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A hybrid memory architecture supporting fine-grained data migration

Frontiers of computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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A Dual Discriminator Method for Generalized Zero-Shot Learning

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computers, Materials & Continua 2024年第4期79卷 1599-1612页

作者： Tianshu Wei Jinjie Huang School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150006China School of Automation Harbin University of Science and TechnologyHarbin150006China

Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen *** the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.

关键词： Generalized zero-shot learning modality consistent discriminator domain shift problem feature fusion

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Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior

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Neural Regeneration Research 2025年第1期20卷 159-173页

作者： Man Han Deyang Zeng Wei Tan Xingxing Chen Shuyuan Bai Qiong Wu Yushan Chen Zhen Wei Yufei Mei Yan Zeng Brain Science and Advanced Technology Institute Wuhan University of Science and TechnologyWuhanHubei ProvinceChina Geriatric Hospital Affiliated to Wuhan University of Science and Technology WuhanHubei ProvinceChina School of Public Health Wuhan University of Science and TechnologyWuhanHubei ProvinceChina

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral *** studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region–specific,particularly involving the corticolimbic system,including the ventral tegmental area,nucleus accumbens,prefrontal cortex,amygdala,and *** how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress *** this review,we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of *** focused on associated molecular pathways and neural circuits,with special attention to the brain-derived neurotrophic factor–tropomyosin receptor kinase B signaling pathway and the ventral tegmental area–nucleus accumbens dopamine *** determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature,severity,and duration of stress,especially in the above-mentioned brain regions of the corticolimbic ***,BDNF might be a biological indicator regulating stress-related processes in various brain regions.

关键词： amygdala chronic mild stress chronic social defeat stress corticolimbic system depression hippocampus medial prefrontal cortex nucleus accumbens social stress models ventral tegmental area

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APF-GAN:Exploring asymmetric pre-training and fine-tuning strategy for conditional generative adversarial network

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Computational Visual Media 2024年第1期10卷 187-192页

作者： Yuxuan Li Lingfeng Yang Xiang Li College of Computer Science Nankai UniversityTianjinChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited diversity of image style,distortion of detailed texture,unbalanced color tone,and lengthy training *** address these issues,we propose an asymmetric pre-training and fine-tuning(APF)-GAN model.

关键词： tuning network asymmetric

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Augmented FCN: rethinking context modeling for semantic segmentation

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science China(Information sciences) 2023年第4期66卷 193-211页

作者： Dong ZHANG Liyan ZHANG Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggregating content-and position-based object contexts for semantic ***, motivated because each deep feature map is a global, class-wise representation of the input,we first propose an augmented nonlocal interaction(AugNI) to aggregate the global content-based contexts through all feature map interactions. Compared to classical position-wise approaches, AugNI is more efficient. Moreover, to eliminate permutation equivariance and maintain translation equivariance, a learnable,relative position embedding branch is then supportably installed in AugNI to capture the global positionbased contexts. AugFCN is built on a fully convolutional network as the backbone by deploying AugNI before the segmentation head network. Experimental results on two challenging benchmarks verify that AugFCN can achieve a competitive 45.38% mIoU(standard mean intersection over union) and 81.9% mIoU on the ADE20K val set and Cityscapes test set, respectively, with little computational overhead. Additionally, the results of the joint implementation of AugNI and existing context modeling schemes show that AugFCN leads to continuous segmentation improvements in state-of-the-art context modeling. We finally achieve a top performance of 45.43% mIoU on the ADE20K val set and 83.0% mIoU on the Cityscapes test set.

关键词： semantic segmentation context modeling long-range dependencies attention mechanism

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Federated Meta Reinforcement Learning for Personalized Tasks

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Tsinghua science and technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and *** this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by *** FRL implementations presuppose that clients have compatible tasks which a single global model can *** practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task *** may severely hinder the implementation of FRL for practical *** this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and ***,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of ***,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server *** experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

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Influence of Organic Cations on the Crystal and Electronic Structures of Two-dimensional Lead Iodide Perovskites

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Journal of wuhan university of technology(Materials science) 2023年第3期38卷 496-504页

作者：杜定谨甘小燕 LU Shun ZHENG Wei GUO Liling LIU Hanxing School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China International School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China

The crystal structures and electronic structures(including band gap,project density of states,partial charge density,effective mass and electron localization function)of the 2D lead iodide perovskites hybrids with different organic spacer cations of 4-fluorophenylethanaminium(4F-PEA^(+)),ethanolamine(EA^(+)),thienylethylamine(TEA^(+))were investigated using first-principles *** was found the higher dipole moment,the stronger the hydrogen bonding between the organic amino and iodide in the inorganic layer,and the larger the[PbI_(6)]^(4-)octahedral distortions in these crystal *** quantifying the degree of the distortions using OctaDist software showed that the distortion of adjacent[PbI_(6)]^(4-)octahedra had a decisive effect on the band ***,the greater deviation of Pb-I-Pb bond angles from 180°,together with the larger distortion of multiple[PbI_(6)]^(4-)octahedron resulted in a wider band gap,which was verified by calculated band gap using different DFT *** results outlined the relationships of hydrogen bonding,ocathedra distortion and band structure in 2D perovskites,highlighting the importance of the cations on the structural tuning and optoelectronic properties.

