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arXiv 2020年

作者： Maurya, Deepak Chinta, Sivadurgaprasad Sivaram, Abhishek Rengaswamy, Raghunathan Department of Computer Science Indian Institute of Technology Madras Chennai India Department of Chemical Engineering Indian Institute of Technology Madras Chennai India Robert Bosch Centre for Data Science and Artificial Intelligence

Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most techniques for model identification do not provide the freedom to incorporate any partial information such as the structure of the model. In this article, we propose model identification techniques which could leverage such partial information to produce better estimates. Specifically, we propose Structural Principal Component Analysis (SPCA) which improvises over existing methods like PCA by utilizing the essential structural information about the model. Most of the existing methods or closely related methods use sparsity constraints which could be computationally expensive. Our proposed method is a wise modification of PCA to utilize structural information. The efficacy of the proposed approach is demonstrated using synthetic and industrial case-studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemical industry

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Kurdish end-to-end speech synthesis using deep neural networks

Natural Language Processing Journal

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Natural Language Processing Journal 2024年 8卷

作者： Sabat Salih Muhamad Hadi Veisi Aso Mahmudi Abdulhady Abas Abdullah Farhad Rahimi Computer Science Department Faculty of Science Soran University Soran Kurdistan Iraq College of Interdisciplinary Science and Technologies University of Tehran Tehran Iran School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Melbourne Australia Artificial Intelligence and Innovation Centre University of Kurdistan Hewlêr Erbil Kurdistan Region Iraq Department of Computer Engineering Faculty of Engineering and agriculture Islamic Azad University Arak Branch Arak Iran

This article introduces an end-to-end text-to-speech (TTS) system for the low-resourced language of Central Kurdish (CK, also known as Sorani) and tackles the challenges associated with limited data availability. We have compiled a dataset suitable for end-to-end text-to-speech that includes 21 h of CK female voice paired with corresponding texts. To identify the optimal performing system, we employed Tacotron2, an end-to-end deep neural network for speech synthesis, in three training experiments. The process involves training Tacotron2 using a pre-trained English system, followed by training two models from scratch with full and intonationally balanced datasets. We evaluated the effectiveness of these models using Mean Opinion Score (MOS), a subjective evaluation metric. Our findings demonstrate that the model trained from scratch on the full CK dataset surpasses both the model trained with the intonationally balanced dataset and the model trained using a pre-trained English model in terms of naturalness and intelligibility by achieving a MOS of 4.78 out of 5.

关键词： Central Kurdish language Deep learning Speech synthesis Tacotron2 End-to-end WaveGlow vocoder

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Visual object tracking: Review and challenges

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Applied Soft Computing 2025年 177卷

作者： Chen, Zeshi Peng, Caiping Liu, Shuai Ding, Weiping School of Educational Science Hunan Normal University No. 36 Lushan Road Changsha410081 China Institute of Interdisciplinary Studies Hunan Normal University No. 36 Lushan Road Changsha410081 China College of Information Science and Engineering Hunan Normal University No. 36 Lushan Road Changsha410081 China School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China

Visual object tracking is a challenging research topic in computer vision. Numerous visual tracking algorithms have been proposed to solve this problem and achieved promising results. Traditional visual tracking algorithms can be categorized into generative and discriminative algorithms. Recently, deep learning based visual tracking algorithms attracted great attention from researchers due to their excellent performance. In order to summarize the development of visual object tracking, some studys have analyzed non-deep learning and deep learning visual tracking algorithms. In this paper, the most advanced tracking algorithms are comprehensively summarized, including both non-deep learning and deep learning based algorithms. First, traditional non-deep learning based tracking algorithms are categorized into generative and discriminative methods. The generative algorithms are summarized from three perspectives: kernel series, subspace series and sparse representation series, and the discriminative algorithms are summarized from two perspectives: correlation filtering series and deep features series. Then, deep learning based algorithms are divided into Siamese network series and Transformer series. Siamese network based algorithms are summarized from different innovation directions, and Transformer based algorithms are summarized from two perspectives: CNN-Transformer and Fully-Transformer. Moreover, the commonly used datasets and evaluation indicators are introduced in visual object tracking, as well as the results and analysis of representative algorithms. Finally, the challenges faced in visual object tracking were summarized and its future development trends were pointed out. © 2025 Elsevier B.V.

