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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination that pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift z  = 7 and 11. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2 's independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with |${\rm AUC}=95~{{\ \rm per\ cent}}$| is possible for 8 < z < 10. While the network prediction lacks the ability to correctly identify ionized regions at higher redshift and differentiate well the few remaining neutral regions at lower redshift due to low contrast between 21-cm signal, noise, and foreground residual in images. Moreover, as the photon sources driving reionization are expected to be located inside ionized regions, we show that SegU-Net v2 can be used to correctly identify and measure the volume of isolated bubbles with |$V_{\rm ion}\gt (10\, {\rm cMpc})^3$| at z > 9, for follow-up studies with infrared/optical telescopes to detect these sources. [ABSTRACT FROM AUTHOR]

Objectives To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. Methods An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n  = 127 030). For validation, 112 radiographs—including those with pneumothorax (n  = 17), nodules (n  = 20), consolidations (n  = 18), and ground-glass opacity (GGO; n  = 16)—were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. Results The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P =.02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (P s <.01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P =.19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P =.26), consolidation (100% [54/54] vs. 96.3% [52/54]; P =.50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P =.69). Conclusions SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. Advances in knowledge This is the first study applying super-resolution in data reduction of chest radiographs. [ABSTRACT FROM AUTHOR]

Determination of individual age is one essential step in the accurate assessment of fish stocks. In non-tropical environments, the manual count of ring-like growth patterns in fish otoliths (ear stones) is the standard method. It relies on visual means and individual judgment and thus is subject to bias and interpretation errors. The use of automated pattern recognition based on machine learning may help to overcome this problem. Here, we employ two deep learning methods based on Convolutional Neural Networks (CNNs). The first approach utilizes the Mask R-CNN algorithm to perform object detection on the major otolith reading axes. The second approach employs the U-Net architecture to perform semantic segmentation on the otolith image in order to segregate the regions of interest. For both methods, we applied a simple postprocessing to count the rings on the output masks returned, which corresponds to the age prediction. Multiple benchmark tests indicate the promising performance of our implemented approaches, comparable to recently published methods based on classical image processing and traditional CNN implementation. Furthermore, our algorithms showed higher robustness compared to the existing methods, while also having the capacity to extrapolate missing age groups and to adapt to a new domain or data source. [ABSTRACT FROM AUTHOR]

The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual cataloguing methodologies are anticipated to be insufficient to exploit the capabilities of new radio surveys. Radio interferometric images of diffuse sources present a challenge for image segmentation tasks due to noise, artifacts, and embedded radio sources. In response to these challenges, we introduce Radio U-Net, a fully convolutional neural network based on the U-Net architecture. Radio U-Net is designed to detect faint and extended sources in radio surveys, such as radio haloes, relics, and cosmic web filaments. Radio U-Net was trained on synthetic radio observations built upon cosmological simulations and then tested on a sample of galaxy clusters, where the detection of cluster diffuse radio sources relied on customized data reduction and visual inspection of Low-Frequency Array Two metre Sky Survey (LoTSS) data. The 83 per cent of clusters exhibiting diffuse radio emission were accurately identified, and the segmentation successfully recovered the morphology of the sources even in low-quality images. In a test sample comprising 246 galaxy clusters, we achieved a 73 per cent accuracy rate in distinguishing between clusters with and without diffuse radio emission. Our results establish the applicability of Radio U-Net to extensive radio survey data sets, probing its efficiency on cutting-edge high-performance computing systems. This approach represents an advancement in optimizing the exploitation of forthcoming large radio surveys for scientific exploration. [ABSTRACT FROM AUTHOR]

The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). AD in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increasingly recognize the significance of employing intelligent systems to detect flaws and defects in product parts. However, industrial settings pose several challenges: diverse categories, limited abnormal samples and vagueness. Hence, there is a growing demand for advanced image AD techniques within industrial product manufacturing. In this paper, an intelligent industrial defective chips detection framework is proposed which mainly consists of three core components. First, the convolutional features of the efficient backbone model is effectively utilized to balance the computational complexity and performance of industrial resource-constrained devices. Secondly, a novel inverse feature matching followed by masking method is proposed to enhance the explanability that localizes the abnormal regions of the abnormal chips. Finally, to evaluate our proposed method a comprehensive ablation study is conducted, where different machine learning and deep learning algorithms are analysed to claim the superiority of our method. Furthermore, to help the research community, a benchmark dataset is collected from real-world industry manufacturing for defective chip detection. The empirical results from the dataset demonstrate the strength and effectiveness of the proposed model compared to the other models. [ABSTRACT FROM AUTHOR]

