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Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.
We consider the maximum-depth problem for mixed 2-sided orthants in R^d: each region imposes one lower bound and one upper bound on distinct coordinates, and the task is to find a point contained in as many regions as possible. We show that the corresponding decision problem is W[1]-hard when parameterized by the dimension. Our reduction from MultiColoredClique uses two coordinates per color class and only polynomially many orthants.
Grid-forming (GFM) inverters are gaining attention as a promising alternative for conventional synchronous generators in the modern power systems. Unlike conventional synchronous generators, GFM inverters have limited overcurrent capability that makes them vulnerable during large disturbances. During disturbances i.e., voltage and frequency dips, GFM inverters are pushed into current-limited operation to protect the semiconductor switches. This causes the internal angle of the GFM inverters to accelerate and lose synchronism with the rest of the grid. To address this limitation, this article proposes a transient triggering grid-forming (TTGFM) synchronization control to enhance the synchronization stability performance under voltage and frequency dips. This method uses two feedback signals; terminal voltage and the difference between unsaturated and saturated power to adjust the internal angle which is generated by power synchronization loop (PSL) of the GFM inverters. These two signals manipulates the internal reference angle generation that act as a virtual braking mechanism. This mechanism limits the angle acceleration during voltage and frequency dips without requiring an extra supervisory signal or parameters to tune. The proposed method is benchmarked against two state-of-the-art synchronization enhancement schemes and validated through high-fidelity electromagnetic transient (EMT) simulations with a grid dynamic equivalent (GDE) model in MATLAB/Simulink\textsuperscript{\textregistered}. An analytical framework is developed to derive the synchronization instability mechanism and the critical limits of the stability margins. Generalization of the GDE model further shows that the TTGFM control is not restricted to a single configuration but is applicable to any standard benchmark system.
We briefly review the predictions of holographic QCD in hard-wall AdS/QCD models for the contribution of pseudoscalars and axial-vector mesons to the hadronic light-by-light amplitude and consequently to the muon anomalous magnetic moment, which allows one to satisfy the Melnikov-Vainshtein constraint in a purely hadronic model. Moreover, we discuss the role of tensor mesons in satisfying the remaining short-distance constraints, and provide details for a minimal model of tensor mesons that turns out to agree remarkably well with available data from BELLE as well as preliminary low-$Q^2$ data from BESIII. In an appendix, we also provide analytical results for the short-distance limits in hQCD and compare with the leading-order OPE result as given by the massless quark loop.
We give a new proof of the classification of unipotent representations for split finite groups of Lie type. To do this, we first construct an (infinity, 2)-categorification of Lusztig's map from the Hecke algebra to the asymptotic algebra. Then we use the theory of weights to show that it induces an isomorphism after applying trace of Frobenius.
Synthetic speech attribution aims to identify the generative system responsible for a speech signal, but current approaches typically rely on black-box neural networks that provide limited insight into their decisions. This work investigates prototype-based networks as an interpretable alternative, where predictions are grounded in comparisons with representative training examples. We adapt ProtoPNet to spectrogram-based speech representations and evaluate the proposed framework on the MLAAD dataset under closed-set, cross-lingual, and open-set conditions. Experiments show that prototype-based reasoning achieves competitive or improved attribution performance compared with the baseline while enabling example-based explanations. These results highlight that interpretability and performance can be jointly achieved in synthetic speech attribution through prototype-based modeling.
Strong unrestricted adversarial attacks can distort the primary object of an image, hereafter referred to as the subject. To preserve subject integrity without compromising attack magnitude, we introduce the carrier: a secondary visual element that provides an auxiliary region to facilitate the attack under global classifier guidance. We demonstrate three key findings: 1. A carrier mitigates subject distortion by absorbing a larger share of globally normalized attack updates. 2. A carrier improves cross-model transferability, governed by the strength of target-related features that balance semantic separation and transfer performance. 3. Successful targeted attacks retain the personalized subject as the primary content perceived by humans while successfully misleading the classifier. Our results demonstrate that a visually secondary carrier offers an auxiliary spatial pathway for adversarial changes, enabling strong and transferable attacks while improving subject preservation.
