Atomic-Scale Polycrystal Reconstruction Using Diffusion Models

Data imputation and data generation have important applications across many domains where incomplete or missing data can hinder accurate analysis and decision-making. Diffusion models have emerged as powerful generative models capable of capturing complex data distributions across various data modalities such as image, audio, and time series. Recently, they have been also adapted to generate tabular data. In this article, we propose a diffusion model for tabular data that introduces three key enhancements: (1) a conditioning attention mechanism, (2) an encoder–decoder transformer as the denoising network, and (3) dynamic masking. The conditioning attention mechanism is designed to improve the model’s ability to capture the relationship between the condition and synthetic data. The transformer layers help model interactions within the condition (encoder) or synthetic data (decoder), while dynamic masking enables our model to efficiently handle both missing data imputation and synthetic data generation tasks within a unified framework. We conduct a comprehensive evaluation by comparing the performance of diffusion models with transformer conditioning against state-of-the-art techniques such as Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models, on benchmark datasets. Our evaluation focuses on the assessment of the generated samples with respect to three important criteria, namely: (1) machine learning efficiency, (2) statistical similarity, and (3) privacy risk mitigation. For the task of data imputation, we consider the efficiency of the generated samples across different levels of missing features. The results demonstrate average superior machine learning efficiency and statistical accuracy compared to the baselines, while maintaining privacy risks at a comparable level, particularly showing increased performance in datasets with a large number of features. By conditioning the data generation on a desired target variable, the model can mitigate systemic biases, generate augmented datasets to address data imbalance issues, and improve data quality for subsequent analysis. This has significant implications for domains such as healthcare and finance, where accurate, unbiased, and privacy-preserving data are critical for informed decision-making and fair model outcomes.

By formulating the data generation as a sequence procedure of denoising autoencoding, diffusion models have achieved superior in-painting performance on image data and beyond. Nevertheless, it is not trivial when capitalizing on diffusion models to generate missing 3D points. The difficulty originates from the intrinsic structure where 3D point cloud is a set of unordered and irregular coordinates. That motivates us to delve into the 3D structural information for designing point cloud encoder-decoder and shape latent generator, to precisely formulate the latent distribution of the complete point cloud and partial observation. In this paper, we propose Point cloud completion with Latent Diffusion Models (PointLDM), a new approach that leverages the conditional denoising diffusion probabilistic modeling (DDPM) in the 3D latent space for shape reconstruction. The architecture of PointLDM consists of a transformer-based variational auto-encoder (VAE) to model the complete shape latent, and a diffusion network for shape latent prediction. The encoder of VAE exploits both of global shape latent and local point features in shape distribution learning. With the learnt shape latent, the decoder first decodes the shape latent into coarse points, and then recovers the fine-grained details around each coarse point by deforming a 2D grid. To reconstruct the shape latent from partial observation, the diffusion network treats the partial observation as the conditional input and generates the shape latent via DDPM. Extensive experiments conducted on MVP, Completion3D, and KITTI quantitatively and qualitatively demonstrate the efficacy of PointLDM over the state-of-the-art shape completion approaches.

This paper reviews the progress in text-to-image generation, which enables the creation of images from textual descriptions. This technology holds promise across various fields, including creative arts, gaming, and healthcare. The main approaches in this area are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DM). While GANs initially made significant advancements in realistic image generation, they faced issues with stability and diversity. VAEs introduced a probabilistic approach, allowing for diverse outputs but often at the cost of image quality. The development of DM, like Stable Diffusion, Imagen, and DALL-E 2, has addressed many limitations, producing high-quality, coherent images through iterative denoising. DM stands out for its stability and ability to generate detailed, semantically accurate images. This review explores the strengths and limitations of each approach, with an emphasis on the advantages of DM. It also discusses future directions, including improving efficiency, enhancing multimodal capabilities, and reducing data requirements to make these models more accessible and versatile for various applications.

Diffusion models have emerged as a popular method for 3D generation. However, it is still challenging for diffusion models to efficiently generate diverse and high‐quality 3D shapes. In this paper, we introduce OctFusion, which can generate 3D shapes with arbitrary resolutions in 2.5 seconds on a single Nvidia 4090 GPU, and the extracted meshes are guaranteed to be continuous and manifold. The key components of OctFusion are the octree‐based latent representation and the accompanying diffusion models. The representation combines the benefits of both implicit neural representations and explicit spatial octrees and is learned with an octree‐based variational autoencoder. The proposed diffusion model is a unified multi‐scale U‐Net that enables weights and computation sharing across different octree levels and avoids the complexity of widely used cascaded diffusion schemes. We verify the effectiveness of OctFusion on the ShapeNet and Objaverse datasets and achieve state‐of‐the‐art performances on shape generation tasks. We demonstrate that OctFusion is extendable and flexible by generating high‐quality color fields for textured mesh generation and high‐quality 3D shapes conditioned on text prompts, sketches, or category labels. Our code and pre‐trained models are available at https://github.com/octree‐nn/octfusion.

Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict mutation effects. Despite encouraging results, current model evaluation metrics leave unclear whether GPSMs faithfully reproduce the complex multi-residue mutational patterns observed in natural sequences due to epistasis. Here, we develop a set of sequence statistics to assess the “generative capacity” of three current GPSMs: the pairwise Potts Hamiltonian, the VAE, and the site-independent model. We show that the Potts model’s generative capacity is largest, as the higher-order mutational statistics generated by the model agree with those observed for natural sequences, while the VAE’s lies between the Potts and site-independent models. Importantly, our work provides a new framework for evaluating and interpreting GPSM accuracy which emphasizes the role of higher-order covariation and epistasis, with broader implications for probabilistic sequence models in general.

With rapid advances in image-generative AI, interest has grown in applying these technologies across diverse domains. While current models excel at producing high-quality final images, they rarely model the intermediate stages of the drawing process. This study proposes a drawing support system that leverages generative AI to sequentially generate and modify images during the drawing process. To train the system, we constructed a custom dataset of time-series drawing images, which are typically unavailable in existing AI models. We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. To address error accumulation during recursive generation, we introduced a noise-augmented training method, incorporating noisy images into the dataset. Experimental results show that standard training suffers from image degradation over time, while the noise-augmented approach significantly improves stability and quality throughout the sequence. Averaged across all generated frames, the noise-augmented training achieved a PSNR of 20.48, SSIM of 0.81, and LPIPS of 0.13. As a benchmark, we compared this approach to PredRNN, a representative temporal model, which achieved a PSNR of 24.05, SSIM of 0.88, and LPIPS of 0.08. These results demonstrate the effectiveness of noise-augmented learning while also highlighting potential performance gains using temporal architectures. PredRNN also required more computation, with 6.41 M parameters and a 0.188 s inference time per sequence, compared to 5.20 M and 0.005 s for our model. Furthermore, in a sample-level analysis of 10 final images, the proposed model outperformed PredRNN in three cases across all metrics, suggesting its robustness in certain sequences despite architectural simplicity. Future directions include using more advanced models such as Variational Autoencoders and diffusion models, and enhancing consistency in long-term sequences. Our work confirms the feasibility of generating interactive drawing sequences using AI and sets the foundation for more robust and creative drawing support tools.

Medical image translation with deep learning: Advances, datasets and perspectives.

Distributing datasets containing DDoS attack patterns in software-defined networks (SDNs) is an important task for improving threat detection methods. This paper examines machine learning (MO) approaches for generating synthetic data based on the HLD-DDoSDN dataset. Generative adversarial networks (GAN), variational autoencoders (VAE), resampling methods (SMOTE, ADASYN), and diffusion models are considered. A comparative analysis of methods is carried out in terms of generation quality, computational complexity, and applicability in DDoS attack detection tasks. The results show that hybrid methods combining GAN and SMOTE demonstrate the best balance between data quality and efficiency.

Synthetic artificial intelligence (AI) is rapidly redefining biomedical research-yet in cardiovascular medicine, its clinical relevance remains obscure, underexplored, and underestimated. Unlike traditional AI, which interprets data, synthetic AI generates entirely new, patient-like information: from realistic ECG signals to cardiac imaging and virtual cohorts that simulate disease progression. While recent publications have addressed specific synthetic AI tools in cardiology, no prior review has comprehensively synthesized their architectures, clinical applications, and implementation challenges within a single, practice-oriented framework. This State-of-the-Art Review fills that gap. We provide a clear, critical synthesis of core architectures-Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, Transformers, Autoregressive Models, Digital Twins, and Synthetic Cohort Simulators-and map their emerging cardiovascular applications. We examine technical barriers, ethical concerns, regulatory uncertainties, and integration challenges, while anchoring the discussion in real-world clinical priorities. This review is not only a scientific analysis-it is a call to engagement. For academic researchers, it offers conceptual and technical clarity. For clinicians in resource-constrained settings, it presents synthetic AI not as abstract innovation, but as a practical opportunity to enhance diagnostic precision, optimize workflows, and extend clinical insight. Traditional AI supports cardiologists by interpreting data; synthetic AI extends this paradigm by creating new, clinically coherent information that enhances decision-making without replacing physician expertise. As investment grows and methods mature, cardiologists must shape this evolution-not as passive adopters, but as active drivers. This review invites them to take the wheel.

