Pub Date : 2022-03-03DOI: 10.48550/arXiv.2203.16343
Georgios Bakirtzis, U. Topcu
Autonomous systems require the management of several model views to assure properties such as safety and security among oth-ers. A crucial issue in autonomous systems design assurance is the notion of emergent behavior; we cannot use their parts in isolation to examine their overall behavior or performance. Compositional verification attempts to combat emergence by implementing model transformation as structure-preserving maps between model views. AlgebraicDynamics relies on categorical semantics to draw relation-ships between algebras and model views. We propose AlgebraicSystems, a conglomeration of algebraic methods to assign semantics and categorical primitives to give computational meaning to relationships between models so that the formalisms and resulting tools are interoperable through vertical and horizontal composition.
{"title":"AlgebraicSystems: Compositional Verification for Autonomous System Design","authors":"Georgios Bakirtzis, U. Topcu","doi":"10.48550/arXiv.2203.16343","DOIUrl":"https://doi.org/10.48550/arXiv.2203.16343","url":null,"abstract":"Autonomous systems require the management of several model views to assure properties such as safety and security among oth-ers. A crucial issue in autonomous systems design assurance is the notion of emergent behavior; we cannot use their parts in isolation to examine their overall behavior or performance. Compositional verification attempts to combat emergence by implementing model transformation as structure-preserving maps between model views. AlgebraicDynamics relies on categorical semantics to draw relation-ships between algebras and model views. We propose AlgebraicSystems, a conglomeration of algebraic methods to assign semantics and categorical primitives to give computational meaning to relationships between models so that the formalisms and resulting tools are interoperable through vertical and horizontal composition.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123425948","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}
Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performance under complex scenarios. However, it is very challenging to formally analyze the behavior of neural network based planners for ensuring system safety, which significantly impedes their applications in safety-critical domains such as autonomous driving. In this work, we propose a hierarchical neural network based planner that analyzes the underlying physical scenarios of the system and learns a system-level behavior planning scheme with multiple scenario-specific motion-planning strategies. We then develop an efficient verification method that incorporates overapproximation of the system state reachable set and novel partition and union techniques for formally ensuring system safety under our physics-aware planner. With theoretical analysis, we show that considering the different physical scenarios and building a hierarchical planner based on such analysis may improve system safety and verifiability. We also empirically demonstrate the effectiveness of our approach and its advantage over other baselines in practical case studies of unprotected left turn and highway merging, two common challenging safety-critical tasks in autonomous driving.
{"title":"Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner","authors":"Xiangguo Liu, Chao Huang, Yixuan Wang, Bowen Zheng, Qi Zhu","doi":"10.1109/iccps54341.2022.00019","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00019","url":null,"abstract":"Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving performance under complex scenarios. However, it is very challenging to formally analyze the behavior of neural network based planners for ensuring system safety, which significantly impedes their applications in safety-critical domains such as autonomous driving. In this work, we propose a hierarchical neural network based planner that analyzes the underlying physical scenarios of the system and learns a system-level behavior planning scheme with multiple scenario-specific motion-planning strategies. We then develop an efficient verification method that incorporates overapproximation of the system state reachable set and novel partition and union techniques for formally ensuring system safety under our physics-aware planner. With theoretical analysis, we show that considering the different physical scenarios and building a hierarchical planner based on such analysis may improve system safety and verifiability. We also empirically demonstrate the effectiveness of our approach and its advantage over other baselines in practical case studies of unprotected left turn and highway merging, two common challenging safety-critical tasks in autonomous driving.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128630495","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 : 2022-01-17DOI: 10.1109/iccps54341.2022.00013
A. Malawade, Trier Mortlock, M. A. Faruque
Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perception across a wide range of driving contexts remains a significant challenge. Techniques to fuse sensor data from camera, radar, and lidar sensors have been proposed to improve AV perception. However, existing methods are insufficiently robust in difficult driving contexts (e.g., bad weather, low light, sensor obstruction) due to rigidity in their fusion implementations. These methods fall into two broad categories: (i) early fusion, which fails when sensor data is noisy or obscured, and (ii) late fusion, which cannot leverage features from multiple sensors and thus produces worse estimates. To address these limitations, we propose HydraFusion: a selective sensor fusion framework that learns to identify the current driving context and fuses the best combination of sensors to maximize robustness without compromising efficiency. HydraFusion is the first approach to propose dynamically adjusting between early fusion, late fusion, and combinations in-between, thus varying both how and when fusion is applied. We show that, on average, Hydrafusionoutperforms early and late fusion approaches by 13.66% and 14.54%, respectively, without increasing computational complexity or energy consumption on the industry-standard Nvidia Drive PX2 AV hardware platform. We also propose and evaluate both static and deep-learning-based context identification strategies. Our open-source code and model implementation are available at https://github.com/AICPS/hydrafusion.
