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arXiv 2021年

作者： Zhang, Yizhen Choi, Minkyu Han, Kuan Liu, Zhongming Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Neurological Surgery University of California San Francisco San FranciscoCA94143 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States

In natural language processing, most models try to learn semantic representations merely from texts. The learned representations encode the "distributional semantics" but fail to connect to any knowledge about the physical world. In contrast, humans learn language by grounding concepts in perception and action and the brain encodes "grounded semantics" for cognition. Inspired by this notion and recent work in vision-language learning, we design a two-stream model for grounding language learning in vision. The model includes a VGG-based visual stream and a Bert-based language stream. The two streams merge into a joint representational space. Through cross-modal contrastive learning, the model first learns to align visual and language representations with the MS COCO dataset. The model further learns to retrieve visual objects with language queries through a cross-modal attention module and to infer the visual relations between the retrieved objects through a bilinear operator with the Visual Genome dataset. After training, the model's language stream is a stand-alone language model capable of embedding concepts in a visually grounded semantic space. This semantic space manifests principal dimensions explainable with human intuition and neurobiological knowledge. Word embeddings in this semantic space are predictive of human-defined norms of semantic features and are segregated into perceptually distinctive clusters. Furthermore, the visually grounded language model also enables compositional language understanding based on visual knowledge and multimodal image search with queries based on images, texts, or their combinations. © 2021, CC BY.

关键词： Embeddings

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Layer-dependent optically induced spin polarization in InSe

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Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

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Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Feasibility of Inferring Spatial Transcriptomics from Single...

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18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Fatemi, Michael Y. Lu, Yunrui Azher, Zarif L. Sharma, Cyril Feng, Eric Diallo, Alos B. Srinivasan, Gokul Rosner, Grace M. Pointer, Kelli B. Christensen, Brock C. Salas, Lucas A. Tsongalis, Gregory J. Palisoul, Scott M. Perreard, Laurent Kolling, Fred W. Vaickus, Louis J. Levy, Joshua J. University of Virginia CharlottesvilleVA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health LebanonNH United States Department of Computer Science Purdue University West LafayetteIN United States Thomas Jefferson High School for Science and Technology AlexandriaVA United States Department of Epidemiology Dartmouth College Geisel School of Medicine HanoverNH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine HanoverNH United States Department of Medicine Section of Radiology Oncology Dartmouth Health LebanonNH United States Department of Molecular and Cell Biology Dartmouth College Geisel School of Medicine HanoverNH United States Genomics Shared Resource Dartmouth Cancer Center LebanonNH United States Department of Dermatology Dartmouth Health LebanonNH United States Department of Pathology and Laboratory Medicine Cedars Sinai Medical Center Los AngelesCA United States Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Biology and Biological Engineering California Institute of Technology PasadenaCA United States

Spatial transcriptomics (ST) enables studying spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. Recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. Elucidating such information from histology alone presents a significant challenge, but if solved can enable spatial molecular analysis at cellular resolution even where ST data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in colorectal cancer whole slide images. A cell-graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA, facilitating the analysis of cellular groupings and gene relationships. We demonstrate that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within it. Our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional computer vision methods which did not incorporate single cell expression during the model training. This innovative approach augments the resolution of spatial molecular assays utilizing histology as sole input through co-mapping of histological and transcriptomic datasets at the single-cell level. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Tumors

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Realistic ultrasound image synthesis for improved classification of liver disease

arXiv

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arXiv 2021年

作者： Che, Hui Ramanathan, Sumana Foran, David J. Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker Department of Biomedical Engineering Rutgers University NJ United States Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States Department of Electrical and Computer Engineering Johns Hopkins University MD United States Rutgers Cancer Institute of New Jersey NJ United States

With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver disease from US data. In this work, we propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis. We adopt the concept of stacking to synthesize realistic liver US data. Quantitative and qualitative evaluation is performed on 550 in-vivo B-mode liver US images collected from 55 subjects. We also show that the synthesized images, together with real in vivo data, can be used to significantly improve the performance of traditional CNN architectures for Nonalcoholic fatty liver disease (NAFLD) classification. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

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Kirchhoff's Current Law with Displacement Current

arXiv

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arXiv 2022年

作者： Eisenberg, Robert Oriols, Xavier Ferry, David K. Department of Physiology and Biophysics Rush University Medical Center ChicagoIL United States Department of Applied Mathematics Illinois Institute of Technology ChicagoIL United States Department of Biomedical Engineering University of Illinois Chicago ChicagoIL United States Departament d’Enginyeria Electrònica Universitat Autònoma de Barcelona Barcelona08193 Spain School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287USA United States

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Decentralized dynamic state estimation with bimodal Gaussian mixture measurement noise

arXiv

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arXiv 2020年

作者： Sarfi, Vahid Ghasemkhani, Amir Niazazari, Iman Livani, Hanif Yang, Lei Department of Electrical and Biomedical Engineering University of Nevada Reno United States Department of Computer Science and Engineering University of Nevada Reno United States

This paper proposes a decentralized dynamic state estimation (DSE) algorithm with bimodal Gaussian mixture measurement noise. The decentralized DSE is formulated using the Ensemble Kalman Filter (EnKF) and then compared with the unscented Kalman filter (UKF). The performance of the proposed framework is verified using the WSCC 9-bus system simulated in the Real Time Digital Simulator (RTDS). The phasor measurement unit (PMU) measurements are streamed in real-time from the RTDS runtime environment to MATLAB for real-time visualization and estimation. To consider the data corruption scenario in the streaming process, a bi-modal distribution containing two normal distributions with different weights and variances are added to the measurements as the noise component. The performances of both UKF and EnKF are then compared for by calculating the mean-squared-errors (MSEs) between the actual and estimated states. Copyright © 2020, The Authors. All rights reserved.

