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Gammapy: A Python package for gamma-ray astronomy

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arXiv 2023年

作者： Donath, Axel Terrier, Régis Remy, Quentin Sinha, Atreyee Nigro, Cosimo Pintore, Fabio Khélifi, Bruno Olivera-Nieto, Laura Ruiz, Jose Enrique Brügge, Kai Linhoff, Maximilian Contreras, Jose Luis Acero, Fabio Aguasca-Cabot, Arnau Berge, David Bhattacharjee, Pooja Buchner, Johannes Boisson, Catherine Fidalgo, David Carreto Chen, Andrew de Lavergne, Mathieu de Bony de Miranda Cardoso, José Vinícius Deil, Christoph Füßling, Matthias Funk, Stefan Giunti, Luca Hinton, Jim Jouvin, Léa King, Johannes Lefaucheur, Julien Lemoine-Goumard, Marianne Lenain, Jean-Philippe López-Coto, Rubén Mohrmann, Lars Morcuende, Daniel Panny, Sebastian Regeard, Maxime Saha, Lab Siejkowski, Hubert Siemiginowska, Aneta Sipőcz, Brigitta M. Unbehaun, Tim van Eldik, Christopher Vuillaume, Thomas Zanin, Roberta Center for Astrophysics Harvard and Smithsonian United States Université de Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Max-Planck-Institut für Kernphysik P.O. Box 103980 HeidelbergD 69029 Germany IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain INAF/IASF Palermo Via U. La Malfa 153 PA Palermo90146 Italy Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Point8 GmbH Germany Astroparticle Physics Department of Physics TU Dortmund University Otto-Hahn-Str. 4a DortmundD-44227 Germany Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 France c. Martí i Franqués 1 Barcelona08028 Spain c. Martí i Franqués 1 Barcelona08028 Spain c. Gran Capità 2-4 Barcelona08034 Spain Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Martí i Franquès 1 Barcelona08028 Spain ZeuthenD-15738 Germany Institute of physics Humboldt-University of Berlin BerlinD-12489 Germany Université Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 Annecy74000 France Max Planck Institute for extraterrestrial Physics Giessenbachstrasse Garching85748 Germany Laboratoire Univers et Théories Observatoire de Paris Université PSL Université Paris Cité CNRS MeudonF-92190 France Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany School of Physics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg2050 South Africa The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Saupfercheckweg Heidelberg1 69117 Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Nikolaus-Fiebiger Strasse 2 Erlangen91058 Germany IRFU CEA Université Pa

Context. Traditionally, TeV-γ-ray astronomy has been conducted by experiments employing proprietary data and analysis software. However, the next generation of γ-ray instruments, such as the Cherenkov Telescope Array Observatory (CTAO), will be operated as open observatories. Alongside the data, they will also make the associated software tools available to a wider community. This necessity prompted the development of open, high-level, astronomical software customized for high-energy astrophysics. Aims. In this article, we present Gammapy, an open-source Python package for the analysis of astronomical γ-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different γ-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Methods. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting γ-ray instruments. After the data are binned, the flux and morphology of one or more γ-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. Results. After describing the structure of the package, we show, using publicly available gamma-ray data, the capabilities of Gammapy in multiple traditional and novel γ-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexib

关键词： Gamma rays

A question answering approach to emotion cause extraction

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arXiv 2017年

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

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. Copyright © 2017, The Authors. All rights reserved.

关键词： Extraction

Internet Traffic Analysis Using Community Detection and Apache Spark

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Internet Traffic Analysis Using Community Detection and Apac...

第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

作者： Jiake Ni Weitao Weng Jiayu Chen Kai Lei Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronic and Computer Engineering(SECE)Peking University SHENZHEN 518055P.R.CHINA

With the rapid development of Internet,Internet traffic and end hosts continue to grow in *** behavior analysis for a large-scale network is becoming more and more *** address these challenges,this paper proposes an Internet traffic analysis approach based on community detection to discover community consisted of end hosts with similar traffic behavior in a large campus ***,we use only the IP-to-IP information without packet payloads to model the similarity of end hosts in campus *** the similarity graph which represent the social behavior similarity of all end hosts is ***,we leverage Label Propagation algorithm to discover end hosts community on the similarity *** satisfy demands for the scalable analysis of evergrowing Internet traffic data,a Spark-based Internet traffic analysis system is developed,including implementing the above *** experimental results based on real campus network traffic show the benefits of the proposed approach in analyzing traffic behavior of a large-scale network on host community level and detecting potential anomalous traffic *** proposed approach reduces the complexity of analyzing the traffic behavior of a large network compare with analyzing individual *** addition,the experimental results also demonstrate the Spark-based Internet traffic analysis system can analyze Internet traffic efficiently.

