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Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by Active-Learning-Potential Molecular-Dynamics Simulation

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Angewandte Chemie 2023年第32期135卷

作者： Pengfei Hou Yumiao Tian Prof. Yu Xie Prof. Fei Du Prof. Gang Chen Dr. Aleksandra Vojvodic Prof. Jianzhong Wu Dr. Xing Meng Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education) College of Physics Jilin University 130012 Changchun P. R. China Department of Chemical and Biomolecular Engineering University of Pennsylvania PA 19104 Philadelphia USA Department of Chemical and Environmental Engineering University of California CA 92521 Riverside USA Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University 130012 Changchun P. R. China

MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super-sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes.

关键词： MXenes Degradation Neural NetworksActive Learning Oxidation Protection

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Motion deblur of QR code based on generative adversative network 2

Motion deblur of QR code based on generative adversative net...

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2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

作者： Wang, Benzhi Xu, Jingben Zhang, Junke Li, Guancheng Wang, Xin College of Computer Science and Technology Jilin University Changchun Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineer Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9781450372619

Aiming at the problem that the QR codes cannot be recognized due to factors such as motion blur caused by the hand tremor and environmental noise in the QR codes of the packaged product, this paper proposes an algorithm based on the Generative Adversative Network to remove QR codes motion blur. Compared with the traditional deblurring algorithm, this method eliminates the process of blur kernel estimation, directly restores the motion blurred image. Gradient L1 regularization is added on the basis of the traditional GAN, which enhances the edge features of the restored image and adds the perceptual loss term, so that the image generated by the generator is closer to the global structure of the target clear image. The results show that the generator can remove the blur and generate a QR code with high recognition rate. © 2019 ACM.

关键词： Image enhancement

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Joint optimization QoS and security of wireless communication networks 7

Joint optimization QoS and security of wireless communicatio...

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7th International Conference on Information Technology: IoT and Smart City, ICIT 2019

作者： Su, Xiaochun Liu, Yanheng Wan, Jian College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Jilin University Changchun130012 China Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9781450376631

Currently, security and quality of service (QoS) are critical for any network. The main purpose of security mechanisms is to provide identity authentication, protect information, and ensure that the received information is not tampered with;the aim of QoS is to provide better network communication services. Security and QoS are not independent of each other. The security mechanism affects QoS and vice versa. Both QoS and security pose resource management problems and are conflicting when resources are limited. QoS requires security mechanisms to ensure proper allocation and service cost, and an inappropriate security mechanism will reduce the performance of the communication quality. Currently, numerous real-time, big data, and diverse applications and services have emerged. Moreover, the number of potential threats is expected to increase dramatically. Therefore, combining QoS and security is important. However, as they are difficult to model owing to their internal conflict, QoS and security are normally separated or optimized at a particular point using game theory. From the perspective of resource allocation, this study uses optimization method and considers security and QoS resource optimization by dividing resources into two categories: space (bandwidth) and time (delay). In order to harmonize the QoS and security, in this paper, the two conflicting terms "communication" and "security" are considered as two individual aspect. Furthermore, we construct a model wherein the joint utility is maximized by controlling the packet length. We formulate the utility functions of these two aspects with respect to the effective throughput. The results show that the presented optimization method enables a mobile node to adaptively select specific packet length based on the optimum curve. With the obtained packet length values, the joint utility reach to the maximum so that satisfactory communication quality can be realized without compromising the security, thereby improvin

关键词： Resource allocation

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Copy-Move Forgery Detection Based on Interest Point and Local Search Algorithm

Copy-Move Forgery Detection Based on Interest Point and Loca...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Fang Mei Jing Lyu Yingda Lyu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

Copy-move forgery detection mainly includes block-based and interest point-based methods, but both of them have shortcomings, such as weak resistance to affine transformations, weak ability to locate the forged region. To solve these problems, a novel algorithm is proposed in this paper, which involves two-stage search algorithm. The first stage takes image blocks as the search unit, and in the second stage, the forger region is searched at the pixel level. Experimental results demonstrate that the proposed method performants better compared to recent state-of-the-art methods.

关键词： Rail to rail outputs

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Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems

arXiv

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arXiv 2023年

作者： Xiao, Yubin Wang, Di Li, Boyang Chen, Huanhuan Pang, Wei Wu, Xuan Li, Hao Xu, Dong Liang, Yanchun Zhou, You The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The School of Computer Science University of Science & Technology of China Hefei230026 China The School of Mathematical and Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom College of Computer National University of Defense Technology Hunan Changsha410073 China The Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia United States The School of Computer Science Zhuhai College of Science and Technology Zhuhai519000 China

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recently, neural networks have gained popularity in this research area because as shown in the literature, they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, non-autoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP encoded information as rewards and seamlessly integrates it into reinforcement learning strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSPs demonstrate that NAR4TSP outperforms five state-of-the-art models in terms of solution quality, inference speed, and generalization to unseen scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Traveling salesman problem

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Attention-based multi-level feature fusion for named entity recognition 29

Attention-based multi-level feature fusion for named entity ...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yang, Zhiwei Chen, Hechang Zhang, Jiawei Ma, Jing Chang, Yi College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China IFM Lab Department of Computer Science Florida State University TallahasseeFL United States Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China International Center of Future Science Jilin University Changchun China

ISBN: (纸本)9780999241165

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

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Error Formulas for Lagrange Projectors Determined by Cartesian Sets

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Journal of Systems Science & Complexity 2018年第4期31卷 1090-1102页

作者： LI Zhe ZHANG Shugong DONG Tian GONG Yihe School of Science Changchun University of Science and Technology Changchun 130022 China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun 130012 China School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University Changchun 130012 China Northeast Electric Power University Jilin 132012 China School of Mathematics Jilin University Changchun 130012 China

This paper studies error formulas for Lagrange projectors determined by Cartesian sets. Cartesian sets are properly subgrids of tensor product grids. Given interpolated functions with all order continuous partial derivatives, the authors directly construct the good error formulas for Lagrange projectors determined by Cartesian sets. Owing to the special algebraic structure, such a good error formula is useful for error estimate.

