This coverage, combined with an effective moving-object detection algorithm, has allowed LINEAR to be very productive when searching for near-Earth objects (NEOs), comets, and main-belt asteroids.
Exchange algorithm has allowed to extract a design with only 28 experiments.
Decoupling of equation-oriented, physics-based models from the optimization algorithm has allowed for simulation and optimization of rather complex concepts.
This algorithm has allowed the identification of biologically-relevant modules, such as protein complexes and other functionally-related protein clusters, in different model organisms and humans [ 4, 5, 10, 24].
The introduction of massively parallelized computer architectures [7 9], together with the availability of more efficient algorithms, has allowed for a spectacular increase in terms of the size of the molecular systems studied [10,11] as well as the length of the simulations that can be performed [6,12].
This is not just due to increased resources: a succession of breakthroughs in machine learning algorithms has allowed us to leverage existing resources much more effectively.
While integrationwith continuous glucose monitoring together with integrative algorithms has allowed great improvement in glucose control, factors such as meal initiation, exercise, stress and insulin boluses previously delivered are variables which need to be incorporated into decision-making algorithms to guide improved insulin delivery.
The development of biclustering algorithms has allowed biologists to start unraveling the underlying functional mechanisms in living organisms.
But advances in computing power and novel processing algorithms have allowed scientists to analyze the information captured during an MRI in completely new ways.
The wide availability of increasing computing power coupled with the development of more efficient training algorithms have allowed the implementation of deep learning principles in a manner and span that had not been previously possible.
