arXiv:2607.09295v1 Announce Type: new
Abstract: Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) offers flexible capacity provisioning for heterogeneous network slices, including Hyper-Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). However, guaranteeing slice-level Service-Level Agreements (SLAs) under dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources remains a fundamental challenge. This paper proposes a predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation. A lightweight prediction module forecasts near-future user mobility, enabling UAVs to anticipate congestion and reposition before SLA violations occur. We design an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices, alongside total energy consumption. UAV agents are trained using Multi-Agent Proximal Policy Optimization (MAPPO) with centralized training and decentralized execution, enabling scalable online decision-making. Event-driven simulations with realistic mobility traces demonstrate that the proposed framework significantly improves SLA stability compared with baselines while maintaining competitive energy efficiency and delay performance, approaching oracle-level performance with sufficiently accurate predictive information.
Science Journals
arXiv:2607.09100v1 Announce Type: new
Abstract: The rapid growth of image data has produced large-scale datasets, raising concerns about the time and memory costs of model training. Selecting representative training subsets, however, remains challenging: individual sample contributions are unclear, and model behavior varies across datasets and runs. We address these challenges with a framework that combines coreset selection with an ensemble aggregation over multiple runs. For coreset selection, we propose SCOre-Stratified Selection (SCOSS), which partitions the training data into intervals based on a chosen score and samples from each interval. The ensemble combines predictions from multiple runs, each performed on an independently sampled training subset. As baselines, we use moderate and random selection, each in original and class-balanced versions. We assess the framework with Simple Graph Convolution (SGC) and Support Vector Machine (SVM) classifiers under different sampling ratios. Experiments show that SCOSS is competitive with baselines, often the best choice for SGC, and enables favorable trade-offs between accuracy and efficiency. On the fine-grained dataset, SGC with SCOSS outperforms SVMs when using fewer labeled samples. The code and supplementary materials are publicly available at http://scoss.lucasvalem.com.
arXiv:2603.11928v2 Announce Type: replace-cross
Abstract: The upcoming decade of observational cosmology will be shaped by large sky surveys, such as the ground-based LSST at the Vera C. Rubin Observatory and the space-based Euclid mission. While they promise an unprecedented view of the Universe across depth, resolution, and wavelength, their differences in observational modality, sky coverage, point-spread function, and scanning cadence make joint analysis beneficial, but also challenging. To facilitate joint analysis, we introduce A(stronomical)S(urvey)-Bridge, a bidirectional generative model that translates between ground- and space-based observations. AS-Bridge learns a diffusion model that employs a stochastic Brownian Bridge process between the LSST and Euclid observations. The two surveys have overlapping sky regions, where we can explicitly model the conditional probabilistic distribution between them. We show that this formulation enables new scientific capabilities beyond single-survey analysis, including faithful probabilistic predictions of missing survey observations and inter-survey detection of rare events. These results establish the feasibility of inter-survey generative modeling. AS-Bridge is therefore well-positioned to serve as a complementary component of future LSST-Euclid joint data pipelines, enhancing the scientific return once data from both surveys become available. Data and code are available at https://github.com/ZHANG7DC/AS-Bridge.
3D Plasma plume characterization of an electrodeless thruster cluster in magnetic arch configuration
arXiv:2607.09509v1 Announce Type: new
Abstract: Clustering electrodeless plasma thrusters in pairs with opposing magnetic polarities offers an easy means to scale-up the propulsion system of future missions, and also, to mutually cancel their respective magnetic dipoles. Their magnetic nozzles merges to form a new topology, the `magnetic arch', which can yield a lower plasma plume divergence than two separate magnetic nozzles. This work characterizes the plasma expansion in the fully-closed magnetic arch of a cluster of two electron-cyclotron resonance thrusters with electrostatic probes (Langmuir probes, Faraday cups, and a Retarding Potential Analyzer). Electrostatic potential, plasma density, electron temperature, ion current and energy are measured in the two orthogonal planes of symmetry of the setup for various operating conditions. Results show that a plasma jet can be extracted even from this magnetic configuration, albeit with a reduced ion energy. A slight potential hill and hotter electrons exist in the central part of the arch. Ion current profiles are doubly-peaked in the horizontal plane, likely corresponding to the beamlet of each thruster. Trends with xenon mass flow rate and input power are consistent with the expectations of electrodeless plasma thrusters. The plume experiences an upward or downward deflection depending on the direction of the applied magnetic field, which could be attributed to the effect of the lateral electron drifts in the magnetic arch.
