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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Pei, L"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.04577">arXiv:2501.04577</a> <span> [<a href="https://arxiv.org/pdf/2501.04577">pdf</a>, <a href="https://arxiv.org/format/2501.04577">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Enciso%2C+Z+M">Zephan M. Enciso</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+B">Boyang Cheng</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Likai Pei</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J">Jianbo Liu</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+S">Steven Davis</a>, <a href="/search/cs?searchtype=author&query=Niemier%2C+M">Michael Niemier</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+N">Ningyuan Cao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.04577v2-abstract-short" style="display: inline;"> Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e.g. autonomous vehicles or medical diagnosis. Bayesian neural networks (BNNs) use Bayesian statistics to provide both classification predictions and uncertainty estimation, but they suffer from high computational overhead associated with random number generation and repeated sample iterations. Furt… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.04577v2-abstract-full').style.display = 'inline'; document.getElementById('2501.04577v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.04577v2-abstract-full" style="display: none;"> Uncertainty estimation is an indispensable capability for AI-enabled, safety-critical applications, e.g. autonomous vehicles or medical diagnosis. Bayesian neural networks (BNNs) use Bayesian statistics to provide both classification predictions and uncertainty estimation, but they suffer from high computational overhead associated with random number generation and repeated sample iterations. Furthermore, BNNs are not immediately amenable to acceleration through compute-in-memory architectures due to the frequent memory writes necessary after each RNG operation. To address these challenges, we present an ASIC that integrates 360 fJ/Sample Gaussian RNG directly into the SRAM memory words. This integration reduces RNG overhead and enables fully-parallel compute-in-memory operations for BNNs. The prototype chip achieves 5.12 GSa/s RNG throughput and 102 GOp/s neural network throughput while occupying 0.45 mm2, bringing AI uncertainty estimation to edge computation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.04577v2-abstract-full').style.display = 'none'; document.getElementById('2501.04577v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 12 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> B.7.1; B.3.1; I.2.10; I.2.9 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.10235">arXiv:2412.10235</a> <span> [<a href="https://arxiv.org/pdf/2412.10235">pdf</a>, <a href="https://arxiv.org/format/2412.10235">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> EnvPoser: Environment-aware Realistic Human Motion Estimation from Sparse Observations with Uncertainty Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yu Zhang</a>, <a href="/search/cs?searchtype=author&query=Su%2C+Z">Zhuo Su</a>, <a href="/search/cs?searchtype=author&query=Zheng%2C+X">Xiaozheng Zheng</a>, <a href="/search/cs?searchtype=author&query=Lv%2C+Z">Zheng Lv</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+G">Guidong Wang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yongjie Zhang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.10235v1-abstract-short" style="display: inline;"> Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (i.e., hypotheses). This amplifies uncertainty and ambiguity in full-body motion estimation, especial… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.10235v1-abstract-full').style.display = 'inline'; document.getElementById('2412.10235v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.10235v1-abstract-full" style="display: none;"> Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (i.e., hypotheses). This amplifies uncertainty and ambiguity in full-body motion estimation, especially for the lower-body joints. Therefore, we propose a new method, EnvPoser, that employs a two-stage framework to perform full-body motion estimation using sparse tracking signals and pre-scanned environment from VR devices. EnvPoser models the multi-hypothesis nature of human motion through an uncertainty-aware estimation module in the first stage. In the second stage, we refine these multi-hypothesis estimates by integrating semantic and geometric environmental constraints, ensuring that the final motion estimation aligns realistically with both the environmental context and physical interactions. Qualitative and quantitative experiments on two public datasets demonstrate that our method achieves state-of-the-art performance, highlighting significant improvements in human motion estimation within motion-environment interaction scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.10235v1-abstract-full').style.display = 'none'; document.getElementById('2412.10235v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.19102">arXiv:2411.19102</a> <span> [<a href="https://arxiv.org/pdf/2411.19102">pdf</a>, <a href="https://arxiv.org/format/2411.19102">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> 360Recon: An Accurate Reconstruction Method Based on Depth Fusion from 360 Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yan%2C+Z">Zhongmiao Yan</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Deng%2C+J">Junyuan Deng</a>, <a href="/search/cs?searchtype=author&query=Mu%2C+X">Xiang Mu</a>, <a href="/search/cs?searchtype=author&query=Jin%2C+R">Renbiao Jin</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.19102v1-abstract-short" style="display: inline;"> 360-degree images offer a significantly wider field of view compared to traditional pinhole cameras, enabling sparse sampling and dense 3D reconstruction in low-texture environments. This makes them crucial for applications in VR, AR, and related fields. However, the inherent distortion caused by the wide field of view affects feature extraction and matching, leading to geometric consistency issue… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.19102v1-abstract-full').style.display = 'inline'; document.getElementById('2411.19102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.19102v1-abstract-full" style="display: none;"> 360-degree images offer a significantly wider field of view compared to traditional pinhole cameras, enabling sparse sampling and dense 3D reconstruction in low-texture environments. This makes them crucial for applications in VR, AR, and related fields. However, the inherent distortion caused by the wide field of view affects feature extraction and matching, leading to geometric consistency issues in subsequent multi-view reconstruction. In this work, we propose 360Recon, an innovative MVS algorithm for ERP images. The proposed spherical feature extraction module effectively mitigates distortion effects, and by combining the constructed 3D cost volume with multi-scale enhanced features from ERP images, our approach achieves high-precision scene reconstruction while preserving local geometric consistency. Experimental results demonstrate that 360Recon achieves state-of-the-art performance and high efficiency in depth estimation and 3D reconstruction on existing public panoramic reconstruction datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.19102v1-abstract-full').style.display = 'none'; document.getElementById('2411.19102v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14169">arXiv:2411.14169</a> <span> [<a href="https://arxiv.org/pdf/2411.14169">pdf</a>, <a href="https://arxiv.org/format/2411.14169">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jingyi Xu</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+X">Xieyuanli Chen</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+J">Junyi Ma</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+J">Jiawei Huang</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jintao Xu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yue Wang</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14169v1-abstract-short" style="display: inline;"> The task of occupancy forecasting (OCF) involves utilizing past and present perception data to predict future occupancy states of autonomous vehicle surrounding environments, which is critical for downstream tasks such as obstacle avoidance and path planning. Existing 3D OCF approaches struggle to predict plausible spatial details for movable objects and suffer from slow inference speeds due to ne… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14169v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14169v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14169v1-abstract-full" style="display: none;"> The task of occupancy forecasting (OCF) involves utilizing past and present perception data to predict future occupancy states of autonomous vehicle surrounding environments, which is critical for downstream tasks such as obstacle avoidance and path planning. Existing 3D OCF approaches struggle to predict plausible spatial details for movable objects and suffer from slow inference speeds due to neglecting the bias and uneven distribution of changing occupancy states in both space and time. In this paper, we propose a novel spatiotemporal decoupling vision-based paradigm to explicitly tackle the bias and achieve both effective and efficient 3D OCF. To tackle spatial bias in empty areas, we introduce a novel spatial representation that decouples the conventional dense 3D format into 2D bird's-eye view (BEV) occupancy with corresponding height values, enabling 3D OCF derived only from 2D predictions thus enhancing efficiency. To reduce temporal bias on static voxels, we design temporal decoupling to improve end-to-end OCF by temporally associating instances via predicted flows. We develop an efficient multi-head network EfficientOCF to achieve 3D OCF with our devised spatiotemporally decoupled representation. A new metric, conditional IoU (C-IoU), is also introduced to provide a robust 3D OCF performance assessment, especially in datasets with missing or incomplete annotations. The experimental results demonstrate that EfficientOCF surpasses existing baseline methods on accuracy and efficiency, achieving state-of-the-art performance with a fast inference time of 82.33ms with a single GPU. Our code will be released as open source. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14169v1-abstract-full').style.display = 'none'; document.getElementById('2411.14169v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07828">arXiv:2411.07828</a> <span> [<a href="https://arxiv.org/pdf/2411.07828">pdf</a>, <a href="https://arxiv.org/ps/2411.07828">ps</a>, <a href="https://arxiv.org/format/2411.07828">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+L">Lan Sun</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Deng%2C+J">Junyuan Deng</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jiarui Yang</a>, <a href="/search/cs?searchtype=author&query=Lai%2C+Z">Zengyuan Lai</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07828v1-abstract-short" style="display: inline;"> With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven approaches, though improving accuracy, often lack robustness due to reliance on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07828v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07828v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07828v1-abstract-full" style="display: none;"> With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven approaches, though improving accuracy, often lack robustness due to reliance on a single device.In our work, we attempt to enhance the positioning performance using the low-cost commodity IMUs embedded in the wearable devices. We propose a multi-device deep learning framework named Suite-IN, aggregating motion data from Apple Suite for inertial navigation. Motion data captured by sensors on different body parts contains both local and global motion information, making it essential to reduce the negative effects of localized movements and extract global motion representations from multiple devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07828v1-abstract-full').style.display = 'none'; document.getElementById('2411.07828v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.20342">arXiv:2409.20342</a> <span> [<a href="https://arxiv.org/pdf/2409.20342">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murugesan%2C+G+K">Gowtham Krishnan Murugesan</a>, <a href="/search/cs?searchtype=author&query=McCrumb%2C+D">Diana McCrumb</a>, <a href="/search/cs?searchtype=author&query=Soni%2C+R">Rahul Soni</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+J">Jithendra Kumar</a>, <a href="/search/cs?searchtype=author&query=Nuernberg%2C+L">Leonard Nuernberg</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Linmin Pei</a>, <a href="/search/cs?searchtype=author&query=Wagner%2C+U">Ulrike Wagner</a>, <a href="/search/cs?searchtype=author&query=Granger%2C+S">Sutton Granger</a>, <a href="/search/cs?searchtype=author&query=Fedorov%2C+A+Y">Andrey Y. Fedorov</a>, <a href="/search/cs?searchtype=author&query=Moore%2C+S">Stephen Moore</a>, <a href="/search/cs?searchtype=author&query=Van+Oss%2C+J">Jeff Van Oss</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.20342v1-abstract-short" style="display: inline;"> AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance ima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20342v1-abstract-full').style.display = 'inline'; document.getElementById('2409.20342v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.20342v1-abstract-full" style="display: none;"> AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20342v1-abstract-full').style.display = 'none'; document.getElementById('2409.20342v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.14070">arXiv:2409.14070</a> <span> [<a href="https://arxiv.org/pdf/2409.14070">pdf</a>, <a href="https://arxiv.org/format/2409.14070">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.13140/RG.2.2.33195.86560">10.13140/RG.2.2.33195.86560 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ma%2C+K">Kehui