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Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress

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Chronic Stress 2023年 7卷

作者： Goel, Rahul Tse, Teresa Smith, Lia J. Floren, Andrew Naylor, Bruce Williams, M. Wright Salas, Ramiro Rizzo, Albert S. Ress, David Department of Neuroscience Baylor College of Medicine Houston TX United States Department of Psychology University of Houston Houston TX United States Center for Translational Research on Inflammatory Diseases Michael E. DeBakey VA Medical Center Houston TX United States Department of Electrical and Computer Engineering University of Texas at Austin Austin TX United States Department of Psychiatry and Behavioral Sciences Baylor College of Medicine Houston TX United States The Menninger Clinic Houston TX United States Institute for Creative Technologies University of Southern California Los Angeles CA United States

Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-

关键词： algorithm design and analysis behavioral sciences functional magnetic resonance imaging machine learning algorithms mental disorders posttraumatic stress disorder (PTSD) psychiatry veterans

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A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism 23rd

A Deep-Generative Hybrid Model to Integrate Multimodal and D...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： DSouza, Niharika Shimona Nebel, Mary Beth Crocetti, Deana Wymbs, Nicholas Robinson, Joshua Mostofsky, Stewart Venkataraman, Archana Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Center for Neurodevelopmental and Imaging Research Kennedy Krieger Institute Baltimore United States Department of Neurology Johns Hopkins School of Medicine Baltimore United States Department of Psychiatry and Behavioral Science Johns Hopkins School of Medicine Baltimore United States

ISBN: (纸本)9783030597276

We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract predictive biomarkers of a disease. The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings. This matrix factorization is guided by the DTI tractography matrices to learn anatomically informed connectivity profiles. The deep part of our framework is an LSTM-ANN block, which models the temporal evolution of the patient sr-DDL loadings to predict multidimensional clinical severity. Our coupled optimization procedure collectively estimates the basis networks, the patient-specific dynamic loadings, and the neural network weights. We validate our framework on a multi-score prediction task in 57 patients diagnosed with Autism Spectrum Disorder (ASD). Our hybrid model outperforms state-of-the-art baselines in a five-fold cross validated setting and extracts interpretable multimodal neural signatures of brain dysfunction in ASD. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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65-2: A Light and Fast Branched Neural Network using Perceptual Optimization for High-Quality Visual Restoration of UDC images

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SID Symposium Digest of Technical Papers 2022年第1期53卷

作者： Jaihyun Koh Jangho Lee Junghoon Kim Youngwook Yoo Bonghyun You Sungroh Yoon Display Research Center Samsung Display Corporation Yongin Korea Department of Electrical and Computer Engineering Seoul National University Seoul Korea

We propose a light and fast method to restore degraded images captured from under-display cameras (UDCs) by modifying a branched deep neural network (BNUDC). The existing BNUDC is trained to minimize the per-pixel difference of intensity between a ground truth and a restored image using L1 or L2 loss, and it has a large number of learnable parameters for achieving state-of-theart performance in terms of numerical distances such as PSNR and SSIM. Commercializing a deep-learning-based image restoration requires high perceptual image quality and real-time inference speed. To this end, we downscale the original BNUDC to restore a 2k resolution image within 44ms which is the frame time for a 24Hz video, and train our network to reconstruct perceptually-pleasing images using ‘perceptual optimization’. Our model has less than 1M learnable parameters, its inference time is less than 40ms, and it outperforms the original network in terms of perceptual image quality.

关键词： Branched neural network OLED under-display camera neural network deep learning perceptual optimization

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A Survey of Multi-robot Regular and Adversarial Patrolling

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 894-903页

作者： Li Huang MengChu Zhou Kuangrong Hao Edwin Hou IEEE the Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Educationand the College of Information Science and TechnologyDonghua UniversityShanghai 201620China the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07032 the Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Multi-robot systems can be applied to patrol a concerned environment for security *** to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial *** patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed,while adversarial one focuses on unpredictable robots’moving patterns to maximize adversary detection *** each category,a systematic survey is done including problem statements and modeling,patrolling objectives and evaluation criteria,and representative patrolling strategies and *** problems and open questions are presented accordingly.

