在这一过程中，參與處理區塊的用戶端可以得到一定量新發行的比特幣，以及相關的交易手續費。為了得到這些新產生的比特幣，參與處理區塊的使用者端需要付出大量的時間和計算力（為此社會有專業挖礦機替代電腦等其他低配的網路設備），這個過程非常類似於開採礦業資源，因此中本聰將資料處理者命名為“礦工”，將資料處理活動稱之為“挖礦”。這些新產生出來的比特幣可以報償系統中的資料處理者，他們的計算工作為比特幣對等網路的正常運作提供保障。
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At last, the deep Studying-dependent FFE has extra opportunity for additional usages in other fusion-linked ML tasks. Multi-process Discovering can be an approach to inductive transfer that increases generalization by utilizing the domain info contained while in the schooling alerts of similar responsibilities as area knowledge49. A shared illustration learnt from Each and every job support other tasks learn superior. Even though the function extractor is properly trained for disruption prediction, several of the outcomes can be utilised for an additional fusion-associated purpose, including the classification of tokamak plasma confinement states.
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Along with the databases determined and recognized, normalization is done to remove the numerical discrepancies involving diagnostics, and also to map the inputs to an proper selection to facilitate the initialization from the neural network. In accordance with the benefits by J.X. Zhu et al.19, the functionality of deep neural community is just weakly depending on the normalization parameters provided that all inputs are mapped to appropriate range19. Consequently the normalization approach is done independently for each tokamaks. As for The 2 datasets of EAST, the normalization parameters are calculated independently Based on different instruction sets. The inputs are normalized Using the z-rating system, which ( X _ rm norm =frac X- rm imply (X) rm std (X) ).
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Performances involving the a few styles are revealed in Desk one. The disruption predictor according to FFE outperforms other versions. The product based on the SVM with manual function extraction also beats the general deep neural community (NN) product by a major margin.
比特幣自動櫃員機 硬體錢包是專門處理比特幣的智慧設備，例如只安裝了比特幣用戶端與聯網功能的樹莓派。由于不接入互联网，因此硬體錢包通常可以提供更多的安全保障措施�?線上錢包服務[编辑]
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