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Ferroelectrets:Heterogenous polymer electrets with high piezoelectric sensitivity for transducers

Journal of Advanced Dielectrics 2023年第4期13卷 3-26页

作者： Xunlin Qiu Peng Fang Axel Mellinger Ruy Alberto Pisani Altafim Werner Wirges Gunnar Gidion Dmitry Rychkov Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237P.R.China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055P.R.China Department of Physics Central Michigan University 223 Dow Science ComplexMount PleasantMI 48859USA Computer Systems Department Informatics Center Federal University of ParáıbaJoão Pessoa-PBBrazil Department of Physics and Astronomy University of Potsdam PotsdamBrandenburgGermany Laboratory for Electrical Instrumentation Department of Microsystems Engineering-IMTEK University of FreiburgGeorges-Köhler-Allee 106Freiburg 79110Germany Technology and Study Center Weissenburg Deggendorf Institute of Technology WeissenburgBavariaGermany

Nowadays,the demand for advanced functional materials in transducer technology is growing *** materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice *** are interesting from both fundamental and application points of ***(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric *** exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to *** this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of *** will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.

关键词： Ferroelectret piezoelectret space-charge electret transducer advanced functional materials

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Learning with Group Noise

arXiv

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arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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A semi-supervised approach to monocular depth estimation, depth refinement, and semantic segmentation of driving scenes using a siamese triple decoder architecture

Informatica (Slovenia)

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Informatica (Slovenia) 2020年第4期44卷 437-445页

作者： Yusiong, John Paul T. Naval, Prospero C. Computer Vision and Machine Intelligence Group Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City Philippines Division of Natural Sciences and Mathematics University of the Philippines Visayas Tacloban College Tacloban City Leyte Philippines

Depth estimation and semantic segmentation are two fundamental tasks in scene understanding. These two tasks are usually solved separately, although they have complementary properties and are highly correlated. Jointly solving these two tasks is very beneficial for real-world applications that require both geometric and semantic information. Within this context, the paper presents a unified learning framework for generating a refined depth estimation map and semantic segmentation map given a single image. Specifically, this paper proposes a novel architecture called JDSNet. JDSNet is a Siamese triple decoder architecture that can simultaneously perform depth estimation, depth refinement, and semantic labeling of a scene from an image by exploiting the interaction between depth and semantic information. A semi-supervised method is used to train JDSNet to learn features for both tasks where geometry-based image reconstruction methods are employed instead of ground-truth depth labels for the depth estimation task while ground-truth semantic labels are required for the semantic segmentation task. This work uses the KITTI driving dataset to evaluate the effectiveness of the proposed approach. The experimental results show that the proposed approach achieves excellent performance on both tasks, and these indicate that the model can effectively utilize both geometric and semantic information. © 2020 Slovene Society Informatika. All rights reserved.

关键词： Semantic Segmentation

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on Robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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The application of artificial intelligence and radiomics in lung cancer

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Precision Clinical Medicine 2020年第3期3卷 214-227页

作者： Yaojie Zhou Xiuyuan Xu Lujia Song Chengdi Wang Jixiang Guo Zhang Yi Weimin Li Department of Respiratory and Critical Care Medicine West China School of MedicineWest China HospitalSichuan UniversityChengdu 610041China Machine Intelligence Laboratory College of Computer ScienceSichuan UniversityChengdu 610065China West China School of Public Health Sichuan UniversityChengdu 610041China

Lung cancer is one of the most leading causes of death throughout the world,and there is an urgent requirement for the precision medical management of *** intelligence(AI)consisting of numerous advanced techniques has been widely applied in the field of medical ***,radiomics based on traditional machine learning also does a great job in mining information through medical *** the integration of AI and radiomics,great progress has been made in the early diagnosis,specific characterization,and prognosis of lung cancer,which has aroused attention all over the *** this study,we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

关键词： artificial intelligence radiomics lung cancer machine learning deep learning

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Unsupervised hashing with contrastive information bottleneck

arXiv

引用

arXiv 2021年

作者： Qiu, Zexuan Su, Qinliang Ou, Zijing Yu, Jianxing Chen, Changyou School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangdong China CSE Department SUNY at Buffalo Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible. However, this requirement may force the models spending lots of their effort on reconstructing the unuseful background information, while ignoring to preserve the discriminative semantic information that is more important for the hashing task. To tackle this problem, inspired by the recent success of contrastive learning in learning continuous representations, we propose to adapt this framework to learn binary hashing codes. Specifically, we first propose to modify the objective function to meet the specific requirement of hashing and then introduce a probabilistic binary representation layer into the model to facilitate end-to-end training of the entire model. We further prove the strong connection between the proposed contrastive-learning-based hashing method and the mutual information, and show that the proposed model can be considered under the broader framework of the information bottleneck (IB). Under this perspective, a more general hashing model is naturally obtained. Extensive experimental results on three benchmark image datasets demonstrate that the proposed hashing method significantly outperforms existing baselines. © 2021, CC BY.

