Pub Date : 2016-03-20DOI: 10.1109/ICASSP.2016.7472498
Min Xiang, S. Kanna, S. Douglas, D. Mandic
Widely linear processing has been shown to be superior to the traditional strictly linear processing in quaternion minimum mean square error (MMSE) estimation. However, a quantifiable performance difference between strictly and widely linear processing and the relationship between the performance and quaternion impropriety are still lacking. To this end, we present a proof for the performance advantage of widely linear estimation and relate the performance bounds to signal properties by exploiting the approximate joint diagonalisation of quaternion covariance matrices. In that sense, this work can be seen as a generalisation of complex-valued MMSE estimation, and can thus also be applied to the complex-valued case. Simulations on synthetic signals support the analysis.
{"title":"Performance advantage of quaternion widely linear estimation: An approximate uncorrelating transform approach","authors":"Min Xiang, S. Kanna, S. Douglas, D. Mandic","doi":"10.1109/ICASSP.2016.7472498","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472498","url":null,"abstract":"Widely linear processing has been shown to be superior to the traditional strictly linear processing in quaternion minimum mean square error (MMSE) estimation. However, a quantifiable performance difference between strictly and widely linear processing and the relationship between the performance and quaternion impropriety are still lacking. To this end, we present a proof for the performance advantage of widely linear estimation and relate the performance bounds to signal properties by exploiting the approximate joint diagonalisation of quaternion covariance matrices. In that sense, this work can be seen as a generalisation of complex-valued MMSE estimation, and can thus also be applied to the complex-valued case. Simulations on synthetic signals support the analysis.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121655761","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472275
Zai Yang, Lihua Xie
The authors have recently proposed two kinds of gridless sparse methods for direction of arrival (DOA) estimation that exploit joint sparsity among snapshots and completely resolve the grid mismatch issue of previous grid-based sparse methods. One is based on covariance fitting from a statistical perspective and termed as the gridless SPICE (GL-SPICE, GLS); the other uses deterministic atomic norm optimization which extends the recent super-resolution and continuous compressed sensing framework from the single to the multi-snapshot case. In this paper, we unify the two techniques by interpreting GLS as atomic norm methods in various scenarios. As a byproduct, we are able to provide theoretical guarantees of GLS for DOA estimation in the case of limited snapshots.
{"title":"On gridless sparse methods for multi-snapshot DOA estimation","authors":"Zai Yang, Lihua Xie","doi":"10.1109/ICASSP.2016.7472275","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472275","url":null,"abstract":"The authors have recently proposed two kinds of gridless sparse methods for direction of arrival (DOA) estimation that exploit joint sparsity among snapshots and completely resolve the grid mismatch issue of previous grid-based sparse methods. One is based on covariance fitting from a statistical perspective and termed as the gridless SPICE (GL-SPICE, GLS); the other uses deterministic atomic norm optimization which extends the recent super-resolution and continuous compressed sensing framework from the single to the multi-snapshot case. In this paper, we unify the two techniques by interpreting GLS as atomic norm methods in various scenarios. As a byproduct, we are able to provide theoretical guarantees of GLS for DOA estimation in the case of limited snapshots.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113965608","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472854
Yaodong Tang, Yuchen Huang, Zhiyong Wu, H. Meng, Mingxing Xu, Lianhong Cai
Question detection is of importance for many speech applications. Only parts of the speech utterances can provide useful clues for question detection. Previous work of question detection using acoustic features in Mandarin conversation is weak in capturing such proper time context information, which could be modeled essentially in recurrent neural network (RNN) structure. In this paper, we conduct an investigation on recurrent approaches to cope with this problem. Based on gated recurrent unit (GRU), we build different RNN and bidirectional RNN (BRNN) models to extract efficient features at segment and utterance level. The particular advantage of GRU is it can determine a proper time scale to extract high-level contextual features. Experimental results show that the features extracted within proper time scale make the classifier perform better than the baseline method with pre-designed lexical and acoustic feature set.
{"title":"Question detection from acoustic features using recurrent neural network with gated recurrent unit","authors":"Yaodong Tang, Yuchen Huang, Zhiyong Wu, H. Meng, Mingxing Xu, Lianhong Cai","doi":"10.1109/ICASSP.2016.7472854","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472854","url":null,"abstract":"Question detection is of importance for many speech applications. Only parts of the speech utterances can provide useful clues for question detection. Previous work of question detection using acoustic features in Mandarin conversation is weak in capturing such proper time context information, which could be modeled essentially in recurrent neural network (RNN) structure. In this paper, we conduct an investigation on recurrent approaches to cope with this problem. Based on gated recurrent unit (GRU), we build different RNN and bidirectional RNN (BRNN) models to extract efficient features at segment and utterance level. The particular advantage of GRU is it can determine a proper time scale to extract high-level contextual features. Experimental results show that the features extracted within proper time scale make the classifier perform better than the baseline method with pre-designed lexical and acoustic feature set.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124298166","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7471680
Eita Nakamura, M. Hamanaka, K. Hirata, Kazuyoshi Yoshii
This paper presents a probabilistic formulation of music language modelling based on the generative theory of tonal music (GTTM) named probabilistic GTTM (PGTTM). GTTM is a well-known music theory that describes the tree structure of written music in analogy with the phrase structure grammar of natural language. To develop a computational music language model incorporating GTTM and a machine-learning framework for data-driven music grammar induction, we construct a generative model of monophonic music based on probabilistic context-free grammar, in which the time-span tree proposed in GTTM corresponds to the parse tree. Applying the techniques of natural language processing, we also derive supervised and unsupervised learning algorithms based on the maximal-likelihood estimation, and a Bayesian inference algorithm based on the Gibbs sampling. Despite the conceptual simplicity of the model, we found that the model automatically acquires music grammar from data and reproduces time-span trees of written music as accurately as an analyser that required elaborate manual parameter tuning.
