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Smart Agricultural Technology 2025年 11卷

作者： Eckhardt, Regina Arablouei, Reza Ingham, Aaron McCosker, Kieren Bernhardt, Heinz Chair of Agricultural Systems Engineering TUM School of Life Sciences Technical University of Munich Freising Germany Data61 CSIRO Pullenvale QLD Australia Agriculture and Food CSIRO St Lucia QLD Australia Centre for Animal Science Queensland Alliance for Agriculture and Food Innovation The University of Queensland Gatton QLD Australia

Recent advancements in sensor technology and generative artificial intelligence (AI) are transforming precision livestock farming by enhancing behaviour monitoring and predictive analytics. This study examines the effectiveness of Transformer-type generative AI models in predicting cattle behaviour profiles and imputing missing data from collar accelerometer readings collected during two trials in Queensland, Australia, in 2022 and 2023, alongside climatic data. Each trial involved 60 cattle equipped with collars that classified six core behaviours: grazing, ruminating, walking, resting, drinking, and other over five-second time windows. Hourly behaviour profiles were constructed for each animal and experiment day by aggregating the behaviour predictions over every calendar hour, representing the time spent on each behaviour within each hour. Subsequently, four Transformer-type models (i.e., standard Transformer, Informer, Reformer, and Autoformer) were trained on the hourly behaviour profile data to predict behaviour profiles of the next 24 hours for each animal. Among the considered models, Autoformer showed the highest predictive accuracy when including climate data, achieving a mean absolute error (MAE) of <5.5 min, while the next best model had an MAE of approximately 6 min. For imputing missing data, the standard Transformer outperformed traditional imputation methods, with an MAE of <30 min over 24 hours, compared to 40 to 70 min for traditional methods (mean, median, and linear interpolation). These results highlight the potential of generative AI, particularly Autoformer and Transformer, to enhance predictive accuracy and data imputation in livestock management, thereby supporting regulatory guidance for data-driven decision-making and improved farming practices. © 2025

关键词： Accelerometer data Cattle behaviour data imputation Generative AI Precision agriculture

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Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime

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Scientific reports 2025年第1期15卷 19140页

作者： Cedric Donié Neha Das Satoshi Endo Sandra Hirche TUM School of Computation Information and Technology Department of Computer Engineering Chair of Information-oriented Control Technical University of Munich Munich Germany. cedric.donie@tum.de. TUM School of Computation Information and Technology Department of Computer Engineering Chair of Information-oriented Control Technical University of Munich Munich Germany. Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany. Munich Data Science Institute (MDSI) Munich Germany.

Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.

关键词： Accelerometer Deep learning IMU InceptionTime Machine learning Parkinson’s disease ROCKET Time series classification

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Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network 9th

Efficient SDN-Based Traffic Monitoring in IoT Networks with ...

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9th International Conference on Computational data and Social Networks, CSoNet 2020

作者： Nguyen, Tri Gia Phan, Trung V. Hoang, Dinh Thai Nguyen, Tu N. So-In, Chakchai Faculty of Information Technology Duy Tan University Danang50206 Viet Nam Chair of Communication Networks Technische Universität Chemnitz Chemnitz09126 Germany School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Department of Computer Science Purdue University Fort Wayne Fort WayneIN46805 United States Department of Computer Science Faculty of Science Khon Kaen University Khon Kaen40002 Thailand

ISBN: (纸本)9783030660451

In an Internet of Things (IoT) environment, network traffic monitoring tasks are intractable to achieve due to various IoT traffic types. Recently, the development of Software-Defined Networking (SDN) enables outstanding flexibility and scalability abilities in network control and management, thereby providing a potential approach to mitigate challenges in monitoring the IoT traffic. In this paper, we propose an IoT traffic monitoring approach that implements deep reinforcement learning technique to maximize the fine-grained monitoring capability, i.e., level of traffic statistics details, for several IoT traffic groups. Specifically, we first study a flow-rule matching control system constrained by different expected levels of statistics details and by the flow-table limit of the SDN-based gateway device. We then formulate our control optimization problem by employing the Markov decision process (MDP). Afterwards, we develop Double Deep Q-Network (DDQN) algorithm to quickly obtain the optimal flow-rule matching control policy. Through the extensive experiments, the obtained results verify that the proposed approach yields outstanding improvements in terms of the ability to simultaneously provide different required degrees of statistics details while protecting the gateway devices from being overflowed in comparisons with those of the conventional Q-learning method and the typical SDN flow rule setting. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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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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Erratum to: Incremental algorithms for the maximum internal spanning tree problem

