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arXiv 2024年

作者： Miao, Juzheng Chen, Cheng Zhang, Keli Chuai, Jie Li, Quanzheng Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA United States Huawei Noah’s Ark Lab Shenzhen China Data Science Office Massachusetts General Brigham BostonMA United States Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong Hong Kong

One-shot detection of anatomical landmarks is gaining significant attention for its efficiency in using minimal labeled data to produce promising results. However, the success of current methods heavily relies on the employment of extensive unlabeled data to pre-train an effective feature extractor, which limits their applicability in scenarios where a substantial amount of unlabeled data is unavailable. In this paper, we propose the first foundation model-enabled one-shot landmark detection (FM-OSD) framework for accurate landmark detection in medical images by utilizing solely a single template image without any additional unlabeled data. Specifically, we use the frozen image encoder of visual foundation models as the feature extractor, and introduce dual-branch global and local feature decoders to increase the resolution of extracted features in a coarse-to-fine manner. The introduced feature decoders are efficiently trained with a distance-aware similarity learning loss to incorporate domain knowledge from the single template image. Moreover, a novel bidirectional matching strategy is developed to improve both robustness and accuracy of landmark detection in the case of scattered similarity map obtained by foundation models. We validate our method on two public anatomical landmark detection datasets. By using solely a single template image, our method demonstrates significant superiority over strong state-of-the-art one-shot landmark detection methods. Code is available at: https://***/JuzhengMiao/FM-OSD. © 2024, CC BY.

关键词： Foundations

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Machine Learning for Energy Load Prediction and its Interpretation

Machine Learning for Energy Load Prediction and its Interpre...

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International IEEE Conference on Intelligent Systems, IS

作者： Małgorzata Charytanowicz Anna Olwert Weronika Radziszewska Jolanta Jarnicka Krzysztof Gajowniczek Tomasz Ząbkowski Jacek Brożyna Grzegorz Mentel Grzegorz Matejko Center for Methods of Data Analysis Systems Research Institute PAS Warsaw Poland Department of Computer Science Systems Research Institute PAS Warsaw Poland Center for Computer Modelling Systems Research Institute PAS Warsaw Poland Institute of Information Technology Warsaw University of Life Sciences Warsaw Poland Department of Quantitative Methods Rzeszow University of Technology Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665492768

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking.

关键词： Measurement Additives Load forecasting Tariffs Companies Medium voltage Predictive models

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CoSimLex: A resource for evaluating graded word similarity in context 12

CoSimLex: A resource for evaluating graded word similarity i...

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12th International Conference on Language Resources and Evaluation, LREC 2020

作者： Armendariz, Carlos S. Purver, Matthew Ulcar, Matej Pollak, Senja Ljubešic, Nikola Robnik-Šikonja, Marko Granroth-Wilding, Mark Vaik, Kristiina Cognitive Science Research Group Queen Mary University of London London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia University of Ljubljana Faculty of Computer and Information Science Slovenia Department of Computer Science University of Helsinki Finland Department of Data Analysis Texta Estonia

ISBN: (纸本)9791095546344

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context;standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures;covers not only discrete differences in word sense but more subtle, graded changes in meaning;and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far. © European Language Resources Association (ELRA), licensed under CC-BY-NC

关键词： Semantics

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Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

arXiv

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arXiv 2022年

作者： Zhan, Jingtao Xie, Xiaohui Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Extrapolation

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Protecting Africa's children from extreme risk: a runway of sustainability for PEPFAR programmes

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The Lancet 2025年第10490期405卷 1700-1712页

作者： Cluver, Lucie Makangila, Gibstar Hillis, Susan Flaxman, Seth Unwin, Juliette Imai-Eaton, Jeffrey W Chtimbire, Vuyelwa Sherr, Lorraine Desmond, Chris Toska, Elona Omigbodun, Olayinka Ratmann, Oliver Carey, Galen Mahy, Mary Honermann, Brian Stover, John Centre for Evidence-Based Social Intervention Department of Social Policy and Intervention University of Oxford Oxford United Kingdom Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa Circle of Hope Lusaka Zambia Department of Mathematics Imperial College London London United Kingdom School of Mathematics University of Edinburgh Edinburgh United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Center for Communicable Disease Dynamics Department of Epidemiology Harvard T H Chan School of Public Health Boston MA United States Zimbabwe Association of Church-related Hospitals (ZACH) Harare Zimbabwe Institute of Global Health University College London London United Kingdom Ecumenical Pharmaceutical Network Africa Nairobi Kenya School of Economics and Finance University of the Witwatersrand Johannesburg South Africa Centre for Social Science Research University of Cape Town Cape Town South Africa Department of Sociology University of Cape Town Cape Town South Africa Centre for Child and Adolescent Mental Health College of Medicine University of Ibadan Ibadan Nigeria Department of Psychiatry College of Medicine University of Ibadan Ibadan Nigeria National Association of Evangelicals Washington DC United States Data For Impact UNAIDS Geneva Switzerland The Foundation for AIDS Research Washington DC United States Center for Modelling Planning and Policy Analysis Avenir Health Glastonbury CT United States

