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MAKING OF AN ASTRONAUT
MAKING OF AN OLYMPIAN
Mysterious 'alien megastructure' star may not be so special after all
Astronomers found more than a dozen stars that display similarly mystifying behavior.
This artist's illustration depicts a hypothetical dust ring orbiting KIC 8462852, also known as Boyajian's Star or Tabby's Star.NASA / JPL-Caltech
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Sept. 19, 2019, 8:28 PM UTC / Source: Space.com
By Charles Q. Choi, Space.com
A mysterious star whose repeated bouts of darkening might be due to "alien megastructures," according to some researchers' conjectures, may now have more than a dozen counterparts that display similarly mystifying behavior, a new study finds.
Further research into all of these stars might help solve the puzzle of their bewildering flickering, the study's author said.
In 2015, scientists noticed unusual fluctuations in the light from a star named KIC 8462852. This otherwise-normal F-type star, which is slightly larger and hotter than Earth's sun, sits about 1,480 light-years from Earth, in the constellation Cygnus.
Related: 13 Ways to Hunt Intelligent Aliens
When the researchers analyzed data from NASA's Kepler space telescope, astronomer Tabetha "Tabby" Boyajian, then at Yale University, and her colleagues found dozens of odd instances of KIC 8462852 dimming by up to 22 percent, with such dips lasting anywhere from a few days to a week. These events did not appear to follow any pattern and seemed far too substantial to be caused by planets or dust crossing the star's face.
These analyses of KIC 8462852 — now nicknamed "Boyajian's star" (formerly Tabby's star) after its discoverer — raised the possibility that astronomers had detected signs of intelligent alien life. Specifically, researchers have suggested that the star is surrounded by a Dyson sphere, a hypothetical megastructure that is built around a star to capture as much of its light as possible. Mathematician and physicist Freeman Dyson suggested that such megastructures could help power an advanced civilization. (Science fiction often depicts Dyson spheres as solid shells around stars, but the megastructures also could be globular swarms of giant solar panels.)
The megastructure hypothesis is near the bottom of most astronomers' lists these days when it comes to Boyajian's star, however; further analyses have pointed to more prosaic explanations, such as clouds of dust or comet fragments. Still, scientists have not yet nailed down the precise cause of the odd dimming. The answer remains elusive in part because Boyajian's star seemed unique; there were no known counterparts to provide additional clues that might help researchers solve this cosmic mystery.
Now, study author Edward Schmidt, an astrophysicist at the University of Nebraska-Lincoln, suggests that he may have discovered more than a dozen stars like Boyajian's star.
SpaceHuge, mysterious 'bubbles' discovered at the heart of the Milky Way
Schmidt looked for counterparts of Boyajian's star using software that searched for analogous dimming events from about 14 million objects with varying brightness monitored in the Northern Sky Variable Survey from April 1999 to March 2000. He then followed up on promising candidates by examining their long-term behavior, using data from the All-Sky Automated Survey for Supernovae, ruling out sources whose dimming could be caused by conventional explanations such as an eclipsing companion star or some intrinsic variability in brightness.
Schmidt identified 21 stars that showed possibly unusual dimming. These fell into two distinct categories: 15 were "slow dippers" that dimmed at rates similar to Boyajian's star, and six were "rapid dippers" that showed even more extreme variability in their dimming rates.
"The thing that surprised me the most were these stars that had so many dips, the ones I called 'rapid dippers,'" Schmidt told Space.com. "I expected more occasional dips like Boyajian's star."
Further analysis using data from the European Space Agency's Gaia space observatory found that these potential dippers tended to be either conventional "main-sequence" stars with about the same mass as the sun or red giant stars with about twice the sun's mass. The slow and rapid dippers are seen in both groups, which may suggest that they represent varying degrees of the same mechanism, Schmidt said.
Schmidt noted that the Northern Sky Variable Survey he searched for potential counterparts of Boyajian's star did not contain records of Boyajian's star itself darkening during the year of data in that catalog. This highlights how astronomers may easily miss stars that can darken in this manner if they look only at catalogs that monitor stars for relatively short intervals of time.
"We're obviously missing some of these stars because of the catalogs we have," Schmidt said. "By looking at more catalogs, we may get a better picture of what's going on, even though it won't be a complete picture."
Future research that combs through more catalogs of stellar activity may turn up even more analogues of Boyajian's star, he said.
"I intend to try and follow up on the rapid dippers," Schmidt said. "One thing I noticed about them is that at least one seemed to be slowing way down in its dipping rate over the five years of coverage we have of it. It'd be interesting to find out what happened in its past, which may help give a better idea of what's going on with these stars."
Schmidt detailed his findings July 18 in The Astrophysical Journal Letters.
'Alien Megastructure' Ruled Out for Some of Star's Weird Dimming
10 Exoplanets That Could Host Alien Life
5 Bold Claims of Alien Life
Follow Charles Q. Choi on Twitter @cqchoi. Follow us on Twitter @Spacedotcom and on Facebook.
Sign up for the MACH newsletter and follow NBC News MACH on Twitter and Facebook and Instagram.


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Safety improvements project in Lakeway will see medians placed along RM 620 by end of summer
Texas Department of Transportation engineer Epi Gonzalez updated Lakeway City Council on proposed raised medians that will run through a portion of the city along RM 620. (Brian Rash/Community Impact Newspaper)
By Brian Rash | 2:44 PM Dec. 9, 2019 CST | Updated 2:44 PM Dec. 9, 2019 CST
Lakeway City Council voted to approve a transportation project that has been in the works for almost a year.
Texas Department of Transportation engineer Epi Gonzalez updated Lakeway City Council on proposed raised medians that will run through a portion of the city along RM 620 during council's special session Dec. 9.
According to city documents, the project, which will be funded by TxDOT, will place raised medians on RM 620's center land and span from Lohmans Crossing Boulevard on the southern portion and run slightly past Bella Montangna Circle on the north end.
Gonzalez said the project limits were recently increased by TxDOT and initially stopped at Lakeway Boulevard on the north end.
Gonzalez also discussed travel times that would be increased by adding the medians due to the fact that motorists will no longer be able to turn left or right from RM 620 at any point.
"Once the raised median is in place, you're not going to be able to make that left turn there," Gonzalez said, using the section of RM 620 at Randall's in north Lakeway as an example of how motorists will have to continue to traffic lights to make turns when the project is complete.
Council initially discussed the matter during a special session last December with the goal of making safety improvements ahead of the massive RM 620 widening project that will take the road from five lanes to six lanes and for which construction is scheduled to begin by 2022.
Gonzalez also said the points at which motorists will be able to turn off of RM 620 or make U-turns once the medians are installed will mirror the access points planned as part of the larger widening project.
"Right now, we're doing the design in-house, and we're looking at an April letting," Gonzalez said of when the project would go out for contract. "I checked with the Lake Travis school district, and their last day in May falls in line with the timing of our project."
Most of the construction work during the summer months will be conducted at night, he said, adding he is confident it can be substantially complete by the end of summer, though a final timeline has not been set.
Regarding costs, Gonzalez said the project carries a price tag estimated at $450,000, all of which would be covered by TxDOT. One aspect the department does request help with from the city of Lakeway, he said, is garnering support from businesses along RM 620.
Mayor Sandy Cox said she appreciated the fact that TxDOT is paying for the project.

redpajama_set_name: RedPajamaCommonCrawl


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Anonymous asked in Arts & HumanitiesBooks & Authors · 9 years ago
What are some good book series I can read?
I've just recently finished both The Hunger Games and The inheritance cycle (Eragon books) I'd like another book series to start reading, id prefer something long with many books and it has to be interesting, list any book series and if you can how many books in the series. Please do not list Harry Potter or Twilight as I have no interest in those books. Anything else goes! Thanks!
Ranger's Apprentice series by John Flanagan (10 books + bonus volume)
Fablehaven series by Brandon Mull (5 books)
Anne of Green Gables by L.M. Montgomery (10 books)
The Anybodies by N.E. Bode (only 3 books...but very cute)
Bloody Jack Adventures by L.A. Meyer (9 books--my sister loved these)
His Dark Materials trilogy by Philip Pullman (3 books)
Pendragon Cycle by Stephen Lawhead (5 books + 1 stand-alone)
Chronicles of Narnia by C.S. Lewis (7 books)
Dark is Rising by Susan Cooper (5 books)
Peter and the Starcatchers by Dave Berry (4 books)
Hitchhiker's Guide to the Galaxy by Douglas Adams (8 books)
Unicorn Chronicles by Bruce Coville (4 books)
Enchanted Forest Chronicles by Patricia C. Wrede (4 books)
Wrinkle in Time by Madelein l'Engle (4 books)
Happy reading! :)
Tree Shepherd's Daughter, Gillian Summers (trilogy)
Chronicles of Narnia, C. S. Lewis (six)
Michael Vey: The Prisoner of Cell 25, Richard Evans (first in a projected series of 10)
Alcatraz versus the Evil Librarians, Brandon Sanderson (four in series with one more being written).
The Alchemist by Michael Scott. He has written four books named the following: Alchemist, Magician, Sorcerer, Necromancer. I loved these books! They each have 400-500 pages in them, but I always finished them in a couple days they were that great. Check it out; you liked the Eragon series you'd love these. He creates a world within our world. I cant wait until Scott comes out with the next book.
Source(s): What ive read
THE HUNGER GAMES. Can't stress this enough! They're so good! And anything Ellen Hopkins. Oh, and the Vanished books by Meg cabot
Dark Secrets by Elizabeth Chandler ! House of night novel ! Lying game :)
The Mortal Instruments by Cassandra Clare, really good!
The Bad Blood series. (Three novels.)
The Mageworlds series. (Seven novels.)
Is the world a better place now that the Jewish murderer Phil Spector has died?
Do you believe that Trump and the GOP's biggest mistake was underestimating how skilled the Dems are at cheating?
I Need A Hobby For My Character. What Should It Be?
Does anyone of even modest intelligence seriously believe that the Queen and Prince Philip had the COVID-19 "vaccine"?
World Communism vs Trump?
Which Haruki Murakami book should I start with?
What types of books do people like to read ?
What do you think of the name Sean?
How to read a book without re-reading previous words or sentence?
What books can help teens gain a new perspective on life?


