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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

Journal of Computer Science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label data Stream

Co-training Based on Semi-Supervised Ensemble Classification...

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IEEE International Conference on Big knowledge (ICBK)

作者： Zhe Chu Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education AnHui China School of Computer Science and Information Engineering Hefei University of Technology AnHui China

A large amount of data streams in the form of texts and images has been emerging in many real-world applications. These data streams often present the characteristics such as multi-labels, label missing and new class emerging, which makes the existing data stream classification algorithm face the challenges in precision space and time performance. This is because, on the one hand, it is known that data stream classification algorithms are mostly trained on all labeled single-class data, while there are a large amount of unlabeled data and few labeled data due to it is difficult to obtain labels in the real world. On the other hand, many of existing multi-label data stream classification algorithms mostly focused on the classification with all labeled data and without emerging new classes, and there are few semi-supervised methods. Therefore, this paper proposes a semi-supervised ensemble classification algorithm for multi-label data streams based on co-training. Firstly, the algorithm uses the sliding window mechanism to partition the data stream into data chunks. On the former w data chucks, the multi-label semi-supervised classification algorithm COINS based on co-training is used to training a base classifier on each chunk, and then an ensemble model with w COINS classifiers is generated ensemble model to adapt to the environment of data stream with a large number of unlabeled data. Meanwhile, a new class emerging detection mechanism is introduced, and the w+1 data chunk is predicted by the ensemble model to detect whether there is a new class emerging. When a new label is detected, the classifier is retrained on the current data chunk, and the ensemble model is updated. Finally, experimental results on five real data sets show that: as compared with the classical algorithms, the proposed approach can improve the classification accuracy of multi-label data streams with a large number of missing labels and new labels emerging.

关键词： data models Training Streaming media Adaptation models Partitioning algorithms Correlation knowledge engineering

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Simultaneous Capture of CO2Boosting Its Electroreduction in the Micropores of a Metal–organic Framework

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Angewandte Chemie 2023年第52期135卷

作者： Yuan-Yuan Liu Jia-Run Huang Hao-Lin Zhu Prof. Pei-Qin Liao Prof. Xiao-Ming Chen MOE Key Laboratory of Bioinorganic and Synthetic Chemistry Guangdong Basic Research Center of Excellence for Functional Molecular Engineering School of Chemistry IGCME Sun Yat-Sen University Guangzhou 510275 China Contribution: Data curation (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Contribution: Software (supporting) Contribution: Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Funding acquisition (supporting) Writing - original draft (supporting) Writing - review & editing (supporting)

Integration of CO 2 capture capability from simulated flue gas and electrochemical CO 2 reduction reaction (eCO 2 RR) active sites into a catalyst is a promising cost-effective strategy for carbon neutrality, but is of great difficulty. Herein, combining the mixed gas breakthrough experiments and eCO 2 RR tests, we showed that an Ag 12 cluster-based metal–organic framework ( 1-NH 2 , aka Ag 12 bpy-NH 2 ), simultaneously possessing CO 2 capture sites as “CO 2 relays” and eCO 2 RR active sites, can not only utilize its micropores to efficiently capture CO 2 from simulated flue gas (CO 2 : N 2 =15 : 85, at 298 K), but also catalyze eCO 2 RR of the adsorbed CO 2 into CO with an ultra-high CO 2 conversion of 60 %. More importantly, its eCO 2 RR performance (a Faradaic efficiency (CO) of 96 % with a commercial current density of 120 mA cm −2 at a very low cell voltage of −2.3 V for 300 hours and the full-cell energy conversion efficiency of 56 %) under simulated flue gas atmosphere is close to that under 100 % CO 2 atmosphere, and higher than those of all reported catalysts at higher potentials under 100 % CO 2 atmosphere. This work bridges the gap between CO 2 enrichment/capture and eCO 2 RR.

