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International Conference on Computer Supported Cooperative Work in Design

作者： Yimu Ji Shangdong Liu Lili Lu Xianbo Lang Haichang Yao Ruchuan Wang Institute of High Performance Computing and Big Data Processing Nanjing University of Posts and Telecommunications School of Computer Science Nanjing College of Information and Technology School of Computer Science Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781538614839

Stream join is one of the key operations for real-time stream data query and calculation. In light of changeable velocity of stream data, traditional static stream join methods are not so adaptive that stream data computing performance will be affected. Based on the large quantity and constantly changing velocity of stream data, by considering traditional stream join algorithm, this paper proposes an optimized algorithm for variable update cycle stream based on time window (VC-TWJoin, Variable Cycle Time Window Join). For unsteady stream calculated in stream join, the optimal update cycle will be calculated to reduce the response time of stream join and improve join efficiency and real-time capability. Both theoretical analysis and experiments demonstrate that the algorithm is better than traditional join algorithms in terms of real-time capability, join response time and throughput.

关键词： Microsoft Windows Real-time systems data models Semantics Computer science Telecommunications Wireless sensor networks

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GPRInvNet: Deep learning-based ground penetrating radar data inversion for tunnel lining

arXiv

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arXiv 2019年

作者： Liu, Bin Ren, Yuxiao Liu, Hanchi Xu, Hui Wang, Zhengfang Cohn, Anthony G. Jiang, Peng The School of Qilu Transportation Shandong University Jinan250061 China The Geotechnical and Structural Engineering Techniques Research Center Shandong University Jinan250061 China The Data Science Institute Shandong University Jinan250061 China The School of Control Science and Engineering Shandong University Jinan250061 China The School of Computing University of Leeds LeedsLS2 9JT United Kingdom The School of Qilu Transportation Shandong University

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第5期10卷 2002-2011页

作者： Hang, Renlong Liu, Qingshan Sun, Yubao Yuan, Xiaotong Pei, Hucheng Plaza, Javier Plaza, Antonio Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Beijing Electro-Mechanical Engineering Institute Beijing China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial-spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.

关键词： Hyperspectral image (HSI) classification multiscale fusion robust matrix discriminative analysis (RMDA) spatial-spectral feature extraction (FE)

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Learning knowledge representation across knowledge graphs 31

Learning knowledge representation across knowledge graphs

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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Efficient Algorithms for the Summed Area Tables Primitive on GPUs

Efficient Algorithms for the Summed Area Tables Primitive on...

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IEEE International Conference on Cluster computing

作者： Peng Chen Mohamed Wahib Shinichiro Takizawa Ryousei Takano Satoshi Matsuoka AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology National Institute of Advanced Industrial Science and Technology Tokyo Japan Dept. of Mathematical and Computing Science Tokyo Institute of Technology Tokyo Japan

Two-dimensional Summed Area Tables (SAT) is a fundamental primitive used in image processing and machine learning applications. We present a collection of optimization methods for computing SAT on CUDA-enabled GPUs. Conventional approaches rely on computing the prefix sum in one dimension in parallel, transposing the matrix, then computing the prefix sum for the other dimension in parallel. Additionally, conventional methods use the scratchpad memory as cache. We propose a collection of algorithms that are scalable with respect to problem size. We use the register cache technique instead of the scratchpad memory and also employ a naive serial scan on the thread level for computing the prefix sum for one of the dimensions. Using a novel transpose-in-registers method we increase the inter-thread parallelism and outperform conventional SAT implementations. In addition, we significantly reduce both the communication between threads and the number of arithmetic instructions. On an Nvidia Pascal P100 GPU and Volta V100, our evaluations demonstrate that our implementations outperform state of the art libraries and yield up to 2.3x and 3.2x speedup over OpenCV and Nvidia NPP libraries, respectively.

关键词： Registers Graphics processing units Instruction sets Two dimensional displays Computer architecture Libraries Acceleration

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Neural or statistical: An empirical study on language models for Chinese input recommendation on mobile 23rd

Neural or statistical: An empirical study on language models...

