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Visual Feature Combination Approach for Zero-Shot Learning

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Ruan Jian Xue Bao/Journal of Software 2018年 29卷 16-29页

作者： Yang, Gang Liu, Jin-Lu Li, Xi-Rong Xu, Jie-Ping School of Information Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China

Zero-Shot learning is an important research in the field of machine learning and image recognition. Zero-Shot learning methods normally use the semantic information among unseen classes and seen classes to transfer the knowledge which is learned from examples of seen classes to unseen classes, so as to recognize and classify the examples of unseen classes. In this study, a zero-shot learning approach based on construction of visual feature combination is proposed. The approach generates many examples of unseen class on visual feature level by the way of feature combination, which is first proposed, and thus transforms zero-shot learning problem to be a traditional classification problem solved by supervised learning. The approach mimics human cognition process of associative memory, and includes four steps: feature-attribute relation extraction, example construction, example screening, and domain adaption. On training examples of seen classes, the relationship between class attributes and dimensions of feature is extracted;on visual feature level, examples of unseen classes are generated by visual feature combination;dissimilarity representation is introduced to filter the generated examples of unseen classes;semi-supervised and unsupervised feature domain adaption are proposed to linearly transform the generated examples of unseen classes to be more effective. The proposed approach shows superior performance on three benchmark datasets (AwA, AwA2, and SUN), especially on dataset AwA, it obtains 82.6% top-1 accuracy which is the best result as far as we know. Experiment results demonstrate the effectiveness and superiority of the proposed approach. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Image classification

Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

XTQA: Span-Level Explanations for Textbook Question Answering

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

Unsupervised statistical text simplification using pre-trained language modeling for initialization

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Frontiers of Computer Science 2023年第01期17卷 171303-171303页

作者： QIANG Jipeng ZHANG Feng LI Yun YUAN Yunhao ZHU Yi WU Xindong Department of Computer Science Yangzhou University Yangzhou 225127 China Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei 23009 China Mininglamp Academy of Sciences Mininglamp Beijing 100089 China

Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based machine translation system (UnsupPBMT) achieved good performance, which initializes the phrase tables using the similar words obtained by word embedding modeling. Since word embedding modeling only considers the relevance between words, the phrase table in UnsupPBMT contains a lot of dissimilar words. In this paper, we propose an unsupervised statistical text simplification using pre-trained language modeling BERT for initialization. Specifically, we use BERT as a general linguistic knowledge base for predicting similar words. Experimental results show that our method outperforms the state-of-the-art unsupervised text simplification methods on three benchmarks, even outperforms some supervised baselines.

关键词： text simplification pre-trained language modeling BERT word embeddings

A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

WikiIns: A High-Quality dataset for Controlled Text Editing by Natural Language Instruction

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arXiv 2023年

作者： Chen, Xiang Li, Zheng Wan, Xiaojun Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China The MOE Key Laboratory of Computational Linguistics Peking University China Schools of Electronic Engineering and Computer Science Peking University China

Text editing, i.e., the process of modifying or manipulating text, is a crucial step in human writing process. In this paper, we study the problem of controlled text editing by natural language instruction. According to a given instruction that conveys the edit intention and necessary information, an original draft text is required to be revised into a target text. Existing automatically constructed datasets for this task are limited because they do not have informative natural language instruction. The informativeness requires the information contained in the instruction to be enough to produce the revised text. To address this limitation, we build and release WikiIns, a high-quality controlled text editing dataset with improved informativeness. We first preprocess the Wikipedia edit history database to extract the raw data (WikiIns-Raw). Then we crowdsource high-quality validation and test sets, as well as a small-scale training set (WikiIns-Gold). With the high-quality annotated dataset, we further propose automatic approaches to generate a large-scale "silver" training set (WikiIns-Silver). Finally, we provide some insightful analysis on our WikiIns dataset, including the evaluation results and the edit intention analysis. Our analysis and the experiment results on WikiIns may assist the ongoing research on text editing. The dataset, source code and annotation guideline are available at https: //***/CasparSwift/WikiIns. Copyright © 2023, The Authors. All rights reserved.

