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Quantum approximation of normalized Schatten norms and applications to learning

Physical Review A 2022年第5期106卷 052409-052409页

作者： Yiyou Chen Hideyuki Miyahara Louis-S. Bouchard Vwani Roychowdhury Department of Computer Science University of California Los Angeles California 90095 USA Department of Electrical and Computer Engineering University of California Los Angeles California 90095 USA Department of Chemistry and Biochemistry University of California Los Angeles California 90095 USA Department of Bioengineering University of California Los Angeles California 90095 USA Center for Quantum Science and Engineering University of California Los Angeles California 90095 USA California NanoSystems Institute University of California Los Angeles California 90095 USA The Molecular Biology Institute University of California Los Angeles California 90095 USA

Efficient measures to determine the similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be efficiently estimated. Given two quantum operations, U1 and U2, represented in their circuit forms, we first develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference (∥U1−U2∥S2) with precision ε, using only one clean qubit and one classical random variable. We prove a Poly(1ε) upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states (F): If ∥U1−U2∥S2 is sufficiently small (e.g., ≤ε1+2(1/δ−1)) then the fidelity of states obtained by processing the same randomly and uniformly picked pure state |ψ〉 is as high as needed [F(U1|ψ〉,U2|ψ〉)≥1−ε] with probability exceeding 1−δ. We provide example applications of this efficient similarity metric estimation framework to quantum circuit learning tasks, such as finding the square root of a given unitary operation.

关键词： Machine learning Quantum algorithms Quantum computation

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

引用

17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning

arXiv

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arXiv 2024年

作者： Yang, Xilin Bai, Bijie Zhang, Yijie Aydin, Musa Selcuk, Sahan Yoruc Guo, Zhen Fishbein, Gregory A. Atlan, Karine Wallace, William Dean Pillar, Nir Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los Angeles90095 United States University of California Los AngelesCA United States Department of Computer Engineering Fatih Sultan Mehmet Vakif University Turkiye Istanbul Turkey Department of Pathology and Laboratory Medicine David Geffen School of Medicine The University of California Los AngelesCA90095 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure. Congo red stain is the gold standard chemical stain for the visualization of amyloid deposits in tissue sections, as it forms complexes with the misfolded proteins and shows a birefringence pattern under polarized light microscopy. However, Congo red staining is tedious and costly to perform, and prone to false diagnoses due to variations in the amount of amyloid, staining quality and expert interpretation through manual examination of tissue under a polarization microscope. Here, we report the first demonstration of virtual birefringence imaging and virtual Congo red staining of label-free human tissue to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue sections into brightfield and polarized light microscopy equivalent images, matching the histochemically stained versions of the same samples. We demonstrate the efficacy of our method with blind testing and pathologist evaluations on cardiac tissue where the virtually stained images agreed well with the histochemically stained ground truth images. Our virtually stained polarization and brightfield images highlight amyloid birefringence patterns in a consistent, reproducible manner while mitigating diagnostic challenges due to variations in the quality of chemical staining and manual imaging processes as part of the clinical workflow. Copyright © 2024, The Authors. All rights reserved.

关键词： Tissue

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Hole-Based Stealthy Hyperuniform Semiconductor Metamaterials for the Mid-Infrared

arXiv

引用

arXiv 2024年

作者： Gallego, Manuel Kacmoli, Sara Zhang, Yezhezi Klatt, Michael A. Gmachl, Claire F. Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Omenn-Darling Bioengineering Institute Princeton University PrincetonNJ08544 United States Institute for AI Safety and Security Wilhelm-Runge-Str. 10 Ulm89081 Germany Institute for Material Physics in Space Köln51170 Germany Department of Physics Ludwig-Maximilians-Universität Schellingstr. 4 Munich80799 Germany

Stealthy hyperuniform heterostructures are a novel type of metamaterial with the potential for optical image processing at angles away from normal incidence. These metamaterials show analogous properties to photonic crystals while circumventing the spatial anisotropy often hindering the latter's use. In this paper, we have successfully designed, fabricated, and characterized a hole-based stealthy hyperuniform structure on a quantum cascade layer substrate. The infrared spectral data reveal a sizable gap-midgap ratio of 9.8% for a photonic band gap around 12.0 µm in the form of an enhanced reflection region for increasing incidence angles. The stealthy hyperuniform metamaterial also showed spatial isotropy by its unchanging reflection spectrum for all in-plane rotational angle measurements. © 2024, CC BY-NC-SA.

