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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Yijin Guan Hao Liang Ningyi Xu Wenqiang Wang Shaoshuai Shi Xi Chen Guangyu Sun Wei Zhang Jason Cong Center for Energy-Efficient Computing and Applications Peking University Beijing China PKU/UCLA Joint Research Institute Microsoft Research Asia Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China PKU/UCLA Joint Research Institute in Science and Engineering UCLA PKU/UCLA Joint Research Institute Peking University Computer Science Department University of California Los Angeles USA

DNNs (Deep Neural Networks) have demonstrated great success in numerous applications such as image classification, speech recognition, video analysis, etc. However, DNNs are much more computation-intensive and memory-intensive than previous shallow models. Thus, it is challenging to deploy DNNs in both large-scale data centers and real-time embedded systems. Considering performance, flexibility, and energy efficiency, FPGA-based accelerator for DNNs is a promising solution. Unfortunately, conventional accelerator design flows make it difficult for FPGA developers to keep up with the fast pace of innovations in DNNs. To overcome this problem, we propose FP-DNN (Field Programmable DNN), an end-to-end framework that takes TensorFlow-described DNNs as input, and automatically generates the hardware implementations on FPGA boards with RTL-HLS hybrid templates. FP-DNN performs model inference of DNNs with our high-performance computation engine and carefully-designed communication optimization strategies. We implement CNNs, LSTM-RNNs, and Residual Nets with FPDNN, and experimental results show the great performance and flexibility provided by our proposed FP-DNN framework.

关键词： Computational modeling Field programmable gate arrays Kernel Hardware Analytical models Data models Generators

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Profiling and analysis of object lazy allocation in Java programs

Profiling and analysis of object lazy allocation in Java pro...

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Jianjun Shi Weixing Ji Lulu Zhang Yujin Gao Han Zhang Duzheng Qing School of Computer Science Beijing Institute of Technology Beijing China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

Lazy allocation strategy allows the memory management system to defer the space allocation action of objects until they are being accessed. This paper investigates the potential benefits of a lazy allocator for Java applications. A heap tracing tool is implemented by instrumenting an existing Java virtual machine HotSpot, which records useful object manipulating events at runtime. By profiling and analyzing a large number of benchmarks, we show the potential dynamic memory management optimization opportunity in Java programs. We also designed a simulation system to demonstrate the actual effects of a lazy allocator.

关键词： Resource management Java Benchmark testing Memory management Virtual machining Color Runtime

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Phonetic temporal neural model for language identification

arXiv

引用

arXiv 2017年

作者： Tang, Zhiyuan Wang, Dong Chen, Yixiang Li, Lantian Abel, Andrew Chengdu Institute of Computer Applications Chinese Academy of Sciences and University of Chinese Academy of Sciences Beijing100049 China Center for Speech and Language Technologies Tsinghua University Beijing100084 China Tsinghua National Laboratory for Information Science and Technology and the Center for Speech and Language Technologies Tsinghua University Beijing100084 China Department of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou215123 China

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Radiative Cooling of the Thermally Isolated System in KAGRA Gravitational Wave Telescope

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Journal of Physics: Conference Series 2021年第1期1857卷