Miley Cyrus is facing a lawsuit from the Jamaican dancehall star Flourgon (Michael May) which has been described in the popular press as a $300 million claim for copyright infringement which focuses on the lyrics single Cyrus's 2013 hit single 'We Can't Stop'.
In Flourgon's 'We Run Things' the lyric reads 'We run things, things no run we'. The similarity between the lyrics is the basis of the claim which alleges that this line was utilised when Cyrus sings 'We run things, things don't run we' in her We Can't Stop.
But Cyrus is not the only one in the firing line; the lawsuit also names Sony Records label RCA, the songs co-writers Rock City (Timothy Thomas and Theron Thomas), Mike Will Made It, and manager Larry Rudolph.
The lawsuit goes on to claim that Cyrus copied Flourgon's original, unique and creative lyrical phrase within 'We Can't Stop'. Furthermore, the lawsuit notes that the song is the one in which Cyrus was re-launched, from Hannah Montana to an 'edgy' pop star.
However, Cyrus' 'We Cant Stop 'already features a number of other influences. In fact, Doug E Fresh and Slick Rick are co-writers of the song. Cyrus sampled the song La Di Da Di in her song and obtained the relevant consents and licence before the songs release.
In the wake of the Blurred Lines decision, and the more recent Taylor Swift decision over the alleged copying in the lyrics of 'Shake It Off', this could be one to watch. Interestingly it was Blurred Lines that kept Cyrus' now disputed song from reaching number 1 in the US Billboard Hot 100!
US District Judge Michael Fitzgerald had little sympathy for the claim saying "The lynchpin of this entire case is thus whether or not the lyrics 'Playas, they gonna play / And haters, they gonna hate' are eligible for protection under the Copyright Act," adding "[B]y 2001, American popular culture was heavily steeped in the concepts of players, haters, and player haters. … The concept of actors acting in accordance with their essential nature is not at all creative; it is banal" and "The allegedly infringed lyrics are short phrases that lack the modicum of originality and creativity required for copyright protection".
But here and as with the Blurred Lines case, Flourgon's lawyers are requesting a jury hearing.


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Home › Books › Cirsova Publishing to Reprint Nearly-Lost Julian Hawthorne Planetary Romance, The Cosmic Courtship ›
Cirsova Publishing to Reprint Nearly-Lost Julian Hawthorne Planetary Romance, The Cosmic Courtship
Submitted by Critical Blast Staff on Wed, 03/10/2021 - 17:35
LITTLE ROCK, AR—Cirsova Publishing is proud to announce that it has partnered with Michael Tierney and Robert Allen Lupton to restore and reprint Julian Hawthorne's The Cosmic Courtship [https://www.kickstarter.com/projects/cirsova/the-cosmic-courtship-by-jul..., a never-before-collected pulp Planetary Romance by the son of famed American author Nathaniel Hawthorne.
Mary Faust, a brilliant scientist, has developed a machine that can allow the conscious human soul to explore the cosmos! Her promising young assistant Miriam Mayne has accidentally transferred her consciousness Saturn, where she falls under the enchantment of an evil sorcerer! Jack Paladin, her love, sets out after her on a thrilling celestial journey to the ringed planet! Swashbuckling adventure and high romance await in Julian Hawthorne's The Cosmic Courtship!
While most are at least somewhat familiar with Nathaniel Hawthorne as one of the great American authors, less well known is that his son Julian was an incredibly prolific writer in his own right. Julian published on a wide variety of subjects, ranging from literary analysis of his father's works to poetry to period romances and adventures. Late in his career, Julian even dabbled in the emerging genre of Science Fiction.
The Cosmic Courtship was serialized in Frank A. Munsey's All-Story Weekly across four issues, beginning with the November 24, 1917 issue and running through the December 15, 1917 issue. While this story has been in the public domain for some time, it has never been collected or published elsewhere until now.
Cirsova Publishing has taken on this exciting project with the aim to preserve this story for posterity and ensure that it is not lost to future generations.
Michael Tierney [https://www.thewildstars.com] is a pulp historian and archivist who has written extensively on Edgar Rice Burroughs, having created the massive four volume Edgar Rice Burroughs 100 Year Art Chronology, and is currently working on another Art Chronology about Robert E. Howard. He has been involved in the comic book industry for 40 years, owning two of the oldest comic book stores in Central Arkansas until switching to mail-order only in 2020. He is also an accomplished science fiction writer and artist, having worked on his Wild Stars saga since the 1970s. Michael not only made his pulp library available for this project, he provided the photographic images of these rare magazines so that a manuscript could be produced. He has also lent his years of experience digitally restoring damaged pulp art to restore the original cover by Fred W. Small to create a unique cover for this edition.
Robert Allen Lupton [https://www.facebook.com/people/Robert-Allen-Lupton/100022680383572] is a prolific author, pulp historian, and commercial hot air balloon pilot. He has published nearly 200 short stories across numerous anthologies, including the New York Times Best Selling Chicken Soup For the Soul series, and has published several anthologies and novels. His most recent novel, "Dejanna of the Double Star" was published in December 2020. Robert has been an active Edgar Rice Burroughs historian, researcher, and writer since the 1970s, including at ERBzine [www.erbzine.com/lupton/], where several of his articles and stories are published. Robert has painstakingly recreated the text as it was originally published from the digital images provided from Michael's collection.
Cirsova Publishing [https://www.cirsova.wordpress.com] has been publishing thrilling adventure science fiction and fantasy since 2016. They have published nearly 20 issues of their flagship publication, Cirsova Magazine. Additionally, they have published a number of anthologies, a fully illustrated edition of Leigh Brackett's Planet Stories-era Stark adventures, Jim Breyfogle's Mongoose and Meerkat, and the 35th Anniversary Editions of Michael Tierney's Wild Stars.
This collected edition of The Cosmic Courtship will be released later in 2021.
Vote for the Best of 2022
Lucifer: Sixth and Final Season on DVD (Giveaway)
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Enter to Win Jurassic World: Dominion (Digital Code)
Lucifer: The Complete Fifth Season on DVD


AutomotiveTouchup paint products are custom mixed to perfectly match the color of your 1996 Toyota Previa using a basecoat/clearcoat system just like factory specs. To insure a proper match, you'll need to know your vehicle's color code so you can find it on the chart below. The color code can be located in the driver side door jamb. The code will have a C/TR in front of it. A typical code will look like C/TR: 1D4/FH13, and 1D4 would be the color code in this example. Click here for Toyota paint code location diagrams and label examples.
Your 1996 Toyota Previa is painted at the factory with a high quality basecoat/clearcoat system. This two-step paint system consists of step one, the basecoat, which is your car's actual color, and step two, the clearcoat, the specially formulated clear paint that protects the base color and provides the luster and deep shine your vehicle came with when new. AutomotiveTouchup products faithfully reproduce your vehicle manufacturer's basecoat/clearcoat system.
The color match was perfect.
The pen I received worked pretty well - the color was a little off and I am sure it is because my car is a 1997. I had some pretty deep scratches from a fender bender. From a distance the damage is not noticeable. I kind of wish I had gotten to bottle type but I would definitely recommend this product for minor scratches.


text:
function* fibonacciGenerator() {
    var prev = 0;
    var curr = 1;
    while (true) {
        yield curr;
        curr = curr + prev;
        prev = curr - prev;
    }
}
// Exemplo do uso da função geradora,
// quando o elemento da sequência for menor que 10
for (n of fibonacciGenerator()) {
    if (n > 10)
        break;
    console.log(n);
}

redpajama_set_name: RedPajamaGithub


If you are interested in buying Maryland real estate, you might want to consider your finances and seek counsel from financial advisers. Though the state is a great place to settle in with a bustling business district and good life standards, there are certain issues about Maryland real estate that you must know. The issues are not as bad as property fraud, but they could wreck havoc in your finances. But if you have high income and can afford a high-priced property, then, you are most welcome in Maryland. Have a look at sell my house fast maryland for more info on this.
Maryland real estate prices are continuously rising prompting realty analysts to conclude that housing will be less affordable to Maryland families and others planning to settle in the state. Maryland Association of Realtors surveyed state residents and reviewed the current housing trends in Maryland. The association found that the cost of purchasing a home and its maintenance are further complicated by unmet strong housing demands and supply shortage across the state. Alan Ingraham, the association's top realtor assessed that the current issues will affect real estate for the succeeding 15 years.
Deborah Ford, a professor of economics from Maryland, affirms statements by Ingraham that Maryland real estate has not yet reached its most affordable price. She even asserts housing prices are not likely to go down in the near future due to the law of supply and demand. According to Ford, the demands for housing are not met by developing new properties so there is a housing scarcity. The law of supply and demand understandably functions like this — higher demands with lower supplies merit higher prices. So unless Maryland developers and builders start building and developing new properties, it is unlikely that the cost of Maryland housing will decrease.
The study by the realtors' organization also points out other factors in the rise of housing costs. It is likely that housing affordability in Maryland will be an impossibility if the following are not taken care of: population growth, high interest rates, continued house-value appreciation, slow income growth, and added real estate taxes. Rising costs of energy are also starting to factor in housing costs. Ingraham states that the problem of Maryland real estate is not an isolated case; other states are suffering the same thing.


This is genuine humility as preached in the Gospel.
2. Humility is, in fact, truth. Many think that it is a mental fiction or a pious exaggeration, and that only a few souls can really achieve it.
Our defects and sins are the only things which are really ours. So we have a double motive for humility. Everything good about us is a gift from God. Only our sins belong to us!
3. Some people realise that they can achieve something good or beautiful by making use of their natural gifts along with the grace of God. Meanwhile they see others failing where they succeeded. As a result they are lacking in humility. Theirs is a mistaken attitude. Do we know what difference there is between the gifts God has given us and those He has given to others?
Even if God has granted us greater natural and spiritual gifts, surely this is a motive for humility rather than pride? St. Thérèse of the Child Jesus was accustomed to ask God's forgiveness not only for the sins which she had committed, but also for those which she would have committed if God had not given her such exceptional graces.


