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SSrN 2023年

作者： Ahn, da-Vin Kim, Kyeongdae Choi, Kyujeong Lee, Jin Woong Kim, Jeong-Gil Yu, Jihun Kim, Heung-Sub Seo, Jaho Park, Young-Jun Department of Biosystems Engineering College of Agriculture and Life Sciences Seoul National University Seoul Korea Republic of Convergence Major in Global Smart Farm College of Agriculture and Life Sciences Seoul National University Seoul Korea Republic of Smart Agricultural Machinery R&D Group Korea Institute of Industrial Technology Gimje Korea Republic of Tractor Advanced Development Group LS Mtron Anyang14118 Korea Republic of Department of Smart Industrial Machine Technologies Korea Institute of Machinery and Materials Daejeon34103 Korea Republic of Department of Automotive and Mechatronics Engineering Ontario Tech University 2000 Simcoe Street North OshawaONL1G 0C5 Canada Research Institute for Agriculture and Life Science Seoul National University Seoul Korea Republic of

The effect of clutch control parameters on the performance of tractor hydraulic systems was investigated in this study to improve the launch quality of the power shuttle tractor under launch conditions. A power shuttle tractor model for launch control was designed as an integrated mechanical, hydraulic, and electric system;a mathematical model was designed for each subsystem, and nonlinear elements were modeled based on the test data. The designed model was validated by comparing the results of the tractor field test with the simulation results. Launch quality was evaluated based on response time and mean jerk. The larger the clutch pressure gradient in the torque phase, the larger is the mean jerk of the tractor. Further, an appropriate fill pulse pressure is desired because excessive fill pulse pressure can cause overfilling and adversely affect launch quality. A smooth launch is required to improve launch quality;however, if the clutch pressure is small, the launch completion time becomes longer, which deteriorates the launch response, and necessitates a compromise. © 2023, The Authors. All rights reserved.

关键词： Tractors (truck)

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Fast Proposals for Image and Video Annotation using Modified Echo State Networks 17

Fast Proposals for Image and Video Annotation using Modified...

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17th IEEE International Conference on machine learning and Applications (IEEE ICMLA)

作者： roychowdhury, Sohini Muppirisetty, L. Srikar Volvo Cars R&D Tech Off USA Mountain View CA 94043 USA Volvo Cars Machine Learning & Data Analyt S-43135 Molndal Sweden

ISBN: (纸本)9781538668054

deep learning frameworks for computer-vision applications require fast and scalable annotation systems. Since manually annotated data for semantic segmentation tasks is time-consuming and tough to quality assure, accurate and automated region-based proposals can significantly aid high quality data annotation. In this work, we propose modified Echo State Network (ESN) models that iteratively learn from a small subset of data (20-30% images) and adapt to a variety of semantic segmentation goals without manual supervision on test images. We observe that the modified ESN model that relies on 3 x 3 pixel neighborhood features scales across segmentation tasks with mean segmentation F_scores in the range of 0.58-0.87 for complete foreground and specific foreground segmentation tasks, respectively. Thus, the proposed methods can be specifically useful for fast semantic proposal estimation to enhance the annotation resourcefulness for time sensitive applications in the automotive field.

关键词： Echo state network active learning semantic segmentation automated proposal image annotation

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European Immunogenicity Platform 11th Open Scientific Symposium on immunogenicity of biopharmaceuticals

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BIOANALYSIS 2020年第15期12卷 1043-1048页

作者： Tourdot, Sophie Quaglia, Carolina B. Chamberlain, Paul de Groot, Anne S. dellas, Nikki Guillemare, Eric Kromminga, Arno Lotz, Gregor P. Mingozzi, Federico Piccoli, Luca Pine, Samuel richards, Susan Waxenecker, Guenter Kramer, daniel Pfizer Inc BioMed Design Andover MA 01810 USA Tech Univ Denmark Immunoinformat & Machine Learning DK-2800 Lyngby Denmark NDA Regulatory Sci Ltd NDA Advisory Board Leatherhead KT22 9DF Surrey England EpiVax Providence RI USA Univ Georgia Athens GA 02909 USA Codexis Redwood City CA 94063 USA Translat Med & Early Dev Sanofi Montpellier F-34184 Montpellier France Bioagilytix D-22339 Hamburg Germany Roche Innovat Ctr Large Mol Bioanalyt Sci Roche Pharma Res & Early Dev D-82377 Penzberg Germany Spark Therapeut Philadelphia PA 19104 USA Univ Svizzera Italiana Inst Res Biomed CH-6500 Bellinzona Switzerland Ablynx NV Bioanal & Immunogen B-8052 Zwijnaarde Belgium Sanofi Genzyme TMED Framingham MA 01701 USA Austrian Med & Med Devices Agcy Dept Biol Preclin & Stat Assessment A-1200 Vienna Austria Sanofi R&D Translat Med & Early Dev D-65926 Frankfurt Germany

Given the expanding number of complex therapeutic protein drugs and advanced therapy medicinal products that are being developed, improving our ability to assess the potential immunogenicity of biologics is critical to ensuring treatment efficacy and patient safety. In this context, the European Immunogenicity Platform annual meeting provides opportunities for experts from industry and academia, regulators and clinicians to convene and discuss immunogenicity assessment methods and tools. This report summarizes the key messages on immunogenicity testing, prediction, clinical relevance and advanced therapy medicinal products discussed at the 11th Open Scientific European Immunogenicity Platform Symposium on Immunogenicity of Biopharmaceuticals, Lisbon, Portugal, 17-19 February 2020.

