scholarly journals Learning of Spatial Properties of a Large-scale Virtual City with an Interactive Map

2019 ◽  
Author(s):  
Sabine U. König ◽  
Viviane Kakerbeck ◽  
Debora Nolte ◽  
Laura Duesberg ◽  
Nicolas Kuske ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Training Session ◽  
Spatial Knowledge ◽  
Self Report ◽  
World City ◽  
Large Area ◽  
Pointing Task ◽  
Virtual City ◽  
Active Exploration

AbstractOn the basis of embodied/-enacted theories of the mind, investigations of spatial cognition related to real world environments have become current research interests. How this perspective relates to acquiring spatial knowledge not by active exploration, but through map learning, however, remains unresolved. Therefore, we designed a large virtual city comprised of over 200 houses, suitable for active exploration or spatial learning based on a map. Here, we report our results after single and repeated 30-minute training sessions using an interactive city map. We tested subjects’ knowledge of the orientation of houses towards cardinal north and of two houses relative to each other and the locations of two houses towards each other in a pointing task. Our results revealed that a single training session was sufficient to repeatedly view the majority of houses covering a large area of the city. However, repeated training sessions were necessary to improve the performance level and reveal significant differences between tasks. In contrast to a previous study in a real world city, performance in the two orientation tasks was better than in the pointing task. The lack of distance and the lack of angular difference effects onto task performance suggest the use of a global reference frame. Performance was positively correlated with a self-report on spatial abilities (FRS) in the absolute orientation and pointing task but not the relative orientation task. Overall, our results suggest that training with an interactive city map enhances abstract knowledge, not directly available from an embodied perspective.

scholarly journals Social Presence Outside the Augmented Reality Field of View

2021 ◽  
Vol 2 ◽  
Author(s):  
Mark Roman Miller ◽  
Jeremy N. Bailenson
Keyword(s):  
Augmented Reality ◽  
Task Performance ◽  
Social Presence ◽  
Real World ◽  
Field Of View ◽  
Self Report ◽  
Virtual Humans ◽  
Large Area ◽  
Virtual Character ◽  
Future Work

Augmented reality headsets in use today have a large area in which the real world can be seen, but virtual content cannot be displayed. Users perceptions of content in this area is not well understood. This work studies participants perception of a virtual character in this area by grounding this question in relevant theories of perception and performing a study using both behavioral and self-report measures. We find that virtual characters within the augmented periphery receive lower social presence scores, but we do notfind a difference in task performance. These findings inform application design and encourage future work in theories of AR perception and perception of virtual humans.

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

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 Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets

2021 ◽  
Vol 13 (15) ◽  
pp. 2877
Author(s):  
Yu Tao ◽  
Siting Xiong ◽  
Susan J. Conway ◽  
Jan-Peter Muller ◽  
Anthony Guimpier ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Imaging System ◽  
Landing Site ◽  
Parameter Tuning ◽  
Processing Parameters ◽  
Input Image ◽  
Large Area ◽  
Laser Altimeter ◽  
Imaging Science

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.

A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽  
2021 ◽  
pp. 102442
Author(s):  
Lin Zhou ◽  
Lu Zhen ◽  
Roberto Baldacci ◽  
Marco Boschetti ◽  
Ying Dai ◽  
...  
Keyword(s):  
Heuristic Algorithm ◽  
Real World ◽  
Design Problem ◽  
Large Scale ◽  
Territory Design

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

scholarly journals Monoclonal Antibodies Targeting CGRP: From Clinical Studies to Real-World Evidence—What Do We Know So Far?

2021 ◽  
Vol 14 (7) ◽  
pp. 700
Author(s):  
Theodoros Mavridis ◽  
Christina I. Deligianni ◽  
Georgios Karagiorgis ◽  
Ariadne Daponte ◽  
Marianthi Breza ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
Real World ◽  
Large Scale ◽  
Randomized Clinical Trials ◽  
Clinical Investigation ◽  
Phase Iii ◽  
Real World Evidence ◽  
Symptomatic Management ◽  
Repurposed Drugs ◽  
Cephalic Pain

Now more than ever is the time of monoclonal antibody use in neurology. In headaches, disease-specific and mechanism-based treatments existed only for symptomatic management of migraines (i.e., triptans), while the standard prophylactic anti-migraine treatments consist of non-specific and repurposed drugs that share limited safety profiles and high risk for interactions with other medications, resulting in rundown adherence rates. Recent advances in headache science have increased our understanding of the role of calcitonin gene relate peptide (CGRP) and pituitary adenylate cyclase-activating polypeptide (PACAP) pathways in cephalic pain neurotransmission and peripheral or central sensitization, leading to the development of monoclonal antibodies (mAbs) or small molecules targeting these neuropeptides or their receptors. Large scale randomized clinical trials confirmed that inhibition of the CGRP system attenuates migraine, while the PACAP mediated nociception is still under scientific and clinical investigation. In this review, we provide the latest clinical evidence for the use of anti-CGRP in migraine prevention with emphasis on efficacy and safety outcomes from Phase III and real-world studies.

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

scholarly journals Efficient and large-area all vacuum-deposited perovskite light-emitting diodes via spatial confinement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peipei Du ◽  
Jinghui Li ◽  
Liang Wang ◽  
Liang Sun ◽  
Xi Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
High Efficiency ◽  
Light Emitting Diodes ◽  
Scale Production ◽  
Nonradiative Recombination ◽  
Large Area ◽  
Spatial Confinement ◽  
Light Emitting ◽  
Large Scale Production

AbstractWith rapid advances of perovskite light-emitting diodes (PeLEDs), the large-scale fabrication of patterned PeLEDs towards display panels is of increasing importance. However, most state-of-the-art PeLEDs are fabricated by solution-processed techniques, which are difficult to simultaneously achieve high-resolution pixels and large-scale production. To this end, we construct efficient CsPbBr3 PeLEDs employing a vacuum deposition technique, which has been demonstrated as the most successful route for commercial organic LED displays. By carefully controlling the strength of the spatial confinement in CsPbBr3 film, its radiative recombination is greatly enhanced while the nonradiative recombination is suppressed. As a result, the external quantum efficiency (EQE) of thermally evaporated PeLED reaches 8.0%, a record for vacuum processed PeLEDs. Benefitting from the excellent uniformity and scalability of the thermal evaporation, we demonstrate PeLED with a functional area up to 40.2 cm2 and a peak EQE of 7.1%, representing one of the most efficient large-area PeLEDs. We further achieve high-resolution patterned perovskite film with 100 μm pixels using fine metal masks, laying the foundation for potential display applications. We believe the strategy of confinement strength regulation in thermally evaporated perovskites provides an effective way to process high-efficiency and large-area PeLEDs towards commercial display panels.

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