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arXiv 2021年

作者： Wismüller, Axel Ali Vosoughi, M. Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

The literature manifests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. The lsXGC is a multivariate approach since it identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here lsXGC serves as a biomarker for classifying schizophrenia patients from typical controls using a subset of 62 subjects from the Centers of biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity. We cross-validate 100 different training/test (90%/10%) data split to obtain mean accuracy and a mean Area Under the receiver operating characteristic Curve (AUC) across all tested numbers of features for lsXGC. Our results demonstrate a mean accuracy range of [0.767, 0.940] and a mean AUC range of [0.861, 0.983] for lsXGC. The result of lsXGC is significantly higher than the results obtained with the cross-correlation, namely mean accuracy of [0.721, 0.751] and mean AUC of [0.744, 0.860]. Our results suggest the applicability of lsXGC as a potential biomarker for schizophrenia. © 2021, CC BY.

关键词： Diseases

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Sequential Markov Chain Monte Carlo for Lagrangian Data Assimilation with Applications to Unknown Data Locations

arXiv

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arXiv 2023年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We consider a class of high-dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging as not only is high-dimensional, but the model for the signal yields longer-range time dependencies through the observation locations. Motivated by this model we revisit a lesser-known and provably convergent computational methodology from [4, 11, 28] that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on Linear Gaussian state space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high-dimensional rotating shallow water model (of about 104 - 105 dimensions) with real and synthetic data. The data is provided by the National Oceanic and Atmospheric Administration (NOAA) and contains observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals [-51◦,-41◦], [17◦,27◦] *** Codes 62M20, 60G35, 60J20, 94A12, 93E11, 65C40 © 2023, CC BY.

关键词： Location

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The Sparse Readout RIGEL Application Specific Integrated Circuit for Pixel Silicon Drift Detectors in Soft X-Ray Imaging Space Applications

arXiv

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arXiv 2022年

作者： Gandola, Massimo Grassi, Marco Mele, Filippo Dedolli, Irisa Malcovati, Piero Bertuccio, Giuseppe Politecnico di Milano Department of Electronics Information and Bioengineering Como Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy Italian National Institute of Nuclear Physics INFN sez. Pavia Pavia Italy Italian National Institute of Nuclear Physics INFN sez. Milano Milan Italy

An Application Specific Integrated Circuit (ASIC), called RIGEL, designed for the sparse readout of a Silicon Pixel Drift Detector (PixDD) for space applications is presented. The low leakage current (less than 1 pA at +20 °C) and anode capacitance (less than 40 fF) of each pixel (300 µm × 300 µm) of the detector, combined with a low-noise electronics readout, allow to reach a high spectroscopic resolution performance even at room temperature. The RIGEL ASIC front-end architecture is composed by a 2-D matrix of 128 readout pixel cells (RPCs), arranged to host, in a 300 µm-sided square area, a central octagonal pad (for the PixDD anode bump-bonding), and the full-analog processing chain, providing a full-shaped and stretched signal. In the chip periphery, the back-end electronics features 16 integrated 10–bits Wilkinson ADCs, the configuration register and a trigger management circuit. The characterization of a single RPC has been carried out whose features are: eight selectable peaking times from 0.5 µs to 5 µs, an input charge range equivalent to 30 keV, and a power consumption of less than 550 µW per channel. The RPC has been tested also with a 4×4 prototype PixDD and 167 eV Full Width at Half Maximum (FWHM) at the 5.9 keV line of 55Fe at 0°C and 1.8 µs of peaking time has been measured. © 2022, CC BY-NC-ND.

关键词： Pixels

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Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation

arXiv

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arXiv 2023年

作者： Liu, Xiaofeng Prince, Jerry L. Xing, Fangxu Zhuo, Jiachen Reese, Timothy Stone, Maureen Fakhri, Georges El Woo, Jonghye Gordon Center for Medical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Neural and Pain Sciences University of Maryland School of Dentistry BaltimoreMD United States Athinoula A. Martinos Center for Biomedical Imaging Dept. of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods. © 2023, CC BY.

关键词： Magnetic resonance

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Time-dependent elastic numerical model for Optical Coherence Elastography of the murine retina

Time-dependent elastic numerical model for Optical Coherence...