Ma</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zhen Sun</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+C">Chaoran Xiong</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+Q">Qiumin Zhu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+K">Kewei Wang</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.14070v1-abstract-short" style="display: inline;"> Traversability estimation is the foundation of path planning for a general navigation system. However, complex and dynamic environments pose challenges for the latest methods using self-supervised learning (SSL) technique. Firstly, existing SSL-based methods generate sparse annotations lacking detailed boundary information. Secondly, their strategies focus on hard samples for rapid adaptation, lea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14070v1-abstract-full').style.display = 'inline'; document.getElementById('2409.14070v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.14070v1-abstract-full" style="display: none;"> Traversability estimation is the foundation of path planning for a general navigation system. However, complex and dynamic environments pose challenges for the latest methods using self-supervised learning (SSL) technique. Firstly, existing SSL-based methods generate sparse annotations lacking detailed boundary information. Secondly, their strategies focus on hard samples for rapid adaptation, leading to forgetting and biased predictions. In this work, we propose IMOST, a continual traversability learning framework composed of two key modules: incremental dynamic memory (IDM) and self-supervised annotation (SSA). By mimicking human memory mechanisms, IDM allocates novel data samples to new clusters according to information expansion criterion. It also updates clusters based on diversity rule, ensuring a representative characterization of new scene. This mechanism enhances scene-aware knowledge diversity while maintaining a compact memory capacity. The SSA module, integrating FastSAM, utilizes point prompts to generate complete annotations in real time which reduces training complexity. Furthermore, IMOST has been successfully deployed on the quadruped robot, with performance evaluated during the online learning process. Experimental results on both public and self-collected datasets demonstrate that our IMOST outperforms current state-of-the-art method, maintains robust recognition capabilities and adaptability across various scenarios. The code is available at https://github.com/SJTU-MKH/OCLTrav. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14070v1-abstract-full').style.display = 'none'; document.getElementById('2409.14070v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.18045">arXiv:2406.18045</a> <span> [<a href="https://arxiv.org/pdf/2406.18045">pdf</a>, <a href="https://arxiv.org/format/2406.18045">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+L">Linqing Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+W">Weilei Wang</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+Z">Zilong Bai</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+P">Peng Xu</a>, <a href="/search/cs?searchtype=author&query=Fang%2C+Y">Yan Fang</a>, <a href="/search/cs?searchtype=author&query=Fang%2C+J">Jie Fang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+W">Wentao Wu</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+L">Lizhi Zhou</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+R">Ruiji Zhang</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+Y">Yubin Xia</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+C">Chaobo Xu</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+R">Ran Hu</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+L">Licong Xu</a>, <a href="/search/cs?searchtype=author&query=Cai%2C+Q">Qijun Cai</a>, <a href="/search/cs?searchtype=author&query=Hua%2C+H">Haoran Hua</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+J">Jing Sun</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J">Jin Liu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+T">Tian Qiu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Haowen Liu</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+M">Meng Hu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+X">Xiuwen Li</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+F">Fei Gao</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yufu Wang</a>, <a href="/search/cs?searchtype=author&query=Tie%2C+L">Lin Tie</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+C">Chaochao Wang</a> , et al. (11 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.18045v3-abstract-short" style="display: inline;"> Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by minimizing the need for complex feature engineering. However, the application of LLMs in specialized domains like biopharmaceuticals and chemistry remains largely unexplored. These fields are characterized by intricate terminologies, specialized knowledge, and a high demand for precision areas where general purpo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18045v3-abstract-full').style.display = 'inline'; document.getElementById('2406.18045v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.18045v3-abstract-full" style="display: none;"> Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by minimizing the need for complex feature engineering. However, the application of LLMs in specialized domains like biopharmaceuticals and chemistry remains largely unexplored. These fields are characterized by intricate terminologies, specialized knowledge, and a high demand for precision areas where general purpose LLMs often fall short. In this study, we introduce PharmaGPT, a suite of domain specilized LLMs with 13 billion and 70 billion parameters, specifically trained on a comprehensive corpus tailored to the Bio-Pharmaceutical and Chemical domains. Our evaluation shows that PharmaGPT surpasses existing general models on specific-domain benchmarks such as NAPLEX, demonstrating its exceptional capability in domain-specific tasks. Remarkably, this performance is achieved with a model that has only a fraction, sometimes just one-tenth-of the parameters of general-purpose large models. This advancement establishes a new benchmark for LLMs in the bio-pharmaceutical and chemical fields, addressing the existing gap in specialized language modeling. It also suggests a promising path for enhanced research and development, paving the way for more precise and effective NLP applications in these areas. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18045v3-abstract-full').style.display = 'none'; document.getElementById('2406.18045v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.08187">arXiv:2406.08187</a> <span> [<a href="https://arxiv.org/pdf/2406.08187">pdf</a>, <a href="https://arxiv.org/format/2406.08187">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Learning-based Traversability Costmap for Autonomous Off-road Navigation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhu%2C+Q">Qiumin Zhu</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zhen Sun</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+G">Guoqing Liu</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+K">Kehui Ma</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+Z">Zheng Gong</a>, <a href="/search/cs?searchtype=author&query=Jin%2C+C">Cheng Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.08187v2-abstract-short" style="display: inline;"> Traversability estimation in off-road terrains is an essential procedure for autonomous navigation. However, creating reliable labels for complex interactions between the robot and the surface is still a challenging problem in learning-based costmap generation. To address this, we propose a method that predicts traversability costmaps by leveraging both visual and geometric information of the envi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08187v2-abstract-full').style.display = 'inline'; document.getElementById('2406.08187v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.08187v2-abstract-full" style="display: none;"> Traversability estimation in off-road terrains is an essential procedure for autonomous navigation. However, creating reliable labels for complex interactions between the robot and the surface is still a challenging problem in learning-based costmap generation. To address this, we propose a method that predicts traversability costmaps by leveraging both visual and geometric information of the environment. To quantify the surface properties like roughness and bumpiness, we introduce a novel way of risk-aware labelling with proprioceptive information for network training. We validate our method in costmap prediction and navigation tasks for complex off-road scenarios. Our results demonstrate that our costmap prediction method excels in terms of average accuracy and MSE. The navigation results indicate that using our learned costmaps leads to safer and smoother driving, outperforming previous methods in terms of the highest success rate, lowest normalized trajectory length, lowest time cost, and highest mean stability across two scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08187v2-abstract-full').style.display = 'none'; document.getElementById('2406.08187v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.04649">arXiv:2406.04649</a> <span> [<a href="https://arxiv.org/pdf/2406.04649">pdf</a>, <a href="https://arxiv.org/format/2406.04649">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> SMART: Scene-motion-aware human action recognition framework for mental disorder group </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lai%2C+Z">Zengyuan Lai</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jiarui Yang</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zhen Sun</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.04649v1-abstract-short" style="display: inline;"> Patients with mental disorders often exhibit risky abnormal actions, such as climbing walls or hitting windows, necessitating intelligent video behavior monitoring for smart healthcare with the rising Internet of Things (IoT) technology. However, the development of vision-based Human Action Recognition (HAR) for these actions is hindered by the lack of specialized algorithms and datasets. In this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04649v1-abstract-full').style.display = 'inline'; document.getElementById('2406.04649v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.04649v1-abstract-full" style="display: none;"> Patients with mental disorders often exhibit risky abnormal actions, such as climbing walls or hitting windows, necessitating intelligent video behavior monitoring for smart healthcare with the rising Internet of Things (IoT) technology. However, the development of vision-based Human Action Recognition (HAR) for these actions is hindered by the lack of specialized algorithms and datasets. In this paper, we innovatively propose to build a vision-based HAR dataset including abnormal actions often occurring in the mental disorder group and then introduce a novel Scene-Motion-aware Action Recognition Technology framework, named SMART, consisting of two technical modules. First, we propose a scene perception module to extract human motion trajectory and human-scene interaction features, which introduces additional scene information for a supplementary semantic representation of the above actions. Second, the multi-stage fusion module fuses the skeleton motion, motion trajectory, and human-scene interaction features, enhancing the semantic association between the skeleton motion and the above supplementary representation, thus generating a comprehensive representation with both human motion and scene information. The effectiveness of our proposed method has been validated on our self-collected HAR dataset (MentalHAD), achieving 94.9% and 93.1% accuracy in un-seen subjects and scenes and outperforming state-of-the-art approaches by 6.5% and 13.2%, respectively. The demonstrated subject- and scene- generalizability makes it possible for SMART's migration to practical deployment in smart healthcare systems for mental disorder patients in medical settings. The code and dataset will be released publicly for further research: https://github.com/Inowlzy/SMART.git. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04649v1-abstract-full').style.display = 'none'; document.getElementById('2406.04649v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.07736">arXiv:2405.07736</a> <span> [<a href="https://arxiv.org/pdf/2405.07736">pdf</a>, <a href="https://arxiv.org/format/2405.07736">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Learning to Plan Maneuverable and Agile Flight Trajectory with Optimization Embedded Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Han%2C+Z">Zhichao Han</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+L">Long Xu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Liuao Pei</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+F">Fei Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.07736v4-abstract-short" style="display: inline;"> In recent times, an increasing number of researchers have been devoted to utilizing deep neural networks for end-to-end flight navigation. This approach has gained traction due to its ability to bridge the gap between perception and planning that exists in traditional methods, thereby eliminating delays between modules. However, the practice of replacing original modules with neural networks in a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.07736v4-abstract-full').style.display = 'inline'; document.getElementById('2405.07736v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.07736v4-abstract-full" style="display: none;"> In recent times, an increasing number of researchers have been devoted to utilizing deep neural networks for end-to-end flight navigation. This approach has gained traction due to its ability to bridge the gap between perception and planning that exists in traditional methods, thereby eliminating delays between modules. However, the practice of replacing original modules with neural networks in a black-box manner diminishes the overall system's robustness and stability. It lacks principled explanations and often fails to consistently generate high-quality motion trajectories. Furthermore, such methods often struggle to rigorously account for the robot's kinematic constraints, resulting in the generation of trajectories that cannot be executed satisfactorily. In this work, we combine the advantages of traditional methods and neural networks by proposing an optimization-embedded neural network. This network can learn high-quality trajectories directly from visual inputs without the need of mapping, while ensuring dynamic feasibility. Here, the deep neural network is employed to directly extract environment safety regions from depth images. Subsequently, we employ a model-based approach to represent these regions as safety constraints in trajectory optimization. Leveraging the availability of highly efficient optimization algorithms, our method robustly converges to feasible and optimal solutions that satisfy various user-defined constraints. Moreover, we differentiate the optimization process, allowing it to be trained as a layer within the neural network. This approach facilitates the direct interaction between perception and planning, enabling the network to focus more on the spatial regions where optimal solutions exist. As a result, it further enhances the quality and stability of the generated trajectories. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.07736v4-abstract-full').style.display = 'none'; document.getElementById('2405.07736v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Some statements in the introduction may be controversial</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.18518">arXiv:2404.18518</a> <span> [<a href="https://arxiv.org/pdf/2404.18518">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Digital Libraries">cs.DL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> From ChatGPT, DALL-E 3 to Sora: How has Generative AI Changed Digital Humanities Research and Services? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+J">Jiangfeng Liu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Ziyi Wang</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+J">Jing Xie</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Lei Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.18518v1-abstract-short" style="display: inline;"> Generative large-scale language models create the fifth paradigm of scientific research, organically combine data science and computational intelligence, transform the research paradigm of natural language processing and multimodal information processing, promote the new trend of AI-enabled social science research, and provide new ideas for digital humanities research and application. This article… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18518v1-abstract-full').style.display = 'inline'; document.getElementById('2404.18518v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.18518v1-abstract-full" style="display: none;"> Generative large-scale language models create the fifth paradigm of scientific research, organically combine data science and computational intelligence, transform the research paradigm of natural language processing and multimodal information processing, promote the new trend of AI-enabled social science research, and provide new ideas for digital humanities research and application. This article profoundly explores the application of large-scale language models in digital humanities research, revealing their significant potential in ancient book protection, intelligent processing, and academic innovation. The article first outlines the importance of ancient book resources and the necessity of digital preservation, followed by a detailed introduction to developing large-scale language models, such as ChatGPT, and their applications in document management, content understanding, and cross-cultural research. Through specific cases, the article demonstrates how AI can assist in the organization, classification, and content generation of ancient books. Then, it explores the prospects of AI applications in artistic innovation and cultural heritage preservation. Finally, the article explores the challenges and opportunities in the interaction of technology, information, and society in the digital humanities triggered by AI technologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.18518v1-abstract-full').style.display = 'none'; document.getElementById('2404.18518v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.12504">arXiv:2403.12504</a> <span> [<a href="https://arxiv.org/pdf/2403.12504">pdf</a>, <a href="https://arxiv.org/format/2403.12504">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> TON-VIO: Online Time Offset Modeling Networks for Robust Temporal Alignment in High Dynamic Motion VIO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xiong%2C+C">Chaoran Xiong</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+G">Guoqing Liu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Hua%2C+T">Tong Hua</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+K">Kehui Ma</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zhen Sun</a>, <a href="/search/cs?searchtype=author&query=Xiang%2C+Y">Yan Xiang</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.12504v1-abstract-short" style="display: inline;"> Temporal misalignment (time offset) between sensors is common in low cost visual-inertial odometry (VIO) systems. Such temporal misalignment introduces inconsistent constraints for state estimation, leading to a significant positioning drift especially in high dynamic motion scenarios. In this article, we focus on online temporal calibration to reduce the positioning drift caused by the time offse… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12504v1-abstract-full').style.display = 'inline'; document.getElementById('2403.12504v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.12504v1-abstract-full" style="display: none;"> Temporal misalignment (time offset) between sensors is common in low cost visual-inertial odometry (VIO) systems. Such temporal misalignment introduces inconsistent constraints for state estimation, leading to a significant positioning drift especially in high dynamic motion scenarios. In this article, we focus on online temporal calibration to reduce the positioning drift caused by the time offset for high dynamic motion VIO. For the time offset observation model, most existing methods rely on accurate state estimation or stable visual tracking. For the prediction model, current methods oversimplify the time offset as a constant value with white Gaussian noise. However, these ideal conditions are seldom satisfied in real high dynamic scenarios, resulting in the poor performance. In this paper, we introduce online time offset modeling networks (TON) to enhance real-time temporal calibration. TON improves the accuracy of time offset observation and prediction modeling. Specifically, for observation modeling, we propose feature velocity observation networks to enhance velocity computation for features in unstable visual tracking conditions. For prediction modeling, we present time offset prediction networks to learn its evolution pattern. To highlight the effectiveness of our method, we integrate the proposed TON into both optimization-based and filter-based VIO systems. Simulation and real-world experiments are conducted to demonstrate the enhanced performance of our approach. Additionally, to contribute to the VIO community, we will open-source the code of our method on: https://github.com/Franky-X/FVON-TPN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12504v1-abstract-full').style.display = 'none'; document.getElementById('2403.12504v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.10340">arXiv:2403.10340</a> <span> [<a href="https://arxiv.org/pdf/2403.10340">pdf</a>, <a href="https://arxiv.org/format/2403.10340">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Thermal-NeRF: Neural Radiance Fields from an Infrared Camera </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ye%2C+T">Tianxiang Ye</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Deng%2C+J">Junyuan Deng</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+G">Guoqing Liu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+L">Liu Liu</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Pang%2C+L">Liang Pang</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.10340v1-abstract-short" style="display: inline;"> In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant reliance on RGB imaging presupposes ideal lighting conditions: a premise frequently unmet in roboti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.10340v1-abstract-full').style.display = 'inline'; document.getElementById('2403.10340v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.10340v1-abstract-full" style="display: none;"> In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant reliance on RGB imaging presupposes ideal lighting conditions: a premise frequently unmet in robotic applications plagued by poor lighting or visual obstructions. This limitation overlooks the capabilities of infrared (IR) cameras, which excel in low-light detection and present a robust alternative under such adverse scenarios. To tackle these issues, we introduce Thermal-NeRF, the first method that estimates a volumetric scene representation in the form of a NeRF solely from IR imaging. By leveraging a thermal mapping and structural thermal constraint derived from the thermal characteristics of IR imaging, our method showcasing unparalleled proficiency in recovering NeRFs in visually degraded scenes where RGB-based methods fall short. We conduct extensive experiments to demonstrate that Thermal-NeRF can achieve superior quality compared to existing methods. Furthermore, we contribute a dataset for IR-based NeRF applications, paving the way for future research in IR NeRF reconstruction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.10340v1-abstract-full').style.display = 'none'; document.getElementById('2403.10340v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.17264">arXiv:2402.17264</a> <span> [<a href="https://arxiv.org/pdf/2402.17264">pdf</a>, <a href="https://arxiv.org/format/2402.17264">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Explicit Interaction for Fusion-Based Place Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jingyi Xu</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+J">Junyi Ma</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Z">Zijie Zhou</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yue Wang</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+X">Xieyuanli Chen</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.17264v1-abstract-short" style="display: inline;"> Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. While achieving remarkable results, they do not explicitly consider what the individual modality affor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17264v1-abstract-full').style.display = 'inline'; document.getElementById('2402.17264v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.17264v1-abstract-full" style="display: none;"> Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. While achieving remarkable results, they do not explicitly consider what the individual modality affords in the fusion system. Therefore, the benefit of multi-modal feature fusion may not be fully explored. In this paper, we propose a novel fusion-based network, dubbed EINet, to achieve explicit interaction of the two modalities. EINet uses LiDAR ranges to supervise more robust vision features for long time spans, and simultaneously uses camera RGB data to improve the discrimination of LiDAR point clouds. In addition, we develop a new benchmark for the place recognition task based on the nuScenes dataset. To establish this benchmark for future research with comprehensive comparisons, we introduce both supervised and self-supervised training schemes alongside evaluation protocols. We conduct extensive experiments on the proposed benchmark, and the experimental results show that our EINet exhibits better recognition performance as well as solid generalization ability compared to the state-of-the-art fusion-based place recognition approaches. Our open-source code and benchmark are released at: https://github.com/BIT-XJY/EINet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.17264v1-abstract-full').style.display = 'none'; document.getElementById('2402.17264v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.10346">arXiv:2312.10346</a> <span> [<a href="https://arxiv.org/pdf/2312.10346">pdf</a>, <a href="https://arxiv.org/format/2312.10346">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> MMBaT: A Multi-task Framework for mmWave-based Human Body Reconstruction and Translation Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jiarui Yang</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yifan Song</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.10346v1-abstract-short" style="display: inline;"> Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions. Despite pioneering efforts in this field, two challenges persist. Firstly, raw point clouds contain massive noise points, usually caused by the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10346v1-abstract-full').style.display = 'inline'; document.getElementById('2312.10346v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.10346v1-abstract-full" style="display: none;"> Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions. Despite pioneering efforts in this field, two challenges persist. Firstly, raw point clouds contain massive noise points, usually caused by the ambient objects and multi-path effects of Radio Frequency (RF) signals. Recent approaches typically rely on prior knowledge or elaborate preprocessing methods, limiting their applicability. Secondly, even after noise removal, the sparse and inconsistent body-related points pose an obstacle to accurate human body reconstruction. To address these challenges, we introduce mmBaT, a novel multi-task deep learning framework that concurrently estimates the human body and predicts body translations in subsequent frames to extract body-related point clouds. Our method is evaluated on two public datasets that are collected with different radar devices and noise levels. A comprehensive comparison against other state-of-the-art methods demonstrates our method has a superior reconstruction performance and generalization ability from noisy raw data, even when compared to methods provided with body-related point clouds. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10346v1-abstract-full').style.display = 'none'; document.getElementById('2312.10346v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 2 figures, accepted by IEEE ICASSP 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.02196">arXiv:2312.02196</a> <span> [<a href="https://arxiv.org/pdf/2312.02196">pdf</a>, <a href="https://arxiv.org/format/2312.02196">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yu Zhang</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jiarui Yang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.02196v2-abstract-short" style="display: inline;"> This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02196v2-abstract-full').style.display = 'inline'; document.getElementById('2312.02196v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.02196v2-abstract-full" style="display: none;"> This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors, and a part-based model dividing the body and sensor data into three regions, each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets, notably reducing pose error by 19\% on the DIP-IMU dataset, thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at {\url{https://github.com/dx118/dynaip}}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02196v2-abstract-full').style.display = 'none'; document.getElementById('2312.02196v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by CVPR2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.07100">arXiv:2311.07100</a> <span> [<a href="https://arxiv.org/pdf/2311.07100">pdf</a>, <a href="https://arxiv.org/format/2311.07100">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Collaborative Planning for Catching and Transporting Objects in Unstructured Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pei%2C+L">Liuao Pei</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+J">Junxiao Lin</a>, <a href="/search/cs?searchtype=author&query=Han%2C+Z">Zhichao Han</a>, <a href="/search/cs?searchtype=author&query=Quan%2C+L">Lun Quan</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+Y">Yanjun Cao</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+C">Chao Xu</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+F">Fei Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.07100v1-abstract-short" style="display: inline;"> Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07100v1-abstract-full').style.display = 'inline'; document.getElementById('2311.07100v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.07100v1-abstract-full" style="display: none;"> Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form.This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state.Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints.We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.07100v1-abstract-full').style.display = 'none'; document.getElementById('2311.07100v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.04477">arXiv:2311.04477</a> <span> [<a href="https://arxiv.org/pdf/2311.04477">pdf</a>, <a href="https://arxiv.org/format/2311.04477">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> PLV-IEKF: Consistent Visual-Inertial Odometry using Points, Lines, and Vanishing Points </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hua%2C+T">Tong Hua</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T">Tao Li</a>, <a href="/search/cs?searchtype=author&query=Pang%2C+L">Liang Pang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+G">Guoqing Liu</a>, <a href="/search/cs?searchtype=author&query=Xuanyuan%2C+W">Wencheng Xuanyuan</a>, <a href="/search/cs?searchtype=author&query=Shu%2C+C">Chang Shu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.04477v1-abstract-short" style="display: inline;"> In this paper, we propose an Invariant Extended Kalman Filter (IEKF) based Visual-Inertial Odometry (VIO) using multiple features in man-made environments. Conventional EKF-based VIO usually suffers from system inconsistency and angular drift that naturally occurs in feature-based methods. However, in man-made environments, notable structural regularities, such as lines and vanishing points, offer… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.04477v1-abstract-full').style.display = 'inline'; document.getElementById('2311.04477v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.04477v1-abstract-full" style="display: none;"> In this paper, we propose an Invariant Extended Kalman Filter (IEKF) based Visual-Inertial Odometry (VIO) using multiple features in man-made environments. Conventional EKF-based VIO usually suffers from system inconsistency and angular drift that naturally occurs in feature-based methods. However, in man-made environments, notable structural regularities, such as lines and vanishing points, offer valuable cues for localization. To exploit these structural features effectively and maintain system consistency, we design a right invariant filter-based VIO scheme incorporating point, line, and vanishing point features. We demonstrate that the conventional additive error definition for point features can also preserve system consistency like the invariant error definition by proving a mathematically equivalent measurement model. And a similar conclusion is established for line features. Additionally, we conduct an invariant filter-based observability analysis proving that vanishing point measurement maintains unobservable directions naturally. Both simulation and real-world tests are conducted to validate our methods' pose accuracy and consistency. The experimental results validate the competitive performance of our method, highlighting its ability to deliver accurate and consistent pose estimation in man-made environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.04477v1-abstract-full').style.display = 'none'; document.getElementById('2311.04477v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ROBIO 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.09114">arXiv:2310.09114</a> <span> [<a href="https://arxiv.org/pdf/2310.09114">pdf</a>, <a href="https://arxiv.org/format/2310.09114">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+J">Jiarui Yang</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+R+C">Robert C. Qiu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.09114v1-abstract-short" style="display: inline;"> Human activity recognition (HAR) with wearables is one of the serviceable technologies in ubiquitous and mobile computing applications. The sliding-window scheme is widely adopted while suffering from the multi-class windows problem. As a result, there is a growing focus on joint segmentation and recognition with deep-learning methods, aiming at simultaneously dealing with HAR and time-series segm… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.09114v1-abstract-full').style.display = 'inline'; document.getElementById('2310.09114v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.09114v1-abstract-full" style="display: none;"> Human activity recognition (HAR) with wearables is one of the serviceable technologies in ubiquitous and mobile computing applications. The sliding-window scheme is widely adopted while suffering from the multi-class windows problem. As a result, there is a growing focus on joint segmentation and recognition with deep-learning methods, aiming at simultaneously dealing with HAR and time-series segmentation issues. However, obtaining the full activity annotations of wearable data sequences is resource-intensive or time-consuming, while unsupervised methods yield poor performance. To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment. However, the limited information of sparse annotations exacerbates the gap between recognition and segmentation tasks, leading to sub-optimal model performance. Therefore, the prototypes are estimated by class-activation maps to form a sample-to-prototype contrast module for well-structured embeddings. Moreover, with the optimal transport theory, our approach generates the sample-level pseudo-labels that take advantage of unlabeled data between timestamp annotations for further performance improvement. Comprehensive experiments on four public HAR datasets demonstrate that our model trained with timestamp supervision is superior to the state-of-the-art weakly-supervised methods and achieves comparable performance to the fully-supervised approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.09114v1-abstract-full').style.display = 'none'; document.getElementById('2310.09114v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under Review (submitted to IEEE TMC)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.11195">arXiv:2306.11195</a> <span> [<a href="https://arxiv.org/pdf/2306.11195">pdf</a>, <a href="https://arxiv.org/format/2306.11195">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> </div> <p class="title is-5 mathjax"> New Cross-Core Cache-Agnostic and Prefetcher-based Side-Channels and Covert-Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yun Chen</a>, <a href="/search/cs?searchtype=author&query=Hajiabadi%2C+A">Ali Hajiabadi</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Lingfeng Pei</a>, <a href="/search/cs?searchtype=author&query=Carlson%2C+T+E">Trevor E. Carlson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.11195v1-abstract-short" style="display: inline;"> In this paper, we reveal the existence of a new class of prefetcher, the XPT prefetcher, in the modern Intel processors which has never been officially documented. It speculatively issues a load, bypassing last-level cache (LLC) lookups, when it predicts that a load request will result in an LLC miss. We demonstrate that XPT prefetcher is shared among different cores, which enables an attacker to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.11195v1-abstract-full').style.display = 'inline'; document.getElementById('2306.11195v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.11195v1-abstract-full" style="display: none;"> In this paper, we reveal the existence of a new class of prefetcher, the XPT prefetcher, in the modern Intel processors which has never been officially documented. It speculatively issues a load, bypassing last-level cache (LLC) lookups, when it predicts that a load request will result in an LLC miss. We demonstrate that XPT prefetcher is shared among different cores, which enables an attacker to build cross-core side-channel and covert-channel attacks. We propose PrefetchX, a cross-core attack mechanism, to leak users' sensitive data and activities. We empirically demonstrate that PrefetchX can be used to extract private keys of real-world RSA applications. Furthermore, we show that PrefetchX can enable side-channel attacks that can monitor keystrokes and network traffic patterns of users. Our two cross-core covert-channel attacks also see a low error rate and a 1.7MB/s maximum channel capacity. Due to the cache-independent feature of PrefetchX, current cache-based mitigations are not effective against our attacks. Overall, our work uncovers a significant vulnerability in the XPT prefetcher, which can be exploited to compromise the confidentiality of sensitive information in both crypto and non-crypto-related applications among processor cores. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.11195v1-abstract-full').style.display = 'none'; document.getElementById('2306.11195v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 12 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04754">arXiv:2306.04754</a> <span> [<a href="https://arxiv.org/pdf/2306.04754">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Temtam%2C+A">A. Temtam</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">L. Pei</a>, <a href="/search/cs?searchtype=author&query=Iftekharuddin%2C+K">K. Iftekharuddin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.04754v1-abstract-short" style="display: inline;"> Computational modeling of Multiresolution- Fractional Brownian motion (fBm) has been effective in stochastic multiscale fractal texture feature extraction and machine learning of abnormal brain tissue segmentation. Further, deep multiresolution methods have been used for pixel-wise brain tissue segmentation. Robust tissue segmentation and volumetric measurement may provide more objective quantific… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04754v1-abstract-full').style.display = 'inline'; document.getElementById('2306.04754v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04754v1-abstract-full" style="display: none;"> Computational modeling of Multiresolution- Fractional Brownian motion (fBm) has been effective in stochastic multiscale fractal texture feature extraction and machine learning of abnormal brain tissue segmentation. Further, deep multiresolution methods have been used for pixel-wise brain tissue segmentation. Robust tissue segmentation and volumetric measurement may provide more objective quantification of disease burden and offer improved tracking of treatment response for the disease. However, we posit that computational modeling of deep multiresolution fractal texture features may offer elegant feature learning. Consequently, this work proposes novel modeling of Multiresolution Fractal Deep Neural Network (MFDNN) and its computational implementation that mathematically combines a multiresolution fBm model and deep multiresolution analysis. The proposed full 3D MFDNN model offers the desirable properties of estimating multiresolution stochastic texture features by analyzing large amount of raw MRI image data for brain tumor segmentation. We apply the proposed MFDNN to estimate stochastic deep multiresolution fractal texture features for tumor tissues in brain MRI images. The MFDNN model is evaluated using 1251 patient cases for brain tumor segmentation using the most recent BRATS 2021 Challenges dataset. The evaluation of the proposed model using Dice overlap score, Husdorff distance and associated uncertainty estimation offers either better or comparable performances in abnormal brain tissue segmentation when compared to the state-of-the-art