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Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams 25th

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Developing a Segmentation Model for Microscopic Images of Mi...

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Baek, Ji Yeon de Guzman, Maria Krishna Park, Ho-min Park, Sanghyeon Shin, Boyeon Velickovic, Tanja Cirkovic Van Messem, Arnout De Neve, Wesley Center for Biotech Data Science Ghent University Global Campus Incheon Korea Republic of Center for Food Chemistry and Technology Ghent University Global Campus Incheon Korea Republic of Department of Food Technology Safety and Health Ghent University Ghent Belgium University of Belgrade - Faculty of Chemistry Belgrade Serbia Serbian Academy of Sciences and Arts Belgrade Serbia IDLab Department of Electronics and Information Systems Ghent University Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

ISBN: (纸本)9783030687793

Microplastics (MP) have become a major concern, given the threat they pose to marine-derived food and human health. One way to investigate this threat is to quantify MP found in marine organisms, for instance making use of image analysis to identify ingested MP in fluorescent microscopic images. In this study, we propose a deep learning-based segmentation model to generate binarized images (masks) that make it possible to clearly separate MP from other background elements in the aforementioned type of images. Specifically, we created three variants of the U-Net model with a ResNet-101 encoder, training these variants with 99 high-resolution fluorescent images containing MP, each having a mask that was generated by experts using manual color threshold adjustments in ImageJ. To that end, we leveraged a sliding window and random selection to extract patches from the high-resolution images, making it possible to adhere to input constraints and to increase the number of labeled examples. When measuring effectiveness in terms of accuracy, recall, and F2 -score, all segmentation models exhibited low scores. However, compared to two ImageJ baseline methods, the effectiveness of our segmentation models was better in terms of precision, F0.5 -score, F1 -score, and mIoU: U-Net (1) obtained the highest mIoU of 0.559, U-Net (2) achieved the highest F1 -score of 0.682, and U-Net (3) had the highest precision and F0.5 -score of 0.594 and 0.626, respectively, with our segmentation models, in general, detecting less false positives in the predicted masks. In addition, U-Net (1), which used binary cross-entropy loss and stochastic gradient descent, and U-Net (2), which used dice loss and Adam, were most effective in discriminating MP from other background elements. Overall, our experimental results suggest that U-Net (1) and U-Net (2) allow for more effective MP identification and measurement than the macros currently available in ImageJ. © 2021, Springer Nature Switzerland AG.

关键词： Image segmentation

Optimizing frequency allocation for fixed-frequency superconducting quantum processors

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Physical Review Research 2022年第2期4卷 023079-023079页

作者： Alexis Morvan Larry Chen Jeffrey M. Larson David I. Santiago Irfan Siddiqi Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Mathematics and Computer Science Division Argonne National Laboratory Lemont Illinois 60439 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

Fixed-frequency superconducting quantum processors are one of the most mature quantum computing architectures with high-coherence qubits and simple controls. However, high-fidelity multiqubit gates pose tight requirements on individual qubit frequencies in these processors, and these constraints are difficult to satisfy when constructing larger processors due to the large dispersion in the fabrication of Josephson junctions. In this paper, we propose a mixed-integer-programming-based optimization approach that determines qubit frequencies to maximize the fabrication yield of quantum processors. We study traditional qubit and qutrit (three-level) architectures with cross-resonance interaction processors. We compare these architectures to a differential ac-Stark shift based on entanglement gates and show that our approach greatly improves the fabrication yield and also increases the scalability of these devices. Our approach is general and can be adapted to problems where one must avoid specific frequency collisions.

