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Versatile hydrogel based on polyvinyl alcohol/chitosan/regenerated silk fibroin 5

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Versatile hydrogel based on polyvinyl alcohol/chitosan/regen...

2021 5th International Conference on Green Composite Materials and Nanotechnology, GCMN 2021

作者： Sun, Qinyue Fu, Jizhu Zhang, Heng Yang, Huihui Wang, Yumin Xiong, Yi Zeng, Wei Wang, Faniu Xu, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Hubei Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Hubei Wuhan430072 China

Versatile sensors have broad application prospects in human motion detection, health monitoring, wearable electronic devices and flexible electronic skin and other emerging fields. In this work, the enhanced hydrogel was prepared by freezing and thawing process with calcium ion crosslinking. This composite material shows excellent flexibility and elasticity, and after being cut in half, it can automatically heal well in a short time without external force. It shows great potential in flexible and wearable devices. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Hydrogels

Technological Knowledge Flow Forecasting through A Hierarchical Interactive Graph Neural Network

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Technological Knowledge Flow Forecasting through A Hierarchi...

IEEE International Conference on data Mining (ICDM)

作者： Huijie Liu Han Wu Le Zhang Runlong Yu Ye Liu Chunli Liu Qi Liu Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China School of Management Hefei University of Technology China

ISBN: (纸本)9781665423991

With the accelerated technology development, technological trend forecasting through patent mining has become a hot issue for high-tech companies. In this term, extensive attention has been attracted to forecasting technological knowledge flows (TKF), i.e., predicting the directional flows of knowledge from one technological field to another. However, existing studies either rely on labor intensive empirical analysis or do not consider the intrinsic characteristics inherent in TKF, including the double-faced aspects (i.e., act as both the source and target) of technology nodes, multiple complex relationships among different technologies, and dynamics of the TKF process. To this end, in this paper, we make a further study and propose a data-driven solution, i.e., a Hierarchical Interactive Graph Neural Network (HighTKF), to automatically find the potential flow trends of technologies. Specifically, HighTKF makes final predictions through two kinds of representations of each technology node (a diffusion vector and an absorption vector), which is realized by three components: High-Order Interaction Module (HOI), Hierarchical Delivery Module (HD) and Technology Flow Tracing Module (TFT). For one thing, HOI and HD aim to model high-order network relationships and hierarchical relationships among technologies. For another, TFT is designed for capturing the dynamic feature evolution of technologies with the above relations involved. Also, we design a hybrid loss function and propose a new evaluation metric for better predicting the unprecedented flows between technologies. Finally, we conduct extensive experiments on a real-world patent dataset, the results verify the effectiveness of our approach and reveal some interesting phenomenons on technological knowledge flow trends.

关键词： Measurement Knowledge engineering Patents databases Predictive models Market research Graph neural networks

Multi-Sentence Matching via Exploiting List-level Semantics Expansion

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Multi-Sentence Matching via Exploiting List-level Semantics ...

IEEE International Conference on Big Knowledge (ICBK)

作者： Ruijun Sun Zhi Li Qi Liu Zhefeng Wang Xinyu Duan Baoxing Huai Nicholas Jing Yuan Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Shenzhen International Graduate School Tsinghua University Huawei Cloud

Sentence semantic matching plays a fundamental role in natural language understanding and is widely used in various tasks. Previous methods are mainly based on the One-to-One single pair matching process, while in many real application scenarios, such as user query matching in FAQ, we need to match multiple sentences with the specified query sentence. The reality is that the multi-sentence matching problem is largely unexplored. Although to some extent, we can use multiple One-to-One pair matching on the multi-sentence matching problem, the exploitation of list-level candidate semantics is still limited, which leads to inferior performance. To that end, in this paper, we present a focused study on the novel multi-sentence matching problem. To begin with, we propose a Multi-Sentence Matching Network (MSMN) with a meticulously designed Fusion Module and Contrastive Matching Module to deeply exploit the list-level candidate semantics expansion for the multi-sentence matching task. Then, we construct two multi-sentence matching datasets, i.e., mQuora and mReceipt, to bridge the gap of benchmark data in this area. Finally, we conduct extensive experiments on two datasets. The experimental results clearly demonstrate the effectiveness of our method.

