Papers

Ontology generation from Competency Questions (CQs) is a central yet labor-intensive phase of Ontology Engineering. While large language models (LLMs) offer promising automation capabilities, current evaluations remain fragmented. Task formulations are heterogeneous, gold standards often lack fine-grained CQ provenance, metrics conflate lexical overlap with structural and logical adequacy, and reference ontologies are not always explicitly designed around the evaluation CQs. Here, we address these limitations with CQ4OE, a benchmark for the systematic and reproducible evaluation of LLM-based ontology generation from CQs. For each ontology in the benchmark, we build a CQ-driven gold OWL ontology with explicit provenance linking each CQ to the classes, properties, and axioms required to answer it. From this resource, we define two complementary evaluation tasks. CQ2Term supports term-level evaluation of CQ-specific class and property prediction over 99 CQs, and CQ2Onto supports ontology-level evaluation over 118 CQs, including hierarchy, property modeling, and axiom-level structure. We demonstrate CQ4OE with experiments using nine LLMs under zero-shot, iterative, and multi-agent generation strategies, showing that LLMs recover explicit vocabulary terms more reliably than creating ontologies, particularly in property modeling, hierarchy construction, and axiom generation.

Large audio-language models may mention acoustic events that are absent from the input. A separate audio event detector can verify these mentions, but doing so requires a second audio encoder and a separate forward pass. We propose Reused Encoder States for Verifying Events (REVE), a lightweight method that uses states already computed by the target model. One readout summarizes class scores across audio frames, while another uses pooled states from four consecutive frame intervals. Class-aware score fusion combines their outputs to verify generated event mentions without encoding the audio again. On AudioSet, REVE removes 92.9% of label-unsupported mentions under a faithful-mention recall constraint. With fewer added parameters and no second audio-encoding pass, REVE achieves a reduction comparable to those of CED-Tiny and CED-Base. Its complete verification latency is about 1/18 of the CED-Base path. Results on controlled DESED mixtures and different target-model architectures further confirm the effectiveness of encoder-state reuse.

An inverse seesaw model for neutrino masses and mixing is proposed, based on the spontaneous breaking of a $D_4$ flavor symmetry. The model simultaneously accounts for the neutrino oscillation parameters, the baryon asymmetry of the Universe through TeV-scale leptogenesis, and makes predictions for charged-lepton flavor violation (cLFV) that are consistent with present experimental limits. A phenomenological analysis shows that the model is consistent with the observed neutrino oscillation data for both normal and inverted mass orderings. Successful leptogenesis is realized for the lightest pseudo-Dirac pair in the multi-TeV range. The predicted cLFV branching ratios lie well below experimental upper bounds, ensuring compatibility with existing constraints. These results establish the model as a viable framework that links low-energy neutrino observables to TeV-scale leptogenesis and cLFV phenomenology.

We establish small-energy regularity for stationary weak solutions of the Liouville equation $ -\Delta u=e^u $ in every dimension. It solves a problem left open in Da Lio [Commun. Partial Differ. Equations, 2008] and posed explicitly again by Da Lio and Hyder in [Commun. Contemp. Math. 2025]. The proof relies on the analysis of concentration phenomena for almost stationary fields with structure constraints.

Human feedback can vary in cost and informativeness. Strong feedback can reveal severe errors but is costly, so cheaper quality ratings can help decide which items to annotate. We propose MICRO (Multi-Fidelity Impact Clustered Rollout), an active search framework that allocates a shared budget to these feedback types to maximise confirmed severe error discoveries. MICRO jointly models ratings and annotation losses conditional on item features to steer acquisition. It clusters acquisitions by their predicted impact on severity probabilities to select diverse candidates, then uses rollout to estimate their discovery value. Experiments on WMT20 English-German show that ratings improve both loss reconstruction and severity prediction. MICRO achieves the highest mean discovery count across four budget and rating cost settings, with similar performance to adapted MF-ENS in one and significant gains over all six comparison policies, including two rollout controls, in the other three $(p<.001)$.

