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|


LDC (Linguistic Data Consortium) Spoken Language Sampler - Third Release, LDC catalog number LDC2015S09 and ISBN 1-58563-726-2, contains samples from 20 different corpora published by LDC between 1996 and 2015.
LDC distributes a wide and growing assortment of resources for researchers, engineers and educators whose work is concerned with human languages. Historically, most linguistic resources were not generally available to interested researchers but were restricted to single laboratories or to a limited number of users. Inspired by the success of selected readily-available and well-known data sets, such as the Brown University text corpus, LDC was founded in 1992 to provide a new mechanism for large-scale corpus development and resource sharing. With the support of its members, LDC provides critical services to the language research community that include: maintaining the LDC data archives, producing and distributing data via media or web download, negotiating intellectual property agreements with potential information providers and maintaining relations with other like-minded groups around the world.
Resources available from LDC include speech, text, video data and lexicons in multiple languages, as well as software tools to facilitate the use of corpus materials. For a complete view of LDC's publications, browse the Catalog.
The LDC Spoken Language Sampler - Third Release provides speech and transcript samples and is designed to illustrate the variety and breadth of the speech-related resources available from the LDC Catalog. The sound files included in this release are excerpts that have been modified in various ways relative to the original data as published by LDC:
The link for the catalog number takes you to the catalog entry, and the link for the title takes you to further documentation for that corpus.
| LDC2014S06 | 2009 NIST Language Recognition Evaluation Test Set | The 2009 evaluation contains approximately 215 hours of conversational telephone speech and radio broadcast conversation collected by LDC in the following 23 languages and dialects: Amharic, Bosnian, Cantonese, Creole (Haitian), Croatian, Dari, English (American), English (Indian), Farsi, French, Georgian, Hausa, Hindi, Korean, Mandarin, Pashto, Portuguese, Russian, Spanish, Turkish, Ukrainian, Urdu and Vietnamese. |
| LDC2014S01 | CALLFRIEND Farsi Second Edition Speech | CALLFRIEND Farsi Second Edition Speech was developed by LDC and consists of approximately 42 hours of telephone conversation (100 recordings) among native Farsi speakers. The CALLFRIEND project supported the development of language identification technology. Each CALLFRIEND corpus consists of unscripted telephone conversations lasting between 5-30 minutes. |
| LDC96S37 | CALLHOME Japanese | A corpus of 120 unscripted telephone conversations between native Japanese speakers and a corpus of associated transcripts. |
| LDC2013S09 | CSC Deceptive Speech | CSC Deceptive Speech was developed by Columbia University, SRI International and University of Colorado Boulder. It consists of 32 hours of audio interviews from 32 native speakers of Standard American English (16 male, 16 female) recruited from the Columbia University student population and the community. The purpose of the study was to distinguish deceptive speech from non-deceptive speech using machine learning techniques on extracted features from the corpus. |
| LDC2007S18 | CSLU Kids' Speech | Developed at Oregon State University's Center for Spoken Language Understanding, this corpus is a collection of spontaneous and prompted speech from 1100 children from Kindergarten through Grade 10. |
| LDC2010S01 | Fisher Spanish Speech | Fisher Spanish Speech consists of audio files covering roughly 163 hours of telephone speech from 136 native Caribbean Spanish and non-Caribbean Spanish speakers. |
| LDC2014S02 | King Saud University Arabic Speech Database | King Saud University Arabic Speech Database contains 590 hours of recorded Arabic speech from 269 male and female Saudi and non-Saudi speakers. The utterances include read and spontaneous speech recorded in quiet and noisy environments. The recordings were collected via different microphones and a mobile phone and averaged between 16-19 minutes. |
| LDC2003S07 | Korean Telephone Conversations Complete (S), (T), (L) | The Korean telephone conversations were originally recorded as part of the CALLFRIEND project. Korean Telephone Conversations Speech consists of 100 telephone conversations, 49 of which were published in 1996 as CALLFRIEND Korean, while the remaining 51 are previously unexposed calls. Korean Telephone Conversations Transcripts (LDC2003T08) consists of 100 text files, totaling approximately 190K words and 25K unique words. All files are in Korean orthography: orthographic Korean characters are in Hangul, encoded in KSC5601 (Wansung) system. The complete data set also includes a lexicon (LDC2003L02). |
| LDC2012S04 | Malto Speech and Transcripts | Malto Speech and Transcripts contains approximately 8 hours of Malto speech data collected between 2005 and 2009 from 27 speakers (22 males, 5 females). Also included are accompanying transcripts, English translations and glosses for 6 hours of the collection. Malto is principally spoken in northeastern India and Bangladesh. |
| LDC2015S05 | Mandarin Chinese Phonetic Segmentation and Tone | Mandarin Chinese Phonetic Segmentation and Tone was developed by LDC and contains 7,849 Mandarin Chinese "utterances" and their phonetic segmentation and tone labels separated into training and test sets. The utterances were derived from 1997 Mandarin Broadcast News Speech and Transcripts (HUB4-NE) (LDC98S73 and LDC98T24, respectively). That collection consists of approximately 30 hours of Chinese broadcast news recordings from Voice of America, China Central TV and KAZN-AM, a commercial radio station based in Los Angeles, CA. This corpus was developed to investigate the use of phone boundary models on forced alignment in Mandarin Chinese. |
| LDC2015S04 | Mandarin-English Code-Switching in South-East Asia | Mandarin-English Code-Switching in South-East Asia was developed by Nanyang Technological University and Universiti Sains Malaysia and includes approximately 192 hours of Mandarin-English code-switching speech from 156 speakers with associated transcripts. |
| LDC2013S03 | Mixer 6 Speech | Mixer 6 Speech was developed by LDC and is comprised of 15,863 hours of telephone speech, interviews and transcript readings from 594 distinct native English speakers. This material was collected by LDC in 2009 and 2010 as part of the Mixer project, specifically phase 6, the focus of which was on native American English speakers local to the Philadelphia area. |
| LDC2014S03 | Multi-Channel WSJ Audio | Multi-Channel WSJ Audio was developed by the Centre for Speech Technology Research at The University of Edinburgh and contains approximately 100 hours of recorded speech from 45 British English speakers. Participants read Wall Street Journal texts published in 1987-1989 in three recording scenarios: a single stationary speaker, two stationary overlapping speakers and one single moving speaker. |
| LDC2004S09 | NIST Meeting Pilot Corpus Speech | This data set contains speech and transcriptions from topical discussions in meeting settings, including complete descriptive metadata and detailed descriptions of the physical environment in which the discussions took place. |
| LDC2015S02 | RATS Speech Activity Detection | RATS Speech Activity Detection was developed by LDC and is comprised of approximately 3,000 hours of Levantine Arabic, English, Farsi, Pashto, and Urdu conversational telephone speech with automatic and manual annotation of speech segments. The corpus was created to provide training, development and initial test sets for the Speech Activity Detection (SAD) task in the DARPA RATS (Robust Automatic Transcription of Speech) program. |
| LDC2015S03 | The Subglottal Resonances Database | The Subglottal Resonances Database was developed by Washington University and University of California Los Angeles and consists of 45 hours of simultaneous microphone and subglottal accelerometer recordings of 25 adult male and 25 adult female speakers of American English between 22 and 25 years of age. |
| LDC2012S02 | TORGO Database of Dysarthric Articulation | TORGO contains approximately 23 hours of English speech data, accompanying transcripts and documentation from 8 speakers (5 males, 3 females) with cerebral palsy or amyotrophic lateral sclerosis and from 7 speakers (4 males, 3 females) from a non-dysarthric control group. |
| LDC2012S06 | Turkish Broadcast News Speech and Transcripts | Turkish Broadcast News Speech and Transcripts contains approximately 130 hours of Voice of America Turkish radio broadcasts and corresponding transcripts. |
| LDC2014S08 | United Nations Proceedings Speech | United Nations Proceedings Speech was developed by the United Nations (UN) and contains approximately 8,500 hours of recorded proceedings in the six official UN languages, Arabic, Chinese, English, French, Russian and Spanish. The data was recorded in 2009-2012 from sessions 64-66 of the General Assembly and First Committee (Disarmament and International Security), and meetings 6434-6763 of the Security Council. |
| LDC2014S04 | USC-SFI MALACH Interviews and Transcripts Czech | USC-SFI MALACH Interviews and Transcripts Czech was developed by The University of Southern California Shoah Foundation Institute (USC-SFI) and the University of West Bohemia as part of the MALACH (Multilingual Access to Large Spoken ArCHives) Project. It contains approximately 229 hours of interviews from 420 interviewees along with transcripts and other documentation. |
Portions © 2015 Trustees of the University of Pennsylvania


