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Improving stock market prediction via heterogeneous information fusion

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arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

Unveiling latent behaviors of video viewers with cross-platform information 27

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Unveiling latent behaviors of video viewers with cross-platf...

27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Gu, Xuhong Zhou, Yipeng Wu, Di Chan, Terence H. Chen, Min School of Data and Computer Science Sun Yat-sen University China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450350037

The online video streaming service is of huge market values with billions of worldwide users. For online video providers, e.g., Net-flix, Youku, the crucial question is how to understand users' view behaviors and preferences because this knowledge is important for their business operation. Existing solutions mainly rely on analyzing users' historical view records, which however are not always available, especially for new videos and unprovided videos. Different from existing solutions, we propose to infer user behaviors and preferences by jointly analyzing data collected from multiple platforms (e.g., video streaming systems, video databases, etc.). In particular, we use the movie data crawled from a leading video streaming system (i.e., Youku), and a well-known video database in China (i.e, Douban) for this study. Our investigation points out that movie quality (evaluated in terms of Douban scores) and release date jointly influence viewers' preferences. In addition, we reveal a series of user behaviors, e.g., users are reluctant to post comments or ratings for movies they do not like, and user eyeballs are heavily captured by new movies. Understanding of these user behaviors covered by this study is essential for video recommendation and video popularity prediction which can benefit video procurement and advertisement campaign. © 2017 ACM.

关键词： Video streaming

CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

Intermediate mass black hole formation in compact young massive star clusters

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arXiv 2020年

作者： Rizzuto, Francesco Paolo Naab, Thorsten Spurzem, Rainer Giersz, Mirek Ostriker, J.P. Stone, N.C. Wang, Long Berczik, Peter Rampp, M. Max-Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 GarchingD-85741 Germany National Astronomical Observatories and Key Laboratory of Computational Astrophysics Chinese Academy of Sciences 20A Datun Rd. Chaoyang District Beijing100101 China Astronomisches Rechen-Institut Zentrum fÃijr Astronomie University of Heidelberg MÃűnchhofstrasse 12-14 Heidelberg69120 Germany Kavli Institute for Astronomy and Astrophysics Peking University Yiheyuan Lu 5 Haidian Qu Beijing100871 China Nicolaus Copernicus Astronomical Centre Polish Academy of Sciences ul. Bartycka 18 Warsaw00-716 Poland Department of Astronomy Columbia University New YorkNY10027 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Racah Institute of Physics Hebrew University Jerusalem91904 Israel Department of Astronomy University of Maryland College ParkMD20742 United States Main Astronomical Observatory of Ukrainian National Academy of Sciences Kiev Ukraine Argelander Institut fÃijr Astronomie Auf dem HÃijgel 71 Bonn Germany RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi Chuo-ku Kobe Japan Max Planck Computing and Data Facility GieÃ§enbachstr. 2 Garching Germany Germany

Young dense massive star clusters are a promising environment for the formation of intermediate mass black holes (IMBHs) through collisions. We present a set of 80 simulations carried out with Nbody6++GPU of 10 initial conditions for compact ∼ 7 × 104Mo star clusters with half-mass radii Rh . 1pc, central densities ρcore & 105Mo pc−3, and resolved stellar populations with 10% primordial binaries. Very massive stars (VMSs) with masses up to ∼ 400Mo grow rapidly by binary exchange and three-body scattering events with main sequences stars in hard binaries. Assuming that in VMS - stellar BH collisions all stellar material is accreted onto the BH, IMBHs with masses up to MBH ∼ 350Mo can form on timescales of . 15 Myr. This process was qualitatively predicted from Monte Carlo MOCCA simulations. Despite the stochastic nature of the process - typically not more than 3/8 cluster realisations show IMBH formation - we find indications for higher formation efficiencies in more compact clusters. Assuming a lower accretion fraction of 0.5 for VMS - BH collisions, IMBHs can also form. The process might not work for accretion fractions as low as 0.1. After formation, the IMBHs can experience occasional mergers with stellar mass BHs in intermediate mass-ratio inspiral events (IMRIs) on a 100 Myr timescale. Realised with more than 105 stars, 10 % binaries, the assumed stellar evolution model with all relevant evolution processes included and 300 Myr simulation time, our large suite of simulations indicates that IMBHs of several hundred solar masses might form rapidly in massive star clusters right after their birth while they are still compact. Copyright © 2020, The Authors. All rights reserved.

