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arXiv 2023年

作者： Jungel, Kai Parmentier, Axel Schiffer, Maximilian Vidal, Thibaut TUM School of Management Technical University of Munich Munich80333 Germany CERMICS École des Ponts Marne-la-Vallée France TUM School of Management Munich Data Science Institute Technical University of Munich Munich80333 Germany CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematics and Industrial Engineering École Polytechnique de Montréal Montréal Canada Department of Computer Science Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil

Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

arXiv

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arXiv 2021年

作者： Liang, Tianming Liu, Yang Liu, Xiaoyan Zhang, Hao Sharma, Gaurav Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. © 2021, CC BY.

关键词： Graphic methods

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A new computational approach to the fractional-order Liouville equation arising from mechanics of water waves and meteorological forecasts

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Journal of Ocean Engineering and science 2022年

作者： Yue, Xiao-Guang Zhang, Zeying Akbulut, Arzu Kaabar, Mohammed K.A. Kaplan, Melike Wuchang Institute of Technology Wuhan China Department of Computer Science and Engineering European University Cyprus Nicosia 1516 Cyprus CIICESI-ESTG Politécnico do Porto Felgueiras 4610-156 Portugal Mathematics-Computer Department Art-Science Faculty Eskişehir Osmangazi University Eskişehir Turkey Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics and Statistics Washington State University Pullman 99163 WA United States Jabalia Camp United Nations Relief and Works Agency (UNRWA) Palestinian Refugee Camp Gaza Strip Jabalya Palestine Department of Computer Engineering Faculty of Engineering and Architecture Kastamonu University Kastamonu Turkey

The current analysis employs the improved F-expansion, modified extended tanh, exponential rational function, and (g′)−expansion procedures to find a divergent collection of the fractional Liouville equation's exact solutions in the context of beta-derivative. Also, we have given the graphical representations of the obtained results. These plots are useful in describing the dynamic characteristics of the solutions. The investigated equation is very essential in studying the mechanics of water waves and atmospheric predictability. Therefore, our techniques provide a great help in investigating various nonlinear models formulated in the contexts of fractional derivatives arising from oceanography and mathematical physics. © 2022

关键词： Beta-derivative Exact solutions Fractional differential equations Symbolic computation

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Sustainability in the Internet of Production: Interdisciplinary Opportunities and Challenges

Sustainability in the Internet of Production: Interdisciplin...

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International Symposium on Technology and Society (ISTAS)

作者： Sebastian Bernhard Sebastian Pütz Calvin Röhl Ralph Baier Philipp Brauner Ester Christou Hannah Dammers Roman Flaig Leon M. Gorißen Jan-Christoph Heilinger Christian Hinke István Koren Dirk Lüttgens Michael Millan Kai Müller Alexander Schollemann Luisa Vervier Thomas Gries Alexander Mertens Saskia K. Nagel Frank T. Piller Günther Schuh Martina Ziefle Verena Nitsch Carmen Leicht-Scholten Gender and Diversity in Engineering RWTH Aachen University Aachen Germany Institute of Industrial Engineering and Ergonomics RWTH Aachen University Aachen Germany Human-Computer Interaction Center RWTH Aachen University Aachen Germany Institute for Technology and Innovation Management RWTH Aachen University Aachen Germany Institut für Textiltechnik RWTH Aachen University Aachen Germany Chair for Laser Technology RWTH Aachen University Aachen Germany Human Technology Center/Applied Ethics RWTH Aachen University Aachen Germany Chair of Process and Data Science RWTH Aachen University Aachen Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen Germany

The vision of the Internet of Production (loP) is focused on optimizing manufacturing processes, with the help of Industry 4.0 technologies (14Ts). However, considering global megatrends such as climate change and the need to achieve the Sustainable Development Goals (SDGs), it is a growing imperative for the manufacturing industry to become more sustainable. This opens the door to transforming the IoP into an Internet of Sustainable Production (loSP). Accordingly, this paper proposes a novel four-step loSP-framework that establishes an Information System (IS) allowing researchers and practitioners to acknowledge and adapt to the interconnected nature of sustainability. Further, to test its applicability, an inter- and cross-disciplinary perspective is adopted to illustrate case-related challenges, opportunities, and pathways - revealed by the proposed framework - in the example of a digital economy for sustainability data, strategic design of global production networks, human-robot collaboration, digital photonic production, and the textile industry. Together, the framework demonstrates the usefulness of generating holistic information on the interconnected nature of sustainability derived from process-specific and contextualized data, while simultaneously assessing the framework's utilization for sustainability, as well as the sustainability of its use.

