Modeling complex rigid motion across large spatiotemporal spans remains an unresolved challenge in dynamic reconstruction. Existing paradigms are mainly confined to short-term, small-scale deformation and offer limited consideration for physical consistency. This study proposes PMGS, focusing on reconstructing Projectile Motion via 3D Gaussian Splatting. The workflow comprises two stages: 1) Target Modeling: achieving object-centralized reconstruction through dynamic scene decomposition and an improved point density control; 2) Motion Recovery: restoring full motion sequences by learning per-frame SE(3) poses. We introduce an acceleration consistency constraint to bridge Newtonian mechanics and pose estimation, and design a dynamic simulated annealing strategy that adaptively schedules learning rates based on motion states. Futhermore, we devise a Kalman fusion scheme to optimize error accumulation from multi-source observations to mitigate disturbances. Experiments show PMGS’s superior performance in reconstructing high-speed nonlinear rigid motion compared to mainstream dynamic methods.

Federated Learning (FL) has emerged as a promising paradigm for decentralized machine learning, where a central server coordinates distributed clients to collaboratively train a global model without direct access to raw data. Despite its advantages, heterogeneous and long-tail data distributions across clients remain a major bottleneck, particularly in IoT scenarios with diverse devices and sensing modalities. To address these challenges, we propose FedSM, a novel framework that integrates multimodal semantic knowledge with balanced pseudo features to enhance global model optimization. Unlike conventional approaches that rely on single-modal information, FedSM leverages CLIP’s cross-modal representations and open-vocabulary priors to guide semantic-aware data augmentation. A probabilistic selection mechanism further refines local features by mixing them with global prototypes, ensuring pseudo features are semantically reliable and reducing bias caused by skewed client distributions. Almost all computations are performed locally at the client side, thereby alleviating server overhead and improving scalability in resource-constrained IoT environments. Extensive experiments on long-tail benchmarks including CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT demonstrate the superiority of FedSM over state-of-the-art baselines, highlighting its potential for robust communication-efficient FL in IoT networks.

Vision language models (VLMs) have opened new avenues for satellite remote sensing image analysis and have shown promise across multiple tasks. However, in the absence of a remote sensing-oriented general VLM, existing approaches rely on retraining generic VLMs with remote sensing datasets to adapt to downstream tasks. This practice is inherently affected by two factors: 1) generic VLMs are pretrained on massive web data containing noise, biases, and misinformation; and 2) many remote sensing image-text datasets use VLM-generated annotations, which can introduce hallucinations and factual errors. Such statistical biases exacerbate the alignment gap of remote sensing VLMs, leading to generation bias and degraded task performance. To address this issue, we propose Geo-Contrastive Decoding (Geo-CD) to enhance remote sensing VLMs. Geo-CD reduces over-reliance on statistical biases by contrasting the output distributions produced from distorted versus original visual inputs. This strategy ensures that the generations of VLMs remain well-grounded in the visual input, thereby improving both reliability and accuracy. Extensive experiments demonstrate that Geo-CD can be applied to diverse remote sensing tasks without additional training or external tools. On the selected base VLM, Geo-CD achieves consistent gains across most remote sensing benchmarks and reaches state-of-the-art performance.

The success of Vision Transformers (ViTs) has profoundly reshaped research paradigms in computer vision. However, similar to conventional CNN-based models, ViTs still require input images to be resized to fixed resolution. Mainstream open-source ViT implementations typically resize images into a square shape, which inevitably leads to information loss and increases computational and memory overhead. Moreover, prior studies have highlighted that the visual encoder in multimodal large language models (MLLMs), as the primary source of visual information, plays a crucial role in determining overall model understanding. In the field of satellite remote sensing, a distinctive challenge lies in the ultra-high resolution of imagery. For instance, a 2K remote sensing image may reach a resolution of$2560 \times 1440$, far exceeding the maximum resolution supported by open-source ViTs (e.g.,$336\times 336$). This limitation results in even more severe information degradation for remote sensing scenarios. To address this, Google proposed NaViT, a vision encoder that supports native resolutions and aspect ratios. We argue that NaViT is particularly valuable for remote sensing image understanding. In this work, following the LLaVA paradigm, we replace the standard ViT with NaViT to investigate the benefits of processing images at their native resolution within MLLMs. Experimental results demonstrate that across several main-stream remote sensing benchmarks, native-resolution image understanding consistently delivers notable improvements across multiple evaluation metrics, validating its effectiveness in high-resolution scenarios.

