Towards Realizability Checking of Contracts using Theories

Virtual integration techniques focus on building architectural models of systems that can be analyzed early in the design cycle to try to lower cost, reduce risk, and improve quality of complex embedded systems. Given appropriate architectural descriptions and compositional reasoning rules, these techniques can be used to prove important safety properties about the architecture prior to system construction. Such proofs build from "leaf-level" assume/guarantee component contracts through architectural layers towards top-level safety properties. The proofs are built upon the premise that each leaf-level component contract is realizable; i.e., it is possible to construct a component such that for any input allowed by the contract assumptions, there is some output value that the component can produce that satisfies the contract guarantees. Without engineering support it is all too easy to write leaf-level components that can't be realized. Realizability checking for propositional contracts has been well-studied for many years, both for component synthesis and checking correctness of temporal logic requirements. However, checking realizability for contracts involving infinite theories is still an open problem. In this paper, we describe a new approach for checking realizability of contracts involving theories and demonstrate its usefulness on several examples.Comment: 15 pages, to appear in NASA Formal Methods (NFM) 201

Computational, experimental, and statistical analyses of social learning in humans and animals

Social learning is ubiquitous among animals and humans and is thought to be critical to the widespread success of humans and to the development and evolution of human culture. Evolutionary theory, however, suggests that social learning alone may not be adaptive but that individuals may need to be selective in who and how they copy others. One of the key findings of these evolutionary models (reviewed in Chapter 1) is that social information may be widely adaptive if individuals are able to combine social and asocial sources of information together strategically. However, up until this point the focus of theoretic models has been on the population level consequences of different social learning strategies, and not on how individuals combine social and asocial information on specific tasks. In Chapter 2 I carry out an analysis of how animal learners might incorporate social information into a reinforcement learning framework and find that even limited, low-fidelity copying of actions in an action sequence may combine with asocial learning to result in high fidelity transmission of entire action sequences. In Chapter 3 I describe a series of experiments that find that human learners flexibly use a conformity biased learning strategy to learn from multiple demonstrators depending on demonstrator accuracy, either indicated by environmental cues or past experience with these demonstrators. The chapter reveals close quantitative and qualitative matches between participant's performance and a Bayesian model of social learning. In both Chapters 2 and 3 I find, consistent with previous evolutionary findings, that by combining social and asocial sources of information together individuals are able to learn about the world effectively. Exploring how animals use social learning experimentally can be a substantially more difficult task than exploring human social learning. In Chapter 4, I develop and present a refined version of Network Based Diffusion analysis to provide a statistical framework for inferring social learning mechanisms from animal diffusion experiments. In Chapter 5 I move from examining the effects of social learning at an individual level to examining their population level outcomes and provide an analysis of how fine-grained population structure may alter the spread of novel behaviours through a population. I find that although a learner's social learning strategy and the learnability of a novel behaviour strongly impact how likely the behaviour is to spread through the population, fine grained population structure plays a much smaller role. In Chapter 6 I summarize the results of this thesis, and provide suggestions for future work to understand how individuals, humans and other animals alike, use social information

Fish pool their experience to solve problems collectively

This work was funded by an ERC Advanced grant to KNL (EVOCULTURE, Ref: 232823)Access to information is a key advantage of grouping. Although experienced animals can lead others to solve problems, less is known about whether partially informed individuals can pool experiences to overcome challenges collectively. Here we provide evidence of such ‘experience-pooling’. We presented shoals of sticklebacks (Gasterosteus aculeatus) with a two-stage foraging task requiring them to find and access hidden food. Individual fish were either inexperienced or had knowledge of just one of the stages. Shoals containing individuals trained in each of the stages pooled their expertise, allowing more fish to access the food, and to do so more rapidly, compared with other shoal compositions. Strong social effects were identified: the presence of experienced individuals increased the likelihood of untrained fish completing each stage. These findings demonstrate that animal groups can integrate individual experience to solve multi-stage problems, and have implications for our understanding of social foraging, migration and social systems.PostprintPeer reviewe

Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts

Permafrost coasts are experiencing accelerated erosion in response to above average warming in the Arctic resulting in local, regional, and global consequences. However, Arctic coasts are expansive in scale, constituting 30–34% of Earth’s coastline, and represent a particular challenge for wide-scale, high temporal measurement and monitoring. This study addresses the potential strengths and limitations of an object-based approach to integrate with an automated workflow by assessing the accuracy of coastal classifications and subsequent feature extraction of coastal indicator features. We tested three object-based classifications; thresholding, supervised, and a deep learning model using convolutional neural networks, focusing on a Pleaides satellite scene in the Western Canadian Arctic. Multiple spatial resolutions (0.6, 1, 2.5, 5, 10, and 30 m/pixel) and segmentation scales (100, 200, 300, 400, 500, 600, 700, and 800) were tested to understand the wider applicability across imaging platforms. We achieved classification accuracies greater than 85% for the higher image resolution scenarios using all classification methods. Coastal features, waterline and tundra, or vegetation, line, generated from image classifications were found to be within the image uncertainty 60% of the time when compared to reference features. Further, for very high resolution scenarios, segmentation scale did not affect classification accuracy; however, a smaller segmentation scale (i.e., smaller image objects) led to improved feature extraction. Similar results were generated across classification approaches with a slight improvement observed when using deep learning CNN, which we also suggest has wider applicability. Overall, our study provides a promising contribution towards broad scale monitoring of Arctic coastal erosion.info:eu-repo/semantics/publishedVersio

Observability and Controllability of Nonlinear Networks: The Role of Symmetry

Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. For example, noncontrollable mathematical models of real systems have subspaces that influence model behavior, but cannot be controlled by an input. Such subspaces can be difficult to determine in complex nonlinear networks. Since almost all of the present theory was developed for linear networks without symmetries, here we present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and nonlinear measures of observability and controllability. We numerically observe and theoretically predict that not all symmetries have the same effect on network observation and control. Our analysis shows that the presence of symmetry in a network may decrease observability and controllability, although networks containing only rotational symmetries remain controllable and observable. These results alter our view of the nature of observability and controllability in complex networks, change our understanding of structural controllability, and affect the design of mathematical models to observe and control such networks.Comment: 19 pages, 9 figure