How late will bitcoin transfer arrive
Publish: 2021-05-09 07:13:46
1. After the chain is slow because of the congestion on the chain, the market has fallen like this these two days, it must be slow. Wait a minute
2. In recent years, e to the waste of resources, unstable block output and computing power attack of pow, although POW is recognized as the most widely used and secure consensus mechanism, there is still a great risk of being attacked for the block chain with insufficient computing power in the whole network. More and more consensus mechanisms are proposed to be used in blockchain projects for trial and experiment. Dpos is one of the mainstream consensus mechanisms, and LBTC has adopted this mechanism. Dpos ensures that the voting right is in the hands of the coin holder, so the coin holder will be able to choose whether to pass the motion by voting, so as to determine the development direction of the project. At the same time, it also means that the development direction of the project depends on the hands of the people who care about the project itself, and the power of wisdom will promote the better development of the project. At the same time, the advantages of dpos mechanism are that there is no computing power attack, strictly abide by the time block and save resources
the number of nodes of LBTC is 101. Compared with 21 or 51 nodes, the coin holder can vote for 51 nodes at most once in the wallet of LBTC, which further reces the risk of centralization; LBTC can output blocks stably in 3 seconds, and the block size of 2m ensures that LBTC is 400 times more efficient than bitcoin, and the transaction speed can reach more than 2000 transactions per second; The most important difference between LBTC and other projects is that LBTC's dpos mechanism has the rule of irreversible block. When the number of block agents reaches 90% or more in one round, or when the number of block agents exceeds 70%, the first block in the previous round can be considered as irreversible, so as to prevent bifurcation.
the number of nodes of LBTC is 101. Compared with 21 or 51 nodes, the coin holder can vote for 51 nodes at most once in the wallet of LBTC, which further reces the risk of centralization; LBTC can output blocks stably in 3 seconds, and the block size of 2m ensures that LBTC is 400 times more efficient than bitcoin, and the transaction speed can reach more than 2000 transactions per second; The most important difference between LBTC and other projects is that LBTC's dpos mechanism has the rule of irreversible block. When the number of block agents reaches 90% or more in one round, or when the number of block agents exceeds 70%, the first block in the previous round can be considered as irreversible, so as to prevent bifurcation.
3. If it's a bitcoin wallet, then!!! My colleagues have not been able to log in the bitcoin wallet since August last year (they can only dig for dogs). The customer service is not online, so they can't reflect it, let alone log in, and the 6W principal is gone
4. It's usually a working day
5. Generally, only the payee can return bitcoin after 24 hours. Any bitcoin transaction is irreversible and can only be returned by the payee.
6. If the block is not fully confirmed, please wait patiently. Only after the confirmation is completed can the platform enter the account for you
if the block has been fully confirmed, but the okex account has not arrived, please contact the online customer service or submit the work order for processing.
if the block has been fully confirmed, but the okex account has not arrived, please contact the online customer service or submit the work order for processing.
7. [information]
through the information panel, the guardian can see the current image and detailed attribute values of the character
[attribute stone]
- the guardian will drop various types of [attribute stone] when fighting in the game. After using the [attribute stone] in the backpack, you can add the corresponding attributes
★ the guardian can view the attributes currently added through the attribute stone interface
★ the guardian has an upper limit on the attributes obtained through the attribute stone. After reaching the upper limit, he can't continue to improve the attributes by using the attribute stone; When a character raises the upper limit of an attribute, the attribute stone that was previously used after exceeding the upper limit can continue to raise the attribute
★ the upper limit of the attribute stone can be increased by increasing the level of the character and the advanced level of the character
[advanced]
★ level: there are six stages of character advancement, each stage can be upgraded by 5 levels (each level is added with attributes), and each level can also improve the basic attributes of character upgrading
★ function: after upgrading, the attribute promoted by each level will be higher (the growth value of the protagonist will be increased when upgrading)
progress: Advanced requires the consumption of corresponding levels of advanced essence and gold to enhance the progress. Only increase but not decrease), and the progress will return to 0 after advanced to the next level
★ critical hit: it is possible to generate critical hit ring enhancement, and you will get more progress ring critical hit.