关键词： Object detection

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3DTeethSeg’22: 3D Teeth Scan Segmentation and Labeling Challenge

arXiv

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arXiv 2023年

作者： Ben-Hamadou, Achraf Smaoui, Oussama Rekik, Ahmed Pujades, Sergi Boyer, Edmond Lim, Hoyeon Kim, Minchang Lee, Minkyung Chung, Minyoung Shin, Yeong-Gil Leclercq, Mathieu Cevidanes, Lucia Prieto, Juan Carlos Zhuang, Shaojie Wei, Guangshun Cui, Zhiming Zhou, Yuanfeng Dascalu, Tudor Ibragimov, Bulat Yong, Tae-Hoon Ahn, Hong-Gi Kim, Wan Han, Jae-Hwan Choi, Byungsun van Nistelrooij, Niels Kempers, Steven Vinayahalingam, Shankeeth Strippoli, Julien Thollot, Aurélien Setbon, Hugo Trosset, Cyril Ladroit, Edouard Centre de Recherche en Numérique de Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Technopôle de Sfax Sfax3021 Tunisia Udini 37 BD Aristide Briand Aix-en-Provence13100 France Inria Univ. Grenoble Alpes CNRS Grenoble INP LJK France Department of Computer Science and Engineering Seoul National University Korea Republic of School of Software Soongsil University Korea Republic of University of Michigan Ann Arbor United States University of North Carolina Chapel Hill United States Shandong University China ShanghaiTech University China Department of Computer Science University of Copenhagen Denmark Osstem Implant Co. Ltd. Seoul Korea Republic of Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre P.O. Box 9101 Nijmegen6500 HB Netherlands Department of Computing Science Radboud University Nijmegen Netherlands Department of Artificial Intelligence Radboud University Nijmegen Netherlands Berlin Institute of Health at Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in dental anatomy, imaging protocols, and limited availability of publicly accessible data. To address these challenges, the 3DTeethSeg’22 challenge was organized in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2022, with a call for algorithms tackling teeth localization, segmentation, and labeling from intraoral 3D scans. A dataset comprising a total of 1800 scans from 900 patients was prepared, and each tooth was individually annotated by a human-machine hybrid algorithm. A total of 6 algorithms were evaluated on this dataset. In this study, we present the evaluation results of the 3DTeethSeg’22 challenge. The 3DTeethSeg’22 challenge code can be accessed at: https://***/abenhamadou/3DTeethSeg22 challenge. © 2023, CC BY-SA.

关键词： Object detection

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Revealing Interfacial Properties of Inorganic Molecular Crystal Sb2o3 and Monolayer Mos2

SSRN

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SSRN 2023年

作者： Liu, Qin Zuo, Yang He, Jingyu Zeng, Minggang Yang, Tong Zhou, Jun Yang, Yulin Song, Ting Ting Wang, Shijie Yang, Ming College of Physics and Space Science China West Normal University Nanchong637002 China Department of Applied Physics The Hong Kong Polytechnic University Hung Hom Hong Kong Institute of High-Performance Computing A∗STAR 1 Fusionopolis Way Singapore138632 Singapore Institute of Materials Research and Engineering A ∗ STAR 2 Fusionopolis Way Singapore138634 Singapore Fujian Provincial Key Laboratory of Optoelectronic Technology and Devices School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Research Centre on Data Sciences & Artificial Intelligence The Hong Kong Polytechnic University Hung Hom Hong Kong

Inorganic molecule crystal Sb2O3 has been identified as a promising high-k dielectric for direct integration with the two-dimensional semiconductor MoS2. However, a comprehensive understanding of this van der Waals (vdW) interface is still lacking, impeding its applications in high-performance nanoelectronics. In this study, we employ first-principles calculations to elucidate the interfacial interaction and electronic properties of the Sb2O3/monolayer MoS2 interface. We find that a high-performance quasi-vdW interface can be formed between Sb2O3 and monolayer MoS2, as evidenced by weak interfacial interaction, a dangling-bond-free interface, insignificant electron-hole puddle redistribution, and the preserved semiconducting properties of monolayer MoS2. Notably, the interface exhibits a remarkable defect tolerance capability during integration, as Sb2O3 cluster vacancies (the dominant defect in Sb2O3) neither introduce mid-gap states nor significantly affect the interface properties. Besides, our study reveals an asymmetric type-I band alignment at the interface, where the conduction band offset is sufficiently large to minimize electron tunneling. Our work offers a comprehensive understanding of the vdW interface between Sb2O3 and monolayer MoS2, which could potentially advance the development of inorganic molecular crystals as high-k dielectrics for high-performance two-dimensional electronic devices. © 2023, The Authors. All rights reserved.

