University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

Behavioural outcomes of subthalamic stimulation and medical therapy versus medical therapy alone for Parkinson's disease with early motor complications (EARLYSTIM trial): secondary analysis of an open-label randomised trial.

Abstract: Summary Background Although subthalamic stimulation is a recognised treatment for motor complications in Parkinson's disease, reports on behavioural outcomes are controversial, which represents a major challenge when counselling candidates for subthalamic stimulation. We aimed to assess changes in behaviour in patients with Parkinson's disease receiving combined treatment with subthalamic stimulation and medical therapy over a 2-year follow-up period as compared with the behavioural evolution under medical therapy alone. Methods We did a parallel, open-label study (EARLYSTIM) at 17 surgical centres in France (n=8) and Germany (n=9). We recruited patients with Parkinson's disease who were disabled by early motor complications. Participants were randomly allocated (1:1) to either medical therapy alone or bilateral subthalamic stimulation plus medical therapy. The primary outcome was mean change in quality of life from baseline to 2 years. A secondary analysis was also done to assess behavioural outcomes. We used the Ardouin Scale of Behavior in Parkinson's Disease to assess changes in behaviour between baseline and 2-year follow-up. Apathy was also measured using the Starkstein Apathy Scale, and depression was assessed with the Beck Depression Inventory. The secondary analysis was done in all patients recruited. We used a generalised estimating equations (GEE) regression model for individual items and mixed model regression for subscores of the Ardouin scale and the apathy and depression scales. This trial is registered with ClinicalTrials.gov, number NCT00354133. The primary analysis has been reported elsewhere; this report presents the secondary analysis only. Findings Between July, 2006, and November, 2009, 251 participants were recruited, of whom 127 were allocated medical therapy alone and 124 were assigned bilateral subthalamic stimulation plus medical therapy. At 2-year follow-up, the levodopa-equivalent dose was reduced by 39% (–363·3 mg/day [SE 41·8]) in individuals allocated bilateral subthalamic stimulation plus medical therapy and was increased by 21% (245·8 mg/day [40·4]) in those assigned medical therapy alone (p Interpretation In a large cohort with Parkinson's disease and early motor complications, better overall behavioural outcomes were noted with bilateral subthalamic stimulation plus medical therapy compared with medical therapy alone. The presence of hyperdopaminergic behaviours and neuropsychiatric fluctuations can be judged additional arguments in favour of subthalamic stimulation if surgery is considered for disabling motor complications. Funding German Federal Ministry of Education and Research, French Programme Hospitalier de Recherche Clinique National, and Medtronic.

PrOnto: Privacy Ontology for Legal Reasoning

Abstract: The GDPR (GDPR, REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)) introduces the self-assessment of digital risks and the modulation of duties on the basis of the impact assessment analysis, including specific measures that intend to safeguard the data subject’s human dignity and fundamental rights. Semantic web technologies and legal reasoning tools can support privacy-by-default and legal compliance. In this light, this paper presents a first draft of a legal ontology on the GDPR, called PrOnto, that has the goal of providing a legal knowledge modelling of the privacy agents, data types, types of processing operations, rights and obligations. The methodology used here is based on legal theory analysis joined with ontological patterns.

AVATAR : Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Abstract: Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.