About 3:45 PM on a Friday afternoon, my two friends, M and R, and I decided to take 25C-NBOMe. R and I had previous experience with psychedelics; M, his girlfriend, did not. R is a very level-headed person, who is not affected strongly by cannabis nor psychedelics. We agreed on a dose of 400 ug for each of us, reasoning that if the dose were too high for M, the trip could be easily aborted with a 5-HT2A antagonist we had on-hand.
(T+0:00) I measure out 400 ug in solution for each of us and we administer it nasally. The characteristic tingling sensation is felt.
(T+0:05) The effects of the drug rapidly begin to take hold. R and I find entertainment in the different visual layers of the carpet. M is awestruck.
 We all agree that the effects of this drug are especially pleasant, though it is at times overwhelming for M. We carefully inspect the plant life; our senses are greatly heightened and we notice patterns in various natural objects. This perceptual increase makes the field before the pond seem open, wide, and sprawling. When examined for long enough, patterns in nature begin to warp and breathe. The strange, rolling elevation of the land plays tricks on our minds.
(T+1:15) We return from our walk. We spend the next hour in coherent, lucid, though tangential conversation, on such subjects as that chairlifts are safe even though they appear dangerous, since people very rarely fall forward out of chairs.
(T+2:15) R smokes cannabis, M and I do not. We then go to dinner.
(T+3:15) As we are getting ready to return from dinner, R suddenly walks off without us. When I realized that he had left, I followed and caught up to him. I asked if he was okay, and he replied in a strange voice that he just felt funny and wanted to lie down. We resume walking back, and M catches up to us, asking why he left without her. R explains that he saw M in a conversation and didn't see anything weird about leaving. Later, M figures out that R was in a state of total mental confusion, and had no idea who anyone at the table was, including M. He simply talked to whoever initiated a conversation with him. Even when we returned, R was still very confused, and would ask M strange questions such as 'How do you know my dad?'
After this point the 25C peak dropped quickly into a period of slow leveling off, as it characteristically does. So, there was nothing more remarkable about the trip to note. I enjoyed reading my copy of PiHKAL (and was pleasantly surprised when I realized Nichols wrote the forward to the book) while R recovered from his state of confusion.
All in all, 25C-NBOMe is a very euphoric, low-anxiety, clear-headed drug. I was very surprised by how easy it was to think and speak clearly, considering the level of visual distortion and mental psychedelia. Having also tried mushrooms, LSD, and 2C-I, I would probably recommend this drug over all of those. However, it was frightening to see someone as level-headed and intelligent as R descend into a state of delirium from two drugs that separately would leave him completely lucid. It is well known that either psychedelics or marijuana can trigger psychosis in susceptible individuals, but what is remarkable in this instance is that R bears none of the traits of a personality predisposed to psychosis or any sort of mental imbalance; in fact, I consider R to be the least likely of any person I know to develop some sort of mental illness. For that reason I would advise that everyone take extreme caution when combining 25C and cannabis.
Here is R's account of what happened:
This is R from the above account. I agree with everything said by the person above, who I will refer to as 'J.' The drug had very pleasant effects and I felt that this was the proper dose for us to take, but that M, being a first time user, might have been a little more comfortable with a slightly smaller dose. Everything was going well, until I saw some of my friends smoking cannabis and decided to join them. J, having more experience and knowledge than I do with regard to psychedelics, informed me that in all likelihood smoking would intensify the sensations I was experiencing.
I decided to try it, and at first, did not notice any significant changes. However, about 20 minutes later, when we were at dinner, my visual distortions were greatly intensified, and I became very confused. I did not know who I was friends with at our table, or even that the girl sitting across from me was my girlfriend. I tried to remain calm and I excused myself to go lie down, after informing J that I felt funny. To my surprise, M followed me into my room, which I then found out was our room, and began talking to me. It became clear that I did not know who she was, and when she mentioned something my dad had told her, I became frightened because I had no recollection of M ever meeting my dad. Understandably, she was scared and somewhat upset.
She began talking to me about generic things, such as my favorite foods and other such topics. This kept us occupied and somewhat distracted. About 30 minutes after we began talking, I started to remember what was going on, and became 'normal' again. I have had no residual confusion since, although it has only been a few days. Overall, I was in this confused state for about an hour and 15 minutes, which began 20 minutes after smoking a rather substantial amount. I want to emphasize that this was a completely novel, emergent experience, which is not characteristic of my responses to either drug individually. I greatly enjoy both drugs, but advise people to use caution when combining the two, and to be aware of the possible risks associated with it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Some content for June
Some content for July
Some content for August
Some content for September
Some content for October
Some content for November
Some content for December
Prev
Next
June
Jul
Aug
Sep
Oct
Nov
Dec


