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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhou, Shu Zhao, Rui Zhou, Zhengda Yi, Haohan Zheng, Xuhui Wang, Hao Nanjing University China Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Provincial Universities Nanjing University China University of Technology Sydney Australia

ISBN: (纸本)9798891761964

Multiparty dialogue question answering (QA) within machine reading comprehension (MRC) presents significant challenges due to the complex interplay of information across multiple speakers and the need for advanced logical reasoning. While existing models often focus on separating dialogue information based on speakers and utterances, they rarely address the crucial aspect of logical inference, leading to suboptimal performance in understanding and answering questions. To bridge this gap, we introduce the Logical Inference Memory Network (LIMN), a novel architecture designed for extractive QA in multiparty dialogues. LIMN incorporates a unique inference module pretrained on plain text QA datasets (like SQuAD 2.0), enabling it to transfer robust logical reasoning abilities to the dialogue domain. This module generates representations that are specifically attuned to logical inference, which are then integrated into the dialogue context. Furthermore, we propose a key-utterance-based interaction mechanism that dynamically focuses on the most relevant utterances within the dialogue, enhancing the model's ability to pinpoint answers. To ensure robust performance, LIMN employs a multitask learning strategy that jointly optimizes for answer extraction, answerability prediction, key-utterance identification, and masked speaker prediction. Extensive experiments on the Molweni and FriendsQA benchmarks, encompassing 25,000 and 10,000 questions respectively, demonstrate that LIMN achieves state-of-the-art results, affirming the effectiveness of incorporating logical inference in multiparty dialogue QA. © 2025 Association for Computational Linguistics.

关键词： Memory architecture

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Early Warning Thresholds for Microalgal Blooms and Environmental Drivers of Substrate Microalgae Recovery in Litopenaeus vannamei Aquaculture

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Water, Air, and Soil Pollution 2025年第6期236卷 1-16页

作者： Ma, Weixing Shi, Yuanyuan Ma, Ni Liu, Gaojian Gao, Yuxin Ma, Jiaru Yi, Zhaohui Yang, Min Li, Zhaoxia College of Environmental Science & Engineering Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province Yancheng Institute of Technology Jiangsu Province Yancheng224051 China School of Marine and Biological Engineering Yancheng Institute of Technology Jiangsu Province Yancheng224051 China Jiangsu YIDA TestingTechnology Co. Ltd. Building A- 15 Big Data Industrial Park Chengnan New District Jiangsu Province Yancheng224051 China Shenzhen518055 China

In response to the global demand for high-quality protein, Litopenaeus vannamei aquaculture has rapidly expanded. However, microalgal blooms often degrade water quality, resulting in reduced yields and significant financial losses. This study investigated the recovery dynamics of substrate microalgae, which act as seed banks for microalgal blooms, and identified early warning thresholds for algae under varying environmental conditions. Sediments from bfIGuccess (BS) and breeding failure (BF) sheds were analyzed to evaluate the effects of temperature (6 °C–21 °C), light intensity (50–2,000 lx), and nutrient types (BG11 and raw water). The findings indicated that environmental factors influencing microalgal recovery followed the priority order: temperature > light > nutrient salts. The highest recovery was observed at temperatures of 11 °C and 16 °C under 2,000 lx illumination. key early warning algal species, including Melosira granulata, Cyclotella, and Chlorella, exhibited thresholds of 0.9–42.9 × 104 cells/g, 0.1–28.8 × 104 cells/g, and 1.0–31.7 × 104 cells/g, respectively. This study uniquely linked substrate algae recovery to aquaculture failures, providing quantitative thresholds and actionable insights for proactive microalgal blooms monitoring and control. These findings form a scientific basis for improving water quality management and ensuring sustainable L. vannamei aquaculture © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquaculture

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Research on the detection and analysis of weak signals with low SNR based on the Welch algorithm 24

Research on the detection and analysis of weak signals with ...

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2nd International Conference on Communication Networks and Machine Learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

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IEEE Transactions on Information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Mining

arXiv

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arXiv 2025年

作者： Wang, Haoran Huai, Lian Li, Wenbin Qi, Lei Jiang, Xingqun Shi, Yinghuan National Key Laboratory for Novel Software Technology The National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing China National Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing Drum Tower Hospital Jiangsu Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China Lab School of Computer Science and Engineering Southeast University Jiangsu Nanjing China BOE Technology Group Co. Ltd. Beijing China

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in medical images, where most of them focus on training an adaptor for fine-tuning a large amount of pixel-wise annotated medical images following a fully supervised manner. In this paper, to reduce the labeling cost, we investigate a novel weakly-supervised SAM-based segmentation model, namely WeakMedSAM. Specifically, our proposed WeakMedSAM contains two modules: 1) to mitigate severe co-occurrence in medical images, a sub-class exploration module is introduced to learn accurate feature representations. 2) to improve the quality of the class activation maps, our prompt affinity mining module utilizes the prompt capability of SAM to obtain an affinity map for random-walk refinement. Our method can be applied to any SAM-like backbone, and we conduct experiments with SAMUS and EfficientSAM. The experimental results on three popularly-used benchmark datasets, i.e., BraTS 2019, AbdomenCT-1K, and MSD Cardiac dataset, show the promising results of our proposed WeakMedSAM. Our code is available at https://***/wanghr64/WeakMedSAM. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging

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Spatiotemporal evolution of drought status and its driving factors attribution in China

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Science of the Total Environment 2025年 958卷 178131页

作者： Jin, Haoyu Zhang, Ke Zhang, Pengfei Liu, Guoyan Liu, Moyang Chen, Xiaohong Willems, Patrick National Key Laboratory of Water Disaster Prevention Hohai University Jiangsu Nanjing210024 China Yangtze Institute for Conservation and Development Jiangsu Nanjing210024 China College of Hydrology and Water Resources Hohai University Jiangsu Nanjing210024 China China Meteorological Administration Hydro-Meteorology Key Laboratory Hohai University Jiangsu Nanjing210024 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Jiangsu Nanjing210024 China CanberraACT0200 Australia Zhuhai519082 China Department of Civil Engineering Hydraulics and Geotechnics Section KU Leuven Leuven Belgium

Drought has intensified in China in recent years, and it is urgent to clarify the characteristics of drought evolutions in different regions and its influencing factors. To this end, we selected six main influencing factors and analyzed their impact on drought patterns. The results show that drought is increasing in 78.4 % of China, and more severe droughts have occurred since 2000. From the perspective of drought change distribution, the wet areas in China are getting wetter, and the dry areas are getting drier. The partial correlation coefficient (PACC) between precipitation and standard precipitation evapotranspiration index (SPEI) is the largest in South China (SC), and the PACC between temperature and SPEI is the largest in Northwest China (NWC). Precipitation and temperature have the greatest correlation with drought, accounting for 45.7 % and 30.8 % of China. In 49.9 % of China, temperature contributes the most to the drought trend changes, and in 16.6 % of China, normalized difference vegetation index (NDVI) contributes the most to the drought trend changes. Geographical detector model (GDM) shows temperature has the greatest driving force on SPEI, followed by surface solar radiation (SSR). Principal component analysis (PCA) shows temperature has the greatest positive impact on drought, while soil moisture has the greatest negative impact on drought. This study is helpful to increase the understanding of the diversities in drought drivers in different regions of China. © 2024

关键词： Drought Geographical detector Partial correlation analysis Principal component analysis Trend decomposition

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