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Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

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Materials Genome engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

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Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

arXiv

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arXiv 2023年

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. The School of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Lab. School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The China-Singapore International Joint Research Institute Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. One possible reason is that fully fine-tuned ViT-based models may disrupt the pre-trained features [1], [2] and overfit to some data-specific patterns [3]. To alleviate this issue, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Asymptotic properties of zeros for discrete-time linear systems with backward triangle sampled-and-hold using delta operator

Asymptotic properties of zeros for discrete-time linear syst...

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Chinese Automation Congress (CAC)

作者： Minghui Ou Shan Liang Xiaobin Wu Zhiyong Yang Hehuan Ran School of Big Data and Internet of Things of Chongqing Vocational Institute of Engineering College of Automation of Chongqing University Chongqing China College of Automation of Chongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing China State Grid Chongqing Shiqu Electric Power Supply Branch Chongqing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This article focuses on a well-studied theoretical problem that, for rapid sampling, a continuous-time system with minimum-phase properties may become a non-minimum phase discrete-time system in the discretization process. The properties of zeros for discrete-time linear models in the field of digital control when using the special shift operator was considered in this paper. From the former relevant studies one can know that the backward triangle sample-and-hold (BTSH) as an alternative hold method to discretize the continuous-time systems has an advantage over ZOH in stabilizing sampling zeros for linear system with classical forward shift operator. In this article, we deduce the exact sampled-data model of linear system using delta operator to replace forward shift operator in the case of BTSH. Furthermore, the expressions and properties of zeros for the corresponding discrete-time system are derived.

关键词： Control systems Linear systems Discrete-time systems Process control Numerical models Signal reconstruction Internet of Things

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

引用

arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Micro-supervised disturbance learning: A perspective of representation probability distribution

arXiv

引用

arXiv 2020年

作者： Chu, Jielei Liu, Jing Wang, Hongjun Meng, Hua Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The School of Business Sichuan University Sichuan Chengdu610065 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on the labels as few as possible. To address these issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution. The positive small-perturbation information (SPI) which only depend on two labels of each cluster is used to stimulate the representation probability distribution and then two variant models are proposed to fine-tune the expected representation distribution of RBM, namely, Micro-supervised Disturbance GRBM (Micro-DGRBM) and Micro-supervised Disturbance RBM (Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized in the same cluster to promote the representation probability distributions to become more similar in Contrastive Divergence (CD) learning. In contrast, the KL divergence of SPI is maximized in the different clusters to enforce the representation probability distributions to become more dissimilar in CD learning. To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation. Experimental results demonstrate that the proposed deep Micro-DL architecture shows better performance in comparison to the baseline method, the most related shallow models and deep frameworks for clustering. © 2020, CC0.

关键词： Probability distributions

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Distributed Local Bayesian Network for Gene Regulatory Network Reconstruction

Distributed Local Bayesian Network for Gene Regulatory Netwo...

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International Conference on big data Computing and Communications (bigCOM)

作者： Luxuan Qu Zhiqiong Wang Yueyang Huo Yuezhou Zhou Junchang Xin Wei Qian College of Medicine and Biological Information Engineering Northeastern University Shenyang China College of Medicine and Biological Information Engineering Northeastern University Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang China School of Computer Science and Engineering Key Laboratory of Big Data Management and Analytics (Liaoning Province) Northeastern University Shenyang China College of Engineering The University of Texas at El Paso El Paso USA

ISBN: (数字)9781728182759

ISBN: (纸本)9781728182766

Gene regulatory network provides an effective way to study functional genomes. Reconstruction of gene regulatory network can better help us to reveal the regulatory relationship and complex mechanism between genes, thus exploring the essence of biological systems and lives. At present, the modeling methods of gene regulatory network either solve the problem of accuracy or scale of gene regulatory network by optimizing the model or combining various models. However, with the continuous increase of network scale and the computational complexity, the reconstruction of the network has exceeded the ability of single machine. Thus, we presented the Distributed Local Bayesian Network (D-LBN) method based on MapReduce framework to solve this problem. D-LBN is based on LBN algorithm which uses mutual information method, k-nearest neighbor algorithm, Bayesian network model and conditional mutual information method to reconstruct gene regulatory network. D-LBN realizes distributed computing using MapReduce framework at the step of regulatory direction learning with Bayesian network model to achieve the purpose of reconstructing large-scale gene regulatory network. Experiments based on two scales of dataset is done to verify the performance of D-LBN in reconstructing gene regulatory network at different scales. The results show that D-LBN is consistent with LBN in evaluation index and much more efficient than LBN on large-scale network reconstruction. And the scale of the network that can be reconstructed is also greatly improved.

关键词： Bayes methods Computational modeling Mutual information Mathematical model Biological system modeling Entropy

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Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

arXiv

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arXiv 2024年

作者： Xiong, Qi Tang, Kai Ma, Minbo Zhang, Ji Xu, Jie Li, Tianrui School of Computing and Artificial Intelligence Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China School of Computing The University of Leeds LeedsLS2 9JT United Kingdom

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by 1.47% to 9.19% and change value errors by 4.57% to 15.78%, highlighting its substantial performance improvements. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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s-LWSR: Super lightweight super-resolution network

arXiv

引用

arXiv 2019年

作者： Li, Biao Liu, Jiabin Wang, Bo Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States College of Information Science and Technology University of Nebraska OmahaNE68182 United States

Deep learning (DL) architectures for super-resolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Efficient Biomedical Ontology Meta-matching Based on Interpolation Model Based Hybrid Evolutionary Algorithm

Efficient Biomedical Ontology Meta-matching Based on Interpo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingsi Xue Qi Wu Miao Ye Hai Zhu Yikun Huang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China School of Information and Communication Guilin University of Electronic Technology Guilin China School of Computer Henan University of Engineering Zhengzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

As an advanced biomedical knowledge modeling technology, biomedical ontology models the biomedical domain. However, since the lack of uniform standards for constructing biomedical ontologies, the biomedical ontologies obtained by different construction methods for the same thing may be different, which is known as the biomedical ontology heterogeneity problem. To solve this problem, we need to execute the biomedical ontology matching process, where it is important to integrate different similarity measures to improve the quality of alignment. Evolutionary Algorithm (EA) is an effective algorithm to address the biomedical ontology meta-matching problem. However, the classical EA-based biomedical ontology meta-matching technique needs to traverse reference alignment to evaluate the individuals, which makes the algorithm have high running time. To overcome this drawback, we propose an Interpolation Model (IM) based Hybrid EA (IM-HEA), which combines EA with a problem-specific IM to evaluate the individual and execute the local search process. In particular, we use lattice design to divide the feasible domain into several uniform sub-regions, and on this basis, approximately evaluate the fitness of newly generated individual. In addition, to avoid the algorithm falling into the local optimum, we further introduce an IM-based local search process into EA’s evolving process. In the experiment, we test IM-HEA on OAEI’s Benchmark and Anatomy and compared them with classical EA in terms of alignment’s quality and running time. The experimental results show that IM-HEA greatly enhances the efficiency of EA with little sacrifice on the alignment’s quality.

关键词： Interpolation Biological system modeling Lattices Evolutionary computation Ontologies Biomedical measurement Benchmark testing

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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