Papers

Multimode entangled states of light are key resources for high-dimensional photonic quantum technologies, particularly for continuous-variable quantum computing and quantum information processing. Exploiting their full capabilities requires detection strategies that can efficiently and simultaneously access a large number of modes and their quantum correlations. Multimode optical parametric amplification (MOPA) provides a promising approach by amplifying multiple quadratures prior to detection, rendering the measurement robust against subsequent optical losses while enabling broadband operation. Here, we demonstrate the spectral multimode capability of MOPA by simultaneously characterizing a broadband 60-node dual-rail continuous-variable cluster state. We reconstruct the cluster-state nullifiers across the spectral modes and observe nearly uniform squeezing ranging from -7.2 to -6.5 dB. Furthermore, by evaluating the van Loock-Furusawa criterion across all relevant bipartitions, we verify genuine multipartite entanglement throughout the 60-node state. These results establish MOPA as a promising platform for scalable high-dimensional quantum information processing in the spectral domain.

Using the recently published global census of the biomass of wild terrestrial mammals, we perform a detailed mathematical statistical analysis of its distribution over $N_s=4795$ species, drawing on tools developed in economics to characterize wealth inequality. We show that the Lorenz curve of the mammal biomass distribution is characterized by a large Gini coefficient $G=0.944$ exceeding the inequality reported for wealth distribution among world countries. This distribution is compared to the predictions of the Wealth Thermalization Hypothesis (WTH), in which species biomass values are treated as energy levels populated according to a Rayleigh-Jeans (RJ) steady-state distribution. We show that an RJ extended spectral model reproduces the real Lorenz and Pareto curves over four orders of magnitude of biomass variation, capturing the strong condensation of biomass among the rare, heavy-bodied species and its near-absence among the vast majority of light-bodied ones. These results extend the WTH framework, previously validated on distributions of human economic origin, to a biological distribution shaped by ecological and evolutionary constraints, and place the extreme rarity of large-bodied mammal species within the same statistical mathematical description as the oligarchic concentration of wealth in human societies. We finally discuss possible ecological mechanisms that may contribute to the observed distribution, including interspecific interactions, food availability, and competition.

The necessary and sufficient conditions for the intersection of two real flag manifolds in a complex flag manifold to be discrete are stated including a previous known result. The discrete intersection of real flag manifolds is the orbit of a Weyl group, which is an antipodal set of a complex flag manifold. The triple of two real flag manifolds and a complex flag manifold as an ambient space is constructed from a compact symmetric triad. It is shown that the compact symmetric triad can be taken to be canonical.

Refereeing decisions in professional soccer remain inconsistent because referees cannot easily compare a contentious foul against similar past cases. We cast this as a retrieval problem and introduce SoccerNet-FoulRet, the first benchmark for semantic foul retrieval. Given a query foul, the task is to retrieve past fouls judged to be relevant precedents, regardless of camera angle, teams, or appearance. This differs from prior video-to-video retrieval, which matches clips by visual similarity or a shared event. Here, relevance is defined by refereeing interpretation. We build the benchmark from the SoccerNet-MVFoul dataset and evaluate retrieval ability of zero-shot video and vision-language embedders together with a task-specific fine-tuned baseline on 693 human-verified queries and category-relevance labels. Semantic foul retrieval remains challenging. The strongest zero-shot model achieves under 5% HitRate@10 on human-verified precedents, while category-supervised fine-tuning improves category relevance but transfers only modestly to precedent retrieval. We release SoccerNet-FoulRet to establish semantic foul retrieval as an open problem: https://github.com/SoccerNet/sn-foulret.

We study iterated function systems with large overlaps via the Kajiwara-Watatani C*-algebra and its KMS states. Our main contributions are threefold. First, we introduce Markov partitions for iterated function systems and give an algorithm that constructs a finite Markov partition whenever one exists. Second, for contractive systems admitting a finite Markov partition, we construct an intrinsic analogue of the Hutchinson measure that is independent of any labelling or weighting of the maps. It is the unique Borel probability measure satisfying a self-similar integral equation dictated by the set-valued dynamics of the system, and it recovers the Hutchinson measure with uniform weights when the system satisfies the open set condition. Finally, we give a complete classification of KMS states for the gauge action on the Kajiwara-Watatani algebra of every such system. The critical inverse temperature is the exponential growth rate of the intrinsic orbits, which can be strictly smaller than the natural logarithm of the number of maps, and the unique KMS state at this temperature is given by our intrinsic Hutchinson measure. These systems include many with large overlaps not covered by the results of Izumi-Kajiwara-Watatani.

