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SSRN 2024年

作者： Wang, Feng Guo, Ziwang Liu, Lulu Gong, Rui Jia, Guangri Xu, Yanchao Wei, Shuting Zhang, Dantong Ding, Baofu Cui, Xiaoqiang State Key Laboratory of Automotive Simulation and Control School of Materials Science and Engineering Key Laboratory of Automobile Materials of MOE Jilin University 2699 Qianjin Street Changchun130012 China Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology CAS Shenzhen518055 China Key Laboratory of Materials Design and Quantum Simulation School of Science Changchun University No.6543 Satellite Road Changchun130022 China Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced Technology Guangdong Shenzhen518055 China

Carrier recombination is one of the main factors in lowering charge separation efficiency, and thus severely restricts photocatalytic performance. In this work, we demonstrate a composite system of FAPbI3/MXene/Pt, featuring an internal energy cycling from the non-radiative fluorescence resonance energy transfer (FRET) between perovskite and MXene. That is, the energy of the emitted fluorescent photons is reabsorbed by each other. Recycling process exploits the energy released by carrier recombination and increases efficiency of charge separation. As a result, the composite of FAPbI3/MXene/Pt possesses a rapid H2 evolution rate of 2394 µmol g-1 h-1 under 1.5 AM simulated sunlight, which is more than one order of magnitude higher than that of FAPbI3/Pt (64 µmol g-1 h-1). The proposed insight not only provides an efficient strategy to promote photocatalytic H2 evolution based on perovskite-based composite, but also opens up opportunities to design other novel heterogeneous catalytic materials and promote their application potential in various catalytic fields. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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The Control Design of Spaee Flying Net Robot System Based on RBF Network

The Control Design of Spaee Flying Net Robot System Based on...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Xu Liu, Yuanhe Liang, Yan'gang Li, Kebo Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions College of Aerospace Science and Engineering National University of Defense Technology Changsha China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions College of Aerospace Science National University Defense Technology Changsha China

ISBN: (纸本)9781665426473

Space Flying Net Robot System (SFNRS) is a new capture method of space debris. In this paper, the SFNRS dynamic equations are established by using the lumped mass method. The previous studies about controller design assumed that the mass of the maneuvering unit in SFNRS is known and a constant while it is unknown in fact. In addition, the upper boundary of tension in the space net is often assumed as a known constant, but it is difficult to get the value. In this paper, the radial basis function (RBF) network is used to design a controller in order to overcome the shortcomings above Finally, the simulation is carried out to verify the effectiveness and the robustness of the proposed method. © 2021 IEEE

关键词： Controllers

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FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

FastClass: A Time-Efficient Approach to Weakly-Supervised Te...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Xia, Tingyu Wang, Yue Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China School of Information and Library Science University of North Carolina Chapel Hill United States Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords. Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed. © 2022 Association for computational Linguistics.

关键词： Classification (of information)

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Testing Non-Commutativity of Reduce Functions with Multi-Column Inputs

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SSRN 2024年

作者： Zhang, Xuan Zhu, Chenlu Li, Ning Zhang, Peng Liu, Lei College of Software Jilin University Jilin China College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Jilin China

With the continuous development of the MapReduce programming model, it is necessary to ensure the reliability of MapReduce programs. In practice, the non-commutativity of Reduce functions seriously affects the reliability of the MapReduce program, which is difficult to debug and even causes errors. Current researches on the non-commutability detection of Reduce function consider the case that the input value is a single attribute. However, such researches ignore the situation where inputs to most reduce functions in practical applications consist of multiple columns (such as a table). To test the commutativity of reduce functions where each input record may contain several input attributes, a new testing method is proposed. This approach uses symbolic execution tools to help generate a few input records, and breaks their data dependencies to generate an original test case t0, with a dynamic program slicing technique to lessen the scale of t0. And the ultimate test suite is consisted of different permutations of records in t0. In the end, experiments demonstrate the effectiveness of our testing method and that the permutation method Gm is helpful to reduce its complexity. © 2024, The Authors. All rights reserved.

关键词： MapReduce

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Learning generalizable agents via saliency-guided features decorrelation 23

Learning generalizable agents via saliency-guided features d...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Sili Huang Yanchao Sun Jifeng Hu Siyuan Guo Hechang Chen Yi Chang Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.

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Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion 39

Skeleton-based Action Recognition with Non-linear Dependency...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Haipeng Yang, Yuheng Lyu, Yingda College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Public Computer Education and Research Center Jilin University China

ISBN: (纸本)157735897X

Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most existing approaches distinguish action classes by estimating the probability density of motion representations, yet the high-dimensional nature of human motions invokes inherent difficulties in accomplishing such measurements. In this paper, we seek to tackle these challenges from two directions: (1) We propose a novel dependency refinement approach that explicitly models dependencies between any pair of joints, effectively transcending the limitations imposed by joint distance. (2) We further propose a framework that utilizes the Hilbert-Schmidt Independence Criterion to differentiate action classes without being affected by data dimensionality, and mathematically derive learning objectives guaranteeing precise recognition. Empirically, our approach sets the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning 39

Boosting Short Text Classification with Multi-Source Informa...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yonghao Li, Mengyu Pang, Wei Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University China Mathematical and Computer Sciences Heriot-Watt University United Kingdom University of Trento Italy

ISBN: (纸本)157735897X

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Novel Amorphous Feooh-Modified Co9s8 Nanosheets with Enhanced Oxygen Evolution Reaction Activity

SSRN

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SSRN 2024年

作者： Wang, Chong Tu, Huanlu Hao, Zeyu Li, Yaxin Xu, Jian Hu, Xiaoying Yu, Shansheng Tian, Hongwei Key Laboratory of Automobile Materials of MOE School of Materials Science and Engineering Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials Jilin University Jilin Changchun130012 China College of Science Laboratory of Materials Design and Quantum Simulation Changchun University Jilin Changchun130022 China

Efficient oxygen evolution reaction (OER) is important for water electrolysis and advanced hydrogen production energy. However, the sluggish kinetics of this reaction require significant overpotentials, leading to high energy consumption. Therefore, developing OER electrocatalysts with exceptional performance and long-term durability is crucial for enhancing the energy efficiency and cost-effectiveness of the hydrogen production process. In this paper, novel FeOOH/Co9S8 catalysts were prepared through a two-step hydrothermal reaction followed by one-step electrodeposition on nickel foam substrates for an alkaline oxygen evolution reaction (OER). The as-obtained catalysts possessed abundant non-homogeneous interfaces between FeOOH and Co9S8 nanosheets, conducive to optimized coordination environments of Fe and Co sites through the redistribution of interfacial charges. This strengthened the chemisorption of oxygenated intermediates, leading to accelerated reaction kinetics, abundant active sites, and enhanced OER properties. The optimized electrocatalyst FeOOH/Co9S8 achieved a current density of 10 mA cm-2 at an overpotential of 248 mV coupled with good stability for over 140 h. Overall, a novel approach for producing effective and durable alkaline dielectric OER electrocatalysts was proposed, useful for future manufacturing of advanced energy devices. © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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A novel dual-stage algorithm for capacitated arc routing problems with time-dependent service costs

arXiv

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arXiv 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most significant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Specifically, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modified benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model 1

Elucidating Spatial Complex Structures from Mass Spectrometr...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Cui, Jiangnan Xia, Junjie Li, Xuan Wang, Yan Qiu, Fufang Xu, Yaochen Xu, Keren Zuo, Chunman School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Huashan Hospital Department of Neurosurgery Shanghai China Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Cas Key Laboratory of Systems Biology Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms. © 2023 IEEE.

关键词： Data integration

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