We study weakly Einstein and weakly $\eta$-Einstein structures on almost contact metric three-manifolds. We first obtain a pointwise structural description of the Ricci operator $Q$ on a weakly $\eta$-Einstein almost contact metric three-manifold. More precisely, at each point, either $Q$ has the $\eta$-Einstein form, $Q\xi=0$, or the metric is weakly Einstein. We then prove that if a contact metric three-manifold satisfies $Q\xi=0$, then either $Q=0$ or $\operatorname{rank}Q=1$. Hence, such a metric is either flat or weakly Einstein but not Einstein. We also construct examples showing that the contact metric assumption is essential: there exist almost contact metric three-manifolds satisfying $Q\xi=0$ whose Ricci operators have rank two. Finally, we classify simply connected homogeneous contact metric three-manifolds that are weakly Einstein.

The need for collaboration between diverse fields of research is increasingly recognised as important by research funding agencies. A significant driver of this need is the current revolution in artificial intelligence (AI) and related technologies. There is a growing interest in the potential impact of AI in different fields including the methodologies they use and the resulting advances in new knowledge, new access and enhanced productivity. However, there is also a corresponding increase in concern about the fundamentals of AI technologies and the way in which trans and/or interdisciplinary research is approached. The resulting collaboration too often ends up as a one-way street where the domain partner acts only as an information provider. For example, the contribution of the AHSS partner might be limited to providing insight about ethics and/or the technology partner may only provide a service to build applied AI-based solutions. In response to this problem, we propose a reciprocal approach to collaboration where both partners seek to understand, cooperate and identify jointly significant impacts. In this paper we explore this relationship between cultural heritage institutions (GLAMs), Arts, Humanities & Social Sciences (AHSS) research and technology-led AI research, especially the impact of current technological advances in AI. Drawing from the history of convergence in GLAM studies, we propose five key practices to form a framework for greater understanding across this divide.

We study a finite tiling problem, where tiles are unit squares whose four edges are colored with one of two colors. We ask whether a given rectangle admits a perfect rectangular tiling: every cell of the rectangle is occupied by one tile, neighboring edge colors match, and exactly $n_i$ tiles of type $i$ are used, where rotations of the tiles are allowed. Our problem is related to classical Wang tilings, more general finite tile-placement problems, and edge placement puzzles. But in our problem, the multiplicities of the tile types are part of the input and the tile alphabet is fixed and extremely small; thus the complexity of the problem arises from the interaction between the rectangle dimensions and the prescribed tile multiplicities. We provide a comprehensive study of the perfect rectangular tiling problem. For this we consider all classes of subsets of the six possible tile types for two-colored edges, and we characterize for each class whether multiplicities either always allow a perfect rectangular tiling or whether their existence can be decided efficiently.

Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing optimal locations to shift continuously. Existing dynamic BO (DBO) methods using standard acquisition functions such as Upper Confidence Bound (UCB) fail to explicitly account for temporal variations, leading to suboptimal sample allocation and poor tracking of moving optima. Here, we propose BOBA (Bayesian Optimization through Bayesian Active Inference), a novel acquisition function inspired by free energy principles from active inference that explicitly minimizes predictive uncertainty about future states in dynamic environments. BOBA extends traditional acquisition functions by incorporating a forward-looking uncertainty quantification that estimates uncertainty in function changes, enabling more informed exploration-exploitation trade-offs in non-stationary settings. We evaluate BOBA on synthetic dynamic benchmarks, comparing against state-of-the-art DBO methods. Our experiments demonstrate that BOBA significantly improves regret in query-restricted settings, while remaining competitive in time-limited settings. We further analyze variants of BOBA with different exploration strategies, showing how the exploration-exploitation balance can be tuned for different types of dynamic functions. This work contributes both a free energy-based acquisition function for DBO and insights into how active inference principles can enhance optimization in non-stationary environments, with implications for real-time applications requiring continuous adaptation.

We give an explicit geometric and algebraic description of the index map for AF-by-discrete groupoids. The index records transport between infinite tiles and abelianized returns in the groups of germs, subject to a defect in the dimension group of the associated Bratteli diagram. We determine the kernel of the index map and construct transposition factorizations of zero-index elements with trivial germs at singleton orbits. In particular, in every minimal case, this kernel equals the subgroup generated by dynamical transpositions. We also characterize approximation by the fixed AF core and prove almost finiteness for minimal systems that are compactly generated. We make higher homology explicit in terms of the groups of germs. We express the secondary parity differential by lifted surface relations and by an extension class constructed from balanced boundary data. Under minimality and comparison, lifting finite relations gives a split AH sequence. Explicit boundary and matrix calculations, together with a nonminimal example with nonzero parity differential, illustrate the theory.