引用

23rd China conference on Information Retrieval, CCIR 2017

作者： Zhang, Hainan Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

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Robust Mathematical Formulation and Probabilistic Description of Agent-Based Computational Economic Market Models

arXiv

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arXiv 2019年

作者： Beikirch, Maximilian Cramer, Simon Frank, Martin Otte, Philipp Pabich, Emma Trimborn, Torsten Rwth Aachen University Templergraben 55 Aachen52056 Germany Rwth Aachen University Production Metrology and Quality Management at Wzl Germany Steinbuch Center for Computing Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Forschungszentrum Julich GmbH Julich Supercomputing Centre Institute for Advanced Simulation Julich52425 Germany Institute for Data Science in Mechanical Engineering Rwth Aachen University Germany NRW.BANK Kavalleriestrae 22 Dusseldorf40213 Germany

In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic market models: The Levy-Levy-Solomon model and the Franke-Westerhoff model. We derive time-continuous formulations of both models, and in particular we discuss the impact of the time-scaling on the model behavior for the Levy-Levy-Solomon model. For the Franke-Westerhoff model, we proof that a constraint required in the original model is not necessary for stability of the time-continuous model. It is shown that a semi-implicit discretization of the time-continuous system preserves this unconditional stability. In addition, this semi-implicit discretization can be computed at cost comparable to the original model. Furthermore, we discuss possible probabilistic descriptions of time continuous agent-based computational economic market models. Especially, we present the potential advantages of kinetic theory in order to derive mesoscopic desciptions of agent-based models. Exemplified, we show two probabilistic descriptions of the Levy-Levy-Solomon and Franke-Westerhoff *** Codes 91B69, 91-10, 91B80, 82C40, 37A50 Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Academic access data analysis for literature recommendation 23rd

Academic access data analysis for literature recommendation

引用

23rd China conference on Information Retrieval, CCIR 2017

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Academic reading plays an important role in researchers’ daily life. To alleviate the burden of seeking relevant literature from rapidly growing academic repository, different kinds of recommender systems have been introduced in recent years. However, most existing work focused on adopting traditional recommendation techniques, like content-based filtering or collaborative filtering, in the literature recommendation scenario. Little work has yet been done on analyzing the academic reading behaviors to understand the reading patterns and information needs of real-world academic users, which would be a foundation for improving existing recommender systems or designing new ones. In this paper, we aim to tackle this problem by carrying out empirical analysis over large scale academic access data, which can be viewed as a proxy of academic reading behaviors. We conduct global, group-based and sequence-based analysis to address the following questions: (1) Are there any regularities in users’ academic reading behaviors? (2) Will users with different levels of activeness exhibit different information needs? (3) How to correlate one’s future demands with his/her historical behaviors? By answering these questions, we not only unveil useful patterns and strategies for literature recommendation, but also identify some challenging problems for future development. © Springer International Publishing AG 2017.

关键词： Recommender systems

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AI-enhanced Citizen science Discovery of an Active Asteroid: (410590) 2008 GB140

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Research Notes of the AAS 2024年第2期8卷 50-50页

作者： Colin Orion Chandler Nima Sedaghat William J. Oldroyd Chadwick A. Trujillo William A. Burris Henry H. Hsieh Jay K. Kueny Kennedy A. Farrell Jarod A. DeSpain Mark Jesus Mendoza Magbanua Scott S. Sheppard Michele T. Mazzucato Milton K. D. Bosch Tiffany Shaw-Diaz Virgilio Gonano Al Lamperti José A. da Silva Campos Brian L. Goodwin Ivan A. Terentev Charles J. A. Dukes Dept. of Astronomy & the DiRAC Institute University of Washington 3910 15th Ave NE Seattle WA 98195 USA coc123@uw.edu LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue Tucson AZ 85721 USA Dept. of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 Flagstaff AZ 86011 USA Raw Data Speaks Initiative Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 Tucson AZ 85719 USA Institute of Astronomy and Astrophysics Academia Sinica P.O. Box 23-141 Taipei 10617 Taiwan National Science Foundation Graduate Research Fellow University of Arizona Dept. of Astronomy and Steward Observatory 933 North Cherry Avenue Rm. N204 Tucson AZ 85721 USA Wyant College of Optical Sciences University of Arizona 1630 E. University Blvd. Tucson AZ 85721 USA Dept. of Laboratory Medicine University of California San Francisco 2340 Sutter Street San Francisco CA 94143 USA Dept. of Terrestrial Magnetism Carnegie Institution for Science 5241 Broad Branch Road. NW Washington DC 20015 USA Active Asteroids Citizen Scientist Royal Astronomical Society Burlington House Piccadilly London W1J 0BQ UK Physical Sciences Group Siena Academy of Sciences Piazzetta Silvio Gigli 2 53100 Siena Italy Delaware Valley Amateur Astronomers 112 Pebble Beach Drive Royersford PA 19468 USA Astronomical Society of Southern Africa P.O. Box 9 Observatory 7935 Cape Town South Africa

We report the discovery of cometary activity emanating from Main-belt asteroid 410590 (2008 GB140), a finding facilitated, for the first time, by an artificial intelligence (AI) assistant. The assistant, TailNet, is a...

