Pub Date : 2025-09-22DOI: 10.1109/TIT.2025.3605036
{"title":"IEEE Transactions on Information Theory Publication Information","authors":"","doi":"10.1109/TIT.2025.3605036","DOIUrl":"https://doi.org/10.1109/TIT.2025.3605036","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 10","pages":"C2-C2"},"PeriodicalIF":2.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-22DOI: 10.1109/TIT.2025.3605038
{"title":"IEEE Transactions on Information Theory Information for Authors","authors":"","doi":"10.1109/TIT.2025.3605038","DOIUrl":"https://doi.org/10.1109/TIT.2025.3605038","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 10","pages":"C3-C3"},"PeriodicalIF":2.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TIT.2025.3612319
Hassan ZivariFard;Rémi A. Chou;Xiaodong Wang
We study covert communication and covert secret key generation with positive rates over quantum state-dependent channels. Specifically, we consider fully quantum state-dependent channels when the transmitter shares an entangled state with the channel. We study this problem setting under two security metrics. For the first security metric, the transmitter aims to communicate covertly with the receiver while simultaneously generating a covert secret key, and for the second security metric, the transmitter aims to transmit a secure message covertly and generate a covert secret key with the receiver simultaneously. Our main results include one-shot and asymptotic achievable positive covert-secret key rate pairs for both security metrics. Our results recover as a special case the best-known results for covert communication over state-dependent classical channels. To the best of our knowledge, our results are the first instance of achieving a positive rate for covert secret key generation and the first instance of achieving a positive covert rate over a quantum channel. Additionally, we show that our results are optimal when the channel is classical and the state is available non-causally at both the transmitter and the receiver.
{"title":"Covert Communication and Key Generation Over Quantum State-Dependent Channels","authors":"Hassan ZivariFard;Rémi A. Chou;Xiaodong Wang","doi":"10.1109/TIT.2025.3612319","DOIUrl":"https://doi.org/10.1109/TIT.2025.3612319","url":null,"abstract":"We study covert communication and covert secret key generation with positive rates over quantum state-dependent channels. Specifically, we consider fully quantum state-dependent channels when the transmitter shares an entangled state with the channel. We study this problem setting under two security metrics. For the first security metric, the transmitter aims to communicate covertly with the receiver while simultaneously generating a covert secret key, and for the second security metric, the transmitter aims to transmit a secure message covertly and generate a covert secret key with the receiver simultaneously. Our main results include one-shot and asymptotic achievable positive covert-secret key rate pairs for both security metrics. Our results recover as a special case the best-known results for covert communication over state-dependent classical channels. To the best of our knowledge, our results are the first instance of achieving a positive rate for covert secret key generation and the first instance of achieving a positive covert rate over a quantum channel. Additionally, we show that our results are optimal when the channel is classical and the state is available non-causally at both the transmitter and the receiver.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 12","pages":"9600-9616"},"PeriodicalIF":2.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145595140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the pooled data problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items in the pool belonging to each category. A prominent special case is quantitative group testing (QGT), which is the case of pooled data with two categories. We consider these problems in the non-adaptive and linear regime, where the fraction of items in each category is of constant order. We propose a scheme with a spatially coupled Bernoulli test matrix and an efficient approximate message passing (AMP) algorithm for recovery. We rigorously characterize its asymptotic performance in both the noiseless and noisy settings, and prove that in the noiseless case, the AMP algorithm achieves almost-exact recovery with a number of tests sublinear in the total number of items p. Although there exist other efficient schemes for noiseless QGT and pooled data that achieve recovery with order-optimal sample complexity ($Theta left ({{frac {p}{log p}}}right)$ tests), there are no guarantees on their performance in the presence of noise, even at low noise-levels. In comparison, our scheme achieves recovery in the noiseless case with a number of tests sublinear in p, and its performance degrades gracefully in the presence of noise. Numerical simulations illustrate the benefits of the spatially coupled scheme at finite dimensions, showing that it outperforms i.i.d. test designs as well as other recovery algorithms based on convex programming.
