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MacroTrend: A Write-Efficient Cache Algorithm for NVM-Based Read Cache

Journal of Computer Science & Technology 2022年第1期37卷 207-230页

作者： Ning Bao Yun-Peng Chai Xiao Qin Chuan-Wen Wang Key Laboratory of Data Engineering and Knowledge Engineering Ministry of EducationBeijing 100872China School of Information Renmin University of ChinaBeijing 100872China Samuel Ginn College of Engineering Auburn UniversityAlabama 36830U.S.A.

The future storage systems are expected to contain a wide variety of storage media and layers due to the rapid development of NVM(non-volatile memory)*** NVM-based read caches,many kinds of NVM devices cannot stand frequent data updates due to limited write endurance or high energy consumption of ***,traditional cache algorithms have to update cached blocks frequently because it is difficult for them to predict long-term popularity according to such limited information about data blocks,such as only a single value or a queue that reflects frequency or *** this paper,we propose a new MacroTrend(macroscopic trend)prediction method to discover long-term hot blocks through blocks'macro trends illustrated by their access count *** then a new cache replacement algorithm is designed based on the MacroTrend prediction to greatly reduce the write amount while improving the hit *** conduct extensive experiments driven by a series of real-world traces and find that compared with LRU,MacroTrend can reduce the write amounts of NVM cache devices significantly with similar hit ratios,leading to longer NVM lifetime or less energy consumption.

关键词： non-volatile memory(NVM) solid state disk(SSD) cache endurance

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Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

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Chinese Journal of Electronics 2021年第1期30卷 109-118页

作者： WANG Xuesong ZHAO Jijuan CHENG Yuhu YU Qiang School of Information and Control Engineering China University of Mining and Technology Xuzhou Key Laboratory of Artificial Intelligence and Big Data

In the problem of unsupervised domain adaption Extreme learning machine(ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive *** on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.

关键词： entire ELM learning process unsupervised domain adaption ELM model output layer parameters image representation double functions parameter adaptive ability common subspace classifier parameters joint feature representation unsupervised domain adaption Extreme learning machine data probability distribution learning (artificial intelligence) higher cross-domain classification accuracy target domain probability pattern classification adaptive classifier learning stage traditional matching method target domains unsupervised learning feature representation stage domain adaptation functions

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Application of U-Net and Optimized Clustering in Medical Image Segmentation:A Review

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Computer Modeling in engineering & Sciences 2023年第9期136卷 2173-2219页

作者： Jiaqi Shao Shuwen Chen Jin Zhou Huisheng Zhu Ziyi Wang Mackenzie Brown School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China School of Data Science Perdana UniversitySerdangSelangor43400Malaysia

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so *** with natural images,medical images have a variety of ***,the emphasis of information which is conveyed by images of different modes is quite *** it is time-consuming and inefficient to manually segment medical images only by professional and experienced ***,large quantities of automated medical image segmentation methods have been ***,until now,researchers have not developed a universal method for all types of medical image *** paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent *** the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation *** is the improved strategies based on traditional clustering *** other is the research progress of the improved image segmentation network structure model based on *** power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional *** paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

关键词： Medical image segmentation clustering algorithm U-Net

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Overcoming Debye length limitations:Three-dimensional wrinkled graphene field-effect transistor for ultra-sensitive adenosine triphosphate detection

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Frontiers of physics 2023年第5期18卷 183-192页

作者： Yue Ding Chonghui Li Meng Tian Jihua Wang Zhenxing Wang Xiaohui Lin Guofeng Liu Wanling Cui Xuefan Qi Siyu Li Weiwei Yue Shicai Xu School of Physics and Electronics Shandong Normal UniversityJinan 250014China Shandong Key Laboratory of Biophysics Institute of BiophysicsDezhou UniversityDezhou 253023China Shandong Engineering Laboratory of Swine Herd Health Big Data and Intelligent Monitoring Dezhou UniversityDezhou 253023China

Adenosine triphosphate(ATP)is closely related to the pathogenesis of certain diseases,so the detection of trace ATP is of great significance to disease diagnosis and drug *** field-effect transistors(GFETs)have been proven to be a promising platform for the rapid and accurate detection of small molecules,while the Debye shielding limits the sensitive detection in real ***,a three-dimensional wrinkled graphene field-effect transistor(3D WG-FET)biosensor for ultra-sensitive detection of ATP is *** lowest detection limit of 3D WG-FET for analyzing ATP is down to 3.01 aM,which is much lower than the reported *** addition,the 3D WG-FET biosensor shows a good linear electrical response to ATP concentrations in a broad range of detection from 10 aM to 10 ***,we achieved ultra-sensitive(LOD:10 aM)and quantitative(range from 10 aM to 100 fM)measurements of ATP in human *** 3D WG-FET also exhibits high *** work may provide a novel approach to improve the sensitivity for the detection of ATP in complex biological matrix,showing a broad application value for early clinical diagnosis and food health monitoring.