In this research, our focus lies in exploring the effectiveness of a frequency-velocity convolutional neural network (CNN) in the efficient and non-intrusive acquisition of 2D wave velocity visuals of near-surface geological substances, accomplished through the analysis of data from ground-penetrating radar (GPR). To learn complex correlations between antenna readings and subsurface velocities, the proposed CNN model makes use of the spatial features present in the GPR data. By employing a network architecture capable of accurately detecting both local and global patterns within the data, it becomes feasible to efficiently extract valuable velocity information from GPR readings. The CNN model is trained and validated using a substantial dataset consisting of GPR readings along with corresponding ground truth velocity images. Diverse subsurface settings, encompassing different soil types and geological characteristics, are employed to gather the GPR measurements. In the supervised learning approach employed to train the CNN model, the GPR measurements serve as input, while the associated ground truth velocity images are utilized as target outputs. The model is trained using backpropagation and optimized using a suitable loss function to reduce the difference between the predicted velocity images and the actual images. The experimental results demonstrate the effectiveness of the proposed CNN method in accurately deriving 2D velocity images of near-surface materials from GPR antenna observations. Compared to traditional techniques, the CNN model exhibits superior velocity calculation precision and achieves high levels of accuracy. Moreover, when applied to unseen GPR data, the trained model exhibits promising generalization abilities, highlighting its potential for practical subsurface imaging applications. [ABSTRACT FROM AUTHOR]

A parked vehicle damaged by a hit-and-run can only be repaired at the expense of the owner, unless the fleeing vehicle is identified and the driver apprehended. Identifying the fleeing vehicle involves using a video investigation method that searches for perpetrators through CCTV footage of the crime scene. When the length of the recorded video is long, the investigation may require an extended amount of time from the investigator, resulting in an added burden on their daily work. Some commercial companies are using object recognition and tracking technology to detect hit-and-run incidents; however, detecting small movements of a vehicle during a minor collision still remains a challenge. Therefore, there is a need for a system that can detect small movement in a vehicle in a lengthy video. Automatic recognition and tracking require a sufficient amount of training dataset. However, such a dataset for hit-and-run incidents is not publicly available. One of the reasons behind this scarcity is that it may violate personal information protection acts. On the other hand, instead of using real accident videos, we could use actors to simulate such accident scenes. Although this may be feasible, creating such a dataset would require substantial costs. In this paper, we describe a new dataset for hit-and-run incidents. We collected 833 hit-and-run videos by recreating a parking lot using miniaturized cars. This dataset has been made publicly available through Kaggle. We used three-dimensional convolution neural network, which is frequently used in the field of action recognition, to detect small movements of vehicles during hit-and-run incidents. In addition, the proportion of the area that surrounds the target vehicle to the min-max box of the vehicle itself and the length of the input frame are varied to compare the accuracy. As a result, we were able to achieve better accuracy by using the lowest proportion and the shortest input frame. [ABSTRACT FROM AUTHOR]

In this study, an improved version of aquila optimizer (AO) known as EHAOMPA has been developed by using the marine predators algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and fish aggregating devices mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on 23 standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a convolutional neural network hyperparameter (CNN-hyperparameter) optimization for Corona Virus Disease 19 (COVID-19) computed tomography-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared with other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data, as indicated by the high R 2 score. Consequently, we propose that GANs can be employed as cost-effective models for black hole image generation and reliably augment training data sets for other parametrization algorithms. [ABSTRACT FROM AUTHOR]

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra -like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 per cent at an 89 per cent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 per cent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC 4765, NGC 533, NGC 2300, NGC 3091, NGC 4073, NGC 4125, and NGC 5129) and a number of potential cavity candidates. [ABSTRACT FROM AUTHOR]

We release photometric redshifts, reaching ∼0.7, for ∼14M galaxies at r ≤ 20 in the 11 500 deg2 of the SDSS north and south Galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on ugriz stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA, and BOSS surveys. Representative training sets of ∼370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of luminous red galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signalling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias, and rate of catastrophic failures of the median point estimate are of order σMAD = 0.014, <Δ z norm>=0.0015, |$\eta (|\Delta z_{\rm norm}|\gt 0.05)=4{{\, \rm per\ cent}}$| on a representative test sample at r < 19.8, outperforming currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members. [ABSTRACT FROM AUTHOR]

Direct imaging is an active research topic in astronomy for the detection and the characterization of young substellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this context, post-processing methods combining several images recorded with the pupil tracking mode of telescope are needed. In previous works, we have presented a data-driven algorithm, PACO , capturing locally the spatial correlations of the data with a multivariate Gaussian model. PACO delivers better detection sensitivity and confidence than the standard post-processing methods of the field. However, there is room for improvement due to the approximate fidelity of the PACO statistical model to the time evolving observations. In this paper, we propose to combine the statistical model of PACO with supervised deep learning. The data are first pre-processed with the PACO framework to improve the stationarity and the contrast. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources. Finally, the trained network delivers a detection map. The photometry of detected sources is estimated by a second CNN. We apply the proposed approach to several data sets from the VLT/SPHERE instrument. Our results show that its detection stage performs significantly better than baseline methods (cADI and PCA), and leads to a contrast improvement up to half a magnitude compared to PACO. The characterization stage of the proposed method performs on average on par with or better than the comparative algorithms (PCA and PACO) for angular separation above 0.5 arcsec. [ABSTRACT FROM AUTHOR]