Methodological framework for an integrated multi-scale vulnerability and resilience assessment

The deliverable illustrates the methodological framework to assess vulnerability and resilience across different temporal and spatial scales, acknowledging the different domains where the latter may manifest, and in particular in the natural and the built environment, allocating a large importance to the so called “critical infrastructures”, in social and economic systems. A set of four matrices has been developed to identify what aspects should be looked at before the impact, that is to say what shows the potential ability or inability to cope with an extreme; at the impact, addressing in particular the capacity (or incapacity) to sustain various types of stresses (in the form of acceleration, pressure, heat…); in the time immediately after the impact, as the ability (or inability) to suffer losses and still continue functioning; and in the longer term of recovery, as the capacity to find a new state of equilibrium in which the fragilities manifested during and after the impact are addressed. Developing the framework, a particular attention has been paid to the relationships among systems within the same matrix and among matrices, across spatial and temporal scales. A set of matrices has been developed for different natural hazards, including in particular landslides and floods, trying to include as much as possible what past cases, the international literature and prior experience of involved partners have indicated as relevant parameters and factors to look at. In this regard, the project builds on the state of the art, embedding what has been learned until now in terms of response capacity to a variety of stresses and in the meantime identifying gaps to be addressed by future research

Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction

Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have tremendous potential to aid in KB construction by contributing feedback that identifies spurious and missing entity attributes and relations. However, correctly integrating user feedback with an existing KB is complicated by the necessity to resolve identity uncertainty, i.e., uncertainty regarding to which real-world entity a piece of data refers. Identity uncertainty abounds in the collection of raw evidence from which a KB is built. Moreover, it also gives rise to identity uncertainty in user feedback, when KB entities, which were affected by user feedback, are split or merged. In this dissertation, we present a continuous reasoning framework capable of integrating user feedback with a KB, under identity certainty. To begin, we introduce Grinch, an online entity resolution (ER) algorithm---with provable correctness guarantees---capable of merging and splitting KB entities as new data arrives. We show that Grinch is efficient and achieves state-of-the-art performance in ER as well as in clustering. Next, we propose a method for using Grinch to resolve identity uncertainty in a KB\u27s underlying data as well as in user feedback. Our approach is based on representing user feedback as mentions, i.e., first class KB objects that participate in all parts of KB construction. Furthermore, we introduce a structured representation for feedback comprised of packaging and payload, which facilitates recovery from KB errors that stem from both identity uncertainty and noisy data. Finally, we evaluate our framework\u27s efficacy using data from the KB that supports OpenReview.net---a deployed, conference management system that solicits feedback from users. The demands of OpenReview.net lead us to develop XGrinch-Shallow (XGS), a variant of Grinch that builds trees with arbitrary branching factors, and subsequently instantiates 60% fewer internal nodes than Grinch. Empirically, we show that XGS is efficient, and is able to effectively utilize user feedback to improve the correctness and completeness of the OpenReview.net KB. We conclude with 7 concrete suggestions for future research on this topic

The Impact of Ethnicity on The Trajectory of Depression Symptom Change During Psychological Interventions

Uncovering variations in depression symptom change across ethnic groups during psychological intervention could improve understanding of differences in treatment response. This study aimed to: (1) identify trajectories of change in treatment; (2) ascertain if depression symptom trajectories varied between BAME and White populations; (3) investigate if sociodemographic and treatment variables predicted association with the identified trajectories; and (4) examine if ethnic groups predicted different trajectory memberships. Adults (N = 17109) with depression and recorded ethnicity were included in the analysis. Depressive symptoms were measured using the Patient Health Questionnaire-9, and co-occurring anxiety was measured using the Generalised Anxiety Disorder-7. Growth Mixture Modelling (GMM) was employed to identify trajectories of symptom change, and multinomial logistic regressions were used to identify ethnicity and other pre-treatment variables associated with trajectory membership. GMM resulted in three depression trajectories of change and four anxiety trajectories. There was a high proportion of patients who did not respond to treatment. Pre-treatment variables that predicted Non-response were: ethnic minority, unemployment, deprived areas, prescribed medications, higher baseline anxiety and depression scores, and long-term physical health conditions. Asian patients had higher odds than White patients associated with trajectories that had high severity for both outcome measures. Black, Other, Mixed-heritage, and Chinese populations were no different from White populations in depressive treatment responses after adjusting for an index of multiple deprivations (IMD). Results have implications for identifying patients at risk of non-response such that clinicians can tailor culturally sensitive interventions for ethnic minority patients

Integration of tools for the Design and Assessment of High-Performance, Highly Reliable Computing Systems (DAHPHRS), phase 1

Systems for Space Defense Initiative (SDI) space applications typically require both high performance and very high reliability. These requirements present the systems engineer evaluating such systems with the extremely difficult problem of conducting performance and reliability trade-offs over large design spaces. A controlled development process supported by appropriate automated tools must be used to assure that the system will meet design objectives. This report describes an investigation of methods, tools, and techniques necessary to support performance and reliability modeling for SDI systems development. Models of the JPL Hypercubes, the Encore Multimax, and the C.S. Draper Lab Fault-Tolerant Parallel Processor (FTPP) parallel-computing architectures using candidate SDI weapons-to-target assignment algorithms as workloads were built and analyzed as a means of identifying the necessary system models, how the models interact, and what experiments and analyses should be performed. As a result of this effort, weaknesses in the existing methods and tools were revealed and capabilities that will be required for both individual tools and an integrated toolset were identified