The analysis of observable phenomena (for instance, in biology or physics) allows the detection of dynamical behaviours. If the conditions are ideal and the number of observations is sufficient, we can represent these phenomena by a dynamical system, also called a functional digraph, that is to say a graph where each node has out-degree exactly one. Up to isomorphism, these dynamical systems, equipped with disjoint union as addition and direct product as multiplication, form a commutative semiring. Several previous studies on this semiring have aimed to establish algebraic properties (primality, injectivity) or complexity results (division, factorisation). However, no work has yet been conducted on graphs derived from imperfect observations that result in missing transitions or nodes, in other words, in cases where each node in the graph has an out-degree of at most one. In this case, we say that the system is partial. In this paper, we show that partial dynamical systems, up to isomorphism and equipped with the same addition and multiplication, still form a commutative semiring. We then characterise the prime elements of this semiring, which differ from those of the semiring of dynamical systems. Finally, we highlight two properties shared by both semirings. First, injective univariate polynomials admit the same characterisation in both. Second, division can be computed in polynomial time for partial dynamical systems if and only if it can be for dynamical systems.
Let $N$ be a square-free, positive integer and let $p\ge 5$ be a prime dividing $N$. In this work, we construct semistable models of Atkin--Lehner quotients of $X_0(N)$ over the Witt ring $W(\overline{\mathbb{F}}_p)$ by taking the corresponding quotients of the Deligne--Rapoport model of $X_0(N)$. We give an explicit description of the special fiber of the models and obtain genus formulae for the Atkin--Lehner quotients of $X_0(N)$.
The known proofs of the gradient estimate for complex Hessian equations rely on the second order estimate of Hou-Ma-Wu and on a Liouville theorem of Dinew-Kolodziej. In this paper, we give a direct proof of the gradient estimate for concave fully nonlinear elliptic equations on compact Hermitian manifolds, which uses neither the Hou-Ma-Wu estimate nor the Liouville theorem. Instead, the proof combines a stability estimate with a comparison argument against nearby smooth admissible functions. We also give an independent proof of the complex Hessian estimate on compact Kähler manifolds, based on Dirichlet Green's functions, under additional assumptions on the operator.
Cooperative language-model agents must coordinate over long horizons and adapt to changing environments and to partners with unfamiliar conventions, yet existing agents map observations to actions without separating persistent coordination strategies from their tactical execution. We introduce OverForge, a training-free hierarchical architecture that separates strategic reasoning over roles and divisions of labour from tactical reasoning over actions within each agent's private, partner-conditioned world model. A metacognitive Prefrontal Cortex Module couples the two levels by forming strategy-action branches, imagining their consequences with a forward model, and committing when confident. In OvercookedV2, OverForge delivers 7 soups in a connected kitchen versus 3 for each flat LLM baseline, retains agreed roles, and adopts roles proposed by unfamiliar partners. Ablations and a fixed-strategy probe show that persistent strategies guide tactical adaptation while each reasoning level contributes to coordination. Memory restarts show that cross-episode partner knowledge supports task performance and partner prediction, linking the hierarchy to continual adaptation.
