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In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for short and medium-range weather forecasts are typically organized into a regular spatial grid structure. This arrangement closely resembles images: each weather variable can be visualized as a map or, when considering the temporal axis, as a video. Several classes of generative models, comprising Generative Adversarial Networks, Variational Autoencoders, or the recent Denoising Diffusion Models have largely proved their applicability to the next-frame prediction problem, and is thus natural to test their performance on the weather prediction benchmarks. Diffusion models are particularly appealing in this context, due to the intrinsically probabilistic nature of weather forecasting: what we are really interested to model is the probability distribution of weather indicators, whose expected value is the most likely prediction. In our study, we focus on a specific subset of the ERA-5 dataset, which includes hourly data pertaining to Central Europe from the years 2016 to 2021. Within this context, we examine the efficacy of diffusion models in handling the task of precipitation nowcasting, with a lead time of 1 to 3 hours. Our work is conducted in comparison to the performance of well-established U-Net models, as documented in the existing literature. An additional comparative analysis has been done with the forecasting capabilities of the CERRA system, part of the Copernicus Climate Change Service. The novelty of our approach, Generative Ensemble Diffusion (GED), lies in its innovative use of a diffusion model to generate a diverse set of possible weather scenarios. These scenarios are then amalgamated into a single prediction in a post-processing phase. This approach mimics the usual weather forecasting technique consisting in running an ensemble of numerical simulations under slightly different initial conditions by exploiting instead the intrinsic stochasticity of the generative model. In comparison to recent deep learning models addressing the same problem, our approach results in approximately a 25% reduction in the mean squared error. Reverse diffusion is a core concept in our GED approach, is particularly relevant to weather forecasting. In the context of diffusion models, reverse diffusion refers to the process of iteratively refining a noisy initial prediction into a coherent and realistic forecast. By leveraging reverse diffusion, our model effectively simulates the complex temporal dynamics of weather systems, mirroring the inherent uncertainty and variability in weather patterns. [ABSTRACT FROM AUTHOR]

In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting: the output of a CGNN belongs to an infinite-dimensional function space. The architecture is inspired by DCGAN, with one fully connected layer, several convolutional layers and nonlinear activation functions. In the continuous L2 setting, the dimensions of the spaces of each layer are replaced by the scales of a multiresolution analysis of a compactly supported wavelet. We present conditions on the convolutional filters and on the nonlinearity that guarantee that a CGNN is injective. This theory finds applications to inverse problems, and allows for deriving Lipschitz stability estimates for (possibly nonlinear) infinite-dimensional inverse problems with unknowns belonging to the manifold generated by a CGNN. Several numerical simulations, including signal deblurring, illustrate and validate this approach. [ABSTRACT FROM AUTHOR]

This paper describes how computational generative models can describe aspects of the artistic process, and how these generative models can provide tools for formulating and testing psychological theories of art. The term "generative models" here refers to algorithms that can generate artistic imagery, video, text, or other artistic media, including techniques developed in both computer graphics and AI research. Generative models can both describe artistic processes and offer useful experimental tools. This paper first outlines different ways to understand the types of research in generative models. It then surveys several recent examples of using generative models to develop theories and to perform experiments. The paper then discusses misleading uses of the concept of "AI-generated art" in psychological studies, and the need for study of our relationship with new artistic technologies. Finally, the paper offers a few remarks on pursuing interdisciplinary research across psychology and computer graphics. [ABSTRACT FROM AUTHOR]

Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised concerns about the potential misuse of fake images and cast new pressures on fake image detection. In this work, we pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models. Firstly, we conduct a comprehensive analysis of the performance of contrastive and classification-based visual features, respectively, extracted from CLIP-based models and ResNet or Vision Transformer (ViT)-based architectures trained on image classification datasets. Our results demonstrate that fake images share common low-level cues, which render them easily recognizable. Further, we devise a multimodal setting wherein fake images are synthesized by different textual captions, which are used as seeds for a generator. Under this setting, we quantify the performance of fake detection strategies and introduce a contrastive-based disentangling method that lets us analyze the role of the semantics of textual descriptions and low-level perceptual cues. Finally, we release a new dataset, called COCOFake, containing about 1.2 million images generated from the original COCO image–caption pairs using two recent text-to-image diffusion models, namely Stable Diffusion v1.4 and v2.0. [ABSTRACT FROM AUTHOR]