arXiv:2607.09121v1 Announce Type: new
Abstract: In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.S. Securities and Exchange Commission (SEC) which can be found in EDGAR. We were preprocessing those data and than sending via API to gpt-4o model in a Retrieval-Augmented Generation (RAG) like regime. We prepared as well a document describing an exemplar investor knowledge based on Kitchin cycles. We were scanning data important for analysis of 9 companies for 4 weeks. Using LLM we were producing automatic briefs about them. They were sent to nine participants who are individual investors to evaluate usefulness of such approach to data analysis.
arXiv:2607.09307v1 Announce Type: new
Abstract: Computer-generated holography can shape electromagnetic fields by encoding wavefront information in nanostructured surfaces. However, despite significant progress, hologram designs for visible and near-infrared wavelengths remain largely limited by fabrication constraints. Most implementations rely on lithographic methods that produce discretized surfaces that do not match the wave nature of light. Further advances would benefit from accurate continuous (grayscale) control of interfacial profiles, which would allow straightforward design principles from Fourier optics to be applied. In this work, we introduce optical Fourier surfaces as a versatile, intuitive platform for free-space computer-generated holograms. Exploiting these arbitrarily wavy surfaces, we demonstrate three different design strategies for computer-generated holography. First, we use an analytical linear design approach based on sinusoidal lenses and gratings as fundamental holographic building blocks. Second, we enhance diffraction efficiencies through an iterative Fourier-transform algorithm. Finally, we extend our framework with machine-learning-based inverse design, creating wavelength-multiplexed holograms that reconstruct distinct diffraction responses in a predefined image plane. Our results combine advanced thermal scanning-probe lithography with flexible design strategies to create useful structures for photonic applications, particularly in settings where rapid prototyping is crucial.
arXiv:2607.09309v1 Announce Type: new
Abstract: We present a polynomial-time algorithm for optimally coloring perfect graphs that is based entirely on graph-theoretic operations. At its core, the algorithm decides whether a perfect graph contains a clique of a given size by iteratively counting walks in the graph with certain weights assigned to its edges and nonedges. These weights are initialized according to a uniform scheme and then updated in each iteration based on the walk counts from the previous iteration.
Dual-BEATs: Unlocking Zero-Shot Stereo Audio Perception in Audio Large Language Models via Dithering
arXiv:2607.08800v1 Announce Type: new
Abstract: Multimodal Large Language Models (LLMs) have remarkable semantic audio understanding, yet they remain "spatially agnostic" due to their reliance on mono-channel audio representations. Currently, spatial audio perception methods mainly focus on complex room simulations and custom-trained, geometry-aware stereo encoders, which limits their accessibility and generalizability. In this paper, we introduce the Dual-BEATs architecture, in which the left and right audio channels are routed independently through two identical semantic encoders as an alternative to specialized spatial modules. To circumvent the architectural bottleneck where internal normalization otherwise erases the inter-channel variance of stereo audio, we inject a static, uncorrelated dithering noise floor prior to encoding. This dithering intervention establishes a macro-variance floor that "smuggles" spatial geometry across the normalization layers. Evaluated on a ternary directional classification task (Left, Center, Right), we demonstrate that dithered models achieve exceptional spatial resolution--reaching up to 97.2% localization accuracy even on subtle 0.5 panning amplitudes--and demonstrates robust, zero-shot generalization to entirely unseen spatial configurations. Our results suggest that with the appropriate acoustic regularization, standard multimodal models are natively capable of generalized stereo audio understanding.
arXiv:2607.09125v1 Announce Type: new
Abstract: Existing volumetric capture of dynamic human performance achieves high fidelity with dense camera arrays. However, in real-world scenarios, only a handful of low-overlap cameras are available, which degrades the output quality and leaves large areas unobserved. Recent 4D reconstruction methods have focused on low-overlap settings, yet they still produce noticeable artifacts in under-observed regions. Video diffusion models have emerged as another option, but they show geometrically inconsistent results for humans. To address these limitations, we propose StudioRecon, a pipeline that reconstructs 4D human scenes from sparse, low-overlap cameras by decoupling background and humans. We densify background supervision by synthesizing hundreds of camera-controlled novel views with a video diffusion model. We also robustly initialize deformable Gaussian humans with cross-view identity association and triangulated multi-view keypoint fitting. Finally, our recursive enhancement module with motion-adaptive consistency injection harmonizes the composed output, thereby further avoiding remaining artifacts. We achieve state-of-the-art novel view synthesis across four real-world datasets and demonstrate applications such as novel trajectory rendering and human replacement.