关键词： Quantum information architectures & platforms Quantum information with solid state qubits

Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

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arXiv 2024年

作者： Nguyen, Nghi Hou, Tao Amico, Enrico Zheng, Jingyi Huang, Huajun Kaplan, Alan D. Petri, Giovanni Goñi, Joaquín Zhao, Yize Duong-Tran, Duy Shen, Li Multidisciplinary Brain Research Center Bar-Ilan University Ramat Gan Israel Department of Computer Science University of Oregon EugeneOR United States Institute of Health and Neurodevelopment College of Health and Life Sciences Aston University Birmingham United Kingdom Department of Mathematics and Statistics Auburn University Alabama United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA United States NPLab Network Science Institute Northeastern University London London United Kingdom School of Industrial Engineering Purdue University West LafayetteIN United States School of Biomedical Engineering Purdue University W. LafayetteIN United States School of Public Health Yale University New HeavenCT United States Department of Mathematics U.S. Naval Academy AnnapolisMD United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities’ insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://***/ngcaonghi/scaffold_noise. © 2024, CC BY.

关键词： Brain mapping

Multi-object Graph-based Segmentation with Non-overlapping Surfaces

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Multi-object Graph-based Segmentation with Non-overlapping S...

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Patrick M. Jensen Anders B. Dahl Vedrana A. Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.

关键词： Image segmentation Three-dimensional displays Microscopy Optical microscopy Shape Image edge detection Optimization

P℘N functions, complete mappings and quasigroup difference sets

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arXiv 2022年

作者： Anbar, Nurdagül Kalaycı, Tekgül Meidl, Wilfried Riera, Constanza Stanica, Pantelimon Sabancı University MDBF Orhanlı Tuzla Istanbul34956 Turkey Institut für Mathematik Alpen-Adria-Universität Klagenfurt Austria Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Department of Applied Mathematics Naval Postgraduate School MontereyCA93943–5216 United States

We investigate pairs of permutations F, G of Fpn such that F (x + a) − G(x) is a permutation for every a ∈ Fpn . We show that necessarily G(x) = ℘(F (x)) for some complete mapping −℘ of Fpn, and call the permutation F a perfect ℘ nonlinear (P℘N) function. If ℘(x) = cx, then F is a PcN function, which have been considered in the literature, lately. With a binary operation on Fpn × Fpn involving ℘, we obtain a quasigroup, and show that the graph of a P℘N function F is a difference set in the respective quasigroup. We further point to variants of symmetric designs obtained from such quasigroup difference sets. Finally, we analyze an equivalence (naturally defined via the automorphism group of the respective quasigroup) for P℘N functions, respectively, the difference sets in the corresponding quasigroup. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

Privacy-preserving training of tree ensembles over continuous data

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arXiv 2021年

作者： Adams, Samuel Choudhary, Chaitali de Cock, Martine Dowsley, Rafael Melanson, David Nascimento, Anderson C.A. Railsback, Davis Shen, Jianwei School of Engineering and Technology University of Washington Tacoma TacomaWA98402 United States Dept. of Applied Mathematics Computer Science and Statistics Ghent University Faculty of Information Technology Monash University Clayton Australia Department of Computer Science University of Arizona

Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In real-life applications, features are often numerical. The standard "in the clear" algorithm to grow decision trees on data with continuous values requires sorting of training examples for each feature in the quest for an optimal cut-point in the range of feature values in each node. Sorting is an expensive operation in MPC, hence finding secure protocols that avoid such an expensive step is a relevant problem in privacy-preserving machine learning. In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data;(2) secure discretization of the data, followed by secure training of a random forest over the discretized data;and (3) secure training of extremely randomized trees ("extra-trees") on the original data. Approaches (2) and (3) both involve randomizing feature choices. In addition, in approach (3) cut-points are chosen randomly as well, thereby alleviating the need to sort or to discretize the data up front. We implemented all proposed solutions in the semi-honest setting with additive secret sharing based MPC. In addition to mathematically proving that all proposed approaches are correct and secure, we experimentally evaluated and compared them in terms of classification accuracy and runtime. We privately train tree ensembles over data sets with 1000s of instances or features in a few minutes, with accuracies that are at par with those obtained in the clear. This makes our solution orders of magnitude more efficient than the existing approaches, which are based on oblivious sorting. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision trees

Crystalline superconductor-semiconductor Josephson junctions for compact superconducting qubits

