The goal of www.janmontyncollection.com is to showcase Jan Montyn's world: a world that is clearly etched into his oeuvre. We want to introduce visitors to and further promote his work, but most of all, we want you to enjoy the collections.
In addition, we have striven to open up his body of work, to connect it with themes from his life and to provide relevant background information. Based on these presentations, visitors can select their own favourite pieces and gain further insight into their background and origins. The intention is that this information will form the basis for creating further publications and studies.
The core of this website is formed by the work of Jan Montyn. These works of art have been grouped according to particular themes in his life and work.
Effectively, www.janmontyncollection.com is a user access point to a large database made up of Jan Montyn's entire body of work. The database is structured according to the notes he kept over the years. The individual pieces can be added to this structure in the form of descriptions, information and photos.
The descriptions and photos will feature works from a wide variety of art collections. The number of collections featured will be gradually expanded in order to eventually create a comprehensive picture of the work of Jan Montyn.
For his unique works, such as mono prints, sketches, gouaches and paintings, we can never hope to achieve completeness. For these unique works, it will suffice to simply feature the pieces included in the featured art collections and the descriptions of the pieces as recorded in Jan Montyn's own notes.
The featured works of art are arranged according to a number of themes from his life and work. This classification enables a wide range of choices in the 'Oeuvre' section. These themes were also selected based on drawings in his notes and comments on the reverse side of the pieces.
The current classification is a first draft. This version (version 1.0) does not claim to be either complete nor accurate. The classification of Jan Montyn's work therefore requires phased or periodic improvement. The intention is to improve the quality gradually and over time, to make use of other sources such as catalogues, articles and books.
Anyone in possession of pieces by Jan Montyn or information regarding particular pieces can potentially make a huge contribution to the improvement of the quality. We therefore dearly hope that the owners of pieces not included in the database will get into contact with us. We would also like to hear from you if you have better and/or supplementary information regarding any of the pieces. You can contact us by e-mail at info@janmontyncollection.com.


SEA SCOUTS IS JUST AN AMAZING PROGRAM. IT'S A PROGRAM WHERE YOU GET TO HAVE ADVENTURE, YOU GET TO LEARN ABOUT LEADERSHIP, AND LEARN HOW TO HANDLE A BOAT PROPERLY AND SAFELY.
I'VE LEARNED A LOT ABOUT LEADERSHIP. I WAS REALLY SHY WHEN I WAS LITTLE BUT SINCE I'VE JOINED SEA SCOUTS I'M JUST VERY FRIENDLY WITH EVERYONE. I GET A LOT OF PEOPLE INVOLVED, MEET SOME REALLY NICE PEOPLE, AND LEARN A LOT OF NEW THINGS.
I WAS NERVOUS AT FIRST. I DIDN'T KNOW MUCH ABOUT BOATS. BUT I DEFINITELY LEARNED A LOT. HANDS ON EXPERIENCE... NO KIDDING!


…or how to have an Win32 Apache2 PHP enabled web server alive on a USB key. With minor configuration tweaks, it also could be embedded on Read-Only media.
you'll get here: Attach:WebOnKey.zip .
Then extract each binary distributions into their relevant root directory.
Optionally, edit the different template files located into the config sub-directory tree and used to generate apache/php real configuration files.
Use the HTMLApplication script WebOnKey\Apache-monitor.hta to start and stop Apache as a console application.
WebOnKey Apache-Monitor is an HTML Application able to start, stop and monitor an Apache2/PHP5 web server located on any removable media such as USB key disks.
When launched, Apache-Monitor first determines its current storage path (ROOTDIR) and defines some environment variables according to the location found.
Then, it generates Apache and Php suitable configuration files from templates, by replacing variables tags %<variable_name>% with computed values, and lastly run the server as a console application.
Application window offers a 3 parts splitted zone displaying current script status and running apache logs.
Runs successfully on 2000/XP and with some restrictions on 98/Me (unable to send appropriate key sequence to terminate the server).
Require Internet Explorer and Windows Scripting Host.
- dropped not-so-useful edit button.
+ improved speed with internal string manipulation tweaks.
+ added support of script configuration file for easy configuration.
+ links opened with the user default browser.
+ now able to open an url when the server is started.
+ new option to auto-minimize when the server is (re)started.
last modification time (faster startup).
logs (both apache2 and php messages are handled).
- fixed some ambiguous error messages.
The software is freely available under the GNU Public License and may be downloaded from above link.


Another new gig poster! I've been a busy boy!
A little late on this release, as there was a snag with shipping from the printer. But, following my recent Foo Fighters gig is another gig poster, this time for the band 311. And yes, it's already been an insanely busy year, and still even busier to come. The poster for 311's gig a few weeks ago definitely has a more vintage vibe. Continuing with the experiment to try different stock choices, this one features another Legion paper stock: stone, for the muted buff-colored background for the birdo. I tried staying away from using pure black for this one and really am pleased with the results. Pulling back on the full tonal range for a more subdued, sun drenched look really worked for the poster in my opinion. And, like the previous Foo Fighters poster, there are two shiny versions in addition to the main: rainbow foil and gold miri foil yet again. They turned out great, and the art really pops off the foils.
The 311 posters will go up for purchase this Friday, in the online shop here. As always, to get the exact drop time, you must be signed up to the newsletter in the sidebar of the site. Membership to the FREE newsletter has it's privileges, and is an easy way to vet out those who are serious about collecting the work when it come out, and those who just want to complain that they couldn't score anything on the drop.
Thanks as always for the incredible support! And now on to the posters!
Print size: 18″ x 24″ edition of 20.
Six-color screenprint on rainbow foil stock.
Rainbow foil- super reflective and hard to photograph!
Six-color screenprint on Legion miri gold foil stock.
Gold miri foil- even harder to photograph!
Previous post: New Gig Poster: Foo Fighters!
Next post: New PRIMUS Gig Poster- May 16 Atlanta.


Despite Barcelona being the capital of Catalonia, Spanish is still the most common language in the city. This is due to its large population of people from all over Spain, as well as South American's and Spanish-speaking foreigners. Barcelona is one of the most beautiful places in the world to learn Spanish, and what better way to learn it than by studying at a language school. Below is a list of some of the best language schools in the city.
This centrally located school (just five minutes from Plaza Catalunya) is one of the best in the city and also one of the cheapest. It offers a wide variety of courses, for both longer and shorter periods. The school offers evening courses, cultural courses and also private classes. The teachers also organise various activities every week including bike tours of the city and beach days. This school is very helpful with finding their students accommodation in homestays, apartments or in rooms within student residences. Almost all of the accommodations are within 20 minutes of the school and near public transport.
This school is housed in a beautiful old colonial building, around the corner from Plaza Catalunya. Behind the building is a quiet garden, a little oasis in the busy city centre where students can sit and relax between classes. The average group size of the class is between 3 and 7 people, so the individual attention that you will receive in these classes is excellent. Private classes are also available. When booking on to a course at this school you can also book your airport transfer within the same booking, from either Barcelona or Girona airport.
This school is another outstanding place to study, as the teachers have many years of experience and the group sizes are small. The intensive courses at this school are particularly good, offering 10, 20 or 30 lessons of 50 minutes per week. This is the perfect language school for younger people, as 69% of students who study here are between 17 and 35. If you are a university student, this school can arrange for you to gain credits for your course. The school is located in the Sants area and is close to the Sants Estació and Entença metro stations.
This school is centrally located and offers a wealth of different courses. All teachers are native speakers and the group sizes are perfect. The biggest draw of this school is its accommodation. While most language schools in Barcelona will help their students find accommodation, this is the only school in Barcelona that has its own onsite accommodation. The school has 10 student flats, kitted out with a kitchen, dining room and TV. In addition, the school has several sunny terraces where students can chill out and socialize. If you're looking for a very sociable experience, this is one of the best schools in the city.
This school is located in the buzzy neighbourhood of El Raval. This area is alive both day and night, with its many bars and restaurants. The school itself is an excellent, professional learning space. It has a computer room and a library, where you can take out Spanish books and DVDs to help you with your studies. The school offers intensive courses, evening courses and also more casual conversation lessons. The school is also very flexible and students can extend their courses week by week if they find that they don't want to leave! This is a great school if you are looking for something smaller and more relaxed.


Strong's Hebrew  פֶּה translit:peh phonetic:peh std_def:from «06284»; the mouth (as the means of blowing), whether literal or figurative (particularly speech); specifically edge, portion or side; adverbially (with preposition) according to:--accord(-ing as, -ing to), after, appointment, assent, collar, command(-ment), × eat, edge, end, entry, + file, hole, × in, mind, mouth, part, portion, × (should) say(-ing), sentence, skirt, sound, speech, × spoken, talk, tenor, × to, + two-edged, wish, word. Strong's Hebrew  פֶּה translit:peh phonetic:peh std_def:from «06284»; the mouth (as the means of blowing), whether literal or figurative (particularly speech); specifically edge, portion or side; adverbially (with preposition) according to:--accord(-ing as, -ing to), after, appointment, assent, collar, command(-ment), × eat, edge, end, entry, + file, hole, × in, mind, mouth, part, portion, × (should) say(-ing), sentence, skirt, sound, speech, × spoken, talk, tenor, × to, + two-edged, wish, word.
st_def - from «06284»; the mouth (as the means of blowing), whether literal or figurative (particularly speech); specifically edge, portion or side; adverbially (with preposition) according to:--accord(-ing as, -ing to), after, appointment, assent, collar, command(-ment), × eat, edge, end, entry, + file, hole, × in, mind, mouth, part, portion, × (should) say(-ing), sentence, skirt, sound, speech, × spoken, talk, tenor, × to, + two-edged, wish, word.
Joshua 8:24 - And it came to pass, when Israel had made an end of slaying all the inhabitants of Ai in the field, in the wilderness wherein they chased them, and when they were all fallen on the edge of the sword, until they were consumed, that all the Israelites returned unto Ai, and smote it with the edge of the sword.
יִשְׂרָאֵל Israel כָּלָה Accomplish, cease, cons..
הָרַג Destroy, out of hand, k.. יָשַׁב "to dwell/sit" (make to) abide(-ing), ..
עַי Ai, Aija, Aijath, Hai שָׂדֶה Country, field, ground,..
מִדְבָּר Desert, south, speech, .. רָדַף Chase, put to flight, f..
נָפַל Be accepted, cast (down.. פֶּה Accord(-ing as, -ing to..
חֶרֶב Axe, dagger, knife, mat.. תָּמַם Accomplish, cease, be c..
עַי Ai, Aija, Aijath, Hai נָכָה Beat, cast forth, clap,..
Deuteronomy 23:23 - That which is gone out of thy lips thou shalt keep and perform; even a freewill offering, according as thou hast vowed unto the LORD thy God, which thou hast promised with thy mouth.
מוֹצָא Brought out, bud, that .. שָׂפָה Band, bank, binding, bo..
דָבַר to speak, declare, con... Answer, appoint, bid, c.. פֶּה Accord(-ing as, -ing to..
Ezekiel 31:15 - Thus saith the Lord GOD; In the day when he went down to the grave I caused a mourning: I covered the deep for him, and I restrained the floods thereof, and the great waters were stayed: and I caused Lebanon to mourn for him, and all the trees of the field fainted for him.
יְהֹוִה "The Holy Creator of t... God יוֹם day/time/year Age, always, chronica..
אָבַל Lament, mourn כָּסָה Clad self, close, cloth..
תְּהוֹם deep Deep (place), depth מָנַע Deny, keep (back), refr..
נָהָר Flood, river רַב much, many, great/capt... (in) abound(-undance, -..
מַיִם water Piss, wasting, water(-.. כָּלָא Finish, forbid, keep (b..
לְבָנוֹן Lebanon קָדַר Be black(-ish), be (mak..
עֵץ Carpenter, gallows, he.. שָׂדֶה Country, field, ground,..
Numbers 9:20 - And so it was, when the cloud was a few days upon the tabernacle; according to the commandment of the LORD they abode in their tents, and according to the commandment of the LORD they journeyed.
מִשְׁכָּן Dwelleth, dwelling (pla.. פֶּה Accord(-ing as, -ing to..
יְהֹוָה "The Holy Creator of t... Jehovah, the Lord חָנָה Abide (in tents), camp,..
נָסַע Cause to blow, bring, g..
H6310 פֶּה - 6310 פֶּה from פָּאָה; the mouth (as the means of blowing), whether literal or figurative (particularly speech); specifically edge, portion or side; adverbially (with preposition) according to; accord(-ing as, -ing to), after, appointment, assent, collar, command(-ment), [idiom] eat, edge, end, entry, [phrase] file, hole, [idiom] in, mind, mouth, part, portion, [idiom] (should) say(-ing), sentence, skirt, sound, speech, [idiom] spoken, talk, tenor, [idiom] to, [phrase] two-edged, wish, word.


I'm trying to code up something that could categorise hand strength on the flop including draws.
The motivation is to take the output of PioSolver report_IP_Full.csv files and run them through code to get Check and Bet Frequencies when you have different types of hands, such as for BUvsBB single raised pot how often does Pio think you should CBet TPTK or Pair+GS or OESD or Combo Draw. Basically get a type of decision tree which could form a heuristic for correct play which might be easily for a player to recall in realtime or could form the basis of an online assistance tool.
In terms of the programming I've looked up lot of hand evaluators but they are all concentrated on efficient assessment of who wins at showdown. I've yet to come across something that could take hole cards + Flop (or Turn) and categorise our hand a la Pio's "Range Explorer" or FlopZilla's Statistics.
I have made some naive code but it is pretty slow at the moment (running over +90k combos of starting hand + Flop is painful) and I can imagine someone much better than me at programming might have tackled this problem before and uploaded to GitHub.
I don't know anything about Pio or FlopZilla so I don't really understand the question. When I needed to evaluate strength on the flop at speed I pre-calculated all the hole vs hole strength matrices for all 1755 isomorphic flops and put them on disk. The occupy about 5Gb. You could either load each matrix as required or keep all in memory if memory constrained.
I ran BBvsBU in a single raised flop over 198 flops. PioSolver was then able to give me a CSV file which outputs the optimal decisions for each combination of hole cards and flop. With the ranges I selected this ended up being 92353 combinations for the in position player when facing a check on the flop.
The % numbers say how often you should perform said action to be balanced in your play.
Basically if I had a good function that I could pass my hole cards and the board which outputs what type of made hand/drawing hand you hold I would do multiple solves in PioSolver for different situations then perform slicing and analysis on the outputs based on these categories to infer the correct play, or generate some rules of thumb. All of this stuff is early days for me so I could for sure be thinking about this in the wrong way.
- https://gist.github.com/sntran/8012387 shows a 5 card hand evaluator. You can definitely figure out the made hand categories from this. I don't know if you figure the draws or or not.
Thanks for the link. I'll check it out and see if I can modify it to get the draws.
Nice idea, thanks for sharing. Would you like to share your file where you calculated flop equity?


A campaign centred around all things happy will spread joy on Friday at the Duckworth Centre.
Acts of Happiness is all about gathering stories, images and sharing inspiration about how people are making the world around them happier.
Happy acts will be laid out on a happiness wall, sharing "happy acts" at the University of Winnipeg or on the virtual wall at ActsOfHappiness.org.
The event takes place from 9 a.m. to 6 p.m. For every person that takes the Happy Acts challenge, $1 will be donated to Big Brothers Big Sisters.
The social media hashtag for the campaign is #HappyActs.


Dubai: Etisalat subscribers will be able to enjoy free WiFi during Eid, the telecommunications company announced on Tuesday.
The free internet connection can be accessible in major public locations across the country, including malls, restaurants, cafes, parks, beaches, airports and entertainment and sports venues between June 22, 2017 and July 1, 2017.
"In the festive spirit of Eid Al Fitr, we want to be part of our customers' celebration, ensuring they stay fully connected to their families and friends. Customers will experience high speed and quality free across Etisalat's public WiFi network to exchange greetings, share photos, access social networking websites and surf online," Etisalat's chief consumer officer, Khaled ElKhouly.
In order to use the service, customers visiting key public locations in the UAE should connect to 'UAE WiFi by Etisalat' WiFi SSID signal.
Users need to register first and once the registration is done, Etisalat will send an SMS with PIN number.
Customers need to use their mobile number as login and PIN number as password. Once registered, the user will be given free WiFi access.
After the Eid Al Fitr promo period, users will still be able to continue using UAE WiFi by Etisalat with free access if eligible to it, or with paid packages of their choice.


Above image is a display of an inspected pipe using Technical Industries, Inc.
Imaging the whole pipe including the positions and severity of the defects.
Imaging the whole pipe wall thickness.
The colors represent the condition and the intensity of the defects.
0 to 2% white. 2%+ to 5% gray. 5%+ to 10% yellow. 10%+ to 15% blue. 15%+ to 20% orange. 20%+ red. The colors can be modified to meet customer's specifications.
The latest addition will enhance Technical Industries, Inc. Visonic™ 3D imaging which enable the customer by using proper equations to simulate tensile strength, burst and collapse rate of each pipe. By comparing previous inspections to the latest inspections, one can predict the future life expectancy of the pipe and store pipe images for future references.
proprietary expandable inspection technology recently used by Baker Oil Tools and BP to achieve the first monobore well (Read Baker Oil Tools article attached).
Technical Industries, Inc. was established since May 11, 1971 confirms it's commitment to be the leader in developing the most advanced NDT inspection technology going back to the first full pipe body ultrasonic system favored by the world largest oil companies. The developing of the 64 points wall moving average first used in developing the Mobile Bay and the Visonic 3D imaging used by BP, Texas A&M, Halliburton, Baker Hughes and others. This proprietary technology enables the energy companies to reach energy reserves present technology cannot reach.


The preliminary rules pack is out for the Warhammer 40k GT. The biggest change is aligning our event more closely with the overall community norms. This means more open list construction and the use of the ITC Champions Missions.
Included in the pack is the painting rubric. We want you to know how we score so you can plan accordingly.
The Rules Pack can be found here.
We just sold out our first event! The Warmahordes Steamroller is sold out!
New event added – Guild Ball!
Still looking for a way to fill your Sunday at the Bugeater? Look no more – we are excited to announce we are adding a Guild Ball tournament! This one day event will follow standard Organized Play rules (which are being updated in late March, so we are waiting to provide details until we see what the changes are).
Tickets and hotel block now online!
Tickets for the 2018 Bugeater are now on sale! You can click on "Buy Tickets" to see all the items that are available. The cart has been added to the right sidebar (on some browsers it may appear at the bottom of the page instead) so you can keep track of what you have ordered. If you have any technical difficulties please email RoyersT@gmail.com. We are enforcing limits on entries for our events – but if we sell out we will evaluate what space we have and may open up new spots.
As a reminder, if you attend for multiple days you NEED to buy a weekend pass. This is INCLUDED in the cost of 2 day events (e.g. the 40k GT) and three day packages (e.g. Malifaux). But if you buy 2 or 3 separate individual one day events, you'll need to buy the $15 weekend pass as well!
The hotel block is also available. You can get the group rate as long as you sign up before May 1st. You can find that info here.
Folks the final event info is now online! We are working to add up to three additional events to the Bugeater, and if that happens we will post that information asap.
Tickets go on sale tonight! Be ready to lock in your spot for Bugeater 2018!
2 Day Event Information Online!
Tickets go on sale on Monday!
More information will be uploaded soon! As a reminder, tickets go on sale on Monday!
2018 Bugeater GT Information is here!
We are excited to announce the initial schedule for the 2018 Bugeater! We will be updating our information on the website over the next few days. Tickets will go on sale next week (Monday, November 27th).
We have our initial offering of events, with 7 events on Friday, 10 events on Saturday, 7 events on Sunday. This will easily be our biggest Bugeater yet!
You can find a pdf of the initial schedule here.
Mark the dates down on your calendars – Bugeater #8 will be one for the record books!
Bugeater 2018 Announcement Coming Soon!
All of your favorite events are back! Warhammer 40k, Bolt Action, Horus Heresy, Warmachine & Hordes, Kings of War, Infinity, Dropzone and Dropfleet Commander, and Infinity are back. They'll be joined by several new events. We have added these new events based on player request, and seeking out and finding tournament staff who are experienced with those games that can run a high quality event for you. So if you play any of these games – get ready! New to the 2018 Bugeater will be Age of Sigmar, Batman, Malifaux, Star Wars: Armada, and more!
With 8th edition on the scene for 40k, we are looking to make our largest, longest running event even bigger and better next year. So in a month, if you're in the area, come to the Khornehusker RTT held here in Omaha, Nebraska. We will be testing some things for next year's Bugeater, and the winner gets a free ticket to the 40k GT at the Bugeater! Check our facebook page for more information.


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                  ctime           [0] KerberosTime,
                  cusec           [1] Microseconds,
                  subkey          [2] EncryptionKey OPTIONAL,
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redpajama_set_name: RedPajamaGithub


If one of your teeth has been damaged by gum disease, tooth decay, or an oral injury, it may need to be extracted and replaced with a partial denture, bridge, or a dental implant. The extraction process quite simple, and at Miamisburg Family Dental, Dr. Almoney and Dr. Brown use the latest techniques and numbing agents and offer sedation to help keep you comfortable during your treatment.
There are a few different reasons that you may need to have one or more teeth extracted.
Your tooth is loose due to gum disease – If you have gum disease, your gums become weak, and may not be able to hold your teeth properly in their sockets. Extraction may be recommended for teeth that are loose due to gum disease.
Your tooth is infected and can't be saved with a root canal – It's best to save an infected tooth with a root canal, but if this is not possible, it will need to be extracted to ensure that the infection does not harm the rest of your smile.
Wisdom teeth are growing in improperly – Wisdom teeth typically erupt during the late teens and early twenties, and by this time, most patients do not have enough room in their mouths for these third molars to grow in properly. Improper growth can damage the surrounding teeth, or become infected when erupting. Because of this, most people have their wisdom teeth extracted.
Prepare for a dental implant or a denture – Full arch dentures require the removal of all of your upper, lower, or both arches of teeth. Extractions may also be required in preparation for a dental implant in the future.
While there are some other reasons that a tooth may need to be extracted, these are the most common oral conditions that require removal.
The actual process of a tooth extraction is quite simple, and usually only takes about 5 minutes per tooth. First, your tooth and gums will be cleaned, numbed and disinfected to prevent infection. Then, Dr. Brown or Dr. Almoney will use a "dental elevator" to loosen the tooth in its socket. Once the tooth has been loosened, your tooth will be pulled out as gently as possible by the root.
After this, the site is cleaned to remove any blood or bacteria and disinfected. Finally, the socket is sutured shut, completing the procedure.
Whether your wisdom teeth are causing you pain, or you believe that you may need to have a tooth pulled for any reason, Dr. Almoney and Dr. Brown are here to help.
At Miamisburg Family Dental, we focus on caring, personal treatment for the entire family. Once you schedule an appointment, one of our dentists will examine your mouth to determine whether extraction is the proper treatment for you and answer any questions you may have about the process. Contact us at (937) 866-5501, or come to our office at 306 E Linden Ave, Miamisburg, OH 45342 to schedule your appointment in person.


text:
Mann Jukebox Full Album Songs Aamir Manisha Sanjeev Darshan Mp3 MB
Songs Of Aamir
The title track of the movie Rang De Basanti, it is sure to boost you with patriotism. The film was shot near Bhuj.
Khan has a reputation for avoiding award ceremonies and not accepting any popular Indian film awards. As a result, several multiplex owners stated that they could not provide security to customers. Delhi Belly is an Indian black comedy movie made by producer Aamir Khan under his film banner. Aamir was remade in Tamil as Aal. Press Information Bureau Govt.
Baazi was declared a superhit and Aamir Khan was praised for his acting skills. Are you tired of looking for your favorite Aamir Khan new movie on the web? His performance in the film and in the thriller Raakh earned him a National Film Award in the Special Mention category. Khan also won his second Filmare Award for Best Actor.
Instead of money, they have put a bomb inside it. Zee Cine Award for Best Director. He and Kiran have a son named Aazad Rao Khan. Shunglu Hukmdev Narayan Yadav. It is highly recommended as a must watch movie to all Aamir Khan fans.
Best you genre music
It also starred Sonali Kulkarni and Dimple Kapadia. If you are also a fan of King Khan, then you have come to the right place. Dil Chahta Hai Dil Chahta Hai follows the story of three best friends who go through ups and down in their lives while discovering new things. An Encyclopedia of Life in the Republic. Filmfare Award for Best Male Debut.
It ended up doing good business with good collection in subsequent weeks. Madhavan, Boman Irani and Omi Vaidya. The rating was far lower than those of most other celebrity-hosted shows at the time. Once inside the bus, he is told to put the briefcase underneath his seat and leave. Ratings for the show eventually picked up, and it became very successful.
The Rising which was proved a box office bomb in terms of business. Reena was involved briefly in Khan's career when she worked as a producer for Lagaan.
Aamir Khan is one of the most celebrated and renowned Bollywood actors of the recent times. Follow him on his quest for God as he asks some soul-stirring questions to people in a hilarious way. It was adapted from the Filipino film Cavite. Ministry of Home Affairs, Government of India. He cherished this big success with his next successful movie, Dil Chahta Hai.
It was a box office success and earned Aamir Khan title of Chocolate Hero. But we have to understand that we all have a certain responsibility. He is asked to stay in a lodge, where a lame man asks him to follow him. Fanaa was declared superhit at box office.
The bomb explodes, killing Aamir. He holds on to the briefcase tightly and begins to think of his family. Zee Entertainment Enterprises. Academy of Motion Picture Arts and Sciences.
Taare Zameen Par Accolades. In other projects Wikimedia Commons Wikiquote. Interestingly, Salman is so careful to avoid any media he only takes his driver with him and arrives alone to meet the others. Ishq was a remake of Kannada movie Snehana Preethina. Later Aamir Khan not only agreed to act in Lagaan but also agreed to produce it.
His last released movie is Peekay that performed extremely well on box-office. Breaking stereotypes related to education in India, the movie was released in and is still adored by his fans.
Part 2. Top 5 Aamir Khan Songs
The show is a classic example of that. It earned him title of chocolate hero and made him a youth icon. Shakti Kapoor as crime master Gogo and Paresh rawal as criminal Teja are still popular. After a long break of eleven years he made his acting debut as an adult actor in movie, Holi, directed by Ketan Mehta. He is currently endorsing Vivo.
Aamir Khan Movies List 2019
Prior to pursuing a full-time acting career, Khan was an avid tennis player. Committee member Priyadarshan explained that they did not want to award him because of his refusal to attend the award ceremony. Film was a box office success. For other people with a similar name, see Amir Khan.
Ranbir Kapoor did a cameo at the end. Rishi Kapoor and Tabu are also there in Fanaa. He loved people and gave them a thought of humanity. Aamir Khan portrays the character of a music producer who helps her take an amazing journey that changes everything in her life.
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This style isn't for everyone.
It's random, outside the lines, and my style.
I'm not perfect, and neither is my stitching.
lace scraps and mother of pearl buttons.
but I'm getting tired of gray winter days.
Chores done, a snowy weekend, and time on my hands.
and decided to have a little fun.
which probably was used as a tablecloth, or bedspread.
and added one simple white denim pocket.
A close up of the focal point.
The back pocket needed a little embellishing.
to add dimension to the flower.
It's been a couple weeks since returning from vacation.
and 18 more inches are predicted this weekend.
and see the beautiful rainbows.
last stint of our journey to St. John.
I will post more pictures in the days to come.
in the past 2 weeks.
Early signs of trouble ahead I am afraid.
The joys of New England weather.
after a couple incredible weeks in the Caribbean.
from the Peace Hill Ruins.
and frequented by the natives.
I came down with some kind of stomach bug.
and then a reaction to the codeine cough syrup.
and paid for 7 snow removals.
This morning, pinch me I'm dreaming!
Can it possibly be snowing again?
the memories to reflect on.
of my trip in the days to come.


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text:
Home News NC Blackbeard's ship fundraising group dissolves amid lawsuits
Blackbeard's ship fundraising group dissolves amid lawsuits
A cannon from Queen Anne's Revenge is brought to the surface in MArch 2016. (Photo: WCTI)
BEAUFORT, NC (AP) — The nonprofit group that raised money to help preserve the wreckage of Blackbeard's flagship off the North Carolina coast has disbanded amid lawsuits alleging copyright infringement by the state.
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Queen Anne's Revenge
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Missing man's 'partially skeletonized' body found in marsh


text:
\section*{Acknowledgment}
We would like to thank the authors
of \cite{kay2017kinetics} for the help on the Kinetics dataset and the baseline experiments,
especially Joao Carreira for many constructive discussions.
We also want to thank Abhinav Shrivastava, Jitendra
Malik, and Rahul Sukthankar for valuable feedbacks. S.X. is supported by Google. Z.T. is supported by NSF IIS-1618477 and NSF  IIS-1717431.
\section{Conclusion}
We show that we can significantly improve on the previous state of the art 3D CNN video classification model, known as I3D,
in terms of efficiency,
by combining 3 key ideas:
a top-heavy model design,
temporally separable convolution,
and spatio-temporal feature gating.
Our modifications are simple and can be applied to other architectures.
We hope this will boost performance on a variety of video understanding tasks.
\section{Experiment Setup}
\label{sec:experiments}
\subsection{Datasets}
\label{data}

In this paper, we consider two large video action classification datasets.
The first one is Kinetics \cite{kay2017kinetics},
  which  is a large dataset collected from YouTube,  containing 400 action classes and 240K training examples.
  Each example is temporally trimmed to be around 10 seconds.
  Since the full Kinetics dataset is quite large,
  we have created a smaller dataset
that we call \MK.\footnote{
The original ``Mini-Kinetics'' dataset used in
 \cite{kay2017kinetics} contains videos that are no longer available.
 We created the new \MK\ dataset in collaboration with the original authors.
} %
\MK\ consists of
the 200 categories with most training examples;
for each category, we randomly sample 400 examples from the training set, and 25 examples from the validation set,
resulting in
80K training examples and 5K validation examples in total. The splits are publicly released to enable future comparisons.
We also report some results on the original Kinetics dataset, which we will call Kinetics-Full\ for clarity.

The second main dataset is
Something-something~\cite{Something}. It consists of ~110k videos of 174 different low-level actions, each lasting  between 2 to 6 seconds.
In contrast to Kinetics,
this dataset requires making fine-grained low-level distinctions, such as between ``Pushing something from left to right'' and ``Pushing something from right to left''.
It is therefore an interesting question whether the same principles will hold and the same architectures will work well on both datasets.

We also consider two
smaller action classification datasets to test the transferability of our model,
which we discuss in Section~\ref{sec:otherClassification},
as well as two
action detection datasets, which we discuss in Section~\ref{sec:detection}.

\subsection{Model training}

Our training procedure largely follows~\cite{carreira2017quo}. During training, we densely sample 64 frames from a video and resize input frames to $256\times 256$ and then take random crops of size $224\times 224$. During evaluation, we use all frames and take $224\times 224$ center crops from the resized frames. Our models are implemented with TensorFlow and optimized with a vanilla synchronous SGD algorithm with momentum of 0.9 and on 56 GPUs, batch size is set to 6 per GPU. For \MK, we train our model for 80k steps with an initial learning rate of 0.1. We decay the learning rate at step 60k to 0.01, and step 70k to 0.001. Since Something-something\ is a smaller dataset, we reduce the number of GPUs to 16 and train at learning rate of 0.1 for 10k steps.

\subsection{Measuring speed and accuracy}

We report top-1 and top-5 accuracy. To measure the computational efficiency of our models, we report theoretical FLOPS based on a single input video sequence of 64 frames and spatial size $224\times 224$. We pad the total number of frames to 250 for \MK\ and 64 for Something-something\ when evaluating.
\subsection{Spatio-temporal feature gating}
\label{sec:gating}

In this section we further improve the accuracy of our model by using feature gating.
We start by considering the context feature gating
mechanism first used for video classification in \cite{miech2017learnable}.
They consider an unstructured input feature vector $x \in \mathcal{R}^n$ (usually learned at final embedding layers close to the logit output),
and produce an output feature vector $y \in \mathcal{R}^n$ as follows:
\[
y = \sigma(W x + b) \odot x
\]
where $\odot$ represents elementwise multiplication,
$W \in \mathcal{R}^{n \times n}$ is a weight matrix,
and $b \in \mathcal{R}^n$ is the bias term.
This mechanism allows the model to upweight certain
dimensions of $x$ if the context model $\sigma(W x +b)$ predicts that they are important,
and to downweight irrelevant dimensions; this can be thought of as a ``self-attention'' mechanism.

We now extend this to feature tensors, with spatio-temporal structure.
Let $X \in \mathcal{R}^{T \times W \times H \times D}$ be the input tensor,
and let $Y$ be an output tensor of the same shape.
We replace the matrix product $W x$ with $W \mathrm{pool}(X)$,
where the pooling operation averages the dimensions of $X$ across space and time.
(We found that this worked better than  just averaging across space or just across time.)
We then compute $Y = \sigma(W \mathrm{pool}(X) + b) \odot X$,
where $\odot$ represents multiplication across the feature (channel) dimension,
(i.e., we replicate the attention map $\sigma(W \mathrm{pool}(X) + b)$ across space and time).

We can plug this gating module into any layer of the network.
We experimented with several options, and got the best results by applying it directly after
each of the $[k, 1, 1]$ temporal convolutions in the S3D network.
We call the final model (S3D with gating) \SG.
We see from Table~\ref{tab:FK} that this results in a healthy gain in accuracy
compared to \Sthree\
on the Kinetics-Full\ dataset (72.2\% top-1 to 74.7\%) at a very modest cost increase (66.38 GFLOPS to 71.38). Table~\ref{tab:something} shows that \SG\ also outperforms \Sthree\ and I3D\
on Something-something.
We also significantly outperform the current state of the art method,
which is the Multi-scale TRN of \cite{zhou_trn},
improving top-1 accuracy from 33.6\% to 42.0\%.
\section{Introduction}

The resurgence of convolutional neural networks (CNNs) has led to a wave of unprecedented advances for image classification using end-to-end hierarchical feature learning architectures \cite{krizhevsky2012imagenet,InceptionV1,simonyan2015very,resnet}. The task of video classification, however, has not enjoyed the same level of performance jump as in image classification.
In the past, one limitation was the lack of large-scale labeled video datasets.
However, the recent creation of
Sports-1M \cite{Karpathy2014},
Kinetics \cite{kay2017kinetics},
Something-something\ \cite{Something},
ActivityNet \cite{ActivityNet},
Charades \cite{Charades}, etc.
has partially removed that impediment.

Now we face more fundamental challenges.
In particular, we have three main barriers to overcome: (1)
how best to represent  spatial information (i.e., recognizing the appearances of objects);
(2) how best to represent temporal information
(i.e., recognizing  context, correlation and causation through time);
and (3)
how best to tradeoff model complexity with speed,
 both at training and testing time.

\begin{figure}[t]
\centering
\includegraphics[height=2.2in]{figures/teaser_2.png}
\caption{
Our goal is to classify videos into different categories, as shown in the top row.
We focus on two qualitatively different kinds of datasets:
Something-something\, which requires recognizing low-level physical interactions,
and Kinetics, which requires recognizing high-level activities.
The main question we seek to answer is what kind of network architecture to use.
We consider 4 main variants:
I2D, which is a 2D CNN, operating on multiple frames;
I3D, which is a 3D CNN, convolving over space and time;
Bottom-Heavy I3D, which uses 3D in the lower layers, and 2D in the higher layers;
and
Top-Heavy I3D, which uses 2D in the lower (larger) layers,
and 3D in the upper layers.
}
\label{fig:teaser}
\end{figure}

In this paper, we study these three questions by considering various kinds of 3D CNNs.
Our starting point is the state of the art approach, due to
Carreira and Zisserman~\cite{carreira2017quo},
known as ``I3D''
(since it ``inflates'' the 2D convolutional filters of the
``Inception'' network \cite{InceptionV1} to 3D).

Despite giving good performance,
this model is very computationally expensive.  This prompts several questions, which we seek to
address in this paper:
\begin{itemize}

\item Do we even need 3D convolution? If so, what layers should we make 3D, and what layers can be 2D? Does this depend on the nature of the dataset and task?

\item Is it important that we convolve jointly over time and space, or would it suffice to convolve over these dimensions independently?

\item How can we use answers to the above questions to improve on prior methods in terms of accuracy, speed and memory footprint?
\end{itemize}

To answer the first question,
we apply ``network surgery'' to obtain several
variants of the I3D architecture.
In one family of variants, which we call Bottom-Heavy-I3D, we retain 3D temporal convolutions at the lowest layers of the network
(the ones closest to the pixels), and use 2D convolutions for the higher layers.
In the other family of variants, which we call Top-Heavy-I3D, we do the opposite, and retain 3D temporal convolutions at the top layers, and use 2D for the lower layers
(see Figure~\ref{fig:teaser}).
We then investigate how to trade between accuracy and speed by varying
the number of layers that are ``deflated'' (converted to 2D) in this way.
We find that the Top-Heavy-I3D\ models are faster,
which is not surprising, since they only apply 3D to the abstract feature maps, which are smaller than the low level feature maps due to spatial pooling.
However, we also find that Top-Heavy-I3D\ models are often more accurate, which is surprising since they ignore low-level motion cues.

To answer the second question (about separating space and time), we consider replacing 3D convolutions
with spatial and temporal separable 3D convolutions,
i.e., we replace filters of the form $k_t \times k \times k$
by $1 \times k \times k$ followed by $k_t \times 1 \times 1$,
where $k_t$ is the width of the filter in time,
and $k$ is the height/width of the filter in space.
We call the resulting model \Sep, which stands for ``separable 3D CNN''.
\Sep\ obviously has many fewer parameters than models that use standard 3D convolution,
and it is more computationally efficient.
Surprisingly, we also show that it also has better accuracy than the original I3D model.

Finally, to answer the third question (about putting things together for an efficient and accurate video classification system), we combine what we have learned in answering
the above two questions with a spatio-temporal gating mechanism
to design a new model architecture
which we call \SG.
We show that this model gives significant gains in accuracy over baseline methods on  a variety of challenging video classification datasets, such as Kinetics,
Something-something, UCF-101 and HMDB,
and also outperforms many other methods on other video recognition tasks, such as action localization on JHMDB.

\subsection{Replacing \emph{all} 3D convolutions with 2D}
\label{sec:all3d}
\label{sec:pyramids}

In this section, we seek to determine how much value  3D convolution brings,
motivated by the surprising success of 2D CNN approaches
to video classification (see e.g., \cite{tsn_wang_eccv16}).
We do this by
replacing every 3D filter in the I3D model with a 2D filter.  This yields what we will refer to as the I2D
model.\footnote{
To reduce the memory and time requirements,
and to keep
the training protocol identical to I3D (in terms of the number of clips we use for training in each batch, etc),
we retain two max-pooling layers with temporal stride 2 between
Inception modules.
Hence, strictly speaking, I2D is not a pure 2D model. However, it is very similar to a single-frame 2D classification model.
}

Theoretically, the I2D network should be invariant to the temporal reversal of the input frames, since it is not capable of incorporating global signals. To verify this, we train I2D and the original I3D model on the Kinetics-Full\ and Something-something\ datasets with normal frame order, and apply the trained models on validation data in which the frames are in
normal order and reversed temporal order.
The results of the experiment are shown in Table~\ref{tab:arrowOfTime}.
We see that I2D has the same performance on both versions during testing, as is to be expected. However,
we notice an interesting difference between the Kinetics\ dataset and the Something-something\ dataset.
In the former case, the performance of I3D is indifferent to the  ``arrow of time''~\cite{pickup2014seeing},
whereas in the latter case, reversing the order hurts performance.
We believe this is because Something-something\ dataset requires fine-grained distinctions between visually similar action categories.

\setlength{\tabcolsep}{3pt}
\begin{table}
\begin{center}
\small
\begin{tabular}{c|c|c|c|c}
\hline
& \multicolumn{2}{c|}{Kinetics-Full} & \multicolumn{2}{c}{Something-something} \\
\hline
 Model  & Normal  (\%) & Reversed (\%) & Normal  (\%) & Reversed (\%)\\
\hline
I3D   & 71.1 & 71.1 & 45.8 & 15.2\\
I2D   & 67.0 & 67.2 & 34.4 & 35.2\\
\hline
\end{tabular}
\end{center}
\caption{Top-1 accuracy on Kinetics-Full\ and Something-something\ datasets.
We train on frames in normal order, and then test on frames
in normal order or reverse order.
Not surprisingly, 2D CNNs do not care about the order of the frames.
For 3D CNNs on Kinetics-Full\, the results are the same on normal and reverse order,
indicating
that capturing the ``arrow of time'' is not important on this dataset.
However, on Something-something\, the exact order does matter.
}
\label{tab:arrowOfTime}
\end{table}

\subsection{Replacing \emph{some} 3D convolutions with 2D}
\label{sec:some3d}

Although we have seen that 3D convolution can boost accuracy compared to 2D convolution, it is computationally very expensive.
In this section, we investigate the consequences of only replacing some of the 3D convolutions with 2D.
Specifically, starting with an I2D model, we gradually inflate 2D convolutions into 3D,
from low-level to high-level layers in the network,
to create what we call the Bottom-Heavy-I3D\ model.
We also consider the opposite process, in which we inflate the top layers of the model to 3D, but keep the lower layers 2D;
we call such models Top-Heavy-I3D\ models.

\begin{figure}
\centering
\begin{tabular}{cc}
\includegraphics[height=1.5in]{figures/acc_flops.png}
&
\includegraphics[height=1.5in]{figures/acc-flops-SS.png}
\\
(a) & (b)
\end{tabular}
\caption{Accuracy vs number of FLOPS needed to perform inference on 64 RGB frames.
Left: \MK\ dataset.
Right: Something-something\ dataset.
Solid lines  denote top-heavy models,
dotted lines denote bottom-heavy models.
Orange denotes spatial and temporal separable 3D convolutions, blue denotes full 3D convolutions.}
\label{fig:accuracyVsFlops}
\end{figure}

We train and evaluate the Bottom-Heavy-I3D\ and Top-Heavy-I3D\ models on \MK\ and Something-something,
and show the results in Figures~\ref{fig:accuracyVsFlops}.
We see that the solid blue lines (top heavy I3D) are much better than the dotted blue lines (bottom heavy I3D) under the same FLOPS, which indicates that top heavy models are faster and more accurate.
The speed increase is expected, since  in a top-heavy model,
the feature maps are reduced in size using spatial pooling before being convolved in 3D. For fixed computation budget, Top-Heavy-I3D is often significantly more accurate than Bottom-Heavy-I3D. This suggests that 3D convolutions are more capable and useful to model temporal patterns amongst high level features that are rich in semantics.

\begin{figure}[t]
\centering
  \includegraphics[width=12cm,height=2cm]{figures/weight_stats.png}
  \caption{Statistics of convolutional filter weights of an I3D model trained on Kinetics-Full. Each boxplot shows the distribution of $W_l(t,:,:,:)$ for temporal offset $t$, with $t=0$ being in the middle.
  Results for
  different layers $l$ are shown in different panels,
  with lowest layers on the left.
 All filters with different temporal offset are initialized with the same set of weights. Low-level
 filters essentially ignore the temporal dimension,
 unlike higher level filters,
 where the weights distributed nicely across different temporal offsets. }
  \label{fig:weights}
\end{figure}

\subsection{Analysis of weight distribution of learned filters}

To verify the above intuition, we examined the  weights of an I3D model
which was trained on Kinetics-Full.
Figure~\ref{fig:weights} shows the distribution of these weights across  4 layers of our model, from low-level to high-level. In particular, each boxplot shows the distribution of $W_l(t,:,:,:)$ for temporal offset $t$ and layer $l$.
We use $t=0$ to indicate no offset in time, i.e.,
the center in the temporal kernel.
At initialization, all the filters started with the same set of (2D convolution) weights (derived from an Inception model pre-trained on Imagenet) for each value of $t \in \{-1,0,1\}.$
After training, we see that the temporally offset filters (i.e., for $t \neq 0$) have a weight distribution that is still closely centered on zero in the lower layers (see left panel), whereas the variance of the distribution increases in higher layers (see right panel).
This suggests once again that the higher level temporal patterns are more useful for the Kinetics action classification task.

