--- layout: post title: 'JFK: A Critique' date: '2010-01-13 23:33:41 -0700' mt_id: 1430 blog_id: 1 post_id: 1430 basename: jfk-a-critique categories: - essays ---
Oliver Stone's production of JFK in 1991 represented the final word on the Kennedy assassination for many Americans. Through the practices and devices available to the filmmaker, Stone was able to create a convincing, plausible account of the conspiracy to kill President Kennedy. The assassination has spawned a veritable cottage industry of conspiracy theory and speculation starting from November 22, 1963. Stone's JFK takes three accounts of the conspiracy and melds them together in a masterful and nearly seamless plot line. Jim Garrison's On the Trail of the Assassins forms the core of the story, with additional theories provided by Jim Marrs' Crossfire and John Newman's Kennedy and Vietnam. Each of these works had generated their own controversies before becoming associated with Stone's JFK and engendered even more after the association. The story they collectively tell is one of conspiracy in the highest echelons of government, as well as self-centeredness and hawkishness by the military. Stone planted enough reasonable doubt in the viewer's mind that—even though he might blanch at the mystifying government-wide conspiracy—he would still believe that the Warren Commission was hiding something and that some sort of conspiracy existed.
Oliver Stone asserted that his movie was historical instead of fiction and that he was a "cinematic historian" instead of a filmmaker. He claimed that he was being persecuted by the media, who he argued acted as a "priesthood" and pretended to be the "sole or privileged interpreters of our history." Stone further contended that history was "too important to leave to newsmen."1 The clear implication is that Stone considered himself a historian defending history from its poseurs. Robert Toplin's History by Hollywood: The Use and Abuse of the American Past takes up Stone's challenge and seeks to comprehend JFK's role as history and its mercurial director as historian. Toplin argues that JFK would have elicited much less controversy had Stone kept quiet about his historical aspirations or defended them as artistic license. A more balanced examination of the assassination reveals that JFK was not a historical work by any stretch of the imagination. Stone's claims brought that fact to the forefront and provided a focal point for critics.
The enduring legacy of that fateful day in Dallas is amazing. Hundreds of books have been written, countless documentaries have been filmed, and many symposia have been convened. The Warren Commission published 26 volumes of testimony, analysis, and evidence yet it could never be thorough enough for many. Max Holland speculates that the "disbelief attached to the Warren Report has to be grounded in unfinished business, some yearning that goes well beyond narrow questions like whether all pertinent government documents have been released."2 This paper cannot claim to present anything remotely like a definitive statement on such a well-travelled topic, but it will examine the larger issue of whether Oliver Stone's JFK was a work of history. The problems raised by Stone and the controversy surrounding JFK rise above the narrow questions of who did what, when and why.
The primary issues raised by Stone are what is history and who gets to speak for it. The major criticism of Stone's JFK is that it adopted a historical stance without employing a historical methodology. The historical methodology emphasizes research to discover facts and then provide an analysis of those facts in order to construct an interpretation. Such an interpretation invariably examines contradictory evidence since reality is rarely clean and clear in its facts. Even more rare is the interpretation that asserts certainty or excludes other possible interpretations. This occurs because of the messy nature of evidence—crucial items are lost forever, contradictory testimony points in two different directions without any probable resolution, or time weathers memories, evidence, and artifacts. Stone, on the other hand, does not engage in any such tempering or qualification. For him, the matter is crystal clear: the government killed its Commander-in-Chief in order to escalate the Vietnam War. Never mind that such a conclusion faces the evidentiary problems inherent in historical work; the evidence required to prove such a fantastic conclusion would be considerable and easily thwarted if such a powerful conspiracy existed. The conspiracy would have to involve a carefully coordinated and precisely executed plan spanning many federal, state, and local agencies, as well as the cooperation and silence of hundreds if not thousands of individuals.3
Yet such a conspiracy was asserted in JFK as fact. It is as if Stone thought that a conspiracy was self-evident: the details of which specific agencies, individuals, countries, or criminal organizations were involved are "narrow questions." There is no qualification by Stone, just a flurry of open-ended speculation. The result is a mishmash of concretes and theories that would make a postmodernist's head spin. Coincidences are used as evidence of conspiracy, or, at least, never considered as coincidences. The conclusion—that Kennedy was killed by a conspiracy—is foregone and Stone, using Garrison's tome, throws out concrete after concrete in a shotgun approach to proving his notions. The viewer, unable to separate fact from fiction thanks to Stone's directorial skill, gives up and allows that Oswald had to have help.
And here we have a crucial difference between Stone and historians. Historians, presumably, would have seen coincidence and looked for the simplest explanation that fits the facts. Occam's Razor, which states that the simplest theory is probably the most correct, is a fundamental part of the historical method—at least implicitly. Stone, on the other hand, weaves fanciful and elaborate theories as to how the simplest explanations are really what the conspiracy would have you believe. Stone and his ilk would rather construct convoluted hypotheses than recognize the messy and contradictory nature of contemporaneous statements and evidence. Some of the information that came out of Dallas in the week following the assassination was speculation and contradictory. However, certain things become apparent based on preponderance and weight. Sure, some people thought they heard shots coming from fence by the grassy knoll—but they can be mistaken. When their testimony is coupled with ballistics evidence and other products of forensic science, we can see that they were mistaken. Thus, for Stone and others, no eyewitness account can be discounted and all must be explained. Any theories or hypotheses constructed up to that point were subject to recasting and accommodation of new evidence.
In shooting a feature film that relies on fictional devices and dramatized reenactments, Stone looks at first blush to be yet another Hollywood director making a big-budget movie. His statements both before and after the film's release indicate that he viewed the movie differently. In press conferences, opinion pieces, articles, and interviews, Stone positioned himself as a historian or "cinematic historian." He defended the film's historical accuracy in long, point-by-point rebuttals sent out as press releases and sometimes printed as editorials. He engaged his critics at every juncture possible: in talk radio interviews, in speeches, in press conferences. He also railed against the academic establishment that accused him of historical revisionism and rewriting history.4 Stone's positions exposed him to criticism by historians, both academic and independent.
It is tempting to write off a lot of the criticism by historians as professional jealousy. A similar reaction—though considerably less strident—greeted filmmaker Ken Burns after he claimed to be a member of the historical profession. Envy may have motivated some of the filmmakers' detractors, but it could not possibly explain all of it: there was just too much criticism from a very diverse group of historians and scholars. The issue at stake was one of specialization. These historians perceived that, if people like Stone could call themselves historians, their hold on history in the public mind was very tenuous. Conceivably, anyone anywhere could spout out nonsense and call themselves historians. Since the public is generally deficient in historical skills, this nonsense could be uncritically accepted. The historical profession, lacking an accrediting agency, did not have any means of automatically conferring authority. One could not be a licensed historian and the various historical organizations were professional in nature.
Stone, as we have seen, called himself a historian, even though he flagrantly violated the historical methodology. Such self-description cannot be taken at face value and one must wonder how many members of the general public actually concurred. Ultimately, JFK must be judged on the basis of its comportment with the historical record. This is the same as any piece of historical work, whether it was produced by a well-credentialed professor, a doctoral student, or a journalist. There are plenty of professors and doctoral students who are less objective and more irrational than any journalist. And the reverse is also true. That is why historical works must be judged on their own merits, rather than on the authority and education of the author.
That being said, though, works produced by academics do tend to be of higher quality and of a more scholarly bent than non-historian efforts. That is due mostly to the specialized training that academic writers receive. They are taught how to use and understand historical evidence. They learn how to deal with contradictory records. All of these lessons make the work more consonant with the messiness of reality. Non-academics tend to see the world in binary terms—black and white—rather than the subtleties present in such a complex context. Stone qua artist was perfectly within his rights to present open-ended speculation about the Kennedy assassination. He crafted a masterpiece of speculative fiction—very persuasive and compelling. If he had left it at that, few would have faulted him. Certainly, historians would have been able to enjoy the film as entertainment, rather than as an attack on their profession. As Toplin characterizes it, Stone "fudged the issue [by] claiming to be both a truth-seeking investigator and an artist who operated free of the standards that guide historical interpretation."5
1 Stone, Oliver. "Who is Rewriting History?" JFK: The Book of the Film. Eds. Oliver Stone and Zachary Sklar. (NY: Applause Books, 1992), 276.
2 Holland, Max. "After Thirty Years: Making Sense of the Assassination," Reviews in American History 22:2 (Jun. 1994): 192.
3 Posner, Gerald. Case Closed: Lee Harvey Oswald and the Assassination of JFK. New York: Random House, 1993. Posner's work, called "a masterpiece of solid research, objectivity, and careful reasoning" in a review in the Journal of American History, explodes the conspiracy theories by pursuing the simplest explanations. One is struck, after reading his work, at the tenaciousness with which conspiracy buffs cling to their theories even when their theories contradict the facts. For the best exposition of the case against a conspiracy, Posner's book is unequaled. Reeves, Thomas C. "A Review of Case Closed," Journal of American History 81:3 (Dec. 1994): 1379.
4 Stone, "Who is Rewriting History?"
5 Toplin, Robert B. History by Hollywood: The Use and Abuse of the American Past. (Urbana, IL: University of Illinois Press, 1996), 78.


