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arXiv 2021年

作者： Upendra, Roshan Reddy Wentz, Brian Jamison Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Bioengineering Graduate Program University of Kansas LawrenceKS United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. © 2021, CC BY.

关键词： Image registration

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Efficient sampling of polycyclic aromatic compounds for free energy predictions through active learning

Energy and AI

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Energy and AI 2025年 21卷

作者： Mohammed I. Radaideh Matt Raymond Paolo Elvati Jacob C. Saldinger Majdi I. Radaideh Angela Violi Department of Mechanical Engineering University of Michigan Ann Arbor MI 48109-2125 United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2125 United States Department of Chemical Engineering University of Michigan Ann Arbor MI 48109-2125 United States Department of Nuclear Engineering and Radiological Sciences University of Michigan Ann Arbor MI 48109-1500 United States Biophysics Program University of Michigan Ann Arbor MI 48109-1500 United States

The physical growth of Polycyclic Aromatic Compounds (PACs) to soot particles plays a significant role in understanding the chemistry of soot formation. Insights into the process can be gained from PACs’ free energy of dimerization landscape. However, because the infeasibly large space of possible PAC dimers cannot be exhaustively simulated, researchers must train machine learning models on a subset of data to impute the rest. To this end, we propose and assess an active learning approach to discovering the optimal PACs for training a machine learning model to predict PACs’ association and dissociation free energies. The comparison between active learning and random sampling showed that active learning has faster loss convergence, requiring fewer training samples to reach the same level of accuracy. The trained model accurately modeled unseen PACs and exhibited robustness against changes in the sampling space used to train the model. More broadly, this work shows how active learning can optimize the design and improve the understanding of more expensive models in specific domains.

关键词： Free energy Polycyclic aromatic compounds (PACs) Active learning Gaussian process regression Representer theorem SHAP

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Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

arXiv

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arXiv 2025年

作者： Murthy, Akshay A. Bal, Mustafa Bedzyk, Michael J. Cansizoglu, Hilal Chan, Randall K. Chandrasekhar, Venkat Crisa, Francesco Datta, Amlan Deng, Yanpei Matute Diaz, Celeo D. Dravid, Vinayak P. Garcia-Wetten, David A. Garattoni, Sabrina Ghimire, Sunil Goronzy, Dominic P. de Graaf, Sebastian E. Haeuser, Sam Hersam, Mark C. Isheim, Dieter Joshi, Kamal Kim, Richard Kolachina, Saagar Kopas, Cameron J. Kramer, Matthew J. Lachman, Ella O. Lee, Jaeyel Lim, Peter G. Lunin, Andrei Mah, William Marshall, Jayss Mutus, Josh Y. Oh, Jin-Su Pappas, David P. Park, Joong-Mok Prozorov, Ruslan dos Reis, Roberto Seidman, David N. Sung, Zuhawn Tanatar, Makariy Walker, Mitchell J. Wang, Jigang Wu, Haotian Zhou, Lin Zhu, Shaojiang Grassellino, Anna Romanenko, Alexander BataviaIL60510 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Rigetti Computing BerkeleyCA94710 United States Ames National Laboratory AmesIA50011 United States Department of Physics & Astronomy Iowa State University AmesIA50011 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States The NUANCE Center Northwestern University EvanstonIL60208 United States International Institute of Nanotechnology Northwestern University EvanstonIL60208 United States National Physical Laboratory TeddingtonTW11 0LW United Kingdom Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Applied Physics Graduate Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Iowa State University AmesIA50011 United States

The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information Science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values. © 2025, CC BY.

