Most model checkers provide a useful simulation mode, that allows users to explore the set of possible behaviours by interactively picking at each state which event to execute next. Traditionally this simulation mode cannot take into consideration ad
ditional temporal logic constraints, such as arbitrary fairness restrictions, substantially reducing its usability for debugging the modelled system behaviour. Similarly, when a specification is false, even if all its counter-examples combined also form a set of behaviours, most model checkers only present one of them to the user, providing little or no mechanism to explore alternatives. In this paper, we present a simple on-the-fly verification technique to allow the user to explore the behaviours that satisfy an arbitrary temporal logic specification, with an interactive process akin to simulation. This technique enables a unified interface for simulating the modelled system and exploring its counter-examples. The technique is formalised in the framework of state/event linear temporal logic and a proof of concept was implemented in an event-based variant of the Electrum framework.
This paper presents a new underwater dataset acquired from a visual-inertial-pressure acquisition system and meant to be used to benchmark visual odometry, visual SLAM and multi-sensors SLAM solutions. The dataset is publicly available and contains ground-truth trajectories for evaluation.
This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks. Especially, we investigate generative adversarial networks and their application to the synthesis of consistent labeled spectra. By training such
networks on public datasets, we show that these models are not only able to capture the underlying distribution, but also to generate genuine-looking and physically plausible spectra. Moreover, we experimentally validate that the synthetic samples can be used as an effective data augmentation strategy. We validate our approach on several public hyper-spectral datasets using a variety of deep classifiers.
Consider a general nonlinear optimal control problem in finite dimension, with constant state and/or control delays. By the Pontryagin Maximum Principle, any optimal trajectory is the projection of a Pontryagin extremal. We establish that, under appr
opriate assumptions, Pontryagin extremals depend continuously on the parameter delays, for adequate topologies. The proof of the continuity of the trajectory and of the control is quite easy, however, for the adjoint vector, the proof requires a much finer analysis. The continuity property of the adjoint with respect to the parameter delay opens a new perspective for the numerical implementation of indirect methods, such as the shooting method. We also discuss the sharpness of our assumptions.
The Gas Electron Multiplier (GEM) detector is one of promising particle and radiation detectors that has been improved greatly from previous gas detectors. The improvement includes better spatial resolutions, higher detection rate capabilities, and f
lexibilities in designs. In particular, the 10 cm x 10 cm GEM prototype is designed and provided by the Gas Detectors Development group (GDD) at CERN, Switzerland. With its simplicity in operations and designs, while still maintaining high qualities, the GEM prototype is suitable for both start-up and advanced researches. This article aims to report the investigations on some important properties of the 10 cm x 10 cm GEM detector using current measurement and signal counting. Results have shown that gains of the GEM prototype exponentially increase as voltage supplied to the detector increases, while the detector reaches full efficiency (plateau region) when the voltage is greater than 4100 V. In terms of signal sharing between X and Y strips of the readout, X strips, which is on the top layer of the readout, collect ~57% of the total signal. For the uniformity test, the GEM prototype has slightly higher efficiencies at the center of the detector and decreases as positions are closer to edges.