Image generative models have advanced in many areas to produce synthetic images of high resolution and detail. This success has enabled its use in the biomedical field, paving the way for the generation of videos showing the biological evolution of its content. Despite the power of generative video models, their use has not yet extended to time-based development, focusing almost exclusively on generating motion in space. This situation is largely due to the lack of specific datasets and metrics to measure the individual quality of videos, particularly when there is no ground truth available for comparison. We propose a new dataset, called GoldenDOT, which tracks the evolution of apples cut in parallel over 10 days, allowing to observe their progress over time while remaining static. In addition, four new metrics are proposed that provide different analyses of the generated videos as a whole and individually. In this article, the proposed dataset and measures are used to study three state-of-the-art video generative models and their feasibility for video generation with biological development: Temporal GAN version 2 (TGANv2), Low-Dimensional Video Discriminator Generative Adversarial Network (LDVDGAN), and Video Diffusion Model (VDM). Among them, the TGANv2 model has managed to obtain the best results in most metrics, including those already known in the state of the art, demonstrating the viability of the new proposed metrics and their congruence with these standard measures. [ABSTRACT FROM AUTHOR]

Machine-learning-based predictive maintenance models, i.e. models that predict breakdowns of machines based on condition information, have a high potential to minimize maintenance costs in industrial applications by determining the best possible time to perform maintenance. Modern machines have sensors that can collect all relevant data of the operating condition and for legacy machines which are still widely used in the industry, retrofit sensors are readily, easily and inexpensively available. With the help of this data it is possible to train such a predictive maintenance model. The main problem is that most data is obtained from normal operating conditions, whereas only limited data are from failures. This leads to highly unbalanced data sets, which makes it very difficult, if not impossible, to train a predictive maintenance model that can detect faults reliably and timely. Another issue is the lack of available real data due to privacy concerns. To address these problems, a suitable data generation strategy is needed. In this work, a literature review is conducted to identify a solution approach for a suitable data augmentation strategy that can be applied to our specific use case of hydrogen combustion engines in the automotive field. This literature review shows that, among the different state-of-the-art proposals, the most promising for the generation of reliable synthetic data are the ones based on generative models. The analysis of the different metrics used in the state of the art allows to identify the most suitable ones to evaluate the quality of generated signals. Finally, an open problem in research in this area is identified and it is the need to validate the plausibility of the data generated. The generation of results in this area will contribute decisively to the development of predictive maintenance models. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in generative neural networks have paved the way for transformative capabilities, particularly in their capacity to generate novel data, notably in the realm of images. The integration of these models with the increasingly popular technique of transfer learning, designed for proficient feature extraction, holds the promise of enhancing overall performance. This paper delves into the exploration of employing generative models in conjunction with transfer learning methods for feature extraction, with a specific focus on image classification tasks. Our investigation aims to scrutinize the effectiveness of leveraging generative models alongside pre-trained models as feature extractors in the context of image classification. To the best of our knowledge, our investigation is the first to link transfer learning and generative models for a discriminative task under one roof. The proposed approach undergoes rigorous evaluation on two distinct datasets, employing specific metrics to gauge the model’s performance. The results exhibit a notable nearly 10% enhancement achieved through the integration of generative models, underscoring their potential for achieving heightened accuracy in image classification. These findings highlight significant advancements in image classification accuracy, surpassing the performance of conventional Artificial Neural Network (ANN) models. [ABSTRACT FROM AUTHOR]

