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1How—and Why—to Introduce Visual Note-Taking to Your Students | Edutopia
Visual note-taking allows information to be processed by the brain in three different ways.
3 years ago by @becker
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brain
immermind
map
mind
note
storming
taking
brainimmermindmapmindnotestormingtaking
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1Karpathy wants BibSonomy maps ;)
https://twitter.com/karpathy/status/1345850586969739265?s=19
3 years ago by @becker
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bibsonomy
karpathy
map
maps
paper
reading
research
bibsonomykarpathymapmapspaperreadingresearch
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1Mobile meteorology: smartphone batteries will change the way we measure the weather - OpenSignal
https://opensignal.com/reports/battery-temperature-weather/
6 years ago by @becker
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weather
p2map
map
smartphone
sensor
temp
temperature
data
smart
phone
sensing
weatherp2mapmapsmartphonesensortemptemperaturedatasmartphonesensing
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1London Air Quality Network » Annual Pollution Maps
https://www.londonair.org.uk/london/asp/annualmaps.asp
6 years ago by @becker
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p2map
air
quality
map
london
model
data
dataset
p2mapairqualitymaplondonmodeldatadataset
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1Tile servers - OpenStreetMap Wiki
https://wiki.openstreetmap.org/wiki/Tile_servers
6 years ago by @becker
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map
tiles
free
cc0
cc
creative
commons
openstreetmap
osm
maptilesfreecc0cccreativecommonsopenstreetmaposm
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2Sequential Exceptional Pattern Discovery Using Pattern-Growth: An Extensible Framework for Interpretable Machine Learning on Sequential Data
D. Mollenhauer, and M. Atzmueller. (2020)
3 years ago by @becker
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pattern
mining
gp
growth
prefix
span
parallel
distributed
exceptional
model
sequence
sequential
discovery
emm
map
reduce
mapreduce
spark
patternmininggpgrowthprefixspanparalleldistributedexceptionalmodelsequencesequentialdiscoveryemmmapreducemapreducespark
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2PointCloud Saliency Maps
T. Zheng, C. Chen, J. Yuan, B. Li, and K. Ren. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (October 2019)
4 years ago by @becker
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cell
cloud
feature
importance
map
net
point
pointnet
pointnet2
saliency
single
visualization
cellcloudfeatureimportancemapnetpointpointnetpointnet2saliencysinglevisualization
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2OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery
S. Ma, and Z. Zhang. arXiv preprint arXiv:1804.05283, (2018)
4 years ago by @becker
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convoluational
nn
neural
network
cnn
multi
omics
multiomics
deep
map
tree
citedby:scholar:count:5
citedby:scholar:timestamp:2020-8-31
convoluationalnnneuralnetworkcnnmultiomicsmultiomicsdeepmaptreecitedby:scholar:count:5citedby:scholar:timestamp:2020-8-31
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12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images
M. Steininger, K. Kobs, A. Zehe, F. Lautenschlager, M. Becker, and A. Hotho. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6 (3): 1--24 (2020)
4 years ago by @becker
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air
cnn
image
map
mapping
myown
network
networks
neural
open
p21
prediction
project:bmbf
quality
street
aircnnimagemapmappingmyownnetworknetworksneuralopenp21predictionproject:bmbfqualitystreet
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1From Google Maps to a fine-grained catalog of street trees
S. Branson, J. Wegner, D. Hall, N. Lang, K. Schindler, and P. Perona. ISPRS Journal of Photogrammetry and Remote Sensing, (2018)
5 years ago by @becker
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street
google
maps
map
tree
p2map
eva
visual
image
citedby:scholar:count:4
citedby:scholar:timestamp:2018-12-15
streetgooglemapsmaptreep2mapevavisualimagecitedby:scholar:count:4citedby:scholar:timestamp:2018-12-15
(0)
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2Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies
M. Nieuwenhuijsen, D. Donaire-Gonzalez, I. Rivas, M. de Castro, M. Cirach, G. Hoek, E. Seto, M. Jerrett, and J. Sunyer. Environmental Science & Technology, 49 (5): 2977--2982 (February 2015)
6 years ago by @becker