I tried pomodoro and I also tried habit techniques of successful people start small.
There were 3 standout articles, that I had the highest interest in. With the meditation I was interested in in how the beta waves can change with meditation and the different parts of the brain, such as parietal lobe. The graph shwoing thickness of the brain (meditators vs controls) was also eyecatching.
Pomodoro worked for me, especially when I was going through command line tutorials.
I will experiment further with pomodoro because it's 25 minutes and that's not too long for sustained concentration. I also want to look into growth mindset, as that should come in handy for me in the future.


| Top |
NcXcorLimberKernelWeakLensingNcXcorLimberKernelWeakLensing — implementation of NcXcorLimberKernel for galaxy weak lensing |
Properties
| NcDistance * | dist | Read / Write / Construct |
| NcmSpline * | dndz | Read / Write / Construct |
| double | intr-shear | Read / Write / Construct |
| double | nbar | Read / Write / Construct |
Object Hierarchy
GEnum ├── NcXcorLimberKernelWeakLensingSParams ╰── NcXcorLimberKernelWeakLensingVParams GObject ╰── NcmModel ╰── NcXcorLimberKernel ╰── NcXcorLimberKernelWeakLensing
Description
The kernel is given by \begin{equation} W^{\kappa_{\mathrm{gal}}} = \frac{3}{2} \frac{\Omega_m H_0^2}{c} \frac{(1+z)}{H(z)} \chi(z) \int dz^\prime \frac{dn}{dz^\prime} \frac{\chi(^\prime) - \chi(z)}{\chi(^\prime)} \end{equation}
where $\frac{dn}{dz}$ is the redshift distribution of galaxies.
Functions
nc_xcor_limber_kernel_weak_lensing_new ()
NcXcorLimberKernelWeakLensing * nc_xcor_limber_kernel_weak_lensing_new (gdouble zmin
,gdouble zmax
,NcmSpline *dn_dz
,gdouble nbar
,gdouble intr_shear
,NcDistance *dist
);
Types and Values
Property Details
The “dist”
property
“dist” NcDistance *
Distance object.
Owner: NcXcorLimberKernelWeakLensing
Flags: Read / Write / Construct
The “dndz”
property
“dndz” NcmSpline *
Source redshift distribution.
Owner: NcXcorLimberKernelWeakLensing
Flags: Read / Write / Construct
The “intr-shear”
property
“intr-shear” double
Intrinsic galaxy shear.
Owner: NcXcorLimberKernelWeakLensing
Flags: Read / Write / Construct
Allowed values: [0,20]
Default value: 0


It's here. Take advantage of discounts up to $10,000 off MSRP * at Ramsey Volvo. Offers Like The...
MSRP: $47,485 Your Price: $37,950


bsearch
—binary search#include <stdlib.h> void *bsearch(const void *key, const void *base, size_t nmemb, size_t size, int (*compar)(const void *, const void *));
Description
bsearch
searches an array beginning at base for any element
that matches key, using binary search. nmemb is the element
count of the array; size is the size of each element.
The array must be sorted in ascending order with respect to the
comparison function compar (which you supply as the last argument of
bsearch
).
You must define the comparison function (*
compar)
to have two
arguments; its result must be negative if the first argument is
less than the second, zero if the two arguments match, and
positive if the first argument is greater than the second (where
“less than” and “greater than” refer to whatever arbitrary
ordering is appropriate).
Returns
Portability
bsearch
is ANSI.
No supporting OS subroutines are required.


Contrasts in R
In this post I discuss how to create custom contrasts for factor variables in R
. First lets create some simulated data. Create the data, and factor Disease status:
We want the following contrasts:
- Control versus all 4 diseases combined
- RA versus the combination of (SLE, Scleroderma, Myositis), leaving out the Controls
Default settings
Let \(x_1,x_2,x_3,x_4\) be the indicators for Myositis, RA, Scleroderma and SLE, respectively. The standard linear model R
will fit is given by (for simplicity I am ignoring age and sex, but it won’t make a difference when you add them in the model):
This is the default contrast matrix with unordered factor variables:
| Myositis | RA | Scleroderma | SLE | |
|---|---|---|---|---|
| Control | 0 | 0 | 0 | 0 |
| Myositis | 1 | 0 | 0 | 0 |
| RA | 0 | 1 | 0 | 0 |
| Scleroderma | 0 | 0 | 1 | 0 |
| SLE | 0 | 0 | 0 | 1 |
This compares the mean of the response for the Controls to the mean of the response for Myositis, RA, Scleroderma, and SLE separately. The table can be read by column, and the numbers in the columns represent the weight of the regression coefficient, e.g. in the first column Myositis is being compare to Control.
Custom Contrats
Since we want only two contrasts, we want R
to fit the following model:
where \(\beta_1\) represents the contrast estimate for the comparison between controls and all other diseases, and \(\beta_2\) represents the contrast estimate of RA versus the combination of SLE, Scleroderma, Myositis.
To create custom contrasts, we must specify the contrast matrix as follows:
| Control_vs_All | RA_vs_Myos_Scle_SLE | |
|---|---|---|
| Control | 0.8 | 0.0000000 |
| Myositis | -0.2 | -0.3333333 |
| RA | -0.2 | 1.0000000 |
| Scleroderma | -0.2 | -0.3333333 |
| SLE | -0.2 | -0.3333333 |
Again we look at the above table, column by column. The variables we want to contrast should have opposite signs and the columns should sum to 0. This contrast matrix leads to the following mean response equations for each of the groups:
\[\begin{align} \mu_{control} & = \beta_0 + 0.8 \beta_1\\ \mu_{myos} & = \beta_0 - 0.2 \beta_1 - \frac{1}{3} \beta_2 \\ \mu_{ra} & = \beta_0 - 0.2 \beta_1 + \beta_2 \\ \mu_{scler} & = \beta_0 - 0.2 \beta_1 - \frac{1}{3} \beta_2 \\ \mu_{sle} & = \beta_0 - 0.2 \beta_1 - \frac{1}{3} \beta_2 \\ \end{align}\]To solve for \(\beta_0\) we can add up all the equations to get
\[\begin{align} \mu_{control}+\mu_{myos}+\mu_{ra}+\mu_{scler}+\mu_{sle} & = 5 \beta_0 \\ \beta_0 & = \frac{\mu_{control}+\mu_{myos}+mu_{ra}+\mu_{scler}+\mu_{sle}}{5} \end{align}\]To solve for \(\beta_1\) we substract \(\mu_{control}\) from the combined mean of \(\mu_{myos},\mu_{ra},\mu_{scler}\) and \(\mu_{sle}\) which gives:
\[\begin{align} \mu_{control}-\frac{\mu_{myos}+\mu_{ra}+\mu_{scler}+\mu_{sle}}{4} & = \beta_0 + 0.8 \beta_1 - \frac{4\beta_0 -0.8\beta_1}{4}\\ & = \beta_0 + 0.8 \beta_1 - \beta_0 + 0.2 \beta_1 \\ & = \beta_1 \end{align}\]To solve for \(\beta_2\) we substract \(\mu_{ra}\) from the combined mean of \(\mu_{myos},\mu_{scler}\) and \(\mu_{sle}\) which gives:
\[\begin{align} \mu_{ra}-\frac{\mu_{myos}+\mu_{scler}+\mu_{sle}}{3} & = \beta_0 - 0.2 \beta_1 + \beta_2 - \frac{3\beta_0 -0.6\beta_1 - \beta_2}{3}\\ & = \beta_0 - 0.2 \beta_1 + \beta_2 - \beta_0 + 0.2 \beta_1 +\frac{1}{3}\beta_2 \\ & = \frac{4}{3} \beta_2 \\ \beta_2 & = \frac{3}{4} \left( \mu_{ra}-\frac{\mu_{myos}+\mu_{scler}+\mu_{sle}}{3} \right) \end{align}\]First we create the contrast matrix with appropriate row and column names for clarity:
Then we store the contrasts attribute to the Disease variable. The how.many
argument specifies how many contrasts we want, therefore this should correspond to the number of columns in the contrast matrix.
Here we check to make sure that the lm
fit is giving the same result as the formulas derived above:


I'm an aspiring Android android Developer and innovation lightbulb_outline enthusiast.
Hello, I'm Tanner Hallman and I was born in
Charlotte, North Carolina. I have always been deeply interested and invested in all the latest technology and trends.
From modifying and building computers, tinkering with phones and writing programs,
my knowledge of computers has grown exponentially. It wasn't until after college, however, that I truely found my passion
as a developer.
Enter Android.
After graduating from UNCW with a degree in Business Administration: Marketing,
I stumbled upon the avid Android development community and I was immediately captivated. I had never before experienced a platform that
provided the freedom and influence that Android radiated. Over the past year and a half, I have studied and practiced
Android development through online courses,
podcasts like Fragmented and
Android Developers Backstage. I am incredibly passionate about Android,
and I can't wait to dive deeper into the world of mobile development.
android


go to >> /get_json?username=xxx&from=YYYY-MM-DD|HH:MM:SS&to=YYYY-MM-DD|HH:MM:SS&cnt=x&list=xxA,xxB,xxC..
Api guid in response you will get json
Parameters
Description
username
valid userhandle
from
strating time of the contest
to
ending time of the contest
cnt
No. of Problems in the contest
list
Problems id list [coma as delimiter]


Projects
TuMee pivot PROJECT PRIMO
Searches multiple on-demand delivery services to find the customer the cheapest or fastest deliveries available at a budget set by the user.
Powerful integrations
Postmates + Uber
TuMee utilized delivery providers to fulfill deliveries requested by our customers. A broker would determine the best service available based on the user's constraints and dispatch the delivery using one of our partners delivery APIs.
Slack
TuMee needed a way to notify our brokers of incoming deliveries, but we didn’t have resources to dedicate to building our own solution. The result, meet Brokerbot a slack bot used to notify TuMee brokers of incoming deliveries.
Google
The Google Places API powered the address autocomplete functionality which provided a level of convenience for the users.
Facebook
Facebook provided analytics and social sign-in with future capabilities for a social aspect to TuMee.
Braintree Payments
Fulfilled al payment transactions for TuMee, utilized built-in payment UI for iOS app.
Branch
Deep linking integration with a user-friendly interface for the marketing department.
TuMee
Crowd sourced on-demand delivery marketplace based in the Inland Empire.


