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Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat

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发表于 2025-3-28 18:00:37 | 显示全部楼层
Juan Gonger with the costs of machinery operations, became more expensive. Thus simpler alternatives to conventional plough tillage, termed minimum or conservation tillage, became attractive as offering savings in the cost of establishing crops [.]. A further incentive was the availability of herbicides for
发表于 2025-3-28 21:03:37 | 显示全部楼层
A Novel Binary BCI Systems Based on Non-oddball Auditory and Visual Paradigms-oddball visual and auditory paradigms, respectively, which significantly outperformed the linear classifier model. These results open up novel avenues for practical ERP systems, which could increase the usability of current brain-computer interfaces remarkably.
发表于 2025-3-29 00:28:51 | 显示全部楼层
A Just-In-Time Compilation Approach for Neural Dynamics Simulationvides a friendly and highly flexible interface for users to define an arbitrary dynamical system, and the JIT compilation enables the defined model to run efficiently. We hope that BrainPy can serve as a general software for both research and education in computational neuroscience.
发表于 2025-3-29 05:42:04 | 显示全部楼层
STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognitn STCN-GR to capture spatial-temporal information. Additionally, the connectivity of the graph can be adjusted adaptively in different layers of networks, which increases the flexibility of networks compared with the fixed graph structure used by original GCNs. On two high-density sEMG (HD-sEMG) dat
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DFFCN: Dual Flow Fusion Convolutional Network for Micro Expression Recognitionn datasets: CASME II, SAMM and SMIC with Leave-One-Subject-Out (LOSO) cross-validation. The results demonstrated that our method achieves competitive performance when compared with the existing approaches, with the best UF1 (0.8452) and UAR (0.8465).
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Semantic Perception Swarm Policy with Deep Reinforcement Learningention network is adopted to effectively model individual-level and group-level relational information. The distributed and transferable swarm policy can perceive the information of uncertain number of agents in swarm environments. Various simulations and real-world experiments on several challengin
发表于 2025-3-30 00:43:43 | 显示全部楼层
Open-Set Recognition with Dual Probability Learninga new method called Dual Probability Learning Model (DPLM). The model built a neural Gaussian Mixed Model for probability estimation. To learn this model, we also added the normalized joint probability of latent representations into the objective function in the training stage. The results showed th
发表于 2025-3-30 04:11:12 | 显示全部楼层
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