关键词： Antimony compounds

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Recurrent Feature Reasoning for Image Inpainting

Recurrent Feature Reasoning for Image Inpainting

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jingyuan Li Ning Wang Lefei Zhang Bo Du Dacheng Tao National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence Wuhan University China UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney Darlington NSW Australia

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing inpainting methods have achieved promising performance for recovering regular or small image defects. However, filling in large continuous holes remains difficult due to the lack of constraints for the hole center. In this paper, we devise a Recurrent Feature Reasoning (RFR) network which is mainly constructed by a plug-and-play Recurrent Feature Reasoning module and a Knowledge Consistent Attention (KCA) module. Analogous to how humans solve puzzles (i.e., first solve the easier parts and then use the results as additional information to solve difficult parts), the RFR module recurrently infers the hole boundaries of the convolutional feature maps and then uses them as clues for further inference. The module progressively strengthens the constraints for the hole center and the results become explicit. To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR. Empirically, we first compare the proposed RFR-Net with existing backbones, demonstrating that RFR-Net is more efficient (e.g., a 4% SSIM improvement for the same model size). We then place the network in the context of the current state-of-the-art, where it exhibits improved performance. The corresponding source code is available at: https://***/jingyuanli001/RFR-Inpainting.

关键词： Cognition Convolution Semantics Merging Convolutional codes Correlation Computational efficiency

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GCNLA: Inferring Cell-Cell Interactions From Spatial Transcriptomics With Long Short-Term Memory and Graph Convolutional Networks

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IEEE journal of biomedical and health informatics 2025年 PP卷 PP页

作者： Chao Yang Xiuhao Fu Zhenjie Luo Leyi Wei Jingbing Li Feifei Cui Quan Zou Qingchen Zhang Zilong Zhang Hainan University School of Computer Science and Technology Haikou570228 China Macao Polytechnic University Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science China Xiamen University School of Informatics Xiamen361005 China Hainan University School of Information and Communication Engineering Haikou570228 China University of Electronic Science and Technology of China Institute of Fundamental and Frontier Sciences Chengdu610054 China Quzhou324000 China

Spatial transcriptomics analysis methods offer an opportunity to investigate highly diverse biological tissues. Cell-cell communication is fundamental for maintaining physiological homeostasis in organisms and coordinating complex biological processes. Identifying cell-cell interactions is critical for understanding cellular activities. The interaction of a cell with other cells depends on several factors, and most of the existing methods that consider only gene expression information of neighbouring cells and spatial location information are somewhat limited. In this paper, we propose a network architecture based on graph convolution network and long short-term memory attention module-GCNLA, which contains graph convolution layer, long short-term memory network, attention module, and residual connections. GCNLA not only learns the spatial structure of cells but also captures interaction information between distal cells, the attention module further extracting and enhancing features related to cell-cell interactions. Finally, the inner product decoding calculates the cosine similarity, which is used to infer cell-cell interactions. In addition, GCNLA is capable of reconstructing the complete cell-cell interaction network. The experimental results on seqFISH and MERFISH demonstrate that the GCNLA network structure has better robustness and noise immunity. The potential features learned by GCNLA enable other downstream analyses, including single-cell resolution cell clustering based on spatial information resolving cell heterogeneity.

关键词： Genes

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Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

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Heliyon 2024年第21期10卷 e39735页

作者： Williams, Kathleen A. Numssen, Ole Guerra, Juan David Kopal, Jakub Bzdok, Danilo Hartwigsen, Gesa Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Methods and Development Group “Brain Networks” Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany The Neuro - Montreal Neurological Institute (MNI) McConnell Brain Imaging Centre Department of Biomedical Engineering Faculty of Medicine School of Computer Science McGill University Montreal Canada Mila - Quebec Artificial Intelligence Institute Montreal QC Canada Wilhelm Wundt Institute for Psychology Leipzig University Germany

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition. © 2024 The Authors

关键词： Cognition cTBS fMRI Functional connectivity Resting state TMS

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A novel data-centric AI approach based on sensitivity and correlation analyses: A case study on multi-organ plant disease classification

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Expert Systems with Applications 2025年 290卷

