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arXiv 2024年

作者： Sun, Jingtao Wang, Yaonan Feng, Mingtao Ding, Chao Shou, Mike Zheng Mian, Ajmal Saeed The College of Electrical and Information Engineering The National Engineering Research Centre for Robot Visual Perception and Control Hunan University Changsha410082 China Department of Electrical and Computer Engineering and Show Lab National University of Singapore Singapore117583 Singapore 2006 Australia

Fully-supervised category-level pose estimation aims to determine the 6-DoF poses of unseen instances from known categories, requiring expensive mannual labeling costs. Recently, various self-supervised category-level pose estimation methods have been proposed to reduce the requirement of the annotated datasets. However, most methods rely on synthetic data or 3D CAD model for self-supervised training, and they are typically limited to addressing single-object pose problems without considering multi-objective tasks or shape reconstruction. To overcome these challenges and limitations, we introduce a diffusion-driven self-supervised network for multi-object shape reconstruction and categorical pose estimation, only leveraging the shape priors. Specifically, to capture the SE(3)-equivariant pose features and 3D scale-invariant shape information, we present a Prior-Aware Pyramid 3D Point Transformer in our network. This module adopts a point convolutional layer with radial-kernels for pose-aware learning and a 3D scale-invariant graph convolution layer for object-level shape representation, respectively. Furthermore, we introduce a pretrain-to-refine self-supervised training paradigm to train our network. It enables proposed network to capture the associations between shape priors and observations, addressing the challenge of intra-class shape variations by utilising the diffusion mechanism. Extensive experiments conducted on four public datasets and a self-built dataset demonstrate that our method significantly outperforms state-of-the-art self-supervised category-level baselines and even surpasses some fully-supervised instance-level and category-level methods. The project page is released at Self-SRPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition 60

Self-supervised Semantic-driven Phoneme Discovery for Zero-r...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Wang, Liming Feng, Siyuan Hasegawa-Johnson, Mark Yoo, Chang D. Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Multimedia Computing Group Delft University of Technology Netherlands Artificial Intelligence and Machine Learning Lab KAIST Korea Republic of

ISBN: (纸本)9781955917216

Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resourced speech technology. In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels. Given the availability of phoneme segmentation and some mild conditions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate. Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phoneme-level representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art self-supervised representation learning algorithms. © 2022 Association for Computational Linguistics.

关键词： Speech recognition

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Beyond MobileNet: An Improved MobileNet for Retinal Diseases 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th International Conference on Medical Image computing and computer Assisted Intervention, MICCAI 2023

作者： Zhu, Wenhui Qiu, Peijie Chen, Xiwen Li, Huayu Wang, Hao Lepore, Natasha Dumitrascu, Oana M. Wang, Yalin School of Computing and Augmented Intelligence Arizona State University TempeAZ United States McKeley School of Engineering Washington University in St. Louis St. LouisMO United States School of Computing Clemson University ClemsonSC United States Department of Electrical and Computer Engineering The University of Arizona TucsonAZ United States Department of Neurology Mayo Clinic PhoenixAZ United States CIBORG Lab Department of Radiology Children’s Hospital Los Angeles Los AngelesCA United States

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Myopic Maculopathy (MM) is the leading cause of severe vision loss or blindness. Deep learning-based automated tools are indispensable in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated model designs, however, are prone to be overfitting and hinder their advantages in specific tasks in medical image analysis. In this work, we argue that a well-calibrated CNN model may mitigate these problems. To this end, we empirically investigated the macro and micro designs of a CNN and its training strategies by starting with a standard MobileNet. Based on the investigation, we proposed a lightweight MobileNet training framework equipped with a series of optimal parameters and modules based on retinal images. As a result of performance, our model secured third place in the MICCAI MMAC 2023 Challenge - Classification of Myopic Maculopathy. Our software package is available at https://***/Retinal-Research/NN-MOBILENET. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Action Recognition via Graph Convolutional Networks for the Assisted Living of the Elderly

Action Recognition via Graph Convolutional Networks for the ...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Zois Tsakiris Lazaros Tsochatzidis Ioannis Pratikakis Dimitrios Menychtas Nikolaos Aggelousis Department of Electrical and Computer Engineering Visual Computing Group Democritus University of Thrace Xanthi Greece Department of Physical Education and Sports Science Biomechanics Laboratory Democritus University of Thrace Komotini Greece

