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Acoustic holography (AH) is a technique with significant potential in realms, such as biomedicine, industry, and augmented reality. The implementation of acoustic holograms can be realized by a passive approach or active ones. Although the passive approach (by a 3D printer) can achieve high-quality acoustic field generation, it is constrained by high manufacturing costs and static field control. On the contrary, the active approach with a phased transducer array (PTA) as the latest technique stands out since it supports dynamic, flexible, and reconfigurable acoustic field generation. However, current PTA-based AH techniques face the drawback of inferior acoustic field fineness due to the Spatial Bandwidth Product (SBP) limit of PTA, which hinders the application of PTA in precise tasks, such as neural electrodes and microfluidics control. To address this issue, we propose a super-resolution acoustic holography (SRAH) method inspired by the concept of super-resolution in ultrasonic imaging and computer vision, by which we can generate acoustic fields reaching the physical diffraction limit of acoustic waves regardless SBP of PTA. In other words, this method enables high-SBP acoustic field generation with low-SBP PTA. The method is based on self-supervised learning, integrating a generative adversarial network and a physical model of acoustic wave propagation, specifically the linear accumulation method. Both simulation and experimental results demonstrate that the proposed method can generate high-fidelity acoustic fields suitable for intricate tasks with low-SBP PTA. Moreover, the performance of the algorithm improves as the target SBP increases. Therefore, the proposed SRAH method shows great potential for applications requiring elaborate manipulation. [ABSTRACT FROM AUTHOR]

An accurate estimation of three-dimensional (3D) temperature fields in channel flows is challenging but critical for many important applications such as heat exchangers, radiation energy collectors, and enhanced geothermal systems. In this paper, we demonstrate the possibility of inferring temperature fields from concentration fields for laminar convection flows in a 3D channel using a machine learning (ML) approach. The study involves generation of data using 3D numerical simulations, application of deep learning methodology using conditional generative adversarial networks (cGANs), and analysis of how dataset selection affects model performance. The model is also tested for applicability in different convection scenarios. Results show that cGANs can successfully infer temperature fields from concentration fields, and the reconstruction accuracy is sensitive to the training dataset selected. In this study, we demonstrate how ML can be used to overcome the limitations of traditional heat and mass analogy functions widely used in heat transfer research. [ABSTRACT FROM AUTHOR]

Control over the two-dimensional electron gas (2DEG) in AlGaN/GaN heterostructures is crucial for their practical applications in current semiconducting devices. However, the oxide surface structures inducing 2DEG are still ambiguous because oxide-stoichiometry (OS) matching structures possess occupied surface donor states at 1.0–1.8 eV below the conduction band minimum of bulk but are usually not available in energy than electron counting (EC) rule structures. In this work, a global optimization algorithm was introduced to explore the possible oxidation structures on GaN (0001) and AlN (0001) surfaces; the method was demonstrated to be available due to the fact that the reported oxidized structures were reproduced at each stoichiometry. Interestingly, the two similar oxide structures with close energy were found in each oxide-bilayer, which can be used to clarify the experimental observations of disordered surface oxide layers below 550 °C. Additionally, new stable oxidation structures with low surface energy were proposed. Interestingly, the new OS matching structures are proposed with remarkably lower energy than EC rule structures under cation-rich and oxygen-poor conditions, which is caused by the large formation enthalpy of Al2O3 and Ga2O3. Further electronic structure calculations demonstrate that the new OS structures possess highest occupied states above the half of the gap and are the origin of 2DEG in AlGaN/GaN heterostructures. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques. [ABSTRACT FROM AUTHOR]

Summary: Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively. [ABSTRACT FROM AUTHOR]

Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal–Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods. [ABSTRACT FROM AUTHOR]

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network--HistoXGAN--capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy.". [ABSTRACT FROM AUTHOR]

This paper explores a novel emotional voice conversion (EVC) method based on the style generative adversarial network combined with dynamic fundamental frequency difference compensation. On the one hand, in terms of spectrum mapping, based on the method of EVC using star generative adversarial network, we propose to replace one-hot vectors with the emotion style features extracted from spectrum to represent emotional information. It enables the conversion not only to fully learn emotion style from the target speech, but also to transfer unseen emotion styles to a new utterance, that is, one-shot EVC. On the other hand, given that the traditional logarithm Gaussian normalization transformation and its some variants for prosody transfer are not enough to reflect the fine-grained fundamental frequency distribution difference between the different emotional speech, we propose the improved strategy of dynamic fundamental frequency difference compensation. The experimental results show that our proposed method can accomplish high quality one-shot EVC, significantly outperforming the baseline in terms of speech quality and emotional saturation in both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