through the information panel, the guardian can see the current image and detailed attribute values of the character
[attribute stone]
- the guardian will drop various types of [attribute stone] when fighting in the game. After using the [attribute stone] in the backpack, you can add the corresponding attributes
★ the guardian can view the attributes currently added through the attribute stone interface
★ the guardian has an upper limit on the attributes obtained through the attribute stone. After reaching the upper limit, he can't continue to improve the attributes by using the attribute stone; When a character raises the upper limit of an attribute, the attribute stone that was previously used after exceeding the upper limit can continue to raise the attribute
★ the upper limit of the attribute stone can be increased by increasing the level of the character and the advanced level of the character
[advanced]
★ level: there are six stages of character advancement, each stage can be upgraded by 5 levels (each level is added with attributes), and each level can also improve the basic attributes of character upgrading
★ function: after upgrading, the attribute promoted by each level will be higher (the growth value of the protagonist will be increased when upgrading)
progress: Advanced requires the consumption of corresponding levels of advanced essence and gold to enhance the progress. Only increase but not decrease), and the progress will return to 0 after advanced to the next level
★ critical hit: it is possible to generate critical hit ring enhancement, and you will get more progress ring critical hit.
8. 1，4 matlab是有帮助文档的，我没有明白你所指的去中心化处理是什么，PCA的结果在数组自己的维度
以下是帮助文档，请仔细阅读
coeff = pca(X) returns the principal component coefficients, also known as loadings, for the n-by-p data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is p-by-p. Each column of coeffcontains coefficients for one principal component, and the columns are in descending order of component variance. By default, pca centers the data and uses the singular value decomposition (SVD) algorithm.
example
coeff = pca(X,Name,Value) returns any of the output arguments in the previous syntaxes using additional options for computation and handling of special data types, specified by one or more Name,Value pair arguments.
For example, you can specify the number of principal components pca returns or an algorithm other than SVD to use.
example
[coeff,score,latent] = pca(___) also returns the principal component scores in score and the principal component variances in latent. You can use any of the input arguments in the previous syntaxes.
Principal component scores are the representations of X in the principal component space. Rows of score correspond to observations, and columns correspond to components.
The principal component variances are the eigenvalues of the covariance matrix of X.
example
[coeff,score,latent,tsquared] = pca(___) also returns the Hotelling's T-squared statistic for each observation in X.
example
[coeff,score,latent,tsquared,explained,mu] = pca(___) also returns explained, the percentage of the total variance explained by each principal component and mu, the estimated mean of each variable in X.
2. PCA 和SVD的不同是，他们分解矩阵的方式是不同的我建议你翻看wikipedia里面SVD和PCA的说明，里面公式很清晰了
以下是帮助文档，请仔细阅读
coeff = pca(X) returns the principal component coefficients, also known as loadings, for the n-by-p data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is p-by-p. Each column of coeffcontains coefficients for one principal component, and the columns are in descending order of component variance. By default, pca centers the data and uses the singular value decomposition (SVD) algorithm.
example
coeff = pca(X,Name,Value) returns any of the output arguments in the previous syntaxes using additional options for computation and handling of special data types, specified by one or more Name,Value pair arguments.
For example, you can specify the number of principal components pca returns or an algorithm other than SVD to use.
example
[coeff,score,latent] = pca(___) also returns the principal component scores in score and the principal component variances in latent. You can use any of the input arguments in the previous syntaxes.
Principal component scores are the representations of X in the principal component space. Rows of score correspond to observations, and columns correspond to components.
The principal component variances are the eigenvalues of the covariance matrix of X.
example
[coeff,score,latent,tsquared] = pca(___) also returns the Hotelling's T-squared statistic for each observation in X.
example
[coeff,score,latent,tsquared,explained,mu] = pca(___) also returns explained, the percentage of the total variance explained by each principal component and mu, the estimated mean of each variable in X.
2. PCA 和SVD的不同是，他们分解矩阵的方式是不同的我建议你翻看wikipedia里面SVD和PCA的说明，里面公式很清晰了
9. In a situation like this, you should be alert if there are more than a few times. Some time ago, I couldn't get to the western region. Later, I said that the website was closed and the western region was running away. Now I'm doing investment in Du (DBI). I sign in 3% of the profit every day and 1.5% can be withdrawn at any time. You can register to learn about it. Registration invitee: dream
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