检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Zou, Ke Tao, Tianjin Yuan, Xuedong Shen, Xiaojing Lai, Wenli Long, Hu National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan Chengdu610065 China State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases Department of Orthodontics West China Hospital of Stomatology Sichuan University Sichuan Chengdu610065 China College of Computer Science Sichuan University Sichuan Chengdu610065 China College of Mathematics Sichuan University Sichuan Chengdu610065 China

Automatic and accurate segmentation of the hard palate from CBCT images is a fundamental task for the insertion of orthodontic mini-implants. Baseline (U-Net) and its variants segmentation methods can obtain satisfactory results on normal dental cases. However, they fail to handle it robustly in facing the hard palate with diversity of shape and thickness among individuals with different ages and genders. Further, they may lead to mis-segmentation or missing-segmentation results induced by high-intensity of the hard bones in the hard palate. Motivated by these challenges and the intersection of disciplines, a novel interactive dual-branch network (IDBNet) is developed to achieve automatic and accurate the hard palate segmentation of the oral cavity from CBCT images. IDBNet is designed to capture multi-scale interactive features of the hard palate and gradually mine the unambiguous hard palate edges. Specifically, dual-branch encoder is developed to capture granular-level multi-scale features of hard palate. Then, channel map interaction module is proposed between the dual-branch encoder to reinforce cross-channel and cross-source information interaction, thereby enhancing feature representations for the hard palate. Besides that, the dual-branch decoder adopts a three-step strategy of rough-interactive-fine to mine the edge information of the hard palate gradually. Dual-branch parallel partial decoder first quickly guide a rough global map of the hard palate. Guide map interaction module then model the similarities and differences between the hard palate boundaries. At last, the fine edges of the hard palate are gradually dug out by the parallel reverse attention module. Five-fold cross-validation experiments on the self-collected dataset demonstrate that our method outperforms the Res-UNet method by 8.05% of mean Dice score and 7.97% of structure measure. The utilisation of our proposed segmentation approach for hard palate of the oral cavity warrants further inves

关键词： computerized tomography

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Odyssey: Creation, Analysis and Detection of Trojan Models

arXiv

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arXiv 2020年

作者： Edraki, Marzieh Karim, Nazmul Rahnavard, Nazain Mian, Ajmal Shah, Mubarak Center for Research in Computer Vision of University of Central Florida United States Department of Electrical and Computer Engineering University of Central Florida United States School of Computer Science and Software Engineering University of Western Australia Australia

Along with the success of deep neural network (DNN) models in solving various real world problems, rise the threats to these models that aim to degrade their integrity. Trojan attack is one of the recent variant of data poisoning attacks that involves manipulation or modification of the model to act balefully. This can occur when an attacker interferes with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that are stamped with trigger. Since the knowledge of such triggers is only privy to the attacker, detection of Trojan behaviour is a challenge task. Unlike any of the existing Trojan detectors, a robust detector should not rely on any assumption about Trojan attack. In this paper, we develop a detector based upon the analysis of intrinsic properties of DNN that could get affected by a Trojan attack. To have a comprehensive study, we propose, Odysseus the largest Trojan dataset with over 3,000 trained DNN models, both clean and Trojan. It covers a large spectrum of attacks;generated by leveraging the versatility in designing a trigger and mapping (source to target class) type. Our findings reveal that Trojan attacks affect the classifier margin and shape of decision boundary around the manifold of the clean data. Combining these two factors leads to an efficient Trojan detector;operates irrespective of any knowledge of the Trojan attack;that sets the first baseline for this task with accuracy above 83%. The Odysseus dataset along with the Trojan detector can be downloaded here Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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Comparing feedback linearization and adaptive backstepping control for airborne orientation of agile ground robots using wheel reaction torque

arXiv

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arXiv 2020年

作者： Kim, Jinho Gonzalez, Daniel J. Korpela, Christopher M. Robotics Research Center Department of Electrical Engineering and Computer Science United States Military Academy West PointNY10996 United States

In this paper, two nonlinear methods for stabilizing the orientation of a Four-Wheel Independent Drive and Steering (4WIDS) robot while in the air are analyzed, implemented in simulation, and compared. AGRO (the Agile Ground Robot) is a 4WIDS inspection robot that can be deployed into unsafe environments by being thrown, and can use the reaction torque from its four wheels to command its orientation while in the air. Prior work has demonstrated on a hardware prototype that simple PD control with hand-tuned gains is sufficient, but hardly optimal, to stabilize the orientation in under 500ms. The goal of this work is to decrease the stabilization time and reject disturbances using nonlinear control methods. A model-based Feedback Linearization (FL) was added to compensate for the nonlinear Coriolis terms. However, with external disturbances, model uncertainty and sensor noise, the FL controller does not guarantee stability. As an alternative, a second controller was developed using backstepping methods with an adaptive compensator for external disturbances, model uncertainty, and sensor offset. The controller was designed using Lyapunov analysis. A simulation was written using the full nonlinear dynamics of AGRO in an isotropic steering configuration in which control authority over its pitch and roll are equalized. The PD+FL control method was compared to the backstepping control method using the same initial conditions in simulation. Both the backstepping controller and the PD+FL controller stabilized the system within 250 milliseconds. The adaptive backstepping controller was also able to achieve this performance with the adaptation law enabled and compensating for offset noisy sinusoidal disturbances. Copyright © 2020, The Authors. All rights reserved.

