Video Summarization Generation Based on Graph Structure Reconstruction by Jing Zhang, Guangli Wu, Shanshan Song

“…To address this issue, this paper proposes a video summary generation model based on graph structure reconstruction (VOGNet), in which the model first adopts a variational graph auto-encoders (VGAE) to reconstruct the graph structure to remove redundant information in the graph structure; followed by using the reconstructed graph structure in a graph attention network (GAT), allocating different weights to different shot features in the neighborhood; and lastly, in order to avoid the loss of information during the training of the model, a feature fusion approach is proposed to combine the training obtained shot features with the original shot features as the shot features for generating the summary. …”
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Efficient sampling in fragment-based protein structure prediction using an estimation of distribution algorithm. by David Simoncini, Kam Y J Zhang

“…In this paper we present EdaFold(AA), a fragment-based approach which shares information between the generated models and steers the search towards native-like regions. …”
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A Generative Approach to Person Reidentification by Andrea Asperti, Salvatore Fiorilla, Lorenzo Orsini

“…In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. …”
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Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models by Muhammad Usman Akbar, Måns Larsson, Ida Blystad, Anders Eklund

“…Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.…”
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Decoding Methods in Neural Language Generation: A Survey by Sina Zarrieß, Henrik Voigt, Simeon Schüz

“…This survey paper provides an overview of the different ways of implementing decoding on top of neural network-based generation models. Research into decoding has become a real trend in the area of neural language generation, and numerous recent papers have shown that the choice of decoding method has a considerable impact on the quality and various linguistic properties of the generation output of a neural NLG system. …”
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An Organismal Perspective on the Warburg Effect and Models for Proliferation Studies by Neil W. Blackstone, Weam S. El Rahmany

“…Such organisms are prime candidates for general studies of proliferation in multicellular organisms and could at least complement the short-generation models of modern biology.…”
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Synthbuster: Towards Detection of Diffusion Model Generated Images by Quentin Bammey

“…In this context, the present corpus of synthetic image detection techniques, primarily focusing on older generative models like Generative Adversarial Networks, finds itself ill-equipped to deal with this emerging trend. …”
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RepNet: A Lightweight Human Pose Regression Network Based on Re-Parameterization by Xinjing Zhang, Qixun Zhou

“…The prediction performance is also improved by the output of the maximum likelihood model and the reversible transformation of the underlying distribution learned by the flow generation model. RepNet achieves a recognition accuracy of 66.1 AP on the COCO dataset, at a computational speed of 15 ms on GPU and 40 ms on CPU. …”
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View Synthesis with Scene Recognition for Cross-View Image Localization by Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

“…To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. …”
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Comparative Evaluation of VAEs, VAE-GANs and AAEs for Anomaly Detection in Network Intrusion Data by Mahmoud Mohamed

“…This study provides a comparative evaluation of deep generative models for detecting anomalies in network intrusion data. …”
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A Metaverse: Taxonomy, Components, Applications, and Open Challenges by Sang-Min Park, Young-Gab Kim

“…With the technological development of deep learning-based high-precision recognition models and natural generation models, Metaverse is being strengthened with various factors, from mobile-based always-on access to connectivity with reality using virtual currency. …”
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Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model by Yaodong Xing, Jinquan Huang, Feng Lu

“…The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. …”
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INCO-GAN: Variable-Length Music Generation Method Based on Inception Model-Based Conditional GAN by Shuyu Li, Yunsick Sung

“…At present, the research on music generation via deep learning can be divided into two categories: predictive models and generative models. However, both categories have the same problems that need to be resolved. …”
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The power of artistic challenges (On the 70th birth anniversary of Professor A.M. Sayapova) by R.F. Bekmetov

“…It is based on the cornerstone thesis that the meaning-generating model of Russian classical literature has not exhausted itself, and, therefore, any weighted judgment about it is of a complementary nature. …”
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LP-BT: A location privacy protection algorithm based on ball trees by Lechan Yang, Song Deng

“…Experimental results on public dataset manifest that our model is superior to other models such as random selection model and path-based fake location generation model in terms of privacy protection level, user density and anonymization time overhead.…”
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COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems by Eduardo Mayo Yanes, Sabyasachi Chakraborty, Renana Gershoni-Poranne

“…High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. …”
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Flow Field Reconstruction of 2D Hypersonic Inlets Based on a Variational Autoencoder by Zuwei Tan, Runze Li, Yufei Zhang

“…In recent years, artificial intelligence techniques have been used to improve the efficiency of aerodynamic optimization. Deep generative models, such as variational autoencoder (VAE) and generative adversarial network (GAN), have been used in a variety of flow problems in the last two years, making fast reconstruction and prediction of the full flow field possible. …”
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Risk Aversion, Managerial Reputation, and Debt–Equity Conflict by Anna Dodonova

“…Using the overlapping generations model, this paper shows that managerial reputation building can partially resolve the debt–equity conflict and improve efficiency in both economies; however, such improvement is larger in the entrepreneurial economy.…”
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Inferring effective couplings with restricted Boltzmann machines by Aurélien Decelle, Cyril Furtlehner, Alfonso De Jesús Navas Gómez, Beatriz Seoane

“…Generative models offer a direct way of modeling complex data. …”
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Image-Range Stitching and Semantic-Based Crack Detection Methods for Tunnel Inspection Vehicles by Lin Tian, Qingquan Li, Li He, Dejin Zhang

“…Consequently, we introduce a method that employs a bidirectional heuristic search approach, utilizing randomly generated seed pixels as hints to locate targets that concurrently appear in both the image and the image semantic generation model. The results reveal the potential for cooperation between laser-scanning and camera-imaging technologies and point out a novel approach of crack detection that appears to be more focused on semantic understanding.…”
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