We study intervals in the lattice of additively idempotent semiring varieties associated with power semirings of finite groups. We prove that every variety lying above the nonempty power semiring of a finite group of order at least three and below a variety generated by finitely many full power semirings of finite groups is nonfinitely based. In fact, none of these varieties admits an identity basis with a fixed finite bound on the number of variables. In particular, adjoining the empty set gives an interval consisting entirely of nonfinitely based varieties. We also construct equational upper bounds that are closed under zero adjunction and yield further intervals with the same property. The proof combines finite commutative quotient semirings, a cardinality estimate for kernel blockers over finite modules, and a uniform estimate for fibres of ordered products in finite groups. As a consequence, the full power semiring of a finite group is finitely based precisely when the group has at most two elements.

Many learning problems combine a dominant regularity with systematic exceptions. Motivated by U-shaped learning in language acquisition, we study this interaction in controlled quasiregular regression problems where regular and exceptional solutions are explicitly known. Neural networks can partially acquire exceptions, subsequently regress toward the dominant regularity, and finally recover. This overregularization becomes substantially stronger when exceptions are rare, despite their early acquisition, but does not emerge equally across all regularities considered. Our results isolate a simple form of competition between regularities and exceptions during neural learning.

We study prophet inequalities for a random walk reward stopping problem with sample-based information. The goal is to stop as close as possible to the maximum of a random walk with i.i.d. increments, measuring performance by the ratio between the expected reward when stopping and the expected true maximum. We consider a sample-based model in which the increment distribution is unknown and the decision maker has access to $K$ independent sample paths of the reward process. For the infinite-horizon setting, we establish a sharp prophet inequality with constant $(K/(K+1))^{K+1}$. The guarantee is attained by a randomised stopping rule based on the ladder height decomposition of random walks. As $K\to\infty$, this recovers the classical $1/e$ prophet inequality from the full-information setting. For the finite-horizon setting, where the process terminates after $n$ steps, we first prove a tight no-information prophet inequality with constant $1/H_n$, where $H_n$ is the $n$-th harmonic number. For $K\ge1$ samples, we show that a prophet constant of $1/4$ is attainable. Finally, we prove that, with $K$ samples, the prophet constant is at most $(K/(K+1))^{K+1}+(6+6H_K)/H_n$ for $n\ge 2K^2$, implying convergence to the infinite-horizon constant as $n\to\infty$. Our approach combines random walk theory, including ladder heights and Spitzer's identity, with linear programming duality. Our results contribute to random walk stopping theory and to sample-based prophet inequalities for correlated rewards, an area that remains largely unexplored. To the best of our knowledge, our tight sample-based prophet inequalities are the first whose performance is parameterised exactly, rather than only up to constants, by the number of available samples.

We call the object this paper derives and certifies a minimal sufficient governance context: given a finite reachable-state model, a deterministic declared verdict, and candidate observable attributes, we compute sufficient observation sets, distinguish attributes that are individually indispensable from contracts that are jointly sufficient, and select among sufficient contracts under a cardinality or declared-cost objective. An observation contract is a set of candidate attributes whose values determine the declared verdict on every reachable state; an authority contract is one selected under an objective and bound to a gate schema. We synthesize every inclusion-minimal sufficient contract where exhaustive enumeration is affordable, and a minimum-cardinality or minimum-cost contract by SAT/MaxSAT encoding otherwise, checking sufficiency directly. On a constructed code/cloud domain, the individually-indispensable core is not sufficient as an observation contract and two distinct reducts exist; a preregistered cost model separates them exactly. On a second, larger, constructed domain, the same pattern recurs, but that domain's cost model does not separate the alternatives: a fully explained cost tie, reported as found. We measure discernibility-family scaling where exhaustive enumeration is confirmed infeasible within a registered timeout, while SAT/MaxSAT synthesis solves in well under a second; MaxSAT showed no measured cardinality advantage over plain SAT. AuthorityBench compares four baselines across three domains; the declared-only baseline is not exactly sufficient on any. Every selected contract is checked for sufficiency, with a counterexample on failure and a check summary, not a portable certificate, on success – the compiler-focused scope of a two-scope table; an independently specified end-to-end case study is registered follow-up work, not claimed here.

Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Edit, a video editor that couples a multimodal encoder with a pretrained video diffusion transformer. The encoder jointly processes source frames and the editing instruction, and learnable queries convey their semantic context to the generator through a trainable connector and cross-attention. A complementary visual pathway concatenates source-video latents with noisy target latents before patch embedding, supplying dense spatial and temporal information for content preservation. We train the model with flow matching and a three-stage image–video curriculum comprising semantic alignment, joint adaptation, and high-resolution refinement. To support training, we construct over 1.2 million instruction-conditioned video-editing records covering object addition, removal, replacement, and attribute modification. We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality. Comparative experiments show that SemantiScene-Edit effectively follows editing instructions while retaining source content. At inference, the model requires only a source video and a natural-language instruction.

For $k\geq2$, let $\alpha_k$ be the supremum of the exponents $a$ for which almost every integer $n$ has $k$ distinct divisors in a multiplicative interval of relative length $(\log n)^{-a}$. Select each positive integer $i$ independently with probability $1/i$, forming a random set $\mathbf A$, and let $\beta_k$ be the supremum of the $c<1$ for which, with probability tending to one as $D\to\infty$, the set $\mathbf A\cap[D^c,D]$ has $k$ distinct subsets with the same sum. We prove that $\alpha_k=\beta_k/(1-\beta_k)$, resolving a conjecture of Ford, Green and Koukoulopoulos [Invent. Math. 232 (2023), 1027–1160]. We also prove that their weak and strict entropy thresholds coincide. The proof combines flag refinement and entropy concavity with an upper bound for approximate subset sums that is uniform in arbitrary translations. A model with independent geometric prime exponents then transfers this bound to divisors.

We investigate the effects of super-Gaussian pulse shapes on dynamically assisted pair production in spatially inhomogeneous electric fields with frequency chirping within the framework of the (1+1)-dimensional Dirac-Heisenberg-Wigner formalism. The analysis is carried out for both single-color fields and dynamically assisted two-color combined fields by varying the chirp parameter, super-Gaussian pulse shape, and the spatial scale of the external field. Our results are presented in terms of the reduced momentum distribution and the total particle yield, revealing that the interplay between pulse shaping, spatial inhomogeneity, frequency chirping, and dynamical assistance can significantly modify the pair-production dynamics. In particular, chirping substantially enhances the absolute pair-production yield and reshapes the momentum spectrum, with chirping of the weak component being particularly effective, while simultaneous chirping of both components produces the largest yields. The super-Gaussian order has a relatively modest influence on strong-field production, while increasing the flat-top character of the pulse enhances spectral structures and momentum redistribution in the weak and dynamically assisted regimes, particularly under chirping. Finally, we show that the relative phase between the two field components provides an additional control parameter, with its influence becoming more pronounced for larger spatial scales leading to significant changes in the momentum-space structure. Overall, our results provide a useful reference for optimal control of dynamically assisted pair production in space- and time-dependent electric fields within a prescribed range of field parameters.

We establish a criterion of arithmetic $k$-polynomial spectral localization and $k$-polynomial dynamical localization in expectation for quasi-periodic long-range operators on $\ell^2(\mathbb{Z}^{d})$ with power-law hopping based on the quantitative $C^{k}$-reducibility of the dual Schrödinger cocycle. As the application, we prove both localization properties for power-law long-range perturbations of the Almost Mathieu Operators with sufficiently large couplings and Diophantine frequencies.

