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2012 International Conference on computing, Measurement, Control and Sensor Network, CMCSN 2012

作者： Ma, Wenzhe Zhang, Wendong Li, Pengwei Hu, Jie Li, Gang Sang, Shengbo MicroNano System Research Center Information Engineering College Taiyuan University of Technology Taiyuan Shanxi China Key Lab. of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan Shanxi China

ISBN: (纸本)9780769547381

The electrical characteristic test of nanowires is important in nano-materials research. The metal-semiconductor junction that is fabricated with semiconductor nanowire and metal probe in electrical test system has impact on the test result directly, therefore it's necessary to study I-V characteristics of the metal-semiconductor(M-S) junction. Si,ZnO,GaN have been selected from multiple semiconductor materials. When they connected with tungsten probe, all of them formed Schottky barriers, but the relationships between current and voltage of the junctions are distinct. In the thesis, I-V curves of these M-S junctions are compared, and the best electrical performance of M-S junction is selected. © 2012 IEEE.

关键词： Nanowires

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A Bigtree Index for Resource Space Model

A Bigtree Index for Resource Space Model

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Zhongyi Zeng Hai Zhuge Department of Computer Science and Technology University of Science & Technology of China HefeiChi Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing

This paper presents the Bigtree, which is a hierarchical index for implementing the Resource Space Model.

关键词： Semantics Indexes Data models Humans USA Councils Springs Space technology

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Combat data shift in few-shot learning with knowledge graph

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Frontiers of Computer Science 2023年第1期17卷 101-111页

作者： Yongchun ZHU Fuzhen ZHUANG Xiangliang ZHANG Zhiyuan QI Zhiping SHI Juan CAO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Artificial Intelligence Beihang UniversityBeijing 100191China Xiamen Institute of Data Intelligence Xiamen 361021China Computer Science and Engineering University of Notre DameIN 46556USA University of California San Diego La JollaCA 92093USA College of Information Engineering&Academy for Multidisciplinary Studies Capital Normal UniversityBeijing 100089China

Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.

关键词： few-shot data shift knowledge graph

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Self-powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

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InfoMat 2024年第7期6卷 62-75页

作者： Junjie Zou Yanan Ma Chenxu Liu Yimei Xie Xingyao Dai Xinhui Li Shuxuan Li Shaohui Peng Yang Yue Shuo Wang Ce-Wen Nan Xin Zhang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanthe People's Republic of China Hubei Key Laboratory of Energy Storage and Power Battery School of MathematicsPhysics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanthe People's Republic of China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeithe People's Republic of China State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China

The simultaneous detection of multiple stimuli,such as pressure and temperature,has long been a persistent challenge for developing electronic skin(eskin)to emulate the functionality of human ***,the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and ***,we propose a self-powered dual temperature–pressure(SPDM)sensor,which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al *** SPDM sensor exhibits a rapid and timely response to changes in pressure-induced deformation,while exhibiting a slow and hysteretic response to temperature *** distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning,resulting in an impressive accuracy rate of 99.1%.Furthermore,the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75C,encompassing the environmental conditions encountered in daily human *** dual-mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination,showcasing its potential applications in wearable electronics,intelligent robots,human–machine interactions,and so on.

关键词： e-skin gel electrolyte machine learning self-powered dual-mode sensor

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The design of burglar alarm circuit based on the magnetic field sensor

The design of burglar alarm circuit based on the magnetic fi...

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2012 International Conference on computing, Measurement, Control and Sensor Network, CMCSN 2012

作者： Deng, Lili Sang, Shengbo Zhang, Wendong Li, Pengwei Hu, Jie Li, Gang Feng, Hui Tang, Xiaoliang MicroNano System Research Center Information Engineering College Taiyuan University of Technology Taiyuan Shanxi China Key Lab. of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan Shanxi China

ISBN: (纸本)9780769547381

This paper introduces a design method and a hardware circuit of burglar alarm circuit based on the magnetic field sensor, which can be used to achieve the protection of valuables. The circuit design is ingenious, and has the characteristics of simple structure, light weight, small size, timeliness, high sensitivity and low cost. It has been proved to have a stable and reliable performance by experiment. © 2012 IEEE.

关键词： Timing circuits

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X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation 24

X-Prompt: Multi-modal Visual Prompt for Video Object Segment...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Pinxue Li, Wanyun Huang, Hao Hong, Lingyi Zhou, Xinyu Chen, Zhaoyu Li, Jinglun Jiang, Kaixun Zhang, Wei Zhang, Wenqiang Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology China

ISBN: (纸本)9798400706868

Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and RGB-Event, has garnered attention due to its capability to address challenging scenarios where traditional VOS methods struggle, such as extreme illumination, rapid motion, and background distraction. Existing approaches often involve designing specific additional branches and performing full-parameter fine-tuning for fusion in each task. However, this paradigm not only duplicates research efforts and hardware costs but also risks model collapse with the limited multi-modal annotated data. In this paper, we propose a universal framework named X-Prompt for all multi-modal video object segmentation tasks, designated as RGB+X. The X-Prompt framework first pre-trains a video object segmentation foundation model using RGB data, and then utilize the additional modality of the prompt to adapt it to downstream multi-modal tasks with limited data. Within the X-Prompt framework, we introduce the Multi-modal Visual Prompter (MVP), which allows prompting foundation model with the various modalities to segment objects precisely. We further propose the Multi-modal Adaptation Experts (MAEs) to adapt the foundation model with pluggable modality-specific knowledge without compromising the generalization capacity. To evaluate the effectiveness of the X-Prompt framework, we conduct extensive experiments on 3 tasks across 4 benchmarks. The proposed universal X-Prompt framework consistently outperforms the full fine-tuning paradigm and achieves state-of-the-art performance. Code: https://***/PinxueGuo/*** © 2024 ACM.

