Origin-Destination (OD) flow matrices are critical for urban mobility analysis, supporting traffic forecasting, infrastructure planning, and policy design. Existing methods face two key limitations: (1) reliance on costly auxiliary features (e.g., Points of Interest, socioeconomic statistics) with limited spatial coverage, and (2) fragility to spatial topology changes, where reordering urban regions disrupts the structural coherence of generated flows. We propose Sat2Flow, a structure-aware diffusion framework that generates structurally coherent OD flows using only satellite imagery. Our approach employs a multi-kernel encoder to capture diverse regional interactions and a permutation-aware diffusion process that maintains consistency across regional orderings. Through joint contrastive training linking satellite features with OD patterns and equivariant diffusion training enforcing structural invariance, Sat2Flow ensures topological robustness under arbitrary regional reindexing. Experiments on real-world datasets show that Sat2Flow outperforms physics-based and data-driven baselines in accuracy while preserving flow distributions and spatial structures under index permutations. Sat2Flow offers a globally scalable solution for OD flow generation in data-scarce environments, eliminating region-specific auxiliary data dependencies while maintaining structural robustness for reliable mobility modeling.

Zero-shot stance detection (ZSSD) seeks to determine the stance of text toward previously unseen targets, a task critical for analyzing dynamic and polarized online discourse with limited labeled data. While large language models (LLMs) offer zero-shot capabilities, prompting-based approaches often fall short in handling complex reasoning and lack robust generalization to novel targets. Meanwhile, LLM-enhanced methods still require substantial labeled data and struggle to move beyond instance-level patterns, limiting their interpretability and adaptability. Inspired by cognitive science, we propose the Cognitive Inductive Reasoning Framework (CIRF), a schema-driven method that bridges linguistic inputs and abstract reasoning via automatic induction and application of cognitive reasoning schemas. CIRF abstracts first-order logic patterns from raw text into multi-relational schema graphs in an unsupervised manner, and leverages a schema-enhanced graph kernel model to align input structures with schema templates for robust, interpretable zero-shot inference. Extensive experiments on SemEval-2016, VAST, and COVID-19-Stance benchmarks demonstrate that CIRF not only establishes new state-of-the-art results, but also achieves comparable performance with just 30% of the labeled data, demonstrating its strong generalization and efficiency in low-resource settings.

The increasing diversity and integration of transportation modes is changing urban mobility, resulting in complex spatiotemporal urban flow patterns. The current forecasting models, which typically rely on static or manually defined graph structures, are inadequate for capturing the dynamic spatial heterogeneity and complex cross-modal interactions that are present in real urban systems. To address these limitations, this study introduces a multimodal dynamic graph neural network (MM-DyGNN), a novel deep learning model that is designed for urban multimodal flow prediction. MM-DyGNN introduces three key innovations: (i) a time-varying multimodal graph learning module based on Tucker decomposition that adaptively constructs mode- and time-specific diffusion graphs; (ii) a sparse cross-modal interaction module that employs a top-k strategy to capture the most relevant region–mode dependencies; and (iii) an adaptive multitask learning strategy with uncertainty weighting to balance heterogeneous modal objectives. Comprehensive experiments conducted on real-world urban mobility datasets demonstrate that the MM-DyGNN significantly outperforms the baseline models in terms of forecasting accuracy. Ablation studies further validate the effectiveness of each component and demonstrate the ability of the model to interpret dynamic spatiotemporal dependencies and cross-modal interactions. This work provides a methodological foundation for understanding and managing the evolving complexities of urban mobility.

Human mobility nowcasting is crucial for public safety, especially during disasters when human mobility significantly differs from normal patterns, posing unique challenges. Recent studies have shown a correlation between disaster-related social media information and abnormal patterns in human mobility. However, these studies mainly focus on text counts while neglecting semantic text, which limits the effective use of social media data and reduces model prediction performance. The social text semantics reveal inherent non-pairwise relationships between regions in human mobility, posing a challenge to traditional graph neural network approaches. Thus, we propose a Semantics-Guided Dynamic Hypergraph Convolutional Network (SG-DyHGCN) for human mobility nowcasting in disaster. The model leverages semantic information to guide dynamic hyper-graph construction, enabling flexible adjustments to the hyper-graph structure, effectively capturing non-pairwise relationships between regions, and enhancing prediction performance. Experimental results validate the effectiveness of our method.