{"title":"Tree-structured probabilistic model of monophonic written music based on the generative theory of tonal music","authors":"Eita Nakamura, M. Hamanaka, K. Hirata, Kazuyoshi Yoshii","doi":"10.1109/ICASSP.2016.7471680","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471680","url":null,"abstract":"This paper presents a probabilistic formulation of music language modelling based on the generative theory of tonal music (GTTM) named probabilistic GTTM (PGTTM). GTTM is a well-known music theory that describes the tree structure of written music in analogy with the phrase structure grammar of natural language. To develop a computational music language model incorporating GTTM and a machine-learning framework for data-driven music grammar induction, we construct a generative model of monophonic music based on probabilistic context-free grammar, in which the time-span tree proposed in GTTM corresponds to the parse tree. Applying the techniques of natural language processing, we also derive supervised and unsupervised learning algorithms based on the maximal-likelihood estimation, and a Bayesian inference algorithm based on the Gibbs sampling. Despite the conceptual simplicity of the model, we found that the model automatically acquires music grammar from data and reproduces time-span trees of written music as accurately as an analyser that required elaborate manual parameter tuning.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127604925","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472448
Santiago Segarra, A. Marques, G. Mateos, Alejandro Ribeiro
Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of linear graph filters modeling underlying network dynamics. This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured graph domain. Given a graph signal y modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients h, and the input signal x which is assumed to be sparse. While y is a bilinear function of x and h, the filtered graph signal is also a linear combination of the entries of the "lifted" rank-one, row-sparse matrix xhT. The blind graph filter identification problem can be thus tackled via rank and sparsity minimization subject to linear constraints, an approach amenable to convex relaxation. An algorithm for jointly processing multiple output signals corresponding to different sparse inputs is also developed. Numerical tests with synthetic and real-world networks illustrate the merits of the proposed algorithm, as well as the benefits of leveraging multiple signals to aid the blind identification task.
{"title":"Blind identification of graph filters with multiple sparse inputs","authors":"Santiago Segarra, A. Marques, G. Mateos, Alejandro Ribeiro","doi":"10.1109/ICASSP.2016.7472448","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472448","url":null,"abstract":"Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of linear graph filters modeling underlying network dynamics. This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured graph domain. Given a graph signal y modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients h, and the input signal x which is assumed to be sparse. While y is a bilinear function of x and h, the filtered graph signal is also a linear combination of the entries of the \"lifted\" rank-one, row-sparse matrix xhT. The blind graph filter identification problem can be thus tackled via rank and sparsity minimization subject to linear constraints, an approach amenable to convex relaxation. An algorithm for jointly processing multiple output signals corresponding to different sparse inputs is also developed. Numerical tests with synthetic and real-world networks illustrate the merits of the proposed algorithm, as well as the benefits of leveraging multiple signals to aid the blind identification task.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126499070","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7471811
Dae Hoe Kim, Seong-Tae Kim, Yong Man Ro
In clinical studies of breast cancer, masses appear as asymmetric densities between the left and the right breasts, which show different breast tissue structures. For classifying breast masses, most researchers have developed hand-crafted bilateral features by extracting the asymmetric information in 2-D mammograms. In digital breast tomosynthesis (DBT), which has 3D volume data, effective bilateral features are needed to detect masses. In this paper, we propose latent bilateral feature representation with 3-D multi-view deep convolutional neural network (DCNN) in the DBT reconstructed volume. The proposed DCNN is designed to discover hidden or latent bilateral feature representation of masses in self-taught learning. Experimental results show that the proposed latent bilateral feature representation outperforms conventional hand-crafted features by achieving a high area under the receiver operating characteristic curve.
