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arXiv 2022年

作者： Bian, Junyi Huang, Xiaodi Zhou, Hong Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia Atypon Systems LLC United Kingdom Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization. In particular, GoSum encodes sentence states in reinforcement learning by building a heterogeneous graph for each input document at different discourse levels. An edge in the graph reflects the discourse hierarchy of a document for restraining the semantic drifts across section boundaries. We evaluate GoSum on two datasets of scientific articles summarization: PubMed and arXiv. The experimental results have demonstrated that GoSum achieve state-of-the-art results compared with strong baselines of both extractive and abstractive models. The ablation studies further validate that the performance of our GoSum benefits from the use of discourse information. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Data Replication Placement Strategy for the Distributed Storage System in Cloud-Edge-Terminal Orchestrated Computing Environments

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IEEE Internet of Things Journal 2025年

作者： Chen, Peng Zheng, Mengke Du, Xin Bilal, Muhammad Lu, Zhihui Duan, Qiang Xu, Xiaolong Nanjing University of Information Science and Technology School of Software and Technology Nanjing210044 China Fudan University School of Computer Science Shanghai200433 China Zhejiang University School of Software Technology Zhejiang 310027 China Zhejiang University State Key Lab of Brain-Machine Intelligence Zhejiang 310027 China Lancaster University School of Computing and Communications LancasterLA1 4WA United Kingdom Shanghai Blockchain Engineering Research Center Shanghai200433 China Pennsylvania State University School of Information Sciences and Technology State CollegePA16802 United States

Cloud-edge-terminal orchestrated computing, as an expansion of cloud computing, has sunk resources to the edge nodes and terminal equipment, which can provide high-quality services for delay-sensitive applications and reduce the cost of network communication. Due to the high volume of data generated by Internet of Things (IoT) devices and the limited storage capacities of edge nodes, a significant number of terminal devices are now being considered for utilization as storage nodes. However, because of the heterogeneous storage capacity and reliability of these hardware devices and the different data requirements of user services, the performance and storage reliability of applications deployed in cloud-edge-terminal orchestrated computing environments have become urgent problems to be solved. Especially, for a distributed storage system in these environments, it is required to ensure reliable storage of the generated data and its’ replications. In this paper, we first implement a distributed storage system and construct a data replication placement model. Then, based on the constructed model, we formulate the data replication placement problem and design a data replication placement strategy called DRPS to solve it. The DRPS covers a ranks-based replication storage node selection algorithm and a greedy load balancing algorithm, which can select appropriate hardware devices for different data requirements of services and is implemented in the data storage system to store replications and balance loads. We design extensive experiments to verify the effectiveness of DRPS. The results indicate that the proposed strategy outperforms other state-of-the-art algorithms in terms of system delay reduction by 39.9%, an increase of 43.3% in the replication numbers, a 27.5% improvement in memory utilization, and a reduction of unreliability rate by 82.0%. © 2014 IEEE.

关键词： Cloud storage

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liao, Zhirui Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan University School of Computer Science Shanghai200433 China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Uji611-0011 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9781665401265

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI). © 2021 IEEE.

关键词： Molecules

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Deep Treatment-Adaptive Network for Causal Inference

arXiv

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arXiv 2021年

作者： Li, Qian Wang, Zhichao Liu, Shaowu Li, Gang Xu, Guandong School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Data Science and Machine Intelligence Lab School of Computer Science University of Technology Sydney Sydney Australia Centre for Cyber Security Research and Innovation Deakin University GeelongVIC3216 Australia

Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome) to benefit the decision making in various domains. One fundamental challenge in this research is that the treatment assignment bias in observational data. To increase the validity of observational studies on causal inference, representation based methods as the state-of-the-art have demonstrated the superior performance of treatment effect estimation. Most representation based methods assume all observed covariates are pre-treatment (i.e., not affected by the treatment), and learn a balanced representation from these observed covariates for estimating treatment effect. Unfortunately, this assumption is often too strict a requirement in practice, as some covariates are changed by doing an intervention on treatment (i.e., post-treatment). By contrast, the balanced representation learned from unchanged covariates thus biases the treatment effect estimation. In light of this, we propose a deep treatment-adaptive architecture (DTANet) that can address the post-treatment covariates and provide a unbiased treatment effect estimation. Generally speaking, the contributions of this work are threefold. First, our theoretical results guarantee DTANet can identify treatment effect from observations. Second, we introduce a novel regularization of orthogonality projection to ensure that the learned confounding representation is invariant and not being contaminated by the treatment, meanwhile mediate variable representation is informative and discriminative for predicting the outcome. Finally, we build on the optimal transport and learn a treatment-invariant representation for the unobserved confounders to alleviate the confounding bias. © 2021, CC BY-NC-SA.

关键词： Deep neural networks

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

关键词：

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Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma

Reler: Relearning Controversial Regions to Accurately Segmen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guihua Tao Haojiang Li Dandan Lu Ziqin Ling Lizhi Liu Hongmin Cai School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-sen University Cancer Center Sun Yat-sen University Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients’ survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features’ estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods.

