arXiv:2608. 00050v1 Announce Type: cross Abstract: Source apportionment from sparse urban air-quality sensors is an inverse problem limited by sensor placement, wind-driven transport, background variation, and noise.
arXiv:2608.24531v1 Announce Type: cross Abstract: Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse...
arXiv:2608. 09775v1 Announce Type: new Abstract: Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes.
arXiv:2606. 24989v1 Announce Type: new Abstract: Urban flow and air-quality simulations generate high-dimensional datasets describing velocity and pollutant transport across multiple spatial, temporal, and physical-variable dimensions.
arXiv:2608. 06340v1 Announce Type: cross Abstract: Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series.
The study evaluates a data‑driven sparse sensing (DSS) workflow for monitoring urban drainage networks, using a 77‑node model to optimize sensor placement and reconstruct flow conditions. By applying singular value decomposition, pivoted QR selection, and a reconstruction decoder to 225 simulated scenarios, a 3‑node layout (4 % of the network) achieved median Nash‑Sutcliffe efficiency of 0.791, with all cases exceeding 0.700. The method matched performance of Greedy D‑optimal and genetic algorithm approaches, proved robust to Gaussian noise, and identified that sensor loss sensitivity correlates with upstream drainage area and conduit characteristics.
arXiv:2608. 01547v1 Announce Type: cross Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations.
AsyncCouple-Flow introduces a new framework for multi‑modal spatio‑temporal forecasting that tackles three key challenges: differing sampling rates, missing modalities, and autoregressive error accumulation. It employs a Modality‑Aware Token Sparsification module to produce equal‑length sequences, an Asynchronous Cross‑Modal Coupling Graph to fuse data under arbitrary asynchrony and missingness, and a Flow‑Matching Forecasting Head that models multi‑step prediction as a conditional ODE. Experiments on weather and traffic datasets demonstrate that the method outperforms state‑of‑the‑art baselines and remains robust even when up to two modalities are missing.
arXiv:2607. 01746v1 Announce Type: new Abstract: Recurrent representations are trajectories, but representation geometry is often measured from static snapshots.
Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series. Yet their moving-average term captures with a few parameters what a pure autoregression matches only with many lags.
arXiv:2606. 24347v1 Announce Type: new Abstract: Accurate short-term PM$_{2.
Diff-SPORT is a diffusion-based framework that integrates a generative diffusion prior, maximum a posteriori inference, and Shapley-value attribution to achieve high-fidelity reconstruction of turbulent flows and optimal sensor placement in urban environments. By training the diffusion prior once for a domain, it enables non-linear sensor placement and near-real-time flow reconstruction from sparse measurements, outperforming state-of-the-art methods and running orders of magnitude faster than RANS or LES simulations. The approach also generalizes to experimental passive scalar concentration data, demonstrating up to 57% lower reconstruction error than random sensor placement under extreme sparsity and providing compact, physically interpretable sensor configurations.
arXiv:2606. 16210v1 Announce Type: new Abstract: Learned representations in intelligent sensing systems are often evaluated by reconstruction fidelity or downstream prediction accuracy, but these criteria do not specify which latent distinctions are justified by the sensing process.