text:
OSA Publishing > Applied Optics > Volume 6 > Issue 11 > Page 1981
A Method for Obtaining Differential 8–13 μ Spectra of the Moon and Other Extended Objects
Alexander F. H. Goetz and James A. Westphal
Alexander F. H. Goetz1,2 and James A. Westphal1
1The authors were both with the Division of Geological Sciences, California Institute of Technology, Pasadena, California, when this work was done
2F. H. Goetz is now with Bellcomm Inc., 1100 17th St. N.W., Washington, D. C.
A Goetz
J Westphal
•https://doi.org/10.1364/AO.6.001981
Alexander F. H. Goetz and James A. Westphal, "A Method for Obtaining Differential 8–13 μ Spectra of the Moon and Other Extended Objects," Appl. Opt. 6, 1981-1983 (1967)
Atmospheric absorption
Emissivity
Infrared spectroscopy
Original Manuscript: June 2, 1967
A method is described for measuring spectral emissivity differences in the 8–13 μ region between two points on the lunar surface which may have different temperatures. Spectral emissivity differences of 0.5% on the moon can be detected in this wavelength region, excluding the strong 9.2–10 μ atmospheric ozone band where the accuracy is reduced to 2%. The application of this method to the bright planets is discussed.
An 8–14 Micron Infrared Astronomical Photometer
James A. Westphal, Bruce C. Murray, and Dowell E. Martz
Appl. Opt. 2(7) 749-753 (1963)
Approximate Normal Emissivity Spectra in the Infrared at Elevated Temperatures of Single-Crystal and Polycrystalline Calcium Fluoride
W. Fussell and J. Geist
Spectral Emissivity Calculations by the Statistical Model Applied to the 4.3-μ Bands of CO2 at High Temperatures
Y. Ben-Aryeh
Appl. Opt. 6(6) 1049-1055 (1967)
An Earth-Based, Infrared Lunar Mapper for Thermal and Composition Studies
John D. Rehnberg, John R. Yoder, and Graham R. Hunt
Balloon Telescope Optics


Matt is the Director Head of Design and Marketing at Safepoint and former Head of Digital Marketing at BookLife Publishing. He began Matt Rumbelow Design as a way to work on a wider range of projects.
After leaving his old role in 2016 and beginning some incredibly successful and exciting new projects, Matt took the decision to work as a full time freelancer.
In 2017, Matt began collaborating with some of the most exciting developers in the field to meet the ever-more demanding scope of his new clients.
Partnering with amazing talents from iOS, Android, Web and backend development, as well as marketing, has meant that Matt can now build projects from the ground up and cover every aspect of the creative process. Ask us what we can do for your team or use the form below.


text:
namespace arangodb {
namespace consensus {
class AgentInterface {
 public:
  /// @brief Possible outcome of write process
  enum raft_commit_t { OK, UNKNOWN, TIMEOUT };

  struct WriteMode {
    bool _discardStartup;
    bool _privileged;
    WriteMode(bool d = false, bool p = false)
        : _discardStartup(d), _privileged(p) {}
    bool privileged() const { return _privileged; }
    bool discardStartup() const { return _discardStartup; }
  };

  /// @brief Attempt write
  virtual write_ret_t write(query_t const&, WriteMode const& mode = WriteMode()) = 0;

  /// @brief Attempt write
  virtual trans_ret_t transient(query_t const&) = 0;

  /// @brief Attempt write
  virtual trans_ret_t transact(query_t const&) = 0;

  /// @brief Wait for slaves to confirm appended entries
  virtual raft_commit_t waitFor(index_t last_entry, double timeout = 2.0) = 0;

  /// @brief Wait for slaves to confirm appended entries
  virtual bool isCommitted(index_t last_entry) = 0;

  // Suffice warnings
  virtual ~AgentInterface() = default;
};
}  // namespace consensus
}  // namespace arangodb
#endif


Local under 40's connect and want a voice in the province's future.
November 26, 2014 – Meagan Gray, Kristina Lane, Adam Burr, James Krikorian and Jillian McCormick from Quinte Young Professionals joined dozens of under forties from all corners of the province today to announce the launch of Young Professionals Network of Ontario (YPNO). YPNO is a central hub to support young people to do business, network, advocate, have a voice at provincial and national tables, and build economic development opportunities.
YPNO represents over 30 Young Professionals Networks with thousands of members dispersed across large cities like Toronto, as well as in small northern communities like Kenora. Members also come from all sectors and walks of life including private business, public entities, trades, not-for profit agencies, and Aboriginal organizations.
An advocacy campaign called; Meet Us, Hear Us was also announced to Parliament at Queen's Park.


I don't think I can ever eat French Toast made with regular bread again. Or banana bread not drizzled in warm maple syrup.
Basically take your favorite banana bread, or whichever kind of banana bread you happen to have lying around the house (cookie butter banana bread, I'm looking at you) if you are like me and never bake the exact same thing twice.
Leave a few thick slices (at least 1 1/2″) of old banana bread covered with a towel overnight, so they get slightly stale but aren't too exposed to the elements. The next morning, heat up the griddle, make a mixture of two eggs, 1/2 cup milk, and a little bit of cinnamon and sugar to submerge the bread slices into. Once the bread has absorbed the mixture, throw those babies on griddle and watch them brown.
In the mean time, warm up some maple syrup and whip up some sweetened almond-orange sour cream. Normally I wouldn't use sour cream for something sweet, but we had it in the fridge, and I'm trying to do a better job using everything we buy each week. So use a hand mixer to whip up 1/2 cup of sour cream with 1/4 cup granulated sugar along with some orange zest and a dash of almond extract. Taste as you go along, adding more sugar, almond extract, etc. Dustin wasn't sold on the idea of putting sour cream on his French Toast at first, but once he tasted he was definitely on board. After the success of Banana Bread French Toast, I think every time I make a bread or cake, we should turn the leftovers into an indulgent breakfast!
Top everything with banana slices, and you have a rich and decadent breakfast that comes together super quickly on a weekday morning (given you've already baked the banana bread, of course).


text:
> Charles Manson: The Final Words
> About Charles Manson: The Final Words
Charles Manson: The Final Words
About Charles Manson: The Final Words
Even after his death, Charles Manson continues to be a beacon of public fascination. How could a no-account drifter and ex-con become a charismatic leader of what prosecutors called a diabolical murder cult?
Music producer Phil Kaufman.
Why did the killers target the home of a Hollywood sex symbol? And where did the prosecution come up with the theory that Manson aimed to start a race war called Helter Skelter based on the music of The Beatles?
Some have proposed that the prosecution made-up the race war theory in order to more easily convict Manson through a sensational conspiracy narrative.
Former Manson Family member Barbara Hoyt.
If the prosecution did construct the Helter Skelter theory, is convicting a man like Manson so essential that any means of achieving it is acceptable?
Exclusive interviews include accused Manson Family members Bobby Beausoleil, Barbara Hoyt and Catherine 'Cappi' Gillies, prosecutor Stephen Kay, defence lawyer Gary Fleischman, and music producer Phil Kaufman. The film also features rare footage and audio recordings of Leslie Van Houten and Susan Atkins.
The film also uncovers shocking new details, never-seen-before materials and digitally restored audio recordings from the original investigation to reconstruct a path of events that led to the brutal murders of movie star Sharon Tate and four of her friends on 8th August 1969.
Defence lawyer Gary Fleischman.
But the most riveting elements come from Manson himself through never seen before pictures, documents, artwork, writings and recordings from inside California State Prison. Recordings that include eerie conversations about modern society, justice and his decades behind bars. Recordings that will help you get to know the man they call simply, Charlie.
Mummy's Little Murderer
This true crime documentary tells the story of a brutal murder and the attempted cover up.
Nurses Who Kill
Nurses Who Kill tells the stories of some of the murderers inside our hospitals...


The validity of the key is determined by the exchange.
When the API is overloaded, it breaks off mid transfer. Thus an error message appears even though the key is correct.
We apologize for the inconvenience, but there is little we can do from our side. If no data is given, we can not import anything.


Apparently the pair have been dating for two years!
Blake Garvey's relationship history has been interesting, to say the least. The former Bachelor made headlines after he proposed to Sam Frost on national television in 2014, only to admit later he had chosen the wrong girl and his heart belonged to Bachelor second runner-up, Louise Pillidge. However, in recent times, Blake has been spotted dining in Perth with an unknown brunette, who sleuths have identified as 23-year-old Tania Mondon.
The Daily Mail allege the couple have been together for two years, meaning the pair could have got together shortly after Blake, 35, ended his relationship with Louise back in April 2016.
Tania's LinkedIn lists her as the 'front office supervisor' at Crown Towers in Pert, however, the gorgeous brunette also had a modelling background as seen from posed shots uploaded to her private Instagram account.
Tania's has an Instagram following of 2000 and her bio lends insight into her interests: 'Do everything with love and passion. Fashion x Family x Friends x Music x Travel Perth, Western Australia. Little Creole Mix' it reads.
Blake Garvey has kept a low public profile since ending his 18 month relationship with Louise. At the time, the pair revealed the severe effects of public scrutiny on their mental health had an irreconcilable effect on their relationship.
"I've gone through bouts of depression and I know we've both gone through some acute anxiety," Blake said in an interview with Today Tonight.
"You get looks and you know that you're starting a series of conversations in the room just because you've entered it. That and many [other] factors make you really very self-conscious."
It's thought Blake and Tania started dating soon after the former Bachie star ended his 18 month relationship with Louise.
Both Blake and Tania are yet to confirm their relationship, however another reality star has put up her hand to claim she had dated the former Bachelor. Speaking with OK! magazine, Married At First Sight star Tracey Jewel admitted to dating Blake when they were younger. She was even asked to go on his series of The Bachelor.
"It was actually to go on Blake Garvey's season, which is weird because we've actually dated before," Tracey explained.
"We met during high school, we were 17."
"Yeah," she laughed, "we were both each other's firsts."
Well that's one reality TV couple we never expected, Blake Garvey and Tracey Jewel!


text:
\section{Introduction}

Recent progress on deep learning based trackers \cite{bergmann2019tracking, meinhardt2021trackformer, shuai2021siammot, zhou2020tracking} has elevated the performance of online multiple-object tracking (MOT) to a level that is comparable to offline trackers \cite{berclaz2011multiple,braso2020learning,tang2017multiple,xiang2015learning,xu2019spatial,zhang2008global}.
This new family of trackers includes two fundamental components: detection of objects in a frame and association of detected objects across frames into tracks (often by estimating object motion).
Training these trackers requires both a large number of bounding box annotations for the detection component and a large number of instance-level correspondence annotations for the motion model.
Unfortunately, large-scale instance correspondence annotations are extremely expensive to obtain. As a consequence, the current MOT datasets are usually restricted to a limited number of videos (e.g.~7 training videos for MOT17 \cite{MOT16}). While these trackers can be trained with a large set of image-based bounding box annotations, the amount of tracking annotations available for training is relatively small. This raises the question: \textit{What if we had large-scale tracking datasets available for training?}

We conjecture that a large-scale dataset is essential to training robust deep neural models for tracking, as is common in training deep networks for other vision tasks \cite{deng2009imagenet,he2016deep,lin2014microsoft,ren2015faster}. Thus, in this work, we explore ways to auto-annotate large sets of unlabeled videos such that we can leverage them for training a deep neural tracker. Specifically, we explore auto-annotation in a weakly supervised setting, where we have bounding box annotations for images and a large pool of unlabeled videos.

\begin{figure}
    \centering
    \includegraphics[width=0.5\textwidth]{figures/hvrv_teaser_2.pdf}
    \caption{\small We first generate a dataset of hallucinated videos (HV) from images on which we train an HV tracking model. Next, we mine a set of hard examples from unlabeled real videos (RV). We train the tracking model jointly on the HV and RV datasets. We hallucinate a short video from a single image with a recurrent zoom-in/out motion transformation as well as with other random motion effect transformations. Hard examples refer to videos where the HV-trained tracker fails, such as a video in which a person's pose deforms dramatically.
    }
    \label{fig:teaser}
\end{figure}

Starting with a single image with bounding box annotations, we hallucinate a short video with bounding box instance correspondences.  To do this, we apply a zoom-in/out transformation to the image and bounding boxes respectively. Our zoom-in/out transformation is similar to the scaling and translation operation used in previous tracker training \cite{held2016learning, zhou2020tracking}, but we extend this notion with advanced motion blur, lighting effect changes, and other video artifact simulations. We train a deep neural tracker on this set of hallucinated video data (HV) and show that it already performs competitively with the state of the art.

While useful for training, our hallucinated videos still lack visual characteristics of real videos which are hard to simulate.
Therefore, we mine on a set of unlabeled videos for hard examples where the HV tracker fails. For example, the predicted tracking trajectories are often broken when object instances are occluded, or their poses suddenly deform. We identify these hard examples by looking for tracklets that are potentially broken from a single object instance's trajectory. To this end, we optimize a video-level cost function that takes into account the spatial and temporal coherence between tracklets.
High-confidence tracklets that are compatible with each other will be merged, generating rectified pseudo labels for the identified hard examples. We show that adding this set of rectified hard examples (RV) to our existing HV data is
important for training our weakly supervised deep neural tracker, as these examples guide the model to track through challenging scenarios in real videos.

We study the effectiveness of our mining method on the multi-person tracking task. To this end, we generate hallucinated videos from person detection image datasets: COCO \cite{lin2014microsoft} and CrowdHuman \cite{shao2018crowdhuman}, and we mine hard examples from the Kinetics \cite{kay2017kinetics} person-centric video dataset.  By testing our model on two well-studied datasets, MOT17 \cite{MOT16} and TAO-Person \cite{dave2020tao}, we make three key findings: 1) our weakly-trained tracker achieves state-of-the-art results on both the MOT17 \cite{MOT16} and TAO-person \cite{dave2020tao} datasets; 2) on the MOT17 \cite{MOT16} dataset, the tracker trained with large-scale self-generated data and annotations outperforms its counterpart that is trained on the provided MOT17 tracking annotation and importantly 3) adding our self-generated annotations to the MOT17 training dataset significantly improves the tracker's performance.  These results clearly demonstrate the value of our self-generated data and annotations even when a small amount of manual annotations are available. Moreover, we conduct thorough ablation experiments to investigate the optimal settings for hard example mining as well as how to effectively train a weakly supervised deep tracker with self-generated tracking annotations.

To summarize, we propose a deep neural tracker training framework to train from only detection annotations and unlabeled videos. The framework relies on two data-generation components: video hallucination and unlabeled video track mining.
Our hallucinated video technique builds on data generation in previous works by introducing more advanced video appearance simulation.
Our mining process differs from previous MOT video mining approaches in that it:
1) performs a tracklet matching optimization based on spatiotemporal coherence rather than matching detections solely on spatial coherence, and 2) acts as a hard example mining process by identifying and rectifying spatiotemporal discontinuities between tracklets.
Our experimental results validate each of these contributions and show state-of-the-art results on the MOT17 and TAO-Person datasets.

\section{Related Work}

\paragraph{Multi-Object Tracking.}
The multi-object tracking (MOT) task involves detecting objects of interest in every frame and then temporally linking them to form trajectories. Most recent MOT works fall into the ``tracking-by-detection" paradigm \cite{berclaz2011multiple,bewley2016simple,tang2017multiple,wojke2017simple,xiang2015learning,xu2019spatial,zhang2008global} which poses MOT as a data association problem. There are two different ways to to tackle this formulation. One is an ``offline tracker" approach, in which a large offline graph is first built \cite{andriyenko2011multi, berclaz2006robust, tang2017multiple, xiang2015learning, xu2019spatial} that encodes the visual similarity and geometric consistency between detected bounding boxes. Next, the trajectories are derived by optimizing the graph-induced cost function. Although these offline trackers can output trajectories with high temporal consistency, they are usually inefficient and less applicable to real-time tracking. The second approach is an ``online tracker" which performs data association on the fly \cite{bergmann2019tracking, bewley2016simple, meinhardt2021trackformer, shuai2021siammot, wojke2017simple, zhou2020tracking}.
Recently, deep neural network based multi-object trackers \cite{bergmann2019tracking, meinhardt2021trackformer, shuai2021siammot, sun2020transtrack, zhou2020tracking} have pushed the online tracking performance to a level that is comparable to offline tracking.

In general, existing deep neural trackers consist of two key components: 1) a detector to detect new object instances and 2) a motion model to estimate the instance's movement between frames such that the detected instances can be linked across time. For example, Bergmann et al. \cite{bergmann2019tracking} reuse the second stage of Faster-RCNN \cite{ren2015faster} -- the detection branch -- as the motion model; Zhou et al. \cite{zhou2020tracking} estimate the object instance's motion by feeding the model with location maps at the previous frame together with feature maps from both previous and current frames. Shuai et al. \cite{shuai2021siammot} leverage a single object tracker to predict the instance's motion. Both Meinhardt et al. \cite{meinhardt2021trackformer} and Sun et al. \cite{sun2020transtrack} use attention-based transformers to model the instance's movement across frames.
All of these trackers are trained with manual tracking annotations.
In this work, we explore training deep neural trackers in a weakly supervised setting by using bounding box annotations from images and a pool of unlabeled videos.

\begin{figure}
    \centering
    \includegraphics[width=0.5\textwidth]{figures/training_examples_3.pdf}
    \caption{\small We show examples from our self-generated training data consisting of hallucinated videos (top) and mined real videos (bottom) with their corresponding pseudo-label tracks. For the real video examples we visualize pseudo-labels of mined hard examples with broken tracks (dotted box) that were identified and corrected by our track rectification process. }
    \label{fig:hv_rv}
\end{figure}

\vspace{0.5em}
\noindent\textbf{Self-training}
Self-training has long been a well-known semi-supervised learning technique \cite{scudder1965probability}. Recently, self-training has been successfully applied to improve a number of modern deep learning models on vision tasks such as image classification \cite{xie2020self,zou2019confidence}, object detection \cite{li2020improving,zoph2020rethinking}, and segmentation \cite{zhu2020improving,zoph2020rethinking,zou2018unsupervised,zou2019confidence} as well as tasks in NLP \cite{he2019revisiting,kahn2020self} and speech recognition \cite{kahn2020self}. Since we must address the gap between image and video frames, our method is especially related to self-training for domain adaptation \cite{wang2018deep,zou2018unsupervised}. However, our work differs in that self-training is also ``across-task" since we start with object detection annotations and use these to obtain tracking annotations in video.

\vspace{0.5em}
\noindent\textbf{Video Hallucination}
Hallucinating videos from a single image has been explored in different video-based tasks. Gao et al. \cite{gao2018im2flow} propose to hallucinate a flow map from a single image, which is used to improve action recognition performance. Kanazawa et al. \cite{kanazawa2019learning} learn to hallucinate temporal context from a single image, which is used for 3D human pose estimation. Li et al. \cite{li2018flow}  propose a generative model to hallucinate an RGB image from a flow map. Held et al. \cite{held2016learning} and Zhou et al. \cite{zhou2020tracking} adopt scaling and translation operations to simulate object's geometric movement across frames. In this work, we hallucinate person's trajectories in a simulated video with different motion effects. Next, we use those videos and pseudo labels to train a deep neural tracker. The closest hallucination techniques to ours appear in Held et al. \cite{held2016learning} and Zhou et al. \cite{zhou2020tracking}, which use a special case of our video hallucination where only camera zoom-in motion is applied and only two frames are generated per video sequence.

\vspace{0.5em}
\noindent\textbf{Mining Tracks from Unlabeled Video}
While there are a variety of previous works that have used tracking through unlabeled video to improve model training, most works have used mined tracks to improve object detection performance \cite{jin2018unsupervised,misra2015watch,DBLP:conf/icra/OsepVLBL19,roychowdhury2019automatic}. Work focused on improving MOT through mining on unlabeled data is very limited. The most closely related work to ours is MOTSNet \cite{porzi2020learning} which proposes to generate a dataset automatically on unlabeled video by predicting image-level segmentation. They link these segmentations into a linear assignment process based on temporal coherence between masks to form annotations.

A key difference between this work and ours is that the track generation process for MOTSNet is based only on spatial coherence between object masks in adjoining frames. Thus, the mined tracklets will be broken in frames where the detection or segmentation model fails. In contrast, we propose a spatiotemporal connecting process linking tracklets over short-term failures, which is capable of identifying and then rectifying brief discontinuities in detection outputs.  Moreover, these rectified tracklets are used as hard examples, which play a pivotal role in training a better tracking model.

\section{Tracking Architecture: DTracker}
\label{sec:dnt}

In this paper, we adopt a tracker architecture similar to the implicit model from SiamMOT \cite{shuai2021siammot}, which achieves state-of-the-art results on the MOT17 dataset. Hereafter, we refer to this specific deep neural tracker as \textbf{DTracker}.

DTracker is a two-stage deep neural tracker that is based on two-stage Faster-RCNN \cite{ren2015faster}, so it includes three major functional sub-networks: region proposal network (RPN), detection branch, and track branch (motion model). Specifically, DTracker uses a single-object tracker -- GOTURN \cite{held2016learning} -- to model the object instance's motion movement between two frames. DTracker is trained with the loss $\ell = \ell_{rpn} + \ell_{detect} + \ell_{track}$, where $\ell_{rpn}$, $\ell_{detect}$ are standard losses related to RPN and detection branch in Faster-RCNN \cite{ren2015faster}, and $\ell_{track}$ is the motion model training loss.