引用

Bioengineering (ENBENG), Portuguese Meeting in

作者： Carlos Correia Ana Batista Sílvia Barbeiro João Cardoso José P. Domingues Rafael Henriques Custódio Loureiro Mário J. Santos Pedro Serranho Rui Bernardes Miguel Morgado Institute for Biomedical Imaging and Translational Research (CIBIT) University of Coimbra Coimbra Portugal Center of Mathematics of the University of Coimbra (CMUC) Coimbra Portugal Department of Physics Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Science and Technology Mathematics Section Universidade Aberta Lisbon Portugal Faculty of Medicine Clinical Academic Center of Coimbra (CACC) University of Coimbra Portugal

We present the initial stages of development of a Finite Element Method-based time-dependent elastic numerical model which seeks to support the employment of our Optical Coherence Elastography system for assessing murine retinal elasticity. The current model is able to reconstruct displacement maps in both homogeneous and heterogeneous domains with errors up to a few hundredths relatively to a known exact displacement map, within 1 millisecond. The results demonstrate the robustness of the numerical algorithm under different elastic domains, and model parametrization with real Optical Coherence Elastography data is already in progress.

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Capstone during COVID-19: Medical Device Development at Home to Solve Global Health Problems

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biomedical engineering education 2021年第1期1卷 209-213页

作者： Kelsey P Kubelick Rudolph L Gleason James K Rains James B Stubbs Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of Medicine Atlanta Georgia USA. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA. The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta Georgia USA.

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Towards a Prosodic Model for Synthesized Speech of Mathematical Expressions in MathML 2020

Towards a Prosodic Model for Synthesized Speech of Mathemati...

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9th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

作者： Adriana Souza Diamantino Freitas Doctoral Program in Digital Media Faculty of Engineering - University of Porto Porto Portugal Bahia Federal Institute of Education Science and Technology Porto Seguro Brazil BR Department of Electrical and Computer EngineeringFaculty of Engineering - University of PortoPorto Portugal PT

ISBN: (纸本)9781450389372

The use of the MathML language made possible to improve the accessibility of mathematics for blind or low-vision persons in digital media. Synthetic speech technologies have advanced significantly using MathML, however, the speech synthesizers' standard reading style is still not suitable for mathematics. Making mathematical reading of the speech synthesizers more natural and expressive is still a challenge. The creation of models to produce the appropriate prosody in the synthesized speech of math content is therefore necessary, as shown in previous research. This article presents a proposal for a model to improve prosody in the synthesized speech of mathematical expressions based on MathML. A corpus of mathematical expressions spoken by Mathematics teachers was created to support the model's development. The Fujisaki intonation model was adopted for intonation control, accent and phrase commands have been extracted from the corpus, and some adjustments have been made to manipulate prosodic parameters in the speech of mathematical expression in correlation with the MathML tree; additionally, a pattern of pauses control is being created.

关键词： Accessibility Mathematics Visual Impairment

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FPGA Design of Digital Circuits for Phonocardiogram Pre-Processing Enabling Real-Time and Low-Power AI Processing

FPGA Design of Digital Circuits for Phonocardiogram Pre-Proc...

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Euromicro Symposium on Digital System Design

作者： Domenico Ragusa Antonio J. Rodriguez-Almeida Marco Gazzoni Emanuele Torti Elisa Marenzi Himar Fabelo Gustavo M. Callico Francesco Leporati Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Institutc for Applied Microelectronics (IUMA) University of Las Palmas de Gran Canaria Las Palmas de G.C. Spain Fundación Canaria Instituto de Investigación Sanitaria de Canaria Las Palmas de G.C. Spain Research Unit Hospital Universitario de Gran Canaria Dr. Negrin Las Palmas de Gran Canaria Spain

ISBN: (数字)9798350380385

ISBN: (纸本)9798350380392

Cardiovascular Diseases (CVDs) stand as the leading cause of mortality worldwide. Detecting subtle heart sounds alterations in the early stages of CVDs can be crucial for an initial effective treatment. Accordingly, the analysis of Phonocardiograms (PCGs) through segmentation could be helpful for CVDs screening. A well-established algorithm for this task is based on a Convolutional Neural Network (CNN) with an encoding-decoding topology. Prior to the CNN processing., a computationally intensive input pre-processing., based on envelopes extraction, is needed. Thus., achieving real-time performance can be challenging. The main goal of this study is the hardware design., implementation, and evaluation of four PCG pre-processing circuits to be employed together in the design of a low-power point-of-care device for real-time Artificial Intelligence (AI)-based PCG segmentation. Results have shown that the approximations introduced by the fixed-point format and this innovative architecture have a negligible impact on the AI segmentation quality. Finally, the pre-processing chain is real-time compliant., achieving a maximum latency of 257 ms for an available processing window of 1.28 s., while dissipating only 61 mW of power.

