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Moral Pragmatism and Public
Perception: The "Bad Cop" in Insomnia
by Paul R Joseph
CAUTION: CONTAINS "SPOILERS"
By the time legendary detective
Will Dormer (Al Pacino) arrives in a backwater Alaska town to
investigate a murder that has stumped the local talent, he is
already doomed. Internal Affairs in his hometown, Los Angeles,
is sniffing close to his secret, that he planted evidence to
gain the conviction of an alleged murderer. His partner, Hap
(Martin Donovan) is about to cut a deal and spill his guts.  When Dormer accidently shoots his partner in the Alaskan
fog, witnessed only by the murderer he is trying to identify
and bring to justice (author Walter Finch, played with understated
calm evil by Robin Williams), Dormer is locked into a sleazy
dance of deception and cover-up which exposes the price of the
initial evil and leads to Dormer's eventual death. By the time
he cautions straight-arrow Alaskan cop Ellie Burr (Hilary Swank)
not to lose her own way as he has lost his, morality triumphs.
Or does it?
In order for Insomnia
to work, Pacino's Will Dormer must be a believable and a sympathetic
character. He is both-and perhaps too much so.
To
be believable, we must accept that Dormer, who is revered in
police circles, whose cases are studied and whose words are memorized
by worshipful Alaskan cop Ellie Burr, is also a law-breaker who
planted evidence to obtain a conviction. We believe. A combination
of real-life events and media popular culture make this leap
no problem at all. The Ramparts Scandal in Los Angeles and the
lingering doubt about the blood samples in the O.J. trial remind
us that cops don't only get it wrong, sometimes they make it
up. Popular culture responds with N.Y.P.D. Blue's Officer
Andy Sipowicz (Dennis Franz), a cop very willing to use force-and
who knows what else?--to get to what he understands to be the
right outcome.
As believable as Dormer is,
he is also sympathetic and the only clear voice in the film.
Like Sipowicz, we know that Dormer is not a bad cop. In fact,
he is a very good one. The people he arrests are guilty and he
sees what most others do not. Yes, he planted evidence, but it
was in a case where the evidence of guilt was clear to him (but
might not be to a jury). Nowhere in Insomnia is there
a hint that the defendant was innocent. The jailed criminals
who might "walk" if Internal Affairs brings Dormer
down are guilty, too. Do we want the evil criminals to walk free?
Not really.
Dormer argues, with all the
power that Al Pacino can bring to a character, that the end justifies
the means.  Perhaps,
in a different context, the argument would be that if a little
torture has to be used to catch a terrorist . . .
Internal Affairs is that department
which is charged with investigating police wrong-doing and law-breaking.
They are the cops who protect us from the cops. They ought to
be our heroes, but they are not. Pacino's Dormer saves his greatest
contempt for the Internal Affairs officers who are on his trail.
To him they are cops who aren't on the streets, aren't facing
the dangers and aren't protecting the public. They are the cops
who don't have the stomach for the job real cops do. Dormer makes
this case several times in long and impassioned soliloquies.
There is not much presented to dispute him. The Internal Affairs
cop appears only by telephone from L.A. and comes across as unlikable,
a tormentor seemingly more concerned with self- promotion than
morality and justice. He's a weak voice compared to Dormer's
eloquent, angry street-wise outbursts.
The arguments against Dormer's
position are presented non-verbally: first through the dedicated
professionalism of Ellie Burr; second, through Dormer's own self-torment
and his progressive breakdown, as sleeplessness and guilt swirl
together; third, by the sleazy bargain between cop and criminal
to preserve Dormer's secret; and, finally, by the fact that it
all leads to Dormer's destruction and his one-line plea to Ellie.
But is this enough to make the case for good or is it all susceptible
to another more subversive explanation?
Obviously, it's Dormer's prior
misdeed that leads to his partner's death and to his own; or
is it? Causation being the slippery little devil that it is,
could it not be argued that without the intervention of Internal
Affairs, Hap would not have been about to turn State's evidence
and Dormer would have been able to admit his accidental shooting
of his partner? There would have been no need to do anything
but arrest Walter Finch. The events leading to Dormer's death
would themselves never have taken that path and Dormer would
be putting more evil-doers away on the streets of Los Angeles.
Is the real wrong his law-breaking or is it the fact that we
wouldn't turn a blind eye to it? Despite everything, it's for
Dormer that we feel emotion, not the off-camera one-dimensional
Internal Affairs pursuer.
Dormer changes his view only
at the moment of his death, cautioning Ellie not to lose her
way, not to throw away a shell casing that will bring the true
story out. Perhaps she shouldn't toss it because even such a
small act would lead her inexorably to her doom just as Dormer
has been led to his. Yet, it could be argued that by that point
both Finch and Dormer were or shortly would be dead and the only
result of Ellie's moral rectitude, aside from possibly saving
her soul and integrity, would be to open up exactly the parade
of horribles-masses of criminals going free to prey on society-that
Dormer has railed against throughout the film. Perhaps it could
be argued that Ellie's inner peace isn't worth our safety. At
least, an audience might come to that conclusion.
This isn't what the film explicitly
presents to us, but the elements of this counter-analysis are
there-lurking just out of reach. Is the quiet professionalism
of Ellie Burr enough to convince us that the bad guys can be
brought to justice without breaking the rules? Is the destruction
of Will Dormer enough to convince us that our law enforcers must
avoid the lawbreakers' road? Possibly, but Insomnia leaves
room for doubt and for much soul-searching late at night when
it's impossible to sleep.
Posted: May 29, 2002
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Amaterra Group inks deal for first all-inclusive Marriott hotel in Jamaica
The Amaterra Group has signed a deal with Marriott International for the first all-inclusive Marriott resort in the Caribbean to be constructed in Jamaica.
The 800-room hotel is to be built in Trelawny with completion set for 2022.
Amaterra’s Keith and Paula Russell and Marriott’s Laurent de Kousemaeker signed the deal on Wednesday afternoon at the Hard Rock Café in Montego Bay, St James.
This is the third Marriott hotel to be built in Jamaica.
The other two are located in New Kingston.