关键词： Qubits

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Wind energy electret-based electrostatic harvester 21

Wind energy electret-based electrostatic harvester

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21st European Conference on Power Electronics and Applications, EPE 2019 ECCE Europe

作者： Coronado, Daniel Augusto Castellanos Romano, Emanuele Dallago, Enrico Juan de Castellanos University Foundation School of Engineering and Basic Sciences Research Group in Science Innovation and Technology Electronic Engineering Program Tunja Colombia University of Pavia Power Electronics Laboratory Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9789075815313

In this paper an electret-based electrostatic induction wind harvester is presented. The induction electrostatic field is produced with an electret. It is described how the electret and the harvester prototype have been obtained. A complete electrical characterization of the harvester and experimental results are then reported. © 2019 EPE Association.

关键词： Wind power

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Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes

arXiv

引用

arXiv 2023年

作者： Mahbub, Maria Goethert, Ian Danciu, Ioana Knight, Kathryn Srinivasan, Sudarshan Tamang, Suzanne Rozenberg-Ben-Dror, Karine Solares, Hugo Martins, Susana Trafton, Jodie Begoli, Edmon Peterson, Gregory D. Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States Information Technology Services Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Biomedical Informatics Vanderbilt University NashvilleTN United States Program Evaluation and Resource Center Office of Mental Health and Suicide Prevention Department of Veterans Affairs United States Department of Medicine Stanford University School of Medicine StanfordCA United States Veterans Affairs Great Lakes Health Care System WestchesterIL United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville KnoxvilleTN United States

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. © 2023, CC BY-NC-ND.

关键词： Data warehouses

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Gmr Biosensing with Magnetic Nanowires as Labels for the Detection of Osteosarcoma Cells

SSRN

引用

SSRN 2022年

作者： Su, Diqing Um, Joseph Moreno, Julian Nemati, Zohreh Srinivasan, Karthik Chen, Junyang Kouhpanji, M. Reza Zamani Shore, Daniel Wu, Kai Kosel, Jürgen Modiano, Jaime F. Franklin, Rhonda Wang, Jian-Ping Stadler, Bethanie Department of Chemical Engineering and Materials Science University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Electrical Engineering Department King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia Animal Cancer Care and Research Program University of Minnesota St. PaulMN55108 United States Department of Veterinary Clinical Sciences College of Veterinary Medicine University of Minnesota St. PaulMN55108 United States Masonic Cancer Center University of Minnesota MinneapolisMN55455 United States Center for Immunology University of Minnesota MinneapolisMN55455 United States Stem Cell Institute University of Minnesota MinneapolisMN55455 United States Institute for Engineering in Medicine University of Minnesota MinneapolisMN55455 United States

Magnetic nanowires (MNWs) were explored as potential magnetic tags for cell detection with giant magnetoresistance (GMR) biosensors based on a handheld system. Due to size, shape anisotropy and higher moment materials, the signal detected from a single MNW was 2500 times larger than that from a single magnetic iron oxide nanobead, which is important for ultra-low concentration cell detection. A model was used to determine how the MNW orientation with respect to the GMR sensor impacts detection performance, and the results aligned well with the experimental results. As a proof of concept OSCA-8 cells tagged with Ni MNWs were also detected using the same handheld system. The limit of detection (LOD) in aqueous solution appeared to be 133 cells, and single-cell detection can be realized if the cell is in direct contact with the sensor surface. Since MNWs are already employed in magnetic separation of cells, directly using MNWs as tags in cell detection eliminates the need of additional functionalization with other labels. This largely simplifies the detection process and reduces the risk of contamination during sample preparation. © 2022, The Authors. All rights reserved.

关键词： Biosensors

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Numerical solution and linear stability analysis of fractional nonlinear Schrödinger equation (2+1) dimensions based on Newton-Raphson method

引用

AIP Conference Proceedings 2024年第1期3082卷

作者： Harish A. Mardi Marwan Ramli Nasaruddin Nasaruddin Said Munzir Muhammad Ikhwan Mathematics Graduate Program Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Electrical and Computer Engineering Department Universitas Syiah Kuala Banda Aceh 23111 Indonesia.