Other weeks Flawless runs Guardians participated Total matches Total kills Weapon usage by type Kills Diff. to avg % % Top weapons from Guardian.gg Guardian.gg does not have any weapon stats to show (yet!). % First fireteams to reach the lighthouse Platform Both Xbox PlayStation The race is still on... {{lb.rank}}


About Me
Well, What can i say...
My Name is Israel Shevkolenko and Im a 14 Years
old teen and 15 by second july, and coding wasnt always my thing to be honest...
You can call me creator or an editor but in my life i engaged a lot in everything,
photos, videos, 3d objects and animation, music and starting from mid 2017
- coding/programming. Both Editing and creating.
I even working with virtual machines and a bit servers,
I one time tried to setup my own server and host a domain and a website on my PC and many more program related Things.
My interest in computers started from age 6
and electronics a bit later. i was disassembling and assembling many many electronics -
PC's, monitors, printers, phones, and many other. always when something broke
i fixed it myself instead of paying someone a lot of precious money. only one time
i took my pc for repairs by someone because one of the chips burned. also my phone
i changed the screen two times because im a bit clumsy and forgot to put an actual case with
armor instead of a nice case i loved. Even at school i showed my talent at school
when it's school projects and niche presentations or funny and professionally made videos,
or helping teachers with the projectors failing or other configurations like internet and display.
i cant even remember how many times i was called to fix the computer in another class but it was a lot.
basically im a lot of time in my computer and i do everything i also game and a lot, sometimes research
something, but overall im not some nerd or something, you wouldnt wanna go to a roast contest with me,
trust me, you shouldn't. im a good preson but if you betray me or do something very bad to me i can be so
cruel you wouldnt even think its me. somehow i had 3 girlfriends in middle school which i just finished 2 days ago
By the time i wrote this. for the end ill quote one sentence from one of my favorite artist of my music i listen,
i listen to bass house and riddim.


Lab DB
SQL Tool
Image Database
Image Search
Help
Developers
Project Supervisor
Admin Login
Content based Image Retrieval
Image Search
Feature algorithm :
Inception-V3
Vgg-16
Vgg-19
Distance algorithm :
Euclidean
Cosine
Drag the slider to set number of clusters.
Number of Clusters:


Tasks newly added in the backlog.
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ALL ABOUT MY MOTHER
Reviewed by Harvey Karten Sony Pictures Classics Director: Pedro Almodovar Writer: Pedro Almovovar Cast: Cecilia Roth, Eloy Azorin, Marisa Paredes, Penelope Cruz, Candela Pena, Antonia San Juan, Rosa Maria Sarda, Toni Canto
In James L. Brooks's comedy "As Good As It Gets," when a receptionist asks celebrated novelist Melvin Udall how he writes women so authentically, he replies, "I think of a man. Then I take away reason." By contrast, Pedro Almodovar bears no malice toward the female sex. His latest film, which won him the best director prize at the last Cannes Film Festival, is billed as a homage to all women and to all people who want to be women. While Almodovar--perhaps Spain's greatest director since Luis Bunuel--breaks with his past custom of portraying off-the-wall characters against surreal settings, he retains an affection for unusual people. In "All About My Mother," a moving, sometimes melodramatic meditation upon a group of women who become good friends and support one another through their heartaches and troubles, the prolific forty-eight year old regisseur creates a somewhat autobiographical picture across a boldly-colored canvas. In a movie flawed only by an unusual number of coincidences, he focuses on a nurse whose tragedies began when her husband ran away from their Madrid home to Barcelona to become a woman, later to become tragically involved with yet another woman whom he callously abandons as well.
Directing "All About My Father" at an often fevered emotional pitch, Almodovar centers on the 38-year-old Manuela (Cecilia Roth) who has a mutually dependent relationship with her 17-year-old son and would-be novelist, Esteban (Eloy Azorin). When the boy, a Truman Capote fan, is run down and killed by a car while seeking the autograph of actress Huma Rojo (Marisa Paredes), Manuela is traumatized, quits her job, and goes in search of the boy's father (Toni Canto)--who has become a transsexual living in Barcelona. After running into a transsexual friend from Madrid, La Agrado (Antonia San Juan), whom she saves from a beating in a red light district of Barcelona, she is hired as an assistant to actress Huma Rojo, who is currently performing the role of Blanche DuBois in Tennessee Williams' "Streetcar Named Desire." Manuela also meets Huma's significant other, Nina (Candela Pena) who is in the role of Stella, and Sister Rosa (Penelope Cruz) who prepares to go to El Salvador to replace a murdered nun when she is hindered from the assignment by an unusual predicament.
A kind of tagline to the movie which is utilized by Almodovar is Truman Capote's quote, "When God gives you a gift, He hands you a whip." This becomes clear as a calamity falls upon one women after another some time following her attainment of a blessing. Despite their misfortunes--or perhaps because of them--Almodovar treats these people whom only a mother could love with the greatest respect and dignity, showing the special spot he carries in his heart for the walking wounded and society's outcasts. Drug addicts, abused nuns, transvestites and transsexuals, whores, lesbians--all fall under the man's umbrella and are accorded compassion and affection.
"You're more authentic the more you resemble what you dream you are," is promoted as a manifesto of the film's comic genius, Antonia San Juan (in the role of the heavily plasticized La Agrado). This pronouncement is obviously the driving force of the warmth we feel for each of the personalities portrayed, and interpreted by Almodovar with camera work that shows an attention to detail and a fancy for luminous colors. The jazzy soundtrack gives an upbeat tone to a film which is serious about the plight of the women while remaining always heedlessly optimistic.
Not Rated. Running Time: 100 minutes. (C) 1999 Harvey Karten
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


Planet Sick-Boy: http://www.sick-boy.com "We Put the SIN in Cinema"
© Copyright 2001 Planet Sick-Boy. All Rights Reserved.
 Like those pictures, it throws a lot of jokes at the audience, but most of them stick, which makes it better than all of the recent SNL debacles. Zoolander, like Superstar and Ladies' Man, was born in sketch comedy, expanding a character created by Stiller for the 1996 and 1997 VH-1 Fashion Awards.
Stiller plays three-time Male Model of the Year Derek Zoolander, a brain-dead pretty boy we meet as he's being interviewed for Time by a reporter named Matilda Jeffries (Christine Taylor, Marcia from the Brady film updates) just before the VH-1 Fashion Awards (the cable channel produced the film). Matilda, intent on writing a scathing piece on the modeling industry, gets plenty of good stuff, topped off by Derek showing her his catalogue of different "looks" that all look the same.
Derek is up for an unprecedented fourth straight MMOY Award but loses to red-hot newcomer Hansel (Owen Wilson, Stiller's Meet the Parents co-star), who tools around with a yo-yo, a scooter and an entourage that seems to grow in each scene. Crushed, Derek considers retiring, but he is talked into one last campaign by agent Maury Ballstein (Jerry Stiller).  Derek is hypnotized by a Bond-like baddie (Will Ferrell, SNL) named Jacobim Mugatu, who has a foxy Russian henchwoman (Milla Jovovich, The Claim) available to do his bidding.
Most of Zoolander's laughs come at the expense of male models, but the film's biggest gag might be its attempt to push Stiller (who's practically a chimp) and Wilson (who has the worst nose in Hollywood) as the cream of the world's modeling crop. But Zoolander is far from a one-joke flick. There are plenty of very funny set pieces (including Derek's return to his coal-mining roots) and a ton of nods to other films (most notably and hysterically, 2001: A Space Odyssey). Stiller also casts half of the industry as both themselves and fictional characters (including a nearly unrecognizable Andy Dick), as well as most of his family.
Wilson, once again, steals the show in the acting department (he's been believable as a cowboy, a serial killer, and now, a rock-star-like model). As far as Stiller's direction (which I've thought has been overrated ever since Reality Bites), he's far from being what even the dimmest person could consider accomplished, but he gets the job done by keeping the film's pace quick and steady. Stiller co-wrote Zoolander's script with Drake Sather, who has worked on some of television's funniest shows (NewsRadio, The Larry Sanders Show). And Zoolander is yet another film that previously featured shots of the World Trade Center, which have been removed from the prints that will make their way to theatres near you.
1:30 - PG-13 on appeal for sexual content and drug references
========== X-RAMR-ID: 29616 X-Language: en X-RT-ReviewID: 251904 X-RT-TitleID: 1110145 X-RT-SourceID: 595 X-RT-AuthorID: 1146
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


WebNavigationActionManager()
A manager that have some actions to process. This actions should be added before load the url in WebWrapper. The wrapper has a client to override the loading of any url in the web view and use WebWrapper.overrideUrlLoading to pass the responsibility to this class to process that Uri.
The actions must be a WebNavigationAction and your aim is catch only the url that need and the result is a navigation inside or outside your application.
Any another action that does not imply a navigation must be implements with WebJsEventManager.
See Also