关键词： Multi-robot systems regular patrolling adversarial patrolling coordination methods surveillance

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Ultrathin TiO 2 -interfaced hafnia ferroelectric transistor for large-scale neuromorphic computing

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Nano Energy 2025年 142卷

作者： Changhyeon Han Ryun-Han Koo Wonjun Shin Jangsaeng Kim Been Kwak Jiseong Im Sojin Kim Seung-Yong Lee Youngho Kang Daewoong Kwon Department of Electrical Engineering Hanyang University Seoul 04763 Republic of Korea Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center Seoul National University Seoul 08826 Republic of Korea Department of Semiconductor Convergence Engineering Sungkyunkwan University Suwon 03063 Republic of Korea Department of Electronic Engineering Sogang University Seoul Republic of Korea Division of Materials Science and Engineering Hanyang University Seoul 04763 Republic of Korea Department of Battery Engineering Hanyang University Seoul 04763 Republic of Korea Department of Material Science and Engineering Incheon National University Incheon 22012 Republic of Korea

The growing demand for large-scale neuromorphic computing necessitates the development of innovative memory devices capable of supporting high-density synaptic arrays with frequent, low-power weight updates. Among the promising candidates, hafnia-based ferroelectric field-effect transistors (FeFETs) have emerged due to their low-power switching and CMOS compatibility. However, conventional hafnia FeFETs are limited by their poor endurance and switching dynamics-both of which are attributed to the degradation mechanisms arising from the ferroelectric/dielectric interface-impeding the realization of large-scale neuromorphic computing. Herein, we propose a synergistic ferroelectric polarization-interface dipole modulation (IDM) switching in hafnium-zirconium oxide (HZO) FeFETs to improve switching dynamics and endurance. Integration of an ultrathin (< 0.5 nm) TiO 2 layer into the gate stack has three critical functions: (i) reducing the oxygen vacancies in HZO; (ii) mitigating trapping at the ferroelectric/dielectric interface; and (iii) improving the switching dynamics through the polarization coupling effect via IDM. Consequently, this synergistic improvement significantly enhances the FeFET performance with 10 6 -fold endurance enhancement. Moreover, by demonstrating large-scale neuromorphic integration that meets the update demands required for CIFAR-100 dataset, our work underscores the transformative potential of this approach for realizing reliable and energy-efficient systems capable of real-time learning.

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Study of Oxygen Reduction Reaction on Bi-Nuclear-Phthalocyanine with Fe-Fe, Co-Co, and Fe-Co Dual-Atom-Active Sites Using Density Functional Theory

SSRN

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SSRN 2022年

作者： Kumar, Anuj Ibraheem, Shumaila Das, Dipak Kumar Nadda, Ashok Kumar Zhang, Guoxin Maiyalagan, T. Yasin, Ghulam Gupta, Ram K. Nano-Technology Research Laboratory Department of Chemistry GLA University Uttar Pradesh Mathura281406 India Institute for Advanced Study Shenzhen University Guangdong Shenzhen518060 China Department of Chemistry Kansas Polymer Research Center Pittsburg State University PittsburgKS66762 United States Department of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China Department of Biotechnology and Bioinformatics Jaypee University of Information Technology Solan Waknaghat173 234 India Electrochemical Energy Laboratory Department of Chemistry SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