The Fractional Nonlinear Schrödinger Equation (FNLS) is an equation that is generally used to describe the interaction of boson particles in the trap potential. This paper discusses the stability of the stationary solution of the FNLS (2+1) dimensional equation using the Newton-Raphson method as a method for determining the stationary solution. The stability of the stationary solution of the FNLS is determined by analyzing the distribution of real-part and imaginary-part of eigenvalues listed on the complex plane. The results obtained in this study are found that the solution to the 1-dimensional FNLS is a stable system. This solution is obtained by analyzing the distribution of eigenvalues in the complex plane, where in the solution to the 1-dimensional FNLS there are only imaginary-part eigenvalues without any real-part eigenvalues. Unlike the case with the solution of the 2-dimensional FNLS equation, the eigenvalues obtained have a real-part of eigenvalues. These eigenvalues represent that the solution to the 2-dimensional FNLS equation is an unstable system. It is an indicator that the soliton which is the solution of the FNLS equation for the 2-dimensional case will be damaged for a certain time.

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Multi-label learning embedding approach based on multi-temporal spectral signature for hyperspectral images classification

Multi-label learning embedding approach based on multi-tempo...

引用

International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Selim Hemissi Abdullah Alhumaidi Alotaibi Sattam Alotaibi RIADI laboratory University of Manouba Tunisia Taif University KSA Computer Science program Turabah univeristy college Taif Saudi Arabia Department of Electrical Engineering Taif University Saudi Arabia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175140

Currently, Hyperspectral signal processing is a crucial area of research. Respectively, various techniques have been investigated to apprehend features combination and multi-label classification issues. Indeed, significant consideration has been given to approaches supporting the use of a single type of features. Moreover, few efforts have been dedicated to model the multi-label aspect of hyperspectral pixels and to integrate simultaneously divergent kinds of interdependent features. In this paper, we propose a novel embedding multi-label learning approach integrating complementary weighted features. The proposed framework combines the singular statistical characteristics of each feature to accomplish a physically meaningful cooperative low-dimensional representation of extracted features. This will grant, in one hand, the refinement of classification process and the propagation of narrow class information to unlabeled sample, in the other hand, when only partial labeling knowledge is available. This paper makes the following contributions: (i) the extraction of multi-view features based on the 3D model of the spectral signature and (ii) an embedding multi-label based approach by better tackling unbalanced and dimensionality issues. A set of complementary spatial/spectral features is extracted in the experimental section from a series of hyperspectral images. The obtained results reflect the efficiency of the proposed classification schema while maintaining a reasonable computational complexity.

关键词： Feature extraction Three-dimensional displays Solid modeling Hyperspectral imaging Computational modeling Shape Data models

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Plug-and-play stability for intracortical brain-computer interfaces: a one-year demonstration of seamless brain-to-text communication 23

Plug-and-play stability for intracortical brain-computer int...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett Department of Computer Science Stanford University Department of Neurosurgery Stanford University Howard Hughes Medical Institute at Stanford University Department of Neuroscience Stanford University School of Engineering and Carney Institute for Brain Science Brown University and VA RR & D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System and Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Bio-X Program Stanford University and Department of Neurobiology Stanford University and Department of Bioengineering Stanford University and Wu Tsai Neurosciences Institute Stanford University and Howard Hughes Medical Institute at Stanford University and Department of Electrical Engineering Stanford University Department of Neurosurgery Stanford University and Wu Tsai Neurosciences Institute Stanford University

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

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Imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron Microscopy

arXiv

引用

arXiv 2022年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Fielitz, Alyssa J. Ha, Don-Hyung Perera, Sanjaya D. Baggari, Ismail El Robinson, Richard D. Fessler, Jeffrey A. Ophus, Colin Rozeveld, Steve Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Dow Chemical Co. MidlandMI United States Department of Material Science and Engineering Cornell University IthacaNY United States School of Integrative Engineering Chung-Ang University Seoul Korea Republic of Department of Physics Cornell University IthacaNY United States The Rowland Institute at Harvard CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic- resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. © 2022, CC BY.

关键词： Electron microscopy

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