Progress in chemistry has been driven by the streamlining of inverse problem-solving methods. In the history of chemistry, several revolutionary technologies have led to leaps forward: the establishment of atomistic theory in the 19th century, structural analysis by spectroscopy in the 20th century, and the development of simulation by theoretical chemistry. Currently, chemistry is about to make a significant leap forward by integrating generative artificial intelligence (AI). In 2016, deep learning techniques were introduced in this domain, leading to explosive development. This paper reviews the development path, including traditional models such as variational autoencoders and more up-to-date models such as large language models and diffusion models. We also discuss how AI can have a real impact on chemistry, including the possibilities and problems associated with synthesizing AI-generated molecules.

Generative AI has revolutionized computer vision by enabling machines to synthesize and enhance visual data. Advances in deep learning, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, have led to high-quality image generation with applications in medical imaging, data augmentation, and autonomous systems. Despite its potential, challenges like ethical concerns, dataset biases, and computational costs remain critical for future research and implementation. This paper explores the evolution of generative AI, its methodologies, applications, and the ethical and computational challenges it presents.

Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research.

Currently, generative artificial intelligence technologies are being actively developed and widely implemented. These technologies are used to solve various tasks in many areas of human activity, including image stylization in digital art, design, advertising, entertainment, and other fields. Image stylization consists in the transformation of its content while maintaining the semantic structure, but with a change in the visual style set by the sample or model parameters. Various methods are used to generate images, of which neural style transfer, variational autoencoders, generative-adversarial networks and diffusion models are considered in the article. The results of their analysis showed that the best approach for creating artistically stylized images is the use of generative-adversarial networks. There are a number of such network architectures, among which Pix2Pix, CycleGAN, StyleGAN, StarGANv and UGATIT have been investigated. Their comparative analysis showed that the CycleGAN architecture best meets the requirements of the task of stylizing images in the style of a particular painter. It does not require paired data, demonstrates resilience to different image structures, and provides stable and reproducible learning. Taken together, these advantages make CycleGAN a preferred choice for building an artistic stylization system. To conduct the experiments, an image stylization system was developed, trained on a set of images with copies of artworks by Vincent Van Gogh, Claude Monet, Henri Matisse, Ivan Aivazovsky and Ilya Repin. These authors were selected based on a set of criteria: popularity, recognition of style, genre diversity and the availability of a sufficient number of digital copies of artworks. Metrics were used to evaluate the quality of image generation. To evaluate the quality of generated images, metrics such as Kernel Inception Distance, Inception Score, and Contrastive Language-Image Pretraining based cosine similarity were used. The experimental results showed that generative models demonstrate the potential to mimic common features of artistic styles. However, in order to achieve a higher level of reliability and accuracy, further research is needed to adapt them to the given styles.

This paper presents a comprehensive survey of the applications, challenges, and limitations of Generative AI (GenAI) in enhancing threat intelligence within cybersecurity, supported by real-world case studies. We examine a wide range of data sources in Cyber Threat Intelligence (CTI), including security reports, blogs, social media, network traffic, malware samples, dark web data, and threat intelligence platforms (TIPs). This survey provides a full reference for integrating GenAI into CTI. We discuss various GenAI models such as Large Language Models (LLMs) and Deep Generative Models (DGMs) like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, explaining their roles in detecting and addressing complex cyber threats. The survey highlights key applications in areas such as malware detection, network traffic analysis, phishing detection, threat actor attribution, and social engineering defense. We also explore critical challenges in deploying GenAI, including data privacy, security concerns, and the need for interpretable and transparent models. As regulations like the European Commission’s AI Act emerge, ensuring trustworthy AI solutions is becoming more crucial. Real-world case studies, such as the impact of the WannaCry ransomware, the rise of deepfakes, and AI-driven social engineering, demonstrate both the potential and current limitations of GenAI in CTI. Our goal is to provide foundational insights and strategic direction for advancing GenAI’s role in future cybersecurity frameworks, emphasizing the importance of innovation, adaptability, and ongoing learning to enhance resilience against evolving cyber threats. Ultimately, this survey offers critical insights into how GenAI can shape the future of cybersecurity by addressing key challenges and providing actionable guidance for effective implementation.

pattern design needs breadth without chaos and repeat safety, yet current generators blur structure and drift in colour.This paper introduces a hybrid generator that separates structure from surface using a latent scaffold refined by diffusion and guided by differentiable priors.In the scheme, first a variational encoder learns a compact scaffold for repetition, alignment, and palette allocation; then a latent denoiser sharpens edges and adds micro variation; finally lightweight priors keep symmetry on beat, close borders for seamless repeats, and hold the palette steady with simple knobs.Experiments on two curated corpora show structural coherence rises by 11%, seam energy drops by 51%, and palette error falls by 26% against strong baselines, while sampling stays about half a second at 512 pixels and human preference reaches 71%.