We study a high-dimensional Kuramoto model with attractive pairwise coupling and a repulsive higher-order effect. Although the pairwise interaction favors synchronization, the higher-order interaction prevents complete synchronization and selects an intermediate level of coherence corresponding to the balanced equilibria. For the unit sphere of dimension at least two, we provide an explicit basin of attraction for the balanced equilibria. We also classify nonzero-mean equilibria and show that all non-balanced equilibria are linearly unstable. This indicates that balanced states are the only natural stable candidates in the higher dimensions. In contrast, on the circle, the same model reduces to a higher-order Kuramoto-type model which exhibits a qualitatively different selection mechanism that depends fundamentally on the dimension. In this case, population imbalance at finite $N$ prevents exact stationary balance and instead generates a common angular drift producing phase-locked states. We construct basins of attraction for two-cluster locked states, in which the phases split into two groups, and identify the corresponding finite-$N$ selected values of the order parameters. This phenomenon is called continuous-versus-quantized order-parameter selection. We further demonstrate, through a root-selection mechanism, why such two-cluster locked states are typically observed in most simulations. Finally, we show that two-cluster locked states with a large population imbalance are linearly unstable, which explains why only certain locked states are dynamically robust.
In the context of a spatially flat FLRW background, we perform a symmetry classification of $k$-essence models with Lagrangian densities of the form $f_1(ϕ)R+f_2(ϕ,X)$. The kinetic term $X$ is introduced as an independent degree of freedom via a Lagrange multiplier, and the lapse function is treated as a dynamical variable. The symmetry analysis is applied to the constrained system prior to any gauge fixing. This approach reveals symmetries which otherwise are lost when the lapse is fixed at the level of the action. The derived families of $k$-essence models admitting nontrivial symmetries fall to two general classes: minimally coupled and nonminimally coupled theories to gravity. We use the corresponding Noetherian conservation laws to derive exact cosmological solutions. We find that, when the numerical value of the conserved charges is zero, the field equations reduce to an algebraic relation. We subsequently derive power-law expressions for the scale factor with exponents determined by the particular $k$-essence function.
Coherent anti-Stokes Raman scattering (CARS) microscopy enables label-free chemical imaging of biological samples and materials. Conventionally, CARS is implemented using a point-scanning approach that probes a single vibrational mode at a time. Hyperspectral CARS enhances chemical specificity by sequentially addressing multiple Raman modes. However, the measured CARS intensity is distorted by an undesired non-resonant background, which broadens and shifts the Raman peaks, thereby hindering the interpretability of hyperspectral images. Here, we introduce a phase-sensitive, wide-field hyperspectral CARS microscopy scheme that suppresses the non-resonant background. The proposed approach combines three key components: a high-power picosecond tunable optical parametric amplifier (OPA), speckle illumination, and quantitative phase imaging based on quadriwave lateral shearing interferometry (QLSI). The high-power OPA provides the peak power required for efficient nonlinear excitation. Speckle illumination distributes the optical energy over the objective back pupil and mitigates coherent imaging artifacts. QLSI enables the simultaneous measurement of the amplitude and phase of the CARS field, thereby allowing separation of resonant and non-resonant contributions, without the need for an external reference beam. This unique combination enables practical phase-resolved CARS imaging over a field of view exceeding $60 \times 60~\text{\textmu m}^2$ at a frame rate of 1.4 Hz. We illustrate the approach by acquiring hyperspectral images of microplastics and liver steatosis across the entire CH-stretching region.
Supernova remnants (SNRs) are extended radio sources whose morphology and evolution are shaped by the interaction of strong shocks with the surrounding medium. We combined the 0.944 GHz Australian Square Kilometre Array Pathfinder (ASKAP) image from the Evolutionary Map of the Universe (EMU) survey with 0.088-0.200 GHz MWA-GLEAM data to characterize the integrated radio spectrum of G343.1$-$00.7 and perform its first sensitive spatially resolved spectral analysis. We derived an integrated spectral index of $α=-0.50\pm0.01$, confirming the previously reported value with improved accuracy. We redefined the radio morphology of the remnant, providing the first morphological and spectral evidence that filamentary structures overlapping the nearby \hii\ G343.147$-$00.44 are associated with the SNR. Local brightness-brightness analysis revealed flatter-spectrum filaments along the south-eastern shell, consistent with a possible thermal bremsstrahlung contribution from radiative or partially radiative shocks. Comparison with \hi\ and CO data identified gas components kinematically consistent with the SNR distance, although no clear morphological evidence of interaction was found. The gas distribution around the \hii\ suggests an expanding \hi\ shell partially embedded within its parent molecular cloud. Theoretical models place G343.1$-$00.7 between the late Sedov and early pressure-driven phases, suggesting different evolutionary stages across the remnant. These results highlight the potential of high-resolution ASKAP observations for spatially resolved studies of poorly characterized SNRs in complex Galactic environments.