关键词： Learning systems Deep learning Image segmentation Neural networks Feature extraction Transformers Generators

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VMAgent: Scheduling simulator for reinforcement learning

arXiv

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arXiv 2021年

作者： Sheng, Junjie Cai, Shengliang Cui, Haochuan Li, Wenhao Hua, Yun Jin, Bo Zhou, Wenli Hu, Yiqiu Zhu, Lei Peng, Qian Zha, Hongyuan Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai China Algorithm Innovation Lab Cloud BU Huawei Technologies Co. Ltd China Alkaid Lab Cloud BU Huawei Technologies Co. Ltd China The Chinese University of Hong Kong Shenzhen China

A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling. VMAgent is inspired by practical virtual machine (VM) scheduling tasks and provides an efficient simulation platform that can reflect the real situations of cloud computing. Three scenarios (fading, recovering, and expansion) are concluded from practical cloud computing and corresponds to many reinforcement learning challenges (high dimensional state and action spaces, high non-stationarity, and life-long demand). VMAgent provides flexible configurations for RL researchers to design their customized scheduling environments considering different problem features. From the VM scheduling perspective, VMAgent also helps to explore better learning-based scheduling solutions. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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Synergistic engineering of hole transport materials in perovskite solar cells

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InfoMat 2020年第5期2卷 928-941页

作者： Sally Mabrouk Behzad Bahrami Hytham Elbohy Khan Mamun Reza Ashim Gurung Mao Liang Fan Wu Mingtai Wang Shangfeng Yang Qiquan Qiao Center for Advanced Photovoltaics Department of Electrical Engineering and Computer ScienceSouth Dakota State UniversityBrookingsSouth Dakota Physics Department Damietta UniversityNew Damietta CityEgypt Tianjin Key Laboratory of Organic Solar Cells and Photochemical Conversion Department of Applied ChemistryTianjin University of TechnologyTianjinChina Key Lab of Optoelectronic Materials and Devices School of ScienceHuzhou UniversityHuzhouChina Institute of Applied Technology Hefei Institutes of Physical ScienceChinese Academy of SciencesHefeiChina Hefei National Laboratory for Physical Sciences at Microscale CAS Key Laboratory of Materials for Energy ConversionDepartment of Materials Science and EngineeringSynergetic Innovation Center of Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefeiChina

In this work,methylammonium lead triiodide(CH3NH3PbI3)perovskite solar cells with efficiencies higher than 18%were achieved using a new nanocomposite hole transport layer(HTL)by doping poly(ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)with a mixed dopant of polyaniline(PANI)and graphene oxide(GO).A synergistic engineering between GO,PANI,and PEDOT:PSS was accomplished to introduce additional energy levels between perovskite and PEDOT:PSS and increase the conductivity of PEDOT:*** probe force microscope results confirmed that adding GO to PEDOT:PSS/PANI composite significantly reduced the average surface *** increased the open circuit voltage(Voc)to 1.05 V for the GO/PEDOT:PSS/PANI nanocomposite perovskite solar cells from the pristine PEDOT:PSS(Voc=0.95 V)and PEDOT:PSS/PANI(Voc=0.99 V).In addition,adding PANI to the HTLs substantially enhanced short circuit current density(Jsc).This was supported by the current sensing-atomic force microscopy(CS-AFM)and conductivity *** PANI doped films showed superior electrical conductivity compared with those without PANI as indicated by CS-AFM *** can fill the gaps between the microflakes of GO and give rise to more compact hole transport material(HTM)*** led to a higher Jsc after doping with PANI,which was consistent with the incident photon-to-current efficiency and electrochemical impedance spectroscopy *** results of X-ray diffraction(XRD)and AFM indicated the GO/PANI doped HTMs significantly improved the crystallinity,topography,and crystal size of the perovskite film grown on their surface.A higher efficiency of 18.12%for p-i-n perovskite solar cells has been obtained by adding the mixed dopant of GO,PANI,and PEDOT:PSS,demonstrating better stability than the pristine PEDOT:PSS cell.

关键词： composite hole transport material perovskite solar cell polyaniline and graphene oxide

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Afnet: Local Aggregation and Context Fusion Network for Plant Point Cloud Part Segmentation

SSRN

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SSRN 2023年

作者： Qi, Yanyu Guo, Ruohao Li, Zhenbo Qu, Liao Niu, Dantong College of Information and Electrical Engineering China Agricultural University Beijing100083 China National Innovation Center for Digital Fishery Beijing100083 China Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural Affairs Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China School of Intelligence Science and Technology Peking University Beijing100871 China Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States BAIR Lab University of California BerkeleyCA94709 United States

Plant phenotype is crucial for understanding plant biology and improving agricultural production efficiency. However, current phenotype research based on deep learning suffers from insufficient annotated data, especially in 3-dimension scenarios. Furthermore, despite the emergence of convolution-like models in recent years to address various point cloud processing tasks, a significant challenge remains in capturing subtle spatial relationships and contextual semantics essential for attaining a comprehensive understanding of point clouds. This challenge becomes particularly salient when dealing with the intricate and latent patterns frequently encountered in the segmentation and classification of plant organs. In view of the problems above, this work presents PlantSeg3D, a point cloud dataset of 3D plant models for plant phenotyping, to alleviate the scarcity of plant datasets in the community. This study utilizes Arabidopsis as the representative model plant and includes segmentation annotations for 3,600 Arabidopsis plants. Meanwhile, we further propose AFNet, a local aggregation and context fusion network, to capture complicated shape structures, global semantics and contextual representations. Extensive experiments showcase that AFNet performs well on several public benchmarks across different tasks, surpassing previous state-of-the-art approaches with an additional increase of +4.1 mIoU. The impressive performance on both part segmentation and classification tasks demonstrates noteworthy generalization capability. © 2023, The Authors. All rights reserved.

关键词： Semantics

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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