Papers

A pseudohemisphere hypergraph is a hypergraph $\mathcal{H}$ with an ordered set of vertices $V$ for which there exists an $ABA$-free hypergraph $\mathcal{F}$ on $V$ and a subset $X$ of $V$ such that the hyperedge set of $\mathcal{H}$ is a subset of $\{F\Delta X: F\in \mathcal{F}\cup \overline{\mathcal{F}}\}$. We prove that the chromatic number of pseudohemisphere hypergraphs is at most four.

The EU AI Act (Art. 27) requires deployers of high-risk AI systems to conduct Fundamental Rights Impact Assessments (FRIAs) before deployment, yet the evidence needed for credible assessments is fragmented across incompatible incident repositories, risk vocabularies, and legal texts. We present a reusable Semantic Web-based framework that consolidates this evidence for two high-risk public sector categories: employment and worker management (Annex III(4)) and access to essential public services (Annex III(5)(a)). A curated 150-record corpus is annotated along four axes using keyword, LLM, and hybrid methods and serialised as a SPARQL-queryable knowledge graph of 1,351 RDF triples. Five FRIA demonstration scenarios surface 103 records (68.7% coverage). Evaluation against a 69-record gold standard reveals that LLM-assisted classification of the employment domain achieves only $\kappa = 0.045$, a cautionary result for automated fairness-related evidence retrieval in this domain. All artefacts are released openly to support adoption by regulators, national authorities, and SMEs.

Tilting mutation provides a natural way to construct derived-equivalent algebras by replacing an indecomposable summand of a tilting object. Holm, Schröer and Zimmermann~\cite{HSZ01} (see also the appendix of~\cite{AA08}) have described combinatorial transformations of the quiver and relations of a gentle algebra that yield derived equivalences via tilting complexes. In this paper, we develop a self-contained module-theoretic framework for tilting mutation via generalized $\mathrm{BB}$-tilting modules. For a gentle algebra $A=\mathbb{k} Q/\langle I\rangle$ and a vertex $k\in Q_0$, we first give a necessary and sufficient condition, expressed in terms of the arrows and relations incident with $k$, for the corresponding minimal left approximation of $P(k)$ to yield a tilting mutation. This criterion applies uniformly to vertices with or without loops. When the mutation exists, we determine the irreducible morphisms between the indecomposable summands of the mutated tilting module and use them to construct explicitly the Gabriel quiver and defining relations of its endomorphism algebra. In particular, we also provide a combinatorial mutation $(Q,I)\mapsto(Q',I')$ of gentle pairs such that $\mu_k^+(A)\cong \mathbb{k} Q'/\langle I'\rangle,$ so the resulting algebra is again gentle and derived equivalent to $A$. Finally, we formulate the dual cotilting mutation and its combinatorial description via opposite gentle pairs.

While color centers in diamond and other three-dimensional crystals are nowadays key elements of several types of sensors, color centers in two-dimensional materials are rapidly developing and promise high-tech applications. One of the interesting centers in 2D materials is the negatively charged boron vacancy in hexagonal boron nitride (h-BN), which has already shown some potential for magnetometry, but the reliable creation of boron vacancies remains challenging. Here, we demonstrate the fabrication of negatively charged boron vacancy color centers in bulk h-BN via implantation of helium ions at ~1 MeV, as confirmed by characteristic photoluminescence and optically detected magnetic resonance. The resonance has a width of 180 MHz and exhibits the expected Zeeman shift of the resonance lines. The depth of the color center along the c-axis of the h-BN crystal was measured and compared with predictions from Stopping and Range of Ions in Matter modeling. The experimental value and the modeled prediction are consistent within the reported uncertainties, as their 1{\sigma} intervals overlap, although the experiment shows a greater depth and a wider distribution.