Accurate forecasting of granular socioeconomic indicators, such as GDP, is essential for informed economic decision-making. Despite pioneering efforts to harness multi-modal data using traditional supervised or self-supervised learning methods for economic prediction, effectively integrating their features remains a significant hurdle. To address the challenge, we propose a semi-supervised enhanced graph learning framework, SemiGPS, which leverages multi-modal geospatial big data to extract features linked to regional economic development. By integrating multiple data modalities, such as street view images and POIs, and adopting a semi-supervised learning paradigm, SemiGPS strikes a balance between spatial-interacted learning from self-supervision and the fitting capabilities from supervised learning, thereby empowering the model to learn more informative and effective representations. Experiments in the Pearl River Delta region of China demonstrate the effectiveness of our approach, with R2scores of 0.85, 0.95, and 0.88 for the primary, secondary, and tertiary sectors, respectively. Our results highlight the potential of SemiGPS to capture nuanced regional economics and pave the way for more accurate economic forecasting using geospatial data.

The development of information and communication technologies has produced massive human sensing data sets, such as point of interest, mobile phone data, and social media data sets. These data sets provide alternative human perceptions of urban spaces; therefore, they have become effective supplements for remote sensing tasks. This letter presents an exploratory framework to examine the scale effect of fusing remote sensing and human sensing. The physical and social semantics are extracted from raw remote sensing images and human sensing data, respectively. A dynamic weighting strategy is developed to explore the fusion of remote sensing and human sensing. Taking urban function inference as an example, the scale effect is evaluated by weighting remote sensing and human sensing. The experiment demonstrates that fusing remote sensing and human sensing enables us to recognize multiple types of urban functions. Meanwhile, the results are significantly affected by the scale.

Understanding urban functions and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we present a novel approach to uncovering urban functions by aggregating human activities inferred from mobile phone positioning and social media data. First, the homes and workplaces (of travelers) are estimated from mobile phone positioning data to annotate the activities conducted at these locations. The remaining activities (such as shopping, schooling, transportation, recreation and entertainment) are labeled using a hidden Markov model with social knowledge learned from social media check-in data over a lengthy period. By aggregating identified human activities, hourly urban functions are inferred, and the diurnal dynamics of those functions are revealed. An empirical analysis was conducted for the case of Shenzhen, China. The results indicate that the proposed approach can capture citywide dynamics of both human activities and urban functions. It also suggests that although many urban areas have been officially labeled with a single land-use type, they may provide different functions over time depending on the types and range of human activities. The study demonstrates that combining different data on human activities could yield an improved understanding of urban functions, which would benefit short-term urban decision-making and long-term urban policy making.

A digital calibration method is presented for delta-sigma ADCs with focus on feedback DAC mismatch error correction. The DAC mismatch information is acquired, and then the nonlinearity is cancelled in the digital domain while the ADC operates. Unlike dynamic element matching which is commonly run between non-overlapping clock phases, the proposed method does not have the restriction and introduces no excess loop delay. The effectiveness of the proposed scheme was demonstrated with implementation of a third-order 15-level delta-sigma ADC.

A multi-channel mixed-signal noise source with uniform amplitude distribution is presented. Cross-coupled, counter-propagating linear feedback shift registers are used to produce mutually independent binary distributed noises, which in turn generates mutually independent uniformly-distributed discrete-analog noises after digital-to-analog conversion. A stochastic comparator offset cancellation technique based on the proposed analog noise source is demonstrated. Applications include energy-efficient stochastic ADCs and high-density analog built-in self-test.

A radix-based digital technique for compensating capacitor mismatch in a two-capacitor DAC is described. Digital input words are pre-distorted with a serial ADC-like algorithm before they are fed into the DAC. The proposed methods provide excellent linearity performance when the DAC is converting a high number of bits. Extra peripheral circuits to achieve the digital compensation are briefly discussed.