Task name is required!
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Managed By Q Headquarters / Studio A+H
- Curated by María Francisca González
- 1 minute
Offices Interiors · New York, United States
- version="1.0" encoding="UTF-8"? Architects: Studio A+H
- AreaArea of this architecture projectArea: 2787.1 ft2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2017
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: Eduard Hueber / archphoto
- version="1.0" encoding="UTF-8"?ManufacturersBrands with products used in this architecture project Manufacturers: A+L Lighting, Acoustical Solutions, Blinds.com, Curran Floor
- Architect / Designer Matthias Altwicker, Jens Holm
- Contractor A101 Inc. / Irek Maslanka
- Engineering Jack Green Associates / Andrei Pascalovici
- Lighting Design LOOP Lighting / John Newman
- IT INNYCIT / Ian Newman
- Client Dan Teran / Managed By Q.
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Text description provided by the architects. Managed by Q merges two sides of a business that rarely meet: the high tech IT headquarters with the daily grit of office cleaning and management. Located in SoHo, New York, the Managed By Q HQ office is designed to reflect the Q brand directly: emphasizing simultaneously hard work and an inclusive and inviting office environment.
Designed around two main ideas, the flexible office space and the program specific wall, the new MBQ-HQ creates a balance that caters to core of the MBQ brand: an office that has the ability to evolve and change on a daily basis while still maintaining its core design identity.
Throughout the main space, natural materials and an array of flexible and operable objects allow for a light and playful interior that can change at any given moment on both larger and smaller scales. The flexible office space allows for a continuous ebb and flow of activity throughout the day with the only fixed element being the centrally placed Q station, a combined standing meeting and reception desk that act as a fulcrum for the diversity around it.
The Q-HQ is a 30,000 SF, full-floor located at the corner of Sixth Avenue and Spring Street in SoHo, New York. The floor has windows on the South, East and West sides while the main entry situated on the west wall opens creates access to a new elevator core.
The key to the design of the Managed by Q-HQ is flexibility - creating a design that both defines the brand as it stands today while also providing possibilities for the company to expand and evolve over time. To allow for this level of flexibility, the floor plate was divided into three distinct zones: The Core, The Actors and The Exchange. Each of the three zones defines and optimizes specific aspects of the overall program while simultaneously providing new opportunities for interaction.
To maximize flexibility, the office is designed as a series of specific elements that occupies each zone of the plan and each element is optimized to maximize possibilities for flexibility at a variety of scales. While each element has a primary function, the interplay between elements create ever evolving possibilities of how to work, engage, meet and play.


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{{> ../layouts/head.html }} Ids Input (Masked) with guides/positions retained {{> ../layouts/footer.html }}


{% extends "base.html" %} <<<<<<< Updated upstream ======= {% import "bootstrap/wtf.html" as wtf %} >>>>>>> Stashed changes {% block navbar %}
In this page, you can upload your video clips. And we will send our encoded file to your emailbox. Please notice that file MUST be H.264 contents, or it will not work.


---
layout: thirteen
title: Nickel City Ruby Conference - September 20-21, 2013 - Buffalo, NY
---
We'd like to invite you to visit Buffalo, NY
for 2 days of Ruby, learning, and discussion on September 20th & 21st
We have 4 days packed with 3 different events for you:
7pm-9pm, Pan-American Grill & Bar, 2 hours of lightning talks for free!
9am-6pm, Buffalo & Erie County Library - Mason O. Damon Auditorium
Jeff Casimir
Sam Kottler
Greg Baugues
Austin Seraphin
Matt Gauger
Julie Pagano
Jeff Hsu
Zach Holman
9am-6pm, Buffalo & Erie County Library - Mason O. Damon Auditorium
Sara Chipps
Corey Ehmke
Zachary Scott
Flip Sasser
Katie Hagerty
Everyone! 1 & 5 minute slots, signup day of.
Neal Sales-Griffin
10am, z80 Labs, an intensive day of honing your craft through code katas. $10 deposit required.
We're proud to bring these talented people and their ideas to Buffalo:
15 speakers, 2 days, and plenty of breaks to absorb these great talks:
| Greg Baugues | Devs and Depression |
| Corey Ehmke | Smash the Monolith: Refactoring into SOAs |
| Matt Gauger | Software Craftmanship’s Missing Link: Apprenticeship |
| Katie Hagerty | My KidsRuby Journey |
| Jeff Hsu | BIO 351: Getting Science done in Ruby |
| Sam Kottler | Rebuilding the heart of the Ruby community in 60 hours |
| Julie Pagano | It's Dangerous to Go Alone: Battling the Invisible Monsters in Tech |
| Flip Sasser | Actually Invented Here: A paean to reinventing the wheel |
| Zachary Scott | The Esoteric Library |
| Austin Seraphin | RubyMotion and Accessibility |
We couldn't have picked a better place for learning and discussion: a library!
Downtown Buffalo is full of architectural gems, and our conference is set in the middle of it: The Buffalo & Erie County Public Library's Mason O. Damon Auditorium. Right outside is Lafayette Square, which has long been a center for the city's activity.
We're really excited to welcome you to the great tech community here in Buffalo. From meetups like OpenHack, to StartupWeekend, and BarCampBuffalo, we love our programmers, designers, and technologists of all kinds.
We want you not only come to the conference, but see some of what our region has to offer. There's plenty of attractions to check out for you in the area while you're in town:
Need a hotel room? We have rooms for you 3 blocks away from the venue at the Comfort Suites. Use Nickel City Ruby
as the group code. We also suggest the Hotel Lafayette, Hyatt Regency, or the Embassy Suites. There's also some great places in local neighborhoods on AirBNB.
Thank you to the following sponsors that make this conference possible:
Interested in sponsoring NickelCityRuby?
Please email the organizers and we'll get in touch!


3D!
try: import vpython #A full GlowScript and WebGL based 3D Python Graphics library
except ImportError:
import os
os.system("pip install vpython") #If the import fails, install vpython.
import vpython
box=vpython.box()
when green flag clicked set rotation style [don't rotate v] set [a v] to [-5] repeat (40) create clone of [myself v] wait (.) secs change [a v] by (0.25) end forever point towards [mouse pointer v] end when i start as a clone set [b v] to (a) go to front forever go to x: ((b) * ([sin v] of ([direction v] of [Sprite1 v]))) y: ((b) * ([cos v] of ([direction v] of [Sprite1 v]))) end
Project


Write a function, AddTen, that returns the input number plus the Integer 10.
Make a function call to AddTen with the input 3.5.
Try calling your function by writing "AddTen(number)" as shown below.
Get the value of res at time=0, after which we plot the variable.


#include <RuyiNetClient.h>
◆ RuyiNetClient()
| Ruyi::SDK::Online::RuyiNetClient::RuyiNetClient |
( |
const std::shared_ptr< TProtocol1 > & |
protocol | ) |
|
◆ ~RuyiNetClient()
| Ruyi::SDK::Online::RuyiNetClient::~RuyiNetClient |
( |
| ) |
|
◆ ActivePlayerIndex()
| int Ruyi::SDK::Online::RuyiNetClient::ActivePlayerIndex |
( |
| ) |
|
Returns the index of the first active player available.
◆ GetAppId()
| const std::string& Ruyi::SDK::Online::RuyiNetClient::GetAppId |
( |
| ) |
|
|
inline |
◆ GetAppSecret()
| const std::string& Ruyi::SDK::Online::RuyiNetClient::GetAppSecret |
( |
| ) |
|
|
inline |
return appSecret of the game
◆ GetBCService()
| SDK::BrainCloudApi::BrainCloudServiceClient* const Ruyi::SDK::Online::RuyiNetClient::GetBCService |
( |
| ) |
|
|
inline |
◆ GetCloudService()
Handles backing up data to the cloud.
◆ GetFriendService()
Provides operations for managing Friend Lists.
◆ GetGamificationService()
◆ GetLeaderboardService()
Provides operations to retrieve leaderboard data and submit scores.
◆ GetLobbyService()
Manages lobbies for network games.
◆ GetPartyService()
Allows players to gather together in a party.
◆ GetPatchService()
Get manifest info for a game.
◆ GetPlayer()
| const struct RuyiNetProfile* Ruyi::SDK::Online::RuyiNetClient::GetPlayer |
( |
int |
index | ) |
|
|
inline |
return pointer of player profile data by index
- Parameters
-
| index | range from 0~3, if the player profile doesn't exit, it'll return nullptr, please check safety before using the pointer if index is not inside 0~3, it'll return 0 index player profile |
◆ GetTelemetryService()
Handles pushing telemetry data to the cloud.
◆ GetUserFileService()
Allows users to upload files to their individual accounts
◆ GetVideoService()
Allows users to upload videos to their individual accounts.
◆ Initialise()
| void Ruyi::SDK::Online::RuyiNetClient::Initialise |
( |
const std::string & |
appId, |
|
|
const std::string & |
appSecret |
|
) |
| |
Initialise the RUYI net client and switch to the game context.
- Parameters
-
| appId | The App ID of the game to initialise for. |
| appSecret | The App secret of the game. NOTE: This is a password and should be treated as such. |
◆ LogoutAccount()
| void Ruyi::SDK::Online::RuyiNetClient::LogoutAccount |
( |
| ) |
|
◆ MAX_PLAYERS
| const int Ruyi::SDK::Online::RuyiNetClient::MAX_PLAYERS = 4 |
|
static |
The documentation for this class was generated from the following files:


The Man who can't be Saved
My father passed from colon cancer five years ago. When he was diagnosed he decided not to have treatment which is a decision that was really difficult to support but being my father I did. One way I learned to process this is by writing this song:
It's amazing how your life can change in a moment; all it takes is one phone call.
So take me back to that February morning when I didn't know what was in store.

And I just wish that I could call you. I just wish that I could say:
Aren't you tired of being the man that can't be saved?
I know you thought that you deserved it, bit I think you were just afraid.
I guess it seems appropriate that a sailor gets taken by the big C.
But the irony doesn't make it easier and most days it's lost on me.
And I just wish that I could call you. I just wish that I could say...
Aren't you tired of being the man who can't be saved.
I know you think that you deserved it but I know you were just afraid.