在池化数据问题中，目标是通过一系列池化测试确定与大型项目集合相关的类别。每个池测试显示池中属于每个类别的项目数量。一个突出的特殊情况是定量组测试（QGT），它是两类数据池的情况。我们考虑这些问题在非自适应和线性状态下，其中每个类别的项目的比例是恒定的顺序。我们提出了一种利用空间耦合伯努利测试矩阵和高效近似消息传递（AMP）算法的恢复方案。我们严格地描述了其在无噪声和有噪声设置下的渐进性能，并证明了在无噪声情况下，AMP算法在项目总数p的次线性测试中实现了几乎精确的恢复。尽管存在其他有效的无噪声QGT和池数据方案，这些方案实现了有序最优样本复杂度的恢复（$Theta left ({{frac {p}{log p}}}right)$测试），但在存在噪声时，它们的性能不能保证。即使在低噪音水平。相比之下，我们的方案在无噪声情况下实现了恢复，在p中有许多次线性的测试，并且在存在噪声的情况下性能下降得很好。数值模拟说明了有限维空间耦合方案的优点，表明它优于i.i.d测试设计以及其他基于凸规划的恢复算法。
{"title":"Quantitative Group Testing and Pooled Data in the Linear Regime With Sublinear Tests","authors":"Nelvin Tan;Pablo Pascual Cobo;Ramji Venkataramanan","doi":"10.1109/TIT.2025.3611276","DOIUrl":"https://doi.org/10.1109/TIT.2025.3611276","url":null,"abstract":"In the <italic>pooled data</i> problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items in the pool belonging to each category. A prominent special case is quantitative group testing (QGT), which is the case of pooled data with two categories. We consider these problems in the non-adaptive and linear regime, where the fraction of items in each category is of constant order. We propose a scheme with a <italic>spatially coupled</i> Bernoulli test matrix and an efficient approximate message passing (AMP) algorithm for recovery. We rigorously characterize its asymptotic performance in both the noiseless and noisy settings, and prove that in the noiseless case, the AMP algorithm achieves <italic>almost-exact</i> recovery with a number of tests sublinear in the total number of items <italic>p</i>. Although there exist other efficient schemes for noiseless QGT and pooled data that achieve recovery with order-optimal sample complexity (<inline-formula> <tex-math>$Theta left ({{frac {p}{log p}}}right)$ </tex-math></inline-formula> tests), there are no guarantees on their performance in the presence of noise, even at low noise-levels. In comparison, our scheme achieves recovery in the noiseless case with a number of tests sublinear in <italic>p</i>, and its performance degrades gracefully in the presence of noise. Numerical simulations illustrate the benefits of the spatially coupled scheme at finite dimensions, showing that it outperforms i.i.d. test designs as well as other recovery algorithms based on convex programming.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 11","pages":"9074-9099"},"PeriodicalIF":2.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-12DOI: 10.1109/TIT.2025.3609564
Adarsh Barik;Anand Krishna;Vincent Y. F. Tan
In this work, we study the robust phase retrieval problem where the task is to recover an unknown signal $boldsymbol {theta }^{*} in mathbb {R} ^{d}$ in the presence of potentially arbitrarily corrupted magnitude-only linear measurements. We propose an alternating minimization approach that incorporates an oracle solver for a non-convex optimization problem as a subroutine. Our algorithm guarantees convergence to $boldsymbol {theta }^{*}$ and provides an explicit polynomial dependence of the convergence rate on the fraction of corrupted measurements. We then provide an efficient construction of the aforementioned oracle under a sparse arbitrary outliers model and offer valuable insights into the geometric properties of the loss landscape in phase retrieval with corrupted measurements. Our proposed oracle avoids the need for computationally intensive spectral initialization, using a simple gradient descent algorithm with a constant step size and random initialization instead. Additionally, our overall algorithm achieves nearly linear sample complexity, $mathcal {O}(d {,}mathrm {polylog}(d))$ .
