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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Magnetoresistance hysteresis in the superconducting state of kagome CsV_(3)Sb_(5)

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Chinese Physics B 2024年第10期33卷 30-34页

作者： Tian Le Jinjin Liu Zhiwei Wang Xiao Lin Institute of Natural Sciences Westlake Institute for Advanced StudyHangzhou 310024China Key Laboratory for Quantum Materials of Zhejiang Province Department of PhysicsSchool of Science and Research Center for Industries of the FutureWestlake UniversityHangzhou 310030China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of TechnologyBeijing 100081China Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of TechnologyBeijing 100081China Material Science Center Yangtze Delta Region Academy of Beijing Institute of TechnologyJiaxing 314011China

The hysteresis of magnetoresistance observed in superconductors is of great interest due to its potential connectionwith unconventional *** this study,we perform electrical transport measurements on kagome superconductorCsV_(3)Sb_(5)nanoflakes and uncover unusual hysteretic behavior of magnetoresistance in the superconducting *** hysteresis can be induced by applying either a large DC or AC current at temperatures(T)well below the superconductingtransition temperature(T_(c)).As T approaches T_(c),similar weak hysteresis is also detected by applying a *** scenarios are discussed,with particular focus on the effects of vortex pinning and the presence of timereversal-symmtery-breaking superconducting *** findings support the latter,hinting at chiral superconductivityin kagome superconductors.

关键词： hysteresis magnetoresistance kagome superconductor chiral superconductor

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Well-structured 3D channels within GO-based membranes enable ultrafast wastewater treatment

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Nano research 2023年第2期16卷 1826-1834页

作者： Huaqiang Fu Zhe Wang Peng Li Wei Qian Zixin Zhang Xin Zhao Hao Feng Zhugen Yang Zongkui Kou Daping He School of Materials Science and Engineering Wuhan University of TechnologyWuhan 430070China Hubei Engineering Research Center of RF-Microwave Technology and Application School of ScienceWuhan University of TechnologyWuhan 430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China School of Water Energy and EnvironmentCranfield UniversityCranfield MK430ALUK

Graphene oxide(GO)-based membranes have been widely studied for realizing efficient wastewater treatment,due to their easily functionalizeable surfaces and tunable interlayer ***,the irregular structure of water channels within GO-based membrane has largely confined water permeance and prevented the simultaneously improvement of purification ***,we purposely construct the well-structured three-dimensional(3D)water channels featuring regular and negatively-charged properties in the GO/SiO_(2)composite membrane via in situ close-packing assembly of SiO_(2)nanoparticles onto GO *** regular 3D channels can improve the water permeance to a record-high value of 33,431.5±559.9 L·m^(−2)·h−1(LMH)bar−1,which is several-fold higher than those of current state-of-the-art GO-based *** further demonstrate that benefiting from negative charges on both GO and SiO2,these negatively-charged 3D channels enable the charge selectivity well toward dye in wastewater where the rejection for positive-charged and negative-charged dye molecules is 99.6%vs.7.2%,*** 3D channels can also accelerate oil/water(O/W)separation process,in which the O/W permeance and oil rejection can reach 19,589.2±1,189.7 LMH bar−1 and 98.2%,*** present work unveils the positive role of well-structured 3D channels on synchronizing the remarkable improvement of both water permeance and purification performance for highly efficient wastewater treatment.

关键词： graphene oxide membranes wastewater treatment water channels charge-selectivity

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Aggregation-enhanced direct S_(0)-T_(n) transitions and room-temperature phosphorescence in gold(I)-complex single crystals

Aggregate

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Aggregate 2022年第1期3卷 159-167页

作者： Akihiro Ando Kazuhisa Ozaki Urara Shiina Eikichi Nagao Kyohei Hisano Kenji Kamada Osamu Tsutsumi Department of Applied Chemistry Ritsumeikan UniversityKusatsuJapan Nanomaterials Research Institute(NMRI) National Institute of Advanced Industrial Science and Technology(AIST)IkedaJapan