关键词： CO2 Enrichment/Capture CO2 Relays Electrochemical CO2 Reduction Metal–Organic Framework Simulated Flue Gas

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SIoTFog:Byzantine-resilient IoT fog networking

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Frontiers of Information Technology & Electronic engineering 2018年第12期19卷 1546-1557页

作者： Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI Department of Information and Electronic Engineering Muroran Institute of Technology School of Information Science and Engineering Central South University MOE Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University

The current boom in the Internet of Things(IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called "SIoTFog," to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

关键词： Byzantine fault tolerance Fog computing Resource allocation Internet of Things(IoT)

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Unveiling the corrosion evolution and pitting mechanism of X70 pipeline steel in typical near-neutral/bentonite soil environment

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Journal of Materials Science & Technology 2025年

作者： Guangming Yang Lulu Zeng Hua Shen Huaiyun Cui Tianqi Chen Zengyao Chen Chengwei Xu Chao Liu Feng Huang Zhiyong Liu Zhichao Che Cuiwei Du Xiaogang Li Key Laboratory for Corrosion and Protection (MOE) Institute of Advanced Materials and Technology University of Science and Technology Beijing Beijing 100083 China National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing Beijing 100083 China Jilin Oilfield Company China National Petroleum Corporation Songyuan 138000 China Corrosion and control Research Center Branch of Science and Technology Research Institute of National Oil and Gas Pipeline Network Group Co Tianjin 300457 China The State Key Laboratory of Refractories and Metallurgy Wuhan University of Science and Technology Wuhan 430081 China School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing 100083 China

This work investigated the effect of dissolved oxygen (DO) on the corrosion behavior and pitting mechanism of X70 pipeline steel in near-neutral soil-bentonite simulated environment. The localized corrosion evolution to uniform corrosion was quantitatively analyzed using statistical methods. In situ characterization reveals that the localized corrosion mechanism is induced by core-shell inclusions. The microcracks occur between MnS and core oxides, the galvanic effect exists between MnS and matrix, and the high distortion region of the matrix promotes the localized corrosion initiation, while the galvanic effect between ferrite and pearlite aids propagation. Interestingly, the pitting growth rates are higher at low DO concentrations. Furthermore, the lower DO and longer immersion time enhance rust layer protection and reduce the corrosion rate of X70 pipeline steel.

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Machine Memory Intelligence: Inspired by Human Memory Mechanisms

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engineering 2025年

作者： Qinghua Zheng Huan Liu Xiaoqing Zhang Caixia Yan Xiangyong Cao Tieliang Gong Yong-Jin Liu Bin Shi Zhen Peng Xiaocen Fan Ying Cai Jun Liu School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an 710049 China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Shanghai 200434 China School of Medicine Tongji University Shanghai 200092 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science Iowa State University Ames IA 50011 USA

Large models, exemplified by ChatGPT, have reached the pinnacle of contemporary artificial intelligence (AI). However, they are plagued by three inherent drawbacks: excessive training data and computing power consumption, susceptibility to catastrophic forgetting, and a deficiency in logical reasoning capabilities within black-box models. To address these challenges, we draw insights from human memory mechanisms to introduce “machine memory,” which we define as a storage structure formed by encoding external information into a machine-representable and computable format. Centered on machine memory, we propose the brand-new machine memory intelligence (M 2 I) framework, which encompasses representation, learning, and reasoning modules and loops. We explore the key issues and recent advances in the four core aspects of M 2 I, including neural mechanisms, associative representation, continual learning, and collaborative reasoning within machine memory. M 2 I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models, driving a qualitative leap from weak to strong AI.

关键词： Machine memory intelligence Neural mechanism Associative representation Continual learning Collaborative reasoning

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Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG

npj Cardiovascular Health

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npj Cardiovascular Health 2024年第1期1卷 1-13页

作者： Jiarong Chen Shenda Hong Wanqing Wu Tong Liu National Institute of Health Data Science Peking University Beijing China School of Biomedical Engineering Sun Yat-sen University Shenzhen Guangdong China Department of Micro/Nano Electronics and MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Institute of Medical Technology Health Science Center of Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin China

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0175 and 0.0654, Pearson correlation coefficients of 0.7772 and 0.7287. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.

