Pub Date : 2016-12-20DOI: 10.1109/ICASSP.2016.7472719
Pegah Ghahremani, J. Droppo
Deep neural network models have been successfully applied to many tasks such as image labeling and speech recognition. Mini-batch stochastic gradient descent is the most prevalent method for training these models. A critical part of successfully applying this method is choosing appropriate initial values, as well as local and global learning rate scheduling algorithms. In this paper, we present a method which is less sensitive to choice of initial values, works better than popular learning rate adjustment algorithms, and speeds convergence on model parameters. We show that using the Self-stabilized DNN method, we no longer require initial learning rate tuning and training converges quickly with a fixed global learning rate. The proposed method provides promising results over conventional DNN structure with better convergence rate.
{"title":"Self-stabilized deep neural network","authors":"Pegah Ghahremani, J. Droppo","doi":"10.1109/ICASSP.2016.7472719","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472719","url":null,"abstract":"Deep neural network models have been successfully applied to many tasks such as image labeling and speech recognition. Mini-batch stochastic gradient descent is the most prevalent method for training these models. A critical part of successfully applying this method is choosing appropriate initial values, as well as local and global learning rate scheduling algorithms. In this paper, we present a method which is less sensitive to choice of initial values, works better than popular learning rate adjustment algorithms, and speeds convergence on model parameters. We show that using the Self-stabilized DNN method, we no longer require initial learning rate tuning and training converges quickly with a fixed global learning rate. The proposed method provides promising results over conventional DNN structure with better convergence rate.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-18DOI: 10.1109/ICASSP.2016.7471748
H. Buchner, J. Skoglund, S. Godsill
In many teleconferencing applications using modern laptop and net-book devices it is common to encounter annoying keyboard typing noise. In this paper we propose an acoustic keystroke transient canceler for speech communication terminals as a novel broadband adaptive filter application in such a hands-free scenario. We present this approach in the context of the Google Chromebook Pixel device which is equipped with a special audio reference channel providing various new signal processing possibilities. Our novel semi-blind/semi-supervised approach exploiting this new degree of freedom, combined with the system-based broadband estimation and a novel adaptation control yields a high-quality speech enhancement even under challenging acoustic conditions.
{"title":"An acoustic keystroke transient canceler for speech communication terminals using a semi-blind adaptive filter model","authors":"H. Buchner, J. Skoglund, S. Godsill","doi":"10.1109/ICASSP.2016.7471748","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471748","url":null,"abstract":"In many teleconferencing applications using modern laptop and net-book devices it is common to encounter annoying keyboard typing noise. In this paper we propose an acoustic keystroke transient canceler for speech communication terminals as a novel broadband adaptive filter application in such a hands-free scenario. We present this approach in the context of the Google Chromebook Pixel device which is equipped with a special audio reference channel providing various new signal processing possibilities. Our novel semi-blind/semi-supervised approach exploiting this new degree of freedom, combined with the system-based broadband estimation and a novel adaptation control yields a high-quality speech enhancement even under challenging acoustic conditions.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129245874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-27DOI: 10.1109/TSP.2017.2691662
Dimitris Berberidis, G. Giannakis
In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper puts forth approaches to render tracking of large-scale dynamic processes affordable, by processing a reduced number of data. Two distinct methods are introduced for reducing the number of data involved per time step. The first method builds on reduction using low-complexity random projections, while the second performs censoring for data-adaptive measurement selection. Simulations on synthetic data, compare the proposed methods with competing alternatives, and corroborate their efficacy in terms of estimation accuracy over complexity reduction.