PEPFAR (President's Emergency Plan for AIDS Relief), a landmark US foreign health policy, is recognised for saving 26 million lives from HIV. PEPFAR investments have also had life-saving impacts for children across sub-Saharan Africa through childhood HIV prevention, care, and treatment, ensuring 7·8 million babies were born HIV-free, supporting 13 million orphaned and vulnerable children, and protecting 10·3 million girls from sexual abuse. In this Health Policy, we review data from UNAIDS, UNICEF, World Bank, Violence Against Children Surveys, SPECTRUM model data, and Population-based HIV Impact Assessments;synthesise PEPFAR reports;conduct in-depth interviews;search PubMed for programme effectiveness evidence;and review economic reports. PEPFAR support is associated with substantial collateral benefits for the USA and Africa, including a four-fold increase in export of US goods to Africa, and US$71·6 billion in total goods trade between the USA and Africa in 2024. PEPFAR-supported countries in Africa are committed to ownership of HIV responses by 2030—overall, PEPFAR-supported countries in sub-Saharan Africa have progressively increased their co-financing of their health systems through domestic government and private expenditure from $13·7 billion per year in 2004 to $42·6 billion per year in 2021. The feasibility of a 5-year transition to country-led sustainability is supported by evidence of innovative cost-saving models of delivery, including through faith-based and community-based organisations, and high return-on-investment for PEPFAR programmes. There are also collateral benefits of PEPFAR for US and Africa national security and health security, for example, reducing forced migration and increasing capacity to control emerging transborder infectious disease threats. Risks in sub-Saharan Africa remain acute: one in five girls (younger than 18 years) experience rape or sexual assault;one in ten children (younger than 18 years) are orphaned;and a child (young

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When to update your model: constrained model-based reinforcement learning 22

When to update your model: constrained model-based reinforce...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Tianying Ji Yu Luo Fuchun Sun Mingxuan Jing Fengxiang He Wenbing Huang Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences JD Explore Academy *** Inc Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9781713871088

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed.

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Synergistic Interface Effects in Composite Dielectrics: Insights into Charge Trapping Regulation through Multiscale Modeling

arXiv

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arXiv 2024年

作者： Zhao, Haoxiang An, Lixuan Zhang, Daning Yang, Xiong Yao, Huanmin Zhang, Guanjun Mu, Haibao Baumeier, Björn State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University Xi'An710049 China Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600MB Netherlands Department of Applied Physics Eindhoven University of Technology Eindhoven5600MB Netherlands KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Ghent9000 Belgium Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven5600MB Netherlands

The rapid development of modern energy applications drives an urgent need to enhance the dielectric strength of energy storage dielectrics for higher power density. Interface design is a promising strategy to regulate the crucial charge transport process determining dielectric strength. However, the targeted exploitation of interface effects on charge transport is limited due to a lack of fundamental understanding of the underlying mechanisms involving elementary electronic processes and details of the intricate interplay of characteristics of molecular building blocks and the interfacial morphology - details that cannot fully be resolved with experimental methods. Here we employ a multiscale modeling approach linking the quantum properties of the charge carriers with nano- and mesoscale structural details of complex interfaces. Applied to a prototypical application-proven cellulose-oil composite with interfaces formed between oil, disordered, and crystalline cellulose regions, this approach demonstrates that charges are trapped in the disordered region. Specifically, it unveils this trapping as a synergistic effect of two transport-regulating interface mechanisms: back-transfer to the oil region is suppressed by energetic factors, while forward-transfer to the crystalline cellulose is suppressed by low electronic coupling. The insight into the molecular origins of interface effects via dual-interface regulation offers new development paths for advanced energy materials. © 2024, CC BY.