The generation and enhancement of satellite imagery are critical in remote sensing, requiring high-quality, detailed images for accurate analysis. This research introduces a two-stage diffusion model methodology for synthesizing high-resolution satellite images from textual prompts. The pipeline comprises a low-resolution diffusion model (LRDM) that generates initial images based on text inputs and a super-resolution diffusion model (SRDM) that refines these images into high-resolution outputs. The LRDM merges text and image embeddings within a shared latent space, capturing essential scene content and structure. The SRDM then enhances these images, focusing on spatial features and visual clarity. Experiments conducted using the Remote Sensing Image Captioning Dataset demonstrate that our method outperforms existing models, producing satellite images with accurate geographical details and improved spatial resolution. [ABSTRACT FROM AUTHOR]

We present a mathematical framework for analyzing fractal patterns in AI-generated images using persistent homology. Given a text-to-image mapping M : T → I , we demonstrate that the persistent homology groups H k (t) of sublevel set filtrations { f − 1 ((− ∞ , t ]) } t ∈ R characterize multi-scale geometric structures, where f : M (p) → R is the grayscale intensity function of a generated image. The primary challenge lies in quantifying self-similarity in scales, which we address by analyzing birth–death pairs (b i , d i) in the persistence diagram P D (M (p)) . Our contribution extends beyond applying the stability theorem to AI-generated fractals; we establish how the self-similarity inherent in fractal patterns manifests in the persistence diagrams of generated images. We validate our approach using the Stable Diffusion 3.5 model for four fractal categories: ferns, trees, spirals, and crystals. An analysis of guidance scale effects γ ∈ [ 4.0 , 8.0 ] reveals monotonic relationships between model parameters and topological features. Stability testing confirms robustness under noise perturbations η ≤ 0.2 , with feature count variations Δ μ f < 0.5 . Our framework provides a foundation for enhancing generative models and evaluating their geometric fidelity in fractal pattern synthesis. [ABSTRACT FROM AUTHOR]

In today's digital age, ensuring the appropriateness of content for children is crucial for their cognitive and emotional development. The rise of automated text generation technologies, such as Large Language Models like LLaMA, Mistral, and Zephyr, has created a pressing need for effective tools to filter and classify suitable content. However, the existing methods often fail to effectively address the intricate details and unique characteristics of children's literature. This study aims to bridge this gap by developing a robust framework that utilizes fine-tuned language models, classification techniques, and contextual story generation to generate and classify children's stories based on their suitability. Employing a combination of fine-tuning techniques on models such as LLaMA, Mistral, and Zephyr, alongside a BERT-based classifier, we evaluated the generated stories against established metrics like ROUGE, METEOR, and BERT Scores. The fine-tuned Mistral-7B model achieved a ROUGE-1 score of 0.4785, significantly higher than the base model's 0.3185, while Zephyr-7B-Beta achieved a METEOR score of 0.4154 compared to its base counterpart's score of 0.3602. The results indicated that the fine-tuned models outperformed base models, generating content more aligned with human standards. Moreover, the BERT Classifier exhibited high precision (0.95) and recall (0.97) for identifying unsuitable content, further enhancing the reliability of content classification. These findings highlight the potential of advanced language models in generating age-appropriate stories and enhancing content moderation strategies. This research has broader implications for educational technology, content curation, and parental control systems, offering a scalable approach to ensuring children's exposure to safe and enriching narratives. [ABSTRACT FROM AUTHOR]

