The potential of cast glass in structural applications. Lessons learned from large-scale castings and state-of-the art load-bearing cast glass in architecture

2018 ◽  
Vol 20 ◽  
pp. 213-234 ◽  
Author(s):  
F. Oikonomopoulou ◽  
T. Bristogianni ◽  
L. Barou ◽  
F.A. Veer ◽  
R. Nijsse
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Lessons Learned ◽  
Load Bearing ◽  
Structural Applications ◽  
Cast Glass

Experiences With and Lessons Learned From Developing, Implementing, and Evaluating a Support Program for Older Hearing Aid Users and Their Communication Partners in the Hearing Aid Dispensing Setting

2020 ◽  
Vol 29 (3S) ◽  
pp. 638-647 ◽  
Author(s):  
Janine F. J. Meijerink ◽  
Marieke Pronk ◽  
Sophia E. Kramer
Keyword(s):  
Large Scale ◽  
Hearing Aid ◽  
Critical Discussion ◽  
Lessons Learned ◽  
Research Note ◽  
Support Program ◽  
Large Sample Size ◽  
Long Term Effects ◽  
Communication Program ◽  
Communication Partners

Purpose The SUpport PRogram (SUPR) study was carried out in the context of a private academic partnership and is the first study to evaluate the long-term effects of a communication program (SUPR) for older hearing aid users and their communication partners on a large scale in a hearing aid dispensing setting. The purpose of this research note is to reflect on the lessons that we learned during the different development, implementation, and evaluation phases of the SUPR project. Procedure This research note describes the procedures that were followed during the different phases of the SUPR project and provides a critical discussion to describe the strengths and weaknesses of the approach taken. Conclusion This research note might provide researchers and intervention developers with useful insights as to how aural rehabilitation interventions, such as the SUPR, can be developed by incorporating the needs of the different stakeholders, evaluated by using a robust research design (including a large sample size and a longer term follow-up assessment), and implemented widely by collaborating with a private partner (hearing aid dispensing practice chain).

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

scholarly journals Scaling Up Delivery of Biofortified Staple Food Crops Globally: Paths to Nourishing Millions

2021 ◽  
pp. 037957212098250
Author(s):  
Jennifer K. Foley ◽  
Kristina D. Michaux ◽  
Bho Mudyahoto ◽  
Laira Kyazike ◽  
Binu Cherian ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Large Scale ◽  
Scale Up ◽  
Lessons Learned ◽  
Raw Material ◽  
Micronutrient Deficiencies ◽  
Seed Systems ◽  
Public And Private ◽  
Context Specific ◽  
Public And Private Sector

Background: Micronutrient deficiencies affect over one quarter of the world’s population. Biofortification is an evidence-based nutrition strategy that addresses some of the most common and preventable global micronutrient gaps and can help improve the health of millions of people. Since 2013, HarvestPlus and a consortium of collaborators have made impressive progress in the enrichment of staple crops with essential micronutrients through conventional plant breeding. Objective: To review and highlight lessons learned from multiple large-scale delivery strategies used by HarvestPlus to scale up biofortification across different country and crop contexts. Results: India has strong public and private sector pearl millet breeding programs and a robust commercial seed sector. To scale-up pearl millet, HarvestPlus established partnerships with public and private seed companies, which facilitated the rapid commercialization of products and engagement of farmers in delivery activities. In Nigeria, HarvestPlus stimulated the initial acceptance and popularization of vitamin A cassava using a host of creative approaches, including “crowding in” delivery partners, innovative promotional programs, and development of intermediate raw material for industry and novel food products. In Uganda, orange sweet potato (OSP) is a traditional subsistence crop. Due to this, and the lack of formal seed systems and markets, HarvestPlus established a network of partnerships with community-based nongovernmental organizations and vine multipliers to popularize and scale-up delivery of OSP. Conclusions: Impact of biofortification ultimately depends on the development of sustainable markets for biofortified seeds and products. Results illustrate the need for context-specific, innovative solutions to promote widespread adoption.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

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