关键词： Metamaterials

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Time-varying EEG spectral power predicts evoked and spontaneous fMRI motor brain activity

arXiv

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arXiv 2025年

作者： Mehta, Neil Gonçalves, Inês Montagna, Alberto Fleury, Mathis Caetano, Gustavo Esteves, Inês Vourvopoulos, Athanasios Grover, Pulkit Figueiredo, Patrícia Institute for Systems and Robotics Department of Bioengineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Electrical and Computer Engineering Department Carnegie Mellon University United States

Simultaneous EEG-fMRI recordings are increasingly used to investigate brain activity by leveraging the complementary high spatial and high temporal resolution of fMRI and EEG signals respectively. It remains unclear, however, to what degree these two imaging modalities capture shared information about neural activity. Here, we investigate whether it is possible to predict both task-evoked and spontaneous fMRI signals of motor brain networks from EEG time-varying spectral power using interpretable models trained for individual subjects with Sparse Group Lasso regularization. Critically, we test the trained models on data acquired from each subject on a different day and obtain statistical validation by comparison with appropriate null models as well as the conventional EEG sensorimotor rhythm. We find significant prediction results in most subjects, although less frequently for resting-state compared to task-based conditions. Furthermore, we interpret the model learned parameters to understand representations of EEG-fMRI coupling in terms of predictive EEG channels, frequencies, and haemodynamic delays. In conclusion, our work provides evidence of the ability to predict fMRI motor brain activity from EEG recordings alone across different days, in both task-evoked and spontaneous conditions, with statistical significance in individual subjects. These results present great potential for translation to EEG neurofeedback applications. © 2025, CC BY.

关键词： Image coding

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Traffic control for the improvement of sustainability in freeway networks: a bibliometric analysis

引用

IFAC-PapersOnLine 2020年第2期53卷 17505-17510页

作者： S. Sacone C. Pasquale S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

Sustainability is becoming a key factor in the design and control of traffic systems. In the field of traffic control for freeway networks, the performance criteria explicitly included in traffic control schemes to address sustainability are mainly referred to the reduction of the environmental impact and the decrease of accidents. These control objectives are generally considered together with the conventional goal of traffic control, i.e. the minimization of the total traveling times for drivers. This paper reports a bibliometric study based on keyword counting regarding the research works on freeway traffic control developed to improve sustainability. The main goal of such analysis is to evaluate how the interest of researchers on sustainability-related freeway traffic control has evolved over the last decades and which traffic control measures have been mostly used to address such issues.

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Autonomous Quality and Hallucination Assessment for Virtual Tissue Staining and Digital Pathology

arXiv

引用

arXiv 2024年

作者： Huang, Luzhe Li, Yuzhu Pillar, Nir Haran, Tal Keidar Wallace, William Dean Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Pathology Hadassah Hebrew University Medical Center Jerusalem91120 Israel Department of Pathology Keck School of Medicine University of Southern California Los AngelesCA90033 United States Department of Surgery University of California Los AngelesCA90095 United States

Histopathological staining of human tissue slides is essential in the diagnosis of various diseases. The recent advances in virtual tissue staining technologies using AI alleviate some of the costly and tedious steps involved in the traditional histochemical staining process, permitting multiplexed rapid staining of label-free tissue without using staining reagents, while also preserving the tissue block. However, potential hallucinations and artifacts in these virtually stained tissue images pose concerns, especially for the clinical utility of these AI-driven approaches. Quality assessment of histology images is, in general, performed by human experts, which can be subjective and depends on the training level of the expert. Here, we present an autonomous quality and hallucination assessment method (termed AQuA), mainly designed for virtual tissue staining, while also being applicable to histochemical staining. Leveraging a novel architecture, AQuA autonomously achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to ground truth, also presenting an agreement of 98.5% with the manual assessments made by a group of board-certified pathologists. Besides, AQuA achieves super-human performance in identifying realistic-looking, virtually stained hallucinatory images that would normally mislead human diagnosticians by deceiving them into diagnosing patients that never existed. We further demonstrate the wide adaptability of AQuA across various virtually and histochemically stained human tissue images and showcase its strong external generalization to detect unseen hallucination patterns of virtual staining network models as well as artifacts observed in the traditional histochemical staining workflow. This framework creates new opportunities to greatly enhance the reliability of virtual staining and will provide quality assurance for various image generation and transformation tasks in the fields of digital path

