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Try to visualize a beautiful woman. Give it some seconds serious consideration. I shall wait patiently as long...
Now the woman you picture in you head, not only has form and shape, she most likely even have mannerisms and perhaps are wearing clothes. If so we can even with wisdom assume that the clothes she is wearing is consistent with, and thus reflect, the person she is and here is my point. She is to the mental effect almost a person, as your imagination is a VERY powerfull tool. - Mind you not even the Tihane-2 (The Chinese supercomputer) would be able to create in memory what you just did in seconds. (Further more, the Tihane-2 would probably answer that beauty is subjective and continue in long explanations to explain the beauty of binary simplicity, but be quite indifferent as in regard to the beauty of women.)
In your head, from the quest was launched, you started drawing upon your feminine resources of data in your eyes putting together a pretty much ”perfect” women ;o) But make no mistake, in a way she is VERY real, as she is created only and alone from YOUR subconscious imaginative spectrum of what a ”beautiful women” consist of. Those perceptions are not only VERY real, to you it is the whole world and thus, the very definition of beauty.
If you are a crossdresser, transvestite or transexual, you know very well what powers are to be drawn from within that imaginative spectrum, but make no mistake. When ordinary macho heterosexual men watch Expendables 1 (Macho hetero classic - 5 stars from my male side, Lisa says ”No comment!” shaking her head) they very much identify them self, with being amongst such group of battle scarred veterans, knowing each others weaknesses and strengths, using them in unison, like a team, working like clockwork and on backbone alone beating odds no sane person would bet a single dime on.
Women as well have their own visual identification spectrum and I stand accused making following statement without statistic documentation, but I have notion practically all women at some time, have imagined them self walking into a crowded room drawing all attention, dazzling everyone with the mere presence of their radiant beauty. But again, I might be mistaken and women not only may, but trust me will rightfully claim ”What the hell do I REALLY know about women.” and it is in fact quite true.
Never the less there is still much to be obtained from within, the almost magical imaginative spectrum.
You see, something happens to macho heterosexual men, watching not ONLY Expendables 1, but every film made in modern times that has to do with war, fighting, death, violence and murder (several times). Slowly, we find, such identification change such individuals. The same thing happens to T-girls who spend much time in the imaginative female spectrum, they change slowly, becoming more like that in reality as well, changing slowly.
Thus watching many movies on war identifying with being a vengeful warmachine, might actually in a stressfull situation, combined with a life crisis, trigger the hidden imaginative being nurtured by such imagination, making that person pick up a riffle going into warmode showing the world a thing or two. Where as a T-girl in same stressfull life crisis, very well might say ”Fuck it all.” pick up a pair of stilettos and wearing a tight skirt ”showing” (though in a more practical sense) the world a thing or two as well.
Try to visualize a beautiful woman. Give it some seconds serious consideration. I shall wait patiently as long...
Now the woman you picture in you head, not only has form and shape, she most likely even have mannerisms and perhaps are wearing clothes. If so we can even with wisdom assume that the clothes she is wearing is consistent with, and thus reflect, the person she is and here is my point. She is to the mental effect almost a person, as your imagination is a VERY powerfull tool. - Mind you not even the Tihane-2 (The Chinese supercomputer) would be able to create in memory what you just did in seconds. (Further more, the Tihane-2 would probably answer that beauty is subjective and continue in long explanations to explain the beauty of binary simplicity, but be quite indifferent as in regard to the beauty of women.)
In your head, from the quest was launched, you started drawing upon your feminine resources of data in your eyes putting together a pretty much ”perfect” women ;o) But make no mistake, in a way she is VERY real, as she is created only and alone from YOUR subconscious imaginative spectrum of what a ”beautiful women” consist of. Those perceptions are not only VERY real, to you it is the whole world and thus, the very definition of beauty.
If you are a crossdresser, transvestite or transexual, you know very well what powers are to be drawn from within that imaginative spectrum, but make no mistake. When ordinary macho heterosexual men watch Expendables 1 (Macho hetero classic - 5 stars from my male side, Lisa says ”No comment!” shaking her head) they very much identify them self, with being amongst such group of battle scarred veterans, knowing each others weaknesses and strengths, using them in unison, like a team, working like clockwork and on backbone alone beating odds no sane person would bet a single dime on.
