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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

Across Scales and Across Dimensions: Temporal Super-Resolution Using Deep Internal Learning 1

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16th European Conference on computer Vision, ECCV 2020

作者： Zuckerman, Liad Pollak Naor, Eyal Pisha, George Bagon, Shai Irani, Michal Department of Computer Science and Applied Math The Weizmann Institute of Science Rehovot Israel Rehovot Israel Technion Israel Institute of Technology Haifa Israel

ISBN: (数字)9783030585716

ISBN: (纸本)9783030585709

When a very fast dynamic event is recorded with a low-framerate camera, the resulting video suffers from severe motion blur (due to exposure time) and motion aliasing (due to low sampling rate in time). True Temporal Super-Resolution (TSR) is more than just Temporal-Interpolation (increasing framerate). It can also recover new high temporal frequencies beyond the temporal Nyquist limit of the input video, thus resolving both motion-blur and motion-aliasing – effects that temporal frame interpolation (as sophisticated as it may be) cannot undo. In this paper we propose a "Deep Internal Learning" approach for true TSR. We train a video-specific CNN on examples extracted directly from the low-framerate input video. Our method exploits the strong recurrence of small space-time patches inside a single video sequence, both within and across different spatio-temporal scales of the video. We further observe (for the first time) that small space-time patches recur also across-dimensions of the video sequence – i.e., by swapping the spatial and temporal dimensions. In particular, the higher spatial resolution of video frames provides strong examples as to how to increase the temporal resolution of that video. Such internal video-specific examples give rise to strong self-supervision, requiring no data but the input video itself. This results in Zero-Shot Temporal-SR of complex videos, which removes both motion blur and motion aliasing, outperforming previous supervised methods trained on external video datasets. © 2020, Springer Nature Switzerland AG.

关键词： Interpolation

Uncertainty Estimation in Cardio Landmark Detection and Heart Disease Diagnosis on Chest X-Ray Images

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IEEE Access 2025年 13卷 87229-87240页

作者： Lvov, Dmitry Stebakov, Ivan Kornaev, Alexei Pershin, Ilya Mustafaev, Tamerlan Afonchikov, Danil Kuleev, Ramil Bariev, Iskander Ibragimov, Bulat Innopolis University Research Center for Artificial Intelligence Innopolis420500 Russia Research Center for Artificial Intelligence N. N. Blokhin National Medical Research Center of Oncology Moscow115478 Russia Kazan Federal University Department of Applied Mathematics and Artificial Intelligence Kazan420008 Russia University of Pittsburgh Swanson School of Engineering PittsburghPA15261 United States University of Copenhagen Department of Computer Science Copenhagen1172 Denmark

Landmark detection using chest X-ray images is often a time-consuming task performed by radiologists, followed by the calculation of cardiometric indices and the diagnosis of heart diseases. The goal of this research is to estimate uncertainty in data and knowledge, which leads to increased accuracy and interpretability in models that predict landmark positions and classify human body states. To achieve this, data from about 800 X-ray images labeled by four practicing radiologists was utilized. An uncertainty-aware negative log-likelihood loss was proposed to address both regression and classification problems. The proposed models were trained to predict the mean and log variance of a normal distribution for each input image, with the variance value estimating the uncertainty of the prediction. Natural data uncertainty was also measured from labels provided by different radiologists and implemented in the form of a label transform for the coordinates of the landmarks. The classification models demonstrated good correspondence with methods such as Monte Carlo dropout and deep ensembling, which account for knowledge uncertainty. The proposed uncertainty-aware models achieved the best results in both landmark detection and heart disease diagnosis, with accuracy improvements of 3% and 4.61%, respectively. The proposed methods are effective and easy to use, making their application to other medical problems a subject of interest for further research. © 2013 IEEE.

关键词： Deep learning

Programmable Mode/Wavelength DEMULTIPLEXER using Inverse-designed Low-loss Phase-change Material

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Programmable Mode/Wavelength DEMULTIPLEXER using Inverse-des...

Conference on Lasers and Electro-Optics (CLEO)

作者： Ziyu Jiao Changming Wu Heshan Yu Ichiro Takeuchi Carlos Ríos Mo Li Department of Electrical and Computer Engineering University of Washington Seattle WA USA School of the Gifted Young University of Science and Technology of China Hefei P. R. China Department of Materials Science and Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Department of Physics University of Washington Seattle WA USA

We demonstrate a non-volatile optically programmable inverse-designed demultiplexer based on low-loss phase change material Sb 2 Se 3 integrated MMI that can be reconfigured both as mode and wavelength demultiplexers.

ISBN: (纸本)9781957171258

关键词： Demultiplexers Extinction ratios Multimode interference Photonic devices Refractive index Subwavelength structures

Understanding Dynamic Associations: Gait Recognition via Cross-view Spatiotemporal Aggregation Network

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IEEE Transactions on Circuits and Systems for Video Technology 2022年 1-1页