The SimpleTexturedObject class represents an object with Phong lighting information and a single texture.
More...
#include <simpletexturedobject.h>
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| | SimpleTexturedObject () |
| | Creates an empty object. Use the setter methods below to set up data, and then call commit() when done. More...
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| |
| virtual | ~SimpleTexturedObject () |
| |
| void | commit () |
| | Call this method after setting all of the object's information. This will commit the information and allow it to be rendered. More...
|
| |
| void | setTriangleInfo (QVector< QVector3D > positions, QVector< QVector3D > normals, QVector< Triangle > triangles) |
| | Sets up face information for the object. More...
|
| |
| void | setMaterialInfo (float ambient, float diffuse, float specular, float shininess) |
| | Sets up material information for the object. Uses the same material for each vertex. More...
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| |
| void | setMaterialInfo (QVector< float > ambient, QVector< float > diffuse, QVector< float > specular, QVector< float > shininess) |
| | Sets up per-vertex materials for the object. More...
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| |
| void | setTextureInfo (QVector< TriangleTexCoords > texCoords, SharedImageAndSource image) |
| | Sets up texture information for the object. More...
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| |
| bool | isCommitted () const |
| |
| int | getNumVertices () const |
| | Returns the number of vertices in the vertex array. This may not be the same as the number of vertices when the object is rendered (it may be that the triangle array is unrolled). More...
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| |
| int | getNumTriangles () const |
| |
| const QVector< QVector3D > & | getVertices () const |
| |
| const QVector< QVector3D > & | getFaceNormals () const |
| |
| const QVector< Triangle > & | getTriangles () const |
| |
| const QVector< float > & | getVertexAmbient () const |
| |
| const QVector< float > & | getVertexDiffuse () const |
| |
| const QVector< float > & | getVertexSpecular () const |
| |
| const QVector< float > & | getVertexShininess () const |
| |
| const QVector< TriangleTexCoords > & | getFaceTexCoords () const |
| |
| const QImage & | getImage () const |
| |
| float | getAmbient () const |
| |
| float | getDiffuse () const |
| |
| float | getSpecular () const |
| |
| float | getShininess () const |
| |
| QString | getMaterialName () const |
| |
| SharedImageAndSource | getImageAndSource () const |
| |
The SimpleTexturedObject class represents an object with Phong lighting information and a single texture.
◆ Triangle
◆ TriangleTexCoords
◆ SimpleTexturedObject()
| SimpleTexturedObject::SimpleTexturedObject |
( |
| ) |
|
Creates an empty object. Use the setter methods below to set up data, and then call commit() when done.
◆ ~SimpleTexturedObject()
| SimpleTexturedObject::~SimpleTexturedObject |
( |
| ) |
|
|
virtual |
◆ commit()
| void SimpleTexturedObject::commit |
( |
| ) |
|
Call this method after setting all of the object's information. This will commit the information and allow it to be rendered.
◆ getAmbient()
| float SimpleTexturedObject::getAmbient |
( |
| ) |
const |
◆ getDiffuse()
| float SimpleTexturedObject::getDiffuse |
( |
| ) |
const |
◆ getFaceNormals()
| const QVector< QVector3D > & SimpleTexturedObject::getFaceNormals |
( |
| ) |
const |
◆ getFaceTexCoords()
◆ getImage()
| const QImage & SimpleTexturedObject::getImage |
( |
| ) |
const |
◆ getImageAndSource()
◆ getMaterialName()
| QString SimpleTexturedObject::getMaterialName |
( |
| ) |
const |
◆ getNumTriangles()
| int SimpleTexturedObject::getNumTriangles |
( |
| ) |
const |
◆ getNumVertices()
| int SimpleTexturedObject::getNumVertices |
( |
| ) |
const |
Returns the number of vertices in the vertex array. This may not be the same as the number of vertices when the object is rendered (it may be that the triangle array is unrolled).
- Returns
- Equal to getVertices().size() if isCommitted() is true. In any case, equal to the size of the positions array last passed to setTriangleInfo().
◆ getShininess()
| float SimpleTexturedObject::getShininess |
( |
| ) |
const |
◆ getSpecular()
| float SimpleTexturedObject::getSpecular |
( |
| ) |
const |
◆ getTriangles()
◆ getVertexAmbient()
| const QVector< float > & SimpleTexturedObject::getVertexAmbient |
( |
| ) |
const |
◆ getVertexDiffuse()
| const QVector< float > & SimpleTexturedObject::getVertexDiffuse |
( |
| ) |
const |
◆ getVertexShininess()
| const QVector< float > & SimpleTexturedObject::getVertexShininess |
( |
| ) |
const |
◆ getVertexSpecular()
| const QVector< float > & SimpleTexturedObject::getVertexSpecular |
( |
| ) |
const |
◆ getVertices()
| const QVector< QVector3D > & SimpleTexturedObject::getVertices |
( |
| ) |
const |
◆ isCommitted()
| bool SimpleTexturedObject::isCommitted |
( |
| ) |
const |
◆ objectUpdated
| void SimpleTexturedObject::objectUpdated |
( |
| ) |
|
|
signal |
Emitted when the object is updated. It is assumed that isCommitted() is true.
◆ setMaterialInfo() [1/2]
| void SimpleTexturedObject::setMaterialInfo |
( |
float |
ambient, |
|
|
float |
diffuse, |
|
|
float |
specular, |
|
|
float |
shininess |
|
) |
| |
Sets up material information for the object. Uses the same material for each vertex.
Assumes setTriangleInfo() has been called.
- Parameters
-
| ambient | The ambient parameter. |
| diffuse | The diffuse parameter. |
| specular | The specular parameter. |
| shininess | The shininess parameter. |
◆ setMaterialInfo() [2/2]
| void SimpleTexturedObject::setMaterialInfo |
( |
QVector< float > |
ambient, |
|
|
QVector< float > |
diffuse, |
|
|
QVector< float > |
specular, |
|
|
QVector< float > |
shininess |
|
) |
| |
Sets up per-vertex materials for the object.
Assumes setTriangleInfo() has been called.
- Parameters
-
| ambient | The ambient parameter. |
| diffuse | The diffuse parameter. |
| specular | The specular parameter. |
| shininess | The shininess parameter. |
◆ setTextureInfo()
Sets up texture information for the object.
Assumes setTriangleInfo() has been called.
- Parameters
-
| texCoords | Texture coordinates for each triangle. |
| image | The image (texture) for the object. |
◆ setTriangleInfo()
| void SimpleTexturedObject::setTriangleInfo |
( |
QVector< QVector3D > |
positions, |
|
|
QVector< QVector3D > |
normals, |
|
|
QVector< Triangle > |
triangles |
|
) |
| |
Sets up face information for the object.
- Parameters
-
| positions | A list of vertex positions. |
| normals | A list of face normals. Should be parallel to triangles array. |
| triangles | A list of triangles. Each triangle is a triplet of indices into the positions array. |
The documentation for this class was generated from the following files:


Pay to click: is it really worth it?
Today I will be reviewing what seems to be the XXI century plague: the pay to click systems. The basic notion is that you are paid to watch a set of advertisement links for a certain period of time. After you do so, you get a credit of a few dollar cents in your account and additionally, when you reach a certain minimum amount of balance in your account you can ask for a checkout.
Now, when I said a few dollar cents, it really is a few dollar cents, as in usually you get paid $0.01 for each page-view which is a pretty meagre amount. Add this to the fact that at best, you get 10 to 20 ad-links to click upon (this varies between PTC systems) and it does not take a genius to do the math and know that you will not be making that much profit out of this system.
So why is it so widespread and why do people adhere to these systems so massively?
Mostly because there is more to it. All of the pay to click systems have a referal system. Actually, this is what keeps them in business.
Referrals work in the following way: for each referral I have, I will also earn a certain amount of money for each click they make. On some PTCs this amount is actually the same as the amount I get for my own clicks, but on some systems this amount is downsized.
Typically it will be hard to find a decent amount of referrals. It will be hard to find an amount big enough to actually render you more than just one or two dozens of dollars, but once again, most or all the PTC systems have a system to fight this: a referral market. Basically, you just buy referrals for money, and then it will be like if those referrals had in fact registered on that PTC site with your name as their referral. Do you see where this is getting now?
Let us consider the following scenario: you are consistently getting 5 ad-links per day, each will give you $0.01, so you on your own make $0.05 per day.  Each of those referrals will also have the 5 links and they will also click on them; as a result, for each click, it will be as if you had clicked as well, so on the first day here’s how much you will make:
5 * 0.01 * 1000 = $50
Sounds better than a simple $0.05 per day, does it not? With an income like that, you can expect something in the order of $1500 per month, give or take.
Maybe at this point some people who did not know about pay to click systems are jumping up and down, wanting to take the plunge and jump right into the scene, but I am sure that many other readers will be skeptical. I know that the first question that came to my mind when I was introduced to this system was: where does the money come from?
Easy. It comes from advertisers who pay a fee to the PTC systems, so that they will publish their advertisements as part of the ad-links. Usually the advertisers pay an amount per page-views (i.e. per amount of times their link was clicked) which is just slightly higher than the amount of money that we, clickers, get for clicking – allowing for huge profit for PTC owners.
So now that you know their source of income, are you still skeptic? If you are, you are rightfully so. The essential fail on these systems is how saturated the web is with them; so much that the advertisers are in fact…. other PTC companies! No matter how much you look around, you will always find that PTC Site A has ad-links for PTC sites B, C and D – and the site B has links for A, C and D and so on… So in reality, this is actually a mangle of websites linking to one another, using profits of theirs to feed the profits of their “opponents” and we get to a point where we are recycling money between companies, in a very closed system, where the only fresh money that gets in, is actually money put in by clickers.
Why don’t real, honest, business companies want to advertise in PTC websites? Simple: whoever is in the PTC deal as a clicker, simply isn’t in it for watching the ads. People will click the ad, keep browsing until the timer runs down, then close that tab, click the next ad and continue the process. This simply does not serve the purpose of advertising. Companies advertise their products in the hopes of increasing their sales, because people will look at their ads and feel the urge to buy their products or to subscribe their services. This simply does not happen when people are in it for the money!
It is somewhat transitive: people click the ads but do not buy. Since people do not buy, the companies do not get the expected income. Let us face it, when a company invests X money in advertising, they are expecting an income of X+Y where they get back the amount they spent, plus a profit. If this profit does not appear, the companies will be less inclined towards advertising and they stop publishing their ads and it all crumbles like a building being demolished.
This is the crux of the PTC systems having failed over and over and over. They are doomed to fail because the notion is flawed to begin with. The punch-line is: why being advertised at for free, whilst you can be get paid to watch advertisements?
Well, this is all really pretty, but you will never be looking at ads from real companies like Microsoft, Yahoo, Google or even of popular online games like World of Warcraft or EvE Online. Instead, you will be looking at ads from companies who also make a living out of PTC deals.
So yeah, I am indeed registered on a few PTC sites and I do click regularly on all of them, but at this point, they are all self-managed. I get profit from PTC, I use it to buy referrals and hopefully at some point I will be leeching the money of people who have invested and given up. Truth is that to make big bucks in this sort of business, you must have big bucks to start with, and they must be disposable. It must be something like venture capital; money that you can simply lose, because there is a high risk involved. There is a risk of it not paying out as well as you expected, or simply the risk of the PTC company going bankrupt and then you have nothing. It has happened the past, many many companies in this field have gone belly-up and then, all those who had invested the big bucks are all of a sudden wiped out.
Obviously, if the company lives long enough for you to make enough profit, so that it gets to be auto-recyclable, then it might be a good deal, but still with a very risky launching pad.
So, in sum, it really isn’t worth it unless you invest at least some money and if you do invest, do be ready to lose the money. You might hit jackpot in the long run, but the most likely ending is that you get no profit and you say goodbye to your money. Truth is: it is not something you can live from. If you had the starting money so that it could become good enough to live from it, then you are probably rich enough to not need this sort of programs to make a living and you will stay out of it.
This is the core of pay to click websites, although there are some new services out there coming up with new innovative strategies that at a first glance, they all look like perfect ways to get more money. Still, none change the fact that these systems are all like a closed environment. Very little money gets in and tons of money seem to be getting out, not to mention the profit that the companies involved make.
To finalize, if you want to make really good money, start your own pay to click service and when you have a few thousand bucks in balance, run away with it and never show up again 😛 otherwise, you might join the few poor ones and help out a bloke by trying out the service. These two services seem to be the most credible and faster payers out there. Be careful though, do not join every PTC service out there. Services like bux.to are simply a no-go due to it taking months from the time you ask for your money until you actually get it.
So if you want to give it a try, just click on these banners and get started. It is not much hard. 🙂
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A passionate individual who always thrive to work on end to end products which develop sustainable and scalable social and technical systems to create impact.