Research in data visualization of artificial intelligence (AI) models has historically focused on enhancing trust through visual explanations of AI. The trustworthiness line of work was built at least partially on an assumption that humans were critical users unlikely to adopt AI technology. It is increasingly clear that human trust levels in AI span, in fact, a wide range from critical to over-reliant. There is an urgent need to support both trust and healthy skepticism in AI solutions. We argue that it is healthy for humans to adopt a skeptical view both on the results of AI models and on the use of such AI models. We share our thoughts on the rising phenomenon of over-reliance on AI models, the risks and opportunities in using AI models, and the role of data visualization in over-reliance situations where humans are not motivated to engage in critical thinking.

In this work we investigate a relative notion of protomodularity. We say that a finitely complete category $\mathsf{C}$ satisfies the \defn{$\mathscr{M}$-version of protomodularity} when the change-of-base functors (concerning the fibration of points) are conservative with respect to a class of morphisms $\mathscr{M}$ of $\mathsf{C}$. If $\mathscr{M}$ is the class of all morphisms or the class of all monomorphisms of $\mathsf{C}$, then this notion is precisely that of an “ordinary” protomodular category. We analyse what conditions the class $\mathscr{M}$ should have to obtain the relative $\mathscr{M}$-versions of some well-known properties of protomodular categories, such as the Split Short Five Lemma (when $\mathsf{C}$ is also pointed) and their relation with Mal'tsev categories. We also give diverse examples of categories satisfying the $\mathscr{M}$-version of protomodularity for different choices of $\mathscr{M}$.

We study the gap between the integrated direction-wise kinetic minima and the minimum expected Dirichlet energy of a joint particle realization of a prescribed two-parameter family of conditional probability measures. Under regular coarea assumptions with compact connected fibres and a star-shaped parameter domain, we derive a curvature lower bound valid for every admissible law of Sobolev maps realizing the prescribed smooth positive conditional measures. Our method combines the weighted-Poisson characterization of local optimal-transport velocities with an exact energy-residual identity, a weak Stokes formula, and explicit transport constructions. The minimum excess vanishes exactly when the Lie-bracket curvature vanishes throughout the parameter domain, whereas at a nonzero-curvature centre it is of order $r^4$ as $r\to0$ on squares of half-side length $r$ with unnormalized area measure. In a two-phase torus model fitted to electricity-demand data, numerical tests illustrate this quartic order for explicit couplings, with radial transport approximately halving the ordered construction's excess on the tested small squares.

When does distribution alignment help a frozen foundation-model embedding generalize across acoustic domains? For cross-domain mosquito-species classification we report a strength-monotonic law: the stronger an encoder is on the target task, the more its unseen-domain generalization relies on a distribution-alignment (MMD) term, and the more it is harmed by domain-rebalanced sampling. Across four encoder families and a within-encoder HuBERT layer sweep (n=8), the rebalancing leg orders exactly with encoder strength (Spearman -1.000), while the MMD-benefit leg is monotonic within each stream and -0.857 pooled; fixing architecture and varying only representation strength flips the rebalancing effect from benefit to collapse. The law is actionable: a single MMD term is the sole lever on a strong encoder, so we reduce the field's default recipe to a frozen Perch 2.0 embedding, a lightweight probe, cross-entropy, one MMD, and input augmentation. The reduced recipe stays within seed noise of the full composite (BA_unseen 0.299+/-0.006 vs. 0.307+/-0.014). As boundary conditions of the same law, three community defaults (backbone fine-tuning, multi-modal fusion, and domain rebalancing) each hurt unseen-domain accuracy under a leave-domain protocol, shown with single-variable, multi-seed evidence. We present a mechanism and the recipe it explains, not a leaderboard entry.

