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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Noaeen Somayeh Amini Shveta Bhasker Zohreh Ghezelsefli Aisha Ahmed Omid Jafarinezhad Zahra Shakeri Hossein Abad Harvard Business School USA Dalla Lana School of Public Health University of Toronto Canada Department of Psychology University of Toronto Canada Department of Computer Engineering Sharif University of Technology Iran Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto Canada

The integration of Electronic Health Records (EHRs) with Machine Learning (ML) models has become imperative in examining patient outcomes due to the vast amounts of clinical data they provide. However, critical information regarding social and behavioral factors that affect health, such as social isolation, stress, and mental health complexities, is often recorded in unstructured clinical notes, hindering its accessibility. This has resulted in an over-reliance on clinical data in current EHR-based research, potentially leading to disparities in health outcomes. This study aims to evaluate the impact of incorporating patient-specific context from unstructured EHR data on the accuracy and stability of ML algorithms for predicting mortality, using the MIMIC III database. Results from the study confirmed the significance of incorporating patient-specific information into prediction models, leading to a notable improvement in the discriminatory power and robustness of the ML algorithms. Furthermore, the findings underline the importance of considering non-clinical factors related to a patient’s daily life, in addition to clinical factors, when making predictions about patient outcomes. The advent of advanced generative models, such as GPT-4, presents new opportunities for effectively extracting social and behavioral factors from unstructured clinical notes, further enhancing the accuracy and stability of ML algorithms in predicting patient outcomes. The results of our study have significant ramifications for improving ML in clinical decision support and patient outcome predictions, specifically highlighting the potential role of generative models like GPT-4 in advancing ML-based outcome predictions.

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Advancing Problem-Based Learning in Biomedical engineering in the Era of Generative AI

arXiv

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arXiv 2025年

作者： Nnamdi, Micky C. Tamo, J. Ben Shi, Wenqi Wang, May D. Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Health Data Science and Biostatistics Peter O’Donnell Jr School of Public Health UT Southwestern Medical Center DallasTX United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking and real-world knowledge application among students. With biomedical engineering rapidly converging with artificial intelligence (AI), integrating effective AI education into established curricula has become challenging yet increasingly necessary. Recent advancements, including AI’s recognition by the 2024 Nobel Prize, have highlighted the importance of training students comprehensively in biomedical AI. However, effective biomedical AI education faces substantial obstacles, such as diverse student backgrounds, limited personalized mentoring, constrained computational resources, and difficulties in safely scaling hands-on practical experiments due to privacy and ethical concerns associated with biomedical data. To overcome these issues, we conducted a three-year (2021–2023) case study implementing an advanced PBL framework tailored specifically for biomedical AI education, involving 92 undergraduate and 156 graduate students from the joint Biomedical engineering program of Georgia Institute of Technology and Emory University. Our approach emphasizes collaborative, interdisciplinary problem-solving through authentic biomedical AI challenges. The implementation led to measurable improvements in learning outcomes, evidenced by high research productivity (16 student-authored publications), consistently positive peer evaluations, and successful development of innovative computational methods addressing real biomedical challenges. Additionally, we examined the role of generative AI both as a teaching subject and an educational support tool within the PBL framework. Our study presents a practical and scalable roadmap for biomedical engineering departments aiming to integrate robust AI education into their curricula. This strategy not only prepares students effectively for future healthcare innovati

关键词： Contrastive Learning

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Hardware Software Codesign Using an Open-Source Authentication Algorithm

Hardware Software Codesign Using an Open-Source Authenticati...

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Applied Computing (URC), Annual Undergraduate Research Conference on

作者： Sadoon L. Albader Haseeb Ur Rehman Ashraf A. Zaher Computer and Information Center Public Authority for Applied Education & Training Adailiya Kuwait Faculty of Engineering University of Ottawa Ottawa Canada Department of Electrical Engineering German International University Cairo Egypt

ISBN: (纸本)9798350346817

This paper explores the use of hardware/software codesign to implement algorithms for open-source message authentication code while investigating the required tools, the problems to be overcome, and satisfying the industry standards for synthesizing the hardware/software codesign. Technical issues related to using the DE10-Nano were carefully analysed, which include the very poor documentation, and the needed specific Linux kernel patches that only work with certain versions of the Linux kernel. This research builds the fundamental blocks for using the DE10-Nano for hardware/software codesign. The results are expected to pave the way to focus on promoting the current trends of hardware/software codesigns and to encourage the hardware manufacturers to offer more help for programmers and engineers.

