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Experimental observation of double-peak structure of coincidence spectra in ultrafast spontaneous parametric down-conversion

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Physical Review A 2013年第1期87卷 013803-013803页

作者： Dmitry A. Kalashnikov Mikhail V. Fedorov Leonid A. Krivitsky Data Storage Institute Agency for Science Technology and Research (A-STAR) 5 Engineering Drive I 117608 Singapore A. M. Prokhorov General Physics Institute Russian Academy of Sciences 38 Vavilov Street Moscow 119991 Russia

The requirement for symmetry of the biphoton wave function results in a double-peak structure of the coincidence spectra of spontaneous parametric down-conversion (SPDC) [Fedorov, Mikhailova, and Volkov, Phys. Scr. 85, 058105 (2012)]. In this work we report the experimental observation of this effect by carefully tailoring parameters of the SPDC crystal, the ultrafast pump laser, and the measurement setup. The results are shown to be relevant to quantum state engineering of ultrafast polarization or frequency-entangled mixed states.

关键词： SYMMETRY (Physics) PARAMETER estimation PHOTONS WAVE functions CRYSTAL structure PICOSECOND pulses LASER beams OPTICAL pumping SPECTRUM analysis

Generalized Category Discovery with Decoupled Prototypical Network

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arXiv 2022年

作者： An, Wenbin Tian, Feng Zheng, Qinghua Ding, Wei Wang, QianYing Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics China Department of Computer Science University of Massachusetts Boston United States Lenovo Research Department of Engineering University of Massachusetts Boston United States

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model’s generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://***/Lackel/DPN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

Multi-stage network embedding for exploring heterogeneous edges

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arXiv 2021年

作者： Huang, Hong Song, Yu Ye, Fanghua Xie, Xing Shi, Xuanhua Jin, Hai The National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computer Science University College London London United Kingdom Microsoft Research Asia Beijing China

The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on exploring the heterogeneous edges for network representation learning. By considering each relationship as a view that depicts a specific type of proximity between nodes, we propose a multi-stage non-negative matrix factorization (MNMF) model, committed to utilizing abundant information in multiple views to learn robust network representations. In fact, most existing network embedding methods are closely related to implicitly factorizing the complex proximity matrix. However, the approximation error is usually quite large, since a single low-rank matrix is insufficient to capture the original information. Through a multi-stage matrix factorization process motivated by gradient boosting, our MNMF model achieves lower approximation error. Meanwhile, the multi-stage structure of MNMF gives the feasibility of designing two kinds of non-negative matrix factorization (NMF) manners to preserve network information better. The united NMF aims to preserve the consensus information between different views, and the independent NMF aims to preserve unique information of each view. Concrete experimental results on realistic datasets indicate that our model outperforms three types of baselines in practical applications. © 2021, CC BY.

关键词： Non-negative matrix factorization

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

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arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

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Contactless Respiratory Rate Monitoring For ICU Patients Bas...

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 IEEE.

关键词： Intensive care units

Nanoelectrokinetic purification device for a continuous peritoneal dialysate recycler

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Nanoelectrokinetic purification device for a continuous peri...

IEEE International Conference on Micro Electro Mechanical Systems

作者： Wonseok Kim Kihong Kim Hajeong Lee Yon Su Kim Jung Chan Lee Gun Yong Sung Sung Jae Kim Department of Electrical and Computer Engineering Seoul National University Department of Internal Medicine Seoul National University Hospital Department of Biomedical Engineering Seoul National University Department of Material Science and Engineering Hallym University Big Data Institute Seoul National University

ISBN: (纸本)9781509050796

Ion concentration polarization (ICP) phenomenon has been extensively researched for electro-desalination and purification applications because of its efficient capability of separating any charged species from brines. In this work, the ICP purifier, for the first time, was applied for a novel continuous peritoneal dialysate recycler. Most of charged hazardous substances were sufficiently removed by conventional ICP mechanisms. Moreover, slightly charged toxic molecule (creatinine) was mostly eliminated through nanoporous membrane, and uncharged toxic molecule (urea) was perfectly decomposed by electrochemical reactions. Based on the combined removal mechanisms confirmed in a micro/nanofluidic platform, a macro ICP purifier was finally demonstrated for practical applicability. We would expect significant advances in a quality of life for chronic kidney disease patients by this ICP purifier.