arXiv:2607.09587v1 Announce Type: new
Abstract: Decentralized multi-robot coordination under partial observability remains challenging, especially in communication-free settings where agents must act solely from local sensor observations. Privileged policy distillation provides a promising approach by transferring knowledge from a globally informed oracle to sensor-constrained students. However, in multi-agent systems, the same local observation may correspond to multiple global configurations requiring qualitatively different cooperative actions, making the conditional action distribution inherently multi-modal. Standard deterministic distillation collapses these modes to their mean, often yielding invalid or hesitant actions. To address this issue, we propose CoDiMAD, a three-stage framework that trains a privileged oracle with MAPPO, constructs an offline dataset of local-observation-oracle-action pairs, and distills the oracle into decentralized students parameterized as conditional denoising diffusion probabilistic models. By approximating the conditional oracle-action distribution through the diffusion reverse process, CoDiMAD samples decisive actions from coherent coordination modes rather than averaging across them. Theoretical analysis characterizes the mode-averaging failure of deterministic distillation and the distributional recovery property of diffusion-based distillation. Experiments on three cooperative tasks show that CoDiMAD consistently outperforms direct local MARL and deterministic distillation baselines. The source code will be made publicly available upon acceptance.
arXiv:2607.09126v1 Announce Type: new
Abstract: Sign language translation (SLT) converts continuous sign videos into spoken language text. Gloss-free approaches leverage pre-trained visual encoders and language models but rely on implicit cross-modal alignment from translation supervision alone. We present VTaMo, a framework that introduces explicit multi-granularity alignment at three levels: (1) local alignment via entropy-regularized optimal transport with a learnable null token for fine-grained frame-to-token correspondences; (2) global alignment via a learnable orthogonal transformation that calibrates embedding space geometry through Earth Mover's Distance; and (3) position-aligned contrastive learning for discriminative token-level representations. Experiments on Phoenix-2014T, CSL-Daily, How2Sign, and OpenASL demonstrate consistent state-of-the-art performance, with ablations confirming the complementary contributions of each component. Code is available at https://github.com/junyi2005/vtamo.
arXiv:2607.09127v1 Announce Type: new
Abstract: We study conditions under which stability of the origin of stochastic differential equations is robust to small perturbations. We express robustness in two ways, firstly in the sense that stochastic stability is maintained under small parametric perturbations not exceeding a state-dependent bound vanishing at the origin but positive elsewhere, and secondly via stochastic input-to-state stability (ISS) which allows non-zero perturbations everywhere. We prove the former property assuming the existence of a Lyapunov function certifying stochastic stability of the nominal system. Under the same assumption, stochastic ISS holds under a suitable state-dependent perturbation scaling. Stochastic exponential stability is maintained under proportionally bounded perturbations and implies exponential ISS even without perturbation scaling. Finally, we propose a novel approach to stochastic integrator backstepping in pure-feedback form that uses the tools from our robustness analysis.
arXiv:2607.09318v1 Announce Type: new
Abstract: Energy demand from cloud and edge computing is rising rapidly, with AI workloads further intensifying electricity use and associated carbon emissions. In hybrid edge-cloud settings, sustainability impact depends on time- and location-varying grid Carbon Intensity (CI), site Power Usage Effectiveness (PUE), and heterogeneous hardware characteristics. Existing carbon-aware work explores solutions such as temporal elasticity, spatio-temporal workload shifting, and carbon-aware placement across distributed sites. However, these solutions do not provide a consistent and reproducible workflow for evaluating sustainability-aware scheduling policies on heterogeneous, federated edge-cloud topologies. We present EcoKube: a configurable simulation framework for the reproducible evaluation of sustainability-aware scheduling policies in heterogeneous edge-cloud environments. The framework includes an event-driven deterministic simulator, policy hooks, and a heterogeneity-aware reference policy. We evaluate the framework with synthetic batch workloads, comparing the reference policy against the default Kubernetes scheduler, KEIDS, and TOPSIS/KCSS. The contribution is architectural and experimental: EcoKube provides a reproducible way to compare sustainability-aware policies before deployment.