Accurate and timely bearing fault detection is imperative for optimal system functioning and the implementation of preventative maintenance measures. Deep learning models provide viable solutions to these malfunctions, however, the lack of labelled data makes the training both expensive and cumbersome. To remedy this, various semi-supervised approaches have surfaced in the last decade, significantly mitigating the need for extensive labelled data but with added computational cost. This study proposes one such approach by leveraging generative adversarial networks (GAN) trained on a time-frequency based representation. The proposed Parallel Convolutions Semi-Supervised GAN, namely PC-SSGAN, uses bottleneck parallel convolutions blocks to capture multi-scale features in both local and global contexts, lacing both the generator and discriminator with enhanced feature extraction capabilities and simultaneously reducing the parameters and training time. The Proposed framework is evaluated on two distinct open-source datasets. The classification accuracy for both models exceeded 99.50%. Moreover, the proposed parallel convolutions-based architecture spent approximately 33% less time on training than the normal convolutional layers. It has been foreseen that the proposed fault detection system can be integrated into the motor fault tolerant control system to produce a unified framework that can make informed decisions to handle the bearing faults effectively. [ABSTRACT FROM AUTHOR]

Background and Aim Methods Results Conclusion Computed tomography of the abdomen exhibits subtle and complex features of liver lesions, subjectively interpreted by physicians. We developed a deep learning‐based localization and classification (DLLC) system for focal liver lesions (FLLs) in computed tomography imaging that could assist physicians in more robust clinical decision‐making.We conducted a retrospective study (approval no. EMRP‐109‐058) on 1589 patients with 17 335 slices with 3195 FLLs using data from January 2004 to December 2020. The training set included 1272 patients (male: 776, mean age 62 ± 10.9), and the test set included 317 patients (male: 228, mean age 57 ± 11.8). The slices were annotated by annotators with different experience levels, and the DLLC system was developed using generative adversarial networks for data augmentation. A comparative analysis was performed for the DLLC system versus physicians using external data.Our DLLC system demonstrated mean average precision at 0.81 for localization. The system's overall accuracy for multiclass classifications was 0.97 (95% confidence interval [CI]: 0.95–0.99). Considering FLLs ≤ 3 cm, the system achieved an accuracy of 0.83 (95% CI: 0.68–0.98), and for size > 3 cm, the accuracy was 0.87 (95% CI: 0.77–0.97) for localization. Furthermore, during classification, the accuracy was 0.95 (95% CI: 0.92–0.98) for FLLs ≤ 3 cm and 0.97 (95% CI: 0.94–1.00) for FLLs > 3 cm.This system can provide an accurate and non‐invasive method for diagnosing liver conditions, making it a valuable tool for hepatologists and radiologists. [ABSTRACT FROM AUTHOR]

The current quantitative retrieval of Aerosol Optical Depth (AOD) typically uses Top-of-atmosphere (TOA) reflectance data obtained by the scale and offset terms of radiometric calibration. Errors can be introduced during the conversion of Digital Number (DN) values to TOA reflectance, restraining the accurate retrieval of AOD. Particularly when surface reflectance is high, conversion errors can significantly distort the retrieval of Aerosol Optical Depth (AOD), given the minimal impact of aerosols on the radiation detected by satellite sensors. To counteract this, we introduce an innovative retrieval algorithm that harnesses a Generative Adversarial Network (GANN), a deep learning (DL) model, to accurately derive AOD from the scaled digital number (DN) values of multi-spectral satellite imagery. The DL technology enables the use of scaled DN values for AOD retrieval, bypassing the conversion errors associated with TOA reflectance since it relies on a sample learning approach instead of radiative transfer calculations. To ensure the number and representativeness of training samples, a total of 37,103 samples from different underlying surfaces from 2014 to 2018 were obtained using a space-time matching strategy. Our K-cross validation demonstrates that employing DN values for AOD retrieval promises to yield higher accuracy compared to using TOA reflectance. Independent 2019 data were used to estimate GANN, MCD19A2 and MOD04_3K aerosol products. Our model showed superior performance compared to the other products in terms of a higher correlation (R = 0.8184) with AERONET AOD and obtaining more reliable retrievals (7601). The sensitivity experiment suggests that GANN exhibits limitations in areas characterized by high aerosol loads and heterogeneity. Our study can give a novel insight into AOD retrieval based on multi-spectral satellite and DL models. [ABSTRACT FROM AUTHOR]

Advancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective image processing system adept at deblurring and segmentation, employing a generative adversarial network (GAN) with UNet as the generator and Wasserstein GAN with Gradient Penalty (WGAN‐gp) as the loss function. This approach performs exceptionally in deblurring pavement crack images and improves segmentation accuracy. Models were trained with sharp and artificially blurred images, with WGAN‐gp surpassing other loss functions in effectiveness. This research innovatively suggests assessing deblurring quality through segmentation accuracy in addition to peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), revealing that PSNR and SSIM may not fully capture deblurring effectiveness for pavement crack images. An extensive evaluation of various generators, including UNet, lightweight UNet, TransUNet, DeblurGAN, DeblurGAN‐v2, and MIMO‐UNet, identifies the superior performance of UNet on simulated motion blur. Validation with actual motion‐blurred images confirms the effectiveness of the proposed model. These findings demonstrate that GAN‐based models have great potential in overcoming motion blur challenges in pavement crack detection systems, marking a notable advancement in the field. [ABSTRACT FROM AUTHOR]