Women as well have their own visual identification spectrum and I stand accused making following statement without statistic documentation, but I have notion practically all women at some time, have imagined them self walking into a crowded room drawing all attention, dazzling everyone with the mere presence of their radiant beauty. But again, I might be mistaken and women not only may, but trust me will rightfully claim ”What the hell do I REALLY know about women.” and it is in fact quite true.
Never the less there is still much to be obtained from within, the almost magical imaginative spectrum.
You see, something happens to macho heterosexual men, watching not ONLY Expendables 1, but every film made in modern times that has to do with war, fighting, death, violence and murder (several times). Slowly, we find, such identification change such individuals. The same thing happens to T-girls who spend much time in the imaginative female spectrum, they change slowly, becoming more like that in reality as well, changing slowly.
Thus watching many movies on war identifying with being a vengeful warmachine, might actually in a stressfull situation, combined with a life crisis, trigger the hidden imaginative being nurtured by such imagination, making that person pick up a riffle going into warmode showing the world a thing or two. Where as a T-girl in same stressfull life crisis, very well might say ”Fuck it all.” pick up a pair of stilettos and wearing a tight skirt ”showing” (though in a more practical sense) the world a thing or two as well.
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
I have so many connections on LinkedIn it became almost unusable and started becoming a repository of business contacts. This visualization, though mesmerizing to me at first and to others as well, is interesting though the groups are spread around time, location, profession and education.
The top and left is more related to my design expertise, the lower right is more my personal life.
Also the edges are filled with people I barely know. Then again in the center it is often the same.
Visualizing the various features of the SwiftRiver distributed reputation and veracity functionality.
Things like Time, Location, Activeness as well as Global and Local interaction, are all considered in scoring. Time (green) and Location (dark grey) are optional, for scenarios like a conflict or war. The content producer’s location, or proximity to ‘ground zero’ tells the system to factor this in to its score. Also the length of time that content is produced after the initial event may also tell us a lot. Things like ‘time’ and ‘location’ are optional because if your Swift instance is tracking something like a political scandal, time and proximity may not actually add any value to authority calculations.
Purple represents how active Users 1 and 2 are. In and of itself how much someone uses a Swift instance is irrelevants. It could mean that they are an eager member providing valuable assistance, or it could mean they are attempting a brute force attack on the system similar to the Figure 1 scenario. However, when coupled with other factors, frequency of interaction is considered and can positively or negatively weight the score for a user.
Illustrative Visualization of a german climate change adaption research network – using processing and a metaball force field fpr moving agents
The Ars Electronica Futurelab made a high-profile guest appearance in Los Angeles. As part of the Walt Disney Concert Hall’s IN/SIGHT series, Esa Pekka Salonen conducted the L.A. Philharmonic Orchestra in a performance of Ravel’s “Mother Goose” that featured impressive visualizations designed by the Linz-based media art lab.
Credit: Ars Electronica Futurelab
This is a visualization of a blog community. It's one of the end results of our project. In the visualization, thicker lines suggest a stronger connection between the two blogs. If you want to know more, or play with it, hop over to www.blogslikethis.com/
Hydrogen accounts for about 74 percent of the normal matter in the Universe. This visualization shows the electron clouds of hydrogen through the probability density function when the principal quantum number, N, is 1 and 2. The probability density illustrates where the electron is most likely to be found if measured, red indicates high probability, blue indicates low probability.
Update: 2020/06/22: A 16k version is now available.
Update: 2020/07/06: A visualization showing all electron orbitals for N=1 to 6 is also available on Youtube: youtu.be/HyRHT4yOvms
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Ascent Penthouse
Client: Mr Dung - IAM Architecture
---
@ Long Nguyen & Thu Nguyen
Architecture - Interior Design & 3D Visualization
0979 962 864, Ho Chi Minh City
advlongnguyen@gmail.com
We used a dual eddy covariance approach to avoid the placement of flux sensors within the actively moving disturbance. The two towers were located on the border of the disturbance, a few metres from the active scar. Systems measures carbon-dioxide exchange from the disturbance and the surrounding undisturbed tundra. Background photo: A. Cassidy, UBC Geography.