Radioactive iodine species, particularly I$_2$ and CH$_3$I, pose significant environmental and technological hazards owing to their high volatility, chemical stability, and relatively weak interaction with traditional substrate and sorption materials. In this work, we proposed a series of transition-metal (TM) (Fe, Ni, Cu, Zn) decorated boron-doped graphene (BDG) and 2D-MoTe2 substrates for efficient adsorptive capture and mitigation of such volatile Iodine species. Using systematic first-principles density functional theory (DFT) calculations, we elucidate the microscopic origin of the enhanced adsorption by analyzing the changes in the electronic structure upon adsorption. More importantly, we analyzed the thermodynamic and kinetic feasibility of adsorption on these newly designed substrates using Climbing-Image Nudged Elastic band (CI-NEB) calculations and found that TM decoration serves as an effective catalytic center, thereby making the reaction thermodynamically and kinetically feasible. Conversely, the reaction becomes thermodynamically and kinetically unfavorable on pristine substrates in the absence of a TM atom as a catalytic center. This work deepens our understanding of the electronic origin of the enhanced adsorption and reaction kinetics, and the predictions made will be useful for experimental realization.

During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high rewards without improving the intended capabilities, i.e., reward hacking. Despite its risks to training efficiency and safety, monitoring and mitigating reward hacking during training remain challenging, which is limited by a lack of testbeds that reproduce hacking and reliably identify it. We introduce CATCH, a controllable testbed for studying reward hacking in coding RL. CATCH deliberately exposes environmental loopholes and provides execution-based gold labels by comparing success under a vulnerable evaluator with task correctness under an independent audit. It also can control the model's initial hacking tendency through supervised fine-tuning data mixtures and the difficulty of earning rewards through reward designing, enabling systematic comparisons of hacking dynamics and interventions. Experiments show that CATCH can produce diverse RL training trajectories with clear reward hacking, and analyses demonstrate that both initial models and reward difficulties shape the emergence of reward hacking. We further evaluate the effectiveness of different reward hacking detection and mitigation methods. A key finding is that a chain-of-thought monitor initially suppresses hacking, but this protection erodes as the policy model learn to mislead the monitor with code comments. This highlights the need to evaluate hacking mitigations throughout training with CATCH. The source code and resources are publicly released at https://github.com/THUAIS-Lab/CATCH.

We study the efficiency of auctions with budget-constrained bidders and derive tight price of anarchy (PoA) bounds for coarse correlated equilibria (CCE). To this end, we develop a general framework that reduces proving PoA guarantees to finding feasible solutions to the dual of a linear program. The LP is formulated over a small number of per-bidder equilibrium statistics, incorporates constraints implied by the auction setting and equilibrium behavior, and captures a novel smoothness notion tailored to budget-constrained environments; we provide a reduction from classical smoothness to this new notion. Our approach enables simple and tight PoA analyses across a broad range of auction formats. Using our framework, we obtain tight liquid welfare guarantees for simultaneous first-price, second-price, and all-pay auctions, simultaneous auctions with restricted uniform bidding interfaces, discriminatory and uniform price multi-unit auctions, and generalized first-price position auctions. Beyond budget-constrained settings, we derive new lower bounds for the budget-free uniform price auction and generalized second-price auction; in particular, our lower bound for the uniform price auction matches the upper bound of $3.146$ by de Keijzer et al. (2013).

We study the problem of estimating the guidance that steers the distribution learned by a diffusion generative model toward a tilted target $q_0 \propto w\,p_0$ at inference time. Relying on the stochastic optimal control approach, we observe that the exact drift correction is the gradient of the logarithm of Doob's $h$-function, and we study the problem of estimating it from a sample. In the present paper, we assume that the score of the pretrained model is available, that the tilting weight is bounded and positive, and that the reference distribution has a bounded support, no smoothness of the weight is required. Introducing a penalized least-squares risk in which the penalty is the residual of the space-time harmonicity equation satisfied by the $h$-function, measured in a dual Sobolev norm, we derive high-probability bounds on the squared error of the resulting guidance estimate. Since the penalty vanishes at the target, the estimator is free of regularization bias, and in favourable scenarios its rate of convergence is faster than the minimax rate of estimating first-order derivatives of a smooth regression function. Assuming that $w$ is bounded and positive with $\mathbb{E}_{p_0}[w^{-\mathrm{s}}] < \infty$ for some $\mathrm{s} \in (0,\infty]$, and that the reference data are compactly supported, we prove that the guidance is estimable in squared $L^2$ at rate $\varepsilon_n^{\mathrm{s}/(\mathrm{s}+4)}$, where $\varepsilon_n = n^{-2(\beta-1)/(2(\beta-1)+d)}.$ We also transfer the obtained bounds to the total variation distance between the marginals of the estimated and the exactly guided samplers, and illustrate the performance of the suggested approach with numerical experiments.