This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and young healthy subjects. Synthetic data for the three classes is generated based on class conditions and random noise by leveraging a combination of conditional adversarial generative neural networks and reconstruction networks. This synthetic data, alongside real data, is utilized as training data for the patient classification model to enhance classification accuracy. The fidelity of the synthetic data is assessed visually to measure the validity and diversity of the generated data qualitatively while quantitatively evaluating distribution similarity to real data. Furthermore, the clinical efficacy of the patient classification model employing synthetic data is demonstrated by showcasing improved classification accuracy through a comparative analysis between results obtained using solely real data and those obtained when both real and synthetic data are utilized. This methodological approach holds promise in addressing data insufficiency in the digital healthcare domain, employing deep learning methodologies, and developing early disease diagnosis tools. [ABSTRACT FROM AUTHOR]

Cyber-physical system is one of the essential components of wireless sensor network (WSN). WSN conjointly senses, collects, analyzes and communicates data on recognized items at service part before transmitting this data to the owner of the network. Blackhole, Grayhole, Flooding, Scheduling are the common WSN attacks that rapidly damage the system. Intrusion detection systems, such as WSN have drawbacks, like less detection rates, high computation costs and high percentage of false alarms, because sensor nodes have limited resources. Therefore, a Self Attention Generative Adversarial Capsule Network as an intrusion detection scheme for WSN (SAGACN-ID-WSN) is proposed in this paper to address the aforementioned issues. Initially, the data are amassed from WSN-DS dataset. Then, the data are sent for pre-processing. The input data are preprocessed using the Guided Box Filtering (GBF) method. Then, the preprocessed data are provided to the Atomic Orbital Search (AOS), Tasmanian Devil Optimization (TDO) and Ebola Optimization Search (EOS) algorithms for feature selection. The attacks are divided into four categories: normal, Grayhole attack, Black hole attack, flooding attack, and scheduling attack. The proposed ID-WSN is implemented in Network simulator 2 (NS-2) utilizing the dataset of WSN-DS. The metrics, like accuracy, precision, recall, F-measure, ROC and computational time are analyzed. The proposed SAGACN-ID-WSN technique attains 25.5%, 20.12% and 20.7% high accuracy, 51.136%, 59.04% and 32.81%; higher precision, 2.292%, 1.51% and 3.915% lower error rate compared to the existing models, like Dataset for Intrusion Detection Systems in WSN (SLGBM-ID-WSN), multi-layer machine learning-based ID-WSN (NB-RF-ID-WSN), deep learning and entity embedding-base intrusion detection systems in WSN, respectively. [ABSTRACT FROM AUTHOR]

The surface quality of aluminium alloy castings is crucial to quality control. Aiming to address the challenges of limited samples and extensive computation in deep learning‐based surface defect detection for aluminium alloy castings, this paper proposes a surface defect detection method based on data enhancement and the Casting Real‐Time DEtection TRansformer. First, to tackle the issue of small sample sizes and uneven distribution in surface defect data sets of aluminium alloy castings, ECA‐MetaAconC Deep Convolution Generative Adversarial Networks is proposed for generating defects with fewer samples and employ the image augmentation (IMGAUG) library for sample enhancement. Second, building upon the Real‐Time DEtection TRansformer (RT‐DETR), a lightweight partial‐rep convolution is designed to decrease the network's parameter count. Simultaneously, the Deformable attention module and the DRBC3 module are introduced to enhance the neck network, thereby improving the model's capability to capture information and enhancing its detection performance. Compared to RT‐DETR, this method reduces the number of model parameters by 38.7%, increases mAP by 1.5%, and achieves a frame rate that is 1.58 times higher than the original model. The experimental results demonstrate that this method can effectively and accurately detect surface defects in aluminium alloy castings, satisfying industrial requirements. [ABSTRACT FROM AUTHOR]

Insulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems. [ABSTRACT FROM AUTHOR]

Background: Bioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment. Results: This study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model—evolutionary scale modeling (ESM-2)—to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models—CapsuleGAN, GRU, and CNN—while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach. Conclusion: In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Automatic primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) is a quite challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution. Therefore, most recently proposed methods based on radiomics or deep learning (DL) is difficult to achieve good results on CT datasets.A peritumoral radiomics‐guided generative adversarial network (PRG‐GAN) was proposed to address this challenge.A total of 157 NPC patients with CT images was collected and divided into training, validation, and testing cohorts of 108, 9, and 30 patients, respectively. The proposed model was based on a standard GAN consisting of a generator network and a discriminator network. Morphological dilation on the initial segmentation results from GAN was first conducted to delineate annular peritumoral region, in which radiomics features were extracted as priori guide knowledge. Then, radiomics features were fused with semantic features by the discriminator's fully connected layer to achieve the voxel‐level classification and segmentation. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were used to evaluate the segmentation performance using a paired samples t‐test with Bonferroni correction and Cohen's d (d) effect sizes. A two‐sided p‐value of less than 0.05 was considered statistically significant.The model‐generated predictions had a high overlap ratio with the ground truth. The average DSC, HD95, and ASSD were significantly improved from 0.80 ± 0.12, 4.65 ± 4.71  mm, and 1.35 ± 1.15  mm of GAN to 0.85 ± 0.18 (p = 0.001, d = 0.71), 4.15 ± 7.56 mm (p = 0.002, d = 0.67), and 1.11 ± 1.65 mm (p < 0.001, d = 0.46) of PRG‐GAN, respectively.Integrating radiomics features into GAN is promising to solve unclear border limitations and increase the delineation accuracy of GTVp for patients with NPC. [ABSTRACT FROM AUTHOR]