In order to train DTracker, an image pair is sampled from a short video sequence (e.g. one second long). Formally, $B_t$ and $B_{t+\delta}$ denote bounding boxes that correspond to the same object instance at times $t$ and $t+\delta$ respectively.
In order to track $B_t$  from $t$ to $t+\delta$, the motion model predicts $(\hat{v}, \hat{m})$ where $\hat{v}$ is a visibility score indicating whether or not the object instance is still visible at $t+\delta$ and $\hat{m}$ is the estimated motion offset for the object from $t$ to  $t+\delta$.  The motion model is trained with the following loss:

\begin{equation} \label{eq:train_loss}
    \ell_{track} = \ell_{cls}(\hat{v}, v^{gt}) +  \mathbbm{1}[v^{gt}] \ell_{reg}(\hat{m}, m^{gt})
\end{equation}
in which $\ell_{cls}$ is the binary cross entropy loss; $\ell_{reg}$ is a smooth $\ell_1$ regression loss; $v^{gt} = 1$ if $B_{t+\delta} \neq \emptyset$, otherwise $v^{gt} = 0$; and $m^{gt}$ is the ground truth motion representation that is derived from $B_t$ and $B_{t+\delta}$ in the same way as regression targets in Faster-RCNN \cite{ren2015faster}.
We refer the readers to  \cite{shuai2021siammot} for a more detailed description.

\section{Weakly supervised DTracker}

A large corpus of labeled tracking annotations is required to train a generalized DTracker. To be specific, bounding box annotations are needed for training the detection model in DTracker, and instance correspondence annotations within videos are needed to train the motion model. However, acquiring large-scale and diverse video-level instance correspondence annotations is expensive. Consequently, current multi-object tracking (MOT) datasets are limited to very few videos. In practice, the detection model in DTracker is trained with large-scale image datasets, while the motion model is trained with a limited video dataset.  Therefore, the feature backbone of DTracker is heavily tuned for the detection task, and the motion model struggles to generalize in different scenes or motion patterns.

Towards the goal of training a generalized tracker without annotated instance correspondences, we adopt a weakly supervised setting.
Specifically, the input to our weakly supervised DTracker training is a set of annotated images with bounding boxes for objects of interest and a pool of unlabeled videos.
In Sec. \ref{sec:hv}, we first introduce hallucinated videos and trajectories that are used for training an initial DTracker. Then we leverage this DTracker to auto-annotate and mine for hard examples from a large corpus of unlabeled videos, as described in Sec. \ref{sec:rv}. We demonstrate that these two data sources are complementary and both substantially contribute to improving our DTracker performance.

\subsection{Video Hallucination From Images}
\label{sec:hv}

To hallucinate short annotated videos, we simulate a zoom-in/out visual effect on images with cropping and scaling operations ($\operatorname{crop\_and\_scale}$). Specifically, for an image $\mathbf{I}_1$,  we generate a $T$ frame video sequence  $[\mathbf{I}_1, \ldots, \mathbf{I}_t, \ldots, \mathbf{I}_{T}]$, in which $\mathbf{I}_t = \operatorname{crop\_and\_scale}(\mathbf{I}_{t-1}, r*(t-1))$ for $t \geq 2$. Here, $r (\geq \frac{0.9}{T-1})$  denotes the relative size of image crop w.r.t. that of the original image $\mathbf{I}_1$.
This zoom-in/out transformation is similar to the scaling and translation operation used in \cite{held2016learning, zhou2020tracking}. To simulate more realistic appearance in videos, we inject visual effects such as motion blur, color/lighting variations, and compression artifacts during video hallucination.

We generate tracking annotations for the hallucinated videos by applying the same geometric zoom-in/out transformation to bounding boxes.

As shown in Figure \ref{fig:hv_rv}, the zoom-in/out motion effects mimic the geometric and scale changes of object instances that result from natural camera and object motion. Such generated training examples can be used to learn a motion model robust to fast camera or object motion as well as common video appearance artifacts.
As each image represents a distinct scene, the large quantity of hallucinated videos offers a great diversity of scenes, far outnumbering the existing tracking datasets.

\subsection{Hard Example Mining from Unlabeled Videos}
\label{sec:rv}

Our hallucinated videos allow us to train a DTracker with no video supervision but they do not fully capture all forms of motion found in real video.
First, all object instances go through the same transformation, so there are no instances of objects with different trajectories crossing in front of each other. Second, our hallucinated videos have no deformations of object instances (i.e. a person changing pose). And third, there are no cases of changing occlusion in our hallucinated videos. To overcome these data limitations, we look to real video data.

To generate this real video training data, we start with a pool of unlabeled videos and run our hallucinated video trained DTracker over these videos. Our DTracker produces high confidence short tracks (tracklets) but fails to track objects through the challenging video specific scenarios such as occlusion or articulation of the object, as demonstrated in Figure \ref{fig:hv_rv}. Thus, we target our hard example mining method to these gaps in the tracking output. By doing so we are able to mine examples from our unlabeled set of videos that complement the hallucinated video training dataset.

Inspired by other works which mine hard examples or rectify pseudo-labels to improve self-training for object detection \cite{jin2018unsupervised,li2020improving,roychowdhury2019automatic,zou2019confidence}, we propose a mining process that first locates hard tracking examples and then rectifies them to output pseudo labels. As Figure \ref{fig:hv_rv} shows, the hard examples usually refer to tracklets that are broken from an otherwise complete trajectory.
Those broken tracklets that belong to the same object instance are usually spatially and temporally coherent, especially in videos with the less-crowded scenes. Therefore, we propose the pairwise matching cost between two tracklets as follows:
\begin{equation} \label{eq:objective}
    c(i, j) = tIoU(\tau_i, \tau_j) + (1 - \frac{t(\tau_i, \tau_j)}{\gamma}) + \chi(\tau_i, \tau_j)
\end{equation}
where $tIoU(\tau_i, \tau_j)$ computes the spatial IoU between the end of track $\tau_i$ and the beginning of track $\tau_j$ while  $t(\tau_i, \tau_j)$ computes the time gap between the two tracks. $\chi(\tau_i, \tau_j)$ is a characteristic function to impose hard constraints on matching between tracks, defined as:
\vspace{-8pt}
\begin{equation}
\chi(\tau_i, \tau_j) =
     \begin{cases}
       -\infty &\quad\text{if} \quad tIoU(\tau_i, \tau_j) < \mu \\
        &\quad\quad\text{or} \quad t(\tau_i, \tau_j) > \gamma\\
        &\quad\quad \text{or} \quad i \leq j \\
       0 &\quad\text{otherwise} \\
     \end{cases}
\label{eq:cost_function}
\end{equation}

In order to identify and rectify the broken tracklets, we propose an optimization process that connects the broken tracklets that belong to the same object instance. We define the optimization problem as follows:
\begin{equation}
\begin{aligned}
    \max_{x} \quad \sum_{i,j} c(i, j) x_{ij} \\
    \textrm{s.t.} \sum_i x_{i,j}  \leq 1 \quad \forall  j \\
    \sum_j x_{i,j} \leq 1 \quad  \forall  i  \\
    x_{ij} \in \{0,1\} \quad \forall i, j \\
\end{aligned}
\end{equation}
in which we use a binary variable $x_{ij}$ to denote the connectivity between two tracks $(\tau_i, \tau_j)$. A value of 1 corresponds to joining two tracks $(\tau_i, \tau_j)$. At each step, we impose constraints that a track may only be joined to one later track and one earlier track. This prevents cases where a single track might be split into multiple separate overlapping tracks.
We optimize this process in an iterative manner:  first running the optimization process to obtain a set of tracks to join, then updating all tracks, and repeating until convergence when no additional tracks are matched.
$x_{ij} = 1$ indicates the identification of a hard example, which is the video sequence covering both tracklet $\tau_i$ and $\tau_j$. Connecting tracklets ($\tau_i, \tau_j$ for $x_{ij}=1$)  gives rise to the rectified pseudo labels. We show some mined hard examples in Figure \ref{fig:hv_rv}, in which broken tracklets are connected in challenging scenarios.  In this way, we generate a training dataset that is focused on the failure modes of the hallucinated video trained DTracker.

\paragraph{Noise in pseudo labels.} Our hard example mining process will inevitably have noise from the pseudo labels through either tracklet prediction errors or tracklet association errors in our optimization process. To mitigate model corruption due to this noise, we propose to regularize the model training with a joint training scheme, in which every batch includes a proportion of hard examples and the rest are hallucinated videos.  As pseudo labels for hallucinated videos are free of noise, they help to balance out corruption in model accuracy due to mined video noise.

\section{Experiments}
\subsection{Implementation details}
We use DLA-34 \cite{yu2018deep} as the feature backbone of DTracker, which adds the motion model -- GOTURN \cite{he2016deep} -- to the 2$^{nd}$ stage of Faster-RCNN \cite{ren2015faster}.

\vspace{0.5em}
\noindent\textbf{Motion model.}
Given a tracked target at time $t$ with bounding box $B_t$, we define the target's corresponding contextual regions at times $t$ and $t+\delta$ to be $C_t$ and $C_{t+\delta}$ respectively. The contextual regions share the same geometric center as that of $B_t$, but with $2 \times$ the width and height of $B_t$. In order to track the target from time $t$ to $t+\delta$, the motion model in DTracker takes the visual features of $C_t$ ($\mathbf{f}_{C}^t$) and $C_{t+\delta}$ ($\mathbf{f}_{C}^{t+\delta}$) and outputs its visibility score $\hat{v}$ and motion offset $\hat{m}$. Note that the ROIAlign operator \cite{he2017mask} is used to pool $\mathbf{f}_C^{t}$ from backbone feature maps with $C_t$. We use two fully connected layers with 512 hidden neurons to represent the motion model, and it is jointly trained with the rest of Faster-RCNN.

\vspace{0.5em}
\noindent\textbf{Video hallucination and mining.}
We generate hallucinated videos from COCO-2017 \cite{lin2014microsoft} and CrowdHuman \cite{shao2018crowdhuman} image datasets. We only consider training images that include person bounding box annotations, which yields a total combined set of 78,832 images. The hallucinated videos are set to be $T=16$ frames in temporal length, and we use the public $img\_aug$ package to implement the visual transformation functions. We mine hard examples from the train split of the Kinetics-700 dataset \cite{carreira2019short,kay2017kinetics} that consists of around 545k videos. The Kinetics dataset is primarily used for human activity recognition, so almost all videos include at least one person. We empirically set $\mu$ and $\gamma$ (in Eq. \ref{eq:cost_function}) to be 0.1 and 0.5 during the hard example mining process.