We report the discovery of cometary activity emanating from Main-belt asteroid 410590 (2008 GB₁₄₀), a finding facilitated, for the first time, by an artificial intelligence (AI) assistant. The assistant, TailNet, is a prototype we designed to enhance volunteer efforts of our Citizen science project Active Asteroids, a NASA Partner program hosted on the Zooniverse platform. Our follow-up investigation revealed eight Dark Energy Camera images showing 2008 GB₁₄₀ with a tail spanning UT 2023 April 23–UT 2023 July 3, when the object was inbound to perihelion. We classify 2008 GB₁₄₀ as an active asteroid and a candidate Main-belt comet (MBC)—a main-belt asteroid that undergoes volatile sublimation-driven activity. Notably, 2008 GB₁₄₀ is presently near perihelion, thus the object is a prime target for follow-up observations to further characterize its activity.

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DP03 Melanoma detection in whole-slide images using a convolutional neural network for objective prognostic biomarker generation

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British Journal of Dermatology 2024年第SUPPLEMENT_1期191卷 i108–i108页

作者： Clarke, Emily L Magee, Derek Newton-Bishop, Julia Merchant, William Hall, Marlous Insall, Robert Maher, Nigel G Scolyer, Richard A Farnworth, Grace Ali, Anisah Saldanha, Gerald Bamford, Mark Sitcova, Eva Kujal, Petr O’Shea, Sally Treanor, Darren Department of Histopathology Leeds Teaching Hospitals NHS Trust Leeds UK Division of Pathology and Data Analytics University of Leeds Leeds UK School of Computing University of Leeds Leeds UK Leeds Institute for Medical Research University of Leeds Leeds UK Department of Clinical and Population Sciences Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Department of Cell and Developmental Biology University College London London UK Melanoma Institute Australia The University of Sydney Sydney Australia Faculty of Medicine and Health The University of Sydney Sydney Australia Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital and NSW Health Pathology Sydney Australia Charles Perkins Centre The University of Sydney Sydney NSW Australia Leicester Cancer Research Centre University of Leicester Leicester UK Department of Histopathology University Hospitals of Leicester NHS Trust Leicester UK Department of Pathology Third Faculty of Medicine Charles University and Teaching Hospital Královské Vinohrady Prague Czechia Dermatology Department South Infirmary Victoria University Hospital Cork Ireland School of Medicine University College Cork Ireland Department of Clinical Pathology and Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of interobserver and intraobserver variation. Melanoma morphology appears to be of greater significance than in other solid tumours, with Breslow thickness remaining the strongest prognostic indicator. The application of convolutional neural networks (CNNs) to whole-slide images (WSIs) may reveal new insights into tumour morphology and therefore patient prognosis. This work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, to enable the creation of objective prognostic biomarkers based on the tissue morphology. In total, 1157 WSIs containing cutaneous melanoma from five sources have been used in the initial development and evaluation of a custom-designed two-class tumour segmentation network with a fully convolutional architecture. The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9%, respectively across, the five test sets (three external). There were no statistical differences between tumour dimensions generated by the CNN compared with manual annotation. Similarly, there were no statistically significant differences between CNN-generated tumour dimensions across three scanning platforms. Furthermore, we have identified that this CNN can be used to calculate the ‘digital Breslow thickness’, which is a strong independent prognostic predictor of overall survival and melanoma-specific survival, across three test sets with follow-up data (hazard ratio 1.26, 95% confidence interval 1.19–1.34,P< 0.001). We have also shown that the ‘nodularity index’ determined by the tumour shape, independently of size, is predictive of survival; the rounder the tumour the worse the outcome (hazard ratio 0.71, 95% confidence interval 0.60–0.83,P< 0.001). We have developed and performed initial evaluation of a CN

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