Inspired by biological magic, how nature creates active sites for oxygen reduction reaction (ORR) has been understood and exploited by constructing synthetic molecular systems like metallo-phthalocyanines and metallo-porphyrins, as efficient SACs. Herein, a bi-nuclear phthalocyanine (bN-Pc) was explored as a theoretical model for FeFe-bN-Pc, CoCo-bN-Pc, and FeCo-bN-Pc dual-atom-sites configurations and their ORR activity using density functional theory. The FeCo-bN-Pc showed the highest working potential of 0.99 V, followed by FeFe-bN-Pc (0.95 V), and CoCo-bN-Pc, demonstrating FeCo-bN-Pc as the best ORR catalyst. The activation of O2 was proposed to be the rate-determining step for FeCo-bN-Pc, with an energy barrier of 0.37 eV, as suggested by Gibbs free energy calculations. The reason being the synergistic interaction between Co and Fe central atoms optimized the overall electron density transfer from the highest occupied molecular orbital of the FeCo-bN-Pc system into π* orbital of O2, however, it is difficult to regulate the Fe-Co combination in the experimental condition precisely. © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Multimodal objective assessment of impulsivity in healthy and mood disorder participants

NPP—Digital Psychiatry and Neuroscience

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NPP—Digital Psychiatry and Neuroscience 2025年第1期3卷 1-9页

作者： Bishal Lamichhane Ashutosh Sabharwal Nidal Moukaddam Ramiro Salas Wayne Goodman Electrical and Computer Engineering Rice University Houston TX USA Menninger Department of Psychiatry Baylor College of Medicine Houston TX USA Center for Translational Research on Inflammatory Diseases Michael E DeBakey VA Medical Center Houston TX USA The Menninger Clinic Houston TX USA

Impulsivity represents an individual’s tendency to act on urges without sufficient forethought. Heightened impulsivity is a hallmark of many mental health disorders. Objective impulsivity assessments could improve risk evaluation, diagnosis, and behavioral outcome monitoring in impulsivity-related health disorders. Towards objective impulsivity assessment, in this work, we identify impulsivity correlates in objective measurements, investigate their complementarity, and contrast impulsivity mechanisms across health conditions. We analyzed behavioral tests, heart rate variability (HRV), and fMRI-based brain connectivity in 227 healthy participants and 34 participants with mood disorders. Impulsivity dimensions had complementary correlates in objective measurements, with fMRI providing the strongest correlates. Multimodal assessment provided high r-squared (adjusted) values in modeling impulsivity of the mood disorder participants (e.g., r-squared of 0.73,p< 0.001 for attentional impulsivity) but low r-squared for healthy participants (the best r-squared being 0.17,p< 0.001 for sensation seeking impulsivity). The differing association between impulsivity dimensions across the two populations likely indicates a health condition-specific impulsivity mechanism across populations. The complementary nature of objective impulsivity correlates across populations demonstrates the distributed signature of multidimensional impulsivity, likely capturing the complexity of behavioral modeling.

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Ultra Compact, Ultra Wideband, DC-1GHz CMOS Circulator Based on Quasi-Electrostatic Wave Propagation in Commutated Switched Capacitor Networks

Ultra Compact, Ultra Wideband, DC-1GHz CMOS Circulator Based...

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IEEE Symposium on Radio Frequency Integrated Circuits (RFIC)

作者： Aravind Nagulu Mykhailo Tymchenko Andrea Alù Harish Krishnaswamy Department of Electrical Engineering Columbia University USA Department of Electrical & Computer Engineering The University of Texas at Austin USA Photonics Initiative Advanced Science Research Center City University of New York USA

ISBN: (数字)9781728168098

ISBN: (纸本)9781728168104

Recent research has revealed the possibility to achieve non-magnetic non-reciprocity using time-variance. However, prior CMOS-based circulators rely on the interference between non-reciprocal switched-capacitor/transmission-line gyrators and reciprocal transmission-line rings, which increases form factor and restricts frequency tunability and bandwidth. On the other hand, recent works on quasi-electrostatic wave propagation in switched-capacitor media have demonstrated a new regime in multipath switched-capacitor network operation that enables an ultra-broadband, ultra-compact reciprocal/non-reciprocal true-time-delay element. In this work, we apply synthetic rotation across this quasi-electrostatic medium to realize an ultra-broadband N-port circulator with ultra-compact form-factor. This new architecture is showcased in a wideband 3-port circulator implemented in a standard 65nm CMOS process. This circulator exhibits symmetric performance across all 3 ports and DC-1GHz operation for a modulation frequency of 500MHz. The measured transmission losses range between 3.1-4.3dB, matching is 18dB and NF is consistent with the insertion loss. This device occupies an area of 0.19mm 2 (λ center 2 /1.9 × 10 6 ), representing about 100-1000× higher miniaturization compared to the prior art.