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2.5
air
bc
black
carbon
eva
map
mapping
model
p2map
pm
pm2.5
pm25
quality
school
2.5airbcblackcarbonevamapmappingmodelp2mappmpm2.5pm25qualityschool
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2Geographic knowledge extraction and semantic similarity in OpenStreetMap
A. Ballatore, M. Bertolotto, and D. Wilson. Knowledge and Information Systems, 37 (1): 61--81 (2013)
6 years ago by @becker
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semantic
similarity
open
street
map
osm
knowledge
geo
p2map
extraction
citedby:scholar:count:90
citedby:scholar:timestamp:2018-3-14
semanticsimilarityopenstreetmaposmknowledgegeop2mapextractioncitedby:scholar:count:90citedby:scholar:timestamp:2018-3-14
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2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, and Z. He. Sustainability, 8 (8): 786 (2016)
6 years ago by @becker
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p2map
eva
osm
lur
land
use
regression
open
street
map
air
quality
pollution
prediction
linear
citedby:scholar:count:4
citedby:scholar:timestamp:2018-2-21
p2mapevaosmlurlanduseregressionopenstreetmapairqualitypollutionpredictionlinearcitedby:scholar:count:4citedby:scholar:timestamp:2018-2-21
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2Land use regression models for ultrafine particles in six European areas
E. van Nunen, R. Vermeulen, M. Tsai, N. Probst-Hensch, A. Ineichen, M. Davey, M. Imboden, R. Ducret-Stich, A. Naccarati, D. Raffaele and 1 other author(s). Environmental science & technology, 51 (6): 3336--3345 (2017)
6 years ago by @becker
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air
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
eva
features
land
lur
map
open
osm
p2map
pollution
quality
regression
street
use
aircitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21evafeatureslandlurmapopenosmp2mappollutionqualityregressionstreetuse
(0)
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, and A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
6 years ago by @becker
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air
eva
features
land
lur
map
open
osm
p2map
pollution
prediction
quality
regression
roads
street
use
airevafeatureslandlurmapopenosmp2mappollutionpredictionqualityregressionroadsstreetuse
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2Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes
M. Venanzi, A. Rogers, and N. Jennings. First AAAI Conference on Human Computation and Crowdsourcing, (2013)
7 years ago by @becker
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p2map
heatmap
interpolation
gauss
map
bc
regression
bayes
crowdsourcing
citedby:scholar:count:14
citedby:scholar:timestamp:2017-5-26
p2mapheatmapinterpolationgaussmapbcregressionbayescrowdsourcingcitedby:scholar:count:14citedby:scholar:timestamp:2017-5-26
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5Fast subgroup discovery for continuous target concepts
M. Atzmueller, and F. Lemmerich. International Symposium on Methodologies for Intelligent Systems (ISMIS), page 35--44. Berlin, Heidelberg, Springer Berlin Heidelberg, (2009)
7 years ago by @becker
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citedby:scholar:count:57
citedby:scholar:timestamp:2017-4-19
discovery
diss
fp
fp-growth
growth
inthesis
map
sd
sd-map
subgroup
citedby:scholar:count:57citedby:scholar:timestamp:2017-4-19discoverydissfpfp-growthgrowthinthesismapsdsd-mapsubgroup
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6SD-Map--A fast algorithm for exhaustive subgroup discovery
M. Atzmueller, and F. Puppe. European Conference on Principles of Data Mining and Knowledge Discovery, page 6--17. Springer, (2006)
7 years ago by @becker
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inthesis
diss
sd-map
sd
map
fp-growth
fp
growth
subgroup
discovery
citedby:scholar:count:131
citedby:scholar:timestamp:2017-4-19
inthesisdisssd-mapsdmapfp-growthfpgrowthsubgroupdiscoverycitedby:scholar:count:131citedby:scholar:timestamp:2017-4-19
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7Efficient Descriptive Community Mining
M. Atzmueller, and F. Mitzlaff. International Florida Artificial Intelligence Research Society Conference (FLAIRS), page 459--464. AAAI Press, (2011)
7 years ago by @becker
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citedby:scholar:count:38
citedby:scholar:timestamp:2017-4-19
community
discovery
diss
inthesis
map
mining
sd
sd-map
subgroup
citedby:scholar:count:38citedby:scholar:timestamp:2017-4-19communitydiscoverydissinthesismapminingsdsd-mapsubgroup
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2A neural network architecture for noise prediction
