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programMER-TRANSPARENT COORDINATION OF RECOVERING CONCURRENT PROCESSES - PHILOSOPHY AND RULES FOR EFFICIENT IMPLEMENTATION

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IEEE TRANSACTIONS ON SOFTWARE engineering 1988年第6期14卷 810-821页

作者： KIM, KH Computer Engineering Program Department of Electrical Engineering University of California CA92717

An approach to coordination of cooperating concurrent processes, each capable of error direction and recovery, is presented. Error detection, rollback, and retry in a process are specified by a well-structured language construct called recovery block. Recovery points of processes must be properly coordinated to prevent a disastrous avalanche of process rollbacks. The approach relies on an intelligent processor system (that runs processes) capable of establishing and discarding the recovery points of interacting processes in a well coordinated manner such that a process never makes two consecutive rollbacks without making a retry between the two, and every process rollback becomes a minimum-distance rollback. Following a discussion of the underlying philosophy of the author's approach, basic rules of reducing storage and time overhead in such a processor system are discussed. Examples are drawn from the systems in which processes communicate through monitors.

关键词： PROCESSOR Philosophy concurrent process RECOVERING MONITOR

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Porous silicon on paper sensors: Factors affecting biosensing performance

Porous silicon on paper sensors: Factors affecting biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

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An insect-based bioelectronic sensing system combining flexible dual-sided microelectrode array and insect olfactory circuitry for human lung cancer detection

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Biosensors and Bioelectronics 2025年 281卷 117356页

作者： Liu, Xiang Sanchez, Simon W. Gong, Yan Riddle, Roksana Jiang, Zebin Trevor, Stevens Contag, Christopher H. Saha, Debajit Li, Wen Neuroscience Program Department of Physiology Michigan State University East LansingMI United States East LansingMI United States Department of Biomedical Engineering Michigan State University East LansingMI United States Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Microbiology Genetics & Immunology Michigan State University East LansingMI United States

Early detection of lung cancer significantly enhances treatment outcomes, yet current screening methods are limited by accessibility, sensitivity, and cost. This study introduces a bioelectronic sensing platform that integrates the highly sensitive locust olfactory system with a flexible dual-sided microelectrode array (MEA), for robust, noninvasive, and label-free detection of volatile lung cancer biomarkers. Using an innovative folding-annealing fabrication technique and PEDOT:PSS surface functionalization, we developed flexible, dual-sided MEAs with high electrode densities of 463, 687, and 766 channels/mm2 across prototypes, maintaining low impedance (within 4 × 104 Ω). These MEAs demonstrated mechanical flexibility and stability, enabling direct insertion into locust brain tissue without mechanical reinforcement and facilitating precise recording of neural activity in the antennal lobe triggered by lung cancer-related volatile organic compounds (VOCs) from low concentration (1 ppm). Advanced dimensionality reduction techniques applied to the electrophysiological recordings identified distinct neural response patterns to each VOC biomarker and the complex "scent" emitted from various cell lines. Using high-dimensional population neuronal response analysis with a leave-one-trial-out approach, the platform achieved a 100 % classification success rate for unknown VOCs. Additionally, varying concentrations (ppm-ppb) of individual VOC biomarkers were detected and classified with an accuracy of 86 %. The system was further tested for its ability to detect and classify human lung cancer cell lines based on the unique "scent" of cultured cells, including two non-small cell lung cancer (NSCLC) and two small cell lung cancer (SCLC) types. Quantitative assessments demonstrated that the platform achieved a classification accuracy of 85 % across these cell lines. These results substantiate the platform's potential for enhancing clinical diagnostics through the accurate ident