关键词： Industries Linux Software algorithms Documentation Market research Hardware Software

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The Effect of Epsilon-Near-Zero (ENZ) Modes on the Casimir Interaction between Ultrathin Films

arXiv

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arXiv 2022年

作者： Gong, Tao Liberal, Iñigo Spreng, Benjamin Camacho, Miguel Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California Davis United States Department of Materials Science and Engineering University of California Davis United States Institute of Smart Cities Public University of Navarre Spain Department of Electronics and Electromagnetism University of Seville Spain Department of Electrical and Systems Engineering University of Pennsylvania United States

Vacuum fluctuation-induced interactions between macroscopic metallic objects result in an attractive force between them, a phenomenon known as the Casimir effect. This force is the result of both plasmonic and photonic modes. For very thin films, field penetration through the films will modify the allowed modes. Here, we investigate the Casimir interaction between two ultrathin films from the perspective of the force distribution over real frequencies for the first time and find pronounced repulsive contributions to the force due to the highly confined and nearly dispersion-free epsilon-near-zero (ENZ) modes that only exist in ultrathin films. These contributions are found to persistently occur around the ENZ frequency of the film and are irrespective of the inter-film separation. We further associate the ENZ modes with a striking thickness dependence in the averaged force density for conductive thin films, a metric signifying a thin-film's acceleration due to Casimir effect. Our results shed light on the role of the unique vacuum fluctuation modes existing in ultrathin ENZ materials, which may offer significant potential for engineering the motion of objects in nanomechanical systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Ultrathin films

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

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Journal of King Saud University - computer and Information Sciences 2024年第2期36卷

作者： Yousefi, Shamim Najjar-Ghabel, Samad Danehchin, Ramin Band, Shahab S. Hsu, Chung-Chian Mosavi, Amir Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning data. This paper proposes a hybrid mechanism for automated melanoma detection on dermoscopic images based on Discrete Cosine Transform features and metadata. It is composed of three steps. First, extra information/artifacts are deleted;the remaining pixels are standardized for accurate processing. Second, the reliability of the mechanism is improved by the Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of features is reduced by Locality Sensitive Discriminant Analysis. The third step divides the images into learning and test ones to create image-based models using learning data. Finally, the best model is selected based on test data and improved by a metadata-based model. Simulation results show that the decision tree provides the most practical image-based model by improving accuracy and sensitivity. Besides, the comparison results demonstrate that our model improves the F-Value to detect melanoma superior to other approaches. © 2024 The Author(s)

关键词： Artificial intelligence Big data Data science Dermoscopic image Discrete cosine transform features Machine learning Melanoma detection Metadata

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Gene Name Recognition in Gene Pathway Figures Using Siamese Networks

Gene Name Recognition in Gene Pathway Figures Using Siamese ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Micheal Olaolu Arowolo Muhammad Azam Fei He Mihail Popescu Dong Xu Department of Public Health Sciences Health Informatics Program Xavier University of Louisiana New Orleans Louisiana USA Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia Missouri USA Department of Health Management and Informatics University of Missouri Columbia Missouri USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://***/MuhammadAzam636/Siamese-Network.

关键词： Image recognition Accuracy Sensitivity Text recognition Biological system modeling Optical character recognition Neural networks Libraries Object recognition Biological information theory

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Detecting Multimedia Generated by Large AI Models: A Survey

arXiv

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arXiv 2024年

作者： Lin, Li Gupta, Neeraj Zhang, Yue Ren, Hainan Liu, Chun-Hao Ding, Feng Wang, Xin Li, Xin Verdoliva, Luisa Hu, Shu Department of Computer and Information Technology Purdue University IN46202 United States School of Software Nanchang University Jiangxi Nanchang330031 China Amazon Prime Video SunnyvaleCA94089 United States Department of Epidemiology and Biostatistics School of Public Health Department of Computer Science College of Nanotechnology Science and Engineering both at University at Albany SUNY AlbanyNY12222 United States Department of Electrical Engineering and Information Technology University Federico II of Naples Naples80125 Italy

