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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lin, Licheng Yang, Zhaohui Wu, Yusen Liu, Yuchen Chen, Mingzhe Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798350351255

In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training. First, the server has limited FL training information (i.e., the parameter server can only obtain the FL model information of each device) and limited computational power for finding the differences among a large amount of devices. Second, each device does not have the data information of other devices for device clustering and can only use global FL model parameters received from the server and its data information to determine its cluster identity, which will increase the difficulty of device clustering. To overcome these two challenges, we propose a joint gradient and loss based distributed clustering method in which each device determines its cluster identity considering the gradient similarity and training loss. The proposed clustering method not only considers how a local FL model of one device contributes to each cluster but also the direction of gradient descent thus improving clustering speed. By delegating clustering decisions to edge devices, each device can fully leverage its private data information to determine its own cluster identity, thereby reducing clustering overhead and improving overall clustering performance. Simulation results demonstrate that our proposed clustered FL algorithm can reduce clustering iterations by up to 99% compared to the existing baseline. © 2024 IEEE.

关键词： Federated learning

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Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning 59

Joint Optimization of Model Splitting and Device Task Assign...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Wei, Dongyu Zhang, Qianqian Liu, Yuchen Chen, Mingzhe Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Nc State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331513269

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm. © 2025 IEEE.

关键词： Deep reinforcement learning

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Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports

arXiv

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arXiv 2023年

作者： Deng, Jamie Wu, Yusen Hayssen, Hilary Englum, Brain Kankaria, Aman Mayorga-Carlin, Minerva Sahoo, Shalini Sorkin, John Lal, Brajesh Yesha, Yelena Nguyen, Phuong Frost Institute for Data Science & Computing University of Miami FL United States School of Medicine University of Maryland MD United States

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (freetext) radiology reports. Our experimental results demonstrate the model’s efficacy, achieving an impressive 97% accuracy and 97% F1 score in predicting DVT, and an outstanding 98.3% accuracy and 98.4% F1 score in predicting PE. These findings emphasize the model’s robustness and its potential to significantly contribute to VTE research. © 2023, CC BY-NC-ND.

关键词： Natural language processing systems

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Apptainer-Based Containers for Legacy and Platform Dependent Machine Learning Applications on HPC Systems

Apptainer-Based Containers for Legacy and Platform Dependent...

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IEEE International Conference on Big data

作者： Rajesh Pasupuleti Ravi Vadapalli Christopher Mader Victor J. Milenkovic Frost Institute for Data Science and Computing(IDSC) University of Miami Coral Gables Miami FL USA Department of Computer Science University of Miami Coral Gables Miami FL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Apptainer (Formerly known as Singularity) is a secure, portable, and easy-to-use container system that provides absolute trust and security. It is widely used across industry and academia and suitable for filling the gaps in integration between running applications on new software technologies and legacy hardware using the optimized resource utilization of CPU and memory. It runs complex applications on HPC clusters in a simple, reproducible way. In this paper we are discussing about various implementations of Artificial Intelligence and Machine learning container-based applications running on Pegasus Supercomputing Nodes using Singularity, Nextflow. It reduces configuration setup work manually by singularity applications and it increases current workflows of High-Performance computing (HPC), High Throughput computing (HTC) and run time performance by 3X. we also incorporated comparative based evaluation analytical results of running an application through normal LSF job with singularity container CPU, GPU utilization and its tradeoffs.

关键词： Industries High performance computing Graphics processing units Machine learning Containers Throughput Software Hardware Security Resource management

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Revisiting the hydrodynamic modulation of short surface waves by longer waves

arXiv

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arXiv 2024年

作者： Curcic, Milan Rosenstiel School of Marine Atmospheric and Earth Science University of Miami MiamiFL United States Frost Institute for Data Science and Computing University of Miami Coral GablesFL United States