关键词： Semantics

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Cross-modal consensus network for weakly supervised temporal action localization

arXiv

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arXiv 2021年

作者： Hong, Fa-Ting Feng, Jia-Chang Xu, Dan Shan, Ying Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Tencent PCG Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science and Engineering HKUST Hong Kong Pazhou Lab Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Previous works use the appearance and motion features extracted from pre-trained feature encoder directly, e.g., feature concatenation or score-level fusion. In this work, we argue that the features extracted from the pre-trained extractors, e.g., I3D, which are trained for trimmed video action classification, but not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization. Therefore, the feature re-calibration is needed for reducing the task-irrelevant information redundancy. Here, we propose a cross-modal consensus network (CO2-Net) to tackle this problem. In CO2-Net, we mainly introduce two identical proposed cross-modal consensus modules (CCM) that design a cross-modal attention mechanism to filter out the task-irrelevant information redundancy using the global information from the main modality and the cross-modal local information from the auxiliary modality. Moreover, we further explore inter-modality consistency, where we treat the attention weights derived from each CCM as the pseudo targets of the attention weights derived from another CCM to maintain the consistency between the predictions derived from two CCMs, forming a mutual learning manner. Finally, we conduct extensive experiments on two commonly used temporal action localization datasets, THUMOS14 and ActivityNet1.2, to verify our method, which we achieve the state-of-the-art results. The experimental results show that our proposed cross-modal consensus module can produce more representative features for temporal action localization. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

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Corrections to “RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning”

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IEEE Access 2024年 12卷 118831-118831页

作者： Ahmed Sharshar Muhammad Sharshar Hosam Elhady Ahmed Aboeitta Youssef Nafea Yasser Ashraf Mohammad Yaqub Mohsen Guizani Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Cyber-Physical Laboratory Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt Department of Natural Language Processing MBZUAI Abu Dhabi United Arab Emirates Department of Machine Learning MBZUAI Abu Dhabi United Arab Emirates

Presents corrections to the paper, RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.

关键词： Deep learning Lung

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IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology

arXiv

引用

arXiv 2021年

作者： Tran, Cong Shin, Won-Yong Spitz, Andreas Department of Computer Science and Engineering Dankook University Yongin16890 Korea Republic of Machine Intelligence & Data Science Laboratory Yonsei University Seoul03722 Korea Republic of Faculty of Information Technology Posts and Telecommunications Institute of Technology Hanoi100000 Viet Nam Yonsei University Seoul03722 Korea Republic of Pohang37673 Korea Republic of Department of Computer and Information Science University of Konstanz Konstanz78457 Germany

Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries. We propose IM-META, a solution to influence maximization (IM) in networks with unknown topology by retrieving information from queries and node metadata. Since using such metadata is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference, we formulate a new IM problem that aims to find both seed nodes and queried nodes. In IM-META, we develop an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network, 2) we select a number of inferred confident edges to construct a reinforced graph, and 3) we identify the next node to query by maximizing the inferred influence spread using our topology-aware ranking strategy. Through experimental evaluation of IM-META on four real-world datasets, we demonstrate a) the speed of network exploration via node queries, b) the effectiveness of each module, c) the superiority over benchmark methods, d) the robustness to more difficult settings, e) the hyperparameter sensitivity, and f) the scalability. © 2021, CC BY.

关键词： Metadata

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Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes

arXiv

引用

arXiv 2021年

作者： Shin, Yong-Min Tran, Cong Shin, Won-Yong Cao, Xin Yonsei University Seoul03722 Korea Republic of The Department of Computer Science and Engineering Dankook University Yongin16890 Korea Republic of The Machine Intelligence & Data Science Laboratory Yonsei University Seoul03722 Korea Republic of The Faculty of Information Technology Posts and Telecommunications Institute of Technology Hanoi100000 Viet Nam The School of Computer Science and Engineering The University of New South Wales Sydney2052 Australia

We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs. Our study is motivated by the fact that GNNs cannot be straightforwardly adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose Edgeless-GNN, a novel inductive framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised learning. Specifically, we start by constructing a proxy graph based on the similarity of node attributes as the GNN's computation graph defined by the underlying network. The known network structure is used to train model parameters, whereas a topology-aware loss function is established in such a way that our model judiciously learns the network structure by encoding positive, negative, and second-order relations between nodes. For the edgeless nodes, we inductively infer embeddings by expanding the computation graph. By evaluating the performance of various downstream machine learning tasks, we empirically demonstrate that Edgeless-GNN exhibits (a) superiority over state-of-the-art inductive network embedding methods for edgeless nodes, (b) effectiveness of our topology-aware loss function, (c) robustness to incomplete node attributes, and (d) a linear scaling with the graph size. © 2021, CC BY.

关键词： Graph neural networks

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