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arXiv 2024年

作者： Surace, Federica Maria Lerose, Alessio Katz, Or Bennewitz, Elizabeth R. Schuckert, Alexander Luo, De De, Arinjoy Ware, Brayden Morong, William Collins, Kate Monroe, Christopher Davoudi, Zohreh Gorshkov, Alexey V. Department of Physics Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States Rudolf Peierls Centre for Theoretical Physics Clarendon Laboratory OxfordOX1 3PU United Kingdom Institute for Theoretical Physics KU Leuven Celestijnenlaan 200D Leuven3001 Belgium Duke Quantum Center Departments of Physics and Electrical and Computer Engineering Duke University DurhamNC27708 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States

Confinement prohibits isolation of color charges, e.g., quarks, in nature via a process called string breaking: the separation of two charges results in an increase in the energy of a color flux, visualized as a string, connecting those charges. Eventually, creating additional charges is energetically favored, hence breaking the string. Such a phenomenon can be probed in simpler models, including quantum spin chains, enabling enhanced understanding of string-breaking dynamics. A challenging task is to understand how string breaking occurs as time elapses, in an out-of-equilibrium setting. This work establishes the phenomenology of dynamical string breaking induced by a gradual increase of string tension over time. It, thus, goes beyond instantaneous quench processes and enables tracking the real-time evolution of strings in a more controlled setting. We focus on domain-wall confinement in a family of quantum Ising chains. Our results indicate that, for sufficiently short strings and slow evolution, string breaking can be described by the transition dynamics of a two-state quantum system akin to a Landau-Zener process. For longer strings, a more intricate spatiotemporal pattern emerges: the string breaks by forming a superposition of bubbles (domains of flipped spins of varying sizes), which involve highly excited states. We finally demonstrate that string breaking driven only by quantum fluctuations can be realized in the presence of sufficiently long-ranged interactions. This work holds immediate relevance for studying string breaking in quantum-simulation experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Excited states

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Dynamic optical contrast imaging for real-time delineation of tumor resection margins using head and neck cancer as a model

arXiv

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arXiv 2022年

作者： Hu, Yong Huang, Shan Han, Albert Y. Moon, Seong Krane, Jeffrey F. Stafsudd, Oscar Grundfest, Warren St. John, Maie A. Los AngelesCA United States Department of Bioengineering UCLA Los AngelesCA United States Department of Materials Science and Engineering UCLA Los AngelesCA United States UCLA Head and Neck Cancer Program UCLA Medical Center Los AngelesCA United States Jonsson Comprehensive Cancer Center UCLA Medical Center Los AngelesCA United States Department of Pathology & Laboratory Medicine UCLA Medical Center Los AngelesCA United States Department of Electrical and Computer Engineering UCLA Los AngelesCA United States

Complete surgical resection of the tumor for Head and neck squamous cell carcinoma (HNSCC) remains challenging, given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. To address the clinical challenges, we introduce a widefield, label-free imaging tool that can assist surgeons delineate tumor margins real-time. We assume that autofluorescence lifetime is a natural indicator of the health level of tissues, and ratio-metric measurement of the emission-decay state to the emission-peak state of excited fluorophores will enable rapid lifetime mapping of tissues. Here, we describe the principle, instrumentation, characterization of the imager and the intraoperative imaging of resected tissues from 13 patients undergoing head and neck cancer resection. 20 x 20 mm2 imaging takes 2 second/frame with a working distance of 50 mm, and characterization shows that the spatial resolution reached 70 μm and the least distinguishable fluorescence lifetime difference is 0.14 ns. Tissue imaging and Hematoxylin-Eosin stain slides comparison reveals its capability of delineating cancerous boundaries with submillimeter accuracy and a sensitivity of 91.86% and specificity of 84.38%. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Accelerating adoption of species-agnostic plant sensors for precision farming

Nature Reviews Electrical Engineering

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Nature Reviews electrical engineering 2025年 58-70页

作者： Abdullah Bukhamsin Khaled Nabil Salama Jürgen Kosel Ikram Blilou Bioengineering Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Center of Excellence — Sustainable Food Security (SFS) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Sensor Systems Division Silicon Austria Labs Villach Austria Plant Science Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Precision farming is an optimized management farming scheme that seeks to link the real-time needs of crops with the nutrients to be administered. Sensing platforms that can monitor the physiological status of crops in situ are key to enabling timely and localized interventions. However, the underdevelopment of plant sensing strategies limits the potential of precision farming. In this Review, we discuss the challenges and advancements in phyto-monitoring, focusing on strategies that are applicable to a wide range of plant species and suitable for field deployment. We explore species-agnostic sensors, including optical and electrochemical sensors, whose operation is based on principles that are widely applicable to all plant species. These platforms enable real-time monitoring of the physiological state of crops by assessing key biomarkers, such as plant hormones, and metabolites such as salicylic acid and reactive oxygen species. Evaluating these systems, we conclude that an integrative sensing approach is necessary to compensate for the limitations of the individual methods and can provide a holistic view of crop health. Cost-effective species-agnostic sensors are thus needed to provide information that can be used to minimize the resource footprint of farming and meet the growing global demand.