关键词： Learning systems Semantics Benchmark testing Natural language processing Task analysis

ICSTrace: A Malicious IP Traceback Model for Attacking data of Industrial Control System

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arXiv 2019年

作者： Xiao, Feng Xu, Qiang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Electronic Engineering Institute of Hefei China

Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new services. The model extracts the function codes and their parameters from the attack data according to the format of industrial control protocol, and employs a short sequence probability method to transform the function codes and their parameter into a vector, which characterizes the attack pattern of malicious IP addresses. Furthermore, a Partial Seeded K-Means algorithm is proposed for the pattern’s clustering, which helps in tracing the attacks back to an organization. ICSTrace is evaluated basing on the attack data captured by the large-scale deployed honeypots for industrial control system, and the results demonstrate that ICSTrace is effective on malicious IP traceback in industrial control system. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

Communication-Efficient Distributed Learning with Local Immediate Error Compensation

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arXiv 2024年

作者： Cheng, Yifei Shen, Li Xu, Linli Qian, Xun Wu, Shiwei Zhou, Yiming Zhang, Tie Tao, Dacheng Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Data Science School of Computer Science University of Science and Technology China The JD Explore Academy China The Shanghai Artificial Intelligence Laboratory China

Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate. In this work, we propose the Local Immediate Error Compensated SGD (LIEC-SGD) optimization algorithm to break the above bottlenecks based on bidirectional compression and carefully designed compensation approaches. Specifically, the bidirectional compression technique is to reduce the communication cost, and the compensation technique compensates the local compression error to the model update immediately while only maintaining the global error variable on the server throughout the iterations to boost its efficacy. Theoretically, we prove that LIEC-SGD is superior to previous works in either the convergence rate or the communication cost, which indicates that LIEC-SGD could inherit the dual advantages from unidirectional compression and bidirectional compression. Finally, experiments of training deep neural networks validate the effectiveness of the proposed LIEC-SGD algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Error compensation

FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification

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arXiv 2023年

作者： Cheng, Mingyue Liu, Qi Liu, Zhiding Li, Zhi Luo, Yucong Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Shenzhen International Graduate School Tsinghua University Shenzhen China

Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the limitation in modeling long-range dependence due to the nature of convolution operations. Recent advancements have shown the potential of transformers to capture long-range dependence. However, it would incur severe issues, such as fixed scale representations, temporal-invariant and quadratic time complexity, with transformers directly applicable to the MTSC task because of the distinct properties of time series data. To tackle these issues, we propose FormerTime, an hierarchical representation model for improving the classification capacity for the MTSC task. In the proposed FormerTime, we employ a hierarchical network architecture to perform multi-scale feature maps. Besides, a novel transformer encoder is further designed, in which an efficient temporal reduction attention layer and a well-informed contextual positional encoding generating strategy are developed. To sum up, FormerTime exhibits three aspects of merits: (1) learning hierarchical multi-scale representations from time series data, (2) inheriting the strength of both transformers and convolutional networks, and (3) tacking the efficiency challenges incurred by the self-attention mechanism. Extensive experiments performed on 10 publicly available datasets from UEA archive verify the superiorities of the FormerTime compared to previous competitive baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning

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arXiv 2023年

作者： Liu, Jiayu Huang, Zhenya Zhai, Chengxiang Liu, Qi Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China University of Illinois at Urbana-Champaign United States

Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge "learning" and "applying" organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking

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arXiv 2023年

作者： Yu, Yang Liu, Qi Zhang, Kai Zhang, Yuren Song, Chao Hou, Min Yuan, Yuqing Ye, Zhihao Zhang, Zaixi Yu, Sanshi Lei Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China OPPO Research Institute China Hefei University of Technology China

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance