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Nyen-Gyud Samten Choe-Ling
April 13, 2009
This short documentary film is dedicated to all
those brave martyrs who sacrificed their precious
lives for the Free Tibet cause. Even though exile
Tibetans doesn't have the resources like China,
Tibetans in exile came up as a winner and
Challenge the People republic of China. They may
build some infrastructures in Tibet but we came
up as a winner in education for Tibetan children.

subset: commoncrawl


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Basilique St-Denis
Time Out says
Friendly warning! We're working hard to be accurate. But these are unusual times, so please check that venues remain open.
Legend has it that when St Denis was beheaded, he picked up his noggin and walked with it to Vicus Catulliacus (now St-Denis) to be buried. The first church, parts of which can be seen in the crypt, was built over his tomb in around 475. The present edifice was begun in the 1130s by Abbot Suger, the powerful minister of Louis VI and Louis VII. It is considered the first example of Gothic architecture, uniting the elements of pointed arches, ogival vaulting and flying buttresses. In the 13th century, master mason Pierre de Montreuil erected the spire and rebuilt the choir nave and transept. St-Denis was the burial place for all but three French monarchs between 996 and the end of the ancien régime, so the ambulatory is a museum of French funerary sculpture. It includes a fanciful Gothic tomb for Dagobert, the austere effigy of Charles V, and the sculpted Renaissance tomb of Louis XII and his wife Anne de Bretagne. In 1792 these tombs were desecrated, and the royal remains thrown into a pit.
1 rue de la Légion d'Honneur
|Métro line 13 to Basilique St-Dénis
|€7.50; €4.50 reductions; free under-18s. PMP
|Opening hours:
|Apr-Sept 10am-6.15pm Mon-Sat; noon-6.15pm Sun. Oct-Mar 10am-5.15pm Mon-Sat; noon-5.15pm Sun. Tours 10.30am, 3pm Mon-Sat; 12.15pm, 3pm Sun


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Tue, 13 June 2017
- Mizuho Drops Apple from "Buy" to "Neutral"
- Drexel Hamilton: Don't Lump Apple in with FANGs
- Sacconaghi: China iPhone Sales Trouble Just a "Pause"
- Apple Allows Discounts on Older iPhones in India
- Foxconn CEO Says Apple and Dell in on its Toshiba NAND Bid
- Apple Hires Stanford's Dr. Sumbul Desai for Digital Health Team
- Blue Apron: A better way to cook. Get your first three meals free by going to BlueApron.com/MACOSKEN
- Power Mac OS Ken through Patreon at Patreon.com/macosken!
- Send me an email: firstname.lastname@example.com or call (716)780-4080!
Direct download: macosken170613.mp3
Category:Tech News -- posted at: 2:42am EDT


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Micro Football Fun Award
height approx 3"
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Ivor Baker Awards | Unit 2, Rear of Longreach, Bramshill Road, Eversley, Hampshire, RG27 0PS | All prices are in GBP | Copyright 2020 Ivor Baker Awards | Sitemap


text: Rhythm, balance, lyricism and tone in Betsy Eby's paintings are influenced by her lifelong devotion to music. Her paintings are informed by the natural world, drawing on forms found on both microscopic and macroscopic scales. Her calligraphic compositions draw on the movement of living things and visual forms echoed throughout nature.


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Are you not watching Breaking Bad? That's a damn shame because it's probably the best show on television right now. I'm still shaken from last night's season three finale. The show just has such a sublime blend of emotional weight, dark comedy, heart-pounding action, and powerhouse performances that it demands to be seen.
If you've been holding off on watching the show, you can now get Season 1 for $12.49 and Season 2 for $17.49 at Amazon.com for the grand total of $29.98. This is a show worth buying sight-unseen, especially for less than $30. Here's season 1 Breaking Bad – The Complete First Season and here's season 2 Breaking Bad: The Complete Second Season. Last night, Breaking Bad was picked up for a fourth season. Catch up on the show and join in the agonizing wait.


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I thought all hell had broken loose the other night… my worst fear, a Trojan on my PC.
"Oh noes" I cried to the Gods of PC protection, "Please, please make it go away" The error message from AVG told me that in my WoW files there was a Trojan pretending to be a WoW sound file – the hide of it all.
So off I went like a good boy and ran just about every anti-stuff program I had… I even did a Windows Update, just to be sure (not sure why but I was lost in the heady clouds of fear and terror brought about by the threat of VIRUS)
After all the anti-stuff programs had finished, purged my poor machine of the malicious nastiest I went to run WoW…
I clicked it again, up pops the loader, hit play…
So after much contemplation and thought (a day later) Frank suggested I run the repair.exe in the WoW directory (before I uninstall and re-install WoW) – Thank the Gods that one worked!! I can play WoW again… but what of the Trojan… did I kill it? Did it wreck my WoW loader? Was it to blame for all this?
It would seem not.
AVG did it! Yes that is right Grisoft's anti-virus software program killed my much loved WoW. A False Positive they call it… a False Positive… They thought that WoW was bad, evil and nasty… How can AVG sleep at night? How can it heartlessly murder my WoW and sit back and say… "ops False Positive… Gotta watch those ones… hehehe"
Of course WoW is so terrific that it rebuilt itself, pulled the broken limbs back together and reformed into bigger and better WoW… but it was the guardian, the safe guard of all things (at least on my C, D and E drives) that was the culprit, not some evil Trojan thought up in the mind of a drug abused 13 year old – who was told he couldn't play WoW because it was a school night (it could happen – right?).
Joystiq told me the news today… I will now have to sit in judgement on what I shall do to AVG in response to this most horriffic of crimes…
I weep for the millions that have had similar problems and for the few that probably did a full re-install… you will never get those gaming hours back… I trully weep for you.


text: The Keystone XL pipeline is one of the most controversial projects of 2014. It is due to run from Hardisty, Alberta and extend almost 2 thousand kilometres across the US-Canadian border to Steele city Nebraska. And then on to East Texas - where the southern leg of the pipeline is set to become operational in January. Al Jazeera's Andy Gallacher reports from East Texas. SOURCE: Al Jazeera US & Canada Canada United States


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What New Jersey residents need to know about state capital gains taxes
In addition to federal capital gains taxes, some states also levy a state capital gains tax. California has the highest marginal tax rate, which combines both federal and state capital gains, of (37.3%), followed by Oregon (34.9%), and Minnesota (34.85%).
The average state capital gains tax rate is 28.7%, and nine states have no capital gains tax at all.
Click here to see what your state's capital gains tax rates are.
You may also be interested in:
What Taxes Need to Be Filed After Someone Dies?
There are several different taxes that need to be filed after there's been a death. Learn more about which taxes to file and how to file them.
What New Jersey Residents Need To Know About Federal Capital Gains Taxes
When beneficiaries inherit assets, those assets generally receive what's called a "step up"in basis. Understand how this saves beneficiaries taxes on appreciated assets.
How to Get a Tax ID Number for a Trust or Estate in New Jersey
In order to settle an estate or a trust, you'll need to get a tax identification number for it. Find out how to do it.


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The Museum at Fashion Institute of Technology (MFIT) CollectionsState University of New York, New York, United States
Description A descriptive note detailing the content and context of the digital collection.
A division of the Fashion Institute of Technology, the Museum at FIT (MFIT) is one of the specialised fashion museums accredited by the American Alliance of Museum. The museum's permanent collection contains about 50,000 garments and accessories from the 18th century to present. The works include pertinent designers such as Chanel, Dior and Balenciaga. Users can search or browse by time periods and types.
Collection Type Broad terms that define the type of digital collection
Subject Broad terms or phrases that describe, identify, or interpret the digital image collection and what it depicts or expresses. Values were obtained using the Art and Architecture Thesaurus (AAT) by the Getty Research Institute.
Style Period Terms that define the styles, art periods, movements, etc. whose characteristics are represented in digital image collection. Values were obtained using the Art and Architecture Thesaurus (AAT) by the Getty Research Institute
Work Types Terms that identify the kinds of works in the digital collection being described. It typically refers to a work's physical form, function, or medium in digital image collection. Values were obtained using the Thesaurus For Graphic Materials (TGM) by Library of Congress.
Openness Openness of the digital collection determined using the 4R activities by Wiley (2010).
Notes on Usage Additional notes on the terms of use on the digital images in the collection.
Images on this site are made available "for educational and personal use that is non-commercial in nature", as defined under Fair Use of the United States copyright laws. Images cannot be modified or altered and citation is required. Images should be cited by including the author and source of the website content as they would material from any printed work, and the citations should include the URL, "www.fitnyc.edu/museum".
e.g. Saint Laurent, Yves. Top, 1968. Cotton. New York, Museum at the Fashion Institute of Technology. http://fashionmuseum.fitnyc.edu.
For more information on acknowledging images with other citation styles, refer here.


