Is welfare dependency inherited? Estimating the causal welfare transmission effects using Swedish sibling data

This study tests whether individuals who grow up with parents on welfare benefits are themselves more (or less) likely to be welfare recipients as young adults, compared to individuals who grow up in non-welfare households. We use the sibling difference method to identify causal effects separately from the effects of correlated factors. While a descriptive analysis reveals a fairly high positive intergenerational correlation, especially in the late teens and conditional on a large set of household level factors, the sibling analysis provides no support for a causal effect of parents’ welfare benefit receipt on children’s future welfare use.Welfare benefits; intergenerational mobility; sibling approach

Type-based Dependency Analysis for JavaScript

Dependency analysis is a program analysis that determines potential data flow between program points. While it is not a security analysis per se, it is a viable basis for investigating data integrity, for ensuring confidentiality, and for guaranteeing sanitization. A noninterference property can be stated and proved for the dependency analysis. We have designed and implemented a dependency analysis for JavaScript. We formalize this analysis as an abstraction of a tainting semantics. We prove the correctness of the tainting semantics, the soundness of the abstraction, a noninterference property, and the termination of the analysis.Comment: Technical Repor

Detecting and Refactoring Operational Smells within the Domain Name System

The Domain Name System (DNS) is one of the most important components of the Internet infrastructure. DNS relies on a delegation-based architecture, where resolution of names to their IP addresses requires resolving the names of the servers responsible for those names. The recursive structures of the inter dependencies that exist between name servers associated with each zone are called dependency graphs. System administrators' operational decisions have far reaching effects on the DNSs qualities. They need to be soundly made to create a balance between the availability, security and resilience of the system. We utilize dependency graphs to identify, detect and catalogue operational bad smells. Our method deals with smells on a high-level of abstraction using a consistent taxonomy and reusable vocabulary, defined by a DNS Operational Model. The method will be used to build a diagnostic advisory tool that will detect configuration changes that might decrease the robustness or security posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244

A Generative Model for Score Normalization in Speaker Recognition

We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions. If, however, these conditions are not met, e.g. under data-set shift between training and runtime, our theory reveals dependencies between scores that could be exploited by strategies such as score normalization. Indeed, it has been demonstrated over and over experimentally, that various ad-hoc score normalization recipes do work. We present a first attempt at using probability theory to design a generative score-space normalization model which gives similar improvements to ZT-norm on the text-dependent RSR 2015 database

The Spanish Long-term Care System. ENEPRI Research Report No. 88, 15 June 2010

Launched in January 2009, ANCIEN is a research project that runs for a 44-month period and involves 20 partners from EU member states. The project principally concerns the future of long-term care (LTC) for the elderly in Europe and addresses two questions in particular: 1) How will need, demand, supply and use of LTC develop? 2) How do different systems of LTC perform? This case study on Spain is part of the first stage in the project aimed at collecting the basic data and necessary information to portray long-term care in each country of the EU. It will be followed by analysis and projections of future scenarios on long-term care needs, use, quality assurance and system performance. State-of-the-art demographic, epidemiologic and econometric modelling will be used to interpret and project needs, supply and use of long-term care over future time periods for different LTC systems