Private 5G networks are a key enabler for flexible industrial automation, especially when used in conjunction with Time-Sensitive Networking (TSN) technology. In this context, radio schedulers must multiplex safety-critical control traffic with bandwidth-hungry sensing streams over a fixed spectrum allocation. This paper proposes a Head-of-Line (HoL) Enhanced Hybrid Lyapunov scheduler for 5G-TSN networks that augments a drift-plus-penalty queue-stability core with an explicit head-of-line delay term and a class-isolation mechanism. The scheduler is evaluated in a 3GPP Indoor Factory scenario with standardized 3GPP fading, spatial consistency, and clutter blockage, using Automated Guided Vehicles (AGVs) generating concurrent URLLC, eMBB, and mMTC flows mapped to dedicated QoS-flow bearers. A fleet-size sweep of 5–30 AGVs on a fixed 20\,MHz carrier reveals a scheduler-independent capacity threshold at approximately 12 vehicles, verified by resource-block saturation. Below the threshold, the proposed scheduler is competitive with the strongest delay-aware baselines and its head-of-line term halves the URLLC deadline-miss ratio relative to the plain Lyapunov formulation. Beyond the threshold, it degrades selectively where the baselines collapse: at $2.5\times$ overload it delivers $1.8\times$ more URLLC traffic than the proportional-fair and delay-budget-aware baselines with a $\approx 4$–$7\times$ shorter 99th-percentile latency, resolving the capacity shortfall in favour of the critical classes instead of spreading it across the traffic mix, at a quantified cost in aggregate cell throughput. The results position Lyapunov-based scheduling as an attractive overload-robustness mechanism for industrial 5G deployments that must remain dependable under unexpected load conditions.

Multi-agent reinforcement learning (MARL) commonly trains decentralized policies from scratch, requiring agents to acquire individual task competence and coordination simultaneously. Yet many multi-agent problems admit a compatible single-agent counterpart in which the underlying task can be learned in isolation. We introduce Multi-Agent Observation Transformation for Existing Single-Agent Policies (MATES), an input-side adaptation framework for tasks whose multi-agent observations preserve the solo-task information while exposing separately identifiable neighbor information. From multi-agent experience, MATES learns a small adapter that maps this observation into the format expected by a frozen single-agent policy, inducing actions suited to the shared environment without updating the single-agent policy itself. MATES leaves the pretrained policy's internal architecture unchanged and retains the objectives and update procedures of the underlying MARL algorithm. We evaluate MATES using both on- and off-policy algorithms on lifelong pathfinding, navigation, and cooperative discovery, spanning discrete and continuous observation and action spaces. Across all evaluated settings, MATES optimizes only 3.5-7.3% as many parameters as full-policy training while consistently outperforming MARL training from scratch. It approaches the performance of full fine-tuning, remains competitive overall with demonstration-based baselines, and retains strong task performance at team sizes not encountered during training. These results provide evidence that, under this observation structure, effective multi-agent behavior can be learned without modifying the policy that encodes individual competence.

The increasing complexity in design and manufacturing has driven the need for advanced techniques for fast modeling problems with large-dimensional parameter spaces. Avoiding high-fidelity finite element models while achieving fast and accurate simulations in such contexts is challenging. Parametric model order reduction (pMOR) has drawn significant attention in recent years. Nevertheless, the curse of dimensionality in parameter spaces has severely limited its effectiveness. The active subspace (AS) approach has been successfully applied to pMOR for systems with many parameters. However, the balance between accuracy and compactness of the reduced model remains problematic for such systems with high-dimensional parameter spaces. It often results in models that are either not small enough or not accurate enough. In this paper, we propose an iterative active subspace (IAS) approach for parametric model order reduction, which, to some extent, addresses the trade-off between accuracy and reduced model size and achieves substantial computational gains compared to the original active subspace method.

Understanding how electrolyte composition influences charge storage in Prussian blue analogues (PBAs) requires clarifying the coupled effects of structural disorder and ion transport. Here, we investigate Fe-, Co-, and Ni-based hexacyanoferrate thin films electrodeposited from KCl, NaCl, NH4Cl, and LiCl electrolytes under identical electrochemical conditions. Although all films retain the cubic PBA structure, electrolyte identity produces pronounced differences in lattice parameters, defect concentration, and local coordination environments. Na+ promotes lattice expansion accompanied by increased vacancy formation, microstrain, and structural heterogeneity, whereas K+ and NH4+ yield more structurally coherent frameworks. Raman spectroscopy shows that increasing structural disorder broadens the distribution of local coordination environments and correlates with increasingly dispersed electrochemical behavior. Electrochemical impedance spectroscopy further reveals coupled ion-electron transport, with impedance increasing by more than one order of magnitude from FeHCF to NiHCF. Notably, the CPE exponent decreases systematically with Raman band broadening, establishing a direct correlation between structural disorder and transport dispersion. These results demonstrate that electrolyte identity controls not only lattice dimensions but also the organization and connectivity of defect networks, highlighting structural coherence and accessible transport pathways as key factors governing electrochemical performance in PBA thin films.