arXiv:2607.09330v1 Announce Type: new
Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics. To enable collaboration among such an agent team, efficient coordination mechanisms that operate reliably under limited network resources are required. However, existing heterogeneous LLM-agent coordination frameworks that rely on multi-round natural-language-based conversations introduce three coupled challenges. First, inter-agent dialogue incurs communication overhead that grows rapidly with team size. Second, the quality of coordination is constrained by the heterogeneous capabilities of the agent team's LLMs. Third, agents may suffer from action delays due to iterative negotiation. To address these challenges, we propose LDT-Coord, a networked coordination framework built upon a lightweight digital twin (DT). Specifically, each agent independently selects its intended action and reports both the action decision and a structured temporal constraint over shared resources to the DT server, thereby decoupling coordination performance from natural-language reasoning ability. Then, DT executes a training-free, rule-based orchestrator algorithm to resolve cross-agent conflicts and returns coordination instructions to prevent such conflicts. To further reduce communication overhead, we formulate agent reporting control as a constrained partially observable Markov decision process (C-POMDP) and solve it with the PPO-Lagrangian algorithm. Simulation results show that LDT-Coord achieves a task success rate comparable to conventional coordination methods while reducing communication overhead by more than 70x and maintaining robustness under LLM heterogeneity.
arXiv:2502.08645v4 Announce Type: replace
Abstract: Real-world data collection for robotics is costly and resource-intensive, requiring skilled operators and expensive hardware. Simulations offer a scalable alternative but often fail to achieve sim-to-real generalization due to geometric and visual gaps. To address these challenges, we propose a 3D-photorealistic real-to-sim system, namely, RE$^3$SIM, addressing geometric and visual sim-to-real gaps. RE$^3$SIM employs advanced 3D reconstruction and neural rendering techniques to faithfully recreate real-world scenarios, enabling real-time rendering of simulated cross-view cameras within a physics-based simulator. By utilizing privileged information to collect expert demonstrations efficiently in simulation, and train robot policies with imitation learning, we validate the effectiveness of the real-to-sim-to-real pipeline across various manipulation task scenarios. Notably, with only simulated data, we can achieve zero-shot sim-to-real transfer with an average success rate exceeding 58%. To push the limit of real-to-sim, we further generate a large-scale simulation dataset, demonstrating how a robust policy can be built from simulation data that generalizes across various objects. Codes and demos are available at: https://re3sim.github.io/.
arXiv:2607.09128v1 Announce Type: new
Abstract: We study local completeness and incompleteness of abstract interpretations from a recursion-theoretic perspective. Local completeness weakens global completeness and captures the absence of precision loss for a specific precondition: abstract computation yields exactly what is obtained by abstracting the corresponding concrete computation. This enables compositional reasoning and rules out false positives in verification. We characterize the distinction between static and dynamic program analysis in terms of uniformly decidable operations and observe that the latter is uniformly decidable only for trivial abstractions. We then prove that the class of programs inducing a predicate transformer that is locally complete for a given non-trivial abstract domain is elusive in a precise recursion-theoretic sense: it is a productive set, hence not computably enumerable, and, under mild hypotheses, the same holds for its complement. In particular, the first class lies in $\Pi^0_2$ and the second in $\Sigma^0_2$. Unlike the usual examples of $\Pi^0_2$ properties, we show that the classes of locally complete programs admit decidable coverings. This makes it possible to construct, via program transformation, an effective enumeration of a representative subset of programs that entirely covers this class -- capturing from the outside a class that eludes enumeration from within.
arXiv:2607.09338v1 Announce Type: new
Abstract: Generative AI is profoundly transforming the core technologies behind conversational systems, shifting from component-based to end-to-end approaches. However, Large Language Models (LLMs) may still generate inconsistencies, a critical issue particularly in Task-Oriented Dialogues (TODs), where system responses must strictly adhere to information from a domain knowledge base (e.g., restaurants in a city). A single hallucination (e.g., suggesting a non-existent restaurant) can lead to severe task failures. We investigate a method for automatically detecting inconsistencies by conceptualizing TODs as a Constraint Satisfaction Problem (CSP), where variables represent dialogue segments referencing the conversational domain, and constraints among variables capture dialogue properties such as turn coherence and adherence to domain knowledge. We propose a pipeline that first identifies variables in a target dialogue and then applies a CSP solver to identify valid solutions. By comparing the target dialogue with valid variable assignments, we can detect inconsistencies and suggest minimal changes to ensure dialogue consistency. We demonstrate the high accuracy of the CSP-based approach in detecting inconsistencies, and provide a detailed analysis of our findings.