Chapter 20 Generalized linear model
20.1 Load packages and set plotting theme
library("knitr") # for knitting RMarkdown
library("titanic") # titanic dataset
library("kableExtra") # for making nice tables
library("janitor") # for cleaning column names
library("broom") # for tidying up linear models
library("lme4") # for linear mixed effects models
library("boot") # for bootstrapping (also has an inverse logit function)
library("effects") # for showing effects in linear, generalized linear, and other models
library("tidyverse") # for wrangling, plotting, etc.
opts_chunk$set(
comment = "",
results = "hold",
fig.show = "hold"
)
theme_set(
theme_classic() + #set the theme
theme(text = element_text(size = 20)) #set the default text size
)
20.2 Load data set
df.titanic = titanic_train %>%
clean_names() %>%
mutate(sex = as.factor(sex))
Let’s take a quick look at the data:
df.titanic %>% glimpse()
Observations: 891
Variables: 12
$ passenger_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, …
$ survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, …
$ pclass <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, …
$ name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Brad…
$ sex <fct> male, female, female, female, male, male, male, mal…
$ age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 3…
$ sib_sp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, …
$ parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, …
$ ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "11380…
$ fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 5…
$ cabin <chr> "", "C85", "", "C123", "", "", "E46", "", "", "", "…
$ embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "…
# Table of the first 10 entries
df.titanic %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| passenger_id | survived | pclass | name | sex | age | sib_sp | parch | ticket | fare | cabin | embarked |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | S | |
| 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.28 | C85 | C |
| 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.92 | S | |
| 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.10 | C123 | S |
| 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.05 | S | |
| 6 | 0 | 3 | Moran, Mr. James | male | NA | 0 | 0 | 330877 | 8.46 | Q | |
| 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54 | 0 | 0 | 17463 | 51.86 | E46 | S |
| 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2 | 3 | 1 | 349909 | 21.07 | S | |
| 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27 | 0 | 2 | 347742 | 11.13 | S | |
| 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14 | 1 | 0 | 237736 | 30.07 | C |
20.3 Logistic regression
Let’s see if we can predict whether or not a passenger survived based on the price of their ticket.
Let’s run a simple regression first:
# fit a linear model
fit.lm = lm(formula = survived ~ 1 + fare,
data = df.titanic)
# summarize the results
fit.lm %>% summary()
Call:
lm(formula = survived ~ 1 + fare, data = df.titanic)
Residuals:
Min 1Q Median 3Q Max
-0.9653 -0.3391 -0.3222 0.6044 0.6973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3026994 0.0187849 16.114 < 2e-16 ***
fare 0.0025195 0.0003174 7.939 6.12e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4705 on 889 degrees of freedom
Multiple R-squared: 0.06621, Adjusted R-squared: 0.06516
F-statistic: 63.03 on 1 and 889 DF, p-value: 6.12e-15
Look’s like fare
is a significant predictor of whether or not a person survived. Let’s visualize the model’s predictions:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived)) +
geom_smooth(method = "lm") +
geom_point() +
labs(y = "survived")
This doesn’t look good! The model predicts intermediate values of survived
(which doesn’t make sense given that a person either survived or didn’t survive). Furthermore, the model predicts values greater than 1 for fares greather than ~ 300.
Let’s run a logistic regression instead.
# fit a logistic regression
fit.glm = glm(formula = survived ~ 1 + fare,
family = "binomial",
data = df.titanic)
fit.glm %>% summary()
Call:
glm(formula = survived ~ 1 + fare, family = "binomial", data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4906 -0.8878 -0.8531 1.3429 1.5942
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.941330 0.095129 -9.895 < 2e-16 ***
fare 0.015197 0.002232 6.810 9.79e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.7 on 890 degrees of freedom
Residual deviance: 1117.6 on 889 degrees of freedom
AIC: 1121.6
Number of Fisher Scoring iterations: 4
And let’s visualize the predictions of the logistic regression:
ggplot(data = df.titanic,
mapping = aes(x = fare,
y = survived)) +
geom_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_point() +
labs(y = "p(survived)")
Much better! Note that we’ve changed the interpretation of our dependent variable. We are now predicting the probability that a person survived based on their fare. The model now only predicts values between 0 and 1. To achieve this, we apply a logit transform to the outcome variable like so:
\[ \ln(\frac{\pi_i}{1-\pi_i}) = b_0 + b_1 \cdot X_i + e_i \] where \(\pi_i\) is the probability of passenger \(i\) having survived. Importantly, this affects our interpretation of the model parameters. They are now defined in log-odds, and can apply an inverse logit transformation to turn this back into a probability:
With
\[ \pi = P(Y = 1) \] and the logit transformation
\[ \ln(\frac{\pi}{1-\pi}) = V, \] where \(V\) is just a placeholder for our linear model formula, we can go back to \(\pi\) through the inverse logit transformation like so:
\[
\pi = \frac{e^V}{1 + e^V}
\]
In R, we can use log(x)
to calculate the natural logarithm \(\ln(x)\), and exp(x)
to calculate e^x
.
20.3.1 Interpreting the parameters
fit.glm %>% summary()
Call:
glm(formula = survived ~ 1 + fare, family = "binomial", data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4906 -0.8878 -0.8531 1.3429 1.5942
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.941330 0.095129 -9.895 < 2e-16 ***
fare 0.015197 0.002232 6.810 9.79e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.7 on 890 degrees of freedom
Residual deviance: 1117.6 on 889 degrees of freedom
AIC: 1121.6
Number of Fisher Scoring iterations: 4
The estimate for the intercept and fare are in log-odds. We apply the inverse logit transformation to turn these into probabilities:
fit.glm$coefficients[1] %>% inv.logit()
(Intercept)
0.2806318
Here, we see that the intercept is \(p = 0.28\). That is, the predicted chance of survival for someone who didn’t pay any fare at all is 28% according to the model. Interpreting the slope is a little more tricky. Let’s look at a situation first where we have a binary predictor.
<IP_ADDRESS> Binary predictor
Let’s see whether the probability of survival differed between male and female passengers.
fit.glm2 = glm(formula = survived ~ sex,
family = "binomial",
data = df.titanic)
fit.glm2 %>% summary()
Call:
glm(formula = survived ~ sex, family = "binomial", data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6462 -0.6471 -0.6471 0.7725 1.8256
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.0566 0.1290 8.191 2.58e-16 ***
sexmale -2.5137 0.1672 -15.036 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.7 on 890 degrees of freedom
Residual deviance: 917.8 on 889 degrees of freedom
AIC: 921.8
Number of Fisher Scoring iterations: 4
It looks like it did! Let’s visualize:
df.titanic %>%
mutate(survived = factor(survived, labels = c("died", "survived"))) %>%
ggplot(data = .,
mapping = aes(x = sex,
fill = survived)) +
geom_bar(position = "fill",
color = "black") +
scale_fill_brewer(palette = "Set1") +
labs(x = "", fill = "", y = "probability")
And let’s interpret the parameters by applying the inverse logit transform. To get the prediction for female passengers we get
\[ \widehat{\ln(\frac{\pi_i}{1-\pi_i})} = b_0 + b_1 \cdot \text{sex}_i = b_0 + b_1 \cdot 0 = b_0 \]
since we dummy coded the predictor and female is our reference category. To get the predicted probability of surival for women we do the logit transform:
\[ \pi = \frac{e^{b_0}}{1 + e^{b_0}} \] The predicted probability is:
fit.glm2$coefficients[1] %>% inv.logit()
(Intercept)
0.7420382
To get the prediction for male passengers we have:
\[ \widehat{\ln(\frac{\pi_i}{1-\pi_i})} = b_0 + b_1 \cdot \text{sex}_i = b_0 + b_1 \cdot 1 = b_0 + b_1 \] Applying the logit transform like so
\[ \pi = \frac{e^{b_0 + b_1}}{1 + e^{b_0 + b_1}} \]
The predicted probability of male passengers surviving is:
sum(fit.glm2$coefficients) %>% inv.logit()
[1] 0.1889081
Here is the same information in a table:
df.titanic %>%
count(sex, survived) %>%
mutate(p = n/sum(n)) %>%
group_by(sex) %>%
mutate(`p(survived|sex)` = p/sum(p)) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| sex | survived | n | p | p(survived|sex) |
|---|---|---|---|---|
| female | 0 | 81 | 0.09 | 0.26 |
| female | 1 | 233 | 0.26 | 0.74 |
| male | 0 | 468 | 0.53 | 0.81 |
| male | 1 | 109 | 0.12 | 0.19 |
<IP_ADDRESS> Continuous predictor
To interpret the predictions when a continuous predictor is invovled, it’s easiest to consider a few concrete cases. Here, I use the augment()
function from the “broom” package to get the model’s predictions for some values of interest:
fit.glm %>%
augment(newdata = tibble(fare = c(0, 10, 50, 100, 500))) %>%
clean_names() %>%
select(fare, prediction = fitted) %>%
mutate(`p(survival)` = prediction %>% inv.logit()) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| fare | prediction | p(survival) |
|---|---|---|
| 0 | -0.94 | 0.28 |
| 10 | -0.79 | 0.31 |
| 50 | -0.18 | 0.45 |
| 100 | 0.58 | 0.64 |
| 500 | 6.66 | 1.00 |
<IP_ADDRESS> Several predictors
Let’s fit a logistic regression that predicts the probability of survival based both on the passenger’s sex and what fare they paid (allowing for an interaction of the two predictors):
fit.glm = glm(formula = survived ~ 1 + sex * fare,
family = "binomial",
data = df.titanic)
fit.glm %>% summary()
Call:
glm(formula = survived ~ 1 + sex * fare, family = "binomial",
data = df.titanic)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6280 -0.6279 -0.5991 0.8172 1.9288
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.408428 0.189999 2.150 0.031584 *
sexmale -2.099345 0.230291 -9.116 < 2e-16 ***
fare 0.019878 0.005372 3.701 0.000215 ***
sexmale:fare -0.011617 0.005934 -1.958 0.050252 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1186.66 on 890 degrees of freedom
Residual deviance: 879.85 on 887 degrees of freedom
AIC: 887.85
Number of Fisher Scoring iterations: 5
And let’s visualize the result:
df.titanic %>%
mutate(sex = as.factor(sex)) %>%
ggplot(data = .,
mapping = aes(x = fare,
y = survived,
color = sex,
group = sex)) +
geom_point(alpha = 0.1, size = 2) +
geom_smooth(method = "glm",
method.args = list(family = "binomial"),
alpha = 0.2,
aes(fill = sex)) +
scale_color_brewer(palette = "Set1")
We notice that there is one outlier who was male and paid a $500 fare (or maybe this is a mistake in the data entry?!). Let’s remove this outlier and see what happens:
df.titanic %>%
filter(fare < 500) %>%
mutate(sex = as.factor(sex)) %>%
ggplot(data = .,
mapping = aes(x = fare,
y = survived,
color = sex,
group = sex)) +
geom_point(alpha = 0.1, size = 2) +
geom_smooth(method = "glm",
method.args = list(family = "binomial"),
alpha = 0.2,
aes(fill = sex)) +
scale_color_brewer(palette = "Set1")
There is still a clear difference between female and male passengers, but the prediction for male passengers has changed a bit.  It applies the inverse logit transform for us, and shows the predictions for a range of cases.
# show effects
allEffects(mod = fit.glm, xlevels = list(fare = c(0, 100, 200, 300, 400, 500)))
model: survived ~ 1 + sex * fare
sex*fare effect
fare
sex 0 100 200 300 400 500
female 0.6007108 0.9165428 0.9876799 0.9982941 0.9997660 0.9999679
male 0.1556552 0.2963415 0.4903402 0.6872927 0.8339147 0.9198098
I’ve used the xlevels argument to specify for what values of the predictor fare
, I’d like get the predicted values.
20.4 Simulate a logistic regression
As always, to better understand a statistical modeling procedure, it’s helpful to simulate data from the assumed data-generating process, fit the model, and see whether we can reconstruct the parameters.
# make example reproducible
set.seed(1)
# set parameters
sample_size = 1000
b0 = 0
b1 = 1
# b1 = 8
# generate data
df.data = tibble(
x = rnorm(n = sample_size),
y = b0 + b1 * x,
p = inv.logit(y)) %>%
mutate(response = rbinom(n(), size = 1, p = p))
# fit model
fit = glm(formula = response ~ 1 + x,
family = "binomial",
data = df.data)
# model summary
fit %>% summary()
Call:
glm(formula = response ~ 1 + x, family = "binomial", data = df.data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1137 -1.0118 -0.4591 1.0287 2.2591
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.06214 0.06918 -0.898 0.369
x 0.92905 0.07937 11.705 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1385.4 on 999 degrees of freedom
Residual deviance: 1209.6 on 998 degrees of freedom
AIC: 1213.6
Number of Fisher Scoring iterations: 3
Nice! The inferred estimates are very close to the parameter values we used to simulate the data.
Let’s visualize the result:
ggplot(data = df.data,
mapping = aes(x = x,
y = response)) +
geom_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_point(alpha = 0.1) +
labs(y = "p(response)")
<IP_ADDRESS> Calculate the model’s likelihood
To calculate the likelihood of the data for a given logistic model, we look at the actual response, and the probability of the predicted response, and then determine the likelihood of the observation assuming a bernoulli process. To get the overall likelihood of the data, we then multiply the likelihood of each data point (or take the logs first and then the sum to get the log-likelihood).
This table illustrate the steps involved:
fit %>%
augment() %>%
clean_names() %>%
mutate(p = inv.logit(fitted)) %>%
select(response, p) %>%
mutate(p_response = ifelse(response == 1, p, 1-p),
log_p = log(p_response)) %>%
rename(`p(Y = 1)` = p, `p(Y = response)` = p_response,
`log(p(Y = response))` = log_p) %>%
head(10) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| response | p(Y = 1) | p(Y = response) | log(p(Y = response)) |
|---|---|---|---|
| 1 | 0.34 | 0.34 | -1.07 |
| 0 | 0.53 | 0.47 | -0.75 |
| 1 | 0.30 | 0.30 | -1.20 |
| 1 | 0.81 | 0.81 | -0.22 |
| 1 | 0.56 | 0.56 | -0.58 |
| 0 | 0.30 | 0.70 | -0.36 |
| 1 | 0.60 | 0.60 | -0.52 |
| 1 | 0.65 | 0.65 | -0.43 |
| 1 | 0.62 | 0.62 | -0.48 |
| 0 | 0.41 | 0.59 | -0.54 |
Let’s calculate the log-likelihood by hand:
fit %>%
augment() %>%
clean_names() %>%
mutate(p = inv.logit(fitted),
log_likelihood = response * log(p) + (1 - response) * log(1 - p)) %>%
summarize(log_likelihood = sum(log_likelihood))
# A tibble: 1 x 1
log_likelihood
<dbl>
1 -605.
And compare it with the model summary
fit %>%
glance() %>%
select(logLik, AIC, BIC) %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = "striped",
full_width = F)
| logLik | AIC | BIC |
|---|---|---|
| -604.82 | 1213.64 | 1223.45 |
We’re getting the same result – neat! Now we know how the likelihood of the data is calculated for a logistic regression model.
20.5 Testing hypotheses
To test hypotheses, we use our gold old model comparison approach:
# fit compact model
fit.compact = glm(formula = survived ~ 1 + fare,
family = "binomial",
data = df.titanic)
# fit augmented model
fit.augmented = glm(formula = survived ~ 1 + sex + fare,
family = "binomial",
data = df.titanic)
# likelihood ratio test
anova(fit.compact, fit.augmented, test = "LRT")
Analysis of Deviance Table
Model 1: survived ~ 1 + fare
Model 2: survived ~ 1 + sex + fare
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 889 1117.57
2 888 884.31 1 233.26 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Note that in order to get a p-value out of this, we need to specify what statistical test we’d like to run. In this case, we use the likelihood ratio test (“LRT”).
20.6 Logistic mixed effects model
Just like we can build linear mixed effects models using lmer()
instead of lm()
, we can also build a logistic mixed effects regression using glmer()
instead of glm()
.
Let’s read in some data:
# load bdf data set from nlme package
data(bdf, package = "nlme")
df.language = bdf %>%
clean_names() %>%
filter(repeatgr != 2) %>%
mutate(repeatgr = repeatgr %>% as.character() %>% as.numeric())
rm(bdf)
Fit the model, and print out the results:
fit = glmer(repeatgr ~ 1 + ses * minority + (1 | school_nr),
data = df.language,
family = "binomial")
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv, : Model failed to converge with max|grad| = 0.00172186
(tol = 0.001, component 1)
fit %>% summary()
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: repeatgr ~ 1 + ses * minority + (1 | school_nr)
Data: df.language
AIC BIC logLik deviance df.resid
1660.9 1689.6 -825.5 1650.9 2278
Scaled residuals:
Min 1Q Median 3Q Max
-0.8943 -0.4062 -0.3151 -0.2233 5.9156
Random effects:
Groups Name Variance Std.Dev.
school_nr (Intercept) 0.2464 0.4964
Number of obs: 2283, groups: school_nr, 131
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.478689 0.206057 -2.323 0.0202 *
ses -0.061214 0.007915 -7.733 1.05e-14 ***
minorityY 0.482829 0.472314 1.022 0.3067
ses:minorityY 0.010820 0.022867 0.473 0.6361
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) ses mnrtyY
ses -0.907
minorityY -0.400 0.368
ses:minrtyY 0.298 -0.318 -0.865
convergence code: 0
Model failed to converge with max|grad| = 0.00172186 (tol = 0.001, component 1)