{"title":"A Sample Efficient Alternating Minimization-Based Algorithm for Robust Phase Retrieval","authors":"Adarsh Barik;Anand Krishna;Vincent Y. F. Tan","doi":"10.1109/TIT.2025.3609564","DOIUrl":"https://doi.org/10.1109/TIT.2025.3609564","url":null,"abstract":"In this work, we study the robust phase retrieval problem where the task is to recover an unknown signal <inline-formula> <tex-math>$boldsymbol {theta }^{*} in mathbb {R} ^{d}$ </tex-math></inline-formula> in the presence of potentially arbitrarily corrupted magnitude-only linear measurements. We propose an alternating minimization approach that incorporates an oracle solver for a non-convex optimization problem as a subroutine. Our algorithm guarantees convergence to <inline-formula> <tex-math>$boldsymbol {theta }^{*}$ </tex-math></inline-formula> and provides an explicit polynomial dependence of the convergence rate on the fraction of corrupted measurements. We then provide an efficient construction of the aforementioned oracle under a sparse arbitrary outliers model and offer valuable insights into the geometric properties of the loss landscape in phase retrieval with corrupted measurements. Our proposed oracle avoids the need for computationally intensive spectral initialization, using a simple gradient descent algorithm with a constant step size and random initialization instead. Additionally, our overall algorithm achieves nearly linear sample complexity, <inline-formula> <tex-math>$mathcal {O}(d {,}mathrm {polylog}(d))$ </tex-math></inline-formula>.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 11","pages":"9116-9133"},"PeriodicalIF":2.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1109/TIT.2025.3604602
Giuseppe Serra;Photios A. Stavrou;Marios Kountouris
We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint belonging to the family of f-divergences. In this setting, the RDPF forms a convex programming problem for which we characterize optimal parametric solutions. We employ the developed solutions in an alternating minimization scheme, namely Optimal Alternating Minimization (OAM), for which we provide convergence guarantees. Nevertheless, the OAM scheme does not lead to a direct implementation of a generalized Blahut-Arimoto (BA) type of algorithm due to implicit equations in the iteration’s structure. To overcome this difficulty, we propose two alternative minimization approaches whose applicability depends on the smoothness of the used perception metric: a Newton-based Alternating Minimization (NAM) scheme, relying on Newton’s root-finding method for the approximation of the optimal solution of the iteration, and a Relaxed Alternating Minimization (RAM) scheme, based on relaxing the OAM iterates. We show, by deriving necessary and sufficient conditions, that both schemes guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and perception constraints, which guarantee that the proposed algorithms converge exponentially fast in the number of iteration steps. We corroborate our theoretical results with numerical simulations and establish connections with existing results.
{"title":"Alternating Minimization Schemes for Computing Rate-Distortion-Perception Functions With f-Divergence Perception Constraints","authors":"Giuseppe Serra;Photios A. Stavrou;Marios Kountouris","doi":"10.1109/TIT.2025.3604602","DOIUrl":"https://doi.org/10.1109/TIT.2025.3604602","url":null,"abstract":"We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint belonging to the family of <italic>f</i>-divergences. In this setting, the RDPF forms a convex programming problem for which we characterize optimal parametric solutions. We employ the developed solutions in an alternating minimization scheme, namely Optimal Alternating Minimization (OAM), for which we provide convergence guarantees. Nevertheless, the OAM scheme does not lead to a direct implementation of a generalized Blahut-Arimoto (BA) type of algorithm due to implicit equations in the iteration’s structure. To overcome this difficulty, we propose two alternative minimization approaches whose applicability depends on the smoothness of the used perception metric: a Newton-based Alternating Minimization (NAM) scheme, relying on Newton’s root-finding method for the approximation of the optimal solution of the iteration, and a Relaxed Alternating Minimization (RAM) scheme, based on relaxing the OAM iterates. We show, by deriving necessary and sufficient conditions, that both schemes guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and perception constraints, which guarantee that the proposed algorithms converge exponentially fast in the number of iteration steps. We corroborate our theoretical results with numerical simulations and establish connections with existing results.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 11","pages":"9100-9115"},"PeriodicalIF":2.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-25DOI: 10.1109/TIT.2025.3602119
Xiuxiu Ma;Abla Kammoun;Mohamed-Slim Alouini;Tareq Y. Al-Naffouri
This paper presents a performance analysis of two distinct techniques for antenna selection and precoding in downlink multi-user massive multiple-input single-output systems with limited dynamic range power amplifiers. Both techniques are derived from the original formulation of the regularized-zero forcing precoder, designed as the solution to minimizing a regularized distortion. Based on this, the first technique, called the $ell _{1}$ -norm precoder, adopts an $ell _{1}$ -norm regularization term to encourage sparse solutions, thereby enabling antenna selection. The second technique, termed the thresholded $ell _{1}$ -norm precoder, involves post-processing the precoder solution obtained from the first method by applying an entry-wise thresholding operation. This work conducts a precise performance analysis to compare these two techniques. The analysis leverages the Gaussian min-max theorem which is effective for examining the asymptotic behavior of optimization problems without explicit solutions. While the analysis of the $ell _{1}$ -norm precoder follows from the conventional convex Gaussian min-max theorem framework, understanding the thresholded $ell _{1}$ -norm precoder is more complex due to the non-linear behavior introduced by the thresholding operation. To address this complexity, we develop a novel Gaussian min-max theorem tailored to these scenarios. We provide precise asymptotic behavior analysis of the precoders, focusing on metrics such as received signal-to-noise and distortion ratio and bit error rate. Our analysis demonstrates that the thresholded $ell _{1}$ -norm precoder can offer superior performance when the threshold parameter is carefully selected. Simulations confirm that the asymptotic results are accurate for systems equipped with hundreds of antennas at the base station, serving dozens of user terminals.
