Runtime complexity refers to the computational time required by an algorithm to process each new observed timestep, with a complexity similar to the forward probability extension in the CHMM model, denoted as O(D|S|2). Here, D represents the depth of the deepest possible goal chain in the ...
Firstly, the design of small peptides that mimic proteins in complexity, but are sufficiently small to allow detailed simulation studies [1–4]. Secondly, the development of fast (nanosecond) time-resolved spectroscopy methods to study peptide folding dynamics on the same timescale as compute...
The time complexity of an algorithm is an important aspect to consider34,35. The computational complexity of the PSO algorithm is difficult to calculate precisely. It is mainly composed of the swarm size, the maximum number of iterations, and the complexity of the problem to be solved36. Accor...
Our community structure detection algorithm includes two parts. The first part of the algorithm finds the shortest path length. The time complexity of this part is\(\mathcal {O}(\left| E\right| + N\log {}N)\)49, where\(\left| E \right|\)is the number of edges andNis the number ...
diegoferigoaddedcomplexity::mediumpriority::normalseverity::normalplatform::linuxtype::bugcomponent::blockslabelsAug 30, 2018 Thank guys for the support @traversaro Are you using a SSD or a classical hard disk? SSD Do you think it make sense to add two YARP time blocks, one that is execute...
To further decrease the barrier of recording this type of content, it would be preferable if the pipeline could skip the step of using a green screen and also work robustly under natural lighting. These are for many applications solved problems but would of course increase the complexity and co...
Public transport assignment models have increased in complexity in order to describe passengers' route choices as detailed and correctly as possible. Important trends in the development are (1) timetable-based assignment, (2) inclusion of feeder modes, (3) use of stochastic components to describe ...
With the aim of designing low complexity prioritized IDNC algorithms, after selecting a packet combination over a given feasible window ω ℓ at time slot t, we compute the resulting upper bound on the probability that the individual completion times of all receivers for the first ℓ video ...
As we know, time-varying equations have been well solved by zeroing neural networks, and some of these can even achieve finite-time convergence. However, in terms of solving time-varying inequalities, the current neural networks can only make its error function exponentially converge to zero, ...
Other examples include turbulence analysis87,111, stream flows89, flame front dynamics57 and laser beam wandering112. Still, one has to note that the line dividing this multi-scale approach from the standard complexity–entropy plane is fuzzy, as many works using the latter include some analysis...