The Experts below are selected from a list of 38727 Experts worldwide ranked by ideXlab platform
Dali L Yang - One of the best experts on this subject based on the ideXlab platform.
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lying or believing measuring preference Falsification from a political purge in china
Comparative Political Studies, 2016Co-Authors: Junyan Jiang, Dali L YangAbstract:Despite its wide usage in explaining political dynamics of non-democracies, preference Falsification remains an empirical myth for students of authoritarian politics due to the challenge of measurement. We offer the first quantitative study of this phenomenon in a non-democratic setting by exploiting a rare coincidence between a major political purge in Shanghai, China, and the administration of a nationwide survey in 2006. We construct two synthetic measures for expressed and actual political support and track their changes before and after the purge. We find that the purge caused a dramatic increase in expressed support among Shanghai respondents, yet the increase was paralleled by an equally evident decline in actual support. We interpret this divergence as evidence for preference Falsification and conduct a number of robustness checks to rule out alternative explanations. We also show that Falsification was most intense among groups that had access to alternative information but were vulnerable to pol...
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lying or believing measuring preference Falsification from a political purge in china
2015Co-Authors: Junyan Jiang, Dali L YangAbstract:Despite its wide usage in explaining some nontrivial dynamics in nondemocratic systems, preference Falsification remains an empirical myth for students of authoritarian politics. We provide to our knowledge the first quantitative study of preference Falsification in an authoritarian setting using a rare coincidence between a major political purge in Shanghai, China, and the administration of a nationwide survey in 2006. We construct two synthetic measures for expressed and actual support from a set of survey questions and track their changes before and after the purge. We find that after the purge there was a dramatic increase in expressed support among Shanghai respondents, yet the increase was paralleled by an equally evident decline in actual support. We interpret this divergence as evidence for the presence of preference Falsification. We also find that variations in the degree of preference Falsification are jointly predicted by one’s access to alternative source of information and vulnerability to state sanctions. Using two additional surveys conducted over the span of a year, we further show that there was substantial deterioration in political trust in Shanghai six months after the purge, which suggests that Falsification could not sustain public support in the long run.
Ichiro Hasuo - One of the best experts on this subject based on the ideXlab platform.
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Constrained Optimization for Falsification and Conjunctive Synthesis.
2020Co-Authors: Sota Sato, Masaki Waga, Ichiro HasuoAbstract:The synthesis problem of a cyber-physical system (CPS) is to find an input signal under which the system's behavior satisfies a given specification. Our setting is that the specification is a formula of signal temporal logic, and furthermore, that the specification is a conjunction of different and often conflicting requirements. Conjunctive specifications are often challenging for optimization-based Falsification -- an established method for CPS analysis that can also be used for synthesis -- since the usual framework (especially how its robust semantics handles Boolean connectives) is not suited for finding delicate trade-offs between different requirements. Our proposed method consists of the combination of optimization-based Falsification and constrained optimization. Specifically, we show that the state-of-the-art multiple constraint ranking method can be combined with Falsification powered by CMA-ES optimization; its performance advantage is demonstrated in experiments.
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Time-Staging Enhancement of Hybrid System Falsification
arXiv: Systems and Control, 2018Co-Authors: Gidon Ernst, Ichiro Hasuo, Zhenya Zhang, Sean SedwardsAbstract:Optimization-based Falsification employs stochastic optimization algorithms to search for error input of hybrid systems. In this paper we introduce a simple idea to enhance Falsification, namely time staging, that allows the time-causal structure of time-dependent signals to be exploited by the optimizers. Time staging consists of running a Falsification solver multiple times, from one interval to another, incrementally constructing an input signal candidate. Our experiments show that time staging can dramatically increase performance in some realistic examples. We also present theoretical results that suggest the kinds of models and specifications for which time staging is likely to be effective.
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FVAV@iFM - Causality-Aided Falsification.
Electronic Proceedings in Theoretical Computer Science, 2017Co-Authors: Takumi Akazaki, Yoshihiro Kumazawa, Ichiro HasuoAbstract:Falsification is drawing attention in quality assurance of heterogeneous systems whose complexities are beyond most verification techniques' scalability. In this paper we introduce the idea of causality aid in Falsification: by providing a Falsification solver -- that relies on stochastic optimization of a certain cost function -- with suitable causal information expressed by a Bayesian network, search for a falsifying input value can be efficient. Our experiment results show the idea's viability.
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time robustness in mtl and expressivity in hybrid system Falsification
Computer Aided Verification, 2015Co-Authors: Takumi Akazaki, Ichiro HasuoAbstract:Building on the work by Fainekos and Pappas and the one by Donze and Maler, we introduce \(\mathbf{AvSTL }\), an extension of metric interval temporal logic by averaged temporal operators. Its expressivity in capturing both space and time robustness helps solving Falsification problems (searching for a critical path in hybrid system models); it does so by communicating a designer’s intention more faithfully to the stochastic optimization engine employed in a Falsification solver. We also introduce a sliding window-like algorithm that keeps the cost of computing truth/robustness values tractable.