# 当页面有删除元素影响高度时，需要调用 .scrollspy('refresh') 重新刷新 $('[data-spy="scroll"]').each(function () { var $spy = $(this).scrollspy('refresh') })
$('#myScrollspy').on('activate.bs.scrollspy', function () { // do something… })
Php is good!
Php is good!
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Php is good!
Php is good!
Php is good!
Php is good!
Php is good!
Php is good!
Php is good!
Php is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Linux is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
Javascript is good!
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Javascript is good!
Css is good!
Css is good!
Css is good!
Css is good!
Css is good!
Css is good!
Css is good!
Css is good!
Css is good!
Css is good!
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com.pump.geom.Spiral2D
shape.
You can construct a spiral by calling:
Shape spiral = Spiral2D(centerX, centerY, coilGap, coils, angularOffset, coilOffset, isClockwise)
This demo uses some static helper methods in Spiral2D
that let you define a spiral by its starting and ending coordinates. The static helper methods are:
public static Spiral2D createWithFixedCoilGap( Point2D center, Point2D end, double coilGap); public static Spiral2D createWithFixedCoilCount( Point2D center, Point2D end, double numCoils);
This relies on two other classes:
AbstractShape
: this class takes care of a lot of the details of implementing a java.awt.Shape
object. As long as you provide the PathIterator
this class will take care of methods like Shape#contains(Rectangle2D)
and Shape#intersects(Rectangle2D)
.ParametricPathIterator
: If you supply the x(t) and y(t) function, a function for dx/dt(t) and dy/dt(t), and a model for how to increment t, then this class can convert your parametric graph to a java.awt.geom.PathIterator
. For example a spiral can be expressed as:
x(t) = centerX + coilGap * (t + coilOffset) * cos(2*pi*t + angularOffset) y(t) = centerY + coilGap * (t + coilOffset) * sin(2*pi*t + angularOffset)
The value t
increments by 1.0/8.0 (so there are eight bezier segments per coil), and never exceeds Spiral2D#getCoils()
.
It may be possible to further improve this class by automating the calculation of the derivative and scanning for changes to the control points, but for the time being this implementation satisfies the original requirements for this class.


--- layout: post title: ISL 1 (Interesting Stuff List) redirect_from: "2004/04/07/isl-1-(interesting-stuff-list)/" ---
And here we go for the first list (please remember my backlog go back to October 2k3 so some links might look a bit old. They are nevertheless quite interesting in my opinion).
Development
· From Jon, an IIS SSL Diagnostic tool.
· For all your COM+ fans, in this comments, how to enable the spinning ball in the Component Services snap-on for your component.
· Sort Comparison from Raymond. Very interesting as always.
· The Logging Application block is available, thanks to Fabrice for the link. Fabrice is one of the most dedicated application blocks promoters I’ve seen.
· Monitoring asp.net application on MSDN, something not done enough in many companies.
· From Sam Gentile
o A paper on a CLR and JVM JIT compiler from Intel.
· From Craig, a snippet of the new add-in deployment model in Visual Studio 2005 (must reckon the page break, or lack of, is annoying).
· All you need to know about WS-Transactions from Benjamin.
· Monad, that recently reached a new milestone according to Neowin, and is now named Command Shell (After being Microsoft Shell for a while)
Blogging
· From Ian White from Marc, an RSS for outlook utility that lets you publish your outlook calendar as an RSS feed. Wonder if it integrates well with the ESF extension for newsgator
· From Dave Winer, about the future of blogging software.
· On the verge of it… Vault generates RSS feeds, and so does the 2.0 version for that matter. Very very useful (like when you want to know up to the minute if one dev try to sneak in some code where he shouldn’t).
Fun
· From Ian White, The Meowtrix.
· From Rob Howard, Software Development Maladies
· From Julien, Top 20 Coder excuses.
And that’s all for today.


