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Development of the high-strength ductile ferritic alloys via regulating the intragranular and grain boundary precipitation of G-phase

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Journal of Materials science & technology 2023年第5期136卷 180-199页

作者： Mujin Yang Chao Huang Jiajia Han Haichen Wu Yilu Zhao Tao Yang Shenbao Jin Chenglei Wang Zhou Li Ruiying Shu Cuiping Wang Huanming Lu Gang Sha Xingjun Liu School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China College of Materials and Fujian Provincial Key Laboratory of Materials Genome Xiamen UniversityXiamen 361005China Test Center Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Department of Materials Science and Engineering City University of Hong KongHong KongChina Department of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 150001China Institute of Materials Genome and Big Data Harbin Institute of TechnologyShenzhen 518055China

A typical G-phase strengthened ferritic model alloy(1Ti:Fe-20Cr-3Ni-1Ti-3Si,wt.%)has been carefully studied using both advanced experimental(EBSD,TEM and APT)and theoretical(DFT)*** the classic“solid solution and aging”process,the superfine(Fe,Ni)_(2)TiSi-L2_(1)particles densely precipitate within the ferritic grain and subsequently transform into the(Ni,Fe)_(16)Ti_(6)Si_(7)-G *** the meanwhile,the elemental segregation at grain boundaries and the resulting precipitation of a large amount of the(Ni,Fe)_(16)Ti_(6)Si_(7)-G phase are also *** nanoscale microstructural evolutions result in a remarkable increase in hardness(100-300 HV)and severe *** the“cold rolling and aging”process is used,the brittle fracture is effectively suppressed without loss of nano-precipitation strengthening *** yield strength of 1700 MPa with 4%elongation at break is *** key improvement in mechanical properties is mainly attributed to the pre-cold rolling process which effectively avoids the dense precipitation of the G-phase at the grain *** findings could shed light on the further exploration of the precipitation site via optimal processing strategies.

关键词： G-phase Precipitation strengthening Grain boundary segregation Nano-precipitates

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CHSTS: Chameleon Hash based on Schnorr threshold signature on Editable Blockchain

CHSTS: Chameleon Hash based on Schnorr threshold signature o...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and big data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Yuan, Linlin Yang, Yuxiang Tan, Chaoyue Dou, Hui Luo, Yun Chen, Yuling College of Information Engineering Guizhou Open University Guiyang China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350304602

Blockchain's immutability, while a core feature, can pose challenges in cases involving sensitive information or compliance with legal regulations, hindering its development. Many subsequent works designed editable blockchains based on the chameleon hash function proposed by H. Krawczyk et al. to solve the above problems. However, existing editable blockchain solutions lack security and are inefficient. To address the security and efficiency issues, we introduce the novel concept of Chameleon hashing based on Schnorr threshold signatures (CHSTS). The CHSTS scheme has double trapdoors, the publisher of the message owns the long-term trapdoor, and the editorial committee owns the ephemeral trapdoor. Message publishers can collaborate with the editorial committee to modify their published information, preventing the exposure of private data, facilitating oversight during the editing process, and enabling the tracking of malicious users. We have designed an updatable ephemeral trapdoor based on Schnorr signatures, effectively avoiding the inefficiencies associated with bilinear pairing. Experimental results show that our CHSTS scheme has a significant advantage in computational cost. © 2023 IEEE.

关键词： Blockchain

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MUN: Image Forgery Localization Based on M3 Encoder and UN Decoder 39

MUN: Image Forgery Localization Based on M3 Encoder and UN D...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yaqi Chen, Shuhuan Shi, Haichao Zhang, Xiao-Yu Xiao, Song Cai, Qiang Beijing Electronic Science and Technology Institute China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University China