Steven D. Pizer - One of the best experts on this subject based on the ideXlab platform.
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Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research
Health Services Research, 2015Co-Authors: Steven D. PizerAbstract:Objectives To demonstrate how Falsification tests can be used to evaluate instrumental variables methods applicable to a wide variety of comparative effectiveness research questions. Study Design Brief conceptual review of instrumental variables and Falsification testing principles and techniques accompanied by an empirical application. Sample STATA code related to the empirical application is provided in the Appendix. Empirical Application Comparative long-term risks of sulfonylureas and thiazolidinediones for management of type 2 diabetes. Outcomes include mortality and hospitalization for an ambulatory care–sensitive condition. Prescribing pattern variations are used as instrumental variables. Conclusions Falsification testing is an easily computed and powerful way to evaluate the validity of the key assumption underlying instrumental variables analysis. If Falsification tests are used, instrumental variables techniques can help answer a multitude of important clinical questions.
Luca Bortolussi - One of the best experts on this subject based on the ideXlab platform.
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an active learning approach to the Falsification of black box cyber physical systems
Integrated Formal Methods, 2017Co-Authors: Simone Silvetti, Alberto Policriti, Luca BortolussiAbstract:Search-based testing is widely used to find bugs in models of complex Cyber-Physical Systems. Latest research efforts have improved this approach by casting it as a Falsification procedure of formally specified temporal properties, exploiting the robustness semantics of Signal Temporal Logic. The scaling of this approach to highly complex engineering systems requires efficient Falsification procedures, which should be applicable also to black box models. Falsification is also exacerbated by the fact that inputs are often time-dependent functions. We tackle the Falsification of formal properties of complex black box models of Cyber-Physical Systems, leveraging machine learning techniques from the area of Active Learning. Tailoring these techniques to the Falsification problem with time-dependent, functional inputs, we show a considerable gain in computational effort, by reducing the number of model simulations needed. The effectiveness of the proposed approach is discussed on a challenging industrial-level benchmark from automotive.
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an active learning approach to the Falsification of black box cyber physical systems
arXiv: Logic in Computer Science, 2017Co-Authors: Simone Silvetti, Alberto Policriti, Luca BortolussiAbstract:Search-based testing is widely used to find bugs in models of complex Cyber-Physical Systems. Latest research efforts have improved this approach by casting it as a Falsification procedure of formally specified temporal properties, exploiting the robustness semantics of Signal Temporal Logic. The scaling of this approach to highly complex engineering systems requires efficient Falsification procedures, which should be applicable also to black box models. Falsification is also exacerbated by the fact that inputs are often time-dependent functions. We tackle the Falsification of formal properties of complex black box models of Cyber-Physical Systems, leveraging machine learning techniques from the area of Active Learning. Tailoring these techniques to the Falsification problem with time-dependent, functional inputs, we show a considerable gain in computational effort, by reducing the number of model simulations needed. The goodness of the proposed approach is discussed on a challenging industrial-level benchmark from automotive.
Junyan Jiang - One of the best experts on this subject based on the ideXlab platform.
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lying or believing measuring preference Falsification from a political purge in china
Comparative Political Studies, 2016Co-Authors: Junyan Jiang, Dali L YangAbstract:Despite its wide usage in explaining political dynamics of non-democracies, preference Falsification remains an empirical myth for students of authoritarian politics due to the challenge of measurement. We offer the first quantitative study of this phenomenon in a non-democratic setting by exploiting a rare coincidence between a major political purge in Shanghai, China, and the administration of a nationwide survey in 2006. We construct two synthetic measures for expressed and actual political support and track their changes before and after the purge. We find that the purge caused a dramatic increase in expressed support among Shanghai respondents, yet the increase was paralleled by an equally evident decline in actual support. We interpret this divergence as evidence for preference Falsification and conduct a number of robustness checks to rule out alternative explanations. We also show that Falsification was most intense among groups that had access to alternative information but were vulnerable to pol...
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lying or believing measuring preference Falsification from a political purge in china
2015Co-Authors: Junyan Jiang, Dali L YangAbstract:Despite its wide usage in explaining some nontrivial dynamics in nondemocratic systems, preference Falsification remains an empirical myth for students of authoritarian politics. We provide to our knowledge the first quantitative study of preference Falsification in an authoritarian setting using a rare coincidence between a major political purge in Shanghai, China, and the administration of a nationwide survey in 2006. We construct two synthetic measures for expressed and actual support from a set of survey questions and track their changes before and after the purge. We find that after the purge there was a dramatic increase in expressed support among Shanghai respondents, yet the increase was paralleled by an equally evident decline in actual support. We interpret this divergence as evidence for the presence of preference Falsification. We also find that variations in the degree of preference Falsification are jointly predicted by one’s access to alternative source of information and vulnerability to state sanctions. Using two additional surveys conducted over the span of a year, we further show that there was substantial deterioration in political trust in Shanghai six months after the purge, which suggests that Falsification could not sustain public support in the long run.