ABSTRACT The rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT applications (DMA‐ACWGAN‐IoT). Here, the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial‐based Multiple‐Input Multiple‐Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR‐4 material. The proposed DMA‐ACWGAN‐IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA‐PRF‐ML), machine learning verification depending on a distinctive SWB multiple slotted four‐port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB‐MIMO‐IOT‐ML), and dual‐band miniaturized composite right–left‐handed transmission line ZOR antenna along machine learning method for microwave communication (DB‐ZORA‐MC‐ML). [ABSTRACT FROM AUTHOR]

A novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases. [ABSTRACT FROM AUTHOR]

The availability of comprehensive datasets is a crucial challenge for developing artificial intelligence (AI) models in various applications and fields. The lack of large and diverse public fabric defect datasets forms a major obstacle to properly and accurately developing and training AI models for detecting and classifying fabric defects in real-life applications. Models trained on limited datasets struggle to identify underrepresented defects, reducing their practicality. To address these issues, this study suggests using a conditional generative adversarial network (cGAN) for fabric defect data augmentation. The proposed image-to-image translator GAN features a conditional U-Net generator and a 6-layered PatchGAN discriminator. The conditional U-Network (U-Net) generator can produce highly realistic synthetic defective samples and offers the ability to control various characteristics of the generated samples by taking two input images: a segmented defect mask and a clean fabric image. The segmented defect mask provides information about various aspects of the defects to be added to the clean fabric sample, including their type, shape, size, and location. By augmenting the training dataset with diverse and realistic synthetic samples, the AI models can learn to identify a broader range of defects more accurately. This technique helps overcome the limitations of small or unvaried datasets, leading to improved defect detection accuracy and generalizability. Moreover, this proposed augmentation method can find applications in other challenging fields, such as generating synthetic samples for medical imaging datasets related to brain and lung tumors. [ABSTRACT FROM AUTHOR]

ABSTRACT In practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high‐quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data. [ABSTRACT FROM AUTHOR]

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities. Here, for the first time, we propose an innovative approach that combines Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of viral oncoprotein prediction. Our methodology evaluated multiple machine learning models, including Random Forest, Multilayer Perceptron, Light Gradient Boosting Machine, eXtreme Gradient Boosting, and Support Vector Machine. In ten-fold cross-validation on our training dataset, the GAN-enhanced Random Forest model demonstrated superior performance metrics: 0.976 accuracy, 0.976 F1 score, 0.977 precision, 0.976 sensitivity, and 1.0 AUC. During independent testing, this model achieved 0.982 accuracy, 0.982 F1 score, 0.982 precision, 0.982 sensitivity, and 1.0 AUC. These results establish our new tool, VirOncoTarget, accessible via a web application. We anticipate that VirOncoTarget will be a valuable resource for researchers, enabling rapid and reliable viral oncoprotein prediction and advancing our understanding of their role in cancer biology. [ABSTRACT FROM AUTHOR]

Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification by incorporating synthetic data. This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability. [ABSTRACT FROM AUTHOR]

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design. [ABSTRACT FROM AUTHOR]

In image dehazing, non-uniform haze leads to issues such as image blurring, distortion, low contrast, and surface detail loss. Existing dehazing techniques struggle with the uneven distribution of haze, especially in scenes with varying depth, making it difficult to restore high-quality images. This research introduces the Central Difference Embedding Convoluted Generative Adversarial Network (CDECGAN), which enhances local contrast variations to address haze-induced blur and low-contrast areas. To further improve adaptability, the Updated Human Memory Algorithm (UHMA) is integrated to optimise hyperparameters and model performance. The approach also combines image denoising and dehazing, using the Generic Edge Attention (GAE) architecture to denoise while preserving edge sharpness. The Weighted Median Channel Prior (WMCP) model helps focus on high-frequency details and estimate scene depth, solving issues related to fixed-size local patches. Performance metrics confirm the effectiveness of CDECGAN, achieving an average peak signal-to-noise ratio of 25.58 dB, a structural similarity index of 0.884, a natural image quality evaluator score of 11.18, and other notable results. The CDECGAN method outperforms existing techniques by successfully handling non-uniform haze and depth-related challenges in image dehazing. [ABSTRACT FROM AUTHOR]

Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data. [ABSTRACT FROM AUTHOR]

