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arXiv 2025年

作者： Löhrer, Bastian Krause, Rolf Fröhlich, Jochen Institute of Fluid Mechanics Technische Universität Dresden Dresden Germany Euler Institute Università della Svizzera italiana Lugano Switzerland Division Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

The paper presents a constraint-based collision model for Cosserat rods, able to handle dynamic or static contact between a large number of highly flexible structures. The model provides the required collision impulses prior to updating the solution of the rods, with the impulses accounted for as external loads. The procedure avoids the need to modify the structure solver itself and circumvents any iteration between the collision model and the solver for the Cosserat rods, maintaining the efficiency of any chosen Cosserat solver. The collision model is adopted from Tschisgale et al. (Arch. Appl. Mech., 2019) and extended towards higher stability, which is found necessary in the case of very flexible rods. Furthermore, the model is supplemented with additional terms that arise when the colliding rods are immersed in a fluid. The latter is accounted for by an immersed-boundary method. A large number of tests are conducted to demonstrate the functionality of the final model. © 2025, CC BY.

关键词： Aerodynamics

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Forgetting User Preference in Recommendation Systems with Label-Flipping

Forgetting User Preference in Recommendation Systems with La...

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IEEE International Conference on Big Data

作者： Manal A. Alshehri Xiangliang Zhang Computer Science CEMSE King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Rabigh Kingdom of Saudi Arabia Department of Computer Science and Engineering University of Notre Dame Notre Dame USA

Recommendation systems play a crucial role in identifying users’ preferences based on their historical interaction records and those of other users. However, the ability to “forget” certain users’ preferences is indispensable for ensuring user privacy and maintaining recommendation accuracy. It is essential to accommodate a user’s request to exclude their behavioral data from the recommendation system when necessary. Likewise, if certain data corrupts the system, its impact should be removed to restore system performance. In this paper, we propose FlipRec, a general and efficient framework for recommendation models to “forget” the preferences of specific users while retaining the model’s performance for all other users. Our concept of forgetting user preferences is inspired by the label-flipping attack, a technique where the labels of some training samples are inverted to adversarially manipulate the weights of the trained model. FlipRec adjusts the recommendation model weights to forget the targeted users by flipping their interaction records $y \in \{ 0,1\}$. To preserve the model’s performance for the remaining users, we augment the fine-tuning data with samples from users who have interacted with the same items as the targeted users. This ensures minimal impact on these users during the “forgetting” process. FlipRec has been validated on both contentbased recommendation models and collaborative filtering models. The experimental results show that FlipRec outperforms stateof-the-art unlearning methods in terms of efficiency, the ability to forget targeted users, and the preservation of performance for the remaining users.

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Skyrmion-based Leaky Integrate and Fire Neurons for Neuromorphic Applications

arXiv

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arXiv 2022年

作者： Lone, Aijaz H. Amara, Selma Aguirre, Fernando Lanza, Mario Fariborzi, H. Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Dept. D'Enginyeria Electronica Universitat Autonoma de Barcelona Spain Physical Science and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

Spintronics is an important emerging technology for data storage and computation. In this field, magnetic skyrmion-based devices are attractive due to their small size and energy consumption. However, controlling the creation, deletion and motion of skyrmions is challenging. Here we propose a novel energy-efficient skyrmion-based device structure, and demonstrate its use as leaky integrate (LIF) and fire neuron for neuromorphic computing. Here we show that skyrmions can be confined by patterning the geometry of the free layer in a magnetic tunnel junction (MTJ), and demonstrate that the size of the skyrmion can be adjusted by applying pulsed voltage stresses. A spiking neural network (SNN) made of such skyrmion-based LIF neurons shows the capability of classifying images from the Modified National Institute of Standards and technology (MNIST) dataset. © 2022, CC BY.