\vspace{0.5em}
\noindent\textbf{Training.}
We stochastically sample two frames from either a hallucinated video or a short real video clip to be the training example. We train our model with batches of 16 training examples for a total of 25k iterations, unless specified.  We train with SGD using momentum 0.9 and weight decay 0.0001. The learning rate is set to 0.02 and decreased by a factor of 10 at 60\% and 80\% of its full iterations. As we mine hard examples from unlabeled videos primarily for training the motion model in DTracker, we ignore the detection losses calculated from them. During training, every video is resized to have a shorter dimension randomly sampled from (640, 720, 800, 880, 960), while its longer dimension is capped at 1500 pixels. For every training batch, we sample 50\% examples from mined real videos, and the rest from hallucinated videos.

\vspace{0.5em}
\noindent\textbf{Inference.}
We use the standard online inference based on SORT. A track (output from motion model) is continued if its visibility score is above threshold ($ vis \geq 0.3$) and it spatially matches with a detection (IOU $\geq 0.5$). A track is terminated if its visibility score is below $0.3$. Finally, a new track is spawned for an unmatched high-confidence detection ( $\geq 0.5$).

\input{tables/mot_results}
\input{tables/tao_sota}

\subsection{Evaluation Datasets}
\paragraph{MOT17.} The MOT17 dataset includes 7 training and 7 test videos, each video has a duration ranging from 20 to 90 seconds. It is the de-facto standard benchmark for multi-person tracking and is used in the MOTChallenge \cite{MOTChallenge2015,MOT16}. MOT17 videos focus on tracking persons in crowded scenes captured at locations such as outdoor plazas or indoor shopping malls. Therefore, it is of great difficulty to track a person consistently due to the common occlusions in crowds.

\paragraph{TAO-Person.} The TAO dataset \cite{dave2020tao} was recently released for large-scale general object tracking. Each video is around 30 seconds in duration. In this work, we only use the TAO-Person subset as we specifically focus on person tracking. In detail, TAO-Person includes 418 train videos and 826 validation videos. However the training videos are not exhaustively annotated, therefore they are not designed for model training but primarily for parameter tuning for inference. Similar to earlier works \cite{dave2020tao}, we report evaluation on the validation split, and we only use the train set for tuning hyperparameters. Tracking people in TAO-Person videos is particularly challenging due to large person pose deformation, heterogeneous person motion, fast camera motion, and motion blur artifacts, etc.

\subsection{Evaluation metrics}
We report standard tracking metrics on both TAO-person and MOT17. Concretely, by following \cite{dave2020tao}, we report Federated Track AP \cite{dave2020tao} on the TAO-person dataset. Following the same protocol with earlier work \cite{bergmann2019tracking, meinhardt2021trackformer, MOT16, shuai2021siammot, zhou2020tracking}, we report MOTA, IDF1, and related metrics on MOT17. Note that TrackAP highlights temporal consistency of the underlying tracks, whereas MOTA usually emphasizes the frame-wise recall and precision of the per-frame bounding boxes.

\section{Results} \label{sec:results}

\begin{figure}
    \centering
    \begin{tabular}{c}
    \includegraphics[width=0.5\textwidth]{figures/mot17-12.png} \\
    \includegraphics[width=0.5\textwidth]{figures/tao_tug_of_war_wide.png} \\
    \includegraphics[width=0.5\textwidth]{figures/tao_futsal.png} \\
    \end{tabular}
    \captionof{figure}{ \small
    The tracker trained with our hallucinated and auto-annotated videos is able to track through very challenging scenarios including occlusion in crowded scenes, large pose deformations, and fast object motion. The example in the first row is from the MOT17 test set, and remaining examples are from the TAO-person validation set. }
    \label{fig:qual-results}
\end{figure}

In this section, we present our Weakly supervised DTracker (WDTracker) on public multi-person tracking datasets including MOT17 \cite{MOT16} and TAO-person \cite{dave2020tao}.

\subsection{Results on MOT17}
We start by training a DTracker on the 7 training videos in MOT17 following common practice in other methods.
For our WDTracker, as person bounding boxes in MOT17 are amodal, we use images from CrowdHuman dataset to generate our hallucinated videos as they include amodal bounding box annotations as well. That offers us 15,000 hallucinated videos with diverse scenes and settings compared to the limited 7 training videos available in MOT17. Our WDTracker is then trained with both hallucinated videos and hard mined self-generated annotations from real videos. We report the results on the MOT17 test set with standard metrics \cite{MOT16} including MOTA, IDF1, false positives, false negatives, and identity switches. In order to separate out the effects of detection, we generate the results with the public detections \cite{MOT16}.

In Table \ref{mot-results} we present our MOT17 trained DTracker along with our WDTracker trained on both hallucinated and real mined video data. DTracker performs on par with other state-of-the-art trackers but our WDTracker achieves state-of-the-art results (64.7 MOTA / 61.4 IDF1), which  outperforms existing best models (i.e. CenterTrack \cite{zhou2020tracking}) by a significant margin (3.2+ MOTA, 1.8+ IDF1). It's important to note that state-of-the-art models are all trained on the MOT17 dataset. This result suggests that our large-scale self-generated datasets are as valuable as small-scale manually-annotated datasets. To further validate this hypothesis, we report results for another variant of our model, WDTracker$^*$, that trains the underlying DTracker jointly on our self-generated dataset and MOT17. Interestingly, we observe another non-trivial performance boost (1.1+ MOTA and 2.3+ IDF1), which indicates that our self-generated datasets are complementary to existing tracking manual annotations.

\subsection{Results on TAO-Person}
We train our WDTracker with hallucinated videos from the COCO-17 and CrowdHuman datasets together with hard examples mined from the Kinetics dataset. We also adopt a larger feature backbone DLA-102, which is still a lighter network compared to ResNet-101 used for Tracktor++ \cite{dave2020tao}. We train the model for 50K iterations in total (2x our default training).
As shown in Table \ref{tao-sota}, our WDTracker outperforms Tracktor and Tracktor++ by a substantial margin. We also apply the same person re-identification network and techniques as in \cite{dave2020tao} to link tracklets, our WDTracker is further improved to 44.8\% AP@IOU=0.5, which sets a new state-of-the-art performance.  These results show the clear advantage of our WDTracker as a generalizable tracker on the highly diverse and challenging examples in TAO. It's important to note that we are unable to present results of DTracker or WDTracker$^*$ analogous to those in Table \ref{mot-results}, as TAO-person dataset does not have appropriate annotations for deep model training. We show some qualitative tracking results in Figure \ref{fig:qual-results}.

\input{tables/main_results_tao}
\input{tables/hard_resampling}

\section{Ablation Analysis}
\label{sec:abaltion}

We conduct our ablation analysis on the large-scale TAO-Person dataset, which covers a diverse set of tracking challenges including fast camera motion, dramatic human pose deformation, motion blur, severe occlusion in crowds, etc. These diverse and challenging motion patterns are not as represented in the MOT17 dataset, making TAO more appealing to validate the usefulness of hard example mining.

As a first step, we train a DLA-34 based Tracktor on hallucinated videos (HV) generated from COCO-17 and CrowdHuman datasets. As listed in Table \ref{main-results}, our implemented Tracktor achieves 28.1\% Track AP @ IOU=0.5, which significantly outperforms the one reported in \cite{dave2020tao} (i.e. 25.9\% in Table \ref{tao-sota}).  Furthermore, we adopt a competitive baseline by estimating the person's movement with optical flow. In detail, we estimate the person's motion offset between two frames by taking the median motion vector of all constituent pixels within the person's bounding boxes.  To extract optical flow, we use PWC-Net that is trained on FlyingChairs \cite{IMKDB17}, FlyingThings3 \cite{IMKDB17}, Sintel \cite{Butler:ECCV:2012} and KITTI \cite{Menze2015CVPR}. As expected, adding flow to Tracktor improves Track AP @IOU=0.5 by a substantial 4.4\%.

\subsection{Hard example mining}
We train our WDTracker, which is effectively Tracktor plus a motion model, on our hallucinated video (HV) dataset. As demonstrated in Table \ref{main-results}, it achieves 32.0 \% AP@IOU=0.5, which is 3.9\% higher than Tracktor. This result shows that the motion model learned from hallucinated videos is able to generalize to real videos and improve tracking. We expect that the motion model is capable of following the person's trajectories when the subject is moving fast, a condition in which Tracktor fails.  Moreover, it performs comparably to our strong baseline -- Tracktor + Flow, which suggests that the learned motion model is as good as a fully supervised flow model to predict the person's movement. We also train our WDTracker on a simpler set of hallucinated videos (HV$^-$) that don't have our advanced motion effect simulation (i.e. motion blur, light changes, video compression artifacts). The model under-performs the one trained on HV by a clear 1.6\% AP@IOU=0.5 margin, which shows that advanced video augmentation is an important aspect of training a high performing DTracker.

Furthermore, we re-train WDTracker with hard examples mined from our real video (RV) dataset, and we observe a significant 3.4\% AP improvement. This improvement demonstrates the significance of mined hard examples  from real videos, which enables the learned model to track through occlusion, large pose deformation, and motion blur. We also show qualitative examples in Fig. \ref{fig:comp-results}.

\begin{figure}
    \centering
        \includegraphics[width=0.5\textwidth]{figures/badminton_baton_twirl_combined.pdf} \\
    \captionof{figure}{\small In both qualitative examples from TAO-Person, the WDTracker trained only on hallucinated videos (top) fails in a case of significant pose change or movement while the model trained with hard examples from our RV dataset is robust to these challenges. In the upper half, the track is broken in the HV model for the man running across the court. In the bottom half, a track is broken during significant pose changes by the dancer on the right.}
    \label{fig:comp-results}
\end{figure}

\subsection{Biased sampling of hard examples} \label{sec:hard-sampling}

We next analyze whether focusing on the hard examples that we mine from unlabeled videos might help to improve the training of our tracker.
We define a hard example to be a short video clip that includes a broken track which has been identified and rectified by our optimization process. Next, we adopt biased sampling for hard examples during model training, and we list the tracker's performance with different sampling rates in Table \ref{hard-resample}. For every training batch, we sample 50\% examples from RV and the rest from HV.

As shown in Table \ref{hard-resample}, there is a significant increase from introducing biased sampling of hard examples, which clearly illustrates the value of hard example mining. We empirically observe that the tracker achieves the best performance at a hard example sampling rate of around 75\%, which leads to a substantial 3\% AP@0.5 improvement over non-biased sampling. As hard examples are relatively sparse in the dataset, training without biased sampling performs nearly the same as sampling all easy examples.

\subsection{Balancing between hallucinated and real videos}
\input{tables/video_ratio}

In this section, we further investigate the optimal balancing ratio between sampling from HV and RV during training.
We define video balancing ratio as $\frac{|RV|}{|RV| + |HV|}$, in which $|HV|$ and $|RV|$ refers to the quantity of HV and RV in a training epoch respectively. We use the optimal biased sampling rate (i.e. 75\%) during model training.

As suggested in Table \ref{video-ratio}, there is a significant benefit to adopting a video balancing ratio between 50\% and 75\%. This result indicates the equal importance of both HV and RV. On one hand, HV provides high-quality and high-diversity of pseudo labels for learning robust geometric correspondence of the same instance. On the other hand, RV offers precious hard real videos that enable the tracker to track through dramatic human pose deformation, partial occlusion, etc.  Therefore, it is important to train the tracker on both HV and RV.
While training on either set of data alone does not work as well, training on hard examples from RV alone causes a more significant performance drop, indicating the importance of regularization from HV during model training.

\section{Conclusion}

In this paper, we explored training a deep neural tracker in a weakly supervised setting where we have bounding box annotations and a large pool of unlabeled videos. This is important as manual tracking annotations are extremely expensive to obtain. In detail, we first hallucinated a video sequence by applying recurrent zoom-in/out motion transformations to an image and later injecting various random motion effect transformations to the sequence. Next, we proposed to mine hard training examples from unlabeled videos. We considered hard examples to be video clips where a tracker trained only on hallucinated video would fail. We proposed an optimization-based connecting process to identify and rectify the broken tracklets, generating mined hard examples and their corresponding pseudo labels. Using our hallucinated videos and mined hard videos, we trained a deep neural tracker. The weakly supervised tracker achieved state-of-the-art performance on the challenging MOT17 and TAO-Person datasets, demonstrating the value of our self-generated dataset. On MOT17, we further showed that our self-generated data was complementary to the existing manually-annotated dataset, allowing for further improvements when combined.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


You can sign up today and learn more about Match.com's great features!
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text:
North of Portugal to lose 400,000 jobs until 2030
Impossible to deny is the fact that automation will certainly change the future of labour. What will be its real impact? The Portuguese Confederation of Enterprises (CIP) gives us a clue.
Impossible to deny is the fact that automation will certainly change the future of labour. What will be its real impact? Little is known, but the Portuguese Confederation of Enterprises (CIP) gives us a clue. In the north of Portugal, 421,000 jobs will be lost by 2030, especially in the manufacturing industry.
According to CIP's report, revealed this Wednesday, the North of Portugal is presenting a negative balance regarding the automation of professional activities. On one end, CIP forecasts the loss of 421,000 jobs, but on the other hand, it predicts the creation of 227,000 new jobs in the region.
Doing all the maths, the net loss of jobs as a direct consequence of automation would stand at around 190,000. For António Saraiva, President of CIP, this number is not a prophecy. "The merit of this study is to raise awareness in the government, unions and enterprises for this most-likely scenario", told ECO.
The textiles will be the most affected by the automation of work, representing half of the total job losses in the region. "Part of this problem is that the textile industry is still highly-specialised on manufacturing", Saraiva adds. The pharmaceutical sector will be the least affected.
Low qualifications and the advanced age range of workers will be the biggest challenges in the process of requalification. But António Saraiva is optimistic about it: "I refuse to believe that we will face that problem as we live in very modern times and people adapted to computers. The complexity (of that shift) is lesser than before."
A trilogy called requalification
To mitigate the elimination of jobs, António Saraiva advises on betting on the intensive requalification of human resources. According to CIP, just in the north of Portugal, "234,000 workers will need to be requalified (the equivalent to 14% of the workforce". "Only by requalifying them is that we can avoid the worst-case scenario", the President of CIP states.
As companies will be benefiting the most from it, they should take the lead. For António Saraiva, they have to accelerate their pace. "There is some work done, but it needs to be faster. Change is fast and we need to follow it."
CIP also remembers that this is not just a concern for the companies. The requalification of resources "should be understood as a shared national purpose where the State must be involved in". "The country will benefit from having better-qualified workers. We cannot just leave that to the companies to do it alone", argues.
According to the CIP report, educators are the last element of this requalification trilogy. On one hand, the government needs "mobilising and preparing the so-called elements of change" as well as the employers need planning strategies for their workforce by implementing those initiatives of requalification". On the other hand, educators have the mission of "adopting new methodologies and focusing on skills-training".
https://econews.pt/2019/06/26/north-of-portugal-to-lose-400000-jobs-until-2030/


text:
High School Senior Creates Viral Campaign to Stop Cyber Bullies
by Asha Job | Sep 11, 2012 | Cyber Bullying News | 1 comment
Tired of seeing his classmates taunted on Twitter, a Minnesota teenager decided he would fight cyber bullying in his own way: by starting a Twitter account that would only spread positivity in his school community. Kevin Curwick, football captain and Senior at Osseo High School, is the mastermind behind @OsseoNiceThings, a Twitter account that singles out the positive qualities of his classmates.
"I wasn't personally attacked but it just hit me the wrong way, these were coming out about my friends. I want them to feel welcomed, be happy about what they are and what they contribute to Osseo." Curwick's idea has inspired similar Twitter accounts all across the state. Minnetonka, Chanhassen, and other metro area schools have followed Curwick's example with similar accounts.
http://www.usatoday.com/news/health/wellness/story/2012-08-17/teen-twitter-cyberbullies/57120166/1
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text:
Q: Angularjs, Directive, ampersand I study AngularJS, And today i run into weird case, So this is the code: Plunker
And there are some things i just can't explain:

*

*If you can take a look at the value of console.log(scope.done) from the Plunker link above, This is the value function (a){return l(e,a)} this value looks like compressed function version, After i played with the code a bit i found that the above function return(and i assume) the call expression logChore(chore) we pass into done attribute done="logChore(chore)", So by executing the function (a){return l(e,a)} we executing the logChore(chore) function ?

*Another thing is why using object map done({chore:chore}), I do understand that the chore property value will be insert into the parameter with the same name  logChore(chore), But why can't i just do that: done(chore).

Well if some one can help me understand all of this i will be very thankful, Thank you all and have a nice day.

A:
I study AngularJS, And today i run into weird case.

I don't understand why it's weird, it's just how angular.js works, and it's great!

this value looks like compressed function version...

You are right, If I run Console.log(scope.done) with a non minified code I get:
function (locals) {
  return parentGet(scope, locals);
}

It's just the callback from compile.js source code:
case '&':
  parentGet = $parse(attrs[attrName]);
  isolateScope[scopeName] = function(locals) {
    return parentGet(scope, locals);
  };

As you can see, when using scope: { done : "&"} , angular parses the expression inside the attribute with $parse , and returns a function that when run will evaluate against the parent  scope.
You must understand that this expression : done="logChore(chore)" will eventually run against the outer scope, but the outer scope may not have a chore property.
A good example is angular events directives like ng-click="do($event)" , the outer scope doesn't have a $event property, so where is it coming from?
Angular solves this problem by providing a locals object as the second argument which allows you to run an expression against an extended scope.

why can't i just do that: done(chore)?

The reason is because angular doesn't treat the attribute with & as a function, it treats it as an expression. Actually, you can put any expression inside that attribute, examples:

*

*done = "logChore(chore); x = 1"

*done = "logChore(otherVar, chore)"

*done = "isLogged || logChore(chore)"

*done = "logChore(chore + 1)"
When you invoke the function inside your directive, the argument you pass is not the same as the argument that will be passed into the controller's function, it's just happens that in your case it's the same.
What you do pass is a locals object that extends the scope in which the expression will be evaluated against.

redpajama_set_name: RedPajamaStackExchange


We are both down to earth. I'm a full time photographer and Eliza does part time child care and acting roles when she gets the opportunity. We like to share a drink, food and cooking with our housemates as well as having our own space and respect other people's space. We enjoy books , music , movies and art in general.


How Important is Personalisation for Content Marketing?
Posted in Content on 14 February 2017, 08:30 , 0 Comments.
The digital revolution has transformed the way brands engage and communicate with their audiences. In an age where advertisements are declining and social media is rising, content marketing has become a go-to marketing strategy for brands. Content is evolving around the consumers needs and brands are struggling to keep up with the high demand of this insatiable appetite for content consumption.
Readers expect content to be informative, relevant and engaging and if it's not, they turn to a brand that is. This 'always - on' world is surrounded by huge amounts of content every day on the internet. Consumers are now beginning to expect tailored content based on their personal preferences (or specific location) to be delivered wherever they are.
Personalisation can be a very powerful tool to boost engagement, drive value and ultimately to make sure readers keep coming back. This year, delivering a personalised experience for customers is a top priority for businesses. In a recent study by PWC's Digital Services group, 94% of senior level executives believe that delivering personalisation is critical to reaching and retaining customers. As content marketing becomes more successful, personalisation becomes more popular, and it's also going to become more competitive.
Organisations in many sectors struggle to find the data and content that drives personalisation to its full capacity. From subscriptions and visitor data, businesses know who their consumers are, what interests them and the types of content they are likely to read, but this insight has little advantages if isn't in real time and actionable. Consumers expect a personalised experience whether online or on their mobile devices and businesses need to prioritise personalisation to help grow their digital revenue.
The brands that will stand out in the coming years will be those who have harnessed the power of user insights and data to drive engagement and sales for their business. Tailoring websites and mobile apps to the individual user now seems to be the main focus for a number of large brands. For example, brands such as Amazon, Netflix and a majority of social media platforms excel at customising the individual user experience by putting their needs first through personalised offers and recommendations. According to a report from Gartner, organisations that have fully invested in all types of online personalisation will outsell companies that have not by more than 30% in 2018.
Personalisation will no longer be a be a nice to have but a need to have strategy to generate a loyal audience. One of the big promises of online personalisation is to reduce bounce rates on their websites, emails and mobile apps and to extend the time visitors spend on their digital platforms. Businesses need to understand that they are giving up a part of their control when they implement personalisation.
Brands have a tendency to be overtly brand-centric in their marketing output. By giving digital tools to consumers to personalise how they are talked to by brands will enable a more intimate relationship between brand and customer. The consumer is in control of what they read and where they read it, the business just needs to make it easily accessible and timely.
Often, brands have a large amount of data already but doesn't offer any insight that can be actioned quickly. On the other hand, personalisation can be easy to implement at first by starting small and then investing in personalisation providers and analytics to enhance the data collection and execution of content. The Harvard Business Review states that personalisation can reduce extra costs by 50%, lift revenues by 5-15%, and increase the efficiency of marketing spend by up to 10-30%. The advantages of valuable and tailored content for the customer can translate into significant commercial benefits for the business.


