Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing fie...
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Collective behaviors by self-organization are ubiquitous in nature and human society and extensive efforts have been made to explore the mechanisms behind them. Artificial intelligence (AI) as a rapidly developing field is of great potential for these tasks. By combining reinforcement learning with evolutionary game (RLEG), we numerically discover a rich spectrum of collective behaviors—explosive events, oscillation, and stable states, etc., that are also often observed in the human society. In this work, we aim to provide a theoretical framework to investigate the RLEGs systematically. Specifically, we formalize AI-agents' learning processes in terms of belief switches and behavior modes defined as a series of actions following beliefs. Based on the preliminary results in the time-independent environment, we investigate the stability at the mixed equilibrium points in RLEGs generally, in which agents reside in one of the optimal behavior modes. Moreover, we adopt the maximum entropy principle to infer the composition of agents residing in each mode at a strictly stable point. When the theoretical analysis is applied to the 2×2 game setting, we can explain the uncovered collective behaviors and are able to construct equivalent systems intuitively. Also, the inferred composition of different modes is consistent with simulations. Our work may be helpful to understand the related collective emergence in human society as well as behavioral patterns at the individual level and potentially facilitate human-computer interactions in the future.
In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by diffe...
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A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patte...
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Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrat...
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The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the co...
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How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grain...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
How to learn a good fine-grained image representation is a key problem for fine-grained tasks. Most previous supervised methods suffer from insufficient training data, which require laborious annotations of fine-grained objects. In this paper, we propose an annotation-free method for fine-grained image representation, dubbed Multi-Grained Selection and Fusion (MGSF). The proposed MGSF extracts two types of visual features, i.e., fine-grained discriminative features that highlight informative convolutional parts by spatial selection and channel selection, and coarse-grained scene-level features that provide context information for fine-grained objects. Extensive experiments in fine-grained image retrieval demonstrate the superiority of our proposed representation compared with the state-of-the-art approaches on several fine-grained datasets.
This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle,which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time *** roles o...
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This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle,which realizes a sampled-data driven approach between a continuous-time signal and its discrete-time *** roles of certain parameters,such as sampling time interval or model order,and signal characteristics,i.e.,its curvature,on the approximation are *** errors in one and multiple-step predictions are ***,time-variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.
Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigati...
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Existing evapotranspiration ( ET ) estimation models face inherent limitations when relying solely on physics-based or data-driven paradigms. To address this issue, we propose three data-physics hybrid modeling method...
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Existing evapotranspiration ( ET ) estimation models face inherent limitations when relying solely on physics-based or data-driven paradigms. To address this issue, we propose three data-physics hybrid modeling methods for improving instantaneous ET estimation in this study. A Physics-data Learning (PDL) model is first formed by adding a complementary physical variable generated by Penman–Monteith (PM) equation to a deep learning (DL) model along with driving variables to regress latent heat flux. Building on the PDL, a Physics-Augmented Learning (PAL) model is then formulated by introducing a physics-augmented term into the loss function. Finally, a Physics-Augmented Residual Learning (PARL) model is developed by using the residual learning technique to deeply integrate the PM and pure DL baseline models. Using the FLUXNET dataset, three proposed models are compared with the baselines on ten vegetation types (VTs) across the globe. The results show that all proposed models improve the accuracy of two baselines and reduce the uncertainty of pure DL to different extents. Among them, the PARL achieves the highest accuracy and robustness, with NSE (RMSE) ranging from 0.71–0.82 (22.40–43.14 W/m 2 ) across ten VTs. The PAL ranks second and effectively mitigates the PDL’s sensitivity to imperfect physical knowledge. Although three proposed models show better extrapolation ability than the pure DL under conditions of limited data, the PARL stands out for its superior generalization under four created extreme climate scenarios. These results confirm the potential of data-physics hybrid modeling in ET estimation, which is conducive to supporting efficient irrigation water management.
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