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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lu, Yi Zhou, Xin He, Wei Zhao, Jun Ji, Tao Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798891761681

Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length generalization and attention's quadratic computational demands. Many sought to mitigate this by restricting the attention window within the pre-trained length. However, these methods introduce new issues such as ignoring the middle context and requiring additional training. To address these problems, we propose LONGHEADS, a training-free framework that enhances LLM's long context ability by unlocking multi-head attention's untapped potential. Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences, we allow each head to process in-distribution length by selecting and attending to important context chunks. To this end, we propose a chunk selection strategy that relies on the inherent correlation between the query and the key representations, efficiently distributing context chunks to different heads. In this way, each head ensures it can effectively process attended tokens within the trained length, while different heads in different layers can collectively process longer contexts. LONGHEADS works efficiently and fits seamlessly with many LLMs that use relative positional encoding. LONGHEADS achieves 100% accuracy at the 128k length on passkey retrieval task, verifying LONGHEADS's efficacy in extending the usable context window for existing models. We release our code at https://***/LuLuLuyi/LongHeads. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Survey of Character Recognition in Tibetan Historical Documents 4

A Survey of Character Recognition in Tibetan Historical Docu...

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4th International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Gongqu, Zhuome Cairang, Jia Duojie, Cairang Jiayang, Dongzhou Luo, Peng The College of Computer Qinghai Normal University Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining810008 China

ISBN: (纸本)9781510683242

Text recognition for Tibetan historical document images is the automated process of extracting and identifying text from these photos utilizing image processing, computer vision, and natural language processing techniques. It is generally understood to be a method of extracting text from visual data. In order to create editable, searchable, and analytical text forms that readers, researchers, and academics may study, preserve, and pass on, it attempts to analyze, identify, and change the text found in traditional Tibetan historical document images. Recent years have seen significant advancements in the effectiveness and performance of Tibetan historical document image and text identification thanks to the quick development of artificial intelligence technology. However, issues with low image quality, non-standard formatting, and notable font style inconsistencies persist in the field of image recognition of Tibetan historical document texts. As a result, the accuracy and universality of current recognizers are low. First, this article gives a general overview of the basic knowledge of Tibetan historical documents, which are the research objects in this field. This helps to better summarize the work of predecessors and support future *** methods of integrating deep learning and conventional methods for Tibetan historical document image text recognition were then arranged, categorized, summarized, and introduced around the four subtasks of dataset construction, text detection, text recognition, and layout analysis. This served as the basis for sorting through the pertinent statistics and assessment indicators and summarizing the state and advancement of the research. Lastly, future technical development patterns are projected based on the primary issues and problems in this subject that require immediate attention. © 2024 SPIE.

关键词： History

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding 23

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Liu, Chao Wang, Xizhao Liu, Qiang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug). © 2024 IEEE.

关键词： Knowledge graph

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A Novel Frequency Reconfigurable Mode Reconfigurable Ultra-Wideband Antenna

A Novel Frequency Reconfigurable Mode Reconfigurable Ultra-W...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Guo, Chen. Wu, Xianliang. Huang, Zhixiang. Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

Aiming at the inability to reconstruct the frequency and the pattern of the traditional monopole antenna, an ultra-wideband monopole antenna with frequency reconstruction and pattern reconstruction is proposed. The antenna consists of a monopole antenna and a metasurface with a distance H1 between them. Using HFSS simulation software to simulate, the size of the monopole antenna is 30mm×30mm×1.6mm, and the center frequency is 4.3 GHz. The simulation results show that the impedance bandwidth of the antenna when S11 is less than -10 dB is 3.21-13.64 GHz. In this design, the antenna widens the bandwidth while realizing the frequency reconstruction and pattern reconstruction, and basically does not affect other performances. The simulation results are basically consistent with the measured results, indicating that the antenna has good gain and radiation characteristics in the working frequency band, and can be widely used in ultra-wideband communication systems. © 2022 IEEE.

关键词： Monopole antennas

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Design of Broadband Asymmetric Multisection Directional Coupler

Design of Broadband Asymmetric Multisection Directional Coup...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Geng, Xiaolei Wu, Xianliang Huang, Zhixiang Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781665452366

The multi-section coupled transmission line is equivalent to the cascade of multi-section ladder impedance filters, which provides a basis for the research of broadband directional couplers and meets the needs of microwave systems. Using this method, a multi-section -15 dB stripline directional coupler was designed in the application frequency range of 0.7-3.7 GHz, and the directional coupler was improved by loading the branch compensation capacitor. And through the electromagnetic simulation software HFSS for simulation optimization. The simulation results show that the coupling degree of the coupler is about -15 dB in 0.7-3.7 GHz, the return loss is greater than 20 dB and the isolation is greater than 30 dB, the insertion loss is less than 0.33 dB, the in-band fluctuation is less than 1 dB, and the physical size is 38mm × 6 mm, which meets the design requirements of the -15 dB coupler. © 2022 IEEE.

关键词： Electromagnetic simulation

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of computer Science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features

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Frontiers of computer Science 2023年第1期17卷 123-132页

作者： Ke-Jia CHEN Mingyu WU Yibo ZHANG Zhiwei CHEN School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China

Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency component upsampling, named SR-AFU. The network is composed of multiple cascaded dilated convolution residual blocks (CDCRB) to extract multi-resolution features representing image semantics, and multiple multi-size convolutional upsampling blocks (MCUB) to adaptively upsample different frequency components using CDCRB features. The paper also defines a new loss function based on the discrete wavelet transform, making the reconstructed SR images closer to human perception. Experiments on the benchmark datasets show that SR-AFU has higher peak signal to noise ratio (PSNR), significantly faster training speed and more realistic visual effects compared with the existing methods.

关键词： super-resolution multi-resolution features adaptive frequency upsampling wavelet transformation

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Pencil beam based on the computational holography method 2

Pencil beam based on the computational holography method

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Luo, Tianlei Lv, Anzhi Liu, Minghao Dong, Jiaqing Liu, Guolin Li, Zilong Song, Xianlin School of Information Engineering Nanchang University Nanchang330031 China School of Advanced Manufacture Nanchang University Nanchang330031 China Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

This paper presents a novel pencil beam generation method based on computational holography. The method generates intensity maps through computer simulation, obtains a computed hologram using a computational holographic method, and simulates near-field diffraction using computer simulation to simulate the role of a spatial light modulator (SLM) in a real holographic system. The theoretical effects of the computed hologram are simulated using computer simulation, and finally, a pencil-shaped beam with a focus position that can be flexibly adjusted at all depths is generated. Through experimental simulations, the feasibility of adjusting the focus position of the beam within a certain depth range is verified, showing its potential application value in fluorescence excitation and biomedical fields. © 2025 SPIE.

关键词： Holograms

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Improving viewpoint-independent object-centric representations through active viewpoint selection 24

Improving viewpoint-independent object-centric representatio...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Yinxuan Huang Chengmin Gao Bin Li Xiangyang Xue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

ISBN: (纸本)9798331314385

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints.

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DisenGCD: a meta multigraph-assisted disentangled graph learning framework for cognitive diagnosis 24

DisenGCD: a meta multigraph-assisted disentangled graph lear...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Shangshang Yang Mingyang Chen Ziwen Wang Xiaoshan Yu Panpan Zhang Haiping Ma Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China and Anhui Province Key Laboratory of Intelligent Computing and Applications Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China and Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui P. R. China

ISBN: (纸本)9798331314385

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned; the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https://***/BIMK/intelligent-Education/tree/main/DisenGCD.

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