https://scholarworks.unist.ac.kr/handle/201301/80 2026-05-13T06:17:44Z 2026-05-13T06:17:44Z Kim, Jooyeon https://scholarworks.unist.ac.kr/handle/201301/91692 2026-05-13T01:30:09Z 2026-04-30T15:00:00Z

Title: Probing the Impact of Scale on Data-Efficient, Generalist Transformer World Models for Atari Author(s): Kim, Jooyeon

2026-04-30T15:00:00Z Kim, Seong Hyeon Ahn, Hyemin https://scholarworks.unist.ac.kr/handle/201301/91452 2026-04-23T00:30:43Z 2025-05-31T15:00:00Z

Title: Proprioception-conditioned Visual Scene Generation for Robot World Modeling via Contrastive Learning and Diffusion Author(s): Kim, Seong Hyeon; Ahn, Hyemin Abstract: A world model allows robots to understand and predict the interplay between their actions and environmental dynamics. Recent advancements in diffusion models have significantly improved the quality of image frame generation in simulated environments, contributing to the development of more robust and generalized world models. However, these diffusion-based world models often depend on discrete inputs, such as keyboard commands, which limit their applicability to continuous real-world robotic control. To address this limitation, we propose a novel framework that integrates contrastive learning to align visual and proprioceptive modalities (e.g., joint positions) within a shared latent space. This shared latent space facilitates accurate cross-modal predictions between visual scenes and proprioceptive states. By combining this latent representation with a diffusion model, our world model can generate long-term future visual scenes by leveraging both initial visual observations and proprioceptive states. Experimental results demonstrate that the proposed framework generates high-fidelity, long-term future visual scenes when provided with target proprioceptive data. This capability enables robots to plan their motions solely based on the generated images, enabling imagination-based planning. © ICROS 2025.

2025-05-31T15:00:00Z Jin, Howon Yoon, Sangwoong Park, Frank C. Ahn, Kyung Hyun https://scholarworks.unist.ac.kr/handle/201301/91295 2026-04-07T04:09:58Z 2023-09-30T15:00:00Z

Title: Data-driven constitutive model of complex fluids using recurrent neural networks Author(s): Jin, Howon; Yoon, Sangwoong; Park, Frank C.; Ahn, Kyung Hyun Abstract: This study introduces the Constitutive Neural Network (ConNN) model, a machine learning algorithm that accurately predicts the temporal response of complex fluids under specific deformations. The ConNN model utilizes a recurrent neural network architecture to capture the time dependent stress responses, and the recurrent units are specifically designed to reflect the characteristics of complex fluids (fading memory, finite elastic deformation, and relaxation spectrum), without presuming any equation of motion of the fluid. We demonstrate that the ConNN model can effectively replicate the temporal data generated by the Giesekus model and the Thixotropic-Elasto-Visco-Plastic (TEVP) fluid model under varying shear rates. To test the performance of the trained model, we subject it to an oscillatory shear flow, with periodic reversals in flow direction, which has not been trained on. The ConNN model successfully replicates the shear moduli of the original models, and the trained values of the recurrent parameters match the physical prediction of the original models. However, we do observe a slight deviation in the normal stresses, indicating that further improvements are necessary to achieve more rigorous physical symmetry and improve the model prediction.

2023-09-30T15:00:00Z Khanal, Shalil Liu, Yuanhang Bamidele, Adebowale O. Wixom, Alexander Q. Washington, Alexander M. Jalan-Sakrikar, Nidhi Cooper, Shawna A. Vuckovic, Ivan Zhang, Song Zhong, Jun Johnson, Kenneth L. Charlesworth, M. Cristine Kim, Iljung Yeon, Yubin Yoon, Sangwoong Noh, Yung-Kyun Meroueh, Chady Timbilla, Abdul Aziz Yaqoob, Usman Gao, Jinhang Kim, Yohan Lucien, Fabrice Huebert, Robert C. Hay, Nissim Simons, Michael Shah, Vijay H. Kostallari, Enis https://scholarworks.unist.ac.kr/handle/201301/91292 2026-04-07T04:03:37Z 2024-05-31T15:00:00Z

Title: Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release spatially to amplify liver fibrosis Author(s): Khanal, Shalil; Liu, Yuanhang; Bamidele, Adebowale O.; Wixom, Alexander Q.; Washington, Alexander M.; Jalan-Sakrikar, Nidhi; Cooper, Shawna A.; Vuckovic, Ivan; Zhang, Song; Zhong, Jun; Johnson, Kenneth L.; Charlesworth, M. Cristine; Kim, Iljung; Yeon, Yubin; Yoon, Sangwoong; Noh, Yung-Kyun; Meroueh, Chady; Timbilla, Abdul Aziz; Yaqoob, Usman; Gao, Jinhang; Kim, Yohan; Lucien, Fabrice; Huebert, Robert C.; Hay, Nissim; Simons, Michael; Shah, Vijay H.; Kostallari, Enis Abstract: Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 (RAB31) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.

2024-05-31T15:00:00Z