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221 results on '"Shan, Shuo"'

1. MobileFlow: A Multimodal LLM For Mobile GUI Agent

Author

Nong, Songqin, Zhu, Jiali, Wu, Rui, Jin, Jiongchao, Shan, Shuo, Huang, Xiutian, and Xu, Wenhao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Currently, the integration of mobile Graphical User Interfaces (GUIs) is ubiquitous in most people's daily lives. And the ongoing evolution of multimodal large-scale models, such as GPT-4v, Qwen-VL-Max, has significantly bolstered the capabilities of GUI comprehension and user action analysis, showcasing the potentiality of intelligent GUI assistants. However, current GUI Agents often need to access page layout information through calling system APIs, which may pose privacy risks. Fixing GUI (such as mobile interfaces) to a certain low resolution might result in the loss of fine-grained image details. At the same time, the multimodal large models built for GUI Agents currently have poor understanding and decision-making abilities for Chinese GUI interfaces, making them difficult to apply to a large number of Chinese apps. This paper introduces MobileFlow, a multimodal large language model meticulously crafted for mobile GUI agents. Transforming from the open-source model Qwen-VL-Chat into GUI domain, MobileFlow contains approximately 21 billion parameters and is equipped with novel hybrid visual encoders, making it possible for variable resolutions of image inputs and good support for multilingual GUI. By incorporating Mixture of Experts (MoE) expansions and pioneering alignment training strategies, MobileFlow has the capacity to fully interpret image data and comprehend user instructions for GUI interaction tasks. Finally, MobileFlow outperforms Qwen-VL-Max and GPT-4v in terms of task execution by GUI agents on both public and our proposed evaluation metrics, and has been successfully deployed in real-world business contexts, proving its effectiveness for practical applications.

Published
2024

24. Kernel Mean Estimation by Marginalized Corrupted Distributions

Author

Xia, Xiaobo, Shan, Shuo, Gong, Mingming, Wang, Nannan, Gao, Fei, Wei, Haikun, and Liu, Tongliang

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms. Given a finite sample, the standard estimate of the target kernel mean is the empirical average. Previous works have shown that better estimators can be constructed by shrinkage methods. In this work, we propose to corrupt data examples with noise from known distributions and present a new kernel mean estimator, called the marginalized kernel mean estimator, which estimates kernel mean under the corrupted distribution. Theoretically, we show that the marginalized kernel mean estimator introduces implicit regularization in kernel mean estimation. Empirically, we show on a variety of datasets that the marginalized kernel mean estimator obtains much lower estimation error than the existing estimators.

Published
2021

31. Research Progress on the Toxicity and Mechanism of Action of Patulin

Author

LI Yong, SHAN Shuo, WU Danzhou, HU Shuheng, XU Zimu, ZHU Ruiyu

Subjects
patulin, contamination, toxicity, toxic mechanism, Food processing and manufacture, TP368-456
Abstract

Patulin, a toxic secondary metabolite of fungi, exists widely and stably in fruits, vegetables, grains, nuts and Chinese medicinal materials. Patulin enters the body through skin contact or food intake, posing a great threat to the health of humans and animals. Previous studies have shown that patulin can induce genotoxicity, immunotoxicity and reproductive toxicity, damaging the intestine, kidney, liver and other organs in the body. The toxic mechanism of patulin involves inducing damage to biological macromolecular structures, oxidative stress injury and cell autophagy as well as damaging intestinal flora homeostasis. In this article, the recent status of patulin contamination in foods is reviewed, with an emphasis on the latest progress in research on the toxicity and mechanism of action of patulin. It is hoped that this review will provide a theoretical basis for patulin detoxification.

Published
2023
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41. Online Learning‐Based Event‐Triggered Model Predictive Control With Shrinking Prediction Horizon for Perturbed Nonlinear Systems.

Author

Lin, Min, Shan, Shuo, Sun, Zhongqi, and Xia, Yuanqing

Subjects
*ONLINE education, *PREDICTION models, *NONLINEAR systems, *PROBLEM solving, *CONSERVATISM
Abstract

This article proposes an online learning‐based event‐triggered model predictive control (OLEMPC) scheme for constrained nonlinear systems with state‐dependent uncertainties. The scheme incorporates both the nominal and the learned models to ensure favorable theoretical properties during online learning. A composite measurement‐triggering strategy is devised to reduce the number of state measurements as well as solving the optimization problems. This strategy attenuates the conservatism in measurement and triggering through combining the event‐ and self‐triggering approaches. By implementing the algorithm, both state measurement and triggering frequency further decrease with the online refinement of the prediction model, and the prediction horizon adaptively shrinks as the state approaches the terminal region. It is shown that the feasibility of the optimization problem and stability of the closed‐loop system are guaranteed. Simulation results verify the effectiveness of this scheme in ensuring closed‐loop performance and alleviating computational burden. [ABSTRACT FROM AUTHOR]

Published
2025
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46. Semi-supervised Early Event Detection

Author

Xie, Liping, Gong, Chen, Zhang, Jinxia, Shan, Shuo, Wei, Haikun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Palaiahnakote, Shivakumara, editor, Sanniti di Baja, Gabriella, editor, Wang, Liang, editor, and Yan, Wei Qi, editor

Published
2020
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