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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Luca Beber Edoardo Lamon Giacomo Moretti Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

关键词： Dimensionality reduction Biological system modeling Heuristic algorithms Biological tissues Estimation Collaboration Robot sensing systems Information filters End effectors Kalman filters

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning

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eLight 2023年第1期3卷 94-104页

作者： Shilian Dong Dong He Qian Zhang Chaoning Huang Zhiheng Hu Chenyang Zhang Lei Nie Kun Wang Wei Luo Jing Yu Bin Tian Wei Wu Xu Chen Fubing Wang Jing Hu Xiangheng Xiao Department of Physics National Demonstration Center for Experimental Physics EducationWuhan UniversityWuhan 430072China Department of Laboratory Medicine Zhongnan Hospital of Wuhan UniversityWuhan 430071China School of Computer Science Wuhan UniversityWuhan 430072China Department of Hepatobiliary Pancreatic Surgery Hubei Cancer HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430079China Department of Clinical Laboratory Tianjin Medical University General HospitalTianjin 300052China Department of Blood Transfusion Wuhan Hospital of Traditional Chinese and Western MedicineTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Laboratory of Printable Functional Materials and Printed Electronics Research Center for Graphic CommunicationPrinting and PackagingWuhan UniversityWuhan 430072China Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics Department of Laboratory MedicineSichuan Academy of Medical Sciences&Sichuan Provincial People’s HospitalUniversity of Electronic Science and TechnologyChengdu 611731China School of Medicine University of Electronic Science and Technology of ChinaChengdu 611731China Wuhan Research Center for Infectious Diseases and Cancer Chinese Academy of Medical SciencesWuhan 430071China

Label-free surface-enhanced Raman scattering(SERS)technique with ultra-sensitivity becomes more and more desirable in biomedical analysis,which is yet hindered by inefficient follow-up data *** we report an integrative method based on SERS and Artificial Intelligence for Cancer Screening(SERS-AICS)for liquid biopsy such as serum via silver nanowires,combining molecular vibrational signals processing with large-scale data mining *** to 382 healthy controls and 1582 patients from two independent cohorts,SERS-AICS not only distinguishes pan-cancer patients from health controls with 95.81% overall accuracy and 95.87% sensitivity at 95.40% specificity,but also screens out those samples at early cancer *** supereminent efficiency potentiates SERS-AICS a promising tool for detecting cancer with broader types at earlier stage,accompanying with the establishment of a data platform for further deep analysis.

关键词： analysis specificity silver

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Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

arXiv

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arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

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PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization

arXiv

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arXiv 2023年

作者： Ma, Xinbei Gong, Yeyun He, Pengcheng Zhao, Hai Duan, Nan Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Microsoft Research Asia China

Based on the remarkable achievements of pretrained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work proposes PROM, a new PhRase-level cOpying Mechanism that enhances attention on n-grams, which can be applied to zero-shot summarization with pre-training. PROM adds an indicator layer to explicitly pick up tokens in n-gram that can be copied from the source, and calculates an auxiliary loss for the copying prediction. Empirical studies show that PROM makes significant improvements in fine-tuning on benchmarks. In the zero-shot setting, PROM is utilized in the self-supervised pre-training on raw corpora and provides new general baselines on a wide range of summarization datasets. Further analysis shows that PROM performs more reasonable copying and contributes to faithfulness. Our code is publicly available at https://***/xbmxb/PROM. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

A Passivity-Based Variable Impedance Controller for Incremen...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Matteo Dalle Vedove Edoardo Lamon Daniele Fontanelli Luigi Palopoli Matteo Saveriano Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator.

关键词： Surface impedance Incremental learning Tracking Education End effectors Safety Impedance Usability Robots Oscillators

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Machine learning-based prediction of low-value care for hospitalized patients

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Intelligence-Based Medicine 2023年 8卷 100115-100115页

作者： King, Andrew J. Tang, Lu Davis, Billie S. Preum, Sarah M. Bukowski, Leigh A. Zimmerman, John Kahn, Jeremy M. Department of Critical Care Medicine University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA United States Department of Computer Science Dartmouth College Hanover NH United States Human-Computer Interaction Institute Carnegie Mellon University School of Computer Science Pittsburgh PA United States

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period;and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system. © 2023 The Authors

关键词： Clinical decision support Evidence-based medicine Low-value care Machine learning Resuscitation

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ON ACCELERATING EDGE AI: OPTIMIZING RESOURCE-CONSTRAINED ENVIRONMENTS

arXiv

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arXiv 2025年

作者： Sander, Jacob Cohen, Achraf Dasari, Venkat R. Venable, Brent Jalaian, Brian Department of Computer Science University of West Florida PensacolaFL United States Department of Mathematics and Statistics University of West Florida PensacolaFL United States DEVCOM Army Research Laboratory Aberdeen Proving Ground MD United States Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States Department of Computer Science Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge. © 2025, CC BY.

关键词： Budget control

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FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures 23

FlexiFed: Personalized Federated Learning for Edge Clients w...

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research

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Biomedicine and Pharmacotherapy 2024年 181卷 117702-117702页

作者： Anwar, Faheem Zhang, Kuo Sun, Changcheng Pang, Meijun Zhou, Wanqi Li, Haodong He, Runnan Liu, Xiuyun Ming, Dong Medical School Tianjin University Tianjin 300072 China School of Pharmaceutical Science and Technology Tianjin University 300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300380 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin 300072 China

Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus. © 2024 The Authors

关键词： Clinical trials Hydrocephalus Non-surgical treatment Pathophysiology Pharmacological treatment Surgical interventions

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