关键词： 2D perovskites density functional theory(DFT) octahedral distortion 4F-PEA EA TEA

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First Principle Study of Cesium-based Lead-free Halide Double Perovskites

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Journal of wuhan university of technology(Materials science) 2023年第3期38卷 520-529页

作者：郑伟甘小燕 DU Dingjin WANG Yajie DAI Siqi GUO Liling LIU Hanxing School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China International School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China

Inorganic halide double perovskites A_(2)B'B"X_(6) have gained significant interests for their diverse composition,stable physicochemical properties,and potential for photoelectric *** influences of trivalent and monovalent cations on the formation energy,decomposition energy,electronic structure and optical properties of cesium-based lead-free Cs^(+)_(2)B'B"Br_(6) (B'=Na^(+),In^(+)Cu^(+),or Ag^(+);B"=Bi^(3),Sb^(3+),In^(3+)) are systematically *** view of the analysis and results of the selected double perovskites,for the double perovskites with different B-site trivalent cation,the band gap increases in the order of Cs_(2)AgInBr_(6),Cs_(2)AgSbBr_(6) and Cs_(2)AgBiBr_(6),with Cs_(2)AgBiBr_(6) possessing the highest thermodynamic ***,the Bi-based perovskites are further studied to elucidate the effect of monovalent cation on their stability and *** show that the thermodynamic stability rises in the sequence of Cs_(2)NaBiBr_(6),Cs_(2)InBiBr_(6),Cs_(2)AgBiBr_(6) and Cs_(2)CuBiBr_(6).Notably,Cs_(2)CuBiBr_(6) exhibits a relatively narrow and appropriate band gap of 1.4634 eV,together with the highest absorption coefficient than other compounds,suggesting that Cs_(2)CuBiBr_(6) is a promising light absorbing material that can be further explored experimentally and be applied to optoelectronic *** research offers theoretical backing for the potential optoelectronic application of cesium-based lead-free halide double perovskites in solar energy conversion.

关键词： lead-free double perovskites density functional theory Cs_(2)B'B"Br_(6) stability electronic properties optical property stability

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A data-driven rolling optimization method for trajectory tracking error prediction of CNC machine tools

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science China(Technological sciences) 2025年第1期68卷 270-285页

作者： Yinxin GUAN Jixiang YANG Shizhong TAN Han DING School of Mechanical Science and Engineering State Key Laboratory of Intelligent Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

The dynamic performance of the feed-drive system in CNC machine tools directly influences the accuracy of machined *** enhance the motion control performance of CNC machine tools,a high-precision model of the feed-drive system is ***,current modeling methods for feed-drive systems seldom consider time-varying factors such as loads,wear,and *** a result,the model accuracy degrades when the system characteristics are affected by these time-varying *** this paper,a rolling optimization method with partial weights frozen is developed to realize quick iterative learning of a data-driven model for a feed drive system with time-varying characteristics using a small amount of ***,the long short-term memory fully connected(LSTM-FC)network is built and divided into feature extraction and output fitting parts based on their ***,a weight freezing-based rolling optimization method is *** weights in the feature extraction part are frozen,which preserves the learned common knowledge and patterns by solidifying the way that high-dimensional features are extracted from the *** adjusting the weights in the output fitting part,the extracted highdimensional features are remapped to the new data distribution changed by time-varying ***,the performance of the developed rolling optimization method is confirmed by *** results show that the proposed rolling optimization method reduces the maximum prediction errors by 49.5%and the total training time by 96.3%compared with existing methods,which demonstrates that the proposed method can restore model accuracy when the system characteristics change due to timevarying factors,and significantly accelerate the optimization process by rolling optimization.

关键词： rolling optimization feed-drive system data-driven time-varying machine tool

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High-Superelasticity NiTi Shape Memory Alloy by Directed Energy Deposition-Arc and Solution Heat Treatment

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Acta Metallurgica Sinica(English Letters) 2024年第1期37卷 132-144页

作者： Junyi Ma Lin Yu Qing Yang Jie Liu Lei Yang State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan430070China School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430074China

Directed energy deposition-arc(DED-Arc)technology has the advantages of simple equipment,low manufacturing cost and high deposition rate,while the use of DED-Arc has problems of microstructure inhomogeneity,position dependence of macroscopic mechanical properties and ***,it is necessary to carry out a subsequent heat treatment to improve its microstructure uniformity,mechanical properties and *** this investigation,the DED-Arc 15-layer NiTi alloy thin-walled parts with the solution treatment at different process parameters were studied to analyze the effects of solution heat treatment on microstructure,phase composition,phase transformation,microhardness,tensile and *** temperature range of solution treatment is 800-1050℃,and the treatment time range is 1-5.5 *** results show that after solution treatment at 800℃/1 h,the content of precipitated phase decreases,the grain is refined,the microhardness increases,and the mechanical properties in the 0°direction are *** strain recovery rate after 10 tensile cycles has increased from 37.13%(as-built)to 49.25%(solid solution treatment).This research provides an effective post treatment method for high-performance DED-Arc NiTi shape memory alloys.

关键词： Directed energy deposition-arc(DED-Arc) Cold metal transfer(CMT) NiTi shape memory alloys Microstructure Phase transformation Solution heat treatment

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