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arXiv 2023年

作者： Schaefer, Kristina Weickert, Joachim Mathematical Image Analysis Group Dept. of Mathematics and Computer Science Saarland University E1.7 Saarbrücken66123 Germany

We propose diffusion–shock (DS) inpainting as a hitherto unexplored integrodifferential equation for filling in missing structures in images. It combines two carefully chosen components that have proven their usefulness in different applications: homogeneous diffusion inpainting and coherence-enhancing shock filtering. DS inpainting enjoys the complementary synergy of its building blocks: It offers a high degree of anisotropy along an eigendirection of the structure tensor. This enables it to connect interrupted structures over large distances. Moreover, it benefits from the sharp edge structure generated by the shock filter, and it exploits the efficient filling-in effect of homogeneous diffusion. The second order equation that underlies DS inpainting inherits a continuous maximum–minimum principle from its constituents. In contrast to other attractive second order inpainting equations such as edge-enhancing anisotropic diffusion, we can guarantee this property also for the proposed discrete algorithm. Our experiments show a performance that is comparable to or better than many linear or nonlinear, isotropic or anisotropic processes of second or fourth order. They include homogeneous diffusion, biharmonic interpolation, TV inpainting, edge-enhancing anisotropic diffusion, the methods of Tschumperlé and of Bornemann and März, Cahn–Hilliard inpainting, and Euler’s elastica. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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DEEPLIO: DEEP LIDAR INERTIAL SENSOR FUSION for ODOMETRY ESTIMATION 24

DEEPLIO: DEEP LIDAR INERTIAL SENSOR FUSION for ODOMETRY ESTI...

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2021 24th ISPRS Congress "Imaging Today, Foreseeing Tomorrow", Commission I

作者： Javanmard-Gh, Arash Iwaszczuk, Dorota Roth, Stefan Remote Sensing and Image Analysis Dept. of Civil and Environmental Engineering Sciences Technical University of Darmstadt Germany Visual Inference Lab Dept. of Computer Science Technical University of Darmstadt Germany

Having a good estimate of the position and orientation of a mobile agent is essential for many application domains such as robotics, autonomous driving, and virtual and augmented reality. In particular, when using LiDAR and IMU sensors as the inputs, most existing methods still use classical filter-based fusion methods to achieve this task. In this work, we propose DeepLIO, a modular, end-to-end learning-based fusion framework for odometry estimation using LiDAR and IMU sensors. For this task, our network learns an appropriate fusion function by considering different modalities of its input latent feature vectors. We also formulate a loss function, where we combine both global and local pose information over an input sequence to improve the accuracy of the network predictions. Furthermore, we design three sub-networks with different modules and architectures derived from DeepLIO to analyze the effect of each sensory input on the task of odometry estimation. Experiments on the benchmark dataset demonstrate that DeepLIO outperforms existing learning-based and model-based methods regarding orientation estimation and shows a marginal position accuracy difference. © Author(s) 2021.

关键词： Mobile agents

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EEG Context Fusion for AI-Based Object Detection and Drone Navigation in Situationally Aware Brain-computer Interfaces

EEG Context Fusion for AI-Based Object Detection and Drone N...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： T. Warren de Wit Vineetha Menon Nathan Tenhundfeld Thomas Davis Dept. of Computer Science University of Alabama Huntsville Huntsville USA Dept. of Psychology University of Alabama Huntsville Huntsville USA Human Systems Integration Division DEVCOM Data and Analysis Center Huntsville USA

We are interested in the utility that artificially intelligent mobile systems such as drones offer to personnel in fast-paced situations such as hostage rescue and disaster relief for real-time situational awareness. Ideally, these assistive systems place no additional cognitive or physical burden on their user; rather, they should respond to the user’s intent with minimal physical and cognitive burden. To this end, brain-computer interface (BCI) and in particular electroencephalography (EEG) offers a novel way to capture intent. EEG-based drone control has been explored, but typically relies on the domain of motor imagery (MI), which still requires somewhat-manual drone piloting for micro directional movement, adding cognitive burden. We propose leveraging the robust computer-vision based AI that exists on modern drones to use objects as waypoints and fly mostly autonomously, with EEG in an object recognition paradigm for passively selecting the waypoint. While existing techniques for EEG object recognition-including steady-state visually evoked potentials (SSVEP), rapid serial visual presentation (RSVP), and eye tracking - rely on “marked” input or a controlled environment, we need one that will work on passive objects. In this work, we propose merging available scene imagery in a vision network with deep-learning based EEG processing to achieve passive intent recognition. The proposed context-scene fusion for a situational awareness paradigm presents accuracy in the same ranges as existing object recognition without requiring SSVEP, RSVP, eye tracking, or MI techniques and serves as proof-of-concept for this approach to real-world BCI applications.

