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Cascaded adaptive aberration-eliminating multimode fiber imaging

Advanced Photonics 2025年第02期 135-143页

作者： Zhong Wen Qilin Deng Quanzhi Li Yizhou Tan Jingshan Zhong Chiming Zhang Jiahe Zhang Clemens F.Kaminski Ying Gu Xu Liu Qing Yang Zhejiang University College of Optical Science and Engineering State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University International Research Center for Advanced Photonics Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Chinese PLA General Hospital The First Medical Center Department of Laser Medicine Chinese PLA General Hospital Hainan HospitalLaser Medicine Center Zhejiang Laboratory Research Center for Intelligent Manufacturing Computing University of Cambridge Department of Chemical Engineering and Biotechnology

In vivo microscopic imaging inside a biological lumen such as the gastrointestinal tract, respiratory airways, or within blood vessels has faced significant technological challenges for decades. A promising candidate technology is the multimode fiber(MMF) endoscope, which enables minimally invasive diagnostics at a resolution reaching the cellular level. However, for in vivo imaging applications deep inside a biological lumen, sample-induced aberrations and the dynamic dispersion in the MMF make the MMF endoscope a chaotic system with many unknowns, where multiple minor fluctuations can couple and compound into intractable problems. We introduce a dynamically encoding, cascaded, optical, and ultrathin polychromatic light-field endoscopy(DECOUPLE) to tackle this challenge. DECOUPLE includes an adaptive aberration correction that can accurately track and control MMF behavior in the spatial-frequency domain to compensate for chaos introduced during complex dynamic imaging processes. We demonstrate the flexibility and practicality of DECOUPLE for noninvasive volumetric imaging in two colors for light passing through various highly aberrating samples including 120-μm-thick onion epidermal slices and 80-μm-thick layers of fat emulsions. To summarize, we represent a significant step toward practical in vivo imaging deep within biological tissue.

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Task-Driven Priority-Aware Computation Offloading Using Deep Reinforcement Learning

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IEEE Transactions on Wireless Communications 2025年

作者： Hao, Hao Xu, Changqiao Zhang, Wei Yang, Shujie Muntean, Gabriel-Miro Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Dublin City University Performance Engineering Laboratory School of Electronic Engineering Dublin Ireland

Computation offloading is an effective method for reducing the pressure put on networks and improving the service experience. However, most existing research on computation offloading is timeslot-driven and treats all tasks equally, resulting in decision waiting delays and failure to complete some important tasks. In this paper, we propose a novel priority-aware task-driven computation offloading model with system performance gain as the optimization objective based on a combination of task delay and energy consumption aspects. The new model is formulated as a Markov decision process (MDP). Considering the discrete-continuous hybrid action space of the optimization problem, we construct a dependence-aware latent space and propose a novel algorithm based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). Additionally, we present the neural network structure and analyze the complexity of the algorithm. Extensive simulations show how our algorithm achieves superior performance compared to three state-of-the-art alternative approaches. © 2002-2012 IEEE.

关键词： Optimization

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Statically-Directed Dynamic Taint Analysis

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Chinese Journal of Electronics 2025年第1期23卷 18-24页

作者： Kai Chen Yingjun Zhang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences Beijing China

Taint analysis is a popular method in software analysis field including vulnerability/malware analysis. By identifying taint source and making suitable taint propagation rules, we could directly know whether variables in software have any relationship with input data. Static taint analysis method is efficient, but it is imprecise since runtime information is lacked. Dynamic taint analysis method usually instruments every instruction in software to catch the taint propagation process. However, this is inefficient since it usually takes lots of time for context switches between original code and instrumenting code. In this paper, we propose a statically-directed dynamic taint analysis method to increase the efficiency of taint analysis process without any loss of accuracy. In this way, there is no need to instrument every instruction. Several experiments are made on our prototype SDTaint and the results show that our method is several times more efficient than traditional dynamic taint analysis method.

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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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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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Catalytic Enantioselective α-Ethynylation of Oxindoles: Total Synthesis of (-)-Corynoxine, (-)-Isorhynchophylline, (-)-Aspidospermidine, and (-)-Limaspermidine

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Angewandte Chemie - International Edition 2025年 e202506065页

作者： Guo, Bao-Kuan Zhang, Yu-Dong Yang, Ju-Song Tian, Jin-Rui Zhang, Xiao-Ming Zhang, Fu-Min Tu, Yong-Qiang State Key Laboratory of Applied Organic Chemistry College of Chemistry and Chemical Engineering Lanzhou University Lanzhou730000 China School of Chemistry and Chemical Engineering Frontier Scientific Center of Transformative Molecules Shanghai Key Laboratory of Chiral Drugs and Engineering National Key Laboratory of Innovative Immunotherapy Shanghai Jiao Tong University Shanghai200240 China

The all-carbon quaternary stereogenic center of oxindoles is a crucial structural element of a broad spectrum of indole alkaloids, imparting these molecules with rigid three-dimensional configurations essential for their biological activities. Here, we present a catalytic asymmetric α-ethynylation reaction of oxindoles taking advantage of the catalysis of a spiropyrrolidine amide (SPA) triazolium. This transformation enables the enantioselective construction of the C3 quaternary carbon stereocenter of oxindoles while introducing a versatile ethynyl functionality. Employment of this methodology has been demonstrated in the divergent total synthesis of indole alkaloids (-)-corynoxine, (-)-isorhynchophylline, (-)-aspidospermidine, and (-)-limaspermidine, featuring a protecting group-dependent 1,6-Michael addition or an aminolysis/1,6-Michael addition sequence to generate two distinct types of spiro-indoles, tailored for different late-stage synthetic purposes. © 2025 Wiley-VCH GmbH.

关键词： Stereocenters

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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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Advanced defense against GAN-based facial manipulation: A multi-domain and multi-dimensional feature fusion approach

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Displays 2025年 89卷

作者： Liu, Yunqi Ouyang, Xue Cui, Xiaohui Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430000 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430000 China

Powerful facial image manipulation offered by encoder-based GAN inversion techniques raises concerns about potential misuse in identity fraud and misinformation. This study introduces the Multi-Domain and Multi-Dimensional Feature Fusion (MDFusion) method, a novel approach that counters encoder-based GAN inversion by generating adversarial samples. Firstly, MDFusion transforms the luminance channel of the target image into spatial, frequency, and spatial-frequency hybrid domains. Secondly, we use the specifically adapted Feature Pyramid Network (FPN) to extract and fuse high-dimensional and low-dimensional features that enhance the robustness of adversarial noise generation. Then, we embed adversarial noise into the spatial-frequency hybrid domain to produce effective adversarial samples. Finally, the adversarial samples are guided by our designed hybrid training loss to achieve a balance between imperceptibility and effectiveness. Tests were conducted on five encoder-based GAN inversion models using ASR, LPIPS, and FID metrics. These tests demonstrated the superiority of MDFusion over 13 baseline methods, highlighting its robust defense and generalization abilities. The implementation code is available at https://***/LuckAlex/MDFusion. © 2025

关键词： Image coding

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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 Wuhan430072 China National Key Laboratory on Ship Vibration & Noise China Ship Scientific Research Center Wuxi214082 China Taihu Laboratory of Deepsea Technological Science Wuxi214082 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. © China Ship scientific Research Center 2025.

关键词： Energy Intensity

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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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