Homo-junction bottom-gate amorphous In-Ga-Zn-O TFTs with metal induced source /drain regions

A fabrication process for homo-junction bottom-gate (HJBG) amorphous In–Ga–Zn–O (a-IGZO) thin-film transistors (TFTs) is proposed, in which the a-IGZO section as source/drain (S/D) region is induced to a low resistance state by coating a thin metal Al film and then performing a thermal annealing in oxygen, and that as channel region is protected from back etching by depositing and patterning a protective layer. Experimental results show that with a 5 nm Al film and a 200 ºC annealing, the sheet resistance of the S/D a-IGZO is 803 Ω/□ and keeps stable during subsequent thermal treatment. In addition, the annealing generated thin Al2O3 film contributes to improve the thermal stability and ambient atmosphere immunity of the fabricated HJBG TFTs. Please click Additional Files below to see the full abstract

The Design of Reference Service System in Cordova-based Hybrid Frameworks

With the rise of mobile technology, the library reference service has dramatically changed. Targeting the new requirements, this paper aims to design a new library reference service system in Cordova-based hybrid frameworks, which caters to the web service embedded in two major mobile platforms, iOS and Android, as well as the PC platform. The new system adopts the WebSocket based technology to realize the function of independent online reference, which improves the quality of the normal digital reference service. The newly designed system also applies the ECS cloud server technology, thereby significantly slashing the hardware setup cost, extending the basic reference service, and improving its fitness-for-use and convenience, and optimizing the allocation of local resources

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs

Solving partial differential equations (PDEs) is a central task in scientific computing. Recently, neural network approximation of PDEs has received increasing attention due to its flexible meshless discretization and its potential for high-dimensional problems. One fundamental numerical difficulty is that random samples in the training set introduce statistical errors into the discretization of loss functional which may become the dominant error in the final approximation, and therefore overshadow the modeling capability of the neural network. In this work, we propose a new minmax formulation to optimize simultaneously the approximate solution, given by a neural network model, and the random samples in the training set, provided by a deep generative model. The key idea is to use a deep generative model to adjust random samples in the training set such that the residual induced by the approximate PDE solution can maintain a smooth profile when it is being minimized. Such an idea is achieved by implicitly embedding the Wasserstein distance between the residual-induced distribution and the uniform distribution into the loss, which is then minimized together with the residual. A nearly uniform residual profile means that its variance is small for any normalized weight function such that the Monte Carlo approximation error of the loss functional is reduced significantly for a certain sample size. The adversarial adaptive sampling (AAS) approach proposed in this work is the first attempt to formulate two essential components, minimizing the residual and seeking the optimal training set, into one minmax objective functional for the neural network approximation of PDEs

A heterogeneous-agent model of growth and inequality for the UK

This paper analyses the effect of wealth inequality on UK economic growth in recent decades with a heterogeneous-agent growth model where agents can enhance individual productivity growth by allocating time to entrepreneurship. Entrepreneurship cost is negatively correlated to individual wealth which originates from the fact that the rich are more likely to undertake entrepreneurship than the poor. An appropriate wealth concentration to the rich theoretically stimulates their entrepreneurship incentives and then aggregate growth. Given UK quarterly data from 1978 to 2015, our model cannot be rejected to be true using the Indirect Inference method. The empirical study finds that our structural model could generate a stable relation between inequality and growth and model simulations could fit main properties of UK economy. Wealth inequality is found to stimulate economic growth, especially in a long term. Policy makers have to face a trade-off when conduct a redistribution policy like taxation because inequality reduction will be followed by a slow-down of economic growth. Moreover, as redistribution tax rate increases, growth reduction has a gradiently increasing trend and thus a moderate tax rate is a priority option for policy makers. Our comparison between tax regimes shows that the tax transferring income from the rich to the poor is preferred to others

Wealth inequality and social mobility: A simulation-based modelling approach

We design a series of simulation-based thought experiments to deductively evaluate the causal effects of various factors on wealth inequality (the distribution) and social mobility (dynamics of the distribution). We find that uncertainty per se can lead to a “natural” degree of inequality and returns-related factors contribute more than earnings-related factors. Based on these identified factors, we construct an empirical, hybrid agent-based model to match the observed wealth inequality measures of the G7 countries and China. The estimated model can generate a power-law wealth distribution for the rich and a positively sloped intra-generational Great Gatsby curve. We also demonstrate how this hybrid model can be extended to a wide range of questions such as redistributive effects of tax and finance