https://scholarworks.unist.ac.kr/handle/201301/79 2026-04-19T03:17:55Z https://scholarworks.unist.ac.kr/handle/201301/91295 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 https://scholarworks.unist.ac.kr/handle/201301/91292 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 https://scholarworks.unist.ac.kr/handle/201301/91136 Title: Task-Aware Quantization Network for JPEG Image Compression Author(s): Choi, Jinyoung; Han, Bohyung Abstract: We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective function of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approximates bitrates using simple network blocks—two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses—two for semantic image understanding and another two for conventional image compression—and demonstrate the effectiveness of our approach to the individual tasks. 2020-08-22T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/91135 Title: Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform Author(s): Song, Myungseo; Choi, Jinyoung; Han, Bohyung Abstract: We propose a versatile deep image compression network based on Spatial Feature Transform (SFT) [45], which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Ourmodel covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training. The code is available at the project website 2021-10-10T15:00:00Z