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arXiv 2020年

作者： Maher, Mohamed Ngoy, Perseverance Munga Rebriks, Aleksandrs Ozcinar, Cagri Cuevas, Josue Sanagavarapu, Rajasekhar Anbarjafari, Gholamreza University of Tartu Tartu Estonia iCV Lab University of Tartu Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Rakuten Inc. Big Data Department Machine Learning Group Tokyo Japan

Boosting sales of e-commerce services is guaranteed once users find more matching items to their interests in a short time. Consequently, recommendation systems have become a crucial part of any successful e-commerce services. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-based recommendation systems during the recent few years. This growing interest is due to the security concerns in collecting personalized user behavior data, especially after the recent general data protection regulations 1. In this work, we present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation. In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items that are more likely to correlate with his/her preferences. Our extensive experiments investigate baseline techniques (e.g., nearest neighbors and pattern mining algorithms) and deep learning approaches (e.g., recurrent neural networks, graph neural networks, and attention-based networks). Our evaluations show that advanced neural-based models and session-based nearest neighbor algorithms outperform the baseline techniques in most of the scenarios. However, we found that these models suffer more in case of long sessions when there exists drift in user interests, and when there is no enough data to model different items correctly during training. Our study suggests that using hybrid models of different approaches combined with baseline algorithms could lead to substantial results in session-based recommendations based on dataset characteristics. We also discuss the drawbacks of current session-based recommendation algorithms and further open research directions in this field. Copyright © 2020, The Authors. All rights reserved.

关键词： Recommender systems

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Botnet Detection Employing a Dilated Convolutional Autoencoder Classifier with the Aid of Hybrid Shark and Bear Smell Optimization Algorithm-Based Feature Selection in FANETs

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big data AND COGNITIVE COMPUTING 2022年第4期6卷 112页

作者： Abdulsattar, Nejood Faisal Abedi, Firas Ghanimi, Hayder M. A. Kumar, Sachin Abbas, Ali Hashim Abosinnee, Ali S. Alkhayyat, Ahmed Hassan, Mustafa Hamid Abbas, Fatima Hashim Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Al Zahraa Univ Women Coll Educ Dept Math Karbala 56001 Iraq Univ Warith Al Anbiyaa Coll Engn Biomed Engn Dept Karbala 56001 Iraq South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk 454080 Russia Altoosi Univ Coll World Rankings Unit Najaf 54001 Iraq Islamic Univ Coll Tech Engn Najaf 54001 Iraq Al Mustaqbal Univ Coll Med Labs Tech Dept Hillah 51001 Iraq

Flying ad hoc networks (FANETs) or drone technologies have attracted great focus recently because of their crucial implementations. Hence, diverse research has been performed on establishing FANET implementations in disparate disciplines. Indeed, civil airspaces have progressively embraced FANET technology in their systems. Nevertheless, the FANETs' distinct characteristics can be tuned and reinforced for evolving security threats (STs), specifically for intrusion detection (ID). In this study, we introduce a deep learning approach to detect botnet threats in FANET. The proposed approach uses a hybrid shark and bear smell optimization algorithm (HSBSOA) to extract the essential features. This hybrid algorithm allows for searching different feature solutions within the search space regions to guarantee a superior solution. Then, a dilated convolutional autoencoder classifier is used to detect and classify the security threats. Some of the most common botnet attacks use the N-BaIoT dataset, which automatically learns features from raw data to capture a malicious file. The proposed framework is named the hybrid shark and bear smell optimized dilated convolutional autoencoder (HSBSOpt_DCA). The experiments show that the proposed approach outperforms existing models such as CNN-SSDI, BI-LSTM, ODNN, and RPCO-BCNN. The proposed HSBSOpt_DCA can achieve improvements of 97% accuracy, 89% precision, 98% recall, and 98% F1-score as compared with those existing models.

关键词： FANETs intrusion detection botnet attack deep neural network feature selection optimization

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Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years

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NATURE 2023年第7985期623卷 106-+页

