The Thick Market Effect on Local Unemployment Rate Fluctuations

This paper studies how the thick market effect influences local unemployment rate fluctuations. The paper presents a model to demonstrate that the average matching quality improves as the number of workers and firms increases. Unemployed workers accumulate in a city until the local labor market reaches a critical minimum size, which leads to cyclical fluctuations in the local unemployment rates. Since larger cities attain the critical market size more frequently, they have shorter unemployment cycles, lower peak unemployment rates, and lower mean unemployment rates. Our empirical tests are consisten with the predictions of the model. In particular, we find that an increase of two standard deviations in city size shortens the unemployment cycles by about 0.72 months, lowers the peak unemployment rates by 0.33 percentage points, and lowers the mean unemployment rates by 0.16 percentage points.

SNP analysis reveals an evolutionary acceleration of the human-specific microRNAs

MicroRNAs are one class of important gene regulators at the post-transcriptional level by binding to the 3’UTRs of target mRNAs. It has been reported that human microRNAs are evolutionary conserved and show lower single nucleotide polymorphisms (SNPs) than their flanking regions. However, in this study, we report that the human-specific microRNAs show a higher SNP density than both the conserved microRNAs and other control regions, suggesting rapid evolution and positive selection has occurred in these regions. Furthermore, we observe that the human-specific microRNAs show greater SNPs minor allele frequency and the SNPs in the human-specific microRNAs show fewer effects on the stability of the microRNA secondary structure, indicating that the SNPs in the human-specific microRNAs tend to be less deleterious. Finally, two microRNAs hsa-mir-423 (SNP: rs6505162), hsa-mir-608 (SNP: rs4919510) and 288 target genes that have apparently been under recent positive selection are identified. These findings will improve our understanding of the functions, evolution, and population disease susceptibility of human microRNAs

Global existence of the ϵ\epsilon-regular solution for the strongly damping wave equation

In this paper, we deal with the semilinear wave equation with strong damping. By choosing a suitable state space, we characterize the interpolation and extrapolation spaces of the operator matrix $\mathbf{A}_{\theta}$, analysis the criticality of the $\varepsilon$-regular nonlinearity with critical growth. Finally, we investigate the global existence of the $\varepsilon$-regular solutions which have bounded $X^{1/2}\times X$ norms on their existence intervals

Who are Taxable?—Basic Problems in Definition Under the Illinois Retailers’ Occupation Tax Act

Identification of non-linear systems is a challenge, due to the richness of both model structures and estimation approaches. As a case study, in this paper we test a number of methods on a data set collected from an electrical circuit at the Free University of Brussels. These methods are based on black box and grey box model structures or on a mixture of them, which are all implemented in a forthcoming Matlab toolbox. The results of this case study illustrate the importance of the use of custom (user defined) regressors in a black box model. Based on physical knowledge or on insights gained through experience, such custom regressors allow to build efficient models with a relatively simple model structure.

Outer Approximation Algorithms for DC Programs and Beyond

We consider the well-known Canonical DC (CDC) optimization problem, relying on an alternative equivalent formulation based on a polar characterization of the constraint, and a novel generalization of this problem, which we name Single Reverse Polar problem (SRP). We study the theoretical properties of the new class of (SRP) problems, and contrast them with those of (CDC)problems. We introduce of the concept of ``approximate oracle'' for the optimality conditions of (CDC) and (SRP), and make a thorough study of the impact of approximations in the optimality conditions onto the quality of the approximate optimal solutions, that is the feasible solutions which satisfy them. Afterwards, we develop very general hierarchies of convergence conditions, similar but not identical for (CDC) and (SRP), starting from very abstract ones and moving towards more readily implementable ones. Six and three different sets of conditions are proposed for (CDC) and (SRP), respectively. As a result, we propose very general algorithmic schemes, based on approximate oracles and the developed hierarchies, giving rise to many different implementable algorithms, which can be proven to generate an approximate optimal value in a finite number of steps, where the error can be managed and controlled. Among them, six different implementable algorithms for (CDC) problems, four of which are new and can't be reduced to the original cutting plane algorithm for (CDC) and its modifications; the connections of our results with the existing algorithms in the literature are outlined. Also, three cutting plane algorithms for solving (SRP) problems are proposed, which seem to be new and cannot be reduced to each other