User-generated content has become one of the most-consumed content types. However, capturing spatial audio with consumer hardware is still challenging. Given the widespread success of smart earbuds, binaural audio could be a promising option to capture spatial audio on consumer devices, but its inherent signal characteristic limits its usability as a recording format. In this paper, we propose and define a new task, Binaural to Ambisonics conversion (Bin2Ambi). In our proposed system, we exploit simultaneously captured head-tracking data provided from the motion sensors in smart earbuds. We show that this motion data help resolve the inherent directional uncertainty of two-channel binaural audio due to front-back localization ambiguities and lateral errors in the cone of confusion. Our results show that our system learns directional and diffuse-field information and that head-tracking especially reduces extreme localization errors. Objective metrics and a subjective listening test suggest that the converted Ambisonics soundfield achieves an average directional error of up to $11.8^\circ$ and a perceived spatial quality similar to a DirAC ground-truth model. The proposed algorithm can serve as a baseline for future improvements to this novel Bin2Ambi task.
We show that every irreducible linear differential equations on the Riemann sphere with regular and/or unramified irregular singularities can be transformed into either the trivial equation or a Fuchsian system of $E_8$-fundamental spectral type by a sequence of invertible transformations consisting of confluences, unfoldings, Laplace transformations, gauge transformations and Möbius transformations. The $E_8$-spectral type is uniquely determined by the original equation. A Fuchsian system of $E_8$-fundamental spectral type has three singular points, 0, 1, and $\infty$. The degrees of the minimal polynomials of the residue matrices at 0 and 1 are two and three, respectively, and the sum of the maximal dimensions of eigenspaces of the three residue matrices is not greater than the size of the matrices. The result follows from the correspondence between spectral types of Fuchsian systems and roots of a star-shaped Kac-Moody root system.
Claims of quantum advantage rest on the classical hardness of simulating quantum circuits. Magic, operator scrambling, anticoncentration, and non-Gaussianity for fermionic circuits are standard diagnostics of complex quantum dynamics. For pure states, some of these have been rigorously connected to classical simulability. Whether these diagnostics reliably track the limits of efficient classical simulation under noise remains unclear. Here, we show that in noisy Clifford+$T$ and nearest-neighbour matchgate+SWAP circuits, dynamical diagnostics and classical simulability can separate in both directions. In particular, the diagnostics can remain nontrivial after classical simulation becomes efficient, or become trivial before known efficient classical algorithms apply. We trace this mismatch to the different statistical properties they probe: magic and scrambling depend on fourth-order statistics of the Pauli spectrum, whereas the simulation algorithms depend primarily on second-order moments, which local noise suppresses at different rates. Thus, dynamical diagnostics measured on a noisy quantum device do not by themselves constitute evidence for classical hardness.
Causal loops escape paradoxes if they generate valid probabilities under arbitrary independent local interventions. We show that a deterministic classical process is logically consistent if and only if it contains no signaling loops: cyclic signaling is forbidden but cyclic causation is not, allowing indefinite causal order. Logical consistency is then found to hold exactly when the list of events of a process is pairwise exclusive and complete, providing the first event-based, intervention-free, non-recursive characterization of consistent causal loops. Our results repair a previously introduced intervention-free criterion that misses global loops and reveal that what makes deterministic classical communications without causal order consistent is a multipartite form of Specker's principle: if any two of several questions can be answered jointly, so can all of them.