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Boosted Storage Kinetics in Thick Hierarchical Micro-Nano Carbon Architectures for High Areal Capacity Li-Ion Batteries

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Energy & Environmental Materials 2022年第4期5卷 1251-1259页

作者： Yang Wu Ting Ouyang Tuzhi Xiong Zhao Jiang Yuwen Hu Jianqiu Deng Zhongmin Wang Yongchao Huang M.-Sadeeq(Jie Tang)Balogun College of Materials Science and Engineering Hunan UniversityChangsha 410082China Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan UniversityChangsha 410082China School of Chemistry Sun Yat-sen UniversityGuangzhou 510275China Institute of Environmental Research at Greater Bay Key Laboratory for Water Quality and Conservation of the Pearl River DeltaMinistry of EducationGuangzhou UniversityGuangzhou 510006China Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Hunan UniversityChangsha 410082China School of Materials Science and Engineering Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Information Materials Guilin University of Electronic TechnologyGuilin 541004China

A practical and effective approach to increase the energy storage capacity of lithium ion batteries(LIBs)is to boost their areal *** thick electrodes is one of the most crucial ways to achieve high areal capacity but limited by sluggish ion/electron transport,poor mechanical stability,and high-cost manufacturing *** we address these constraints by engineering a unique hierarchical-networked 10 mm thick all-carbon electrode,providing a scalable strategy to produce high areal capacity LIB *** hierarchical-networked structure utilizes micrometer-sized carbon fibers(MCFs)as building blocks,nano-sized carbon nanotubes(CNTs)as good continuous network with excellent electrical conductivity,and pyrolytic carbon as the binder and active material with excellent storage *** combination of the above features endows our HNT-MCF/CNT/PC electrode with excellent performance including high reversible capacity of 15.44 mAh cm^(-2) at 2.0 mA cm^(-2) and exhibits excellent rate capability of 2.50 mAh cm^(-2) under 10.0 mA cm^(-2) current *** Li-ion storage mechanism in HNT-MCF/CNT/PC involves dual-storage mechanism including intercalation and surface adsorption(pseudocapacitance)confirmed by the cyclic voltammetry and symmetric cell *** work provides insights into the construction of high mechanical stability thick electrode for the next generation high areal capacity LIBs and beyond.

关键词： dual storage mechanism high-areal-capacity micro-nano carbon architecture rapid kinetics and high mechanical stability thick electrode

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Noise-robust Cross-modal Interactive Learning with Text2image Mask for Multi-modal Neural Machine Translation 29

Noise-robust Cross-modal Interactive Learning with Text2imag...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Ye, Junjie Guo, Junjun Xiang, Yan Tan, Kaiwen Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

Multi-modal neural machine translation (MNMT) aims to improve textual level machine translation performance in the presence of text-related images. Most of the previous works on MNMT focus on multi-modal fusion methods with full visual features. However, text and its corresponding image may not match exactly, visual noise is generally inevitable. The irrelevant image regions may mislead or distract the textual attention and cause model performance degradation. This paper proposes a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT. A text-image relation-aware attention module is constructed through the cross-modal interaction mask mechanism, and visual features are extracted based on the text-image interaction mask knowledge. Then a noise-robust multi-modal adaptive fusion approach is presented by fusion the relevant visual and textual features for machine translation. We validate our method on the Multi30K dataset. The experimental results show the superiority of our proposed model, and achieve the state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Neural machine translation

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HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTENT AND STYLE 11

HARNESSING OUT-OF-DISTRIBUTION EXAMPLES VIA AUGMENTING CONTE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Huang, Zhuo Xia, Xiaobo Shen, Li Han, Bo Gong, Mingming Gong, Chen Liu, Tongliang Syndey AI Centre The University of Sydney Australia JD Explore Academy Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes a HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. The benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Learning systems

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UZNER: A Benchmark for Named Entity Recognition in Uzbek 12th

UZNER: A Benchmark for Named Entity Recognition in Uzbek

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Yusufu, Aizihaierjiang Jiang, Liu Ainiwaer, Abidan Teng, Chong Yusufu, Aizierguli Li, Fei Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China School of Computer Science and Technology Xinjiang Normal University Urumqi China

ISBN: (纸本)9783031446924

Named entity recognition (NER) is a key task in natural language processing, and entity recognition can provide necessary semantic information for many downstream tasks. However, the performance of NER is often limited by the richness of language resources. For low-resource languages, NER usually performs poorly due to the lack of sufficient labeled data and pre-trained models. To address this issue, we manually constructed a large-scale, high-quality Uzbek NER corpus of Uzbek, and experimented with various NER methods. We improved state-of-the-art baseline models by introducing additional features and data translations. Data translation enables the model to learn richer syntactic structure and semantic information. Affix features provide knowledge at the morphological level and play an important role in identifying oversimplified low-frequency entity labels. Our data and models will be available to facilitate low-resource NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Identification of Unknown Malicious Flow Based on Adaptive Annotation and Deep Neural Networks

Identification of Unknown Malicious Flow Based on Adaptive A...