The design of efficient aggregation-induced emission materials requires an improved understanding of photophysical processes in aggregated ***,the photophysical behavior of an Au(I)complex(R6)that exhibits intense roomtemperature phosphorescence(RTP)in crystals is *** addition,the photophysical processes related to RTP are discussed based on the structure of the molecular aggregates and the primary structure of the *** extremely efficient S_(0)-T_(n) direct transition is found to occur in the R6 ***,intermolecular Au-Au interactions and the internal/external heavy-atom effects of Au atoms are demonstrated to enhance the electronic transitions involving intersystem crossing,namely,direct S_(0)-T_(n) excitation,radiative T_(1)-S_(0) transition(phosphorescence),and S_(1)-T_(n) intersystem *** of the dense molecular packing,both Au-Au interactions and heavy-atom effects play important roles in the *** a result,R6 shows more efficient RTP in crystals than in *** insights into the mechanism of highly efficient RTP in Au(I)-complex crystals are expected to advance the development of new luminogens for a variety of sensing and imaging applications.

关键词： aggregation-induced emission aurophilic interaction gold(I)complex phosphorescence spin-forbidden transition

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TT@CIM: A Tensor-Train In-Memory-Computing Processor Using Bit-Level-Sparsity Optimization and Variable Precision Quantization

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IEEE Journal of Solid-State Circuits 2023年第3期58卷 852-866页

作者： Guo, Ruiqi Yue, Zhiheng Si, Xin Li, Hao Hu, Te Tang, Limei Wang, Yabing Sun, Hao Liu, Leibo Chang, Meng-Fan Li, Qiang Wei, Shaojun Yin, Shouyi Tsinghua University Beijing National Research Center for Information Science and Technology School of Integrated Circuits and the Beijing Advanced Innovation Center Beijing100084 China Southeast University National ASIC System Engineering Research Center School of Electronic Science and Engineering Nanjing210096 China Department of Electrical Engineering Hsinchu300 Taiwan Chengdu610054 China

Computing-in-memory (CIM) is an attractive approach for energy-efficient deep neural network (DNN) processing, especially for low-power edge devices. However, today's typical DNNs usually exceed CIM-static random access memory (SRAM) capacity. The introduced off-chip communication covers up the benefits of CIM technique, meaning that CIM processors still encounter the memory bottleneck. To eliminate this bottleneck, we propose a CIM processor, called TT@CIM, which applies the tensor-Train decomposition (TTD) method to compress the entire DNN to fit within CIM-SRAM. However, the cost of storage reduction by TTD is to introduce multiple serial small-size matrix multiplications, resulting in massive inefficient multiply-And-Accumulate (MAC) and quantization operations (QuantOps). To achieve high energy efficiency, three optimization techniques are proposed in TT@CIM. First, TTD-CIM-matched dataflow is proposed to maximize CIM utilization and minimize additional MAC operations. Second, a bit-level-sparsity-optimized CIM macro with high bit-level-sparsity encoding scheme is designed to reduce the power consumption of one MAC operation. Third, a variable precision quantization method and a lookup table-based quantization unit are presented to improve the performance and energy efficiency of QuantOp. Fabricated in 28-nm CMOS and tested on 4/8-bit decomposed DNNs, TT@CIM achieves 5.99-To-691.13-TOPS/W peak energy efficiency depending on the operating voltage. © 1966-2012 IEEE.

关键词： Deep neural networks

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A Collaborative Training Using Crowdsourcing and Neural Networks on Small and Difficult Image Classification Datasets

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SN Computer science 2022年第2期3卷 178页

作者： Takase, Tomoumi Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan

Although machine learning has recently achieved performance that exceeds human capacity in prediction, humans still have an advantage in difficult tasks when the number of training samples is small or when human knowledge is required to identify features that are included in samples. However, if specialized knowledge is required, the number of humans that can perform those tasks is limited. In this study, we effectively use crowdsourcing to incorporate domain knowledge in neural network trainings;specifically, we decide feature values by asking crowdsourcing workers to answer easy questions prepared based on dictionary. We evaluated this method on a single type of task that is intuitive and relevant for non-specialists, which is binary classification of dog image datasets with similar breeds, and found that using crowdsourcing tended to improve the performance of machine-learning models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： Crowdsourcing Feature extraction Image classification Machine learning Neural network

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Microstructure and Mechanical Properties of Ti–Ta Composites Prepared Through Cold Spray Additive Manufacturing

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Acta Metallurgica Sinica(English Letters) 2022年第9期35卷 1465-1476页

