In affective neuroscience and emotion-aware AI, understanding how complex auditory stimuli drive emotion arousal dynamics remains unresolved. This study introduces a neurobiologically informed computational framework to model the brain's encoding of naturalistic auditory inputs into dynamic behavioral/neural responses using four datasets (SEED, LIRIS, self-collected SEED Annotation and BAVE). Guided by neurobiological principles of parallel auditory hierarchy, we decompose audio into multilevel auditory features (through classical algorithms and wav2vec 2.0/Hubert) from the original and isolated human voice/background soundtrack elements, mapping them to emotion-related responses via cross-dataset analyses. Our analysis reveals that high-level semantic representations (derived from the final layer of wav2vec 2.0/Hubert) exert a dominant role in emotion encoding, outperforming low-level acoustic features with significantly stronger mappings to behavioral annotations and dynamic neural synchrony across most brain regions ($p \lt 0.05$). Notably, middle layers of wav2vec 2.0/hubert (balancing acoustic-semantic information) surpass the final layers in emotion induction across datasets. Moreover, human voices and soundtracks show dataset-dependent emotion-evoking biases aligned with stimulus energy distribution (e.g., LIRIS favors soundtracks due to higher background energy), with neural analyses indicating voices dominate prefrontal/temporal activity while soundtracks excel in limbic regions. By integrating affective computing and neuroscience, this work uncovers hierarchical mechanisms of auditory-emotion encoding, providing a foundation for adaptive emotion-aware systems and cross-disciplinary explorations of audio-affective interactions.

Federated Learning (FL) is an emerging decentralized machine learning paradigm that addresses the data-silo problem through privacy-preserving collaborative model training, attracting significant attention from academia and industry. However, model trading in FL involves multiple stakeholders, including data owners, model requesters, and the cloud service platform, whose conflicting interests hinder the sustainability and stability of FL. To address these challenges, this paper considers the bounded rationality of the three parties involved in long-term dynamic decision-making and constructs a tripartite evolutionary game model based on evolutionary game theory, further taking into account collusion between data owners and cloud service platforms. We analyze the evolutionary dynamics involved, theoretically revealing the social dilemma of dishonesty in FL model trading. To prevent dishonest behaviors such as free-riding and false reporting, we apply the replicator dynamics and Lyapunov method to analyze the impact of rewards, punishments, and collusion costs on the evolutionary stable strategies of the three parties and propose incentive strategies. Simulation experimental results validate that our incentive model is effective in alleviating dishonest social dilemmas and improving social welfare.

Environmental changes play a critical role in determining the evolution of social dilemmas in many natural or social systems. Generally, the environmental changes include two prominent aspects: the global time-dependent fluctuations and the local strategy-dependent feedbacks. However, the impacts of these two types of environmental changes have only been studied separately, a complete picture of the environmental effects exerted by the combination of these two aspects remains unclear. Here we develop a theoretical framework that integrates group strategic behaviors with their general dynamic environments, where the global environmental fluctuations are associated with a nonlinear factor in public goods game and the local environmental feedbacks are described by the 'eco-evolutionary game'. We show how the coupled dynamics of local game-environment evolution differ in static and dynamic global environments. In particular, we find the emergence of cyclic evolution of group cooperation and local environment, which forms an interior irregular loop in the phase plane, depending on the relative changing speed of both global and local environments compared to the strategic change. Further, we observe that this cyclic evolution disappears and transforms into an interior stable equilibrium when the global environment is frequency-dependent. Our results provide important insights into how diverse evolutionary outcomes could emerge from the nonlinear interactions between strategies and the changing environments.

Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.

In this paper, we propose a powerful weak learner (Vector Decision Tree (VDT)) and a new Boosted Vector Decision Tree (BVDT) algorithm framework for the task of multi-class classification. Unlike the traditional scalar valued boosting algorithms, the BVDT algorithm directly maps the feature space to the decision space in the multi-class setting, which facilitates convenient implementations of the multi-class classification algorithms using diverse loss functions. By viewing the explicit hard threshold on the leaf node value applied in the LogitBoost as a constraint optimization problem, we further develop two new variants of the BVDT algorithm: the [Formula: see text]-BVDT and the [Formula: see text]-BVDT. The performance of the proposed algorithm is evaluated on different datasets and compared with three state-of-the-art boosting algorithms, [Formula: see text]-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The results show that the performance of the proposed algorithm ranks first in all but one dataset and reduces the test error rate by 4% up to 58% with respect to the state-of-the-art boosting algorithms based on the scalar-valued weak learner. Furthermore, we present a case study on the Abalone dataset by designing a new loss function that combines the negative log-likelihood loss function of classification problem and square loss function of regression problem.