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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Yu Wang, Hui Hou, Meijie Wang, Yaojun Bu, Dongbo Zhang, Chunming Huang, Chuncui Sun, Shiwei Institute of Computing Technology Chinese Academy of Sciences Key Lab of Intelligent Information Processing 6 Kexueyuan South Road Beijing100190 China Zhongke Big Data Academy Henan Zhengzhou450046 China University of Chinese Academy of Sciences 19 Yuquan Road Beijing100049 China Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing100101 China College of Information and Electrical Engineering China Agricultural University 17 Qinghua Donglu Beijing100083 China Phil Rivers Technology Beijing China

ISBN: (纸本)9781665401265

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%. © 2021 IEEE.

关键词： Message passing

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A Trajectory Tracking System for Zebrafish Based on Embedded Edge Artificial Intelligence

A Trajectory Tracking System for Zebrafish Based on Embedded...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chuanhao Zang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Trajectory tracking of zebrafish is an important requirement in studying neurological disorders and developing new psychoactive drug. However, many challenges emerge for stable tracking, since zebrafish are similar in appearance, occlusion, agile, non-linear in moving and easy to swarm, all of which will lead to mistrack for multiple fish. And there is no precedent for tracking zebrafish through embedded edge artificial intelligence device. To overcome these difficulties, we present a tracking system for zebrafish based on RK3588-S. First, We construct an embedded edge AI hardware system consisting of two cameras driven by the RK3588-S, one for the front view and the other for top view of the fish. Then, we develop a 2D tracking algorithm based on YOLOv5 and the Observation-Centric Simple online and real-time tracking (OC-SORT) algorithm, which are transplanted to the RK3588-S for tracking the top and front views of the fish at the edge device. Compared with the previous methods, our method has fewer ID exchanges and highly real-time. Moreover, we apply the MQTT mechanism to establish communication links between the edge and the cloud to reliably transmit data to the cloud. The correspondence between cloud server and embedded AI is 1: N. Finally, we design a multi-view data fusion association algorithm to fuse the data of the two views in the cloud, which are further utilized to build the 3D tracklets of the zebrafish.

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Railway wagon flow routing locus pattern intelligent recognition algorithm based on SST

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Smart and Resilient Transportation 2020年第1期2卷 3-21页

作者： Xiaodong Zhang Ping Li Xiaoning Ma Yanjun Liu Research and Application Innovation Center for Big Data Technology in Railway China Academy of Railway Sciences Beijing China Institute of Computing Technology China Academy of Railway Sciences Beijing China

Purpose The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm. Design/methodology/approach Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized. Findings This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is propos

关键词： Intelligent transportation Pattern recognition Simhash algorithm Wagon flow routing Similarity matrix Clustering analysis

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Structure Embedded Nucleus Classification for Histopathology Images

arXiv

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arXiv 2023年

作者： Lou, Wei Wan, Xiang Li, Guanbin Lou, Xiaoying Li, Chenghang Gao, Feng Li, Haofeng Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China Pazhou Lab Guangzhou510330 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Pathology Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Artificial Intelligence Thrust The Hong Kong University of Science and Technology at Guangzhou Guangzhou510030 China Department of Colorectal Surgery Department of General Surgery Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou510655 China Shanghai Artificial Intelligence Laboratory Shanghai China

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Understanding Online Public Sentiments: A Machine Learning-Based Analysis of English and Chinese Twitter Discourse during the 2019 Chinese National Day 2

Understanding Online Public Sentiments: A Machine Learning-B...

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2nd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2020

作者： Xu, Yekai He, Qingqian Ni, Shiguang Big Data Lab Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen China Tsinghua International Relations Data Computing Lab School of Social Sciences Tsinghua University Beijing China School of Political Science and Public Administration China University of Political Science and Law Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9781728195209

As the Internet gradually penetrates people's daily lives, individual citizens are empowered to demonstrate and exchange opinions and sentiments at any time anywhere. Online communities are increasingly participating in the agenda-setting of public affairs and official policies. However, how to depict online public opinion and to what degree does it influence the real world are still unclear. This study addresses the above problems by analyzing Twitter discourse during the 2019 Chinese National Day with a machine learning-based approach. Over 300,000 English and Chinese tweets were collected between Sept 30 and Oct 3, and a hybrid method of support vector machine (SVM) and dictionary was applied to evaluate the sentiments of the collected tweets. This method avoids complex structures while yielding an average accuracy of over 96% in most classifiers used in the study. The results indicate alignment between the time of National Day celebration activities and the peak of sentiments revealed in both English and Chinese tweets, although the sentiments of the two languages tend to be in opposite directions. The sentiments of tweets also diverge from nation to nation, but are generally consistent with the country's official relations with China. The linguistic features of the tweets suggest different concerns for Twitter users who have different sentiments towards China. Future studies should prolong the collecting period and refine the algorithms used. Specific attention can also be paid to important countries pairs like China and the United States. © 2020 IEEE.

关键词： Sentiment analysis

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Intertwined formation of H2, dust, and stars in cosmological simulations

arXiv

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arXiv 2024年

作者： Ragone-Figueroa, Cinthia Granato, Gian Luigi Parente, Massimiliano Murante, Giuseppe Valentini, Milena Borgani, Stefano Maio, Umberto Universidad Nacional de Córdoba Laprida 854 CórdobaX5000BGR Argentina INAF Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy SISSA Via Bonomea 265 TriesteI-34136 Italy Dipartimento di Fisica Università di Trieste Sez. di Astronomia via Tiepolo 11 TriesteI-34131 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy

Context. Molecular hydrogen (H2) plays a crucial role in the formation and evolution of galaxies, serving as the primary fuel reservoir for star formation. In a metal-enriched Universe, H2 forms mostly through catalysis on interstellar dust grain surfaces. However, due to the complexities of modelling this process, star formation in cosmological simulations often relies on empirical or theoretical frameworks that have only been validated in the local Universe to estimate the abundance of H2. Aims. The goal of this work is to model the connection between the processes of star, dust, and H2 formation in our cosmological simulations. Methods. Building upon our recent integration of a dust evolution model into the star formation and feedback model MUPPI, we included the formation of molecular hydrogen on the surfaces of dust grains. We also accounted for the destruction of molecules and their shielding from harmful radiation. Results. The model reproduces, reasonably well, the main statistical properties of the observed galaxy population for the stellar, dust, and H2 components. The evolution of the molecular hydrogen cosmic density (ρH2) in our simulated boxes peaks around redshift z = 1.5, consistent with observations. Following its peak, ρH2 decreases by a factor of two towards z = 0, which is a milder evolution than observed. Similarly, the evolution of the molecular hydrogen mass function since z = 2 displays a gentler evolution when compared to observations. Our model recovers satisfactorily the integrated molecular Kennicut-Schmidt (mKS) law between the surface star formation rate (ΣSFR) and surface H2 density (ΣH2) at z = 0. This relationship is already evident at z = 2, albeit with a higher normalization. We find hints of a broken power law with a steeper slope at higher ΣH2. We also study the H2-to-dust mass ratio in galaxies as a function of their gas metallicity and stellar mass, observing a decreasing trend with respect to both quantities. The H2-to-dust ma

关键词： Cosmology

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Deep Active Contour Network for Medical Image Segmentation 23rd

Deep Active Contour Network for Medical Image Segmentation

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Mo Dong, Bin Li, Quanzheng Center for Data Science Peking University Beijing100871 China Center for Data Science in Health and Medicine Peking University Beijing100871 China Laboratory for Biomedical Image Analysis Beijing Institute of Big Data Research Beijing100871 China Peking University Beijing100871 China Institute for Artificial Intelligence Peking University Beijing100871 China Department of Radiology Center for Advanced Medical Computing and Analysis MGH/BWH Center for Clinical Data Science Massachusetts General Hospital Harvard Medical School BostonMA02115 United States

ISBN: (纸本)9783030597184

Image segmentation is vital to medical image analysis and clinical diagnosis. Recently, convolutional neural networks (CNNs) have achieved tremendous success in this task, however, it performs poorly at recognizing precise object boundary due to the information loss in the successive downsampling layers. To overcome this problem, we integrate an active contour model (convexified Chan-Vese model) into the CNN structure (DenseUNet), forming a new framework called deep active contour network (DACN). Instead of manual setting, DACN applies a CNN backbone to learn the initialization and parameters of active contour model (ACM) automatically. The proposed DACN leverages the advantage of ACM to detect object boundaries accurately, which can be trained in an end-to-end differential manner. The experimental results on two public datasets demonstrate the effectiveness of DACN, and the trimap experiment confirms the superior ability of DACN to obtain precise boundary delineation. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Spintronic Terahertz Polarization Programmable System for Information Encoding

SSRN

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng Beijing Advanced Innovation Center for Big Data and Brain Computing China School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China

