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Temperature-controlled multisensory neuromorphic devices for artificial visual dynamic capture enhancement

Nano Research 2023年第5期16卷 7661-7670页

作者： Gengxu Chen Xipeng Yu Changsong Gao Yan Dai Yanxue Hao Rengjian Yu Huipeng Chen Tailiang Guo National&Local United Engineering Laboratory of Flat Panel Display Technology Institute of Optoelectronic DisplayCollege of Physics and Information EngineeringFuzhou UniversityFuzhou 350108China Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350100China

Multi-sensory neuromorphic devices(MND)have broad potential in overcoming the structural bottleneck of von Neumann in the era of big ***,the current multisensory artificial neuromorphic system is mainly based on unitary nonvolatile memory or volatile synaptic devices without intrinsic thermal sensitivity,which limits the range of biological multisensory perception and the flexibility and computational efficiency of the neural morphological computing ***,a temperature-dependent memory/synaptic hybrid artificial neuromorphic device based on floating gate phototransistors(FGT)is *** CsPbBr_(3)/TiO_(2)core–shell nanocrystals(NCs)prepared by in-situ pre-protection low-temperature solvothermal method were used as the photosensitive *** device exhibits remarkable multi-level visual memory with a large memory window of 59.6 V at room ***,when the temperature varies from 20 to 120℃back and forth,the device can switch between nonvolatile memory and volatile synaptic device with reconfigurable and reversible behaviors,which contributes to the efficient visual/thermal fusion *** work expands the sensory range of multisensory devices and promotes the development of memory and neuromorphic devices based on organic field-effect transistors(OFET).

关键词： floating gate phototransistors perovskite nanocrystals temperature multisensory neuromorphic devices

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Towards Privacy-Aware Causal Structure Learning in Federated Setting

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IEEE Transactions on Big Data 2023年第6期9卷 1525-1535页

作者： Huang, Jianli Guo, Xianjie Yu, Kui Cao, Fuyuan Liang, Jiye Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attached much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in federated learning setting. © 2023 IEEE.

关键词： Big data

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Multifunctional light-field modulation based on hybrid nonlinear metasurfaces

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Chinese Physics B 2023年第10期32卷 73-89页

作者：钱树航王凯杨加兴关超龙华陆培祥 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074China School of Electronic and Information Engineering Hubei University of Science and TechnologyXianning 437100China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205China

The generation characteristics of nonlinear optical signals and their multi-dimensional modulation at micro-nano scale have become a prominent research area in nanophotonics,and also the key to developing various novel nonlinear photonics *** recent years,the demand for higher nonlinear conversion efficiency and device integration has led to the rapid progress of hybrid nonlinear metasurfaces composed of nanostructures and nonlinear *** a joint platform of stable wavefront modulation,nonlinear metasurface and efficient frequency conversion,hybrid nonlinear metasurfaces offer a splendid opportunity for developing the next-generation of multipurpose flat-optics *** article provides a comprehensive review of recent advances in hybrid nonlinear metasurfaces for light-field *** advantages of hybrid systems are discussed from the perspectives of multifunctional light-field modulation,valleytronic modulation,and quantum ***,the remaining challenges of hybrid metasurfaces are summarized and future developments are also prospected.

关键词： light-field modulation hybrid metasurfaces nonlinear optics two-dimensional materials

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Novel Batch Polishing Method of Ceramic Cutting Inserts for Reducing Tool Wear

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Chinese Journal of Mechanical engineering 2024年第4期37卷 74-83页

作者： Rui Gao Chunjin Wang Yee Man Loh Xiaoliang Liang Chen Jiang Chi Fai Cheung State Key Laboratory of Ultra-precision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China College of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Ceramic cutting inserts are a type of cutting tool commonly used in high-speed metal cutting ***,the wear of these inserts caused by friction between the workpiece and cutting inserts limits their overall *** order to improve the tool life and reduce wear,this study introduces an emerging method called magnetic field-assisted batch polishing(MABP)for simultaneously polishing multiple ceramic cutting *** polishing experiments were conducted under different conditions,and the wear characteristics were clarified by cutting S136H *** results showed that after 15 min of polishing,the surface roughness at the flank face,edge,and nose of the inserts was reduced to below 2.5 nm,6.25 nm,and 45.8 nm,***,the nose radii of the inserts did not change significantly,and there were no significant changes in the weight percentage of elements before and after ***,the tool life of the batch polished inserts was found to be up to 1.75 times longer than that of unpolished *** findings suggest that the MABP method is an effective way to mass polish ceramic cutting inserts,resulting in significantly reduced tool ***,this novel method offers new possibilities for polishing other tools.