作者： Chen, Jianyu Wang, Zhongyuan Zheng, Caixia Zeng, Kangli Zou, Qin Xiong, Zixiang School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Wuhan University Wuhan China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Gait recognition is a technique to recognize subjects under long-distance conditions. Existing gait recognition methods generally rely on extracting different motion representations of body parts to capture the gait information. However, the differences in appearance silhouettes between subjects are not discriminative enough to identify the distinct gaits. Therefore, exploring the representations of unique motion differences of subjects from the temporal domain is an effective way to boost gait recognition performance. In this paper, we propose a cross-view spatiotemporal aggregation network (CSTAN) to learn the most discriminative motion representations of subjects from multi-scale features. Particularly, CSTAN uses temporal contextual feature aggregation (TCFA) block to interact multi-scale features, increasing the diversity of temporal representations in feature sequences and producing suitable motion representations. Besides, a potential spatial feature assessment (PSFA) block is proposed to address the problem of missing silhouettes due to occlusion. Considering the multi-view context, we propose a cross-view prediction learning (CVPL) block to mitigate the impact of the view itself. By aggregating features in the spatiotemporal and view domains, CSTAN can learn a more appropriate representation of human motion differences. Extensive experiments on two public gait recognition datasets demonstrate the state-of-the-art performance of our proposed CSTAN. It is worth mentioning that the performance of CSTAN achieves a breakthrough in some complex environments, e.g., the subjects wearing different coats or carrying a bag. IEEE

关键词： Data mining

Quantum Linear System Solvers: A Survey of Algorithms and Applications

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

An all Mach number semi-implicit hybrid Finite Volume/ Virtual Element method for compressible viscous flows on Voronoi meshes

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arXiv 2024年

作者： Boscheri, Walter Busto, Saray Dumbser, Michael Laboratoire de Mathématiques UMR 5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Ferrara44121 Italy Department of Applied Mathematics Universidade de Santiago de Compostela Santiago de Compostela15782 Spain Galician Center for Mathematical Research and Technology CITMAga Santiago de Compostela15782 Spain Department of Civil Environmental and Mechanical Engineering University of Trento Trento38123 Italy

We present a novel high order semi-implicit hybrid finite volume/virtual element numerical scheme for the solution of compressible flows on Voronoi tessellations. The method relies on the flux splitting of the compressible Navier-Stokes equations into three sub-systems: a convective sub-system solved explicitly using a finite volume (FV) scheme, and the viscous and pressure sub-systems which are discretized implicitly at the aid of a virtual element method (VEM). Consequently, the time step restriction of the overall algorithm depends only on the mean flow velocity and not on the fast pressure waves nor on the viscous eigenvalues. As such, the proposed methodology is well suited for the solution of low Mach number flows at all Reynolds numbers. Moreover, the scheme is proven to be globally energy conserving so that shock capturing properties are retrieved in high Mach number flows. To reach high order of accuracy in time and space, an IMEX Runge-Kutta time stepping strategy is employed together with high order spatial reconstructions in terms of CWENO polynomials and virtual element space basis functions. The chosen discretization techniques allow the use of general polygonal grids, a useful tool when dealing with complex domain configurations. The new scheme is carefully validated in both the incompressible limit and the high Mach number regime through a large set of classical benchmarks for fluid dynamics, assessing robustness and *** Codes 65M08, 65M60, 76N06 © 2024, CC BY-NC-ND.

关键词： Mach number

Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction

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Comparative Analysis of Machine Learning Models in Online Pa...

Intelligent Methods, Systems, and Applications (IMSA)

作者： Ahmed Younes Shdefat Mennatallah Mohamed Shahd Khaled Farrah Hany Hanaa Fathi Diaa Salama AbdElminaam College of Engineering and Technology American University of the Middle East Egaila Kuwait Faculty of Computer Science Misr International University Cairo Egypt Applied Science Research Center Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid Jordan Information Systems department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime—online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists.

关键词： Support vector machines Accuracy Machine learning algorithms Predictive models Prediction algorithms Fraud Bayes methods

Streaming Algorithms via Local Algorithms for Maximum Directed Cut

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arXiv 2024年

作者： Saxena, Raghuvansh R. Singer, Noah G. Sudan, Madhu Velusamy, Santhoshini Tata Institute of Fundamental Research Maharashtra Mumbai India Department of Computer Science Carnegie Mellon University PittsburghPA United States School of Engineering and Applied Sciences Harvard University CambridgeMA United States Toyota Technological Institute ChicagoIL United States

We explore the use of local algorithms in the design of streaming algorithms for the Maximum Directed Cut problem. Specifically, building on the local algorithm of Buchbinder, Feldman, Seffi, and Schwartz [BFSS15] and Censor-Hillel, Levy, and Shachnai [CLS17], we develop streaming algorithms for both adversarially and randomly ordered streams that approximate the value of maximum directed cut in bounded-degree graphs. In n-vertex graphs, for adversarially ordered streams, our algorithm uses O(n1−Ω(1)) (sub-linear) space and for randomly ordered streams, our algorithm uses logarithmic space. Moreover, both algorithms require only one pass over the input stream. With a constant number of passes, we give a logarithmic-space algorithm which works even on graphs with unbounded degree on adversarially ordered streams. Our algorithms achieve any fixed constant approximation factor less than 21. In the single-pass setting, this is tight: known lower bounds show that obtaining any constant approximation factor greater than 12 is impossible without using linear space in adversarially ordered streams Kapralov and Krachun [KK19] and Ω(√n) space in randomly ordered streams, even on bounded degree graphs Kapralov, Khanna, and Sudan [KKS15]. In terms of techniques, our algorithms partition the vertices into a small number of different types based on the structure of their local neighborhood, ensuring that each type carries enough information about the structure to approximately simulate the local algorithm on a vertex with that type. We then develop tools to accurately estimate the frequency of each type. This allows us to simulate an execution of the local algorithm on all vertices, and thereby approximate the value of the maximum directed cut. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm