Capturing the rich, hierarchical information embedded in complex multivariate time series (MTS) necessitates modeling at multiple temporal scales. However, prevalent multi-scale forecasting methods suffer from critical limitations: a uniform scaling approach that overlooks individual variable characteristics, inconsistent modeling of temporal patterns and inter-variable dependency leading to modeling spurious correlations, and a neglect of global context. To overcome these challenges, we introduce LMSNet, a Lightweight Multi-Scale graph learning framework. Specifically, LMSNet introduces a novel variable-specific scaling mechanism, which leverages frequency-domain analysis to assign a tailored analytical window to each variable based on its intrinsic dynamics, establishing a robust foundation for accurately modeling multi-scale inter-variable dependencies. Furthermore, LMSNet synergizes global and local information and performs concurrent temporal and inter-variable dependency modeling at every network layer, aided by a momentum update mechanism that refines deep-layer graph node representations to mitigate the risk of modeling spurious correlations. LMSNet establishes a new state-of-the-art on eight benchmark datasets. Crucially, it achieves a 2.94% reduction in MAE with reducing GPU memory usage by 83% and accelerating training by 60% compared to strong baseline-TimeMixer. Our source code is provided at https://github.com/05Pikachu24/TSF-LMSNet.
Concerns over the potential over-pathologization of generative AI (GenAI) use and the lack of conceptual clarity surrounding GenAI addiction call for empirical tools and theoretical refinement. This study developed and validated the PUGenAIS-9 (Problematic Use of Generative Artificial Intelligence Scale-9 items) and examined whether PUGenAIS reflects addiction-like patterns under the Internet Gaming Disorder (IGD) framework. Using samples from China and the United States (N = 1508), we conducted confirmatory factor analysis and identified a robust 31-item structure across nine IGD-based dimensions. We then derived the PUGenAIS-9 by selecting the highest-loading items from each dimension and validated its structure in an independent sample (N = 1426). Measurement invariance tests confirmed its stability across nationality and gender. Person-centered (latent profile analysis) and variable-centered (network analysis) approaches revealed a 5–10% prevalence rate, a symptom network structure similar to IGD, and predictive factors related to psychological distress and functional impairment. These findings indicate that PUGenAI shares features of the emotionally vulnerable subtype of IGD rather than the competence-based type. These results support using PUGenAIS-9 to identify problematic GenAI use and show the need to rethink digital addiction with an IDC (Infrastructure-Device-Content) model. This keeps addiction research responsive to new media while avoiding over-pathologizing.
We investigate traveling wave fronts for an acid-mediated tumor invasion model with density-dependent degenerate diffusion. The model is a partially diffusive PDE–ODE system of Gatenby–Gawlinski type, in which the tumor diffusion coefficient D(U) is allowed to be a general decreasing function satisfying D(1)=0. This degeneracy causes the traveling wave equation for the tumor component to lose uniform ellipticity near the healthy state, and hence standard arguments for nondegenerate reaction diffusion systems are not directly applicable. To overcome this difficulty, we introduce a nonlinear change of variables which removes the degeneracy from the highest-order term of the tumor equation. For each fixed admissible tumor profile, the acid profile is represented by a Green kernel, while the healthy-tissue profile is obtained from an explicit integral formula. The transformed tumor profile is then constructed as the stationary limit of a uniformly parabolic auxiliary problem. By combining comparison principles, local Schauder estimates, carefully chosen super- and sub-solutions, and the Schauder fixed-point theorem, we prove the existence of traveling wave fronts for every wave speed θ≥2rD(0). The resulting wave connects the tumor-dominant state (0,1,1) at z=−∞ to the healthy state (1,0,0) at z=+∞. We further establish strict pointwise bounds, monotonicity of all wave components, and one-sided exponential asymptotic estimates in both the transformed variable and the original traveling-wave variable.
Visual–inertial odometry (VIO) can be viewed as a structured multimodal fusion problem, where heterogeneous sensory streams with time-varying reliability must be integrated to estimate ego-motion. Most learning-based VIO methods implicitly assume that all modalities are equally reliable at inference time and perform unconditional or softly gated fusion. However, when reliability becomes asymmetric or intermittently uncertain, such strategies may propagate corrupted information across modalities and degrade estimation stability. In this work, we propose WormVIO, a reliability-aware deep VIO framework motivated by compact sensorimotor decision processes, which formulates adaptive inference as a hierarchical discrete fusion problem. At its core, the Instinct-Bias Module is a hierarchical reliability-aware fusion mechanism that explicitly disentangles two decision factors: (i) fusion participation, which determines whether multimodal aggregation is beneficial at a given time step, and (ii) modality dominance, which determines which modality should guide estimation when reliability is imbalanced. These discrete decisions are implemented using a compact Neural Circuit Policy (NCP) combined with differentiable Gumbel-Softmax sampling, enabling end-to-end optimization of structured fusion control. Experiments on the KITTI benchmark demonstrate that the proposed decoupled fusion mechanism improves trajectory stability under intermittent visual and inertial perturbations while preserving competitive performance under nominal conditions. Furthermore, without fine-tuning, WormVIO transfers more consistently to the EuRoC MAV dataset, indicating that disentangling fusion participation and modality dominance enhances robustness to reliability shifts and cross-domain motion variations. The source code is available at: https://github.com/zRiverBird/worm_vio.git.