ISBN: (数字)9798350308921

ISBN: (纸本)9798350308938

Automated monitoring systems can play a crucial role in the effective assisted living of the elderly. Such systems aim to detect specific actions or activities of the individual which may indicate discomfort, pain or danger. Towards this end, we employ a skeleton-based representation for the human body, as a graph of interconnected nodes, that can be used as the means of meaningful spatial-temporal information of an action. In this paper, a novel methodology for human action recognition is proposed in the context of Graph Convolutional Networks that, along with the conventional full-body human graph, utilizes an arm-specific graph representation to better capture the fine-grained motion of the arms. For experimental evaluation, a new dataset is created that comprises a variety of actions, performed by multiple elderly individuals, specifically for the purpose of monitoring everyday life activities. Additionally, experimentation is extended to a well-established large dataset, namely NTU RGB+D 120, focusing on the actions that correspond to medical conditions. Experimental results demonstrate the effectiveness of our combined approach, compared to the single graph approach, in terms of recognition accuracy.

关键词： Medical conditions Graph convolutional networks Pain Conferences Focusing Nanoscale devices Assisted living

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The Design and Development of a GPU-accelerated Radar Simulator for Space Debris Monitoring 21

The Design and Development of a GPU-accelerated Radar Simula...

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5th High Performance computing and Cluster Technologies Conference, HPCCT 2021

作者： Martin, Mogamat Yaaseen Winberg, Simon Lucas Gaffar, Mohammed Yunus Abdul Macleod, David Department of Electrical Engineering University of Cape Town South Africa Advanced Computer Engineering Lab Centre for High Performance Computing South Africa

ISBN: (纸本)9781450390132

The problem of space debris represents a major topic of concern in astronomy as the threat of space junk continues to grow, and the accuracy of its tracking is greatly restricted by the insufficiency and limitations of current surveillance sensors. This article presents the development of an open-source, high-performance, signal-level radar simulator to assist in modelling the detection and tracking of space debris from terrestrial radar stations, including multistatic installations where the transmitter and receiver may be separated by many kilometers. This tool is expected to aid astronomers and researchers in space situational awareness, supporting the modelling of radar interactions in this context and simulation-based exploration of radar designs for space surveillance. It makes use of an accelerated orbit propagation technique with measured two-line element datasets being used to define space debris objects. The software has been named the Space Object Astrodynamics and Radar Simulator - or SOARS - and both the transmitted and received signals generated by the application have been shown to agree with theoretical expectations. Additionally, SOARS is presently undergoing continued development, extension and optimization for heterogeneous computing platforms, enabling the use of the NVIDIA® Compute Unified Device Architecture (CUDA) interface. Results have demonstrated promising speed-ups in simulation runtimes when using the CUDA version of the application over the original sequential version, even on lower-end graphics processors. It is anticipated that the developed application will be used for the design and testing of radar sensors for space situational awareness applications, as well as for use in research, teaching and training environments. © 2021 ACM.

关键词： Application programs

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China Nanyang Technological University College of Computing and Data Science Singapore Singapore University of New South Wales School of Electrical Engineering and Telecommunications NSW2052 Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem by jointly optimizing the beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices. The problem is constrained by total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, the maximum ratio transmission (MRT) and zero-forcing (ZF) are adopted to design the beamforming vectors. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches. Moreover, the implementation of the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2002-2012 IEEE.

关键词： Budget control

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IoT based Smart Fan Dimmer with suppressed Humming Sound and Nonlinear Effect of Inverter 15

IoT based Smart Fan Dimmer with suppressed Humming Sound and...

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15th International Conference on Open Source Systems and Technologies, ICOSST 2021

作者： Ikram, Ataul Aziz Imran, Hamza Ali Ikram, Ahmed Jamal Hamza, Kiran Sehgal, Khawaja Usman Riaz FAST-National University of Computer Emerging Sciences EPIC Lab Islamabad Pakistan School of Electrical Engineering Computer Science National University of Science and Technology Department of Computing Islamabad Pakistan School of Electrical Engineering Computer Science National University of Science and Technology Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9781665413053

To address power shortages in third-world countries, other energy sources such as uninterrupted power supplies that charge batteries while line voltage is available are employed. Inverters on the market contain output voltage waveforms that do not provide pure sine waves, and these modified sine waves are non-linear and high-frequency components that impair connected appliances such as fan dimmers. With the rising smart home and IoT industries in mind, this article presents a revised fan dimmer that does not generate humming noises, is resilient, and can be connected to the internet via WiFi using the ESP8266 board. This allows the user to control the speed of the fan from a smartphone via voice command or user-interface © 2021 IEEE.