关键词： Phasor measurement units

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Miniaturized Microarray-Format Digital ELISA Enabled by Lithographic Protein Patterning

SSRN

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SSRN 2023年

作者： Stephens, Andrew D. Song, Yujing McClellan, Brandon L. Su, Shiuan-Haur Xu, Sonnet Chen, Kevin Castro, Maria G. Singer, Benjamin H. Kurabayashi, Katsuo Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Neurosurgery University of Michigan Medical School Ann ArborMI48109 United States Department of Cell and Developmental Biology University of Michigan Medical School Ann ArborMI48109 United States Graduate Program in Immunology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Rogel Cancer Center University of Michigan Ann ArborMI48109 United States Department of Internal Medicine Division of Pulmonary and Critical Care Medicine University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to © 2023, The Authors. All rights reserved.

关键词： Biomarkers

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Thompson-Sampling-Based Wireless Transmission for Panoramic Video Streaming

Thompson-Sampling-Based Wireless Transmission for Panoramic ...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Jiangong Chen Bin Li R. Srikant Department of Electrical Computer and Biomedical Engineering at the University of Rhode Island Kingston RI USA Department of Electrical and Computer Engineering Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783903176294

ISBN: (纸本)9781728193892

Panoramic video streaming has received great attention recently due to its immersive experience. Different from traditional video streaming, it typically consumes 4 ~ 6× larger bandwidth with the same resolution. Fortunately, users can only see a portion (roughly 20%) of 360° scenes at each time and thus it is sufficient to deliver such a portion, namely Field of View (FoV), if we can accurately predict user's motion. In practice, we usually deliver a portion larger than FoV to tolerate inaccurate prediction. Intuitively, the larger the delivered portion, the higher the prediction accuracy. This however leads to a lower transmission success probability. The goal is to select an appropriate delivered portion to maximize system throughput, which can be formulated as a multi-armed bandit problem, where each arm represents the delivered portion. Different from traditional bandit problems with single feedback information, we have two-level feedback information (i.e., both prediction and transmission outcomes) after each decision on the selected portion. As such, we propose a Thompson Sampling algorithm based on two-level feedback information, and demonstrate its superior performance than its traditional counterpart via simulations.

关键词： Prediction algorithms Streaming media Throughput Wireless communication Predictive models Bandwidth Adaptation models

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Spatially-varying microscope calibration from unstructured sparse inputs

Spatially-varying microscope calibration from unstructured s...

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Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Kuo, Grace Monakhova, Kristina Yanny, Kyrollos Ng, Ren Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley BerkeleyCA94720 United States

We propose a method based on blind deconvolution to calibrate the spatially-varying point spread functions of a coded-aperture microscope system. From easy-to-acquire measurements of unstructured fluorescent beads, we... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical transfer function

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Model Technology Service E-Participation - Voting (E-PV) for Political Communication between the Council of Regional Representatives (DPRD) and the Citizens Using the Framework Information Technology Infrastructure Library (ITIL V.3) Transition Domain Service 7

Model Technology Service E-Participation - Voting (E-PV) for...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Hilabi, Shofa Shofiah Harso Supangat, Suhono Hendric Spits Warnars, Harco Leslie Prabowo, Harjanto Mursitama, Tirta Nugraha Universitas Buana Perjuangan Faculty of Engineering and Computer Science Karawang Indonesia Institute of Technology Bandung School of Electrical Engineering and Informatics Bandung Indonesia Nusantara University Doctor of Computer Science Bina Computer Science Department BINUS Graduate Program Jakarta11530 Indonesia Bina Nusantara University Doctor of Computer Science Jakarta11530 Indonesia Bina Nusantara University Faculty of Humanities International Relations Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

E-voting is an electronic voting information technology service, while E-participation is an information technology service for citizens of the public to provide reports, polls, or suggestions on government policies electronically. The Technology Services E-Participation-VOT (EPV) model is a combined E-Participation and E-Voting service model. The concept of this service model is that citizens transform from E-participation and E-voting services into an EPV model. The purpose of this model is to provide information to participants (citizens) about online-based voting services. This method of developing the EP-Voting Anticipation (EPV) technology model uses agile methods that are effective and flexible in various cases of designing Information Technology models for Information Technology (IT) services that apply a Framework Information Technology Infrastructure Library (ITIL). The results obtained from this study are an Information Technology service system model that provides recommendations for participation in conducting electronic elections (E-Voting). © 2020 IEEE.

关键词： Surveys

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