关键词： Wheels

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Can reinforcement learning for continuous control generalize across physics engines?

arXiv

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arXiv 2020年

作者： Mohammed, Aaqib Parvez Valdenegro-Toro, Matias Department of Computer Science Hochschule Bonn-Rhein-Sieg Sankt Augustin Germany Robotics Innovation Center German Research Center for Artificial Intelligence Bremen Germany

Reinforcement learning (RL) algorithms should learn as much as possible about the environment but not the properties of the physics engines that generate the environment. There are multiple algorithms that solve the task in a physics engine based environment but there is no work done so far to understand if the RL algorithms can generalize across physics engines. In this work, we compare the generalization performance of various deep reinforcement learning algorithms on a variety of control tasks. Our results show that MuJoCo is the best engine to transfer the learning to other engines. On the other hand, none of the algorithms generalize when trained on PyBullet. We also found out that various algorithms have a promising generalizability if the effect of random seeds can be minimized on their performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Explainable deep classification models for domain generalization

arXiv

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arXiv 2020年

作者： Zunino, Andrea Bargal, Sarah Adel Volpi, Riccardo Sameki, Mehrnoosh Zhang, Jianming Sclaroff, Stan Murino, Vittorio Saenko, Kate Huawei Ireland Research Center Department of Computer Science Boston University Naver Labs Europe Microsoft Adobe Research Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Università di Verona

Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision. This is represented in the form of a saliency map conveying how much each pixel contributed to the network's decision. Our training strategy enforces a periodic saliency-based feedback to encourage the model to focus on the image regions that directly correspond to the ground-truth object. We quantify explainability using an automated metric, and using human judgement. We propose explainability as a means for bridging the visual-semantic gap between different domains where model explanations are used as a means of disentagling domain specific information from otherwise relevant features. We demonstrate that this leads to improved generalization to new domains without hindering performance on the original domain. Copyright © 2020, The Authors. All rights reserved.

关键词： Conveying

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Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 24

Binding in hippocampal-entorhinal circuits enables compositi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Christopher J. Kymn Sonia Mazelet Anthony Thomas Denis Kleyko E. Paxon Frady Friedrich T. Sommer Bruno A. Olshausen Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Department of Electrical and Computer Engineering UC Davis Davis Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden and Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley

ISBN: (纸本)9798331314385

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. Code is available at https://***/SoniaMaz8/Hippocampal_enthorinal_circuit

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Improving Ancient Cham Glyph Recognition from Cham Inscription Images Using Data Augmentation and Transfer Learning 2nd

Improving Ancient Cham Glyph Recognition from Cham Inscripti...

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2nd International Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st International Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st International Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, International Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th International Conference on Image Analysis and Processing, ICIAP 2019

作者： Nguyen, Minh-Thang Schweyer, Anne-Valérie Le, Thi-Lan Tran, Thanh-Hai Vu, Hai Computer Vision Department MICA International Research Institute Hanoi University of Science and Technology Hanoi Viet Nam French National Center for Scientific Research Paris France

ISBN: (纸本)9783030307530

Ancient Cham glyphs have mostly appeared in inscriptions on stones at some museums in Vietnam. Unfortunately, these inscriptions are being abrasive by the time. To conserve Cham heritage as well as to make them widely accessible and readable by users, digitization and recognition of ancient Cham glyphs become necessary. In our previous work, we have built the first dataset of champ inscription images, manually segmented them in glyphs and annotated by an ancient Cham expert. We adapted some automatic recognition methods and conducted experiments on the manually denoised dataset. The aim of this paper is to extend that earlier research to work on noising data. To this end, we face two main issues. Firstly, the current pre-built dataset is still small which is usually a main drawback for deep learning based methods. Therefore, some data augmentation techniques will be evaluated and investigated to increase the number and variation of samples in the dataset. Second, even with the augmented dataset, the fact of training a deep model from scratch could be very long and sometimes cannot meet a good local minimum. Therefore, we use a simple transfer learning procedure which inherits knowledge from similar or of the same family language. Experiments on both the raw test set and its denoised version show very promising results and of F1-score on two test sets respectively). © 2019, Springer Nature Switzerland AG.

关键词： Image classification

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