Corruption in public finances, and the effects on inflation, taxation, and growth

In this paper, we study the effects of bureaucratic corruption on inflation, taxation, and growth. Here corruption takes three forms: (i) it reduces the tax revenues that are raised from households, (ii) it inflates the volume of government spending, and (iii) it reduces the productivity of ‘effective’ government expenditure. Our policy experiments reveal that the effect of (i) is to increase both seigniorage and the income tax rate, and to decrease the steady-state growth rate. The effect of (ii) is to increase seigniorage, which leads to lower growth, although the effect on the income tax rate is ambiguous. The effect of (iii) is to increase seigniorage and decrease the income tax rate. The former yields a lower growth rate, while the latter has an ambiguous effect on growth. These findings, from our unified framework involving corruption in public finances, could rationalise the apparently conflicting evidence on the impact of corruption on economic growth provided in the literature, highlighting the presence of conditional corruption effects

Single photons from a gain medium below threshold

The emission from a nonlinear photonic mode coupled weakly to a gain medium operating below threshold is predicted to exhibit antibunching. In the steady state regime, analytical solutions for the relevant observable quantities are found in accurate agreement with exact numerical results. Under pulsed excitation, the unequal time second order correlation function demonstrates the triggered probabilistic generation of single photons well separated in time.Comment: Submitte

An investigation of magnetic field distortions in accretion discs around neutron stars. I. Analysis of the poloidal field component

We report results from calculations investigating stationary magnetic field configurations in accretion discs around magnetised neutron stars. Our strategy is to start with a very simple model and then progressively improve it providing complementary insight into results obtained with large numerical simulations. In our first model, presented here, we work in the kinematic approximation and consider the stellar magnetic field as being a dipole aligned with the stellar rotation axis and perpendicular to the disc plane, while the flow in the disc is taken to be steady and axisymmetric. The behaviour in the radial direction is then independent of that in the azimuthal direction. We investigate the distortion of the field caused by interaction with the disc matter, solving the induction equation numerically in full 2D. The influence of turbulent diffusivity and fluid velocity on the poloidal field configuration is analysed, including discussion of outflows from the top and bottom of the disc. We find that the distortions increase with increasing magnetic Reynolds number R_m (calculated using the radial velocity). However, a single global parameter does not give an adequate description in different parts of the disc and we use instead a `magnetic distortion function' D_m(r,\theta) (a magnetic Reynolds number defined locally). Where D_m<<1 (near to the inner edge of the disc) there is little distortion, but where D_m>1 (most of the rest of the disc), there is considerable distortion and the field becomes weaker than the dipole would have been. Between these two regions, there is a transition zone where the field is amplified and can have a local minimum and maximum. The location of this zone depends sensitively on the diffusivity. The results depend very little on the boundary conditions at the top of the disc.Comment: Published in A&A; 10 pages and 8 figures; ver. 4: compactification of content

Risk Prediction of a Multiple Sclerosis Diagnosis

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disabling disease. Although medications have been developed to slow the disease progression and help manage symptoms, MS research has yet to result in a cure. Early diagnosis and treatment of the disease have been shown to be effective at slowing the development of disabilities. However, early MS diagnosis is difficult because symptoms are intermittent and shared with other diseases. Thus most previous works have focused on uncovering the risk factors associated with MS and predicting the progression of disease after a diagnosis rather than disease prediction. This paper investigates the use of data available in electronic medical records (EMRs) to create a risk prediction model; thereby helping clinicians perform the difficult task of diagnosing an MS patient. Our results demonstrate that even given a limited time window of patient data, one can achieve reasonable classification with an area under the receiver operating characteristic curve of 0.724. By restricting our features to common EMR components, the developed models also generalize to other healthcare systems

Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach

Three-qubit quantum gates are key ingredients for quantum error correction and quantum information processing. We generate quantum-control procedures to design three types of three-qubit gates, namely Toffoli, Controlled-Not-Not and Fredkin gates. The design procedures are applicable to a system comprising three nearest-neighbor-coupled superconducting artificial atoms. For each three-qubit gate, the numerical simulation of the proposed scheme achieves 99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant quantum computing. We test our procedure in the presence of decoherence-induced noise as well as show its robustness against random external noise generated by the control electronics. The three-qubit gates are designed via the machine learning algorithm called Subspace-Selective Self-Adaptive Differential Evolution (SuSSADE).Comment: 18 pages, 13 figures. Accepted for publication in Phys. Rev. Applie