Electronic health records (EHR) data in the medical field contain a wealth of valuable biomedical knowledge and provide a crucial resource for healthcare data analysis. However, privacy protection and data sharing constraints have become significant bottlenecks for researchers, hindering the application and development of data analysis and machine learning techniques in healthcare. To address these challenges, researchers have been exploring the use of generative modeling methods to generate EHR data. Firstly, this paper introduced and summarized the origins and evolution of diffusion models. Next, it delved into various existing diffusion model methods, providing a detailed analysis of each approach. Then it listed and compared different generative modeling methods applied in EHR data generation, emphasized the advantages and limitations of diffusion models. Finally, it summarized the current applications of diffusion models in EHR data generation, discussed the limitations of current research, and presented an outlook on the future development and application of diffusion models in this field [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is pivotal in advancing science, including nanomaterial studies. This review explores AI‐based image processing in nanoscience, focusing on algorithms to enhance characterization results from instruments like scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, atomic force microscopy etc. It addresses the significance of AI in nanoscience, challenges in advancing AI‐based image processing for nano material characterization, and AI's role in structural analysis, property prediction, deriving structure‐property relations, dataset augmentation, and improving model robustness. Key AI techniques such as Graph Neural Networks, adversarial training, transfer learning, generative models, attention mechanisms, and federated learning are highlighted for their contributions to nano science studies. The review concludes by outlining persisting challenges and thrust areas for future research, aiming to propel nanoscience with AI. This comprehensive analysis underscores the importance of AI‐powered image processing in nanomaterial characterization, offering valuable insights for researchers. [ABSTRACT FROM AUTHOR]

In anomaly detection applications, anomalies might come from multiple sources and there might be many reasons why a sample is considered to be anomalous. However, most novel anomaly detection methods do not consider this. In our work, we describe a novel approach that is demonstrated on the problem of detection of anomalies in image data. We propose the SGVAEGAN model, which decomposes the image into three independent components—the shape of an object and its foreground and background textures—and provides anomaly scores for each of those factors separately. The overall anomaly score of an image is a weighted combination of the individual factor scores. The anomaly scores are learned in an unsupervised manner, and the weights are considered as hyperparameters that can be learned in the validation stage. The approach allows the identification of the source of the anomaly using factor scores, as well as the detection of semantic anomalies where the semantic meaning is encoded in the weights and learned from very few samples of validation anomalies. On classical anomaly detection benchmarks, the proposed model outperforms all baseline models. This is shown in a rigorous experimental study that covers the behavior of the model under a varying range of conditions. [ABSTRACT FROM AUTHOR]

The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for the analysis of large private datasets. This article is a review of data analysis techniques based on generative models, with a focus on large language models (LLMs). It covers the strengths, limitations, and applications of methods like the fine-tuning of LLMs and retrieval-augmented generation (RAG). This study consolidates, analyzes, and interprets the findings from the literature to provide a coherent overview of the current research landscape on this topic, aiming to guide effective, privacy-conscious data analysis and exploring future improvements, especially for low-resource languages. [ABSTRACT FROM AUTHOR]

Modern 3D content creation heavily relies on procedural assets. In particular, procedural materials are ubiquitous in the industry, but their manipulation remains challenging. Previous work [Hu et al. 2023] conditionally generates procedural graphs that match a given input image. However, the parameter generation step limits how accurately the generated graph matches the input image, due to a reliance on supervision with scarcely available procedural data. We propose to improve parameter prediction accuracy for image-conditioned procedural material generation by leveraging reinforcement learning (RL) and present the first RL approach for procedural materials. RL circumvents the limited availability of procedural data, the domain gap between real and synthetic materials, and the need for end-to-end differentiable loss functions. Given a target image, we retrieve a procedural material and use an RL-trained transformer model to predict a set of parameters that reconstruct the target image as closely as possible. We show that using RL significantly improves parameter prediction to match a given target image compared to supervised methods on both synthetic and real target images. [ABSTRACT FROM AUTHOR]