关键词： Lung cancer

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DiffEGG: Diffusion-Driven Edge Generation as a Pixel-Annotation-Free Alternative for Instance Annotation

arXiv

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arXiv 2025年

作者： Jo, Sanghyun Lee, Ziseok Lee, Wooyeol Kim, Kyungsu OGQ Seoul Korea Republic of Department of Biomedical Sciences Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of School of Transdisciplinary Innovations Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Achieving precise panoptic segmentation heavily relies on pixel-wise instance annotations, but obtaining such datasets is costly. Unsupervised instance segmentation (UIS) eliminates annotation requirements but struggles with adjacent instance merging and single-instance fragmentation, largely due to the limitations of DINO-based backbones, which lack strong instance separation cues. Weakly-supervised panoptic segmentation (WPS) reduces annotation costs by using sparse labels (e.g., points, boxes), yet these annotations remain expensive and introduce human bias and boundary errors, limiting performance. To address these challenges, we propose DiffEGG (Diffusion-Driven EdGe Generation), a fully annotation-free method that extracts instance-aware features from pretrained diffusion models to generate precise instance edge maps. DiffEGG fundamentally differs from prior UIS and WPS approaches by leveraging pretrained diffusion models as a backbone. Unlike DINO-based UIS methods, diffusion models inherently capture fine-grained, instance-aware features, enabling more precise boundary delineation. DiffEGG further enhances edge connectivity through self-supervised fine-tuning, progressively improving instance separation. For WPS, DiffEGG completely eliminates annotation costs and human bias by operating without any form of manual supervision, addressing the key limitations of prior best methods. Additionally, we introduce RIP, a post-processing technique that fuses DiffEGG’s edge maps with segmentation masks in a task-agnostic manner. This broad applicability allows DiffEGG to be seamlessly integrated into various segmentation frameworks. When applied to UIS, DiffEGG and RIP achieve an average +4.4 AP improvement over prior best UIS methods. When combined with weakly-supervised semantic segmentation (WSS), DiffEGG enables WPS without instance annotations, outperforming prior best point-supervised WPS methods by +1.7 PQ. These results demonstrate that DiffEGG’s edge maps serv

关键词： Semantic Segmentation

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KNU-DTI: KNowledge United Drug-Target Interaction prediction

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computers in Biology and Medicine 2025年 189卷 109927-109927页

作者： Heo, Ryong Lee, Dahyeon Kim, Byung Ju Seo, Sangmin Park, Sanghyun Park, Chihyun Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Gangwon-do Chuncheon-si24341 Korea Republic of Department of Data Science Kangwon National University Korea Republic of UBLBio Corporation Yeongtong-ro 237 Gyeonggi-do Suwon16679 Korea Republic of Department of Computer Science Yonsei University Yonsei-ro 50 Seodaemun-gu Seoul03722 Korea Republic of Department of Computer Science and Engineering Kangwon National University Korea Republic of

Motivation: Accurately predicting drug-target protein interactions (DTI) is a cornerstone of drug discovery, enabling the identification of potential therapeutic compounds. Sequence-based prediction models, despite their simplicity, hold great promise in extracting essential information directly from raw sequences. However, the focus in recent DTI studies has increasingly shifted toward enhancing algorithmic complexity, often at the expense of fully leveraging robust sequence representation learning methods. This shift has led to the underestimation and gradual neglect of methodologies aimed at effectively capturing discriminative features from sequences. Our work seeks to address this oversight by emphasizing the value of well-constructed sequence representation algorithms, demonstrating that even with simple interaction mapping algorithm techniques, accurate DTI models can be achieved. By prioritizing meaningful information extraction over excessive model complexity, we aim to advance the development of practical and generalizable DTI prediction frameworks. Results: We developed the KNowledge Uniting DTI model (KNU-DTI), which retrieves structural information and unites them. Protein structural properties were obtained using structural property sequence (SPS). Extended-connectivity fingerprint (ECFP) was used to estimate the structure-activity relationship in molecules. Including these two features, a total of five latent vectors were derived from protein and molecule via various neural networks and integrated by elemental-wise addition to predict binding interactions or affinity. Using four test concepts to evaluate the model, we show that the model outperforms recently published competitors. Finally, a case study indicated that our model has a competitive edge over existing docking simulations in some cases. © 2025

