A privacy-preserving, decentralized and functional Bitcoin e-voting protocol

Bitcoin, as a decentralized digital currency, has caused extensive research interest. There are many studies based on related protocols on Bitcoin, Bitcoin-based voting protocols also received attention in related literature. In this paper, we propose a Bitcoin-based decentralized privacy-preserving voting mechanism. It is assumed that there are n voters and m candidates. The candidate who obtains t ballots can get x Bitcoins from each voter, namely nx Bitcoins in total. We use a shuffling mechanism to protect voter's voting privacy, at the same time, decentralized threshold signatures were used to guarantee security and assign voting rights. The protocol can achieve correctness, decentralization and privacy-preservings. By contrast with other schemes, our protocol has a smaller number of transactions and can achieve a more functional voting method.Comment: 5 pages;3 figures;Smartworld 201

Full Waveform Inversion Guided Wave Tomography Based on Recurrent Neural Network

Corrosion quantitative detection of plate or plate-like structures is a critical and challenging topic in industrial Non-Destructive Testing (NDT) research which determines the remaining life of material. Compared with other methods (X-ray, magnetic powder, eddy current), ultrasonic guided wave tomography has the advantages of non-invasiveness, high efficiency, high precision and low cost. Among various ultrasonic guided wave tomography algorithms, travel time or diffraction algorithms can be used to reconstruct defect or corrosion model, but the accuracy is low and heavily influenced by the noise. Full Waveform Inversion (FWI) can build accurate reconstructions of physical properties in plate structures, however, it requires a relatively accurate initial model, and there is still room for improvement in the convergence speed, imaging resolution and robustness. This thesis starting with the physical principle of ultrasonic guided waves, the dispersion characteristic curve of the guided wave propagating in the plate structure converts the change of the remaining thickness of the plate structure material into the wave velocity variation when the ultrasonic guided wave propagates in it, and provides a physical principle for obtaining the thickness distribution map from the velocity reconstruction. Secondly, a guided wave tomography method based on Recurrent Neural Network Full Waveform Inversion (RNN-FWI) is proposed. Finally, the efficiency of the above method is verified through practical experiments. The main work of the thesis includes: The feasibility of conventional full waveform inversion for guided wave tomography is introduced and verified. An FWI algorithm based on RNN is proposed. In the framework of RNN-FWI, the effects of different optimization algorithms on imaging performance and the effects of different sensor numbers and positions on imaging performance are analyzed. The quadratic Wasserstein distance is used as the objective equation to further reduce the dependence on the initial model. The depth image prior (DIP) based on convolutional neural network (CNN) is used as the regularization method to further improve the conventional FWI algorithm, and the effectiveness of the improved algorithm is verified by simulation and actual experiments

2019-1 Trading Motives in Asset Markets

I study how trading motives in asset markets affect equilibrium outcomes and welfare. I focus on two types of trading motives – informational and allocational. I show that while a fully separating equilibrium is the unique equilibrium when trading motives are known, multiple equilibria exist when trading motives are unknown. Moreover, forcing traders to reveal their trading motives may harm welfare. I also use this model to study how an asset market may exit a fire sale equilibrium and how government programs may eliminate private information and improve agents’ welfare

Essays on Information Frictions in Macroeconomics

No abstract availableI explore the interaction between financial and information frictions when firms set prices under rational inattention. Facing both aggregate monetary policy shock and idiosyncratic productivity shock, firms with binding financial constraint pay weakly more attention to aggregate shock than unconstrained firms due to strategic complementarity. Using recent survey data, I find new cross-sectional facts that are consistent with the model's predictions regarding firm's attention to different Macroeconomic variables. Generally, monetary non-neutrality is decreasing with the fraction of constrained firms. I provide quantitative calibration disciplined by new survey data to show how a rational inattention model with financial heterogeneity can explain the state-dependent effectiveness of monetary policy which is consistent with recent empirical findings. During recession, increasing fraction of constrained firms will lead to less powerful monetary stimulus, which standard rational inattention model can hardly perform

A moment substitution approach to fitting linear regression models with categorical covariates subject to randomized response.

Master'sMASTER OF SCIENC