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So here are our 9 top tips to help get you started..............happy tweeting!
#2 Shorten links on sites such as tinyurl.com or bitly.com & free up additional character space in your tweet.
#3 Don't overload your tweets with #hashtags, it can make the tweet difficult to read & disengage the reader.
#4 Remember, that no one has to share your content on Twitter. If someone does RT or favourite your tweet, remember to thank them.
#5 Beware automation overload! Automated DM, excessive RSS feeds & Facebook to Twitter links make you look like a bot & not you.
#6 Maximise your Twitter profile to tell potential followers about you & your business.
#7 Ensure you connect your company website address to your Twitter profile......you would be amazed how many companies don't!
#9 Timelines that only shout "Me, Me, Me" or "Sell, Sell, Sell" are most likely to turn followers away.


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Author Archives: blogger
Migrate Debian 6+ to another server with minimal downtime
Recently I had to migrate services from a running Debian server to another one, with minimal downtime of services. I usually do this to P2V or V2V Linux systems, as this allows me to resize the new virtual machine to … Continue reading
Posted in Computer, Linux Leave a comment
SNOM phones and presence monitoring
If you have issues with your SNOM phones and presence monitoring (the little LEDs that display when another extension is ringing or busy), then maybe this will help. We had this problem at work. When we checked the SIP traces, … Continue reading
Posted in Computer, VoIP Leave a comment
Nagios and LSI RAID cards
To monitor the status of a LSI RAID card, say for example a Dell PERC card, you will need to install NRPE, sudo, mpt-status and check_mpt.sh . Install sudo and NRPE via your package manager. You can grab mpt-status via … Continue reading
Posted in Computer, Linux Leave a comment
VMware and Windows poor TCP performance
I have had a strange issue lately with Windows servers on VMware vSphere 5.1 hosts. Throughput of TCP connections between some virtual machines were very very slow, barely 10 mbit/s . The behavior was easily reproducible : just start an … Continue reading
Posted in Computer, Microsoft, Virtualization, VMware Leave a comment
Using Windows domain resources while VPN is active with different credentials
If you are frustrated to get your Domain account locked while you are logged on a VPN with different credentials than your Domain credentials, then this is for you. Edit the file %userprofile%\AppData\Roaming\Microsoft\Network\Connections\Pbk\rasphone.pbk with your favorite text editor. All the … Continue reading
Posted in Computer, Microsoft, Networking Leave a comment
Exim GnuTLS Diffie-Hellman errors
I have been getting increasing number of errors in my Exim log related to GnuTLS Diffie-Hellman prime number. That prime number being too small causes Exim to abort the connection and initiate a new one to send the email over … Continue reading
Posted in Computer, Linux, Networking Tagged email, exim, security Leave a comment
rsyslogd auto-configuration
To have rsyslogd automatically create directories and files with whatever you send at it, just put this in your rsyslog.conf file (or a .conf file in /etc/rsyslog.d): # provide UDP syslog reception $ModLoad imudp $UDPServerRun 514 $template DynFile,"/var/log/syslogs/%fromhost-ip%-%fromhost%/%$YEAR%-%$MONTH%-%syslogfacility-text%" # log … Continue reading
Posted in Computer, Linux Tagged linux, monitoring, network, ubuntu Leave a comment
Active Directory export script to import into SME Server
I had to migrate users from an Active Directory/Exchange combo to a SME server for temporary disaster recovery event. Here's the script I wrote to create the export and recreate the users and their aliases in the SME server. The … Continue reading
VMware vCSA 5.1 password expiration
A few months ago, I was unable to login on my vCSA. At that time, I thought it ws a glitch, I rebooted (didn't fix the issue) and changed the password of the users, which fixed the issue. Recently, I … Continue reading
Posted in Computer, Linux, Virtualization, VMware Tagged security, suse, vcenter, vcsa Leave a comment
Nagios and Exim queue size
A simple script to check Exim queue size for Nagios NRPE: #!/bin/bash #arg1 = warning count #arg2 = critical count if [ -z "$1" ] || [ -z "$2" ] then echo "Usage: ${0} warningcount criticalcount" else if … Continue reading


text: Sage Vaughn | Wild Flowers 29/04/2015 Blueshift New York presents "Wild Flowers" a solo exhibition by ANP Artist Sage Vaughn. This new body of work is a meditation on gardens and our incessant desire to control nature and fabricate beauty. The garden serves as metaphor for the natural beauty that strangely coexists with our inherent decline and mortality. Opening Reception is May 6th from 6-8pm and will be on view till June 21st. Blueshift New York196 BoweryNew York, NY 10012For more info call 212.219.4095 or email [email protected]


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Go Karting Near Eston, Redcar & Cleveland
Every Adventure ® » Go Karting » Near Eston, Redcar & Cleveland
Lowest price £29.99 near Eston, Redcar & Cleveland
Durham, County Durham
£ 29.998
Taking on a plethora of sharp bends and breathtaking straights, your driving skills will be put to the test when you visit our epic circuit in Durham. As you race around our 200m indoor track, you can...
- 200m Indoor track
- 200cc karts
commute22 miles
Latest Go Karting Reviews For Eston, Redcar & Cleveland
"The venue was superb and i went with group booking and they enjoyed it.
The track was great the lads loved it.
Would recomend it to others.
I plan a event every year and had noproblems with the venue at all."
Thomas Little , Near Eston, Redcar & Cleveland
"We had the best day ever so much fun. The staff in and off the track were amazing very friendly took so many photos and videos for us all amazing experience defanitly go again x "
Jade Morris, Near Eston, Redcar & Cleveland
"Great day had by all."
oliver neal, Near Eston, Redcar & Cleveland
Go Karting Close to Eston, Redcar & Cleveland
Your session at the Eston, Redcar & Cleveland circuit of your choosing will begin with a quick safety briefing, before it is time to get changed and strap into one of our high speed karts. With 2 karting circuits serving Eston, Redcar & Cleveland to choose from and the closest being only 2 miles from Eston, Redcar & Cleveland center you really are spoilt for choice. You may not be driving like Lewis Hamilton straight away, but with the help of our instructors, you'll be more that ready to beat your mates to the finish line.
Eston, Redcar & Cleveland Go Karting facts
- Beginners Welcome
- No Licence Required
- 148 Go Karting Locations across The UK and Ireland
- The closest Go Karting circuit to Eston, Redcar & Cleveland is only 2 miles away.
- 2 Go Karting venues within 31 miles of Eston, Redcar & Cleveland
- Available for all ages
- Prices From £15.00 across The UK and Ireland
- Lowest price of Go Karting near Eston, Redcar & Cleveland is £29.99
- Minimum Age for Go Karting around Eston, Redcar & Cleveland is from 8 years old.
Frequently Asked Questions
How many Go Karting locations are there in Eston?
There are 2 Go Karting centers to choose from within 30 miles of Eston.
How much does it cost to do Go Karting in Eston?
The average price of Go Karting in Eston is £43.28. The lowest price is £29.99, with the most expensive option being £70.00.
What is the minimum age for Go Karting in Eston?
The minimum age for Go Karting in Eston is 16 years old.
What is the average time needed for Go Karting?
In Eston, the average time needed for a Go Karting session is between 30 and 60 minutes.
Where is the closest Go Karting center to Eston?
The closest Go Karting center to Eston is at Middlesbrough, Teesside and is 1.9 miles away.
Customer First Experiences 100% enjoyment from beginning to end
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We work with thousands of providers across the UK to find the best value experiences on the market.
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Find your adventure, book your adventure and then manage your bookings easily on our website or mobile apps.
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If you change your mind within 24 hours of making a booking we offer 100% money back guarantee. Applies to bookings made at least 14 days in advance of event date.
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Adventures Near Eston, Redcar & Cleveland
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Features and Benefits:
High-power, long-life motor offers optimal performance and durability. Kit Includes: (1) D1410 screwdriver (2) BL1203 12V Li-ion batteries, (1) BC1110 12V charger, (1) tool bag. The Ingersoll Rand D1410 IQV12 Series™ screwdriver aids assembly and disassembly operations with a magnetic, pin-detent bit holder that makes changing bits a fast, one-handed operation, so operators can Do the Job, Do it Fast, Do it Every Time.


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By Shelly Harris
University of Hawai`i system officials are working side-by-side with the Hawai`i Board of Education and Department of Education to ensure public-school students are better prepared for the competitive and arduous task of succeeding in college.
UH President David Lassner is helping to strengthen the pipeline to higher education by serving as a member of the recently formed Advisory Group to the Hawaii Board of Education Search Committee for Superintendent. The BOE says the Advisory Group is composed of "individuals representing stakeholder groups, such as parents, teachers, and school administrators."
A Feb. 14 UH press release said Lassner "encourages input from the university" in the process to select a replacement for outgoing Superintendent Kathryn Matayoshi.
"Members of the UH community have a unique perspective on the state's only public school system: many UH students are recent graduates who understand how public school prepared them for higher education; the faculty have many public school graduates in their classes and labs; and many faculty and staff are parents of public school students," the release said.
The Advisory Group was established by the Search Committee to assist the committee in identifying final candidates for superintendent, according to BOE website. Lassner was unavailable for comment at press time and didn't attend the BOE General Business Meeting on Tuesday night on O`ahu.
In an interview with Ho`oulu on Tuesday night, UH Director for Communications Dan Meisenzahl said Lassner has no direct role in the hiring decision of the new superintendent and is not part of the BOE's policy-making process.
Meisenzahl said Lassner's participation is similar to other UH-DOE partnerships. For instance, UH Manoa's School of Education collaborates with many Hawai`i public schools.
The university leadership recognizes collaboration with the public-school system is important to Hawai`i's students, Meisenzahl said.
New and innovative teaching methods and curricula are made possible through the Hawai`i P-20 Partnerships for Education program. Hawaii P-20 exposes elementary-school students to the university, "so more Hawaii students are thinking of college from a young age," Meisenzahl said.
The UH system also provides "dual credit" classes, which allows many high-school students to take college-level classes, saving both time and finances spent on college.
Dr. Patricia Halagao, an associate professor at the UH Manoa College of Education, serves with Lassner on the Advisory Group. She jointly submitted written testimony for the BOE's meeting on Tuesday night, successfully advocating for an emphasis on experience in "educational equity" in the new job description for state superintendent.
The testimony signatories included Cheri Nakamura, who also serves as a member of the Advisory Group and is Director of the Hui for Excellence in Education, a coalition committed to working collaboratively to identify opportunities to improve public education in Hawai`i. Nakamura, a graduate of UH Manoa's Shidler College of Business, attended Tuesday's BOE meeting and provided an oral version of the testimony.