关键词： Iterative closest point algorithm Ions Microchannels Purification Electrodes Abdomen Nanoscale devices

The monkey microbial biobank brings previously uncultivated bioresources for nonhuman primate and human gut microbiomes

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mLife 2022年第2期1卷 210-217页

作者： Danhua Li Chang Liu Rexiding Abuduaini Mengxuan Du Yujing Wang Haizhen Zhu Honghe Chen Nan Zhou Yuhua Xin Linhuan Wu Juncai Ma Yuguang Zhou Yong Lu Chengying Jiang Qiang Sun Shuang-Jiang Liu State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology Chinese Academy of SciencesBeijingChina College of Life Science University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Microbial Biotechnology Shandong UniversityQingdaoChina China General Microorganism Culture Collection Institute of MicrobiologyChinese Academy of SciencesBeijingChina Microbial Resources and Big Data Center Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Neuroscience CAS Key Laboratory of Primate NeurobiologyCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesInstitute of NeuroscienceShanghaiChina

Impact statement Nonhuman primates(NHPs)such as monkeys are the closest living relatives to humans and are the best available models for causative studies of human health and *** microbiomes are intensively involved in host *** this study,by large-scale cultivation of microbes from fecal samples of monkeys,we obtained previously uncultured bacterial species and constructed a Macaca fascicularis Gut Microbial Biobank(MfGMB).The MfGMB consisted of 250 strains that represent 97 species of 63 genera,25 families,and 4 *** information of the 250 strains and the genomes of 97 cultured species are publicly *** MfGMB represented nearly 50% of core gut microbial compositions at the genus level and covered over 80% of the KO-based known gut microbiome functions of *** mining showed that the bacterial species in the MfGMB were prevalent not only in NHPs gut microbiomes but also in human gut *** study will help the understanding and future investigations on how gut microbiomes interact with their mammalian hosts.

关键词： statement monkey gut

A mean-field optimal control formulation of deep learning

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arXiv 2018年

作者： Weinan, E. Han, Jiequn Li, Qianxiao Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research and Peking University Beijing100871 China Institute of High Performance Computing Agency for Science Technology and Research Connexis North 1 Fusionopolis Way Singapore138632 Singapore

Recent work linking deep neural networks and dynamical systems opened up new avenues to analyze deep learning. In particular, it is observed that new insights can be obtained by recasting deep learning as an optimal control problem on difference or differential equations. However, the mathematical aspects of such a formulation have not been systematically explored. This paper introduces the mathematical formulation of the population risk minimization problem in deep learning as a mean-field optimal control problem. Mirroring the development of classical optimal control, we state and prove optimality conditions of both the Hamilton-Jacobi-Bellman type and the Pontryagin type. These mean-field results reflect the probabilistic nature of the learning problem. In addition, by appealing to the mean-field Pontryagin's maximum principle, we establish some quantitative relationships between population and empirical learning problems. This serves to establish a mathematical foundation for investigating the algorithmic and theoretical connections between optimal control and deep learning. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimal control systems

Entropy-Based Model Generalization for Sleep Stage Classification 13

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Entropy-Based Model Generalization for Sleep Stage Classific...

13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Kim, Dongyoung Na, Younghoon Woo, Yunhee Kim, Dong-Kyu Lee, Jeong-Gun Hallym University Dept. of Computer Engineering Chuncheon Korea Republic of Hallym University Division of Software Chuncheon Korea Republic of Chuncheon Sacred Heart Hospital Hallym University College of Medicine Department of Otorhinolaryngology-Head and Neck Surgery Chuncheon Korea Republic of Institute of New Frontier Research and Division of Big Data and Artificial Intelligence Chuncheon Sacred Heart Hospital Chuncheon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The most recent researches on sleep stage classification have been focused on a model architecture to attain high classification accuracy. Sleep stage classification is performed by human sleep experts and consequently there are some inconsistencies of labels among the scoring experts. To improve robustness of the sleep stage classification model, it is essential to consider those noisy labels which have been commonly mentioned in typical image classification problems as well. To partly solve the problem, we employ 'entropy minimization' in a loss function. By including the entropy, we can make a generalized model with regard to the label noises. To validate the effectiveness of the use of entropy on model generalization, we use two different datasets gathered from two institutions. We train a model only using a dataset obtained from one institution. Then we test the model using the dataset of another institution to investigate the accuracy improvement by the generalization. © 2022 IEEE.

关键词： Entropy