\section{Related work}
\label{sec:related}

2D CNNs have achieved state of the art results for image classification,
so, not surprisingly,
 there have been many recent attempts to extend these successes
to video classification.
 The Inception 3D (I3D) architecture~\cite{carreira2017quo} proposed by Carreira and Zisserman is one of the current
 state-of-the-art models. There are three key ingredients for its success: first, they ``inflate'' all the 2D convolution filters used by the Inception V1 architecture~\cite{InceptionV1} into 3D convolutions, and carefully choose the temporal kernel size in the earlier layers. Second, they initialize the inflated model weights by duplicating weights
 that were pre-trained on ImageNet classification
 over the temporal dimension. Finally, they train the network on
  the large-scale Kinetics dataset~\cite{kay2017kinetics}.

Unfortunately, 3D CNNs are computationally expensive, so there has been recent interest in more efficient variants.
In concurrent work, \cite{Tran2018}
has recently proposed a variety of models based on top
of the ResNet architecture \cite{resnet}.
In particular, they consider models that use 3D convolution in either the bottom or top layers, and 2D in the rest; they call these ``mixed convolutional'' models.
This is similar to our top-heavy and bottom-heavy models.
They conclude that bottom heavy networks are more accurate,
which contradicts our finding.
However, the differences they find between top heavy and bottom heavy are fairly small,
and are conflated with changes in computational complexity.
By studying the entire speed-accuracy tradeoff curve (of Inception variants),
we show that there are clear benefits to using a top-heavy design for a given computational budget (see Section~\ref{sec:some3d}).

Another way to save computation is to replace 3D convolutions with separable convolutions, in which we first convolve spatially in 2D, and then convolve temporally in 1D.
We call the resulting model S3D.
This factorization is similar in spirit to the depth-wise separable convolutions
 used in \cite{xception,mobilenet,resnext}, except that we apply the idea
 to the temporal dimension instead of the feature dimension.
This idea has been used in a variety of recent papers,
including \cite{Tran2018} (who call it ``R(2+1)D''),
\cite{P3D} (who call it ``Pseudo-3D network''),
\cite{Sun2015} (who call it ``factorized spatio-temporal convolutional networks''),
etc.
We use the same method, but combine it with both top-heavy and bottom-heavy designs, which is a combination that leads to a very efficient video classification system.
We show that the gains from separable convolution are complementary to the gains from using a top-heavy design (see Section~\ref{sec:sepconv}).

An efficient way to improve accuracy is to use feature gating, which captures dependencies between feature channels with a simple but effective multiplicative transformation.
This can be viewed as an efficient approximation to second-order pooling
as shown in \cite{Girdhar_17b_AttentionalPoolingAction}.
Feature gating has been used for many tasks,
such as machine translation~\cite{dauphin2016language},
VQA~\cite{perez2017film},
reinforcement learning~\cite{elfwing2018sigmoid}, image classification~\cite{ramachandran2017swish,hu2017squeeze},
and action recognition~\cite{miech2017learnable}.
We consider a variant of the above techniques in
which we place the feature gating module after each of the temporal convolutions in an S3D network,
and show that this results in substantial gains in accuracy (see Section~\ref{sec:gating}).

Another way to improve accuracy (at somewhat higher cost)
is to use precomputed optical flow features.
This idea was successfully used in \cite{TwoStreamVGG},
who proposed a two-stream architecture where one CNN stream handles raw RGB input,  and the other
 handles  precomputed optical flow.
Since then, many video classification methods follow the same multi-stream 2D CNN design, and have made improvements in terms of new representations~\cite{bilen2016dynamic,bilen2017action}, different backbone architecture~\cite{FeichtenhoferCVPR17,TwoStreamCUHK,ng_cvpr15,Girdhar_17b_AttentionalPoolingAction}, fusion of the streams~\cite{Feichtenhofer16,feichtenhofer2017,FeichtenhoferNIPS16,chained_fusion} and exploiting richer temporal structures~\cite{lrcn2014,Wang_Transformation,tsn_wang_eccv16}.
We will study the benefits of using optical flow in Section~\ref{sec:flow}.

\subsection{Separating temporal convolution from spatial convolutions}
\label{sec:sepconv}

In this section, we study the effect of replacing  standard 3D convolution with a
factored version which disentangles this operation into a temporal part and a spatial part.
In more detail, our method is to
 replace each 3D convolution with two consecutive convolution layers: one 2D convolution layer to learn spatial features, followed by a 1D convolution layer purely on the temporal axis.
This can be implemented  by running two 3D convolutions, where the first (spatial) convolution has filter shape $[1,k,k]$ and the
second (temporal) convolution has filter shape $[k, 1, 1]$.
By applying this factorization to I3D, we obtain a
model which we refer to as S3D. For a detailed illustration of the architecture, please refer to Figure~\ref{fig:s3d}.\footnote{
There are 4 branches in an Inception block, but only two of them have 3x3 convolutions (the other two being pointwise 1x1 convolutions), as shown in Figure~\ref{fig:inc_blocks}.
As such, when I3D inflates the convolutions to 3D, only some of the features contain temporal information.
However, by using  separable temporal convolution, we can add temporal information  to all 4 branches.
This improves the performance from $78.4\%$ to $78.9\%$ on \MK.
In the following sections, whenever we refer to an S3D model, we mean S3D with such configuration.
}

\begin{figure}[!htp]
\begin{center}
\includegraphics[height=30mm]{figures/s3dg.pdf}
\end{center}
\caption{
An illustration of the S3D model.
Dark red boxes are
temporal separable convolutions (sep-conv),
and pink boxes are temporal separable
inception blocks, shown in
Figure~\ref{fig:sep_inc_block}.
}
\label{fig:s3d}
\end{figure}

\label{sec:resultsSeparable}

Table~\ref{tab:FK}
compares the results of S3D and I3D on Kinetics-Full. Table~\ref{tab:something} shows that S3D also outperforms I3D on the Something-something\ dataset.
The results show that, despite a substantial compression in model size
(12.06M parameters for I3D reduced to 8.77M for S3D),
and a large speed-up ($107.9$ GFLOPS for I3D reduced to $66.38$ GFLOPS for
S3D), the separable model is even more accurate
(top-1 accuracy improved from 71.1\% to 72.2\% for Kinetics-Full, and from 45.8\% to 47.3\% for Something-something).
We believe the gain in accuracy is because the
spatio-temporal
factorization reduces overfitting, in a way without sacrificing the expressiveness of the representation, as we find that simply reducing the parameters of the network does not help with the performance.

Note that we can apply this separable transformation to any place where 3D convolution is used;
thus this idea is orthogonal to the question of which layers should contain 3D convolution, which we discussed
in Section~\ref{sec:pyramids}.
We denote
the separable version of the
Bottom-Heavy-I3D\ models by Bottom-Heavy-S3D,
and the separable version of the
Top-Heavy-I3D\ models by Top-Heavy-S3D,
thus
giving us 4 families of models.

We plot the speed vs accuracy of these models
in Figure~\ref{fig:accuracyVsFlops}. We see that separable top-heavy models offer the best speed-accuracy trade-off.
In particular, the model in which we keep the top 2 layers as separable 3D convolutions,
and make the rest 2D convolutions,
seems to be a kind of ``sweet spot''.
We call this model ``Fast-S3D'',
since it is is 2.5 times more efficient than I3D
(43.47 vs 107.9 GFLOPS), and yet has comparable
accuracy (78.0\% vs 78.4\% on \MK).

\begin{figure}[htp]
\begin{center}
\includegraphics[height=59mm]{figures/tsne_new.pdf}
\end{center}
\caption{tSNE projection of activation maps
derived from images in the Something-something\ dataset.
Colors and numbers represent the 10 action groups defined in \cite{Something}.
The top row shows increasing semantic separation as we move to higher layers of S3D.
The bottom row shows activations at level Max5a for 4 different models.
We see that Top-Heavy-S3D has better semantic separation
than Bottom-Heavy-S3D, especially for visually similar categories inside the red box.
}
\label{fig:tSNE}
\end{figure}

\subsection{tSNE analysis of the features}
\label{sec:tSNE}

Here we explore the spatiotemporal representations learned by different levels
of the S3D model
on the Something-something\ dataset,
using the tool of tSNE projection \cite{tSNE}.
The behavior of the I3D models is very similar.
Instead of using samples from all 174 categories, we use a smaller vocabulary, namely the ``10 action groups'' defined in \cite{Something}.\footnote{
The labels are as follows.
0: Dropping [something],
1: Moving [something] from right to left,
2: Moving [something] from left to right,
3: Picking [something],
4:Putting [something],
5: Poking [something],
6: Tearing [something],
7: Pouring [something],
8: Holding [something],
9: Showing [something].
} %
We sample ~2,200 data points from the validation set.
In Figure~\ref{fig:tSNE}, the top row shows representations learned by a S3D model,  at levels from Max3a to Max5c.
The class separation becomed increasingly clearer at higher levels.

The bottom row shows representations learned at a certain feature level (Max5a), but across different models including I2D, Bottom-Heavy-S3D and Top-Heavy-S3D (both have a 2D-3D transition point at Max4b layer),
as well as a full S3D model. Comparing the bottom-heavy and top-heavy models, for subtle actions such as ``3: Picking'', ``4: Putting'' and ``5: Poking'' something, representations learned with a top-heavy model are more discriminative than that in a bottom-heavy model, thus leading to  better class separations with the tSNE projection (highlighted with the red box). A top-heavy model can learn features that are as good as those learned with a full 3D model, and significantly better than those from the 2D model, without much sacrifice in processing speed.
This observation further supports our hypothesis that temporal information modeling is most effective at top levels in the feature hierarchy for action classification tasks.

\begin{table}[!htp]
\begin{center}
\begin{tabular}{c|c|c|c|c}
\hline
Model  &  Top-1 (\%) & Top-5 (\%)  & Params (M) & FLOPS (G) \\
\hline
I3D  & 71.1 & 89.3 & $12.06$ & $107.89$ \\
\Sthree & 72.2 & 90.6 & $8.77$ & $66.38$ \\
\SG  & {\bf 74.7} & {\bf 93.4} & $11.56$ & $71.38$ \\
\hline
\end{tabular}
\end{center}
\caption{Effect of separable convolution and feature gating on the Kinetics-Full\ validation set using RGB features.
}
\label{tab:FK}
\end{table}

\begin{table}[!htp]
\begin{center}
\begin{tabular}{c|c|c|c|c}
\hline
Model & Backbone & Val Top-1 (\%) & Val Top-5 (\%) & Test Top-1 (\%)\\
\hline
Pre-3D CNN + Avg~\cite{Something} & VGG-16 & - & - & 11.5 \\
Multi-scale TRN~\cite{zhou_trn} & Inception & 34.4 & 63.2 & 33.6 \\
\hline
I2D & Inception & 34.4 & 69.0 & - \\
I3D & Inception & 45.8 & 76.5 & - \\
\Sthree & Inception & 47.3 & 78.1 & - \\
\SG  & Inception & {\bf 48.2} & {\bf 78.7} & {\bf 42.0} \\
\hline
\end{tabular}
\end{center}
\caption{
Effect of separable convolution and feature gating on
the Something-something\ validation and test sets  using RGB features.
}
\label{tab:something}
\end{table}

\section{Network surgery}
\label{sec:surgery}

In this section, we report the results of various ``network surgery'' experiments, where we vary different aspects of the I3D model to study the effects on speed and accuracy.

\begin{figure}[!htp]
\begin{center}
\subfigure[I3D]{
\includegraphics[height=25mm]{figures/i3d.pdf}
\label{fig:detail_i3d}
}
\subfigure[I2D]{
\includegraphics[height=25mm]{figures/i2d.pdf}
\label{fig:detail_i2d}
}
\subfigure[Bottom-heavy I3D]{
\includegraphics[height=25mm]{figures/bottomheavy_i3d.pdf}
\label{fig:detail_bottomheavy_i3d}
}
\subfigure[Top-heavy I3D]{
\includegraphics[height=25mm]{figures/topheavy_i3d.pdf}
\label{fig:detail_topheavy_i3d}
}
\end{center}
\caption{
Network architecture details for (a) I3D, (b) I2D, (c) Bottom-Heavy and (d) Top-Heavy variants. $K$ indexes the spatio-temporal convolutional layers. The ``2D Inc.'' and ``3D Inc.'' blocks
refer to 2D and 3D inception blocks, defined in Figure~\ref{fig:inc_blocks}.}
\end{figure}

\begin{figure}
\begin{center}
\subfigure[]{
\includegraphics[height=35mm]{figures/inc_block_2d.pdf}
\label{fig:inc_block_2d}
}\qquad
\subfigure[]{
\includegraphics[height=35mm]{figures/inc_block_3d.pdf}
\label{fig:inc_block_3d}
}\qquad
\subfigure[]{
\includegraphics[height=35mm]{figures/sep_inc_block.pdf}
\label{fig:sep_inc_block}
}
\end{center}
\caption{\subref{fig:inc_block_2d} 2D Inception block;
\subref{fig:inc_block_3d} 3D Inception block;
\subref{fig:sep_inc_block} 3D temporal separable Inception block used in S3D networks.
}
\label{fig:inc_blocks}
\end{figure}

\input{pyramids}
\input{sepconv}
\input{gating}
\section{Generalization to other modalities, data and tasks}

In this section,  we evaluate the generality and robustness of the proposed \SG\ architecture by conducting transfer learning experiments on different input modalities, video datasets, and tasks.

\subsection{Using optical flow features}
\label{sec:flow}

We first verify if \SG\ also works with optical flow inputs.
For these experiments, we follow the standard setup as described in~\cite{carreira2017quo} and extract optical flow features with the TV-L1 approach~\cite{tvl1}. We truncate the flow magnitude at $[-20, 20]$ and store them as encoded JPEG files. Other experiment settings are the same as the RGB experiments. From Table~\ref{tab:flow}, we can see that the improvement of \SG\ over I3D is consistent
with the gain we saw with RGB inputs,
bringing the performance up from $63.91\%$ to $68.00\%$.
By ensembling
 the two streams of RGB and flow, we obtain
 a performance of 77.22\%,
 which is a 3\% boost over the I3D network when trained on the same data. We note that even though we focus on the speed-accuracy trade-offs in action classification network design, the performance is competitive compared with recent Kinetics Challenge winners and concurrent works; notably \cite{kinetics_winner} and \cite{wang2018non} use heavier backbone architectures (e.g. ResNet 101 has ~8.5x more FLOPS than our S3D-G architecture)

\begin{table*}[htp]
\begin{center}
\begin{tabular}{c|c|c|c|c|c}
\hline
Model & Inputs & Backbone & Pre-train & Top-1 (\%) & Top-5 (\%)\\
\hline
NL I3D \cite{wang2018non} & RGB & ResNet-101 & ImNet & {\bf 77.7} & {\bf 93.3} \\
SAN \cite{kinetics_winner} & RGB+Flow+Audio & Inception-ResNet-v2 & ImNet & {\bf 77.7} & {\bf 93.2} \\
TSN \cite{tsn_wang_eccv16} & RGB+Flow & Inception & ImNet & 73.9 & 91.1 \\
ARTNet \cite{ARTNet} & RGB+Flow & ResNet-18 & ImNet & 72.4 & 90.4\\
R(2+1)D \cite{Tran2018} & RGB+Flow & ResNet-34 & Sports-1M & 75.4 & 91.9 \\
\hline
I3D & Flow & Inception & ImNet & 63.9 & 85.0 \\
I3D & RGB & Inception & ImNet & 71.1 & 89.3 \\
I3D & RGB+Flow & Inception & ImNet & 74.1 & 91.6 \\
\hline
\SG & Flow & Inception & ImNet & 68.0 & 87.6 \\
\SG & RGB & Inception & ImNet & 74.7 & {\bf 93.4} \\
\SG & RGB+Flow & Inception & ImNet & {\bf 77.2}  & {\bf 93.0} \\
\hline
\end{tabular}
\end{center}
\caption{Benefits of using optical flow.
We report results
on the Kinetics-Full\ validation set.
We report I3D performance based on our implementation, as~\cite{carreira2017quo} only report results on the held-out test set (where they get a top-1 accuracy of 74.2\% using RGB+flow and ImNet pretraining).
}
\label{tab:flow}
\end{table*}

\subsection{Fine-tuning on other video classification datasets}
\label{sec:otherClassification}

Next we conduct transfer learning experiments from Kinetics to other video classification datasets,
namely HMDB-51~\cite{hmdb_kuehne2011} and UCF-101~\cite{ucf101}.
HMDB-51 contains around 7,000 videos spanning over 51 categories, while UCF-101 has 13,320 videos spanning over 101 categories.
 Both datasets  consist of short video clips that are temporally trimmed, and contain
 3 training and validation splits.
 We follow the standard setup as used in previous work and report average accuracy across all splits.