Pig Dice is a game in which two players race to reach 100 points.
How to play:
- Each turn, a player repeatedly rolls a die until either a 1 is rolled or the player holds and scores the sum of the rolls.
- If the player rolls a 1, the player scores nothing and it becomes the opponent's turn.
- If the player rolls 2 - 6, the number is added to the player's turn total and the player's turn continues.
- If the player holds, the turn total is added to the player's score and it becomes the opponent's turn.


WITNESS LIST Pensions & Investments Committee May 7, 2001-8:00A SB 230 Registering, but not testifying: For: Kouri, Richard (Texas State Teachers Association) Romo, Joel (Association Of Texas Professional Educators) SB 292 For: Anderson, Gary (Texas Public Empoyees Association) Raab, Ted (Texas State Employees Union) On: Beckett, Sheila (Employees Retirement System) Registering, but not testifying: For: Johnson, Julia (Self) McGinnis, Larry (Texas Department Of Public Safety Officers Association) SB 477 For: Romo, Joel (Association Of Texas Professional Educators) On: Dunlap, Charles (Teacher Retirement System) Registering, but not testifying: For: Fickel, Ann (Texas Classroom Teachers Association) Floyd, Beamon (Texas Association Of School Administrators) Kouri, Richard (Texas State Teachers Association) SB 593 For: Sheffield, Mike (Austin Police Association) Wood, Chesley (City Of Austin Police Retirement System) Registering, but not testifying: For: Haggerty, Catherine (Self and Austin Police Retirement System) Morin, Peter (Self) Wilkinson, Charley (Combined Law Enforcement Association Of Texas) On: Jordan, Sampson (Self and City Of Austin Police Retirement System) May, Robert (Austin Police Retirement System)


Manchester United weighing up move for Dortmund’s Jadon Sancho
by Jamie Jackson
• Ole Gunnar Solskjær alerted by recent troubles in Germany
• United may face competition from Liverpool and Real Madrid
Manchester United are weighing up a move in January for Jadon Sancho, with Ole Gunnar Solskjær alerted by the forward’s recent troubles at Borussia Dortmund.
The manager has identified Sancho as a summer target but he and Ed Woodward, United’s executive vice-chairman, have a policy of trying to sign players of interest in the preceding winter window should there be a chance of doing so.
The 19-year-old was last month dropped and fined by Dortmund for being late back to training after England duty. Last Saturday he was taken off after 36 minutes of the 4-0 defeat by Bayern Munich, then criticised by his manager, Lucien Favre, who confirmed Sancho had not been injured.
Sancho started England’s 7-0 win over Montenegro on Thursday. If United firm up their interest in January they may face competition from clubs including Liverpool and Real Madrid.


Going to an Upcoming Anime Film!
Just bought tickets to go see Seishun Buta Yarō wa Yumemiru Shōjo no Yume o Minai (Rascal Does Not Dream of a Dreaming Girl) at AMC River East 21 in Chicago for Thursday, October 3, 2019.
I really enjoyed the main series anime this show was based off, Seishun Buta Yarou wa Bunny Girl-senpai no Yume wo Minai, and I just have to go see the movie. If anyone else is interested in going with me, I would be happy to drive up to 3 other people to see the movie with me, all you need to do is purchase your ticket. I know the middle of the week isn’t the best day to go see a movie nearly 2 hours away, but there are no other closer threaters. I also am aware that a lot of people have exams during this week (because I do), but all mine are earlier in the week.
If you are at all interested in going, please fill out this Google Form: https://forms.gle/BtX8TuxYF2bU5wgj7.
Thanks for reading!


First things first: in a startup of the size that we concerned with in this book (perhaps 3-4 to 30-40 technology team size), CTO is either the technical co founder, or else a person hired by the early stage company to head the technology team. He may also be referred by VP Engineering, which emphasizes the managerial role, and is usually suitable when the team size is on the higher side of the above mentioned ranges.
The title of the chapter is presumptive: it assumes that I am a good CTO. But how can I say that? If there were a way to measure it quantitatively, like you get 70 out of 100 in CTO-goodness, then I would have tried to prove myself. But in absence of such a scale, we will just assume the above statement because I am the writer of this book.
Note that the opinions expressed here are predicated to my experience: I have served as a CTO or CTO-like role in a companies where technology team sizes has varied from 5 to 20 people. The companies have employed engineers which I would rate as 90 percentilers: they are clearly above average, but by no means superstars. Further, the work my companies have done is fairly tech intensive: dealing with billions of requests per day, have processed terabytes of data in Hadoop and have written sub millisecond response time servers, but they are by no means on the cutting edge of computer science: we have not coded up self driving cars or brand new operating systems or compiler. Further, since I have worked in ad-tech and related fields for long time, speed of development and savings in infrastructure costs have been important considerations. While the software needs to be accurate, accuracy concerns are not as pressing, as they would be, for example when you are coding up a medical equipment or self driving car. Read the rest of the chapter with above caveats in mind.
A common dilemma for the CTO is: should you be involved in the ground level coding, or should you rather zoom out and look at the big picture?
My suggestion is for the CTO to necessarily look at the big picture (If you don't, who will?), and be involved in ground level coding to extent which your capabilities allow.
In any startup that is off the ground, and has more than a couple of engineers, there will inevitably be a large number of components. At my company we have a bunch of SDKs: an android SDK, an iOS SDK and a web SDK, written respectively in Objective C, Java and javascript. Our data ingestion system is written in Java Jetty and core backend is written Java and Python. We use React + Redux for our web app and use Python Django web server as the backend for UI.
It is not possible to get a deep knowledge of each of the components. Indeed there are people who specialize in a single component: there are frontend developers who will argue about merits and demerits of using webpack, and there are Python gurus who will talk about Python's metaclasses and reflection capabilities.
I think, to the extent your capabilities allow, you should try to learn everything. Being a full time engineer, it is good to learn various aspects of Computer Science: from backend to frontend to deployment to unit testing and continuous integration. It is hard task, but you have signed up for it. As they say, delegation is the art of second best.
At some point, you will max out and no longer be up to date on all the aspects. At that point you should continue being involved in fewer components. Perhaps you can stop tracking less critical components, or the components which you do not know well any way, or if there is a person who can handle it better than you.


