scholarly journals Large Scaled Topographic Mapping and Issues in Depicting VGI and Open Data

2021 ◽  
Vol 4 ◽  
pp. 1-6
Author(s):  
Benedikt Hajek ◽  
Karel Kriz
Keyword(s):  
Large Scale ◽  
Open Data ◽  
Quality Standard ◽  
Topographic Mapping ◽  
Quality Research ◽  
Contour Lines ◽  
New Challenges ◽  
New Processing ◽  
Automated Methods ◽  
Insight Into

Abstract. Open data and geospatial data collected by volunteers are nowadays easy to obtain and available with worldwide coverage through projects like OpenStreetMap. However, the use of these datasets leads to new challenges in depiction especially in the field of large scale topographic cartography. In addition to quality research, new processing and depiction methods for integrating these data are emerging. In the course of this work, specific problems of maps based on OpenStreetMap and Open Data elevation models are pointed out and possible solutions are introduced. In addition, a method for the preprocessing of contour lines is presented and the process flow is described in more detail. The goal of this work is to give insight into a toolbox of specially adapted and (semi-)automated methods. In this way, the quality standard of the depiction of topographic maps based on Open Data is to be increased, but also limitations are being shown.

Linguistically Annotated Reordering: Evaluation and Analysis

2010 ◽  
Vol 36 (3) ◽  
pp. 535-568 ◽  
Author(s):  
Deyi Xiong ◽  
Min Zhang ◽  
Aiti Aw ◽  
Haizhou Li
Keyword(s):  
Large Scale ◽  
Statistical Machine Translation ◽  
Training Data ◽  
Linguistic Knowledge ◽  
Analysis Method ◽  
Complementary Information ◽  
New Approach ◽  
Parse Trees ◽  
New Challenges ◽  
Insight Into

Linguistic knowledge plays an important role in phrase movement in statistical machine translation. To efficiently incorporate linguistic knowledge into phrase reordering, we propose a new approach: Linguistically Annotated Reordering (LAR). In LAR, we build hard hierarchical skeletons and inject soft linguistic knowledge from source parse trees to nodes of hard skeletons during translation. The experimental results on large-scale training data show that LAR is comparable to boundary word-based reordering (BWR) (Xiong, Liu, and Lin 2006), which is a very competitive lexicalized reordering approach. When combined with BWR, LAR provides complementary information for phrase reordering, which collectively improves the BLEU score significantly. To further understand the contribution of linguistic knowledge in LAR to phrase reordering, we introduce a syntax-based analysis method to automatically detect constituent movement in both reference and system translations, and summarize syntactic reordering patterns that are captured by reordering models. With the proposed analysis method, we conduct a comparative analysis that not only provides the insight into how linguistic knowledge affects phrase movement but also reveals new challenges in phrase reordering.

scholarly journals Detecting and Correcting False Transients in Calcium Imaging

2018 ◽  
Author(s):  
Jeff L. Gauthier ◽  
Sue Ann Koay ◽  
Edward H. Nieh ◽  
David W. Tank ◽  
Jonathan W. Pillow ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Large Scale ◽  
Time Course ◽  
Robust Estimator ◽  
Neural Function ◽  
Visualization Tool ◽  
Large Dataset ◽  
Large Populations ◽  
Automated Methods ◽  
Insight Into

AbstractPopulation recordings of calcium activity are a major source of insight into neural function. Large dataset sizes often require automated methods, but automation can introduce errors that are difficult to detect. Here we show that automatic time course estimation can sometimes lead to significant misattribution errors, in which fluorescence is ascribed to the wrong cell. Misattribution arises when the shapes of overlapping cells are imperfectly defined, or when entire cells or processes are not identified, and misattribution can even be produced by methods specifically designed to handle overlap. To diagnose this problem, we develop a transient-by-transient metric and a visualization tool that allow users to quickly assess the degree of misattribution in large populations. To filter out misattribution, we also design a robust estimator that explicitly accounts for contaminating signals in a generative model. Our methods can be combined with essentially any cell finding technique, empowering users to diagnose and correct at large scale a problem that has the potential to significantly alter scientific conclusions.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

scholarly journals The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4127
Author(s):  
Will Farlessyost ◽  
Kelsey-Ryan Grant ◽  
Sara R. Davis ◽  
David Feil-Seifer ◽  
Emily M. Hand
Keyword(s):  
Social Interactions ◽  
First Impressions ◽  
Social Traits ◽  
Judicial Proceedings ◽  
Facial Attributes ◽  
Future Work ◽  
Automated Methods ◽  
Insight Into ◽  
Social Trait ◽  
Better Than

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.

scholarly journals Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

2021 ◽  
Vol 10 (7) ◽  
pp. 432
Author(s):  
Nicolai Moos ◽  
Carsten Juergens ◽  
Andreas P. Redecker
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Methodological Approach ◽  
Spatial Relationship ◽  
Postal Code ◽  
Annual Income ◽  
Choropleth Maps ◽  
Clustering Approach ◽  
Relationship Of ◽  
Insight Into

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.

scholarly journals Fragment-centric topographic mapping method guides the understanding of ABCG2-inhibitor interactions

RSC Advances ◽  
2019 ◽  
Vol 9 (14) ◽  
pp. 7757-7766 ◽  
Author(s):  
Yao Wu ◽  
Xin-Ying Gao ◽  
Xin-Hui Chen ◽  
Shao-Long Zhang ◽  
Wen-Juan Wang ◽  
...  
Keyword(s):  
Mapping Method ◽  
Topographic Mapping ◽  
Ligand Interaction ◽  
Mapping Tool ◽  
Protein Ligand Interaction ◽  
Insight Into ◽  
Computational Strategy

Our study gains insight into the development of novel specific ABCG2 inhibitors, and develops a comprehensive computational strategy to understand protein ligand interaction with the help of AlphaSpace, a fragment-centric topographic mapping tool.

scholarly journals Metachronal waves in magnetic micro-robotic paddles for artificial cilia

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Matthew T. Bryan ◽  
Elizabeth L. Martin ◽  
Aleksandra Pac ◽  
Andrew D. Gilbert ◽  
Feodor Y. Ogrin
Keyword(s):  
Large Scale ◽  
Point Of Care ◽  
Hydrodynamic Response ◽  
Flow Speeds ◽  
Break Time ◽  
Large Scale Manufacture ◽  
Fresh Insight ◽  
Metachronal Waves ◽  
Insight Into ◽  
Phase Differences

AbstractBiological cilia generate fluid movement within viscosity-dominated environments using beating motions that break time-reversal symmetry. This creates a metachronal wave, which enhances flow efficiency. Artificially mimicking this behaviour could improve microfluidic point-of-care devices, since viscosity-dominated fluid dynamics impede fluid flow and mixing of reagents, limiting potential for multiplexing diagnostic tests. However, current biomimicry schemes require either variation in the hydrodynamic response across a cilia array or a complex magnetic anisotropy configuration to synchronise the actuation sequence with the driving field. Here, we show that simple modifications to the structural design introduce phase differences between individual actuators, leading to the spontaneous formation of metachronal waves. This generates flow speeds of up to 16 μm/s as far as 675 μm above the actuator plane. By introducing metachronal waves through lithographic structuring, large scale manufacture becomes feasible. Additionally, by demonstrating that metachronal waves emerge from non-uniformity in internal structural mechanics, we offer fresh insight into the mechanics of cilia coordination.

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