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No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine

AI and ethics 2022年第4期2卷 635-643页

作者： Tayo Obafemi-Ajayi Andy Perkins Bindu Nanduri Donald C Wunsch Ii James A Foster Joan Peckham Engineering Program Missouri State University Springfield MO USA. Department of Computer Science and Engineering Mississippi State University Starkville MS USA. Department of Comparative Biomedical Sciences College of Veterinary Medicine Mississippi State University Starkville MS USA. Electrical & Computer Engineering Department Missouri University of Science and Technology Rolla MO USA. Biological Sciences Department University of Idaho Moscow ID USA. Computer Science & Statistics Department University of Rhode Island Kingston RI USA.

Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.

关键词： Artificial intelligence Data science Ethics Precision medicine

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Characterizing biomolecular structure features through an innovative elliptical dichroism spectrometry for cancer detection

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Heliyon 2024年第19期10卷 e38399页

作者： Asad, Yusuf Jangili, Keerthi Priya Arshad, Amara Elma, Maliha Rasuleva, Komila Akinlalu, Alfred Gao, Tommy Tida, Umamaheswara Rao Xia, Wenjie Sun, Dali Biomedical Engineering Program North Dakota State University 1401 Centennial Blvd Engineering Administration Room 203 Fargo 58102 ND United States Materials and Nanotechnology North Dakota State University Fargo 58108 ND United States Department of Aerospace Engineering Iowa State University Ames 50011 IA United States Department of Electrical and Computer Engineering North Dakota State University 1411 Centennial Blvd. 101S Fargo 58102 ND United States Department of Electrical & Computer Engineering University of Denver 2155 E. Wesley Ave. Denver 80210 CO United States Knoebel Institute for Healthy Aging University of Denver United States

This research introduces a novel method for evaluating the structural features of biomolecules, utilizing our innovative Elliptical Dichroism (ED) spectrometer specifically designed for stereochemical analysis. By integrating ED spectrometry with autocorrelation (AC) analysis, we investigate the conformational characteristics of biological molecules such as amino acids, proteins, and extracellular vesicles (EVs) induced by elliptically polarized UV absorption. Our streamlined approach offers a cost-effective and portable solution with minimal sample consumption and supports multiple working modes to efficiently characterize biomolecular structures. The insight from this new approach demonstrates potential applications in using biomolecular characterization for cancer detection. © 2024

关键词： Autocorrelation function Density-functional theory Elliptical dichroism Molecular dynamics simulation Spectrometer Stereochemical analysis Tumor differentiation

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Vacuum Ultraviolet Light-Generating Metalens

Vacuum Ultraviolet Light-Generating Metalens

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Tseng, Ming Lun Semmlinger, Michael Zhang, Ming Arndt, Catherine Huang, Tzu-Ting Yang, Jian Kuo, Hsin Yu Su, Vin-Cent Chen, Mu Ku Chu, Cheng Hung Cerjan, Benjamin Tsai, Din Ping Nordlander, Peter Halas, Naomi J. Institute of Electronics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Research Center for Applied Sciences Academia Sinica Taipei115 Taiwan Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Laboratory for Nanophotonics Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering National United University Miaoli36003 Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong Department of Chemistry Rice University HoustonTX77005 United States

Vacuum ultraviolet (VUV) light is of great importance to many applications such as photochemistry and nanolithography. In this talk, a nonlinear metalens composed of ZnO nanoantennas for VUV generation and focusing wi...

ISBN: (纸本)9798350350012

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Vacuum Ultraviolet Light-Generating Metalens

Vacuum Ultraviolet Light-Generating Metalens

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2022 International Conference on Optics-Photonics Design and Fabrication, ODF 2022

关键词： Nanoclay

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ECG heart-beat classification using multimodal image fusion

arXiv

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arXiv 2021年

作者： Ahmad, Zeeshan Tabassum, Anika Guan, Ling Khan, Naimul Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Master of Data Science program Ryerson University TorontoON Canada

In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end-to-end deep learning. We perform experiments on PhysioNet's MIT-BIH dataset for five different arrhythmias in accordance with the AAMI EC57 standard and on PTB diagnostics dataset for myocardial infarction (MI) classification. We achieved an state-of-an-art results in terms of prediction accuracy, precision and recall. © 2021, CC BY.

关键词： Extraction

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Analysis of temperature in an air-cooled combustion motorcycle engine using fiber Bragg grating sensors

Analysis of temperature in an air-cooled combustion motorcyc...

