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SSRN 2024年

作者： Zheng, Cenyang Gong, Xun Fan, Lin Li, Jiao School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Department of Gastroenterology The Third People’s Hospital of Chendu Affiliated Hospital of Southwest Jiaotong University Chengdu610031 China

Endoscopic ultrasound (EUS) is the primary diagnostic imaging technique for gastrointestinal stromal tumors. However, accurately identifying these tumors through EUS imaging alone remains challenging, even for experienced clinicians. They often rely on additional data, such as white light endoscopy (WLE) and EUS text reports, to aid in the diagnosis. Unfortunately, in practice, these supplementary modalities are sometimes overlooked during patient evaluation. To address these issues, we propose the Cross-modal Feature Reconstruction Network (CFRN), which integrates multimodal data modeling for medical diagnostic processes and performs specific learning for different modalities with missing data. The network fuses the multi-modal features based on the clinical medical process, realizes the feature-level missing modality reconstruction, and learns the refined features of different modal true cases by decoding the mode-specific missing. We trained and tested the model on two publicly available datasets, Audiovision-MNIST, MM-IMDb, and data sets collected from real clinical scenarios at three hospitals. Our results show that CFRN outperforms state-of-the-art methods, achieving higher prediction accuracy than using EUS alone. By simultaneously incorporating multimodal or reconstructed modal data for disease diagnosis, our single-modal EUS model proves to be more suitable for clinical scenarios. © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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A Multi-Source Information Fusion Model for Outlier Detection

SSRN

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SSRN 2022年

作者： Zhang, Pengfei Li, Tianrui Wang, Guoqiang Wang, Dexian Lai, Pei Zhang, Fan School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Institute of Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China

Multi-source information fusion (MSIF) is a useful strategy for combining complimentary data from numerous information sources to produce an overall precise description, which can help with effective decision-making, prediction, and categorization, etc. In order to find the objects that are different from the expected ones after fusion, i.e., anomalies, or outliers, an MSIF model is put forward for outlier detection. First, the concept of information set (ISet) is developed, which is a collection of the sum of the products of information source values (ISVs) and the corresponding information gains (Gaussian membership degrees). Moreover, it is possible to capture the uncertainty in the information gains itself, besides that in the ISVs. Then, on the basis of the ISet, Shannon source transform and Shannon inverse source transform of ISVs are presented, and their sum is taken as the total uncertainty measure (TUM) of ISVs. Using TUM-based minimal measure fusion strategy, a multi-source information system can be fused into a single-source information system. Further, in the framework of a single-source information system, the Gaussian kernel function is utilized to map the newly fused data to a fuzzy approximation space, and a novel fuzzy knowledge measure (FKM) is proposed. Several metrics are derived on the basis of FKM to describe the outlier degree of objects after fusion. Finally, the FKM-based outlier factor is integrated to implement outlier detection, and the relevant FKMbased outlier detection algorithm (FKMOD) is designed. The experimental results help quantify the performance of the FKMOD. © 2022, The Authors. All rights reserved.

关键词： Anomaly detection

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Exploring the Non-linear Relationship between E-scooter Ridership and the Built Environment

Exploring the Non-linear Relationship between E-scooter Ride...

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International Conference on Transportation Information and Safety (ICTIS)

作者： Zhongquan Qiu Hongxia Yuan Chao Wang Shideng Dai Yamin Huo Yi Wen School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Hi-Tech Industrial Development Zone Chengdu China School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Transportation Southwest Jiaotong University Hi-Tech Industrial Development Zone Chengdu China Department of Civil and Environmental Engineering University of Tennessee Knoxville TN USA

ISBN: (纸本)9781665497145

Shared e-scooter has become a popular mode of transportation in the United States. Despite the increasing attention paid to the e-scooter patterns and the influencing factors of e-scooter trips, few studies have explored the relative importance of the built environment and its threshold effect on e-scooters. Using the trip records of all the shared e-scooters in Austin, Texas, USA from August 2018 to March 2021, the origins and destinations of which are recorded by census tract, this study employs gradient boosting decision trees (GBDT) to examine the non-linear effects of the built environment on the e-scooter ridership. The results show that the relative importance of all built environment factors, with a contribution of 70.45%, is higher than that of socio-demographics (29.54%), and the distance from census tract to city center and bike lane density are the two most essential attributes to e-scooter ridership. Besides, all built environment factors have salient non-linear and threshold effects on the e-scooter ridership. For example, distance to the city center has a significant negative impact on e-scooter trips with an effective range of 6.5 kilometers, and the bike lane density is positively associated with the e-scooter ridership in a certain range. By quantifying each built environment factor's relative importance and threshold effect on e-scooters, this paper can provide e-scooter operators with fleet deployment and operation management suggestions and guide planning departments to build an e-scooter-friendly environment.