G. Cammarata, S. Cavalieri, and A. Fichera. Neural Networks, 8 (6): 963--973 (1995)
9 years ago by @becker
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eva21
map
model
networks
neural
noise
prediction
proposal
widenoise
eva21mapmodelnetworksneuralnoisepredictionproposalwidenoise
(0)
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1Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities
I. Aguilera, M. Foraster, X. Basagaña, E. Corradi, A. Deltell, X. Morelli, H. Phuleria, M. Ragettli, M. Rivera, A. Thomasson and 2 other author(s). Journal of Exposure Science and Environmental Epidemiology, 25 (1): 97--105 (December 2015)
9 years ago by @becker
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eva21
lur
map
model
noise
proposal
eva21lurmapmodelnoiseproposal
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4A review of land-use regression models to assess spatial variation of outdoor air pollution
G. Hoek, R. Beelen, K. de Hoogh, D. Vienneau, J. Gulliver, P. Fischer, and D. Briggs. Atmospheric environment, 42 (33): 7561--7578 (2008)
9 years ago by @becker
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LUR
air
eva21
map
model
proposal
quality
LURaireva21mapmodelproposalquality
(0)
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2CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system
N. Nikzad, N. Verma, C. Ziftci, E. Bales, N. Quick, P. Zappi, K. Patrick, S. Dasgupta, I. Krueger, T. Rosing and 1 other author(s). Proceedings of the conference on Wireless Health, page 11:1--11:8. (2012)
9 years ago by @becker
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air
citisense
collective
eva
eva21
map
participatory
proposal
quality
sensing
aircitisensecollectiveevaeva21mapparticipatoryproposalqualitysensing
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8Pushing the spatio-temporal resolution limit of urban air pollution maps
D. Hasenfratz, O. Saukh, C. Walser, C. Hueglin, M. Fierz, and L. Thiele. Proceedings of the 12th International Conference on Pervasive Computing and Communications, page 69--77. IEEE, (2014)
10 years ago by @becker
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air
airprobe
cat:sensormodelling
eva21
everyaware
map
pmx
pollution
proposal
sensor
sitc
ufp
urban
airairprobecat:sensormodellingeva21everyawaremappmxpollutionproposalsensorsitcufpurban
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1NoiseMap-real-time participatory noise maps
I. Schweizer, R. Bärtl, A. Schulz, F. Probst, and M. Mühläuser. Proceedings of the 2nd International Workshop on Sensing Applications on Mobile Phones, page 1--5. (2011)
10 years ago by @becker
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eva
eva21
everyaware
map
noise
participatory
proposal
sensing
system
widenoise
evaeva21everyawaremapnoiseparticipatoryproposalsensingsystemwidenoise
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BibSonomy

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1How—and Why—to Introduce Visual Note-Taking to Your Students | Edutopia

1Karpathy wants BibSonomy maps ;)

1Mobile meteorology: smartphone batteries will change the way we measure the weather - OpenSignal

1London Air Quality Network » Annual Pollution Maps

1Tile servers - OpenStreetMap Wiki

publications (hide)23
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2Sequential Exceptional Pattern Discovery Using Pattern-Growth: An Extensible Framework for Interpretable Machine Learning on Sequential Data

2PointCloud Saliency Maps

2OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery

12MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images

1From Google Maps to a fine-grained catalog of street trees

2Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies

2Geographic knowledge extraction and semantic similarity in OpenStreetMap

2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China

2Land use regression models for ultrafine particles in six European areas

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

2Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes

5Fast subgroup discovery for continuous target concepts

6SD-Map--A fast algorithm for exhaustive subgroup discovery

7Efficient Descriptive Community Mining

2A neural network architecture for noise prediction

1Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities

4A review of land-use regression models to assess spatial variation of outdoor air pollution

2CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system

8Pushing the spatio-temporal resolution limit of urban air pollution maps

1NoiseMap-real-time participatory noise maps

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