The Tibet AS$\gamma$ experiment observes cosmic gamma rays from several teraelectron volts to the petaelectron volt range using an array of ground-based surface air-shower detectors (Tibet-III), which is composed of plastic scintillators. The arrival direction is conventionally reconstructed by fitting the shower front to detector hit-timing data, and the resulting angular resolution is approximately 0.5$^\circ$ to 0.2$^\circ$ in the energy range from 10 to 100 TeV. In this study, to further improve the directional reconstruction accuracy, we developed a new arrival-direction reconstruction method that integrates a convolutional neural network (CNN) with the conventional method. Evaluations using gamma-ray events generated by Monte Carlo simulations show that the angular resolution is improved by approximately 10\,%–15\,% compared with that achieved with the conventional method. In addition, this performance improvement shows little dependence on the zenith angle up to 40$^\circ$. The proposed method provides a performance gain equivalent to increasing the Tibet-III array, which consists of approximately 600 detectors, by roughly 150 to 200 detectors.

Let $F \in \mathbb{Z}[x_1, \ldots, x_n]$ be a homogeneous form of degree $d \geq 4$ which defines a smooth hypersurface in $\mathbb{P}^{n-1}_{\mathbb{C}}$. For $n \geq 24 d^4 2^d$, we prove an asymptotic formula for the number of prime solutions to the equation $F(x_1, \ldots, x_n) = 0$, provided $F$ satisfies suitable local conditions.

World Action Models (WAM) improve robot policies by augmenting sparse action supervision with dense future prediction. However, much of the predictable future is dominated by appearance and scene persistence rather than action-dependent dynamics. We observe that several recent WAM designs, including optical flow, motion-centric representations, and latent actions, can be understood from a common perspective in which world supervision becomes more efficient as it contains a higher proportion of action-relevant variation. Based on this insight, we introduce Action Tangent Fields, which reformulate world supervision through a local Taylor expansion of how actions induce changes in future dynamics. We represent future dynamics in Residual-VAE space, where the future latent remains recoverable from the current latent and its residual, and use a strong action-conditioned world model (ACWM) to probe the local correspondence between action variations and residual-world variations. This local first-order structure is distilled into the WAM to guide its denoising supervision toward dynamics that are more tightly coupled to action, rather than merely predictable from appearance. Across LIBERO-Plus, RoboTwin, and RoboTwin2.0-Plus, our method consistently improves robustness to lighting, background, camera, layout, and other environmental perturbations. Despite using no large-scale embodied pretraining, it achieves stronger robustness under several distribution shifts than pretrained policies. We further distill multi-step VideoDiT denoising into a single step for efficient inference. Our results suggest that effective WAM supervision should remain information-rich while concentrating its predictive capacity on the directions along which actions change the future.

Multilingual LLMs answer the same multi-hop reasoning question across languages, but we lack a mechanistic account of whether they share an internal circuit. We identify Bridge Routing Heads (BRH) in two large multilingual LLMs through a three-stage pipeline. The resulting language-specific head sets exhibit near-complete mutual exclusivity across the five languages, with a mean Jaccard similarity of only 0.017 for Llama 3.1 70B and 0.057 for Qwen 2.5 72B, revealing language-idiosyncratic circuits. Ablating general BRH increases two-hop Negative Log-Likelihood (NLL) by 39-89x the random-head baseline, providing direct causal evidence of their role. Amplifying these heads in a failing target-language pass rescues up to 51.7% of cross-lingual failures, with no training. The two models share this dual-circuit pattern but allocate heads differently: Llama concentrates chaining in a large general pool, while Qwen leans on larger language-specific pools. Together these results show that activation-level intervention alone can recover correct answers from cross-lingual reasoning failures.

We provide a description of the real variable Hardy spaces $H^p$, $p\in (0,1]$ associated to a regular polyhedral open convex cone, devised as real variable analogues of the holomorphic Hardy spaces on the corresponding tube domain.