 That's been the question on my mind ever since I became a critic, as amateur as I am, since the announcement that a new Star Wars film came out long before I even began to write these things. I'm a Star Wars fan, I may admit it; though I'm not a "die hard fan" (amongst the scariest people in the world, I assure you...though a quaint second compared to Star Trek fans), and because of this personal problem, I have a huge bias against judging this film on a fair basis. I love the other three, though hardly on equal levels, and would easily and without much trepidation award each of them the coveted four star rating - yes, even 'Jedi' - but how does one even begin to put into logical order the thoughts concerning the viewing of the newest installment in this saga, let alone the first in nearly 16 years? I pictured early drafts of this review that would lapse into considerable amounts of hyperbole, virtual years before seeing the actual movie, and I swore tonight, a mere hour after leaving the theater, that I would: a) write the review and get it out there before I passed out from fatigue; and b) try not to do what I feared I would do under pressure.
But though the temptation may have been strong to over-dramaticize the greatness thereof before, it's remarkably easy not to after actually having seen it. As people talked about how incredible it was on the way out, remarked about the action sequences with flustered enthusiasm, and already began speculated about the way everything we saw is incorporated with the other three, I kept a mild and reserved contain on my emotions. A big grin plastered on my face, not merely because I had actually seen the most anticipated movie of all time two weeks before it is released to the general public in America (a feat I am almost certain I would have been killed for, or at the very least tortured inhumanely for), but also because it did in fact work for me. Thanks to several unexpected disappointments within the past couple years with highly anticipated movies that had actually blown, I made a conscious decision, say about six months ago, to not get too pumped up for any movie (and this includes new films from the likes of Greenaway, Egoyan, and Leigh), and thus lessen the impact of disappointment on me. Instead of building this new film up to be the Most Incredible Motion Picture Experience Or Else, I kept a handle on things, and by walking into the movie, I was more excited about actually being there, seeing a big exec from 20th Century Fox in person, et. al., than I was about the fact that what would unfold in front of me in a very short amount of time was yet another movie in the saga that is the most embraced cinematic saga of all time, and a personal favorite of mine. I was prepared for the mediocre, to put it bluntly.
That may have backfired on me though. Instead of either really loving it above all or hating it above all, I just really liked it. I sat there watching the movie, nearly forgot that it was a Star Wars movie from time to time, and was just really entertained. The story was well told, the characters were for the most part nicely drawn, and the action was phenomenal. Even the CGI effects, which I swore would be its downfall (I mean, it looks so...fake...), worked extremely well and didn't bother me for a second. So in short, very recommended. But my big problem is this: do I really like it because I wasn't expecting much and it delivered more than I thought it would; and if so, am I sure this movie isn't really the mediocre mess my friend made it out to be (a non-Star Wars fan, for the record, who must be glad I haven't targeted him for scorn in this)? There were things that bugged me, namely the fact that instead of running smoothly like the other three, it seems to have adopted an ebb-n-flow technique that isn't incredibly satisfying, at least not as satisfying as it should be. It introduces elements to us that are terrific and mesmerizing in theory, but does it work as a whole in execution? I'd answer that with a reasonable "sort of, for the most part." Looking at the plot structure of the film from afar makes it sound incredibly incredible. The Federation may be overtaking the Republic with an illegal attack that may run its course due to corrupt politicians and legal technicalities. The Queen of the Republic of Naboo is saved by two Jedi Knights who stop by Tatooine on the voyage to safety due to a damaged ship and stumble upon no less than Annakin Skywalker at the ripe age of about eight.
The rest is, of course, predictable, but just look at the situation, by god. Annakin, the same guy who becomes Darth Vadar even before the original 'Star Wars' (or probably to be forever recognized under the more dour title, 'A New Hope'), becomes the completely ironic hero of the film! Star Wars geeks get to have a field day with this situation! If the most prominently figured character - a Jedi Knight named Qui-Gon Jinn (played marvelously by the ever-reliable Liam Neeson) - is the most wise character in the film, and he fingers Annakin as the messianic one of the Jedis who will bring everything together in peace and harmony, he's in fact dead wrong, isn't he? Or is he? Isn't by training Annakin to become a Jedi Knight only to have him turn to the dreaded Dark Side the good option since his becoming evil actually causes everything to come together so neatly in 'Return of the Jedi?' This is loads of fun to think through, especially for anyone who's ever given any serious thought to the 'Star Wars' saga in terms of novelistic entrapments, but does this automatically make this new film - called 'The Phantom Menace,' by the way, though there doesn't appear to be an 'official official' title for it - a brilliant piece of art? Is it great merely because it comes fourth in being made but first in terms of order, thus giving it a weight it wouldn't have to begin with? Lucas may have stumbled onto something quite clever by making the fourth episode first, but it doesn't automatically make this film anything great.
Yet what I admire, perhaps most about this film, is how it doesn't at all play up to this contrivance. There isn't a lot of Lucas pointing out irony here in the delicious situation he's cooked up for us; no clever asides that gawk at the brilliance of himself, how he managed to make an entire movie that could potentially hold onto the reins of the previous moneymaking machines without doing any of the steering for itself. In fact, it seems, quite refreshingly, that Lucas is actually working to make this good. He actually cares about the storyline and isn't automatically set to shoot anything in front of us and feel confident that everyone will like it because it's Star Wars. No, Lucas is the real deal.  He makes it all seem incredibly real and fresh and new. In fact, it hardly seems a thing like the other three. The irony is here, not only with Annakin, but with Emperor Palpatine (Ian McDiarmid), the gentle, kind man who becomes the head of the counsel, but who by the next trilogy becomes the dreaded Emperor who shows up prominently in 'Jedi' and is done away with nicely by Vadar/Annakin (McDiarmid, by the way, is great). This paradox hadn't even occurred to me during the run of the film (though Diarmid also portrays Darth Sidious, who like Emperor Palpatine in the other films, is only seen with a cloak brought down over his head mysteriously), and only afterwards did this hit me. This kind of thing makes me excited, not only because this means we also get to follow his assured path to evil, but because that means this film is for real because it doesn't play it up. Even Annakin isn't overblown.  There's no sign of Vadar in him yet, and if you hadn't seen the other three (and had subsequently been living on Mars in a cave with fingers placed firmly in ears), you'd have no idea of the tragedy about to ensnare him. That he plays this completely straight is marvelously commendable for anyone else would have wanted to brag about the fact that they've developed something so immensely adroit.