作者： Junrong Tang Naeem ul Haq Tariq Zhipo Zhao Mingxiao Guo Hanhui Liu Yupeng Ren Xinyu Cui Yanfang Shen Jiqiang Wang Tianying Xiong Shi-changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang110016China Department of Metallurgy and Materials Engineering Pakistan Institute of Engineering and Applied SciencesNiloreIslamabadPakistan

In this study, an innovative approach was used to fabricate Ti–Ta composite biomaterials through cold spray additive manufacturing followed by a diff usion treatment. The microstructure and mechanical properties of the composites were investigated in detail using field emission scanning electron microscopy, electron backscatter diff raction, 3D X-ray computed tomography, tensile test, nanohardness test and resonance vibration test. The obtained results indicated that the prepared composites have inhomogeneity in their microstructure and composition. A unique microstructure, composed of Ti-rich, Ta-rich and diff usion regions, was evolved in the composites due to incomplete diff usion between Ti and Ta splats. Further, Kirkendall pores were formed in the composites due to uneven diff usion of the two phases(of Ti and Ta) during high-temperature heat treatment. The prepared composites simultaneously showed low elastic modulus and high tensile strength which is required for a good biomaterial. Low elastic modulus was associated with the residual pores and the alloying eff ect of Ta in Ti, while high tensile strength was related to the solid solution strengthening eff ects. The obtained results indicated that the prepared Ti–Ta composites have a great potential to become a new candidate for biomedical applications.

关键词： Ti–Ta composites Cold spray additive manufacturing(CSAM) Biomaterials Mechanical properties X-ray computed tomography(XCT)

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Atomistic Investigation of the Influence of Hydrogen on Mechanical Response during Nanoindentation in Pure Iron

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Acta Metallurgica Sinica(English Letters) 2023年第7期36卷 1179-1192页

作者： Wenjing Lou Lin Cheng Runsheng Wang Chengyang Hu Kaiming Wu The State Key Laboratory of Refractories and Metallurgy Hubei Province Key Laboratory of Systems Science on Metallurgical ProcessingInternational Research Institute for Steel TechnologyCollaborative Center on Advanced SteelsWuhan University of Science and TechnologyWuhan430081China Metals Valley and Band(Foshan)Metallic Composite Materials Co. Ltd.Foshan528000China

The effects of hydrogen on the mechanical response of pure iron with randomly distributed hydrogen atoms under nanoindentation were systematically investigated by molecular dynamics simulations with the aim to further understand hydrogen embrittlement mechanism in the *** simulations results revealed that,for the three models with[001],[110]and[111]surface normal,hydrogen reduced the critical load of the pop-in event,promoted the dislocation slipping and reduced the plastic region size and dislocation density around the indenter compared to the hydrogen free ***,the different mechanical responses of the three models with different surface normal were further explained in the perspective of Schmid factor.

关键词： Hydrogen embrittlement Dislocations Nanoindentation Molecular dynamics

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Ceramic composites toughened by vat photopolymerization 3D printing technology

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Journal of Materials science & technology 2023年第15期146卷 42-48页

作者： Qiaolei Li Zhenxue Pan Jingjing Liang Zongbo Zhang Jinguo Li Yizhou Zhou Xiaofeng Sun School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Shi-changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China Research/Education Centre for Excellence in Molecular Sciences Institute of ChemistryChinese Academy of SciencesBeijing 100190China Space Manufacturing Technology(CAS Key Lab) Beijing 100094China

High strength and high toughness are mutually exclusive in structural *** ceramic materials,increasing toughness usually depends on the introduction of a ductile phase that reduces the strength and high-temperature stability of the *** this work,vat photopolymerization 3D printing technology was used to achieve toughening of ceramic composite *** friction sliding of the 3D-printed ceramic macrolayer structure results in effective energy dissipation and redistribution of strain in the whole structure,and macroscale toughening of the ceramic material is *** addition,the bridging and elongation of the crack in situ amorphous ceramic whiskers were significant microscopic toughening results,coupled with the toughening of the crack tip of nano-ZrO_(2).Multiscale collaborative toughening methods based on 3D-printed ceramics should find wide applications for materials in service at extreme high temperatures.

关键词： Ceramic composites 3D printing Flexure strength Fracture toughness Polysilazane

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