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License Plate Detection and Recognition Technology for Complex Real Scenarios 16th

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License Plate Detection and Recognition Technology for Compl...

16th International Conference on intelligent Computing, ICIC 2020

作者： Li, Zhipeng Wang, Fei Taleb, Hamdan Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai201804 China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China

ISBN: (纸本)9783030607982

At present, Automatic License Plate Recognition(ALPR) technology has been widely used in residential parking, high-speed intersection toll stations, roadside illegal parking, smart transportation and other fields. Although automatic license plate technology has been widely used in various fields, at present, whether it is commercial or academic methods, it is to explore the license plate recognition research of approximate frontal images in specific regions or specific countries (such as China, Brazil, and the United States). Aiming at real and complex scenarios, this paper builds a dataset for countries along the Belt and Road (such as Kenya, Nigeria, Togo, Ghana, etc.), called BR-ALPR dataset, designed to ALPR. We use yolov3 to complete the license plate detection. For license plate recognition, we use an improved Convolutional Recurrent Neural Network (CRNN) algorithm, which inserts the Spatial Transformation Network (STN) into the CRNN. In addition, we still use the method of template paste to complete the enhancement of the dataset. Experimental results show that our method is superior to advanced commercial methods for the detection and recognition of license plates in complex real Scenarios. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

Trust-region variational inference with gaussian mixture models

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arXiv 2019年

作者： Arenz, Oleg Zhong, Mingjun Neumann, Gerhard Intelligent Autonomous Systems TU Darmstadt Machine Learning Group University of Lincoln Bosch Center for Artificial Intelligence Renningen Germany Center for Autonomous Systems University of Lincoln

Many methods for machine learning rely on approximate inference from in- tractable probability distributions. Variational inference approximates such distri- butions by tractable models that can be subsequently used for approximate infer- ence. learning sufficiently accurate approximations requires a rich model family and careful exploration of the relevant modes of the target distribution. We pro- pose a method for learning accurate GMM approximations of intractable probabil- ity distributions based on insights from policy search by establishing information- geometric trust regions for principled exploration. For efficient improvement of the GMM approximation, we derive a lower bound on the corresponding optimization objective enabling us to update the components independently. The use of the lower bound ensures convergence to a local optimum of the original objective. The number of components is adapted online by adding new components in promising regions and by deleting components with negligible weight. We demonstrate on several domains that we can learn approximations of complex, multi-modal distri- butions with a quality that is unmet by previous variational inference methods, and that the GMM approximation can be used for drawing samples that are on par with samples created by state-of-the-art MCMC samplers while requiring up to three orders of magnitude less computational resources. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability distributions

Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers machine learning Random Forest

Trajectory-based off-policy deep reinforcement learning

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arXiv 2019年

作者： Doerr, Andreas Volpp, Michael Toussaint, Marc Trimpe, Sebastian Daniel, Christian Bosch Center for Artificial Intelligence Renningen Germany Max Planck Institute for Intelligent Systems Stuttgart/Tubingen Germany Machine Learning and Robotics Lab University of Stuttgart Germany

Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining recent improvements in the reuse of off-policy data and exploration in parameter space with deterministic behavioral policies. The resulting objective is amenable to standard neural network optimization strategies like stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo. Incorporation of previous rollouts via importance sampling greatly improves data-efficiency, whilst stochastic optimization schemes facilitate the escape from local optima. We evaluate the proposed approach on a series of continuous control benchmark tasks. The results show that the proposed algorithm is able to successfully and reliably learn solutions using fewer system interactions than standard policy gradient methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Plant Leaf Recognition Based on Conditional Generative Adversarial Nets 15th

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Plant Leaf Recognition Based on Conditional Generative Adver...

15th International Conference on intelligent Computing, ICIC 2019

作者： Jiao, Zhihao Zhang, Lijun Yuan, Chang-An Qin, Xiao Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle Shanghai China School of Automotive Studies Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030267629

Plants play an important role in human life, Identifying and protecting plants has far-reaching implications for the sustainable development of the ecological environment. Plant leaves can often reflect important characteristics of plants, so it is scientific and feasible to effectively identify plant species through plant leaves. With the rapid development of deep learning in recent years, it has been widely applied in plant leaf recognition. Compared with the traditional method, deep learning based plant leaf recognition algorithm can extract plant leaf features more effectively and can greatly improve the performance. Based on the detailed analysis of the structural characteristics of three classical convolutional neural network models, this paper comprehensively compares the recognition performance of three convolutional neural network models on ICL plant leaf datasets. The experimental results show that the Conditional Generative Adversarial Net with optimized output layer has better recognition results on the plant leaf dataset than other convolutional neural network models. © 2019, Springer Nature Switzerland AG.

关键词： Neural network models

The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ICA

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arXiv 2019年

作者： Gresele, Luigi Rubenstein, Paul K. Mehrjou, Arash Locatello, Francesco Schölkopf, Bernhard Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Machine Learning Group University of Cambridge United Kingdom Max Planck ETH Center for Learning Systems Zürich Switzerland BMI Dept. for Computer Science ETH Zürich Switzerland

We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple experimental modalities, and where the goal is to synthesize the disparate measurements into a single unified representation. We consider the case that the observed views are a nonlinear mixing of component-wise corruptions of the sources. When the views are considered separately, this reduces to nonlinear Independent Component Analysis (ICA) for which it is provably impossible to undo the mixing. We present novel identifiability proofs that this is possible when the multiple views are considered jointly, showing that the mixing can theoretically be undone using function approximators such as deep neural networks. In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available. Copyright © 2019, The Authors. All rights reserved.

关键词： Mixing

Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

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Random Occlusion Recovery with Noise Channel for Person Re-i...

16th International Conference on intelligent Computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Analytical probabilistic modeling of dose-volume histograms

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arXiv 2020年

作者： Wahl, Niklas Hennig, Philipp Wieser, Hans-Peter Bangert, Mark German Cancer Research Center – DKFZ Im Neuenheimer Feld 280 Heidelberg69120 Germany Heidelberg Institute for Radiation Oncology – HIRO Im Neuenheimer Feld 280 Heidelberg69120 Germany Department of Physics and Astronomy Ruprecht Karls University Heidelberg Grabengasse 1 Heidelberg69117 Germany Probabilistics Numerics Max Planck Institute for Intelligent Systems Tübingen72076 Germany Chair for the Methods of Machine Learning Eberhard Karls University Tübingen Tübingen72024 Germany Medical Faculty Ruprecht Karls University Heidelberg Grabengasse 1 Heidelberg69117 Germany Department of Physics Ludwig-Maximilians-University of Munich Munich80539 Germany

Purpose: Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, e.g., dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data. Methods: We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, i.e., α-DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and single-fraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling. Results: The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90 % of all computed expected DVH-points and their standard deviations agree within 1 % volume with their empirical counterpart from sampling computations, for both fractionated and single fraction treatments. When computing α-DVHs, the assumption of a beta distribution achieved better agreement with empirical percentiles than the assumption of a normal distribution: While in both cases probabilities

关键词： Normal distribution