关键词： Circulators Capacitors Bandwidth Switches Delays Modulation Inductors

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Quantum repeaters: From quantum networks to the quantum internet

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Reviews of Modern Physics 2023年第4期95卷 045006-045006页

作者： Koji Azuma Sophia E. Economou David Elkouss Paul Hilaire Liang Jiang Hoi-Kwong Lo Ilan Tzitrin NTT Basic Research Laboratories NTT Corporation 3-1 Morinosato-Wakamiya Atsugi Kanagawa 243-0198 Japan and NTT Research Center for Theoretical Quantum Information NTT Corporation 3-1 Morinosato-Wakamiya Atsugi Kanagawa 243-0198 Japan Department of Physics Virginia Tech Blacksburg Virginia 24061 USA QuTech Delft University of Technology Lorentzweg 1 2628 CJ Delft The Netherlands and Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan Department of Physics Virginia Tech Blacksburg Virginia 24061 USA and Quandela SAS 10 Boulevard Thomas Gobert 91120 Palaiseau France Pritzker School of Molecular Engineering The University of Chicago Chicago Illinois 60637 USA Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong and Center for Quantum Information and Quantum Control Department of Physics and Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Toronto Toronto Ontario Canada

A quantum internet is the holy grail of quantum information processing, enabling the deployment of a broad range of quantum technologies and protocols on a global scale. However, numerous challenges must be addressed before the quantum internet can become a reality. Perhaps the most crucial of these is the realization of a quantum repeater, an essential component in the long-distance transmission of quantum information. As the analog of a classical repeater, extender, or booster, the quantum repeater works to overcome loss and noise in the quantum channels constituting a quantum network. Here the conceptual frameworks and architectures for quantum repeaters, as well as the experimental progress toward their realization, are reviewed. Various near-term proposals to overcome the limits to the communication rates set by point-to-point quantum communication are also discussed. Finally, the manner in which quantum repeaters fit within the broader challenge of designing and implementing a quantum internet is overviewed.

关键词： Quantum communication Quantum memories Quantum networks Quantum repeaters Internet

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Cavity-Enhanced 2D Material Quantum Emitters Deterministically Integrated with Silicon Nitride Microresonators

arXiv

引用

arXiv 2022年

作者： Parto, K. Azzam, S.I. Lewis, N. Patel, S.D. Umezawa, S. Watanabe, K. Taniguchi, T. Moody, G. Electrical and Computer Engineering Department University of California Santa BarbaraCA93106 United States California Nanosystems Institute University of California Santa BarbaraCA93106 United States Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan International Center for Materials Nanoarchitectures National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan

Optically active defects in 2D materials, such as hexagonal boron nitride (hBN) and transition metal dichalcogenides (TMDs), are an attractive class of single-photon emitters with high brightness, room-temperature operation, site-specific engineering of emitter arrays, and tunability with external strain and electric fields. In this work, we demonstrate a novel approach to precisely align and embed hBN and TMDs within background-free silicon nitride microring resonators. Through the Purcell effect, high-purity hBN emitters exhibit a cavity-enhanced spectral coupling efficiency up to 46% at room temperature, which exceeds the theoretical limit for cavity-free waveguide-emitter coupling and previous demonstrations by nearly an order-of-magnitude. The devices are fabricated with a CMOS-compatible process and exhibit no degradation of the 2D material optical properties, robustness to thermal annealing, and 100 nm positioning accuracy of quantum emitters within single-mode waveguides, opening a path for scalable quantum photonic chips with on-demand single-photon sources. © 2022, CC BY.

关键词： Boron nitride

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