Hydrodynamic modulation of short ocean surface waves by longer ambient waves is a well-known ocean surface process that affects remote sensing, the interpretation of in situ wave measurements, and numerical wave forecasting. In this paper, we revisit the linear wave theory and derive higher-order steady solutions for the change of short-wave wavenumber, action density, and gravitational acceleration due to the presence of longer waves. We validate the analytical solutions with numerical simulations of the full wave crest and action conservation equations. The nonlinear analytical solutions of short-wave wavenumber, amplitude, and steepness modulation significantly deviate from the linear analytical solutions of Longuet-Higgins & Stewart (1960), and are similar to the nonlinear numerical solutions by Longuet-Higgins (1987) and Zhang & Melville (1990). The short-wave steepness modulation is attributed to be 5/8 due to the wavenumber, 1/4 due to the wave action, and 1/8 due to the effective gravity. We further examine the result of Peureux et al. (2021) who found through numerical simulations that the short-wave modulation grows unsteadily with each long-wave passage. We show that this unsteady growth only occurs only for homogeneous initial conditions as a special case and does not generally occur, for example in more realistic long-wave groups. The proposed steady solutions are a good approximation of the fully nonlinear numerical solutions in long-wave steepness up to ≈0.2. Except for a subset of initial conditions, the solutions to the fully nonlinear crest-action conservation equations are mostly steady in the reference frame of the long *** Codes 76 (Primary) © 2024, CC BY.

关键词： Water waves

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Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

Improving VTE Identification through Language Models from Ra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jamie Deng Yusen Wu Yelena Yesha Phuong Nguyen Department of Computer Science University of Miami Miami FL USA Frost Institute for Data Science & Computing University of Miami Miami FL USA Department of Radiology University of Miami Miami FL USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Venous thromboembolism (VTE) is a critical cardio-vascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97% accuracy and F1 score on the DVT dataset and a 98% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches.

关键词： Deep learning Training Accuracy Computational modeling Biological system modeling Computer architecture Feature extraction Complexity theory Thrombosis Biomedical imaging

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Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hanzhi Yu Yuchen Liu Mingzhe Chen Department of Electrical and Computer Engineering and Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Accuracy Simulation Optimization methods Estimation Information processing Logic gates Prediction algorithms Servers Synchronization Resource management

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Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

arXiv

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arXiv 2024年

作者： Deng, Jamie Wu, Yusen Yesha, Yelena Nguyen, Phuong Department of Computer Science University of Miami MiamiFL United States Frost Institute for Data Science & Computing University of Miami MiamiFL United States Department of Radiology University of Miami MiamiFL United States

Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97% accuracy and F1 score on the DVT dataset and a 98% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches. © 2024, CC BY.

关键词： Classification (of information)

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Optimizing Vehicle Positioning via Multi-Model Image and Radio Frequency Fusion

Optimizing Vehicle Positioning via Multi-Model Image and Rad...

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IEEE International Conference on Communications (ICC)

作者： Ouwen Huan Mingzhe Chen Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

关键词： Training Deep learning Base stations Simulation Fingerprint recognition Cameras data models

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Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization

arXiv

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arXiv 2024年

作者： Yu, Hanzhi Chen, Mingzhe Liu, Yuchen The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

In this article, the use of channel state information (CSI) for indoor positioning is studied. In the considered model, a server equipped with several antennas sends pilot signals to users, while each user uses the received pilot signals to estimate channel states for user positioning. To this end, we formulate the positioning problem as an optimization problem aiming to minimize the gap between the estimated positions and the ground truth positions of users. To solve this problem, we design a complex-valued neural network (CVNN) model based federated learning (FL) algorithm. Compared to standard real-valued centralized machine learning (ML) methods, our proposed algorithm has two main advantages. First, our proposed algorithm can directly process complex-valued CSI data without data transformation. Second, our proposed algorithm is a distributed ML method that does not require users to send their CSI data to the server. Since the output of our proposed algorithm is complex-valued which consists of the real and imaginary parts, we study the use of the CVNN to implement two learning tasks. First, the proposed algorithm directly outputs the estimated positions of a user. Here, the real and imaginary parts of an output neuron represent the 2D coordinates of the user. Second, the proposed method can output two CSI features (i.e., line-of-sight/non-line-of-sight transmission link classification and time of arrival (TOA) prediction) which can be used in traditional positioning algorithms. Simulation results demonstrate that our designed CVNN based FL can reduce the mean positioning error between the estimated position and the actual position by up to 36%, compared to a RVNN based FL which requires to transform CSI data into real-valued data. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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