作者： T. Akutsu M. Ando K. Arai Y. Arai S. Araki A. Araya N. Aritomi Y. Aso S.-W. Bae Y.-B. Bae L. Baiotti R. Bajpai M. A. Barton K. Cannon E. Capocasa M.-L. Chan C.-S. Chen K.-H. Chen Y.-R. Chen H.-Y. Chu Y.-K. Chu S. Eguchi Y. Enomoto R. Flaminio Y. Fujii M. Fukunaga M. Fukushima G.-G. Ge A. Hagiwara S. Haino K. Hasegawa H. Hayakawa K. Hayama Y. Himemoto Y. Hiranuma N. Hirata E. Hirose Z. Hong B.-H. Hsieh G.-Z. Huang P.-W. Huang Y.-J. Huang B. Ikenoue S. Imam K. Inayoshi Y. Inoue K. Ioka Y. Itoh K. Izumi K. Jung P. Jung T. Kajita M. Kamiizumi N. Kanda G.-W. Kang K. Kawaguchi N. Kawai T. Kawasaki C. Kim J. Kim W. Kim Y.-M. Kim N. Kimura N. Kita H. Kitazawa Y. Kojima K. Kokeyama K. Komori A. K. H. Kong K. Kotake C. Kozakai R. Kozu R. Kumar J. Kume C.-M. Kuo H.-S. Kuo S. Kuroyanagi K. Kusayanagi K. Kwak H.-K. Lee H.-W. Lee R.-K. Lee M. Leonardi C.-Y. Lin F.-L. Lin L. C.-C. Lin G.-C. Liu L.-W. Luo M. Marchio Y. Michimura N. Mio O. Miyakawa A. Miyamoto Y. Miyazaki K. Miyo S. Miyoki S. Morisaki Y. Moriwaki K. Nagano S. Nagano K. Nakamura H. Nakano M. Nakano R. Nakashima T. Narikawa R. Negishi W.-T. Ni A. Nishizawa Y. Obuchi W. Ogaki J. J. Oh S.-H. Oh M. Ohashi N. Ohishi M. Ohkawa K. Okutomi K. Oohara C.-P. Ooi S. Oshino K.-C. Pan H.-F. Pang J. Park F. E. Peiia Arellano I. Pinto N. Sago S. Saito Y. Saito K. Sakai Y. Sakai Y. Sakuno S. Sato T. Sato T. Sawada T. Sekiguchi Y. Sekiguchi S. Shibagaki R. Shimizu T. Shimoda K. Shimode H. Shinkai T. Shishido A. Shoda K. Somiya E. J. Son H. Sotani R. Sugimoto T. Suzuki H. Tagoshi H. Takahashi R. Takahashi A. Takamori S. Takano H. Takeda M. Takeda H. Tanaka K. Tanaka T. Tanaka S. Tanioka E. N. Tapia San Martin S. Telada T. Tomaru Y. Tomigami T. Tomura F. Travasso L. Trozzo T. T. L. Tsang K. Tsubono S. Tsuchida T. Tsuzuki D. Tuyenbayev N. Uchikata T. Uchiyama A. Ueda T. Uehara K. Ueno G. Ueshima F. Uraguchi T. Ushiba M. H. P. M. van Putten H. Vocca J. Wang C.-M. Wu H.-C. Wu S.-R. Wu W.-R. Xu T. Yamada K. Yamamoto T. Yamamoto K. Yokogawa J. Yokoyama T. Yokozawa T. Yoshioka H. Yuzurihar National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka-City Tokyo 181-8588 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa-City Chiba 277-8582Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 OhoTsukuba-City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1Yayoi Bunkyo-ku Tokyo 113-0032Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida-City Gifu Pref. Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka-City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 South Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 beon-gil Yuseong-gu Daejeon 34047 South Korea Graduate School of Science Osaka University 1-1 Machikaneyama-cho Toyonaka-City Osaka560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba-City Ibaraki 305-0801 Japan Institute for Gravitational Research University of Glasgow Kelvin Building G12 8QQ UK Department of Applied Physics Fukuoka University Fukuoka Jonan Nanakuma 814-0180 Japan Department of Physics Tamkang university No.151 Yingzhuan Rd. Danshui Dist. New Taipei-City 25137 Taiwan Department of Physics the Center for High Energy and High Field Physics National Central University No. 300 Zhongda Road Zhongyi District Taiyuan-City 320 Taiwan Institute of Astronomy National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan Department of Physics National Tsing Hua University No. 101 Section 2

Radiative cooling of the thermally isolated system is investigated in KAGRA gravitational wave telescope. KAGRA is a laser interferometer-based detector and main mirrors constituting optical cavities are cool down to 20K to reduce noises caused by the thermal fluctuation. The mirror is suspended with the multi-stage pendulum to isolate any vibration. Therefore, this mirror suspension system has few heat links to reduce vibration injection. Thus, this system is mainly cooled down with thermal radiation. In order to understand the process of radiative cooling of the mirror, we analyzed cooling curve based on mass and specific heat. As a result, it was newly found that a cryogenic part called 'cryogenic duct-shield' seems to have large contribution in the beginning of the mirror cooling. This finding will help to design new cooling system for the next generation cryogenic gravitational wave detector.

