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Unsupervised Domain-Adaptive Semantic Segmentation for Surgical Instruments Leveraging Dropout-Enhanced Dual Heads and Coarse-Grained Classification Branch

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年

作者： Li, Ziqian Wang, Zhengyu Xu, Xinzhou Chen, Yongfa Schuller, Bjorn W. Hefei University of Technology School of Mechanical Engineering Hefei China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China Graz University of Technology Signal Processing and Speech Communication Laboratory Graz Austria Chair of Health Informatics Munich Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany Imperial College London GLAM – the Group on Language Audio and Music London United Kingdom

Accurate semantic segmentation for surgical instruments is crucial in robot-assisted minimally invasive surgery, mainly regarded as a core module in surgical-instrument tracking and operation guidance. Nevertheless, it is usually difficult for existing semantic surgical-instrument segmentation approaches to adapt to unknown surgical scenes, particularly due to their insufficient consideration for reducing the domain gaps across different scenes. To address this issue, we propose an unsupervised domain-adaptive semantic segmentation approach for surgical instruments, leveraging Dropout-enhanced Dual Heads and Coarse-Grained classification branch (D2HCG). The proposed approach comprises dropout-enhanced dual heads for diverse feature representation, and a coarse-grained classification branch for capturing complexities across varying granularities. This incorporates consistency loss functions targeting fine-grained features and coarse-grained granularities, aiming to reduce crossscene domain gaps. Afterwards, we perform experiments in crossscene surgical-instrument semantic segmentation cases, with the experimental results reporting the effectiveness for the proposed approach, compared with state-of-the-art semantic segmentation ones. © 2018 IEEE.

关键词： coarse-grained classification dropout enhancement Surgical instruments unsupervised domain-adaptive semantic segmentation

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Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

arXiv

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arXiv 2023年

作者： Jungel, Kai Parmentier, Axel Schiffer, Maximilian Vidal, Thibaut TUM School of Management Technical University of Munich Munich80333 Germany CERMICS École des Ponts Marne-la-Vallée France TUM School of Management Munich Data Science Institute Technical University of Munich Munich80333 Germany CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematics and Industrial Engineering École Polytechnique de Montréal Montréal Canada Department of Computer Science Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil

Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Digital earth:yesterday,today,and tomorrow

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International Journal of Digital Earth 2023年第1期16卷 1022-1072页

作者： Alessandro Annoni Stefano Nativi ArzuÇöltekin Cheryl Desha Eugene Eremchenko Caroline M.Gevaert Gregory Giuliani Min Chen Luis Perez-Mora Joseph Strobl Stephanie Tumampos International Society for Digital Earth BeijingPeople’s Republic of China National Research Council RomeItaly University of Applied Sciences and Arts Northwestern Switzerland FHNW School of EngineeringInstitute of Interactive Technologies IITBrugg-WindischSwitzerland Nathan Campus Griffith UniversityCities Research InstituteBrisbaneAustralia Lomonosov Moscow State University MoscowRussia Department of Earth Observation Science University of TwenteTwenteThe Netherland Institute for Environmental Sciences/enviroSPACE University of GenevaGenevaSwitzerland Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjingPeople’s Republic of China Cities Research Institute Nathan CampusGriffith UniversityBrisbaneAustralia Department of Geoinformatics University of SalzburgSalzburgAustria Chair of Remote Sensing Technology Technical University of MunichMunichGermany Big Geospatial Data Management Technical University of MunichMunichGermany

The concept of Digital Earth(DE)was formalized by Al Gore in *** that time the technologies needed for its implementation were in an embryonic stage and the concept was quite *** then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our *** creates new opportunities and challenges for the realization of DE.‘What is DE today?’,‘What could DE be in the future?’,and‘What is needed to make DE a reality?’.To answer these questions it is necessary to examine DE considering all the technological,scientific,social,and economic aspects,but also bearing in mind the principles that inspired its *** understanding the lessons learned from the past,it becomes possible to identify the remaining scientific and technological challenges,and the actions needed to achieve the ultimate goal of a‘Digital Earth for all’.This article reviews the evolution of the DE vision and its multiple definitions,illustrates what has been achieved so far,explains the impact of digital transformation,illustrates the new vision,and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.

关键词： Digital Earth digital transformation sustainable development data governance and ethic citizen empowerment and industry engagement

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Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text data

Exploring the Capabilities of a Language Model-Only Approach...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Misha Sadeghi Bernhard Egger Reza Agahi Robert Richer Klara Capito Lydia Helene Rupp Lena Schindler-Gmelch Matthias Berking Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen- Nürnberg (FAU) Erlangen Germany Department of Computer Science Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Syenah GMBH Eschborn Germany Chair of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.

