Public Ownership of Banks and Economic Growth – The Role of Heterogeneity

In an influential paper, La Porta, Lopez-De-Silanes and Shleifer (2002) argued that public ownership of banks is associated with lower GDP growth. We show that this relationship does not hold for all countries, but depends on a country’s financial development and political institutions. Public ownership is harmful only if a country has low financial development and low institutional quality. The negative impact of public ownership on growth fades quickly as the financial and political system develops. In highly developed countries, we find no or even positive effects. Policy conclusions for individual countries are likely to be misleading if such heterogeneity is ignored.Public banks, economic growth, financial development, quality of governance, political institutions

Stable control of 10 dB two-mode squeezed vacuum states of light

Continuous variable entanglement is a fundamental resource for many quantum information tasks. Important protocols like superactivation of zero-capacity channels and finite-size quantum cryptography that provides security against most general attacks, require about 10 dB two-mode squeezing. Additionally, stable phase control mechanisms are necessary but are difficult to achieve because the total amount of optical loss to the entangled beams needs to be small. Here, we experimentally demonstrate a control scheme for two-mode squeezed vacuum states at the telecommunication wavelength of 1550 nm. Our states exhibited an Einstein-Podolsky-Rosen covariance product of 0.0309 \pm 0.0002, where 1 is the critical value, and a Duan inseparability value of 0.360 \pm 0.001, where 4 is the critical value. The latter corresponds to 10.45 \pm 0.01 dB which reflects the average non-classical noise suppression of the two squeezed vacuum states used to generate the entanglement. With the results of this work demanding quantum information protocols will become feasible.Comment: 8 pages, 4 figure

A graphical description of optical parametric generation of squeezed states of light

The standard process for the production of strongly squeezed states of light is optical parametric amplification (OPA) below threshold in dielectric media such as LiNbO3 or periodically poled KTP. Here, we present a graphical description of squeezed light generation via OPA. It visualizes the interaction between the nonlinear dielectric polarization of the medium and the electromagnetic quantum field. We explicitly focus on the transfer from the field's ground state to a squeezed vacuum state and from a coherent state to a bright squeezed state by the medium's secondorder nonlinearity, respectively. Our pictures visualize the phase dependent amplification and deamplification of quantum uncertainties and give the phase relations between all propagating electro-magnetic fields as well as the internally induced dielectric polarizations. The graphical description can also be used to describe the generation of nonclassical states of light via higherorder effects of the non-linear dielectric polarization such as four-wave mixing and the optical Kerr effect

Where Do We Go From Here? Guidelines For Offline Recommender Evaluation

Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. However, there has been little research into what set of practices should serve as a starting point during experimentation. In this paper, we examine four larger issues in recommender system research regarding uncertainty estimation, generalization, hyperparameter optimization and dataset pre-processing in more detail to arrive at a set of guidelines. We present a TrainRec, a lightweight and flexible toolkit for offline training and evaluation of recommender systems that implements these guidelines. Different from other frameworks, TrainRec is a toolkit that focuses on experimentation alone, offering flexible modules that can be can be used together or in isolation. Finally, we demonstrate TrainRec's usefulness by evaluating a diverse set of twelve baselines across ten datasets. Our results show that (i) many results on smaller datasets are likely not statistically significant, (ii) there are at least three baselines that perform well on most datasets and should be considered in future experiments, and (iii) improved uncertainty quantification (via nested CV and statistical testing) rules out some reported differences between linear and neural methods. Given these results, we advocate that future research should standardize evaluation using our suggested guidelines.Comment: 8 page

Strong Einstein-Podolsky-Rosen steering with unconditional entangled states

In 1935 Schr\"odinger introduced the terms entanglement and steering in the context of the famous gedanken experiment discussed by Einstein, Podolsky, and Rosen (EPR). Here, we report on a sixfold increase of the observed EPR-steering effect as quantified by the Reid-criterion. We achieved an unprecedented low conditional variance product of about 0.04 < 1, where 1 is the upper bound below which steering is present. The steering effect was observed on an unconditional two-mode-squeezed entangled state that contained a total vacuum state contribution of less than 8%, including detection imperfections. Together with the achieved high interference contrast between the entangled state and a bright coherent laser field, our state is compatible with efficient applications in high-power laser interferometers and fiber-based networks for entanglement distribution.Comment: 5 pages, 3 figure

Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While the conventional approach to evaluation relies on online A/B tests, recent work has shown that counterfactual estimators can provide an inexpensive and fast alternative, since they can be applied offline using log data that was collected from a different policy fielded in the past. In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies. This question is of great relevance in practice, since policies get updated frequently in most online systems. We show that naively combining data from multiple logging policies can be highly suboptimal. In particular, we find that the standard Inverse Propensity Score (IPS) estimator suffers especially when logging and target policies diverge -- to a point where throwing away data improves the variance of the estimator. We therefore propose two alternative estimators which we characterize theoretically and compare experimentally. We find that the new estimators can provide substantially improved estimation accuracy.Comment: KDD 201