Terahertz (THz) wave as an important electromagnetic wave has been increasingly applied in wireless communication and imaging sensing technology. It has been an increasingly practical application. Ultra-high data rates in communication technology push toward a broadband, controllable polarization, and multiple transmission channels signal sources. Here, we propose a programmable polarization spintronics terahertz source (PP-STS), in which contains a controllable polarization spintronic THz emitter and a microcontroller unit. The PP-STS not only can generate THz waves with wide frequency bandwidth, but also has the function of switching the orthogonal linear polarization state of the generated THz waves flexibly. By coding the microcontroller unit, the designed PP-SPS can directly program the orthogonal linear polarization states as a series of information to transmit. To achieve the interconversions between digital codes and the THz polarization information, an optimum matching THz polarization beam splitter (THz-PBS) is also designed via simulations and experiments. The THz-PBS, as a decoder, can accurately split the transverse electric (TE) and transverse magnetic (TM) modes into two THz detectors. At last, a coding and decoding prototype system of spintronic THz polarization is built by integrating the PP-STS, the THz-PBS and two THz detectors. The send and receive information with high speed and accuracy are verified in this prototype system as well. We believe that our work takes THz applications for multiplexing communications one step further and sheds light on commercializing the PP-STS with exciting manufacturing lines for efficient and economical THz polarization technology. © 2023, The Authors. All rights reserved.

关键词： Microcontrollers

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Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm

Predicting Large-scale Protein-protein Interactions by Extra...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lun Hu Bo-Wei Zhao Shicheng Yang Xin Luo MengChu Zhou Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

Protein-protein interactions are of great significance for us to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technology, the amount of protein-protein interaction data has become so big that most of existing prediction algorithms are no longer applicable. To address this problem, we develop a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, by using MapReduce. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption cased by the huge sequence information of proteins. After that, the procedure of CoFex is modified by following the paradigm of MapReduce such that the prediction task can be completed in a distributed manner, thus fulfilling the demanding requirements of large-scale protein-protein interaction prediction. A series of experiments have been conducted to evaluate the performance of the proposed distributed framework in terms of both efficiency and effectiveness. Experimental results demonstrate that the proposed framework can considerably improve the efficiency of CoFex by achieving more than two-orders-of-magnitude improvement in computational efficiency while retaining a comparable level of accuracy.

关键词： Proteins Memory management Genomics Predictive models Prediction algorithms data structures Computational efficiency

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Cosmological constraints from abundance, weak-lensing and clustering of galaxy clusters: application to the SDSS

arXiv

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arXiv 2023年

作者： Fumagalli, Alessandra Costanzi, Matteo Saro, Alexandro Castro, Tiago Borgani, Stefano Dipartimento di Fisica - Sezione di Astronomia Universitá di Trieste Via Tiepolo 11 Trieste34131 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy University Observatory Faculty of Physics Ludwig-MaximiliansUniversität Scheinerstr. 1 Munich81679 Germany INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Italian Research Center on High-Performance Computing Big Data and Quantum Computing Italy

The clustering of galaxy clusters is a powerful cosmological tool, which can help to break degeneracies between parameters when combined with other cosmological observables. We aim to demonstrate its potential in constraining cosmological parameters and scaling relations when combined with cluster counts and weak lensing mass information, using as a case study the redMaPPer cluster catalog derived from the Sloan Digital Sky Survey (SDSS). We extend the analysis of number counts and weak lensing signal performed by Costanzi et al. 2019a, with the addition of the real-space 2-point correlation function. We derive cosmological and scaling relation posteriors for all the possible combinations of the three observables to assess their constraining power, parameter degeneracies, and possible internal tensions. We find no evidence for tensions between the three data set analyzed. We demonstrate that the inclusion of the cluster clustering statistic can greatly enhance the constraining power of the sample thanks to its capability of breaking the Ωm – σ8 degeneracy characteristic of cluster abundance studies. In particular, for a flat ΛCDM model with massive neutrinos, we obtain Ωm = 0.28 ± 0.03 and σ8 = 0.82 ± 0.05, a 33 % and 50 % improvement compared to the posteriors derived combining cluster abundance and weak lensing analyses. Our results are consistent with cosmological posteriors from other cluster surveys, as well as with Planck CMB results and DES-Y3 galaxy clustering and weak-lensing *** Codes 85A40 © 2023, CC BY-NC-ND.

关键词： Galaxies

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