Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost

Buccheri, Enrico; Dell’Aquila, D.; Russo, Marco

doi:10.1016/j.obmed.2022.100398

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…BP has been successfully used so far in a number of research fields, see e.g. [36][37][38][39], but this is the first time it is used in the field of GW physics.…”

Section: The Bpmentioning

confidence: 99%

“…Such techniques are well-developed in other research fields (see e.g. [36][37][38][39][40]) but were not previously proposed for GW searches. The model described in this paper, and used in the first layer, is trained and tested exploiting a bank of simulated BBH waveforms [41,42] in synthetic Gaussian noise, produced according to the aLIGO design sensitivity curve 8 previously used in [31].…”

Section: Introductionmentioning

confidence: 99%

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A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals

Barone,

Dell’Aquila,

Russo

2023

Mach. Learn.: Sci. Technol.

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Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective GW detection algorithms is crucial. We propose a novel layered framework for real-time detection of GWs inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g. it identifies 45 % of low signal-to-noise-ration GW signals, against 65 % of the state-of-the-art, at a false alarm probability of 10−2), but has a much lower computational complexity (it exploits only 4 numerical features in the present implementation) and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of GW signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.

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“…BP has been successfully used so far in a number of research fields, see e.g. [36][37][38][39], but this is the first time it is used in the field of GW physics.…”

Section: The Bpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals

Barone,

Dell’Aquila,

Russo

2023

Mach. Learn.: Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This tool was previously successfully used in a number of investigations in several research 44th Symposium on Nuclear Physics Cocoyoc Journal of Physics: Conference Series 2619 (2023) 012004 fields (see e.g. [53,54,55,56]). BP belongs to the field of Evolutionary Computation [57], a widely used programming scheme that exploits concepts derived from the Darwinian theory of evolution to solve optimization problems (see also Ref.…”

Section: Dataset and Modelingmentioning

confidence: 99%

Understanding Heavy-ion Fusion Cross Section Data Using Novel Artificial Intelligence Approaches

Dell’Aquila,

Gnoffo,

Lombardo

et al. 2023

J. Phys.: Conf. Ser.

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An unprecedentedly extensive dataset of complete fusion cross section data is modeled via a novel artificial intelligence approach. The analysis was focused on light-to-medium-mass nuclei, where fission-like phenomena are more difficult to occur. The method used to derive the models exploits a state-of-the-art hybridization of genetic programming and artificial neural networks and is capable to derive, in a data-driven way, an analytical expression that serves to predict integrated cross section values. We analyzed a comprehensive set of nuclear variables, including quantities related to the nuclear structure of projectile and target. In this paper, we describe the derivation of two computationally simple models that can satisfactorily describe, with a reduced number of variables and only a few parameters, a large variety of light-to-intermediate-mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the oncet of multi-fragmentation phenomena. The underlying methods are of potential use for a broad domain of applications in the nuclear field.

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“…Refs. [40][41][42]57], but this is the first time it is used in the field of gravitational wave physics.…”

Section: The Brain Projectmentioning

confidence: 99%

“…Refs. [40][41][42][43]) but were not previously proposed for GW searches. The model described in this paper, and used in the first layer, is trained and tested exploiting a bank of simulated BBH waveforms [44,45] in synthetic Gaussian noise, produced according to the aLIGO design sensitivity curve previously used in [35].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Layer Modular Approach for Real-Time Gravitational-Wave Detection

Barone¹,

Dell’Aquila²,

Russo³

2022

Preprint

Self Cite

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Advanced LIGO and Advanced Virgo ground-based interferometers are poised to probe an unprecedentedly large volume of space, enhancing the discovery power of the observations to even new sources of gravitational wave emitters. In this scenario, the development of highly optimized gravitational wave detection algorithms is crucial. We propose a novel layered framework for realtime detection of gravitational waves inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. To train and test the models, we used simulated binary black hole gravitational wave waveforms in synthetic Gaussian noise representative of Advanced LIGO sensitivity design. Compared to more complex approaches, such as convolutional neural networks, our framework, even using the simple ground model described in the paper, has similar performance but with a much lower computational complexity and a higher degree of modularity. Furthermore, the underlying exploitation of short-term features makes the results of the new framework virtually independent against time-position of gravitational wave signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with second generation interferometers.

show abstract

Stratified analysis of the age-related waist circumference cut-off model for the screening of dysglycemia at zero-cost

Cited by 8 publications

References 18 publications

A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals

A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals

Understanding Heavy-ion Fusion Cross Section Data Using Novel Artificial Intelligence Approaches

A Novel Multi-Layer Modular Approach for Real-Time Gravitational-Wave Detection

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