Machine-learning nonstationary noise out of gravitational-wave detectors

Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

BQIABC: A new Quantum-Inspired Artificial Bee Colony Algorithm for Binary Optimization Problems

Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper a new binary version of the ABC algorithm inspired by quantum computing, called binary quantum-inspired artificial bee colony algorithm (BQIABC), is proposed. The BQIABC combines the main structure of ABC with the concepts and principles of quantum computing such as, quantum bit, quantum superposition state and rotation Q-gates strategy to make an algorithm with more exploration ability. The proposed algorithm due to its higher exploration ability can provide a robust tool to solve binary optimization problems. To evaluate the effectiveness of the proposed algorithm, several experiments are conducted on the 0/1 knapsack problem, Max-Ones and Royal-Road functions. The results produced by BQIABC are compared with those of ten state-of-the-art binary optimization algorithms. Comparisons show that BQIABC presents the better results than or similar to other algorithms. The proposed algorithm can be regarded as a promising algorithm to solve binary optimization problems

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

Deep learning para a classificação de ruídos transitórios e sinais nos detetores LIGO

In this work, data from the aLIGO detectores collected during the first two aLIGO and AdV observing runs (O1 and O2), in the form of spectrograms, were classified using Deep Learning models based on Convolutional Neural Networks. As well as training models from scratch, pre-trained models were also employed, and their performance compared. Initially, a brief theoretical introduction on gravitational wave detection was performed, focusing on the LIGO detectors. In addition, the foundations of Deep Learning and current best practices for the training of image classification models were also presented. The computational experiments showed that encoding information from different time windows in the different colour channels enhanced the performance of the models and that small architectures were capable of separating the 22 classes present in the Gravity Spy dataset. Moreover, transfer learning was able to accelerate the training process and achieve classifiers with competitive performance. The best models obtained a macro-averaged F1 score of 96.84% (fine-tuned model) and 97.18% (baseline trained from scratch), which are in line with the best results in the literature for the same dataset. In addition, these models were evaluated on real gravitational wave signals from Compact Binary Coalescences from the first two aLIGO and AdV observing runs, and they achieved recalls of 75% and 25%, respectively, while only having been trained with a small number of signals from gravitational wave simulations.Neste trabalho, dados dos detetores aLIGO recolhidos nos dois primeiros períodos de observação de LIGO e Virgo (O1 e O2), na forma de espectrogramas, foram classificados usando modelos de Deep Learning baseados em redes neuronais convolucionais. Além de serem usados modelos treinados do zero, também se testaram modelos pré-treinados, e os resultados foram comparados. Para isso, começou por se fazer uma breve introdução às ondas gravitacionais e sua deteção nos detetores de LIGO. Foram também introduzidos os fundamentos relacionados com algoritmos de Deep Learning e das boas práticas para o treino de modelos para a classificação de imagens. Verificou-se que usar os diferentes canais de cor das imagens para apresentar informação com diferentes janelas temporais melhora os resultados dos modelos e que, além disso, arquiteturas pequenas são capazes de separar eficazmente as 22 classes presentes no dataset Gravity Spy. Adicionalmente, a técnica de transfer learning permite acelerar a fase de treino e obter classificadores com um desempenho competitivo. Os melhores modelos obtiveram um F1-score médio (macro) de 96.84% para o modelo pré-treinado e de 97.18% para o modelo base treinado do zero. Estes resultados estão em linha com os melhores resultados encontrados na literatura para o mesmo dataset. Adicionalmente, os modelos foram testados em sinais reais de ondas gravitacionais de Coalescências Binárias Compactas detetadas por LIGO, obtendo sensibilidades de, respetivamente, 25% e 75%, apesar de terem sido treinados com um número reduzido de sinais provenientes de simulações de ondas gravitacionais.Mestrado em Engenharia Físic

Machine-learning nonstationary noise out of gravitational-wave detectors

Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation

Applications of gravitational search algorithm in engineering

Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems

A Quantum Computational Approach to Correspondence Problems on Point Sets

Modern adiabatic quantum computers (AQC) are already used to solve difficult combinatorial optimisation problems in various domains of science. Currently, only a few applications of AQC in computer vision have been demonstrated. We review modern AQC and derive the first algorithm for transformation estimation and point set alignment suitable for AQC. Our algorithm has a subquadratic computational complexity of state preparation. We perform a systematic experimental analysis of the proposed approach and show several examples of successful point set alignment by simulated sampling. With this paper, we hope to boost the research on AQC for computer vision