text:
DealBook | Manchester United Plans an I.P.O.
Manchester United Plans an I.P.O.
By Julia Werdigier
August 16, 2011 10:43 am August 16, 2011 10:43 am
Wolfgang Rattay/ReutersManchester United's Wayne Rooney.
Manchester United, the English soccer club, is preparing to sell shares in an initial public offering on the Singapore exchange later this year, a person with direct knowledge of the plan said on Tuesday.
The club, which is controlled by the Glazer family, owners of the Tampa Bay Buccaneers of the National Football League, might raise about $1 billion in the share sale, said the person, who declined to be identified before the details of the sale were made public.
Manchester United picked the Asian market for the share sale because many of its fans are now based in the region.
The club's share sale would be the latest of a European company in Asia. The commodities trader Glencore International and the fashion group Prada have all sold shares on Asian stock exchanges this year. L'Occitane went public in Hong Kong last year.
Manchester United, an English Premier League club whose players include Wayne Rooney and Ryan Giggs, has hired Credit Suisse to manage the share sale and is in the process of appointing other banks to assist, the person said.
Representatives for Manchester United and Credit Suisse declined to comment.
A sale would be the latest episode in a rocky relationship between the Glazers and Manchester United fans ever since the American family bought the club — at the time a publicly traded company on the London Stock Exchange — more than five years ago. Some fans had criticized the buyout for putting a huge debt burden on the club that it had to pay off with its revenue, hurting investments in the club.
Manchester United's parent company, Red Football Joint Venture, said in March that it had a record pretax loss of £109 million, or $178 million, in the year that ended in June 2010. The loss was 36 percent larger than what the company had reported in October, according to a public filing of its accounts.
The Glazer family rejected speculation as recently as February that it was interested in selling the club. The Daily Mail newspaper had reported that the Qatar royal family was close to making a £1.5 billion bid.
Michael J. de la Merced contributed reporting.
The Case for Harbinger Group as the Next Berkshire


text:
Watch Robin Hood: Men in Tights
"The legend had it coming..."
Robin Hood: Men In Tights is a 1993 spoof of previous Robin Hood theatrical releases. Part musical and part adventure comedy, the film was produced and directed by Mel Brooks. Starring Cary Elwes as Robin of Loxley, and Richard Lewis as Prince John, the film also marks the motion picture debut of Dave Chappelle as Ahchoo, and includes an hilarious performance by Mark Blankfield as Blinkin - the family's blind servant.
Robin of Luxley escapes from Jerusalem's Khalil Prison, where he had been incarcerated after being captured in the Crusades. Robin is aided in his escape by another inmate named Asneeze, played by Isaac Hayes, who requests that Robin find his son Ahchoo once he is free.
Robin tracks down Ahchoo upon his return to England but also finds Prince John abusing the control he had been granted while King Richard has been fighting in the Crusades. For instance, Robin's family home, Luxley Hall, has been repossessed by Prince John's gang. Robin is informed by family servant Blinkin that his family and pets have all been killed by Prince John's men. Robin forms a union with Little John, played by Eric Allan Kramer, and Will Scarlett Ohara, played by Matthew Porretta, to regain his family's land and dethrone Prince John.
Robin also meets Maid Marian, played by Amy Yasbeck, as she seeks a man with the key to her heart and chastity belt. Marian is eventually kidnapped by the Sheriff of Rottingham, played by Roger Rees, who vows to deflower her. Robin saves the day by winning a sword fight. Robin and Marian marry and Ahchoo becomes the new sheriff.
The film is full of Mel Brooks styled comedy, with multiple references to Hollywood and other Mel Brooks movies. Brooks makes a cameo in the film as Rabbi Tuckman. The film, while not a box office success when released, has earned a cult following over time.
Cary Elwes, Richard Lewis, Roger Rees, Amy Yasbeck
More Like Robin Hood: Men in Tights
Wayne's World 2
Also directed by Mel Brooks
Life Stinks
Dracula: Dead and Loving It
High Anxiety
Get Smart, Again!
Also starring Cary Elwes
The Riverman
A Haunting In Cawdor
Wish You Were Dead
The Alphabet Killer
Also starring Richard Lewis
Richard Lewis: I'm Exhausted
Watch Robin Hood: Men in Tights Trailer
Robin Hood: Men in Tights (1993) Trailer|1:33
Watch Robin Hood: Men in Tights Online - Watch online anytime: Stream, Buy
Robin Hood: Men in Tights is available to watch and stream, buy on demand at Amazon, Vudu, Google Play, FandangoNow, iTunes, YouTube VOD online.


text:
Saipan Chickens are commonly called Jungle Fowl; however, they are not to be confused with true Jungle Fowl. They are quite different in appearance and temperament from many true Jungle Fowl, and are valued for their beauty.
Saipan Chickens, particularly the cockerels, are quite territorial. Males will attack people or other birds in their enclosures, and will defend their females fiercely. Conversely, female Saipan Chickens are reported to be docile, calm, and easy to handle. They may be kept with a male as long as he does not bully them, although males may be easier to handle when they have no hens to defend. Male Saipan Chickens should not be placed in the same enclosure. They may bully other birds of different species in their enclosures, particularly territorial birds. Saipan Chickens are, however, naturally gregarious, and may be less stressed when kept in a harem situation.
Standing two to three feet tall, Saipan Chickens are quite large. Females are often smaller than males, which usually weigh 10 to 12 pounds. Female Saipan Chickens have duller coloration than males. Their bodies are usually colored with brown and buff shades and white. Males, however, have impressive colorations. Their feathers have metallic sheens, and their bodies are golden or bronzed. Necks are golden to green, and the crown is usually bright red or rusty in color.
During the Second World War, Saipan Chickens were brought to the United States from southeastern Asia and India, where they originate. Saipan Chickens were once used to breed with fighting cocks because of their aggressive natures and their tall size.


It's Chocolate Day! Of course, it calls for a special celebration. And if you are thinking of surprising your sweetheart and loved ones with some sweet delights, what better than chocolates?...Don't wait and them these Chocolate Day images and make their day sweet.
Every meal should end with something sweet. Maybe it's jelly on toast at breakfast, or a small piece of chocolate at dinner - but it always helps my brain bring a close to the meal.


text:
Q: Python (scikit learn) lda collapsing to single dimension I'm very new to scikit learn and machine learning in general.
I am currently designing a SVM to predict if a specific amino acid sequence will be cut by a protease. So far the the SVM method seems to be working quite well:

I'd like to visualize the distance between the two categories (cut and uncut), so I'm trying to use the linear discrimination analysis, which is similar to the principal component analysis, using the following code:
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda = LinearDiscriminantAnalysis(n_components=2)
targs = np.array([1 if _ else 0 for _ in XOR_list])
DATA = np.array(data_list)
X_r2 = lda.fit(DATA, targs).transform(DATA)
plt.figure()
for c, i, target_name in zip("rg", [1, 0],["Cleaved","Not Cleaved"]):
    plt.scatter(X_r2[targs == i], X_r2[targs == i], c=c, label=target_name)
plt.legend()
plt.title('LDA of cleavage_site dataset')

However, the LDA is only giving a 1D result
In: print X_r2[:5]
Out: [[ 6.74369996]
 [ 4.14254941]
 [ 5.19537896]
 [ 7.00884032]
 [ 3.54707676]]

However, the pca analysis will give 2 dimensions with the data I am inputting:
pca = PCA(n_components=2)
X_r = pca.fit(DATA).transform(DATA)
print X_r[:5]
Out: [[ 0.05474151  0.38401203]
 [ 0.39244191  0.74113729]
 [-0.56785236 -0.30109694]
 [-0.55633116 -0.30267444]
 [ 0.41311866 -0.25501662]]

edit: here is a link to two google-docs with the input data. I am not using the sequence information, just the numerical information that follows. The files are split up between positive and negative control data.
Input data:
file1
file2

A: LDA is not a dimensionality reduction technique. LDA is a classifier, the fact that people visualize decision function is just a side effect, and - unfortunately for your use case - decision function for binary problem (2 classes) is 1 dimensional. There is nothing wrong with your code, this is how every single decision function of a linear binary classifier looks like.
In general for 2 classes you get at most 1-dim projection and for K>2 classes you can get up to K-dim projection. With other decomposition techniques (like 1 vs 1) you can go up to K(K-1)/2 but again, only for more than 2 classes.


Arms & Armour is a two-piece band. It is loud. We're something between electronic and sludge metal. That is all.
Istvan Medgyesi is a working musician in Cleveland, Ohio. He plays somewhere between the dark heart of song in the tradition of the 1970's greats and the experimental urges of the seriously avant-garde. His work is urgent and immediate. One gets the sense that he's living the songs as much as playing them.
Performing on various acoustic and electronic instruments, listeners can hear the echoes of an array of musical traditions intertwined with modal explorations and occasional forays into noise and drone. From raga based acoustic compositions to random voltage generated melodies, the focus is on the unifying factors of sound and style.
Oregon Space Trail Of Doom with Arms & Armour, Istvan Medgyesi, Sean Holt.


"We are pleased to announce that the construction cost of Phases 1 and 2 of the ECRL has now been reduced to RM44 billion". But the project's cost ballooned as construction went underway, with the finance ministry estimating that the price tag could reach as much as 81 billion ringgit.
The ECRL supplementary agreement, which has been the subject of ongoing negotiations from July past year, was signed here at 11am local time Friday.
The deal follows months of vacillating over the East Coast Rail Link, which connects Malaysia's west coast to eastern rural states and is a key part of China's Belt and Road infrastructure initiative. But this new government is concerned about cost.
According to earlier reports, Phase 1 of the rail line will be from Klang Valley to Kuantan, while Phase 2 will cover Kuantan to Kuala Terengganu.
The Prime Minister's Special Envoy Tun Daim Zainuddin is in Beijing to meet with representatives from China on the ECRL. It is not a new agreement or contract, but an improved deal, he said at a press conference at the Malaysian embassy.
Malaysia said Friday a major Chinese-backed railway had been revived following a deal to cut costs, lifting a suspension slapped on the project when a corruption-plagued regime lost power a year ago.
He said the stop at Gombak in Selangor has been cancelled.
Malaysia's government said Friday it has made a decision to resume a China-backed rail link project, after the Chinese contractor agreed to cut the construction cost by one-third. The lower cost also means the government can save in terms of interest on a lower loan, he said. FYI, our current prime minister Tun Dr Mahathir Mohamad is expected reveal those information in the press conference.