Health and social problems associated with recent Novel Psychoactive Substance (NPS) use amongst marginalised, nightlife and online users in six European countries.

Continued diversification and use of new psychoactive substances (NPS) across Europe remains a public health challenge. The study describes health and social consequences of recent NPS use as reported in a survey of marginalised, nightlife and online NPS users in the Netherlands, Hungary, Portugal, Ireland, Germany and Poland (n = 3023). Some respondents were unable to categorise NPS they had used. Use of ‘herbal blends’ and ‘synthetic cannabinoids obtained pure’ was most reported in Germany, Poland and Hungary, and use of ‘branded stimulants’ and ‘stimulants/empathogens/nootropics obtained pure’ was most reported in the Netherlands. Increased heart rate and palpitation, dizziness, anxiety, horror trips and headaches were most commonly reported acute side effects. Marginalised users reported substantially more acute side effects, more mid- and long-term mental and physical problems, and more social problems. Development of country-specific NPS awareness raising initiatives, health and social service needs assessments, and targeted responses are warranted

Statistical Debugging for Real-World Performance Problems

Design and implementation defects that lead to inefficient computation widely exist in software. These defects are difficult to avoid and discover. They lead to severe performance degradation and energy waste during production runs, and are becoming increasingly critical with the meager increase of single-core hardware performance and the increasing concerns about energy constraints. Effective tools that diagnose performance problems and point out the inefficiency root cause is sorely needed. The state of the art of performance diagnosis is preliminary. Profiling can tell where computation resources are spent, but not where and why the resources are wasted. Performance-bug detectors can identify specific type of inefficient computation, but are not suited for diagnosing general performance problems. Effective failure diagnosis techniques, such as statistical debugging, have been proposed for functional bugs. However, whether they work for performance problems is still an open question. In this paper, we first conduct an empirical study to understand how performance problems are observed and reported by real-world users. Our study shows that statistical debugging is a natural fit for diagnosing performance problems, which are often observed through comparison-based approaches and reported together with both good and bad inputs. We then thoroughly investigate different design points in statistical debugging, including three different predicates and two different types of statistical models, to understand which design point works the best for performance diagnosis. Finally, we study how some unique nature of performance bugs allows sampling techniques to lower the overhead of run-time performance diagnosis without extending the diagnosis latency