关键词： Stars

Extractive Adversarial Networks for Network Embedding

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Extractive Adversarial Networks for Network Embedding

International Conference on Behavior, Economic and Social computing (BESC)

作者： Runxuan Chen Haoran Xie Jiaxing Chen Yanghui Rao Yingchao Zhao Fu Lee Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Mathematics and Information Technology The Education University of Hong Kong Tai Po Hong Kong School of Computing and Information Sciences Caritas Institute of Higher Education Tseung kwan O Hong Kong School of Science and Technology The Open University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781728102085;9781728102078

Network embedding has attracted more and more researchers recently. Although many algorithms focus on topological information, there exists a disadvantage. Nodes in many real-world network often have their own attributes, which are potentially valuable information, but algorithms focusing on structure ignore these messages, which will decrease the accuracy of the embedding space. To solve this problem, we consider a deep learning model. In this paper, we propose a novel model named Extractive Adversarial Networks for network embedding, which aims to extract the latent space from labels, attributed information and topological structure (e.g. low-order proximity), based on generative adversarial network model in Domain-Adversarial Training of Neural Networks. The goal of EAN is extracting the same features from the topological structure and attributed information according to our extractive network, while distinguishing the extractive features from either the topological structure or attributed information, which becomes an adversarial models. First, we train our extractive network with labels information, which is feeded with attributed information and nodes structure in vector forms. The second step is training the adversarial network to distinguish the embedding representation from either the topological structure or attributed information, where the embedding representation is from the hidden layer in extractive network. We conduct experiments on six real-world datasets, which illustrates that the proposed models outperforms state-of-the-art embedding algorithms.

关键词： Predictive models data mining Feature extraction data models Mathematical model Neural networks Computer science

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

Private model compression via knowledge distillation

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arXiv 2018年

作者： Wang, Ji Bao, Weidong Sun, Lichao Zhu, Xiaomin Cao, Bokai Yu, Philip S. College of Systems Engineering National University of Defense Technology Changsha China Department of Computer Science University of Illinois at Chicago Chicago United States State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Facebook Inc. Menlo Park United States Institute for Data Science Tsinghua University Beijing China

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9:83;10-6)-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0.97% accuracy loss. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

A question answering approach to emotion cause extraction

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A question answering approach to emotion cause extraction

2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Gui, Lin Hu, Jiannan He, Yulan Xu, Ruifeng Lu, Qin Du, Jiachen Shenzhen Graduate School Harbin Institute of Technology China College of Mathematics and Computer Science Fuzhou University China School of Engineering and Applied Science Aston University United Kingdom Guangdong Provincial Engineering Technology Research Center for Data Science China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781945626838

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure. © 2017 Association for Computational Linguistics.

关键词： Extraction

Learning Visual Features from Snapshots for Web Search

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arXiv 2017年

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Pang, Liang Cheng, Xueqi University of Chinese Academy of Sciences CAS Key Lab of Network Data Science and Technology Institute of Computing Technology

When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based on the extracted textual elements, link analysis, and user logs. However, Web pages are not solely linked texts, but have structured layout organizing a large variety of elements in different styles. Such layout itself can convey useful visual information, indicating the relevance of a Web page. For example, the query-independent layout (i.e., raw page layout) can help identify the page quality, while the query-dependent layout (i.e., page rendered with matched query words) can further tell rich structural information (e.g., size, position and proximity) of the matching signals. However, such visual information of layout has been seldom utilized in Web search in the past. In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking. Both query-independent and query-dependent snapshots are considered as the new inputs. We then propose a novel visual perception model inspired by human’s visual search behaviors on page viewing to extract the visual features. This model can be learned end-to-end together with traditional human-crafted features. We also show that such visual features can be efficiently acquired in the online setting with an extended inverted indexing scheme. Experiments on benchmark collections demonstrate that learning visual features from Web page snapshots can significantly improve the performance of relevance ranking in ad-hoc Web retrieval tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Websites