关键词： Neural networks

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α1,3-fucosylation treatment improves cord blood CD34 negative hematopoietic stem cell navigation

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iscience 2024年第2期27卷 108882页

作者： Al-Amoodi, Asma S. Kai, Jing Li, Yanyan Malki, Jana S. Alghamdi, abdullah Al-Ghuneim, Arwa Saera-Vila, Alfonso Habuchi, Satoshi Merzaban, Jasmeen S. Bioscience Program Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Sequentia Biotech SL Barcelona Spain KAUST Smart-Health Initiative King Abdullah University of Science and Technology Thuwal Saudi Arabia

For almost two decades, clinicians have overlooked the diagnostic potential of CD34neg hematopoietic stem cells because of their limited homing capacity relative to CD34posHSCs when injected intravenously. This has co... 详细信息

For almost two decades, clinicians have overlooked the diagnostic potential of CD34^neg hematopoietic stem cells because of their limited homing capacity relative to CD34^posHSCs when injected intravenously. This has contributed to the lack of appeal of using umbilical cord blood in HSC transplantation because its stem cell count is lower than bone marrow. The present study reveals that the homing and engraftment of CD34^negHSCs can be improved by adding the Sialyl Lewis X molecule via α1,3-fucosylation. This unlocks the potential for using this more primitive stem cell to treat blood disorders because our findings show CD34^negHSCs have the capacity to regenerate cells in the bone marrow of mice for several months. Furthermore, our RNA sequencing analysis revealed that CD34^negHSCs have unique adhesion pathways, downregulated in CD34^posHSCs, that facilitate interaction with the bone marrow niche. Our findings suggest that CD34^neg cells will best thrive when the HSC resides in its microenvironment. © 2024 The Author(s)

关键词： Immunology Stem cells research Transcriptomics

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Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

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computers, Materials & Continua 2021年第8期68卷 2413-2429页

作者： Tapan Kumar Das Pradeep Kumar Roy Mohy Uddin Kathiravan Srinivasan Chuan-Yu Chang Shabbir Syed-Abdul School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Indian Institute of Information TechnologySurat395007India Research Quality Management Section King Abdullah International Medical Research CenterKing Saud bin Abdulaziz University for Health SciencesMinistry of National Guard-Health AffairsRiyadh11426Kingdom of Saudi Arabia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Graduate Institute of Biomedical Informatics Taipei Medical UniversityTaipeiTaiwan

Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s ***,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion *** this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is *** research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high *** research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the *** system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR *** results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of *** the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.

关键词： Deep learning convolutional neural network brain tumor magnetic resonance imaging

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An Offline Assistance Tool for Visually Impaired People Based on Image Captioning

An Offline Assistance Tool for Visually Impaired People Base...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Guo, Yu Chen, Yue Xie, Yuanyan Ban, Xiaojuan Obaidat, Mohammad S. University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China University of Science and Technology Beijing Shunde Innovation School Foshan China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China King Abdullah Ii School of Information Technology University of Jordan Amman Jordan Amity University UP Noida India

ISBN: (纸本)9781665468190

Eye defects of visually impaired people bring great inconvenience to daily lives. With the rapid development of computers and artificial intelligence, some assistance tools and wearable devices have provided convenience to a certain extent, but they still possess many shortcomings, such as high prices, limited functionality, poor real-time performance, the inability to be used offline and so on. This paper designs an offline assistance tool specifically for visually impaired people that can help them perceive their surrounding environments by taking photos and reading the descriptions output by an image captioning model. Considering that the photos taken by visually impaired people may be incomplete, an improved image stitching method based on a genetic algorithm is proposed to obtain high-quality stitched images as the inputs of the image captioning model. An improved model pruning algorithm is also designed to compress and accelerate the image captioning model to meet the offline and real-time deployment requirements of portable devices. Experimental results and a practical tool show that the developed system based on an image captioning model and pruning can quickly and accurately describe environments, thus assisting visually impaired people anytime and anywhere. © 2022 IEEE.

