diagonalization and resolve low-energy modes localized at opposite boundaries, together with their increasing overlap near the topological transition of the non-interacting system. These findings establish HMC as an efficient sampling framework for Pfaffian auxiliary-field simulations.

Transferable hexagonal boron nitride (hBN) hosting negatively charged boron-vacancy (VB$^{-}$) spin defects offers a versatile platform for integrated quantum magnetometry, yet quantitative imaging of magnetic microstructures remains challenging. Here, we integrate a transferred hBN flake with a 4 $\mu$m-diameter permalloy (Py = Ni${81}$Fe${19}$) microdisk and perform spatially resolved optically detected magnetic resonance measurements at room temperature. An applied in-plane magnetic field distorts the vortex-state magnetization, generating edge-localized magnetic surface charges and pronounced stray-field signatures at opposite disk edges. By referencing each pixel to its local zero-field splitting and correcting for a residual out-of-plane bias field, we quantitatively reconstruct the out-of-plane stray-field distribution, revealing peak fields of approximately 11.2 mT. An edge-charge model reproduces the spatial distribution and amplitude of the reconstructed field, linking the ODMR response to the field-driven evolution of the vortex state. These results establish transferred hBN VB$^{-}$ sensors for quantitative magnetometry of magnetic microstructures.

Multi-Channel imaging (MCI) data differs fundamentally from natural images, as each channel records a semantically distinct signal rather than a colour band. To adapt vision encoders to MCI data, Multi-Channel Vision Transformers (MC-ViTs) tokenize each channel independently and concatenate the resulting tokens into one sequence, and the channel count is no longer fixed by the architecture. Self-attention is then computed across all channel-patch tokens with no restriction on which channels attend to which, which dilutes the features of individual channels. The Decoupled Vision Transformer (DC-ViT) regulates this by separating updates computed within a channel from updates computed across channels, and by forming a representation per channel before the channels are combined. Its formulation, however, pairs tokens by spatial position, and thus requires the same visible tokens in every channel. Correspondence under independent per-channel masking is recovered by solving a linear assignment between the retained patches of each channel, which allows decoupled attention to be combined with current masked multi-channel training in its standard configuration rather than a restricted one. Across three classification and three segmentation benchmarks spanning fluorescence microscopy, imaging mass cytometry and satellite imaging, including dense prediction at high channel counts, the resulting formulation outperforms the strongest MC-ViT baseline.

Automatic tooth segmentation and FDI numbering on panoramic radiographs underpins computer-assisted dental diagnosis, yet which factors govern performance remains unclear. We assemble a corpus of 1,422 panoramic radiographs containing 42,142 expert-delineated tooth polygons across the 32-class FDI taxonomy, annotated by 30 dental practitioners and independently reviewed by two others, and use it to isolate input resolution, architecture and anatomical priors under a single evaluation protocol. First, resolution dominates: across a controlled 640/1024/1280 ablation, mask mAP50-95 rises 0.656 -> 0.710 -> 0.717 while mAP50 stays flat at ~0.982. Both gains are significant under a paired bootstrap over images (p < 0.001, p = 0.024); neither mAP50 change is distinguishable from zero. Added resolution buys boundary precision, not detection. Second, architecture is nearly irrelevant in-domain: a query-based transformer with 2.1x the parameters is statistically equivalent to a one-stage detector (95% CI [-0.0064, +0.0064]), only marginally better under domain shift, 5.5x slower on CPU and not executable under standard ONNX runtimes. Third, three targeted interventions fail: a LoRA-adapted self-supervised encoder underperforms, a promptable foundation segmenter degrades masks by 39%, and globally optimal anatomical label assignment yields +0.0007 despite correcting a constraint violated in 40% of out-of-domain predictions. Zero-shot transfer to an independent multi-centre cohort, verified overlap-free, costs 62% of mask mAP50-95 but only 18% of mAP50, reproducing the dissociation. Decomposing masks along the tooth axis localises the residual error to the apical third. Boundary precision is therefore the binding constraint, and effort is better directed at resolution and acquisition diversity than at architectural novelty.

Consensus protocols ensure the robustness and scalability of blockchains and decentralized applications built on them. However, existing consensus mechanisms often impose high computational cost or require heavy communication overhead. To address these challenges, we propose proof of verifiable delay (PoVD), a lightweight consensus protocol based on the verifiable delay function (VDF). We present the detailed protocol of PoVD, including the block mining and verification rules, and illustrate how PoVD can flexibly adjust the block time distribution according to the network condition. Through mathematical proof, we point out that, under a relatively weak network assumption, PoVD can achieve a lower fork rate than PoW while maintaining the same throughput. Our experiments verify that PoVD exhibits low computation and communication complexity and can also improve the blockchain consistency, making it particularly suitable for resource-constrained nodes and bandwidth-limited networks.

Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized differently for each user. Existing prompt optimization methods, however, rely on a single prompt optimized against a global objective, which is misaligned with the inherent user heterogeneity of real workplaces. We formulate enterprise IE as per-user prompt adaptation under interaction feedback and propose Self-Meta-Evolve, a hierarchical framework that maintains a dedicated prompt for each user and continuously refines it through a dual-loop process: an inner loop that edits structured prompts based on persona-conditioned feedback, and an outer loop that evolves the meta-prompt itself by distilling successful editing patterns. To enable scalable training and evaluation, we release a persona-driven IE benchmark of 292 simulated enterprise users, paired with a reproducible persona-generation pipeline grounded in O*NET occupational taxonomies. On this benchmark, Self-Meta-Evolve achieves a 74.58% success rate, outperforming the strongest prompt-optimization baseline by 13.56 absolute points, and reaches 52.54% within only two iterations. A double-blind human study with twenty real professionals further confirms that prompts adapted by our framework win against static baselines in 71% of pairwise comparisons.

The dissipationless quantum-thermal escape rate from a metastable well is often written as a Boltzmann average of the Hill-Wheeler flux over a continuum of energies. This continuum treatment diverges exponentially at low temperature, because it pairs the quantum partition function of the well with a classical flux integral. Retaining the discrete nature of the quasibound spectrum removes the divergence exactly, and the resulting rate is obtained in closed form on both sides of the crossover temperature $T_0$. Evaluating a uniform Kemble transmission probability on Bohr-Sommerfeld levels further removes the parabolic-barrier error and recovers the WKB decay rate of the lowest resonance at zero temperature. Benchmarked against the resonances of cubic and quintic potentials, obtained by complex scaling, this uniform semiclassical rate stays within $9\%$ of the exact one over eleven orders of magnitude. Below $T_0$, the rate rises above its zero-temperature plateau with the level spacing as activation energy, and the plateau ends at a temperature $T_1$ noticeably below $T_0$. In an isolated well, whose levels are not replenished, the thermal rate is only the initial slope of a decay which ceases to be single exponential at $T_1$, while at $T_0$ the initial escape moves from tunneling inside the well to the barrier top. We discuss implications for Josephson junctions, single-electron pumps, and nuclear burning in dense stellar matter.

Problematic internet use affects a growing share of the population, yet common interventions, e.g., time limits, blocking, forced breaks, are coercive and easily circumvented. We explore a less restrictive alternative: adapting the emotional intensity of visual content. Prior work has shown that optimization can steer an image's affective content, but its per-image optimization cost makes it impractical for real-time deployment. We instead learn a model that predicts this transformation in a single forward pass: a MobileNetV4 backbone with FiLM-based emotion conditioning outputs parameters for differentiable global transformations. This replaces prior iterative optimization (80 s per image) with a single 3.7 ms forward pass. In a user study (N = 54), the model reduced viewer-reported arousal relative to unedited images, comparably to the grayscale well-being filter, while being rated higher in perceived quality. We integrate the model into an Android app that adapts Instagram video in real time, sustaining 60 fps on a Samsung Galaxy S23.

In ultrafast spin dynamics additional modes in the THz range appear which cannot be captured by conventional Landau-Lifshitz equations. Extending these equations by further time derivatives (inertial Landau-Lifshitz-Gilbert equation) solves the issue only partly. Here we suggest to explain these modes by retarded Lindbladian relaxation. In spite of the simplicity of this phenomenological approach the agreement with the experimental data is promising: physically plausible parameters describe the position and the large width of these resonances. The salient feature of several almost equidistant resonance peaks is in-line with experimental evidence as well.

Comparing two populations at the same physical covariate value requires more than conditional means or isolated target-point decisions: researchers may need evidence about an entire conditional-distribution ordering over a continuum, even when covariate margins differ. This paper makes that common-value comparison estimable under an explicit structure–flexibility tradeoff and turns the resulting surface into simultaneous evidence for first-order stochastic dominance. Population-specific margins map the common covariate value into each group, while a fitted copula-derivative representation links conditional distributions across the region. Uniform inference propagates uncertainty from both the margins and dependence model through a one-sided statistic with unknown binding locations. Under correct specification within a finite copula class, smoothness and trimming conditions, and a uniquely best candidate family, the procedure admits uniform control and consistent calibration. Simulations show increasing rejection as alternatives become more distinguishable, alongside model-selection sensitivity and small-sample size distortion. In a descriptive PSID application, the high–low parental-education comparison satisfies the two-direction criterion after multiplicity adjustment, whereas adjacent education-group comparisons remain inconclusive. The framework therefore supports region-wide distributional comparison while making its structural and inferential boundaries explicit.