You run the TLC model checker on a model of your spec. The model tells TLC what it should do. Here are the parts of the model that you must explicitly choose:
The Model Overview, Spec Options, and TLC Options help pages describe all the things that can go into a model.
A specification may contain multiple models. You can create a new model, open an existing one, or create a clone (a copy with a new name) of an existing model by clicking on the TLC Model Checker menu. Alternatively, you can use the Spec Explorer. To create a new model, open the Spec Explorer and right-click on the currently open spec. To open an existing model, open the spec's menu item (by clicking on the + ) and double-clicking on the model. Right-clicking on the model also gives you the options of renaming it, deleting it, and creating a clone.
A convenient way to examine all the spec's models
is by selecting Quick Access on the Window menu
or typing Control+Shift+A
. This raises a window showing the models
and the beginning of their Model Description fields. Double-clicking
on a model opens it.
You can also open
imported modules from that window. Check out the window's options menu.
When you model check a spec, you should begin with a very small model--that is, a model for which the values substituted for constants (and perhaps a constraint) yield a small set of reachable states. Before you've model checked it, your spec almost surely contains quite a few errors. Using a small model, TLC can find the trivial ones very quickly. When it finds no errors in a small model, you can then gradually increase the size of the model.
As you run your spec on larger and larger models, it's easy to forget what you've done. Instead of changing a model and running it again, create a new model and keep the old one. (This is easy to do by cloning the model, as described above.) You can identify your models with the Model Description field, which is shown It will be easier to identify your models if you put the important contents of the model in the model name--for example, you can name a model
N=3,Proc={a1,a2,a3}The model is always locked while it is being run by TLC. This means that you can't view information in sections of a running model that are closed. It's therefore a good idea to open all sections of the model that you might want to look at before starting a TLC run that will take a significant amount of time. If you forget to do this, you can clone the running model and examine the clone.
The Toolbox saves a copy of the version of the spec on which the model was run or checked, so you can look at it even after you have changed the spec. You can examine any modules in the saved version by clicking on the Open Saved Module command on the TLC Model Checker menu when the model is selected. (The model is selected when one of its pages has the focus.) The How TLC is Run help-page section tells you where those saved modules are saved.


OBFundsConfirmation1_Data
- ConsentId : String
Unique identification as assigned by the ASPSP to uniquely identify the funds confirmation consent resource.
- InstructedAmount : OBFundsConfirmation1_Data_InstructedAmount
- Reference : String
Unique reference, as assigned by the CBPII, to unambiguously refer to the request related to the payment transaction.


DROP PUBLIC SYNONYM -- remove a public synonym
DROP PUBLIC SYNONYM name
DROP PUBLIC SYNONYM drops existing public synonyms. To execute this command you must be a superuser or the owner of the public synonym.
See Section 2.2.4 for additional information on public synonyms.
name
The name of a public synonym to be removed
This command will remove the public synonym named, personnel:
DROP PUBLIC SYNONYM personnel;
See Also


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This project is an attempt to combat the spread of fake news articles on social networks and encouraging people to take information with a grain of salt.


Ravenous (1999) Guy Pearce, Robert Carlyle, Jeremy Davies, Jeffrey Jones, John Spencer, Stephen Spinella, Neal McDonough, David Arquette, Sheila Tousey, Bill Brochtrup, Joseph Running Fox. Score by Michael Nyman and Damon Albarn. Cinematography: Anthony Richmond. Screenplay by Ted Griffin. Directed by Antonia Bird. 103 minutes. Rated R, 2 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo-online.com Archive reviews at http://us.imdb.com/M/reviews_by?Edward+Johnson-ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>Both visually and thematically, "Ravenous" is a messy little film. The tale of cannibalism in the Old West can't decide whether to be a horror movie, a black comedy or a sweeping metaphor about American imperialism, and tries in vain to be all three. The result is a grisly curiosity that rarely works on any of its levels.
Set in 1847, we follow the morose Captain Boyd (Guy Pearce) to his reassignment at a remote fort in the Sierra Nevadas. Shortly after his arrival, Colqhoun (Robert Carlyle), a half-frozen Scotsman, staggers to the encampment and collapses. Nursed back to health, he recounts the nightmarish story of his group of settlers, forced into cannibalism while snowbound in the mountains.
The soldiers immediately launch a search for survivors, only to discover that Colqhoun, hungry for fresh meat, has led them into a trap. Boyd escapes by leaping from a cliff, then lands on the body of a fallen comrade and, incapacitated by injury, is forced into cannibalism himself. Strengthened by the food, he goes back to the fort, only to see Colqhoun return, this time in the guise of a commanding officer. Shunned by disbelieving fellow soldiers, Boyd tries to convince them of the truth, even while fighting an ever-increasing hunger for more human flesh.
After a somber opening, "Ravenous" takes a turn towards black comedy, with Jeffrey Jones, looking like some grand Dickens' character, drolly introducing the odd ducks who staff the fort. Jeremy Davies (Corporal Upham from "Saving Private Ryan") is intriguing as the skittering, giddy camp chaplain (despite the fact that, for about the fifth film in a row, he plays a minor variation of the same character) and Neal McDonough, whose wolf-like blue eyes are mesmerizing, is effective as a soldier experiencing the 1847 equivalent of 'roid rage. David Arquette, playing a zoned-out loco-weed aficionado, is fun during his brief time onscreen.
Instead of focusing on these interesting folks, the plot dispatches them far too quickly as it veers towards straightforward horror. We learn of Weendigo, a Native American myth which claims that if you eat the flesh of another, you gain their strength and spirit. The wild-eyed Colqhoun is a walking testimonial to the belief, displaying super-human strength and amazing healing powers generated by his taste for humans. As Colqhoun, Robert Carlyle gives an enthusiastic performance, but Pearce's morose Captain Boyd is an uninspiring foe for the monster. Almost any of the other cast members would have been far more entertaining adversaries.
As if things weren't going bad enough, the script takes a self-important turn, making pompous comparisons between cannibalism and the country's expansion. In case anyone misses the leaden metaphors, Colqhoun editorializes about Manifest Destiny, helpfully stating that America "wants to be whole, stretching out its arms and consuming all it can."
Despite some stunning photography and a powerhouse score, "Ravenous" succumbs to its own pretensions and lack of focus. Had the filmmakers ditched the political commentary and followed either the black comedy or the horror theme, this could have been a nasty treat. Instead, "Ravenous" is merely an exercise in bad taste. And yes, the pun is intentional.
© 1999 Ed Johnson-Ott
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