For most of it, it's an exciting, engrossing motion picture, mostly because it's slow paced enough to take in the situations and interestingly enough structured. Neeson commands most of the movie, bringing a realistic and natural feel to the first act and it's more standard escape-from-the-bad-guys stuff, and thus to the rest of the movie which plays out a whole lot smoother. In fact, once Annakin has popped up for the first time by around the 40 minute mark, the film begins to really take off. He becomes a source of selfless aid to Neeson and company (including his apprentice Obi-Wan Kenobi - played with dour temperament by Ewan McGregor - and Queen Amidala - played with reserve, dignity, and head dresses galore by Natalie Portman) during their tenure on Tatooine, and though some of the plot points are contrived (as are their explanations: "Nothing happens by accident." - gimme a break), it at least feels fresh while happening, mostly due to a state of intellectual tension that's there (read: if you think about the fact that if they don't hurry up and get the Queen to her rendezvous, more and more people will die, though they hardly keep reminding you). I was even impressed by the way Lucas seems to purposely gloss over the inclusions of characters from the next trilogy, including a brief appearance by Jabba the Hut, a couple scenes with Yoda (as a senator, walking cane firmly in hand), and introductions to both R2-D2 and C3P0. R2-D2 is even glanced over when he first appears, and C3P0 is given such a brief role that it's refreshing that Lucas didn't pander too much to his audience. The same goes for the inclusion of Samuel L. Jackson and Terence Stamp, two terrific actors given unglamorous cameo-sized roles.
Still, a lot of the film feels like it was pasted together out of neat ideas that don't gel terribly well together, something not present in the other films. There's also an odd air of "we should be completely enthralled though we aren't that enthralled" throughout. My reason for this is because, with the exception of Annakin and Qui-Gon Jinn, there's no real source of emotional appeal. Neeson's likable just because he's commanding and wise and confident, and Annakin's likable because Lloyd makes him so darned appealing (also, kudos to Pernilla August for playing his mother so damn well, adding a considerable amount of emotional weight to the film). But most of everyone else is a bit disappointing. Amidala is underwritten, and the same times two goes for Obi-Wan Kenobi (McGregor turns in a 'not bad but not great either, is it?' performance, consisting of trying too hard to appear like a young Alec Guiness and coming up with vacuousness). The other films, even the first one, had sharply drawn characters that were full of personality if nothing else and only got better as the series went on. With these characters, I doubt much can be done, though. Amidala possesses some of Princess Leia's feistiness from time to time (probably because she's her mother), but she's mostly authoritative and kinda cold; I can already picture her reasons for wanting to marry Anakin in a later episode. And Obi-Wan's future seems painfully clear already: he'll learn more and more and become stern and wise as he gets older. For Annakin, though, it's wonderfully vague. I'm nearly anticipating the second episode already, just so I can see where Lucas takes his character on his journey to assured evil.
While this all factors in the fact that I shan't be giving this a four star rating like the other ones (sorry, guys), I was at least totally thrilled with the film's final act, a juxtaposition a la 'Jedi' where three battles go on simultaneously and are shown via cutaways at key moments, that was exciting, filled with originality, and pulse-pounding. The inclusion of the character Darth Maul plays a key role in the impact of this section as he's probably the most incredibly fascinating minor character in the saga since Boba Fett: his face is painted red and black stripes, he has horns on his head that aren't revealed till the finale (and with lots of 'oo's and 'ah's from the audience), and he says all of one or two lines. A mysterious character like this, especially one so menacing, is all this film needs, and his final battle with both Obi-Wan and Qui-Gon is nothing short of breathtaking. The orchestra adds choir voices and then pounds away with intensity, the acrobatics and choreography are probably better than the stuff in 'The Matrix,' and there's so much tension, namely in the survival of Qui-Gon, that it's sublime. Only problem is the other two aren't nearly as captivating, though still terrific, and every cutaway is like a massive jolt that lasts until the next time they come back to it. Same thing happened with 'Jedi'; why this structure wasn't avoided at all costs by Lucas is beyond me (well, really not; he probably thinks 'Jedi' is just flawless stuff).
But still, this entire section, plus a nifty (and short) denouement cap the film off with an enjoyable feeling, leaving the audience pumped up.  It doesn't try and play like one of those films, and by playing it so differently, the CGI effects actually work. They don't look too unreal, as I feared, but they do give it a whole other feeling that wasn't there in the other films, which relied mostly on puppets and real creations, not CGI. But that all works with this film. The characters seem to have more life in them, and we're able to visit places where more diverse creatures dwell in large numbers. This didn't work at all in the re-releases (where you could visibly tell where they injected CGI), but it works here because it's consistent and clearly defines its reality (the flip-side ot this is it seems to have given Lucas the chance to add broad comedy to the brew - mostly in the form of 'comic relief' Jar Jar Binks - most of which falls flat and proves quite annoying). It'd be nice for a return, just because I prefer the look of those films, but if this is the direction Lucas is taking this trilogy in, I'm glad to follow along. His dialogue may still be shit, and he's lost a bit of his storytelling magic, but 'The Phantom Menace' is at the very least incredibly satisfying, which is commendable since this is, in fact, the most highly anticipated film of all time. It's nice not to be letdown by such a film, even if you have turned skeptical.
MY RATING (out of 4): ***1/2
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Since every programming paradigm needs to solve the tough problem of printing a well-known greeting to the console we’ll introduce you to the actor-based version.
Open HelloWorld.java
The HelloWorld
actor is the application’s “main” class; when it terminates
the application will shut down—more on that later. The main business logic
happens in the preStart
method, where a Greeter
actor is created
and instructed to issue that greeting we crave for. When the greeter is done it
will tell us so by sending back a message, and when that message has been
received it will be passed into the behavior described by the receive
method where we can conclude the demonstration by stopping the HelloWorld
actor.
You will be very curious to see how the Greeter
actor performs the
actual task. Open Greeter.java.
This is extremely simple now: after its creation this actor will not do
anything until someone sends it a message, and if that happens to be an
invitation to greet the world then the Greeter
complies and informs the
requester that the deed has been done.
Go to the Run tab, and start the application main class
sample.hello.Main
. In the log output you can see the "Hello World!" greeting.
Main.java
is actually just a small wrapper around the generic launcher class akka.Main
,
which expects only one argument: the class name of the application’s main actor. This main
method will then create the infrastructure needed for running the actors, start the
given main actor and arrange for the whole application to shut down once the
main actor terminates. Thus you will be able to run the application with a
command similar to the following:
java -classpath akka.Main sample.hello.HelloWorld
This conveniently assumes placement of the above class definitions in package
sample.hello
and it further assumes that you have the required JAR files for
scala-library
, typesafe-config
and akka-actor
available.
The easiest would be to manage these dependencies with a
build tool.
If you need more control of the startup code than what is provided by akka.Main
you can easily write your own main class such as
Main2.java
Try to run the sample.hello.Main2
class
by selecting it in the 'Main class' menu in the Run tab.


