Quantitative Information Flow as Safety and Liveness Hyperproperties

We employ Clarkson and Schneider's "hyperproperties" to classify various verification problems of quantitative information flow. The results of this paper unify and extend the previous results on the hardness of checking and inferring quantitative information flow. In particular, we identify a subclass of liveness hyperproperties, which we call "k-observable hyperproperties", that can be checked relative to a reachability oracle via self composition.Comment: In Proceedings QAPL 2012, arXiv:1207.055

Verifying Temporal Properties of Reactive Systems by Transformation

We show how program transformation techniques can be used for the verification of both safety and liveness properties of reactive systems. In particular, we show how the program transformation technique distillation can be used to transform reactive systems specified in a functional language into a simplified form that can subsequently be analysed to verify temporal properties of the systems. Example systems which are intended to model mutual exclusion are analysed using these techniques with respect to both safety (mutual exclusion) and liveness (non-starvation), with the errors they contain being correctly identified.Comment: In Proceedings VPT 2015, arXiv:1512.02215. This work was supported, in part, by Science Foundation Ireland grant 10/CE/I1855 to Lero - the Irish Software Engineering Research Centre (www.lero.ie), and by the School of Computing, Dublin City Universit

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis}, \textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.Comment: PPoPP '2018: 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programmin

Qualitative Analysis of POMDPs with Temporal Logic Specifications for Robotics Applications

We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as parity objectives. We study the qualitative analysis problem for POMDPs with parity objectives that asks whether there is a controller (policy) to ensure that the objective holds with probability 1 (almost-surely). While the qualitative analysis of POMDPs with parity objectives is undecidable, recent results show that when restricted to finite-memory policies the problem is EXPTIME-complete. While the problem is intractable in theory, we present a practical approach to solve the qualitative analysis problem. We designed several heuristics to deal with the exponential complexity, and have used our implementation on a number of well-known POMDP examples for robotics applications. Our results provide the first practical approach to solve the qualitative analysis of robot motion planning with LTL properties in the presence of uncertainty

Observation and abstract behaviour in specification and implementation of state-based systems

Classical algebraic specification is an accepted framework for specification. A criticism which applies is the fact that it is functional, not based on a notion of state as most software development and implementation languages are. We formalise the idea of a state-based object or abstract machine using algebraic means. In contrast to similar approaches we consider dynamic logic instead of equational logic as the framework for specification and implementation. The advantage is a more expressive language allowing us to specify safety and liveness conditions. It also allows a clearer distinction of functional and state-based parts which require different treatment in order to achieve behavioural abstraction when necessary. We shall in particular focus on abstract behaviour and observation. A behavioural notion of satisfaction for state-elements is needed in order to abstract from irrelevant details of the state realisation