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Qpplab: A Generally Applicable Software Package for Detecting, Analyzing, and Visualizing Large-Scale Quasiperiodic Spatiotemporal Patterns (Qpps) of Brain Activity

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SSRN

SSRN 2024年

作者： Xu, Nan Yousefi, Behnaz Anumba, Nmachi LaGrow, Theodore J. Zhang, Xiaodi Keilholz, Shella Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Georgia Institute of Technology United States

One prominent brain dynamic process detected in functional neuroimaging data is large-scale quasi-periodic patterns (QPPs) which display spatiotemporal propagations along brain cortical gradients. QPP associates with the infraslow neural activity related to attention and arousal fluctuations and has been identified in both resting and task-evoked brains across various species. Several QPP detection and analysis tools were developed for distinct applications with study-specific parameters methods. This MATLAB package provides a simplified and user-friendly generally applicable toolbox for detecting, analyzing, and visualizing QPPs from fMRI timeseries of the brain. This paper describes the software functions and presents its ease of use on any brain datasets. © 2024, The Authors. All rights reserved.

关键词： Brain

6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

EvoResUNet: A Lesion Segmentation Network for Primary Liver Cancer from 3D CECT Volume

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EvoResUNet: A Lesion Segmentation Network for Primary Liver ...

Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Shixin Huang Jiawei Luo Xiaoyu Wan Xixi Nie Department of Scientific Research The People's Hospital of Yubei District of Chongqing City School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China West China Biomedical Big Data Center West China Hospital Med-X Center for Informatics Sichuan University Sichuan China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Early accurate diagnosis of focal liver lesions is crucial for treatment and prognosis. To this end, we propose an evolved ResUNet algorithm (EvoResUnet) specifically designed to accurately segment focal regions in CECT 3D volume of primary liver cancer. The EvoResUNet model innovatively introduces the convolutional block attention module to achieve accurate focusing on the region of liver tumors. To further optimize the model, we design a DCLoss that combines the Dice loss with the improved cross-entropy loss, which is able to optimize the model from both local and global perspectives, and improves the stability and convergence of the model. In addition, we propose a conditional random field-based segmentation post-processing technique, which makes full use of the neighborhood context and related information in the 3D CECT volume to further improve the accuracy and performance of liver tumor segmentation. Experimental results demonstrate the superior performance of the proposed EvoResUnet compared to state-of-the-art methods.

关键词： Image segmentation Liver cancer Three-dimensional displays Accuracy Liver Feature extraction Stability analysis Lesions Prognostics and health management Convergence

DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

Localization of Dummy data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

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arXiv 2024年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong Zang, Jiye Xu, Qi School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China School of Computer Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

关键词： Convolution

Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra

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arXiv 2023年

作者： Kumazoe, Hiroyuki Iwamitsu, Kazunori Imamura, Masaki Takahashi, Kazutoshi Mototake, Yoh-Ichi Okada, Masato Akai, Ichiro Graduate School of Social Data Science Hitotsubashi University Tokyo Kunitachi186-8601 Japan Technical Division Kumamoto University Kumamoto860-8555 Japan Synchrotron Light Application Center Saga University Saga Tosu841-0005 Japan Department of Complexity Science and Engineering The University of Tokyo Chiba277-8561 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki305-0047 Japan Institute of Industrial Nanomaterials Kumamoto University Kumamoto860-8555 Japan

We analyzed the X-ray photoemission spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly accurate spectral decomposition of the XPS spectra, we proposed a framework for discovering physical constraints from the absence of prior quantified physical knowledge, in which we designed the prior probabilities based on the found constraints and the physically required conditions. This suppresses the exchange of peak components during replica exchange Monte Carlo iterations and makes possible to decompose XPS in the case where a reliable structure model or a presumable number of components is not known. As a result, we have successfully decomposed XPS of one monolayer (1ML), two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene samples deposited on SiC substrates with the meV order precision of the binding energy, in which the posterior probability distributions of the binding energies were obtained distinguishably between the different components of buffer layer even though they are observed as hump and shoulder structures because of their overlapping. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

Unconventional spin polarization at Argon ion milled SrTiO3 Interfaces.