KURT & COURTNEY (Roxie) Featuring: Nick Broomfield, Courtney Love, Hank Harrison, Tom Grant. Written, Produced and Directed by Nick Broomfield. MPAA Rating: Unrated (could be R for profanity, nudity and adult themes) Running Time: 97 minutes. Reviewed by Scott Renshaw.
By the end of Nick Broomfield's controversy-plagued documentary KURT & COURTNEY, I think I knew as much about Broomfield himself as I knew about Kurt Cobain or Courtney Love. The film might have begun as a biographical study, exploring the tempestuous tives of Nirvana lead singer Cobain and riot grrrl-cum-actress Love both before and after their 1992 marriage, but that's not the story it ends up telling. Instead, it becomes the tale of Broomfield's ongoing battle with Love over the making of the film -- her successful efforts to persuade MTV to withdraw funding, her objection to the use of several songs, her attempts to intimidate anyone involved in the production.
No one can blame Broomfield for being infuriated at Love, particularly when her 11th-hour legal stunt forced KURT & COURTNEY out of the Sundance Film Festival lineup. It might have been easier to sympathize with him, however, if his presence in the film weren't so consistently intrusive. Like Michael Moore, Broomfield has no qualms about filming himself in the process of making his documentary film; unlike Moore, Broomfield isn't entertaining enough to warrant all the screen time. In fact, he's just plain annoying at times, interjecting commentary over virtually every musical selection (or lack thereof) and casually referring to Cobain's hometown of Aberdeen, Washington as a "redneck logging town." Even his habit of shooting from inside the car as it pulls up to various locations feels pretentious, an attempt at you-are-there urgency which conveniently ignores that automotive jaunts hardly make for compelling cinema.
Lucky for Broomfield that his subject is such a wild and provocative one. KURT & COURTNEY features as wonderfully twisted a cast of characters as any recent film has compiled: Love's estranged father Hank Harrison, whose casual aggression towards his daughter provides a frightening insight into her childhood; Love's ex-boyfriend and former Portland-area singer Rozz Rezabek, seething bitterness at the career he might have had if Courtney hadn't driven him to hate it; a one-time friend of Love and Cobain named Amy who may or may not have photos of them doing heroin together, but certainly is trying way too hard to look like Courtney.  This angle introduces us to El Duce, a demented, drunken punk band frontman who claims Love once offered him "50 grant to whack Kurt Cobain." Watching the kind of people with whom Kurt and Courtney surrounded themselves makes you wonder why both of them didn't end up dead long before.
Ultimately, KURT & COURTNEY doesn't seem out to convict Courtney Love of soliciting murder, debunking many of the contentions of conspiracy theorists like private detective Tom Grant. It's far more damning in its contention that Love's drive for fame and success tends to crush everything in its path, and that her history of verbal and physical assaults is just a hair's breadth removed from her obsessive need to control her public image. It would have been chilling to watch that personality connected more directly to Cobain's psychological disintegration, but the troubled soul has little more than a cameo role in the film. Instead, Courtney is primarily connected to Nick Broomfield, who finally follows Love on-stage at an ACLU benefit to chastise her hypocrisy. KURT & COURTNEY tends to drift away from its potentially fascinating subject; it's a record of celebrity turned caustic, intercut with a record of a film-maker's pique. You almost wonder why he didn't call the film KURT & COURTNEY & NICK.
On the Renshaw scale of 0 to 10 burnin' Loves: 6.
Visit Scott Renshaw's MoviePage http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the MoviePage for details, or reply to this message with subject line "Subscribe".
The review above was posted to the
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| HDG | 000 | ° |
| SPD | 00.0 | kt |
| VMG | -00.0 | kt |
| LWY | 0.0 | °S |
| DIR | 000 | ° |
| SPD | 000 | kt |
| FPS | 0 |
| DRV | 000 | |
| HLG | 000 | |
| FRC | 000 | |
| RES | 000 | |
| IKL | 000 | |
| ISL | 000 |
| It's Albert's sailing simulator |
| By The Lee! |
| Wind menu: change strength, direction | |
| settings_overscan | View menu: standard views, in boat, at boat |
| fullscreen | Toggle full screen |
| open_with | Toggle rotate/pan view with drag |
| pause | Pause/run |
| assistant_photo | Racing |
| map | Display map (not yet working) |
| chevron_right | Display Picture-in-Picture view, lower left |
| chevron_left | Display Picture-in-Picture view, lower right |
| Credits... | ||||||
|
| Racing Time: | 00:00:00.0 |
| Penalty Time: | 00:00:00.0 |
| Final Time: | 00:00:00.0 |