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science China Information sciences 2022年第10期65卷 1-1页

作者： Zhu, Xianbin Li, Wenjun Yang, Yongjie Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Chair of Economic Theory Saarland University Saarbrücken Germany

In the original paper [1], there are two errors on page 5. The rules in the algorithm are not sufficient since we omitted one rule during publication. The following rule should be added to the paper. Rule 3: If P1 = (u, v) and P2 = (x, y) are two maximal leaf-paths both with two vertices in T, where u and x are leaves in T and (v, y) ∈ E(G), we remove (y′, y) and then add (v, y) to T where y′ is the non-leaf neighbor of y in T. The expression of Algorithm 2 is not accurate. It should be modified as follows. Line 3: Exhaustively apply Rules 1–3, and for i ∈ {2, 3} only apply Rule i when none of Rules j where 1 ⩽ j < i is applicable. Line 8: Determine the longest path P among all paths whose endpoints have nonempty neighbors in T \ V(T′).

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GWSkyNet-Multi II: an updated deep learning model for rapid classification of gravitational-wave events

arXiv

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arXiv 2025年

作者： Raza, Nayyer Chan, Man Leong Haggard, Daryl Mahabal, Ashish McIver, Jess Durand, Audrey Larouche, Alexandre Moazen, Hadi Department of Physics McGill University 3600 rue University MontréalQCH3A2T8 Canada Trottier Space Institute at McGill 3550 rue University MontréalQCH3A2A7 Canada Department of Physics and Astronomy University of British Columbia VancouverBCV6T1Z1 Canada Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Computer Science and Software Engineering Université Laval QuébecQCG1V0A6 Canada Canada CIFAR AI Chair Mila MontréalQCH2S3H1 Canada

Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formation of heavy elements, and the expansion rate of the Universe. With the LIGO-Virgo-KAGRA (LVK) gravitational-wave detectors currently in their fourth observing run (O4), it is an exciting time for detecting these mergers. However, assessing whether to follow up a candidate gravitational-wave event given limited telescope time and resources is challenging;the candidate can be a false alert due to detector glitches, or may not have any detectable electromagnetic counterpart even if it is real. GWSkyNet-Multi is a deep learning model developed to facilitate follow-up decisions by providing real-time classification of candidate events, using localization information released in LVK rapid public alerts. Here we introduce GWSkyNet-Multi II, an updated model targeted towards providing more robust and informative predictions during O4 and beyond. Specifically, the model now provides normalized probability scores and associated uncertainties for each of the four corresponding source categories released by the LVK: glitch, binary black hole, neutron star-black hole, and binary neutron star. Informed by explainability studies of the original model, the updated model architecture is also significantly simplified, including replacing input images with intuitive summary values, making it more interpretable. For significant O4 event alerts issued between May 2023 and December 2024, GWSkyNet-Multi II produces a prediction that is consistent with the updated LVK classification for 93% of events. The updated model can be used by the community to help make time-critical follow-up decisions. © 2025, CC BY.