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arXiv 2023年

作者： Kumar, Amrendra Shashank, Utkarsh Maharana, Suman Kumar Mohan, John Rex Gupta, Surbhi Asada, Hironori Fukuma, Yasuhiro Medwal, Rohit Department of Physics Indian Institute of Technology Kanpur Kanpur208016 India Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka820-8502 Japan Department of Physics Motilal Nehru National Institute of Technology Barrister Mullah Colony Uttar PradeshTeliarganj Prayagraj211004 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

Interfacial two-dimensional electron gas (2DEG) formed at the perovskite-type oxide, such as SrTiO3, has attracted significant attention due to its properties of ferromagnetism, superconductivity, and its potential application in oxide-based low-power consumption electronics. Recent studies have investigated spin-to-charge conversion at the STO interface with different materials, which could affect the efficiency of this 2DEG interface. In this report, we presented a Ar+ ion milling method to create a 2DEG at STO directly by inducing oxygen vacancies. To quantify the spin-to-charge conversion of this interface, we measured the angular-dependent spin torque ferromagnetic resonance (ST-FMR) spectra, revealing an unconventional spin polarization at the interface of of Argon ion milled STO and NiFe . Furthermore, a micromagnetic simulation for angular dependent spin torque ferromagnetic resonance (ST-FMR) has been performed, confirming the large unconventional spin type spin polarization at the interface. Copyright © 2023, The Authors. All rights reserved.

关键词： Ferromagnetism

PlaneSAM: Multimodal Plane Instance Segmentation Using the Segment Anything Model

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arXiv 2024年

作者： Deng, Zhongchen Yang, Zhechen Chen, Chi Zeng, Cheng Meng, Yan Yang, Bisheng School of Computer Science and Information Engineering Hubei University Hubei Wuhan430062 China School of Artificial Intelligence Hubei University Hubei Wuhan430062 China Ministry of Education Hubei Wuhan430062 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Hubei Wuhan430072 China Engineering Research Center of Space-Time Data Capturing and Smart Application The Ministry of Education of P.R.C. Hubei Wuhan430072 China Institute of Geospatial Intelligence Wuhan University Hubei Wuhan430072 China

Plane instance segmentation from RGB-D data is a crucial research topic for many downstream tasks, such as indoor 3D reconstruction. However, most existing deep-learning-based methods utilize only information within the RGB bands, neglecting the important role of the depth band in plane instance segmentation. Based on EfficientSAM, a fast version of the Segment Anything Model (SAM), we propose a plane instance segmentation network called PlaneSAM, which can fully integrate the information of the RGB bands (spectral bands) and the D band (geometric band), thereby improving the effectiveness of plane instance segmentation in a multimodal manner. Specifically, we use a dual-complexity backbone, with primarily the simpler branch learning D-band features and primarily the more complex branch learning RGB-band features. Consequently, the backbone can effectively learn D-band feature representations even when D-band training data is limited in scale, retain the powerful RGB-band feature representations of EfficientSAM, and allow the original backbone branch to be fine-tuned for the current task. To enhance the adaptability of our PlaneSAM to the RGB-D domain, we pretrain our dual-complexity backbone using the segment anything task on large-scale RGB-D data through a self-supervised pretraining strategy based on imperfect pseudo-labels. To support the segmentation of large planes, we optimize the loss function combination ratio of EfficientSAM. In addition, Faster R-CNN is used as a plane detector, and its predicted bounding boxes are fed into our dual-complexity network as prompts, thereby enabling fully automatic plane instance segmentation. Experimental results show that the proposed PlaneSAM sets a new state-of-the-art (SOTA) performance on the ScanNet dataset, and outperforms previous SOTA approaches in zero-shot transfer on the 2D-3D-S, Matterport3D, and ICL-NUIM RGB-D datasets, while only incurring a 10% increase in computational overhead compared to EfficientSAM. Ou

关键词： 3D reconstruction

A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

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A deep learning approach to Lung Nodule Growth Prediction us...

7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Li, ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting