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YOLOStereo3D: A step back to 2D for efficient stereo 3D detection

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arXiv 2021年

作者： Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering Robotics and Multi-Perception Laborotary Hong Kong University of Science and Technology Hong Kong Cloud Computing Lab of Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D imagebased detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://***/Owen-Liuyuxuan/visualDet3D. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

An assessment of measuring local levels of homelessness through proxy social media signals

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arXiv 2023年

作者： Bird, Yoshi Meke Grobe, Sarah E. Arnold, Michael V. Rogers, Sean P. Fudolig, Mikaela I. Zimmerman, Julia Witte Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States University of Vermont BurlingtonVT05405 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States

Although nearly 600,000 people experience homelessness in the United States every year, efforts to address this public health crisis are limited by the underperformance of standard methods to estimate localized and nationwide homelessness. Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through straightforward applications of natural language processing. We present results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring "homeless." Correlations between homelessness-related tweet counts and ranked per capita homelessness volume, but not general-population densities, suggest a relationship between the likelihood of Twitter users to personally encounter or observe homelessness in their everyday lives and their likelihood to communicate about it online. An increase to the log-odds of the word "homeless" appearing in an English-language tweet, as well as an acceleration in the increase in average tweet sentiment, suggest that tweets about homelessness are also affected by trends at the nation-scale. Additionally, changes to the lexical content of tweets over time suggest that reversals to the polarity of national or state-level trends may be detectable through an increase in political or service-sector language over the semantics of charity or direct appeals. Although tweet sentiment does not correlate to changes in homelessness volume, an analysis of user account type undertaken to explain nationwide sentiment dynamics revealed changes to Twitter-use patterns by accounts authored by individuals versus entities that may provide an additional signal to confirm changes to homelessness density in a given jurisdiction. While a computational approach to social media analysis may provide a low-cost, real-time dataset rich with information

关键词： Public health

SABER: Data-driven motion planner for autonomously navigating heterogeneous robots

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arXiv 2021年

作者： Schperberg, Alexander Tsuei, Stephanie Soatto, Stefano Hong, Dennis The Robotics and Mechanisms Laboratory Department of Mechanical and Aerospace Engineering University of California Los AngelesCA90095 United States The UCLA Vision Lab Department of Computer Science University of California Los AngelesCA90095 United States

We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in uncertain environments. First, we use stochastic model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. Second, recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution, which are trained on uncertainty outputs of various simultaneous localization and mapping algorithms. When two or more robots are in communication range, these uncertainties are then updated using a distributed Kalman filtering approach. Lastly, a Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal. Our complete methods are demonstrated on a ground and aerial robot simultaneously (code available at: https://***/AlexS28/SABER). Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Socially Pertinent Robots in Gerontological Healthcare

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arXiv 2024年

作者： Alameda-Pineda, Xavier Addlesee, Angus García, Daniel Hernández Reinke, Chris Arias, Soraya Arrigoni, Federica Auternaud, Alex Blavette, Lauriane Beyan, Cigdem Camara, Luis Gomez Cohen, Ohad Conti, Alessandro Dacunha, Sébastien Dondrup, Christian Ellinson, Yoav Ferro, Francesco Gannot, Sharon Gras, Florian Gunson, Nancie Horaud, Radu D’Incà, Moreno Kimouche, Imad Lemaignan, Séverin Lemon, Oliver Liotard, Cyril Marchionni, Luca Moradi, Mordehay Pajdla, Tomas Pino, Maribel Polic, Michal Py, Matthieu Rado, Ariel Ren, Bin Ricci, Elisa Rigaud, Anne-Sophie Rota, Paolo Romeo, Marta Sebe, Nicu Sieińska, Weronika Tandeitnik, Pinchas Tonini, Francesco Turro, Nicolas Wintz, Timothée Yu, Yanchao RobotLearn Team Inria at Univ. Grenoble Alpes CNRS LJK 655 Avenue de l’Europe Montbonnot38334 France Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 Dejvice 160 00 Czech Republic Acoustic Signal Processing Laboratory Bar-Ilan University Ramat-Gan5290002 Israel Department of Information and Computer Science University of Trento Via Sommarive 9 Trento38123 Italy Interaction Lab Mathematical and Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom ERM Automatismes 561 allée Bellecour Carpentras84200 France PAL Robotics C/ Pujades 77-79 Barcelona08005 Spain Lusage Living Lab Assistance Publique - Hopitaux de Paris 54-56 Rue Pascal Paris75013 France