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ASAP offering dance, music collaboration
Under the direction of professional teaching artists, students in grades six to 12 will collaboratively create dance and music pieces which will later be performed in their communities.
ASAP will provide transportation for students from regional school districts 12 and 14 to the Waterbury Arts Magnet School, where the project will be offered.
Master percussionist John Marshall will lead the musical portion of the project, while Pilobolus performers will teach the dance portion of the project.
A "talk back" time will follow the performances, which will be held Feb. 1 at several locations.
For more information, call (860) 868-0740.


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Cyber Intelligence Centre has been saved
Cyber Intelligence Centre
Business relevant insight and client-centric intelligence
Deloitte's Cyber Intelligence Centre integrates state-of-the-art technology with industry insight to provide round-the-clock business-focused operational security.
Explore content
The rapid pace of digital development allows organisations to develop new business models, products and services. While this digital revolution has evolved the way we do business, it has also created sophisticated and complex security issues.
Business assets that were once physical are now accessible online, in the cloud and from multiple, individual, bring-your-own (BYO) devices. Customer databases and portals are vulnerable to cyber-threats with cyber criminals presenting new opportunities for theft and fraud.
If your business is online, it is a target for cyber-attacks. With relevant insight and business intelligence you can prepare to address these cyber threats before they arise and respond quickly and effectively.
This is where Deloitte's Cyber Intelligence Centre can help. Integrating state-of-the-art technology with industry insight, Deloitte's Cyber Intelligence Centre provides round-the-clock business-focused operational security.
With 24x7 coverage, we monitor and assess cyber-threats specific to your business, using our globally connected network of cyber intelligence centres, enabling you to swiftly and effectively mitigate risk and strengthen your businesses cyber resilience.
Cyber Intelligence Center service solutions:
Deloitte's Cyber Intelligence Centre integrates leading technology with the experience and knowledge of our cyber-security analysts to provide relevant business intelligence to help protect your organisation against cyber-threats. The Cyber Intelligence Center offers clients the following services and solutions:
- Cyber Watch
Adopt an intelligence-led approach to managing cyber-risk. Cyber Watch provides advanced online internet surveillance and reconnaissance so you're aware of active or growing external threats targeting your organisation and detailed information leakage. Find out more
- Cyber Monitor
Building on Cyber Watch, Cyber Monitor provides an advanced security information and event management (SIEM) solution that consumes and monitors your logs for the presence of compliance and cyber-security issues providing the added internal threat analysis and business context to enable response. Find out more
- Cyber Respond
Effective outsourced retained or on-demand cyber-incident response service coordinating fast, thorough, decisive response activity including advanced malware, cyber incident response team (CIRT) and forensic investigation capability to minimise the impact of a breach and likelihood of future cyber-attacks. Find out more
- Cyber Check
Cyber Check conducts personalised online vulnerability scanning and management to discover and fix these vulnerabilities before hackers do, allowing you to secure critical business assets. Find out more
- Cyber Govern
A managed governance, risk and compliance capability, which can capture, coordinate, and assess the security risk and compliance issues within a business, as well as benchmark across industries.
- Cyber Prepare
Methods, processes, capability and enablers to ensure cyber readiness and preparation at all levels of the organisation. This includes executive reporting, resiliency, and other programs.
- Cyber Protect
A data loss preventions and advanced persistent threat (APT) solution that monitors sensitive information as it flows across and out of your network alerting you to compliance or cyber security issues.
- Digital Identity Services
A managed identity service that provides organisations with effective and efficient lifecycle processes, privileged access management and also builds online digital trust between an organisation and it's customers.
Key benefits to your organisation
Whether you are looking for a fully managed cyber security solution or a way to replace or augment your existing systems, Deloitte's Cyber Intelligence Centre can help you do the following:
Improve security: By adopting a risk-based approach to cyber crime prevention, you gain access to timely, actionable threat intelligence and position your business to improve the effectiveness of its security controls.
Become more proactive: With a customised approach to cyber intelligence that takes your specific environment into account, you can more readily predict and prevent security incidents, strengthen your organisation's threat management profile and reduce vulnerabilities to criminal attacks.
Heighten your resilience: Some cyber incidents can cause serious business crises. If you enhance your ability to detect, manage and respond to threats, you can minimise losses and get back to business-as-usual faster.
Cover off cyber risks — from end-to-end: With access to skilled resources, proven processes and effective tools that cross the entire security spectrum, you can keep pace with a constantly evolving threat landscape and proactively monitor, manage and respond to incidents.
Global scope, customised focus
Identified as a Kennedy Vanguard Leader for having the most comprehensive competency strengths across the cyber spectrum, Deloitte provides security services to some of the world's largest organisations.*
Globally, we have over 900 Certified Information Systems Security Professionals (CISSP), 1,500 Certified Information Systems Auditors (CISA), 150 Certified Information Security Managers (CISM) and 65 Certified International Privacy Professionals (CIPP). We also have Cyber Intelligence Centres strategically located across Australia and around the world.
The combination of our team, methodologies and cumulative experience offer the people, processes, tools and technology to help your organisation manage the full spectrum of cyber threats and risks.
* Source: Kennedy Consulting Research & Advisory; Cyber Security Consulting 2013; Kennedy Consulting Research & Advisory estimates © 2013 Kennedy Information, LLC. Reproduced under license


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Welcome to MadModder!
Only about 2 months late.
Now that has released some hobby time ehh....?
And to cure Bernds fetish for fingerprints, the second shot shows that I am not anonymous, but do in fact have paw prints.Bogs
I have got hands like a gorilla.
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On the recent episode of Star King aired on Febuary 6th, SHINee's MinHo is forced to choose his favorite SNSD member. Choosing in a member vs. member style, the final two was SeoHyun vs. Yuri. In the end he chose SeoHyun.
Everyone loves maknae these days. ^^
Filed under: kpop, shinee, snsd, tv | Tagged: kpop, MinHo, shinee, snsd, star king | 2 Comments »


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Podcast: Download
Two segments on this episode of the podcast; the first [6 mins. 50 secs.] with documentary filmmakers Alex Hammond & Ian Mankiewicz (Better Than Something: Jay Reatard) are back with a new film about Mexican Lucha Libre wrestlers called Lucha Mexico. Currently in theaters in NYC, LA plus 20 more cities around the US. Also, actor David Call returns to the podcast with the director of a new film, Americana, Zachary Shedd. The noir drama will be at Montreal's Fantasia International Film Festival on July 31st.
This episode of the podcast is sponsored by Zipcar.com. Earn $25 of free driving credit at joinzipcar.com/filmwaxradio.
Filmwax Radio is part of the ShowBriz Studios podcast network and presented by Rooftop Films. Like us on Facebook.


