UniSTOK: Uniform Inductive Spatio-Temporal Kriging

• URL: http://arxiv.org/abs/2603.05301v1
• Date: Thu, 05 Mar 2026 15:42:03 GMT
• Title: UniSTOK: Uniform Inductive Spatio-Temporal Kriging
• Abstract summary: We propose a plug-and-play framework that enhances inductive kriging backbones under missing observation.<n>Our framework forms a dual-branch input consisting of the original observations and a jigsaw a-augmented counterpart that synthesizes proxy signals only at missing entries.<n>Experiments on multiple real-world datasets under diverse missing patterns demonstrate significant improvements.
• Score: 18.058350565240637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-temporal kriging aims to infer signals at unobserved locations from observed sensors and is critical to applications such as transportation and environmental monitoring. In practice, however, observed sensors themselves often exhibit heterogeneous missingness, forcing inductive kriging models to rely on crudely imputed inputs. This setting brings three key challenges: (1) it is unclear whether an value is a true signal or a missingness-induced artifact; (2) missingness is highly heterogeneous across sensors and time; (3) missing observations distort the local spatio-temporal structure. To address these issues, we propose Uniform Inductive Spatio-Temporal Kriging (UniSTOK), a plug-and-play framework that enhances existing inductive kriging backbones under missing observation. Our framework forms a dual-branch input consisting of the original observations and a jigsaw-augmented counterpart that synthesizes proxy signals only at missing entries. The two branches are then processed in parallel by a shared spatio-temporal backbone with explicit missingness mask modulation. Their outputs are finally adaptively fused via dual-channel attention. Experiments on multiple real-world datasets under diverse missing patterns demonstrate consistent and significant improvements.