ISBN: (纸本)157735897X

Image forgeries can entirely change the semantic information of an image, and can be used for unscrupulous purposes. In this paper, we propose a novel image forgery localization network named as MUN, which consists of an M3 encoder and a UN decoder. Firstly, the M3 encoder is constructed based on a Multi-scale Max-pooling query module to extract Multi-clue forged features. Noiseprint++ is adopted to assist the RGB clue, and its deployment methodology is discussed. A Multi-scale Max-pooling Query (MMQ) module is proposed to integrate RGB and noise features. Secondly, a novel UN decoder is proposed to extract hierarchical features from both top-down and bottom-up directions, reconstructing both high-level and low-level features at the same time. Thirdly, we formulate an IoU-recalibrated Dynamic Cross-Entropy (IoUDCE) loss to dynamically adjust the weights on forged regions according to IoU which can adaptively balance the influence of authentic and forged regions. Last but not least, we propose a data augmentation method, i.e., Deviation Noise Augmentation (DNA), which acquires accessible prior knowledge of RGB distribution to improve the generalization ability. Extensive experiments on publicly available datasets show that MUN outperforms the state-of-the-art works. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

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Bio-inspired visual neural network for stop-and-go wave perception in high-density crowd 4

Bio-inspired visual neural network for stop-and-go wave perc...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Jiang, Wei Hu, Bin State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Computer Science College of Computer Science & Technology Guizhou University Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In high-density crowd, a unique visual motion effect called stop-and-go wave occurs, which could evolve to trampling and compression incidents. However, few computational models have been reported for stop-and-go wave perception form the angle of computer vision. On the bases of the neural structures of the locusts’ vision systems and the frequency resonance properties of the interneuronal (INs) neurons in locust’s brains, this paper investigates a bio-inspired visual neural network (sgWPNN) for stop-and-go wave perception in high-density crowd scenes. The proposed sgWPNN firstly visually stabilizes the image frames;and then converts the spatial intensity visual information to the frequency domain for extracting crowd’s motion cues;finally, sgWPNN output membrane potential impulses to identify the occurring of stop-and-go wave in the field of view. Videos filmed high-density crowd activities in two scenarios demonstrate the effectiveness of sgWPNN in the perception against to the unique stop-and-go wave effect in moving crowd scenes. This work discusses the biological inspired processing of dynamic visual information in crowd activity perception, which can provide some new ideas for crowd activity detection and behavior analysis. © 2025 SPIE.

关键词： Frequency domain analysis

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Characterization and modeling studies towards Al_(3)Ti/Mg interfaces in Ti reinforced AZ31 alloys

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Journal of Materials science & technology 2023年第16期147卷 197-206页

作者： Longke Bao Shengli Han Shengkun Xi Yuhui Zhang Cuiping Wang Kaihong Zheng Rongpei Shi Xingjun Liu Fusheng Pan State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650China College of Materials Science and Engineering Xiamen UniversityXiamen 361005China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China

The interfaces between in-situ Al3Ti particles and magnesium(Mg)matrix are crucial role in highperformance titanium(Ti)reinforced AZ31 ***,the interfaces between Al3Ti particles and the Mg matrix are fabricated and investigated using advanced characterization tools and first-principles *** orientation relationship(OR)and atomic interface structure between the Al_(3)Ti particles and matrix are characterized using a high-resolution transmission electron *** OR is determined to be(1010)Mg//(001)Al3Ti;[1213]Mg//[100]*** on the characterized OR,the interface properties(including atomic structure,work of adhesion,interface energy,and fracture mechanism)are investigated using first-principles *** relaxed interface structure indicates that the TiAl-terminated bridge site configurations(MT1)and hollow site configurations(HCP1)are unstable and would convert into other bridge site configurations(MT).Furthermore,the work of adhesion and interface energy suggests that Al-terminated hollow site configurations(HCP)and bridge site configurations(MT)are more stable than other *** addition,the calculations of work of fracture show that fracture of the interfaces with Al-MT1,Al-HCP,and TiAl-MT configurations may initiate from bulk Mg *** findings may help to understand and tailor the deformation mechanisms and mechanical properties of Ti-reinforced Mg alloys.

关键词： Ti/AZ31 alloy Mg/Al_(3)Ti interface HAADF-STEM First-principles computation

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K-ON: Stacking Knowledge On the Head Layer of Large Language Model 39

K-ON: Stacking Knowledge On the Head Layer of Large Language...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Lingbing Zhang, Yichi Bo, Zhongpu Chen, Zhuo Sun, Mengshu Zhang, Zhiqiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