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Creating Synthetic Geospatial Patient Data to Mimic Real Data Whilst Preserving Privacy: *2022 35th International Symposium on computer-Based Medical Systems (CBMS)

Creating Synthetic Geospatial Patient Data to Mimic Real Dat...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Dima R. Alattal Zhenchen Wang Puja Myles Allan Tucker Dept. of Computer Science Intelligent Data Analysis Group Brunel University London London UK CPRD Medicines and Healthcare products Regulatory Agency London UK

Synthetic Individual-Level Geospatial Data (SIL-GSD) offers a number of advantages in Spatial Epidemiology when compared to census data or surveys conducted on regional or global levels. The use of SILGSD could bring a new dimension to the study of the patterns and causes of diseases in a particular location while minimizing the risk of patient identity disclosure, especially for rare conditions. Additionally, it could help in building and monitoring regional machine learning models, improving the quality and effectiveness of local healthcare services. Finally, SILGSD could help in controlling the spread and causes of diseases by studying disease movement across areas through the travelling patterns of populations. To our knowledge, no synthetic health records data containing synthesised geographic locations for patients has been published for research purposes so far. Therefore, in this paper we explore generating SILGSD by allocating synthetic patients to general practices (healthcare providers) in the UK using the demographics and prevalence of health conditions in each practice. The assigned general practice locations can be used as proxies for patient locations due to people being registered to their nearest practice from home. We use high-fidelity synthetic primary care patients from the Clinical Practice Research Datalink (CPRD) and allocate them to England's general practices (GPs), using the publicly available GP health conditions statistics from the Quality and Outcomes Framework (QOF). The allocation relies on similarities between patients in different locations without using real location information for the patients. We demonstrate that the Allocation Data is able to accurately mimic the real health conditions distribution in the general practices and also preserves the underlying distribution of the original primary care patients data from CPRD (Gold Standard).

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Learning Superconductivity from Ordered and Disordered Material Structures 38

Learning Superconductivity from Ordered and Disordered Mater...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Pin Peng, Luoxuan Jiao, Rui Mo, Qing Wang, Zhen Huang, Wenbing Liu, Yang Lu, Yutong National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

arXiv

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arXiv 2024年

作者： Wang, Han Kvich, Yhonatan Pérez, Eduardo Römer, Florian Eldar, Yonina C. Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA). Copyright © 2024, The Authors. All rights reserved.

关键词： Ultrasonic testing

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Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

Efficient Convolutional Forward Modeling and Sparse Coding i...

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European Signal Processing Conference (EUSIPCO)

作者： Han Wang Yhonatan Kvich Eduardo Pérez Florian Römer Yonina C. Eldar Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing Saarbrücken Germany Dept. Electronic Measurements and Signal Processing Technische Universität Ilmenau Ilmenau Germany Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

关键词： Convolutional codes Adaptation models Convolution Computational modeling Signal processing algorithms Imaging Transforms Encoding Computational efficiency Sparse matrices

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Equivariant Diffusion for Crystal Structure Prediction 41

Equivariant Diffusion for Crystal Structure Prediction

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41st International Conference on Machine Learning, ICML 2024

作者： Lin, Peijia Chen, Pin Jiao, Rui Mo, Qing Cen, Jianhuan Huang, Wenbing Liu, Yang Huang, Dan Lu, Yutong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Supercomputer Center Guangzhou China Dept. of Comp. Sci. Tech. Institute for AI Tsinghua University Beijing China Institute for AIR Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Code is available at https://***/EmperorJia/EquiCSP. Copyright 2024 by the author(s)

关键词： Crystal symmetry

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern

Classification of Awareness in Disorders of Consciousness Us...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Chaewon Lee Sunho Lee Jitka Annen Ji-Hoon Jeong Marcello Massimini Silvia Casarotto Melanie Boly Olivier Bodart Aurore Thibaut Mario Rosanova Steven Laureys Olivia Gosseries Minji Lee Dept. Data Science The Catholic University of Korea Bucheon Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent Ghent Belgium Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical and Clinical Sciences “L. Sacco” University of Milan Milan Italy Dept. Psychiatry Wisconsin Institute for Sleep and Consciousness University of Wisconsin Madison USA Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future.

关键词： Deep learning Accuracy Biological system modeling Pattern classification Focusing Signal processing Brain modeling Particle measurements Electroencephalography Robustness

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Predicting Grinding Mill Power Consumption in Mining: A Comparative Study

Predicting Grinding Mill Power Consumption in Mining: A Comp...

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International Conference on Information science and Technology (ICIST)

作者： Chaimae Loudari Moha Cherkaoui Rachid Bennani Imad El Harraki Omar Fares Mohamed El Adnani El Hassan Abdelwahed Intissar Benzakour François Bourzeix Karim Baina dept. LMAID Laboratory National School of Mines of Rabat (ENSMR) Rabat Morocco dept. Digitalization and Microelectronic Smart Devices Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. EIA Reminex Research Center MANAGEM Group Casablanca Morocco dept. LISI Laboratory University of Cadi Ayyad (UCA) Marrakech Morocco dept. Embedded Systems and Artificial Intelligence Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. Al-Qualsadi Research & Development Team National Higher School for Computer Science and System analysis (ENSIAS) Rabat Morocco

The mining industry deals with complexity and high energy consumption in mineral production. Artificial intelligence technology has been integrated to manage and optimize energy usage in mining equipment. A special focus is given to grinding mills, which are essential but difficult to model due to their energy-intensive nature. Data-driven machine learning approaches have emerged as effective solutions, enabling energy consumption prediction and optimization. These methods offer valuable insights to improve the overall energy efficiency of grinding mills in mining. In this article, a comparative study is conducted to investigate the use of machine learning models for predicting the power consumption of grinding mills. The study starts by providing a detailed description of the dataset used and the predictive models employed in the research. Notably, the results of power prediction for grinding mills are compared, with the Random Forest model emerging as the top performer, boasting an impressive R2 value of 0.94. The article underscores the significance of this predictive capability in helping mining experts make informed decisions to optimize energy consumption in grinding mills. Finally, the article concludes by offering insightful recommendations and identifying promising avenues for future research in this field. Overall, this study sheds light on the potential of machine learning to enhance energy efficiency in the mining industry.

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