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  1. Federated learning (FL)-based object detection systems provide many advantages, such as efficiency and privacy. However, performance degradation due to the data heterogeneity issue remains a critical yet often overlooked challenge in recent FL research. In this paper, we address the data heterogeneity issue by introducing model contrastive loss, which significantly improves performance compared to baseline methods. In addition, focal loss is applied to further enhance the prediction accuracy on minority-class objects. Experimental results demonstrate the effectiveness of the proposed federated training framework, achieving approximately 20% improvement in mean average precision over the baseline FedAvg. Furthermore, extensive ablation studies on different hyperparameters in the model contrastive loss are conducted, providing deeper insights into the impact of parameter selection. 
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    Free, publicly-accessible full text available November 1, 2026
  2. Cytochromes P460 oxidise hydroxylamine within the nitrogen cycle and contain as their active site an unusual catalyticc-type haem where the porphyrin is crosslinked to the protein via a lysine... 
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  5. Abstract In 1983, Bouchet conjectured that every flow‐admissible signed graph admits a nowhere‐zero 6‐flow. By Seymour's 6‐flow theorem, Bouchet's conjecture holds for signed graphs with all edges positive. Recently, Rollová et al proved that every flow‐admissible signed cubic graph with two negative edges admits a nowhere‐zero 7‐flow, and admits a nowhere‐zero 6‐flow if its underlying graph either contains a bridge, or is 3‐edge‐colorable, or is critical. In this paper, we improve and extend these results, and confirm Bouchet's conjecture for signed graphs with frustration number at most two, where the frustration number of a signed graph is the smallest number of vertices whose deletion leaves a balanced signed graph. 
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