Kernel methods are among the most powerful tools in machine learning and statistics, with a large number of successful applications. Their immense success stems from the flexible function class associated to each kernel—its reproducing kernel Hilbert space (RKHS)—which facilitates statistical analysis, as well as from their computational tractability and applicability to many domains. Multiple notions (such as characteristic, $L_p$-universal, and integrally strictly positive definite) capture the expressivity of kernels and their RKHSs and play a key role in understanding the statistical properties of kernel methods; these concepts and their relations are well-understood for bounded kernels. Even though unbounded kernels have received significant attention over the past decade (for instance, in the construction of kernel-based discrepancy and dependence measures such as the maximum mean discrepancy, the Hilbert-Schmidt independence criterion, and the kernel Stein discrepancy), surprisingly little is known about the relations of these notions in the unbounded case. In the present paper we tackle this severe bottleneck, establishing their relations under mild assumptions.

Protocol verifiers check whether a protocol meets a security goal under stated trust assumptions, such as that a key is never leaked, a value is fresh, or a channel is authentic. They do not say which of those assumptions the goal needs. Rowe, Guttman and Liskov asked for the weakest assumptions under which a protocol achieves a goal and left the question open. We answer it for assumptions about keys, values and channels. Call a run that violates the goal an attack, and the assumptions that would rule it out its stopping set. The least a protocol must trust to meet a goal is exactly the set of minimal ways to stop all of its minimal attacks. Several such sets may exist. The answer becomes unique once either/or assumptions are allowed, and it is a single set exactly when every minimal attack is stopped by a single assumption. A Galois connection between assumptions and goals explains why: a goal may end in "or", but its hypothesis may not. The same structure gives the trust needed by a conjunction of goals and an exact condition under which composed protocols need no extra trust. To compute the answer, a loop asks a verifier whether a candidate suffices, records what stops each attack it reports, and recomputes the candidates. Deciding whether some trust of at most k assumptions suffices is NP-hard. With a bounded analyser of our own and with CPSA, the loop finds the weakest trust for 18 goals over ten protocols and their variants, and on each it agrees with evaluating every trust using the same verifier. The answers include an assumption that our model of the adopted fix of Kerberos PKINIT states but the client's authentication of the server does not need, and channel assumptions under which Needham-Schroeder meets its goal in a bounded model.

We prove global rigidity of smooth asymptotically flat stationary vacuum black holes with sufficiently small ADM angular momentum, under global regularity, bifurcation, and maximal-hypersurface assumptions, with a threshold depending on fixed geometric bounds. This answers the question of Alexakis, Ionescu, and Klainerman concerning replacement of their smallness assumption on the stationary field at the bifurcation sphere by smallness of the angular momentum at infinity. The main step establishes Kerr rigidity near infinity for arbitrary angular momentum. We transform the vacuum metric into an Einstein–Maxwell family and compare null focusing at its limiting end with the area–charge inequality on compact maximal hypersurfaces, constructed by continuation through the family. Equality determines both the transformed metric and its stationary field, from which the inverse transformation recovers Kerr. Two Kerr identities then give the horizon estimate required for the global conclusion.

We study commutativity and zero products of slant Toeplitz operators on the Bergman space of the unit disk. For bounded polyanalytic symbols and for bounded harmonic symbols with boundary values in $W^{1,2}(\mathbb T)$, we prove that two such operators commute if and only if their symbols are linearly dependent over $\mathbb C$, with both symbols belonging to the same class. In the polyanalytic case, no boundedness assumption is imposed on the individual analytic components. We also show that every finite-rank commutator in this class is zero and that every finite product associated with nonzero polyanalytic symbols has infinite rank. Finally, for $s\ge2$, we prove that a product of $s$ slant Toeplitz operators with bounded harmonic symbols whose boundary values belong to $W^{s-1,2}(\mathbb T)$ is zero if and only if at least one symbol vanishes identically.

The non-collinear antiferromagnet (AFM), Mn3Sn, exhibits ultrafast spin dynamics and negligible stray fields, making it a promising candidate for fast and energy-efficient spintronic devices. However, its practical applications remain limited by the relatively small Hall response compared with ferromagnets. Here, we demonstrate through both first-principles calculations and experiments that Cr-doping is a promising strategy to enhance the anomalous Hall transport in Mn3Sn without breaking the antiferromagnetic order. Our first-principles calculations reveal an effective Cr-doping window that enhances the anomalous Hall conductivity while largely preserving the host coplanar inverse triangular AFM order. Berry curvature and Wannier Hamiltonian analyses further reveal that Cr substitution amplifies the pre-existing Berry curvature hotspots through coordinated changes in the energy alignment, local interband gap, and Mn-derived orbital weight of the relevant bands. Experimentally, we have found that Cr doping not only supports our computational predictions (enhancement of the anomalous Hall conductivity), but also suppresses the transition to helical or spin-glass phases at low temperatures. These findings demonstrate that Cr-doping is an effective strategy for stabilizing the AFM order while simultaneously improving the anomalous Hall transport properties over a wide temperature range in Mn3Sn.

