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arXiv 2025年

作者： Dahlbom, David Zhang, Hao Miles, Cole Quinn, Sam Niraula, Alin Thipe, Bhushan Wilson, Matthew Matin, Sakib Mankad, Het Hahn, Steven Pajerowski, Daniel Johnston, Steve Wang, Zhentao Lane, Harry Li, Ying Wai Bai, Xiaojian Mourigal, Martin Batista, Cristian D. Barros, Kipton Neutron Scattering Division Oak Ridge National Laboratory United States Department of Physics and Astronomy University of Tennessee United States Theoretical Division CNLS Los Alamos National Laboratory United States Kodiak Robotics School of Physics Georgia Institute of Technology United States Department of Physics and Astronomy Univeriy of California Los Angeles United States Department of Physics and Astronomy Louisiana State University United States X-Computational Physics Division Los Alamos National Laboratory United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States Institute for Advanced Materials and Manufacturing Unversity of Tennessee United States Center for Correlated Matter School of Physics Zhejiang University China Department of Physics and Astronomy University of Manchester United Kingdom The University of Manchester at Harwell University of Manchester United Kingdom School of Physics and Astronomy University of St Andrews United Kingdom Computer Computational and Statistical Sciences Division Los Alamos National Laboratory United States

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of numerical solvers. These include powerful methods for simulating spin dynamics both in and out of equilibrium. Uniquely, it features a broad generalization of classical and semiclassical approaches to SU(N) coherent states, which is useful for studying systems exhibiting strong spin-orbit coupling or local entanglement effects. Sunny also offers a well-developed framework for calculating the dynamical spin structure factor, enabling direct comparison with scattering experiments. Ease of use is a priority, with tools for symmetry-guided modeling and interactive visualization. © 2025, CC BY.

关键词： Spin dynamics

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Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

arXiv

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arXiv 2021年

作者： Lazo, Jorge F. Marzullo, Aldo Moccia, Sara Catellani, Michele Rosa, Benoit de Mathelin, Michel de Momi, Elena DEIB Politecnico di Milano Milan Italy ICube UMR 7357 CNRS-Université de Strasbourg Strasbourg France Department of Mathematics and Computer Science University of Calabria Rende Italy The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Milan Italy

Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on Convolutional Neural Networks (CNNs). Methods: The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks(m1) and Mask-RCNN(m2), which are fed with single still-frames I(t). The other two models (M1, M2) are modifications of the former ones consisting on the addition of a stage which makes use of 3D Convolutions to process temporal information. M1, M2 are fed with triplets of frames (I(t - 1), I(t), I(t + 1)) to produce the segmentation for I(t). Results: The proposed method was evaluated using a custom dataset of 11 videos (2,673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion: The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in presence of poor visibility, occasional bleeding, or specular reflections. © 2021, CC BY.

关键词： Convolutional neural networks

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On the self-testing (m,n)-code checker design

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IOP Conference Series: Materials science and Engineering 2021年第1期1019卷

作者： N Butorina Yu Burkatovskaya E Pakhomova Institute of Applied Mathematics and Computer Science National Research Tomsk State University Tomsk Russia School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia

We propose an approach to a self-testing (m, n)-code checker design, based on subdividing the set of all code words into special subsets called segments. The checker circuit is constructed by using one- and two-output configurable logic blocks (CLB). Previously, in each output of a CLB, a function representing exactly one segment was implemented. In the proposed approach, at each CLBs output, it is possible to implement functions that represent several segments and to provide the self-testing property. It allows reducing the number of CLBs and simplifying the circuit of the checker.

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A transfer-learning approach for lesion detection in endoscopic images from the urinary tract

arXiv

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arXiv 2021年

作者： Lazo, Jorge F. Moccia, Sara Marzullo, Aldo Catellani, Michele de Cobelli, Ottavio Rosa, Benoit de Mathelin, Michel de Momi, Elena Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy ICube UMR 7357 CNRS-Université de Strasbourg Strasbourg France The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Department of Mathematics and Computer Science University of Calabria RendeCS Italy Milan Italy

Ureteroscopy and cystoscopy are the gold standard methods to identify and treat tumors along the urinary tract. It has been reported that during a normal procedure a rate of 10-20 % of the lesions could be missed. In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions. A total of 6,101 images from ureteroscopy and cystoscopy procedures were collected. The CNNs were trained and tested using transfer learning in a two-steps fashion on 3 datasets. The datasets used were: 1) only ureteroscopy images, 2) only cystoscopy images and 3) the combination of both of them. For cystoscopy data, VGG performed better obtaining an Area Under the ROC Curve (AUC) value of 0.846. In the cases of ureteroscopy and the combination of both datasets, ResNet50 achieved the better results with AUC values of 0.987 and 0.940. The use of a training dataset which comprehends both domains results in general better performances, but performing a second stage of transfer learning achieves comparable ones. There is no single model which performs better in all scenarios, but ResNet50 is the network that achieves the better performances in most of them. The obtained results open the opportunity for further investigation with a view for improving lesion detection in endoscopic images of the urinary system. © 2021, CC BY-NC-SA.