{"title":"HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception","authors":"A. Malawade, Trier Mortlock, M. A. Faruque","doi":"10.1109/iccps54341.2022.00013","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00013","url":null,"abstract":"Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perception across a wide range of driving contexts remains a significant challenge. Techniques to fuse sensor data from camera, radar, and lidar sensors have been proposed to improve AV perception. However, existing methods are insufficiently robust in difficult driving contexts (e.g., bad weather, low light, sensor obstruction) due to rigidity in their fusion implementations. These methods fall into two broad categories: (i) early fusion, which fails when sensor data is noisy or obscured, and (ii) late fusion, which cannot leverage features from multiple sensors and thus produces worse estimates. To address these limitations, we propose HydraFusion: a selective sensor fusion framework that learns to identify the current driving context and fuses the best combination of sensors to maximize robustness without compromising efficiency. HydraFusion is the first approach to propose dynamically adjusting between early fusion, late fusion, and combinations in-between, thus varying both how and when fusion is applied. We show that, on average, Hydrafusionoutperforms early and late fusion approaches by 13.66% and 14.54%, respectively, without increasing computational complexity or energy consumption on the industry-standard Nvidia Drive PX2 AV hardware platform. We also propose and evaluate both static and deep-learning-based context identification strategies. Our open-source code and model implementation are available at https://github.com/AICPS/hydrafusion.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130898435","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 : 2021-12-01DOI: 10.1109/iccps54341.2022.00010
Edward Kim, Jay Shenoy, Sebastian Junges, Daniel J. Fremont, A. Sangiovanni-Vincentelli, S. Seshia
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety. Conse-quently, substantial research has focused on searching for failure scenarios in simulation. However, a fundamental question remains: are AV failure scenarios identified in simulation meaningful in re-ality - i.e., are they reproducible on the real system? Due to the sim-to-real gap arising from discrepancies between simulated and real sensor data, a failure scenario identified in simulation can be either a spurious artifact of the synthetic sensor data or an actual failure that persists with real sensor data. An approach to validate simulated failure scenarios is to identify instances of the scenario in a corpus of real data, and check if the failure persists on the real data. To this end, we propose a formal definition of what it means for a labelled data item to match an abstract scenario, encoded as a scenario program using the Scenic probabilistic programming language. Using this definition, we develop a querying algorithm which, given a scenario program and a labelled dataset, finds the subset of data matching the scenario. Experiments demonstrate that our algorithm is accurate and efficient on a variety of realistic traffic scenarios, and scales to a reasonable number of agents.
{"title":"Querying Labelled Data with Scenario Programs for Sim-to-Real Validation","authors":"Edward Kim, Jay Shenoy, Sebastian Junges, Daniel J. Fremont, A. Sangiovanni-Vincentelli, S. Seshia","doi":"10.1109/iccps54341.2022.00010","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00010","url":null,"abstract":"Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety. Conse-quently, substantial research has focused on searching for failure scenarios in simulation. However, a fundamental question remains: are AV failure scenarios identified in simulation meaningful in re-ality - i.e., are they reproducible on the real system? Due to the sim-to-real gap arising from discrepancies between simulated and real sensor data, a failure scenario identified in simulation can be either a spurious artifact of the synthetic sensor data or an actual failure that persists with real sensor data. An approach to validate simulated failure scenarios is to identify instances of the scenario in a corpus of real data, and check if the failure persists on the real data. To this end, we propose a formal definition of what it means for a labelled data item to match an abstract scenario, encoded as a scenario program using the Scenic probabilistic programming language. Using this definition, we develop a querying algorithm which, given a scenario program and a labelled dataset, finds the subset of data matching the scenario. Experiments demonstrate that our algorithm is accurate and efficient on a variety of realistic traffic scenarios, and scales to a reasonable number of agents.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116055899","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 : 2021-11-04DOI: 10.1109/iccps54341.2022.00015
Matthew Cleaveland, I. Ruchkin, O. Sokolsky, Insup Lee
Autonomous systems with machine learning-based perception can exhibit unpredictable behaviors that are difficult to quantify, let alone verify. Such behaviors are convenient to capture in proba-bilistic models, but probabilistic model checking of such models is difficult to scale - largely due to the non-determinism added to models as a prerequisite for provable conservatism. Statistical model checking (SMC) has been proposed to address the scalabil-ity issue. However it requires large amounts of data to account for the aforementioned non-determinism, which in turn limits its scalability. This work introduces a general technique for reduction of non-determinism based on assumptions of “monotonic safety”, which define a partial order between system states in terms of their probabilities of being safe. We exploit these assumptions to remove non-determinism from controller/plant models to drasti-cally speed up probabilistic model checking and statistical model checking while providing provably conservative estimates as long as the safety is indeed monotonic. Our experiments demonstrate model-checking speed-ups of an order of magnitude while main-taining acceptable accuracy and require much less data for accurate estimates when running SMC - even when monotonic safety does not perfectly hold and provable conservatism is not achieved.