ISBN: (纸本)157735897X

Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an entity requires at least several tokens. This leads to a granularity mismatch between KGs and natural languages. To address this issue, we propose K-ON, which integrates KG knowledge into the LLM by employing multiple head layers for next k-step prediction. K-ON can not only generate entity-level results in one step, but also enables contrastive loss against entities, which is the most powerful tool in KG representation learning. Experimental results show that K-ON outperforms state-of-the-art methods that incorporate text and even the other modalities. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Large Language Model-based Human-Agent Collaboration for Complex Task Solving

Large Language Model-based Human-Agent Collaboration for Com...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Feng, Xueyang Chen, Zhi-Yuan Qin, Yujia Lin, Yankai Chen, Xu Liu, Zhiyuan Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761681

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. To tackle the problem, we propose a Reinforcement Learning-based HumanAgent Collaboration method, ReHAC, which trains a policy model designed to determine the most opportune stages for human intervention within the task-solving process. We conduct experiments under real and simulated human-agent collaboration scenarios. Experimental results demonstrate that the synergistic efforts of humans and LLM-based agents significantly improve performance in complex tasks, primarily through well-planned, limited human intervention. datasets and code are available at: https://***/XueyangFeng/ReHAC/. © 2024 Association for Computational Linguistics.

关键词： Autonomous agents

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A First Look at LLM-powered Smartphones

A First Look at LLM-powered Smartphones

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IEEE/ACM International Conference on Automated Software Engineering - Workshops (ASE Workshops)

作者： Liangxuan Wu Yanjie Zhao Chao Wang Tianming Liu Haoyu Wang Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology

ISBN: (数字)9798400712494

ISBN: (纸本)9798331523169

The integration of Large Language Models (LLMs) into edge devices such as smartphones represents a significant leap in mobile technology, promising enhanced user experiences and novel functionalities. This paper presents a first look at LLM-powered smartphones, addressing four key aspects: the current market landscape, core functions enabled by integrated LLMs, potential security risks, and user perceptions. The findings reveal a rapidly evolving market with major manufacturers competing to integrate LLMs, innovative features that improve user interaction, significant security challenges, and mixed user perceptions that balance enthusiasm for new capabilities with privacy concerns. This study contributes to understanding LLM integration in mobile devices and its implications for users, manufacturers, and the broader technological landscape.

关键词： Industries Privacy Technological innovation Ethics Large language models Conferences User experience Security Smart phones Software engineering

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THE BLESSING OF RANDOMNESS: SDE BEATS ODE IN GENERAL DIFFUSION-BASED IMAGE EDITING 12

THE BLESSING OF RANDOMNESS: SDE BEATS ODE IN GENERAL DIFFUSI...

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12th International Conference on Learning Representations, ICLR 2024

作者： Nie, Shen Guo, Hanzhong Allan Lu, Cheng Zhou, Yuhao Zheng, Chenyu Li, Chongxuan Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

We present a unified probabilistic formulation for diffusion-based image editing, where a latent variable is edited in a task-specific manner and generally deviates from the corresponding marginal distribution induced by the original stochastic or ordinary differential equation (SDE or ODE). Instead, it defines a corresponding SDE or ODE for editing. In the formulation, we prove that the Kullback-Leibler divergence between the marginal distributions of the two SDEs gradually decreases while that for the ODEs remains as the time approaches zero, which shows the promise of SDE in image editing. Inspired by it, we provide the SDE counterparts for widely used ODE baselines in various tasks including inpainting and image-to-image translation, where SDE shows a consistent and substantial improvement. Moreover, we propose SDE-Drag - a simple yet effective method built upon the SDE formulation for point-based content dragging. We build a challenging benchmark (termed DragBench) with open-set natural, art, and AI-generated images for evaluation. A user study on DragBench indicates that SDE-Drag significantly outperforms our ODE baseline, existing diffusion-based methods, and the renowned DragGAN. Our results demonstrate the superiority and versatility of SDE in image editing and push the boundary of diffusion-based editing methods. See the project page https://***/SDE-Drag-demo/for the code and DragBench dataset. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation 39

Tokenization, Fusion, and Augmentation: Towards Fine-grained...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yichi Chen, Zhuo Guo, Lingbing Xu, Yajing Hu, Binbin Liu, Ziqi Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

ISBN: (纸本)157735897X

Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models, resulting in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interactions. To tackle this shortfall, we introduce a novel framework MYGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities and enhance the MMKGC performance. Motivated by the tokenization technology, MYGO tokenizes multi-modal entity information as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MYGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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