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Vibration isolation system with a compact damping system for power recycling mirrors of KAGRA

arXiv

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arXiv 2019年

作者： Akiyama, Y. Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Baiotti, L. Barton, M.A. Cannon, K. Capocasa, E. Chen, C.-S. Chiu, T.-W. Cho, K. Chu, Y.-K. Craig, K. Dattilo, V. Doi, K. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Fukunaga, M. Fukushima, M. Furuhata, T. Haino, S. Hasegawa, K. Hashimoto, Y. Hashino, K. Hayama, K. Hirayama, T. Hirose, E. Hsieh, B.H. Huang, C.-Z. Ikenoue, B. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kang, G. Kasuya, J. Kawai, N. Kawasaki, T. Kim, C. Kim, W.S. Kim, J. Kim, J.C. Kimura, N. Kirii, S. Kitaoka, Y. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. Kotake, K. Kozu, R. Kumar, R. Kuo, H.-S. Kuroki, S. Kuroyanagi, S. Lee, H.K. Lee, H.M. Lee, H.W. Leonardi, M. Lin, C.-Y. Lin, F.-L. Liu, G.C. Marchio, M. Matsui, T. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Musha, M. Nagano, S. Nagano, K. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Narikawa, T. Quynh, L. Nguyen Ni, W.-T. Nishizawa, A. Obuchi, Y. Oh, J. Oh, S.H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Ono, K. Oohara, K. Ooi, C.P. Pan, S.-S. Paoletti, F. Park, J. Passaquieti, R. Peña Arellano, F.E. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sasai, M. Sato, S. Sato, T. Sekiguchi, T. Sekiguchi, Y. Shibata, M. Shimoda, T. Shinkai, H. Shishido, T. Shoda, A. Someya, N. Somiya, K. Son, E.J. Suemasa, A. Suzuki, T. Suzuki, T. Tagoshi, H. Tahara, H. Takahashi, H. Takahashi, R. Takeda, H. Tanaka, H. Tanaka, K. Tanaka, T. Tanioka, S. San Martin, E.N. Tapia Tomaru, T. Tomura, T. Travasso, F. Tsubono, K. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Uraguchi, F. Ushiba, T. van Putten, M.H.P.M. Vocca, H. Wakamatsu, T. Watanabe, Y. Xu, W.-R. Yamada, T. Yamamoto, K. Yamamoto, K. Yamamoto, S. Yamamoto, T. Yokogawa, K. Yokoyama, J. Yokozawa, T. Yoshioka, T. Yuzurihara, H. Zeidler, S. Zhu, Z.-H. Graduate School of Science and Engineering Hosei University TokyoKoganei184-8584 Japan National Astronomical Observatory of Japan TokyoMitaka181-8588 Japan Department of Physics University of Tokyo TokyoBunkyo113-0033 Japan University of Tokyo KashiwaChiba277-8582 Japan High Energy Accelerator Research Organization TsukubaIbaraki305-0801 Japan Earthquake Research Institute University of Tokyo TokyoBunkyo113-0032 Japan Department of Physics University of Tokyo TokyoBunkyo113-0033 Japan Department of Mathematics and Physics Hirosaki University AomoriHirosaki036-8561 Japan National Astronomical Observatory of Japan GifuHida506-1205 Japan Korea Institute of Science and Technology Information Yuseong Daejeon34141 Korea Republic of Department of Physics Osaka University ToyonakaOsaka560-0043 Japan University of Tokyo TokyoBunkyo113-0033 Japan Department of Physics National Taiwan Normal University Taipei116 Taiwan Department of Physics Sogang University Seoul121-742 Korea Republic of PisaCascinaI-56021 Italy Department of Physics University of Toyama ToyamaToyama930-8555 Japan Department of Astronomy University of Tokyo TokyoBunkyo113-0032 Japan Advanced Technology Center National Astronomical Observatory of Japan TokyoMitaka181-8588 Japan Institute of Physics Academia Sinica TaipeiNankang11529 Taiwan Department of Applied Physics Fukuoka University FukuokaJonan814-0180 Japan Yukawa Institute for Theoretical Physics Kyoto University KyotoSakyo606-8502 Japan Graduate School of Science Osaka City University OsakaSumiyosi558-8585 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency SagamiharaKanagawa252-5210 Japan University of Tokyo GifuHida506-1205 Japan Graduate Schoool of Science and Technology Tokyo Institute of Technology MeguroTokyo152-8551 Japan Department of Physics Ewha Womans University SeoulSeodaemun-gu03760 Korea Republic of National Institute for