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Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs

arXiv

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arXiv 2020年

作者： Wang, Xueying Li, Guangli Dong, Xiao Li, Jiansong Liu, Lei Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

Accelerating the deep learning inference is very important for real-time applications. In this paper, we propose a novel method to fuse the layers of convolutional neural networks (CNNs) on Graphics Processing Units (GPUs), which applies data reuse analysis and access optimization in different levels of the memory hierarchy. To achieve the balance between computation and memory access, we explore the fusion opportunities in the CNN computation graph and propose three fusion modes of convolutional neural networks: straight, merge and split. Then, an approach for generating efficient fused code is designed, which goes deeper in multi-level memory usage for cross-layer data reuse. The effectiveness of our method is evaluated with the network layers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02 × on representative structures of CNNs, and 1.57× on end-to-end inference of SqueezeNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Memory architecture

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ECG-DPM: Electrocardiogram Generation via a Spectrogram-based Diffusion Probabilistic Model

ECG-DPM: Electrocardiogram Generation via a Spectrogram-base...

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Smart World Congress (SWC), IEEE

作者： Lujundong Li Tong Xia Haojie Zhang Dongchen He Kun Qian Bin Hu Yoshiharu Yamamoto Björn W. Schuller Cecilia Mascolo Key Laboratory of Brain Health Intelligent Evaluation and Intervention Ministry of Education Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Info Hub AI Thrust the Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Technology University of Cambridge UK School of Life Sciences Peking University Beijing China Guaduate School of Frontier Sciences Computational Biology and Medical Sciences The University of Tokyo Japan Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan GLAM – the Group on Language Audio & Music Imperial College London UK CHI – Chair of Health Informatics MRI Technical University of Munich Germany

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

An electrocardiogram (ECG) records the electrical signals from the heart to assess various cardiovascular conditions. Deep learning methods have been proposed to model ECGs, but the insufficient availability of ECG data and annotations often hinders their performance. To address this challenge, this paper explores the latest data synthesis technique, i.e., diffusion probabilistic models (DPMs), to enable the generation of an unlimited number of ECGs representing various cardiovascular conditions. In contrast to previous approaches that treat ECGs as time series data or convert them into power spectrograms, we introduce a novel multi-channel spectrogram-based diffusion framework. In our method, the diffusion model enhances generation diversity, while the multi-channel spectrogram preserves both magnitude and phase information, ensuring high fidelity in the reconstructed ECGs. Extensive experiments conducted on real-world ECG data demonstrate the superiority of our approach. Notably, our ECG-DPM outperforms the best baseline by a margin ranging from ${1 2. 5 \%}$ to ${6 2. 5 \%}$ when generating ECGs for 30 seconds.

关键词： Training Heart Biological system modeling Time series analysis Electrocardiography Diffusion models Brain modeling Data models Distance measurement Spectrogram

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Towards an Advanced Control Approach for Energy Management in Distributed Micro-Grid Systems

SSRN

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SSRN 2022年

作者： Elouadoud, Houda Ouladsine, Radouane Elmouatamid, Abdellatif Bakhouya, Mohamed Pierfederici, Serge International University of Rabat College of Engineering and Architecture LERMA-Lab Sala El Jadida 11103 Morocco Université de Lorraine ENSEM LEMTA 2 Avenue de la Foret de Haye BP 90161 VandoeuvreCedex 54505 France New Jersey Institute of Technology United States Faculty of Computer Science and Logistic United States

Distributed microgrid systems represent the next generation of smart grids. It will allow the development of electrical network in rural or isolated areas without sophisticated infrastructures. However, achieving energy management and power sharing among microgrids (MG) is the main challenge, which needs to be addressed. For instance, it is required to keep the ability of each MG to feed its own load as long as possible while maintaining the state of the charge (SoC) on the safety limits. This paper introduces an advanced control for energy management in distributed MG systems. First, a strategy, using predictive functional control techniques, is developed within each MG system. Results are reported to show its effectiveness for standalone MG control. A simple power flows sharing algorithm is then developed for distributed energy management of MG systems. Simulations have been conducted using MATLAB/SIMULINK and results are reported to show the effectiveness of the proposed algorithm in managing energy-flows exchange between MG systems. It is mainly based on several indicators, in particular the power consumption of each MGs, the battery SoC, and the power generated by renewable energy sources. © 2022, The Authors. All rights reserved.

关键词： Renewable energy resources

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Ray tracing structured AMR data using ExaBricks

arXiv

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arXiv 2020年

作者： Wald, Ingo Zellmann, Stefan Usher, Will Morrical, Nate Lang, Ulrich Pascucci, Valerio NVIDIA University of Cologne Chair of Computer Science SCI Institute University of Utah and Intel Corp SCI Institute University of Utah United States

Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations;however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling;and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations. Copyright © 2020, The Authors. All rights reserved.

关键词： Data structures

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RETRACTED ARTICLE: A research study on the cervical cerclage to deal with cervical insufficiency using machine learning

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Soft Computing 2023年第2期28卷 643-643页

作者： Kaur, Mandeep Khedkar, Ganesh Sakhare, Sachin Rogulj, Katarina Department of Computer Science Savitribai Phule Pune University Pune India Siddhant College of Engineering Pune India Computer Engineering Department Vishwakarma Institute of Information Technology Pune India Faculty of Civil Engineering Architecture and Geodesy University of Split Split Croatia

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