{"title":"Data sketching for large-scale Kalman filtering","authors":"Dimitris Berberidis, G. Giannakis","doi":"10.1109/TSP.2017.2691662","DOIUrl":"https://doi.org/10.1109/TSP.2017.2691662","url":null,"abstract":"In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper puts forth approaches to render tracking of large-scale dynamic processes affordable, by processing a reduced number of data. Two distinct methods are introduced for reducing the number of data involved per time step. The first method builds on reduction using low-complexity random projections, while the second performs censoring for data-adaptive measurement selection. Simulations on synthetic data, compare the proposed methods with competing alternatives, and corroborate their efficacy in terms of estimation accuracy over complexity reduction.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128729440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-16DOI: 10.1109/ICASSP.2016.7472400
Tim Schmitz, P. Jax, P. Vary
A drawback of digital transmission of analog signals is the unavoidable quantization error which leads to a limited quality even for good channel conditions. This saturation can be avoided by using analog transmission systems with discrete-time and quasi-continuous-amplitude encoding and decoding, e.g., Analog Modulo Block codes (AMB codes). The AMB code vectors are produced by multiplying a real-valued information vector with a real-valued generator matrix using a modulo arithmetic. Here, algorithms for improving the decoding performance are presented. The Lattice Maximum Likelihood (LML) decoder, a variant of the Discrete Maximum Likelihood (DML) decoder, is derived and analyzed. It refines the Zero Forcing (ZF) result if necessary, thus achieving near-ML signal quality with a reduced decoding complexity. A reduced complexity is essential for decoding high-dimensional code words. Additionally, pre- and post-processing methods are presented and analyzed, which increase the signal-to-distortion ratio (SDR) of the received symbols.
{"title":"Improved decoding of analog modulo block codes for noise mitigation","authors":"Tim Schmitz, P. Jax, P. Vary","doi":"10.1109/ICASSP.2016.7472400","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472400","url":null,"abstract":"A drawback of digital transmission of analog signals is the unavoidable quantization error which leads to a limited quality even for good channel conditions. This saturation can be avoided by using analog transmission systems with discrete-time and quasi-continuous-amplitude encoding and decoding, e.g., Analog Modulo Block codes (AMB codes). The AMB code vectors are produced by multiplying a real-valued information vector with a real-valued generator matrix using a modulo arithmetic. Here, algorithms for improving the decoding performance are presented. The Lattice Maximum Likelihood (LML) decoder, a variant of the Discrete Maximum Likelihood (DML) decoder, is derived and analyzed. It refines the Zero Forcing (ZF) result if necessary, thus achieving near-ML signal quality with a reduced decoding complexity. A reduced complexity is essential for decoding high-dimensional code words. Additionally, pre- and post-processing methods are presented and analyzed, which increase the signal-to-distortion ratio (SDR) of the received symbols.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130872105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-19DOI: 10.1109/ICASSP.2016.7471766
Marisel Villafañe-Delgado, Selin Aviyente
Functional connectivity brain networks have been shown to demonstrate interesting complex network behavior such as small-worldness. Transforming networks to time series has provided an alternative way of characterizing the structure of complex networks. However, previously proposed deterministic methods are limited to unweighted graphs. In this paper, we propose to employ the resistance distance matrix of weighted graphs as the distance matrix for transforming networks to signals based on classical multidimensional scaling. We present a framework for obtaining information about the network's structure through the mapped signals and recovering the original network using properties of the resistance matrix. Finally, the proposed method is applied to characterizing functional connectivity networks constructed from electroencephalogram data.
{"title":"Functional connectivity brain network analysis through network to signal transform based on the resistance distance","authors":"Marisel Villafañe-Delgado, Selin Aviyente","doi":"10.1109/ICASSP.2016.7471766","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471766","url":null,"abstract":"Functional connectivity brain networks have been shown to demonstrate interesting complex network behavior such as small-worldness. Transforming networks to time series has provided an alternative way of characterizing the structure of complex networks. However, previously proposed deterministic methods are limited to unweighted graphs. In this paper, we propose to employ the resistance distance matrix of weighted graphs as the distance matrix for transforming networks to signals based on classical multidimensional scaling. We present a framework for obtaining information about the network's structure through the mapped signals and recovering the original network using properties of the resistance matrix. Finally, the proposed method is applied to characterizing functional connectivity networks constructed from electroencephalogram data.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131751401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-19DOI: 10.1109/ICASSP.2016.7472519
D. Valsesia, P. Boufounos
We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scalar quantization. Our approach can be interpreted as a variation of quantized compressed sensing, where the most significant bits are discarded at the encoder and recovered at the decoder from the side information. The image is reconstructed using weighted total variation minimization, incorporating side information in the weights while enforcing consistency with the recovered quantized coefficient values. Our experiments validate our approach and confirm the improvements in rate-distortion performance.