eBPF observability of non-standard execution environments (NEEs) like TEEs or LibOSes is hindered by their unconventional exception-handling and memory-access mechanisms that limit standard Linux tooling. This work introduces two mechanisms that enable eBPF-based observability for NEEs: (1) kernel memory extensions for safely accessing NEE memory from eBPF programs, and (2) a lightweight flexible probe performance measurement unit (LWFP PMU) that provides flexible and generic probing, for NEEs, through the following LWFP: simple (SLWFP), enclave (ELWFP) and extended (ExLWFP) probes. We demonstrate the practicality of these extensions by developing tooling for tracing, stack sampling with Flame Graphs, dynamic instrumentation, timing analysis, and USDT support for Intel SGX enclaves and LibOSes. Performance measurements show that SLWFP probes achieve a latency of 394 ns, outperforming uprobes, which exhibit 25% higher latency, while ELWFP probes incur a latency ~2.8 microseconds, which is practical for enclave observability. The addition of SLWFP introduces negligible overhead to existing uprobe performance. Taken together, this work lays the foundation for closing the long-standing gap between NEE tooling and Linux observability tooling by enabling generic and reusable eBPF tooling for diverse NEE hardware and software architectures.

The Lux-Zeplin collaboration recently reported one unusual event which was interpreted as first evidence for a WIMP (arXiv:2609.02823[hep-ex]). In this note we offer an alternative explanation of this event, following our earlier proposal that dark matter could consist of fractionally charged supermassive gravitinos. A main difference between the two interpretations is that one expects small cross sections and large flux rates for WIMPs, whereas it is the opposite for supermassive gravitinos, namely large (electromagnetic) cross sections and very low flux rates. The observed nuclear recoil energy of about 250 keV and the occurrence of one event in one year fit well with the estimates quoted in (K.A. Meissner, H.~Nicolai, Eur.Phys.J.C 84 (2024) 3, 269). A main characteristic of the new interpretation distinguishing it unequivocally from the WIMP hypothesis would be the sequential occurrence of two or more recoils within a few microseconds along the gravitino's straight trajectory, a prediction that remains to be checked. Such a measurement would also allow to overcome the `neutrino floor'. In this note we refine our previous estimates and propose some extra steps so as to enable the experiment to discriminate between the WIMP and gravitino hypotheses.

We prove a local amplitude-ratio bound for positive ground states and an explicit reverse comparison for fidelity under finite-region erasure. An exact lattice calculation for a connected critical Ising chain gives amplitude exponent $1/8$, preserved by arbitrary optimal control on any fixed spin set and, at the level of logarithmic exponents, on $o(\log N)$ spins. We derive the finite Cauchy product and its uniform strip asymptotics, and document exact two-spin optimization and an interferometric protocol for measuring the recovery. A separate finite-mediator theorem quantifies local compression and a decoder while retaining the rest of the environment exactly. We also give a ground-preparation Ramsey bound, counterexamples to stronger projected-state inferences, and independent full-Hamiltonian calculations.

The presence of lens flares in images can significantly reduce the quality of downstream application results for tasks such as 3D scene reconstruction. This is because lens flares are a property of the camera imaging system, and not a part of the underlying scene being modeled. There are previous methods that tackle the removal of small flares focused around a light source. However, existing methods struggle with large flares, such as those that fill the entire image. In this work, we compile a novel dataset for large-flare removal, combining publicly available real-world data with a procedural generation pipeline. We fine-tune a diffusion-based model on our dataset to remove complex, large lens flares. On the other hand, lens flares remain effective artistic tools, widely used in the media. While there are ways to simulate 2D flares, representing and reconstructing lens flares consistently across multiple views has not yet been explored. To achieve this, we introduce a flare representation model that leverages the symmetry of lens flares about the camera's principal point. We propose a computational pipeline to jointly optimize this flare model and a Gaussian splatting model (3DGS). This enables the decomposition of a 3D scene into lens flares and the scene itself, using our flare-removal model. Because the reconstructed flare is explicit and re-renderable, it can be edited and transferred to novel images and new 3D scenes. We evaluate removal on an established benchmark and a new one for large reflective flares, quantify the flare/scene decomposition directly, and show that the pipeline is robust to errors in automatic light-source localization.

Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete action tokens provide compact structural representations but sacrifice precision, while continuous action tokens offer high precision but often require multiple denoising steps. We introduce Discrete Forcing, a flow-matching framework that combines these representations through an explicit coarse-to-fine generation process. It first predicts discrete action tokens to establish a coarse action structure, then uses them to guide continuous action refinement. The discrete and continuous components share a common diffusion transformer backbone with specialized branches, maintaining a parameter count comparable to a conventional single-branch model while requiring only one forward pass per branch. Extensive evaluations across multiple benchmarks demonstrate improved performance and faster inference over a parameter-matched continuous action expert, with consistent performance gains as model capacity increases. Real-world experiments further demonstrate improvements on high-precision and dynamic manipulation tasks.

Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intractable statistical models and offers near instantaneous inference for new datasets after prepaying the training cost. Although theory guarantees faithfulness under ideal convergence, practical amortized inference still requires iterating over architectures and optimization choices and ultimately “satisficing” under finite simulation, compute, and time budgets. Even the best-performing solution may thus retain avoidable representation gaps that typically require problem-specific fixes. Here, we propose a generic alternative which improves training dynamics with auxiliary guidance losses applied to internal representations. Specifically, we show how such guidance leads to faster convergence when training data is abundant and to better performance when it is scarce. We formalize representation gaps as getting stuck in a local optimum at the information bottleneck between the parts of the network tasked with feature learning and those tasked with conditional distribution learning, and offer a generic diagnostic to separate summary failures from inference failures. Finally, we demonstrate that auxiliary supervision improves convergence speed and accuracy on a range of challenging real-world inference problems.

In this article, we investigate the Assouad spectrum and Assouad dimension of graphs of fractal functions generated by generalized affine iterated function systems. We establish upper and lower bounds for the Assouad spectrum in terms of the scaling functions and the underlying partition of the generalized affine construction. If the scaling function is Lipschitz continuous and the partition is uniform, we obtain an explicit expression for the Assouad spectrum of the associated graph. These results provide a connection between the parameters defining the generalized affine fractal function and the local multiscale geometry of its graph. As applications, we consider two classic examples of generalized affine fractal functions, namely the Weierstrass and Takagi functions. For the classical Weierstrass function $W$, whose graph $\Gamma_W$ has the box dimension $2+\log_N\lambda$, we obtain \[ \dim_A^\theta(\Gamma_W) \leq \frac{2+\log_N\lambda-\theta}{1-\theta}, \qquad \theta\in \left(0,\log_N\frac{1}{\lambda}\right). \] and if $\lambda^2 N <1$, we get \[ \dim_A(\Gamma_W)\geq 1 +\log_N\left(\frac{1}{\lambda}\right). \] For the classical Takagi function $T$ with graph $\Gamma_T$, we show that \[ \dim_A^\theta(\Gamma_T)=1, \theta\in(0,1), \] and consequently its quasi-Assouad dimension is equal to $1.$ These results settle an open problem on dimension of graphs posed by Fraser.

Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experiment on a specific variable. However, observational data alone may leave multiple causal models compatible with available evidence, while experimental data with interventions on exactly the variable of interest might be unavailable. This work studies CFMs as a method to combine finite observational and surrogate-interventional datasets in order to predict a target conditional interventional distribution (CID) more accurately than with observational data alone. We first formalise the conceptual benefits of surrogate experiments. Building on this analysis, we introduce Foundation Models for Causal Inference from Diverse Experimental Regimes (CIDER-FM), a causal foundation model that uses an intervention-aware representation and hierarchical three-axis attention to exchange information across variables, samples, and experimental regimes. We evaluate CIDER-FM against a wide range of baselines across diverse synthetic graph and mechanism families, as well as on both simulated and real-world data from Causal Chambers. Our results demonstrate strong CID prediction performance and show that incorporating experimental context can improve predictions over observational data alone.