We introduce ToonCrafter, a novel approach that transcends traditional correspondence-based cartoon video interpolation, paving the way for generative interpolation. Traditional methods, that implicitly assume linear motion and the absence of complicated phenomena like dis-occlusion, often struggle with the exaggerated non-linear and large motions with occlusion commonly found in cartoons, resulting in implausible or even failed interpolation results. To overcome these limitations, we explore the potential of adapting live-action video priors to better suit cartoon interpolation within a generative framework. ToonCrafter effectively addresses the challenges faced when applying live-action video motion priors to generative cartoon interpolation. First, we design a toon rectification learning strategy that seamlessly adapts live-action video priors to the cartoon domain, resolving the domain gap and content leakage issues. Next, we introduce a dual-reference-based 3D decoder to compensate for lost details due to the highly compressed latent prior spaces, ensuring the preservation of fine details in interpolation results. Finally, we design a flexible sketch encoder that empowers users with interactive control over the interpolation results. Experimental results demonstrate that our proposed method not only produces visually convincing and more natural dynamics, but also effectively handles dis-occlusion. The comparative evaluation demonstrates the notable superiority of our approach over existing competitors. Code and model weights are available at https://doubiiu.github.io/projects/ToonCrafter [ABSTRACT FROM AUTHOR]

Normalizing flows (NF) use a continuous generator to map a simple latent (e.g. Gaussian) distribution, towards an empirical target distribution associated with a training data set. Once trained by minimizing a variational objective, the learnt map provides an approximate generative model of the target distribution. Since standard NF implement differentiable maps, they may suffer from pathological behaviors when targeting complex distributions. For instance, such problems may appear for distributions on multi-component topologies or characterized by multiple modes with high probability regions separated by very unlikely areas. A typical symptom is the explosion of the Jacobian norm of the transformation in very low probability areas. This paper proposes to overcome this issue thanks to a new Markov chain Monte Carlo algorithm to sample from the target distribution in the latent domain before transporting it back to the target domain. The approach relies on a Metropolis adjusted Langevin algorithm whose dynamics explicitly exploits the Jacobian of the transformation. Contrary to alternative approaches, the proposed strategy preserves the tractability of the likelihood and it does not require a specific training. Notably, it can be straightforwardly used with any pre-trained NF network, regardless of the architecture. Experiments conducted on synthetic and high-dimensional real data sets illustrate the efficiency of the method. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Micro RNAs (miRNA) are a type of non-coding RNA involved in gene regulation and can be associated with diseases such as cancer, cardiovascular, and neurological diseases. As such, identifying the entire genome of miRNA can be of great relevance. Since experimental methods for novel precursor miRNA (pre-miRNA) detection are complex and expensive, computational detection using Machine Learning (ML) could be useful. Existing ML methods are often complex black boxes that do not create an interpretable structural description of pre-miRNA. In this paper, we propose a novel framework that makes use of generative modeling through Variational Auto-Encoders to uncover the generative factors of pre-miRNA. After training the VAE, the pre-miRNA description is developed using a decision tree on the lower dimensional latent space. Applying the framework to miRNA classification, we obtain a high reconstruction and classification performance while also developing an accurate miRNA description. [ABSTRACT FROM AUTHOR]

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models' understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design. [ABSTRACT FROM AUTHOR]

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality. [ABSTRACT FROM AUTHOR]

Goal: To accurately detect infections in Diabetic Foot Ulcers (DFUs) using photographs taken at the Point of Care (POC). Achieving high performance is critical for preventing complications and amputations, as well as minimizing unnecessary emergency department visits and referrals. Methods: This paper proposes the Guided Conditional Diffusion Classifier (ConDiff). This novel deep-learning framework combines guided image synthesis with a denoising diffusion model and distance-based classification. The process involves (1) generating guided conditional synthetic images by injecting Gaussian noise to a guide (input) image, followed by denoising the noise-perturbed image through a reverse diffusion process, conditioned on infection status and (2) classifying infections based on the minimum Euclidean distance between synthesized images and the original guide image in embedding space. Results: ConDiff demonstrated superior performance with an average accuracy of 81% that outperformed state-of-the-art (SOTA) models by at least 3%. It also achieved the highest sensitivity of 85.4%, which is crucial in clinical domains while significantly improving specificity to 74.4%, surpassing the best SOTA model. Conclusions: ConDiff not only improves the diagnosis of DFU infections but also pioneers the use of generative discriminative models for detailed medical image analysis, offering a promising approach for improving patient outcomes.

