Klaus-Dieter Schewe

Klaus-Dieter Schewe, Flavio Ferrarotti, Peter Rivière et al.

The verification of liveness conditions is an important aspect of state-based rigorous methods. This article addresses the extension of the logic of Event-B to a powerful logic, in which properties of traces of an Event-B machine can be expressed. However, all formulae of this logic are still interpreted over states of an Event-B machine rather than traces. The logic exploits that for an Event-B machine $M$ a state $S$ determines all traces of $M$ starting in $S$. We identify a fragment called TREBL of this logic, in which all liveness conditions of interest can be expressed, and define a set of sound derivation rules for the fragment. We further show relative completeness of these derivation rules in the sense that for every valid entailment of a formula $φ$ one can find a derivation, provided the machine $M$ is sufficiently refined. The decisive property is that certain variant terms must be definable in the refined machine. We show that such refinements always exist. Throughout the article several examples from the field of security are used to illustrate the theory.

Kanya Mo, Shen Zheng, Xiwei Wang et al.

The fully connected (FC) layer, one of the most fundamental modules in artificial neural networks (ANN), is often considered difficult and inefficient to train due to issues including the risk of overfitting caused by its large amount of parameters. Based on previous work studying ANN from linear spline perspectives, we propose a spline-based approach that eases the difficulty of training FC layers. Given some dataset, we first obtain a continuous piece-wise linear (CPWL) fit through spline methods such as multivariate adaptive regression spline (MARS). Next, we construct an ANN model from the linear spline model and continue to train the ANN model on the dataset using gradient descent optimization algorithms. Our experimental results and theoretical analysis show that our approach reduces the computational cost, accelerates the convergence of FC layers, and significantly increases the interpretability of the resulting model (FC layers) compared with standard ANN training with random parameter initialization followed by gradient descent optimizations.

Egon Börger, Klaus-Dieter Schewe

We define a programming language independent transaction controller and an operator which when applied to concurrent programs with shared locations turns their behavior with respect to some abstract termination criterion into a transactional behavior. We prove the correctness property that concurrent runs under the transaction controller are serialisable. We specify the transaction controller TaCtl and the operator TA in terms of Abstract State Machines. This makes TaCtl applicable to a wide range of programs and in particular provides the possibility to use it as a plug-in when specifying concurrent system components in terms of Abstract State Machines.