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Who is Robert Zinsser?
Get to know what makes him tick!
Robert is President of Get in the Game SE, a professional sales organization that offers a broad range of products related to optimizing organizational efficiency and creating positive workplaces. Get In The Game SE provides services to build businesses with a system to achieve dramatic and permanent improvements in sales productivity.
Robert has proven success helping organizations succeed through developing their people. He applies his business development skills in situational analysis to identify needs and leverage skill building as a springboard to innovation, best practices, productivity, and sales skills development. By his peers, he is known for accepting challenging projects while developing a process to achieve desired results.
- Builds and leverages relationships to get work done
- Proactive in driving to reach goals
- Eager for results, swift implementation, resourcefully works around most obstacles blocking completion of what needs accomplished
- Impatient for results
- Invigorating impact on people
Robert recently joined Fortegra in Jacksonville, FL as a Client Delivery Specialist. He is looking forward to contributing to this new opportunity in many ways both internally and externally
Who I Help
I established the Dugout Club to provide Sales Professionals with a convenient and affordable resource to improve their Sales Performance. We do this by working on specific issues unique to the individual, not an "off the shelf" approach.
Every Sales Professional is at a different stage in their career, their focus is on many different industries, and the environment in which they sell is unique to their company, their product, and their approach.
The Dugout Club is delivered in three different monthly membership levels, depending on the needed coaching and extensiveness of work to be done. Visit the homepage to learn more.
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8 Case study: mining NASA metadata
There are over 32,000 datasets hosted and/or maintained by NASA; these datasets cover topics from Earth science to aerospace engineering to management of NASA itself. We can use the metadata for these datasets to understand the connections between them.
What is metadata? Metadata is a term that refers to data that gives information about other data; in this case, the metadata informs users about what is in these numerous NASA datasets but does not include the content of the datasets themselves.
The metadata includes information like the title of the dataset, a description field, what organization(s) within NASA is responsible for the dataset, keywords for the dataset that have been assigned by a human being, and so forth. NASA places a high priority on making its data open and accessible, even requiring all NASA-funded research to be openly accessible online. The metadata for all its datasets is publicly available online in JSON format.
In this chapter, we will treat the NASA metadata as a text dataset and show how to implement several tidy text approaches with this real-life text. We will use word co-occurrences and correlations, tf-idf, and topic modeling to explore the connections between the datasets. Can we find datasets that are related to each other? Can we find clusters of similar datasets? Since we have several text fields in the NASA metadata, most importantly the title, description, and keyword fields, we can explore the connections between the fields to better understand the complex world of data at NASA. This type of approach can be extended to any domain that deals with text, so let's take a look at this metadata and get started.
8.1 How data is organized at NASA
First, let's download the JSON file and take a look at the names of what is stored in the metadata.
## [1] "_id" "@type" "accessLevel" "accrualPeriodicity"
## [5] "bureauCode" "contactPoint" "description" "distribution"
## [9] "identifier" "issued" "keyword" "landingPage"
## [13] "language" "modified" "programCode" "publisher"
## [17] "spatial" "temporal" "theme" "title"
## [21] "license" "isPartOf" "references" "rights"
## [25] "describedBy"
We see here that we could extract information from who publishes each dataset to what license they are released under.
It seems likely that the title, description, and keywords for each dataset may be most fruitful for drawing connections between datasets. Let's check them out.
## [1] "character"
## [1] "character"
## [1] "list"
The title and description fields are stored as character vectors, but the keywords are stored as a list of character vectors.
8.1.1 Wrangling and tidying the data
Let's set up separate tidy data frames for title, description, and keyword, keeping the dataset ids for each so that we can connect them later in the analysis if necessary.
nasa_title <- tibble(id = metadata$dataset$`_id`$`$oid`,
title = metadata$dataset$title)
## # A tibble: 32,089 x 2
## id title
## <chr> <chr>
## 1 55942a57c63a7fe59b495a77 15 Minute Stream Flow Data: USGS (FIFE)
## 2 55942a57c63a7fe59b495a78 15 Minute Stream Flow Data: USGS (FIFE)
## 3 55942a58c63a7fe59b495a79 15 Minute Stream Flow Data: USGS (FIFE)
## 4 55942a58c63a7fe59b495a7a 2000 Pilot Environmental Sustainability Index (ESI)
## 5 55942a58c63a7fe59b495a7b 2000 Pilot Environmental Sustainability Index (ESI)
## 6 55942a58c63a7fe59b495a7c 2000 Pilot Environmental Sustainability Index (ESI)
## 7 55942a58c63a7fe59b495a7d 2001 Environmental Sustainability Index (ESI)
## 8 55942a58c63a7fe59b495a7e 2001 Environmental Sustainability Index (ESI)
## 9 55942a58c63a7fe59b495a7f 2001 Environmental Sustainability Index (ESI)
## 10 55942a58c63a7fe59b495a80 2001 Environmental Sustainability Index (ESI)
## # … with 32,079 more rows
These are just a few example titles from the datasets we will be exploring. Notice that we have the NASA-assigned ids here, and also that there are duplicate titles on separate datasets.
nasa_desc <- tibble(id = metadata$dataset$`_id`$`$oid`,
desc = metadata$dataset$description)
nasa_desc %>%
select(desc) %>%
## # A tibble: 5 x 1
## desc
## <chr>
## 1 "The objective of this proposal is to create new gas chromatographs (GCs)\nfor contaminant monito…
## 2 "3GPROF products provide global gridded monthly/daily precipitation averages from multiple satell…
## 3 "MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the\nTerra (…
## 4 "MODIS/Terra Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG"
## 5 "The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to\nthe measur…
Here we see the first part of several selected description fields from the metadata.
Now we can build the tidy data frame for the keywords. For this one, we need to use
unnest() from tidyr, because they are in a list-column.
nasa_keyword <- tibble(id = metadata$dataset$`_id`$`$oid`,
keyword = metadata$dataset$keyword) %>%
## # A tibble: 126,814 x 2
## id keyword
## <chr> <chr>
## 1 55942a57c63a7fe59b495a77 EARTH SCIENCE
## 2 55942a57c63a7fe59b495a77 HYDROSPHERE
## 3 55942a57c63a7fe59b495a77 SURFACE WATER
## 4 55942a57c63a7fe59b495a78 EARTH SCIENCE
## 5 55942a57c63a7fe59b495a78 HYDROSPHERE
## 6 55942a57c63a7fe59b495a78 SURFACE WATER
## 7 55942a58c63a7fe59b495a79 EARTH SCIENCE
## 8 55942a58c63a7fe59b495a79 HYDROSPHERE
## 9 55942a58c63a7fe59b495a79 SURFACE WATER
## 10 55942a58c63a7fe59b495a7a EARTH SCIENCE
## # … with 126,804 more rows
This is a tidy data frame because we have one row for each keyword; this means we will have multiple rows for each dataset because a dataset can have more than one keyword.
Now it is time to use tidytext's
unnest_tokens() for the title and description fields so we can do the text analysis. Let's also remove stop words from the titles and descriptions. We will not remove stop words from the keywords, because those are short, human-assigned keywords like "RADIATION" or "CLIMATE INDICATORS".
nasa_title <- nasa_title %>%
unnest_tokens(word, title) %>%
nasa_desc <- nasa_desc %>%
unnest_tokens(word, desc) %>%
These are now in the tidy text format that we have been working with throughout this book, with one token (word, in this case) per row; let's take a look before we move on in our analysis.
## # A tibble: 210,914 x 2
## id word
## <chr> <chr>
## 1 55942a57c63a7fe59b495a77 15
## 2 55942a57c63a7fe59b495a77 minute
## 3 55942a57c63a7fe59b495a77 stream
## 4 55942a57c63a7fe59b495a77 flow
## 5 55942a57c63a7fe59b495a77 data
## 6 55942a57c63a7fe59b495a77 usgs
## 7 55942a57c63a7fe59b495a77 fife
## 8 55942a57c63a7fe59b495a78 15
## 9 55942a57c63a7fe59b495a78 minute
## 10 55942a57c63a7fe59b495a78 stream
## # … with 210,904 more rows
## # A tibble: 2,677,811 x 2
## id word
## <chr> <chr>
## 1 55942a57c63a7fe59b495a77 usgs
## 2 55942a57c63a7fe59b495a77 15
## 3 55942a57c63a7fe59b495a77 minute
## 4 55942a57c63a7fe59b495a77 stream
## 5 55942a57c63a7fe59b495a77 flow
## 6 55942a57c63a7fe59b495a77 data
## 7 55942a57c63a7fe59b495a77 kings
## 8 55942a57c63a7fe59b495a77 creek
## 9 55942a57c63a7fe59b495a77 konza
## 10 55942a57c63a7fe59b495a77 prairie
## # … with 2,677,801 more rows
8.1.2 Some initial simple exploration
What are the most common words in the NASA dataset titles? We can use
count() from dplyr to check this out.
## # A tibble: 11,614 x 2
## word n
## <chr> <int>
## 1 project 7735
## 2 data 3354
## 3 1 2841
## 4 level 2400
## 5 global 1809
## 6 v1 1478
## 7 daily 1397
## 8 3 1364
## 9 aura 1363
## 10 l2 1311
## # … with 11,604 more rows
What about the descriptions?
## # A tibble: 35,940 x 2
## word n
## <chr> <int>
## 1 data 68871
## 2 modis 24420
## 3 global 23028
## 4 2 16599
## 5 1 15770
## 6 system 15480
## 7 product 14780
## 8 aqua 14738
## 9 earth 14373
## 10 resolution 13879
## # … with 35,930 more rows
Words like "data" and "global" are used very often in NASA titles and descriptions. We may want to remove digits and some "words" like "v1" from these data frames for many types of analyses; they are not too meaningful for most audiences.
We can do this by making a list of custom stop words and using
anti_join() to remove them from the data frame, just like we removed the default stop words that are in the tidytext package. This approach can be used in many instances and is a great tool to bear in mind.
my_stopwords <- tibble(word = c(as.character(1:10),
"v1", "v03", "l2", "l3", "l4", "v5.2.0",
"v003", "v004", "v005", "v006", "v7"))
nasa_title <- nasa_title %>%
nasa_desc <- nasa_desc %>%
What are the most common keywords?
## # A tibble: 1,774 x 2
## # Groups: keyword [1,774]
## keyword n
## <chr> <int>
## 2 Project 7452
## 4 Ocean Color 7268
## 5 Ocean Optics 7268
## 6 Oceans 7268
## 7 completed 6452
## # … with 1,764 more rows
We likely want to change all of the keywords to either lower or upper case to get rid of duplicates like "OCEANS" and "Oceans". Let's do that here.
8.2 Word co-ocurrences and correlations
As a next step, let's examine which words commonly occur together in the titles, descriptions, and keywords of NASA datasets, as described in Chapter 4. We can then examine word networks for these fields; this may help us see, for example, which datasets are related to each other.
8.2.1 Networks of Description and Title Words
We can use
pairwise_count() from the widyr package to count how many times each pair of words occurs together in a title or description field.
title_word_pairs <- nasa_title %>%
pairwise_count(word, id, sort = TRUE, upper = FALSE)
## # A tibble: 156,689 x 3
## item1 item2 n
## <chr> <chr> <dbl>
## 1 system project 796
## 2 lba eco 683
## 3 airs aqua 641
## 4 level aqua 623
## 5 level airs 612
## 6 aura omi 607
## 7 global grid 597
## 8 global daily 574
## 9 data boreas 551
## 10 ground gpm 550
## # … with 156,679 more rows
These are the pairs of words that occur together most often in title fields. Some of these words are obviously acronyms used within NASA, and we see how often words like "project" and "system" are used.
desc_word_pairs <- nasa_desc %>%
pairwise_count(word, id, sort = TRUE, upper = FALSE)
## # A tibble: 10,889,084 x 3
## item1 item2 n
## <chr> <chr> <dbl>
## 1 data global 9864
## 2 data resolution 9302
## 3 instrument resolution 8189
## 4 data surface 8180
## 5 global resolution 8139
## 6 data instrument 7994
## 7 data system 7870
## 8 resolution bands 7584
## 9 data earth 7576
## 10 orbit resolution 7462
## # … with 10,889,074 more rows
These are the pairs of words that occur together most often in description fields. "Data" is a very common word in description fields; there is no shortage of data in the datasets at NASA!
Let's plot networks of these co-occurring words so we can see these relationships better in Figure 8.1. We will again use the ggraph package for visualizing our networks.
title_word_pairs %>%
filter(n >= 250) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n, edge_width = n), edge_colour = "cyan4") +
geom_node_point(size = 5) +
geom_node_text(aes(label = name), repel = TRUE,
point.padding = unit(0.2, "lines")) +
We see some clear clustering in this network of title words; words in NASA dataset titles are largely organized into several families of words that tend to go together.
What about the words from the description fields?
desc_word_pairs %>%
filter(n >= 5000) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n, edge_width = n), edge_colour = "darkred") +
geom_node_point(size = 5) +
geom_node_text(aes(label = name), repel = TRUE,
point.padding = unit(0.2, "lines")) +
Figure 8.2 shows such strong connections between the top dozen or so words (words like "data", "global", "resolution", and "instrument") that we do not see clear clustering structure in the network. We may want to use tf-idf (as described in detail in Chapter 3) as a metric to find characteristic words for each description field, instead of looking at counts of words.
8.2.2 Networks of Keywords
Next, let's make a network of the keywords in Figure 8.3 to see which keywords commonly occur together in the same datasets.
keyword_pairs <- nasa_keyword %>%
pairwise_count(keyword, id, sort = TRUE, upper = FALSE)
## # A tibble: 13,390 x 3
## item1 item2 n
## <chr> <chr> <dbl>
## # … with 13,380 more rows
keyword_pairs %>%
filter(n >= 700) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n, edge_width = n), edge_colour = "royalblue") +
geom_node_point(size = 5) +
geom_node_text(aes(label = name), repel = TRUE,
point.padding = unit(0.2, "lines")) +
We definitely see clustering here, and strong connections between keywords like "OCEANS", "OCEAN OPTICS", and "OCEAN COLOR", or "PROJECT" and "COMPLETED".
These are the most commonly co-occurring words, but also just the most common keywords in general.
To examine the relationships among keywords in a different way, we can find the correlation among the keywords as described in Chapter 4. This looks for those keywords that are more likely to occur together than with other keywords for a dataset.
keyword_cors <- nasa_keyword %>%
group_by(keyword) %>%
filter(n() >= 50) %>%
pairwise_cor(keyword, id, sort = TRUE, upper = FALSE)
## # A tibble: 7,875 x 3
## item1 item2 correlation
## <chr> <chr> <dbl>
## # … with 7,865 more rows
Notice that these keywords at the top of this sorted data frame have correlation coefficients equal to 1; they always occur together. This means these are redundant keywords. It may not make sense to continue to use both of the keywords in these sets of pairs; instead, just one keyword could be used.
Let's visualize the network of keyword correlations, just as we did for keyword co-occurences.
keyword_cors %>%
filter(correlation > .6) %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = correlation, edge_width = correlation), edge_colour = "royalblue") +
geom_node_point(size = 5) +
geom_node_text(aes(label = name), repel = TRUE,
point.padding = unit(0.2, "lines")) +
This network in Figure 8.4 appears much different than the co-occurence network. The difference is that the co-occurrence network asks a question about which keyword pairs occur most often, and the correlation network asks a question about which keywords occur more often together than with other keywords. Notice here the high number of small clusters of keywords; the network structure can be extracted (for further analysis) from the
graph_from_data_frame() function above.
8.3 Calculating tf-idf for the description fields
The network graph in Figure 8.2 showed us that the description fields are dominated by a few common words like "data", "global", and "resolution"; this would be an excellent opportunity to use tf-idf as a statistic to find characteristic words for individual description fields. As discussed in Chapter 3, we can use tf-idf, the term frequency times inverse document frequency, to identify words that are especially important to a document within a collection of documents. Let's apply that approach to the description fields of these NASA datasets.
