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 computer...
The second part of the paper (Section 8) is devoted to relating the complexity of SAT(⋅) problems to the exponential time hypothesis (ETH) [18], i.e., the hypothesis that k-SAT cannot be solved in subexponential time for k≥3. The ETH has recently gained popularity when studying the...
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...
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...
domain have been largely dominated by the explanatory power provided by shapelets, which are class-discriminatory subsequences extracted from training examples [23,40,42]. However, a clear gap has been present within the time series domain regardingexplainability, which this study has sought to ...
leading to global and fast convergence. compared with ee, tsee adds an additional repulsive factor in the objective function, which results in a simple modification of w and l with no effect on computational performance, making the complexity of tsee comparable to that of ee. in terms of the...
MLPs have also been successfully used for imputing missing time series values across areas of differing complexity and applications. For example, [16, 17] showed that time delayed deep neural network models can impute missing values in univariate hourly traffic volumes. The referenced works focus on...
Before complexity science, variation in repeatedly measured values was divided into two categories: regular changes from one measured value to another, or random changes. Regular changes were thought to be the explainable variance, while random variance was equated with measurement error. In cognitive ...
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...
The minimum number is dictated by the model complexity. Fundamentally, there must be at least as many observations as model parameters to undertake the model fitting, and in this study, at least 8 observations were required because a 7-parameter linear harmonic model fitted by least squares ...