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Seismic impedance inversion based on cycle-consistent generative adversarial network

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Petroleum Science 2022年第1期19卷 147-161页

作者： Yu-Qing wang Qi wang Wen-Kai Lu Qiang ge Xin-fei Yan The Institute for Artificial Intelligence Tsinghua University(THUAI)Beijing100084China State Key Laboratory of Intelligent Technology and Systems Tsinghua UniversityBeijing100084China Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing100084China The Department of Automation Tsinghua UniversityBeijing100084China The Research Institute of Petroleum Exploration and Development China National Petroleum Corporation(CNPC)Beijing100083China

Deep learning has achieved great success in a variety of research fields and industrial ***,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based *** order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative *** kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training *** from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real ***,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN *** robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most *** the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.

关键词： Seismic inversion Cycle GAN Deep learning Semi-supervised learning Neural network visualization

Near-perfect replication on amorphous alloys through active force modulation based on machine learning/neural network parameter prediction

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 181-191页

作者： Senkuan Meng Zheng wang Ruisong Zhu Ruijie Liu Jiang Ma Lina Hu Weihua wang Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials (Ministry of Education) School of Materials Science and Engineering Shandong University College of Mechatronics and Control Engineering Shenzhen University Songshan Lake Materials Laboratory Institute of Physics Chinese Academy of Sciences

As a microforming technique, micro/nano-structural replication possesses advantages of high precision and efficiency. With the remarkable superplasticity in the supercooled liquid region, amorphous alloys or metallic glasses(MGs) are regarded as ideal materials for miniature fabrication. However, due to the intrinsic metastable nature of supercooled liquids, the design of imprinting processes for MGs poses a challenge. In the past, process parameters have largely relied on trial-and-error *** this work, a low-frequency active force modulation system is employed to apply a stable, precise, and real-time feedback stress field for imprinting of MG samples. Low-frequency vibrations can facilitate the filling of microstructures on the template surface by reducing the effective viscosity of the supercooled liquid. With the dataset composed of orthogonal experiments, a machine learning strategy based on back-propagation(BP) neural networks was utilized to construct a 3D parameter space for temperature, stress, and time, and to predict the corresponding filling ratio. Furthermore, the optimal combination of imprinting process parameters was identified, and its filling ratio was experimentally validated to reach as high as 0.94. The near-perfect replication of microstructures confirms the superiority of the active force modulation system and the data-driven strategy of machine learning-assisted parameter design. At the same time, this one-step microforming process provides a new approach to addressing the accuracy-cost trade-off dilemma in precision manufacturing.

关键词： metallic glass amorphous alloy microforming imprinting machine learning

Local-contrastive-learning machine with both generalization and adversarial robustness: A statistical physics analysis

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 109-123页

作者： Mingshan Xie Yuchen wang Haiping Huang PMI Lab School of Physics Sun Yat-sen University Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices Sun Yat-sen University

Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful representation learning, we discard backpropagation, and instead, propose a local contrastive learning, where the representations for the inputs bearing the same label shrink(akin to boson) in hidden layers,while those of different labels repel(akin to fermion). This layer-wise learning is local in nature, being biologically plausible.A statistical mechanics analysis shows that the target fermion-pair-distance is a key parameter. Moreover, the application of this local contrastive learning to MNIST benchmark dataset demonstrates that the adversarial vulnerability of standard perceptron can be greatly mitigated by tuning the target distance, i.e., controlling the geometric separation of prototype manifolds.

关键词： machine learning statistical mechanics adversarial robustness neural networks

Dissecting the cell microenvironment of ovarian endometrioma through single-cell RNA sequencing

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Science China Life Sciences 2025年第1期 116-129页

作者： Jiangpeng Wu Siyu Xia Wenting Ye Yan Sun Jing Cai Fubing Yu Haiping Wen Xiuwei Yi Taikang Li Mingwei Chen Jiayun Chen ge Song Chuanbin Yang Yali Song Jigang wang Department of Reproductive Medicine Dongguan Maternal and Child Health Care Hospital Department of Urology and Shenzhen Clinical Research Centre for GeriatricsShenzhen People's HospitalSouthern University of Science and Technology State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs Artemisinin Research Centerand Institute of Chinese Materia MedicaChina Academy of Chinese Medical Sciences Reproductive Medicine Center Foshan Women and Children Hospital State Key Laboratory of Antiviral Drugs School of PharmacyHenan University