{"title":"Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis","authors":"Matthew Cleaveland, I. Ruchkin, O. Sokolsky, Insup Lee","doi":"10.1109/iccps54341.2022.00015","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00015","url":null,"abstract":"Autonomous systems with machine learning-based perception can exhibit unpredictable behaviors that are difficult to quantify, let alone verify. Such behaviors are convenient to capture in proba-bilistic models, but probabilistic model checking of such models is difficult to scale - largely due to the non-determinism added to models as a prerequisite for provable conservatism. Statistical model checking (SMC) has been proposed to address the scalabil-ity issue. However it requires large amounts of data to account for the aforementioned non-determinism, which in turn limits its scalability. This work introduces a general technique for reduction of non-determinism based on assumptions of “monotonic safety”, which define a partial order between system states in terms of their probabilities of being safe. We exploit these assumptions to remove non-determinism from controller/plant models to drasti-cally speed up probabilistic model checking and statistical model checking while providing provably conservative estimates as long as the safety is indeed monotonic. Our experiments demonstrate model-checking speed-ups of an order of magnitude while main-taining acceptable accuracy and require much less data for accurate estimates when running SMC - even when monotonic safety does not perfectly hold and provable conservatism is not achieved.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133301748","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 : 2021-11-03DOI: 10.1109/iccps54341.2022.00007
I. Ruchkin, Matthew Cleaveland, Radoslav Ivanov, Pengyuan Lu, Taylor J. Carpenter, O. Sokolsky, Insup Lee
Closed-loop verification of cyberphysical systems with neural network controllers offers strong safety guarantees under certain assumptions. It is, however, difficult to determine whether these guar-antees apply at run time because verification assumptions may be violated. To predict safety violations in a verified system, we propose a three-step confidence composition (CoCo) framework for monitoring verification assumptions. First, we represent the sufficient condition for verified safety with a propositional logical formula over assumptions. Second, we build calibrated confidence monitors that evaluate the probability that each assumption holds. Third, we obtain the confidence in the verification guarantees by composing the assumption monitors using a composition function suitable for the logical formula. Our CoCo framework provides theoretical bounds on the calibration and conservatism of compositional monitors. Two case studies show that compositional monitors are calibrated better than their constituents and successfully predict safety violations.
