Safety-Aware Apprenticeship Learning

Apprenticeship learning (AL) is a kind of Learning from Demonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations. In this paper, we study the problem of how to make AL algorithms inherently safe while still meeting its learning objective. We consider a setting where the unknown reward function is assumed to be a linear combination of a set of state features, and the safety property is specified in Probabilistic Computation Tree Logic (PCTL). By embedding probabilistic model checking inside AL, we propose a novel counterexample-guided approach that can ensure safety while retaining performance of the learnt policy. We demonstrate the effectiveness of our approach on several challenging AL scenarios where safety is essential.Comment: Accepted by International Conference on Computer Aided Verification (CAV) 201

Evaluating the reliability of NAND multiplexing with PRISM

Probabilistic-model checking is a formal verification technique for analyzing the reliability and performance of systems exhibiting stochastic behavior. In this paper, we demonstrate the applicability of this approach and, in particular, the probabilistic-model-checking tool PRISM to the evaluation of reliability and redundancy of defect-tolerant systems in the field of computer-aided design. We illustrate the technique with an example due to von Neumann, namely NAND multiplexing. We show how, having constructed a model of a defect-tolerant system incorporating probabilistic assumptions about its defects, it is straightforward to compute a range of reliability measures and investigate how they are affected by slight variations in the behavior of the system. This allows a designer to evaluate, for example, the tradeoff between redundancy and reliability in the design. We also highlight errors in analytically computed reliability bounds, recently published for the same case study

Explicit Model Checking of Very Large MDP using Partitioning and Secondary Storage

The applicability of model checking is hindered by the state space explosion problem in combination with limited amounts of main memory. To extend its reach, the large available capacities of secondary storage such as hard disks can be exploited. Due to the specific performance characteristics of secondary storage technologies, specialised algorithms are required. In this paper, we present a technique to use secondary storage for probabilistic model checking of Markov decision processes. It combines state space exploration based on partitioning with a block-iterative variant of value iteration over the same partitions for the analysis of probabilistic reachability and expected-reward properties. A sparse matrix-like representation is used to store partitions on secondary storage in a compact format. All file accesses are sequential, and compression can be used without affecting runtime. The technique has been implemented within the Modest Toolset. We evaluate its performance on several benchmark models of up to 3.5 billion states. In the analysis of time-bounded properties on real-time models, our method neutralises the state space explosion induced by the time bound in its entirety.Comment: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24953-7_1

Quantitative Analysis of DoS Attacks and Client Puzzles in IoT Systems

Denial of Service (DoS) attacks constitute a major security threat to today's Internet. This challenge is especially pertinent to the Internet of Things (IoT) as devices have less computing power, memory and security mechanisms to mitigate DoS attacks. This paper presents a model that mimics the unique characteristics of a network of IoT devices, including components of the system implementing `Crypto Puzzles' - a DoS mitigation technique. We created an imitation of a DoS attack on the system, and conducted a quantitative analysis to simulate the impact such an attack may potentially exert upon the system, assessing the trade off between security and throughput in the IoT system. We model this through stochastic model checking in PRISM and provide evidence that supports this as a valuable method to compare the efficiency of different implementations of IoT systems, exemplified by a case study

Value Iteration for Long-run Average Reward in Markov Decision Processes

Markov decision processes (MDPs) are standard models for probabilistic systems with non-deterministic behaviours. Long-run average rewards provide a mathematically elegant formalism for expressing long term performance. Value iteration (VI) is one of the simplest and most efficient algorithmic approaches to MDPs with other properties, such as reachability objectives. Unfortunately, a naive extension of VI does not work for MDPs with long-run average rewards, as there is no known stopping criterion. In this work our contributions are threefold. (1) We refute a conjecture related to stopping criteria for MDPs with long-run average rewards. (2) We present two practical algorithms for MDPs with long-run average rewards based on VI. First, we show that a combination of applying VI locally for each maximal end-component (MEC) and VI for reachability objectives can provide approximation guarantees. Second, extending the above approach with a simulation-guided on-demand variant of VI, we present an anytime algorithm that is able to deal with very large models. (3) Finally, we present experimental results showing that our methods significantly outperform the standard approaches on several benchmarks

Giant cardiac tumours in the newborn: an unusual image

Primary heart tumours in the paediatric population are very rare and they range from 0.01% to 0.04%. Most are benign lesions of which about half are rhabdomyomas. Rhabdomyoma tumour diagnosis is associated with a 75–80% risk of tuberous sclerosis complex (TSC). TSC are characterised with numerous changes of hamartoma-type located in the brain, kidneys, skin and other organs including the heart. More than two-thirds of newborns with TSC present rhabdomyomas in the heart. These changes may be asymptomatic, but in some cases they may cause heart failure, arrhythmias and death. We present a case report of an infant with giant rhabdomyoma tumours in the course of TSC

Fermi surface of an important nano-sized metastable phase: Al3_3Li

Nanoscale particles embedded in a metallic matrix are of considerable interest as a route towards identifying and tailoring material properties. We present a detailed investigation of the electronic structure, and in particular the Fermi surface, of a nanoscale phase ($L1_2$ Al$_3$Li) that has so far been inaccessible with conventional techniques, despite playing a key role in determining the favorable material properties of the alloy (Al\nobreakdash-9 at. %\nobreakdash-Li). The ordered precipitates only form within the stabilizing Al matrix and do not exist in the bulk; here, we take advantage of the strong positron affinity of Li to directly probe the Fermi surface of Al$_3$Li. Through comparison with band structure calculations, we demonstrate that the positron uniquely probes these precipitates, and present a 'tuned' Fermi surface for this elusive phase

Automated recognition of sleep arousal using multimodal and personalized deep ensembles of neural networks

Background and Aim: Monitoring physiological signals during sleep can have substantial impact on detecting temporary intrusion of wakefulness, referred to as sleep arousals. To overcome the problems associated with the cubersome visual inspection of these events by experts, sleep arousal recognition algorithms have been proposed. Method: As part of the Physionet/Computing in Cardiology Challenge 2018, this study proposes a deep ensemble neural network architecture for automatic arousal recognition from multi-modal sensor signals. Separate branches of the neural network extract features from electro-encephalography, electrooculography, electromyogram, breathing patterns and oxygen saturation level; and a final fully-connected neural network combines features computed from the signal sources to estimate the probability of arousal in each region of interest. We investigate the use of shared-parameter Siamese architectures for effective feature calibration. Namely, at each forward and backward pass through the network we concatenate to the input a user-specific template signal that is processed by an identical copy of the network. Result: The proposed architecture obtains an AUPR score of 0.40 on the test set of the official phase of Physionet/CbiC Challenge 2018. A score of 0.45 is obtained by means of 10 -fold cross-validation on the training set