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arXiv 2025年

作者： Balgley, Jesse Park, Jinho Chu, Xuanjing Arnault, Ethan G. Gustafsson, Martin V. Ranzani, Leonardo Holbrook, Madisen Watanabe, Kenji Taniguchi, Takashi Perebeinos, Vasili Hone, James Fong, Kin Chung Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Applied Physics and Mathematics Columbia University New YorkNY10027 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States RTX BBN Technologies Quantum Engineering and Computing Group CambridgeMA02138 United States Department of Physics Columbia University New YorkNY10027 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0044 Japan Department of Electrical Engineering University at Buffalo The State University of New York BuffaloNY14260 United States

The narrow bandgap of semiconductors allows for thick, uniform Josephson junction barriers, potentially enabling reproducible, stable, and compact superconducting qubits. We study vertically stacked van der Waals Josephson junctions with semiconducting weak links, whose crystalline structures and clean interfaces offer a promising platform for quantum devices. We observe robust Josephson coupling across 2–12 nm (3–18 atomic layers) of semiconducting WSe2 and, notably, a crossover from proximity- to tunneling-type behavior with increasing weak link thickness. Building on these results, we fabricate a prototype all-crystalline merged-element transmon qubit with transmon frequency and anharmonicity closely matching design parameters. We demonstrate dispersive coupling between this transmon and a microwave resonator, highlighting the potential of crystalline superconductor-semiconductor structures for compact, tailored superconducting quantum devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Josephson junction devices

Pass-Efficient randomized lu algorithms for computing low-rank matrix approximation

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arXiv 2020年

作者： Zhang, Bolong Mascagniy, Michael Department of Computer Science Florida State University FL United States Applied and Computational Mathematics Division NIST GaithersburgMD United States

Low-rank matrix approximation is extremely useful in the analysis of data that arises in scientific computing, engineering applications, and data science. However, as data sizes grow, traditional low-rank matrix approximation methods, such as singular value decomposition (SVD) and column pivoting QR decomposition (CPQR), are either prohibitively expensive or cannot provide sufficiently accurate results. A solution is to use randomized low-rank matrix approximation methods such as randomized SVD , and randomized LU decomposition on extremely large data sets. In this paper, we focus on the randomized LU decomposition method. First, we employ a reorthogonalization procedure to perform the power iteration of the existing randomized LU algorithm to compensate for the rounding errors caused by the power method. Then we propose a novel randomized LU algorithm, called PowerLU, for the fixed low-rank approximation problem. PowerLU allows for an arbitrary number of passes of the input matrix, v = 2. Recall that the existing randomized LU decomposition only allows an even number of passes. We prove the theoretical relationship between PowerLU and the existing randomized LU. Numerical experiments show that our proposed PowerLU is generally faster than the existing randomized LU decomposition, while remaining accurate. We also propose a version of PowerLU, called PowerLU FP, for the fixed precision low-rank matrix approximation problem. PowerLU FP is based on an efficient blocked adaptive rank determination Algorithm 4.1 proposed in this paper. We present numerical experiments that show that PowerLU FP can achieve almost the same accuracy and is faster than the randomized blocked QB algorithm by Martinsson and Voronin. We finally propose a single-pass algorithm based on LU factorization. Tests show that the accuracy of our single-pass algorithm is comparable with the existing single-pass *** Codes 65F99, 65C99 Copyright © 2020, The Authors. All rights reserved.

关键词： Singular value decomposition

Correction: Discovering novel soliton solutions for (3 + 1)-modified fractional Zakharov–Kuznetsov equation in electrical engineering through an analytical approach

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Optical and Quantum Electronics 2024年第5期56卷 1-1页

作者： Alqhtani, Manal Saad, Khaled M. Shah, Rasool Hamanah, Waleed M. Department of Mathematics College of Sciences and Arts Najran University Najran Kingdom of Saudi Arabia Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics Abdul Wali Khan University Mardan Mardan Pakistan Applied Research Center for Metrology Standards and Testing KFUPM Dhahran Kingdom of Saudi Arabia