33,184 research outputs found
On some sequencing problems in finite groups
AbstractA finite group is called Z-sequenceable if its non-identity elements can be listed x1, x2, …, xn so that xixi+1 for i = 1, 2, …, n − 1. Various necessary and sufficient conditions are determined for such sequencings to exist. In particular, it is proved that if n ⩾ 3, then the symmetric group Sn is not Z-sequenceable
Accurate Profiling of Microbial Communities from Massively Parallel Sequencing using Convex Optimization
We describe the Microbial Community Reconstruction ({\bf MCR}) Problem, which
is fundamental for microbiome analysis. In this problem, the goal is to
reconstruct the identity and frequency of species comprising a microbial
community, using short sequence reads from Massively Parallel Sequencing (MPS)
data obtained for specified genomic regions. We formulate the problem
mathematically as a convex optimization problem and provide sufficient
conditions for identifiability, namely the ability to reconstruct species
identity and frequency correctly when the data size (number of reads) grows to
infinity. We discuss different metrics for assessing the quality of the
reconstructed solution, including a novel phylogenetically-aware metric based
on the Mahalanobis distance, and give upper-bounds on the reconstruction error
for a finite number of reads under different metrics. We propose a scalable
divide-and-conquer algorithm for the problem using convex optimization, which
enables us to handle large problems (with species). We show using
numerical simulations that for realistic scenarios, where the microbial
communities are sparse, our algorithm gives solutions with high accuracy, both
in terms of obtaining accurate frequency, and in terms of species phylogenetic
resolution.Comment: To appear in SPIRE 1
Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines
Air Traffic Control (ATC) is a complex safety critical environment. A tower
controller would be making many decisions in real-time to sequence aircraft.
While some optimization tools exist to help the controller in some airports,
even in these situations, the real sequence of the aircraft adopted by the
controller is significantly different from the one proposed by the optimization
algorithm. This is due to the very dynamic nature of the environment. The
objective of this paper is to test the hypothesis that one can learn from the
sequence adopted by the controller some strategies that can act as heuristics
in decision support tools for aircraft sequencing. This aim is tested in this
paper by attempting to learn sequences generated from a well-known sequencing
method that is being used in the real world. The approach relies on a genetic
algorithm (GA) to learn these sequences using a society Probabilistic
Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus,
decomposing the learning problem into a group of agents that need to work
together to learn the overall problem. Three sequence metrics (Levenshtein,
Hamming and Position distances) are compared as the fitness functions in GA. As
the results suggest, it is possible to learn the behavior of the
algorithm/heuristic that generated the original sequence from very limited
information
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