{"title":"Universal encoding of multispectral images","authors":"D. Valsesia, P. Boufounos","doi":"10.1109/ICASSP.2016.7472519","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472519","url":null,"abstract":"We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scalar quantization. Our approach can be interpreted as a variation of quantized compressed sensing, where the most significant bits are discarded at the encoder and recovered at the decoder from the side information. The image is reconstructed using weighted total variation minimization, incorporating side information in the weights while enforcing consistency with the recovered quantized coefficient values. Our experiments validate our approach and confirm the improvements in rate-distortion performance.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122740739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-19DOI: 10.1109/ICASSP.2016.7472358
Chuili Kong, C. Zhong, M. Matthaiou, Emil Björnson, Zhaoyang Zhang
We consider a multi-pair two-way amplify-and-forward relaying system with a massive antenna array at the relay and estimated channel state information, assuming maximum-ratio combining/transmission processing. Closed-form approximations of the sum spectral efficiency are developed and simple analytical power scaling laws are presented, which reveal a fundamental trade-off between the transmit powers of each user/the relay and of each pilot symbol. Finally, the optimal power allocation problem is studied.
{"title":"Multi-pair two-way AF relaying systems with massive arrays and imperfect CSI","authors":"Chuili Kong, C. Zhong, M. Matthaiou, Emil Björnson, Zhaoyang Zhang","doi":"10.1109/ICASSP.2016.7472358","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472358","url":null,"abstract":"We consider a multi-pair two-way amplify-and-forward relaying system with a massive antenna array at the relay and estimated channel state information, assuming maximum-ratio combining/transmission processing. Closed-form approximations of the sum spectral efficiency are developed and simple analytical power scaling laws are presented, which reveal a fundamental trade-off between the transmit powers of each user/the relay and of each pilot symbol. Finally, the optimal power allocation problem is studied.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130720195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-19DOI: 10.1109/ICASSP.2016.7472386
Yunyan Chang, P. Jung, Chan Zhou, S. Stańczak
In this paper, we utilize the framework of compressed sensing (CS) for device detection and distributed resource allocation in large-scale machine-to-machine (M2M) communication networks. The devices are partitioned into clusters according to some pre-defined criteria, e.g., proximity or service type. Moreover, by the sparse nature of the event occurrence in M2M communications, the activation pattern of the M2M devices can be formulated as a particular block sparse signal with additional in-block structure in CS based applications. This paper introduces a novel scheme for distributed resource allocation to the M2M devices based on block-CS related techniques, which mainly consists of three phases: (1) In a full-duplex acquisition phase, the network activation pattern is collected in a distributed manner. (2) The base station detects the active clusters and the number of active devices in each cluster, and then assigns a certain amount of resources accordingly. (3) Each active device detects the order of its index among all the active devices in the cluster and accesses the corresponding resource for transmission. The proposed scheme can efficiently reduce the acquisition time with much less computation complexity compared with standard CS algorithms. Finally, extensive simulations confirm the robustness of the proposed scheme under noisy conditions.
{"title":"Block compressed sensing based distributed resource allocation for M2M communications","authors":"Yunyan Chang, P. Jung, Chan Zhou, S. Stańczak","doi":"10.1109/ICASSP.2016.7472386","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472386","url":null,"abstract":"In this paper, we utilize the framework of compressed sensing (CS) for device detection and distributed resource allocation in large-scale machine-to-machine (M2M) communication networks. The devices are partitioned into clusters according to some pre-defined criteria, e.g., proximity or service type. Moreover, by the sparse nature of the event occurrence in M2M communications, the activation pattern of the M2M devices can be formulated as a particular block sparse signal with additional in-block structure in CS based applications. This paper introduces a novel scheme for distributed resource allocation to the M2M devices based on block-CS related techniques, which mainly consists of three phases: (1) In a full-duplex acquisition phase, the network activation pattern is collected in a distributed manner. (2) The base station detects the active clusters and the number of active devices in each cluster, and then assigns a certain amount of resources accordingly. (3) Each active device detects the order of its index among all the active devices in the cluster and accesses the corresponding resource for transmission. The proposed scheme can efficiently reduce the acquisition time with much less computation complexity compared with standard CS algorithms. Finally, extensive simulations confirm the robustness of the proposed scheme under noisy conditions.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-19DOI: 10.1109/ICASSP.2016.7472895
Xiong Xiao, Shengkui Zhao, Thi Ngoc Tho Nguyen, Douglas L. Jones, Chng Eng Siong, Haizhou Li
This paper presents an eigenvector clustering approach for estimating the direction of arrival (DOA) of multiple speech signals using a microphone array. Existing clustering approaches usually only use low frequencies to avoid spatial aliasing. In this study, we propose a probabilistic eigenvector clustering approach to use all frequencies. In our work, time-frequency (TF) bins dominated by only one source are first detected using a combination of noise-floor tracking, onset detection and coherence test. For each selected TF bin, the largest eigenvector of its spatial covariance matrix is extracted for clustering. A mixture density model is introduced to model the distribution of the eigenvectors, where each component distribution corresponds to one source and is parameterized by the source DOA. To use eigenvectors of all frequencies, the steering vectors of all frequencies of the sources are used in the distribution function. The DOAs of the sources can be estimated by maximizing the likelihood of the eigenvectors using an expectation-maximization (EM) algorithm. Simulation and experimental results show that the proposed approach significantly improves the root-mean-square error (RMSE) for DOA estimation of multiple speech sources compared to the MUSIC algorithm implemented on the single-source dominated TF bins and our previous clustering approach.