Estimating remaining shelf life (RSL) from images could provide affordable decision support for perishable produce, but evaluation protocols can substantially affect reported performance when repeated images are available from the same biological specimen. We use the Hass Avocado Ripening dataset, comprising 8,834 image-RSL pairs from 426 fruits across three storage regimes, to evaluate a frozen ImageNet-pretrained visual backbone with a lightweight regression head. Our contributions are threefold: we quantify the effect of observation-level versus specimen-disjoint evaluation, compare lightweight and heavier visual backbones under specimen-disjoint cross-validation, and examine their spatial attributions using Grad-CAM. Across ten observation-level random splits, the model achieves a mean RMSE of 2.37 days with a standard deviation of 0.03 days, whereas specimen-disjoint 5-fold cross-validation yields a mean RMSE of 3.12 days with a standard deviation of 0.11 days. The corresponding mean coefficient of determination is 0.553. A matched per-specimen comparison confirms higher error under specimen-disjoint evaluation, with a probability value below 0.001 across 426 specimens, showing that observation-level partitioning gives a substantially more optimistic estimate for this dataset and model configuration. Under specimen-disjoint evaluation, MobileNetV3-Small (0.93 million parameters) achieves accuracy comparable to ResNet-18 while providing substantially higher throughput, and Grad-CAM reveals differences in spatial attribution between the lightweight backbones. These results support specimen-disjoint evaluation and attribution analysis when assessing lightweight vision models for longitudinal shelf-life prediction.

Let $f_n:\{0,1\}^n\to\{0,1\}$ be Boolean functions, and let $C_n$ count their changes in a unit time interval under stationary dynamics in which each coordinate is independently resampled at rate one from the Bernoulli$(p_n)$ distribution. We prove that, if $f_n$ is transitive and $n\min\{p_n,1-p_n\}^{r}\to\infty$ for every $r>0$, then tightness of $(C_n)$ implies $\mathrm{Var}(f_n)\to0$. This settles Forsström's conjecture in the $p_n\le 1/2$ regime and extends it symmetrically to arbitrary biases. The proof uses a mixed jump sum whose second moment is bounded by the generator of a low degree Fourier projection.

Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relevant in modern retrieval systems, including those based on large language models (LLMs). However, no single QE method consistently outperforms others across all queries. This work seeks to explain the variation in QE performance through two complementary perspectives. The first is the concept of an Ideal Expanded Query (IEQ)–a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model. The second is a separability perspective, which quantifies how distinctly relevant and non-relevant documents are scored for a given expanded query using Cohen's (d). We develop a separability measure and practical formulations to approximate the IEQ and investigate how these factors relate to retrieval effectiveness. Extensive experiments on the TREC Robust collection, TREC DL 2019-2022 passage collections, and TREC DL 2019-2020 document collections reveal several interesting patterns. In particular, we find that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval effectiveness. We further show that the separability of relevant and non-relevant documents provides a complementary perspective for understanding QE performance.

Prior artisan mesh generation works largely predict face tokens autoregressively, which makes inference slow. Recent methods instead flow match continuous latents built by Variational AutoEncoders (VAEs), but reconstruction quality drops significantly when geometry and topology are jointly encoded, and further when the latent space is compressed. We present MeshCarve, a flow matching method that generates entirely in compact latent spaces, generating vertex positions and edge connections separately and sidestepping the difficulty of a joint compact latent. To shorten the token sequence, we propose a hierarchical sparse transformer backbone, instantiated as VertexVAE and EdgeVAE. Instead of encoding fields over the surface voxels, both VAEs anchor on discrete vertices in their latent spaces, which drastically reduces the token sequence length, and our spatial-aware compression shortens it further without costing reconstruction. VertexVAE directly encodes vertex occupancy. For connectivity, we propose vertex-link encoding, which turns arbitrary connectivity between vertices into fixed-length continuous per-vertex embeddings and recovers complex artistic topology faithfully. MeshCarve combines these VAEs with an anchor generator and flow matches on the shortened token sequences. It shows advantages over state-of-the-art autoregressive and flow matching methods on Objaverse and generalizes to Toys4K. To the best of our knowledge, it is among the first artisan mesh generation methods whose every generative stage runs in a spatially compressed latent, with a token sequence only a fraction of the most compressed previous autoregressive and flow matching works.