Results of this measurement campaign have been published in Biogeosciences, 13, 2291-2303.
Part of album "Carbon fluxes over permafrost disturbances in the High Arctic".
This visualization shows the wave functions of hydrogen when the principal quantum number, N, is between 1 and 2. The wave function is the solution of the Schrödinger equation and describes the electron in its wave form. Yellow and red colors show positive, while blue and purple denote negative values. Its complex square is the probability density, which actually shows where the electron might be found in the atom when measured.
That visualization can be found here: www.flickr.com/photos/188522613@N05/49924325132/in/datepo...
Update: 2020/06/22: A 16k version is now available.
From: www.connectedaction.net
Link: www.flickr.com/photos/marc_smith/6903608625/sizes/l/
Data set: nodexlgraphgallery.org/Pages/Graph.aspx?graphID=415
These are the connections among the Twitter users who recently tweeted the word obama AND SOTU when queried on January 25, 2012, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another. The data set starts on 1/25/2012 13:41 and ends on 1/25/2012 14:07 UTC. Green lines are "follows" relationships, blue lines are "reply" or "mentions" relationships.
Layout created with the "Group Layout" feature of NodeXL which tiles bounded regions for each cluster. The Harel-Koren layout algorithm positioned each vertex: en.wikipedia.org/wiki/Force-based_algorithms_(graph_drawing).
Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color. Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111
A larger version of the image is here: www.flickr.com/photos/marc_smith/6903608625/sizes/l/
Betweenness Centrality is defined here: en.Wikipedia.org/wiki/Centrality#Betweenness_centrality
Top most between users:
@time
@ronpaul
@dougbenson
@borowitzreport
@theatlantic
@thinkprogress
@cbsnews
@yahoonews
@markknoller
@foxnation
Top word pairs by frequency of mention
last, night, 117
president, obama, 108
union, address, 63
union, speech, 53
ron, paul, 51
paul, responds, 48
#sotu, #gop, 45
#tcot, #ronpaul, 45
#sotu, speech, 39
obama, made, 36
second, term, 34
#sotu, address, 34
great, speaker, 31
obama, plagiarized, 30
million, people, 28
unemployed, #sotu, 28
#sotu, #jobs, 28
full, text, 26
barack, obama, 25
Graph Metric, Value
Graph Type, Directed
Vertices, 1000
Unique Edges, 2653
Edges With Duplicates, 690
Total Edges, 3343
Self-Loops, 718
Connected Components, 264
Single-Vertex Connected Components, 256
Maximum Vertices in a Connected Component, 728
Maximum Edges in a Connected Component, 3038
Maximum Geodesic Distance (Diameter), 8
Average Geodesic Distance, 3.285081
Graph Density, 0.002319319
Modularity, 0.440592
NodeXL Version, 1.0.1.199
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/ and here:
www.nodexlgraphgallery.org/Pages/Default.aspx
A gallery of NodeXL network data sets is available here: nodexlgraphgallery.org/Pages/Default.aspx?search=twitter
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) - which is dedicated to open tools, open data, and open scholarship.
Donations to support NodeXL are welcome through PayPal: www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_bu...
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.
Marc Smith on Twitter.
Here's a quick and dirty attempt to automatically determine arterial streets without any prior knowledge about the arterial status of a street. The idea is: a shortest path tree is taken for a bunch of random points, and the trunkyness of each branch was determined. The trunkynesses of the roads are summed, and this is the resulting map. It sort of works.
Although originally designed to measure atmospheric water vapor and temperature profiles for weather forecasting, data from the Atmospheric Infrared Sounder (AIRS) instrument on NASA's Aqua spacecraft are now also being used by scientists to observe atmospheric carbon dioxide.
This visualization shows Aqua/AIRS mid-tropospheric carbon dioxide from July 2003. Low concentrations, 360 ppm, are shown in blue and high concentrations, 385 ppm, are shown in red. Notice that despite carbon dioxide's high degree of mixing, the regional patterns of atmospheric sources and sinks are still apparent in mid-troposphere carbon dioxide concentrations.