{"title":"Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis","authors":"Dae Hoe Kim, Seong-Tae Kim, Yong Man Ro","doi":"10.1109/ICASSP.2016.7471811","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7471811","url":null,"abstract":"In clinical studies of breast cancer, masses appear as asymmetric densities between the left and the right breasts, which show different breast tissue structures. For classifying breast masses, most researchers have developed hand-crafted bilateral features by extracting the asymmetric information in 2-D mammograms. In digital breast tomosynthesis (DBT), which has 3D volume data, effective bilateral features are needed to detect masses. In this paper, we propose latent bilateral feature representation with 3-D multi-view deep convolutional neural network (DCNN) in the DBT reconstructed volume. The proposed DCNN is designed to discover hidden or latent bilateral feature representation of masses in self-taught learning. Experimental results show that the proposed latent bilateral feature representation outperforms conventional hand-crafted features by achieving a high area under the receiver operating characteristic curve.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612997","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472944
Ondrej Daniel, J. Raasakka, Pekka Peltola, Markus Fröhle, A. Rodriguez, H. Wymeersch, J. Nurmi
A satellite navigation receiver traditionally searches for positioning signals using an acquisition procedure. In situations, in which the required information is only a binary decision whether at least one positioning signal is present or absent, the procedure represents an unnecessarily complex solution. This paper presents a different approach for the binary detection problem with significantly reduced computational complexity. The approach is based on a novel decision metric which is utilized to design two binary detectors. The first detector operates under the theoretical assumption of additive white Gaussian noise and is evaluated by means of Receiver Operating Characteristics. The second one considers also additional interferences and is suitable to operate in a real environment. Its performance is verified using a signal captured by a receiver front-end.
{"title":"Blind sub-Nyquist GNSS signal detection","authors":"Ondrej Daniel, J. Raasakka, Pekka Peltola, Markus Fröhle, A. Rodriguez, H. Wymeersch, J. Nurmi","doi":"10.1109/ICASSP.2016.7472944","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472944","url":null,"abstract":"A satellite navigation receiver traditionally searches for positioning signals using an acquisition procedure. In situations, in which the required information is only a binary decision whether at least one positioning signal is present or absent, the procedure represents an unnecessarily complex solution. This paper presents a different approach for the binary detection problem with significantly reduced computational complexity. The approach is based on a novel decision metric which is utilized to design two binary detectors. The first detector operates under the theoretical assumption of additive white Gaussian noise and is evaluated by means of Receiver Operating Characteristics. The second one considers also additional interferences and is suitable to operate in a real environment. Its performance is verified using a signal captured by a receiver front-end.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115883263","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472426
S. Särkkä, É. Moulines
We analyze the Lp-convergence of a previously proposed Girsanov theorem based particle filter for discretely observed stochastic differential equation (SDE) models. We prove the convergence of the algorithm with the number of particles tending to infinity by requiring a moment condition and a step-wise initial condition boundedness for the stochastic exponential process giving the likelihood ratio of the SDEs. The practical implications of the condition are illustrated with an Ornstein-Uhlenbeck model and with a non-linear Benes model.
{"title":"On the LP-convergence of a Girsanov theorem based particle filter","authors":"S. Särkkä, É. Moulines","doi":"10.1109/ICASSP.2016.7472426","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472426","url":null,"abstract":"We analyze the Lp-convergence of a previously proposed Girsanov theorem based particle filter for discretely observed stochastic differential equation (SDE) models. We prove the convergence of the algorithm with the number of particles tending to infinity by requiring a moment condition and a step-wise initial condition boundedness for the stochastic exponential process giving the likelihood ratio of the SDEs. The practical implications of the condition are illustrated with an Ornstein-Uhlenbeck model and with a non-linear Benes model.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131346376","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472495
Pouria Tohidi, E. Bostan, P. Pad, M. Unser
We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is an iterative algorithm that combines standard wavelet-based thresholding with an optimized non-linearity and cycle-spinning. This method is more computationally efficient than the former and appears to provide the same optimal denoising results in practice. We illustrate the superior performance of both methods through numerical simulations by comparing them with other well-known denoising schemes.
{"title":"MMSE denoising of sparse and non-Gaussian AR(1) processes","authors":"Pouria Tohidi, E. Bostan, P. Pad, M. Unser","doi":"10.1109/ICASSP.2016.7472495","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472495","url":null,"abstract":"We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is an iterative algorithm that combines standard wavelet-based thresholding with an optimized non-linearity and cycle-spinning. This method is more computationally efficient than the former and appears to provide the same optimal denoising results in practice. We illustrate the superior performance of both methods through numerical simulations by comparing them with other well-known denoising schemes.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451045","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 : 2016-03-20DOI: 10.1109/ICASSP.2016.7472476
Liming Wang, F. Renna, Xin Yuan, M. Rodrigues, A. Calderbank, L. Carin
We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.
{"title":"A general framework for reconstruction and classification from compressive measurements with side information","authors":"Liming Wang, F. Renna, Xin Yuan, M. Rodrigues, A. Calderbank, L. Carin","doi":"10.1109/ICASSP.2016.7472476","DOIUrl":"https://doi.org/10.1109/ICASSP.2016.7472476","url":null,"abstract":"We develop a general framework for compressive linear-projection measurements with side information. Side information is an additional signal correlated with the signal of interest. We investigate the impact of side information on classification and signal recovery from low-dimensional measurements. Motivated by real applications, two special cases of the general model are studied. In the first, a joint Gaussian mixture model is manifested on the signal and side information. The second example again employs a Gaussian mixture model for the signal, with side information drawn from a mixture in the exponential family. Theoretical results on recovery and classification accuracy are derived. The presence of side information is shown to yield improved performance, both theoretically and experimentally.","PeriodicalId":165321,"journal":{"name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451395","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：