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An Empirical Study on Information Extraction using Large Language Models

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arXiv 2023年

作者： Han, Ridong Yang, Chaohao Peng, Tao Tiwari, Prayag Wan, Xiang Liu, Lu Wang, Benyou College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Information Technology Halmstad University Sweden

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI’s GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs’ information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs’ human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods’ effectiveness and some of their remaining issues in improving GPT-4’s information extraction ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults

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SSRN

SSRN 2022年

作者： Xin, Ruihao Wang, Tiantian Miao, Fengbo Yu, Cuinan Feng, Xin College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Science Jilin Institute of Chemical Technology Jilin Jilin132000 China

The use of deep learning for fault diagnosis is already a commonly used method. However, complex operating conditions under different working conditions significantly impact bearing vibration signals. Integrating the discriminative information of fault type and size into the deep learning model to achieve rich multi-task fault feature diagnosis still deserves attention. This study proposes a deep multi-task-based multi-scale feature fusion network MEAT(multi-task-based deep multi-scale feature fusion network) model to address these issues. Solve the limitations and poor adaptability of traditional convolutional neural network models under complex work. This model considers two sub-tasks, fault scale discrimination, and fault type discrimination, in the fault diagnosis multi-classification task through multi-task mapping decomposition. First, multi-dimensional features are extracted by convolution at different scales to obtain multi-dimensional fault information at different levels. Then these features are weighted and fused using a multi-layer attention mechanism to get accurate diagnosis results of bearing faults. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding

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arXiv 2024年

作者： Zhang, Ruochi Wu, Haoran Liu, Chang Li, Huaping Wu, Yuqian Li, Kewei Wang, Yifan Deng, Yifan Chen, Jiahui Zhou, Fengfeng Gao, Xin Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Syneron Technology Guangzhou510700 China Beijing Life Science Academy Beijing102209 China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia College of Software Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/PepHarmony or http://***/supp/. © 2024, CC BY-NC-ND.

关键词： Peptides

Variational Wasserstein Barycenters with c-Cyclical Monotonicity Regularization

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arXiv 2021年

作者： Chi, Jinjin Yang, Zhiyao Li, Ximing Ouyang, Jihong Guan, Renchu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Wasserstein barycenter, built on the theory of Optimal Transport (OT), provides a powerful framework to aggregate probability distributions, and it has increasingly attracted great attention within the machine learning community. However, it is often intractable to precisely compute, especially for high dimensional and continuous settings. To alleviate this problem, we develop a novel regularization by using the fact that c-cyclical monotonicity is often necessary and sufficient conditions for optimality in OT problems, and incorporate it into the dual formulation of Wasserstein barycenters. For efficient computations, we adopt a variational distribution as the approximation of the true continuous barycenter, so as to frame the Wasserstein barycenters problem as an optimization problem with respect to variational parameters. Upon those ideas, we propose a novel end-to-end continuous approximation method, namely Variational Wasserstein Barycenters with c-Cyclical Monotonicity Regularization (VWB-CMR), given sample access to the input distributions. We show theoretical convergence analysis and demonstrate the superior performance of VWB-CMR on synthetic data and real applications of subset posterior aggregation. Copyright © 2021, The Authors. All rights reserved.

关键词： Probability distributions

JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

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arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

Rule-based modeling and simulation of gene expression process 10

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Rule-based modeling and simulation of gene expression proces...

2020 10th International Workshop on Computer Science and engineering, WCSE 2020

作者： Zhao, Tianqi Luo, Cuntong Yang, Yi Feng, Liwei Lu, Xinhua College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789811447877

A process of gene expression which is designed and modeled by machine language, and by using to process complex data. At the molecular biological level, classifying and sorting the related substances involved in the transcription and translation processes, and using a rule-based modeling method to simulate the gene expression process. By entering the initial data of the designed gene into the program, the computer can give the result of the gene expression process. The experimental data shows that this gene expression result is correct. In this paper, we summarize the rules of the transcription and translation of the gene expression process and the basic part of the central dogma, and then formalize it, and then establish a model to realize the biological information processing in uncomplicated situations, and then realize the gene expression process on the computer. © WCSE 2020.

关键词： Transcription

A Survey of Sum-Product Networks Structural Learning

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SSRN

SSRN 2022年

作者： Xia, Riting Zhang, Yan Liu, Xueyan Yang, Bo School of Artificial Intelligence Jilin University Jilin Changchun130012 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Jilin Changchun130012 China College of Communication Engineering Jilin University Jilin Changchun130012 China

Sum-product networks (SPNs) in deep probabilistic models have made great progress in computer vision, robotics, neuro-symbolic artificial intelligence, natural language processing, probabilistic programming languages, and other fields. Compared with probabilistic graphical models and deep probabilistic models, SPNs can balance the tractability and expressive efficiency. In addition, SPNs remain more interpretable than deep neural models. The expressiveness and complexity of SPNs depend on its own structure. Thus, how to design an effective SPN structure learning algorithm that can balance expressiveness and complexity has become a hot research topic in recent years. In this paper, we review SPN structure learning comprehensively, including the motivation of SPN structure learning, a systematic review of related theories, the proper categorization of different SPN structure learning algorithms, several evaluation approaches and some helpful online resources. Moreover, we discuss some open issues and research directions for SPN structure learning. To our knowledge, this is the first survey to focus specifically on SPN structure learning, and we hope to provide useful references for researchers in related fields. © 2022, The Authors. All rights reserved.

关键词： Efficiency

UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

UNIQUE DETERMINATION BY A SINGLE FAR-FIELD MEASUREMENT FOR AN INVERSE ELASTIC PROBLEM

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arXiv 2023年

作者： Diao, Huaian Tang, Ruixiang Liu, Hongyu Tang, Jiexin School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun China School of Mathematics Jilin University Jilin Changchun130012 China Department of Mathematics City University of Hong Kong Kowloon Hong Kong The No. 3 Junior High School of Yantai Economic and Technological Development Zone No. 1 Changzhou Street Shandong Yantai265503 China

This paper is concerned with the unique identification of the shape of a scatterer through a single far-field pattern in an inverse elastic medium scattering problem with a generalized transmission boundary condition. The uniqueness issue by a single far-field measurement is a challenging problem in inverse scattering theory, which has a long and colorful history. In this paper, we demonstrate the well-posedness of the direct problem by the variational approach. We establish the uniqueness results by a single far-field measurement under a generic scenario when dealing with underlying elastic scatterers exhibiting polygonal-nest or polygonal-cell structures. Furthermore, for a polygonal-nest or polygonal-cell structure scatterer associated with density and boundary impedance parameters as piecewise constants, we show that these physical quantities can be uniquely determined simultaneously by a single far-field measurement. The corresponding proof relies heavily on examining the singular behaviour of a coupled PDE system near a corner in a microlocal *** Codes 35R30, 74J20, 86A22 © 2023, CC BY.

关键词： Boundary conditions