In the southern hemisphere the jet stream flow is more directly West to East, and during the period from July to October the carbon dioxide concentration is enhanced in a belt delineated by the jet stream and lofting of carbon dioxide into the free troposphere by the high Andes is visible in this period. The zonal flow of carbon dioxide around the globe at the latitude of South Africa, southern Australia and southern South America is readily apparent.
______________________________________________________________________
About AIRS
The Atmospheric Infrared Sounder, AIRS, in conjunction with the Advanced Microwave Sounding Unit, AMSU, sense emitted infrared and microwave radiation from the Earth to provide a three-dimensional look at Earth's weather and climate. Working in tandem, the two instruments make simultaneous observations all the way down to the Earth's surface, even in the presence of heavy clouds. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, 3-dimensional map of atmospheric temperature and humidity, cloud amounts and heights, greenhouse gas concentrations, and many other atmospheric phenomena. The AIRS and AMSU fly onboard NASA's Aqua spacecraft and are managed by the Jet Propulsion Laboratory, Pasadena, California, under contract to NASA. JPL is a division of the California Institute of Technology in Pasadena.
Credit
NASA/JPL AIRS Project
NASA/Goddard Space Flight Center Scientific Visualization Studio
Download the image
Various sizes of the image are available, and there are two ways to download:
1) Right-click on the image. Click on a size next to "View all sizes".
2) Click on the "Actions" menu located above the image. Select "View all sizes".
Resources
Additional formats and stills ›
Atmospheric Infrared Sounder web site ›
How to get AIRS data
Ascent Penthouse
Client: Mr Dung - IAM Architecture
---
@ Long Nguyen & Thu Nguyen
Architecture - Interior Design & 3D Visualization
0979 962 864, Ho Chi Minh City
advlongnguyen@gmail.com
Money plays a central role to life in EVE. What does 200B ISK in damage actually look like? When I lose a Battleship, how much is that setting me back? How many Logistics ships could you buy instead of one Dreadnaught?
More details on how I collected the data, and why, in my post on this visualization.
You'll need to zoom in on this to see the details.
This image is part my new blog about EVE Online that tries to explain and document the world, with a focus on making it accessible for non-players and new players.
Visualization of ragas based on 1000+ features derived from:
- vadi and samvadi
- which notes are included
- bigram and tetragrams in the raga
- distinction of all the above features wrt aaroha vs avaroha
- distinction of all the above features wrt extended notes
- distinction of all the above features wrt second-octave notes
Raw data is available as json and csv:
github.com/kylemcdonald/ragaDB/blob/master/ragasdb/ragas....
github.com/kylemcdonald/ragaDB/blob/master/ragasdb/ragas.csv
And a script is available for generating the derived features: github.com/kylemcdonald/ragaDB/blob/master/ragasdb/make-t...
Layout and coloring was found using t-sne, with scripts in this repository github.com/kylemcdonald/EmbeddingScripts
There are more variations on the visualization above with different parameters (and varying accuracy in representing the space) here: github.com/kylemcdonald/ragaDB/tree/master/tsne
June 18, 2013 - NREL Senior Scientists Ross Larsen and Travis Kemper examine a molecular model of Polymeric organic nitroxide radical (PTMA) film for battery applications using a 3D model at the Insight Collaboration Laboratory during a tour of the Energy Systems Integration Facility (ESIF) at the National Renewable Energy Laboratory (NREL) in Golden, Colorado. (Photo by Dennis Schroeder / NREL)
visualization in-progress - this is a dataset of twitter messages taken from the #140conf that jeff pulver organized in Tel Aviv last December.
I'm playing with a new theme for my blog, and wanted a better way to let people quickly scan for how many posts I had. So I used a background image of black, offset with background-position in css to let a graph "show through" to show the count.
I've also posted the PHP and CSS code for WordPress to make this work: http://gist.github.com/304290
It looks better larger: http://www.flickr.com/photos/artlung/4356884087/sizes/o/