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Improving the microenvironment of tracking photovoltaic systems promotes soil organic carbon accumulation by mediating plant carbon inputs and microbial necromass retention

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Journal of Environmental Management 2025年 388卷 125937页

作者： Siqi Zhang Jirui Gong Rui Wang Kai Zheng Weiyuan Zhang Xuede Dong Yuxia Hu Guisen Yang Chenyi Yan Shangpeng Zhang Ruijing Wang Tong Wang State Key Laboratory of Earth Surface Processes and Resource Ecology MOE Engineering Research Center of Desertification and Blown-sand Control Faculty of Geographical Science Beijing Normal University No. 19 Xinjiekouwai Street Haidian District Beijing 100875 China CGN New Energy Holdings Co. Ltd. Hanwei International Plaza Fengtai District Beijing 100071 China Inner Mongolia Ganze Desert Technology Co. Ltd. High-tech industrial park cloud computing big data enterprise headquarters Kangbashi District Ordos 017004 China

Photovoltaic systems greatly reduce greenhouse gas emissions. However, the microenvironmental changes of photovoltaic modules affect soil organic carbon (SOC) and improve carbon sequestration in terrestrial ecosystems to mitigate global climate change is unclear. We analyzed the effects of different photovoltaic systems on SOC and its fractions in semi-arid grassland, and revealed the SOC formation and stability mechanisms. Tracking photovoltaic systems increased carbon stock, especially proportion of mineral-associated organic carbon (MAOC) in SOC by more than 10 %, and depended on the responses of plant inputs and microbial necromass to microenvironmental changes. Suitable amounts of light and water in the systems improved litter biomass and quality, and promoted MAOC accumulation by increasing the exogenous carbon supply and microbial activity. Such systems also reduced soil carbon loss by reducing heterotrophic respiration caused by low levels of organic acids and recruiting effective microorganisms such as Schizothecium and Lactobacillus . Moreover, changes in litter and root exudates promoted microbial biomass and enzyme activity, mediating the retention of microbial necromass and SOC. The contribution of bacterial necromass carbon to SOC increased by more than 8.5 %. However, fixed photovoltaic systems reduced soil carbon stock by 0.46 kg m −2 due to water limitations that decreased both plant carbon inputs and microbial necromass. Our results revealed the importance of plant inputs and microbial necromass in regulating SOC in photovoltaic systems, and demonstrated that photovoltaic systems can achieve synergies between CO 2 emission reduction and soil carbon sequestration. This provides new insights for formulating carbon management policy and promoting sustainable eco-economic development.

关键词： Litter quantity and quality Microbial necromass Photovoltaic system Root exudates Soil microbes Soil organic carbon

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Self-Accelerating Effect in a Covalent–Organic Framework with Imidazole Groups Boosts Electroreduction of CO2to CO

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Angewandte Chemie 2023年第42期135卷

作者： Meng-Di Zhang Jia-Run Huang Dr. Wen Shi Prof. Pei-Qin Liao Prof. Xiao-Ming Chen MOE Key Laboratory of Bioinorganic and Synthetic Chemistry Guangdong Basic Research Center of Excellence for Functional Molecular Engineering School of Chemistry IGCME Sun Yat-Sen University Guangzhou 510275 China Contribution: Data curation (supporting) Writing - original draft (supporting) Contribution: Software (supporting) Contribution: Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Funding acquisition (supporting) Writing - original draft (supporting) Writing - review & editing (supporting)

Solvent effect plays an important role in catalytic reaction, but there is little research and attention on it in electrochemical CO 2 reduction reaction (eCO 2 RR). Herein, we report a stable covalent-organic framework (denoted as PcNi-im ) with imidazole groups as a new electrocatalyst for eCO 2 RR to CO. Interestingly, compared with neutral conditions, PcNi-im not only showed high Faraday efficiency of CO product (≈100 %) under acidic conditions (pH ≈ 1), but also the partial current density was increased from 258 to 320 mA cm −2 . No obvious degradation was observed over 10 hours of continuous operation at the current density of 250 mA cm −2 . The mechanism study shows that the imidazole group on the framework can be protonated to form an imidazole cation in acidic media, hence reducing the surface work function and charge density of the active metal center. As a result, CO poisoning effect is weakened and the key intermediate *COOH is also stabilized, thus accelerating the catalytic reaction rate.

关键词： Acidic Electrolyte Electrochemical CO2 Reduction Reaction Covalent-Organic Framework Electrocatalyst CO Poisoning Effect

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Dense residual network: Enhancing global dense feature flow for character recognition

arXiv

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arXiv 2020年

作者： Zhang, Zhao Tang, Zemin Wang, Yang Zhang, Zheng Zhan, Choujun Zha, Zhengjun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China School of Computer Science and Technology Soochow University Suzhou215006 China Shenzhen China School of Computer South China Normal University Guangzhou510631 China Deparmtment of Computer Science and Technology University of Science and Technology of China Hefei China

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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