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An Adjusted Gray Map Technique for Constructing Large Four-Level Uniform Designs

Journal of Systems Science & Complexity 2023年第1期36卷 433-456页

作者： ELSAWAH A M VISHWAKARMA G K MOHAMED H S FANG Kai-Tai Department of Statistics and Data Science Faculty of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 44519Egypt Department of Mathematics&Computing Indian Institute of Technology DhanbadDhanbad 826004India College of Transportation and Civil Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China The Key Lab of Random Complex Structures and Data Analysis The Chinese Academy of SciencesBeijing 100190China

A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are *** large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not *** paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation *** justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level *** results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.

关键词： Aberration adjusted Gray map technique Gray map technique Hamming distance moment aberration threshold accepting algorithm uniform design

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Unified Uncertainty Estimation for Cognitive Diagnosis Models 24

Unified Uncertainty Estimation for Cognitive Diagnosis Model...

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33rd ACM Web Conference, WWW 2024

作者： Wang, Fei Liu, Qi Chen, Enhong Liu, Chuanren Huang, Zhenya Wu, Jinze Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China 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 Business Analytics and Statistics The University of Tennessee Knoxville United States IFLYTEK Research State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis. © 2024 ACM.

关键词： Large datasets

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application of a neural network model with multimodal fusion for fluorescence spectroscopy

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Nuclear Science and Techniques 2024年第10期35卷 135-148页

作者： Lin Tang Shuang Zhou Kai-Bo Shi Hong-Tao Shen Lei You College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province Panzhihua UniversityPanzhihua 617000China Key Laboratory of Interior Layout optimization and Security Institutions of Higher Education of Sichuan ProvinceChengdu Normal UniversityChengdu 611130China

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum *** error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured *** eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet *** UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude *** model is trained using simulated pulse datasets with different amplitudes and distortion *** the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping *** processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM *** estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI *** results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

关键词： UNet Long-and short-term memory Pulse distortion Pulse height estimation Fluorescent spectroscopy

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An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

An improved method of algal-bloom discrimination in Taihu La...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

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Boosting the Electrochemical Performance of Li-and Mn-Rich Cathodes by a Three-in-One Strategy

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Nano-Micro Letters 2021年第12期13卷 311-321页

作者： Wei He Fangjun Ye Jie Lin Qian Wang Qingshui Xie Fei Pei Chenying Zhang Pengfei Liu Xiuwan Li Laisen Wang Baihua Qu Dong-Liang Peng State Key Lab of Physical Chemistry of Solid Surface Collaborative Innovation Center of Chemistry for Energy MaterialsCollege of Materials and Pen-Tung Sah Institute of Micro-Nano Science and TechnologyXiamen UniversityXiamen 361005People’s Republic of China College of Chemistry and Chemical Engineering Xiamen UniversityXiamen 361005People’s Republic of China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big DataZhengzhou UniversityZhengzhou 450002People’s Republic of China Fujian Provincial Key Laboratory of Light Propagation and Transformation College of Information Science and EngineeringHuaqiao UniversityXiamen 361021People’s Republic of China Shenzhen Research Institute of Xiamen University Shenzhen 518000People’s Republic of China

There are plenty of issues need to be solved before the practi-cal application of Li-and Mn-rich cathodes,including the detrimental voltage decay and mediocre rate capability,*** doping can e ectively solve the above problems,but cause the loss of *** introduction of appropriate defects can compensate the capacity loss;however,it will lead to structural mismatch and stress ***,a three-in-one method that combines cation–polyanion co-doping,defect construction,and stress engineering is *** co-doped Na^(+)/SO_(4)^(2-)can stabilize the layer framework and enhance the capacity and voltage *** induced defects would activate more reac-tion sites and promote the electrochemical ***,the unique alternately distributed defect bands and crystal bands structure can alleviate the stress accumulation caused by changes of cell parameters upon ***,the modified sample retains a capacity of 273 mAh g^(-1)with a high-capacity retention of 94.1%after 100 cycles at 0.2 C,and 152 mAh g^(-1)after 1000 cycles at 2 C,the corresponding voltage attenuation is less than 0.907 mV per cycle.

关键词： Li-and Mn-rich cathodes Cation–polyanion co-doping Defect and stress engineering Good structure stability Electrochemical performance

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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Learning the compositional visual coherence for complementary recommendations 29

Learning the compositional visual coherence for complementar...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Zhi Wu, Bo Liu, Qi Wu, Likang Zhao, Hongke Mei, Tao Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China China Columbia University United States School of Computer Science and Technology University of Science and Technology of China China College of Management and Economics Tianjin University China JD AI Research

ISBN: (纸本)9780999241165

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Networks (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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A Knowledge-Injected Curriculum Pretraining Framework for Question Answering 24

A Knowledge-Injected Curriculum Pretraining Framework for Qu...

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33rd ACM Web Conference, WWW 2024

作者： Lin, Xin Su, Tianhuang Huang, Zhenya Xue, Shangzi Liu, Haifeng Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen China University of Science and Technology of China Hefei China 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

ISBN: (纸本)9798400701719

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks. © 2024 ACM.

关键词： Curricula

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Option Pricing Based on GA-BP neural network

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Procedia Computer Science 2022年 199卷 1340-1354页

作者： Long Qian Jianbin Zhao Yue Ma International College of Zhengzhou University Zhengzhou University Zhengzhou 450052 China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big Data Zhengzhou University Zhengzhou 450052 China

Machine Learning has received widespread attention in the financial field, especially in the issue of option pricing. Compared with the original BSM (Black-Scholes-Merton) option pricing method, this paper introduces the genetic algorithm into the option pricing process based on the characteristics of financial data, and constructs a GA-BP neural network (Neural Networks) option pricing Model and empirical analysis of European call options based on the Shanghai and Shenzhen 300 Index. The research results show that the option price calculation result of the GA-BP neural network algorithm has better accuracy than the BP neural network model or the classic BSM method, while taking into account efficiency, which is helpful to predicting option prices in financial practice to a certain extent.

关键词： BP Neural Network Genetic Algorithm Option Pricing BSM Model

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