Train scuffles and Christmas tree alight as Hong Kong protests go on
by Nathalie TexierTempers were short on Hong Kong transport on Wednesday.
A confrontation occurred after sharp clashes overnight between police and anti-government protesters.
Many subway and rail stations were closed after the protesters blocked commuters and vandalised trains.
Classes were suspended at schools and universities.
Overnight, police and protesters battled on multiple fronts at the Chinese University of Hong Kong.
Gasoline bombs and fires lit the nighttime scene, and the situation remained tense in the morning.
Pro-democracy protesters set a Christmas tree on fire in a Hong Kong shopping mall on Tuesday (November 12).


*params "me", "at" what's that ??? follow manual of jQuery UI position
Include javascript and css:
create two html elements, one for tooltip and second to positioning on them:
and execute script:


Ruby and JavaScript: If Statements
March 25, 2016
What are If statements
If statements are widely used in Ruby and JavaScript as conditional statements for control flow.
If we look at the above image, we see Homer Simpson have 2 actions. The
first action is to say "mmm.. Keep it coming" and the second action is to say "I can't eat this!!!"
Which action he takes depend on if food == 'junk food'
, this means if the
food is junk food then he will say "mmm.. Keep it coming" otherwise (else) he'll say "I can't eat this!!!"
In the above example, only junk food will trigger Homer's first action, everything else will trigger his second action. Also there are only 2 conditions but we could easily have 5, 10 or more conditions by adding elsif or else if conditions which we will talk about below.
See If in Action
Overall, the logic behind Ruby If and JavaScript If statements are the same but the syntax has some differences. Let's take a look at the code examples:
Ruby JavaScript Example 1: if true Example 1: if (true) puts "this is true!" { console.log("this is true!"); } end Example 2: puts "this is true!" if true Example 2: if (true) console.log("this is true!"); Example 3: if food == "junk food" Example 3: if (food === 'junk food') puts "mmm.. Keep it coming" { console.log("mmm.. Keep it coming"); } elsif food == "steak" else if (food === "steak") puts "mm.. ssssssteak" { console.log("mm.. ssssssteak"); } else else puts "I can't eat this!!!" { console.log("I can't eat this!!!"); } end food == "junk food" ? "mmm.. Keep it coming" : "I can't eat this!!!"
-
Differences:
- Ruby does not require parenthesis while JavaScript does: if true vs if (true)
- Ruby allows a short one-line way of writing an if statement (without elsif or else) by putting the result before the if while JavaScript does not allow this syntax
- Ruby uses elsif while JavaScript uses else if
- Ruby requires the word "end" to close the if statement while JavaScript uses { } brackets but code will pass without the brackets
The good news is both Ruby and JavaScript can use
food == "junk food" ? "mmm.. Keep it coming" : "I can't eat this!!!"
. This is the
ternary operator which reads: condition ? result if true : result if false. The question
mark and colon are required as part of the syntax. Learn more about ternary operators
here.