8.3.1 What is tf-idf for the description field words?
We will consider each description field a document, and the whole set of description fields the collection or corpus of documents. We have already used
unnest_tokens() earlier in this chapter to make a tidy data frame of the words in the description fields, so now we can use
bind_tf_idf() to calculate tf-idf for each word.
desc_tf_idf <- nasa_desc %>%
count(id, word, sort = TRUE) %>%
ungroup() %>%
bind_tf_idf(word, id, n)
What are the highest tf-idf words in the NASA description fields?
## # A tibble: 1,913,224 x 6
## id word n tf idf tf_idf
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 55942a7cc63a7fe59b49774a rdr 1 1 10.4 10.4
## 2 55942ac9c63a7fe59b49b688 palsar_radiometric_terrain_corrected_high_res 1 1 10.4 10.4
## 3 55942ac9c63a7fe59b49b689 palsar_radiometric_terrain_corrected_low_res 1 1 10.4 10.4
## 4 55942a7bc63a7fe59b4976ca lgrs 1 1 8.77 8.77
## 5 55942a7bc63a7fe59b4976d2 lgrs 1 1 8.77 8.77
## 6 55942a7bc63a7fe59b4976e3 lgrs 1 1 8.77 8.77
## 7 55942a7dc63a7fe59b497820 mri 1 1 8.58 8.58
## 8 55942ad8c63a7fe59b49cf6c template_proddescription 1 1 8.30 8.30
## 9 55942ad8c63a7fe59b49cf6d template_proddescription 1 1 8.30 8.30
## 10 55942ad8c63a7fe59b49cf6e template_proddescription 1 1 8.30 8.30
## # … with 1,913,214 more rows
These are the most important words in the description fields as measured by tf-idf, meaning they are common but not too common.
Notice we have run into an issue here; both \(n\) and term frequency are equal to 1 for these terms, meaning that these were description fields that only had a single word in them. If a description field only contains one word, the tf-idf algorithm will think that is a very important word.
Depending on our analytic goals, it might be a good idea to throw out all description fields that have very few words.
8.3.2 Connecting description fields to keywords
We now know which words in the descriptions have high tf-idf, and we also have labels for these descriptions in the keywords. Let's do a full join of the keyword data frame and the data frame of description words with tf-idf, and then find the highest tf-idf words for a given keyword.
Let's plot some of the most important words, as measured by tf-idf, for a few example keywords used on NASA datasets. First, let's use dplyr operations to filter for the keywords we want to examine and take just the top 15 words for each keyword. Then, let's plot those words in Figure 8.5.
desc_tf_idf %>%
filter(!near(tf, 1)) %>%
filter(keyword %in% c("SOLAR ACTIVITY", "CLOUDS",
arrange(desc(tf_idf)) %>%
group_by(keyword) %>%
distinct(word, keyword, .keep_all = TRUE) %>%
top_n(15, tf_idf) %>%
ungroup() %>%
mutate(word = factor(word, levels = rev(unique(word)))) %>%
ggplot(aes(word, tf_idf, fill = keyword)) +
geom_col(show.legend = FALSE) +
facet_wrap(~keyword, ncol = 3, scales = "free") +
coord_flip() +
labs(title = "Highest tf-idf words in NASA metadata description fields",
caption = "NASA metadata from https://data.nasa.gov/data.json",
x = NULL, y = "tf-idf")
Using tf-idf has allowed us to identify important description words for each of these keywords. Datasets labeled with the keyword "SEISMOLOGY" have words like "earthquake", "risk", and "hazard" in their description, while those labeled with "HUMAN HEALTH" have descriptions characterized by words like "wellbeing", "vulnerability", and "children." Most of the combinations of letters that are not English words are certainly acronyms (like OMB for the Office of Management and Budget), and the examples of years and numbers are important for these topics. The tf-idf statistic has identified the kinds of words it is intended to, important words for individual documents within a collection of documents.
8.4 Topic modeling
Using tf-idf as a statistic has already given us insight into the content of NASA description fields, but let's try an additional approach to the question of what the NASA descriptions fields are about. We can use topic modeling as described in Chapter 6 to model each document (description field) as a mixture of topics and each topic as a mixture of words. As in earlier chapters, we will use latent Dirichlet allocation (LDA) for our topic modeling; there are other possible approaches for topic modeling.
8.4.1 Casting to a document-term matrix
To do the topic modeling as implemented here, we need to make a
DocumentTermMatrix, a special kind of matrix from the tm package (of course, this is just a specific implementation of the general concept of a "document-term matrix"). Rows correspond to documents (description texts in our case) and columns correspond to terms (i.e., words); it is a sparse matrix and the values are word counts.
Let's clean up the text a bit using stop words to remove some of the nonsense "words" leftover from HTML or other character encoding. We can use
bind_rows() to add our custom stop words to the list of default stop words from the tidytext package, and then all at once use
anti_join() to remove them all from our data frame.
my_stop_words <- bind_rows(stop_words,
tibble(word = c("nbsp", "amp", "gt", "lt",
"timesnewromanpsmt", "font",
"td", "li", "br", "tr", "quot",
"st", "img", "src", "strong",
"http", "file", "files",
lexicon = rep("custom", 30)))
word_counts <- nasa_desc %>%
anti_join(my_stop_words) %>%
count(id, word, sort = TRUE) %>%
## # A tibble: 1,895,310 x 3
## id word n
## <chr> <chr> <int>
## 1 55942a8ec63a7fe59b4986ef suit 82
## 2 55942a8ec63a7fe59b4986ef space 69
## 3 56cf5b00a759fdadc44e564a data 41
## 4 56cf5b00a759fdadc44e564a leak 40
## 5 56cf5b00a759fdadc44e564a tree 39
## 6 55942a8ec63a7fe59b4986ef pressure 34
## 7 55942a8ec63a7fe59b4986ef system 34
## 8 55942a89c63a7fe59b4982d9 em 32
## 9 55942a8ec63a7fe59b4986ef al 32
## 10 55942a8ec63a7fe59b4986ef human 31
## # … with 1,895,300 more rows
This is the information we need, the number of times each word is used in each document, to make a
DocumentTermMatrix. We can
cast() from our tidy text format to this non-tidy format as described in detail in Chapter 5.
## <<DocumentTermMatrix (documents: 32003, terms: 35901)>>
## Non-/sparse entries: 1895310/1147044393
## Sparsity : 100%
## Maximal term length: 166
## Weighting : term frequency (tf)
We see that this dataset contains documents (each of them a NASA description field) and terms (words). Notice that this example document-term matrix is (very close to) 100% sparse, meaning that almost all of the entries in this matrix are zero. Each non-zero entry corresponds to a certain word appearing in a certain document.
8.4.2 Ready for topic modeling
Now let's use the topicmodels package to create an LDA model. How many topics will we tell the algorithm to make? This is a question much like in \(k\)-means clustering; we don't really know ahead of time. We tried the following modeling procedure using 8, 16, 24, 32, and 64 topics; we found that at 24 topics, documents are still getting sorted into topics cleanly but going much beyond that caused the distributions of \(\gamma\), the probability that each document belongs in each topic, to look worrisome. We will show more details on this later.
# be aware that running this model is time intensive
desc_lda <- LDA(desc_dtm, k = 24, control = list(seed = 1234))
## A LDA_VEM topic model with 24 topics.
This is a stochastic algorithm that could have different results depending on where the algorithm starts, so we need to specify a
seed for reproducibility as shown here.
8.4.3 Interpreting the topic model
Now that we have built the model, let's
tidy() the results of the model, i.e., construct a tidy data frame that summarizes the results of the model. The tidytext package includes a tidying method for LDA models from the topicmodels package.
## # A tibble: 861,624 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 suit 1.00e-121
## 2 2 suit 2.63e-145
## 3 3 suit 1.92e- 79
## 4 4 suit 6.72e- 45
## 5 5 suit 1.74e- 85
## 6 6 suit 7.69e- 84
## 7 7 suit 3.28e- 4
## 8 8 suit 3.74e- 20
## 9 9 suit 4.85e- 15
## 10 10 suit 4.77e- 10
## # … with 861,614 more rows
The column \(\beta\) tells us the probability of that term being generated from that topic for that document. It is the probability of that term (word) belonging to that topic. Notice that some of the values for \(\beta\) are very, very low, and some are not so low.
What is each topic about? Let's examine the top 10 terms for each topic.
top_terms <- tidy_lda %>%
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
arrange(topic, -beta)
## # A tibble: 240 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 data 0.0449
## 2 1 soil 0.0368
## 3 1 moisture 0.0295
## 4 1 amsr 0.0244
## 5 1 sst 0.0168
## 6 1 validation 0.0132
## 7 1 temperature 0.0132
## 8 1 surface 0.0129
## 9 1 accuracy 0.0123
## 10 1 set 0.0116
## # … with 230 more rows
It is not very easy to interpret what the topics are about from a data frame like this so let's look at this information visually in Figure 8.6.
top_terms %>%
mutate(term = reorder_within(term, beta, topic)) %>%
group_by(topic, term) %>%
arrange(desc(beta)) %>%
ungroup() %>%
ggplot(aes(term, beta, fill = as.factor(topic))) +
geom_col(show.legend = FALSE) +
coord_flip() +
scale_x_reordered() +
labs(title = "Top 10 terms in each LDA topic",
x = NULL, y = expression(beta)) +
facet_wrap(~ topic, ncol = 4, scales = "free")
We can see what a dominant word "data" is in these description texts. In addition, there are meaningful differences between these collections of terms, from terms about soil, forests, and biomass in topic 12 to terms about design, systems, and technology in topic 21. The topic modeling process has identified groupings of terms that we can understand as human readers of these description fields.
We just explored which words are associated with which topics. Next, let's examine which topics are associated with which description fields (i.e., documents). We will look at a different probability for this, \(\gamma\), the probability that each document belongs in each topic, again using the
tidy verb.
## # A tibble: 768,072 x 3
## document topic gamma
## <chr> <int> <dbl>
## 1 55942a8ec63a7fe59b4986ef 1 0.00000645
## 2 56cf5b00a759fdadc44e564a 1 0.0000116
## 3 55942a89c63a7fe59b4982d9 1 0.0492
## 4 56cf5b00a759fdadc44e55cd 1 0.0000225
## 5 55942a89c63a7fe59b4982c6 1 0.0000661
## 6 55942a86c63a7fe59b498077 1 0.0000567
## 7 56cf5b00a759fdadc44e56f8 1 0.0000475
## 8 55942a8bc63a7fe59b4984b5 1 0.0000431
## 9 55942a6ec63a7fe59b496bf7 1 0.0000441
## 10 55942a8ec63a7fe59b4986f6 1 0.0000288
## # … with 768,062 more rows
Notice that some of the probabilities visible at the top of the data frame are low and some are higher. Our model has assigned a probability to each description belonging to each of the topics we constructed from the sets of words. How are the probabilities distributed? Let's visualize them (Figure 8.7).
ggplot(lda_gamma, aes(gamma)) +
geom_histogram() +
scale_y_log10() +
labs(title = "Distribution of probabilities for all topics",
y = "Number of documents", x = expression(gamma))
First notice that the y-axis is plotted on a log scale; otherwise it is difficult to make out any detail in the plot. Next, notice that \(\gamma\) runs from 0 to 1; remember that this is the probability that a given document belongs in a given topic. There are many values near zero, which means there are many documents that do not belong in each topic. Also, there are many values near \(\gamma = 1\); these are the documents that do belong in those topics. This distribution shows that documents are being well discriminated as belonging to a topic or not. We can also look at how the probabilities are distributed within each topic, as shown in Figure 8.8.
ggplot(lda_gamma, aes(gamma, fill = as.factor(topic))) +
geom_histogram(show.legend = FALSE) +
facet_wrap(~ topic, ncol = 4) +
scale_y_log10() +
labs(title = "Distribution of probability for each topic",
y = "Number of documents", x = expression(gamma))
Let's look specifically at topic 18 in Figure 8.8, a topic that had documents cleanly sorted in and out of it. There are many documents with \(\gamma\) close to 1; these are the documents that do belong to topic 18 according to the model. There are also many documents with \(\gamma\) close to 0; these are the documents that do not belong to topic 18. Each document appears in each panel in this plot, and its \(\gamma\) for that topic tells us that document's probability of belonging in that topic.
This plot displays the type of information we used to choose how many topics for our topic modeling procedure. When we tried options higher than 24 (such as 32 or 64), the distributions for \(\gamma\) started to look very flat toward \(\gamma = 1\); documents were not getting sorted into topics very well.
8.4.4 Connecting topic modeling with keywords
Let's connect these topic models with the keywords and see what relationships we can find. We can
full_join() this to the human-tagged keywords and discover which keywords are associated with which topic.
## # A tibble: 3,037,671 x 4
## document topic gamma keyword
## <chr> <int> <dbl> <chr>
## 1 55942a8ec63a7fe59b4986ef 1 0.00000645 JOHNSON SPACE CENTER
## 2 55942a8ec63a7fe59b4986ef 1 0.00000645 PROJECT
## 3 55942a8ec63a7fe59b4986ef 1 0.00000645 COMPLETED
## 4 56cf5b00a759fdadc44e564a 1 0.0000116 DASHLINK
## 5 56cf5b00a759fdadc44e564a 1 0.0000116 AMES
## 6 56cf5b00a759fdadc44e564a 1 0.0000116 NASA
## 7 55942a89c63a7fe59b4982d9 1 0.0492 GODDARD SPACE FLIGHT CENTER
## 8 55942a89c63a7fe59b4982d9 1 0.0492 PROJECT
## 9 55942a89c63a7fe59b4982d9 1 0.0492 COMPLETED
## 10 56cf5b00a759fdadc44e55cd 1 0.0000225 DASHLINK
## # … with 3,037,661 more rows
Now we can use
filter() to keep only the document-topic entries that have probabilities (\(\gamma\)) greater than some cut-off value; let's use 0.9.
top_keywords <- lda_gamma %>%
filter(gamma > 0.9) %>%
count(topic, keyword, sort = TRUE)
## # A tibble: 1,022 x 3
## topic keyword n
## <int> <chr> <int>
## # … with 1,012 more rows
What are the top keywords for each topic?
top_keywords %>%
group_by(topic) %>%
top_n(5, n) %>%
ungroup %>%
mutate(keyword = reorder_within(keyword, n, topic)) %>%
ggplot(aes(keyword, n, fill = as.factor(topic))) +
geom_col(show.legend = FALSE) +
labs(title = "Top keywords for each LDA topic",
x = NULL, y = "Number of documents") +
coord_flip() +
scale_x_reordered() +
facet_wrap(~ topic, ncol = 4, scales = "free")
Let's take a step back and remind ourselves what Figure 8.9 is telling us. NASA datasets are tagged with keywords by human beings, and we have built an LDA topic model (with 24 topics) for the description fields of the NASA datasets. This plot answers the question, "For the datasets with description fields that have a high probability of belonging to a given topic, what are the most common human-assigned keywords?"
It's interesting that the keywords for topics 13, 16, and 18 are essentially duplicates of each other ("OCEAN COLOR", "OCEAN OPTICS", "OCEANS"), because the top words in those topics do exhibit meaningful differences, as shown in Figure 8.6. Also note that by number of documents, the combination of 13, 16, and 18 is quite a large percentage of the total number of datasets represented in this plot, and even more if we were to include topic 11. By number, there are many datasets at NASA that deal with oceans, ocean color, and ocean optics. We see "PROJECT COMPLETED" in topics 9, 10, and 21, along with the names of NASA laboratories and research centers. Other important subject areas that stand out are groups of keywords about atmospheric science, budget/finance, and population/human dimensions. We can go back to Figure 8.6 on terms and topics to see which words in the description fields are driving datasets being assigned to these topics. For example, topic 4 is associated with keywords about population and human dimensions, and some of the top terms for that topic are "population", "international", "center", and "university".
8.5 Summary
By using a combination of network analysis, tf-idf, and topic modeling, we have come to a greater understanding of how datasets are related at NASA. Specifically, we have more information now about how keywords are connected to each other and which datasets are likely to be related. The topic model could be used to suggest keywords based on the words in the description field, or the work on the keywords could suggest the most important combination of keywords for certain areas of study.