With the formation of a new technological paradigm and global competition for leadership in the digital space, the attention of experts is shifting towards the growth of political, economic and R&D influence of Eastern countries, which imposes new demands on research methods and tools. The article summarizes the experience of enhancing the academic research process through involving young IT professionals in the Oriental studies in competitive mode. During two weeks of the “Hackathon” contest organized by Yandex Cloud, Napoleon IT and AI Talent Hub in collaboration with the experts from the Institute on Oriental Studies and the Institute of China and Contemporary Asia of the Russian Academy of Sciences, contestants were challenged to develop a chat bot employing generative models and machine translation to analyze news flows of East Asian countries, primarily China and Japan. A review of the winners’ approaches and solutions proves overall feasibility of the idea and shows that many specific linguistic and engineering tasks that were relevant only recently have already been successfully solved. Therefore, when planning and developing next-generation digital tools, it is necessary to operate at functionally and semantically higher levels of generalization closer to human reasoning.

The development of generative models of artificial intelligence (AI) poses new challenges for legal science and practice. This requires understanding of the legal nature of prompts (queries to AI) and development of appropriate legal regulation. The article aims to determine the legal significance of prompts and outlines the prospects for their research in the context of the interaction between law and AI. The study is based on the analysis of contemporary scientific literature devoted to the problems of legal regulation of AI, as well as investigation of the first cases of the use of generative AI models in legal practice and education. Methods of legal qualification, comparative legal analysis, and legal modeling are applied. Prompts are qualified as legal actions (legal facts in the strict sense), which opens the path to addressing the applicability of copyright criteria to them. The potential and risks of using prompts in legal practice and education are identified, and the need for standardizing prompts and developing specialized methods for teaching lawyers to interact with AI is substantiated. Prompts, as a tool for human-AI interaction, represent a fundamentally important subject of legal research, upon which the prospects for AI application in law largely rely. The article concludes that interdisciplinary and international studies are necessary to unite the efforts of legal professionals, AI specialists, and the generative models themselves in developing optimal legal solutions.

Artificial Intelligence (AI) plays an increasingly prominent role in various spheres of life in today’s world, including generation of a variety of visual content from selfie stream processing to creating works of digital art. The present paper raises the question of whether AI is capable of creating real art or it just imitates its external form. The paper examines the specificity of prompts: from concrete named ones to interpretive descriptive queries in linguistic, artistic and socio-cultural contexts. The article dwells upon some important aspects of evaluating the quality of keyword extraction algorithms and their relation to artistic practice. The authors rely on semiotic analysis to uncover encoded meanings and imports in the text. The article emphasises that the literary text is at the top of the hierarchy of cultural texts; it is characterised by intentionality and coherence and represents a complex semantic field where key words and images interact with the explicit and implicit contexts. The study examines and analyses the visualised images of Cheshire Cat, Cat Behemoth and Tomcat Murr created by the authors with the use of three generative neural networks: Stable Diffusion, Dall‑E and Kandinsky. Understanding and visualising the literary text by generative systems and models realising specific algorithms requires the ability to reveal its multilayered semantics and connection with the cultural context, which ultimately helps to understand the in-depth meanings of the work and its place in culture. Consideration of the operational quality of algorithms for keyword system extraction and image generation is deemed possible from the point of view of their structural organisation. Generative algorithms create an imitative reality, while the immanence of the artistic value determines the uniqueness and meanings of the created figurative world. The article can be useful to anyone interested in the substance and specificity of digital art, the relationship between technological innovations and socio-cultural context, the creation and visualisation of artistic images in generative AI models, their conceptualisation and interpretation.

