Essays on macroeconomic policy

This dissertation consists of four self-contained chapters which discuss topics related to challenges and the design for macroeconomic policy in the aftermath of the Global Financial Crisis 2007-2008. The first two chapters consider the implications of the global financial cycle (GFC) to monetary policy in two-country open market dynamic stochastic general equilibrium models where the dominant role of the reserve currency in trade and global financial transactions can account for the cross-border transmission of U.S. monetary policy shocks. Chapter 1 evaluates the gains from international monetary policy cooperation between the financial centre and periphery countries. The key finding is that although monetary policy cooperation may improve the global welfare, the asymmetric policy spill-overs through the global financial markets create winners and losers from cooperation at the individual country level. Chapter 2 discusses whether exchange rate regime matters in the presence of the GFC with a similar model estimated to match the empirical evidence from a panel vector autoregressive (VAR) model. We find that even with a GFC, a peg substantially increases macroeconomic volatility. Conversely, the introduction of an additional policy instrument to manage capital flows dampens economic fluctuations. A tax on domestic credit achieves nearly equivalent results. Chapter 3 examines the effectiveness of unconventional monetary policy (UMP). We develop a theoretical model that underpins the `irrelevance hypothesis,' which states that UMP may make the effective lower bound (ELB) on the short-term interest rate irrelevant. We then test and firmly reject this hypothesis for Japan and the United States using a structural VAR model with the ELB. However, we find that UMP has had strong delayed effects. Chapter 4 analyses the factors that hinder the implementation of structural reforms using a macroeconomic political economy model. In the model, the effects of product market deregulation create its own resistance, and the resistance generates endogenous political costs to impair deregulation. In addition, adverse initial conditions, better risk-sharing among workers, and a lower share of temporary workers weaken opposition and help deregulation

Testing the effectiveness of unconventional monetary policy in Japan and the United States

The effective lower bound on a short term interest rate may not constrain a central bank's capacity to achieve its objectives if unconventional monetary policy (UMP) is powerful enough. We formalize this `irrelevance hypothesis' using a dynamic stochastic general equilibrium model with UMP and test it empirically for the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints. The hypothesis is strongly rejected for both countries. However, a comparison of the impulse responses to a monetary policy shock across regimes shows that UMP has had strong delayed effects in each country

Novel Tet(L) Efflux Pump Variants Conferring Resistance to Tigecycline and Eravacycline in Staphylococcus Spp.

Tigecycline is regarded as one of the few important last-resort antibiotics to treat complicated skin and intra-abdominal infections. Members of the genus Staphylococcus are zoonotic pathogens and pose a serious threat to public health. Tigecycline resistance in this species appears to be a rare phenomenon, and the mechanisms underlying tigecycline resistance have not been fully elucidated. Here, we report two novel variants of the tet(L) gene in Staphylococcus spp. from swine in China, designed as tet(L)F58L and tet(L)A117V. The tet(L)F58L was located within a 18,720 bp chromosomal multidrug resistance gene cluster flanked by two copies of IS257 in Staphylococcus cohnii 11-B-312, while the tet(L)A117V was located on a 6,292 bp plasmid in S. haemolyticus 11-B-93, which could be transferred to S. aureus by electrotransformation. Cloning of each of the two tet(L) variants into S. aureus RN4220 showed 16- or 8-fold increases in the minimal inhibition concentrations (MICs), which can fully confer the resistance to tigecycline (MICs from 0.125 to 2 mg/liter) and eravacycline (MICs from 0.125 to 1 or 2 mg/liter), but no increase in the MICs of omadacycline, compared with the MICs of the recipient strain S. aureus RN4220. In the in vivo murine sepsis and in the murine pneumonia models, an increase in CFU of S. aureus 29213_pT93 carrying the tet(L)A117V was seen despite tigecycline treatment. This observation suggests that the tet(L)A117V and its associated gene product compromise the efficacy of tigecycline treatment in vivo and may lead to clinical treatment failure. Our finding, that novel Tet(L) efflux pump variants which confer tigecycline and eravacycline resistance have been identified in Staphylococcus spp., requires urgent attention. IMPORTANCE Tigecycline and eravacycline are both important last-resort broad spectrum antimicrobial agents. The presence of novel Tet(L) efflux pump variants conferring the resistance to tigecycline and eravacycline in Staphylococcus spp. and its potential transmission to S. aureus will compromise the efficacy of tigecycline and eravacycline treatment for S. aureus associated infection in vivo and may lead to clinical treatment failure

Optimization and Noise Analysis of the Quantum Algorithm for Solving One-Dimensional Poisson Equation

Solving differential equations is one of the most promising applications of quantum computing. Recently we proposed an efficient quantum algorithm for solving one-dimensional Poisson equation avoiding the need to perform quantum arithmetic or Hamiltonian simulation. In this letter, we further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. To this end, we first develop a new way of performing the sine transformation, and based on it the algorithm is optimized by reducing the depth of the circuit from n2 to n. Then, we analyze the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit. We find that the phase damping noise has little effect on our algorithm, while the bit flip noise has the greatest impact. In addition, threshold errors of the quantum gates are obtained to make the fidelity of the circuit output being greater than 90%. The results of noise analysis will provide a good guidance for the subsequent work of error mitigation and error correction for our algorithm. The noise-analysis method developed in this work can be used for other algorithms to be executed on the NISQ devices.Comment: 20 pages, 9 figure

Quantum-inspired Complex Convolutional Neural Networks

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found.Comment: 12pages, 6 figure

Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure

SMA1, a homolog of the splicing factor Prp28, has a multifaceted role in miRNA biogenesis in Arabidopsis

MicroRNAs (miRNAs) are a class of small non-coding RNAs that repress gene expression. In plants, the RNase III enzyme Dicer-like (DCL1) processes primary miRNAs (pri-miRNAs) into miRNAs. Here, we show that SMALL1 (SMA1), a homolog of the DEADbox pre-mRNA splicing factor Prp28, plays essential roles in miRNA biogenesis in Arabidopsis. A hypomorphic sma1-1 mutation causes growth defects and reduces miRNA accumulation correlated with increased target transcript levels. SMA1 interacts with the DCL1 complex and positively influences primiRNA processing. Moreover, SMA1 binds the promoter region of genes encoding pri-miRNAs (MIRs) and is required for MIR transcription. Furthermore, SMA1 also enhances the abundance of the DCL1 protein levels through promoting the splicing of the DCL1 pre-mRNAs. Collectively, our data provide new insights into the function of SMA1/Prp28 in regulating miRNA abundance in plants

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science