关键词： Internet traffic analysis community detection Apache Spark data mining

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Online hashing

arXiv 2017年

作者： Huang, Long-Kai Yang, Qiang Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China School of Computer Science and Engineering Nanyang Technological University Singapre Singapore Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this work proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space. Then, a structured hash model is derived and optimized in a passive-aggressive way. Theoretical analysis on the upper bound of the cumulative loss for the proposed online hash model is provided. Furthermore, we extend our online hashing from a single-model to a multi-model online hashing that trains multiple models so as to retain diverse online hashing models in order to avoid biased update. The competitive efficiency and effectiveness of the proposed online hash models are verified through extensive experiments on several large-scale datasets as compared to related hashing methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Hash functions

Publisher Correction: Extreme risk induced by communities in interdependent networks

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Communications Physics 2019年第1期2卷 1页

作者： Jiachen Sun Rui Zhang Ling Feng Christopher Monterola Xiao Ma Céline Rozenblat H. Eugene Stanley Boris Podobnik Yanqing Hu School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Institute for Molecular Engineering University of Chicago Chicago USA Institute of High Performance Computing A*STAR Singapore Singapore Department of Physics National University of Singapore Singapore Singapore Asian Institute of Management Metro Manila Philippines School of Data and Computer Science Sun Yat-sen University Guangzhou China Institute of Geography University of Lausanne Lausanne Switzerland Department of Physics Boston University Boston USA Faculty of Civil Engineering University of Rijeka Rijeka Croatia Faculty of Information Studies Novo Mesto Slovenia Zagreb School of Economics and Management Zagreb Croatia Luxembourg School of Business Luxembourg Luxembourg

Decentralizing MVCC by leveraging visibility

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arXiv 2017年

作者： Zhou, Xuan Zhou, Xin Yu, Zhengtai Guo, Hua Tan, Kian-Lee School of Data Science and Engineering East China Normal University 3663 Zhongshan North Road Shanghai200062 China DEKE Lab Renmin University of China 59 Zhongguancun Street Beijing100872 China School of Computing National University of Singapore 13 Computing Dr Singapore117417 Singapore

Educational video classification by using a transcript to image transform and supervised learning

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Educational video classification by using a transcript to im...

Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Houssem Chatbri Marlon Oliveira Kevin McGuinness Suzanne Little Keisuke Kameyama Paul Kwan Alistair Sutherland Noel E. O’Connor Insight Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland Faculty of Engineering Information and Systems University of Tsukuba Japan School of Science & Technology University of New England NSW Australia

In this work, we present a method for automatic topic classification of educational videos using a speech transcript transform. Our method works as follows: First, speech recognition is used to generate video transcripts. Then, the transcripts are converted into images using a statistical cooccurrence transformation that we designed. Finally, a classifier is used to produce video category labels for a transcript image input. For our classifiers, we report results using a convolutional neural network (CNN) and a principal component analysis (PCA) model. In order to evaluate our method, we used the Khan Academy on a Stick dataset that contains 2,545 videos, where each video is labeled with one or two of 13 categories. Experiments show that our method is effective and strongly competitive against other supervised learning-based methods.

关键词： Principal component analysis Transforms Visualization Feature extraction Training Convolutional neural networks Histograms

Feature extraction and evaluation of electricity load data with high precision

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Feature extraction and evaluation of electricity load data w...

IEEE International Conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Jiangqi Chen Ting Zhao Yang Yang Di Zhang Advanced Computing and Big Data Technology Laboratory of SGCC Global Energy Interconnection Research Institute Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781509045006

This paper summarizes the characteristics of the electricity load data collected every 15 minutes of power users. The single-day load data of single power user is taken as a 96-length vector, and the single-day data of n users is taken as a 96-column set. Single-user monthly data and annual data are described as a 30 (31) × 96 matrix and a 365 (366) × 96 matrix respectively. By comparing the wavelet transforms of multiple wavelet basis functions, the Daubechies 4 wavelet basis function is chosen as the wavelet transform basis function of the electricity load data according to the Shannon entropy of the obtained wavelet coefficients. The difference between the inverse wavelet transformed data and the original data is compared under various characteristic wavelet coefficients. Besides, the number of the characteristic wavelet coefficients and the error of the wavelet transform are determined. The results show that there is a high redundancy in the original power load data, and the storage space can be greatly compressed by wavelet transform to achieve feature extraction and data desensitization. Therefore, the feature extraction technology of power load data based on wavelet transform in this paper has potential application value and popularization value, and can produce high economic efficiency.

关键词： Wavelet transforms Load modeling Analytical models data models Entropy Monitoring

A note on semidefinite programming relaxations for polynomial optimization over a single sphere

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Science China Mathematics 2016年第8期59卷 1543-1560页

作者： HU Jiang JIANG Bo LIU Xin WEN ZaiWen Beijing International Center for Mathematical Research Peking University Research Center for Management Science and Data Analytics School of Information Management and EngineeringShanghai University of Finance and Economics LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems Science Chinese Academy of Sciences

We study two instances of polynomial optimization problem over a single sphere. The first problem is to compute the best rank-1 tensor approximation. We show the equivalence between two recent semidefinite relaxations methods. The other one arises from Bose-Einstein condensates(BEC), whose objective function is a summation of a probably nonconvex quadratic function and a quartic term. These two polynomial optimization problems are closely connected since the BEC problem can be viewed as a structured fourth-order best rank-1 tensor approximation. We show that the BEC problem is NP-hard and propose a semidefinite relaxation with both deterministic and randomized rounding procedures. Explicit approximation ratios for these rounding procedures are presented. The performance of these semidefinite relaxations are illustrated on a few preliminary numerical experiments.

关键词： polynomial optimization over a single sphere semidefinite programming best rank-1 tensor ap-proximation Bose-Einstein condensates