作者： Muhammad Hammad Saleem Fakhia Hammad Muhammad Taha Shivakumara Palaiahnakote Sadaqat ur Rehman Mohamad Saraee School of Science Engineering and Environment University of Salford Salford M5 4WT United Kingdom Data Science and Artificial Intelligence Hub University of Salford Salford M5 4WT United Kingdom Department of Biomedical Engineering Ziauddin University Karachi 74600 Pakistan Department of Electrical Engineering National University of Computer & Emerging Sciences Karachi 75030 Pakistan

With advancements in deep learning (DL), most research on classification problems has focused on developing or modifying DL models, which are known as model-centric artificial intelligence (AI) approaches. However, these approaches are time-consuming and overlook the exploration of available resources and expertise required to address industrial problems. This study proposes a new data-centric AI-based approach by thoroughly investigating dataset complexities using multi-organ plant disease classification as a case study. To the best of authors’ knowledge, this study is the first to perform comprehensive sensitivity and correlation analyses to evaluate the relationship between dataset complexity exclusion and the accuracy of the DL classifier. In contrast to conventional sensitivity analyses which only evaluate changes in model output with respect to input changes, this study introduces a novel Sensitivity Correlation Score (SC-score). The SC-score combines sensitivity and correlation analyses into a single metric formulated as the product of the Absolute Sensitivity Function and Pearson Correlation Coefficient which is normalised for interpretability. This formulation rewards positive sensitivity and strong correlation while neutralising the effects of negative correlation. The SC-score successfully evaluated both the responsiveness and consistency of the performance enhancement of the DL model owing to the elimination of dataset complexities. To demonstrate the robustness of this study, the proposed data-centric AI-based approach was validated on an external testing dataset from diverse agricultural environments and achieved an accuracy improvement of 10.94%. These results highlight the strength of data-centric AI in solving industry-oriented problems in real-world environments.

关键词： Model-centric AI data-centric AI Deep learning Convolutional neural networks Plant disease classification

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Discontinuous stochastic forcing in Greenland ice core data

arXiv

引用

arXiv 2025年

作者： Riechers, Keno Morr, Andreas Lehnertz, Klaus Lind, Pedro G. Boers, Niklas Witthaut, Dirk Gorjão, Leonardo Rydin Climate Physics Max Planck Institute for Meteorology Germany Earth System Modelling School of Engineering & Design Technical University of Munich Germany Research Domain IV – Complexity Science Potsdam Institute for Climate Impact Research Potsdam14473 Germany Department of Epileptology University of Bonn Medical Center Bonn53105 Germany Helmholtz Institute for Radiation and Nuclear Physics University of Bonn Bonn53115 Germany Interdisciplinary Centre for Complex Systems University of Bonn Bonn53175 Germany Department of Computer Science OsloMet – Oslo Metropolitan University Oslo0130 Norway OsloMet Artificial Intelligence lab OsloMet Oslo0166 Norway Global Systems Institute Department of Mathematics University of Exeter United Kingdom Jülich52428 Germany Institute for Theoretical Physics University of Cologne Köln50937 Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås1432 Norway

Paleoclimate proxy records from Greenland ice cores, archiving e.g. δ18O as a proxy for surface temperature, show that sudden climatic shifts called Dansgaard–Oeschger events (DO) occurred repeatedly during the last glacial interval. They comprised substantial warming of the Arctic region from cold to milder conditions. Concomitant abrupt changes in the dust concentrations of the same ice cores suggest that sudden reorganisations of the hemispheric-scale atmospheric circulation have accompanied the warming events. Genuine bistability of the North Atlantic climate system is commonly hypothesised to explain the existence of stadial (cold) and interstadial (milder) periods in Greenland. However, the physical mechanisms that drove abrupt transitions from the stadial to the interstadial state, and more gradual yet still abrupt reverse transitions, remain debated. Here, we conduct a one-dimensional data-driven analysis of the Greenland temperature and atmospheric circulation proxies under the purview of stochastic processes. We take the Kramers–Moyal equation to estimate each proxy’s drift and diffusion terms within a Markovian model framework. We then assess noise contributions beyond Gaussian white noise. The resulting stochastic differential equation (SDE) models feature a monostable drift for the Greenland temperature proxy and a bistable one for the atmospheric circulation proxy. Indicators of discontinuity in stochastic processes suggest to include higher-order terms of the Kramers–Moyal equation when modelling the Greenland temperature proxy’s evolution. This constitutes a qualitative difference in the characteristics of the two time series, which should be further investigated from the standpoint of climate dynamics. © 2025, CC BY-NC-SA.

关键词： Stochastic systems

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