{"title":"Confidence Composition for Monitors of Verification Assumptions","authors":"I. Ruchkin, Matthew Cleaveland, Radoslav Ivanov, Pengyuan Lu, Taylor J. Carpenter, O. Sokolsky, Insup Lee","doi":"10.1109/iccps54341.2022.00007","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00007","url":null,"abstract":"Closed-loop verification of cyberphysical systems with neural network controllers offers strong safety guarantees under certain assumptions. It is, however, difficult to determine whether these guar-antees apply at run time because verification assumptions may be violated. To predict safety violations in a verified system, we propose a three-step confidence composition (CoCo) framework for monitoring verification assumptions. First, we represent the sufficient condition for verified safety with a propositional logical formula over assumptions. Second, we build calibrated confidence monitors that evaluate the probability that each assumption holds. Third, we obtain the confidence in the verification guarantees by composing the assumption monitors using a composition function suitable for the logical formula. Our CoCo framework provides theoretical bounds on the calibration and conservatism of compositional monitors. Two case studies show that compositional monitors are calibrated better than their constituents and successfully predict safety violations.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797595","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 : 2021-05-10DOI: 10.1109/iccps54341.2022.00022
Shenghui Chen, Kayla Boggess, D. Parker, Lu Feng
Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which con-cerns the problem of computing an optimal controller subject to multiple (possibly conflicting) criteria. The relative importance of objectives is often specified by human decision-makers. However, there is inherent uncertainty in human preferences (e.g., due to artifacts resulting from different preference elicitation methods). In this paper, we formalize the notion of uncertain human preferences, and present a novel approach that accounts for this uncertainty in the context of multi-objective controller synthesis for Markov decision processes (MDPs). Our approach is based on mixed-integer linear programming and synthesizes an optimally permissive multi-strategy that satisfies uncertain human preferences with respect to a multi-objective property. Experimental results on a range of large case studies show that the proposed approach is feasible and scalable across varying MDP model sizes and uncertainty levels of human preferences. Evaluation via an online user study also demon-strates the quality and benefits of the synthesized controllers.
{"title":"Multi-Objective Controller Synthesis with Uncertain Human Preferences","authors":"Shenghui Chen, Kayla Boggess, D. Parker, Lu Feng","doi":"10.1109/iccps54341.2022.00022","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00022","url":null,"abstract":"Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which con-cerns the problem of computing an optimal controller subject to multiple (possibly conflicting) criteria. The relative importance of objectives is often specified by human decision-makers. However, there is inherent uncertainty in human preferences (e.g., due to artifacts resulting from different preference elicitation methods). In this paper, we formalize the notion of uncertain human preferences, and present a novel approach that accounts for this uncertainty in the context of multi-objective controller synthesis for Markov decision processes (MDPs). Our approach is based on mixed-integer linear programming and synthesizes an optimally permissive multi-strategy that satisfies uncertain human preferences with respect to a multi-objective property. Experimental results on a range of large case studies show that the proposed approach is feasible and scalable across varying MDP model sizes and uncertainty levels of human preferences. Evaluation via an online user study also demon-strates the quality and benefits of the synthesized controllers.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134188300","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 : 2021-03-10DOI: 10.1109/iccps54341.2022.00030
Amir Khazraei, S. Hallyburton, Qitong Gao, Yu Wang, M. Pajic
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS, where the low-level control is based on a feedback controller and an observer (e.g., the extended Kalman filter (EKF)), while also employing an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have on systems with general nonlinear dynamics, we develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where only parts of the runtime information are known to the attacker. We then introduce two methods for generating effective stealthy attacks, based on feed-forward neural networks (FNN) and recurrent neural networks (RNN). Both types of attack-generator models are trained offline, using a cost function that combines the attack impact on the estimation error (and thus control) and the residual signal used for anomaly detection; this enables the trained models to recursively generate effective yet stealthy sensor attacks in real-time while requiring different levels of system information at runtime. The effectiveness of the proposed methods is demonstrated on several case studies with varying levels of complexity and nonlinearity: inverted pendulum, autonomous driving vehicles (ADV), and unmanned areal vehicles (UAVs).
{"title":"Learning-Based Vulnerability Analysis of Cyber-Physical Systems","authors":"Amir Khazraei, S. Hallyburton, Qitong Gao, Yu Wang, M. Pajic","doi":"10.1109/iccps54341.2022.00030","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00030","url":null,"abstract":"This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS, where the low-level control is based on a feedback controller and an observer (e.g., the extended Kalman filter (EKF)), while also employing an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have on systems with general nonlinear dynamics, we develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where only parts of the runtime information are known to the attacker. We then introduce two methods for generating effective stealthy attacks, based on feed-forward neural networks (FNN) and recurrent neural networks (RNN). Both types of attack-generator models are trained offline, using a cost function that combines the attack impact on the estimation error (and thus control) and the residual signal used for anomaly detection; this enables the trained models to recursively generate effective yet stealthy sensor attacks in real-time while requiring different levels of system information at runtime. The effectiveness of the proposed methods is demonstrated on several case studies with varying levels of complexity and nonlinearity: inverted pendulum, autonomous driving vehicles (ADV), and unmanned areal vehicles (UAVs).","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114710084","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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