ABSTRACT Energy efficiency and secure data transmission are considered as the main design targets of wireless sensor network (WSN). Previous energy‐aware cluster head (CH) selection approaches could not provide secured data transmission. To address this problem, a self‐attention–based cycle‐consistent generative adversarial network (SaCyCsGAN) with honey badger algorithm (HyBA)–adopted energy‐aware routing (EgAR) in WSN is proposed. Initially, the proposed system uses a CH to carry out the routing practice. Accordingly, SaCyCsGAN classifier is exploited for CH selection under firm fitness function: delay, distance, energy, cluster density, and traffic rate. Afterward CH selection, a spiteful node may enter the cluster and acts as a CH. Hence, best path selection is needed. For that, HyBA is utilized, it selects the best path depending upon triplet parameter: trust, connectivity, and service degree. Finally, data are conveying into base station (BS) and vice versa under best path. Finally, the proposed EgAR‐WSN‐SaCyCsGAN‐HyBA method attains 24.13%, 28.57%, 5.88%, and 20.37% higher throughput and 18.50%, 21.67%, 13.33%, and 22.22% lesser energy consumption evaluated to the existing methods such as multiple‐objective CH utilizing self‐attention basis progressive generative adversarial network for safe data aggregation (EgAR‐WSN‐SAPrGAN‐ArVOA), reinforcement learning–based energy‐efficient optimized routing protocol in WSN (EgAR‐WSN‐RLGT‐BCSA), data aggregation including clustering protocol in WSN under machine learning (EgAR‐WSN‐AfNN), and optimum cluster along trusted path for routing formation with categorization of intrusion under machine learning categorization (EgAR‐WSN‐RsBPDT‐HoCSOA). [ABSTRACT FROM AUTHOR]

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurately predicting the degradation and remaining useful life (RUL) of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for enhancing their reliability, particularly in automotive and energy sectors. Traditional models often fail to capture the complex, non-linear degradation patterns of PEMFCs, resulting in suboptimal predictions. This study addresses these technological gaps by introducing a novel approach, the Self-Attention Temporal Dual Discriminator Generative Adversarial Network (SAT-DD-GAN). This model is designed to overcome the limitations of existing methods by integrating advanced Long Short-Term Memory (LSTM) networks for time-series analysis, a self-attention mechanism to focus on critical degradation signals, and dual discriminators to improve prediction accuracy and robustness. By addressing the gap in handling long-term dependencies and complex degradation patterns, the proposed SAT-DD-GAN bridges the disconnect between traditional models and the real-world degradation behavior of PEMFCs. The SAT-DD-GAN achieved groundbreaking predictive performance, with the lowest RMSE (0.983E-3 for static load, 1.012E-3 for dynamic load), MAPE (0.036E-1 and 0.253E-1), MAE (0.776E-3 and 0.806E-3), and the highest R2-score (0.9998 and 0.9983), alongside zero relative error for both datasets. These results demonstrate the model's ability to capture the intricate degradation dynamics more effectively than existing state-of-the-art methods. By filling the technology gap of accurately modeling PEMFC degradation under varying load conditions, this study establishes a new standard for PEMFC prognostics and paves the way for future innovations in optimizing fuel cell performance and longevity. • Introduced a novel SAT-DD-GAN model for accurate prediction of PEMFC stack voltage degradation and Remaining Useful Life (RUL). • Innovatively combined LSTM, self-attention, and dual discriminator mechanisms for enhanced predictive robustness. • Achieved superior accuracy, with RMSE as low as 0.000983 (static load) and R2-score of 0.9998 (static load). • Demonstrated advanced handling of static and dynamic loads, outperforming recent models in key prediction metrics. • Set a new benchmark in predictive maintenance for PEMFCs, offering potential advancements in neural network-driven prognostics. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, defect detection is pivotal for enhancing productivity and yield. This paper introduces a novel weakly supervised method, the Implicit Cross Domain Diffusion Model (ICDDM), designed to tackle defect pattern segmentation challenges in the absence of detailed pixel-wise annotations. ICDDM employs a generative model to estimate the joint distribution of images depicting defect patterns and background circuits, formulating this estimation as a Markov Chain and optimizing it through denoising score matching. Building on this, we propose the Cross Domain Latent Diffusion Model (CDLDM), inspired by the Latent Diffusion Model, which simplifies the diffusion process into a lower-dimensional latent space to boost detection efficiency. Further enhancing our model, we introduce the Knowledge Distillation Cross Domain Diffusion Model (KDCDDM), which utilizes CDLDM as a teacher model and a Generative Adversarial Network (GAN) as a student model. This approach significantly accelerates the diffusion process by reducing the number of necessary denoising iterations while maintaining robust model performance. This suite of techniques offers a comprehensive solution for efficient and effective defect detection in semiconductor production environments. [ABSTRACT FROM AUTHOR]

