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arXiv 2020年

作者： Rodríguez-Piñeiro, José Domínguez-Bolaño, Tomás Cai, Xuesong Huang, Zeyu Yin, Xuefeng The College of Electronics and Information Engineering Tongji University Shanghai China The Department of Computer Engineering The CITIC Research Center University of A Coruña A Coruña Spain The Wireless Communication Networks Section Department of Electronic Systems Aalborg University Aalborg Denmark The College of Electronics and Information Engineering The National Computer and Information Technology Practical Education Demonstration Center Tongji University Shanghai China

Due to the decrease in cost, size and weight, Unmanned Aerial Vehicles (UAVs) are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to UAVs both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this paper, an approach to the air-to-ground channel characterization for low-height UAVs based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics like path loss, shadow fading, root mean square delay and Doppler frequency spreads and the K-factor are statistically characterized for different suburban scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Proteomic clustering reveals the kinetics of disease biomarkers in bovine and human models of post-traumatic osteoarthritis

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Osteoarthritis and Cartilage Open 2021年第4期3卷 100191-100191页

作者： Black, Rebecca Mae Wang, Yang Struglics, André Lorenzo, Pilar Chubinskaya, Susan Grodzinsky, Alan J. Önnerfjord, Patrik Departments of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Orthopaedics Department of Clinical Sciences Lund Faculty of Medicine Lund University Lund Sweden Rheumatology and Molecular Skeletal Biology Department of Clinical Sciences Lund Faculty of Medicine Lund University Lund Sweden Departments of Pediatrics Orthopedic Surgery and Medicine (Section of Rheumatology) Rush University Medical Center Chicago IL United States Mechanical Engineering Massachusetts Institute of Technology Cambridge MA United States Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States

Objectives: In this study, we apply a clustering method to proteomic data sets from bovine and human models of post-traumatic osteoarthritis (PTOA) to distinguish clusters of proteins based on their kinetics of release from cartilage and examined these groups for PTOA biomarker candidates. We then quantified the effects of dexamethasone (Dex) on the kinetics of release of the cartilage media proteome. Design: Mass spectrometry was performed on sample medium collected from two separate experiments using juvenile bovine and human cartilage explants (3 samples/treatment condition) during 20- or 21-day treatment with inflammatory cytokines (TNF-α, IL-6, sIL-6R) with or without a single compressive mechanical injury. All samples were incubated with or without 100 nM Dex. Clustering was performed on the correlation between normalized averaged release vectors for each protein. Results: Our proteomic method identified the presence of distinct clusters of proteins based on the kinetics of their release over three weeks of culture. Clusters of proteins with peak release after one to two weeks had biomarker candidates with increased release compared to control. Dex rescued some of the changes in protein release kinetics the level of control, and in all conditions except control, there was late release of immune-related proteins. Conclusions: We demonstrate a clustering method applied to proteomic data sets to identify and validate biomarkers of early PTOA progression and explore the relationships between the release of spatially related matrix components. Dex restored the kinetics of release to many matrix components, but not all factors that contribute to cartilage homeostasis. © 2021 The Author(s)

关键词： Cartilage matrix Cytokines Dexamethasone Mass spectrometry Post-traumatic osteoarthritis Proteomics

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Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

arXiv

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arXiv 2021年

作者： Llopis, Hermes Sampedro Engsig-Karup, Allan P. Jeong, Cheol-Ho Pind, Finnur Hesthaven, Jan S. Rambøll Denmark Copenhagen Denmark Acoustic Technology Group Department of Electrical Engineering Technical University of Denmark Kongens Lyngby Denmark Scientific Computing Section Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Treble Technologies Reykjavík Iceland Computational Mathematics and Simulation Science Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

The use of model-based numerical simulation of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iteratively and this is computationally expensive. We present a reduced basis methods (RBM) to achieve a computational cost reduction relative to a traditional full order model (FOM), for wave-based room acoustic simulations with parametrized boundary conditions. In this study, the FOM solver is based on the spectral element method, however other numerical methods could be applied. The RBM reduces the computational burden by solving the problem in a low-dimensional subspace for parametrized frequency-independent and frequency-dependent boundary conditions. The problem is formulated and solved in the Laplace domain, which ensures the stability of the reduced order model based on the RBM approach. We study the potential of the proposed RBM framework in terms of computational efficiency, accuracy and storage requirements and we show that the RBM leads to 100-fold speed-ups for a 2D case with an upper frequency of 2kHz and around 1000-fold speed-ups for an analogous 3D case with an upper frequency of 1kHz. While the FOM simulations needed to construct the ROM are expensive, we demonstrate that despite this cost, the ROM has a potential of three orders of magnitude faster than the FOM when four different boundary conditions are simulated per room surface. Moreover, results show that the storage model for the ROM is relatively high but affordable for the presented 2D and 3D cases. © 2021, CC BY-NC-ND.

