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On a vectorized basic linear algebra package for prototyping codes in MATLAB

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arXiv 2024年

作者： Moskovka, Alexej Rahman, Talal Valdman, Jan Vatne, Jon Eivind Department of Mathematics Faculty of Applied Sciences University of West Bohemia Technická 8 Pilsen30100 Czech Republic Faculty of Engineering and Science Western Norway University of Applied Sciences Inndalsveien 28 Bergen5063 Norway Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 Č. Budějovice37005 Czech Republic Department of Economics BI Norwegian Business School Kong Christian Frederiks plass 5 Bergen5006 Norway The Czech Academy of Sciences Institute of Information Theory and Automation Pod Vodárenskou věží 4 Prague18208 Czech Republic

When writing a high-performance code for numerical computation in a scripting language like MATLAB, it is crucial to have the operations in a large for-loop vectorized. If not, the code becomes too slow to be of any use, even for a moderately large problem. However, in the process of vectorizing, it often happens that the code loses its original structure and becomes less readable. This is particularly true in the case of a finite element implementation, even though finite element methods are inherently structured. A basic remedy to this is the separation of the vectorization part from the mathematics part in the code, which is easily achieved through building the code on top of the basic linear algebra subprograms that are already vectorized codes, an idea which has been used in a series of papers over the last fifteen years, developing codes that are fast and still structured and readable. We discuss the vectorized basic linear algebra package, and introduce a formalism using multi-linear algebra to explain and define formally the functions in the package, as well as MATLAB’s pagetime functions. We provide examples from computations of varying complexity, including the computation of normal vectors, volumes, and finite element methods. Benchmarking shows that we also get fast computations. Using the library, we can write codes that closely follow our mathematical thinking, making it easier to write, follow, reuse, and extend the code. © 2024, CC BY.

关键词： MATLAB

Proceedings of the 2016 International Conference on Image Pr...

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Foreword

Proceedings of the 2016 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

Large margin multi-modal multi-task feature extraction for image classification

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

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arXiv 2023年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Liu, Zhiwei Wu, Jia Yu, Zhengtao Yu, Philip S. The Institute of Information Engineering Chinese Academy of Sciences the School of Cyber Security University of Chinese Academy of Sciences Beijing China The School of Cyber Science and Technology Beihang University Beijing1000191 China Salesforce AI Research CA94301 United States The School of Computing Macquarie University Sydney Australia The Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China The Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCLSED, and its variant - UCL-ECSED, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

Effects of carbon dioxide on different PSK modulation formats of Optical Wireless Communications using USRP

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Effects of carbon dioxide on different PSK modulation format...

International Conference on Transparent Optical Networks

作者： Jan Vitasek Radek Martinek Ales Vanderka Lukas Hajek Jan Latal Zhenli Lu Bin Li Department of Telecommunications VSB-Technical University of Ostrava Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 15 708 33 Czech Republic State key Laboratoryof Robotics Chinese Academy of Sciences Shenyang China School of Electrical Engineering and Automation Changshu Instituteof Technology Changshu China

ISBN: (纸本)9781509014682

This article explores the influence of the concentrated carbon dioxide (CO 2 ) gas on the different Phase Shift Keying (PSK) modulations used for Optical Wireless Communication (OWC) links. In this work, the OWC consisted of a laser diode, a photodetector, and a transmission medium. The Universal Software Radio Peripheral (USRP) unit created and evaluated the PSK modulations. This unit modulated the laser diode on the transmitter side and demodulated the data which were received by the photodetector on the receiver side. The CO 2 gas was emitted as a consequence of sublimation of the solid carbon dioxide in hot water. The measurements were done in a closed box in laboratory. The CO 2 gas is a one element of smog which pollutes cities now and then. Our experimental results with CO 2 gas could help to choose a suitable modulation scheme for OWC which are installed in cities with smog-polluted atmospheric conditions.