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A systematic review on recommender systems in process mining

arXiv

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arXiv 2021年

作者： Eili, Mansoureh Yari Rezaeenour, Jalal Sani, Mohammadreza Fani Department of Computer Engineering Faculty of Technology and Engineering University of Qom Iran Department of Industrial Engineering Faculty of Technology and Engineering University of Qom Iran Process and Data Science Chair RWTH Aachen University Aachen Germany

Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender system is assessed in a few works. One reason can be the lack of knowledge on process mining potential for recommendation problems. Therefore, this paper aims to identify and analyze the published studies on process-aware recommender system techniques in business process management and process mining domain. A systematic review was conducted on 33 academic articles published between 2008 and 2020 according to several aspects. In this regard, we provide a state-of-the-art review with critical details and researchers with a better perception of which path to pursue in this field. Moreover, based on a knowledge base and holistic perspective, we discuss some research gaps and open challenges in this field. © 2021, CC BY-SA.

关键词： data mining

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Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?

arXiv

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arXiv 2022年

作者： Santana, Ítalo Lodi, Andrea Vidal, Thibaut Department of Computer Science Pontifical Catholic University of Rio de Janeiro Brazil Jacobs Technion-Cornell Institute Cornell Tech and Technion - IIT Canada CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematical and Industrial Engineering Polytechnique Montréal Canada

Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms. In this study, we investigate the use of predictions from graph neural networks (GNNs) in the form of heatmaps to improve the Hybrid Genetic Search (HGS), a state-of-the-art algorithm for the Capacitated Vehicle Routing Problem (CVRP). The crossover and local-search components of HGS are instrumental in finding improved solutions, yet these components essentially rely on simple greedy or random choices. It seems intuitive to attempt to incorporate additional knowledge at these levels. Throughout a vast experimental campaign on more than 10,000 problem instances, we show that exploiting more sophisticated strategies using measures of node relatedness (heatmaps, or simply distance) within these algorithmic components can significantly enhance performance. However, contrary to initial expectations, we also observed that heatmaps did not present significant advantages over simpler distance measures for these purposes. Therefore, we faced a common —though rarely documented— situation of overkill: GNNs can indeed improve performance on an important optimization task, but an ablation analysis demonstrated that simpler alternatives perform equally well. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Trapping-influenced photoluminescence intensity decay in semiconductor nanoplatelets 6

Trapping-influenced photoluminescence intensity decay in sem...

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6th International Conference on Metamaterials and Nanophotonics, METANANO 2021

作者： Kurilovich, A.A. Mantsevich, V.N. Stevenson, K.J. Chechkin, A.V. Palyulin, V.V. Center for Energy Science and Technology Skolkovo Institute of Science and Technology Moscow121205 Russia Chair of Semiconductors and Cryoelectronics and Quantum Technology Center Physics department Lomonosov Moscow State University Moscow119991 Russia Institute for Physics and Astronomy University of Potsdam Potsdam-GolmD-14476 Germany Akhiezer Institute for Theoretical Physics National Science Center Kharkov Institute of Physics and Technology" Kharkov61108 Ukraine Center for Computational and Data-intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow121205 Russia

We present a diffusion-based simulation model for explanation of long time power-law decay of photoluminescence (PL) emission intensity in semiconductor nanoplatelets. In our model the shape of emission curves is an outcome of interplay of recombination, diffusion and trapping of excitons. At short times the excitons diffuse freely following the normal diffusion behaviour. The emission decay is purely exponential and is defined by recombination. At long times the transition into the subdiffusive motion happens and the emission occurs due to the release of excitons from surface traps. A power-law tail for intensity is a consequence of the release. The crossover from onelimit to another is controlled by diffusion properties. The approach reproduces the properties of experimental curves measured for different nanoplatelet systems. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Excitons

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Question answering over knowledge graphs: a graph-driven approach

Question answering over knowledge graphs: a graph-driven app...

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2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Sareh Aghaei Sepide Masoudi Tek Raj Chhetri Anna Fensel Department of Computer Science University of Innsbruck Innsbruck Austria Faculty of Computer Engineering Islamic Azad University Najafabad Iran Wageningen Data Competence Center Wageningen University and Research Wageningen The Netherlands Consumption and Healthy Lifestyles Chair Group Wageningen University & Research Wageningen The Netherlands Semantic Technology Institute University of Innsbruck Innsbruck Austria

With the growth of knowledge graphs (KGs), question answering systems make the KGs easily accessible for end-users. Question answering over KGs aims to provide crisp answers to natural language questions across facts stored in the KGs. This paper proposes a graph-driven approach to answer questions over a KG through four steps, including (1) knowledge subgraph construction, (2) question graph construction, (3) graph matching, and (4) query execution. Given an input question, a knowledge subgraph, which is likely to include the answer is extracted to reduce the KG’s search space. A graph, named question graph, is built to represent the question’s intention. Then, the question graph is matched over the knowledge subgraph to find a query graph corresponding to a SPARQL query. Finally, the corresponding SPARQL is executed to return the answers to the question. The performance of the proposed approach is empirically evaluated using the 6th Question Answering over Linked data Challenge (QALD-6). Experimental results show that the proposed approach improves the performance compared to the-state-of-art in terms of recall, precision, and F1-score.