As a fundamental task in food computing, food detection played a crucial role in locating and identifying food items from input images, particularly in applications such as intelligent canteen settlement and dietary health management. However, food categories were constantly updating in practical scenarios, making it difficult for food detectors trained on fixed categories to accurately detect previously unseen food categories. To address this issue, this paper proposed a zero-shot food image detection method. Firstly, a Transformer-based food primitive generator was constructed, where each primitive contained fine-grained attributes relevant to food categories. These primitives could be selectively assembled based on the food characteristics to synthesize new food features. Secondly, an enhancement component of visual feature disentanglement was proposed in order to impose more constraints on the visual features of unseen food categories. The visual features of food images were decomposed into semantically related features and semantically unrelated features, thereby better transferring semantic knowledge of food categories to their visual features. The proposed method was extensively evaluated on the ZSFooD and UEC-FOOD256 datasets through numerous experiments and ablation studies. Under the zero-shot detection (ZSD) setting, optimal average precision on unseen classes reached 4.9% and 24.1%, respectively, demonstrating the effectiveness of the proposed approach. Under the generalized zero-shot detection (GZSD) setting, the harmonic mean of visible and unseen classes reaches 5.8% and 22.0%, respectively, further validating the effectiveness of the proposed method.

Abstract Generative machine learning models have shown notable success in identifying architectures for metamaterials—materials whose behavior is determined primarily by their internal organization—that match specific target properties. By examining kirigami metamaterials, in which dependencies between cuts yield complex design restrictions, we demonstrate that this perceived success in the employment of generative models for metamaterials might be akin to survivorship bias. We assess the performance of the four most popular generative models—the Variational Autoencoder (VAE), the Generative Adversarial Network (GAN), the Wasserstein GAN (WGAN), and the Denoising Diffusion Probabilistic Model (DDPM)—in generating kirigami structures. Prohibiting cut intersections can prevent the identification of an appropriate similarity measure for kirigami metamaterials, significantly impacting the effectiveness of VAE and WGAN, which rely on the Euclidean distance—a metric shown to be unsuitable for considered geometries. This imposes significant limitations on employing modern generative models for the creation of diverse metamaterials.

The object of the study is the value chain of the bank. The purpose of the study is to identify the possibility of applying artificial intelligence (AI) technologies in the value chain stages of commercial banks and transform value chains under the influence of these technologies. It uses both general scientific methods — analysis, synthesis, abstraction, induction and deduction, and graphical and statistical analysis, the methodology of value chain creation. The main approaches to the formation of the value chain in the banking industry, as well as the key characteristics of the business processes included in it, were studied. Particular attention is paid to the technological component as the basis for the development of modern digital banking. During the research, the main directions for the implementation of modern artificial intelligence technologies, both applied and generative. Analysis of the value chain showed that the creation and use of AI models is an independent supporting process, the work of which not only affects the core activities of the bank, but also requires a certain level of technology development and risk-management in the bank. Data from the AI Russia case library demonstrates the actual impact of AI models on the value chain phases of marketing and sales, customer support and communications, operational processing and risk management. Based on the results of the study, it was concluded that the introduction of innovations in the field of artificial intelligence increases the value of the company by increasing the efficiency of business processes. The introduction of artificial intelligence into processes requires the technological maturity of the enterprise, and its use is an independent technological process that requires the participation of auxiliary processes, for example, risk management. The results of the study are of practical importance for companies in the banking industry, since methods for analyzing the impact of AI technologies on the value chain can be used when making decisions about their implementation.

Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximising an evidence lower bound. There has been much progress in improving the expressiveness of the variational distribution to obtain tighter variational bounds and increased generative performance. Whilst previous work has leveraged Markov chain Monte Carlo methods for constructing variational densities, gradient-based methods for adapting the proposal distributions for deep latent variable models have received less attention. This work suggests an entropy-based adaptation for a short-run metropolis-adjusted Langevin or Hamiltonian Monte Carlo (HMC) chain while optimising a tighter variational bound to the log-evidence. Experiments show that this approach yields higher held-out log-likelihoods as well as improved generative metrics. Our implicit variational density can adapt to complicated posterior geometries of latent hierarchical representations arising in hierarchical VAEs. [ABSTRACT FROM AUTHOR]

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials that could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space optimization methods, and we carefully validate our diffusion process design choices.1 [ABSTRACT FROM AUTHOR]