Hope you have gone through all the tutorials and are familiar with all the controls. It is time to put your skills to the test. The map has been expanded to include 3 floors and a ground floor. Your objective is to trap at least one minion in the fire by manipulating the respective controls. There will be a fixed number of 50 minions in the game. You will be given a score if you manage to successfully trap at least one minion during the game. You must be thinking: "This is easy, I just need to increase the incremental-fire-spread-chance to 1, set the fire to start at a stairwell-exit and increase the delay mean and sd to a very large value!" However, your score will be very low. Your score will be recorded in a Google Form and displayed on a scoreboard! So, if you want to be at the top of the scoreboard, do your best in setting more realistic variables!
Due to the large file size of our game model, we are unable to upload it to the website. Instead, we have created a Google Drive folder that would allow anyone to access the files. Before that, you need to download Netlogo, the modelling software that was used to create this. Downloading the software is very simple. Here are the steps that you need to take:
(1) Download BC2Netlogo from this link and unzip the downloaded file. (2) Now, go to the Google Drive link and download all the files in that folder. There should be 2 .nlogo and 1 .png files. (3) The last step is to place the .nlogo files into the BC2Netlogo Folder, and also placing the .png file into the Netlogo Data folder. Double click on the Game of Death.nlogo and you can access the game that we have created!
The game interface is similar to the models you have explored before, except now there are 3 floors. The objective as aforementioned is to trap at least 1 minion. When you fail to do so, a message will pop out indicating "YOU HAVE FAILED IN YOUR MISSION. PLEASE TRY AGAIN". Upon success however, a message will pop ouT indicating "YOU HAVE WON.  " If you are happy with this score, do update your score in the Google Form provided in the Game Statistics page ! (We know this is rather primitive and cumbersome, but we do seek your patience as we continually improve the model in Netlogo and we promise that an automatic update of scores is not far away!)
Hope you enjoy the game and ... may the odds be ever in your favour:)