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The internals of Agave. It is not necessary to understand the content of this package to use Agave, therefore the docmentation of it are slimmer. The descriptions from the other packages are sufficient information to use it.


Hi there! I'm a Senior Software Engineer who recently started creating this website as an opportunity to try out some newer technologies, e.g. Nx, Angular standalone components, and other such fun and innovative tools.
I'm still in the early stages of development, but I'm excited to see what I can create. I hope you'll follow along and give me some feedback.
Thanks for visiting!


Congratulations on reaching the final project of the Robotics Nanodegree!
Previously, you worked on the Semantic Segmentation lab where you built a deep learning network that locates a particular human target within an image. For this project, you will utilize what you implemented and learned from that lab and extend it to train a deep learning model that will allow a simulated quadcopter to follow around the person that it detects!
Most of the code below is similar to the lab with some minor modifications. You can start with your existing solution, and modify and improve upon it to train the best possible model for this task.
You can click on any of the following to quickly jump to that part of this notebook:
We have provided you with a starting dataset for this project. Download instructions can be found in the README for this project's repo. Alternatively, you can collect additional data of your own to improve your model. Check out the "Collecting Data" section in the Project Lesson in the Classroom for more details!
import os
import glob
import sys
import tensorflow as tf
from scipy import misc
import numpy as np
from tensorflow.contrib.keras.python import keras
from tensorflow.contrib.keras.python.keras import layers, models
from tensorflow import image
from utils import scoring_utils
from utils.separable_conv2d import SeparableConv2DKeras, BilinearUpSampling2D
from utils import data_iterator
from utils import plotting_tools
from utils import model_tools
In the Classroom, we discussed the different layers that constitute a fully convolutional network (FCN). The following code will introduce you to the functions that you need to build your semantic segmentation model.
The Encoder for your FCN will essentially require separable convolution layers, due to their advantages as explained in the classroom. The 1x1 convolution layer in the FCN, however, is a regular convolution. Implementations for both are provided below for your use. Each includes batch normalization with the ReLU activation function applied to the layers.
def separable_conv2d_batchnorm(input_layer, filters, strides=1):
output_layer = SeparableConv2DKeras(filters=filters,kernel_size=3, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
def conv2d_batchnorm(input_layer, filters, kernel_size=3, strides=1):
output_layer = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
The following helper function implements the bilinear upsampling layer. Upsampling by a factor of 2 is generally recommended, but you can try out different factors as well. Upsampling is used in the decoder block of the FCN.
def bilinear_upsample(input_layer):
output_layer = BilinearUpSampling2D((2,2))(input_layer)
return output_layer
In the following cells, you will build an FCN to train a model to detect and locate the hero target within an image. The steps are:
encoder_block
decoder_block
Create an encoder block that includes a separable convolution layer using the separable_conv2d_batchnorm()
function. The filters
parameter defines the size or depth of the output layer. For example, 32 or 64.
def encoder_block(input_layer, filters, strides):
# TODO Create a separable convolution layer using the separable_conv2d_batchnorm() function.
output_layer = separable_conv2d_batchnorm(input_layer, filters, strides)
return output_layer
The decoder block is comprised of three parts:
def decoder_block(small_ip_layer, large_ip_layer, filters):
# TODO Upsample the small input layer using the bilinear_upsample() function.
upsampled_layer = bilinear_upsample(small_ip_layer)
# TODO Concatenate the upsampled and large input layers using layers.concatenate
concatened_layers = layers.concatenate([upsampled_layer, large_ip_layer])
# TODO Add some number of separable convolution layers
separable_layer = separable_conv2d_batchnorm(concatened_layers, filters, 1)
output_layer = separable_conv2d_batchnorm(separable_layer, filters, 1)
return output_layer
Now that you have the encoder and decoder blocks ready, go ahead and build your FCN architecture!
There are three steps:
def fcn_model(inputs, num_classes):
# TODO Add Encoder Blocks.
# Remember that with each encoder layer, the depth of your model (the number of filters) increases.
encoder_1_layer = encoder_block(inputs, 32, 2)
encoder_2_layer = encoder_block(encoder_1_layer, 64, 2)
encoder_3_layer = encoder_block(encoder_2_layer, 128, 2)
print("Encoder 1", encoder_1_layer.shape)
print("Encoder 2", encoder_2_layer.shape)
print("Encoder 3", encoder_3_layer.shape)
# TODO Add 1x1 Convolution layer using conv2d_batchnorm().
conv_1_1_layer = conv2d_batchnorm(encoder_3_layer, 128, 1, 1)
print("1x1 Conv", conv_1_1_layer.shape)
# TODO: Add the same number of Decoder Blocks as the number of Encoder Blocks
decoder_1_layer = decoder_block(conv_1_1_layer, encoder_2_layer, 128)
decoder_2_layer = decoder_block(decoder_1_layer, encoder_1_layer, 64)
decoder_3_layer = decoder_block(decoder_2_layer, inputs, 32)
print("Decoder 1", decoder_1_layer.shape)
print("Decoder 2", decoder_2_layer.shape)
print("Decoder 3", decoder_2_layer.shape)
# The function returns the output layer of your model. "x" is the final layer obtained from the last decoder_block()
return layers.Conv2D(num_classes, 1, activation='softmax', padding='same')(decoder_3_layer)
The following cells will use the FCN you created and define an ouput layer based on the size of the processed image and the number of classes recognized. You will define the hyperparameters to compile and train your model.
Please Note: For this project, the helper code in data_iterator.py
will resize the copter images to 160x160x3 to speed up training.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes)
Define and tune your hyperparameters.
learning_rate = 0.01
batch_size = 40
num_epochs = 110
steps_per_epoch = int(4131/batch_size)-1
validation_steps = int(1000/batch_size)-1
workers = 2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss='categorical_crossentropy')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
callbacks = [logger_cb]
model.fit_generator(train_iter,
steps_per_epoch = steps_per_epoch, # the number of batches per epoch,
epochs = num_epochs, # the number of epochs to train for,
validation_data = val_iter, # validation iterator
validation_steps = validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers = workers)
print("Done")
# Save your trained model weights
weight_file_name = 'model_weights'
model_tools.save_network(model, weight_file_name)
Now that you have your model trained and saved, you can make predictions on your validation dataset. These predictions can be compared to the mask images, which are the ground truth labels, to evaluate how well your model is doing under different conditions.
There are three different predictions available from the helper code provided:
# If you need to load a model which you previously trained you can uncomment the codeline that calls the function below.
# weight_file_name = 'model_weights'
# restored_model = model_tools.load_network(weight_file_name)
The following cell will write predictions to files and return paths to the appropriate directories.
The run_num
parameter is used to define or group all the data for a particular model run. You can change it for different runs. For example, 'run_1', 'run_2' etc.
run_num = 'run_1'
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
Now lets look at your predictions, and compare them to the ground truth labels and original images. Run each of the following cells to visualize some sample images from the predictions in the validation set.
# images while following the target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','following_images', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol without target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_non_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol with target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_with_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
Evaluate your model! The following cells include several different scores to help you evaluate your model under the different conditions discussed during the Prediction step.
# Scores for while the quad is following behind the target.
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(val_following, pred_following)
# Scores for images while the quad is on patrol and the target is not visable
true_pos2, false_pos2, false_neg2, iou2 = scoring_utils.score_run_iou(val_no_targ, pred_no_targ)
# This score measures how well the neural network can detect the target from far away
true_pos3, false_pos3, false_neg3, iou3 = scoring_utils.score_run_iou(val_with_targ, pred_with_targ)
# Sum all the true positives, etc from the three datasets to get a weight for the score
true_pos = true_pos1 + true_pos2 + true_pos3
false_pos = false_pos1 + false_pos2 + false_pos3
false_neg = false_neg1 + false_neg2 + false_neg3
weight = true_pos/(true_pos+false_neg+false_pos)
print(weight)
# The IoU for the dataset that never includes the hero is excluded from grading
final_IoU = (iou1 + iou3)/2
print(final_IoU)
# And the final grade score is
final_score = final_IoU * weight
print(final_score)