关键词： Knowledge engineering Structured Query Language Linked data IEEE Sections Semantics Natural languages Knowledge graphs

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data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture

arXiv

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arXiv 2022年

作者： Steinberger, Dominik Issa, Inas Strobl, Rachel Imrich, Peter J. Kiener, Daniel Sandfeld, Stefan Institute of Mechanics and Fluid Dynamics Freiberg University of Mining and Technology Freiberg Germany Department Materials Science Chair of Materials Physics Montanuniversität Leoben Leoben Austria Forschungszentrum Jülich GmbH Jülich52425 Germany Now at Kompetenzzentrum Automobil- und Industrieelektronik GmbH Europastraße 8 Villach9524 Austria Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering RWTH Aachen University Aachen52056 Germany

The lifetime and performance of any engineering component, from nanoscale sensors to macroscopic structures, are strongly influenced by fracture processes. Fracture itself is a highly localized event;originating at the atomic scale by bond breaking between individual atoms close to the crack tip. These processes, however, interact with defects such as dislocations or grain boundaries and influence phenomena on much larger length scales, ultimately giving rise to macroscopic behavior and engineering-scale fracture properties. This complex interplay is the fundamental reason why identifying the atomistic structural and energetic processes occurring at a crack tip remains a longstanding and still unsolved challenge. We develop a new analysis approach for combining quantitative in-situ observations of nanoscale deformation processes at a crack tip with three-dimensional reconstruction of the dislocation structure and advanced computational analysis to address plasticity and fracture initiation in a ductile metal. Our combinatorial approach reveals details of dislocation nucleation, their interaction process, and the local internal stress state, all of which were previously inaccessible to experiments. This enables us to describe fracture processes based on local crack driving forces on a dislocation level with a high fidelity that paves the way towards a better understanding and control of local failure processes in materials. Copyright © 2022, The Authors. All rights reserved.

关键词： Crack tips

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Sustainability in the Internet of Production: Interdisciplinary Opportunities and Challenges

Sustainability in the Internet of Production: Interdisciplin...

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International Symposium on Technology and Society (ISTAS)

作者： Sebastian Bernhard Sebastian Pütz Calvin Röhl Ralph Baier Philipp Brauner Ester Christou Hannah Dammers Roman Flaig Leon M. Gorißen Jan-Christoph Heilinger Christian Hinke István Koren Dirk Lüttgens Michael Millan Kai Müller Alexander Schollemann Luisa Vervier Thomas Gries Alexander Mertens Saskia K. Nagel Frank T. Piller Günther Schuh Martina Ziefle Verena Nitsch Carmen Leicht-Scholten Gender and Diversity in Engineering RWTH Aachen University Aachen Germany Institute of Industrial Engineering and Ergonomics RWTH Aachen University Aachen Germany Human-Computer Interaction Center RWTH Aachen University Aachen Germany Institute for Technology and Innovation Management RWTH Aachen University Aachen Germany Institut für Textiltechnik RWTH Aachen University Aachen Germany Chair for Laser Technology RWTH Aachen University Aachen Germany Human Technology Center/Applied Ethics RWTH Aachen University Aachen Germany Chair of Process and Data Science RWTH Aachen University Aachen Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen Germany

The vision of the Internet of Production (loP) is focused on optimizing manufacturing processes, with the help of Industry 4.0 technologies (14Ts). However, considering global megatrends such as climate change and the need to achieve the Sustainable Development Goals (SDGs), it is a growing imperative for the manufacturing industry to become more sustainable. This opens the door to transforming the IoP into an Internet of Sustainable Production (loSP). Accordingly, this paper proposes a novel four-step loSP-framework that establishes an Information System (IS) allowing researchers and practitioners to acknowledge and adapt to the interconnected nature of sustainability. Further, to test its applicability, an inter- and cross-disciplinary perspective is adopted to illustrate case-related challenges, opportunities, and pathways - revealed by the proposed framework - in the example of a digital economy for sustainability data, strategic design of global production networks, human-robot collaboration, digital photonic production, and the textile industry. Together, the framework demonstrates the usefulness of generating holistic information on the interconnected nature of sustainability derived from process-specific and contextualized data, while simultaneously assessing the framework's utilization for sustainability, as well as the sustainability of its use.

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