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.

Local negative permittivity and topological phase transition in polar skyrmions

Abstract: Topological solitons such as magnetic skyrmions have drawn attention as stable quasi-particle-like objects. The recent discovery of polar vortices and skyrmions in ferroelectric oxide superlattices has opened up new vistas to explore topology, emergent phenomena and approaches for manipulating such features with electric fields. Using macroscopic dielectric measurements, coupled with direct scanning convergent beam electron diffraction imaging on the atomic scale, theoretical phase-field simulations and second-principles calculations, we demonstrate that polar skyrmions in (PbTiO3)n/(SrTiO3)n superlattices are distinguished by a sheath of negative permittivity at the periphery of each skyrmion. This enhances the effective dielectric permittivity compared with the individual SrTiO3 and PbTiO3 layers. Moreover, the response of these topologically protected structures to electric field and temperature shows a reversible phase transition from the skyrmion state to a trivial uniform ferroelectric state, accompanied by large tunability of the dielectric permittivity. Pulsed switching measurements show a time-dependent evolution and recovery of the skyrmion state (and macroscopic dielectric response). The interrelationship between topological and dielectric properties presents an opportunity to simultaneously manipulate both by a single, and easily controlled, stimulus, the applied electric field.

Mining SQL injection and cross site scripting vulnerabilities using hybrid program analysis

Abstract: In previous work, we proposed a set of static attributes that characterize input validation and input sanitization code patterns. We showed that some of the proposed static attributes are significant predictors of SQL injection and cross site scripting vulnerabilities. Static attributes have the advantage of reflecting general properties of a program. Yet, dynamic attributes collected from execution traces may reflect more specific code characteristics that are complementary to static attributes. Hence, to improve our initial work, in this paper, we propose the use of dynamic attributes to complement static attributes in vulnerability prediction. Furthermore, since existing work relies on supervised learning, it is dependent on the availability of training data labeled with known vulnerabilities. This paper presents prediction models that are based on both classification and clustering in order to predict vulnerabilities, working in the presence or absence of labeled training data, respectively. In our experiments across six applications, our new supervised vulnerability predictors based on hybrid (static and dynamic) attributes achieved, on average, 90% recall and 85% precision, that is a sharp increase in recall when compared to static analysis-based predictions. Though not nearly as accurate, our unsupervised predictors based on clustering achieved, on average, 76% recall and 39% precision, thus suggesting they can be useful in the absence of labeled training data.

Acceptability and Acceptance of Autonomous Mobility on Demand: The Impact of an Immersive Experience

Abstract: Autonomous vehicles have the potential to fundamentally change existing transportation systems. Beyond legal concerns, these societal evolutions will critically depend on user acceptance. As an emerging mode of public transportation [7], Autonomous mobility on demand (AMoD) is of particular interest in this context. The aim of the present study is to identify the main components of acceptability (before first use) and acceptance (after first use) of AMoD, following a user experience (UX) framework. To address this goal, we conducted three workshops (N=14) involving open discussions and a ride in an experimental autonomous shuttle. Using a mixed-methods approach, we measured pre-immersion acceptability before immersing the participants in an on-demand transport scenario, and eventually measured post-immersion acceptance of AMoD. Results show that participants were reassured about safety concerns, however they perceived the AMoD experience as ineffective. Our findings highlight key factors to be taken into account when designing AMoD experiences.