Conformal off-policy prediction

Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. In this paper, we develop a novel procedure to produce reliable interval estimators for a target policy’s return starting from any initial state. Our proposal accounts for the variability of the return around its expectation, focuses on the individual effect and offers valid uncertainty quantification. Our main idea lies in designing a pseudo policy that generates subsamples as if they were sampled from the target policy so that existing conformal prediction algorithms are applicable to prediction interval construction. Our methods are justified by theories, synthetic data and real data from short-video platforms

New testing and calculation method for determination viscoelasticity of optical glass

Viscoelastic properties of glass within molding temperatures, such as shear relaxation modulus and bulk relaxation modulus, are key factors to build successful numerical model, predict forming process, and determine optimal process parameters for precision glass molding. However, traditional uniaxial compression creep tests with large strains are very limited in obtaining high-accuracy viscoelastic data of glass, due to the declining compressive stress caused by the increasing cross-sectional area of specimen in testing process. Besides, existing calculation method has limitation in transforming creep data to viscoelasticity data, especially when Poisson's ratio is unknown at molding temperature, which further induces a block to characterize viscoelastic parameter. This study proposes a systematic acquisition method tbr high-precision viscoelastic data, including creep testing, viscoelasticity calculation, and finite element verification. A minimal uniaxial creep testing (MUCT) method based on thermo-mechanical analysis (TMA) instrument is first built to obtain ideal and accurate creep data, by keeping compressive stress as a constant. A new calculation method on viscoelasticity determination is then proposed to derive shear relaxation modulus without the need of knowing bulk modulus or Poisson's ratio, which, compared with traditional method, extends the application range of viscoelasticity calculation. After determination, the obtained viscoelastic data are further incorporated into a numerical simulation model of MUCT to verify the accuracy of the determined viscoelasticity. Base on the great consistence between simulated and measured results (uniaxial creep displacement), the proposed systematic acquisition method can be used as a high accuracy viscoelasticity determination method.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The green GDP accounting system based on the BP neural network: an environmental pollution perspective

Introduction: The green GDP accounting system has become the focus of sustainable development, but a comprehensive accounting of environmental pollution cost and resource depletion cost has not yet been formed.Methods: This study measures environmental pollution cost and resource loss cost, and establishes the green GDP accounting system based on the SEEA-2012. To analyze the environmental effects brought by the adoption of green GDP accounting system, a BP neural network model including green GDP, traditional GDP and global climate indicators is constructed to predict the global climate changes.Results: The empirical results show that after the adoption of the green GDP accounting system, the global climate extreme weather can be reduced, the sea level will be lowered, and the climate problem is thus alleviated