Layer codes are three-dimensional local CSS codes of check weight at most six, obtained by quasi-concatenating a CSS input code with unrotated surface code patches. What is established about them describes the code, not the circuit that measures it. In this work, we evaluate them as active memories under circuit-level noise. Doing so first requires a syndrome extraction circuit, and the junction stabilizers that couple the patches rule out borrowing a surface code patch's CNOT schedule. We present a decoder-free scheduler that constrains hook propagation first and compresses depth afterwards, generate circuits for eighteen layer codes across four input codes, and compare them with surface codes of the same distance, simulated and decoded in the same way. The circuits lose no distance to hook errors on [[4,2,2]]-input codes wherever a circuit-distance proof is affordable, and reach a lower logical error rate than those of a general-purpose scheduler for CSS codes at roughly half its CNOT depth per round. Our primary family built from a [[4,2,2]] input code reaches a circuit-level threshold of $5.06(7)\times10^{-3}$. The layer code outperforms the surface code in terms of idle robustness: at the same distance it tolerates an interval between syndrome extraction rounds 1.7-3.0 times longer than the rotated surface code. This comes at the cost of 1.7-5.0 times more operations per unit time and approximately five times more physical qubits, both per logical qubit. Finally, we model the cost of the beyond-2D cross-plane CNOTs that the construction demands, and find that their speed matters far more than their fidelity.

The wide-bandgap compound semiconductor ZnSe is a promising host material for the realization of electron spin-qubits. Its non-degenerate conduction band and the potential for isotopic nuclear spin purification promise long spin coherence times. Key requirements for such devices include reliable electrostatic control of electrons in ZnSe and low-resistance ohmic contacts that remain functional at cryogenic temperatures. In this work, we utilize a novel Shadow Wall technique for molecular-beam epitaxy combined with in-situ deposition of Al ohmic contacts to realize normally-off ZnSe-based field-effect transistors. The devices exhibit linear output characteristics and effective gate control of the channel from room temperature down to 5 K, confirming low-resistance ohmic contacts to the undoped ZnSe channel. The drain current can be modulated by several orders of magnitude through electrostatic gating, with threshold voltages of approximately 3 V and field-effect mobilities exceeding 100 cm2/Vs over the investigated temperature range. Self-consistent Schrödinger-Poisson and drift-diffusion simulations reproduce the measured transfer characteristics and provide insight into the role of interface electrostatics in determining the channel formation and threshold voltage. These results demonstrate the potential of gated ZnSe heterostructures for future spin-qubit applications.

We discuss exclusive production of $\pi^+ \pi^-$ pairs in diffractive $\gamma p$ and in $pp$ collisions at high energies, considering resonant ($\rho(770)$, $\omega$, $f_2(1270)$) and non-resonant (Drell-Söding) contributions within the tensor-pomeron approach. For the $\gamma p \to \pi^+ \pi^- p$ reaction, the model describes well the H1 data in the region $M_{\pi \pi} \lesssim 1.2$ GeV. We discuss the important role of the Drell-Söding mechanism in shaping the resonance line. We also predict differential cross sections for the $pp \to pp \pi^+ \pi^-$ reaction at $\sqrt{s} = 13$ TeV where at least one proton emits a virtual photon. These findings are relevant for the central exclusive production of $\pi^+ \pi^-$ pairs in the context of ALICE, ATLAS, CMS, and LHCb measurements in hadron-hadron collisions at the LHC, especially under experimental selections using only rapidity-gap conditions. Additionally, our results are also applicable to $\pi^+ \pi^-$ production in ultraperipheral $p$A/AA collisions at the LHC and to photo- and electroproduction of $\pi^+ \pi^-$ at future electron-proton/ion colliders (EIC, LHeC).