关键词： ATP in complex human serum three-dimensional wrinkled graphene field effect transistor Debye shielding ultra-sensitive

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Phase equilibria in Co-Ti-Re ternary system

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Rare Metals 2023年第8期42卷 2736-2746页

作者： Ling-Ling Li Cui-Ping Wang Shui-Yuan Yang Jia-Jia Han Yong Lu Jin-Bin Zhang Xing-Jun Liu College of Materials and Fujian Provincial Key Laboratory of Materials Genome Xiamen UniversityXiamen 361005China College of Materials Science and Engineering Guangdong Ocean UniversityYangjiang 529500China Xiamen Key Laboratory of High-Performance Metals and Materials Xiamen UniversityXiamen 361005China School of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China

The phase equilibria in the Co-Ti-Re ternary system at 800,1000,1100 and 12000C were studied combined with electron probe microanalyzer(EPMA),X-ray diffraction(XRD),scanning electron microscope(SEM)and transmission electron microscope(TEM).The new phaseτwith a large solubility was discovered at all four isothermal sections,and its structure still needs a further study.A large solubility of Re in the CoTi phase was *** solubility of Re in the Co_(3)Ti phase is6.1%at 800℃,8.7%at 1000℃and 7.7%at 1100℃.The present work could be significant for alloy design of Co-based alloys and thermodynamic analysis of the Co-TiRe ternary system.

关键词： Isothermal section Phase equilibria Co-Ti-Re Co-based supperalloys

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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Untangling the association between urban mobility and urban elements

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地球空间信息科学学报（英文版） 2024年第4期27卷 1071-1089页

作者： Jinzhou Cao Wei Tu Rui Cao Qili Gao Guanzhou Chen Qingquan Li College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina Key Laboratory of National Geographic Census and Monitoring Ministry of Natural ResourcesWuhan UniversityWuhanChina School of Architecture and Urban Planning & Guangdong Key Laboratory of Urban Informatics Shenzhen UniversityShenzhenChina Department of Land Surveying and Geo-Informatics & Smart Cities Research Institute The Hong Kong Polytechnic UniversityHong KongChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan UniversityWuhanChina

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population,infrastructure,and socioeconomic *** laws are basic but effective rules for evaluating a city's internal growth logic and assessing its efficiency by investigating whether urban indicators scale with *** date,only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few *** multiple urban-sensed and human-sensed data,this study proposes a thorough framework for quantifying the scaling laws in a *** begin,urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking *** demonstrate the spatiotemporal variability of urban mobility,various network-based mobility measures are *** that,three different features of urban mobility laws are exposed,explaining spatial agglomeration,spatial hierarchical structures,and the temporal growth *** scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed *** this framework to the case study of Shenzhen,China revealed(a)spatial travel heterogeneity,hierarchical spatial structures,and mobility growth,and(b)not only a robust sub-linear relationship between infrastructure volume and population,but also a sub-linear relationship for socioeconomic *** identified scaling laws,both in terms of mobility measures and urban indicators,provide a multi-faceted portrait of the spatio-temporal varia-tions of urban settings,allowing us to better understand intra-urban developments and,consequently,provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future.

关键词： Scaling law urban scaling urban mobility intra-urban system mobile phone data

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Low-light image enhancement combining dehazing and auxiliary channels 3

Low-light image enhancement combining dehazing and auxiliary...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wu, Yiting Yang, Chuansheng Wang, Chao Chen, Hongming Ye, Qihong School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province China

ISBN: (纸本)9781510668522

Image enhancement tasks have garnered significant attention in recent years, particularly regarding images captured in low-light conditions. Due to their inherent under-illumination characteristics, these images often suffer from challenges such as low contrast, low brightness, and noise that hinder their quality. With the recent advancements in deep learning, convolutional neural networks (CNNs) have demonstrated a high potential in addressing these challenges. In this paper, we propose a deep CNN-based approach that enhances color components a and b through Lab-space decomposition of images, utilizing a deep unsupervised dehazing network model as a baseline. Our approach considers both algorithmic and image decomposition, splitting the low-light image enhancement task into two parallel subtasks. Extensive experiments demonstrate that our proposed approach outperforms previous methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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An Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation 3

An Enhanced-Precision Bandgap Reference Incorporating Temper...

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3rd International Symposium on Semiconductor and Electronic Technology, ISSET 2024

作者： Xiang, Tianzi Xu, Quanyuan Liu, Haorui Lu, Ying Southwest Forestry University College of Big Data and Intelligent Engineering Yunnan Kunming China Southwest Forestry University Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Yunnan Kunming China Changchun University of Science and Technology School of Physics Jilin Changchun China

ISBN: (纸本)9798350390285

In this work, an Enhanced-Precision Bandgap Reference Incorporating Temperature-Compensation was proposed. Unlike traditional bandgap references, the reference voltage output of 0.701 V is realized by combining reference current and resistor. Meanwhile, an innovative Linear-piecewise current compensation was proposed, which aimed at optimizing the temperature drift of the bandgap reference. The simulation results shows that the proposed curvature-compensation technique improves the TC from 24.96 to 2.14 ppm°C in the temperature range of -40 °C to 120 °C;the PSRR of Enhanced-Precision Bandgap Reference at low frequency is 72.40 dB;the line regulation is about 0.08 %/V for input voltages between 3.2 and 3.7 V in standard 0.18 μm 1P6M CMOS process. The proposal of this new circuit provides a new idea for the realization of high- performance integrated circuits in the future. © 2024 IEEE.

关键词： Piecewise linear techniques

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