{"title":"An expectation-maximization eigenvector clustering approach to direction of arrival estimation of multiple speech sources","authors":"Xiong Xiao, Shengkui Zhao, Thi Ngoc Tho Nguyen, Douglas L. Jones, Chng Eng Siong, Haizhou Li","doi":"10.1109/ICASSP.2016.7472895","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472895","url":null,"abstract":"This paper presents an eigenvector clustering approach for estimating the direction of arrival (DOA) of multiple speech signals using a microphone array. Existing clustering approaches usually only use low frequencies to avoid spatial aliasing. In this study, we propose a probabilistic eigenvector clustering approach to use all frequencies. In our work, time-frequency (TF) bins dominated by only one source are first detected using a combination of noise-floor tracking, onset detection and coherence test. For each selected TF bin, the largest eigenvector of its spatial covariance matrix is extracted for clustering. A mixture density model is introduced to model the distribution of the eigenvectors, where each component distribution corresponds to one source and is parameterized by the source DOA. To use eigenvectors of all frequencies, the steering vectors of all frequencies of the sources are used in the distribution function. The DOAs of the sources can be estimated by maximizing the likelihood of the eigenvectors using an expectation-maximization (EM) algorithm. Simulation and experimental results show that the proposed approach significantly improves the root-mean-square error (RMSE) for DOA estimation of multiple speech sources compared to the MUSIC algorithm implemented on the single-source dominated TF bins and our previous clustering approach.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115146069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-05-19DOI: 10.1109/ICASSP.2016.7471715
Matt McVicar, Raúl Santos-Rodríguez, T. D. Bie
Separating the singing from a polyphonic mixed audio signal is a challenging but important task, with a wide range of applications across the music industry and music informatics research. Various methods have been devised over the years, ranging from Deep Learning approaches to dedicated ad hoc solutions. In this paper, we present a novel machine learning method for the task, using a Conditional Random Field (CRF) approach for structured output prediction. We exploit the diversity of previously proposed approaches by using their predictions as input features to our method - thus effectively developing an ensemble method. Our empirical results demonstrate the potential of integrating predictions from different previously-proposed methods into one ensemble method, and additionally show that CRF models with larger complexities generally lead to superior performance.
{"title":"Learning to separate vocals from polyphonic mixtures via ensemble methods and structured output prediction","authors":"Matt McVicar, Raúl Santos-Rodríguez, T. D. Bie","doi":"10.1109/ICASSP.2016.7471715","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471715","url":null,"abstract":"Separating the singing from a polyphonic mixed audio signal is a challenging but important task, with a wide range of applications across the music industry and music informatics research. Various methods have been devised over the years, ranging from Deep Learning approaches to dedicated ad hoc solutions. In this paper, we present a novel machine learning method for the task, using a Conditional Random Field (CRF) approach for structured output prediction. We exploit the diversity of previously proposed approaches by using their predictions as input features to our method - thus effectively developing an ensemble method. Our empirical results demonstrate the potential of integrating predictions from different previously-proposed methods into one ensemble method, and additionally show that CRF models with larger complexities generally lead to superior performance.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116622670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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