Underwater simultaneous localization and mapping (SLAM) is essential for effectively navigating and mapping underwater environments; however, traditional SLAM systems have limitations due to restricted vision and the constantly changing conditions of the underwater environment. This study thoroughly examined the underwater SLAM technology, particularly emphasizing the incorporation of deep learning methods to improve performance. We analyzed the advancements made in underwater SLAM algorithms. We explored the principles behind SLAM and deep learning techniques, examining how these methods tackle the specific difficulties encountered in underwater environments. The main contributions of this work are a thorough assessment of the research into the use of deep learning in underwater image processing and perception and a comparison study of standard and deep learning-based SLAM systems. This paper emphasizes specific deep learning techniques, including generative adversarial networks (GANs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and other advanced methods to enhance feature extraction, data fusion, scene understanding, etc. This study highlights the potential of deep learning in overcoming the constraints of traditional underwater SLAM methods, providing fresh opportunities for exploration and industrial use. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones' features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an R 2 of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. [ABSTRACT FROM AUTHOR]

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency. [ABSTRACT FROM AUTHOR]

The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally derived data to real data to enrich the variability represented in the training set. Traditional augmentation methods, such as rotation, permutation, and time-warping, have shown some benefits in improving classifier performance, but often fail to produce realistic training examples. This study employs Conditional Generative Adversarial Networks (cGANs) to create synthetic kinematic data from a publicly available dataset, closely mimicking the experimentally measured reaching movements of stroke survivors. This approach not only captures the complex temporal dynamics and common movement patterns after stroke, but also significantly enhances the training dataset. By training deep learning models on both synthetic and experimental data, we enhanced task classification accuracy: models incorporating synthetic data attained an overall accuracy of 80.0%, significantly higher than the 66.1% seen in models trained solely with real data. These improvements allow for more precise task classification, offering clinicians the potential to monitor patient progress more accurately and tailor rehabilitation interventions more effectively. [ABSTRACT FROM AUTHOR]

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator's performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures. [ABSTRACT FROM AUTHOR]

Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches (derived from satellite observations). Moreover, numerically simulated emissions are typically easier to obtain, even if they are less reliable than satellite acquisitions, which, being derived from actual measurements, are considered a more trustworthy instrument for performing chemistry and climate investigations. Given the coarseness and relative lack of satellite-derived measurements, fine-grained numerically simulated emissions could be exploited to enhance them. However, simulated (BU) and observed (TD) emissions usually differ regarding value range and spatiotemporal resolution. In this work, we present a novel deep learning (DL)-based approach to increase the spatial resolution of satellite-derived isoprene emissions, investigating the adoption of efficient domain adaptation (DA) techniques to bridge the gap between numerically simulated emissions and satellite-derived emissions, avoiding the need for retraining a specific super-resolution (SR) algorithm on them. For this, we propose a methodology based on the cycle generative adversarial network (CycleGAN) architecture, which has been extensively used for adapting natural images (like digital photographs) of different domains. In our work, we depart from the standard CycleGAN framework, proposing additional loss terms that allow for better DA and emissions' SR. We extensively demonstrate the proposed method's effectiveness and robustness in restoring fine-grained patterns of observed isoprene emissions. Moreover, we compare different setups and validate our approach using different emission inventories from both domains. Eventually, we show that the proposed DA strategy paves the way towards robust SR solutions even in the case of spatial resolution mismatch between the training and testing domains and in the case of unknown testing data. [ABSTRACT FROM AUTHOR]

Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods. [ABSTRACT FROM AUTHOR]

Facial image synthesis using delineated face images is a complex task associated with computer vision. The delineated face image synthesis such as sketch to face generation or thermal to visual face generation using generative adversarial networks (GANs) is a widely accepted task due to its generative capability. The accurate and realistic sample generation by using GAN with an attention network shows a more realistic sample generation. Attention-based network improves the network's learning by prioritizing the learning over a specific region. Motivated by the success of attention mechanism in recent literature, we develop a new inception-based encoder–decoder self-attentive generative adversarial network (ISA-GAN) by incorporating an inception network with self-attention-based learning. The proposed network is embedded with parallel self-attention, which helps to generate high-quality images and converges faster in terms of training epochs. The proposed scenario has been experimented for face synthesis using delineated face images of sketch images over the CUHK dataset. We also test it over thermal to visual face synthesis using WHU-IIP and CVBL-CHILD datasets. The proposed ISA-GAN outperforms the state-of-the-art generative models for face synthesis. The proposed ISA-GAN shows on an average improvement of 9.95 % in SSIM score over CUHK dataset while 10.38 % , 12.58 % for WHU-IIP and CVBL-CHILD datasets, respectively. [ABSTRACT FROM AUTHOR]

Forecasting precipitation accurately poses significant challenges due to various factors affecting its distribution and intensity, including but not limited to subgrid variability. Although higher resolution simulations are often considered to improve precipitation forecasts, it is crucial to note that simply increasing resolution may not suffice without appropriate adjustments to parameterization schemes or tuning. Traditionally, ensembles of simulations are used to generate uncertainty predictions associated with precipitation forecasts, but this approach can be computationally intensive. As an alternative, there is a growing trend towards leveraging neural networks for precipitation prediction, which offers potential computational advantages. We propose a new approach to generating ensemble weather predictions for high‐resolution precipitation without requiring high‐resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble, on which our model was trained on. [ABSTRACT FROM AUTHOR]