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Greg Hurrell
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Greg Hurrell
diff --git a/features/step_definitions/webrat_steps.rb b/features/step_definitions/webrat_steps.rb index caf436d..6178549 100644 --- a/features/step_definitions/webrat_steps.rb +++ b/features/step_definitions/webrat_steps.rb @@ -87,10 +87,18 @@ When /^I attach the file at "(.*)" to "(.*)" $/ do |path, field| end Then /^I should see "(.*)"$/ do |text| + HTML::FullSanitizer.new.sanitize(response.body).should =~/#{text}/m +end + +Then /^The page source should match "(.*)"$/ do |text| response.body.should =~ /#{text}/m end Then /^I should not see "(.*)"$/ do |text| + HTML::FullSanitizer.new.sanitize(response.body).should_not =~/#{text}/m +end + +Then /^The page source should not match "(.*)"$/ do |text| response.body.should_not =~ /#{text}/m end
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Here is the list of world top 100 websites in 2011 list based on a combination of Alexa Rank, content richness, users’ reviews and freshness. Check it out to see how many of your favorite sites fall in this list.
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One of the top US news websites which is well known for its liberal bias.
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50. Financial Times
One of the world most influencial business news websites which report “latest UK and international business, finance, economic and political news,comment and analysis”.
51. EzineArticles A site which allows expert authors in hundreds of niche fields to get massive levels of exposure.
52. Business Week BusinessWeek is also a great source to find business, finance news, business advice and much more.
53. Wall Street Journal Leading business news website in US, also the largest paid-subscription business news site over the world.
54. Metacritic The leading site which aggregates music, game, tv, and movie reviews from the leading critics.
55. The Free Dictionary The Free Dictionary is an online dictionary and encyclopedia which cross references content from many trusted sources.
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62. Sciense Magazine International weekly science journal, published by the American Association for the Advancement of Science.
63. Discovery A excellent site which lets you explore science, history, space, tech, sharks, & more, with videos & news and more.
64. National Geographic A popular site whichprovides free maps, photos, videos and daily news stories, articles about animals, the environment, etc.
65. WebMD.com One of the most professional health websites providing trustworthy and latest health and medical news and information.
66. MedHelp.org Very popular online health site consisting of over 325 communities and forums providing professional medical information by doctors from leading medical centers. 67. MayoClinic.com A trusted health website which offers award-winning medical and health information and tools for healthy living.
68. NIH.gov Official website of the United States National Institutes of Health. NIH is one of the world’s top health sites for foremost medical research.
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73. reddit Similar functions with other social bookmarking websites. The first impression on me is its simple interface, not neat, only simple.
74. Technorati Popular bookmarking site as well as the largest blog search engine.
75. Guardian A news website which provides “Latest news, sport, business, comment, analysis and reviews from the Guardian, the world’s leading liberal voice.”
76. Reuters One of the leading news websites which brings you the latest news from around the world, covering breaking news in business, politics, entertainment, technology and more.
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79. GameSpot GameSpot is the world’s largest source for PC, PlayStation 2, Xbox 360, Wii, PS3, GameCube, PSP, DS, GBA, PS2, PlayStation as well as a huge source to downloads games.
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100. Time Leading US news magazine which provides “Breaking news and analysis, Politics, world news, photos, video, tech reviews, health, science and entertainment news.”
| Rank | Name | Headquarters | Deposits (billions) |
| 1 | JP Morgan Chase & Co. | New York, NY | $1,009 |
| 2 | Bank of America | Charlotte, NC | $884 |
| 3 | Wells Fargo | San Francisco, CA | $785 |
| 4 | Citigroup | New York, NY | $774 |
| 5 | PNC Financial Services Group | Pittsburgh, PA | $193 |
| 6 | HSBC Holdings | London | $161 |
| 7 | Bank of New York Mellon | New York, NY | $160 |
| 8 | U.S. Bancorp | Minneapolis, MN | $159 |
| 9 | SunTrust Bank | Atlanta, GA | $113 |
| 10 | State Street Corp. | Boston, MA | $112 |
| 11 | Capital One Financial | McClean, VA | $109 |
| 12 | BB&T Corporation | Winston-Salem, NC | $98.7 |
| 13 | Toronto-Dominion Bank | Toronto, ON | $95.6 |
| 14 | Royal Bank of Scotland | Edinburgh | $94.8 |
| 15 | Regions Financial | Birmingham, AL | $90.9 |
| 16 | Fifth Third Bancorp | Cincinnati, OH | $78.9 |
| 17 | ING Group | Wilmington, DE | $71.8 |
| 18 | KeyCorp | Cleveland, OH | $65.3 |
| 19 | Northern Trust | Chicago, IL | $62.4 |
| 20 | Bank of Montreal | Toronto, ON | $53.7 |
| 21 | BNP Paribas (Bank of the West) | Paris | $46.6 |
| 22 | Mitsubishi UFJ Financial Group (Union Bank) | Tokyo | $46.1 |
| 23 | Allied Irish Banks | Dublin | $42.6 |
| 24 | Comerica | Dallas, TX | $42.2 |
| 25 | Zions Bancorp | Salt Lake City, UT | $41.3 |
| 26 | Marshall & Ilsley | Milwaukee, WI | $41.1 |
| 27 | Banco Bilbao Vizcaya Argentaria S.A. | Bilbao, Spain | $38.8 |
| 28 | Huntington Bancshares | Columbus, OH | $38.0 |
| 29 | E-Trade Financial | New York | $29.9 |
| 30 | Synovus Financial Corp. | Columbus, GA | $28.6 |
| 31 | Popular Inc. | San Juan, PR | $27.6 |
| 32 | Navy Federal Credit Union | Vienna, VA | $24.7 |
| 33 | Charles Schwab Corp. | San Francisco, CA | $23.9 |
| 34 | Royal Bank of Canada (Centura) | Toronto, ON | $21.1 |
| 35 | Colonial BancGroup Inc. | Montgomery, AL | $18.7 |
| 36 | Hudson City Bancorp | Paramus, NJ | $18.7 |
| 37 | Deutsche Bank | Frankfurt | $17.1 |
| 38 | People’s United Financial Inc. | Bridgeport, CT | $15.6 |
| 39 | State Employees Credit Union | Raleigh, NC | $15.3 |
| 40 | Associated Banc-Corp | Green Bay, WI | $15.2 |
| 41 | BOK Financial Corp. | Tulsa, OK | $15.0 |
| 42 | New York Community Bancorp Inc. | Westbury, NY | $14.6 |
| 43 | First Horizon National Bancorp | Memphis, TN | $14.2 |
| 44 | First Citizens Bancshares | Raleigh, NC | $13.7 |
| 45 | Astoria Financial Corp. | Lake Success, NY | $13.7 |
| 46 | First Bancorp | San Juan, PR | $13.1 |
| 47 | Commerce Bancshares | Kansas City, MO | $12.9 |
| 48 | City National Bank | Beverly Hills, CA | $12.7 |
| 49 | Webster Financial Corp | Waterbury, CT | $11.9 |
| 50 | FBOP Corp | Oak Park, IL | $11.8 |
| 51 | Cullen/Frost Bankers Inc. | San Antonio, TX | $11.6 |
| 52 | Guaranty Financial Group Inc. | Austin, TX | $11.6 |
| 53 | AmTrust Financial Corp. | Cleveland, OH | $11.4 |
| 54 | New York Private Bank & Trust Corp | New York, NY | $11.3 |
| 55 | W Holding Co. Inc. | Mayaguez, PR | $10.9 |
| 56 | Fulton Financial Corp. | Lancaster, PA | $10.6 |
| 57 | Pentagon Federal Credit Union | Alexandria, VA | $10.5 |
| 58 | TCF Financial | Wayzata, MN | $10.3 |
| 59 | Lehman Brothers Holdings Inc. | New York | $10.2 |
| 60 | Bancorpsouth, Inc. | Tupelo, MS | $9.63 |
| 61 | Valley National Bank | Wayne, NJ | $9.52 |
| 62 | South Financial Group | Greenville, SC | $9.45 |
| 63 | Whitney Holding Corp. | New Orleans, LA | $9.27 |
| 64 | Susquehanna Bancshares Inc. | Lititz, PA | $9.07 |
| 65 | Citizens Republic Bancorp Inc. | Flint, MI | $9.06 |
| 66 | UCBH Holdings, Inc. | San Francisco, CA | $8.97 |
| 67 | Raymond James Financial Inc. | St. Petersburg, FL | $8.79 |
| 68 | First Banks Inc. | Clayton, MO | $8.74 |
| 69td> | Wilmington Trust | Wilmington, DE | $8.66 |
| 70 | BankUnited Financial Corp. | Coral Gables, FL | $8.61 |
| 71 | Third Federal S&L | Cleveland, OH | $8.48 |
| 72 | Wintrust Financial Corp | Lake Forest, IL | $8.38 |
| 73 | Sterling Financial Corp. | Spokane, WA | $8.37 |
| 74 | Bank of Hawaii | Honolulu, HI | $8.29 |
| 75 | Arvest Bank Group | Bentonville, AR | $8.23 |
| 76 | East West Bancorp Inc. | Pasadena, CA | $8.15 |
| 77 | FirstBank Holding Co. | Lakewood, CO | $8.11 |
| 78 | PrivateBancorp | Chicago, IL | $8.00 |
| 79 | Flagstar Bancorp Inc. | Troy, MI | $7.91 |
| 80 | Boeing Employees Credit Union | Tukwila, WA | $7.88 |
| 81 | UMB Financial Corp. | Kansas City, MO | $7.73 |
| 82 | Midland Financial | Oklahoma City, OK | $7.64 |
| 83 | Firstmerit Corp. | Akron, OH | $7.60 |
| 84 | Corus Bancshares, Inc. | Chicago, IL | $7.59 |
| 85 | SVB Financial Group | Santa Clara, CA | $7.53 |
| 86 | Prosperity Bancshares Inc. | Houston, TX | $7.33 |
| 87 | Washington Federal, Inc. | Seattle, WA | $7.29 |
| 88 | United Community Banks, Inc. | Blairsville, GA | $7.00 |
| 89 | MetLife Inc. | New York, NY | $6.88 |
| 90 | International Bankshares Corp | Laredo, TX | $6.86 |
| 91 | Cathay General Bancorp | El Monte, CA | $6.84 |
| 92 | Trustmark Corp. | Jackson, MS | $6.82 |
| 93 | Central Bancompany | Jefferson City, MO | $6.79 |
| 94 | Umpqua Holdings Corp. | Portland, OR | $6.60 |
| 95 | Pacific Capital Bancorp | Santa Barbara, CA | $6.59 |
| 96 | SchoolsFirst Federal Credit Union | Santa Ana, CA | $6.55 |
| 97 | MB Financial, Inc. | Chicago, IL | $6.50 |
| 98 | National Penn Bancshares Inc. | Boyertown, PA | $6.39 |
| 99 | Cam-Discount Ltd. | Grand Cayman | $6.21 |
| 100 | Apple Financial Holdings Inc. | New York, NY | $6.09 |
| 101 | F.N.B. Corp | Hermitage, PA | $6.05 |
| 102 | First Niagara Financial | Lockport, NY | $6.05 |
| 103 | Golden 1 Credit Union | Sacramento, CA | $6.00 |
| 104 | Hancock Holding Company | Gulfport, MS | $5.93 |
| 105 | First Citizens Bancorp, Inc. | Columbia, SC | $5.80 |
| 106 | United Bancshares, Inc. | Charleston, WV | $5.65 |
| 107 | First Midwest Bancorp, Inc. | Itasca, IL | $5.59 |
| 108 | Old National Bancorp | Evansville, IN | $5.42 |
| 109 | Otto Bremer Foundation | St. Paul, MN | $5.40 |
| 110 | First Interstate Bank | Billings, MT | $5.17 |
| 111 | Northwest Bancorp MHC | Warren, PA | $5.12 |
| 112 | Eastern Bank Corp. | Boston, MA | $5.10 |
| 113 | Suncoast Schools Federal Credit Union | Tampa, FL | $5.05 |
| 114 | Banco Santander | Santander, Spain | $5.02 |
| 115 | EverBank Financial Corp. | Jacksonville, FL | $5.02 |
| 116 | Dickinson Financial Corp. | Kansas City, MO | $4.98 |
| 117 | Boston Private Financial Holdings, Inc. | Boston, MA | $4.92 |
| 118 | FirstFed Financial Corp. | Los Angeles, CA | $4.91 |
| 119 | Bank Leumi Ie-Israel, BM | Tel-Aviv, Israel | $4.82 |
| 120 | American Airlines Federal Credit Union | Fort Worth, TX | $4.81 |
| 121 | Rabobank Nederland | Utrecht | $4.79 |
| 122 | Park National Corp. | Newark, OH | $4.76 |
| 123 | Provident Bancshares | Baltimore, MD | $4.75 |
| 124 | Alliant Credit Union | Chicago, IL | $4.67 |
| 125 | Capitol Bancorp Ltd. | Lansing, MI | $4.50 |
| 126 | NewAlliance Bancshares Inc | New Haven, CT | $4.47 |
| 127 | Johnson Financial Group | Racine, WI | $4.43 |
| 128 | R&G Financial Corp. | Hato Rey, PR | $4.43 |
| 129 | Doral Financial Corp | San Juan, Puerto Rico | $4.40 |
| 130 | Mercantil Servicios Financieros | Caracas | $4.36 |
| 131 | First Commonwealth Financial Corporation | Indiana, PA | $4.28 |
| 132 | Pinnacle Bancorp Inc. | Central City, NE | $4.27 |
| 133 | Investors Bancorp MHC | Short Hills, NJ | $4.26 |
| 134 | Provident Financial Services Inc. | Jersey City, NJ | $4.25 |
| 135 | Ocean Bankshares, Inc. | Miami, FL | $4.17 |
| 136 | Security Service Federal Credit Union | San Antonio, TX | $4.07 |
| 137 | Capitol Federal Savings Bank MHC | Topeka, KS | $4.01 |
| 138 | Iberiabank Corp. | Lafayette, LA | $4.00 |
| 139 | SNBNY Holdings | Gibraltar | $3.99 |
| 140 | San Diego County Credit Union | San Diego, CA | $3.97 |
| 141 | America First Federal Credit Union | Riverdale, UT | $3.97 |
| 142 | Harleysville National Corp. | Harleysville, PA | $3.94 |
| 143 | BFC Financial Corp | Fort Lauderdale, FL | $3.93 |
| 144 | Amcore Financial Inc. | Rockford, IL | $3.93 |
| 145 | NBT Bancorp Inc. | Norwich, NY | $3.92 |
| 146 | Central Pacific Financial Corp. | Honolulu, HI | $3.91 |
| 147 | Sterling Bancshares | Houston, TX | $3.82 |
| 148 | Banner Corp. | Walla Walla, WA | $3.78 |
| 149 | First Merchants Corp | Muncie, IN | $3.72 |
| 150 | Community Bank System | De Witt, NY | $3.70 |