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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object eu.cassandra.sim.Event
public class Event
A simulation event.
| Field Summary | |
|---|---|
java.lang.String |
hashcode
The event's hashcode. |
static int |
SWITCH_OFF
The Constant SWITCH_OFF. |
static int |
SWITCH_ON
The Constant SWITCH_ON. |
| Constructor Summary | |
|---|---|
Event(int atick,
int aaction,
Appliance aapp,
java.lang.String ahashcode,
Activity aact)
Instantiates a new event. |
| Method Summary | |
|---|---|
boolean |
apply()
Apply the event. |
int |
compareTo(Event o)
|
int |
getAction()
Gets the event's action. |
Activity |
getActivity()
Gets the event's activity. |
Appliance |
getAppliance()
Gets the event's appliance. |
int |
getTick()
Gets the event's tick. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public java.lang.String hashcode
public static final int SWITCH_OFF
public static final int SWITCH_ON
| Constructor Detail |
|---|
public Event(int atick, int aaction, Appliance aapp, java.lang.String ahashcode, Activity aact)
atick
- the event's tickaaction
- the event's actionaapp
- the event's applianceahashcode
- the event's hashcodeaact
- the event's activity| Method Detail |
|---|
public Appliance getAppliance()
public Activity getActivity()
public int getAction()
public int getTick()
public boolean apply()
public int compareTo(Event o)
compareTo
in interface java.lang.Comparable<Event>
|
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Welcome to BatCat’s documentation!
BatCat is designed to make the data scientist’s life easier when practicing machine learning operations (MLOps) on Amazon Web Services (AWS).
Services of AWS included:
AWS Lambda: a serverless, event-driven compute service
AWS S3 (Simple Storage Service): provides object storage service
AWS Glue: is a serverless data integration service that makes data preparation simpler, faster, and cheaper.
Amazon Athena: a serverless, interactive query service on S3
Amazon Redshift: a data warehouse product
Amazon SageMaker Processing: allows you to run steps for data pre- or post-processing, feature engineering, data validation, or model evaluation workloads on Amazon SageMaker.
Elastic Container Registry (ECR): is a fully managed Docker container registry that makes it easy to store, share, and deploy container images.
AWS Step Functions: a low-code, visual workflow service that developers use to build distributed applications, automate IT and business processes, and build data and machine learning pipelines using AWS services.
Secrets Manager: is a secure and convenient storage system for API keys, passwords, certificates, and other sensitive data.


public
constructors in non-public
classes.
Usually, there is no reason for creating a public
constructor in a class with a lower access level.
Please note, however, that this inspection changes the behavior of some reflection calls. In particular,
Class.getConstructor()
won't be able to find the updated constructor
(Class.getDeclaredConstructor()
should be used instead). Do not use the inspection if your code
or code of some used frameworks relies on constructor accessibility via getConstructor()
.
Example:
class House {
public House() {}
}
After the quick-fix is applied:
class House {
House() {}
}


Shipping and Billing
{% csrf_token %}
Your shipping address
{{ shipping_address.as_p }}
Your billing address
{{ billing_address.as_p }}
Save


Teams: USMC vs. OpFor
Place: An urban desert town; location unknown
Terrain: Urban desert town with many streets and enterable buildings
Type of Combat: Medium range to close quarters combat with some long range fighting
Strike is a very urban map with the largest open space being the two roads that run through the map. There are many small buildings that see little combat, although a good sniper can control a large area of the map while being safely planted inside one. Light Machine Guns are favored here.
"Taliban Avenue" and "Al-Qaeda Road", the two main intersecting streets (in the middle of the map creating a "T", below the marketplace) are where the most choke points for snipers and Assault classes are located. The apartment block to the south provides sniper points covering nearly the entire map. But beware of the Al-Jazeera towers to the West, they provide a blind corner for rushers. There are long lines of view, and most fighting occurs building-to-building across the streets. People tend to enjoy this map most with Team Deathmatch or Headquarters settings. Desert Camouflage is often preferred.
Sniping is very effective in the apartment at the top right corner of the map, on account of the overview of the intersections, good cover, and the exits can be covered by claymores. The use of claymores can also get kills if positioned properly. The corners are sharp, so no one sees them.
It is possible, on Domination, to hide in the far back corner on the bottom floor of the building closest to flag C and take it without being spotted/attacked. It has been stated by Robert Bowling that this is not possible in the Modern Warfare 2 version of the map.
The walled in area near the Marine spawn can be used to snipe and secretly kill someone in the building across the street.
Low recoil and/or high power weapons are recommended for this map, due to most of the combat occurring in the streets or at long ranges.


{% extends "base.html" %} {% block content %}
This service is work-in-progress and as such is provided with no guarantees regarding stability, reliability, and/or support.
Source code and documentation on GitHub.


No Indication of Foul Play in Death of White Helmets Founder - Source
ANKARA (Sputnik) - The police have not found any traces that would indicate that additional people were present at the Istanbul house of James Le Mesurier, a founder of the White Helmets organisation, at the moment of his death earlier in the day, a source close to the investigation said.
"According to the preliminary data, UK citizen Le Mesurier died as a result of his fall from height at about 05:30 a.m. on Monday. Their body has numerous fractures ... According to the preliminary data, no strangers were seen in the house at the time of his death. After the body was examined by forensic experts, it was sent to a morgue," the source said.
Turkish media reported citing police sources that Le Mesurier's neighbours found his dead body in the early hours of Monday in the garden of his house.
A law enforcement source confirmed later the death of Le Mesurier though it remains "unclear whether he was murdered, or committed suicide."
Le Mesurier, who was also a former officer of the UK Secret Intelligence Service, created the controversial 'urban search and rescue' White Helmet organisation in 2013.


TOGETHER (Tillsammans) ----------------------
When Elisabeth's (Lisa Lindgren) husband Rolf (Michael Nyqvist) hits her for the second time, she bundles up her kids Stefan (Sam Kessel) and Eva (Emma Samuelsson) and moves into her brother Goran's (Gustaf Hammarsten, "The Best Intentions") commune. The wildly divergent commune members and the people Rolf meets now that he's alone will take each member of this family on a unique journey that ultimately brings them all back "Together."
Writer/director Lukas Moodysson ("Show Me Love") creates a warm comedy about a group of people forced to reconsider preconceived notions and how only those willing to take a chance on change are able to make the connections that count in life. While Moodysson doesn't give equal weight to all his characters, making his film's structure unbalanced, his fine cast and true human observations are ultimately what count.
 Medical student Lasse (Ola Norell) humorously speaks his mind, summing up situations in a no nonsense manner that,
while it may sting, is always on target.  Erik, (Olle Sarri) a strident member of the Communist, Marxist, Leninist, Revolutionary League, argues that his welding job keeps him from 'rotting at university.' Signe (Cecilia Frode) and Sigvard (Lars Frode) are hippies who reject meat and materialism.
When Elisabeth and her kids arrive from the bourgeois world (just as Lasse's dropped his pants in protest of Anna's bottomless kitchen attire!), everything shifts. Anna immediately sets her sights on Elisabeth, loosening her up and strengthening her resolve to stay away from Rolf. The kids suffer until Stefan connects with Tet (Axel Zuber), Lasse and Anna's Vietnam War named son, over forbidden Legos, war games and 'playing Pinochet' and Eva begins a tentative romance with Fredrik (Henrik Lundstrom), the equally four-eyed outcast son of next door's disapproving neighbors Margit (Therese Brunnander) and Ragnar (Claes Hartelius). Meanwhile, after a disastrous evening with his kids which ends with him drunk in prison, Rolf quits the booze
and strikes up a friendship with lonely widower Birger (Sten Ljunggren), who urges him to woo back his wife.
Moodysson's nostalgic look back at 1975 Stockholm of 'ugly clothes and bad music' proves that styles may change, but people pretty much stay the same. His gradual reconfiguration of the Tillsammans household is canny and uplifting and his cast are uniformly excellent. Except for its small imbalances (the action shifts away from the household for too long a stretch, Signe and Sigvard exist only to leave in protest), the film's lone drawback is the amateurish cinematography by Ulf Brantas ("Show Me Love"). Brantas zooms and pans like someone with their first videocamera, although editors Michal Leszczylowski and Fredrik Abrahamsen make the most with the footage they have.
"Together" is a life-affirming comedy.
B-
For more Reeling reviews visit www.reelingreviews.com
<EMAIL_ADDRESS><EMAIL_ADDRESS>========== X-RAMR-ID: 29732 X-Language: en X-RT-ReviewID: 253326 X-RT-TitleID: 1109600 X-RT-SourceID: 386 X-RT-AuthorID: 1487
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


