Some Advances in the Control of Tooth Decay

Author Institution: College of Dentistry, The Ohio State Universit

Living labs and vacancy in the neoliberal city

© 2017 Elsevier Ltd This paper evaluates smart city (SC) initiatives in the context of re-using vacant property, focusing on the role of living labs (LL). LL utilise Lo-Fi technologies to foster local digital innovation and support community-focused civic hacking, running various kinds of workshops and engaging with local citizens to co-create digital interventions and apps aimed at 'solving’ local issues. Five approaches to LL are outlined and discussed in relation to vacancy and gentrification: pop-up initiatives, university-led activities, community organised venues/activities, citizen sensing and crowdsourcing, and tech-led regeneration initiatives. Notwithstanding the potential for generating temporary and independent spaces for transferring digital competences and increasing citizens' participation in the SC, we argue LL foster largely a form of participation framed within a model of civic stewardship for 'smart citizens’. While presented as horizontal, open, and participative, LL and civic hacking are rooted often in pragmatic and paternalistic discourses and practices related to the production of a creative economy and a technocratic version of SC. As such, by encouraging a particular kind of re-use of vacant space, LLs are used actively to bolster the Smart City discourse, as part of the more general neoliberalization of urban political economy. We discuss these approaches and issues generally, drawing on previous fieldwork and with respect to a case study of Dublin, Ireland

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks

Orchestrating Tuple-based Languages

The World Wide Web can be thought of as a global computing architecture supporting the deployment of distributed networked applications. Currently, such applications can be programmed by resorting mainly to two distinct paradigms: one devised for orchestrating distributed services, and the other designed for coordinating distributed (possibly mobile) agents. In this paper, the issue of designing a pro- gramming language aiming at reconciling orchestration and coordination is investigated. Taking as starting point the orchestration calculus Orc and the tuple-based coordination language Klaim, a new formalism is introduced combining concepts and primitives of the original calculi. To demonstrate feasibility and effectiveness of the proposed approach, a prototype implementation of the new formalism is described and it is then used to tackle a case study dealing with a simplified but realistic electronic marketplace, where a number of on-line stores allow client applications to access information about their goods and to place orders

Systematic infrared image quality improvement using deep learning based techniques

This is the final version. Available from SPIE via the DOI in this recordInfrared thermography (IRT, or thermal video) uses thermographic cameras to detect and record radiation in the longwavelength infrared range of the electromagnetic spectrum. It allows sensing environments beyond the visual perception limitations, and thus has been widely used in many civilian and military applications. Even though current thermal cameras are able to provide high resolution and bit-depth images, there are significant challenges to be addressed in specific applications such as poor contrast, low target signature resolution, etc. This paper addresses quality improvement in IRT images for object recognition. A systematic approach based on image bias correction and deep learning is proposed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. Our main objective is to maximise the useful information on the object to be detected even when the number of pixels on target is adversely small. The experimental results show that our approach can significantly improve target resolution and thus helps making object recognition more efficient in automatic target detection/recognition systems (ATD/R).Centre for Excellence for Sensor and Imaging System (CENSIS)Scottish Funding CouncilDigital Health and Care Institute (DHI)Royal Society of EdinburghNational Science Foundation of Chin

Urban Megatrends: Towards a European Research Agenda

The report presents the urban megatrends both worlwide and in Europe

Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

Data journeys: Capturing the socio-material constitution of data objects and flows

In this paper, we discuss the development and piloting of a new methodology for illuminating the socio-material con- stitution of data objects and flows as data move between different sites of practice. The data journeys approach contributes to the development of critical, qualitative methodologies that can address the geographic and temporal scale of emerging knowledge infrastructures, and capture the ‘life of data’ from their initial generation through to re-use in different contexts. We discuss the theoretical development of the data journeys methodology and the application of the approach on a project examining meteorological data on their journey from initial production through to being re- used in climate science and financial markets. We then discuss three key conceptual findings from this project about: (1) the socio-material constitution of digital data objects, (2) ‘friction’ in the movement of data through space and time and (3) the mutability of digital data as a material property that contributes to driving the movement of data between different sites of practice

Steam reforming on transition-metal carbides from density-functional theory

A screening study of the steam reforming reaction (CH_4 + H_2O -> CO + 3H_2) on early transition-metal carbides (TMC's) is performed by means of density-functional theory calculations. The set of considered surfaces includes the alpha-Mo_2C(100) surfaces, the low-index (111) and (100) surfaces of TiC, VC, and delta-MoC, and the oxygenated alpha-Mo_2C(100) and TMC(111) surfaces. It is found that carbides provide a wide spectrum of reactivities towards the steam reforming reaction, from too reactive via suitable to too inert. The reactivity is discussed in terms of the electronic structure of the clean surfaces. Two surfaces, the delta-MoC(100) and the oxygen passivated alpha-Mo_2C(100) surfaces, are identified as promising steam reforming catalysts. These findings suggest that carbides provide a playground for reactivity tuning, comparable to the one for pure metals.Comment: 6 pages, 4 figure