Ovarian endometrioma（OE）, also known as “chocolate cysts,” is a cystic mass that develops in the ovaries due to endometriosis and is a common gynecological condition characterized by the growth of endometrial tissue outside the uterus, leading to symptoms such as dysmenorrhea, pelvic pain, and infertility. However, the precise molecular and cellular mechanisms driving this pathophysiology remain largely unknown, posing challenges for diagnosis and treatment. Here, we employed integrated single-cell transcriptomic profiling of over 52,000 individual cells from endometrial tissues of OE patients and healthy donors and identified twelve major cell populations. We identified notable alterations in cell type-specific proportions and molecular signatures associated with OE. Notably, the activation of IGFBP5+macrophages with pro-inflammatory properties, NK cell exhaustion, and aberrant proliferation of IQCG+and KLF2+epithelium are key features and may be the potential mechanisms underlying the pathogenesis of OE. Collectively, our data contribute to a better understanding of OE at the single cell level and may pave the way for the development of novel therapeutic strategies.

关键词： ovary high-throughput sequencing transcriptomics gene expression analysis ligand-receptor analysis

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一种应用随机森林的代码混淆路径分支技术

信息技术 2019年第8期43卷 71-73,78页

作者：胡浔惠葛王飞段文强郑通国电南瑞南京控制系统有限公司南京210000 中国民航大学信息安全测评中心天津300300

为了降低代码混淆路径信息泄露以及提高运算效率,文中提出了一种应用随机森林的代码混淆路径分支技术。以随机森林作为分支混淆算法,经过混淆后的路径条件将被随机森林内部规则所代替。分别对随机森林路径混淆方法开展了效能与安全性评... 详细信息

为了降低代码混淆路径信息泄露以及提高运算效率,文中提出了一种应用随机森林的代码混淆路径分支技术。以随机森林作为分支混淆算法,经过混淆后的路径条件将被随机森林内部规则所代替。分别对随机森林路径混淆方法开展了效能与安全性评估,结果表明:选择常规逆向分析方法,通过多次运行再求解平均值的方法来得到被混淆程序时间开销,时间开销随着执行次数的增加而增大,bzip2达到了最大开销,对应的被混淆分支执行次数最大。

关键词：代码混淆路径分支随机森林评估

Flexible Graphene Field‑Effect Transistors and Their Application in Flexible Biomedical Sensing

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Nano-Micro Letters 2025年第2期17卷 252-313页

作者： Mingyuan Sun Shuai wang Yanbo Liang Chao wang Yunhong Zhang Hong Liu Yu Zhang Lin Han Institute of Marine Science and Technology Shandong UniversityQingdao 266237ShandongPeople’s Republic of China State Key Laboratory of Crystal Materials Shandong UniversityJinan 250100ShandongPeople’s Republic of China School of Integrated Circuits Shandong UniversityJinan 250100ShandongPeople’s Republic of China Shandong Engineering Research Center of Biomarker and Artificial Intelligence Application Jinan250100ShandongPeople’s Republic of China

Flexible electronics are transforming our lives by making daily activities more *** to this innovation are field-effect transistors(FETs),valued for their efficient signal processing,nanoscale fabrication,low-power consumption,fast response times,and ***,known for its exceptional mechanical properties,high electron mobility,and biocompatibility,is an ideal material for FET channels and *** combination of graphene and FETs has given rise to flexible graphene field-effect transistors(FGFETs),driving significant advances in flexible electronics and sparked a strong interest in flexible biomedical ***,we first provide a brief overview of the basic structure,operating mechanism,and evaluation parameters of FGFETs,and delve into their material selection and patterning *** ability of FGFETs to sense strains and biomolecular charges opens up diverse application *** specifically analyze the latest strategies for integrating FGFETs into wearable and implantable flexible biomedical sensors,focusing on the key aspects of constructing high-quality flexible biomedical ***,we discuss the current challenges and prospects of FGFETs and their applications in biomedical *** review will provide valuable insights and inspiration for ongoing research to improve the quality of FGFETs and broaden their application prospects in flexible biomedical sensing.