Biography
My name is Mike Falk. I grew up in Albuquerque, NM and Reno, NV. I moved to Seatlle 12 years ago and love the PNW. My wife, Mary, and I have a son named Max who is almost 2, a dog named Chloe, and cat named Kitty.
I enjoy playing golf, skiing, hiking, cycling, and cooking. I'm also pationate about seeing the world. I've visted over 20 countries so far and I've lived and worked in Japan and attended school in Spain
My favorite movie is The Big Lebowski with Bill and Ted's Excellent adventure as a close second. My favorite band is the Beastie Boys and my favorite novel is The Razor's Edge by W. Somerset Maugham, but my favorite literary genre is Magical Realism.
Education History
- Code Fellows, Seattle, WA September 2020 - February 2020 Certificate - Advanced Software Development in Full-Stack Python
- University of Nevada, Reno, NV August 1996 - June 2002 BA - English Literature & History
- Oxford House School, Barcelona, Spain September 2001 - September 2001 Trinity College TESOL, Teaching English as a Second Language
Work Experience
- Amazon, Seattle - Senior Program Manager, Global Mile FBA Automation Team December 2018 - August 2020
- Amazon, Seattle - Senior Product Manager, Amazon Global Logistics Retail Expansion April 2017 - December 2018
- Amazon, Seattle - Senior Product Manager, Prime Now Expansion April 2016 - April 2017
Goals
I enrolled in the Code Fellows program because I wanted to build coding skill set nessecary to become a professional coder.
Mike's Top 10 Favorite Movies
- The Big Lebowski
- Bill and Ted's Excellent Adventure
- Spectre
- Layer Cake
- The Empire Strikes Back
- Monty Python's Search for the Holy Grail
- Ghostbusers
- Batman Begins
- Thor: Ragnarok
- The Godfather II


Contact Us
You can get in contact with us by emailing Ellie Hamilton at<EMAIL_ADDRESS>or by calling 01225 300 300
4 North Parade, Bath, Somerset BA1 1LF
Fill in Contact Form
You can email us at<EMAIL_ADDRESS>alternatively you can fill in this form and well get back to you.


NAME's Profile
Age: 20
Gender: Female
Mental Health Concern: Depression, Anxiety
Councellor Evaluation: Last
Mental Status:
"Counseclor Evaluation:"
{% extends "layouts/base.html" %} {% block title %} Page Blank {% endblock %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
Age: 20
Gender: Female
Mental Health Concern: Depression, Anxiety
Councellor Evaluation: Last
Mental Status:
"Counseclor Evaluation:"


"x" "1" "GEORGE,SE HUGGINS-CLARK,G AND BROOKS,LR, USE OF A SALMONELLAMICROSUSPENSION BIOASSAY TO DETECT THE MUTAGENICITY OF MUNITIONS COMPOUNDS ATLOW CONCENTRATIONS, MUTAT. RES. 490(1):45-56, 2001"


Your browser does not support the video tag.
Card Title
Your browser does not support the video tag.
-{{index+1}}-
{{caption}} {{ $route.params.id }}


[midterm]
URL Canonicalization index URLS. All the content within the website that are associated with the indexed URL is brought to the search engine database, but Uniform Resource Locator or URL’s are what possess ranking. What complicates a certain matter in search is that the identical content page can have a single or multiple URLS associated with it.
The Status-Code element in a server response, is a 3-digit integer where the first digit of the Status-Code defines the class of response and the last two digits do not have any categorization role.
here are the http status codes from tutorialspoint.com
HTTP - Status Codes| S.N. | Code and Description |
|---|---|
| 1 | 1xx: Informational It means the request has been received and the process is continuing. |
| 2 | 2xx: Success It means the action was successfully received, understood, and accepted. |
| 3 | 3xx: Redirection It means further action must be taken in order to complete the request. |
| 4 | 4xx: Client Error It means the request contains incorrect syntax or cannot be fulfilled. |
| 5 | 5xx: Server Error It means the server failed to fulfill an apparently valid request. |
HTTP status codes are extensible and HTTP applications are not required to understand the meaning of all the registered status codes. Given below is a list of all the status codes.
| Message | Description |
|---|---|
| 100 Continue | Only a part of the request has been received by the server, but as long as it has not been rejected, the client should continue with the request. |
| 101 Switching Protocols | The server switches protocol. |
| Message | Description |
|---|---|
| 200 OK | The request is OK. |
| 201 Created | The request is complete, and a new resource is created . |
| 202 Accepted | The request is accepted for processing, but the processing is not complete. |
| 203 Non-authoritative Information | The information in the entity header is from a local or third-party copy, not from the original server. |
| 204 No Content | A status code and a header are given in the response, but there is no entity-body in the reply. |
| 205 Reset Content | The browser should clear the form used for this transaction for additional input. |
| 206 Partial Content | The server is returning partial data of the size requested. Used in response to a request specifying a Range header. The server must specify the range included in the response with the Content-Range header. |
| Message | Description |
|---|---|
| 300 Multiple Choices | A link list. The user can select a link and go to that location. Maximum five addresses . |
| 301 Moved Permanently | The requested page has moved to a new url . |
| 302 Found | The requested page has moved temporarily to a new url . |
| 303 See Other | The requested page can be found under a different url . |
| 304 Not Modified | This is the response code to an If-Modified-Since or If-None-Match header, where the URL has not been modified since the specified date. |
| 305 Use Proxy | The requested URL must be accessed through the proxy mentioned in the Location header. |
| 306 Unused | This code was used in a previous version. It is no longer used, but the code is reserved. |
| 307 Temporary Redirect | The requested page has moved temporarily to a new url. |
| Message | Description |
|---|---|
| 400 Bad Request | The server did not understand the request. |
| 401 Unauthorized | The requested page needs a username and a password. |
| 402 Payment Required | You can not use this code yet. |
| 403 Forbidden | Access is forbidden to the requested page. |
| 404 Not Found | The server can not find the requested page. |
| 405 Method Not Allowed | The method specified in the request is not allowed. |
| 406 Not Acceptable | The server can only generate a response that is not accepted by the client. |
| 407 Proxy Authentication Required | You must authenticate with a proxy server before this request can be served. |
| 408 Request Timeout | The request took longer than the server was prepared to wait. |
| 409 Conflict | The request could not be completed because of a conflict. |
| 410 Gone | The requested page is no longer available . |
| 411 Length Required | The "Content-Length" is not defined. The server will not accept the request without it . |
| 412 Precondition Failed | The pre condition given in the request evaluated to false by the server. |
| 413 Request Entity Too Large | The server will not accept the request, because the request entity is too large. |
| 414 Request-url Too Long | The server will not accept the request, because the url is too long. Occurs when you convert a "post" request to a "get" request with a long query information . |
| 415 Unsupported Media Type | The server will not accept the request, because the mediatype is not supported . |
| 416 Requested Range Not Satisfiable | The requested byte range is not available and is out of bounds. |
| 417 Expectation Failed | The expectation given in an Expect request-header field could not be met by this server. |
| Message | Description |
|---|---|
| 500 Internal Server Error | The request was not completed. The server met an unexpected condition. |
| 501 Not Implemented | The request was not completed. The server did not support the functionality required. |
| 502 Bad Gateway | The request was not completed. The server received an invalid response from the upstream server. |
| 503 Service Unavailable | The request was not completed. The server is temporarily overloading or down. |
| 504 Gateway Timeout | The gateway has timed out. |
| 505 HTTP Version Not Supported | The server does not support the "http protocol" version. |