Fine-grained object (FO) manipulation requires robots to distinguish a specified FO from visually similar objects and execute actions reliably despite scene distractors. However, scene-level visual conditioning lacks explicit object selection, while category-level guidance cannot reliably distinguish FOs within the same category. We present a SAM3-guided visuomotor policy framework that addresses these challenges through persistent object memory and focused visual conditioning. First, we introduce FO Memory-driven SAM3 (FOM-SAM3), which learns reusable FO memory tokens from limited multi-view registration images, while keeping SAM3 fully frozen. Through one-vs-rest learning, these tokens encode persistent memories for localizing target FOs and rejecting similar alternatives, which can be stored in a memory bank for reuse. Second, we propose Focused Spatial-Appearance Encoding (FSAE), which combines in-FO appearance features with explicit bounding boxes to condition action policies, including Diffusion Policy (DP) and Action Chunking with Transformers (ACT). The effectiveness of the proposed FOM-SAM3 is validated on the FO-30 dataset comprising 30 physical objects across four coarse categories. Across three real-robot FO manipulation tasks, our FOM-SAM3-guided policies demonstrated robustness against distractors, discriminative ability of similar FOs, and extendibility to new FOs.

In this paper, we present a refinement of the symplectic Lidskii theorem for directional derivatives of symplectic eigenvalues, which is inspired by the work of Sendov [Electron. J. Linear Algebra 41(2025), 338-341]. We show that for a $2n \times 2n$ real positive definite matrix $A$ and symmetric matrices $H, K$ \begin{align*} d'(A;H+K)-d'(A;H) \prec_{\pi_A} d'(A;K). \end{align*} Here $\prec_{\pi_A}$ represents block-majorization corresponding to the partition $\pi_A$ of $\{1,\ldots, n\}$ given by the equality blocks of symplectic eigenvalues of $A$. We know that Hermitianization of a symmetric matrix occurs in the directional derivative expression of symplectic eigenvalues. We establish a componentwise inequality comparing the symplectic eigenvalues of a positive definite matrix with the ordinary eigenvalues of its associated Hermitianization, and also characterize the equality case. By using the aforementioned inequality, we then show that the symplectic Lidskii theorem follows from the directional derivative Lidskii theorem.

On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an on-policy student. However, this discrepancy does not purely reflect the capability gap between the teacher and the student: it also contains deviations arising from the teacher itself, which are consequently mixed into the observed teacher–student discrepancy and indiscriminately learned by standard OPD during training. This issue is further exacerbated by privileged OPD, where privileged information induces larger teacher-side likelihood shifts, thereby encouraging the student to learn more of the teacher's own deviation. We introduce Calibrated On-Policy Distillation (Cal-OPD), which estimates the teacher's self-deviation region through positive and negative privileged interventions and calibrates the original teacher–student discrepancy by retaining only the component that lies beyond this region. Experiments on mathematical reasoning benchmarks show that, while retaining only about 52–65% of the original teacher–student discrepancy as the optimization signal, Cal-OPD consistently outperforms standard OPD and its variants across model scales.

We have developed a compact fibre-coupled active beamstop for simultaneous interception and time-resolved monitoring of the direct X-ray beam in synchrotron diffraction experiments. The complete beamstop tip has a maximum transverse dimension of 1 mm and incorporates a 700 $\mu$m-wide Ce:YAG scintillator. The scintillator converts the intercepted X-rays into visible light, which is transported through a multimode optical fibre to a silicon photodiode at the bottom of the blade that is connected to a dedicated fast amplifier electronics. Tests with a pulsed laser yielded a system decay time of 69.4 ns, consistent with reported luminescence decay times for Ce:YAG. Measurements at the P14.EH2 end station of PETRA III demonstrated the detection of individual bunch signals separated by 192 ns in the 40-bunch operating mode. The active beamstop also provided an in-situ timing signal for synchronising an experimental sequence with the approximately 190 $\mu$s transmission window of a mechanical X-ray chopper. The device therefore combines a compact obstruction of the direct beam with fast monitoring of X-ray timing and flux during diffraction data acquisition.