Setting Up iOS Logic Tests [Part 3]
This is part 3 in a multi-part series on iOS unit testing and integration testing. In the last post, we discussed setting up SVProgressHUD.
Magical Record
Magical Record is an excellent library that compliments the Core Data framework. I’m going to assume some knowledge of Core Data here. If you need a reference, the Core Data book by Marcus Zarra is excellent, and just hit its 2nd edition. We are going to build out a very simple data model, with just one entity. This will allow us to set up the core data stack and verify that Magical Record is working.
Let’s get core data set up:
- Add the Core Data framework in the Link Binary with Libraries step
- Click the project node in the project navigator sidebar on the left
- Select the app target
- Select the Build Phases tab
- Open the Link Binary with Libraries list
- Click the ‘+’ button
- Search for ‘Core Data’
- Select
CoreData.framework
- Click the Add button
Add an import for Core Data to the precompiled header file to make Core Data available for the entire project
- Open the precompiled header file - This file is usually placed in the Supporting Files group, and is named [project-name]-Prefix.pch
- Inside the
#ifdef __OBJC__
block, add#import <CoreData/CoreData.h>
. This will make core data available throughout the project without having to import the framework everywhere it needs to be used.
Add a data model to your app
- Right click on the group in the project navigator that represents your app. For me, this is LogicTests
- Select New File
- Select Core Data => Data Model
- Select Create
- Your new data model should open in the graphical editor. By default, it will be named Model
Create a Person entity
- On the bottom toolbar, click the Add Entity button
- A new entity will be created and the name will be editable immediately in the left sidebar. Type Person and hit Return
- We now have a new entity. We need to create attributes for it.
- Under the Attributes section, click the ‘+’ symbol. Name the new attribute
firstName
and make it aString
type - Repeat this process, and create a
lastName
property, also of typeString
- When you finish, your entity should look like this:
Create an NSManagedObject subclass1
- With your newly created Person entity selected, go to the menu bar, and select Editor => Create NSManagedObject Subclass
- Leave everything as-is, and click Create
- You should now have a
Person.h
andPerson.m
file in your project navigator. Inspecting these files will just show some standard property declarations in the header, and@dynamic
declarations in the implementation. Without diving too deep here, the@dynamic
declarations simply tell the compiler that the implementation is there forNSManagedObject
subclasses and prevent compiler warnings. - Let’s take a sanity break here and hit ⌘B to make sure the app builds.
Use Magical Record to set up the Core Data stack
Magical Record is an excellent helper library that makes interacting with Core Data much easier. It adds syntax that allows you to perform common operations in 1 line of code instead of 3 or 4. Let’s add Magical Record into the mix and use it to bootstrap our Core Data stack.
- Add an import to your .pch file so that Magical Record is available project wide. My .pch file now looks like this:
1 2 3 4 5 6 7 8 9 |
|
- The
#define
statement allows you to use Magical Record calls without a prefix. Without this define, you would have to prefix all of your Magical Record calls withMR_
The
#import "CoreData+MagicalRecord.h"
statement adds categories to several Core Data classes, which allow you to use the Magical Record extensionsIn your app delegate, add the following statement to set up the core data stack:
[MagicalRecord setupCoreDataStack];
- Build to make sure everything is wired up correctly and you get no compiler errors.
Ok, now we’re all set up. Next, we need to create the unit test bundle and begin to meet the challenges of creating unit tests against our code.
I’ve put my progress so far up on GitHub for reference. Please feel free to check it out!
I always create
NSManagedObject
subclasses. I prefer to use Mogenerator. Mogenerator is a much more elegant and destruction-proof way of doing this, but for the sake of brevity, we’ll just use the built-in class generation from the Core Data modeler. ↩


--- layout: post status: publish published: true title: Kerry Wood's Nintendo Finger author: display_name: Andy login: Andy email<EMAIL_ADDRESS>url: '' author_login: Andy author_email<EMAIL_ADDRESS>excerpt: "\n\nMy friend James pointed out that we haven't really talked about Kerry Wood's blister issue much yet. Many Cubs fans are wondering how it is possible that he has been sidelined with this blister since June 11th. In my opinion, there have to be factors other than baseball involved. " wordpress_id: 153 wordpress_url: http://ivyenvy.wordpress.com/?p=153 date: '2008-07-29 11:28:14 +0000' date_gmt: '2008-07-29 16:28:14 +0000' categories: - Uncategorized - Relief Pitching tags: - blister - Carlos Marmol - Jeff Samardzija - Kerry Wood - Nintendo finger comments: - id: 76 author: dezigner80 author_email<EMAIL_ADDRESS>author_url: '' date: '2008-07-29 13:47:56 +0000' date_gmt: '2008-07-29 18:47:56 +0000' content: It's just too bad he can't be fixed with a good, hard blow in the cartridge. - id: 77 author: andy author_email<EMAIL_ADDRESS>author_url: http://journalofweightsandmeasures.blogspot.com/ date: '2008-07-29 14:58:15 +0000' date_gmt: '2008-07-29 19:58:15 +0000' content: I've tried that approach on many of life's problems, and it never seems to work. - id: 312 author: Ivy Envy » Injuries and Cardinals author_email: '' author_url: http://ivyenvy.com/?p=1108 date: '2009-07-09 21:37:21 +0000' date_gmt: '2009-07-10 03:37:21 +0000' content: "[...] our system then. This business about Ryan Dempster’s toe is just about as frustrating as Kerry Wood’s Nintendo finger from last season. However, Dempster hasn’t seemed extraordinarly sharp this season [...]" ---
My friend James pointed out that we haven't really talked about Kerry Wood's blister issue much yet. Many Cubs fans are wondering how it is possible that he has been sidelined with this blister since June 11th. In my opinion, there have to be factors other than baseball involved.
Potential contributing factors to the Cubs doing without their closer since June 11th:
But things aren't all gloom and doom in the Cubs' closer situation. Marmol looked confident against the Brewers in last night's series-opener, and Samardzija could also easily put opposing comeback attempts to death. It will be nice to have Wood back, though. So lay off the NES a bit please, Kerry.
Now listening to: The Advantage


