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arXiv 2024年

作者： Bhattacharjee, Abhinab Popov, Andrey A. Sarshar, Arash Sandu, Adrian Computational Science Lab Department of Computer Science Virginia Tech BlacksburgVA24060 United States The Oden Institute for Computational Engineering and Sciences The University of Texas AustinTX78712 United States Department of Computer Engineering and Computer Science California State University Long BeachCA90840 United States

The ADAM optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical ADAM algorithm is a first-order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization point of view, we propose new extensions of the ADAM scheme obtained by using higher-order IMEX methods to solve the ODE. Based on this approach, we derive a new optimization algorithm for neural network training that performs better than classical ADAM on several regression and classification problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yanzhou Wang Lidia Al-Zogbi Jiawei Liu Lauren Shepard Ahmed Ghazi Junichi Tokuda Simon Leonard Axel Krieger Iulian Iordachita Department of Mechanical Engineering and the Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Brady Urological Institute Johns Hopkins University Baltimore MD USA Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation.

关键词： Uncertain systems Uncertainty Parameter estimation Medical robotics Shape Biological system modeling Biopsy Needles Prostate cancer Standards

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Failure Explanation in Privacy-Sensitive Contexts: An Integrated Systems Approach

Failure Explanation in Privacy-Sensitive Contexts: An Integr...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sihui Li Sriram Siva Terran Mott Tom Williams Hao Zhang Neil Dantam Department of Computer Science Colorado School of Mines Human-Centered Robotics Lab University of Massachusetts Amherst

In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment.

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Single Robot Multitasking Through Dynamic Resource Allocation

Single Robot Multitasking Through Dynamic Resource Allocatio...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tyler Becker Song Jiang David Feil-Seifer Monica Nicolescu Department of Computer Science & Engineering Robotics Research lab University of Nevada Reno Reno NV USA Department of Computer Science & Engineering Socially Assistive Robotics Group University of Nevada Reno Reno NV USA

This paper addresses the problem of dynamic allocation of robot resources to tasks with hierarchical representations and multiple types of execution constraints, with the goal of enabling single-robot multitasking capabilities. Although the vast majority of robot platforms are equipped with more than one sensor (cameras, lasers, sonars) and several actuators (wheels/legs, two arms), which would in principle allow the robot to concurrently work on multiple tasks, existing methods are limited to allocating robots in their entirety to only one task at a time. This approach employs only a subset of a robot's sensors and actuators, leaving other robot resources unused. Our aim is to enable a robot to make full use of its capabilities by having an individual robot multitask, distributing its sensors and actuators to multiple concurrent activities. We propose a new architectural framework based on Hierarchical Task Trees that supports multitasking through a new representation of robot behaviors that explicitly encodes the robot resources (sensors and actuators) and the environmental conditions needed for execution. This architecture was validated on a two-arm, mobile, PR2 humanoid robot, performing tasks with multiple types of execution constraints.

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The ATTUNE Model for Artificial Trust Towards Human Operators

The ATTUNE Model for Artificial Trust Towards Human Operator...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Giannis Petousakis Angelo Cangelosi Rustam Stolkin Manolis Chiou Department of Computer Science Cognitive Robotics Lab University of Manchester United Kingdom Extreme Robotics Lab University of Birmingham School of Metallurgy and Materials United Kingdom Queen Mary University of London United Kingdom

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach.

关键词： Measurement Statistical analysis Navigation Disasters Human-robot interaction Real-time systems Recording Artificial intelligence Robots Monitoring

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A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Artificial Intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance

Fairness-Sensitive Policy-Gradient Reinforcement Learning fo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jie Zhu Mengsha Hu Amy Zhang Ruoming Jin Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science George Washington University Department of Computer Science Kent State University