关键词： Boundary conditions

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Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

arXiv

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arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms

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A predictive surrogate model for heat transfer of an impinging jet on a concave surface

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International Journal of Heat and Mass Transfer 2025年 251卷

作者： Sajad Salavatidezfouli Saeed Rakhsha Armin Sheidani Giovanni Stabile Gianluigi Rozza Mathematics Area MathLab International School for Advanced Studies (SISSA) Trieste Italy Department of Electrical and Computer Engineering - Signal Processing and Machine Learning Aarhus University Aarhus Denmark Department of Mechanical Engineering Semnan University Semnan Iran Department of Applied Physics Eindhoven University of Technology The Netherlands Department of Pure and Applied Sciences Informatics and Mathematics Section University of Urbino Carlo Bo Urbino Italy The Biorobotics Institute Sant’Anna School of Advanced Studies Pisa Italy

This paper aims to comprehensively investigate the efficacy of model order reduction and deep learning techniques in predicting heat transfer of pulsatile impinging jets on a concave surface. We introduce two predictive approaches: one employing a Fast Fourier Transform-augmented Artificial Neural Network for predicting the area-averaged Nusselt number under constant-frequency jet scenarios, and another comparing the performance of LSTM and Transformer neural networks for random-frequency jets. Results indicate that the Transformer significantly outperforms the LSTM, achieving higher accuracy and robustness in predicting Nusselt numbers — covering up to 50% of the cycle with precision, whereas the LSTM covers only 20% with greater error margins. Additionally, the integration of Proper Orthogonal Decomposition with Transformer networks yields a novel strategy for predicting local Nusselt number distributions. This method significantly reduces computational complexity while maintaining high accuracy, with a maximum prediction error of 5%. These findings demonstrate the efficacy of advanced deep learning techniques for temporal prediction of the Nusselt number on complex surfaces, suggesting further applicability in broader fluid dynamics and heat transfer analyses.

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The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII (vol 2024, baae069, 2024)

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DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2024年第2024期2024卷 baae069页

作者： Islamaj, Rezarta Lai, Po-Ting Wei, Chih-Hsuan Luo, Ling National Center for Biotechnology Information (NCBI) National Library of Medicine (NLM) National Institutes of Health (NIH) 8600 Rockville Pike Bethesda MD 20894 United States School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian 116024 China Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Departamento de Informática Faculdade de Ciências da Universidade de Lisboa Edifício C6 Campo Grande Lisbon 1749-016 Portugal Department of Computer Science and Information Engineering National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Republic of China Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City 32001 Taiwan Republic of China Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd. Nangang District Taoyuan City 115201 Taiwan Republic of China School of Information Sciences University of Illinois at Urbana-Champaign 614 E. Daniel St Champaign IL 61820 United States Higher School of Economics University 20 Myasnitskaya st Moscow 101000 Russia Kazan Federal University 18 Kremlevskaya st Kazan Russia 420008 Russia Miin Wu School of Computing National Cheng Kung University No. 1 University Road Tainan 701 Taiwan Republic of China School of Computing Technologies RMIT University 124 La Trobe Street Melbourne Victoria 3000 Australia

The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents. The challenge consisted of two subtasks: (i) in Subtask 1, participants were given the article text and human expert annotated entities, and were asked to extract the relation pairs, identify their semantic type and the novelty factor, and (ii) in Subtask 2, participants were given only the article text, and were asked to build an end-to-end system that could identify and categorize the relationships and their novelty. We received a total of 94 submissions from 14 teams worldwide. The highest F-score performances achieved for the Subtask 1 were: 77.17% for relation pair identification, 58.95% for relation type identification, 59.22% for novelty identification, and 44.55% when evaluating all of the above aspects of the comprehensive relation extraction. The highest F-score performances achieved for the Subtask 2 were: 55.84% for relation pair, 43.03% for relation type, 42.74% for novelty, and 32.75% for comprehensive relation extraction. The entire BioRED track dataset and other challenge materials are available athttps://***/pub/lu/BC8-BioRED-track/andhttps://***/competitions/13377andhttps://***/competitions/*** URL:https://***/pub/lu/BC8-BioRED-track/https://***/competitions/13377https://***/competitions/13378

