Multimodal cross-subject emotion recognition has gained increasing attention due to the complementary nature of physiological and behavioral signals, such as electroencephalography (EEG) and eye movements (EM). However, modality heterogeneity and subject variability remain major obstacles, especially in few-shot scenarios. Existing approaches often combine increasingly complex architectures with unsupervised domain adaptation to address these issues, but they typically require large amounts of unlabeled target data and struggle to generalize when samples are scarce. To overcome these limitations, this article proposes a unified multimodal representation learning (UMRL) framework with prototype calibration under the few-shot paradigm. Specifically, the UMRL strategy is designed from an optimization perspective rather than architectural complexity to learn a compact and discriminative joint representation. It encourages compact representations by jointly minimizing cross-modal inconsistency and penalizing modality dominance, ensuring that complementary cues are effectively integrated in a unified embedding space. Nevertheless, emotional prototypes constructed from a few labeled samples of novel subjects are often unstable and sensitive to subject-dependent variability. To further alleviate this issue, this article introduces an emotional prototype calibration mechanism that aligns novel emotional prototypes with semantic priors learned from seen subjects to suppress noise and improve cross-subject generalization. Extensive experiments on multiple multimodal emotion recognition benchmarks with EEG and EM demonstrate that the proposed framework achieves state-of-the-art performance and delivers consistent robustness under few-shot conditions.

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods.

This paper addresses the problem of drop-point constrained state modeling in the set-valued framework, where only drop-point information of state trajectories is available. Due to the lack of complete trajectory information, existing estimation methods with linear equality constraints struggle to achieve effective state prediction. This paper primarily investigates the integration of drop-point constraint information into the entire system state evolution process via convex optimization projection in the set-valued framework, aiming to reconstruct a linear dynamic system model. Subsequently, by employing multiple affine transformations of ellipsoids and designing a weight matrix, the smoothness of state trajectories is enhanced to better align with real motion patterns. Finally, through simulation experiments, we validate the significant advantages of the reconstructed system model over traditional unconstrained system models under the set-valued framework. Additionally, we demonstrate the impact of drop-point constraints on state trajectory evolution under different initial point conditions with the same drop point.

Convolutional Neural Networks (CNNs) have ex-hibited great power in various vision tasks. However, the lack of transform-invariant property limits their further applications in complicated real-world scenarios. In this work, we pro-posed a novel generalized one-dimension convolutional operator (OneDConv), which dynamically transforms the convolution kernels based on the input features in a computationally and parametrically efficient manner. The proposed operator can extract the transform-invariant features naturally. It improves the robustness and generalization of convolution without sac-rificing the performance of common images. The proposed OneDConv operator can substitute the vanilla convolution. Thus, it can readily be incorporated into popular convolutional architectures, supporting end-to-end training. Empirical evaluations on popular benchmarks reveal OneDConv's superior performance over the standard convolution and competitive models in handling canonical and distorted images.

Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches, which may not sufficiently facilitate meaningful object-specific semantic understanding, leading to a reliance on apparent background correlations. Moreover, they primarily rely on high-dimensional local descriptors to construct complex embedding space, potentially limiting the generalization. To address the above challenges, this article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition. RSaD introduces additional saliency-aware supervision via saliency detection to guide the model toward focusing on the intrinsic discriminative regions. Specifically, RSaD utilizes the saliency detection model to emphasize the critical regions of each sub-category, providing additional object-specific information for fine-grained prediction. RSaD transfers such information with two symmetric branches in a mutual learning paradigm. Furthermore, RSaD exploits inter-regional relationships to enhance the informativeness of the representation and subsequently summarize the highlighted details into contextual embeddings to facilitate the effective transfer, enabling quick generalization to novel sub-categories. The proposed approach is empirically evaluated on three widely used benchmarks, demonstrating its superior performance.

The study region includes 312 counties of 11 provinces and autonomous regions in Northern China. The methodology is mainly the combination of remote sensing, geographic information system and spatial database technology with field investigation of the grassland. The main conclusions are as follows. (1) the monitoring indicates that grass production of the major grasslands in Northern China is decreasing in recent decades, at a rate of 10-40%. (2) Compared with the grass production, over-grazing is a common phenomenon in grazing region. Among the 153 counties in the grazing region, over 50% is with over-loading of herd. Similar phenomenon is also observed in semi-grazing region where over 80% of the 159 counties have various degrees of herd over-loading. (3) Over-loading is severe in semi-grazing area than in grazing area. Comparison has been done to the loading balance between grazing and semi-grazing regions by the end of 1990's. In grazing region. there were 12 counties (banners) with extreme over-loading, accounting for 7.79% of total county numbers in this region. In semi-grazing region, there were 82 counties showing sign of overloading, accounting for 51.5% of total county numbers in this region. The proportion of extreme over-loading grazing region to the total regions occupies 4.82% in grazing region. This proportion is 34.85% in semi-grazing. Obviously, the severity of extreme overloading in semi-grazing highly surpasses that in grazing region. Understanding this animal loading difference among various types of region may help to facilitate proper administration of grazing for sustainable development of the grassland.