Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

Let the Citizens Map—Public Participation GIS as a Planning Support System in the Helsinki Master Plan Process

Abstract: Current public participation methods are laborious, reach few participants and are ineffective at gathering usable information for planning. This situation leads often to mistrust and dissatisfaction in the process and outcome. This article identifies the critical conditions for meaningful use of public participation GIS (PPGIS) tools to support the making of master plan in Helsinki. With PPGIS tools, residents’ insight of their living environment can be reached and utilized along the planning process. The results are divided to conceptual and empirical points. Whereas the conceptual points emphasize better understanding of the locus of the PPGIS tools in planning process, the empirical findings reveal new ways to study how residents’ perceptions align with the plan proposal. Though new tools, data and analysis can support representativeness, independence, early involvement, influence and transparency, planners and residents need more understanding of the benefits of these tools. The study indicat...

Localized surface plasmon resonance in silver nanoparticles: Atomistic first-principles time-dependent density-functional theory calculations

Abstract: We observe using ab initio methods that localized surface plasmon resonances in icosahedral silver nanoparticles enter the asymptotic region already between diameters of 1 and 2 nm, converging close to the classical quasistatic limit around 3.4 eV. We base the observation on time-dependent density-functional theory simulations of the icosahedral silver clusters Ag-55 (1.06 nm), Ag-147 (1.60 nm), Ag-309 (2.14 nm), and Ag-561 (2.68 nm). The simulation method combines the adiabatic GLLB-SC exchange-correlation functional with real time propagation in an atomic orbital basis set using the projector-augmented wave method. The method has been implemented for the electron structure code GPAW within the scope of this work. We obtain good agreement with experimental data and modeled results, including photoemission and plasmon resonance. Moreover, we can extrapolate the ab initio results to the classical quasistatically modeled icosahedral clusters.

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.

Abstract: Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.