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Dental Age Estimation: A Machine Learning Perspective 4th

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4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Tao, Jiang Wang, Jian Wang, Andrew Xie, Zhangqian Wang, Ziheng Wu, Shaozhi Hassanien, Aboul Ella Xiao, Kai Department oft of General Dentistry Ninth People’s Hospital Shanghai Jiao Tong University School of Medicine No. 639 Zhi Zao Ju Road Shanghai200011 China Department of Electrical Engineering and Computer Sciences University of California Berkeley Soda Hall BerkeleyCA94720-1776 United States School of Mathematics Shandong University 27 Shanda Nanlu Jinan250100 China The School of Aerospace Engineering and Applied Mechanics Tongji University No. 1239 Siping Road Shanghai200000 China School of Computer Science and Engineering University of Electronic Science and Technology of China No. 4 Section 2 North Jianshe Road Chengdu610054 China Information Technology Department Cairo University 5 Ahmed Zewal Street Orman Giza Cairo Egypt School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China

ISBN: (纸本)9783030141172

Dental age estimation is important for determining the actual age of an individual. In this paper, for the purpose of improving the accuracy of dental age estimation, we present several machine learning algorithms. We apply Demirjian’s method, Willem’s method, and our methods to a dataset with 1636 cases;787 males and 849 females. The Multi-layer Perceptron algorithm is used to predict dental age in our experiments. In order to avoid overfitting, we use Leave-one-out cross-validation when training the model. Meanwhile, we employ root-mean-square error, mean-square-error and mean-absolute-error to measure the error of the results. Through experiments, we verify that this algorithm is more accurate than traditional dental methods. In addition, we try to use a new set of features that are converted by traditional dental methods. Specifically, we find that using Demirjian’s method converted data for males and using Willem’s method converted data for females can improve the accuracy of the dental age predictions. © 2020, Springer Nature Switzerland AG.

关键词： Mean square error

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Origin of the Large Entropy Change in the Molecular Caloric and Ferroelectric Ammonium Sulfate

arXiv

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arXiv 2022年

作者： Yuan, Shurong Meijer, Bernet E. Cai, Guanqun Dixey, Richard J.C. Demmel, Franz Dove, Martin T. Liu, Jiaxun Playford, Helen Y. Walker, Helen C. Phillips, Anthony E. School of Physical and Chemical Sciences Queen Mary University of London Mile End Rd LondonE1 4NS United Kingdom ISIS Neutron and Muon Source Rutherford Appleton Laboratory DidcotOX11 0QX United Kingdom School of Computer Sciences Sichuan University No 24 South Section 1 Yihuan Road Chengdu610065 China Department of Physics Wuhan University of Technology 205 Luoshi Road Hongshan district Hubei Wuhan430070 China School of Mechanical Engineering Dongguan University of Technology 1st Daxue Road Songshan Lake Guangdong Dongguan523000 China

The deceptively simple inorganic salt ammonium sulfate undergoes a ferroelectric phase transition associated with a very large entropy change and both electrocaloric and barocaloric functionality. While the structural origins of the electrical polarisation are now well established, those of the entropy change have been controversial for over fifty years. This question is resolved here using a combination of DFT phonon calculations with inelastic neutron scattering under variable temperature and pressure, supported by complementary total and quasielastic neutron scattering experiments. A simple model of the entropy in which each molecular ion is disordered across the mirror plane in the high symmetry phase, although widely used in the literature, proves to be untenable. Instead, the entropy arises from low-frequency librations of ammonium ions in this phase, with harmonic terms that are very small or even negative. These results suggest that, in the search for molecular materials with functionality derived from large entropy changes, vibrational entropy arising from broad energy minima is likely to be just as important as configurational entropy arising from crystallographic disorder. © 2022, CC BY.

