اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRestoration and extrapolation of structural transformation by dynamical general equilibrium feedbacks
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We model sectoral production by serially nesting (cascading) binary compounding processes. The sequence of processes is discovered in a self-similar hierarchical structure stylized in macroscopic input-output transactions. The feedback system of unit cost functions, with recursively estimated nest-wise CES parameters, is calibrated for sectoral productivities to replicate two temporally distant cost share structures, observed in a set of linked input--output tables. We model representative households by multifactor CES, with parameters estimated by fixed effects regressions. By the integrated dynamic general equilibrium model, we extrapolate potential structural transformations, and measure the associated welfare changes, caused by exogenous sectoral productivity shocks.
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Sector specific multifactor CES elasticity of substitution and the corresponding productivity growths are jointly measured by regressing the growths of factor-wise cost shares against the growths of factor prices. We use linked input-output tables fo
r Japan and the Republic of Korea as the data source for factor price and cost shares in two temporally distant states. We then construct a multi-sectoral general equilibrium model using the system of estimated CES unit cost functions, and evaluate the economy-wide propagation of an exogenous productivity stimuli, in terms of welfare. Further, we examine the differences between models based on a priori elasticity such as Leontief and Cobb-Douglas.
I tell about different mathematical tool that is important in general relativity. The text of the book includes definition of geometrical object, concept of reference frame, geometry of metric-affinne manifold. Using this concept I learn few physical
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Disaster recovery is widely regarded as the least understood phase of the disaster cycle. In particular, the literature around lifeline infrastructure restoration modeling frequently mentions the lack of empirical quantitative data available. Despite
limitations, there is a growing body of research on modeling lifeline infrastructure restoration, often developed using empirical quantitative data. This study reviews this body of literature and identifies the data collection and usage patterns present across modeling approaches to inform future efforts using empirical quantitative data. We classify the modeling approaches into simulation, optimization, and statistical modeling. The number of publications in this domain has increased over time with the most rapid growth of statistical modeling. Electricity infrastructure restoration is most frequently modeled, followed by the restoration of multiple infrastructures, water infrastructure, and transportation infrastructure. Interdependency between multiple infrastructures is increasingly considered in recent literature. Researchers gather the data from a variety of sources, including collaborations with utility companies, national databases, and post-event damage and restoration reports. This study provides discussion and recommendations around data usage practices within the lifeline restoration modeling field. Following the recommendations would facilitate the development of a community of practice around restoration modeling and provide greater opportunities for future data sharing.
Primordial cosmological perturbations are the seeds that were cultivated by inflation and the succeeding dynamical processes, eventually leading to the current Universe. In this work, we investigate the behavior of the gauge-invariant scalar and tens
or perturbations under the general extended disformal transformation, namely, $g_{mu u} rightarrow A(X,Y,Z)g_{mu u} + Phi_muPhi_ u$, where $X equiv -tfrac{1}{2}phi^{;mu}phi_{;mu}, Y equiv phi^{;mu}X_{;mu}, Z equiv X^{;mu}X_{;mu} $ and $Phi_mu equiv Cphi_{;mu} + DX_{;mu}$, with $C$ and $D$ being a general functional of $(phi,X,Y,Z)$. We find that the tensor perturbation is invariant under this transformation. On the other hand, the scalar curvature perturbation receives a correction due the conformal term only; it is independent of the disformal term at least up to linear order. Within the framework of the full Horndeski theory, the correction terms turn out to depend linearly on the gauge-invariant comoving density perturbation and the first time-derivative thereof. In the superhorizon limit, all these correction terms vanish, leaving only the original scalar curvature perturbation. In other words, it is invariant under the general extended disformal transformation in the superhorizon limit, in the context of full Horndeski theory. Our work encompasses a chain of research studies on the transformation or invariance of the primordial cosmological perturbations, generalizing their results under our general extended disformal transformation.
Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images. In this paper, we pro
pose a new progressive semantic-aware style transformation framework, named PSFR-GAN, for face restoration. Specifically, instead of using an encoder-decoder framework as previous methods, we formulate the restoration of LQ face images as a multi-scale progressive restoration procedure through semantic-aware style transformation. Given a pair of LQ face image and its corresponding parsing map, we first generate a multi-scale pyramid of the inputs, and then progressively modulate different scale features from coarse-to-fine in a semantic-aware style transfer way. Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs. In addition, we further introduce a semantic aware style loss which calculates the feature style loss for each semantic region individually to improve the details of face textures. Finally, we pretrain a face parsing network which can generate decent parsing maps from real-world LQ face images. Experiment results show that our model trained with synthetic data can not only produce more realistic high-resolution results for synthetic LQ inputs and but also generalize better to natural LQ face images compared with state-of-the-art methods. Codes are available at https://github.com/chaofengc/PSFRGAN.
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