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We present the Gaussian Mixture Periodicity Detection Algorithm (GMPDA), a novel method for detecting periodicity in the binary time series of event onsets. The GMPDA addresses the periodicity detection problem by inferring parameters of a generative model. We introduce two models, the Clock Model and the Random Walk Model, which describe distinct periodic phenomena and provide a comprehensive generative framework. The GMPDA demonstrates robust performance in test cases involving single and multiple periodicities, as well as varying noise levels. Additionally, we evaluate the GMPDA on real-world data from recorded leg movements during sleep, where it successfully identifies expected periodicities despite high noise levels. The primary contributions of this paper include the development of two new models for generating periodic event behavior and the GMPDA, which exhibits high accuracy in detecting multiple periodicities even in noisy environments. [ABSTRACT FROM AUTHOR]

Histological staining plays a crucial role in anatomic pathology for the analysis of biological tissues and the formulation of diagnostic reports. Traditional methods like hematoxylin and eosin (H&E) primarily offer morphological information but lack insight into functional details, such as the expression of biomarkers indicative of cellular activity. To overcome this limitation, we propose a computational approach to synthesize virtual immunohistochemical (IHC) stains from H&E input, transferring imaging features across staining domains. Our approach comprises two stages: (i) a multi‐stage registration framework ensuring precise alignment of cellular and subcellular structures between the source H&E and target IHC stains, and (ii) a deep learning‐based generative model which incorporates functional attributes from the target IHC stain by learning cell‐to‐cell mappings from paired training data. We evaluated our approach of virtual restaining H&E slides to simulate IHC staining for phospho‐histone H3, on inguinal lymph node and bladder tissues. Blind pathologist assessments and quantitative metrics validated the diagnostic quality of the synthetic slides. Notably, mitotic counts derived from synthetic images exhibited a strong correlation with physical staining. Moreover, global and stain‐specific metrics confirmed the high quality of the synthetic IHC images generated by our approach. This methodology represents an important advance in automated functional restaining, achieved through robust registration and a model trained on precisely paired H&E and IHC data to transfer functions cell‐by‐cell. Our approach forms the basis for multiparameter histology analysis and comprehensive cohort staining using only digitized H&E slides. [ABSTRACT FROM AUTHOR]

Research background: Over the last ten years, a substantial amount of scholarly research has delved into energy communities (ECs) from diverse viewpoints. These ECs are extremely important in setting the pathway to a clean energy transition. Purpose of the article: Our objective is to glean valuable insights from publications indexed in the Web of Science (WoS) database to deepen our comprehension of ECs and their academic discourse. Methods: Data analytics, factorial analysis, and more complex natural language processing (NLP) techniques such as latent Dirichlet allocation (LDA) are implemented to extract valuable insights from over 1000 WoS publications relevant in the EC field. The primary contribution of this study lies in furnishing details regarding the key contributors to the EC scholarly landscape, including authors, their affiliations, universities, and countries of origin. Additionally, we aim to elucidate the prevalent keywords and thematic approaches employed in their research endeavors. Findings & value added: Considering the extracted dataset, an annual growth rate of 21.15% has been recorded, highlighting the research community’s interest in the field of ECs. Furthermore, three topics are optimally obtained. Overall, a coherence score of 0.44 suggests that the LDA model performs adequately in terms of topic interpretation. Topic 1 relates to community- based energy initiatives. Topic 2, featuring terms like “grid,” “study” and “EU” alongside “energy” and “community,” suggests a focus on energy systems. Topic 3 includes terms such as “generation,” “analysis” and “consumption,” indicating a topic that is centered around the technical or analytical aspects of energy production and usage. This study underscores how the alignment between state laws and EU directives in supporting ECs can serve as a model for other regions. The findings suggest that similar policy frameworks could be effectively adapted to different national contexts, providing valuable insights for countries looking to enhance their renewable energy strategies. [ABSTRACT FROM AUTHOR]