关键词： Endoscopy

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Robust facial landmark detection by multi-order multi-constraint deep networks

arXiv

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arXiv 2020年

作者： Wan, Jun Lai, Zhihui Li, Jing Zhou, Jie Gao, Can College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China School of Mathematics and Statistics Hanshan Normal University Chaozhou521041 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China School of Computer Science Wuhan University Wuhan430072 China

Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature correlations, which is very important to learn more representative features and enhance shape constraints. Moreover, no explicit global shape constraints have been added to the final predicted landmarks, which leads to a reduction in accuracy. To address these issues, in this paper, we propose a Multi-order Multi-constraint Deep Network (MMDN) for more powerful feature correlations and shape constraints learning. Specifically, an Implicit Multi-order Correlating Geometry-aware (IMCG) model is proposed to introduce the multi-order spatial correlations and multi-order channel correlations for more discriminative representations. Furthermore, an Explicit Probability-based Boundary-adaptive Regression (EPBR) method is developed to enhance the global shape constraints and further search the semantically consistent landmarks in the predicted boundary for robust facial landmark detection. It’s interesting to show that the proposed MMDN can generate more accurate boundary-adaptive landmark heatmaps and effectively enhance shape constraints to the predicted landmarks for faces with large pose variations and heavy occlusions. Experimental results on challenging benchmark datasets demonstrate the superiority of our MMDN over state-of-the-art facial landmark detection methods. The code has been publicly available at https://github. com/junwan2014/MMDN-master. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Regularization with multilevel non-stationary tight framelets for image restoration

arXiv

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arXiv 2021年

作者： Li, Yan-Ran Chan, Raymond H.F. Shen, Lixin Zhuang, Xiaosheng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong Department of Mathematics Syracuse University SyracuseNY13244 United States

Variational regularization models are one of the popular and efficient approaches for image restoration. The regularization functional in the model carries prior knowledge about the image to be restored. The prior knowledge, in particular for natural images, are the first-order (i.e. variance in luminance) and second-order (i.e. contrast and texture) information. In this paper, we propose a model for image restoration, using a multilevel non-stationary tight framelet system that can capture the image's first-order and second-order information. We develop an algorithm to solve the proposed model and the numerical experiments show that the model is effective and efficient as compared to other higher-order models. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

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Robust facial landmark detection by cross-order cross-semantic deep network

arXiv

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arXiv 2020年

作者： Wan, Jun Lai, Zhihui Shen, Linlin Zhou, Jie Gao, Can Xiao, Gang Hou, Xianxu College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hanshan Normal University Chaozhou521041 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China

Recently, convolutional neural networks (CNNs)-based facial landmark detection methods have achieved great success. However, most of existing CNN-based facial landmark detection methods have not attempted to activate multiple correlated facial parts and learn different semantic features from them that they can not accurately model the relationships among the local details and can not fully explore more discriminative and fine semantic features, thus they suffer from partial occlusions and large pose variations. To address these problems, we propose a cross-order cross-semantic deep network (CCDN) to boost the semantic features learning for robust facial landmark detection. Specifically, a cross-order two-squeeze multi-excitation (CTM) module is proposed to introduce the cross-order channel correlations for more discriminative representations learning and multiple attention-specific part activation. Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection. It is interesting to show that by integrating the CTM module and COCS regularizer, the proposed CCDN can effectively activate and learn more © 2020, CC-BY.

关键词： Semantics

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An automatic approach for the classification of ancient clay statuettes based on heads features recognition

An automatic approach for the classification of ancient clay...

引用

2019 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2019

作者： Scalas, A. Vassallo, V. Mortara, M. Spagnuolo, M. Hermon, S. Institute of Applied Mathematics and Information Technologies 'Enrico Magenes National Research Council Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Italy Science and Technology for Archaeology Research Center Cyprus Institute Cyprus Department of Archaeology and Ancient History Lund University Sweden

ISBN: (纸本)9783038680826

In recent years, quantitative approaches based on mathematical theories and ICT tools, known under the terms of digital, computational, and virtual archaeology, are more and more involved in the traditional archaeological research. In this paper, we apply shape analysis techniques to 3D digital replicas of archaeological findings to support their interpretation. In particular, our study focuses on a collection of small terracotta figurines from the ancient sanctuary of Ayia Irini, Cyprus, and it aims at re-analysing the material utilising a quantitative approach. We experiment state of the art techniques (meshSIFT and DBSCAN) to cluster statuettes according to the similarity of their heads, to investigate their production process. © 2019 Eurographics Association.