A vibration isolation system called Type-Bp system used for power recycling mirrors has been developed for KAGRA, the interferometric gravitational-wave observatory in Japan. A suspension of the Type-Bp system passively isolates an optic from seismic vibration using three main pendulum stages equipped with two vertical vibration isolation systems. A compact reaction mass around each of the main stages allows for achieving sufficient damping performance with a simple feedback as well as vibration isolation ratio. Three Type-Bp systems were installed in KAGRA, and were proved to satisfy the requirements on the damping performance, and also on estimated residual displacement of the optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Mirrors

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Scattering center modelling based on compressed sensing principle from under-sampling scattering field data

Scattering center modelling based on compressed sensing prin...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qi-Feng Li Kun-Yi Guo Bo Tang Xin-Qing Sheng Center for Electromagnetic Simulation Beijing Institute of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509033331

It has been proved that scattering center modeling for narrow band signals can be achieved through the optimum image matching of time-frequency representation (TFR). In order to obtain TFR, however, the azimuth sampling interval should be quite small for electrically large target, which results in a huge amount of electromagnetic computation. Under the situation of under-sampling scattered waves, aliasing distortions of the Doppler curves of scattering centers will be caused in TFR, which makes it hard to estimate the parameters of corresponding scattering centers from TFR. To deal with this problem, an approach for scattering center modelling from under-sampling scattered waves are presented in this paper. The random under-sampling scattered waves based on the principle of compressive sensing are applied to acquire the non-aliasing Doppler curves of scattering centers in TFR. The feasibility of this method has been validated by the simulation results.

关键词： Doppler effect Time-frequency analysis Radar imaging Radar scattering Synthetic aperture radar

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Enhanced null message algorithm for hybrid parallel simulation systems with large disparity in time step

Enhanced null message algorithm for hybrid parallel simulati...

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International Workshop/Symposium on Distributed simulation and Real-Time applications (DS-RT)

作者： Bin Wang Yanlong Zhai Zishuo Wang Han Zhang Duzheng Qing Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Applications Bijing Institute of Technology Beijing China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center China

ISBN: (纸本)9781509035069

Modeling and simulation has received significant attention over the years for its ability to understand and transform real world systems. With the increasing of complexity and scale of the simulated systems, more computational power is required. Parallel discrete event simulation(PDES) is an important technology to integrate parallel computing into modeling and simulation. Time synchronization algorithm is the core of PDES, which determines the correctness and efficiency of the simulation. Traditional conservative synchronization algorithms, such as Chandy-Misra-Bryant(CMB) null message algorithm, use null message to synchronize the state and avoid deadlock among logical processes(LPs). But hybrid simulation systems contains both continuous models and discrete models, continuously changing states is typically modeled using time steps in hybrid simulation system. Therefore, hybrid systems with large disparity in time step are very common in real simulation systems. However, current conservative synchronization algorithm has not been fully investigated for this type of simulation systems and will generate massive synchronization messages. This paper proposed an enhanced null message algorithm for hybrid simulation systems with large disparity in time step. It extended the CMB algorithm by adding simulation time in null message request messages. This could significantly reduce the number of synchronization messages. Experimental results showed that our synchronization algorithm outperforms the CMB algorithm by reducing a great number of synchronization messages.

关键词： Synchronization Computational modeling Atmospheric modeling Adaptation models System recovery Ports (computers) Complex systems

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A system-of-systems architecture-driven modeling method for combat system effectiveness simulation 2

A system-of-systems architecture-driven modeling method for ...

