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When liver disease diagnosis encounters deep learning:Analysis,challenges,and prospects

iLIVER 2023年第1期2卷 73-87页

作者： Yingjie Tian Minghao Liu Yu Sun Saiji Fu School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of SciencesBeijing 100190China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190China Tsinghua University Press Beijing 102611China School of Economics and Management Beijing University of Posts and TelecommunicationsBeijing 100876China

The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic ***,obesity,alcohol use,and other factors can damage the liver and cause liver *** diagnosis of liver disease used to depend on the clinical experience of doctors,which made it subjective,difficult,and *** learning has made breakthroughs in various fields;thus,there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and *** this paper,we provide an overview of deep learning in liver research using 139 papers from the last 5 *** also show the relationship between data modalities,liver topics,and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic,in addition to the relations and trends between these ***,we discuss the challenges of and expectations for deep learning in liver research.

关键词： Liver Deep learning data modality Liver topic Application

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Rethink Video Retrieval Representation for Video Captioning

SSRN

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SSRN 2024年

作者： Tian, Mingkai Li, Guorong Qi, Yuankai Wang, Shuhui Sheng, Quan Z. Huang, Qingming School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computing Macquarie University Australia Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences China

Video captioning, a challenging task targeting the automatic generation of accurate and comprehensive descriptions based on video content, has witnessed substantial success recently driven by bridging video representations and textual semantics. Inspired by the nature of the video retrieval task, which learns visual features strongly related to text queries, we propose to take advantage of visual representation learning from the video retrieval framework to tackle video captioning tasks. However, a simple direct application of video retrieval models performs poorly due to the weak ability to capture sufficient video details and temporal information required for video captioning. To increase the attention on details, we propose a learnable token shift module, which flexibly captures subtle movements in local regions across the temporal sequence. Furthermore, we devise a Refineformer, which learns to integrate local video tokens strongly related to desired captions via a cross-attention mechanism. Extensive experiments on MSVD, MSR-VTT and VATEX demonstrate the favorable performance of our method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Iterative Privileged Learning for Multi-view Classification 10

Iterative Privileged Learning for Multi-view Classification

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Fu, Saiji Tian, Yingjie Tang, Jingjing School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Sichuan 611130 China

Multi-view data is closely associated with privileged information, presenting opportunities for advancements in current studies. Firstly, they often overlook the dynamic nature of privileged information;Secondly, the current state-of-the-art privileged support vector machine, dSVM+, is primarily designed for single-view learning;Thirdly, some methods only address two-view classification problems;Finally, the performance enhancements achieved by these approaches remain unsatisfactory. To this end, this paper proposes a multi-view privileged support vector machine called MVdSVM+. The iterative learning strategy is put forward to update the privileged information dynamically. To our best knowledge, this is the first attempt of dSVM+ in multi-view learning. Meanwhile, it is designed for multiple views. Experiments show the effectiveness of MVdSVM+ over the benchmark methods. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Classification (of information)

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MambaLCT: Boosting Tracking via Long-term Context State Space Model

arXiv

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arXiv 2024年

作者： Li, Xiaohai Zhong, Bineng Liang, Qihua Li, Guorong Mo, Zhiyi Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Colleges Universities Key Laboratory of Intelligent Software Wuzhou University Wuzhou543002 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100101 China

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model’s ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time running speeds. Code and models are available at https://***/GXNU-ZhongLab/MambaLCT. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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Types and Evolutionary Boundaries of Agents 5

Types and Evolutionary Boundaries of Agents

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Liu, Feng Liu, Ying Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Economics and Management Beijing China

ISBN: (纸本)9781665426305

Through the analysis of the characteristics of natural intelligent systems, the three laws of intelligence are exploringly proposed. The most important point is that any agent is a system with knowledge input, output, (dynamic) storage and creation abilities Since an agent has four abilities, and each ability has three states, including zero, infinity, and finite (between zero and infinity), 81 types of agents can be formed. Therefore, it can be inferred that Alpha point (No. 1), where all the four abilities of an agent are zero, and Omega point (No. 81), where all four abilities are infinite are the boundary of the evolution of Agent. This paper designs experiments to detect these 81 agents, and finds that 11 of them can be confirmed by experiments at present. Through the discussion of the states of Agents No. 64 to No. 81, fresh thinking is proposed for the generation and realization of the paradox. © 2021 IEEE.

关键词： Evolution of Agents Types of Agents α Point Ω Point

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Credit and guarantee insurance: A consistent pricing approach in supply chain finance 9th

Credit and guarantee insurance: A consistent pricing approac...

引用

9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Zhang, Linzi Xue, Jiayu Shang, Mengyu Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Credit insurance is a new loan guarantee contract which can effectively stimulate domestic demand and promote consumption. According to different modes of payment, individual credit loan guarantees mainly include credit insurance and guarantee insurance. In this paper, we apply the Complete Information Game to analyze the strategies of the insured subject and the risk subject under different scenarios of personal loan guarantee, and obtain the influencing factors of each decision-making subject in supply chain finance. The conclusion shows that the credit status of individual loan applicants greatly affects the decision-making of supply chain financial entities such as banks. Based on this, this paper puts forward corresponding suggestions for the credit insurance business management. © 2022 The Authors. Published by Elsevier B.V.

关键词： Costs

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Pyramid scheme in stock market:A kind of financial market simulation

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Chinese Physics B 2021年第9期30卷 624-632页

作者： Yong Shi Bo Li Guang-Le Du School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial stock market simulation based on agent is an important means to study financial *** on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid *** simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main *** work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

关键词： pyramid scheme agent-based model artificial stock market herd behavior

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The Further Development of Intelligent knowledge for Wisdom 9th

The Further Development of Intelligent Knowledge for Wisdom

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Gu, Jifa Xiao, Xiao Zhang, Lingling Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Moe Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at Ucas Beijing100190 China

This paper introduces some modes from D, I, K to W, M. From different angles we investigate the various process. Especially we utilize the intelligent knowledge to develop the whole process. Finally we design a new medical hall for workshop of meta-synthetic engineering for helping patient to get the appropriate treatment and useful knowledge for health. © 2022 The Authors. Published by Elsevier B.V.

关键词： Patient treatment

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A Study on The Multi-Attribute Decision Theory and Methods 9th

A Study on The Multi-Attribute Decision Theory and Methods

引用

9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Feng, Jinyuan Yan, Yunxi Huang, Manling Du, Yang Lu, Zhichen Li, Biao School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Dance Minzu University of China Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision theory

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A Brief Survey of Distribution Robust Graph Neural Networks

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Procedia Computer Science 2024年 242卷 1281-1286页

作者： Lei Zheng Pei Quan Yong Shi Lingfeng Niu The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China The College of Economics and Management Beijing University of Technology Beijing 100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing 1001090 China

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction.

关键词： Graph neural networks Shift robust Distribution generalization

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