BZEXE(1) BZEXE(1) NAME bzexe - compress executable files in place SYNOPSIS bzexe [ name ... ] DESCRIPTION The bzexe utility allows you to compress executables in place and have them automatically uncompress and execute when you run them (at a penalty in performance). For example if you execute ``bzexe /bin/cat'' it will create the following two files: -r-xr-xr-x 1 root bin 9644 Feb 11 11:16 /bin/cat -r-xr-xr-x 1 bin bin 24576 Nov 23 13:21 /bin/cat~ /bin/cat~ is the original file and /bin/cat is the self-uncompressing executable file. You can remove /bin/cat~ once you are sure that /bin/cat works properly. This utility is most useful on systems with very small disks. OPTIONS -d Decompress the given executables instead of compressing them. SEE ALSO bzip2(1), znew(1), zmore(1), zcmp(1), zforce(1) CAVEATS The compressed executable is a shell script. This may create some secu‐ rity holes. In particular, the compressed executable relies on the PATH environment variable to find gzip and some other utilities (tail, chmod, ln, sleep). BUGS bzexe attempts to retain the original file attributes on the compressed executable, but you may have to fix them manually in some cases, using chmod or chown. BZEXE(1)


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Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
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Grandmaster Richard de Coeur
Welcome, Brother! I am Richard de Coeur, engineering student and star quarterback of the SAIT Lionhearts. I have been the proud leader of the Plantagenet Society since my first year. Since that time, the Society’s membership has grown to include a wide diversity of students in terms of their educational background, aspirations, and goals. You are more than welcome to join us!
In The Beginning
The Plantagenet Society traces its origins right at the founding of SAIT. At the time of the school’s establishment, two prominent fraternities popped up: the Lancaster Fraternity and the York Brotherhood. Rivalry between the two organizations was fierce. Everything, from academia to the sports fields, was a battlefield.
What Came Next
Things eventually came to a head when it was decreed that the school could only have one football team. At the time, the school was sporting two: the Royals and the Nobles. Of course, each of the two fraternities supported differing teams. It was only after the efforts of legendary coach William Kingmaker that not only did both teams unite, but the two fraternities also became one.


V_LEARN
C
Quiz
Course Material
Course Videos
Complier
Logout
QUIZ1
Quiz2
Quiz3
QUIZ4
Quiz5
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Welcome to Quiz section
copyright saravanan0298@gmail.com


Tools
I have all the tools and gadgets. I tell my son, who's a producer, 'You never work for the machine; the machine works for you.'
- Quincy Jones
Magic
I always feel like there's something magic in recording studios. There's a reason good music continues to be made in them. It's just some mojo element.
- Rick Rubin
Music
Music washes away from the soul the dust of everyday life.
- Berthold Auerbach
Please note
Since we have made the transition to a private facility, we are no longer taking on outside projects. We would like to express our gratitude and appreciation to our many clients over the years, wishing them all of the best in their endeavours.
Thank you all!
Contact
Upload Your Files
To send your audio files to SoundMaster Studios, read the instructions below and then click the "Upload Now" button. Do let us know if you have any questions. Thanks!
Instructions For Uploading
- Place all of the files you wish to upload into a folder on your computer. Please also include a text document containing your notes and instructions in the same folder.
- When you are ready, click the "Upload Now" button below. This will open a new page in your browser at pCloud, our file handling service.
- Once you are on the pCloud page, select all of the files you would like to upload (including the document with your instructions), and drag them onto the screen.
- Enter your name AND email address into the field labeled "Your name". There is no separate field for email address.
- Click the "UPLOAD" button and wait for the confirmation message: "Files are uploaded!" NOTE: It is not necessary to add a password or create an account after uploading. That is merely an option provided by the PCloud service.
Upload Now


{% extends "base.html" %} {% block body %} BitMask Support Sign Up
BitMask
Support Sign Up
Donate to {{ wallet.title }}
Paying {{ address }} {{ btc }}
Copy