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2nd Annual IEEE International Symposium on Systems Engineering, ISSE 2016

作者： Li, Xiaobo Wang, Weiping Shu, Zhe Zhu, Ning He, Hua Liao, Tianjun College of Information Systems and Management National University of Defense Technology Changsha Hunan410073 China Department of Mathematics and Computer Science University of Antwerp Antwerp2020 Belgium State Key Laboratory of Complex System Simulation Beijing Institute of Systems Engineering Beijing China

ISBN: (纸本)9781509007936

Under the background of system-of-systems (SoS) counterwork, it is more apparent that the simulation modeling of the equipment should be domain-specific, formal, automatic and composable. Current equipment effectiveness simulation modeling approaches are mainly based on the methods and techniques from the modeling and simulation (M&S) community and software engineering community, and they are not capable to satisfy these four requirements systematically. Thus the systems engineering architecture model and domain knowledge should be used to support effectiveness simulation modeling more effectively. In this research, we propose an architecture-driven modeling method which conforms to the principle of architecture-driven development, uses ontology techniques to build equipment SoS architecture model and sub-domain ontology. This method performs architecture driven simulation modeling to realize the transformation from architecture models to simulation model frameworks;employs ontological metamodeling to design domain-specific modeling languages (DSML) based on the comprehensive usage of architecture models, sub-domain ontologies and M&S formalisms;integrates domain-specific simulation models from various domains using the model framework, and supports the composable development of simulation applications. © 2016 IEEE.

关键词： System of systems

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OC16-CE80: A Chinese-English mixlingual database and a speech recognition baseline 19

OC16-CE80: A Chinese-English mixlingual database and a speec...

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19th Annual Conference of the Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2016

作者： Wang, Dong Tang, Zhiyuan Tang, Difei Chen, Qing Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Center for Speech and Language Technologies Research Institute of Information Technology Tsinghua University China Chengdu Institute of Computer Applications Chinese Academy of Sciences China SpeechOcean Beijing China

ISBN: (纸本)9781509035168

We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixhngual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded from more than 1,400 speakers, where the utterances are in Chinese but each involves one or several Enghsh words. Based on the database and another two free data resources (THCHS30 and the CMU dictionary), a speech recognition (ASR) baseline was constructed with the deep neural network-hidden Markov model (DNN-HMM) hybrid system. We then report the baseline results following the MixASR-CHEN evaluation rules and demonstrate that OC16-CE80 is a reasonable data resource for mixlingual research. © 2016 IEEE.

关键词： Database systems

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Correlation-compressed direct coupling analysis

arXiv

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arXiv 2017年

作者： Gao, Chen-Yi Zhou, Hai-Jun Aurell, Erik Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Department of Computational Biology KTH-Royal Institute of Technology StockholmSE-10044 Sweden School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Depts of Applied Physics and Computer Science Aalto University AaltoFIN-00076 Finland Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha Hunan410081 China

Learning Ising or Potts models from data has become an important topic in statistical physics and computational biology, with applications to predictions of structural contacts in proteins and other areas of biological data analysis. The corresponding inference problems are challenging since the normalization constant (partition function) of the Ising/Potts distributions cannot be computed efficiently on large instances. Different ways to address this issue have hence given size to a substantial methodological literature. In this paper we investigate how these methods could be used on much larger datasets than studied previously. We focus on a central aspect, that in practice these inference problems are almost always severely under-sampled, and the operational result is almost always a small set of leading (largest) predictions. We therefore explore an approach where the data is pre-filtered based on empirical correlations, which can be computed directly even for very large problems. Inference is only used on the much smaller instance in a subsequent step of the analysis. We show that in several relevant model classes such a combined approach gives results of almost the same quality as the computationally much more demanding inference on the whole dataset. We also show that results on whole-genome epistatic couplings that were obtained in a recent computation-intensive study can be retrieved by the new approach. The method of this paper hence opens up the possibility to learn parameters describing pair-wise dependencies in whole genomes in a computationally feasible and expedient manner. Copyright © 2017, The Authors. All rights reserved.

关键词： Potts model

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