关键词： Nanocrystals

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Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

arXiv

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arXiv 2023年

作者： Azad, Reza Jia, Yiwei Aghdam, Ehsan Khodapanah Cohen-Adad, Julien Merhof, Dorit The Institute of Imaging and Computer Vision RWTH Aachen University Aachen52074 Germany The Department of Electrical Engineering Shahid Beheshti University Tehran*** Iran The Mila Quebec AI Institute Canada The Functional Neuroimaging Unit CRIUGM University of Montreal Canada The NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal MontrealH3T 1J4 Canada The Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany The Fraunhofer Institute for Digital Medicine MEVIS Bremen28359 Germany

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suffer from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available on GitHub. © 2023, CC BY.

关键词： Merging

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Variable Star Light Curves in Koopman Space

arXiv

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arXiv 2024年

作者： Mekhaël, Nicolas Pasquato, Mario Carenini, Gaia Braga, Vittorio F. Trevisan, Piero Bono, Giuseppe Hezaveh, Yashar Dèpartment de Physique Universitè de Montrèal Montrèal Canada Mila – Quebec Artificial Intelligence Institute Montrèal Canada Dèpartment de Physique Université de Sherbrooke Sherbrooke Canada INAF IASF-Milano Milan Italy Ciela – Computation and Astrophysical Data Analysis Institute Montrèal Canada Paris France Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Space Science Data Center Rome Italy INAF-Osservatorio Astronomico di Roma Monte Porzio Catone Italy Departimento di Fisica Università di Roma Tor Vergata Rome Italy Center for Computational Astrophysics Flatiron Institute New York United States

We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variables, in the Galactic globular cluster ω Centauri. Light curves are thus summarized by a handful of complex eigenvalues, corresponding to oscillatory or fading dynamical modes. We find that variable stars of the RRc subclass can be summarized in terms of fewer (≈ 8) eigenvalues, while RRab need comparatively more (≈ 12). This result can be leveraged for classification and reflects the simpler structure of RRc light curves. We then consider variable stars displaying secular variations due to the Tseraskaya–Blazhko effect and find a change in relevant eigenvalues with time, with possible implications for the physical interpretation of the effect. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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Unsupervised many-to-many stain translation for histological image augmentation to improve classification accuracy

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Journal of Pathology Informatics 2023年 14卷 100195-100195页

作者： Berijanian, Maryam Schaadt, Nadine S. Huang, Boqiang Lotz, Johannes Feuerhake, Friedrich Merhof, Dorit Department of Computational Mathematics Science and Engineering (CMSE) Michigan State University East Lansing United States Institute for Pathology Hannover Medical School Hannover Germany Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany Institute for Neuropathology University Clinic Freiburg Freiburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Background: Deep learning tasks, which require large numbers of images, are widely applied in digital pathology. This poses challenges especially for supervised tasks since manual image annotation is an expensive and laborious process. This situation deteriorates even more in the case of a large variability of images. Coping with this problem requires methods such as image augmentation and synthetic image generation. In this regard, unsupervised stain translation via GANs has gained much attention recently, but a separate network must be trained for each pair of source and target domains. This work enables unsupervised many-to-many translation of histopathological stains with a single network while seeking to maintain the shape and structure of the tissues. Methods: StarGAN-v2 is adapted for unsupervised many-to-many stain translation of histopathology images of breast tissues. An edge detector is incorporated to motivate the network to maintain the shape and structure of the tissues and to have an edge-preserving translation. Additionally, a subjective test is conducted on medical and technical experts in the field of digital pathology to evaluate the quality of generated images and to verify that they are indistinguishable from real images. As a proof of concept, breast cancer classifiers are trained with and without the generated images to quantify the effect of image augmentation using the synthetized images on classification accuracy. Results: The results show that adding an edge detector helps to improve the quality of translated images and to preserve the general structure of tissues. Quality control and subjective tests on our medical and technical experts show that the real and artificial images cannot be distinguished, thereby confirming that the synthetic images are technically plausible. Moreover, this research shows that, by augmenting the training dataset with the outputs of the proposed stain translation method, the accuracy of breast cancer classifie

关键词： Adversarial networks Breast cancer classifier Deep learning Digital pathology Stain translation Unsupervised

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