arXiv:2004.05813v3 Announce Type: replace
Abstract: Suppose that we are given independent, identically distributed random samples $x_1,\cdots,x_n$ from a mixture at most $k$ many $d$-dimensional spherical Gaussian distributions $\mu_1,\cdots,\mu_{k_0}$ of identical and known variance $\sigma^2$ in each coordinate, such that the minimum $\ell^2$ distance between two distinct centers $y_l$ and $y_j$ is greater than $2\Delta\sigma \min\{\sqrt{d},\sqrt k\}$, where $\Delta>C_0$, and $C_0$ is a sufficiently large universal constant. We develop a randomized algorithm that learns the centers $y_l$'s of the Gaussian components to within an $\ell^2$ distance of $k^{-\tilde C_0}$ -- in presence of arbitrarily large number of components and in arbitrary dimension, when the weights are known to be uniform. Furthermore, if the number of components is $k= \Omega(2^d)$, then for arbitrary universal constant $c>0$, even for unknown weights, the algorithm learns the centers to within an $\ell^2$ distance of $d^{-\tilde C_0}$ and the weights up to an accuracy of $cw_{min}$, with probability greater than $1 - \exp(-k/c)$, provided that the weights lie in $[c/k,1/ck]$, and the minimum separation is just $2c\sqrt d$. The number of samples and the computational time is bounded above by $\mathrm{poly}(k, d)$ in either case. Such a bound on the sample and computational complexity was previously unknown in the regime of non-constant dimension, and in particular, when $d$ is not $O(1)$. When $d = O(1)$, this complexity bound follows from work of Regev and Vijayaraghavan, where it has also been shown that the sample complexity of learning a random mixture of Gaussians in a ball of radius $o(\sqrt{d})$ in $d$ dimensions, when $d$ is $\Theta( \log k)$, is at least super-polynomial in $k, d$, showing that our result is tight in this case.
arXiv:2602.04600v2 Announce Type: replace
Abstract: Achieving generalizable manipulation in unconstrained environments requires the robot to proactively resolve information uncertainty, i.e., the capability of active perception. However, existing methods are often confined in limited types of sensing behaviors, restricting their applicability to complex environments. In this work, we formalize active perception as a history-dependent perception-action loop driven by information-seeking action and decision branching, providing a structured categorization of visual active perception paradigms. Building on this perspective, we introduce CoMe-VLA, a cognitive and memory-aware vision-language-action (VLA) framework that leverages large-scale human egocentric data to learn versatile exploration and manipulation priors. Our framework integrates a cognitive auxiliary head for autonomous sub-task transitions and a dual-track memory system to maintain consistent self and environmental awareness by fusing proprioceptive and visual temporal contexts. By aligning human and robot hand-eye coordination behaviors in a unified egocentric action space, we train the model progressively in three stages. Extensive experiments on a wheel-based humanoid have demonstrated strong robustness and adaptability of our proposed method across diverse long-horizon tasks spanning multiple active perception scenarios.
arXiv:2603.05711v2 Announce Type: replace
Abstract: Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-fused depth completion methods learn priors jointly conditioned on training RGB distribution and specific depth patterns, limiting domain generalization and robustness to various depth patterns. Recent efforts leverage monocular depth estimation (MDE) models to introduce domain-general geometric priors, but current two-stage integration strategies relying on explicit relative-to-metric alignment incur additional computation and introduce structured distortions. To this end, we present Any2Full, a one-stage, domain-general, and pattern-agnostic framework that reformulates completion as a scale-prompting adaptation of a pretrained MDE model. To address varying depth sparsity levels and irregular spatial distributions, we design a Scale-Aware Prompt Encoder. It distills scale cues from sparse inputs into unified scale prompts, guiding the MDE model toward globally scale-consistent predictions while preserving its geometric priors. Extensive experiments demonstrate that Any2Full achieves superior robustness and efficiency. It outperforms OMNI-DC by 32.2\% in average AbsREL and delivers a 1.4$\times$ speedup over PriorDA with the same MDE backbone, establishing a new paradigm for universal depth completion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.