This paper studies transfer learning for high-dimensional quantile regression with limited target samples and heterogeneous source domains. We propose a sample-level transfer learning method (SL-TL) that combines sample selection and importance weighting. Unlike existing source-level approaches that include or exclude entire source domains, SL-TL identifies transferable samples within each source domain and incorporates them through adaptive importance weights. The proposed weights rely only on one-dimensional densities associated with the quantile loss, avoiding the estimation of high-dimensional density ratios. We establish error bounds for SL-TL estimators and characterize the effective sample size contributed by source domains. In the oracle setting, we show that SL-TL achieves faster $\ell_2$-convergence rates than existing competitors under the considered regimes. For the unknown setting, we develop an implementable algorithm based on sample splitting and cross-fitting procedures. Extensive simulations and a real data analysis demonstrate the finite-sample performance and robustness of SL-TL.

We establish an $L^p$–$L^q$-type Carleman estimate for the Schrödinger operator $H_{A,V}$ with scaling-critical electromagnetic singular potentials on a conical singular space $(X,g)$ of dimension $d\geq3$, where the metric is $g=dr^2+r^2h$ and $X=C(Y)=(0,\infty)_r\times Y$ is a product cone over the closed Riemannian manifold $(Y,h)$. More precisely, we prove $$ \|e^{\tau\phi}u\|_{L^{2d/(d-2)}(X)}\le C\,\|e^{\tau\phi}H_{A,V}u\|_{L^{2d/(d+2)}(X)} $$ with the quadratic weight $\phi(r)=\tfrac14(\log r)^2$, uniformly in $\tau$. As an application of this Carleman estimate, we prove the unique continuation property for this electromagnetic Schrödinger operator when the cross-section is $Y=\mathbb{S}^{d-1}$.

Online reconstruction of dynamic 4D scenes from long, unposed streaming videos requires both continuous processing and photorealistic rendering, which existing methods struggle to achieve simultaneously. Existing feed-forward Gaussian methods are restricted to offline processing, whereas online point-cloud approaches struggle to maintain dense geometry and high-fidelity rendering. We present DynStream, a framework for streaming 4D Gaussian reconstruction from long, unposed videos. Given a continuous video stream, DynStream reconstructs the scene within local temporal windows and incrementally aligns and fuses these local reconstructions into a globally consistent scene, enabling online 4D reconstruction without per-scene optimization. By jointly enforcing cross-window geometric consistency and modeling time-varying scene content, DynStream supports efficient reconstruction and photorealistic rendering over extended video streams. Experiments demonstrate that DynStream enables high-fidelity online dynamic reconstruction and rendering from long video streams, achieving state-of-the-art performance across diverse dynamic indoor and outdoor scenes.

In this work, we have systematically investigated the structural stability, electronic properties, and photocatalytic performance of the 2D h-Be3N2 monolayer for water splitting application, utilizing a first-principles density functional theory. Electronic structure calculations based on the PBE functional reveal that h-Be3N2 is a direct band-gap semiconductor with a band gap value of 1.74 eV, making it well suited for visible-light-driven photocatalysis. Furthermore, the alignment of band-edges illustrates that h-Be3N2 has suitable redox potential that facilitate overall photocatalytic water splitting in neutral and alkaline environment. Conversely, in acidic condition, the position of valence band is an adequate to enable the OER, thereby leading to photocathodic behavior. At neutral pH, the HER process is still possible with the application of an external potential of 0.77 V. For the OER process, it is thermodynamically favourable only under strongly alkaline conditions, while an external potential of 0.43 V is needed to promote the reaction at neutral pH. These results clearly underscore the significant impact of solution pH and photogenerated charge carriers on the photocatalytic effectiveness of the h-Be3N2 monolayer. Furthermore, the study of H coverage indicates that the amount of H that adheres to the surface has a considerable impact on the efficiency of the HER in h-Be3N2. The favourable photocatalytic capabilities of the h-Be3N2 monolayer imply that it might be a viable option for upcoming solar-powered green hydrogen generation and may assist in realizing the goals of the National Green Hydrogen Mission by facilitating effective and sustainable hydrogen production.