关键词： Neutrons

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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science China(Information sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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Towards a widespread usage of computational fluid dynamics simulations for automated virtual nasal surgery planning

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Future Generation Computer Systems 2026年 174卷

作者： Mario Rüttgers Moritz Waldmann Fabian Hübenthal Klaus Vogt Makoto Tsubokura Sangseung Lee Andreas Lintermann Data-Driven Fluid Engineering (DDFE) Laboratory Inha University Incheon South Korea Institute of Aerodynamics and Chair of Fluid Mechanics (AIA) RWTH Aachen University Germany Faculty of Medicine Center of Experimental Surgery University of Latvia 1586 Riga Latvia Department of Computational Science Graduate School of System Informatics Kobe University Japan Complex Phenomena Unified Simulation Research Team RIKEN Center for Computational Science Kobe Japan Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich GmbH Germany

Efficient computational approaches are crucial for advancing computational fluid dynamics (CFD)-based automated planning in nasal surgeries, such as septoplasties and turbinectomies. This study introduces a hybrid lattice-Boltzmann and level-set method to address the trade-off between computational cost and automation. By interpolating geometry variations in discrete steps between pre-surgical and target states, the approach achieves computational efficiency with only 21 surface variations per intervention. Previous methods rely on more costly coupling strategies, such as reinforcement learning or thermal modeling, which may still be appropriate for complex planning scenarios involving multiple intervention sites or thermal flow analysis. In contrast, the presented method reduces complexity while retaining key predictive capabilities, making it particularly suitable for widespread, time-sensitive clinical use focused on a single surgical intervention. Fluid mechanical metrics, including pressure loss and volume flow rate balance, are evaluated alongside tissue removal volume to recommend optimized surgical plans. Case studies on three patients demonstrate tissue savings of 12–25% without compromising key flow parameters. Additionally, a non-linear regression model trained on as few as 11 CFD simulations predicts pressure loss and flow rates with errors below 4%, and reduces computational costs by 50%. The proposed framework represents a significant step toward making CFD-based virtual nasal surgery planning more accessible and practical.

关键词： Septoplasty Turbinectomy Non-linear regression

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Harnessing multimodal approaches for depression detection using large language models and facial expressions

Npj mental health research

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Npj mental health research 2024年第1期3卷 66页

作者： Misha Sadeghi Robert Richer Bernhard Egger Lena Schindler-Gmelch Lydia Helene Rupp Farnaz Rahimi Matthias Berking Bjoern M Eskofier Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. misha.sadeghi@fau.de. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany. Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany.

Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.

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Determination of residual stresses in processes with multiple thermal loads

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Procedia CIRP 2020年 87卷 509-514页

作者： J. Vorspohl F. Frerichs S. Schneider M. Meinke W. Schröder Th. Lübben A. Klink Institute of Aerodynamics and Chair of Fluid Dynamics (AIA) of RWTH Aachen University Wüllnerstr. 5a 52062 Aachen Germany Leibniz Institute for Materials Engineering IWT Badgasteiner Str. 3 28359 Bremen Germany Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany JARA Center for Simulation and Data Science RWTH Aachen Univeristy Seffenter Weg 23 52074 Aachen Germany

Numerical models are used to identify correlations between internal material loads and material modifications. Such correlations are called Process Signatures, which are based on the idea that different processes inducing equal internal material loads produce the same material modifications. Here, machining processes involving repeated thermal loads at high frequency as they occur in laser ablation or electric discharge machining are considered. As an example for the material modifications, the resulting residual stress distribution is determined as a function of the temperature. First, the temperature field in the work piece is computed for about 1000 consecutive heat pulses generated by the laser or electric discharges. An in-house finite-volume method is used to solve the time dependent enthalpy equation on a hierarchical Cartesian grid, which allows a high degree of parallelization such that high-performance computing hardware can be used. The resulting temperature fields and the heating rates for several load numbers are used to determine the residual stress state in the material. This is done in a second step by finding an appropriate model for the thermal loads, which provides a boundary condition for a finite element simulation of the surface near modification process. In this step, phase transformation during heating (austenite formation) and cooling (martensitic formation) are taken into account. Finally, the displacements and residual stresses are calculated by the phase and temperature distributions determined in the previous step. The results are compared with measurements and enable the determination of a Process Signature component describing the relation between repeated heat loads and the resulting residual stresses in the surface layer of the work piece.

关键词： Process Signature residual stress phase transformation multiple thermal loads

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