text:
'The new agency, which would be called the National Preparedness and Response Authority, or NPRA, would have responsibility both for natural disasters, such as a hurricane, and a possible terror attack.'
'While the NPRA would be within DHS, it should be a "distinct entity" and should be protected from diminution by the department. Numerous witnesses testified that FEMA had been stripped of resources and power in recent years, making it less nimble and effective.'
'"The Director would have a direct line of communications to the President during catastrophes," the report reads.'
'The committee's recommendations went into great organizational detail, calling for more regional offices than the 10 FEMA now has, the consolidation of three interagency coordinating groups into one and greater funding for preparedness at the state and federal levels.'
Amazing how things come full circle. They're saying what many are saying without really saying it. huh? Yeah that's return FEMA to an independent agency. Why? Well FEMA used to have a direct line to the president. FEMA used to have resources and power. FEMA seemed to work fine during the september 11th terrorism attacks. Amazing what FEMA has asked for (noted back in the post-katrina days) is now being said should be given to some other agency?
Of course now I can't find the quote regarding how FEMA is broken due to a lack of leadership and funding…a quote made by a senate committee member. Think about that for a second. Read into that a bit. Who decides the leadership? Who decides the funding? So…who's broken?
BUT, take the cash cow out of Homeland Security? Take away the Homeland Security privelege tax from the FEMA cash cow? Preposterous.
Its a wholesale housecleaning…..of an acronym.
Of course the problem isn't this crazy DHS thing. Duh.
FEMA's a good idea. DHS is not. Its creation was political, a 'yes we're doing something to change things due to sept 11th'. Why create an independent agency within a department? How does that make sense? I'd love to be there when this 'independent agency within a department' goes straight to the pres and the chertoff-esque guy is left out. That doesn't work within the framework of politics nor the National Incident Management System.
Its the regulations that are the freaking problem, but they can't admit that. That would mean votes.