关键词： Entropy

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Sharing a secret image in the cloud using two shadows

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International Journal of Network Security 2020年第4期22卷 551-560页

作者： Chen, Yu Lin, Jiang-Yi Chang, Chin-Chen Hu, Yu-Chen School of Information Science and Engineering Fujian University of Technology 33 Xuefu South Road Fuzhou350118 China Department of Computer Science Xiamen University of Technology 600 Ligong Road Xiamen361024 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhua Road Taichung40724 Taiwan Department of Computer Science and Information Management Providence University 200 Section 7 Taiwan Boulevard Shalu District Taichung43301 Taiwan

In this paper, we present a novel (2, 2) reversible secret image sharing scheme. Our scheme permits secret mes-sages to be shared with two participants by splitting the marked encrypted image into two shadows. The secret messages can be reconstructed if two participants collab-orate with each other. The proposed scheme chooses suit-able binary blocks of a cover image in which to embed the secret message and divides those blocks into two shadow blocks by executing a logical operation with all of the other binary blocks, thereby producing two shadows. In the data extraction procedure, the secret messages and the cover image can be reconstructed by the logical oper-ation of the corresponding binary blocks of the two shad-ows. A practical application is demonstrated by model-ing our scheme as a reversible watermarking scheme in the Cloud. The experimental results indicated that the proposed method is reversible and that it can restore the image and watermark properly. © 2020, Femto Technique Co., Ltd.

关键词： Image watermarking

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Technologies for Ocean Sensing project developments in imaging and sensing

Technologies for Ocean Sensing project developments in imagi...

引用

OCEANS

作者： Matthew Mowlem Fillipa Carvalho Rudolf Hanz Ehsan Abdi Camile Catalano Katherine Hartle Mougiou Rana Abualhaija Susan Evans Dan Hayes Ahmed Alrefaey Reuben Forrester Jean-Olivier Irisson Martin Arundel Sarah Giering Kevin Köser Nathan Briggs Electra Gizeli Patricia Lopez-Garcia Wahida Bhuiyan Peter Glynne-Jones Jake Ludgate Jonathan Butement Lionel Guidi Miguel Massot Campos Christopher L. Cardwell Weili Guo Jon McQuillan Marina Montresor Efstathios Papadimitriou Allison Schaap C. Mark Moore Matthew Patey Nina Schuback Hywel Morgan Marc Picheral Blair Thornton Mojtaba Masoudi Fiona Regan Martha Valiadi Caroline Murphy Julie Robidart John Walk Andrew Morris Fabrizio Siracusa Xiangyu Weng David Nakath Dan Spenser Euan Wilson Kevin Oxborough National Oceanography Centre Ocean Technology and Engineering Group Southampon UK National Oceanography Centre Ocean Biogeochemistry and Geoscience Southampon UK Cyprus Subsea Consulting and Services Nicosia Cyprus Laboratoire d'Oceanographie de Villefranche France Foundation for Research and Technology Hellas Institute for Molecular Biology and Biotechnology Heraklion Greece Electronics and Computer Science University of Southampton Southampton UK In Situ and Remote Intelligent Sensing University of Southampton Southampton UK School of Biotechnology Dublin City University Dublin UK Section Integrated Marine Ecology Stazione Zoologica Anton Dohrn Napoli Italy School of Ocean and Earth Science University of Southampton Southampton UK Chelsea Technologies Group London UK School of Chemical Sciences Dublin City University Dublin UK Head of Research Minderoo Foundation Perth Western Australia Australia Helmholtz Centre for Ocean Research Oceanic Machine Vision Group GEOMAR Kiel Germany

The TechOceanS project is developing new remote ocean sensing technology supporting wider ocean measurement and a drive to net zero. The project will deliver 5 new sensor classes for biogeochemistry, biology and ecosystems addressing 10 of 19 EOVs, 31 of 73 subvariables, 6 of 9 MSFD targets together with microplastics and a range of biotoxins and contaminants. It will also develop a new image processing workflow for extracting EOVs (9) and MSFD (6) and litter measurements from images. These innovations concentrate on key capability gaps in ocean observing from non-ship systems with a focus on low-cost per measurement through minimised instrument and deployment costs. This paper gives a brief overview of the technologies, and were possible, because of progress or protection of intellectual property, details of our approaches and early results.

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