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

作者： Qiankun Pi Jicang Lu Yepeng Sun Qinlong Fan Xukun Zhou Shouxin Shang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model’s understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model’s adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model’s stance discriminative ability and generalization performance.

关键词： Adaptation models Weak supervision Social networking (online) Annotations Semantics Blogs Transfer learning Signal processing Data models Speech processing

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International research initiative on genomicsguided sugarcane breeding

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Molecular Plant 2025年第2期18卷 171-174页

作者： Xingtan Zhang Jungang Wang Noor-ul-Ain Youxiong Que Jisen Zhang Javeria Abid Peifang Zhao Yuebin Zhang Jishan Lin Li-yu Chen Yixue Bao Robert Herry Sithichoke Tangphatsornruang Qinnan Wang Dongliang Huang Jian Ye Muqing Zhang Ming Luo Mike Butterfield Ren Wang Ray Ming National Key Laboratory of Tropical Crop Breeding Institute of Tropical Bioscience and BiotechnologyChinese Academy of Tropical Agricultural SciencesSanyaHainanChina National Key Laboratory for Tropical Crop Breeding Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenGuangdong 518120China State Key Laboratory for Conservation and Utilization of Subtropical Agri-Biological Resources&Guangxi Key Laboratory for Sugarcane Biology Guangxi UniversityNanningGuangxiChina National Key Laboratory of Tropical Crop Breeding Yunan Academy of Agricultural SciencesKaiyuanYunnanChina National Engineering Research Center for Sugarcane Fujian Provincial Key Laboratory of Haixia Applied Plant Systems BiologyKey Laboratory of GeneticsBreeding and Multiple Utilization of CropsMinistry of EducationFujian Agriculture and Forestry UniversityFuzhouFujianChina Queensland Alliance for Agriculture and Food Innovation University of QueenslandBrisbaneQLDAustralia National Omics Center National Science and Technology Development AgencyPathum ThaniThailand Institute of Nanfan&Seed Industry Guangdong Academy of Sciencesand Ministry of Agriculture of Guangdong Sugarcane Germplasm Resources and Utilization of Scientific Observation Test StationNo.10 Shiliugang RdGuangzhouGuangdong China Guangxi Key Laboratory of Sugarcane Genetic Improvement Guangxi Academy of Agricultural SciencesNanningGuangxiChina State Key Laboratory of Plant Genomics Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Plant Diversity and Specialty Crops Guangdong Provincial Key Laboratory of Applied BotanySouth China Botanical Garden Chinese Academy of SciencesGuangzhou 510650China 12Centro De Tecnologia CanavieiraPiracicabaSão PauloBrazil

Addressing global challenges such as energy sustainability,food security,and environmental conservation requires innovative agricultural *** sugarcane(Saccharum hybrid)is a crucial crop that contributes not only to the sugar industry but also to bioenergy *** its traditional role,sugarcane supports global food security,environmental sustainability,and economic empowerment of *** 80%of the worldwide sugarcane production is dedicated to sugar,a dietary staple(OECD-FAO,2024).The remaining 19%aid biofuel production,with sugarcane accounting for 24%of the global ethanol feedstock by *** a C4 plant,sugarcane efficiently captures atmospheric carbon and acts as an important carbon ***,sugarcane cultivation can lower local temperatures by nearly 1°C compared to pastures/natural vegetation(Loarie et al.,2011).Economically,sugarcane is a high-value cash crop,generating$43 billion annually in Brazil alone(Voora et al.,2023).

关键词： annually sugar breeding

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Functional Testing and Repair for ReRAM-Based Deep-Learning Accelerators

IEEE Design and Test

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IEEE Design and Test 2025年第3期42卷 66-73页

作者： Li, Wen Wang, Ying Li, Huawei Li, Xiaowei Zou, Kaiwei Shanxi University School of Computer and Information Technology Shanxi Taiyuan030006 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Editor’s notes: This article offers an overview of the RRAMedy framework, designed to perform online testing, fault diagnosis, and fault mitigation in resistive random-access memory (ReRAM) devices, specifically in the context of ReRAM crossbar-based implementations for AI hardware acceleration. —Jyotika Athavale, Synopsys, USA —Haralampos-G. Stratigopoulos, Sorbonne Université, CNRS, LIP6, France. © 2013 IEEE.

关键词： Accuracy Fault tolerant systems Fault tolerance Deep learning Image edge detection Fault detection Computational modeling Training Reliability Hafnium

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Influence of hydrofoil characteristic thickness on the cavitation erosion risk distribution of clearance vortex cavitation and its mechanisms

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Journal of Hydrodynamics 2025年第1期37卷 50-62页

作者： Huang, Jia-le Cheng, Huai-yu Cao, Yan-tao Ji, Bin State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan P.R. China National Key Laboratory on Ship Vibration & Noise China Ship Scientific Research Center Wuxi China Taihu Laboratory of Deepsea Technological Science Wuxi China