We present B-GRASP (Bayesian GRAph inference with SPDE priors), a Bayesian framework for inferring uncertain edge weights in stochastic dynamical systems on graphs from noisy observations of nodal states. The unknown edge weights parameterize the graph differential operator and therefore directly govern the evolution of the graph process. Motivated by connections between differential operators in PDEs and SPDEs and their graph counterparts, we construct stochastic graph models incorporating diffusion, reaction dynamics, and stochastic forcing. The resulting hierarchical formulation jointly represents uncertainty in the graph structure and stochastic forcing. Latent graph variables determine positive edge weights and the corresponding graph Laplacian, while latent Brownian variables represent the stochastic forcing. Conditional on these variables, the graph dynamics define a deterministic forward map from which the likelihood and posterior distribution are constructed. We characterize the posterior using maximum a posteriori estimation and the No-U-Turn Sampler, enabling both point estimation and uncertainty quantification. We demonstrate the framework on a one-dimensional inverse heat-conduction problem, stationary and nonstationary graph reaction-diffusion systems with nonlinear dynamics, and state-level COVID-19 data in the United States. The numerical results show that posterior uncertainty provides information not captured by point estimates, particularly for weakly identifiable or highly conductive edges, and enables uncertainty in both graph connectivity and stochastic forcing to be quantified within a Bayesian framework.

Effective human-AI collaboration requires partners to establish references through interaction, which becomes fragile when descriptions are ambiguous, similar referents compete, or partners see different things. We study referential uncertainty - uncertainty over which candidate object a description refers to - in a collaborative puzzle task where a human Helper instructs an AI Worker to place pieces. The Worker must identify and communicate its uncertainty, and the Helper must recognize and act on it. We show that a separately elicited belief distribution over candidate pieces is better calibrated (ECE 0.15) and better discriminates correct from incorrect placements (AUROC 0.65) than raw action-token probabilities, which are severely overconfident (0.97 mean confidence, ECE 0.44). Across three frontier vision-language models (GPT-4.1, GPT-5, GPT-5.5), this elicited uncertainty rises predictably with instruction vagueness, but not with competing referents in context, even when those increase errors. The models seldom externalize it, asking for clarification on only 3.5-16.7% of turns. In a controlled human study (N=210), participants given only the Worker's default message accept 78% of wrong placements and cannot tell right from wrong (AUC 0.50). Precise descriptions and, especially, well-targeted hedges cut wrong-move acceptance to 36% while largely preserving correct-move acceptance, compensating for missing shared awareness such as not seeing the Worker's action. But this benefit depends on targeting: a deployable hedge derived from the model's own belief entropy inherits that signal's weakness and can do more harm than good. Externalized uncertainty helps a human partner only when it is accurately targeted.

We give a full list of translational nets which flex within their class of discrete surfaces of translation, by reducing the classification problem to the one of flexible complete bipartite frameworks on the sphere, for which the solution is known. We also obtained two novel classes which correspond to Bottema's spherical 16-bar mechanisms and the constant diagonal angle frameworks. Based on an algorithm for the construction of all flexible translational nets, we also discuss flexible translational tubes and toroids. Furthermore, we present novel results for both topologies which are implied by Bottema's spherical 16-bar mechanisms.

Shape-morphing materials have the potential to realise simple, tunable telecommunication arrays without the need for complex circuitry. However, existing devices either lack individual cell control, or require an integrated power supply, reducing their beamsteering ability and increasing size, weight and power consumption. This paper presents a simple, photothermally activated 1-bit reflectarray unit cell requiring no integrated power sources. The unit cell consists of dimer elements, which are connected or disconnected via a photothermally activated electrical bridge to achieve modulation. The paper discusses the shape memory materials used for switching, and presents a design for a photothermal switching mechanism. The unit cell is validated by waveguide experiments, showing strong agreement with simulations at the working frequency of 3.7GHz. Finally, a 4-cell array is created. Each cell is activated sequentially, with good agreement between simulation and experiment, demonstrating the ability to control each cell exclusively. This study therefore demonstrates that a 1-bit unit cell can be photothermally configured without complicated electronics or an integrated power supply, opening the door to a new class of shape-morphing reflectarray.