|
Pallas Solver
0.1
C++ Global Optimization Algorithms
|
Contains a summary of the optimization. More...
#include <brute.h>
Public Member Functions | |
| Summary () | |
| Default constructor. | |
| std::string | BriefReport () const |
| std::string | FullReport () const |
Public Attributes | |
| TerminationType | termination_type |
| std::string | message |
| double | final_cost |
| GradientLocalMinimizer::Summary | local_minimization_summary |
| unsigned int | num_parameters |
| unsigned int | num_iterations |
| double | total_time_in_seconds |
| double | local_minimization_time_in_seconds |
| double | permutation_build_time_in_seconds |
| double | cost_evaluation_time_in_seconds |
| bool | was_polished |
| HistorySeries | history |
Contains a summary of the optimization.
This struct contains the result of the optimization and has convenience methods for printing reports of a completed optimization.
| std::string pallas::Brute::Summary::BriefReport | ( | ) | const |
A brief one line description of the state of the solver after termination.
| std::string pallas::Brute::Summary::FullReport | ( | ) | const |
A full multi-line description of the state of the solver after termination.
| double pallas::Brute::Summary::cost_evaluation_time_in_seconds |
time spent evaluating cost function (outside local minimization)
| double pallas::Brute::Summary::final_cost |
Cost of the problem (value of the objective function) after the optimization.
| HistorySeries pallas::Brute::Summary::history |
History of the system saved on the interval specified by the history_save_frequency
option.
| GradientLocalMinimizer::Summary pallas::Brute::Summary::local_minimization_summary |
Summary from the local minimization iteration if the result from pallas::Brute was polished.
| double pallas::Brute::Summary::local_minimization_time_in_seconds |
time spent in local minimizer
| std::string pallas::Brute::Summary::message |
Message describing why the solver terminated.
| unsigned int pallas::Brute::Summary::num_iterations |
Number of iterations
| unsigned int pallas::Brute::Summary::num_parameters |
Number of parameters in the problem.
| double pallas::Brute::Summary::permutation_build_time_in_seconds |
time spent calculating the possible permutations of the input parameter ranges
| TerminationType pallas::Brute::Summary::termination_type |
Reason optimization was terminated
| double pallas::Brute::Summary::total_time_in_seconds |
total time elapsed in global minimizer
| bool pallas::Brute::Summary::was_polished |
specifies whether the output was polished


Man arrested after robbery in Waterloo
Police say he tried to take keys from a man early Sunday morning
by Ben EppelA quick police response and a citizen standing up for themselves has led to an arrest.
Regional Police say a 74-year-old man was leaving his car around 1:00 a.m. Sunday morning in the area of Erb Street East and Moore Avenue when a 27-year-old man tried to take his keys.
The 74-year-old resisted and called for help.
Police say the suspect was quickly arrested.