text:
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Mackintosh combines its rich outerwear heritage with a refined sense of current city style. This updated trench coat has a traditional throat latch and storm flap back, but the concealed, single-breasted fastenings and streamlined shoulders without epaulettes create a more modern look that's no less refined. It's handmade in Scotland from water-repellent bonded cotton with fully taped seams to protect you from the elements.
Product ID: 12569203
Size & Fit
Model wears size 8
Model measures: bust 32"/82cm, height 5'10ft/178cm, waist 22.8"/58cm, hips 35.4"/90 cm
Details & Care
Water-repellent bonded cotton
Fly fronted
Slash front pockets
Underarm eyelet vents
Fully taped seams
Buttoned throat latch
100% cotton (bonded)
Do not wash
Handmade in Scotland
Style ID: RO3455
Returns & Exchange
If you are not happy with your purchase you can return it to us within 14 days for a refund.


text:
The rollout of the 3G iPhone has been a tough one for Apple, plagued by issues ranging from overwhelmed update servers to applications causing browser crashes. Fair enough–though Apple has taken all kinds of heat for the flubbed product launch, if they can't do it no company out there would have. But the latest problem, if it turns out to be widespread is more than inexcusable.
Owners of the white model 3G iPhone have noticed cracks appearing in the handset casing. Exactly how many owners have had this problem isn't known yet, but several have come out of the woodwork over at the Mac Rumors forum. Chances are that if this is happening to the white models, black models are also affected though it's obviously not as easy to tell. The problems started appearing only a week after the July 11 launch and so far Apple hasn't responded in any way to the issue, so your best bet at the moment is to take your phone back to where you got it if cracks appear and get a replacement. We'll keep you updated.


text:
Disclaimer: The information contained herein is subject to change without notice.
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text:
Select multiple tasks and move them to another list
jans.roeber says:
Does anyone have as idea how to do this ?
emily (Remember The Milk) says:
emily (Remember The Milk) says:
We've just launched version 3.0, which allows you to edit multiple tasks at once (including moving multiple tasks to another list). Please see the blog post for more details. :)
andrewski (Remember The Milk) says:
This isn't currently possible; sorry for the inconvenience.
christerolsson says:
This would be really useful!
mhbeals says:
I use the ipod app for my shopping list and my kitchen inventory so this would be really helpful. Please try! Even if I had to do it one at a time with a swipe it would be easier than editing each individually.
(closed account) says:
this would make all the difference in the functionality of the app. Not just moving multiple tasks to other lists, but other batch editing tasks such as tags and due date. PLEASE PLEASE PLEASE PLEASE PLEASE