作者： Namburete, Ana I. L. Papiez, Bartlomiej W. Fernandes, Michelle Wyburd, Madeleine K. Hesse, Linde S. Moser, Felipe A. Ismail, Leila Cheikh Gunier, Robert B. Squier, Waney Ohuma, Eric O. Carvalho, Maria Jaffer, Yasmin Gravett, Michael Wu, Qingqing Lambert, Ann Winsey, Adele Restrepo-Mendez, Maria C. Bertino, Enrico Purwar, Manorama Barros, Fernando C. Stein, Alan Noble, J. Alison Molnar, Zoltan Jenkinson, Mark Bhutta, Zulfiqar A. Papageorghiou, Aris T. Villar, Jose Kennedy, Stephen H. Univ Oxford Dept Comp Sci Oxford Machine Learning Neuroimaging Lab Oxford England Univ Oxford Wellcome Ctr Integrat Neuroimaging Oxford England Univ Oxford Dept Engn Sci Oxford England Univ Oxford Big Data Inst Li Ka Shing Ctr Hlth Informat & Discovery Oxford England Univ Oxford Nuffield Dept Womens & Reprod Hlth Oxford England Univ Southampton Fac Med MRC Lifecourse Epidemiol Ctr Human Dev & Hlth Acad Unit Southampton Hants England Univ Oxford Oxford Maternal & Perinatal Hlth Inst Green Templeton Coll Oxford England Univ Sharjah Dept Clin Nutr & Dietet Coll Hlth Sci Sharjah U Arab Emirates Univ Calif Berkeley Sch Publ Hlth Ctr Environm Res & Childrens Hlth Berkeley CA 94720 USA John Radcliffe Hosp Dept Neuropathol Oxford England London Sch Hyg & Trop Med Maternal Adolescent Reprod & Child Hlth Ctr London England Aga Khan Univ Hosp Dept Obstet & Gynaecol Fac Hlth Sci Nairobi Kenya Minist Hlth Dept Family & Community Hlth Muscat Oman Univ Washington Dept Obstet Seattle WA 98195 USA Univ Washington Dept Gynecol Seattle WA 98195 USA Univ Washington Dept Global Hlth Seattle WA 98195 USA Peking Univ Sch Publ Hlth Beijing Peoples R China Univ Torino Dipartimento Sci Pediat & Adolescen SCDU Neonatol Turin Italy Ketkar Hosp Nagpur INTERGROWTH Res Ctr 21 Nagpur Maharashtra India Univ Catolica Pelotas Programa Posgrad Saude Comportamento Pelotas RS Brazil Univ Oxford Dept Psychiat Oxford England African Hlth Res Inst Kwa Zulu South Africa Univ Witwatersrand Sch Publ Hlth MRC Wits Rural Publ Hlth & Hlth Transit Res Unit Fac Hlth Sci Johannesburg South Africa Univ Oxford Dept Physiol Anat & Genet Oxford England Univ Adelaide Australian Inst Machine Learning Dept Comp Sci Adelaide SA Australia South Australian Hlth & Med Res Inst Adelaide SA Australia Hosp Sick Children Ctr Global Child Hlth Toronto ON Canada

Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function(1). We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women(2), selected using World Health Organization recommendations for growth standards(3). Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age(4,5). The atlas was produced using 1,059 optimal quality, three-dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline(6-8). The atlas corresponds structurally to published magnetic resonance images(9), but with finer anatomical details in deep grey matter. The between-study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks' gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks' gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment.

关键词： Intrauterine growth Medical imaging Neuroscience

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High-Performance and Disruptive Computing in Remote Sensing HDCRS-A New Working Group of the GRSS Earth Science Informatics Technical Committee

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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2022年第2期10卷 329-345页

作者： Cavallaro, Gabriele Heras, Dora B. Wu, Zebin Maskey, Manil Lopez, Sebastian Gawron, Piotr Coca, Mihai Datcu, Mihai Julich Supercomp Ctr AI & ML Remote Sensing Simulat & Data Lab Wilhelm Johnen Str D-52428 Julich Germany Univ Bonn Inst Geodesy & Geoinformat Scalable Machine Learning Remote Sensing Big Dat Bonn Germany Univ Santiago de Compostela Dept Elect & Comp Engn Santiago 15782 Spain Univ Santiago de Compostela Remote Sensing Comp Grp Santiago 15782 Spain Ctr Singular Invest Tecnoloxias Intelixentes CiTI Santiago De Compostela 15782 Spain Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Grenoble Alpes Grenoble Images Parole Signal Automat Lab Grenoble Inst Technol Grenoble France Univ Calif Los Angeles Dept Math Los Angeles CA 90024 USA Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Badajoz Spain NASA Marshall Space Flight Ctr Huntsville AL 35808 USA Univ Las Palmas Gran Canaria Las Palmas Gran Canaria Spain Inst Appl Microelect Integrated Syst Design Div Las Palmas Gran Canaria 35001 Las Palmas Spain Polish Acad Sci Particle Astrophys Sci & Technol Ctr AstroCeNT In Nicolaus Copernicus Astron Ctr PL-00614 Warsaw Poland Polish Acad Sci Quantum Syst Informat Grp Inst Theoret & Appl Informat Gliwice Poland Mil Tech Acad Ferdinand I Comp Sci & Cyber Secur Lab Bucharest 050141 Romania Mil Tech Acad Ferdinand I Bucharest Romania German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany Univ Politech Bucharest Dept Appl Elect & Informat Engn Fac Elect Telecommun & Informat Technol Bucharest 061071 Romania Swiss Fed Inst Technol Swiss Fed Inst Technol Zurich Switzerland Telecom Paris Paris Inst Technol ParisTech Paris France

The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

关键词： Remote sensing

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Gradient Descent Finds Global Minima of Deep Neural Networks 36

Gradient Descent Finds Global Minima of Deep Neural Networks

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36th International Conference on machine learning (ICML)

作者： Du, Simon S. Lee, Jason D. Li, Haochuan Wang, Liwei Zhai, Xiyu Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Southern Calif Data Sci & Operat Dept Los Angeles CA 90007 USA Peking Univ Sch Phys Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Inst Big Data Res Beijing Peoples R China Peking Univ Key Lab Machine Percept MOE Sch EECS Beijing Peoples R China MIT Dept EECS Cambridge MA 02139 USA

ISBN: (纸本)9781510886988

Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result.