关键词： Point of care Signal processing algorithms Transforms Standardization Real-time systems Topology Convolutional neural networks Artificial intelligence Phonocardiography Digital circuits

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Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features

arXiv

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arXiv 2024年

作者： Tshimula, Jean Marie Nkashama, D'Jeff K. Muabila, Jean Tshibangu Galekwa, René Manassé Kanda, Hugues Dialufuma, Maximilien V. Didier, Mbuyi Mukendi Kalala, Kalonji Mundele, Serge Lenye, Patience Kinshie Basele, Tighana Wenge Ilunga, Aristarque Mayemba, Christian N. Kasoro, Nathanaël M. Kasereka, Selain K. Mikese, Hardy Tardif, Pierre-Martin Frappier, Marc Kabanza, Froduald Chikhaoui, Belkacem Wang, Shengrui Sumbu, Ali Mulenda Ndona, Xavier Intudi, Raoul Kienge-Kienge Global Department of Computer Science University of Kinshasa Congo Department of Computer Science Université de Sherbrooke Canada LISV-UVSQ Université Paris-Saclay France University of Klagenfurt Austria Canada Biomedical Research Unit Hospital Monkole Kinshasa Democratic Republic Congo University of Florida United States School of Electrical Engineering and Computer Science University of Ottawa Canada Karlstad University Sweden Institut Supérieur Pédagogique de Kikwit Congo Applied Artificial Intelligence Institute TELUQ University Canada Faculty of Psychology and Education Sciences University of Kinshasa Congo Harrisburg University of Science and Technology United States School of Criminology University of Kinshasa Congo

The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language Models (LLMs) and psycholinguistic features. We investigate the intersection of psychology and cybersecurity, discussing how LLMs can be employed to analyze textual data for identifying psychological traits of threat actors. We explore the incorporation of psycholinguistic features, such as linguistic patterns and emotional cues, into cybersecurity frameworks. Our research underscores the importance of integrating psychological perspectives into cybersecurity practices to bolster defense mechanisms against evolving threats. © 2024, CC BY.

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UAS-Based Tools for Mapping and Monitoring Hydrothermal Systems: An Example from Mammoth Lakes, California

UAS-Based Tools for Mapping and Monitoring Hydrothermal Syst...

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2022 Geothermal Rising Conference: Using the Earth to Save the Earth, GRC 2022

作者： Zielinski, Laurie A. Glen, Jonathan M. Earney, Tait E. Rea-Downing, Grant H. Vaughan, R. Greg Kelly, Peter J. Keller, Gordon H. Dean, Branden J. Schermerhorn, William D. U.S. Geological Survey Geology Minerals Energy and Geophysics Science Center Moffett FieldCA United States University of Utah Department of Geology and Geophysics Salt Lake CityUT United States U.S. Geological Survey Astrogeology Science Center FlagstaffAZ United States U.S. Geological Survey Volcano Hazards Program VancouverWA United States University of California Santa Cruz Department of Electrical and Computer Engineering Santa CruzCA United States Mount Vernon WA United States

ISBN: (纸本)9781713871040

Unoccupied aerial systems (UAS) can accommodate a variety of tools for mapping and monitoring hydrothermal systems (e.g., magnetic, gas, photogrammetry, and thermal infrared [TIR]). These platforms offer increased speed, coverage area, and uniformity compared to ground-based measurements, as well as lower flight height - and therefore higher resolution - than occupied aircraft. We adapted a suite of tools for use with UAS and implemented these methods in a study focused on the area around Shady Rest Park, Mammoth Lakes, California, within the Long Valley Caldera. This location, which contains tree kills, gas vents, soil gas emissions, heated ground, and hydrothermal alteration, is the site of ongoing efforts to monitor changes in the surface expression of the local hydrothermal system. The methods applied in this study included: (1) airborne visible imagery for surficial mapping and the creation of high-resolution digital elevation models;(2) airborne magnetic measurements;(3) airborne TIR imagery;(4) airborne gas emission measurements;and (5) ground-based gravity measurements. We conducted these surveys in May and October of 2021, in part to establish baseline TIR and gas data against which future changes to the hydrothermal system may be assessed. UAS-based magnetic and ground-based gravity data were collected to map subsurface geology and to characterize potential subsurface controls on thermal anomalies and gas emissions. Results of these efforts at mapping and monitoring the hydrothermal system at Mammoth Lakes demonstrate how an integrated UAS- and ground-based approach may be applied more broadly to study other known or potential hydrothermal and volcanic systems. We consider the benefits and limitations of each method, particularly the TIR and gas sensors, which have less well-developed processing techniques in place for UAS applications. By integrating results from several of these different methods, however, the limitations facing each individual approach

关键词： Mapping

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