Optimizer
Updated base class for optimizers.
tf.compat.v1.keras.optimizers.Optimizer
tf.compat.v2.keras.optimizers.Optimizer
tf.compat.v2.optimizers.Optimizer
tf.optimizers.Optimizer
This class defines the API to add Ops to train a model. You never use this
class directly, but instead instantiate one of its subclasses such as
tf.keras.optimizers.SGD
, tf.keras.optimizers.Adam
.
# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 * var1 + 2 * var2 * var2 # In graph mode, returns op that minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2])
In Keras models, sometimes variables are created when the model is first called, instead of construction time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases.
opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid')) loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn)
Calling minimize()
takes care of both computing the gradients and
applying them to the variables. If you want to process the gradients
before applying them you can instead use the optimizer in three steps:
tf.GradientTape
.apply_gradients()
.# Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable). Do whatever you # need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars)
tf.distribute.Strategy
.This optimizer class is tf.distribute.Strategy
aware, which means it
automatically sums gradients across all replicas. To average gradients,
you divide your loss by the global batch size, which is done
automatically if you use tf.keras
built-in training or evaluation loops.
See the reduction
argument of your loss which should be set to
tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
for averaging or
tf.keras.losses.Reduction.SUM
for not.
If you are not using these and you want to average gradients, you should use
tf.math.reduce_sum
to add up your per-example losses and then divide by the
global batch size. Note that when using tf.distribute.Strategy
, the first
component of a tensor's shape is the replica-local batch size, which is off
by a factor equal to the number of replicas being used to compute a single
step. As a result, using tf.math.reduce_mean
will give the wrong answer,
resulting in gradients that can be many times too big.
All Keras optimizers respect variable constraints. If constraint function is passed to any variable, the constraint will be applied to the variable after the gradient has been applied to the variable. Important: If gradient is sparse tensor, variable constraint is not supported.
The entire optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary.
Many optimizer subclasses, such as Adam
and Adagrad
allocate and manage
additional variables associated with the variables to train. These are called
Slots. Slots have names and you can ask the optimizer for the names of
the slots that it uses. Once you have a slot name you can ask the optimizer
for the variable it created to hold the slot value.
This can be useful if you want to log debug a training algorithm, report stats about the slots, etc.
These are arguments passed to the optimizer subclass constructor
(the __init__
method), and then passed to self._set_hyper()
.
They can be either regular Python values (like 1.0), tensors, or
callables. If they are callable, the callable will be called during
apply_gradients()
to get the value for the hyper parameter.
Hyper parameters can be overwritten through user code:
# Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 + 2 * var2 # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2])
If you intend to create your own optimization algorithm, simply inherit from this class and override the following methods:
__init__
__init__( name, **kwargs )
Create a new Optimizer.
This must be called by the constructors of subclasses. Note that Optimizer instances should not bind to a single graph, and so shouldn't keep Tensors as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead.
This class in stateful and thread-compatible.
name
: A non-empty string. The name to use for accumulators created
for the optimizer.**kwargs
: keyword arguments. Allowed to be {clipnorm
, clipvalue
, lr
,
decay
}. clipnorm
is clip gradients by norm; clipvalue
is clip
gradients by value, decay
is included for backward compatibility to
allow time inverse decay of learning rate. lr
is included for backward
compatibility, recommended to use learning_rate
instead.ValueError
: If name is malformed.RuntimeError
: If _create_slots has been overridden instead of
_create_vars.iterations
Variable. The number of training steps this Optimizer has run.
weights
Returns variables of this Optimizer based on the order created.
add_slot
add_slot( var, slot_name, initializer='zeros' )
Add a new slot variable for var
.
add_weight
add_weight( name, shape, dtype=None, initializer='zeros', trainable=None, synchronization=tf.VariableSynchronization.AUTO, aggregation=tf.compat.v1.VariableAggregation.NONE )
apply_gradients
apply_gradients( grads_and_vars, name=None )
Apply gradients to variables.
This is the second part of minimize()
. It returns an Operation
that
applies gradients.
grads_and_vars
: List of (gradient, variable) pairs.name
: Optional name for the returned operation. Default to the name
passed to the Optimizer
constructor.An Operation
that applies the specified gradients. If global_step
was not None, that operation also increments global_step
.
TypeError
: If grads_and_vars
is malformed.ValueError
: If none of the variables have gradients.from_config
@classmethod from_config( cls, config, custom_objects=None )
Creates an optimizer from its config.
This method is the reverse of get_config
,
capable of instantiating the same optimizer from the config
dictionary.
config
: A Python dictionary, typically the output of get_config.custom_objects
: A Python dictionary mapping names to additional Python
objects used to create this optimizer, such as a function used for a
hyperparameter.An optimizer instance.
get_config
get_config()
Returns the config of the optimimizer.
An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.
Python dictionary.
get_gradients
get_gradients( loss, params )
Returns gradients of loss
with respect to params
.
loss
: Loss tensor.params
: List of variables.List of gradient tensors.
ValueError
: In case any gradient cannot be computed (e.g. if gradient
function not implemented).get_slot
get_slot( var, slot_name )
get_slot_names
get_slot_names()
A list of names for this optimizer's slots.
get_updates
get_updates( loss, params )
get_weights
get_weights()
minimize
minimize( loss, var_list, grad_loss=None, name=None )
Minimize loss
by updating var_list
.
This method simply computes gradient using tf.GradientTape
and calls
apply_gradients()
. If you want to process the gradient before applying
then call tf.GradientTape
and apply_gradients()
explicitly instead
of using this function.
loss
: A callable taking no arguments which returns the value to minimize.var_list
: list or tuple of Variable
objects to update to minimize
loss
, or a callable returning the list or tuple of Variable
objects.
Use callable when the variable list would otherwise be incomplete before
minimize
since the variables are created at the first time loss
is
called.grad_loss
: Optional. A Tensor
holding the gradient computed for loss
.name
: Optional name for the returned operation.An Operation that updates the variables in var_list
. If global_step
was not None
, that operation also increments global_step
.
ValueError
: If some of the variables are not Variable
objects.set_weights
set_weights(weights)
variables
variables()
Returns variables of this Optimizer based on the order created.