关键词： Tissue

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Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning

arXiv

引用

arXiv 2025年

作者： Islam, Khayrul Forelli, Ryan F. Han, Jianzhong Bhadane, Deven Huang, Jian Agar, Joshua C. Tran, Nhan Ogrenci, Seda Liu, Yaling Department of Mechanical Engineering Lehigh University BethlehemPA18015 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Coriell Institute for Medical Research CamdenNJ United States Department of Computer Science Lehigh University BethlehemPA18015 United States Cooper Medical School of Rowan University CamdenNJ08103 United States Center for Metabolic Disease Research Temple University Lewis Katz School of Medicine PhiladelphiaPA19122 United States Department of Mechanical Engineering and Mechanics Drexel University PhiladelphiaPA19104 United States Real-time Processing Systems Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States

Precise cell classification is essential in biomedical diagnostics and therapeutic monitoring, particularly for identifying diverse cell types involved in various diseases. Traditional cell classification methods such as flow cytometry depend on molecular labeling which is often costly, time-intensive, and can alter cell integrity. To overcome these limitations, we present a label-free machine learning framework for cell classification, designed for real-time sorting applications using bright-field microscopy images. This approach leverages a teacher-student model architecture enhanced by knowledge distillation, achieving high efficiency and scalability across different cell types. Demonstrated through a use case of classifying lymphocyte subsets, our framework accurately classifies T4, T8, and B cell types with a dataset of 80,000 preprocessed images, accessible via an open-source Python package for easy adaptation. Our teacher model attained 98% accuracy in differentiating T4 cells from B cells and 93% accuracy in zero-shot classification between T8 and B cells. Remarkably, our student model operates with only 0.02% of the teacher model’s parameters, enabling field-programmable gate array (FPGA) deployment. Our FPGA-accelerated student model achieves an ultra-low inference latency of just 14.5 µs and a complete cell detection-to-sorting trigger time of 24.7 µs, delivering 12x and 40x improvements over the previous state-of-the-art real-time cell analysis algorithm in inference and total latency, respectively, while preserving accuracy comparable to the teacher model. This framework provides a scalable, cost-effective solution for lymphocyte classification, as well as a new SOTA real-time cell sorting implementation for rapid identification of subsets using in situ deep learning on off-the-shelf computing hardware. © 2025, CC BY.

关键词： Deep learning

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Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning

arXiv

引用

arXiv 2024年

作者： Işıl, Çağatay Koydemir, Hatice Ceylan Eryilmaz, Merve de Haan, Kevin Pillar, Nir Mentesoglu, Koray Unal, Aras Firat Rivenson, Yair Chandrasekaran, Sukantha Garner, Omai B. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Biomedical Engineering Texas A&M University College StationTX77843 United States Center for Remote Health Technologies and Systems Texas A&M Engineering Experiment Station College StationTX77843 United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los AngelesCA90095 United States

Gram staining has been one of the most frequently used staining protocols in microbiology for over a century, utilized across various fields, including diagnostics, food safety, and environmental monitoring. Its manual procedures make it vulnerable to staining errors and artifacts due to, e.g., operator inexperience and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained deep neural network that digitally transforms darkfield images of unstained bacteria into their Gram-stained equivalents matching brightfield image contrast. After a one-time training effort, the virtual Gram staining model processes an axial stack of darkfield microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of the virtual Gram staining workflow on label-free bacteria samples containing Escherichia coli and Listeria innocua by quantifying the staining accuracy of the virtual Gram staining model and comparing the chromatic and morphological features of the virtually stained bacteria against their chemically stained counterparts. This virtual bacteria staining framework effectively bypasses the traditional Gram staining protocol and its challenges, including stain standardization, operator errors, and sensitivity to chemical variations. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

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Global linearization of asymptotically stable systems without hyperbolicity

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Systems & Control Letters 2025年 203卷

作者： Matthew D. Kvalheim Eduardo D. Sontag Department of Mathematics and Statistics University of Maryland Baltimore County MD USA Departments of Electrical and Computer Engineering and Bioengineering and affiliate of Departments of Mathematics and Chemical Engineering Northeastern University Boston MA USA

We give a proof of an extension of the Hartman-Grobman theorem to nonhyperbolic but asymptotically stable equilibria of vector fields. Moreover, the linearizing topological conjugacy is (i) defined on the entire basin of attraction if the vector field is complete, and (ii) a C k ≥ 1 -diffeomorphism on the complement of the equilibrium if the vector field is C k and the underlying space is not 5-dimensional. We also show that the C k statement in the 5-dimensional case is equivalent to the 4-dimensional smooth Poincaré conjecture.

关键词： Linearization Hartman-Grobman theorem

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