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Before Change
)
def test_filesystem_specs_nonexistent_file(self) -> None:
specs = FilesystemSpecs([FilesystemLiteralSpec("demo/fake.txt")])
with pytest.raises(ExecutionError) as exc:
self.request_single_product(
AddressesWithOrigins, Params(specs, create_options_bootstrapper()),
)
assert "Unmatched glob from file arguments: "demo/fake.txt"" in str(exc.value)
ignore_errors_result = self.request_single_product(
AddressesWithOrigins,
Params(specs, create_options_bootstrapper(args=["--owners-not-found-behavior=ignore"])),
)
assert not ignore_errors_result
def test_filesystem_specs_no_owner(self) -> None:
self.create_file("no_owners/f.txt")
After Change
}
def test_filesystem_specs_nonexistent_file(self) -> None:
spec = FilesystemLiteralSpec("demo/fake.txt")
with pytest.raises(ExecutionError) as exc:
self.resolve_filesystem_specs([spec])
assert "Unmatched glob from file arguments: "demo/fake.txt"" in str(exc.value)
assert not self.resolve_filesystem_specs(


Sunday - Saturday 6am-10pm Open druing major holdays unless otherwise stated
12917 San Pablo Avenue Richmond, CA 94805


Uses of Package
org.apache.sis.xml
-
Packages that use org.apache.sis.xml Package Description org.apache.sis.metadata.iso Root package for ISO 19115 metadata about resources (data or services).org.apache.sis.metadata.iso.acquisition Information about the measuring instruments, the platform carrying them, and the mission to which the data contributes.org.apache.sis.metadata.iso.citation Reference to the data or service (citation, responsible party, contact information).org.apache.sis.metadata.iso.constraint Information about legal and security constraints placed on data.org.apache.sis.metadata.iso.content Description of the dataset content.org.apache.sis.metadata.iso.distribution Information about the options for obtaining a resource (data or service).org.apache.sis.metadata.iso.extent Information about spatial, vertical, and temporal extent.org.apache.sis.metadata.iso.identification Information to uniquely identify the data or service.org.apache.sis.metadata.iso.lineage Information about the sources and production processes of a dataset.org.apache.sis.metadata.iso.maintenance Information about the frequency and scope of data updates.org.apache.sis.metadata.iso.quality Information about data quality, accuracy and consistency of a dataset.org.apache.sis.metadata.iso.spatial Information about the mechanisms (grid or vector) used to represent spatial data.org.apache.sis.xml Provides methods for marshalling and unmarshalling SIS objects in XML. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso Class Description IdentifiedObject The interface for all SIS objects having identifiers.IdentifierMap A map view of some or all identifiers in an identified object. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.acquisition Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.citation Class Description IdentifiedObject The interface for all SIS objects having identifiers.IdentifierSpace Some identifier namespaces that are handled in a special way. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.constraint Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.content Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.distribution Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.extent Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.identification Class Description IdentifiedObject The interface for all SIS objects having identifiers.IdentifierMap A map view of some or all identifiers in an identified object. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.lineage Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.maintenance Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.quality Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.metadata.iso.spatial Class Description IdentifiedObject The interface for all SIS objects having identifiers. -
Classes in org.apache.sis.xml used by org.apache.sis.xml Class Description IdentifierMap A map view of some or all identifiers in an identified object.IdentifierSpace Some identifier namespaces that are handled in a special way.MarshalContext Context of a marshalling or unmarshalling process.NilReason Explanation for a missing XML element.ReferenceResolver Controls the (un)marshaller behavior regarding thexlink
oruuidref
attributes.ValueConverter Performs conversions of XML element or attribute values encountered during XML (un)marshalling.XLink The XML attributes defined by OGC in the xlink schema.XLink.Actuate Communicates the desired timing of traversal from the starting resource to the ending resource.XLink.Show Communicates the desired presentation of the ending resource on traversal from the starting resource.XLink.Type The type of axlink
.


Psychoeducation
Insomnia is more than just getting a poor night’s sleep. Everyone can have difficulty sleeping from time to time. For people with insomnia, difficulty sleeping becomes a regular and persistent problem. This could involve difficulty falling asleep, difficulty staying asleep, or both. Some people with insomnia wake up frequently throughout the night and have trouble getting back to sleep. Others wake up too early in the morning.
Insomnia is not the result of a lack of opportunity to sleep. People with insomnia experience sleep disruptions even though they had a reasonable chance to sleep.
Over the next few weeks you will learn strategies to help resolve this problem.
Usually, insomnia is not caused by a chemical imbalance. Most of the time it is caused by habits, lifestyle, and problematic ways of thinking about sleep. Solutions that focus on these underlying causes of insomnia have the best chance of long-term success.
Sometimes medical conditions interfere with sleep. If these are left untreated, changes to your sleep routine might not be enough. Talk with your health care provider about any medical conditions you may have that may be affecting your sleep.
Health enSuite Insomnia is based on a cognitive behavioural approach to treating insomnia. The goal is to change old ways of thinking and behaving and learn new, more effective ways of thinking and behaving. Every week, you will learn new skills to help you sleep better at night and feel better during the day. Here is an outline of what to expect in the weeks to come.
It may take a few weeks before you start to notice a difference in your sleep quality. Don’t get discouraged. Changing habits can be difficult and take time. The key to success is to stick with the program and practice the skills you are learning every day.
Sleeping pills may seem like an easy way to manage your sleep. Unfortunately sleeping pills only work as a short term remedy for sleep problems. After a few weeks, sleeping pills can become less effective in promoting good sleep. But many people continue using sleeping pills for weeks or even years. This can happen even when there are adverse side effects like daytime drowsiness and difficulty concentrating.
Your health care provider has recommended a plan that will gradually reduce the amount of the sleeping pills you are taking. The goal is for you to eventually be able to stop taking sleeping pills and get a good night’s sleep.
How is it going so far?
Copyright © Designed & Developed by Health enSuite Research Team 2020


