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Visualizing nonlinear resonance in nanomechanical systems via single-electron tunneling

Nano Research 2021年第4期14卷 1156-1161页

作者： Xinhe Wang Lin Cong Dong Zhu Zi Yuan Xiaoyang Lin Weisheng Zhao Zaiqiao Bai Wenjie Liang Ximing Sun Guang-Wei Deng Kaili Jiang Fert Beijing Research Institute School of Microelectronics&Beijing Advanced Innovation Centre for Big Data and Brain Computing(BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics and Tsinghua-Foxconn Nanotechnology Research CenterTsinghua UniversityBeijing 100084China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230026China Department of Physics Beijing Normal UniversityBeijing 100875China Institute of Physics Chinese Academy of SciencesBeijing 100080China Institute of Nuclear and New Energy Technology Collaborative Innovation Center of Advanced Nuclear Energy TechnologyKey Laboratory of Advanced Reactor EngineeringTsinghua UniversityBeijing 100084China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China

Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-electron transport that drives a large amplitude into a nonlinear ***,a CNT hybrid device has been investigated,which comprises a gate-defined quantum dot that is embedded into a mechanical resonator under strong actuation *** Coulomb peak positions synchronously oscillate with the mechanical vibrations,enabling a single-electron Chopper*1 ***,the vibration amplitude of the CNT versus its frequency can be directly visualized via detecting the time-averaged single-electron tunneling *** understand this phenomenon,a general formula is derived for this time-averaged single-electron tunneling current,which agrees well with the experimental *** using this visualization method,a variety of nonlinear motions of a CNT mechanical oscillator have been directly recorded,such as Duffing nonlinearity,parametric resonance,and double-,fractional-,mixed-frequency *** approach opens up burgeoning opportunities for investigating and understanding the nonlinear motion of a nanomechanical system and its interactions with electron transport in quantum regimes.

关键词： carbon nanotube mechanical resonator quantum dot nonlinear coupling

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A tree search algorithm for sequence labeling

arXiv

引用

arXiv 2018年

作者： Lao, Yadi Xu, Jun Lan, Yanyan Guo, Jiafeng Gao, Sheng Cheng, Xueqi Guo, Jun Beijing University of Posts and Telecommunications CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from le. to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of thewhole sentence are produced.e policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a *** search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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Analysis of Private Industrial Control Protocol Format Based on LSTM-FCN Model 2020

Analysis of Private Industrial Control Protocol Format Based...

引用

2020 International Conference on Aviation Safety and Information technology, IcasIT 2020

作者： Zhao, Rui Liu, Zhaohui School of Computer Science. University of South China Key Laboratory of Network Assessment Technology Cas Institute of Information Engineering Chinese Academy of Sciences Beijing Hengyang China School of Computer Science University of South China Hengyang China

ISBN: (纸本)9781450375764

Protocol security is an important element in the field of industrial control system security. For industrial control systems filled with a large number of private protocols, accurate information on the format of private industrial control protocols is the basis of its security analysis. How to effectively analyze the format of private industrial control protocols is the key point to be solved in the security of the current industrial control system. In this paper, a private industrial control protocol formats analysis method based on Long short-term memory Fully convolutional neural network (LSTM-FCN) is proposed. The Long short-term memory Fully convolutional neural network (LSTM-FCN) model is used to learn the field types of public industrial control protocols. The trained model is then applied to the private industrial control protocols field type identification. Finally, the type recognition fields are fused based on the format characteristics of the public protocol fields. The results of the format analysis of the private industrial control protocol were obtained. The experimental results show that the method can accurately analyze the format of private industrial control protocols. © 2020 ACM.

关键词： Long short-term memory

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Graph convolutional networks using heat kernel for semi-supervised learning

arXiv

引用

arXiv 2020年

作者： Xu, Bingbing Shen, Huawei Cao, Qi Cen, Keting Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Graph convolutional networks gain remarkable success in semi-supervised learning on graph-structured data. The key to graph-based semi-supervised learning is capturing the smoothness of labels or features over nodes exerted by graph structure. Previous methods, spectral methods and spatial methods, devote to defining graph convolution as a weighted average over neighboring nodes, and then learn graph convolution kernels to leverage the smoothness to improve the performance of graph-based semi-supervised learning. One open challenge is how to determine appropriate neighborhood that reflects relevant information of smoothness manifested in graph structure. In this paper, we propose GraphHeat, leveraging heat kernel to enhance low-frequency filters and enforce smoothness in the signal variation on the graph. GraphHeat leverages the local structure of target node under heat diffusion to determine its neighboring nodes flexibly, without the constraint of order suffered by previous methods. GraphHeat achieves state-of-the-art results in the task of graph-based semi-supervised classification across three benchmark datasets: Cora, Citeseer and Pubmed. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus

引用

National science Review 2019年第3期6卷 505-514页

作者： Honglin He Shaoqiang Wang Li Zhang Junbang Wang Xiaoli Ren Lei Zhou Shilong Piao Hao Yan Weimin Ju Fengxue Gu Shiyong Yu Yuanhe Yang Miaomiao Wang Zhongen Niu Rong Ge Huimin Yan Mei Huang Guoyi Zhou Yongfei Bai Zongqiang Xie Zhiyao Tang Bingfang Wu Leiming Zhang Nianpeng He Qiufeng Wang Guirui Yu Synthesis Research Center of China's Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences College of Resources and Environment University of Chinese Academy of Sciences Sino-French Institute for Earth System Science College of Urban and Environment Sciences Peking University Key Laboratory of Alpine Ecology and Biodiversity Institute of Tibetan Plateau Research CAS Center for Excellence in Tibetan Plateau Earth Science Chinese Academy of Sciences National Meteorological Center China Meteorological Administration International Institute for Earth System Science and Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Nanjing University Key Laboratory of Dryland Agriculture MOA Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural Sciences School of Geography Geomatics and Planning Jiangsu Normal University State Key Laboratory of Vegetation and Environmental Change Institute of Botany Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical Garden Chinese Academy of Sciences Department of Ecology College of Urban and Environmental Science Key Laboratory for Earth Surface Processes of the Ministry of Education Peking University Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences

The carbon budgets in terrestrial ecosystems in China are strongly coupled with climate changes. Over the past decade, China has experienced dramatic climate changes characterized by enhanced summer monsoon and decelerated warming. However, the changes in the trends of terrestrial net ecosystem production（NEP） in China under climate changes are not well documented. Here, we used three ecosystem models to simulate the spatiotemporal variations in China's NEP during 1982–2010 and quantify the contribution of the strengthened summer monsoon and warming hiatus to the NEP variations in four distinct climatic regions of the country. Our results revealed a decadal-scale shift in NEP from a downtrend of–5.95 TgC/yr2（reduced sink） during 1982–2000 to an uptrend of 14.22 TgC/yr2（enhanced sink） during 2000–10. This shift was essentially induced by the strengthened summer monsoon, which stimulated carbon uptake, and the warming hiatus, which lessened the decrease in the NEP trend. Compared to the contribution of 56.3% by the climate effect, atmospheric CO2concentration and nitrogen deposition had relatively small contributions（8.6 and 11.3%, respectively） to the shift. In conclusion, within the context of the global-warming hiatus, the strengthening of the summer monsoon is a critical climate factor that enhances carbon uptake in China due to the asymmetric response of photosynthesis and respiration. Our study not only revealed the shift in ecosystem carbon sequestration in China in recent decades, but also provides some insight for understanding ecosystem carbon dynamics in other monsoonal areas.

关键词： climate change Asian summer monsoon warming hiatus biogeochemical modeling ecosystem carbon dynamics

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Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

arXiv

引用

arXiv 2024年

作者： Chen, Lu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China

Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. However, existing studies on RAG seldom address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect, resulting in uncontrollable risks in real-world applications. In this work, we emphasize the importance of risk control, ensuring that RAG models proactively refuse to answer questions with low confidence. Our research identifies two critical latent factors affecting RAG’s confidence in its predictions: the quality of the retrieved results and the manner in which these results are utilized. To guide RAG models in assessing their own confidence based on these two latent factors, we develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers. We also introduce a benchmarking procedure to collect answers with the option to abstain, facilitating a series of experiments. For evaluation, we introduce several risk-related metrics and the experimental results demonstrate the effectiveness of our approach. Our code and benchmark dataset are available at https://***/ictbigdatalab/RC-RAG. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Search from history and reason for future: Two-stage reasoning on temporal knowledge graphs

arXiv

引用

arXiv 2021年

作者： Li, Zixuan Jin, Xiaolong Guan, Saiping Li, Wei Guo, Jiafeng Wang, Yuanzhuo Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Baidu Inc

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Semantic Structure Enhanced Event Causality Identification

arXiv

引用

arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Jin, Xiaolong Bai, Long Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure. The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events. Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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The first data release of LAMOST low-resolution single-epoch spectra

引用

Research in Astronomy and Astrophysics 2021年第10期21卷 73-85页

作者： Zhong-Rui Bai Hao-Tong Zhang Hai-Long Yuan Dong-Wei Fan Bo-Liang He Ya-Juan Lei Yi-Qiao Dong Si-Cheng Yu Yong-Heng Zhao Yong Zhang Yong-Hui Hou Yao-Quan Chu CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China National Astronomical Data Center Beijing 100101China Nanjing Institute of Astronomical Optics&Technology National Astronomical ObservatoriesChinese Academy of SciencesNanjing 210042China University of Science and Technology of China Hefei 230026China

LAMOST data Release 5,covering 17000 deg^(2) from-10°to 80°in declination,contains 9 million co-added low-resolution spectra of celestial objects,each spectrum combined from repeat exposure of two to tens of times during Oct 2011 to Jun *** this paper,we present the spectra of individual exposures for all the objects in LAMOST data Release *** each spectrum,the equivalent width of 60lines from 11 different elements are calculated with a new method combining the actual line core and fitted line *** stars earlier than F type,the Balmer lines are fitted with both emission and absorption profiles once two components are *** velocity of each individual exposure is measured by minimizing χ^(2) between the spectrum and its best *** database for equivalent widths of spectral lines and radial velocities of individual spectra are available *** velocity uncertainties with different stellar type and signal-to-noise ratio are quantified by comparing different exposure of the same *** notice that the radial velocity uncertainty depends on the time lag between *** stars observed in the same day and with signal-to-noise ratio higher than 20,the radial velocity uncertainty is below 5 km s^(-1),and increases to 10 km s^(-1) for stars observed in different nights.

关键词： surveys stars:general methods:data analysis

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Modeling topical relevance for multi-turn dialogue generation

arXiv

引用

arXiv 2020年

作者： Zhang, Hainan Lan, Yanyan Pang, Liang Chen, Hongshen Ding, Zhuoye Yin, Dawei CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS China *** Beijing China University of Chinese Academy of Sciences China

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations. Copyright © 2020, The Authors. All rights reserved.

关键词： Stars

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