|
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Object org.jasypt.util.digest.Digester
public final class Digester
Utility class for creating digests without using a salt or iterating
the hash function. This means that digests created by this class will
be compatible (and equivalent) to the ones which could be created by
the user by directly using a MessageDigest
object.
This class can be thought of as convenience wrapper for
MessageDigest
, adding thread-safety and
a more javabean-like interface to it. These two features enable a more
adequate use from an IoC container like Spring.
This class internally holds a StandardByteDigester
configured this way:
This class is thread-safe
| Field Summary | |
|---|---|
static String |
DEFAULT_ALGORITHM
MD5 will be the default algorithm to be used if none is specified. |
| Constructor Summary | |
|---|---|
Digester()
Creates a new instance of Digester. |
|
Digester(String algorithm)
Creates a new instance of Digester, specifying the algorithm to be used. |
|
Digester(String algorithm,
java.security.Provider provider)
Creates a new instance of Digester, specifying the algorithm to be used. |
|
Digester(String algorithm,
String providerName)
Creates a new instance of Digester, specifying the algorithm to be used. |
| Method Summary | |
|---|---|
byte[] |
digest(byte[] binary)
Creates a digest. |
void |
setAlgorithm(String algorithm)
Sets the algorithm to be used for digesting, like MD5 or SHA-1. |
void |
setProvider(java.security.Provider provider)
Sets the security provider to be asked for the digest algorithm. |
void |
setProviderName(String providerName)
Sets the name of the security provider to be asked for the digest algorithm. |
| Methods inherited from class Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final String DEFAULT_ALGORITHM
| Constructor Detail |
|---|
public Digester()
setAlgorithm(String)
.
public Digester(String algorithm)
Creates a new instance of Digester, specifying the algorithm to be used.
public Digester(String algorithm, String providerName)
Creates a new instance of Digester, specifying the algorithm to be used.
public Digester(String algorithm, java.security.Provider provider)
Creates a new instance of Digester, specifying the algorithm to be used.
| Method Detail |
|---|
public void setAlgorithm(String algorithm)
Sets the algorithm to be used for digesting, like MD5 or SHA-1.
This algorithm has to be supported by your security infrastructure, and it should be allowed as an algorithm for creating java.security.MessageDigest instances.
If you are specifying a security provider with setProvider(Provider)
or
setProviderName(String)
, this algorithm should be
supported by your specified provider.
If you are not specifying a provider, you will be able to use those algorithms provided by the default security provider of your JVM vendor. For valid names in the Sun JVM, see Java Cryptography Architecture API Specification & Reference.
algorithm
- the name of the algorithm to be used.
AlreadyInitializedException
- if it has already been initialized,
this is, if digest(byte[])
has been called at least
once.public void setProviderName(String providerName)
Sets the name of the security provider to be asked for the digest algorithm. This security provider has to be registered beforehand at the JVM security framework.
The provider can also be set with the setProvider(Provider)
method, in which case it will not be necessary neither registering
the provider beforehand,
nor calling this setProviderName(String)
method to specify
a provider name.
Note that a call to setProvider(Provider)
overrides any value
set by this method.
If no provider name / provider is explicitly set, the default JVM provider will be used.
providerName
- the name of the security provider to be asked
for the digest algorithm.
AlreadyInitializedException
- if it has already been initialized,
this is, if digest(byte[])
has been called at least
once.public void setProvider(java.security.Provider provider)
Sets the security provider to be asked for the digest algorithm. The provider does not have to be registered at the security infrastructure beforehand, and its being used here will not result in it being registered.
If this method is called, calling setProviderName(String)
becomes unnecessary.
If no provider name / provider is explicitly set, the default JVM provider will be used.
provider
- the provider to be asked for the chosen algorithm
AlreadyInitializedException
- if it has already been initialized,
this is, if digest(byte[])
has been called at least
once.public byte[] digest(byte[] binary)
binary
- the byte array to be digested.
StandardByteDigester.digest(byte[])
|
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ABOUT
HIGHLIGHTS
CONTACT
Using Bootstrap 3
This website is designed using Bootstrap 3 and it is completely responsive
About this website
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Consultation outcome
Summary of the responses we received, along with the government’s response outlining the next steps.
This consultation ran from
to
We are seeking views on proposals for setting up an insolvency regime for further education institutions and sixth-form colleges.
Ref: BIS/16/320 PDF , 723KB , 48 pages
PDF , 213KB , 30 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
PDF , 72.4KB , 8 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
This consultation seeks comments on the proposals to introduce procedures for further education and sixth-form colleges which become insolvent (unable to pay their debts). The proposals include a Special Administration Regime, giving extra protection to ensure continuity of service.
The insolvency regime would be designed to:
We are proposing insolvency procedures for colleges in line with those given to companies under the Insolvency Act 1986 , including administration and liquidation.
The consultation will be of interest to:
Published: 6 July 2016
Updated: 27 October 2016


policytool - PolicyTool Administration GUI Utility
f3policytool reads and writes a plain text policy file based on user input via the utility GUI.
Run the policytool Administrator's utility
f4policytool
Run policytool and load the specified policy file
f4policytoolf2[-file f2filenamef2]
where:
file directs f2policytool to load a local policy file
filename The file name
f3policytool is a GUI that allows users to create and manage policy files. For details, see
f2the Policytool Users Guide @
http://docs.oracle.com/javase/7/docs/technotes/guides/security/PolicyGuide.html.
file Loads f2filename.
f2Default Policy Implementation and Syntax @
http://docs.oracle.com/javase/7/docs/technotes/guides/security/PolicyFiles.html
f2Policy Tool Users' Guide @
http://docs.oracle.com/javase/7/docs/technotes/guides/security/PolicyGuide.html
f2Security Permissions @
http://docs.oracle.com/javase/7/docs/technotes/guides/security/permissions.html
f2Security Overview @
http://docs.oracle.com/javase/7/docs/technotes/guides/security/overview/jsoverview.html


LOSING ISAIAH A film review by Scott Renshaw Copyright 1995 Scott Renshaw
Starring: Jessica Lange, Halle Berry, David Strathairn, Marc John Jefferies, Samuel L. Jackson. Screenplay: Naomi Foner. Director: Stephen Gyllenhaal.
It is one of film criticism's most ubiquitous put-downs--one I frequently use myself--to compare a theatrical feature to a TV-movie. Such a description is intended to describe both subject matter (usually topical or sensationalistic) and manner of presentation (usually didactic or sentimental). LOSING ISAIAH has garnered more TV-movie comparisons than I can count, but the comparison isn't entirely appropriate. True, the subject of birth mother/adoptive mother custody conflict is a television staple; there were probably three such films on the small screen in the last month. But TV-movies generally feature one-dimensional characters and a specific agenda. LOSING ISAIAH features one-dimensional characters and a steadfast refusal to adopt a point of view.
 He is found and brought to a hospital, where social worker Margaret Lewin (Jessica Lange) becomes attached to the child and decides to bring him home. Three years later, Margaret and her husband Charles (David Strathairn) have finalized the adoption, and Khaila is struggling to make ends meet after a stint in a rehab program. Then she discovers that Isaiah is not dead, as she had believed. Enlisting the aid of an activist attorney (Samuel L. Jackson), Khaila decides to attempt to regain custody, prompting a court examination of race, culture and the best environment in which to raise a black child.
The subject of inter-racial adoption is a challenging and complex one, and no one would expect the makers of LOSING ISAIAH to deliver the final word on the matter. It isn't the absence of a patently obvious opinion from writer Naomi Foner and director Stephen Gyllenhaal which is the problem, but that there is only a cursory examination of any of the complexities involved. The inevitable court battle features the predictable array of speeches and finger-pointing, but none of these academic arguments are of much use to the audience. We need to see more of Isaiah (the adorable Marc John Jefferies) interacting with the Lewins, perhaps even being interviewed so his point-of-view could be understood. There is an attempt to deal with Isaiah's state of mind in some very effective late scenes which show him responding to a new environment, but those scenes are too little, too late.
Similarly, the characters of Margaret Lewin and Khaila Richards are not characters so much as they are representatives of the two perspectives. Jessica Lange is a dynamic actress, and she pours tremendous emotion into Margaret; there simply isn't much to her character, particularly a sense of why this particular child, out of all those she has seen over fifteen years as a social worker, won her heart. Some token marital difficulties are meant to fill in the blanks, but they don't do the job. The fact is, LOSING ISAIAH is far less about the Lewins' life with Isaiah than it is about Khaila, whose redemption is the heart of the story. Halle Berry is impressive in the early scenes as Khaila the addict, but in the film's later stages she becomes the 12-step version of the "hooker with the heart of gold," too squeaky clean to be real. It seems to be the film's idea of balanced presentation not to give both mothers a character outside of their fierce conviction that the other one is wrong.
Stephen Gyllenhaal's direction is usually best when no one is speaking. Two scenes are particular standouts: one in which Khaila first sees the 3-year-old Isaiah and follows him to a park, and later when Margaret and Khaila's black attorney (the under-used Samuel L. Jackson) share an uncomfortable cigarette break. Those scenes, and those from Isaiah's perspective, suggest that if LOSING ISAIAH had been more intimate with its characters all the way through, the story could have worked. As it stands, a controversial subject becomes a minor plot device connecting the stories of two rather bland characters. LOSING ISAIAH's contrived ending is somehow appropriate for this wishy-washy effort.
On the Renshaw scale of 0 to 10 custody battles: 4.
-- Scott Renshaw Stanford University Office of the General Counsel
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