KSI's brother Deji branded a "Logan Paul fanboy" by his own parents ahead of rematch
The two siblings have previously been involved in a bitter feud with each other which has only recently calmed
by Darren Wells, https://www.mirror.co.uk/authors/darren-wells/KSI's parents have branded his brother Deji a " Logan Paul fanboy" ahead of their rematch this coming weekend.
The Youtube stars are preparing to touch gloves for a second time at the Los Angeles Staples Center on Saturday in a repeat of their bout last year.
And in the lead up to the much-anticipated fight, Deji quizzed his parents on the contestants, which included questions like 'Who has got more heart?' and others related to size, and number of Youtube viewers on their respective accounts.
After five of the answers in a row were in favour of KSI's opponent, his dad Jide quipped at Deji: "Are you a Logan fanboy? All these questions, all Logan questions."
Wife Yinka agreed, and said: "Sounds like it, yeah!"
Deji was quick to hit back, asking: "How am I a Logan fanboy?!"
The 22-year-old then made threats at posing his parents more taxing questions.
He has gone back on his earlier prediction that Logan has the advantage over his brother, and since claimed that KSI will win the fight.
The two brothers have been involved in a feud with each other in the past, after Deji produced a diss track aimed at KSI's cameraman and fellow Youtuber Randolph.
Deji's track claimed that KSI paid £4000 towards Randolph's wedding, calling him a "b****" which led to him being blocked on social media by his brother.
In response, Deji tweeted proof of his blocking, and wrote: "Family always has your back."
His brother responded by saying: "Get rid of negative troublesome delusional people. Even if they're family."
KSI - real name Olajide ‘JJ’ Olatunji - claimed Deji had made his life hell during the row last Christmas, and it is not known whether their issues are completely resolved.
Meanwhile Billy-Joe Saunders has defended his appearance on the undercard of the celebrity bout, where he will put his WBO world super-middleweight title on the line against Marcelo Esteban Coceres.
"I am looking forward to it because it is my American debut," he said. "I am up for the challenge and I am going over there to put my name out.
"There would not be much point in sending me out there as chief-support on a 4,000-seater fight when I can go to LA in front of a sold-out 20,000 with two guys who had 10million views for their press conference.
"If I can take five or 10 per cent of their crowd with a good performance then I am establishing my name in America and giving myself a better chance of getting a Gennady Golovkin or Canelo Alvarez.
"I hadn’t heard of KSI or Logan Paul but people should take their hat off to them because they have come from nowhere. That demands respect because they have both done great business.


Personal Website Reflection
Challenges
May 16, 2015
At first, I thought the process of creating a website and maintaining it throughout phase zero would be a hinderance. After I actually worked through it incrementally then get an initial version up, it has actually be kind of nice to have a place to share some of what I have learned in just nine short weeks. Normally, I am the type of person who prefers to write things down by hand. Actually being able to add some styling and a different look to my content has really made me appreciate the simplicity behind some basic HTML and CSS.
That's not to say that I can't still improve it more, but I feel proud of what I have created so far. The list entries on my blog index page were the biggest problem. I went back in to the CSS I wrote during week two and realized just how much I didn't know. I was hardcoding several different margins and paddings just to get the elements to appear where I wanted to on my screen. I didn't test this approach with different screen sizes. It was lacking cleanliness and organization. I decided that I wanted to at least apply some better CSS to that page and fix it up. I was using positioning too liberally. I realized that all I needed to do were to isolate the two tables and use float with width percentages to get them to be symmetrical. I gave the tables captions so they could serve more as the titles for the tables as I intended. Before long, I made a few changes that really cleaned up the page and at least made it more respectable. I still cannot decide on a color scheme I like. I always default back to blues and grays. The next step may even be to implement a CSS framework (such as Bootstrap or Jekyll), but I almost prefer to use this blog to continue practicing my own HTML and CSS without having to rely on a tool.
I focused much more on the content of my blog. I wanted to write a useful entry each week for my tech blog (no matter what the length). The personal blog serves as a place to get my thoughts out in to the open. The most challenging part of maintaining my website has revolved around not knowing when to push myself to change it more and not knowing when to be satisfied with what I've created. This is something that I will continue to work on. Maybe after another nine weeks -- I'll have a much more professional blog. For now, I'm proud of what I've been able to do with it.


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- Method resolution order:
- Notifier
- threading.Thread
- threading._Verbose
Methods defined here:
- __init__(self, host, debug=0)
- Create a new Notifier instance.
Keyword arguments:
host -- the Jabber server to connect to
debug -- whether to print debug info or not
- login(self, username, passwd, resource)
- Login to a server.
- messageFunc(self, client, message)
- Recieve and process a jabber.Message from the Jabber server.
- presenceFunc(self, client, presence)
- Recieve and process a jabber.Presence from the Jabber server.
- recieveMessage(self)
- Get a received message, or None.
- run(self)
- Start the processing thread.
Do this after constructing and loggin in, and before
doing other things.
- sendMessage(self, message)
- Send a jabber.Message.
- sendMessageAll(self, message, sensitive=1)
- Send a message to all contacts on our roster.
Keyword arguments:
message -- the jabber.Message to send
sensitive -- whether to avoid sending to contacts in modes xa or dnd
Data and non-method functions defined here:
- __doc__ = 'Notify contacts of interesting events.\n\t\n\tA...to send notifications to interested parties.\n\t'
- __module__ = 'notifier'
- debug = 0
- ibLock = <thread.lock object>
- A lock object is a synchronization primitive. To create a lock,
call the PyThread_allocate_lock() function. Methods are:
acquire() -- lock the lock, possibly blocking until it can be obtained
release() -- unlock of the lock
locked() -- test whether the lock is currently locked
A lock is not owned by the thread that locked it; another thread may
unlock it. A thread attempting to lock a lock that it has already locked
will block until another thread unlocks it. Deadlocks may ensue.
- inbox = []
- obLock = <thread.lock object>
- A lock object is a synchronization primitive. To create a lock,
call the PyThread_allocate_lock() function. Methods are:
acquire() -- lock the lock, possibly blocking until it can be obtained
release() -- unlock of the lock
locked() -- test whether the lock is currently locked
A lock is not owned by the thread that locked it; another thread may
unlock it. A thread attempting to lock a lock that it has already locked
will block until another thread unlocks it. Deadlocks may ensue.
- outbox = []
- running = 1
- sendEv = <threading._Event instance>
- show = {}
Methods inherited from threading.Thread:
- _Thread__bootstrap = __bootstrap(self)
- _Thread__delete = __delete(self)
- _Thread__stop = __stop(self)
- __repr__(self)
- _set_daemon(self)
- getName(self)
- isAlive(self)
- isDaemon(self)
- join(self, timeout=None)
- setDaemon(self, daemonic)
- setName(self, name)
- start(self)
Data and non-method functions inherited from threading.Thread:
- _Thread__initialized = 0
Methods inherited from threading._Verbose:
- _note(self, format, *args)
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