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Optics and Photonics Conference (SBFoton IOPC), SBFoton International

作者： Henrique Ferazza Rodrigo Fiorin Valmir de Oliveira Ilda Abe Hypolito José Kalinowski Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná (UTFPR) Curitiba Brazil Universidade Federal Fluminense RJ Brazil

A fiber Bragg grating sensor is used to monitor the temperature of an air-cooled combustion motorcycle engine in this study. The sensor is attached to one of the aluminum cylinders fins of the motorcycle to characterize the heat transfer process. We present the results of the heating and cooling curves during tests with the motorcycle at rest and in motion. A better understanding of the air-cooling process can lead to the optimization of designs in this area.

关键词： Temperature sensors Temperature measurement Heating systems Motorcycles Optics Combustion Engines

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A 3D GAN Architecture for Volumetric Synthetic Aperture Sonar

A 3D GAN Architecture for Volumetric Synthetic Aperture Sona...

引用

OCEANS

作者： Greg D. Vetaw Albert Reed Daniel C. Brown Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University

ISBN: (纸本)9781665427883

Synthetic aperture sonar (SAS) is used extensively in underwater imaging for visualizing the seafloor and objects present on it. However, processing SAS images can be time-consuming and tedious, with machine learning techniques being ineffective due to the lack of available data. In particular, automated target recognition (ATR) with 3D SAS data for machine learning is challenging in many ways due to the complexity with working with 3D volumetric data. Recently, researchers have introduced generative adversarial networks (GANs) to help perform 2D SAS image generation for data augmentation. Following this line of work in this paper, we introduce a 3D-GAN architecture to generate photorealistic 3D SAS data which matches the fidelity of real data. In particular, we discuss novel latent space sampling and normalization to help 3D GANs overcome mode collapse for generating volumetric SAS information. Experimental results are shown on real 3D SAS data, showing the potential of using 3D GANs for dataset augmentation in the future.

关键词： Three-dimensional displays Target recognition Image synthesis Sea floor Imaging Data visualization Machine learning

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Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

TechRxiv

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TechRxiv 2023年

作者： Petrenko, Sasha Hier, Daniel B. Obafemi-Ajayi, Tayo Bone, Mary Timpson, Erik J. Speight, Michael Marsh, William E. Wunsch, Donald C. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Department of Neurology and Rehabilitation University of Illinois at Chicago ChicagoIL United States University of Southeastern Norway Kongsberg Norway Engineering Program Missouri State University SpringfieldMO United States Honeywell Federal Manufacturing & Technologies Kansas CityMO United States

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multicategorical symbolic data. This article proposes Symbolic Tree Adaptive Resonance Theory (START), with additional supervised, Dual-Vigilance (DV-START), and Distributed Dual-Vigilance (DDV-START) formulations, for the clustering of multicategorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot-Marie-Tooth disease using genomic, phenotypic, and proteomic data. © 2023, CC BY.

关键词： Ontology

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Structural tuning magnetism and topology in a magnetic topological insulator

arXiv

引用

arXiv 2023年

作者： Eckberg, Christopher Qiu, Gang Qu, Tao Kwon, Sohee Liu, Yuhang Tai, Lixuan Graf, David Chong, Su Kong Zhang, Peng Wong, Kin L. Lake, Roger K. Neupane, Mahesh R. Wang, Kang L. Fibertek Inc. HerndonVA20171 United States DEVCOM Army Research Laboratory AdelphiMD20783 United States DEVCOM Army Research Laboratory Playa Vista CA90094 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California RiversideCA92521 United States National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Materials Science and Engineering Program University of California RiversideCA92521 United States

To date, the most widely-studied quantum anomalous Hall insulator (QAHI) platform is achieved by dilute doping of magnetic ions into thin films of the alloyed tetradymite topological insulator (TI) (Bi1−xSbx)2Te3 (BST) [1–4]. In these films, long-range magnetic ordering of the transition metal substituants opens an exchange gap ∆ in the topological surface states, stabilizing spin-polarized, dissipationless edge channels with a nonzero Chern number C. The long-range ordering of the spatially separated magnetic ions is itself mediated by electronic states in the host TI, leading to a sophisticated feedback between magnetic and electronic properties. Here we present a study of the electronic and magnetic response of a BST-based QAHI system to structural tuning via hydrostatic pressure. We identify a systematic closure of the topological gap under compressive strain accompanied by a simultaneous enhancement in the magnetic ordering strength. Combining these experimental results with first-principle calculations we identify structural deformation as a strong tuning parameter to traverse a rich topological phase space and modify magnetism in the magnetically doped BST system. © 2023, CC BY-NC-ND.

关键词： Topology

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

arXiv

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arXiv 2024年

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Statistics Program King Abdullah University of Science and Technology Saudi Arabia Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics The University of Houston HoustonTX United States

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the

关键词： Statistics

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