本文对具有有限动态范围功率放大器的下行多用户大规模多输入单输出系统中的天线选择和预编码两种不同的技术进行了性能分析。这两种技术都源于正则化零强制预编码器的原始公式，设计为最小化正则化失真的解决方案。基于此，第一种技术，称为$ well _{1}$范数预编码器，采用$ well _{1}$范数正则化项来鼓励稀疏解，从而实现天线选择。第二种技术，称为阈值化的$ well _{1}$ -norm预编码器，涉及到通过应用入口式阈值操作对从第一种方法获得的预编码器解进行后处理。这项工作进行了精确的性能分析来比较这两种技术。该分析利用高斯最小-最大定理，该定理对于检查无显式解的优化问题的渐近行为是有效的。虽然对$ well _{1}$ -范数预编码器的分析遵循传统的凸高斯最小-极大定理框架，但由于阈值操作引入的非线性行为，理解有阈值的$ well _{1}$ -范数预编码器更为复杂。为了解决这种复杂性，我们针对这些场景开发了一个新的高斯最小-最大定理。我们提供了精确的渐近行为分析的预编码器，重点指标，如接收信噪比和失真率和误码率。我们的分析表明，当仔细选择阈值参数时，阈值$ well _{1}$ -norm预编码器可以提供更好的性能。仿真结果表明，对于配备了数百个天线的基站，服务于数十个用户终端的系统，渐近结果是准确的。
{"title":"Performance Analysis of Joint Antenna Selection and Precoding Methods in Multi-User Massive MISO","authors":"Xiuxiu Ma;Abla Kammoun;Mohamed-Slim Alouini;Tareq Y. Al-Naffouri","doi":"10.1109/TIT.2025.3602119","DOIUrl":"https://doi.org/10.1109/TIT.2025.3602119","url":null,"abstract":"This paper presents a performance analysis of two distinct techniques for antenna selection and precoding in downlink multi-user massive multiple-input single-output systems with limited dynamic range power amplifiers. Both techniques are derived from the original formulation of the regularized-zero forcing precoder, designed as the solution to minimizing a regularized distortion. Based on this, the first technique, called the <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-norm precoder, adopts an <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-norm regularization term to encourage sparse solutions, thereby enabling antenna selection. The second technique, termed the thresholded <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-norm precoder, involves post-processing the precoder solution obtained from the first method by applying an entry-wise thresholding operation. This work conducts a precise performance analysis to compare these two techniques. The analysis leverages the Gaussian min-max theorem which is effective for examining the asymptotic behavior of optimization problems without explicit solutions. While the analysis of the <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-norm precoder follows from the conventional convex Gaussian min-max theorem framework, understanding the thresholded <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-norm precoder is more complex due to the non-linear behavior introduced by the thresholding operation. To address this complexity, we develop a novel Gaussian min-max theorem tailored to these scenarios. We provide precise asymptotic behavior analysis of the precoders, focusing on metrics such as received signal-to-noise and distortion ratio and bit error rate. Our analysis demonstrates that the thresholded <inline-formula> <tex-math>$ell _{1}$ </tex-math></inline-formula>-norm precoder can offer superior performance when the threshold parameter is carefully selected. Simulations confirm that the asymptotic results are accurate for systems equipped with hundreds of antennas at the base station, serving dozens of user terminals.","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 10","pages":"8099-8148"},"PeriodicalIF":2.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-22DOI: 10.1109/TIT.2025.3598417
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Pub Date : 2025-08-22DOI: 10.1109/TIT.2025.3594587
{"title":"IEEE Transactions on Information Theory Publication Information","authors":"","doi":"10.1109/TIT.2025.3594587","DOIUrl":"https://doi.org/10.1109/TIT.2025.3594587","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 9","pages":"C2-C2"},"PeriodicalIF":2.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-22DOI: 10.1109/TIT.2025.3594589
{"title":"IEEE Transactions on Information Theory Information for Authors","authors":"","doi":"10.1109/TIT.2025.3594589","DOIUrl":"https://doi.org/10.1109/TIT.2025.3594589","url":null,"abstract":"","PeriodicalId":13494,"journal":{"name":"IEEE Transactions on Information Theory","volume":"71 9","pages":"C3-C3"},"PeriodicalIF":2.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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