Accurate and efficient damage recognition of reinforced concrete structures is essential for structural health monitoring and recovery planning. However, most existing deep learning-based approaches treat damage recognition as an isolated visual task, without explicitly considering component-level context or the engineering relevance of different failure modes. This study proposes a multi-task framework, guided by the geometric and engineering characteristics of different damage types, that integrates component recognition with damage recognition to achieve fine-grained post-earthquake component assessment. Component localization is achieved through instance segmentation, region-level damage is obtained via semantic segmentation, and cracks are identified via orientation-aware bounding box detection. To address data scarcity, a data augmentation strategy termed foreground-background regrouping is introduced, which leverages structural domain knowledge to generate physically realistic damage samples. Experimental results demonstrate the effectiveness of the framework, yielding accurate component localization and damage segmentation. The framework shows strong potential for automated, rapid post-earthquake component assessment and decision-making support.

Concrete shrinkage under non-stationary climatic forcing poses an increasing threat to the serviceability and longevity of infrastructure in low-pressure, arid and high-altitude regions. Current models neglect multi-environment interactions and climate-driven risk evolution. This study presents a hybrid modeling and assessment framework that coupled physics-informed empirical priors with optimized machine learning to predict shrinkage evolution, quantify structural risk, and map spatiotemporal vulnerability under future climate scenarios. A curated shrinkage database was fused with high-resolution meteorological projections and downscaled via filtering and cubic interpolation. The empirical CEB-FIP 2010 shrinkage formulation and air pressure parameters were embedded into feature engineering to create temperature-humidity-pressure coupled predictors. An XGBoost (Extreme Gradient Boosting) model was optimized through systematic hyperparameter tuning and physics-guided transfer learning. The optimized coupling model attained R 2 = 0.92 to predict shrinkage evolution, and reduced long-term prediction divergence to within 15% against independent data from three-factor experiments. To translate material-level shrinkage into structural risk, multiphysics finite-element simulations of a representative reinforced-concrete pier incorporated eigenstrain shrinkage fields and reinforcement constraint to resolve strain–stress–damage progression. Four critical normalized strain thresholds were identified that demarcated initiation, stable propagation, accelerated expansion and through-crack stages. A five-tier risk zoning map across China was constructed, covering both historical data and mid-future climate scenario. Plateau and northwestern basins showed marked vulnerability. Using C60 concrete as a representative case study due to its prevalence, results showed the medium-to-high risk area increasing by 65%, with 31.1% of China's territory classified as medium–high risk by 2050.

The use of dry electrode electroencephalography (EEG) systems holds significant importance in advancing the everyday application of emotion recognition. However, adapting it to real-world applications faces unique challenges due to low signal-to-noise ratios and unreliable emotion labels. To address these challenges, we propose a Cross-Device Representation Consistency (CDRC) pre-training paradigm for dry EEG emotion recognition, where the self-supervised signal is provided by the distance between representations embedded in wet and dry EEG components and trained via contrastive estimation. Specifically, we employ a dual-branch embedding prediction task coupled with contrastive feature alignment module to extract robust and distinctive features from dry electrode EEG signals. We evaluate our model on an available emotional dataset PaDWEED, extensive experiments demonstrate that CDRC performs comparably to fully supervised training and achieves state-of-the-art results compared to several self-supervised approaches. Moreover, the remarkable performance on subject-independent tasks highlights its effectiveness in addressing and mitigating subject variability.

Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications. Deep learning approaches greatly improve current lip reading systems. However, lip reading in cross-speaker scenarios where the speaker identity changes, poses a challenging problem due to inter-speaker variability. A well-trained lip reading system may perform poorly when handling a brand new speaker. To learn a speaker-robust lip reading model, a key insight is to reduce visual variations across speakers, avoiding the model overfitting to specific speakers. In this work, in view of both input visual clues and latent representations based on a hybrid CTC/attention architecture, we propose to exploit the lip landmark-guided fine-grained visual clues instead of frequently-used mouth-cropped images as input features, diminishing speaker-specific appearance characteristics. Furthermore, a max-min mutual information regularization approach is proposed to capture speaker-insensitive latent representations. Experimental evaluations on public lip reading datasets demonstrate the effectiveness of the proposed approach under the intra-speaker and inter-speaker conditions.

Autonomous driving (AD) has experienced rapid development in recent years, and the reinforcement learning (RL) pipeline in trial-and-error manner can surpass human driving ability. However, the poor performance in sample efficiency and generalization limits RL applying in the challenging urban traffic scenarios. In this paper, we build a context-enhanced meta-RL framework with data-reused adaptation for challenging urban AD. At both the meta-learning and adaptation stages, the context-enhanced state representation is designed to reduce the perceptual gap in variant urban scenarios, improving the sample efficiency and robustness. At adaptation stage, the meta-training data with context-enhanced features are reused through propensity estimation to constrain the optimization objective of new tasks, aiming to maintain the good driving performance of meta-trained policy and fast adapt to the new tasks. Extensive experiments are conducted in CARLA simulator with various urban environments and task settings. The learning curves and quantitative comparisons validate the good sample efficiency and generalization of our proposed method, with state-of-the-art driving performance on urban AD benchmarks.

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This article presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.