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not an autonomous social interactive robot with multi-modal conversational capabilities will be useful and accepted in real-life facilities is yet to be answered. This paper is an attempt to partially answer this question, via two waves of experiments including patients and companions in a day-care gerontological facility in Paris with a full-sized humanoid robot endowed with social and conversational interaction capabilities. The software architecture, developed during the H2020 SPRING project, together with the experimental protocol, allowed us to evaluate the acceptability (AES) and usability (SUS) with more than 60 end-users. Overall, the users are receptive to this technology, especially when the robot’s perception and action skills are robust to environmental clutter and flexible to handle a plethora of different interactions. We also report and discuss some concerns and general comments of the users. © 2024, CC BY.

关键词： Microrobots

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

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arXiv 2020年

作者： Popov, Andrey A. Sandu, Adrian Nino-Ruiz, Elias D. Evensen, Geir Computational Science Laboratory Department of Computer Science Virginia Tech United States Applied Math and Computer Science Lab Universidad del Norte Colombia Norway

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation. We additionally provide for a way in which this methodology can be localized similar to the state-of-the-art LETKF method, and that for a certain model setup, our methodology significantly outperforms it. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

CoBe - Coded Beacons for Localization, Object Tracking, and SLAM Augmentation

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CoBe - Coded Beacons for Localization, Object Tracking, and ...

IEEE International Conference on Automation science and Engineering (CASE)

作者： Roman Rabinovich Ibrahim Jubran Aaron Wetzler Ron Kimmel Geometric Image Processing (GIP) Lab Computer Science Department Technion - Israel Institute of Technology Israel Robotics & Big Data (RBD) Lab Computer Science Department University of Haifa Israel

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image. The protocol is provably robust to a predefined set of detection and decoding errors and does not require any synchronization between the beacons themselves and the optical sensor. A detailed guide is then given for developing an optical tracking and localization system, which is based on the suggested protocol and readily available hardware. Such a system operates either as a standalone system for recovering the six degrees of freedom of fast moving objects, or integrated with existing SLAM pipelines providing them with error-free and easily identifiable landmarks. Based on this guide, we implemented a low-cost positional tracking system which can run in real-time on an IoT board. We evaluate our system's accuracy and compare it to other popular methods which utilize the same optical hardware, in experiments where the ground truth is known. A companion video containing multiple real-world experiments demonstrates the accuracy, speed, and applicability of the proposed system in a wide range of environments and real-world tasks. Open source code is provided to encourage further development of low-cost localization systems integrating the suggested technology at its navigation core.

关键词： Optical sensors Robustness Simultaneous localization and mapping Protocols Decoding Adaptive optics Optical imaging

A decomposition of book structure through ousiometric fluctuations in cumulative word-time

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arXiv 2022年

作者： Fudolig, Mikaela Irene Alshaabi, Thayer Cramer, Kathryn Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT United States Advanced Bioimaging Center UC Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States

While quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point;we define this measure as "cumulative word-time". Using ousiometrics, a reinterpretation of the valence-arousal-dominance framework of meaning obtained from semantic differentials, we convert text into time series of power and danger scores, with time corresponding to cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes and a non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shuffled text, we find that shorter books exhibit only a general trend, while longer books have fluctuations in addition to the general trend. These fluctuations typically have a period of a few thousand words regardless of the book length or library classification code, but vary depending on the content and structure of the book. Our findings suggest that, in the ousiometric sense, longer books are not expanded versions of shorter books, but rather are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. All together, these results open up avenues for future work in computational literary analysis, particularly the possibility of measuring a basic unit of narrative. Copyright © 2022, The Auth

关键词： Empirical mode decomposition