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Ontology Revision based on Pre-trained Language Models

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arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts

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arXiv 2024年

作者： Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie Bernard and Irene Schwartz Center for Biomedical Imaging Dept. of Radiology NYU School of Medicine NY United States Dept. of Radiology NYU School of Medicine NY United States CUBRIC School of Engineering Cardiff University Cardiff United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany School of Computation Information and Technology Technical University of Munich Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Neurobiology Research Unit Copenhagen University Hospital Copenhagen Denmark

Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics’ robustness to typical pre-processing techniques. Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores. Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. © 2024, CC BY.

关键词： Image quality

Correction to: Deep mantle melting, global water circulation and its implications for the stability of the ocean mass

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Progress in Earth and Planetary science 2021年第1期8卷 1-3页

作者： Karato, Shun-ichiro Karki, Bijaya Park, Jeffrey Department of Earth and Planetary Sciences Yale University New Haven USA School of Electrical Engineering and Computer Science Department of Geology and Geophysics Center for Computation and Technology Louisiana State University Baton Rouge USA

An amendment to this paper has been published and can be accessed via the original article.

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Topological Edge States at Singular Points in Non-Hermitian Plasmonic Waveguide-Cavity Systems

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Topological Edge States at Singular Points in Non-Hermitian ...

Conference on Lasers and Electro-Optics (CLEO)

作者： Yin Huang Yuecheng Shen Georgios Veronis Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Changsha China State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Center for Computation and Technology Louisiana State University Baton Rouge LA USA

We show that non-Hermitian plasmonic waveguide-cavity systems can simultaneously exhibit both topological edge states and singular points. We also show that the sensitivity of the reflected light intensity is greatly ... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Sensitivity Waveguide lasers Lasers and electrooptics Plasmons Electro-optical waveguides

Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning

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arXiv 2022年

作者： Zhang, Qingquan Liu, Jialin Zhang, Zeqi Wen, Junyi Mao, Bifei Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Trustworthiness Theory Research Center Huawei Technologies Co. Ltd. Shenzhen518129 China The School of Computer Science University of Birmingham United Kingdom

In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the training of fair models. It has been theoretically and empirically shown that there exist conflicts and inconsistencies among accuracy and multiple fairness measures. Optimising one or several fairness measures may sacrifice or deteriorate other measures. Two key questions should be considered, how to simultaneously optimise accuracy and multiple fairness measures, and how to optimise all the considered fairness measures more effectively. In this paper, we view the mitigating unfairness problem as a multi-objective learning problem considering the conflicts among fairness measures. A multi-objective evolutionary learning framework is used to simultaneously optimise several metrics (including accuracy and multiple fairness measures) of machine learning models. Then, ensembles are constructed based on the learning models in order to automatically balance different metrics. Empirical results on eight well-known datasets demonstrate that compared with the state-of-the-art approaches for mitigating unfairness, our proposed algorithm can provide decision-makers with better tradeoffs among accuracy and multiple fairness metrics. Furthermore, the high-quality models generated by the framework can be used to construct an ensemble to automatically achieve a better tradeoff among all the considered fairness metrics than other ensemble methods. Our code is publicly available at https://***/qingquan63/FairEMOL. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double IA Harmony,and Causal computation

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 865-893页

作者： Lei Xu IEEE the Center for Cognitive Madmnes and Computational Health(CMaCH) School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Neural Computation Research Centre in Brain and Intelligence Science-Technology Institute Shanghai ZhangJiang National Lab

Advances on bidirectional intelligence are overviewed along three threads,with extensions and new *** first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are ***,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense *** second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY *** getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced *** third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in *** thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY *** contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem *** particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,***,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT ***,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman probl

关键词： Autoencoder LMSER duality outward attention associative recall concept formation imagining pattern transformation STD vs LTD RPCL skip connection feedback variational least redundancy Bayesian Ying Yang IA system best harmony best matching image thinking rational thinking intelligence potential theory Alpha-TSP Alpha-AGM graph matching ME Player BYY Follower constraint satisfaction causal potential theory

Raman Signatures of Single Point Defects in Hexagonal Boron Nitride Quantum Emitters

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arXiv 2025年

作者： Cholsuk, Chanaprom Çakan, Aslı Deckert, Volker Suwanna, Sujin Vogl, Tobias Department of Computer Engineering TUM School of Computation Information and Technology Technical University of Munich Munich80333 Germany Munich80799 Germany Institute of Physical Chemistry Friedrich-Schiller University Jena07743 Germany Leibniz Institute of Photonic Technology Jena07745 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand

Point defects in solid-state quantum systems are vital for enabling single-photon emission at specific wavelengths, making their precise identification essential for advancing applications in quantum technologies. However, pinpointing the microscopic origins of these defects remains a challenge. In this work, we propose Raman spectroscopy as a robust strategy for defect identification. Using density functional theory, we systematically characterize the Raman signatures of 100 defects in hexagonal boron nitride (hBN) spanning periodic groups III to VI, encompassing around 30,000 phonon modes. Our findings reveal that the local atomic environment plays a pivotal role in shaping the Raman lineshape, enabling the narrowing of potential defect candidates. Furthermore, we demonstrate that Raman spectroscopy can differentiate defects based on their spin and charge states as well as strain-induced variations, implying the versatility of this approach. Therefore, this study not only provides a comprehensive theoretical database of Raman spectra for hBN defects but also establishes a novel experiment framework for using tip-enhanced Raman spectroscopy to identify point defects. More broadly, our approach offers a universal method for defect identification in any quantum materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Point defects

Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

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Elucidating Spatial Complex Structures from Mass Spectrometr...

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

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Breakdown of the static dielectric screening approximation of Coulomb interactions in atomically thin semiconductors

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arXiv 2024年

作者： Mhenni, Amine Ben Van Tuan, Dinh Geilen, Leonard Petrić, Marko M. Erdi, Melike Watanabe, Kenji Taniguchi, Takashi Tongay, Sefaattin Müller, Kai Wilson, Nathan P. Finley, Jonathan J. Dery, Hanan Barbone, Matteo Walter Schottky Institute TUM School of Natural Sciences MCQST Technical University of Munich Munich Germany Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Walter Schottky Institute TUM School of Computation Information and Technology MCQST Technical University of Munich Munich Germany School for Engineering of Matter Transport and Energy Arizona State University TempeAZ United States Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Physics and Astronomy University of Rochester RochesterNY United States

Coulomb interactions in atomically thin materials are uniquely sensitive to variations in the dielectric screening of the environment, which can be used to control exotic quantum many-body phases and engineer exciton potential landscapes. A static approximation of the dielectric response, where increased dielectric screening is predicted to cause an energy redshift of the exciton resonance, has been until now sufficient. Here, we use charge-tunable exciton resonances to study screening effects in transition metal dichalcogenide monolayers embedded in materials with dielectric constants ranging from 4 to more than 1000, a range two orders of magnitude larger than previous studies. In contrast to the redshift predicted by static models employed until now, we observe a blueshift of the exciton resonance exceeding 30 meV for larger dielectric constant environments. By introducing a dynamical screening model based on a new solution to the Bethe-Salpeter equation, we find that while the exciton binding energy remains mostly controlled by the static dielectric response, the exciton self-energy is dominated by the high-frequency one. Our results supplant the understanding of screening in layered materials and their heterostructures, introduce a knob to tune selected many-body effects, and open new routes to detect and control correlated quantum many-body states and to design optoelectronic and quantum devices. © 2024, CC BY-NC-ND.

关键词： Nanocrystals