3,144 research outputs found
Super- and Anti-Principal Modes in Multi-Mode Waveguides
We introduce a new type of states for light in multimode waveguides featuring
strongly enhanced or reduced spectral correlations. Based on the experimentally
measured multi-spectral transmission matrix of a multimode fiber, we generate a
set of states that outperform the established "principal modes" in terms of the
spectral stability of their output spatial field profiles. Inverting this
concept also allows us to create states with a minimal spectral correlation
width, whose output profiles are considerably more sensitive to a frequency
change than typical input wavefronts. The resulting "super-" and
"anti-principal" modes are made orthogonal to each other even in the presence
of mode-dependent loss. By decomposing them in the principal mode basis, we
show that the super-principal modes are formed via interference of principal
modes with closeby delay times, whereas the anti-principal modes are a
superposition of principal modes with the most different delay times available
in the fiber. Such novel states are expected to have broad applications in
fiber communication, imaging, and spectroscopy.Comment: 8 pages, 5 figures, plus supplementary materia
Adaptive multimodal continuous ant colony optimization
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima
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Prediction of microbial communities for urban metagenomics using neural network approach.
BACKGROUND:Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS:We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations
Evolutionary Alternating Direction Method of Multipliers for Constrained Multi-Objective Optimization with Unknown Constraints
Constrained multi-objective optimization problems (CMOPs) pervade real-world
applications in science, engineering, and design. Constraint violation has been
a building block in designing evolutionary multi-objective optimization
algorithms for solving constrained multi-objective optimization problems.
However, in certain scenarios, constraint functions might be unknown or
inadequately defined, making constraint violation unattainable and potentially
misleading for conventional constrained evolutionary multi-objective
optimization algorithms. To address this issue, we present the first of its
kind evolutionary optimization framework, inspired by the principles of the
alternating direction method of multipliers that decouples objective and
constraint functions. This framework tackles CMOPs with unknown constraints by
reformulating the original problem into an additive form of two subproblems,
each of which is allotted a dedicated evolutionary population. Notably, these
two populations operate towards complementary evolutionary directions during
their optimization processes. In order to minimize discrepancy, their
evolutionary directions alternate, aiding the discovery of feasible solutions.
Comparative experiments conducted against five state-of-the-art constrained
evolutionary multi-objective optimization algorithms, on 120 benchmark test
problem instances with varying properties, as well as two real-world
engineering optimization problems, demonstrate the effectiveness and
superiority of our proposed framework. Its salient features include faster
convergence and enhanced resilience to various Pareto front shapes.Comment: 29 pages, 17 figure
The Construction of Optimized High-Order Surface Meshes by Energy-Minimization
Despite the increasing popularity of high-order methods in computational fluid dynamics, their application to practical problems still remains challenging. In order to exploit the advantages of high-order methods with geometrically complex computational domains, coarse curved meshes are necessary, i.e. high-order representations of the geometry. This dissertation presents a strategy for the generation of curved high-order surface meshes. The mesh generation method combines least-squares fitting with energy functionals, which approximate physical bending and stretching energies, in an incremental energy-minimizing fitting strategy. Since the energy weighting is reduced in each increment, the resulting surface representation features high accuracy. Nevertheless, the beneficial influence of the energy-minimization is retained. The presented method aims at enabling the utilization of the superior convergence properties of high-order methods by facilitating the construction of coarser meshes, while ensuring accuracy by allowing an arbitrary choice of geometric approximation order. Results show surface meshes of remarkable quality, even for very coarse meshes representing complex domains, e.g. blood vessels
The measurement of profit, profitability, cost and revenue efficiency through data envelopment analysis: A comparison of models using BenchmarkingEconomicEfficiency.jl
We undertake a systematic comparison of existing models measuring and decomposing the economic efficiency
of organizations. For this purpose we introduce the package BenchmarkingEconomicEfficiency.jl for the
open-source Julia language including a set of functions to be used by scholars and professionals working
in the fields of economics, management science, engineering, and operations research. Using mathematical
programming methods known as Data Envelopment Analysis, the software develops code to decompose
economic efficiency considering alternative definitions: profit, profitability, cost and revenue. Economic
efficiency can be decomposed, multiplicative or additively, into a technical (productive) efficiency term and
a residual term representing allocative (or price) efficiency. We include traditional decompositions like the
radial efficiency measures associated with the input (cost) and output (revenue) approaches, as well as
new ones corresponding to the Russell measures, the directional distance function, DDF (including novel
extensions like the reverse DDF, modified DDF, or generalizations based on Hölder norms), the generalized
distance function, and additive measures like the slack based measure, their weighted variants, etc. Moreover,
regardless the underlying economic efficiency model, many of these technical inefficiency measures are
available for calculation in a computer software for the first time. This article details the theoretical methods
and the empirical implementation of the functions, comparing the obtained results using a common dataset
on Taiwanese BanksJosé L. Zofío thanks the grant PID2019-105952 GB-I00 funded by
Ministerìo de Ciencia e Innovación/ Agencia Estatal de Investigación
/10.13039/50110001103
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