TEEN DREAMS A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 8.4
Shown at the Philadelphia Festival of World Cinema Running Length: 1:10 MPAA Classification: Not Rated (Mature themes, profanity, violence)
Feaaturing: Frank Cardon, Adawna, La Traun Parker Directors: Frank Cardon, Adawna, La Traun Parker Producers: Ilan Ziv and Peter Kinoy Cinematography: the directors Distribution unknown
Alongside HOOP DREAMS, this is one of the best documentaries I've seen chronicling the difficulties of coming of age in the inner city amidst pervasive violence, drug use, and prostitution. Directors Frank Cardon, Adawna, and La Traun Parker were given hi-8 video cameras and asked to record the events of their lives over an eight-month period. The results of these taped diaries are at times shocking, often moving, and always gripping. It's one thing for a professional film maker to come into a situation and document what's happening; it's something entirely different for one of the participants to do so. TEEN DREAMS' greatest strength is its perspective.
Taped on the streets of North Philadelphia, Frank Cardon's segment is an examination of teen-on-teen violence and the emotions that lead to such explosive bloodlettings. Frank takes us inside his own mind and allows us to see the hatred, fear, and anger lurking there. "Hate," he says, "allows you to survive." There is an omnipresent frustration on the part of those living in the inner city that they can't have what the people in the suburbs have, and this gives birth to a need to lash out at something. But Frank's story is not without optimism. He believes the people in his neighborhood will eventually band together to fight their oppressors rather than each other.
Adawna and her sister Gloria are two young teens living on Hollywood Boulevard. While their world isn't the same grim place of violence that Frank's is, they would surely understand his feelings. Adawna grew up in Arkansas, where her family still resides, but she fled home at an early age to escape life-threatening abuse by her father. Now, walking the streets, she understands the pressures of loneliness and isolation. Money is scarce, and the temptation is always there to sell her body for a bite to eat. In Adawna's case, there's no time for dreams of the future. She's just trying to survive the present.
La Traun's story is the most upbeat. He's a high school graduate living in Harlem with a single mom. He intends to go to college, but first, he wants to visit South Carolina to locate his father, who he hasn't seen for years. His father and mother split because of his dad's drug use, and La Traun wonders if his long-lost parent still cares for him. His trek takes him to the place of his paternal family's roots, and gives him a new perspective on a man he never really understood. La Traun says the experience represented a maturing process which helped him to realize that "life is good."
TEEN DREAMS successfully accomplishes what it sets out to do: give an unclouded, uncompromising view of some of the difficulties and challenges faced by youths growing up in less-than-privileged conditions. It is a stirring triple portrait, and we come to understand Frank, Adawna, and La Traun through their own words and thoughts. Without the filter of a professional film maker, there is a starkness to this work which is often missing from other, similarly-themed documentaries. If change can begin only through understanding, then TEEN DREAMS is a powerful first step in the process.
- James Berardinelli<EMAIL_ADDRESS>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.
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to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Extension Class
At bare minimum, an extension must have an instance of Zoop\Shard\AbstractExtension
. This class provides all the hooks and configuration required to create your extension. It has these properties, and you can add your own custom configuration properties:
| Name | type | description |
|---|---|---|
| documents | array | An array of document namespaces and directories to register. |
| subscribers | array | An array of subscribers, or subscriber service names to register. |
| cliCommands | array | An array of cli command service names to register with the doctrine cli. |
| cliHelpers | array | An array of cli helper service names to register with the doctrine cli. |
| serviceManagerConfig | array | Any service manager configuration. |
| dependencies | array | An array of service names of other extensions that must be loaded for this extension to work. |
For example, the Color extension class would look like this:
namespace My\Color; use Zoop\Shard\AbstractExtension; class Extension extends AbstractExtension { protected $subscribers = [ 'subscriber.color.annotationsubscriber' //annotation subscriber to listen to @Color annotation events ]; protected $serviceManagerConfig = [ 'invokables' => [ 'subscriber.color.annotationsubscriber' => 'My\Color\AnnotationSubscriber' //register the annotation subscriber service ] ]; protected $filters = [ 'color' => 'My\Color\Filter' //register filter ]; protected $dependencies = [ 'extension.annotation' => true //require the annotation extension to make annotation events work ]; }
Extension Factory
Create a small factory class to return an instance of your extension. Eg:
namespace My\Color; use Zoop\Shard\AbstractExtensionFactory; use Zend\ServiceManager\ServiceLocatorInterface; class ExtensionFactory extends AbstractExtensionFactory { protected $extensionServiceName = 'my.extension.color'; public function createService(ServiceLocatorInterface $serviceLocator) { return new Extension($this->getConfig($serviceLocator)); } }
Annotation
The color extension is using an annotation, so we need to define that annotation:
namespace My\Color; use Doctrine\Common\Annotations\Annotation; final class Color extends Annotation { const event = 'annotationColor'; }
Annotation Subscriber
Add an annotation subscriber to listen to the annotationColor
event and augment the metadata:
namespace My\Color; use Doctrine\Common\EventSubscriber; use Zoop\Shard\Annotation\AnnotationEventArgs; class AnnotationSubscriber implements EventSubscriber { public function getSubscribedEvents(){ return [Color::event]; } public function annotationColorZones(AnnotationEventArgs $eventArgs) { $eventArgs->getMetadata()->color = $eventArgs->getReflection()->getName(); } }
Filter
Add a filter that can filter by color:
namespace My\Color; use Doctrine\ODM\MongoDB\Mapping\ClassMetadata; use Doctrine\ODM\MongoDB\Query\Filter\BsonFilter; class Filter extends BsonFilter { protected $color; public function setColor($color){ $this->color = $color; } public function addFilterCriteria(ClassMetadata $metadata) { if (isset($metadata->color)) { return array($metadata->color => $this->color); } return array(); } }
Using It
That's it!
Config
Now register and enable you extension when you create a manifest:
$manifest = new Zoop\Shard\Manifest([ ... 'extension_configs' => [ 'my.extension.color' => true ], 'service_manager_config' => [ 'factories' => [ 'my.extension.color' => 'My\Color\ExtensionFactory' ], ] ]);
@Color
Use your @Color
annotation in a document:
use Doctrine\ODM\MongoDB\Mapping\Annotations as ODM; use My\Color\Color; /** * @ODM\Document */ class DocWithColor { /** * @ODM\String * @Color */ protected $color; ...