methods in the literature. Index Terms: Computational Modeling, Multiresolution Fractional Brownian Motion (fBm), Deep Multiresolution Analysis, Fractal Dimension (FD), Texture Features, Brain tumor segmentation, Deep Learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04754v1-abstract-full').style.display = 'none'; document.getElementById('2306.04754v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10029">arXiv:2305.10029</a> <span> [<a href="https://arxiv.org/pdf/2305.10029">pdf</a>, <a href="https://arxiv.org/format/2305.10029">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> TextSLAM: Visual SLAM with Semantic Planar Text Features </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+B">Boying Li</a>, <a href="/search/cs?searchtype=author&query=Zou%2C+D">Danping Zou</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+Y">Yuan Huang</a>, <a href="/search/cs?searchtype=author&query=Niu%2C+X">Xinghan Niu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.10029v2-abstract-short" style="display: inline;"> We propose a novel visual SLAM method that integrates text objects tightly by treating them as semantic features via fully exploring their geometric and semantic prior. The text object is modeled as a texture-rich planar patch whose semantic meaning is extracted and updated on the fly for better data association. With the full exploration of locally planar characteristics and semantic meaning of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10029v2-abstract-full').style.display = 'inline'; document.getElementById('2305.10029v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10029v2-abstract-full" style="display: none;"> We propose a novel visual SLAM method that integrates text objects tightly by treating them as semantic features via fully exploring their geometric and semantic prior. The text object is modeled as a texture-rich planar patch whose semantic meaning is extracted and updated on the fly for better data association. With the full exploration of locally planar characteristics and semantic meaning of text objects, the SLAM system becomes more accurate and robust even under challenging conditions such as image blurring, large viewpoint changes, and significant illumination variations (day and night). We tested our method in various scenes with the ground truth data. The results show that integrating texture features leads to a more superior SLAM system that can match images across day and night. The reconstructed semantic 3D text map could be useful for navigation and scene understanding in robotic and mixed reality applications. Our project page: https://github.com/SJTU-ViSYS/TextSLAM . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10029v2-abstract-full').style.display = 'none'; document.getElementById('2305.10029v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 23 figures. Whole project page: https://leeby68.github.io/TextSLAM/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.10709">arXiv:2303.10709</a> <span> [<a href="https://arxiv.org/pdf/2303.10709">pdf</a>, <a href="https://arxiv.org/format/2303.10709">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Deng%2C+J">Junyuan Deng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+X">Xieyuanli Chen</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zhen Sun</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+G">Guoqing Liu</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.10709v1-abstract-short" style="display: inline;"> Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance fields (NeRF) have shown promising advances in implicit reconstruction for indoor environments, the probl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.10709v1-abstract-full').style.display = 'inline'; document.getElementById('2303.10709v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.10709v1-abstract-full" style="display: none;"> Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance fields (NeRF) have shown promising advances in implicit reconstruction for indoor environments, the problem of simultaneous odometry and mapping for large-scale scenarios using incremental LiDAR data remains unexplored. To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction. All these modules utilize our proposed neural signed distance function, which separates LiDAR points into ground and non-ground points to reduce Z-axis drift, optimizes odometry and voxel embeddings concurrently, and in the end generates dense smooth mesh maps of the environment. Moreover, this joint optimization allows our NeRF-LOAM to be pre-trained free and exhibit strong generalization abilities when applied to different environments. Extensive evaluations on three publicly available datasets demonstrate that our approach achieves state-of-the-art odometry and mapping performance, as well as a strong generalization in large-scale environments utilizing LiDAR data. Furthermore, we perform multiple ablation studies to validate the effectiveness of our network design. The implementation of our approach will be made available at https://github.com/JunyuanDeng/NeRF-LOAM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.10709v1-abstract-full').style.display = 'none'; document.getElementById('2303.10709v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.07668">arXiv:2303.07668</a> <span> [<a href="https://arxiv.org/pdf/2303.07668">pdf</a>, <a href="https://arxiv.org/ps/2303.07668">ps</a>, <a href="https://arxiv.org/format/2303.07668">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> PIEKF-VIWO: Visual-Inertial-Wheel Odometry using Partial Invariant Extended Kalman Filter </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hua%2C+T">Tong Hua</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T">Tao Li</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.07668v1-abstract-short" style="display: inline;"> Invariant Extended Kalman Filter (IEKF) has been successfully applied in Visual-inertial Odometry (VIO) as an advanced achievement of Kalman filter, showing great potential in sensor fusion. In this paper, we propose partial IEKF (PIEKF), which only incorporates rotation-velocity state into the Lie group structure and apply it for Visual-Inertial-Wheel Odometry (VIWO) to improve positioning accura… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07668v1-abstract-full').style.display = 'inline'; document.getElementById('2303.07668v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.07668v1-abstract-full" style="display: none;"> Invariant Extended Kalman Filter (IEKF) has been successfully applied in Visual-inertial Odometry (VIO) as an advanced achievement of Kalman filter, showing great potential in sensor fusion. In this paper, we propose partial IEKF (PIEKF), which only incorporates rotation-velocity state into the Lie group structure and apply it for Visual-Inertial-Wheel Odometry (VIWO) to improve positioning accuracy and consistency. Specifically, we derive the rotation-velocity measurement model, which combines wheel measurements with kinematic constraints. The model circumvents the wheel odometer's 3D integration and covariance propagation, which is essential for filter consistency. And a plane constraint is also introduced to enhance the position accuracy. A dynamic outlier detection method is adopted, leveraging the velocity state output. Through the simulation and real-world test, we validate the effectiveness of our approach, which outperforms the standard Multi-State Constraint Kalman Filter (MSCKF) based VIWO in consistency and accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07668v1-abstract-full').style.display = 'none'; document.getElementById('2303.07668v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.05203">arXiv:2303.05203</a> <span> [<a href="https://arxiv.org/pdf/2303.05203">pdf</a>, <a href="https://arxiv.org/format/2303.05203">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+X">Xiuyu Yang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zhuangyan Zhang</a>, <a href="/search/cs?searchtype=author&query=Du%2C+H">Haikuo Du</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+S">Sui Yang</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+F">Fengping Sun</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yanbo Liu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+W">Wenchao Xu</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+W">Weiqi Sun</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Z">Zhengyu Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.05203v3-abstract-short" style="display: inline;"> Autonomous driving has now made great strides thanks to artificial intelligence, and numerous advanced methods have been proposed for vehicle end target detection, including single sensor or multi sensor detection methods. However, the complexity and diversity of real traffic situations necessitate an examination of how to use these methods in real road conditions. In this paper, we propose RMMDet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05203v3-abstract-full').style.display = 'inline'; document.getElementById('2303.05203v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.05203v3-abstract-full" style="display: none;"> Autonomous driving has now made great strides thanks to artificial intelligence, and numerous advanced methods have been proposed for vehicle end target detection, including single sensor or multi sensor detection methods. However, the complexity and diversity of real traffic situations necessitate an examination of how to use these methods in real road conditions. In this paper, we propose RMMDet, a road-side multitype and multigroup sensor detection system for autonomous driving. We use a ROS-based virtual environment to simulate real-world conditions, in particular the physical and functional construction of the sensors. Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion. We produce local datasets and real sand table field, and conduct various experiments. Furthermore, we link a multi-agent collaborative scheduling system to the fusion detection system. Hence, the whole roadside detection system is formed by roadside perception, fusion detection, and scheduling planning. Through the experiments, it can be seen that RMMDet system we built plays an important role in vehicle-road collaboration and its optimization. The code and supplementary materials can be found at: https://github.com/OrangeSodahub/RMMDet <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.05203v3-abstract-full').style.display = 'none'; document.getElementById('2303.05203v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.06060">arXiv:2302.06060</a> <span> [<a href="https://arxiv.org/pdf/2302.06060">pdf</a>, <a href="https://arxiv.org/format/2302.06060">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TGRS.2023.3273287">10.1109/TGRS.2023.3273287 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+X">Xuxiang Sun</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+G">Gong Cheng</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Lei Pei</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Hongda Li</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Junwei Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.06060v1-abstract-short" style="display: inline;"> Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attention has been paid to this topic in Optical Remote Sensing Images (O-RSIs). To this end, we focus on this research, i.e., PAs on object detection in O-RSIs, and propose a more Threatening PA without the scarification of th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.06060v1-abstract-full').style.display = 'inline'; document.getElementById('2302.06060v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.06060v1-abstract-full" style="display: none;"> Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attention has been paid to this topic in Optical Remote Sensing Images (O-RSIs). To this end, we focus on this research, i.e., PAs on object detection in O-RSIs, and propose a more Threatening PA without the scarification of the visual quality, dubbed TPA. Specifically, to address the problem of inconsistency between local and global landscapes in existing patch selection schemes, we propose leveraging the First-Order Difference (FOD) of the objective function before and after masking to select the sub-patches to be attacked. Further, considering the problem of gradient inundation when applying existing coordinate-based loss to PAs directly, we design an IoU-based objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL), which pushes the detected bounding boxes far from the initial ones until there are no intersections between them. Finally, on two widely used benchmarks, i.e., DIOR and DOTA, comprehensive evaluations of our TPA with four typical detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable effectiveness. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.06060v1-abstract-full').style.display = 'none'; document.getElementById('2302.06060v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.13160">arXiv:2208.13160</a> <span> [<a href="https://arxiv.org/pdf/2208.13160">pdf</a>, <a href="https://arxiv.org/format/2208.13160">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Han%2C+Z">Zhichao Han</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yuwei Wu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T">Tong Li</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+L">Lu Zhang</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Liuao Pei</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+L">Long Xu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+C">Chengyang Li</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+C">Changjia Ma</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+C">Chao Xu</a>, <a href="/search/cs?searchtype=author&query=Shen%2C+S">Shaojie Shen</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+F">Fei Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.13160v2-abstract-short" style="display: inline;"> As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization meth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.13160v2-abstract-full').style.display = 'inline'; document.getElementById('2208.13160v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.13160v2-abstract-full" style="display: none;"> As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.13160v2-abstract-full').style.display = 'none'; document.getElementById('2208.13160v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.07547">arXiv:2208.07547</a> <span> [<a href="https://arxiv.org/pdf/2208.07547">pdf</a>, <a href="https://arxiv.org/format/2208.07547">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+R+C">Robert C. Qiu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.07547v1-abstract-short" style="display: inline;"> Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR models have achieved impressive recognition performance. However, most HAR algorithms are susceptible to the multi-class windows problem that is essential yet rarely exploited. In this paper, we propose to relieve this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.07547v1-abstract-full').style.display = 'inline'; document.getElementById('2208.07547v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.07547v1-abstract-full" style="display: none;"> Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR models have achieved impressive recognition performance. However, most HAR algorithms are susceptible to the multi-class windows problem that is essential yet rarely exploited. In this paper, we propose to relieve this challenging problem by introducing the segmentation technology into HAR, yielding joint activity segmentation and recognition. Especially, we introduce the Multi-Stage Temporal Convolutional Network (MS-TCN) architecture for sample-level activity prediction to joint segment and recognize the activity sequence. Furthermore, to enhance the robustness of HAR against the inter-class similarity and intra-class heterogeneity, a multi-level contrastive loss, containing the sample-level and segment-level contrast, has been proposed to learn a well-structured embedding space for better activity segmentation and recognition performance. Finally, with comprehensive experiments, we verify the effectiveness of the proposed method on two public HAR datasets, achieving significant improvements in the various evaluation metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.07547v1-abstract-full').style.display = 'none'; document.getElementById('2208.07547v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by GLOBECOM 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.06331">arXiv:2208.06331</a> <span> [<a href="https://arxiv.org/pdf/2208.06331">pdf</a>, <a href="https://arxiv.org/format/2208.06331">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> A Linear and Exact Algorithm for Whole-Body Collision Evaluation via Scale Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+Q">Qianhao Wang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhepei Wang</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Liuao Pei</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+C">Chao Xu</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+F">Fei Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.06331v2-abstract-short" style="display: inline;"> Collision evaluation is of vital importance in various applications. However, existing methods are either cumbersome to calculate or have a gap with the actual value. In this paper, we propose a zero-gap whole-body collision evaluation which can be formulated as a low dimensional linear program. This evaluation can be solved analytically in O(m) computational time, where m is the total number of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06331v2-abstract-full').style.display = 'inline'; document.getElementById('2208.06331v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.06331v2-abstract-full" style="display: none;"> Collision evaluation is of vital importance in various applications. However, existing methods are either cumbersome to calculate or have a gap with the actual value. In this paper, we propose a zero-gap whole-body collision evaluation which can be formulated as a low dimensional linear program. This evaluation can be solved analytically in O(m) computational time, where m is the total number of the linear inequalities in this linear program. Moreover, the proposed method is efficient in obtaining its gradient, making it easy to apply to optimization-based applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06331v2-abstract-full').style.display = 'none'; document.getElementById('2208.06331v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.10836">arXiv:2204.10836</a> <span> [<a href="https://arxiv.org/pdf/2204.10836">pdf</a>, <a href="https://arxiv.org/format/2204.10836">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41467-022-33407-5">10.1038/s41467-022-33407-5 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Federated Learning Enables Big Data for Rare Cancer Boundary Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pati%2C+S">Sarthak Pati</a>, <a href="/search/cs?searchtype=author&query=Baid%2C+U">Ujjwal Baid</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+B">Brandon Edwards</a>, <a href="/search/cs?searchtype=author&query=Sheller%2C+M">Micah Sheller</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+S">Shih-Han Wang</a>, <a href="/search/cs?searchtype=author&query=Reina%2C+G+A">G Anthony Reina</a>, <a href="/search/cs?searchtype=author&query=Foley%2C+P">Patrick Foley</a>, <a href="/search/cs?searchtype=author&query=Gruzdev%2C+A">Alexey Gruzdev</a>, <a href="/search/cs?searchtype=author&query=Karkada%2C+D">Deepthi Karkada</a>, <a href="/search/cs?searchtype=author&query=Davatzikos%2C+C">Christos Davatzikos</a>, <a href="/search/cs?searchtype=author&query=Sako%2C+C">Chiharu Sako</a>, <a href="/search/cs?searchtype=author&query=Ghodasara%2C+S">Satyam Ghodasara</a>, <a href="/search/cs?searchtype=author&query=Bilello%2C+M">Michel Bilello</a>, <a href="/search/cs?searchtype=author&query=Mohan%2C+S">Suyash Mohan</a>, <a href="/search/cs?searchtype=author&query=Vollmuth%2C+P">Philipp Vollmuth</a>, <a href="/search/cs?searchtype=author&query=Brugnara%2C+G">Gianluca Brugnara</a>, <a href="/search/cs?searchtype=author&query=Preetha%2C+C+J">Chandrakanth J Preetha</a>, <a href="/search/cs?searchtype=author&query=Sahm%2C+F">Felix Sahm</a>, <a href="/search/cs?searchtype=author&query=Maier-Hein%2C+K">Klaus Maier-Hein</a>, <a href="/search/cs?searchtype=author&query=Zenk%2C+M">Maximilian Zenk</a>, <a href="/search/cs?searchtype=author&query=Bendszus%2C+M">Martin Bendszus</a>, <a href="/search/cs?searchtype=author&query=Wick%2C+W">Wolfgang Wick</a>, <a href="/search/cs?searchtype=author&query=Calabrese%2C+E">Evan Calabrese</a>, <a href="/search/cs?searchtype=author&query=Rudie%2C+J">Jeffrey Rudie</a>, <a href="/search/cs?searchtype=author&query=Villanueva-Meyer%2C+J">Javier Villanueva-Meyer</a> , et al. (254 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.10836v2-abstract-short" style="display: inline;"> Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train acc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.10836v2-abstract-full').style.display = 'inline'; document.getElementById('2204.10836v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.10836v2-abstract-full" style="display: none;"> Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.10836v2-abstract-full').style.display = 'none'; document.getElementById('2204.10836v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.10074">arXiv:2112.10074</a> <span> [<a href="https://arxiv.org/pdf/2112.10074">pdf</a>, <a href="https://arxiv.org/format/2112.10074">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.59275/j.melba.2022-354b">10.59275/j.melba.2022-354b <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mehta%2C+R">Raghav Mehta</a>, <a href="/search/cs?searchtype=author&query=Filos%2C+A">Angelos Filos</a>, <a href="/search/cs?searchtype=author&query=Baid%2C+U">Ujjwal Baid</a>, <a href="/search/cs?searchtype=author&query=Sako%2C+C">Chiharu Sako</a>, <a href="/search/cs?searchtype=author&query=McKinley%2C+R">Richard McKinley</a>, <a href="/search/cs?searchtype=author&query=Rebsamen%2C+M">Michael Rebsamen</a>, <a href="/search/cs?searchtype=author&query=Datwyler%2C+K">Katrin Datwyler</a>, <a href="/search/cs?searchtype=author&query=Meier%2C+R">Raphael Meier</a>, <a href="/search/cs?searchtype=author&query=Radojewski%2C+P">Piotr Radojewski</a>, <a href="/search/cs?searchtype=author&query=Murugesan%2C+G+K">Gowtham Krishnan Murugesan</a>, <a href="/search/cs?searchtype=author&query=Nalawade%2C+S">Sahil Nalawade</a>, <a href="/search/cs?searchtype=author&query=Ganesh%2C+C">Chandan Ganesh</a>, <a href="/search/cs?searchtype=author&query=Wagner%2C+B">Ben Wagner</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+F+F">Fang F. Yu</a>, <a href="/search/cs?searchtype=author&query=Fei%2C+B">Baowei Fei</a>, <a href="/search/cs?searchtype=author&query=Madhuranthakam%2C+A+J">Ananth J. Madhuranthakam</a>, <a href="/search/cs?searchtype=author&query=Maldjian%2C+J+A">Joseph A. Maldjian</a>, <a href="/search/cs?searchtype=author&query=Daza%2C+L">Laura Daza</a>, <a href="/search/cs?searchtype=author&query=Gomez%2C+C">Catalina Gomez</a>, <a href="/search/cs?searchtype=author&query=Arbelaez%2C+P">Pablo Arbelaez</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+C">Chengliang Dai</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+S">Shuo Wang</a>, <a href="/search/cs?searchtype=author&query=Reynaud%2C+H">Hadrien Reynaud</a>, <a href="/search/cs?searchtype=author&query=Mo%2C+Y">Yuan-han Mo</a>, <a href="/search/cs?searchtype=author&query=Angelini%2C+E">Elsa Angelini</a> , et al. (67 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.10074v2-abstract-short" style="display: inline;"> Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.10074v2-abstract-full').style.display = 'inline'; document.getElementById('2112.10074v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.10074v2-abstract-full" style="display: none;"> Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at: https://github.com/RagMeh11/QU-BraTS. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.10074v2-abstract-full').style.display = 'none'; document.getElementById('2112.10074v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA): https://www.melba-journal.org/papers/2022:026.html</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Machine.Learning.for.Biomedical.Imaging. 1 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.00474">arXiv:2109.00474</a> <span> [<a href="https://arxiv.org/pdf/2109.00474">pdf</a>, <a href="https://arxiv.org/format/2109.00474">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> </div> <p class="title is-5 mathjax"> Leaking Control Flow Information via the Hardware Prefetcher </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yun Chen</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Lingfeng Pei</a>, <a href="/search/cs?searchtype=author&query=Carlson%2C+T+E">Trevor E. Carlson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.00474v1-abstract-short" style="display: inline;"> Modern processor designs use a variety of microarchitectural methods to achieve high performance. Unfortunately, new side-channels have often been uncovered that exploit these enhanced designs. One area that has received little attention from a security perspective is the processor's hard-ware prefetcher, a critical component used to mitigate DRAM latency in today's systems. Prefetchers, like bran… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.00474v1-abstract-full').style.display = 'inline'; document.getElementById('2109.00474v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.00474v1-abstract-full" style="display: none;"> Modern processor designs use a variety of microarchitectural methods to achieve high performance. Unfortunately, new side-channels have often been uncovered that exploit these enhanced designs. One area that has received little attention from a security perspective is the processor's hard-ware prefetcher, a critical component used to mitigate DRAM latency in today's systems. Prefetchers, like branch predictors, hold critical state related to the execution of the application, and have the potential to leak secret information. But up to now, there has not been a demonstration of a generic prefetcher side-channel that could be actively exploited in today's hardware. In this paper, we present AfterImage, a new side-channel that exploits the Intel Instruction Pointer-based stride prefetcher. We observe that, when the execution of the processor switches between different private domains, the prefetcher trained by one domain can be triggered in another. To the best of our knowledge, this work is the first to publicly demonstrate a methodology that is both algorithm-agnostic and also able to leak kernel data into userspace. AfterImage is different from previous works, as it leaks data on the non-speculative path of execution. Because of this, a large class of work that has focused on protecting transient, branch-outcome-based data will be unable to block this side-channel. By reverse-engineering the IP-stride prefetcher in modern Intel processors, we have successfully developed three variants of AfterImage to leak control flow information across code regions, processes and the user-kernel boundary. We find a high level of accuracy in leaking information with our methodology (from 91%, up to 99%), and propose two mitigation techniques to block this side-channel, one of which can be used on hardware systems today. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.00474v1-abstract-full').style.display = 'none'; document.getElementById('2109.00474v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 14 figures, 8 listings</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.02399">arXiv:2012.02399</a> <span> [<a href="https://arxiv.org/pdf/2012.02399">pdf</a>, <a href="https://arxiv.org/format/2012.02399">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> P3-LOAM: PPP/LiDAR Loosely Coupled SLAM with Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+T">Tao Li</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Xiang%2C+Y">Yan Xiang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Tao%2C+L">Lihao Tao</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.02399v1-abstract-short" style="display: inline;"> Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has drawn increasing interests in autonomous driving. However, LiDAR-SLAM suffers from accumulating errors which can be significantly mitigated by Global Navigation Satellite System (GNSS). Precise Point Positioning (PPP), an accurate GNSS operation mode independent of base stations, gains more popularity in unm… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.02399v1-abstract-full').style.display = 'inline'; document.getElementById('2012.02399v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.02399v1-abstract-full" style="display: none;"> Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has drawn increasing interests in autonomous driving. However, LiDAR-SLAM suffers from accumulating errors which can be significantly mitigated by Global Navigation Satellite System (GNSS). Precise Point Positioning (PPP), an accurate GNSS operation mode independent of base stations, gains more popularity in unmanned systems. Considering the features of the two technologies, LiDAR-SLAM and PPP, this paper proposes a SLAM system, namely P3-LOAM (PPP based LiDAR Odometry and Mapping) which couples LiDAR-SLAM and PPP. For better integration, we derive LiDAR-SLAM positioning covariance by using Singular Value Decomposition (SVD) Jacobian model, since SVD provides an explicit analytic solution of Iterative Closest Point (ICP), which is a key issue in LiDAR-SLAM. A novel method is then proposed to evaluate the estimated LiDAR-SLAM covariance. In addition, to increase the reliability of GNSS in urban canyon environment, we develop a LiDAR-SLAM assisted GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithm. Finally, we validate P$^3$-LOAM with UrbanNav, a challenging public dataset in urban canyon environment. Comprehensive test results prove that P3-LOAM outperforms benchmarks such as Single Point Positioning (SPP), PPP, LeGO-LOAM, SPP-LOAM, and loosely coupled navigation system proposed by the publisher of UrbanNav in terms of accuracy and availability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.02399v1-abstract-full').style.display = 'none'; document.getElementById('2012.02399v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.09404">arXiv:2009.09404</a> <span> [<a href="https://arxiv.org/pdf/2009.09404">pdf</a>, <a href="https://arxiv.org/format/2009.09404">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Xiao%2C+F">Fanyi Xiao</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+R">Robert Qiu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.09404v2-abstract-short" style="display: inline;"> Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas. Recently, deep learning-based methods pave a new way of understanding and performing analysis of the complex data in the HAR system. However, the performance of these methods is mostly based on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.09404v2-abstract-full').style.display = 'inline'; document.getElementById('2009.09404v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.09404v2-abstract-full" style="display: none;"> Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas. Recently, deep learning-based methods pave a new way of understanding and performing analysis of the complex data in the HAR system. However, the performance of these methods is mostly based on the quality and quantity of the collected data. In this paper, we innovatively propose to build a large database based on virtual IMUs and then address technical issues by introducing a multiple-domain deep learning framework consisting of three technical parts. In the first part, we propose to learn the single-frame human activity from the noisy IMU data with hybrid convolutional neural networks (CNNs) in the semi-supervised form. For the second part, the extracted data features are fused according to the principle of uncertainty-aware consistency, which reduces the uncertainty by weighting the importance of the features. The transfer learning is performed in the last part based on the newly released Archive of Motion Capture as Surface Shapes (AMASS) dataset, containing abundant synthetic human poses, which enhances the variety and diversity of the training dataset and is beneficial for the process of training and feature transfer in the proposed method. The efficiency and effectiveness of the proposed method have been demonstrated in the real deep inertial poser (DIP) dataset. The experimental results show that the proposed methods can surprisingly converge within a few iterations and outperform all competing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.09404v2-abstract-full').style.display = 'none'; document.getElementById('2009.09404v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.06886">arXiv:2009.06886</a> <span> [<a href="https://arxiv.org/pdf/2009.06886">pdf</a>, <a href="https://arxiv.org/format/2009.06886">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+J">Jinquan Li</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Zou%2C+D">Danping Zou</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+S">Songpengcheng Xia</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qi Wu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T">Tao Li</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Z">Zhen Sun</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.06886v1-abstract-short" style="display: inline;"> This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM. Most SLAM methods treat all the features extracted from the images as equal importance during the optimization process. However, the salient feature points in scenes h… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.06886v1-abstract-full').style.display = 'inline'; document.getElementById('2009.06886v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.06886v1-abstract-full" style="display: none;"> This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM. Most SLAM methods treat all the features extracted from the images as equal importance during the optimization process. However, the salient feature points in scenes have more significant influence during the human navigation process. Therefore, we first propose a visual saliency model called SalVavNet in which we introduce a correlation module and propose an adaptive Exponential Moving Average (EMA) module. These modules mitigate the center bias to enable the saliency maps generated by SalNavNet to pay more attention to the same salient object. Moreover, the saliency maps simulate the human behavior for the refinement of SLAM results. The feature points extracted from the salient regions have greater importance in optimization process. We add semantic saliency information to the Euroc dataset to generate an open-source saliency SLAM dataset. Comprehensive test results prove that Attention-SLAM outperforms benchmarks such as Direct Sparse Odometry (DSO), ORB-SLAM, and Salient DSO in terms of efficiency, accuracy, and robustness in most test cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.06886v1-abstract-full').style.display = 'none'; document.getElementById('2009.06886v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.03738">arXiv:2004.03738</a> <span> [<a href="https://arxiv.org/pdf/2004.03738">pdf</a>, <a href="https://arxiv.org/format/2004.03738">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/JIOT.2020.3019199">10.1109/JIOT.2020.3019199 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+Y">You Li</a>, <a href="/search/cs?searchtype=author&query=Zhuang%2C+Y">Yuan Zhuang</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+X">Xin Hu</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+Z">Zhouzheng Gao</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+J">Jia Hu</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/cs?searchtype=author&query=He%2C+Z">Zhe He</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+K">Kejie Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+M">Maosong Wang</a>, <a href="/search/cs?searchtype=author&query=Niu%2C+X">Xiaoji Niu</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+R">Ruizhi Chen</a>, <a href="/search/cs?searchtype=author&query=Thompson%2C+J">John Thompson</a>, <a href="/search/cs?searchtype=author&query=Ghannouchi%2C+F">Fadhel Ghannouchi</a>, <a href="/search/cs?searchtype=author&query=El-Sheimy%2C+N">Naser El-Sheimy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.03738v1-abstract-short" style="display: inline;"> The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.03738v1-abstract-full').style.display = 'inline'; document.getElementById('2004.03738v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.03738v1-abstract-full" style="display: none;"> The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.03738v1-abstract-full').style.display = 'none'; document.getElementById('2004.03738v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.01874">arXiv:2003.01874</a> <span> [<a href="https://arxiv.org/pdf/2003.01874">pdf</a>, <a href="https://arxiv.org/format/2003.01874">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xiao%2C+F">Fanyi Xiao</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Zou%2C+D">Danping Zou</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+Y">Yifan Zhu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T">Tao Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.01874v2-abstract-short" style="display: inline;"> Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement units (IMUs), researchers begin to utilize IMU data for HAR. By employing machine learning algorithms, early IMU-based research for HAR can achieve accurate classification results on traditional classical HAR datasets, con… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.01874v2-abstract-full').style.display = 'inline'; document.getElementById('2003.01874v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.01874v2-abstract-full" style="display: none;"> Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement units (IMUs), researchers begin to utilize IMU data for HAR. By employing machine learning algorithms, early IMU-based research for HAR can achieve accurate classification results on traditional classical HAR datasets, containing only simple and repetitive daily activities. However, these datasets rarely display a rich diversity of information in real-scene. In this paper, we propose a novel method based on deep learning for complex HAR in the real-scene. Specially, in the off-line training stage, the AMASS dataset, containing abundant human poses and virtual IMU data, is innovatively adopted for enhancing the variety and diversity. Moreover, a deep convolutional neural network with an unsupervised penalty is proposed to automatically extract the features of AMASS and improve the robustness. In the on-line testing stage, by leveraging advantages of the transfer learning, we obtain the final result by fine-tuning the partial neural network (optimizing the parameters in the fully-connected layers) using the real IMU data. The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91.15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.01874v2-abstract-full').style.display = 'none'; document.getElementById('2003.01874v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.05002">arXiv:1912.05002</a> <span> [<a href="https://arxiv.org/pdf/1912.05002">pdf</a>, <a href="https://arxiv.org/format/1912.05002">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> TextSLAM: Visual SLAM with Planar Text Features </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+B">Boying Li</a>, <a href="/search/cs?searchtype=author&query=Zou%2C+D">Danping Zou</a>, <a href="/search/cs?searchtype=author&query=Sartori%2C+D">Daniele Sartori</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1912.05002v2-abstract-short" style="display: inline;"> We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline. The key idea of our novel text-based visual SLAM is to treat each detected text as a planar feature which is rich of textures and semantic meanings. The text feature is compactly represented by three parameters and integrated into visual SLAM by adopting the illumination-invariant photometric error. We a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.05002v2-abstract-full').style.display = 'inline'; document.getElementById('1912.05002v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.05002v2-abstract-full" style="display: none;"> We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline. The key idea of our novel text-based visual SLAM is to treat each detected text as a planar feature which is rich of textures and semantic meanings. The text feature is compactly represented by three parameters and integrated into visual SLAM by adopting the illumination-invariant photometric error. We also describe important details involved in implementing a full pipeline of text-based visual SLAM. To our best knowledge, this is the first visual SLAM method tightly coupled with the text features. We tested our method in both indoor and outdoor environments. The results show that with text features, the visual SLAM system becomes more robust and produces much more accurate 3D text maps that could be useful for navigation and scene understanding in robotic or augmented reality applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.05002v2-abstract-full').style.display = 'none'; document.getElementById('1912.05002v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by ICRA2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.06329">arXiv:1905.06329</a> <span> [<a href="https://arxiv.org/pdf/1905.06329">pdf</a>, <a href="https://arxiv.org/ps/1905.06329">ps</a>, <a href="https://arxiv.org/format/1905.06329">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chu%2C+L">Lei Chu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Husheng Li</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+R+C">Robert Caiming Qiu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.06329v2-abstract-short" style="display: inline;"> This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the cha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.06329v2-abstract-full').style.display = 'inline'; document.getElementById('1905.06329v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.06329v2-abstract-full" style="display: none;"> This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the channel station information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn a representative function that can predict the targets (constellation points) from the inputs (outputs of the low-resolution ADCs) based on the labeled training data (pilot signals). Especially, our main contributions are two-fold: 1) First, we design a new activation function, whose outputs are close to the constellation points when the parameters are finally optimized, to help us fully exploit the stochastic gradient descent method for the discrete optimization problem. 