Monocular drone navigation requires reaching a goal in an unseen environment from a single forward-facing camera, which offers few cues for depth and scale. World models address this by modelling how observations evolve under actions, but they are built to be executed: the prediction is produced at deployment and fed back into action generation at every control step. We argue that what a policy needs from a world model is not the prediction but the representation required to produce it: in flight the executed action explains almost all of the change between observations, so prediction reduces to reprojecting a static scene under a known displacement. We therefore introduce skytopia, a policy built on an action-conditioned latent world model, and the 3D Gaussian Splatting platform on which it is trained. A forward objective predicts the representation of the next observation from the intended motion, and an inverse objective recovers that motion from the predicted transition. Because the prediction never reaches action generation, the predictor is discarded and one policy serves point-goal, image-goal, and goal-free navigation. Simulation experiments show that skytopia outperforms every baseline under all three specifications, attaining 57.8%, 66.0%, and 49.0% success rate, while discarding the predictor removes 59.4% of the inference cost. The same policy is subsequently deployed on a physical drone without fine-tuning and reaches goals in indoor, open outdoor, and woodland environments.

The Matching-Match puzzle asks whether the vertices of a fixed graph can be colored so that the multiset of color pairs induced by its edges is exactly a prescribed multiset. We study how the complexity of this realization problem depends on the host graph. On the dense side, we give a polynomial-time algorithm for complete $k$-partite graphs for every fixed number $k$ of parts, with arbitrary precoloring and an arbitrary number of colors. We prove a sharp complement-degree threshold: the problem is polynomial-time solvable when $\Delta(\overline G)\le1$, but NP-complete on completely uncolored graphs already when $\Delta(\overline G)=2$. This yields a dichotomy for uniform complete multipartite graphs, and connected diameter two already suffices for NP-completeness. We also prove W[1]-hardness on cographs parameterized by the number of colors. On the sparse side, completely uncolored paths and cycles admit a linear-time characterization by Euler trails and circuits, while counting feasible colorings is $\#P$-complete on both classes. Counting is nevertheless polynomial-time solvable on stars and complete graphs, even with arbitrary precoloring. A decomposition-transfer theorem yields NP-completeness already at tree-depth two. A separate path-decomposition reduction gives a maximum-degree threshold between one and two for completely uncolored disconnected host graphs with unrestrictedly many colors. Components with at most two edges are tractable, while a disjoint union of $P_4$'s is NP-complete. Finally, precoloring restores tractability in several cases: star forests are polynomial when every center is precolored, and two broad precoloring regimes on length-two spiders are polynomial even when the number of colors is unbounded.

When ferromagnets are brought into contact with a superconductor, superconducting proximity effects give rise to a variety of interesting phenomena, including oscillatory singlet Cooper-pair amplitudes and long-ranged odd-frequency triplet correlations induced by the exchange interactions in the ferromagnets. From an electrodynamic perspective, however, it is equally important to understand when and how spontaneous currents can emerge. To investigate the interplay between electromagnetic and conventional superconducting proximity effects, we study clean ferromagnet/ferromagnet/superconductor spin-valve heterostructures in which the relative angle between the two ferromagnetic layers can be tuned. Our approach is based on a self-consistent numerical solution of the coupled Bogoliubov-de Gennes and Maxwell equations, providing a microscopic description capable of resolving physics on atomic length scales. Several notable features emerge. In both the weak- and strong-exchange-field regimes, the central ferromagnetic layer plays a dominant role in generating sizable spontaneous currents when its exchange-field strength is varied. We further find that the resulting electromagnetic response extends across the entire superconducting layer, in sharp contrast to the short-ranged inverse proximity effect. In noncollinear configurations, the electromagnetic proximity effect also reconfigures the local magnetic-field orientation and reduces the angular mismatch between the fields in the two ferromagnetic layers. The long-ranged odd-frequency triplet amplitudes are consequently modified by the orbital response, which alters the underlying quasiparticle states by changing their momentum-space structure. Finally, our framework can be naturally generalized to other superconducting spintronic systems, possibly including Josephson junctions and altermagnet/superconductor heterostructures.

Most research on the manipulation of deformable objects focuses on lightweight systems with negligible mechanical response, effectively restricting attention to quasi-static regimes. This assumption excludes a broad class of practically relevant objects, such as hoses, pipes, and wiring harnesses, whose dynamics cannot be ignored during manipulation. In this work, we address this limitation by introducing a closed-loop control architecture that explicitly accounts for object dynamics and recasts manipulation as a shape-regulation problem. Control is achieved by modulating forces and torques applied at multiple fixed points along the object. This approach builds on three methodological contributions: a fully dynamic model of linear deformable objects based on discrete strain parameterizations; an extension of the notion of actuation coordinates to SE(3), yielding a structured and inherently underactuated control architecture; and nonlinear feedback strategies providing explicit conditions for steady-state convergence to desired configurations. Extensive simulations on representative manipulation tasks demonstrate the performance gains enabled by the proposed modelbased formulation. We finally validate the approach experimentally through a real-time closed-loop implementation with online shape estimation, confirming its practical feasibility and effectiveness

Recent first-principles searches predict that a titanium vacancy in rocksalt TiC is not an empty octahedral site but can reconstruct through the formation of carbon Frenkel pairs and short C-C bonds. We determine the C 1s signatures of these local structures and assess whether they remain specific in the presence of disordered carbon. Site-resolved binding energies were calculated with a self-consistent core-hole method for an unreconstructed vacancy and four representative C-C-bonded reconstructions. The unreconstructed vacancy shifts its nearest-neighbour C 1s level by only -0.36 eV. In contrast, the largest positive shift in each reconstructed model ranges from 2.34 eV to 2.80 eV. The large shifts occur at carbon atoms participating in the reconstructed C-C network and correlate with markedly less negative Mulliken charges, providing candidate local fingerprints of vacancy reconstruction. However, an exploratory TiC/amorphous-C model shows that disordered carbon environments can extend into the same high-binding-energy range. Overall, the calculated shifts provide reference energies for experimental searches for reconstructed Ti vacancies in TiC.

This paper establishes an asymptotic formula for negative Sobolev norms as the fractional order tends to zero. In the Euclidean setting, under a mild boundedness condition, the product of the order and the norm raised to the power $p$ converges to a dimension-independent constant multiple of the corresponding $L^p$ norm. The proof relies on heat-kernel regularization, weak compactness, and an Abelian–Tauberian argument. The result is further extended to a general measure space equipped with a family of bounded and continuous operators that covering nonlinear and non-semigroup settings. We also present several applications in analysis and probability, including an absolute-continuity criterion for measures, a random-distribution regularity result, a construction of square-integrable local times for fractional Brownian motion, and limiting formulas for truncated maximal operators and martingales.

In this paper, an abstract framework is introduced for the derivation of reliable a posteriori error estimators for variational inequalities of the second kind. Under certain conditions (local) efficiency of the error estimators can also be derived. The framework is based on the estimation of a residuum which results from an equivalent mixed formulation and from the replacement of the Lagrange multiplier by its (possibly post-processed) discrete counterpart. The approach is applied to an idealized frictional contact problem and a Mosolov type model problem in an $hp$-finite element discretization setup. Several numerical experiments demonstrate the broad applicability of the theoretical findings.

Speech enhancement (SE) models typically rely on supervised learning with paired data examples where clean speech is synthetically degraded. This paradigm limits performance in real-world scenarios where the target environment's specific acoustic characteristics are unknown. We propose a fully unpaired SE framework that uses principled Diffusion Schrödinger Bridges (DSB) to learn a stochastic transport process between a clean and a degraded speech distribution. Algorithms for learning transport maps are computationally heavy since they require simulating differential equations during training, usually at each training step. Therefore, we propose using a high-efficiency Mamba Diffusion Model designed for end-to-end waveform processing. We compare against state-of-the-art methods for speech enhancement, both paired and unpaired, as well as a classical signal processing algorithm. Experimental results show that we are on par or better than the baselines while being orders of magnitude faster during inference. Furthermore, we show that the flexibility of the DSB formulation allows our model to generalize across SE tasks, offering a robust and efficient solution for real-world speech restoration.