A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery

Abstract: Purpose of Review: Clinical response to brain stimulation treatments for depression is highly variable. A major challenge for the field is predicting an individual patient’s likelihood of response. This review synthesises recent developments in neural predictors of response to targeted brain stimulation in depression. It then proposes a framework to evaluate the clinical potential of putative ‘biomarkers’. Recent Findings: Largely, developments in identifying putative predictors emerge from two approaches: data-driven, including machine learning algorithms applied to resting state or structural neuroimaging data, and theory-driven, including task-based neuroimaging. Theory-driven approaches can also yield mechanistic insight into the cognitive processes altered by the intervention. Summary: A pragmatic framework for discovery and testing of biomarkers of brain stimulation response in depression is proposed, involving (1) identification of a cognitive-neural phenotype; (2) confirming its validity as putative biomarker, including out-of-sample replicability and within-subject reliability; (3) establishing the association between this phenotype and treatment response and/or its modifiability with particular brain stimulation interventions via an early-phase randomised controlled trial RCT; and (4) multi-site RCTs of one or more treatment types measuring the generalisability of the biomarker and confirming the superiority of biomarker-selected patients over randomly allocated groups

Predicting Response to Brain Stimulation in Depression: a Roadmap for Biomarker Discovery

Abstract: Purpose of Review: Clinical response to brain stimulation treatments for depression is highly variable. A major challenge for the field is predicting an individual patient’s likelihood of response. This review synthesises recent developments in neural predictors of response to targeted brain stimulation in depression. It then proposes a framework to evaluate the clinical potential of putative ‘biomarkers’. Recent Findings: Largely, developments in identifying putative predictors emerge from two approaches: data-driven, including machine learning algorithms applied to resting state or structural neuroimaging data, and theory-driven, including task-based neuroimaging. Theory-driven approaches can also yield mechanistic insight into the cognitive processes altered by the intervention. Summary: A pragmatic framework for discovery and testing of biomarkers of brain stimulation response in depression is proposed, involving (1) identification of a cognitive-neural phenotype; (2) confirming its validity as putative biomarker, including out-of-sample replicability and within-subject reliability; (3) establishing the association between this phenotype and treatment response and/or its modifiability with particular brain stimulation interventions via an early-phase randomised controlled trial RCT; and (4) multi-site RCTs of one or more treatment types measuring the generalisability of the biomarker and confirming the superiority of biomarker-selected patients over randomly allocated groups

A vádlottak jogai és a sértettek érdekei a büntető eljárási törvény-javaslatban

Abstract. One of the most classical problems of mathematics is to solve systems of polynomial equations in several unknowns. Today, polynomial models are ubiquitous and widely applied across the sciences. They arise in robotics, coding theory, optimization, mathematical biology, computer vision, game theory, statistics, machine learning, control theory, and numerous other areas. The set of solutions to a system of polynomial equations is an algebraic variety, the basic object of algebraic geometry. The algorithmic study of algebraic varieties is the central theme of computational algebraic geometry. Exciting recent developments in symbolic algebra and numerical software for geometric calculations have revolutionized the field, making formerly inaccessible problems tractable, and providing fertile ground for experimentation and conjecture. The first half of this book furnishes an introduction and represents a snapshot of the state of the art regarding systems of polynomial equations. Afficionados of the well-known text books by Cox, Little, and O’Shea will find familiar themes in the first five chapters: polynomials in one variable, Gröbne

Machine learning challenges in theoretical HEP

In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP. We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools. Finally, we provide an outlook about future applications and developments due to the synergy between ML and HEP-TH.Comment: 7 pages, 3 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017

Community innovation for sustainable energy

As in other countries, there is a growing public, policy and business interest in the UK in the roles and potential of community-led initiatives for sustainable energy consumption and production. Such initiatives include green lifestyle-based activities to reduce energy consumption (e.g. Transition Towns, and Carbon Reduction Action Groups), more traditional behaviour change initiatives such as neighbourhood insulation projects and energy-saving campaigns, as well as renewable energy generation projects such as community-owned windfarms and biofuel projects. Case studies of specific projects identify a variety of rationales amongst participants, whilst policy interest suggests a more instrumental concern for facilitating additional, larger-scale sustainable energy transitions. Amongst participant rationales are ideas that bottom-up, community-based projects deliver energy savings and behaviour changes that top-down policy instruments cannot achieve, due to the greater local knowledge and engagement they embody, the sense of common ownership and empowerment, and the social capital and trust that is generated among local actors. These resources provide organisational and values-based 'grassroots innovations' which experiment with new consumption practices based on alternative 'new economics' values. However, previous research shows 'grassroots innovations' face a series of critical challenges requiring support to overcome, in order to achieve their potential benefits more widely. This includes developing 'niche' networks for mobilising reforms both to highly centralised energy institutions and infrastructures, as well as deeply ingrained social practices of 'normal' energy consumption and everyday life. What makes this experience fascinating for the purposes of the SCORAI workshop is the way these community-based initiatives are trying to develop new energy-related consumption practices with a view to the socio-technical transition to local, renewable or lower carbon energy systems. Understandably, many projects remain practically focused on securing early successes and resourcing their long-term survival. However, the institutional and infrastructure reforms that will help in this endeavour require strategies for addressing the wider (national and international) political economy of consumption which adopts an ecological modernisation approach to sustainability. In surveying the community energy scene in the UK, our paper pays particular attention to this last issue

Computer‐supported experiential learning (Phase One ‐ staff development)

The Computer‐Supported Experiential Learning Project has been established to promote the use of communication and information technologies for teaching and learning within a vocational university. Phase 1 has concentrated upon raising awareness and actively involving academic staff in experiencing these technologies. The project is curriculum‐led, and considers how technology can be applied appropriately to an established curriculum model which links theory and practice (Kolb, 1984). All academic staff were invited to take part by logging onto the university intranet, accessing information about teaching and learning, trying out ideas and emailing their online mentors with their plans and reflections. In addition, all staff could take part in discussion forums concerning a range of issues. The participation of academic staff is reported; which staff registered as having visited the site, which staff actively used the information to experiment with their teaching, and which staff took part in public online discussions. Barriers which limited participation are also reported The outcome of Phase 1 has been to encourage over 40 academic staff to embed the use of learning technologies in their own course modules in Phase 2 with continued support from the Learning Methods Unit