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A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

Petroleum Science 2024年第1期21卷 286-301页

作者： Yan-Wei Wang Zhen-Xue Dai Gui-Sheng Wang Li Chen Yu-Zhou Xia Yu-Hao Zhou College of Construction Engineering Jilin UniversityChangchun130026JilinChina Institute of Intelligent Simulation and Early Warning for Subsurface Environment Jilin UniversityChangchun130026JilinChina School of Energy Resources China University of GeosciencesBeijing100083China Department of Safety Environmental Protection and Quality ManagementShengli OilfieldSinopecDongying257000ShandongChina Key Laboratory of Thermo-Fluid Science and Engineering of MOE School of Energy and Power EngineeringXi'an Jiaotong UniversityXi'an710049ShaanxiChina College of Computer Science and Technology China University of Petroleum(East China)Qingdao266580ShandongChina Department of Physics University of FribourgFribourg1700Switzerland

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport ***,most current CO_(2) sequestration models do not adequately consider multiple transport ***,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and *** this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training *** this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and *** incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion ***,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated *** validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise *** study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

关键词： Deep learning Physics-informed data-driven neural network Depleted shale reservoirs CO_(2)storage Transport mechanisms

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Unsupervised Style Control for Image Captioning

Unsupervised Style Control for Image Captioning

引用

作者： Junyu Tian Zhikun Yang Shumin Shi School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

We propose a novel unsupervised image captioning *** captioning involves two fields of deep learning,natural language processing and computer *** excessive pursuit of model evaluation results makes the caption style generated by the model too monotonous,which is difficult to meet people's demands for vivid and stylized image ***,we propose an image captioning model that combines text style transfer and image emotion recognition methods,with which the model can better understand images and generate controllable stylized *** proposed method can automatically judge the emotion contained in the image through the image emotion recognition module,better understand the image content,and control the description through the text style transfer method,thereby generating captions that meet people's *** our knowledge,this is the first work to use both image emotion recognition and text style control.

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Beyond Overfitting: Doubly Adaptive Dropout for Generalizable AU Detection

arXiv

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arXiv 2025年

作者： Li, Yong Ren, Yi Niu, Xuesong Ding, Yi Wei, Xiu-Shen Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Beijing Institute for General Artificial Intelligence Beijing China School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore

Facial Action Units (AUs) are essential for conveying psychological states and emotional expressions. While automatic AU detection systems leveraging deep learning have progressed, they often overfit to specific datasets and individual features, limiting their cross-domain applicability. To overcome these limitations, we propose a doubly adaptive dropout approach for cross-domain AU detection, which enhances the robustness of convolutional feature maps and spatial tokens against domain shifts. This approach includes a Channel Drop Unit (CD-Unit) and a Token Drop Unit (TD-Unit), which work together to reduce domain-specific noise at both the channel and token levels. The CD-Unit preserves domain-agnostic local patterns in feature maps, while the TD-Unit helps the model identify AU relationships generalizable across domains. An auxiliary domain classifier, integrated at each layer, guides the selective omission of domain-sensitive features. To prevent excessive feature dropout, a progressive training strategy is used, allowing for selective exclusion of sensitive features at any model layer. Our method consistently outperforms existing techniques in cross-domain AU detection, as demonstrated by extensive experimental evaluations. Visualizations of attention maps also highlight clear and meaningful patterns related to both individual and combined AUs, further validating the approach’s effectiveness. © 2025, CC BY-NC-SA.

关键词： Deep learning

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Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmentation

Boosting LLMS with Ontology-Aware Prompt for Ner Data Augmen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhizhao Luo Youchen Wang Wenjun Ke Rui Qi Yikai Guo Peng Wang Beijing Institute of Computer Technology and Application Beijing China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education Nanjing China China Life Property & Casualty Insurance Company Limited Changsha China

Named Entity Recognition (NER) data augmentation (DA) aims to improve the performance and generalization capabilities of NER models by generating scalable training data. The key challenge lies in ensuring the generated samples maintain contextual diversity while preserving label consistency. However, existing dominant methods fail to simultaneously satisfy both criteria. Inspired by the extensive generative capabilities of large language models (LLMs), we propose ANGEL, a frAmework integrating the oNtoloGy structure and instructivE prompting within LLMs. Specifically, the hierarchical ontology structure guides prompt ranking, while instructive prompting enhances LLMs’ mastery of domain knowledge, empowering synthetic sample generation and annotation. Experiments show ANGEL surpasses state-of-the-art (SOTA) baselines, conferring absolute F1 increases of 2.86% and 0.93% on two benchmark datasets, respectively.

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An Improved Dynamic NSGA-Ⅱ for Unrelated Parallel Machine Rescheduling under Machine Breakdowns

An Improved Dynamic NSGA-Ⅱ for Unrelated Parallel Machine R...

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第35届中国控制与决策会议

作者： XiaoLong Li Yang Liu Jiao Yu Wei Sun Department of Intelligent data and Systems Engineering Northeastern University Beijing Institute of Computer Technology and Applications Ecological Environment Bureau of Chifeng City Business School Liaoning University

We consider a rescheduling problem of the unrelated parallel machines under machine *** is triggered whenever machine breakdowns and repairs occur(i.e.,a rescheduling point arrives).At each rescheduling point,we establish a multi-objective mathematical model where the makespan is considered as an efficiency measure and stability is gauged in terms of the sum of deviation in completion times of jobs in the previous and new ***,we develop an improved dynamic NSGA-II to fast-track non-dominated solutions,where a population re-initialization strategy is used to effectively respond to machine breakdowns and *** experiments were conducted to demonstrate the performance of the proposed algorithm.