关键词： Prediction models

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Developing an explainable Deep Neural Network for stress detection using biosignals and human-engineered features

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Biomedical Signal Processing and Control 2025年 109卷

作者： Lee, Hyeokjong Kim, Jaewon Han, Bohyung Park, Sang Min Chang, Jooyoung Department of Biomedical Sciences Seoul National University College of Medicine Seoul03080 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of XAIMED Co. Ltd. Seoul02738 Korea Republic of

Detecting stress is crucial for preventing long-term health issues, and recent research, particularly using the WESAD (WEarable Stress and Affect Detection) dataset, has made significant advances in this area. Deep Neural Networks (DNNs) demonstrate a key advantage by efficiently processing raw signals, unlike traditional machine learning methods that predominantly rely on human-engineered features. In our study, we enhance DNN capabilities by integrating both raw signals and human-engineered features altogether. Our model, augmented with attention mechanisms, significantly improves its performance. This approach yields state-of-the-art results, demonstrating high accuracies across various modalities in the stress classification task: 92.89% in Electrodermal Activity (EDA), 92.16% in Photoplethysmography (PPG), and 96.57% in all modalities. Explaining model decisions in biosignals is challenging compared to the image domain, where images are inherently more intuitive. Many Explainable AI (XAI) techniques, optimized for images, face difficulties when applied to biosignals. To address this, we employ human-engineered features for biosignals, grounded in the ‘Human Comprehensible Space’ concept, making explanations intuitively understandable. Furthermore, a survey conducted to statistically validate the comprehensibility of our XAI methods confirms their effectiveness in enhancing the accessibility of explanations. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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MULTIDATABASE SYSTEMS - AN ADVANCED CONCEPT IN HANDLING DISTRIBUTED DATA

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ADVANCES IN computerS 1991年第C期32卷 149-200页

作者： HURSON, AR BRIGHT, MW Computer Engineering Program Department of Electrical Engineering The Pennsylvania State University University Park Pennsylvania

Multidatabases integrate the data from preexisting, heterogeneous local databases into a distributed environment, and present global users with transparent methods to use the total information in the system. This chapter describes the taxonomy of global information-sharing systems, multidatabase issues, multidatabse design choices, analysis of existing multidatabase systems, and the position of multidatabase systems in the taxonomy. Multidatabase language systems have no constraints on system size, but the tradeoff for achieving this is a multiple-data-source user paradigm and a more complex user interface. Multidatabase issues, such as site autonomy, differences in data representation, and concurrency control are also discussed in the chapter. Site autonomy is a major strength of multidatabases, yet it is also a major constraint on the global system design. Differences in data representation are many and varied, and resolving these differences is a major concern of the global system design. Global updates are a major current restriction in many systems, and the focus of much current research. To make multidatabase systems more useful and efficient, some deficiencies in existing prototypes must be resolved before they can evolve into commercial products, such as global concurrency control, global security, maintenance and administration, and error control and recovery. Multidatabase systems should begin to have a larger impact in the information-processing world as more powerful systems to become generally available in the near future.

关键词： Database Systems

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Three-dimensional computation of light scattering from cells

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 1996年第4期2卷 898-905页

作者： Dunn, A RichardsKortum, R Biomedical Engineering Program Department of Electrical and Computer Engineering University of Technology Austin TX USA

Using the finite-difference time-domain method, three-dimensional scattering patterns are computed for cells containing multiple organelles. The scattering cross section and average cosine of the scattering angle are computed for cells as a function of volume fraction of melanin granules and mitochondria, Results show that small organelles play a significant role in light scattering from cells, and the volume fraction of organelles affects both the total amount of scattered light and the angular distribution of scattered light.

关键词： Light scattering Optical scattering Mie scattering Electromagnetic scattering Cells (biology) Optical refraction Finite difference methods Time domain analysis Biomedical optical imaging High-resolution imaging

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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