The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, platform, and content creators, with the platform interacting with the content creator under a principal-agent model through contracts to encourage better content. Additionally, the platform interacts with the users to recommend new content, receive an evaluation, and ultimately profit from the content, which can be modeled as a recommender system. Our study aims to explore how the platform can jointly optimize the contract and recommender system to maximize the utility in an online learning fashion. We primarily analyze and compare two families of contracts: return-based contracts and feature-based contracts. Return-based contracts pay the content creator a fraction of the reward the platform gains. In contrast, feature-based contracts pay the content creator based on the quality or features of the content, regardless of the reward the platform receives. We show that under smoothness assumptions, the joint optimization of return-based contracts and recommendation policy provides a regret $\Theta(T^{2/3})$. For the feature-based contract, we introduce a definition of intrinsic dimension $d$ to characterize the hardness of learning the contract and provide an upper bound on the regret $\mathcal{O}(T^{(d+1)/(d+2)})$. The upper bound is tight for the linear family.
As financial markets grow increasingly complex and volatile, time-series-based stock price forecasting has become a critical research focus in the field of finance. Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data, while neural networks (NNs) have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities. Although several existing surveys discuss the applications of NNs in stock forecasting, they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments. In response, this paper reviews relevant literature from 2015 to 2025 and classifies time-series-based stock forecasting methods into four categories: NNs, recurrent NNs (RNNs), convolutional NNs (CNNs), Transformers and other models. We analyze their performance under different market conditions, highlight strengths and limitations, and identify recent trends in model design. Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios. This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
In dynamic scenes, the pose estimation and map consistency of visual simultaneous localisation and mapping (visual SLAM) are affected by intermittent changes in object motion states. An adaptive motion-state estimation and feature-reuse mechanism is proposed which restores features once objects become stationary. Camera ego-motion is compensated via projection-based point-to-point red-green-blue-depth (RGB-D) Iterative Closest Point; the alignment residual yields a short-term jitter score. An Extended Kalman Filter fuses the centre-pixel trajectory and depth of the object, using depth innovation as strong evidence to suppress false triggers. Applied adaptive decision thresholds involve resolution, ego-motion intensity, jitter, and reference depth, and are combined with dual/single triggering and hysteresis to achieve robust switching. When an object is considered static, its feature points are reused. On the Bonn RGB-D Dynamic Dataset (BONN) and TUM RGB-D SLAM Dataset and Benchmark (TUM), the proposed method matches or exceeds baselines: In intermittent-motion-dominated BONN sequences Placing_non_box, it reduces the root-mean-square of the absolute trajectory error (ATE-RMSE) by 27% relative to the baseline, remains comparable to Ellipsoid-SLAM on TUM, and consistently outperforms ORB-SLAM3 in dynamic scenes. The hysteresis counter reading on Placing_non_box2 shows that the proposed method can reduce the motion-state misclassification rate by nearly 40%. From the ablation experiment results, we confirm that adaptive thresholds yield the most significant optimisation effect. The approach improves robustness and map completeness in dynamic environments without degrading performance in low-dynamic settings.
The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand. Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply. While complex machine learning models can achieve high accuracy, their “black-box” nature severely limits the model interpretability. In this paper, we explore the trade-off between model accuracy and interpretability for the energy balancing market. Particularly, we take the example of forecasting manual frequency restoration reserve (mFRR) activation price in the balancing market using real market data from different energy price zones. We explore the interpretability of mFRR forecasting using two models: extreme gradient boosting (XGBoost) machine and explainable boosting machine (EBM). We also integrate the two models, and we benchmark all the models against a baseline naive model. Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability. Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price. Importantly, EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics. Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.
This paper presents a comprehensive overview of distributed Nash equilibrium (NE) seeking algorithms in non-cooperative games for multi-agent systems (MASs), with a distinct emphasis on the dynamic control perspective. It specifically focuses on the research addressing distributed NE seeking problems in which agents are governed by heterogeneous dynamics. The paper begins by introducing fundamental concepts of general non-cooperative games and the NE, along with definitions of specific game structures such as aggregative games and multi-cluster games. It then systematically reviews existing studies on distributed NE seeking for various classes of MASs from the viewpoint of agent dynamics, including first-order, second-order, high-order, linear, and Euler-Lagrange (EL) systems. Furthermore, the paper highlights practical applications of these theoretical advances in cooperative control scenarios involving autonomous systems with complex dynamics, such as autonomous surface vessels, autonomous aerial vehicles, and other autonomous vehicles. Finally, the paper outlines several promising directions for future research.