关键词： Genetic algorithms

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Deformable Image Registration Using Vision Transformers for Cardiac Motion Estimation from Cine Cardiac MRI Images 1

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Functional Imaging and Modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Upendra, Roshan Reddy Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Institute for Information Sciences University of Kansas LawrenceKS United States

ISBN: (数字)9783031353024

ISBN: (纸本)9783031353017

Accurate cardiac motion estimation is a crucial step in assessing the kinematic and contractile properties of the cardiac chambers, thereby directly quantifying the regional cardiac function, which plays an important role in understanding myocardial diseases and planning their treatment. Since the cine cardiac magnetic resonance imaging (MRI) provides dynamic, high-resolution 3D images of the heart that depict cardiac motion throughout the cardiac cycle, cardiac motion can be estimated by finding the optical flow representation between the consecutive 3D volumes from a 4D cine cardiac MRI dataset, thereby formulating it as an image registration problem. Therefore, we propose a hybrid convolutional neural network (CNN) and Vision Transformer (ViT) architecture for deformable image registration of 3D cine cardiac MRI images for consistent cardiac motion estimation. We compare the image registration results of our proposed method with those of the VoxelMorph CNN model and conventional B-spline free form deformation (FFD) non-rigid image registration algorithm. We conduct all our experiments on the open-source Automated Cardiac Diagnosis Challenge (ACDC) dataset. Our experiments show that the deformable image registration results obtained using the proposed method outperform the CNN model and the traditional FFD image registration method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image registration

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Directly and instantly seeing through random diffusers by self-imaging in scattering speckles

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PhotoniX 2023年第1期4卷 674-687页

作者： Jietao Liu Wenhong Yang Guofeng Song Qiaoqiang Gan Laboratory of Photonic Integrated Circuits Xiong’an Institute of InnovationChinese Academy of SciencesXiong’an New AreaBeijing 071700Hebei ProvinceChina 2 Sustainability&Photonics Energy Research LaboratoryMaterial Science Engineering programDivision of Physical Science&EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

Imaging through scattering medium is challenging but important for different *** advances rely on computational image reconstruction from scattering *** these conventional investigations,speckles were always treated as scrambled grainy *** seeing through scattering diffusers has never been ***,we report a new strategy to see through random diffusers directly using self-imaging of *** analyzing the physics,a direct observation strategy through scattering media is reported with improved image *** this method,we experimentally demonstrated reconstruction-free real-time imaging of static and moving objects with their actual orientation information under single-wavelength and white light *** also proposed a modified speckle autocorrelation imaging(SAI)method inspired by the self-imaging ***,our strategy requires no pre-calibration or acquisition of point-spread-function,no active control of wavefronts or complicated equipment,nor iterations or carefully adjusted parameters,paving the way towards rapid and high-quality imaging through scattering diffusers.

关键词： speckle random instant

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Red emission from strain-relaxed bulk InGaN active region

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Optics Express 2025年第13期33卷 27245-27254页

作者： Zuojian Pan Zhizhong Chen Haodong Zhang Chuhan Deng Ling Hu Fei Huang Xiaohang Li Qi Wang Guoyi Zhang Bo Shen State Key Laboratory for Artificial Microstructure and Mesoscopic Physics School of Physics Peking University Beijing 100871 China Dongguan Institute of Optoelectronics Peking University Dongguan Guangdong 523808 China Yangtze Delta Institute of Optoelectronics Peking University Nantong Jiangsu 226000 China Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

High-In-content InGaN quantum wells (QWs) in red light-emitting diodes (LEDs) are typically grown at low temperatures to ensure effective In incorporation. In this study, red LEDs based on bulk InGaN active region were demonstrated. The growth temperature of bulk InGaN was ∼800℃, which is over 100℃ higher than the typical growth temperature of red QWs. By introducing high-density trench structures in the underlying green multi-quantum wells (MQWs), the compressive strain in bulk InGaN was relaxed by ∼96%. With strain relaxation, phase separation occurred in the bulk InGaN, forming low-In-content (blue) and high-In-content (red) phases. The red phase acted as carrier localization centers, enabling red light emission under electrical injection. The red LEDs based on bulk InGaN active region exhibited superior wavelength stability, with the peak wavelength shifting slightly from 648.6 nm at 1 mA to 642.4 nm at 100 mA. Meanwhile, the on-wafer peak external quantum efficiency was measured to be 0.32%. To the best of our knowledge, this study presents a new epitaxial strategy for red InGaN LEDs.

关键词： Green light emitting diodes Quantum efficiency Quantum wells Red light emitting diodes Scanning electron microscopy Stark effect

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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