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arXiv 2023年

作者： Fedrigo, Roberto Yousefirizi, Fereshteh Liu, Ziping Jha, Abhinav K. Bergen, Robert V. Rajotte, Jean-Francois Ng, Raymond T. Bloise, Ingrid Harsini, Sara Kadrmas, Dan J. Uribe, Carlos Rahmim, Arman Dept. of Integrative Oncology BC Cancer Research Institute VancouverBC Canada Dept. of Physics and Astronomy Univ. of British Columbia VancouverBC Canada Dept. of Biomedical Engineering Washington Univ. in St. Louis St. LouisMO United States Mallinckrodt Institute of Radiology Washington Univ. School of Med. St. LouisMO United States Univ. of British Columbia Data Science Institute 6339 Stores Road VancouverBC Canada Dept. of Functional Imaging BC Cancer VancouverBC Canada Dept. Of Radiology and Imaging Sciences University of Utah Salt Lake CityUT United States Dept. of Radiology Univ. of British Columbia VancouverBC Canada

The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack of labeled samples, as well as privacy concerns regarding data sharing. To address these issues, we previously developed (Bergen et al. 2022) a synthetic PET dataset for Head & Neck (H&N) cancer using the temporal generative adversarial network (TGAN) architecture and evaluated its performance segmenting lesions and identifying radiomics features in synthesized images. In this work, a two-alternative forced-choice (2AFC) observer study was performed to quantitatively evaluate the ability of human observers to distinguish between real and synthesized oncological PET images. In the study eight trained readers, including two board-certified nuclear medicine physicians, read 170 real/synthetic image pairs presented as 2D-transaxial using a dedicated web app. For each image pair, the observer was asked to identify the "real" image and input their confidence level with a 5-point Likert scale. P-values were computed using the binomial test and Wilcoxon signed-rank test. A heat map was used to compare the response accuracy distribution for the signed-rank test. Response accuracy for all observers ranged from 36.2% [27.9-44.4] to 63.1% [54.8-71.3]. Six out of eight observers did not identify the real image with statistical significance, indicating that the synthetic dataset was reasonably representative of oncological PET images. Overall, this study adds validity to the realism of our simulated H&N cancer dataset, which may be implemented in the future to train AI algorithms while favoring patient confidentiality and privacy protection. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Using zigzag persistent homology to detect hopf bifurcations in dynamical systems

arXiv

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arXiv 2020年

作者： Tymochko, Sarah Munch, Elizabeth Khasawneh, Firas A. Dept. of Computational Mathematics Science and Engineering Dept. of Mathematics Dept. of Mechanical Engineering Michigan State University United States

Bifurcations in dynamical systems characterize qualitative changes in the system behavior. Therefore, their detection is important because they can signal the transition from normal system operation to imminent failure. While standard persistent homology has been used in this setting, it usually requires analyzing a collection of persistence diagrams, which in turn drives up the computational cost considerably. Using zigzag persistence, we can capture topological changes in the state space of the dynamical system in only one persistence diagram. Here we present Bifurcations using ZigZag (BuZZ), a one-step method to study and detect bifurcations using zigzag persistence. The BuZZ method is successfully able to detect this type of behavior in two synthetic examples as well as an example dynamical system. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

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Multilevel Selective Harmonic Modulation by Duality*

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IFAC-PapersOnLine 2022年第16期55卷 56-61页

作者： U. Biccari E. Zuazua Chair of Computational Mathematics - Fundación Deusto Av.da de las Universidades 24 48007 Bilbao Basque Country Spain Chair for Dynamics Control and Numerics Alexander von Humboldt-Professorship Dept. of Data Science Friedrich-Alexander-Universität 91058 Erlangen Germany Departamento de Matemáticas Universidad Autónoma de Madrid 28049 Madrid Spain

We address the Selective Harmonic Modulation (SHM) problem in power electronic engineering, consisting in designing a multilevel staircase control signal with some prescribed frequencies to improve the performances of a converter. In this work, SHM is addressed through an optimal control methodology based on duality, in which the admissible controls are piece-wise constant functions, taking values only in a given finite set. To fulfill this constraint, the cornerstone of our approach is the introduction of a special penalization in the cost functional, in the form of a piece-wise affine approximation of a parabola. In this manner, we build optimal multilevel controls having the desired staircase structure.

