Does Venture Capital Investment Spur Employment Growth?

Anglo-Saxon countries have been successful in the 1990s concerning labor market performance compared to the former role models Germany and Japan. This reversal in relative economic performance might be related to idiosyncracies in financial markets with bank-based financial markets as in Germany and Japan being possibly inferior to stockmarket based financial markets in turbulent times and when approaching the economic frontier. A cleavage is related to venture capital markets which are flourishing on Anglo-Saxon but not on German type financial markets. Venture capital is crucial for financing structural change, new firms and innovations and therefore possibly also nowadays for employment growth.

Does Venture Capital Investment Spur Employment Growth?

Anglo-Saxon countries have been successful in the 1990s concerning labor market performance compared to the former role models Germany and Japan. This reversal in relative economic performance might be related to idiosyncracies in financial markets with bank-based financial markets as in Germany and Japan being possibly inferior to stockmarket based financial markets in turbulent times and when approaching the economic frontier. A cleavage is related to venture capital markets which are flourishing on Anglo-Saxon but not on German type financial markets. Venture capital is crucial for financing structural change, new firms and innovations and therefore possibly also nowadays for employment growth.labor markets, venture capital, unemployment, new economy, panel data analysis

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Coding-theorem Like Behaviour and Emergence of the Universal Distribution from Resource-bounded Algorithmic Probability

Previously referred to as `miraculous' in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.Comment: 27 pages main text, 39 pages including supplement. Online complexity calculator: http://complexitycalculator.com