Vision-Language-Action (VLA) models acquire broad manipulation capabilities via large-scale pretraining, yet eliciting them through language requires fine-grained alignment between instructions and physical interactions. Existing robot demonstrations typically provide only coarse task descriptions, omitting how actions are executed, including which gripper acts, which object is contacted, and how it is grasped and moved. We introduce YUBI-STAG, a framework for Spatio-Temporal Annotation and Grounding that automatically enriches manipulation demonstrations with interaction-rich semantics to align pretrained VLAs with fine-grained manipulation language. Combining contact-object segmentation with vision-language models, YUBI-STAG annotates object identities, attributes and states, per-gripper actions, bimanual coordination, and spatially grounded interactions. To address YUBI-STAG's reliance on localized sequences and multi-stage VLM inference, we distill it into YUBI-VLM. YUBI-VLM directly recovers action structure and annotations from raw, unsegmented video in few inference calls and operates from wrist views alone. We evaluate both frameworks on YUBI-STAG-Bench across temporal, semantic, and spatial grounding tasks. YUBI-VLM retains much of YUBI-STAG's annotation accuracy with fewer inference calls and shorter runtime while generalizing to unseen manipulations. Finally, post-training VLA policies on these annotations aligns them with fine-grained language and contact-aware structure. Bimanual experiments demonstrate improved performance and instruction following, including control over object identity, acting gripper, target location, and spatial relations absent from original labels.

Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantities usually reported separately: how well a kernel performs on the task, and how far its geometry departs from the classical kernels available for the same problem. We introduce a fully unsupervised, multi-objective protocol that optimises simultaneously the normalised pseudo discrepancy (NPD), a label-free proxy for anomaly detection quality, and the geometric difference (GD) to a tuned classical reference kernel, selecting models from the resulting Pareto front. We apply it to malware beaconing detection in real network traffic, using a one-class support vector machine with fidelity and projected quantum kernels over four data encodings, on simulators and on IQM's 20-qubit Garnet processor. NPD-guided selection alone finds a fidelity kernel that beats the tuned classical baseline, but with a geometric difference too small to certify the gain as quantum. Projected kernels reach far larger geometric differences; the Pareto-selected one only marginally exceeds the baseline (AUC $0.782$ versus $0.765$, $g_{C\to Q}\approx 89>\sqrt{N}$ relative to that reference kernel), still below the NPD-selected fidelity kernel ($0.840$).

Quantum emitters in hexagonal boron nitride (hBN) are promising building blocks for solid-state quantum photonics because of their bright room-temperature emission. These emitters strongly interact with lattice vibrations, which partially underpins their excitation efficiency. Here, using complementary cathodoluminescence (CL) and photoluminescence (PL) spectroscopy, we demonstrate cavity-mediated interactions between hBN quantum emitters coupled to the same plasmonic Au void cavity at the room temperature. Cathodoluminescence measurements reveal that a vibronic transition of the 443 nm emitter hybridizes with the cavity mode, giving rise to a spectrally resolved splitting, while the neighboring 537 nm emitter remains in the weak-coupling regime despite spectral overlap. The energy split of the CL peak is at the order of 36.2 meV, whereas the PL-resolved peak is approximately 24.1 meV, which is due to the collective excitations of multiple emitters per electron excitation and their mutual interactions. Maxwell-Bloch simulations reproduce the experimental spectra and identify the observed splitting as the result of cavity-induced hybridization. Excitation-dependent PL further shows that the hybridized state can be accessed through phonon-assisted excitations below the zero-phonon transition. These results demonstrate that vibronic transitions provide an effective pathway for controlling interactions between multiple quantum emitters in hybrid plasmonic nanophotonic systems.