关键词： Computation theory

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Correction to: SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification

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Medical & biological engineering & computing 2024年第9期62卷 2785页

作者： Akib Zaman Shiu Kumar Swakkhar Shatabda Iman Dehzangi Alok Sharma Computer Science and Artificial Intelligence Laboratory (CSAIL) Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge MA USA. School of Electrical & Electronics Engineering Fiji National University Suva Fiji. shiu.kumar@fnu.ac.fj. Centre for Artificial Intelligence and Robotics (CAIR) United International University Dhaka Bangladesh. Department of Computer Science Rutgers University Camden NJ USA. Center for Computational and Integrative Biology Rutgers University Camden USA. Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230‑0045 Japan. Institute for Integrated and Intelligent Systems Griffith University Nathan Brisbane QLD Australia.

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Active interactions between animals and technology: biohybrid approaches for animal behaviour research

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Animal Behaviour 2025年 224卷

作者： Papadopoulou, M. Ball, M. Bartashevich, P. Burns, A.L.J. Chiara, V. Clark, M.A. Costelloe, B.R. Fele, M. French, F. Hauert, S. Heinrich, M.K. Herbert-Read, J.E. Hoitt, J. Ioannou, C.C. Landgraf, T. Matchette, S.R. Polverino, G. Sankey, D.W.E. Scott, D.M. Sridhar, V.H. Strömbom, D. Trianni, V. Vo-Doan, T.T. King, A.J. Department of Biosciences Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Biology Lafayette College Easton PA United States Institute for Theoretical Biology Department of Biology Humboldt-Universität zu Berlin Berlin Germany Cluster of Excellence ‘Science of Intelligence’ Berlin Germany Department Fish Biology Fisheries and Aquaculture Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany Faculty of Life Sciences Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences Humboldt-Universität zu Berlin Berlin Germany Museum and Institute of Zoology Polish Academy of Science Warszawa Poland School of Biological Sciences University of Bristol Bristol United Kingdom School of Natural Sciences Macquarie University Sydney Australia Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Department of Collective Behavior Max Planck Institute of Animal Behavior Konstanz Germany School of Computing and Digital Media London Metropolitan University London United Kingdom Bristol Robotics Laboratory University of Bristol Bristol United Kingdom IRIDIA Université Libre de Bruxelles Brussels Belgium Department of Zoology University of Cambridge Cambridge United Kingdom Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Ecological and Biological Sciences University of Tuscia Viterbo Italy School of Natural and Environmental Science Newcastle University Newcastle upon Tyne United Kingdom Centre for Ecology and Conservation Faculty of Environment Science and Economy University of Exeter Penryn Campus Cornwall United Kingdom School of Animal Rural and Environmental Sciences Nottingham Trent University Nottingham United Kingdom Department for the Ecology of Animal Societies Max Planck Institute of Animal Behavior Konstanz Germany Ins

Biohybrid approaches (where living and engineered components are combined) provide new opportunities for advancing animal behaviour research and its applications. This review article and accompanying special issue explores how different types of novel technologies can be used in the field of animal behaviour from three perspectives: (1) comprehension, (2) application and (3) integration. Under the perspective of ‘comprehension,’ we present examples of how technologies like virtual animals or robots can be used in experimental settings to interact with living animals in a standardized manner. Such interactions can advance our understanding of fundamental topics such as mate choice, social learning and collective behaviour. Under ‘application,’ we investigate the potential for technologies to monitor, react and interact with animals in a variety of scenarios. For example, we discuss how drones can be used to keep large herbivores away from valuable crops and robotic predators to deter invasive species. Under ‘integration,’ we discuss possibilities for the coexistence of engineered and biological systems, augmenting the capacity or resilience of either or both components. Integration can be physical, for example, livestock can have sensors sit in their inner body for temperature monitoring, or within the environment, where sensors or robots monitor and interact with animals, such as a short-term earthquake forecasting method. Based upon these three themes, we discuss and classify existing biohybrid animal behaviour research, including the four articles included in our special issue. We also consider the ethics of this emerging field, highlight the advantages and potential issues associated with using technologies to create biohybrid systems and emphasize how such technologies can support the advancement of animal behaviour research. © 2025 The Association for the Study of Animal Behaviour

关键词： behavioural control behavioural monitoring biomimetic robotics ethorobotics interspecies interaction sensory integration

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