Books Yearwise
First year
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second year books
third year books
fourth year books


Feature Flag for Web Applications
HOL - Feature Flag for Web Applications
Feature flags provide the ability to turn features of your application on and off at a moments notice, no code deployments required. With feature flags you can test your features in production, getting early feedback from a subset of production users, and incrementally enable it to different sets of users. Once the feature is enabled for everyone and becomes part of the product, the flag code can be removed. In this lab you will add the foundation for feature flags to the PartsUnlimited project, and implement a simple feature flag for phone number validation. The users will be able to self-subscribe to this feature. For your own projects you can decide how the flags are enabled and to which customers.
Note: Launch Darkly is a great option for more advanced feature flag management. Version 3.0.0 of the Launch Darkly SDK supports .NET core.
Pre-requisites:
- Visual Studio 2015 Update 3 or higher
Tasks Overview:
Task 1. Add the backend code required for feature flags - in this task we will add in the required code to enable feature flags for our application.
Task 2. Create a new page under the user section for users to enable/disable features - in this task we will add a section for users to opt in for specific features.
Task 3. Try it out! - in this task we will try out our feature flag to ensure it’s working as expected.
###Task 1: Add the backend code required for feature flags
Step 1. Clone the PartsUnlimited repository to a local directory.
-
Open a command line (one that supports Git) and navigate to the directory where you want to store your local repositories. For example in a Windows OS, you can create and navigate to
C:\Source\Repos
. -
Clone the repository with the following command:
git clone https://github.com/Microsoft/PartsUnlimited.git
After a few seconds of downloading, all of the code should now be on your local machine.
-
Move into the repository directory that was just created. In a Windows OS, you can use this command:
cd PartsUnlimited
Step 2. Open the PartsUnlimited solution with Visual Studio
In the command line, type the following.
start PartsUnlimited.sln
Or navigate to where you cloned the repository to e.g. C:\Source\Repos\PartsUnlimited
with explorer and double click on PartsUnlimited.sln
Step 3. Let’s create a folder for our feature flag related classes. On the PartsUnlimitedWebsite solution, right click and create a new folder. We can call it something like ‘FeatureFlag’.
Step 4. Now we will create the first feature flag class, this will represent a feature flag in our application. Right click on the newly created FeatureFlag folder -> select ‘Add’ -> select ‘Class…‘.
Step 5. Name the new class ‘Feature.cs’ then select ‘Add’
Step 6. Add the following properties for the Feature.cs class
public class Feature
{
public string Key { get; set; }
public string Description { get; set; }
public bool Active { get; set; }
}
- Key is going to be the unique identifier for that particular feature flag.
- Description is going to be a brief description of what the feature flag is for.
- Active is going to be the current state for the feature flag.
Step 7. We also want to add a property in ApplicationUser.cs called ActiveFeatures
public class ApplicationUser : IdentityUser
{
public string Name { get; set; }
public string ActiveFeatures { get; set; }
}
Step 8. Now we want to create a simple feature manager class. This will be used later on to toggle our features on and off. Under the same FeatureFlag folder create a new class called FeatureManager.cs
using System;
using System.Collections.Generic;
using System.Linq;
using System.Security.Claims;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Identity;
using PartsUnlimited.Models;
public interface IFeatureManager
{
Task<bool> GetStatusByKey(string key, ClaimsPrincipal userClaim);
Task ChangeFeatureToggles(string[] selectedItems, ClaimsPrincipal userClaim);
Task<IEnumerable<Feature>> GetUserFeatures(ClaimsPrincipal userClaim);
}
public class FeatureManager : IFeatureManager
{
private readonly UserManager<ApplicationUser> _userManager;
public FeatureManager(UserManager<ApplicationUser> userManager)
{
if (userManager == null)
throw new ArgumentNullException(nameof(userManager));
_userManager = userManager;
}
public static IEnumerable<Feature> Features = new List<Feature>
{
new Feature
{
Description = "New screen for adding phone numbers",
Key = PhoneNumberKey,
Active = false
}
};
public async Task<IEnumerable<Feature>> GetUserFeatures(ClaimsPrincipal userClaim)
{
ApplicationUser applicationUser = await _userManager.GetUserAsync(userClaim);
IEnumerable<string> applicationUserActiveFeatures = !string.IsNullOrEmpty(applicationUser.ActiveFeatures) ? applicationUser.ActiveFeatures.Split(',') : Enumerable.Empty<string>();
return applicationUserActiveFeatures.Select(feature => Features.First(f => f.Key == feature)).ToList();
}
public async Task<bool> GetStatusByKey(string key, ClaimsPrincipal userClaim)
{
if (string.IsNullOrEmpty(key))
throw new ArgumentNullException(nameof(key));
ApplicationUser applicationUser = await _userManager.GetUserAsync(userClaim);
return !string.IsNullOrEmpty(applicationUser.ActiveFeatures) && applicationUser.ActiveFeatures.Split(',').Any(f => f == key);
}
public async Task ChangeFeatureToggles(string[] selectedItems, ClaimsPrincipal userClaim)
{
if (selectedItems == null)
throw new ArgumentNullException(nameof(selectedItems));
ApplicationUser applicationUser = await _userManager.GetUserAsync(userClaim);
applicationUser.ActiveFeatures = selectedItems.Any() ? string.Join(",", selectedItems) : string.Empty;
await _userManager.UpdateAsync(applicationUser);
}
public const string PhoneNumberKey = "Phone-Number";
}
Now let’s explain the sections of the above code.
- Features is a list of features we want to use with the application.
- GetStatusByKey is a method we will use to get the current status for a given user for a particular feature key.
- ChangeFeatureToggles takes in a list of selected feature toggles and a user. If the key exists that status will be toggled for that user.
- GetUserFeatures will get the current list of active features for a given user.
Note: In a real world implementation we would store these flags in a database. For simplicity’s sake we have stored them in memory. This also gives you the ability to extenalize the on/off switch for your features
We also want to set up our dependency injection for the FeatureManager. Navigate to Startup.cs and add the service binding shown below.
...
using PartsUnlimited.FeatureFlag;
public class Startup
{
...
public void ConfigureServices(IServiceCollection services) {
...
services.AddScoped<IFeatureManager, FeatureManager>();
}
...
}
Step 9. Now that we have the basics set up let’s create our first actual feature. We want to display a different placeholder and utilize HTML5 validation for updating phone numbers. In ManageViewModels.cs we want to add an additional properties on the AddPhoneNumberViewModel class. This will be used on the view to check if the new phone number feature is active.
...
public class AddPhoneNumberViewModel
{
...
public bool IsPhoneFeatureActive { get; set; }
}
...
In ManageController.cs we want to alter two of the existing methods. This is located here -> .\PartsUnlimited\src\PartsUnlimitedWebsite\Controllers\ManageController.cs
Note the AddPhoneNumber()
method already exists but we need to change a few things. This needs to become an async method that returns Task<IActionResult>
. Replace the whole method with the one below.
Also for the AddPhoneNumber(AddPhoneNumberViewModel model)
we will be replacing the existing return statement with the one displayed below.
using System;
...
using PartsUnlimited.FeatureFlag;
public class ManageController : Controller
{
private readonly IFeatureManager _featureManager;
public ManageController(..., IFeatureManager featureManager)
{
if (featureManager == null)
throw new ArgumentNullException(nameof(featureManager));
_featureManager = featureManager;
...
}
...
//
// GET: /Account/AddPhoneNumber
public async Task<IActionResult> AddPhoneNumber()
{
var statusByKey = await _featureManager.GetStatusByKey(FeatureManager.PhoneNumberKey, HttpContext.User);
return View(new AddPhoneNumberViewModel { IsPhoneFeatureActive = statusByKey });
}
//
// POST: /Account/AddPhoneNumber
[HttpPost]
[ValidateAntiForgeryToken]
public async Task<IActionResult> AddPhoneNumber(AddPhoneNumberViewModel model)
{
...
var statusByKey = await _featureManager.GetStatusByKey(FeatureManager.PhoneNumberKey, HttpContext.User);
return RedirectToAction("VerifyPhoneNumber", new { PhoneNumber = model.Number, IsPhoneFeatureActive = statusByKey });
}
}
Step 10. Now we want to conditionally display a block of code relating to the new feature. Navigate to AddPhoneNumber.cshtml
Find the following line of code in the same file (AddPhoneNumber.cshtml), located at line 20.
@Html.TextBoxFor(m => m.Number, new { @class = "form-control", placeholder = "Phone Number" })
Replace it with this code block
@{
if (Model.IsPhoneFeatureActive)
{
@Html.TextBoxFor(m => m.Number, new { @class = "form-control", placeholder = "555-555-5555", type = "tel", pattern = "\\d{3}[\\-]\\d{3}[\\-]\\d{4}", id = "phone" })
}
else
{
@Html.TextBoxFor(m => m.Number, new { @class = "form-control", placeholder = "Phone Number" })
}
}
The key difference here is inside the input tag properties
- placeholder = “555-555-5555” this will display a sample number for the display to assist users in knowing what format is required.
- type = “tel” this is the new HTML5 input type for phone numbers
- pattern = “\d{3}[\-]\d{3}[\-]\d{4}” this is a regex patter to ensure the user types in a phone number with the format<PHONE_NUMBER>
At the bottom of the file, under the closing section tag, add the following
@{
if (Model.IsPhoneFeatureActive)
{

