The Weakly Connected Components (WCC) algorithm finds the weakly-connected components in a directed graph. A weakly-connected component is a group of nodes in which every node is reachable from every other node when edge directions are ignored. Weakly connected components are the maximal connected ...
In principle, it is possible with Pregel and the Connected Components algorithm. However, the algorithm is designed for undirected graphs. Edges are always directed in ArangoDB, but they can be followed in either direction (INBOUND,OUTBOUND) or be treated as undirected (ANY) in AQL traversals....
However, even the most efficient heuristic algorithm in the literature has a quadratic complexity in terms of the number of states of the automaton, and therefore can only scale up to a couple of thousands of states. It was also shown before that if an automaton is not strongly connected, ...
We define the Algorithm 1 Training of Weakly-Shared Deep Transfer Networks Input: DS={(¯xSj , y¯jS )}Nj=S1, C={(xSi , xTi )}Ni=C1, AT ={(˜xTt , y˜tT )}Nt˜=T1, L1, L2, γ, λ, µ, dimension per layers and maxIter. Output: Parameter set Θ. Initialization:...
The teacher model is updated by the exponential moving average (EMA) algorithm, with a step of learning iterations of the student model. The goal is to train an effective segmentation model (i.e., the student model) to segment the infection regions in the CT image. In the testing phase,...
We present an algorithm for obtaining an emergent hypernetwork from a given network system. Its input consists of the adjacency matrix A, the function h and the phases ω1 through ωn, and we assume the nonresonance conditions of the theorem to hold. The algorithm is as follows: Algorithm ...
In future work [35], we will use some of the approaches developed here to design an efficient algorithm for the identification of these networks. A similar approach may be used for the identification of network representations of lowest deficiency, and allow for wider applicability of classical re...
The first is to conceptualize time series as high-dimensional points, enabling the application of point-based techniques, such as the k-nearest neighbor algorithm (Chaovalitwongse et al. 2007), clustering-based (Rebbapragada et al. 2009; Aghabozorgi et al. 2015) and density-based (Breunig...
The algorithm finds the zero-crossing at the first pair of samples with a sign change of the SDF Φ. The sub-discretization accurate location x(u) of the surface is found through linear interpolation of the depth regarding the corresponding SDF values of these points. The depth at a pixel ...
A simple algorithm or classifier may be configured to predict user context based on measurement “x.” As used herein, such a simple algorithm or classifier may also be referred to as a “public model,”“baseline model,” or “baseline inference model.” However, such predictions of user co...