Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor , a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture (DiTs-based). The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model's superior performance to the current state-of-the-art approaches. Persona-Gestor improves the system's usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology. Supplementary videos and code can be accessed at https://zf223669.github.io/Diffmotion-v2-website/.

The core issue in handling constrained multi-objective optimization problems (CMOP) is how to maintain a balance between objectives and constraints. However, existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to achieve the desired performance when confronted with complex feasible regions. Building upon this theoretical foundation, a two-stage archive-based constrained multi-objective evolutionary algorithm (CMOEA-TA) based on genetic algorithms (GA) is proposed. In CMOEA-TA, First stage: The archive appropriately relaxes constraints based on the proportion of feasible solutions and constraint violations, compelling the population to explore more search space. Second stage: Sharing valuable information between the archive and the population, while embedding constraint dominance principles to enhance the feasibility of solutions. In addition an angle-based selection strategy was used to select more valuable solutions to increase the diversity of the population. To verify its effectiveness, CMOEA-TA was tested on 54 CMOPs in 4 benchmark suites and 7 state-of-the-art algorithms were compared. The experimental results show that it is far superior to seven competitors in inverse generation distance (IGD) and hypervolume (HV) metrics.

When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on a dual-population cooperative correlation (CMOEA-DCC). Under the CMOEA-DDC framework, the system maintains two independently evolving populations: the driving population and the conventional population. These two populations share information through a collaborative interaction mechanism, where the driving population focuses on objective optimization, while the conventional population balances both objectives and constraints. To further enhance the performance of the algorithm, a shift-based density estimation (SDE) method is introduced to maintain the diversity of solutions in the driving population, while a multi-criteria evaluation metric is adopted to improve the feasibility quality of solutions in the normal population. CMOEA-DDC was compared with seven representative constrained multi-objective evolutionary algorithms (CMOEAs) across various test problems and real-world application scenarios. Through an in-depth analysis of a series of experimental results, it can be concluded that CMOEA-DDC significantly outperforms the other competing algorithms in terms of performance.

Infrared and visible image fusion plays a critical role in multimodal perception systems, particularly under challenging conditions such as low illumination, occlusion, or complex backgrounds. However, existing approaches often struggle with global feature modelling, cross-modal dependency learning, and preserving structural details in the fused images. In this paper, we propose a novel adversarial fusion framework driven by a state-space modelling paradigm to address these limitations. In the feature extraction phase, a computationally efficient state-space model is utilized to capture global semantic context from both infrared and visible inputs. A cross-modality state-space architecture is then introduced in the fusion phase to model long-range dependencies between heterogeneous features effectively. Finally, a multi-class discriminator, trained under an adversarial learning scheme, enhances the structural fidelity and detail consistency of the fused output. Extensive experiments conducted on publicly available infrared–visible fusion datasets demonstrate that the proposed method achieves superior performance in terms of information retention, contrast enhancement, and visual realism. The results confirm the robustness and generalizability of our framework for complex scene understanding and downstream tasks such as object detection under adverse conditions.

To address the challenges of photovoltaic (PV) power prediction in highly dynamic environments. We propose an improved Long Short-Term Memory (ILSTM) model. The model uses Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) for feature selection, ensuring key information is preserved while reducing dimensionality. The Depthwise Separable Convolution (DSC) module extracts spatial features, while the Channel-Spatial Attention Mechanism (CBAM) focuses on important time-dependent patterns. Finally, Bidirectional Long Short-Term Memory (BiLSTM) captures nonlinear dynamics and long-term dependencies, boosting prediction performance. The model is called DSC-CBAM-BiLSTM. It selects important features adaptively. It captures key spatial-temporal patterns and improves forecasting performance based on RMSE, MAE, and $R^2$. Extensive experiments using real-world PV datasets under varied meteorological scenarios show the proposed model significantly outperforms traditional approaches. Specifically, RMSE and MAE are reduced by over 70%, and the coefficient of determination ($R^2$) is improved by 8.5%. These results confirm the framework’s effectiveness for real-time, short-term PV forecasting and its applicability in energy dispatching and smart grid operations.

Federated learning offers a powerful approach for training models across decentralized datasets, enabling the creation of machine learning models that respect data privacy. However, federated learning faces significant challenges due to its vulnerability to adversarial attacks, especially when clients have diverse and potentially malicious data distributions. These challenges can lead to severe degradation in the global model’s performance and generalization. In this paper, we present a novel federated image adversarial training framework that combines client-specific adversarial example generation with a robust trimmed aggregation technique. By creating adversarial examples tailored to each client’s local data, our method strengthens individual model defenses against adversarial attacks. Meanwhile, the trimmed aggregation strategy ensures the global model’s robustness by mitigating the impact of harmful or low-quality updates during the model aggregation process. This framework effectively addresses both the issue of data heterogeneity and adversarial threats in federated learning settings. Our experimental results on standard image classification datasets show that the proposed approach significantly enhances model robustness, surpassing existing methods in defending against various adversarial attacks while maintaining high classification accuracy. This framework holds strong promise for real-world applications, particularly in privacy-sensitive domains where both security and model reliability are essential.