Metamaterial Reverse Multiple Prediction Method Based on Deep Learning

Metamaterials and their related research have had a profound impact on many fields, including optics, but designing metamaterial structures on demand is still a challenging task. In recent years, deep learning has been widely used to guide the design of metamaterials, and has achieved outstanding performance. In this work, a metamaterial structure reverse multiple prediction method based on semisupervised learning was proposed, named the partially Conditional Generative Adversarial Network (pCGAN). It could reversely predict multiple sets of metamaterial structures that can meet the needs by inputting the required target spectrum. This model could reach a mean average error (MAE) of 0.03 and showed good generality. Compared with the previous metamaterial design methods, this method could realize reverse design and multiple design at the same time, which opens up a new method for the design of new metamaterials.

Accountability through government alternation: Economic performance and the conditional role of political institutions in fifty countries, 1990–2015

Government alternation is a fundamental aspect of representative democracies because it is the most efficient mechanism of accountability through which voters can steer the course of government. Previous research on alternation has focused either on its conceptualization and measurement or on its role as a determinant of political and economic outcomes. This article attempts to investigate the factors affecting government alternation. We test research hypotheses on the direct effect of the economy on alternation and the conditional role played by political-institutional settings using an original dataset covering 50 countries from 1990 to 2015 and including 304 elections. First, the article provides an overview of the patterns of alternation in the countries we analyse. Second, it shows that alternation is less likely in times of successful macroeconomic performance, and that the effect of macroeconomic conditions on alternation is only partially conditional on the political-institutional context.

Strategic sequencing behavior among owner-occupiers

Purpose – This article aims to study the dual search problem using data on the Norwegian housing market during the financial crisis of 2008 and begin the detailed mapping of the elements in the transmission mechanism from policy to the housing market. Moving owner occupiers face a simultaneous dual search and matching problem, as they must locate both a buyer and a seller with whom to transact. Individual agents solve this optimization under uncertainty by planning to make their bids for a new house partially conditional upon the sale of the old house. Design/methodology/approach – Norway may function as a window into a policy quasi-laboratory since the housing market was turned around in December 2008 in the midst of a worldwide financial crisis and after a year and a half of price decreases. The article proposes that one key dimension in the recovery was the reduced frequency of households with conditional demand involving sell-first strategies and acquires data to shed light on this proposition. Findings – Empirical evidence on the sell-first–buy-first differential, for-sale stock and stock-to-volume supports this proposition, and results indicate that the housing market is affected by sell-first strategies. The article discusses policy alternatives. Originality/value – The article introduces a miniature model of housing trade sequences and a simple apparatus with which to analyze the consequences of sell-first behavior. It also acquires and combines new data on sell-first–buy-first differential, for-sale stock and stock-to-volume ratio. The article analyzes the co-movement between these time series and the house price index.