关键词： antidrug antibody gene therapy immunogenicity risk assessment

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Hierarchical pointer net parsing

arXiv

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arXiv 2019年

作者： Liu, Linlin Lin, Xiang Joty, Shafiq Han, Simeng Bing, Lidong Nanyang Technological University Singapore R&D Center Singapore Machine Intelligence Technology Alibaba DAMO Academy Salesforce Research Asia Singapore

Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this paper, we propose hierarchical pointer network parsers, and apply them to dependency and sentence-level discourse parsing tasks. Our results on standard benchmark datasets demonstrate the effectiveness of our approach, outperforming existing methods and setting a new state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Syntactics

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Chargrid: Towards understanding 2d documents

arXiv

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arXiv 2018年

作者： Katti, Anoop r. reisswig, Christian Guder, Cordula Brarda, Sebastian Bickel, Steffen Höhne, Johannes Faddoul, Jean Baptiste SAP SE Machine Learning R&D Berlin Germany

We introduce a novel type of text representation that preserves the 2d layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Credit Card Fraud detection: A realistic Modeling and a Novel learning Strategy

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IEEE TrANSACTIONS ON NEUrAL NETWOrKS ANd learning SYSTEMS 2018年第8期29卷 3784-3797页

作者： dal Pozzolo, Andrea Boracchi, Giacomo Caelen, Olivier Alippi, Cesare Bontempi, Gianluca Univ Libre Bruxelles Comp Sci Dept Machine Learning Grp B-1050 Brussels Belgium Politecn Milan Dipartimento Elettron Informaz & Bioingegneria I-20133 Milan Italy R&D High Proc & Volume Team B-1130 Brussels Belgium Univ Svizzera Italiana CH-6900 Lugano Switzerland

detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FdS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FdSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.

关键词： Concept drift Credit card fraud detection learning in nonstationary environments unbalanced classification

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machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

arXiv

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arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, ronan Lucor, didier Iooss, Bertrand Brajard, Julien Xiao, dunhui Janjic, Tijana ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

data Assimilation (dA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFd) to geoscience and climate systems. recently, much effort has been given in combining dA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply dA and UQ techniques to improve the accuracy and the interpretability of their models, but also at dA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of dA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： dynamical systems

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deep residual Text detection Network for Scene Text 14

Deep Residual Text Detection Network for Scene Text

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14th IAPr International Conference on document Analysis and recognition (ICdAr)

作者： Zhu, Xiangyu Jiang, Yingying Yang, Shuli Wang, Xiaobing Li, Wei Fu, Pei Wang, Hua Luo, Zhenbo Samsung R&D Inst China Machine Learning Lab Beijing Peoples R China

ISBN: (纸本)9781538635865

Scene text detection is a challenging problem in computer vision. In this paper, we propose a novel text detection network based on prevalent object detection frameworks. In order to obtain stronger semantic feature, we adopt resNet as feature extraction layers and exploit multi-level feature by combining hierarchical convolutional networks. A vertical proposal mechanism is utilized to avoid proposal classification, while regression layer remains working to improve localization accuracy. Our approach evaluated on ICdAr2013 dataset achieves 0.91 F-measure, which outperforms previous state-of-the-art results in scene text detection.

关键词： Scene text detection deep residual Networks CTPN

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End-to-end Scene Text recognition in Videos Based on Multi Frame Tracking 14

End-to-end Scene Text Recognition in Videos Based on Multi F...

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14th IAPr International Conference on document Analysis and recognition (ICdAr)

作者： Wang, Xiaobing Jiang, Yingying Yang, Shuli Zhu, Xiangyu Li, Wei Fu, Pei Wang, Hua Luo, Zhenbo Samsung R&D Inst China Machine Learning Lab Beijing Peoples R China

ISBN: (纸本)9781538635865

Text detection and recognition in scene images and videos attract much attention in computer vision recently. However, most existing text detection and recognition methods only focus on static images. In this paper an end-to-end scene text recognition method based on multi frame tracking is proposed for text in videos, in which temporal information is employed to improve performance. First, an end-to-end text recognition method based on a unified deep neural network is used to detect and recognize text in each frame of the input video. Then, multi frame text tracking is employed through associations of texts in current frame and several previous frames to obtain final results. Experiments on ICdAr datasets demonstrate that the proposed method outperforms the state-of-the-art methods in end-to-end video text recognition.

关键词： end-to-end text recognition text in videos deep neural network multi frame tracking

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Sketch-based Medical Image retrieval

arXiv

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arXiv 2023年

作者： Kobayashi, Kazuma Gu, Lin Hataya, ryuichiro Mizuno, Takaaki Miyake, Mototaka Watanabe, Hirokazu Takahashi, Masamichi Takamizawa, Yasuyuki Yoshida, Yukihiro Nakamura, Satoshi Kouno, Nobuji Bolatkan, Amina Kurose, Yusuke Harada, Tatsuya Hamamoto, ryuji Division of Medical AI Research and Development National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Machine Intelligence for Medical Engineering Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Medical Data Deep Learning Team Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Experimental Therapeutics National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Diagnostic Radiology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Neurosurgery and Neuro-Oncology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Colorectal Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Thoracic Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Radiation Safety and Quality Assurance Division National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Division of Research and Development for Boron Neutron Capture Therapy National Cancer Center Exploratory Oncology Research & Clinical Trial Center 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Medical Physics Laboratory Division of Health Science Graduate School of Medicine Osaka University Yamadaoka 1-7 Osaka Suita-shi565-0871 Japan Department of Surgery Kyoto University Graduate School of Medicine 54 Shogoin Kawahara-cho Sakyo-ku Kyoto606-8507 Japan

The amount of medical images stored in hospitals is increasing faster than ever;however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIr) systems usually require example images to construct query vectors;nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIr) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIr system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIr system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIr system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

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