关键词： Flexible Graphene Field-effect transistor Wearable Implantable Biosensor

Recent Advances in Fibrous Materials for Hydroelectricity generation

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Nano-Micro Letters 2025年第2期17卷 109-133页

作者： Can ge Duo Xu Xiao Feng Xing Yang Zheheng Song Yuhang Song Jingyu Chen Yingcun Liu Chong Gao Yong Du Zhe Sun Weilin Xu Jian Fang College of Textile and Clothing Engineering Soochow UniversitySuzhou 215123People’s Republic of China National Engineering Laboratory for Modern Silk Soochow UniversitySuzhou 215123People’s Republic of China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430200People’s Republic of China Institute of Functional Nano&Soft Materials(FUNSOM) Soochow UniversitySuzhou 215123People’s Republic of China Department of Electrical and Computer Engineering University of MassachusettsAmherstMA 01003USA Department of Materials Imperial College LondonSouth Kensington CampusLondon SW72AZUK College of Textile Science and Engineering Zhejiang Sci-Tech UniversityHangzhou 310018People’s Republic of China School of Materials Science and Engineering Shanghai Institute of Technology100 Haiquan RoadShanghai 201418People’s Republic of China

Depleting fossil energy sources and conventional polluting power generation pose a threat to sustainable *** generation from ubiquitous and spontaneous phase transitions between liquid and gaseous water has been considered a promising strategy for mitigating the energy *** materials with unique flexibility,processability,multifunctionality,and practicability have been widely applied for fibrous materials-based hydroelectricity generation(FHG).In this review,the power generation mechanisms,design principles,and electricity enhancement factors of FHG are first ***,the fabrication strategies and characteristics of varied constructions including 1D fiber,1D yarn,2D fabric,2D membrane,3D fibrous framework,and 3D fibrous gel are ***,the advanced functions of FHG during water harvesting,proton dissociation,ion separation,and charge accumulation processes are analyzed in ***,the potential applications including power supply,energy storage,electrical sensor,and information expression are also ***,some existing challenges are considered and prospects for future development are sincerely proposed.

关键词： Hydroelectricity Fibrous material Streaming potential Ion diffusion

MixerKT:A Knowledge Tracing Model Based on Pure MLP Architecture

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Computers, Materials & Continua 2025年第1期82卷 485-498页

作者： Jun wang Mingjie wang Zijie Li Ken Chen Jiatian Mei Shu Zhang Key Laboratory of Education Informatization for Nationalities Ministry of EducationYunnan Normal UniversityKunming650000China Yunnan Key Laboratory of Smart Education Yunnan Normal UniversityKunming650000China

In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant *** tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional *** knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential ***,these models may neglect crucial short-term dynamics and other relevant *** paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)*** proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature *** architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key *** evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge *** results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.

关键词： Knowledge tracing multilayer perceptron channel mixer sequence mixer

Synthesis and Crystal Structure of Chiral 4-Hydroxy-4-butyrolactam Fused Aziridine

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Chinese Journal of Structural Chemistry 2008年第2期27卷 133-136页

作者： wang Jian-Ping wang Jian-ge FU Yong-Ju LI Ying-fei Department of Chemistry Luoyang Normal University Luoyang 471022 China Chemical Engineering ＆ Pharmaceutics College Henan University of Science and Technology Luoyang 471003 China

A novel compound of chiral 4-hydroxy-4-butyrolactam fused aziridine was synthesized via tandem nucleophilic displacement reaction under mild condition. Its structure was determined by IR, 1H NMR, elemental analysis and X-ray diffraction. The crystal of the new compound is of orthorhombic, space group P212121 with a = 6.4246（6）, b = 13.5081（13）, c = 18.6399（18） A, Mr = 278.39, Z = 4, V = 1617.6（3）A^3, Dc = 1.143 g/cm^3, p（MoKa） = 0.075 mm^-1, F（000） = 608, the final R = 0.0297 and wR = 0.0679.

关键词： butyrolactam aziridine asymmetric synthesis crystal structure