Summary

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Dmitry Ryumin
+Copyright (c) 2023-24 Dmitry Ryumin
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

sections/2024/main/MLSP.md

@@ -61,7 +61,7 @@
 | Exploration of Visual Prompt in Grounded Pre-Trained Open-Set Detection | :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10447589-E4A42C.svg)](https://ieeexplore.ieee.org/document/10447589) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2312.08839-b31b1b.svg)](https://arxiv.org/abs/2312.08839) | :heavy_minus_sign: |
 | Adaptive Quantization with Mixed-Precision based on Low-Cost Proxy | :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10447866-E4A42C.svg)](https://ieeexplore.ieee.org/document/10447866) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2402.17706-b31b1b.svg)](https://arxiv.org/abs/2402.17706) | :heavy_minus_sign: |
 | Exploring the Utility of Clip Priors for Visual Relationship Prediction | :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10447556-E4A42C.svg)](https://ieeexplore.ieee.org/document/10447556) | :heavy_minus_sign: |
-| Knowledge-based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows |  :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10448415-E4A42C.svg)](https://ieeexplore.ieee.org/document/10448415) |  :heavy_minus_sign: |
+| Knowledge-based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows |  :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10448415-E4A42C.svg)](https://ieeexplore.ieee.org/document/10448415) | :heavy_minus_sign: |
 | Asformer: Learning from Adjacent Scale | :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10445968-E4A42C.svg)](https://ieeexplore.ieee.org/document/10445968) | :heavy_minus_sign: |
 | Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement | [![GitHub Page](https://img.shields.io/badge/GitHub-Page-159957.svg)](https://gitlab.au.dk/maleci/gigazoom) | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10446065-E4A42C.svg)](https://ieeexplore.ieee.org/document/10446065) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2305.09271-b31b1b.svg)](https://arxiv.org/abs/2305.09271) | :heavy_minus_sign: |
 | Temporal Inconsistency-based Active Learning | :heavy_minus_sign: | [![IEEE Xplore](https://img.shields.io/badge/IEEE-10446899-E4A42C.svg)](https://ieeexplore.ieee.org/document/10446899) | :heavy_minus_sign: |