关键词： Image segmentation

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ONE2SET + Large Language Model: Best Partners for keyphrase Generation

ONE2SET + Large Language Model: Best Partners for Keyphrase ...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Shao, Liangying Zhang, Liang Peng, Minlong Ma, Guoqi Yue, Hao Sun, Mingming Su, Jinsong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism China Cognitive Computing Lab Baidu Research China Beijing Institute of Mathematical Sciences and Applications China

ISBN: (纸本)9798891761643

keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document. The dominant paradigms in KPG include ONE2SEQ and ONE2SET. Recently, there has been increasing interest in applying large language models (LLMs) to KPG. Our preliminary experiments reveal that it is challenging for a single model to excel in both recall and precision. Further analysis shows that: 1) the ONE2SET paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training;2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections. Given these observations, we introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a ONE2SET-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates. Particularly, we make two important improvements on our generator and selector: 1) we design an Optimal Transport-based assignment strategy to address the above improper assignments;2) we model the keyphrase selection as a sequence labeling task to alleviate redundant selections. Experimental results on multiple benchmark datasets show that our framework significantly surpasses state-of-the-art models, especially in absent keyphrase prediction. We release our code at https://***/DeepLearnXMU/KPG-SetLLM. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A novel theory of business process virtualiztion on E-commerce

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ICIC Express Letters 2010年第2期4卷 381-387页

作者： Hong, Daocheng Ling, Hong Li, Yangmei School of Management Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China Research Center for Marketing Shanghai China Express Co. Ltd Shanghai 200433 China

In practice, many commercial activities that have traditionally beenconducted via physical mechanisms are being conducted virtually by means ofinformation technology (IT). Visualization of business process has beenreceiving more and more attention in the management literatures. However, fewdocuments research IT-enabled business process of e-commerce from theperspective of media interactivity and richness. This paper focuses oncommercial activities and proposes a novel theory of business processvirtualization (BPV) with theory construction methodology. BPV theory provides anew lens for researching e-commerce and process management which are playing acritical role in contemporary research field. With BPV theory, the unique case -development of mobile-banking in China is interpreted properly as a case ofapplication. Additionally, the conceptual framework also has superiorinstructional and explanatory power for both research and practice. ICIC International © 2010.

关键词： Electronic commerce

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de novo sequencing with mirror image protease of LysargiNase and trypsin for large scale proteomics

de novo sequencing with mirror image protease of LysargiNase...

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中国生物化学与分子生物学会第十二届全国会员代表大会暨2018年全国学术会议

作者：徐平杨皓李衍常赵明治张俊令王一豪迟浩贺思敏 National Center for Protein Sciences Beijing Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing TechnologyCAS

De novo peptide sequencing for an individual pure protein has improved remarkably with the progress of mass spectrometry but there still exists incomplete peptide fragmentation and indistingushiable series of fragmented ions,which interrupted the interpretation of amino acid sequence of *** is even worse at proteome ***,we developed a series of high efficiency proteases,including LysargiNase with super activity and acetylated trypsin with high *** advantage of these two enzymes,we developed a novel algorithm,pNovoM for automated de novo sequencing of the mirrored peptides generated from the digestion of trypsin and LysargiNase *** combination of paired mirror image spectra results nearly complete series of product ions,which facilitate the de novo sequencing on both of purified protein and proteasome samples.

关键词： de novo sequencing proteomics mirror enzyme LysargiNase

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Learning from multi metrics for stereoscopic 3D image quality assessment

Learning from multi metrics for stereoscopic 3D image qualit...

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International Conference on 3D Imaging (IC3D)

作者： Jiamei Zhan Yuzhen Niu Yize Huang College of Mathematics and Computer Science. Fuzhou University Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fujian China

Quality assessment of stereoscopic 3D images is much more complex than that of 2D images. Quality assessment metrics for 3D images have achieved good performance on symmetric distorted 3D images, while given poor performance on asymmetric distorted 3D images. To further improve the perception consistency for symmetric and asymmetric distorted 3D images, this paper presents a machine learning-based full-reference 3D image quality assessment method that learns from multi IQA metrics. Considering that changes in left view, right view and depth information all impact human perception, our method considers the quality of two views and depth information. Since symmetric and asymmetric distortions impact left and right views of a 3D image differently, we propose different features for symmetric and asymmetric distorted 3D images. The experimental results show that our method has better performance than both 2D-based 3D quality assessment metrics and the state of the art specially designed 3D quality assessment metrics.

关键词： Three-dimensional displays Measurement Distortion Indexes Two dimensional displays Image quality Stereo image processing

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