关键词： Phase shift keying Carbon dioxide Optical receivers Diode lasers Integrated optics Optical transmitters

Developing and validating measures for AI literacy tests: From self-reported to objective measures

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computers and Education: Artificial Intelligence 2024年 7卷

作者： Chiu, Thomas K.F. Chen, Yifan Yau, King Woon Chai, Ching-sing Meng, Helen King, Irwin Wong, Savio Yam, Yeung Department of Curriculum and Instruction Faculty of Education and Centre for Learning Sciences and Technologies And Centre for University and School Partnership the Chinese University of Hong Kong SAR ShatinNT Hong Kong Faculty of Engineering The Chinese University of Hong Kong SAR ShatinNT Hong Kong Faculty of Education The Chinese University of Hong Kong SAR ShatinNT Hong Kong Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong SAR ShatinNT Hong Kong Department of Computer Science & Engineering The Chinese University of Hong Kong SAR ShatinNT Hong Kong Department of Educational Psychology The Chinese University of Hong Kong SAR ShatinNT Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong SAR ShatinNT Hong Kong

The majority of AI literacy studies have designed and developed self-reported questionnaires to assess AI learning and understanding. These studies assessed students' perceived AI capability rather than AI literacy because self-perceptions are seldom an accurate account of true measures. International assessment programs that use objective measures to assess science, mathematical, digital, and computational literacy back up this argument. Furthermore, because AI education research is still in its infancy, the current definition of AI literacy in the literature may not meet the needs of young students. Therefore, this study aims to develop and validate an AI literacy test for school students within the interdisciplinary project known as AI4future. engineering and education researchers created and selected 25 multiple-choice questions to accomplish this goal, and school teachers validated them while developing an AI curriculum for middle schools. 2390 students in grades 7 to 9 took the test. We used a Rasch model to investigate the discrimination, reliability, and validity of the items. The results showed that the model met the unidimensionality assumption and demonstrated a set of reliable and valid items. They indicate the quality of the test items. The test enables AI education researchers and practitioners to appropriately evaluate their AI-related education interventions. © 2024 The Authors

关键词： AI literacy Instrument K-12 education AI education Co-design process Measures

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

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EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control...

IEEE Conference on Decision and Control

作者： Andre Coelho Alin Albu-Schaeffer Arne Sachtler Hrishik Mishra Davide Bicego Christian Ott Antonio Franchi Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) Oberpfaffenhofen Germany Dextrous Robotics Inc Memphis TN USA Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands Department of Informatics Technical University of Munich Germany Automation and Control Institute (ACIN) Technical University of Vienna Austria LAAS-CNRS University of Toulouse France

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

关键词： Analytical models Aerospace electronics Energy efficiency Trajectory Mechanical systems Oscillators Robots

TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems

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arXiv 2024年

作者： Yang, Jing Zhao, Yu Yang, Linyao Wang, Xiao Chen, Long Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410000 China Zhejiang Lab Hangzhou311121 China School of Artificial Intelligence Anhui University Hefei266114 China Department of Computer and Information Science University of Macau 999078 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems. However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. To elicit more effective prompts for PLMs, we introduce a task-oriented prompt construction approach that thoroughly takes the myriad factors of TRE into consideration for automatic prompt generation. In addition, we design temporal event reasoning in the form of masked language modeling as auxiliary tasks to bolster the model’s focus on events and temporal cues. The experimental results demonstrate that TemPrompt outperforms all compared baselines across the majority of metrics under both standard and few-shot settings. A case study on designing and manufacturing printed circuit boards is provided to validate its effectiveness in crowdsourcing scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview

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arXiv 2024年

作者： Hu, Yingbai Abu-Dakka, Fares J. Chen, Fei Luo, Xiao Li, Zheng Knoll, Alois Ding, Weiping Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong School of Computation Information and Technology Technical University of Munich Munich85748 Germany Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Surgery The Chinese University of Hong Kong Hong Kong School of Information Science and Technology Nantong University Nantong226019 China

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers' comprehension of dynamical systems' foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration. © 2024, CC BY.

关键词： Reinforcement learning