Background: The current mainstream multi‐modal medical image‐to‐image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel‐wise aligned training data to constrain the model optimization. However, obtaining pixel‐wise aligned multi‐modal medical image datasets is challenging. Unsupervised methods can be trained without paired data, but their reliability cannot be guaranteed. At present, there is no ideal multi‐modal medical image‐to‐image translation method that can generate reliable translation results without the need for pixel‐wise aligned data. Purpose: This work aims to develop a novel medical image‐to‐image translation model that is independent of pixel‐wise aligned data (MITIA), enabling reliable multi‐modal medical image‐to‐image translation under the condition of misaligned training data. Methods: The proposed MITIA model utilizes a prior extraction network composed of a multi‐modal medical image registration module and a multi‐modal misalignment error detection module to extract pixel‐level prior information from training data with misalignment errors to the largest extent. The extracted prior information is then used to construct a regularization term to constrain the optimization of the unsupervised cycle‐consistent Generative Adversarial Network model, restricting its solution space and thereby improving the performance and reliability of the generator. We trained the MITIA model using six datasets containing different misalignment errors and two well‐aligned datasets. Subsequently, we conducted quantitative analysis using peak signal‐to‐noise ratio and structural similarity as metrics. Moreover, we compared the proposed method with six other state‐of‐the‐art image‐to‐image translation methods. Results: The results of both quantitative analysis and qualitative visual inspection indicate that MITIA achieves superior performance compared to the competing state‐of‐the‐art methods, both on misaligned data and aligned data. Furthermore, MITIA shows more stability in the presence of misalignment errors in the training data, regardless of their severity or type. Conclusions: The proposed method achieves outstanding performance in multi‐modal medical image‐to‐image translation tasks without aligned training data. Due to the difficulty in obtaining pixel‐wise aligned data for medical image translation tasks, MITIA is expected to generate significant application value in this scenario compared to existing methods. [ABSTRACT FROM AUTHOR]

Background: Deep learning (DL) techniques have been extensively applied in medical image classification. The unique characteristics of medical imaging data present challenges, including small labeled datasets, severely imbalanced class distribution, and significant variations in imaging quality. Recently, generative adversarial network (GAN)‐based classification methods have gained attention for their ability to enhance classification accuracy by incorporating realistic GAN‐generated images as data augmentation. However, the performance of these GAN‐based methods often relies on high‐quality generated images, while large amounts of training data are required to train GAN models to achieve optimal performance. Purpose: In this study, we propose an adversarial learning‐based classification framework to achieve better classification performance. Innovatively, GAN models are employed as supplementary regularization terms to support classification, aiming to address the challenges described above. Methods: The proposed classification framework, GAN‐DL, consists of a feature extraction network (F‐Net), a classifier, and two adversarial networks, specifically a reconstruction network (R‐Net) and a discriminator network (D‐Net). The F‐Net extracts features from input images, and the classifier uses these features for classification tasks. R‐Net and D‐Net have been designed following the GAN architecture. R‐Net employs the extracted feature to reconstruct the original images, while D‐Net is tasked with the discrimination between the reconstructed image and the original images. An iterative adversarial learning strategy is designed to guide model training by incorporating multiple network‐specific loss functions. These loss functions, serving as supplementary regularization, are automatically derived during the reconstruction process and require no additional data annotation. Results: To verify the model's effectiveness, we performed experiments on two datasets, including a COVID‐19 dataset with 13 958 chest x‐ray images and an oropharyngeal squamous cell carcinoma (OPSCC) dataset with 3255 positron emission tomography images. Thirteen classic DL‐based classification methods were implemented on the same datasets for comparison. Performance metrics included precision, sensitivity, specificity, and F1$F_1$‐score. In addition, we conducted ablation studies to assess the effects of various factors on model performance, including the network depth of F‐Net, training image size, training dataset size, and loss function design. Our method achieved superior performance than all comparative methods. On the COVID‐19 dataset, our method achieved 95.4%±0.6%$95.4\%\pm 0.6\%$, 95.3%±0.9%$95.3\%\pm 0.9\%$, 97.7%±0.4%$97.7\%\pm 0.4\%$, and 95.3%±0.9%$95.3\%\pm 0.9\%$ in terms of precision, sensitivity, specificity, and F1$F_1$‐score, respectively. It achieved 96.2%±0.7%$96.2\%\pm 0.7\%$ across all these metrics on the OPSCC dataset. The study to investigate the effects of two adversarial networks highlights the crucial role of D‐Net in improving model performance. Ablation studies further provide an in‐depth understanding of our methodology. Conclusion: Our adversarial‐based classification framework leverages GAN‐based adversarial networks and an iterative adversarial learning strategy to harness supplementary regularization during training. This design significantly enhances classification accuracy and mitigates overfitting issues in medical image datasets. Moreover, its modular design not only demonstrates flexibility but also indicates its potential applicability to various clinical contexts and medical imaging applications. [ABSTRACT FROM AUTHOR]

