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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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A disk I/O optimized system for concurrent graph processing jobs

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Frontiers of computer science 2024年第3期18卷 13-29页

作者： Xianghao XU Fang WANG Hong JIANG Yongli CHENG Dan FENG Peng FANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Computer Science&Engineering University of Texas at ArlingtonArlingtonTX 76019USA College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Zhejiang Lab Hangzhou 311121China

In order to analyze and process the large graphs with high cost efficiency,researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity *** the other hand,with the rapidly growing need of analyzing graphs in the real-world,graph processing systems have to efficiently handle massive concurrent graph processing(CGP)***,due to the inherent design for single graph processing job,existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP *** this paper,we propose GraphCP,a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP *** proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices,which efficiently overcomes above challenges faced by existing out-of-core graph processing ***,GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future *** addition,GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access *** evaluation results show that GraphCP is 20.5×and 8.9×faster than two out-of-core graph processing systems GridGraph and GraphZ,and 3.5×and 1.7×faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.

关键词： graph processing disk I/O concurrent jobs

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Federated Learning for Character Prediction for Text Generation

Federated Learning for Character Prediction for Text Generat...

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2023 Congress in computer science, computer Engineering, and Applied Computing, CSCE 2023

作者： Hu, Guang Fang, Xin School of Statistics and Information Shanghai University of International Business and Economics Shanghai China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (纸本)9798350327595

Modern mobile devices have access to enormous amounts of user data including text, images, speech, etc., which can be utilized to train high-performance learning models and enhance the user experience. However, accessing large amounts of data often raises concerns for user privacy and security. To address this, federated learning (FL) has emerged as a new machine learning approach that trains models on multiple decentralized edge devices (e. g. mobiles) or servers while protecting user privacy. In this paper, we present a distributed learning framework using a practical iterative average-based federated learning algorithm for the text generation task in Natural Language Processing (NLP). Our results show that text generation training under federated learning yields better performance than random guessing, demonstrating the feasibility of FL in language modeling. The study highlights the success of text generation techniques trained using federated learning, while emphasizing the importance of safeguarding user privacy and security. © 2023 IEEE.

关键词： FedAvg Federated Learning Natural Language Processing Text Generation

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Interactive Visualization of Temporal Brain Connectivity data based-on Frequent Feature Mining 35

Interactive Visualization of Temporal Brain Connectivity Dat...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Guangwei Jing, Ming Liu, Yunjing Zhang, Li Dong, Anming Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure. In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest. To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas. This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Brain

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling 38

Structured Multi-Track Accompaniment Arrangement via Style P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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Improved Machine Learning Approach for Shanghai City's Secondhand Housing Price Prediction

Improved Machine Learning Approach for Shanghai City's Secon...

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2023 Congress in computer science, computer Engineering, and Applied Computing, CSCE 2023

作者： Hu, Guang Tang, Yue Yi, Rui School of Statistics and Information Shanghai University of International Business and Economics Shanghai China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (纸本)9798350327595

This paper presents an improved machine learning approach for prediction of second-hand housing prices in Shanghai. It firstly builds the random forest model and the XGboost model with Shanghai second-hand housing transaction data, and then analyses the challenges in the improvement of the models' prediction accuracy and generalization performance. In the light of that, it introduces the Lasso model for variable selection, and deletes three variables with insignificant regression coefficients, and then rebuilds the random forest and XGboost prediction models. The experimental results show that the prediction accuracy and the generalization performance of the models is well improved. © 2023 IEEE.

关键词： Lasso Machine Learning Prediction Random Forest XGboost

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A Framework for Multiclass Contour Visualization

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 IEEE.

关键词： data visualization

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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Learning Empirical Bregman Divergence for Uncertain Distance Representation 26

Learning Empirical Bregman Divergence for Uncertain Distance...

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26th International Conference on Information Fusion, FUSION 2023

作者： Li, Zhiyuan Liuy, Ziru Zouz, Anna Ralescu, Anca L. University of Cincinnati Department of Computer Science United States City University of Hong Kong School of Data Science Hong Kong Social Behavioral and Economic Sciences National Science Foundation United States

ISBN: (纸本)9798890344854

Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems. © 2023 International Society of Information Fusion.

关键词： Embeddings

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