关键词： Selective Harmonic Modulation Optimal Control Theory Duality Piece-wise affine penalization Multilevel controls

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Turbulent burning velocity and thermo-diffusive instability of premixed flames

arXiv

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arXiv 2023年

作者： Lee, Hsu Chew Wu, BuChen Dai, Peng Wan, Minping Lipatnikov, Andrei N. Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Zhejiang Jiaxing314031 China Dept. of Mechanics and Maritime Sciences Chalmers University of Technology Göteborg412 96 Sweden

Reported in the paper are results of unsteady three-dimensional direct numerical simulations of laminar and turbulent, lean hydrogen-air, complex-chemistry flames propagating in forced turbulence in a box. To explore the eventual influence of thermo-diffusive instability of laminar flames on turbulent burning velocity, (i) a critical length scale Λn that bounds regimes of unstable and stable laminar combustion is numerically determined by gradually decreasing the width Λ of computational domain until a stable laminar flame is obtained and (ii) simulations of turbulent flames are performed by varying the width from Λ n (in this case, the instability is suppressed) to Λ > Λn (in this case, the instability may grow). Moreover, simulations are performed either using mixture-averaged transport properties (low Lewis number flames) or setting diffusivities of all species equal to heat diffusivity of the mixture (equidiffusive flames), with all other things being equal. Obtained results show a significant increase in turbulent burning velocity UT when the boundary Λ = Λn is crossed in weak turbulence, but almost equal values of UT are computed at Λ n and Λ > Λn in moderately turbulent flames characterized by Karlovitz number equal to 3.4 or larger. These results imply that thermo-diffusive instability of laminar premixed flames substantially affects burning velocity in weak turbulence only, in line with a simple criterion proposed by Chomiak and Lipatnikov (Phys. Rev. E 107, 015102, 2023). Copyright © 2023, The Authors. All rights reserved.

关键词： Stability

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Constraint-Guided Learning of data-driven Health Indicator Models: An Application on the Pronostia Bearing dataset

arXiv

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arXiv 2025年

作者： Tefera, Yonas Van Baelen, Quinten Meire, Maarten Luca, Stijn Karsmakers, Peter KU Leuven Dept. of Computer Science ADVISE-DTAI Kleinhoefstraat 4 GeelB-2440 Belgium Leuven.AI - KU Leuven Institute for AI Belgium Flanders Make @ KU Leuven Belgium Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia Ghent University Department of Data Analysis and Mathematical Modelling Coupure Links 653 Gent9000 Belgium

This paper presents a constraint-guided deep learning framework to develop physically consistent health indicators in bearing prognostics and health management. Conventional data-driven approaches often lack physical plausibility, while physics-based models are limited by incomplete knowledge of complex systems. To address this, we integrate domain knowledge into deep learning models via constraints, ensuring monotonicity, bounding output ranges between 1 and 0 (representing healthy to failed states respectively), and maintaining consistency between signal energy trends and health indicator estimates. This eliminates the need for complex loss term balancing to incorporate domain knowledge. We implement a constraint-guided gradient descent optimization within an autoencoder architecture, creating a constrained autoencoder. However, the framework is flexible and can be applied to other architectures as well. Using time-frequency representations of accelerometer signals from the Pronostia dataset, the constrained model generates more accurate and reliable representations of bearing health compared to conventional methods. It produces smoother degradation profiles that align with the expected physical behavior. Model performance is assessed using three metrics: trendability, robustness, and consistency. When compared to a conventional baseline model, the constrained model shows significant improvement in all three metrics. Another baseline incorporated the monotonicity behavior directly into the loss function using a soft-ranking approach. While this approach outperforms the constrained model in trendability, due to its explicit monotonicity enforcement, the constrained model performed better in robustness and consistency, providing stable and interpretable health indicator estimates over time. The results of the ablation study confirm that the monotonicity constraint enhances trendability, the boundary constraint ensures consistency, and the energy-health indicator con

关键词： Electronic health record

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LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules

arXiv

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arXiv 2022年

作者： Zhang, He Li, Sizhen Zhang, Liang Mathews, David H. Huang, Liang School of Electrical Engineering & Computer Science Oregon State University CorvallisOR97330 United States Baidu Research USA SunnyvaleCA94089 United States Dept. of Biochemistry & Biophysics United States Center for RNA Biology United States Dept. of Biostatistics & Computational Biology University of Rochester Medical Center RochesterNY14642 United States

Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful. Some existing tools are less accurate due to omitting the competing of intermolecular and intramolecular base pairs, or focus more on predicting the binding region rather than predicting the complete secondary structure of two interacting strands. Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences. To address these issues, we present LinearCoFold, which predicts the complete minimum free energy structure of two strands in linear runtime, and LinearCoPartition, which calculates the cofolding partition function and base pairing probabilities in linear runtime. LinearCoFold and LinearCoPartition follows the concatenation strategy of RNAcofold, but are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode (0.6 minutes vs. 52.1 minutes), and LinearCoPartition is 642.3× faster than RNAcofold partition function mode (1.8 minutes vs. 1156.2 minutes). Different from the local algorithms, LinearCoFold and LinearCoPartition are global cofolding algorithms without restriction on base pair length. Surprisingly, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA-RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, which has been experimentally studied, and observe that LinearCoFold’s prediction correlates better to the wet lab results. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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Enhancing Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication

Enhancing Natural Language Processing in Multilingual Chatbo...

引用

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Myagmarsuren Orosoo Indrajit Goswami Fredrick Ruban Alphonse Gulnaz Fatma Manikandan Rengarajan B Kiran Bala Mongolian National University of Education General Management Strategy and HRM Alliance School of Business Alliance University Bengaluru Department of Languages Institute of Finance and International Management Bengaluru Dept. of English Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

In order to promote smooth cross-cultural communication, this research focuses on improving Natural Language Processing (NLP) features within multilingual Chabots. The need for efficient communication across various linguistic and cultural barriers has grown as the world becomes more interconnected. In this context, multilingual Chabot's are essential tools that facilitate easy communication between users of various cultural backgrounds. In order to better understand and respond to a wide range of linguistic details, idioms, and cultural allusions, the research explores ways to improve NLP algorithms. This research investigates how machine learning techniques can be integrated to improve the Chabot's ability to adapt to various linguistic patterns, enabling it to acquire new language skills over time. By implementing an extensive and flexible approach, the proposed approach seeks to transform Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication. In order to identify language-specific patterns and specifications, the methodology starts with the collection of a diverse dataset that is representative of linguistic variations and cultural contexts. It then uses advanced linguistic analysis techniques like phonetic analysis. The next step is cultural context modelling, which entails building a database enhanced with cultural allusions and language variations unique to a given context that are obtained from various sources. Transfer learning, reinforcement learning for dialogue, and modern algorithms such as Multilingual Unsupervised and Supervised Embeddings (MUSE) for cross-lingual embeddings are all included in machine learning integration. In order to obtain information about language precision and cultural sensitivity, the methodology incorporates interactive surveys and sentiment analysis into a strong user feedback mechanism. The proposed model achieved 98% performance.

关键词：

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

引用

Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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RAPID MODEL TRANSFER for MEDICAL IMAGE SEGMENTATION VIA ITERATIVE HUMAN-IN-THE-LOOP UPDATE: From LABELLED PUBLIC to UNLABELLED CLINICAL dataSETS for MULTI-ORGAN SEGMENTATION in CT

arXiv

引用

arXiv 2022年

作者： Ma, Wenao Zheng, Shuang Zhang, Lei Zhang, Huimao Dou, Qi Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Department of Radiology The First Hospital of Jilin University Jilin China Jilin Provincial Key Laboratory of Medical Imaging & Big Data Jilin China

Despite the remarkable success on medical image analysis with deep learning, it is still under exploration regarding how to rapidly transfer AI models from one dataset to another for clinical applications. This paper presents a novel and generic human-in-the-loop scheme for efficiently transferring a segmentation model from a small-scale labelled dataset to a larger-scale unlabelled dataset for multi-organ segmentation in CT. To achieve this, we propose to use an igniter network which can learn from a small-scale labelled dataset and generate coarse annotations to start the process of human-machine interaction. Then, we use a sustainer network for our largerscale dataset, and iteratively updated it on the new annotated data. Moreover, we propose a flexible labelling strategy for the annotator to reduce the initial annotation workload. The model performance and the time cost of annotation in each subject evaluated on our private dataset are reported and analysed. The results show that our scheme can not only improve the performance by 19.7% on Dice, but also expedite the cost time of manuallabelling from 13.87 min to 1.51 min per CT volume during the model transfer, demonstrating the clinical usefulness with promising potentials. © 2022, CC BY.

关键词： Image segmentation

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