To clarify the influence of the hydrofoil characteristic thickness on the distribution characteristics and mechanisms of clearance cavitation erosion risk, a large eddy simulation (LES) is conducted to study the clearance cavitating flow around NACA0012 and NACA0024 hydrofoils under identical conditions. The study predicts cavitation erosion risk using three methods: The erosive power method (EPM), the improved gray level method (IGLM) and the energy conservation method (ECM). The numerical results are in good agreement with the experiment data and the ECM is applied due to its simplicity in parameter adjustment and low sensitivity. The results indicate that the characteristic thickness significantly influences the flow field, leading to variations in the position and intensity of cavitation collapse, ultimately resulting in notable differences in cavitation erosion risk distribution. The high cavitation erosion risk region on the clearance surface of NACA0012 is concentrated around the midsection, while it is concentrated in the upstream region for the NACA0024, with a lower frequency of extreme events. Tip separation vortex (TSV) cavitation is the main cause of the differences in cavitation erosion risk distribution. On the clearance surface of the NACA0012, TSV cavitation primarily collapses in the central region, whereas for the NACA0024 hydrofoil, TSV cavitation occurs only in the upstream region of the clearance surface and exhibits more stability. The differences in vorticity distribution near the clearance surface partially influence the distribution of TSV cavitation, thereby affecting the characteristics of cavitation erosion risk distribution. The larger characteristic thickness of the NACA0024 reduces the effects of the stretching term and the baroclinic torque term, weakening the effect of vorticity on TSV cavitation, resulting in more stable patterns of the TSV cavitation.

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LSDK-LegalSum: improving legal judgment summarization using logical structure and domain knowledge

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-15页

作者： Gao, Wei Yu, Shuai Qin, Yongbin Yang, Caiwei Huang, Ruizhang Chen, Yanping Lin, Chuan Engineering Research Center of Text Computing & CognitiveIntelligence Ministry of Education Guizhou University Guizhou Huaxi Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Huaxi Guiyang 550025 China College of Computer Science and Technology Guizhou University Guizhou Huaxi Guiyang 550025 China

Legal judgment summarization enhances the efficiency and accuracy of processing and retrieving similar cases by distilling key information from legal judgment documents. Legal judgments, which record the trial process and outcome, tend to have lengthy texts. However, most existing methods for legal judgment summarization struggle to effectively handle long texts and focus only on the serialized text information, neglecting the auxiliary role of the document’s logical structure in generating summaries. Additionally, legal judgment documents contain extensive domain knowledge formed by professional legal terminology, which is crucial for accurately understanding and analyzing the content of the documents. We propose a method to improve the quality of legal judgment summaries by utilizing logical structures and domain knowledge (LSDK-LegalSum). Specifically, we first use a multi-label document classification approach to divide the legal judgment into four parts: ‘Type’, ‘Claim’, ‘Fact’ and ‘Result’, then generate a summary of the ‘Type’ part, and combine it with the ‘Claim’ part to create a new summary, and so forth until the final summary is produced. Moreover, the case descriptions in legal judgments cause substantial textual noise;therefore, we employ a text-denoising method that integrates domain knowledge to further enhance the performance of the summaries. Extensive experiments on the CAIL2020 legal summarization dataset have shown that our method achieves improvements of 8.37, 16.64, and 13.07 percentage points in Rouge-1, Rouge-2, and Rouge-L scores respectively over the state-of-the-art models. This demonstrates that our method not only produces concise and semantically complete summaries but also ensures that the content is logically rigorous and well-organized, meeting practical needs more effectively. © The Author(s) 2025.

关键词： Abstractive summarization Domain knowledge Legal judgment summarization Logical structure Low-source summarization

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Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

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

作者： Pi, Qiankun Lu, Jicang Sun, Yepeng Fan, Qinlong Zhou, Xukun Shang, Shouxin State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798350368741

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model's understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model's adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model's stance discriminative ability and generalization performance. © 2025 IEEE.

关键词： argumentation corpus machine annotation multitask learning stance detection

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On the Cycle Structure of Some Nonlinear Feedback Shift Registers

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Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

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Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks

Time-independent Spiking Neuron via Membrane Potential Estim...

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

作者： Hanqi Chen Lixing Yu Shaojie Zhan Penghui Yao Jiankun Shao Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN.

关键词： Neuromorphics Computational modeling Neurons Membrane potentials Estimation Spiking neural networks Parallel processing Signal processing Computational efficiency Speech processing

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The Distributionally Robust Optimization Model of Sparse Principal Component Analysis

arXiv

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arXiv 2025年

作者： Wang, Lei Liu, Xin Chen, Xiaojun Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

We consider sparse principal component analysis (PCA) under a stochastic setting where the underlying probability distribution of the random parameter is uncertain. This problem is formulated as a distributionally robust optimization (DRO) model based on a constructive approach to capturing uncertainty in the covariance matrix, which constitutes a nonsmooth constrained min-max optimization problem. We further prove that the inner maximization problem admits a closed-form solution, reformulating the original DRO model into an equivalent minimization problem on the Stiefel manifold. This transformation leads to a Riemannian optimization problem with intricate nonsmooth terms, a challenging formulation beyond the reach of existing algorithms. To address this issue, we devise an efficient smoothing manifold proximal gradient algorithm. We prove the Riemannian gradient consistency and global convergence of our algorithm to a stationary point of the nonsmooth minimization problem. Moreover, we establish the iteration complexity of our algorithm. Finally, numerical experiments are conducted to validate the effectiveness and scalability of our algorithm, as well as to highlight the necessity and rationality of adopting the DRO model for sparse PCA. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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