text:
A link to begin - Nameberry today have done an interesting post with a 'Baby Name Timeline', US-focussed but very interesting. Reminds me of the great lists in J Besnard's 'La Cote des Prénoms' which show the most popular names in France over specific decades - an easy way to see the trends.
Wednesday, June 24, 2009
New Names pt 2
Posted by Sarah Francis at 1:27 pm 0 comments
Labels: baby name wizard, camden, camila, dunkling, most popular, new names, popular names
Tuesday, June 23, 2009
'New' Names
What is a 'new' name? A general definition would be a name that has been newly adopted by parents, that was not previously used as a first name - the likes of Cooper, Nevaeh and Addison. These names may have history as surnames (Cooper, Addison), occupations (Cooper, Mason - often also surnames), place names (Brooklyn, Savannah, Sydney) or words (Chase, Destiny, Savannah - and I suppose, Nevaeh). One way of identifying a 'new' name is to look at its popularity over time - when it first emerged. One of my favourite posts from Laura Wattenberg on the Baby Name Wizard was looking at the false antiqueness of Olivia and Ava (also this post) - though they are seen as 'vintage revivals', they were never that popular to begin with.
Emma, Jason and Brian probably appear in Camden
A remark often made is that the top males names are less changeable than the girls, this list also shows that they are more traditional: names that have been in name books since the 17th century are much more frequent on the male list.
Names Not Appearing Until 2000s:
Madison Addison Taylor Kaylee Savannah Nevaeh Makayla Brooklyn Destiny Kaitlyn Mackenzie Trinity Aaliyah Katelyn Camila Payton Genesis
Jayden Landon Brayden Wyatt Brody Jaden Ayden Caden Colton Kaden
Is it a surprise that the girls' list is longer than the male list? Male names are not only traditional, there is also less variety in their 'new' names - 6 of the 10 are 'ayden' names, and only two do not end with 'n'. The girls list is a mix of surnames (Madison, Addison, Taylor), places (Savannah, Brooklyn) and words (Trinity, Destiny, Genesis) as well as respellings/foreign spellings (Makayla, Kaylee, Kaitlyn/Katelyn, Camila and Aaliyah). There isn't a dominant common theme.
A few names with appearances in books that surprised me:
Ava - doesn't appear until Dunkling in 1983, though Gardner was active in the movies until the 1940s.
Olivia - appears in all books except Camden, 'Twelfth Night' was written in 1599. Too new for Camden? Jessica is the same, 'Merchant of Venice' was 1596-8
Alexis - appears as a male name until Dunkling, and a female name thereafter.
Ashley - appears for neither gender until Dunkling, where it is a unisex name.
Lauren - not until Dunkling, though Lauren Bacall in films from 1940s.
Avery - first appears in Dunkling as a m name, as a female name from Baby Name Wizard (BNW) Riley has a similar pattern
Aubrey - a male name until 1990 Oxford
Morgan - a male name until Dunkling
Evelyn - a male name in Yonge, but female in Moody
Ariana - appears in Moody as a Persian name, but not again until BNW in 2005
Ethan - doesn't appear until Withycombe, 1945 but is a Biblical name
Aiden - appears in Dunkling, and then not til BNW
Dylan - not until Dunkling
Angel - in Yonge, not Camden or Moody. 'Previously male' in Oxford 1990 - Angel Clare as in 'Tess of D'Urbervilles' rather than Spanish origin.
Kevin, Connor- in Yonge and Withycombe, but not Camden, Moody or Weekley
Hayden - not until Dunkling, and then under Haydn in Penguin and Oxford 2006
Natalie, Leah, Claire, Jennifer, Sara, Gavin, Isaiah, Justin, Lucas, Jordan, Aidan, Tristan, Alex, Dominic - in Yonge, but not Camden or Moody
Isabella, Abigail, Hannah, Amelia, Grace, Victoria, Julia, Angelina, Ella, Zoe, Alexandra, Vanessa, Charlotte, Faith, Caroline, Isabel, Noah, Nathan, Elijah, Christian, Austin, Jeremiah, Carlos, Sebastian, Ian, Eric, Josiah, Eli - all except Camden
Data here
Posted by Sarah Francis at 7:05 pm 0 comments
Labels: audrey, camden, dunkling, names, popular names, top 100, usa, withycombe


text:
Here is couple tips on how to go green this Valentine's Day:
1. Cook home-made dinner or have a picnic at the park
There is nothing more romantic than somebody taking time to make you dinner. I know it's easier to go out and just pay for food. But do you really wanna be stuck in traffic, wait for the table and then sit in the crowded restaurant? Or consider taking food outside relaxing in the park or by the beach, plenty of privacy and opportunities to connect. Remember to make a vegetarian dinner and use organic produce. Your body will be happy, as well as your loved one. Greens will give you lots of energy to have fun in the evening later…;)
2. Flowers
Flowers are definetely a greener choice. Make sure you buy at your local flower store. With lower transportation costs and with minimum pesticides. Less carbon foot print. You can even order organic flowers online. Another possibility to buy flower in the pot, so it lives longer and produces oxygen.
3. Massage
Everybody loves a good massage. Being touched and cared for is what Valentine's Day all about. Turn on some relaxing music, use organic candles and some natural oil like Almond, Grapeseed or Olive. You will have a blast!
4. Think Recycled or Organic
Whatever you are planning to buy as a gift. Take your time to find recycled version of it. There is plenty recycled gold jewelry, handbags, decorative subjects. Organic clothes or beauty products as such perfume/cologne.
5. Send e-cards online
No need to buy cards anymore. E-card online are so cool, you choose a song, add words from your heart, you can even use a picture of your partner to make it more personal. I love the internet and technology, it gives us chance to be more green and still show that we care.
6. Chocolate
Don't forget to eat some chocolate. Good source of happiness and antioxidants. Visit your local Whole Foods or Trader Joes to find huge selections of fair trade, delicious organic heaven bites:)
Have fun on Valentine's Day. Be green and joyful. Share this with your friends and family. Earth day everyday! Love this life!


text:
Very Rich
From Newbery Honor and National Book Award-winning author Polly Horvath, another quirky novel featuring a time travel adventure (among others).
Ten-year-old Rupert Brown comes from an ordinary family. They live in a small house in the poorest section of Steelville, Ohio, and have little money or food. So when Rupert inadvertently finds himself spending Christmas at Turgid Rivers' house--the richest boy in town--he is blown away to discover a whole other world, including all the food he can eat and wonderful prizes that he wins when the family plays games, prizes he hope to take home to his family so they can have Christmas presents for the very first time. But this windfall is short-lived when Rupert loses it all in one last game and goes home empty-handed. Each member of the Rivers family feels guilty about what happened, and unbeknownst to one another tries to make it up to Rupert in their own unique way, taking him on one unlikely adventure after another.
Polly Horvath has written many books for children including Everything on a Waffle, a Newbery Honor Book; The Canning Season, a National Book Award winner; and The Trolls, a National Book Award finalist. She lives in British Columbia with her family. Visit her at www.PollyHorvath.com.
The Bottle Imp of a Bright House
A delightfully dark middle-grade novel reminiscent of the Newbery-winning classic, The Westing Game.
Think of a wish. Go big with it. A winning lottery ticket. Eternal happiness. School vacation for a year. Now, answer this question: If you know this wish will come true, would you still make it if you knew someone else would somehow have to pay for it?
That's the catch thirteen-year-old Gabe Silver faces when a mysterious millionaire sells him a bottle containing a wish-granting imp. Any time Gabe makes a wish, someone else, somewhere, is going to lose something--maybe something big. That means each of Gabe's wishes should be an ethical dilemma--but as he scores a Ferrari, a hot tub, and all the pizza and sub sandwiches a kid could want, he's certain a guilty conscience is worth it...isn't it?
This thought-provoking book, inspired by a Robert Louis Stevenson novella, is pithy, dark, and very, very funny. It has an endearing, wacked-out cast of characters brought to life by illustrator Gris Grimly, whose cover art will be embossed to highlight the depth and detail of the art.
Tom Llewellyn is a writer, marketer, father, and letterpress poster artist in his hometown of Tacoma, Washington, where The Bottle Imp of Bright House is set. If he were offered a bottle imp, he'd turn it down. So many of his wishes have already come true that he doesn't want to push his luck.
Gris Grimly has been drawing since he was old enough to hold a pencil. Since then, his elegant, haunting, and critically acclaimed art has graced more than two dozen books including The Dangerous Alphabet by Neil Gaiman, a clothing line for a national retailer, and more. Gris lives with his family in California, where he is wary about deals that involve insidious little imps.


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Soil use, depletion of natural resources, loss of biodiversity, pollution and climate change - today's environmental challenges are linked to the development of mankind and the economy.
From multi-national to local government - at Antea Group, we have the means and methodology at our disposal to assist our clients with what they inherited from the past, while at the same time preparing them for a sustainable future.
Sustainability, environmental awareness and the safe use of the space are unavoidable aspects of developments such as the installation of infrastructure, housing construction or the development of a business. At the same time, we are claiming an ever greater portion of the available space, both above ground and below ground. Businesses and government bodies are each facing their own challenges in enabling development.
Innovative starting point
The issues are complex. Sometimes extremely detailed knowledge is required in order to identify an appropriate opening within existing laws and regulations, to work in harmony with the environment and to develop activities that comply with applicable procedures. From help with policy formulation through to implementation, Antea Group's knowledge of techniques, details and the process allows us to provide an innovative starting point for clients in translating great ambitions into total solutions.
Wide perspective
Soil, water, noise, air, safety, nature, sustainability, resource efficiency, biodiversity..., all of these elements combine to determine the quality of our environment. Antea Group therefore considers environmental issues from a wide perspective. We have total solutions in-house for all environmental aspects of the world in which we live, work and play.


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Perry universe
(Covers information from the S.D. Perry novels)
(Covers information from the S.D. Perry novels)
For the Mainstream counterpart, see Ben Bertolucci.
Bertolucci was a Raccoon City reporter. Some time in mid-1998, Police Chief Brian Irons criticised the man for putting him through uncomfortable questions during a private interview. In response, Irons threatened to permanently ban Bertolucci from any further interviews.[1]
- ↑ Perry, Umbrella Conspiracy, p.14.
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