31 research outputs found
Conservative Safety Monitors of Stochastic Dynamical Systems
Generating accurate runtime safety estimates for autonomous systems is vital
to ensuring their continued proliferation. However, exhaustive reasoning about
future behaviors is generally too complex to do at runtime. To provide scalable
and formal safety estimates, we propose a method for leveraging design-time
model checking results at runtime. Specifically, we model the system as a
probabilistic automaton (PA) and compute bounded-time reachability
probabilities over the states of the PA at design time. At runtime, we combine
distributions of state estimates with the model checking results to produce a
bounded time safety estimate. We argue that our approach produces
well-calibrated safety probabilities, assuming the estimated state
distributions are well-calibrated. We evaluate our approach on simulated water
tanks
Curating Naturally Adversarial Datasets for Trustworthy AI in Healthcare
Deep learning models have shown promising predictive accuracy for time-series
healthcare applications. However, ensuring the robustness of these models is
vital for building trustworthy AI systems. Existing research predominantly
focuses on robustness to synthetic adversarial examples, crafted by adding
imperceptible perturbations to clean input data. However, these synthetic
adversarial examples do not accurately reflect the most challenging real-world
scenarios, especially in the context of healthcare data. Consequently,
robustness to synthetic adversarial examples may not necessarily translate to
robustness against naturally occurring adversarial examples, which is highly
desirable for trustworthy AI. We propose a method to curate datasets comprised
of natural adversarial examples to evaluate model robustness. The method relies
on probabilistic labels obtained from automated weakly-supervised labeling that
combines noisy and cheap-to-obtain labeling heuristics. Based on these labels,
our method adversarially orders the input data and uses this ordering to
construct a sequence of increasingly adversarial datasets. Our evaluation on
six medical case studies and three non-medical case studies demonstrates the
efficacy and statistical validity of our approach to generating naturally
adversarial dataset
Compositional Probabilistic Analysis of Temporal Properties over Stochastic Detectors
Run-time monitoring is a vital part of safety-critical systems. However, early-stage assurance of monitoring quality is currently limited: it relies either on complex models that might be inaccurate in unknown ways, or on data that would only be available once the system has been built. To address this issue, we propose a compositional framework for modeling and analysis of noisy monitoring systems. Our novel 3-value detector model uses probability spaces to represent atomic (non-composite) detectors, and it composes them into a temporal logic-based monitor. The error rates of these monitors are estimated by our analysis engine, which combines symbolic probability algebra, independence inference, and estimation from labeled detection data. Our evaluation on an autonomous underwater vehicle found that our framework produces accurate estimates of error rates while using only detector traces, without any monitor traces. Furthermore, when data is scarce, our approach shows higher accuracy than non-compositional data-driven estimates from monitor traces. Thus, this work enables accurate evaluation of logical monitors in early design stages before deploying them
Causal Repair of Learning-enabled Cyber-physical Systems
Models of actual causality leverage domain knowledge to generate convincing
diagnoses of events that caused an outcome. It is promising to apply these
models to diagnose and repair run-time property violations in cyber-physical
systems (CPS) with learning-enabled components (LEC). However, given the high
diversity and complexity of LECs, it is challenging to encode domain knowledge
(e.g., the CPS dynamics) in a scalable actual causality model that could
generate useful repair suggestions. In this paper, we focus causal diagnosis on
the input/output behaviors of LECs. Specifically, we aim to identify which
subset of I/O behaviors of the LEC is an actual cause for a property violation.
An important by-product is a counterfactual version of the LEC that repairs the
run-time property by fixing the identified problematic behaviors. Based on this
insights, we design a two-step diagnostic pipeline: (1) construct and
Halpern-Pearl causality model that reflects the dependency of property outcome
on the component's I/O behaviors, and (2) perform a search for an actual cause
and corresponding repair on the model. We prove that our pipeline has the
following guarantee: if an actual cause is found, the system is guaranteed to
be repaired; otherwise, we have high probabilistic confidence that the LEC
under analysis did not cause the property violation. We demonstrate that our
approach successfully repairs learned controllers on a standard OpenAI Gym
benchmark
Distributionally Robust Statistical Verification with Imprecise Neural Networks
A particularly challenging problem in AI safety is providing guarantees on
the behavior of high-dimensional autonomous systems. Verification approaches
centered around reachability analysis fail to scale, and purely statistical
approaches are constrained by the distributional assumptions about the sampling
process. Instead, we pose a distributionally robust version of the statistical
verification problem for black-box systems, where our performance guarantees
hold over a large family of distributions. This paper proposes a novel approach
based on a combination of active learning, uncertainty quantification, and
neural network verification. A central piece of our approach is an ensemble
technique called Imprecise Neural Networks, which provides the uncertainty to
guide active learning. The active learning uses an exhaustive neural-network
verification tool Sherlock to collect samples. An evaluation on multiple
physical simulators in the openAI gym Mujoco environments with
reinforcement-learned controllers demonstrates that our approach can provide
useful and scalable guarantees for high-dimensional systems
Protective Cr Coatings with ZrO2/Cr Multilayers for Zirconium Fuel Claddings
This article described the protective properties of Cr coatings with a barrier layer composed of ZrO2/Cr multilayers deposited onto E110 zirconium alloy. The coatings with a ZrO2/Cr multilayer thickness of 100, 250, and 750 nm and single-layer (1.5 Β΅m) ZrO2 barrier were obtained by multi-cathode magnetron sputtering in Ar + O2 atmosphere. Then, cracking resistance and oxidation behavior were studied under conditions of thermal cycling (1000 Β°C) in air and high-temperature oxidation at 1200-1400 Β°C in a water steam. The role of the ZrO2/Cr multilayers and multilayer thickness on cracking resistance of the experimental coatings and oxidation resistance of the coated E110 alloy was discussed. It was shown that the coatings with more quantity of the ZrO2/Cr multilayers have higher cracking resistance, but such types of samples have a large amount of coating spallation under thermal cycling. The high-temperature steam oxidation (1200-1400 Β°C) demonstrated that interfaces of the ZrO2/Cr multilayers can act as a source of cavities formed by the Kirkendall mechanism that results in accelerating Cr-Zr interdiffusion for Cr-coated E110 alloy
Single-window integrated development environment
International audienceThis paper addresses the problem of IDE interface complexity by introducing single-window graphical user interface. This approach lies in removing additional child windows from IDE, thus allowing a user to keep only text editor window open. We describe an abstract model of IDE GUI that is based on most popular modern integrated environments and has generalized user interface parts. Then this abstract model is reorganized into single windowed interface model: access to common IDE functions is provided from the code editing window while utility windows are removed without loss of IDE functionality. After that the implementation of single-window GUI on KDevelop 4 is described. And finally tool views and usability of several well- known IDEs are surveyed