关键词： Correlation Employment Urban planning Motorcycles Bicycles Boosting Safety

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Hierarchical Attention Feature Fusion and Refinement Network for Point Cloud Upsampling

Hierarchical Attention Feature Fusion and Refinement Network...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yaori Zhang Shujin Lin Fan Zhou Ruomei Wang National Engineering Research Center of Digital Life School of Computer Science and Engineering Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China School of Communication and Design Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism Sun Yat-sen University Guangzhou China Guangdong Provincial Science and Technology Collaborative Innovation Center for Culture and Tourism National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Engineering Research Center of Digital Life School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

This paper presents a novel hierarchical attention feature fusion and refinement network designed to address challenges in existing deep learning based point cloud upsampling methods. The network combines self-attention layers with a multi-level feature extraction architecture, effectively integrating local and global features, thereby enhancing the robustness and uniformity of the point cloud. Furthermore, a spatial refinement module is employed to predict the offset between the generated coarse dense point clouds and real point clouds, thereby enhancing consistency with the ground truth. Concurrently, a filter function is applied in the loss function to handle outliers of generated point clouds. Extensive experimental results across multiple datasets indicate that our method outperforms existing approaches.

关键词： Point cloud compression Deep learning Feature extraction Robustness Encoding

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Adaptive Convolutional Neural Network for Image Super-resolution

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zhang, Xuanyu Wang, Tao Zhang, Yongjun Zhu, Qi Lin, Chia-Wen The School of Software Northwestern Polytechnical University Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China The Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang215400 China The School of Computer Science Northwestern Polytechnical University Xi’an710129 China The College of Computer Science and Technology Guizhou University Guiyang550025 China The School of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing210016 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Taiwan

Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenges in varying scenes. bigger differences in network architecture are beneficial to extract more diversified structural information to strengthen the robustness of an obtained superresolution model. In this paper, we proposed a adaptive convolutional neural network for image super-resolution (ADSRNet). To capture more information, ADSRNet is implemented by a heterogeneous parallel network. The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers to improve performance of image super-resolution. That can strengthen adaptability of an obtained super-resolution model for different scenes. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution. The relevant experimental results show that the proposed ADSRNet is effective to deal with image resolving. Codes are obtained at https://***/hellloxiaotian/ADSRNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular data

ADVISor: Automatic Visualization Answer for Natural-Language...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Can Liu Yun Han Ruike Jiang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of EECS Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Beijing China

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.

关键词： Annotations Natural languages Pipelines Neural networks data visualization Tools data mining

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Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening

Heterogeneous two-Stream Network with Hierarchical Feature P...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dong Wang Yunpeng Bai Bendu Bai Chanyue Wu Ying Li National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728176055;9781728176062

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Satellites Three-dimensional displays Fuses Streaming media Signal processing Information retrieval Visual effects

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Ripple Effect in China-Europe Railway Transport Network: Operational Failure Risk Propagation and Influence

SSRN

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SSRN 2022年

作者： Lyu, Min Shuai, Bin Zhang, Qinyu Li, Linqing School of Transportation and Logistics Southwest Jiaotong University China National United Engineering Laboratory of Integrated and Intelligent Transportation China National Engineering Laboratory of Integrated Transportation Big Data Application Technology China Market Monitoring and Evaluation Center National Railway Administration of China China School of Transportation and Logistics Southwest Jiaotong University No. 999 Xi'an Road Pidu District Chengdu611756 China

The practice of chain failure in transnational long-haul transport networks has received massive attention. However, less attention is paid to the operational failure risk propagation under the condition that the failed cargo flow need not or cannot be redistributed, which is common in the transportation mode with low flexibility over long distances. Taking the westbound China-Europe Railway transport network (CRN) as the research object, this paper points out the ripple effect phenomenon of forward and backward risk propagation, and novelly illustrates the relationship between flow and operational failure risk. Based on this, we construct a layered flow-risk network and propose a multi-group SIR-like model to delineate the risk propagation dynamics in CRN. Then, we develop an agent-based simulation under different scenarios to examine the effects of forward and backward risk propagation on network performance, moderated by node properties related to function, network topology, and network structure. The attributes of nodes have different impacts on network failures. The descending order is as follows: hubs of high centrality, transit change stations, destination stations, and departure Stations. It is of practical significance to distinguish between forward and backward risk propagation in CRN where eastbound-westbound cargo flow is seriously unbalanced. Forward risk propagation has a more considerable utility on the network's supply side, whereas backward risk propagation has a more considerable utility on the demand side of the network. Operators of China Europe Railway Express and managers of stations can determine early warning and investment priorities according to the supply-demand bias of the node position in the network. We further conduct a regression analysis to discuss the difference between CRN, supply chain network, and random network. We find that the current CRN is still in its infancy in terms of topology, which does not belong to any specific type.

关键词： Network performance

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BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised applications

arXiv

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arXiv 2023年

作者： Lin, Jiatai Han, Guoqiang Xu, Xuemiao Liang, Changhong Wong, Tien-Tsin Chen, C.L. Philip Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Class activation mapping (CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation (WSSS) and object localization (WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores (forward information), gradients (backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data. © 2023, CC BY.

关键词： Learning systems

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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