}
}
Step 11. We also want to make sure that the proper CSS is applied for the HTML5 validation. Navigate to Site.scss (located in ./PartsUnlimited/src/PartsUnlimitedWebsite/Content/Site.scss
)
Step 12. Add the following to Site.scss - this will ensure a red border is applied when the pattern or input field type is invalid.
input:invalid {border:3px solid red;}
input[pattern]:invalid{border:3px solid red;}
###Task 2: Create a new page under the user section for users to enable/disable features Now we need to create an administration view to toggle these features on and off from the site.
Step 1. In order to create a new page on the site, we need to create a ViewModel that will represent the data we need to use on that page.
Under the ./PartsUnlimited/src/PartsUnlimitedWebsite/ViewModels
folder, create a new class called FeaturesViewModel.cs
using System.Collections.Generic;
using PartsUnlimited.FeatureFlag;
namespace PartsUnlimited.ViewModels
{
public class FeaturesViewModel
{
public IEnumerable<Feature> AvailableFeatures { get; set; }
public IEnumerable<Feature> UserFeatures { get; set; }
}
}
Step 2. Navigate to ./PartsUnlimited/src/PartsUnlimitedWebsite/Controllers
and create a new controller called FeaturesController.cs
using System;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Mvc;
using PartsUnlimited.FeatureFlag;
using PartsUnlimited.ViewModels;
namespace PartsUnlimited.Controllers
{
public class FeaturesController : Controller
{
private readonly IFeatureManager _featureManager;
public FeaturesController(IFeatureManager featureManager)
{
if (featureManager == null)
throw new ArgumentNullException(nameof(featureManager));
_featureManager = featureManager;
}
public async Task<IActionResult> Index()
{
FeaturesViewModel featuresViewModel = new FeaturesViewModel
{
UserFeatures = await _featureManager.GetUserFeatures(HttpContext.User),
AvailableFeatures = FeatureManager.Features
};
return View(featuresViewModel);
}
[ValidateAntiForgeryToken]
[HttpPost]
public async Task<ActionResult> SaveFeatureToggles(string[] selectedItems)
{
await _featureManager.ChangeFeatureToggles(selectedItems, HttpContext.User);
return RedirectToAction("Index", "Features");
}
}
}
Step 3. Now to tie everything together we need a view. Navigate to PartsUnlimitedWebsite -> Views and create a new folder called ‘Features’
Step 4. Inside the folder you just created, right click and select ‘Add’. Now add a View called Index.cshtml. Insert the contents shown below in to this new file.
@model PartsUnlimited.ViewModels.FeaturesViewModel

Enable or disable features

@using (Html.BeginForm("SaveFeatureToggles", "Features", FormMethod.Post))
{

Description

Key

Active

@foreach (var item in Model.AvailableFeatures)
{

<span style="font-size<EMAIL_ADDRESS>

<span style="font-size<EMAIL_ADDRESS>

@Html.AntiForgeryToken()
<input type="checkbox" name="selectedItems"<EMAIL_ADDRESS>checked="@Model.UserFeatures.Any(a => a.Key == item.Key)">

}

<input type="submit" value="Save" style="float: right;" />

}

Step 5. We need to link to our new page. Navigate to PartsUnlimitedWebsite -> Views -> Shared -> _Login.cshtml
Add the following list item to the bottom of the menu.

...

 @Html.ActionLink("View Features", "Index", "Features", new { area = string.Empty })

###Task 3: Try it out!
Step 1. Now launch the site. You can do this but pressing f5 or hitting the button shown below in Visual Studio.
Step 2. Now log in with the administrator account. This can be found in config.json.
"AdminRole": {
"UserName"<EMAIL_ADDRESS>"Password": "YouShouldChangeThisPassword1!"
}
Step 3. Navigate to the page we created earlier under Profile -> View Features.
Step 4. Now we can see our active feature flags displayed in a list. We can also toggle them on and off from here!
Step 5. In your browser navigate back to the home page.
Step 6. Select Profile -> Manage Account.
Step 7. Now we need to modify our phone number. Select ‘Add’ next to the ‘Phone Number’ label.
Step 8. Make sure you saved the changes, then navigate back to the feature flag page (Profile -> View Features). Now toggle the flag to be on. Let’s see if our changes are pushed through.
Step 9. Navigate back to the user management page (Profile -> Manage Account)
Notice the placeholder is different to the one before.
Also when you start typing a red border appears around the input. This will continue until you supply a valid input similar to the placeholder pattern.
Try clicking submit, you will notice the error dialog pop up asking the user to enter the correct format.
Now when we add the correct format the red border will disappear.
Next steps
In this lab you have learned how to implement feature flags in a web application.  You can give users access to beta functionality to give you better test coverage and deliver continuous improvement.


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Working with the Identity Team - Privacy/Inclusivity/Diversity/Identity at Twitch.
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In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_image = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name="input_image")
z_dim = tf.placeholder(tf.float32, (None, z_dim), name = "z_dim")
learning_rate = tf.placeholder(tf.float32, name="learning_rate")
return input_image, z_dim, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha=0.2):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
# Input layer is 28x28x3
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
x1 = tf.maximum(alpha * x1, x1)
# 16x16x64
x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=True)
x2 = tf.maximum(alpha * x2, x2)
# 7x7x128
x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=True)
x3 = tf.maximum(alpha * x3, x3)
# 4x4x256
# Flatten it
flat = tf.reshape(x3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
x1 = tf.layers.dense(z, 7*7*256) # First fully connected layer
x1 = tf.reshape(x1, (-1, 7, 7, 256)) # Reshape it to start the convolutional stack
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
# 7x7x256
x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
# 14x14x128
x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
# 28x28x64
# Output layer
logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
# 28x28x5
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
32### Loss
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
_, image_width, image_height, image_channels = data_shape
input_real, input_z, l_rate = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
saver = tf.train.Saver()
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps +=1
batch_images *= 2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
_ = sess.run(d_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_rate: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, l_rate: learning_rate})
if steps % 10 == 0:
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % 100 == 0: show_generator_output(sess, 50, input_z, image_channels, data_image_mode)
show_generator_output(sess, 10, input_z, image_channels, data_image_mode)
saver.save(sess, './generator.ckpt')
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Microsoft Chart Control for .NET Framework supports a large number of
events, giving developers many different ways to customize their charts.
All events are made available by the Chart object. The name of the event usually indicates the nature of the event and the object to which it applies, such as the AxisScrollBarClicked event.
There are two special events that allow for custom painting: PrePaint and PostPaint. The PrePaint event is triggered just before the Chart object is painted, and the PostPaint event is triggered just after the chart is painted. Use these events for custom painting. The figure below demonstrates using PostPaint to place an image in the chart picture.
The following lists chart elements that have one or more associated events:
| Chart Element | Related Events |
|---|---|
| Annotation | AnnotationPlaced, AnnotationPositionChanging, AnnotationPositionChanged, AnnotationSelectionChanged |
| Axis | AxisScrollBarClicked, AxisViewChanging, AxisViewChanged |
| Chart (root object) | PrePaint, PostPaint |
| Cursor | CursorPositionChanging, CursorPositionChanged, SelectionRangeChanging, SelectionRangeChanged |
| multiple elements | GetTooltipText |


Responstable
2.0
by
jordyvanraaij
Main driver
First name
Surname
Date of birth
Relationship
Steve
Foo
01/01/1978
Policyholder
Steffie
Foo
01/01/1978
Spouse
Stan
Foo
01/01/1994
Son
Stella
Foo
01/01/1992
Daughter


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