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robots assist humans in various activities, from daily living to collaborative manufacturing. Because they have biased learning sources (e.g., data, demonstrations, human feedback), robots inevitably have discriminatory performance regarding individual differences (e.g., skin color, mobility, appearance); discriminatory performance will undermine robots’ service quality, causes request ignorance and response delay, and even cause emotional offenses. Therefore, mitigating biases is critically important for delivering fair robotic services. In this paper, we design a bias-mitigation method – Fairness-Sensitive Policy Gradient Reinforcement Learning (FSPGRL), to help robots self-identify and correct biased behaviors. FSP-GRL identifies bias by examining the abnormal updates along particular gradients and updates the policy network to provide fair decisions. To validate FSPGRL’s effectiveness, we designed a human-centered service scenario: a robot serving people in a restaurant. With a user study involving 24 humans and 1,000 service demonstrations, FSPGRL has proven effective in maintaining fairness during robot services.

关键词： Image color analysis Design methodology Collaboration Reinforcement learning Skin Manufacturing Delays Robots

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Bimanual Manipulation of Steady Hand Eye Robots with Adaptive Sclera Force Control: Cooperative vs. Teleoperation Strategies

arXiv

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arXiv 2024年

作者： Esfandiari, Mojtaba Gehlbach, Peter Taylor, Russell H. Iordachita, Iulian I. The Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States The Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States The Department of Computer Science The Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States

Performing retinal vein cannulation (RVC) as a potential treatment for retinal vein occlusion (RVO) without the assistance of a surgical robotic system is very challenging to do safely. The main limitation is the physiological hand tremor of surgeons. Robot-assisted eye surgery technology may resolve the problems of hand tremors and fatigue and improve the safety and precision of RVC. The Steady-Hand Eye Robot (SHER) is an admittance-based robotic system that can filter out hand tremors and enables ophthalmologists to manipulate a surgical instrument inside the eye cooperatively. However, the admittance-based cooperative control mode does not safely minimize the contact force between the surgical instrument and the sclera to prevent tissue damage. Additionally, features like haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. This work presents a bimanual adaptive teleoperation (BMAT) control framework using SHER 2.0 and SHER 2.1 robotic systems. We integrate them with an adaptive force control (AFC) algorithm to automatically minimize the tool-sclera interaction force. The scleral forces are measured using two fiber Bragg grating (FBG)-based force-sensing tools. We compare the performance of the BMAT mode with a bimanual adaptive cooperative (BMAC) mode in a vessel-following experiment under a surgical microscope. Experimental results demonstrate the effectiveness of the proposed BMAT control framework in performing a safe bimanual telemanipulation of the eye without over-stretching it, even in the absence of registration between the two robots. © 2024, CC BY.

关键词： Fiber Bragg gratings

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All Translation Tools Are Not Equal: Investigating the Quality of Language Translation for Forced Migration

All Translation Tools Are Not Equal: Investigating the Quali...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Ameeta Agrawal Lisa Singh Elizabeth Jacobs Yaguang Liu Gwyneth Dunlevy Rhitabrat Pokharel Varun Uppala Department of Computer Science Portland State University Department of Computer Science Georgetown University Digital and Computational Demography Lab Max Planck Institute for Demographic Research

As the volume and complexity of forced movement continues to grow, there is an urgent need to use new data sources to better understand emerging crises. Organic sources, like social media and newspapers, can offer insights in near real time when administrative data are unavailable for timely and detailed analysis. However, in order to flexibly switch to different contexts, we need the ability to contextualize the drivers of movement for different locations and languages. Recent advances in natural language processing and specifically, neural machine translation, have shown impressive results on standard benchmark datasets for well-studied language pairs. However, the effectiveness of these models in a real-world scenario remains less known. To advance our understanding of real-world, contextual translation, we systematically study the performance of multiple widely used off-the-shelf machine translation tools using words associated with drivers of forced movement in both high- and low-resource languages. Our empirical results suggest significant variation between the performance of these machine translation tools in terms of accuracy and efficiency, highlighting a problem that must be faced by those conducting migration research using multilingual contexts. We conclude by suggesting strategies for obtaining reasonable translations from off-the-shelf language tools.

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