For our transfer learning experiments, we use the same setup as training on Kinetics, but change the number of GPUs to 8 and lower the learning rate to 0.01 for 6K steps, and 0.001 for another 2K steps.
For simplicity, we only use RGB (no optical flow).

Table~\ref{tab:cls-transfer} shows the results of this experiment. On UCF-101, our proposed \SG\ architecture,
which only uses Kinetics for pretraining,
outperforms I3D,
and matches R(2+1)D, both of which use largescale datasets (Kinetics and Sports-1M) for pretraining.
On HMDB-51, we outperform all previous methods published to date.

\begin{table}[pt!]
\small
\begin{center}
\begin{tabular}{c|c|c|c|c}
\hline
Model & Inputs & Pre-train & UCF-101 & HMDB-51 \\
\hline
P3D~\cite{Qiu_2017_ICCV} & RGB & Sports-1M & 88.6 & - \\
C3D~\cite{TranC3D} & RGB & Sports-1M & 82.3 & 51.6 \\
Res3D~\cite{TranR3D} & RGB & Sports-1M & 85.8 & 54.9 \\
ARTNet w/ TSN~\cite{ARTNet} & RGB & Kinetics & 94.3 & 70.9 \\
I3D~\cite{carreira2017quo} & RGB & ImNet+Kinetics & 95.6 & 74.8 \\
R(2+1)D \cite{Tran2018} & RGB & Kinetics & {\bf 96.8} & 74.5 \\
\hline
\SG	& RGB & ImNet+Kinetics & {\bf 96.8} & {\bf 75.9} \\
\hline
\end{tabular}
\end{center}
\caption{Results of various methods on action classification on the  UCF-101 and HMDB-51 datasets.
All numbers are computed as the average accuracy across three splits.
}
\label{tab:cls-transfer}
\end{table}

\subsection{Spatio-temporal action detection in video}
\label{sec:detection}

Finally, we demonstrate the effectiveness of \SG\ on action detection tasks, where the inputs are video frames, and the outputs are bounding boxes associated with action labels on the frames.
Similar to the framework proposed in~\cite{peng2016multi}, we use the Faster-RCNN~\cite{ren2015faster} object detection algorithm to jointly perform person localization and action recognition. We use the same approach as described in~\cite{ava} to incorporate temporal context information via 3D networks. To be more specific, the model uses a 2D ResNet-50~\cite{resnet} network that takes the annotated keyframe (frame with box annotations) as input, and extract features for region proposal generation on the keyframe. We then use a 3D network (such as I3D or \SG) that takes the frames surrounding the keyframe as input, and extract feature maps which are then pooled for bounding box classification. The 2D region proposal network (RPN) and 3D action classification network are jointly trained end-to-end. Note that we extend the ROIPooling operation to handle 3D feature maps by simply pooling at the same spatial locations over all time steps.

We report performance on two widely adopted video action detection datasets: JHDMB~\cite{jhmdb} and UCF-101-24~\cite{ucf101}. JHMDB dataset is a subset of HMDB-51, it consists of 928 videos for 21 action categories, and each video clip contains 15 to 40 frames. UCF-101-24 is a subset of UCF-101 with 24 labels and 3207 videos; we use the cleaned bounding box annotations from~\cite{micro_tube2017}. We report performance using the standard frame-AP metric defined in~\cite{gkioxari2015}, which is computed as the average precision of action detection over all individual frames, at the intersection-over-union (IoU) threshold of 0.5. As commonly used by previous work, we report average performance over three splits of JHMDB and the first split for UCF-101-24.

Our implementation is based on the TensorFlow Object Detection API~\cite{huang2016speed}. We train Faster-RCNN with asynchronous SGD on 11 GPUs for 600K iterations. We fix the input resolution to be $320\times400$ pixels. For both training and validation, we fix the size of temporal context to 20 frames. All the other model parameters are set based on the recommended values from~\cite{huang2016speed}. The ResNet-50 networks are initialized with ImageNet pre-trained models, and I3D and \SG are pre-trained from Kinetics. We extract 3D feature maps from the ``\textit{Mixed 4e}'' layer which has a spatial stride of 16.

Table~\ref{tab:det-transfer} shows the comparison between I3D, \SG, and other state-of-the-art methods. We can see that both 3D networks outperform previous architectures by large margins, while \SG~is consistently better than I3D.

\begin{table}[!htp]
\begin{center}
\begin{tabular}{c|c|c|c}
\hline
Model & Inputs & JHMDB & UCF-101 \\
\hline
Gkioxari and Malik~\cite{gkioxari2015} &RGB+Flow & 36.2 & - \\
Weinzaepfel \emph{et al}.~\cite{weinzaepfel2015} &RGB+Flow & 45.8 & 35.8 \\
Peng and Schmid~\cite{peng2016multi} & RGB+Flow & 58.5 & 65.7 \\
Kalogeiton \emph{et al}.~\cite{kalogeiton17iccv} & RGB+Flow & 65.7 & 69.5 \\
\hline
Faster RCNN + I3D~\cite{ava} &RGB+Flow & 73.2 & 76.3 \\
Faster RCNN + \SG & RGB+Flow & {\bf 75.2} & {\bf 78.8} \\
\hline
\end{tabular}
\end{center}
\caption{Results of various methods on action detection in JHMDB and UCF101.
We report frame-mAP at IoU threshold of 0.5 on JHMDB (all splits) and UCF-101-24 (split 1) datasets.
}
\label{tab:det-transfer}
\end{table}

redpajama_set_name: RedPajamaArXiv


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Federal law enforcement professionals, working without pay, say they've been reduced to bargaining chips.
WASHINGTON - An FBI agents group warned Thursday that the partial government shutdown threatens national security, as thousands of federal law enforcement professionals working without pay grow anxious that personal financial hardships could jeopardize their security clearances and investigations slow with the furloughs of their support staff.
The shutdown is the result of President Donald Trump's insistence that more miles of border wall must be built in the interest of national security - to keep out illegal immigrants and drugs - and Democrats' refusal to go along with his demands for $5.7 billion in wall-construction funds. As the shutdown enters it third week, groups representing those who do everything from patrol borders and guard courthouses to make undercover drug buys have expressed alarm that the political drama has reduced them to bargaining chips while they continue doing dangerous jobs that keep Americans safe.
"It's uncharted territory, as this shutdown is going to be the longest in history," said Thomas O'Connor, president of the FBI Agents Association. "For special agents, financial security is national security."
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In a letter to the White House and lawmakers, FBIAA leaders wrote that their agents "are subject to high security standards that include rigorous and routine financial background checks . . . Missing payments on debts could create delays in securing or renewing security clearances, and could even disqualify agents from continuing to serve in some cases."
O'Connor said FBI investigations already are being affected. No one at the FBI is getting paid, but investigators are still working while much of their support staff, including some surveillance experts, is not, O'Connor said. The FBI lab in Quantico, Virginia, has faced significant staff reductions, and the money available for investigative expenses like undercover drug buys is dwindling, he said.
"Operations are being hindered," said O'Connor. "This situation is not sustainable."
The FBI has nearly 13,000 special agents, and already, some families are making tough choices to manage household finances. Several current FBI employees said Thursday they worry the shutdown shows no sign of being resolved soon and that, as a result, they could miss not one but two paychecks.
Gomez, who experienced a shutdown during the Obama administration, said that back then he told his agents not to worry because shutdowns only last a week or two, not long enough to miss a paycheck.
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The shutdown affects all the federal law enforcement agencies within the departments of Justice and Homeland Security. U.S. Marshals guarding the trial of accused drug kingpin Joaquin "El Chapo" Guzman in New York, amid extremely tight security, are working without pay. Secret Service agents, DEA agents, ATF agents, Immigration and Customs Enforcement personnel, and Customs and Border Protection officers all are working without pay and much of their support staff.
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FLEOA has warned its members "to be extra vigilant and cautious performing your duties" because of the increased risks associated with not having support staff available.
The shutdown debate is more complicated within the Border Patrol, because many of those agents support Trump's demand for more wall construction.
Border Patrol union leaders appeared at the White House last week to show their support for Trump despite agents working without pay. And while the president continues to enjoy broad support within the Border Patrol, but some agents say there is growing worry among the rank-and-file about missed car payments and late mortgages. Many of those who are assigned to remote border towns are their families' sole breadwinner.
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