arXiv:2607.08849v1 Announce Type: cross
Abstract: We study how generative AI affects student learning in a randomized experiment. In proctored, in-person sessions, undergraduates learn about an unfamiliar topic and write an analytical essay with or without access to off-the-shelf generative AI, then complete unaided assessments immediately and one week later. We measure learning with knowledge tests (factual and conceptual understanding) and open-ended essays (higher-order skills). AI access raises immediate test scores by 0.27 standard deviations. These gains persist one week later. Essay quality, by contrast, changes little while students have AI access but improves in style and relevance one week later, when students write unaided. These delayed gains are larger among augmentation users-who use AI to explain concepts rather than generate text-whereas automation users' short-run quality gains vanish once AI is removed. We find evidence for two mechanisms behind the learning gains: students shift time away from drafting text and toward reading and searching for information, and they report greater learning enjoyment.
arXiv:2607.09391v1 Announce Type: new
Abstract: This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning (FL) systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information may still be leaked during model updates. To address this issue, homomorphic encryption enables computations on encrypted data, while differential privacy prevents the extraction of individual information through statistical techniques applied to model outputs. The proposed architecture was tested on the Framingham, Pima Indians Diabetes, and Bank Marketing datasets, revealing that enhanced privacy can be achieved without significantly compromising model accuracy. Furthermore, the impact of data heterogeneity among clients on model performance was analyzed, and it was concluded that strategies such as the careful selection of differential privacy parameters and training settings, along with the use of larger datasets, can improve the efficiency of FL. The findings demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensitive domains such as healthcare and finance.
arXiv:2607.09392v1 Announce Type: new
Abstract: We consider the inverse problem for the quantitative reconstruction of physical properties in the context of passive imaging, where ambient wavefields are used to infer a medium. The data are modeled as a superposition of waves generated by stochastic sources. In this work, we focus on time-harmonic acoustic wave propagation and assume that the stochastic sources exciting the medium are zero-mean and spatially uncorrelated. Under these assumptions, the expected value of the cross-correlation between signals recorded at two locations can be related to the deterministic Green's function and the covariance of the source terms. We follow a first-order formulation of the wave equation, which enables the treatment of correlations between different types of wavefields. A numerical framework is developed for the resulting nonlinear inverse problem. The quantitative reconstruction is carried out using an iterative minimization scheme, in which the gradient of the misfit functional is computed via the adjoint-state method. Numerical experiments in two and three dimensions are performed using synthetic data, and inversions based on the expected value of cross-correlations are compared with those relying on direct wavefield measurements from active-source acquisitions.
arXiv:2511.09524v2 Announce Type: replace
Abstract: The concept of a security index quantifies the minimum number of components that must be compromised to carry out a stealth attack. This metric enables system operators to assess the security risk of each component and implement countermeasures accordingly. In this paper, we introduce a data-driven security index that can be computed solely from input/output data when the system model is unknown. We show a sufficient condition under which the data-driven security index coincides with the model-based security index, which implies that the exact risk level of each component can be identified solely from data. We also provide an algorithm for computing the data-driven security index.
arXiv:2607.08853v1 Announce Type: new
Abstract: Mobility systems of people and goods are inherently multi-scale, spanning levels of organization from individual cities to regions and nations. Understanding whether mobility networks exhibit similar patterns across these scales is important. Such similarity would point to common organizing principles, enabling insights gained at one scale to inform planning and management at others. Despite growing efforts to analyze mobility at multiple scales, such cross-scale similarity remains poorly understood, and renormalization provides a natural framework for addressing this question. Here, we propose a Neighbor-Limited Box Covering method to renormalize undirected weighted mobility networks. This method iteratively selects box centers in descending order of node strength, merges each center with a fixed number of its highest-weight neighbors to form a renormalized node, and aggregates edge weights between renormalized nodes to generate the network at the next scale. We apply this technique to uncover multi-scale structures of real-world inter-city human mobility and freight trip networks in China and find that the topological structures, weighted structural features, and dynamic processes all exhibit self-similarity across these multi-scale mobility networks. Moreover, we find that the constituent nodes in most renormalized nodes show a strong spatial cohesion, and the boundaries of them closely follow existing political and socio-economic borders, even though the method does not explicitly incorporate any spatial information. Our study not only reveals the consistency of multi-scale inter-city mobility patterns, but also provides important insights into their spatial organization. Furthermore, our method is applicable to mobility networks of different sizes and has potential as a powerful tool for the multi-scale analysis of various other real-world complex systems.