Immigration status and property crime:an application of estimators for underreported outcomes

This paper studies the individual-level relationship between immigration and property crime in England and Wales using crime self-reports from the Crime and Justice Survey. Models that account for underreporting are used, since this is a major concern in crime self-reports. The results indicate that the reported crime is substantially underreported, but if anything, immigrants are less likely to underreport than natives. More importantly, controlling for underreporting and basic demographics, the estimates across all model specifications, although imprecise, indicate that immigration status and property crime are negatively associated. We finally find that the estimated relationship between immigration status and property crime differs across regions and ethnic groups

Identification issues in models for underreported counts

In this note we study the conditions under which leading models for underreported counts are identified. In particular, we highlight a peculiar identification problem that afflicts two of the most popular models in this class.

Deep Affordance-grounded Sensorimotor Object Recognition

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. The proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201

What's so special about specialization in the euro area? Early evidence of changing economic structures

Euro area countries exhibited modest convergence prior to the financial crisis and diverged thereafter. Such divergence has been examined from many angles, and various narratives of the crisis have developed. Surprisingly, the gradual transformation of the economic structures of euro area countries over the last 15-20 years has, however, received less attention. This paper brings together several strands of evidence - both macro and micro - on such economic transformation. It makes three contributions. First, profound changes are found in the allocation of countries’ resources across sectors as had been predicted prior to the launch of the euro. In some cases, transformation precedes the launch of the euro, such as the industrial sector, and might reflect different comparative advantages. Such specialisation is not problematic, and is generally accompanied by diverse risk sharing channels. Yet, the second contribution of this paper is to show instead that in some euro area countries productive resources were misallocated to less efficient and lower productivity sectors. In order to distinguish between good and bad specialisation, a firm-based database is examined. The third contribution shows that frictions play an important role in preventing the shift of resources towards more productive firms and thus reduce the potential growth of some countries. This might then explain in part the modest convergence and then divergence of euro area countries

Conformational Distribution of Bradykinin [bk + 2H]2+ Revealed by Cold Ion Spectroscopy Coupled with FAIMS

We employ cold ion spectroscopy (CIS) in conjunction with high-field asymmetric waveform ion mobility spectrometry (FAIMS) to study the peptide bradykinin in its doubly protonated charge state ([bk + 2H]2+). Using FAIMS, we partially separate the electrosprayed [bk + 2H]2+ ions into two conformational families and selectively introduce one of them at a time into a cold ion trap mass spectrometer, where we probe them by UV photofragment spectroscopy. Although the two conformational families have distinct electronic spectra, some cross-conformer contamination can be observed under certain conditions. We demonstrate that this contamination comes from isomerization of ions energized during and/or after their separation and not from incomplete separation of the initially electrosprayed conformations in the FAIMS stage. By varying the injection voltage of the ions into our mass spectrometer, we can intentionally induce isomerization to produce what seems to be a gas phase equilibrium distribution of conformers. This distribution is different from the one produced initially by electrospray, indicating that some of the conformers are kinetically trapped and may be related to conformers that are more favored in solutio