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International Conference on Big Data and information Analytics (BigDIA)

作者： Zhaoyun Ding Yi Wang Wangqun Lin National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha Academy of Military Sciences Beijing

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

With the explosive growth of Internet flow, the problems of network security are becoming more and more prominent. The identification and detection of malicious flow in the network have become important topics. Most of the existing classification models are based on closed-world data sets, and can only classify the sampled flow data. In this case, unknown flow, especially the hidden malicious flow, cannot be effectively identified. In order to solve this problem, this paper proposes a recognition framework based on deep neural network to identify unknown flow and detect malicious flow, which can automatically expand the known data set to enhance the classification and detection ability of the model. Firstly, the framework constructs a classification model that can effectively identify the network flow based on the convolutional neural network. Based on this model, the unknown flow data with potential security risks are distinguished and clustered. Then, the unknown flow is maliciously detected according to the clustering results, and the model is updated with the newly discovered flow data.

关键词： Adaptation models Annotations Impurities Manuals Network security Data models Real-time systems Explosives Internet Detection algorithms

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Increasing impacts of fire air pollution on public and ecosystem health

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The Innovation 2024年第3期5卷 11-12页

作者： Xu Yue Yihan Hu Chenguang Tian Rongbin Xu Wenhua Yu Yuming Guo Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVIC 3004Australia

Wildfire episodes have become more frequent and severe in recent years.1 Record-breaking fires devastated the Arctic,Amazon,and Australia in 2019–*** year,fires began in Canada in May and lasted for several months,resulting in an area burned of 16.5 million hectares by early *** size is 6–7 times the annual fire area for a normal year in *** favorable fire weather for burning and spread lasted for months(https://***/maps/fw).Furthermore,most Canadian fires occur in remote regions far from firefighting facilities,causing fire extinction to be ***,such“unprecedented”fire events occurred routinely in boreal regions between 2020 and 2023,although the locations varied from year to year(Figure 1).

关键词： Canada pollution Figure

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Verifiable and Multi-keyword Searchable Encryption Scheme Based on LSSS

Verifiable and Multi-Keyword Searchable Encryption Scheme Ba...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Li, Su Qin, Chengyi Xu, Zihui Zhang, Xinzhe Liu, Zhien Wu, Lei School of Information Science and Engineering Shandong Normal University Jinan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Attribute-based Searchable Encryption (ABSE) schemes allow keyword encryption to be outsourced to cloud servers, and users can securely search for keyword ciphertext documents of interest. However, most existing searchable encryption schemes do not encourage the fine-grained search of users, and attributes are exposed to third parties easily. Moreover, ABSE schemes have high computational costs for resource-constrained clients on the user’s client side. To solve the above problems, a verifiable and multi-keyword searchable encryption scheme based on linear secret sharing schemes (LSSS) is presented by us, which enables users to achieve a more efficient and secure fine-grained search of multi-keyword documents. In this scheme, access control is achieved using LSSS technology, and user attribute hiding is accomplished by hash. Furthermore, user authentication and pre-decryption are outsourced to cloud servers without leaking any information, which lessens the client’s computational burden. It is demonstrated that the scheme can resist selective plaintext and selective keyword attacks in security models. In addition, the performance evaluation shows that our scheme performs well in real application scenarios. © 2022 IEEE.

关键词： Cryptography

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Screening of glucosinolates degrading lactic acid bacteria and their utilization in rapeseed meal fermentation

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Grain & Oil science and technology 2024年第3期7卷 168-176页

作者： Yifang Chen Chong Xie Muhammad Bilal Pei Wang Runqiang Yang College of Food Science and Technology Whole Grain Food Engineering Research CenterNanjing Agricultural UniversityNanjing 210095JiangsuChina The Sanya Institute of Nanjing Agricultural University Sanya 572024HainanChina National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Physiology Ecologyand ManagementMinistry of Agriculture/National Engineering and Technology Center for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina

Rapeseed meal is a promising food ingredient, but its utilization is limited by the presence of some potentially harmful ingredients, such as glucosinolates. Fermentation is a cost-effective method of detoxication but a food-grade starter culture with glucosinolates degradation capacity is required. In this study, 46 strains of lactic acid bacteria from traditional paocai brines were screened for their ability to glucosinolate degradation. The results showed that more than 50% of the strains significantly degraded glucosinolates. Two strains of Lactiplantibacillus(p7 and s7) with high capacity of glucosinolates degradation through producing enzymes were identified. Then,an optimized condition for rapeseed meal fermentation by p7 was established to degrade glucosinolates, which can achieve about 80% degradation. UPLC/Q-TOF-MS analysis showed that the degradation rate of individual glucosinolates was different and the degradation rate of gluconapin and progoitrin in rapeseed meal can reach more than 90%. Meanwhile, fermentation with p7 can improve safety of rapeseed meal by inhibiting the growth of Enterobacteriaceae and improve its nutritional properties by degrading phytic acid. The in vitro digestion experiments showed that the content of glucosinolates in rapeseed meal decreased significantly during gastric digestion. Meanwhile, fermentation with p7 can greatly improve the release of soluble protein and increase the contents of free essential amino acids, such as lysine(increased by 12 folds) and methionine(increased by 10 folds).

关键词： Rapeseed meal Glucosinolates Fermentation Lactic acid bacteria In vitro digestion

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Differentiator-Based Adaptive H8 Tracking Control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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FolkRank++: An optimization of folkrank tag recommendation algorithm integrating user and item information

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KSII Transactions on Internet and information systems 2021年第1期15卷 1-19页

作者： Zhao, Jianli Zhang, Qinzhi Sun, Qiuxia Huo, Huan Xiao, Yu Gong, Maoguo College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China Faculty of Engineering and Information Technology University of Technology SydneyAustralian Australia Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an China

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance. © 2021 Korean Society for Internet information. All rights reserved.

关键词： Behavioral research

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