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Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

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Light(science & Applications) 2021年第9期10卷 1797-1808页

作者： Neha Goswami Yuchen R.He Yu-Heng Deng Chamteut Oh Nahil Sobh Enrique Valera Rashid Bashir Nahed Ismail Hyunjoon Kong Thanh H.Nguyen Catherine Best-Popescu Gabriel Popescu Department of Bioengineering University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Beckman Institute of Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA Department of Civil and Environmental Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA NCSA Center for Artificial Intelligence Innovation University of Illinois at Urbana-ChampaignUrbanaIL61801USA Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois61801USA Biomedical Research Center Carle Foundation Hospital509W University Ave.UrbanaIllinois61801USA Carle Illinois College of Medicine 807 South Wright St.UrbanaIllinois61801USA Mayo-Illinois Alliance for Technology Based Healthcare UrbanaIllinois61801USA Department of Pathology College of MedicineUniversity of Illinois at ChicagoChicagoILUSA

Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future *** propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and *** ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length *** these data with fluorescence images for ground truth,we trained semantic segmentation models based on U-Net,a particular type of convolutional neural *** trained network was applied to classify the viruses from the interferometric images only,containing simultaneously SARS-CoV-2,H1N1(influenza-A virus),HAdV(adenovirus),and ZIKV(Zika virus).Remarkably,due to the nanoscale sensitivity in the input data,the neural network was able to identify SARS-CoV-2 *** other viruses with 96%*** inference time for each image is 60 ms,on a common graphic-processing *** approach of directly imaging unlabeled viral particles may provide an extremely fast test,of less than a minute per *** the imaging instrument operates on regular glass slides,we envision this method as potentially testing on patient breath *** necessary high throughput can be achieved by translating concepts from digital pathology,where a microscope can scan hundreds of slides automatically.

关键词： testing neural hundreds

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cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America. Department of Bioengineering Rice University Houston Texas United States of America. Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America. Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America. Biophysics Graduate Program University of California Berkeley California United States of America. Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America. Department of Electrical and Computer Engineering Rice University Houston Texas United States of America.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells

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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

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Nature biomedical engineering 2025年第2期9卷 283页

作者： Hamidreza Abbaspourazad Eray Erturk Bijan Pesaran Maryam M Shanechi Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. Departments of Neurosurgery Neuroscience and Bioengineering University of Pennsylvania Philadelphia PA USA. Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. shanechi@usc.edu. Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA USA. shanechi@usc.edu.

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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...

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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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Quantum Gates on Individually-Addressed Atomic Qubits Subject to Noisy Transverse Motion

arXiv

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arXiv 2020年

作者： Cetina, M. Egan, L.N. Noel, C.A. Goldman, M.L. Risinger, A.R. Zhu, D. Biswas, D. Monroe, C. Joint Quantum Institute Center for Quantum Information and Computer Science Departments of Physics and Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

Individual trapped atomic qubits represent one of the most promising technologies to scale quantum computers, owing to their negligible idle errors and the ability to implement a full set of reconfigurable gate operations via focused optical fields. However, the fidelity of quantum gate operations can be limited by weak confinement of the atoms transverse to the laser. We present measurements of this effect by performing individually-addressed entangling gates in chains of up to 25 trapped atomic ions that are weakly confined along the chain axis. We present a model that accurately describes the observed decoherence from the residual heating of the ions caused by noisy electric fields. We propose to suppress these effects through the use of ancilla ions interspersed in the chain to sympathetically cool the qubit ions throughout a quantum circuit. Copyright © 2020, The Authors. All rights reserved.

关键词： Logic gates

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CNN-based cardiac motion extraction to generate deformable geometric left ventricle myocardial models from Cine MRI

arXiv

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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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Hybrid Oscillator-Qubit Quantum Processors: Simulating Fermions, Bosons, and Gauge Fields

arXiv

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arXiv 2024年

作者： Crane, Eleanor Smith, Kevin C. Tomesh, Teague Eickbusch, Alec Martyn, John M. Kühn, Stefan Funcke, Lena DeMarco, Michael Austin Chuang, Isaac L. Wiebe, Nathan Schuckert, Alexander Girvin, Steven M. Department of Physics Co-Design Center for Quantum Advantage Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science University of Maryland NIST College ParkMD20742 United States Brookhaven National Laboratory UptonNY11973 United States Yale Quantum Institute PO Box 208 334 17 Hillhouse Ave New HavenCT06520-8263 United States Departments of Applied Physics and Physics Yale University New HavenCT06511 United States Department of Computer Science Princeton University PrincetonNJ08544 United States Infleqtion ChicagoIL60604 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CQTA Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany University of Bonn Nussallee 14-16 Bonn53115 Germany Department of Computer Science University of Toronto Canada Pacific Northwest National Laboratory RichlandWA United States Canadian Institute for Advanced Studies Toronto Canada

We develop a hybrid oscillator-qubit processor framework for quantum simulation of strongly correlated fermions and bosons that avoids the boson-to-qubit mapping overhead encountered in qubit hardware. This framework gives exact decompositions of particle interactions such as density-density terms and gauge-invariant hopping, as well as approximate methods based on the Baker-Campbell Hausdorff formulas including the magnetic field term for the U(1) quantum link model in (2 + 1)D. We use this framework to show how to simulate dynamics using Trotterisation, perform ancilla-free partial error detection using Gauss's law, measure non-local observables, estimate ground state energies using a oscillator-qubit variational quantum eigensolver as well as quantum signal processing, and we numerically study the influence of hardware errors in circuit QED experiments. To show the advantages over all-qubit hardware, we perform an end-to-end comparison of the gate complexity for the gauge-invariant hopping term and find an improvement of the asymptotic scaling with the boson number cutoff S from O(log(S)2) to O(1) in our framework as well as, for bosonic matter, a constant factor improvement of better than 104. We also find an improvement from O(log(S)) to O(1) for the U(1) magnetic field term. While our work focusses on an implementation in superconducting hardware, our framework can also be used in trapped ion, and neutral atom hardware. This work establishes digital quantum simulation with hybrid oscillator-qubit hardware as a viable and advantageous method for the study of qubit-boson models in materials science, chemistry, and high-energy physics. Copyright © 2024, The Authors. All rights reserved.

关键词： Ground state

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