A measurement of the correlation between the splitting angle $\theta_{\rm g}$ and the momentum-sharing fraction $z_{\rm g}$ of the first hard splitting in a parton shower in pp collisions and 0$-$10% central Pb$-$Pb collisions at $\sqrt{s_{\rm NN}}$ = 5 TeV with the ALICE detector is reported. Charged-particle jets are reconstructed using the anti-$k_{\rm T}$ algorithm with a jet resolution parameter $R$ = 0.2, in the transverse-momentum range $60 \leq p_{\rm T,ch\;jet} < 80$ GeV/$c$. The Soft Drop grooming algorithm is used to identify the first splitting in the parton shower. Jets observed in Pb$-$Pb collisions are narrower than in pp collisions. This effect is more pronounced for balanced than for unbalanced jets. No significant modification of the momentum-sharing fraction is observed for jets with small opening angles. In contrast, there is a hint that jets with a large opening angle may be less balanced in transverse momentum in Pb$-$Pb collisions compared to pp collisions. The correlations between $\theta_{\rm g}$ and $z_{\rm g}$ are well described in pp collisions by PYTHIA 8 and POWHEG while HERWIG shows too few jets with large opening angle. In Pb$-$Pb collisions the measurement is compared to a variety of models. The measurement is insensitive to the different implementations of medium response. Inclusion of elastic scatterings, as implemented in the HYBRID model is preferred to describe the unbalanced jets with large $\theta_{\rm g}$ in Pb$-$Pb collisions. The balanced jets, however, are less sensitive to elastic scatterings.

Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. However, most existing models rely on large Transformers, whose latency and energy costs hinder deployment on resource-constrained platforms. Through sparse event-driven computation, spiking neural networks offer a promising paradigm for high-performance and energy-efficient computing. Here, we propose the first Spike-driven VLA framework enabling end-to-end direct training for robotic manipulation, which mainly comprises three core components. First, we develop spiking visual and instruction encoders for multimodal perception, encoding visual observations and language instructions into sparse, reliable spike representations for subsequent cross-modal fusion. Then, we introduce Multi-Winner Spike Fusion for instruction-guided scene understanding, using bidirectional top-$k$ winner-take-all spike routing to suppress background interference and yield fused memory. Finally, we propose a Spike Action Chunking Transformer that incorporates spiking cross-attention over the fused memory and the current robot state, enabling efficient end-to-end generation of continuous action chunks for robotic control. Extensive experiments on LIBERO and Meta-World demonstrate that Spike-driven VLA achieves competitive performance with fewer parameters and lower estimated inference energy than conventional VLA models. This work establishes a foundational framework for neuromorphic VLA modeling, paving the way for future advances in resource-efficient embodied intelligence.

Despite being an emerging credible means of using artificial intelligence (AI) for improving disease surveillance via early detection of outbreaks, epidemics prediction and evidence-based decision-making, there continue to be challenges in the use of AI tools in many low- and middle-income countries (LMICs) due to various factors including fragmented health information system, digital inequality, governance problems and institutional incapability. Most of the research conducted so far has concentrated on the efficacy and accuracy of AI in terms of predicting outbreaks, with little focus on other conditions necessary for its deployment. This study adopts a qualitative problem-discovery research design, integrating thematic analysis with philosophical analysis to examine the structural and normative barriers surrounding AI implementation in disease surveillance. The analysis identifies four interrelated dimensions of implementation readiness: epistemic adequacy, distributive justice, ethics of governance, and institutional legitimacy. These dimensions provide a framework for understanding how limitations in knowledge integration, unequal digital infrastructure, privacy and accountability concerns, and deficits in institutional and public trust can constrain the practical adoption of AI-enabled surveillance. Rather than proposing another predictive model, this study develops a theoretical framework that conceptualizes AI implementation in disease surveillance as simultaneously a socio-technical and normative process. The framework provides a foundation for subsequent empirical investigation and offers a structured perspective for designing more context-sensitive, ethically grounded, and institutionally sustainable AI-enabled disease surveillance systems in resource-constrained healthcare settings.

The small-sample learning problem remains a fundamental challenge in machine learning because limited training data lead to unstable model estimation and generalization. Structural Risk Minimization (SRM) has long been regarded as a principled solution under the classical i.i.d. assumption. However, domain generalization (DG) violates this assumption, leaving the theoretical role of SRM in DG largely unexplored. To bridge this gap, we establish the first theoretical guarantees for SRM in DG under mild assumptions. Specifically, based on the concept of stability, we derive learning consistency and generalization error bounds and prove that these bounds become tight when the hypotheses satisfy the stability condition. Building upon this, under a specific hypothesis space assumption, we establish stability, learning, and generalization bounds for SRM. We further discuss the applicability of these bounds to deep learning. This work establishes theoretical foundations for SRM under distribution shifts and sheds light on the design of robust DG algorithms in small-sample scenarios.