Self-play in high-throughput simulators yields driving policies with robust closed-loop performance, but improvement per unit of simulation diminishes as training scales. Policies learn to handle common situations early, while further rollouts repeatedly encounter unresolved failures. Post-training offers an opportunity to target these failures, but existing methods primarily evaluate alternative actions or continuations at visited states, although successful recovery may require changing driving style earlier. We propose CounterPlay, a counterfactual self-play post-training approach that backtracks from failed tasks and retries them under alternate driving styles. CounterPlay rests on three key components. First, failure-driven backtracking uses the policy's value estimates to select an earlier stored state from which to retry the task. Second, reward conditioning enables a single policy to retry the task from this state using candidate styles ranging from cautious to aggressive. Third, CounterPlay retains task-completing retries only if no other vehicle incurs a new or earlier collision or off-road event relative to the factual branch. Retries that pass verification with fresh randomness are then distilled into the policy under its deployment condition. On BehaviorBench, CounterPlay achieves state-of-the-art scores on both the Interactive and Random splits across all eight traffic regimes using 1B post-training transitions, which is just 1% of the anchor's 100B self-play training budget. Improvements over the anchor hold across all three evaluated driving styles. CounterPlay resolves a substantial fraction of the anchor's timeout cases on BehaviorBench and achieves a balance between task completion and safety that neither continued self-play nor adopting a more aggressive driving style attains.

Defect prediction depends on knowing which commits fix bugs, yet the labels that encode this are produced by routes that each introduce noise. Reused benchmarks carry documented data-quality problems, issue-tracker links are biased and the underlying reports are frequently mistyped, and matching keywords in commit messages is a coarse heuristic. This paper examines whether commits can be labelled as bug fixes from their content alone, using open-weight language models that run locally and therefore keep the process reproducible, inexpensive at corpus scale, usable on proprietary code, and independent of any issue tracker. Against datasets of manually validated and curated bug fixes spanning Java, Python, and JavaScript, we compare a keyword baseline with a set of open-weight models of varying size, prompting each with the commit message and the code diff. On the manually validated corpus the keyword baseline recovers fewer than half of the fixes, whereas the open-weight models recover the large majority and outperform it repository by repository with statistical significance, and larger models do not consistently outperform smaller ones. We further show that evaluation corpora without negative examples cannot support a precision-aware comparison of such classifiers. We release the labelling pipeline together with a labelled, multi-language corpus produced by the recommended configuration, as a reproducible silver-standard resource for building current, project-specific datasets.

The notion of induced packing treewidth aims to unify classes defined by forbidden induced subgraphs or induced minors with classes defined by the existence of certain structured tree decompositions. For a graph $H$, \emph{induced $H$-packing treewidth}, denoted by $\treepi_{H}$, is a tree-decomposition-based graph parameter that, for each bag, measures the maximum number of pairwise anticomplete induced copies of $H$ intersecting that bag. This notion generalizes some previously studied parameters: when $H=P_1$, it is equivalent to tree-independence number, and when $H=P_2$, it is equivalent to induced matching treewidth. We prove the following: \begin{itemize}[itemsep=2mm,leftmargin=6mm] \item For all $a,t\in \mathbb{N}$, $K_{a,a}$-free graphs of bounded induced $P_t$-packing treewidth have bounded tree-independence number. This extends the previous result of Abrishami et al. [SIAM J. Discrete Math., 2025] for $t=2$, and a result of Hajebi and Spirkl who showed that $(P_t,K_{a,a})$-free graphs have bounded tree-independence number. \item If $H$ is any fixed path or a star, then the class of graphs of bounded induced $H$-packing treewidth is $\chi$-bounded. Again, this extends the previous result of Abrishami et al. [SIAM J. Discrete Math., 2025] for $H=P_2$. \item Finally, we study the relationship between induced packing treewidth and sim-width, a width parameter based on branch decompositions. We show that, although sim-width and induced $P_3$-packing treewidth are incomparable, graphs of bounded sim-width that exclude all \emph{$H$-obstructions}—certain graphs that force large induced $H$-packing treewidth—have bounded induced $H$-packing treewidth. This simultaneously generalizes and resolves questions posed by Abrishami et al. [SIAM J. Discrete Math., 2025] and Brettell et al. [European J. Comb., 2025]. \end{itemize}

Global trade is more than a single network of aggregate flows. Beneath the observable exchange of products among economies lies a complex multilayer structure, formed by thousands of product-specific trade relationships that differ in their similarity, interdependence, and temporal evolution. Using the CEPII's BACI database, which records bilateral product-level trade flows between economies, we represent the global trade network from 1995 to 2024 as a temporal multilayer network, with economies as nodes and directed weighted trade flows as edges. To investigate product-level organisation and cross-layer similarity, temporal structural change, and the structural role of individual economies, we introduce a random-walk-based similarity measure that provides a unified framework for comparing weighted and directed trade layers. Our results show that the global trade network remains relatively stable over short periods but undergoes gradual structural change over longer timescales. We also find that similarity-based product communities only partially align with the official product taxonomy, indicating that products assigned to the same official category do not necessarily exhibit similar trade-network structures. Finally, we show that an economy's structural influence is not always determined by its trade volume. These results highlight the value of multilayer network analysis for revealing patterns in global trade that remain hidden at the aggregate level.