关键词： Deep neural networks

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A study on the uncertainty of convolutional layers in deep neural networks

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University ShenzhenGuangdong518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution. © 2020, CC-BY.

关键词： Convolution

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Incorporating Hidden Layer representation into Adversarial Attacks and Defences

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adversarial perturbation. And this defence strategy can be regarded as an activation function which can be applied any kind of neural networks. We also prove theoretically the effectiveness of this defense strategy under certain conditions. Besides, incorporating hidden layer representation we propose three types of adversarial attacks to generate three types of adversarial examples, respectively. The experiments show that our defence method can significantly improve the adversarial robustness of deep neural networks which achieves the state-of-the-art performance even though we do not adopt adversarial training. © 2020, CC BY.

关键词： Deep neural networks

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MedShapeNet - a large-scale dataset of 3D medical shapes for computer vision

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2025年第1期70卷 71-90页

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Viet Duc Vu Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Maechler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Kuestner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Loeffler, Maximilian T. Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nuernberger, Andreas Pedrosa, Joo Ferreira, Carlos Aresta, Guilherme Cunha, Antonio Campilho, Aurelio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Roehrig, Rainer Hoelzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, Andre Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hoerst, Fabian Brehmer, Alexan Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Girardetstr 2 D-45131 Essen Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16c A-8010 Graz Austria Comp Algorithms Med Lab Cafe Graz Austria Univ Med Essen AoR Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany Univ Med Essen Univ Hosp Essen Ctr Virtual & Extended Real Med ZvRM Hufelandstr 55 D-45147 Essen Germany Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Swiss Fed Inst Technol Lausanne EPFL Comp Vis Lab Lausanne Switzerland Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany Shanghai Jiao Tong Univ Renji Hosp Sch Med Dept Radiol Shanghai Peoples R China AGH Univ Sci & Technol Dept Measurement & Elect Krakow Poland Univ Appl Sci Western Switzerland HES SO Valais Inst Informat Syst Sierre Switzerland Univ Basel Dept Biomed Engn Ctr Med Image Anal & Nav CIAN Allschwil Switzerland Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA ZhejiangLab Res Ctr Connected Healthcare Big Data Hangzhou Zhejiang Peoples R China Graz Univ Technol Inst Mech Graz Austria Justus Liebig Univ Giessen Univ Hosp Giessen Dept Diagnost & Intervent Radiol Giessen Germany Mehran Univ Engn & Technol Dept Mech Engn Jamshoro Sindh Pakistan Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Hosp Canadian Surg Technol & Adv Robot CSTAR London ON Canada Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Med Univ Graz Dept Cardiac Surg Graz Austria Techn Univ Munchen Univ Klinikum Geschaftsfuhrender Oberarzt Abt Intervent & Diagn Munich Germany Icometrix Leuven Belgium Tech Univ Munich Dept Informat Garching Germany Univ Zurich Dept Quantitat Biomed Zurich Switzerland Tech Univ Dortmund Fac Stat Dortmund Germany Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Techn Univ Munchen Geschaftsfuhrender Oberarzt Abt Interv

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://***/.

关键词： 3D medical shapes benchmark anatomy education shapeomics augmented reality virtual reality

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Modified inexact Levenberg-Marquardt methods for solving nonlinear least squares problems

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2019年第2期74卷 547-582页

作者： Bao, Jifeng Yu, Carisa Kwok Wai Wang, Jinhua Hu, Yaohua Yao, Jen-Chih Zhejiang Ocean Univ Sch Math Phys & Informat Sci Zhoushan 316022 Zhejiang Peoples R China Key Lab Oceanog Big Data Min & Applicat Zhejiang Zhoushan 316022 Zhejiang Peoples R China Hang Seng Univ Hong Kong Dept Math & Stat Hong Kong Peoples R China Zhejiang Univ Technol Dept Math Hangzhou 310032 Zhejiang Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen 518060 Peoples R China China Med Univ China Med Univ Hosp Res Ctr Interneural Comp Taichung Taiwan Hangzhou Normal Univ Dept Math Hangzhou 311121 Zhejiang Peoples R China

In the present paper, we propose a modified inexact Levenberg-Marquardt method (LMM) and its global version by virtue of Armijo, Wolfe or Goldstein line-search schemes to solve nonlinear least squares problems (NLSP), especially for the underdetermined case. Under a local error bound condition, we show that a sequence generated by the modified inexact LMM converges to a solution superlinearly and even quadratically for some special parameters, which improves the corresponding results of the classical inexact LMM in Dan et al. (Optim Methods Softw 17:605-626, 2002). Furthermore, the quadratical convergence of the global version of the modified inexact LMM is also established. Finally, preliminary numerical experiments on some medium/large scale underdetermined NLSP show that our proposed algorithm outperforms the classical inexact LMM.

关键词： Nonlinear least squares problems Inexact Levenberg-Marquardt method Lipschitz condition Local error bound

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