The geometrically decomposed bases of the polynomial differential form spaces $\mathcal{P}_r\Lambda^1$ and $\mathcal{P}_r^-\Lambda^1$ on a simplex are products of a barycentric scalar polynomial and a directional or Whitney 1-form. These bases depend on an ordering of the simplex vertices. On unstructured meshes, neighbouring cells need not agree on this ordering, making it non-trivial to achieve conformity in finite element software. Existing strategies compute degree-of-freedom transformation matrices numerically, or impose a global vertex ordering through face-bubble spaces or by preprocessing the mesh. We consider the standard basis of the $\mathcal{P}_r^-\Lambda^1$ (trimmed) space and introduce a new basis for the $\mathcal{P}_r\Lambda^1$ (full) space, for arbitrary polynomial order $r \geq 1$ and dimension $D \geq 2$, and prescribe their basis polynomials as shape functions. We show that the pullback of shape functions under the change of coordinates induced by an arbitrary vertex relabelling $\pi$ in the symmetric group $S_{D+1}$ admits a closed-form, combinatorial expression. For most basis functions this pullback is a single basis function of the relabelled ordering, up to sign. On an explicitly characterised "filter-hit" set, this pullback is a signed sum of at most $D$ (full space) or exactly two (trimmed space) relabelled basis functions. All coefficients are in $\{-1, +1\}$ for all $r$, $D$ and $\pi$. The inverse transformation is obtained by computing the formulas at $\pi^{-1}$, so no numerical inversion of a change-of-basis matrix is needed. Conforming assembly and evaluation on simplicial meshes with arbitrary vertex orderings reduce to index manipulation. Our results are compared with the study of relabelling-invariant bases of Berchenko-Kogan and Licht, and verified in an open-source Julia implementation in the Gridap.jl library.

We propose a general characterization of the hazard rate and reversed hazard rate stochastic orders for random variables that are absolutely continuous with respect to a common dominating measure. This framework is useful in giving a unified treatment of continuous, discrete, and mixed distributions without requiring ad hoc approximation techniques or restrictive integrability assumptions. Using this result, we provide direct proofs of the bivariate characterizations of both orders. Additionally, we introduce the class of $\overline{G}$-IFR and $\overline{G}$-DRHR distributions on additive groups, unifying aging properties across different domains and proving their closure under convolution. Finally, we revisit and extend the classic results of Shanthikumar and Yao (1991) on the preservation of hazard rate orders under random sums, simplifying the underlying conditions and accommodating discrete and mixed sum components.

This study mathematically explores wave motion induced by earth-space forces specifically ground actions. Developing and solving governing differential equations provides insights into intricate propagation dynamics caused by these forces. Numerical simulation modeled wave behavior and results were analyzed to understand ground action impacts. Initial wave generation and subsequent propagation were simulated using COMSOL Multiphysics which models wave dynamics in physical media. Partial differential equations describing wave motion alongside Fourier transform techniques were applied to analyze the frequency components. Mathematical derivations yielded wave equations while COMSOL's built-in statistical analysis tool analyzed results. Data visualization employed plotting tools within the software to create detailed graphs. The wave amplitude relative to distance km varied at 0.001 at 0 km and 0.001 at 10 km over 10 km. Amplitudes were recorded at intervals 0.05 0.1 0.15 0.2 0.25 1.05 1.1 1.15 1.2 and 1.25 s. The impact of varying amplitude and frequency on the behavior ranges from 1 s at 0.1 amplitude in Case 1 (0.2, 200) to 1 s at 0.2 amplitude in Case 3 (0.2, 300). Outputs indicate: (1) initial energy input significantly influences the propagation characteristics aiding seismic prediction and mitigation design; (2) propagation details underscore the medium properties' importance in shaping behavior vital for earthquake engineering and accurate ground-motion prediction; (3) sensitivity to amplitude and frequency suggests tailoring parameters can optimize wave-based geophysical surveys and environmental monitoring. This study serves as a policy guide for understanding wave dynamics to inform building codes and disaster preparedness strategies for mitigating seismic impacts.