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Anomaly Detection Based on Dual-threaded Blockchain in Large-scale Intelligent Networks

Anomaly Detection Based on Dual-threaded Blockchain in Large...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Wei Liu Yuefeng Shen Hui Yang Bowen Bao Qiuyan Yao Lvda Wang State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Computer Technology and Applications Beijing China

This paper proposes a dual-threaded blockchain-based architecture to detect large-scale intelligent network abnormalities. Anomaly detection is then implemented using an adaptive encoder. The simulation results show that the proposed anomaly detection scheme achieves efficient anomaly detection while reducing latency and improving throughput.

关键词： Wireless communication Intelligent networks Adaptive systems simulation computer architecture Throughput Blockchains

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OneRel: Joint Entity and Relation Extraction with One Module in One Step

arXiv

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arXiv 2022年

作者： Shang, Yu-Ming Huang, Heyan Mao, Xian-Ling School of Computer Science & Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. Existing approaches usually decompose the joint extraction task into several basic modules or processing steps to make it easy to conduct. However, such a paradigm ignores the fact that the three elements of a triple are interdependent and indivisible. Therefore, previous joint methods suffer from the problems of cascading errors and redundant information. To address these issues, in this paper, we propose a novel joint entity and relation extraction model, named OneRel, which casts joint extraction as a fine-grained triple classification problem. Specifically, our model consists of a scoring-based classifier and a relation-specific horns tagging strategy. The former evaluates whether a token pair and a relation belong to a factual triple. The latter ensures a simple but effective decoding process. Extensive experimental results on two widely used datasets demonstrate that the proposed method performs better than the state-of-the-art baselines, and delivers consistent performance gain on complex scenarios of various overlapping patterns and multiple triples. © 2022, CC BY.

关键词： Extraction

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Ask to Understand: Question Generation for Multi-hop Question Answering

arXiv

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arXiv 2022年

作者： Li, Jiawei Ren, Mucheng Gao, Yang Yang, Yizhe School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the "black-box" reasoning process to capture the potential relationship between entities and sentences, thus achieving good performance. At the same time, the latter provides a clear reasoning logical route by decomposing multi-hop questions into simple single-hop sub-questions. In this paper, we propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of our proposed QG module, human evaluation further clarifies its interpretability quantitatively, and thorough analysis shows that the QG module could generate better sub-questions than QD methods in terms of fluency, consistency, and diversity. Copyright © 2022, The Authors. All rights reserved.

关键词： Decomposition

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ATTAA: Active Text Traffic Analysis Attacks on Secure Messaging applications

ATTAA: Active Text Traffic Analysis Attacks on Secure Messag...

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IEEE International Conference on Communications (ICC)

作者： Fengyan Lv Cheng Liu Xiaofeng Liu Chengyu Hu Zhihao Chen Shanqing Guo Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Qingdao China School of Cyber Science and Technology Shandong University Qingdao China Quancheng Laboratory Jinan China Beijing Institute of Computer Technology and Applications Beijing China

Popular Secure Instant Messaging (SIM) applications like Telegram and WhatsApp have deployed state-of-the-art encryption schemes in recent years to protect the security of user communications. However, SIM applications are still not completely secure. Governments can surveil and censor users who participate in groups on sensitive topics based on the leaked information of their SIM clients. In this paper, we find two types of padding flaws in SIM applications where the padding length is not long enough, thereby exposing users' encrypted traffic characteristics. Furthermore, we first present an Active Text Traffic Analysis Attack (ATTAA) that enables the adversary to obtain sensitive information about target users' clients by merely monitoring their encrypted SIM traffic. Specifically, the adversary can quickly identify the participants of target SIM groups with high accuracy. Our study demonstrates a significant, real-world threat to SIM users due to increasing government regulation on social media. We demonstrate the practicality of our ATTAA through extensive experiments on real-world SIM communications. Although SIM applications have various restrictions on message sending, our results show that only ten text messages in 10 seconds are enough to successfully attack Telegram and WhatsApp with an accuracy of 99.94% and 98.66%, and a false positive rate of $4.3\times 10^{-3}$ and $1.5\times 10^{-4}$ .

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Symbolic Reasoning About Quantum Circuits in Coq

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Journal of computer Science & technology 2021年第6期36卷 1291-1306页

作者： Wen-Jun Shi Qin-Xiang Cao Yu-Xin Deng Han-Ru Jiang Yuan Feng Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China John Hopcroft Center of Computer Science Shanghai Jiao Tong UniversityShanghai 200240China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408China Centre for Quantum Software and Information University of Technology SydneySydney NSW 2007Australia

A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by ***,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes *** this paper,we propose a symbolic approach to reasoning about quantum *** is based on a small set of laws involving some basic manipulations on vectors and *** symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.

关键词： quantum circuit symbolic reasoning Dirac notation Coq

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