If unassigned vertices remain in the set of vertices and no vertices were assigned to the processing order then a cycle exists in the directed graph. These steps are repeated as long as there are vertices in the set of vertices that are unassigned and no cycle has been detected....
If you do this and detect a cycle in the graph, a deadlock has occurred (unless somebody in the cycle is on the timeout plan and might break the deadlock willingly). This is a foolproof strategy, but can be costly in terms of run-time impact to lock acquisitions. In many cases, a...
transaction T1 has a shared (S) lock on row r1 and is waiting to get an exclusive (X) lock on r2. Transaction T2 has a shared (S) lock on r2 and is waiting to get an exclusive (X) lock on row r1. This results in a lock cycle in which T1 and T2 wait for each other to ...
Deciphering the non-trivial interactions and mechanisms driving the evolution of time-varying complex networks (TVCNs) plays a crucial role in designing optimal control strategies for such networks or enhancing their causal predictive capabilities. In th
An anomaly is recognized if there is a difference between these two profiles. • Machine learning based: this technique has the ability of learning and improving its performance over time. It tends to focus on constructing a system which can optimize its performance in a loop cycle and can ...
a complete graph\(G(t)=(V,E,\mathscr {w}(t))\)at timetis a real-valued scalar function\(\mathfrak {C}_G : \big ( V\times V \setminus \{ (u,u) \,|\, u\in V\} \big ) \times [0,\infty ) \mapsto {\mathbb R}\)that maps every pair of nodes ofGat timetto a ...
In reality, however, cellular systems are highly dynamic and responsive to environmental cues [17]. The real PPI network in cell keeps changing over different stages of the cell cycle [18], leading to multiple dynamic protein interaction networks. As such, it is desirable to design novel ...
In our influence-spreading model, the probability matrix is called an influence-spreading matrix, which describes the directed influence between all pairs of nodes in the network. By using the local maxima of an objective function, our method can standardise and help in comparing different ...
As can be seen from the figure, the model improves rapidly in terms of precision, recall, and average precision, and the curve starts to flatten out after 300 iterations, and the model stops after 500 iterations. Figure 7. This shows the graph during the training cycle (where (A,E) are...
{ if(helper(graph, u, visited, recStack)) //checks if the DFS tree from the vertex contains a cycle return true; } } return false; } /* Driver function */ int main() { list<int>* graph = new list<int>[NUM_V]; graph[0].push_back(1); ...