RNA methylation modification influences various processes in the human body and has gained increasing attention from scholars. Predicting genes associated with RNA methylation pathways can significantly aid biologists in studying RNA methylation processes. Several prediction methods have been investigated, but their performance is still limited by the scarcity of positive samples. To address the challenge of data imbalance in RNA methylation-associated gene prediction tasks, this study employed a generative adversarial network to learn the feature distribution of the original dataset. The quality of synthetic samples was controlled using the Classifier Two-Sample Test (CTST). These synthetic samples were then added to the data blocks to mitigate class distribution imbalance. Experimental results demonstrated that integrating the synthetic samples generated by our proposed model with the original data enhances the prediction performance of various classifiers, outperforming other oversampling methods. Moreover, gene ontology (GO) enrichment analyses further demonstrate the effectiveness of the predicted genes associated with RNA methylation pathways. The model generating gene samples with PyTorch is available at https://github.com/heyheyheyheyhey1/WGAN-GP_RNA_methylation [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions The field of artificial intelligence (AI)‐based patient‐specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi‐institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi‐institution data is critical for enabling large‐scale integration and analysis.This study aims to develop a star generative adversarial network (StarGAN) and transformer‐based hybrid classification‐regression PSQA framework to address unification of heterogeneous data from different institutions.A StarGAN and transformer‐based hybrid classification‐regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as “Pass” or “Fail” at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation.For the 3%/3, 3%/2, and 2%/2 mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root‐mean‐square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2 mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework.The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the amount of training data for RS image generation than for natural image generation (Fig. 1). In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data (Fig. 2). Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely uniformity regularization and entropy regularization, to increase the information learned by the GAN model at the distributional and sample levels, respectively. Extensive experiments on eight RS datasets and three natural datasets show the effectiveness and versatility of our methods. The source code is available at https://github.com/rootSue/Causal-RSGAN. [ABSTRACT FROM AUTHOR]

Recent progress in generative models has led to the drastic growth of research in image generation. Existing approaches show visually compelling results by learning multi-modal distributions, but they still lack realism, especially in certain scenarios like medical image synthesis. In this paper, we propose a novel Brain Generative Adversarial Network (BrainGAN) that explores GANs with multi-constraint and transferable property for cross-modal brain image synthesis. We formulate BrainGAN by introducing a unified framework with new constraints that can enhance modal matching, texture details and anatomical structure, simultaneously. We show how BrainGAN can learn meaningful tissue representations with rich variability of brain images. In addition to generating 3D volumes that are visually indistinguishable from real ones, we model adversarial discriminators and segmentors jointly, along with the proposed cost functions, which forces our networks to synthesize brain MRIs with realistic textures conditioned on anatomical structures. BrainGAN is evaluated on three public datasets, where it consistently outperforms the other state-of-the-art approaches by a large margin, advancing cross-modal synthesis of brain images both visually and practically. [ABSTRACT FROM AUTHOR]

Close-up facial images captured at short distances often suffer from perspective distortion, resulting in exaggerated facial features and unnatural/unattractive appearances. We propose a simple yet effective method for correcting perspective distortions in a single close-up face image. We first perform 3D GAN inversion using a perspective-distorted input facial image by jointly optimizing the intrinsic and extrinsic camera parameters and the face latent code. To address the ambiguity inherent in this joint optimization, we develop starting from a short distance, optimization scheduling, reparametrizations, and geometric regularization. Re-rendering the portrait at a proper focal length and camera distance effectively corrects perspective distortions and produces more natural-looking results. We also incorporate a workflow to handle full images rather than limiting our method to cropped faces. Our experiments show that our method compares favorably against previous approaches qualitatively and quantitatively. We showcase numerous examples validating the applicability of our method on in-the-wild portrait photos. Our code is available at https://github.com/lightChaserX/DisCO. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) for ship classification in multi-spectral images (RGB, IR, etc.) is proposed in this paper. Recent developments in deep learning have significantly advanced the field of ship recognition. However, since maritime light intensity is frequently disturbed, multispectral imaging is considered a more robust substitute for RGB imaging. The proposed architectures were fine-tuned after being trained from scratch on the publicly available dataset VAIS (RGB-IR pairs). Unfortunately, the classification results plateaued at 59.74% accuracy, which is unsatisfactory for most real-life applications. Such an accuracy wall was due to the small number of training images. In order to overcome the scarcity of IR ship images, we proposed a novel image data augmentation strategy that translates RGB images to IR. A Pix2Pix model and a Generative Adversarial Network (GAN) network were modified to carry out the generation process as an RGB to IR translator. The KAIST general-purpose RGB-IR image pairs dataset was used to train our RGB-to-IR image translator, whereas the VAIS dataset was held aside for validation purposes. Our proposed network improved the accuracy of the native network by 8% (from 59.74% to 67.74%), which is fairly satisfactory in the field of ship recognition. [ABSTRACT FROM AUTHOR]

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264 nm to 138 nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels. [ABSTRACT FROM AUTHOR]