关键词： Polishing Finishing Magnetic field-assisted Tool wear Ultra-precision machining

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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Superior multiphase interfaces in AgCuTe-based composite with significantly enhanced thermoelectric properties

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Journal of Advanced Ceramics 2023年第8期12卷 1511-1520页

作者： Wenpei Li Zhonghai Yu Chengyan Liu Ying Peng Baoquan Feng Jie Gao Guojing Wu Xiaobo Bai Junliang Chen Xiaoyang Wang Lei Miao Guangxi Key Laboratory of Information Materials Electronical Information Materials and Devices Engineering Research Center of Ministry of EducationSchool of Materials Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Precision Navigation Technology and Application School of Information and CommunicationGuilin University of Electronic TechnologyGuilin 541004China

It is common sense that a phase interface(or grain boundary)could be used to scatter phonons in thermoelectric(TE)materials,resulting in low thermal conductivity(k).However,a large number of impurity phases are always so harmful to the transport of carriers that poor TE performance is ***,we demonstrate that numerous superior multiphase(AgCuTe,Ag_(−2)Te,copper telluride(Cu_(2)Te and Cu_(2−x)Te),and nickel telluride(NiTe))interfaces with simultaneous strong phonon scattering and weak electron scattering could be realized in AgCuTe-based TE *** to the similar chemical bonds in these phases,the depletion region at phase interfaces,which acts as carrier scattering centers,could be ***,the power factor(PF)is obviously enhanced from~609 to~832μW·m^(−1)·K^(−2),and k is simultaneously decreased from~0.52 to~0.43 W·m^(−1)·K^(−1) at 636 ***,a peak figure of merit(zT)of~1.23 at 636 K and an average zT(zTavg)of~1.12 in the temperature range of 523–623 K are achieved,which are one of the best values among the AgCuTe-based TE *** study could provide new guidance to enhance the performance by designing superior multiphase interfaces in the TE materials.

关键词： thermoelectric(TE)materials AgCuTe phase interface carrier scattering phonon scattering

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Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model 30

Distill, Fuse, Pre-train: Towards Effective Event Causality ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Huang, Peixin Zhao, Xiang Hu, Minghao Tan, Zhen Xiao, Weidong National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Information Research Center of Military Science Beijing China

ISBN: (纸本)9782493814104

Event Causality Identification (ECI) aims to detect causal relations between events in unstructured texts. This task is challenged by the lack of data and explicit causal clues. Some methods incorporate explicit knowledge from external knowledge graphs (KGs) into Pre-trained Language Models (PLMs) to tackle these issues, achieving certain accomplishments. However, they ignore that existing KGs usually contain trivial knowledge which may prejudice the performance. Moreover, they simply integrate the concept triplets, underutilizing the deep interaction between the text and external graph. In this paper, we propose an effective pipeline DFP, i.e., Distill, Fuse and Pre-train, to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsense graphs. This pipeline works as follows: (1) To leverage the reliable knowledge, commonsense graph distillation is proposed to distill commonsense graphs and obtain the meta-graph which contain credible task-oriented knowledge. (2) To model the deep interaction between the text and external graph, heterogeneous information fusion is proposed to fuse them through a commonsense-aware memory network. (3) Continual pre-training is proposed to further align and fuse the text and the commonsense meta-graph with three continual pre-training tasks. Through extensive experiments on two benchmarks, we demonstrate the validity of our pipeline. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Pipelines

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A High-Performance Data Verification Mechanism in Blockchain-Based IoT systems 21

A High-Performance Data Verification Mechanism in Blockchain...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Zhou, Pian Sun, Yongtao Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification. © 2023 IEEE.

关键词： Efficiency

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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science China(information sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Vitrification cryopreservation of ligaments based on zwitterionic betaine

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Chinese Journal of Chemical engineering 2024年第8期72卷 1-9页

作者： Liming Zhang Xinmeng Liu Haoyue Li Lei Zhang Department of Biochemical Engineering Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(MOE)School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300350China Haihe Laboratory of Sustainable Chemical Transformations Tianjin 300192China

Ligament cryopreservation enables a prolonged shelf life of allogeneic ligament grafts,which is fundamentally important to ligament ***,conventional cryopreservation techniques fail to eliminate the damage caused by ice crystal growth and the toxicity of cryopreservation agents(CPAs).Here,we report a novel CPA vitrification formulation primarily composed of betaine for ligament *** optimization was conducted on the methods for vitrification and rewarming,as well as the loading and unloading conditions,based on the critical cooling rate(CCR),critical warming rate(CWR),and permeation properties of the *** biomechanical and histological level tests,we demonstrate the superior performance of our method in ligament *** 30 days of vitrification cryopreservation,parameters such as the Young's modulus,tensile stress,denaturation temperature,and glycosaminoglycans content of the ligament remained essentially *** work pioneers the application of ice-free cryopreservation for ligament and holds great potential for improving the long-term storage of ligament,providing valuable insights for future cryopreservation technique development.

关键词： Ligament Betaine Vitrification Cryopreservation

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