关键词： Cartesian sets error formulas ideal interpolation multivariate polynomial interpolation

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Efficient reinforcement learning in continuous state and action spaces with Dyna and policy approximation

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Frontiers of Computer Science 2019年第1期13卷 106-126页

作者： Shan ZHONG Quan LIU Zongzhang ZHANG Qiming FU School of Computer Science and Technology Soochow UniversitySuzhou 215000China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou 215006China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000China College of Electronic &Information Engineering Suzhou University of Science and TechnologySuzhou 215000China

Dyna is an effective reinforcement learning (RL) approach that combines value function evaluation with model ***,existing works on Dyna mostly discuss only its efficiency in RL problems with discrete action *** paper proposes a novel Dyna variant,called Dyna-LSTD-PA,aiming to handle problems with continuous action ***-LSTD-PA stands for Dyna based on least-squares temporal difference (LSTD)and policy ***-LSTD-PA consists of two simultaneous,interacting *** learning process determines the probability distribution over action spaces using the Gaussian distribution;estimates the underlying value function,policy,and model by linear representation;and updates their parameter vectors online by LSTD(,t).The planning process updates the parameter vector of the value function again by using ofttine LSTD(2).Dyna-LSTD-PA also uses the Sherman-Morrison formula to improve the efficiency of LSTD(,t),and weights the parameter vector of the value function to bring the two processes ***,the global error bound is derived by considering approximation,estimation,and model ***,Dyna-LSTD-PA outperforms two representative methods in terms of convergence rate,success rate,and stability performance on four benchmark RL problems.

关键词： problem solving control methods heuristic search methods dynamic programming

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Underwater acoustic intelligent spectrum sensing with multimodal data fusion: An Mul-YOLO approach

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Future Generation Computer Systems 2025年 173卷

作者： Li, Yufang Zhang, Liliang Wang, Kai Xu, Lingwei Gulliver, T. Aaron Department of Information Science and Technology Qingdao University of Science and Technology Qingdao266061 China Xiangyang Sub-center of National Engineering Research Center for Water Transport Safety Hubei University of Arts and Science Xiangyang441053 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Nanning530006 China Department of Electrical and Computer Engineering University of Victoria VictoriaBC V8W 2Y2 Canada

Realizing the Ocean Internet of Things (OIoT) requires diverse and comprehensive real-time marine data. This creates significant demands on spectrum resources for effective underwater communication. Coexisting users in the OIoT can generate massive amounts of data, which results in competition for underwater acoustic spectrum. To address the problem of poor utilization of this spectrum in complex and dynamic ocean environments, this paper proposes an intelligent spectrum sensing scheme based on multimodal data fusion. First, the system obtains time series data for the underwater acoustic signals and a wavelet transform is used to obtain the corresponding image data. The time series and image data constitute multimodal data that provide a more comprehensive time and space perspective and improve the diversity of features. Then, unsupervised learning with a Denoising Autoencoder is used to extract high-quality features from the time series data. Finally, a parallel dual Backbone YOLO network is constructed to compensate for the inability of traditional YOLO to extract multimodal features. One branch captures key temporal features through a network built with deep separable convolutions, while the other branch captures key spatial features using an improved CSPDarknet. This Mul-YOLO network can extract hierarchical structure information from underwater acoustic signals and integrate the diverse multilevel features of the data to achieve accurate spectrum sensing. Compared with Convolutional Neural Network (CNN), ResNet, Swin-Transformer, ConvNext and YOLOv5 algorithms, the average detection probability of the Mul-YOLO algorithm is increased by about 5.9% to 18.9%, and the average false alarm probability is optimized by about 7% to 12.7% in the range of signal-to-noise ratio from −12 dB to 0 dB. The results given show that the proposed algorithm can detect spectrum holes with high accuracy and effectively avoid interference between users. © 2025 Elsevier B.V.

关键词： Image fusion

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TDCA: Improved Optimization Algorithm with Degree Distribution and Communication Traffic for the Deployment of Software Components Base on AUTOSAR Architecture

Research Square

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Research Square 2021年

作者： Zhang, Kunpeng Liu, Yanheng Zhang, Jindong Zhang, Guanhua Jin, Jingyi Li, Yunhao Tang, Fengmin College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering The Ministry of Education Jilin University Changchun China State Key Laboratory of Automobile Simulation and Control Jilin University Changchun China College of Mechanical Engineering Hebei University of Technology Tianjin300132 China China Automotive Technology and Research Center Tianjin300300 China

AUTOSAR (Automotive Open System Architecture), as an open, standardized framework for automotive electronic software development, has gradually become the standard followed by major automotive manufacturers and automotive electronic device suppliers. The electronic software system problem improves the development efficiency and portability of the system by reducing the development cost of automotive electronic software while ensuring the quality of products and services, which is beneficial for subsequent upgrades and updates of the system. In order to improve the reliability of the software component deployment algorithm based on AUTOSAR architecture, we proposed the TDCA algorithm. During the execution of the algorithm, communication volume and communication degree are introduced to improve the accuracy of the deployment plan by optimizing the bus load and ECU balancing. Algorithm comparison experiments show that comparing heuristic and linear optimization algorithms, the TDCA algorithm proposed in this paper has significant advantages in reducing bus load and ECU utilization. The algorithm can reduce the communication between cores and balance ECU load according to the constraints of AUTOSAR architecture. © 2021, CC BY.

关键词： Equalizers

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