关键词： Internet of things

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Expediting Distributed GNN Training with Feature-only Partition and Optimized Communication Planning

Expediting Distributed GNN Training with Feature-only Partit...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Bingqian Du Jun Liu Ziyue Luo Chuan Wu Qiankun Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering The Ohio State University USA Department of Computer Science The University of Hong Kong Hong Kong School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Feature-only partition of large graph data in distributed Graph Neural Network (GNN) training offers advantages over commonly adopted graph structure partition, such as minimal graph preprocessing cost and elimination of cross-worker subgraph sampling burdens. Nonetheless, performance bottleneck of GNN training with feature-only partitions still largely lies in the substantial communication overhead due to cross-worker feature fetching. To reduce the communication overhead and expedite distributed training, we first investigate and answer two key questions on convergence behaviors of GNN model in feature-partition based distribute GNN training: 1) As no worker holds a complete copy of each feature, can gradient exchange among workers compensate for the information loss due to incomplete local features? 2) If the answer to the first question is negative, is feature fetching in every training iteration of the GNN model necessary to ensure model convergence? Based on our theoretical findings on these questions, we derive an optimal communication plan that decides the frequency for feature fetching during the training process, taking into account bandwidth levels among workers and striking a balance between model loss and training time. Extensive evaluation demonstrates consistent results with our theoretical analysis, and the effectiveness of our proposed design.

关键词： Training Time-frequency analysis Costs Computational modeling Distributed databases Bandwidth Graph neural networks

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HyperScore: A unified measure to model hypertension progression using multi-modality measurements and semi-supervised learning

HyperScore: A unified measure to model hypertension progress...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alkhodari, Mohanad Lapidaire, Winok Xiong, Zhaohan Kart, Turkay Iturria-Medina, Yasser Hadjileontiadis, Leontios Khandoker, Ahsan Lewandowski, Adam J. Banerjee, Abhirup Leeson, Paul University of Oxford Cardiovascular Clinical Research Facility Radcliffe Department of Medicine Oxford United Kingdom Khalifa University Healthcare Engineering Innovation Center Department of Biomedical Engineering Abu Dhabi United Arab Emirates Imperial College London Biomedical Image Analysis Group Department of Computing London United Kingdom McGill University Neuroinformatics for Personalized Medicine Lab Montreal Canada Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece University of Oxford Institute of Biomedical Engineering Department of Engineering Science Oxford United Kingdom

ISBN: (纸本)9798350337488

Hypertension is a serious medical condition that affects over a billion people worldwide. The proper management of disease progression requires an extended knowledge of the overall functional and structural changes in the whole body in response to the hypertension. Here, we propose HyperScore, an integrative and unified measure of hypertension progression relative to multi-organ and multi-modality clinical measurements and based on a semi-supervised machine learning (ML) approach. We developed the measure based on a large participating cohort from the UK Biobank database (n=27,099) with over 500 imaging and clinical variables from multiple modalities. The semi-supervised approach was developed based on the contrastive trajectory inference mechanism to provide a score that reflects the proximity of a participant to the disease state (range: 0-1). Modelling revealed that majority of hypertensive participants had scores above 0.25, whereas normotensives had scores below this threshold. The sensitivity and specificity were above 89%, with an area under the receiver operating characteristics of 96.4%. The modelling showed a stable performance when evaluating hidden testing sets on a 10-fold cross-validation scheme with nearly 0.1 error. There was a strong association (r2>0.6) between HyperScore and organs' phenotypic patterns, especially for variables such as white matter hyperintensity and body mass index. This study is the first to potentiate ML-based modelling of hypertension progression from a multi-organ perspective, which could significantly aid in clinical decision making to save lives. © 2023 IEEE.

关键词： blood pressure contrastive PCA Hypertension machine learning semi-supervised learning

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