Related papers

• Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks [68.64050157343334]
  Existing watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution.<n>We introduce an innovative watermark that jointly provides provenance and tamper evidence.<n>The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes.
  arXiv  Detail & Related papers  (2026-08-13T01:55:16Z)
• Three Necessary Principles for Self-Supervised Visual Representation Learning [6.086784305572313]
  We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives.<n>We formalize these as the observation, prediction, and regularization principles and prove (i) that combining observation and prediction without regularization admits the constant encoder as a global minimizer.<n>We pair every theoretical claim with a controlled experiment, including a patch-retrieval evaluation for the spatial consequence of prediction.
  arXiv  Detail & Related papers  (2026-08-08T19:37:05Z)
• LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning [16.92190487823839]
  We propose layout-Aware Road Anomaly Detection (LARAD), which shifts the paradigm from appearance matching to spatial-logic reasoning.<n>First, we introduce the Spatial-Logic Violation Synthesis pipeline, which generates training samples that are texture-consistent yet spatially invalid.<n>Second, we augment a standard closed-set segmentation network with a lightweight, OoD-guided attention branch.
  arXiv  Detail & Related papers  (2026-07-14T15:09:00Z)
• Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts [60.95873409673775]
  identifiability in continuous-time latent differential equation models remains largely open.<n>We study additive-noise latent SDEs observed through an unknown nonlinear diffeomorphism.<n>We propose a two-stage estimator for latent disentanglement and optional graph recovery.
  arXiv  Detail & Related papers  (2026-06-26T16:18:28Z)
• Complex-gauge control of anomalous Floquet corner responses in a non-Hermitian physical-synthetic photonic lattice [0.0]
  We show that anomalous corner pairs at quasienergies zero and (/T) exhibit three distinct layers of physics.<n>The same topological coexistence sector can be bright, skin-dark, or flux-dark in a local optical measurement.
  arXiv  Detail & Related papers  (2026-06-05T08:27:18Z)
• Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement [26.873671102536438]
  Sensor networks govern modern infrastructure, yet the data they lose are rarely missing in the uniform-random patterns assumed by standard imputation benchmarks.<n>We introduce Multi-Scale Hypergraph Laplacians, a framework for learning higher-order structure from incomplete observations.<n>We prove that MSHL represents group-conservation patterns inaccessible to pairwise graph priors, adapts to the best fixed scale up to a logarithmic factor, and admits a one-sided refinement guarantee.
  arXiv  Detail & Related papers  (2026-05-17T08:15:16Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection [53.191369031661885]
  Unsupervised time series anomaly detection aims to build a model for identifying abnormal timestamps without assuming the availability of annotations.<n>We present Learnable Fusion of Tri-view Tokens (LEFT), a unified unsupervised TSAD framework that models anomalies as inconsistencies across complementary representations.<n>Experiments on real-world benchmarks show that LEFT yields the best detection accuracy against SOTA baselines, while achieving a 5x reduction on FLOPs and 8x speed-up for training.
  arXiv  Detail & Related papers  (2026-02-09T13:33:49Z)
• Breaking Semantic Hegemony: Decoupling Principal and Residual Subspaces for Generalized OOD Detection [10.596067236901968]
  We investigate the Simplicity Paradox in state-of-the-art (SOTA) models.<n>Models exhibit keen sensitivity in distinguishing semantically subtle OOD samples but suffer from severe Geometric Blindness.<n>We propose D-KNN, a training-free, plug-and-play geometric decoupling framework.
  arXiv  Detail & Related papers  (2026-02-05T06:32:33Z)
• SMKC: Sketch Based Kernel Correlation Images for Variable Cardinality Time Series Anomaly Detection [0.0]
  In operational environments, monitoring systems frequently experience sensor churn.<n>We propose SMKC, a framework that decouples the dynamic input structure from the anomaly detector.<n>We find that a detector using random projections and nearest neighbors on the SMKC representation performs competitively with fully trained baselines.
  arXiv  Detail & Related papers  (2026-01-28T21:15:11Z)
• GO-OSC and VASH: Geometry-Aware Representation Learning for Early Degradation Detection in Oscillatory Systems [0.0]
  We introduce GO-OSC, a geometry-aware representation learning framework for oscillatory time series.<n>We show that under early phase-only degradation, energy-based statistics have zero first-order detection power, whereas geometric probes achieve strictly positive sensitivity.<n>Our analysis characterizes when and why linear probing fails under non-identifiable representations and shows how canonicalization restores statistical detectability.
  arXiv  Detail & Related papers  (2026-01-24T09:35:57Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Stabilizing Information Flow Entropy: Regularization for Safe and Interpretable Autonomous Driving Perception [8.543667347406286]
  We reconceptualize deep neural encoders as hierarchical communication chains that compress raw sensory inputs into task-relevant latent features.<n>We propose Eloss, a novel entropy-based regularizer designed as a lightweight, plug-and-play training objective.
  arXiv  Detail & Related papers  (2025-09-18T17:01:27Z)
• Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts [80.32933059529135]
  Test-Time Adaptation (TTA) methods have emerged to adapt to target distributions during inference.<n>We propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD.<n>In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues.
  arXiv  Detail & Related papers  (2025-08-28T07:09:21Z)
• Bifurcations of nonlinear dynamics in coupled twin spin masers [0.7136205674624813]
  Recently, a system of coupled twin spin masers placed in dual bias magnetic fields has been studied. Compared with previous spin masers, this setup exhibits new attractors such as quasi-periodic orbits and chaos in addition to the usual limit cycles and the trivial no signal fixed point. Here, to shed light on the nature of the bifurcations, we turn to a closely related system and systematically study the various bifurcations therein along different routes in parameter space.
  arXiv  Detail & Related papers  (2024-10-26T05:41:57Z)
• Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning [52.70210390424605]
  In this work, we consider endowing a neural network autoencoder with three select inductive biases from the literature. In practice, however, naively combining existing techniques instantiating these inductive biases fails to yield significant benefits. We propose adaptations to the three techniques that simplify the learning problem, equip key regularization terms with stabilizing invariances, and quash degenerate incentives. The resulting model, Tripod, achieves state-of-the-art results on a suite of four image disentanglement benchmarks.
  arXiv  Detail & Related papers  (2024-04-16T04:52:41Z)
• Emergence of non-Abelian SU(2) invariance in Abelian frustrated fermionic ladders [37.69303106863453]
  We consider a system of interacting spinless fermions on a two-leg triangular ladder with $pi/2$ magnetic flux per triangular plaquette. Microscopically, the system exhibits a U(1) symmetry corresponding to the conservation of total fermionic charge, and a discrete $mathbbZ$ symmetry. At the intersection of the three phases, the system features a critical point with an emergent SU(2) symmetry.
  arXiv  Detail & Related papers  (2023-05-11T15:57:27Z)
• Macroscopic noise amplification by asymmetric dyads in non-Hermitian optical systems for generative diffusion models [55.2480439325792]
  asymmetric non-Hermitian dyads are promising candidates for efficient sensors and ultra-fast random number generators. integrated light emission from such asymmetric dyads can be efficiently used for all-optical degenerative diffusion models of machine learning.
  arXiv  Detail & Related papers  (2022-06-24T10:19:36Z)
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [68.86835407617778]
  Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
  arXiv  Detail & Related papers  (2021-10-06T10:33:55Z)
• Quantum asymmetry and noisy multi-mode interferometry [55.41644538483948]
  Quantum asymmetry is a physical resource which coincides with the amount of coherence between the eigenspaces of a generator. We show that the asymmetry may emphincrease as a result of a emphdecrease of coherence inside a degenerate subspace.
  arXiv  Detail & Related papers  (2021-07-23T07:30:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.