关键词： Silver

Penetration Effect of Informatization on Technological Progress in Logistics Industry: Empirical Evidence from Inter-provincial Panel data in China 9th

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Penetration Effect of Informatization on Technological Progr...

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

作者： Gong, Yaling Tian, Xin Jiang, Zuoliang Jiao, Yamin School of Economics and Management Yuzhang Normal University Nanchang330103 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 Shanghai Heading Information Engineering Co. Ltd. Shanghai201112 China Shanghai Winjoin Information Technology Co. Ltd. Shanghai200126 China

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology. © 2022 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0

Dissociation mechanism of C2H42+ induced by 18-keV/u Ne8+ impact: Symmetric and isomeric cleavage of the C-C bond

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Physical Review A 2022年第1期106卷 012805-012805页

作者： Baihui Ren Pufang Ma Yu Zhang Long Wei Jie Han Zihan Xia Jiarong Wang Tianming Meng Wandong Yu Yaming Zou Chuan-Lu Yang Baoren Wei Institute of Modern Physics Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE) Fudan University Shanghai 200433 China College of Data Science Jiaxing University Jiaxing 314001 China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics School of Physics Peking University Beijing 100871 China School of Physics and Optoelectronics Engineering Ludong University Yantai 264025 China

The C-C bond cleavage dissociation of ethylene dication produced by 18-keV/u Ne8+ impact is investigated by combining experimental measurement and theoretical calculation. Using cold target recoil ion momentum spectroscopy, two channels, i.e., the symmetric fragmentation, C2H42+ → CH2++CH2+, and the isomeric one, C2H42+ → CH++CH3+, are clearly identified and thus their kinetic-energy release (KER) distributions are determined. The average KER values are then compared with the theoretical results obtained by quantum chemical calculations, which provide reaction paths of both channels on the potential-energy surfaces of different molecular states. It is found that the hydrogen transfer process and excited-state dynamics still play crucial roles in the formation of C-C bond cleavage channels, but in a different way in comparison with the cases of acetylene C2H2 and ethane C2H6.

关键词： Atomic & molecular structure Chemical reactions Density functional theory Ions Molecules

An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection

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arXiv 2025年

作者： Liu, Xian-Xian Wei, Yuanyuan Xu, Mingkun Guo, Yongze Zhang, Hongwei Dong, Huicong Song, Qun Zhao, Qi Luo, Wei Tien, Feng Gao, Juntao Fong, Simon Department of Computer and Information Science University of Macau 999078 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai519031 China Department of Precision Instrument Tsinghua University Beijing100084 China Department of Gastroenterology Affiliated Hospital of Hebei University of Engineering Handan China Institute of Artificial Intelligence Chongqing Technology and Business University CO Chongqing400067 China Cancer Centre Institute of Translational Medicine Faculty of Health Sciences University of Macau Taipa519000 China MoE Frontiers Science Center for Precision Oncology University of Macau Taipa519000 China Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou1528000 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China Tsinghua University Beijing China

Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains hampered by the limitations of current diagnostic technologies, leading to high rates of misdiagnosis and missed diagnoses. To address these challenges, we propose an integrated system that synergizes advanced hardware and software technologies to balance speed-accuracy. Our study introduces the One Class Twin Cross Learning (OCT-X) algorithm. Leveraging a novel fast double-threshold grid search strategy (FDT-GS) and a patch-based deep fully convolutional network, OCT-X maximizes diagnostic accuracy through real-time data processing and seamless lesion surveillance. The hardware component includes an all-in-one point-of-care testing (POCT) device with high-resolution imaging sensors, real-time data processing, and wireless connectivity, facilitated by the NI CompactDAQ and LabVIEW software. Our integrated system achieved an unprecedented diagnostic accuracy of 99.70%, significantly outperforming existing models by up to 4.47%, and demonstrated a 10% improvement in multirate adaptability. These findings underscore the potential of OCT-X as well as the integrated system in clinical diagnostics, offering a path toward more accurate, efficient, and less invasive early gastric cancer detection. Future research will explore broader applications, further advancing oncological diagnostics. Code is available at https://***/ liu37972/*** © 2025, CC BY-NC-ND.

关键词： Computer hardware description languages