Abstract Cross‐modality person re‐identification is a challenging task due to the large visual appearance difference between RGB and infrared images. Existing studies mainly focus on learning local features and ignore the correlation between local features. In this paper, the Integration Graph Attention Network is proposed to learn the completed correlation between local features via the graph structure. To this end, the authors learn the coarse‐fine attention weights to aggregate the local features by considering local detail and global information. Furthermore, the Multi‐Centre Constrained Loss is proposed to optimise the feature similarity by constraining the centres of modality and identity. It simultaneously utilises three kinds of centre constraints, that is intra‐identity centre constraint, modality centre constraint, and inter‐identity centre constraint, in order to reduce the influence of modality information explicitly. The proposed method is evaluated on two standard benchmark datasets, that is SYSU‐MM01 and RegDB, and the results demonstrate that the authors’ method achieves better performance than the state‐of‐the‐art methods, for example, surpassing NFS by 4.8% and 6.0% mAP on the single‐shot setting in All‐search and Indoor‐search modes, respectively.

In this article, we propose a novel deep framework termed local alignment deep network (LADN) for infrared-visible cross-modal person reidentification (IVCM ReID) in 6G-enabled IoT, which could meet the demands of all-day and real-time surveillance. The proposed LADN is designed as a two-stream structure, and it learns shallow interested feature maps and common subspace feature maps to reduce the gap between IR and RGB images. To overcome the challenge of pose and viewpoint variations of pedestrians, we learn the local features in the deep layers. We also propose the local alignment triplet loss (LAT) to align local features, which could capture the consistent local information via comparing noncorresponding local features in a certain range. Furthermore, we learn the global features to provide the global field of vision for the representation. The proposed LADN is optimized in an end-to-end way by combining different cross-modality losses. We evaluate the proposed method on two standard benchmark data sets, i.e., SYSU-MM01 and RegDB, and the results demonstrate the effectiveness of LADN.

Short-term optimal hydrothermal scheduling problem is one of the most popular research issues in power systems optimization. A novel mathematical model of the short-term optimal hydrothermal scheduling is proposed in this paper. This model aims at minimizing the total fuel cost of the thermal generating units while satisfying the various constraints such as power balance, water balance, transmission network and other system's constraints. A modified differential evolution algorithm is also introduced to solve the short-term optimal hydrothermal scheduling problem. In the proposed approach, an operation of migration and a self-adaptive mechanism are presented to improve the searching efficiency. Moreover, four constraint handling rules are proposed to handle the complex constraints of short-term optimal hydrothermal scheduling problem. An IEEE nine buses test system is applied to verify the proposed mathematic model and algorithm. The numerical results show the feasibility and efficiency of the proposed approach to the short-term optimal hydrothermal scheduling problem.

We present a novel robust control scheme that deals with multi-body spacecraft attitude tracking problems. The control scheme consists of a radial basis function network (RBFN) and a robust controller. By using the finite time convergence property of the terminal sliding mode (TSM), we derive a new online learning algorithm for updating all the parameters of the RBFN that ensures the RBFN has fast approximation for the parameter uncertainties and external disturbances. We design a robust controller to compensate RBFN approximation errors and realise the anticipative stability and performance properties. We can also achieve closed-loop system stability using Lyapunov stability theory. No detailed knowledge of the non-linear dynamics of the spacecraft is required at any point in the entire design process, and the proposed robust scheme is simple and effective and can be applied to more complex systems. Simulation results demonstrate the good tracking characteristics of the proposed control scheme in the presence of inertial uncertainties and external disturbances.