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, and online detection tools to provide a valuable resource for researchers and practitioners in this field. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://***/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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electrically switchable Casimir forces using transparent conductive oxides

arXiv

引用

arXiv 2022年

作者： Gong, Tao Spreng, Benjamin Camacho, Miguel Liberal, Inigo Engheta, Nader Munday, Jeremy N. Department of Electrical and Computer Engineering University of California Davis United States Department of Materials Science and Engineering University of California Davis United States Department of Electronics and Electromagnetism University of Seville Spain Institute of Smart Cities Public University of Navarre Spain Department of Electrical and Systems Engineering University of Pennsylvania United States

Casimir forces between charge-neutral bodies originate from quantum vacuum fluctuations of electromagnetic fields, which exhibit a critical dependence on material’s electromagnetic properties. Over the years, in-situ modulation of material’s optical properties has been enabled through various means and has been widely exploited in a plethora of applications such as electro-optical modulation, transient color generation, bio- or chemical sensing, etc. Yet Casimir force modulation has been hindered by difficulty in achieving high modulation signals due to the broadband nature of the Casimir interaction. Here we propose and investigate two configurations that allow for in-situ modulation of Casimir forces through electrical gating of a metal-insulator-semiconductor (MIS) junction comprised of transparent conductive oxide (TCO) materials. By switching the gate voltage on and off, a force modulation of > 400 pN is predicted due to substantive charge carrier accumulation in the TCO layer, which can be easily measured using state-of-the-art force measurement techniques in an atomic force microscope (AFM). We further examine the influence of the oxide layer thickness on the force modulation, suggesting the importance of the fine control of the oxide layer deposition. Our work provides a promising pathway for modulating the Casimir effect in-situ with experimentally measurable force contrast. Copyright © 2022, The Authors. All rights reserved.

关键词： Modulation

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RELD: Regularization by Latent Diffusion Models for Image Restoration

arXiv

引用

arXiv 2025年

作者： Cascarano, Pasquale Stacchio, Lorenzo Sebastiani, Andrea Benfenati, Alessandro Kamilov, Ulugbek S. Marfia, Gustavo Department of the Arts University of Bologna Bologna40123 Italy Department of Political Sciences Communication and International Relations University of Macerata Macerata62100 Italy Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Modena41125 Italy Department of Environmental and Science Policy University of Milan Milan20133 Italy Department of Computer Science & Engineering Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO63130 United States

In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting, exploiting its regularization properties. This approach, under appropriate conditions that can be easily met in various imaging applications, allows for reduced computational cost while achieving high-quality results. The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images, for image denoising, deblurring, and super-resolution tasks. The numerical experiments show that RELD is competitive with other state-of-the-art methods, particularly achieving remarkable results when evaluated using perceptual quality metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Yue, Xianghu Chen, Yiming Zhang, Xueyi Gao, Xiaoxue Feng, Mengling Lao, Mingrui Zhuang, Huiping Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China Institute for Infocomm Research A*STAR 138632 Singapore Saw Swee Hock School of Public Health National University of Singapore 117583 Singapore Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou510006 China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forgetting. While existing studies primarily focus on the integration and utilization of multi-modal information for MMCIL, a critical challenge remains: the issue of missing modalities during incremental learning phases. This oversight can exacerbate severe forgetting and significantly impair model performance. To bridge this gap, we propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios. Concretely, we devise modality-specific prompts to compensate for missing information, facilitating the model to maintain a holistic representation of the data. On this foundation, we reformulate the MMCIL problem into a Recursive Least-Squares task, delivering an analytical linear solution. Building upon these, PAL not only alleviates the inherent under-fitting limitation in analytic learning but also preserves the holistic representation of missing-modality data, achieving superior performance with less forgetting across various multi-modal incremental scenarios. Extensive experiments demonstrate that PAL significantly outperforms competitive methods across various datasets, including UPMC-Food101 and N24News, showcasing its robustness towards modality absence and its anti-forgetting ability to maintain high incremental accuracy. © 2025, CC BY.

关键词： Contrastive Learning

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