 I thought we were starting a long relationship... Or...have you found another? Ha! Do what you want.
I appreciate your hard work and hope to work with you again some day.


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This content should take the whole screen even it only has two sentences. But it didn't, we could tell by the background color.


curl -X GET 'https://api.steamcmd.net/v1/info/740'
Something went wrong while connecting to the API. Please check the status page or Twitter for more information


Union SCM Co., Ltd. was established in December 2015 with the headquarters in Taipei, Taiwan and branch in Shanghai, China.
Repair product lines are located at the Free Trade Zone, Taoyuan, Taiwan and Kun Shan City, Jiang Su Province , China. We perform repair service for both bonded and non-bond products.
We focus on rework, refurbish and repair for DT & SVR & NB PCBA / GPS PND / Smart Phone / Pad /LCD Panel associated with electronic and consumer products.
We also can do Spare Parts trading and worldwide on-side Technical Support and we have strategic repair partners in India, Europe, N. America and S. America to serve customers’ needs.
We have the most advanced IT system to manage working processes related to Warehouse Management, RMA, Rework and Repair etc.


JOSEFA MONTEIRO
I'm a backend programmer, currently working at Digital Factory Angola , I'm still addicted to the code, the feeling of seeing my scenes running, makes me very proud, I'm co-organizer of women techmarkers Luanda, Student of Computer Engineering and I love learning new things .


Swagger Documentation
- has a conflict with @DataJpaTest coniguration
/personcontroller/findAll
(using the PersonController object)
/personcontroller/findByLastName?lastName=Banana
(using the PersonController object)
/persons
(direct call to the PersonRepository)
/persons/3
(direct call to the PersonRepository)


Welcome to Dropular version (3).002.
Things are still being developed and
we guarantee that you will stumble
upon bugs and errors during your visit.
We constantly working on new
features that can extend our service
and make Dropular much more convenient
and ease to use.
For updates,
read our blog
and add us on Twitter.
Dropular is a micro-blogging service which makes it possible for members to save and collect their favorite images found from various sources online. Dropular is currently an invite only service and mostly designed for fashion designers, photographers, architects, graphic designers and artists.


Raptors forward Serge Ibaka ruled out against Hawks
by Sportsnet StaffToronto Raptors forward Serge Ibaka will miss Saturday’s game against the Atlanta Hawks.
Ibaka and point guard Kyle Lowry are close to returning to action, with head coach Nick Nurse saying the team is “shooting for” a Dec. 1 return.
Ibaka (ankle) and Lowry (thumb) have been out since Nov. 8 when they both picked up injuries against the New Orleans Pelicans.
The Raptors (10-4) are aiming for their third straight win after defeating the Orlando Magic and Charlotte Hornets in their previous two matchups.


MicroTrain Websites builds custom web applications for clients across a large number of industries.
MicroTrain Technologies creates programs for clients across a large number of industries.
Front-end and client-side development with HTML, CSS and JavaScript. Work with libraries and frameworks such as jQuery, Bootstrap and Angular. Build tools such as Yarn and Gulp. Use web technology to build hybrid mobile applications using Apache’s Cordova.
Work with third party APIs from vendors such as Google, NASA, Twilio and so on. Server side integration with LAMP (Linux, Apache, MySQL, PHP) and MEAN (MongoDB, Express, Angular, Node,js) stacks. Build, set-up, maintain own cloud based server.
Covering the principles of Agile and Scrum methodology through a combination of robust simulations, hands-on experimentation and high-quality instruction. Currently learning the fundamentals of Scrum and how to facilitate, coach and lead an Agile project.


description = "Production-ComeTogether" [viewBag] ==
Come Together
We work closely with our customers to meet their individual needs.