Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Sakai, Y.; Itoh, Y.; Jung, P.; Kokeyama, K.; Kozakai, C.; Nakahira, Katsuko T.; Shikano, Yutaka; Takahashi, Hideya; Uchiyama, Takashi; Ueshima, G.; Washimi, T.; Yamamoto, Takahiro; Yokozawa, T.

doi:10.1002/andp.202200140

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…'Blip' , 'Power_Line' , 'Koi_Fish' , etc) were assigned through collaboration with detector experts and volunteer citizen scientists. This collaborative effort [22][23][24][25][26][27][28][29][30][31] utilises the Gravity Spy sparkling cloud resources 11 , where both glitch images and labels are saved as datasets [32]. Glitch dataset images and labels used in this study were provided by [32].…”

Section: Datasetsmentioning

confidence: 99%

Enhancing the rationale of convolutional neural networks for glitch classification in gravitational wave detectors: a visual explanation

Koyama,

Sakai,

Sasaoka

et al. 2024

Mach. Learn.: Sci. Technol.

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In the pursuit of detecting gravitational waves, ground-based interferometers (e.g. LIGO, Virgo, and KAGRA) face a significant challenge: achieving the extremely high sensitivity required to detect fluctuations at distances significantly smaller than the diameter of an atomic nucleus. Cutting-edge materials and innovative engineering techniques have been employed to enhance the stability and precision of the interferometer apparatus over the years. These efforts are crucial for reducing the noise that masks the subtle gravitational wave signals. Various sources of interference, such as seismic activity, thermal fluctuations, and other environmental factors, contribute to the total noise spectra characteristic of the detector. Therefore, addressing these sources is essential to enhance the interferometer apparatus's stability and precision. Recent research has emphasised the importance of classifying non-stationary and non-Gaussian glitches, employing sophisticated algorithms and machine learning methods to distinguish genuine gravitational wave signals from instrumental artefacts. The time-frequency-amplitude representation of these transient disturbances exhibits a wide range of new shapes, variability, and features, reflecting the evolution of interferometer technology. In this study, we developed a convolutional neural network model to classify glitches using spectrogram images from the Gravity Spy O1 dataset. We employed score-class activation mapping and the uniform manifold approximation and projection algorithm to visualise and understand the classification decisions made by our model. We assessed the model's validity and investigated the causes of misclassification from these results.

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Section: Datasetsmentioning

confidence: 99%

Enhancing the rationale of convolutional neural networks for glitch classification in gravitational wave detectors: a visual explanation

Koyama,

Sakai,

Sasaoka

et al. 2024

Mach. Learn.: Sci. Technol.

Self Cite

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“…In this study, the dataset used is a sentiment dataset of new energy vehicle reviews, characterized by high noise, limited dimensional information, and unclear topics. The ERNIE pretrained model utilizes a large volume of Chinese data for unsupervised learning [40,41], thereby acquiring high-quality feature word vectors. Consequently, replacing the original word vectors with those generated by ERNIE can enhance the performance of the deep CNN model on the automotive review sentiment dataset, improve the model's ability to capture text semantics, and mitigate collinearity issues associated with similar words.…”

Section: Edc Modelmentioning

confidence: 99%

Sentiment Analysis of Online New Energy Vehicle Reviews

Wang

You

et al. 2023

Applied Sciences

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Massive online reviews of new energy vehicles in China are deemed crucial by companies, as they offer valuable insights into user demands and perceptions. An effective analysis enables companies to swiftly adapt and enhance their products while upholding a positive public image. Nonetheless, the sentiment analysis of online car reviews can pose challenges due to factors such as incomplete context, abrupt information bursts, and specialized vocabulary. In this paper, an enhanced hybrid model is introduced, combining Enhanced Representation through kNowledge IntEgration (ERNIE) and a deep (Convolutional Neural Network) CNN, to tackle these challenges. The model utilizes fine-tuned ERNIE for feature extraction from preprocessed review datasets, generating word vectors that encompass comprehensive semantic information. The deep CNN component captures local features from the text, thereby capturing semantic nuances at multiple levels. To address sudden shifts in public sentiment, a channel attention mechanism is employed to amplify the significance of crucial information within the reviews, facilitating comment relationship classification and sentiment prediction. The experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy rate of 97.39% on the test set and significantly outperforming other models.

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Gravity Spy: lessons learned and a path forward

Zevin,

Jackson,

Doctor

et al. 2024

Eur. Phys. J. Plus

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The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

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Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Cited by 4 publications

References 41 publications

Enhancing the rationale of convolutional neural networks for glitch classification in gravitational wave detectors: a visual explanation

Enhancing the rationale of convolutional neural networks for glitch classification in gravitational wave detectors: a visual explanation

Sentiment Analysis of Online New Energy Vehicle Reviews

Gravity Spy: lessons learned and a path forward

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