2) Second, an unsupervised loss is designed and then added to the optimization objective, aiming to enhance the representation ability (so-called generalization). Lastly, various experimental results confirm the superiority of the proposed equalizer over some existing ones, particularly when the statistics of the channel state information are unclear. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.06329v2-abstract-full').style.display = 'none'; document.getElementById('1905.06329v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.09233">arXiv:1904.09233</a> <span> [<a href="https://arxiv.org/pdf/1904.09233">pdf</a>, <a href="https://arxiv.org/format/1904.09233">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41598-019-47193-6">10.1038/s41598-019-47193-6 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+Y">Yueqin Li</a>, <a href="/search/cs?searchtype=author&query=Mahjoubfar%2C+A">Ata Mahjoubfar</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+C+L">Claire Lifan Chen</a>, <a href="/search/cs?searchtype=author&query=Niazi%2C+K+R">Kayvan Reza Niazi</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Li Pei</a>, <a href="/search/cs?searchtype=author&query=Jalali%2C+B">Bahram Jalali</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.09233v2-abstract-short" style="display: inline;"> Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.09233v2-abstract-full').style.display = 'inline'; document.getElementById('1904.09233v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.09233v2-abstract-full" style="display: none;"> Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.09233v2-abstract-full').style.display = 'none'; document.getElementById('1904.09233v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Scientific Reports 9 (2019) 11088 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.02629">arXiv:1811.02629</a> <span> [<a href="https://arxiv.org/pdf/1811.02629">pdf</a>, <a href="https://arxiv.org/format/1811.02629">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bakas%2C+S">Spyridon Bakas</a>, <a href="/search/cs?searchtype=author&query=Reyes%2C+M">Mauricio Reyes</a>, <a href="/search/cs?searchtype=author&query=Jakab%2C+A">Andras Jakab</a>, <a href="/search/cs?searchtype=author&query=Bauer%2C+S">Stefan Bauer</a>, <a href="/search/cs?searchtype=author&query=Rempfler%2C+M">Markus Rempfler</a>, <a href="/search/cs?searchtype=author&query=Crimi%2C+A">Alessandro Crimi</a>, <a href="/search/cs?searchtype=author&query=Shinohara%2C+R+T">Russell Takeshi Shinohara</a>, <a href="/search/cs?searchtype=author&query=Berger%2C+C">Christoph Berger</a>, <a href="/search/cs?searchtype=author&query=Ha%2C+S+M">Sung Min Ha</a>, <a href="/search/cs?searchtype=author&query=Rozycki%2C+M">Martin Rozycki</a>, <a href="/search/cs?searchtype=author&query=Prastawa%2C+M">Marcel Prastawa</a>, <a href="/search/cs?searchtype=author&query=Alberts%2C+E">Esther Alberts</a>, <a href="/search/cs?searchtype=author&query=Lipkova%2C+J">Jana Lipkova</a>, <a href="/search/cs?searchtype=author&query=Freymann%2C+J">John Freymann</a>, <a href="/search/cs?searchtype=author&query=Kirby%2C+J">Justin Kirby</a>, <a href="/search/cs?searchtype=author&query=Bilello%2C+M">Michel Bilello</a>, <a href="/search/cs?searchtype=author&query=Fathallah-Shaykh%2C+H">Hassan Fathallah-Shaykh</a>, <a href="/search/cs?searchtype=author&query=Wiest%2C+R">Roland Wiest</a>, <a href="/search/cs?searchtype=author&query=Kirschke%2C+J">Jan Kirschke</a>, <a href="/search/cs?searchtype=author&query=Wiestler%2C+B">Benedikt Wiestler</a>, <a href="/search/cs?searchtype=author&query=Colen%2C+R">Rivka Colen</a>, <a href="/search/cs?searchtype=author&query=Kotrotsou%2C+A">Aikaterini Kotrotsou</a>, <a href="/search/cs?searchtype=author&query=Lamontagne%2C+P">Pamela Lamontagne</a>, <a href="/search/cs?searchtype=author&query=Marcus%2C+D">Daniel Marcus</a>, <a href="/search/cs?searchtype=author&query=Milchenko%2C+M">Mikhail Milchenko</a> , et al. (402 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1811.02629v3-abstract-short" style="display: inline;"> Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles dissem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.02629v3-abstract-full').style.display = 'inline'; document.getElementById('1811.02629v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.02629v3-abstract-full" style="display: none;"> Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.02629v3-abstract-full').style.display = 'none'; document.getElementById('1811.02629v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The International Multimodal Brain Tumor Segmentation (BraTS) Challenge</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.06796">arXiv:1810.06796</a> <span> [<a href="https://arxiv.org/pdf/1810.06796">pdf</a>, <a href="https://arxiv.org/format/1810.06796">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zou%2C+D">Danping Zou</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yuanxin Wu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Ling%2C+H">Haibin Ling</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.06796v2-abstract-short" style="display: inline;"> We propose a novel visual-inertial odometry approach that adopts structural regularity in man-made environments. Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity. An Atlanta world is a world that contains multiple local Manhattan worlds with different heading directions. Each local Manhattan world is detected on-the-fly, and their headings are gra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.06796v2-abstract-full').style.display = 'inline'; document.getElementById('1810.06796v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.06796v2-abstract-full" style="display: none;"> We propose a novel visual-inertial odometry approach that adopts structural regularity in man-made environments. Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity. An Atlanta world is a world that contains multiple local Manhattan worlds with different heading directions. Each local Manhattan world is detected on-the-fly, and their headings are gradually refined by the state estimator when new observations are coming. With fully exploration of structural lines that aligned with each local Manhattan worlds, our visual-inertial odometry method become more accurate and robust, as well as much more flexible to different kinds of complex man-made environments. Through extensive benchmark tests and real-world tests, the results show that the proposed approach outperforms existing visual-inertial systems in large-scale man-made environments <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.06796v2-abstract-full').style.display = 'none'; document.getElementById('1810.06796v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages,15 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1707.07082">arXiv:1707.07082</a> <span> [<a href="https://arxiv.org/pdf/1707.07082">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Gyroscope Calibration via Magnetometer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yuanxin Wu</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1707.07082v1-abstract-short" style="display: inline;"> Magnetometers, gyroscopes and accelerometers are commonly used sensors in a variety of applications. The paper proposes a novel gyroscope calibration method in the homogeneous magnetic field by the help of magnetometer. It is shown that, with sufficient rotation excitation, the homogeneous magnetic field vector can be exploited to serve as a good reference for calibrating low-cost gyroscopes. The… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.07082v1-abstract-full').style.display = 'inline'; document.getElementById('1707.07082v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1707.07082v1-abstract-full" style="display: none;"> Magnetometers, gyroscopes and accelerometers are commonly used sensors in a variety of applications. The paper proposes a novel gyroscope calibration method in the homogeneous magnetic field by the help of magnetometer. It is shown that, with sufficient rotation excitation, the homogeneous magnetic field vector can be exploited to serve as a good reference for calibrating low-cost gyroscopes. The calibration parameters include the gyroscope scale factor, non-orthogonal coefficient and bias for three axes, as well as its misalignment to the magnetometer frame. Simulation and field test results demonstrate the method's effectiveness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1707.07082v1-abstract-full').style.display = 'none'; document.getElementById('1707.07082v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 July, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Volume 17, Issue 16, IEEE Sensors Journal, 2017 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.07967">arXiv:1703.07967</a> <span> [<a href="https://arxiv.org/pdf/1703.07967">pdf</a>, <a href="https://arxiv.org/format/1703.07967">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Nonconvex Regularization Based Sparse Recovery and Demixing with Application to Color Image Inpainting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wen%2C+F">Fei Wen</a>, <a href="/search/cs?searchtype=author&query=Adhikari%2C+L">Lasith Adhikari</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Marcia%2C+R+F">Roummel F. Marcia</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+P">Peilin Liu</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+R+C">Robert C. Qiu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1703.07967v1-abstract-short" style="display: inline;"> This work addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, super-resolution, and restoration of signals corrupted by clipping, saturation, impulsive noise, or narrowband interference. We employ the $\ell_q$-norm ($0 \le q < 1$) for sparsity inducing and propose a constrained… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.07967v1-abstract-full').style.display = 'inline'; document.getElementById('1703.07967v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.07967v1-abstract-full" style="display: none;"> This work addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, super-resolution, and restoration of signals corrupted by clipping, saturation, impulsive noise, or narrowband interference. We employ the $\ell_q$-norm ($0 \le q < 1$) for sparsity inducing and propose a constrained $\ell_q$-minimization formulation for the recovery and demixing problem. This nonconvex formulation is approximately solved by two efficient first-order algorithms based on proximal coordinate descent and alternative direction method of multipliers (ADMM), respectively. The new algorithms are convergent in the nonconvex case under some mild conditions and scale well for high-dimensional problems. A convergence condition of the new ADMM algorithm has been derived. Furthermore, extension of the two algorithms for multi-channels joint recovery has been presented, which can further exploit the joint sparsity pattern among multi-channel signals. Various numerical experiments showed that the new algorithms can achieve considerable performance gain over the $\ell_1$-regularized algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.07967v1-abstract-full').style.display = 'none'; document.getElementById('1703.07967v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.07952">arXiv:1703.07952</a> <span> [<a href="https://arxiv.org/pdf/1703.07952">pdf</a>, <a href="https://arxiv.org/format/1703.07952">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wen%2C+F">Fei Wen</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yuan Yang</a>, <a href="/search/cs?searchtype=author&query=Pei%2C+L">Ling Pei</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wenxian Yu</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+P">Peilin Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1703.07952v2-abstract-short" style="display: inline;"> This work addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction which employs the $\ell_1$-norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\ell_1$-loss is less sensitive to outliers in the measurements than the popular $\ell_2$-l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.07952v2-abstract-full').style.display = 'inline'; document.getElementById('1703.07952v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.07952v2-abstract-full" style="display: none;"> This work addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction which employs the $\ell_1$-norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\ell_1$-loss is less sensitive to outliers in the measurements than the popular $\ell_2$-loss, while the nonconvex penalty has the capability of ameliorating the bias problem of the popular convex LASSO penalty and thus can yield more accurate recovery. To solve this nonconvex and nonsmooth minimization formulation efficiently, we propose a first-order algorithm based on alternating direction method of multipliers (ADMM). A smoothing strategy on the $\ell_1$-loss function has been used in deriving the new algorithm to make it convergent. Further, a sufficient condition for the convergence of the new algorithm has been provided for generalized nonconvex regularization. In comparison with several state-of-the-art algorithms, the new algorithm showed better performance in numerical experiments in recovering sparse signals and compressible images. The new algorithm scales well for large-scale problems, as often encountered in image processing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.07952v2-abstract-full').style.display = 'none'; document.getElementById('1703.07952v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 5 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 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