Full name:
com.unic.maven.plugins:aem-maven-plugin:2.0.17-SNAPSHOT:validate-content
Description:
contentDirectory
). This can be used to ensure that JCR
content is syntactically valid.Attributes:
| Name | Type | Since | Description |
|---|---|---|---|
<allowUuids> |
boolean |
- |
UUIDs should not be present in XML content as importing UUIDs may
conflict with existing UUIDs on the target system. set this to
true to allow UUIDs.Default value is: false .User property is: allow.uuids . |
<contentDirectories> |
File[] |
- |
Additional directories containing JCR XML content for import into
AEM that shall be validated by the validate-xml mojo. User property is: content.directories . |
<contentDirectory> |
File |
- |
The standard directly containing content XML files to be validated. Default value is: ${project.basedir}/src/main/content .User property is: content.directory . |
true
to allow UUIDs.boolean
No
allow.uuids
false
java.io.File[]
No
content.directories
java.io.File
No
content.directory
${project.basedir}/src/main/content


| DOSE: |
repeated |
smoked |
Cannabis |
(plant material) |
I had never really smoked pot before. I mean, some of my friends and I had tried some alleged 'really killer shit' a few times in the past, but being rather inexperienced, we didn't know that it takes more than a few hits to get baked on regs. Oh well for pot, I thought.
Then one evening, I was invited to a friend's party. It had been a long week at school and work, so I wwnt to the party planning on leaving sobriety behind until the next morning. However, I didn't drink hardly any alcohol that night, which was quite uncharacteristic of me. A circle of us were standing around the bonfire are suddenly one of the guys breaks out a joint. I thought, cool, I'm game for this, but was expecting that it wouldn't be much different than all the times I tried pot before. Word got around that herb was being passed, and so the time the roach made it to me, I didn't get anything significant off of it. All of us were thouroughly bummed until the host of the party pulled a pipe and an 8th of bud from his pocket. Within 30 seconds, the bowl was filled and the pipe was passed. This time, when it got to me, I got a real hit, filling the lungs.
I held it in as long as I could as I passed the pipe on. After exhaling that first hit, I didn't feel anything at all. Of course, I also didn't know that it takes a few minutes to come up on the high. So, the pipe made several more rounds, and I got 8 full tokes in all in about 5-10 minutes. Everything was cool for about a minute after that and then all of it rushed up on me like a waterfall.
Since I had never really smoked out properly before, I didn't really know what to expect, but when that stuff hit, it hit like a sledgehammer.  I was wearing a ballcap and distinctly remember feeling it tingle my scalp while it was on my head. My balance got a little bit weakened and I found myself slowly swaying side to side, as if I was on a ship, to maintain full control of my posture. Everyone was standing around feeling pretty high and chatting with each other. I remember turning to talk to one guy that I had seen in my class and noting that I was seeing a bit blurry and that it was a little difficult to maintain focus on him while we were talking.
When I was talking to him, I felt as if I was able to maintain a coherent conversation, but still let my mind ponder other thoughts at the same time. Sort of like I could multi-task my brain. It felt like forever, but according to my watch, about 45 minutes had passed since it all had hit me. When I felt like I was soming down, I noticed that my arms, legs, hands, etc., would start to tingle if I thought about it for a few seconds. After a few more minutes of chatting, I drank some more water and sat down with some other people to enjoy the rest of the high and talk for about an hour.
But the night was far from over yet. I suppose everyone else who smoked had come down too, and the pipe come out again and passed around a little bit smaller group of people. This time, I had an idea of what to expect, and took about 6 hits. The come up wasn't as intense this time, but the high was just as nice. We all sat back down and started talking and playing cards.
When I felt myself coming back down again, I realized just how tired I had become and stretched out on the ground next to the bonfire and stared up at the stars. Before I fell asleep, I found that any visualization I created in my mind was accelerated, and much more vivid, changing slightly to the sounds of the fire popping nearby. I woke up refreshed the next day, and also noticed I was still stoned just a touch when I got up.
While that's a pretty hefty amount of pot in one night for a first-timer, I decided right then and there that pot was definitely cool and that I needed to toke up a little more often. I have never been as baked as that first time, but 4 hits of some potent weed (some of you may know it as 'krip' or 'krippie') does me pretty good most of the time.
I'm a successful engineering student at a well-respected school, and have gone to class stoned every now and then. It just sort of helps me see problems from different angles, like 'thinking outside of the box'. And of course, it's one of my favourite relaxation techniques. I havn't been smoking too long, but have found that the cottonmouth and munchies are more of a mental response rather than physical for me, and with practice, have been easy to control.
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.


{% import 'includes/filters/filter-variables.j2' as vars %}
{% with key='Replace-data-%28mapping-table%29-filter' %}{% include "includes/help.html" %}{% endwith %}
Replace values using an external mapping table.
{% include 'includes/filters/label-field.html' %}
{% set name='replace-map-url%02d' % n %}
{% with id='#' + name %}
{% include "includes/selectors.html" %}
{% endwith %}


Application List
ID
Name
View Application
123
Name
Cloud Disposition
Business Value
Application Link 1
555123
Name
Cloud Disposition
Business Value
Application Link 2
123
App Name
Cloud Disposition
Biz Value
View


Aussie researchers mount rescue bid for endangered pygmy possum
SYDNEY: Climate change is threatening to wipe out Australia's critically endangered mountain pygmy possum, but researchers are hoping to save the hibernating species by relocating the last remaining mammals to cooler lowlands.
The tiny mammal lives in alpine regions but less than 2,500 remain in the wild, according to estimates, with winter snowfall declines and warmer weather threatening extinction.
The possums hibernate deep inside humid rock piles that are insulated by snow during winter and provide shelter from high summer temperatures that can prove fatal.
The species - Australia's only hibernating marsupial - needs temperatures to hover just above freezing to hibernate successfully, but without enough snow the cold air outside penetrates the rocks and chills the atmosphere inside, University of New South Wales (UNSW) associate lecturer Hayley Bates said Monday (Nov 4).
"Anything less than 0.6 Celsius will wake them from their hibernation and they can shiver and starve to death," she added.
"You just need two bad winters like this and the species could collapse."
Scientists at UNSW have started a breeding program in a lowland area of New South Wales state in an attempt to acclimatise the possums to a new home, with hopes of establishing an inital colony of 25 animals.
More of the marsupials could be moved from their alpine habitat if the project is successful.
Based on analysis of ancient fossils dating back 25 million years, they believe the mountain pygmy possum's ancestors lived in a more temperate and less extreme environment than it endures today.
Research also showed that other closely related possums had long lived in settings such as lowland rainforests, said UNSW palaeontologist Mike Archer.
"What probably happened is that the modern species followed cool rainforest which invaded the alpine areas during a period of relatively warmer, lush conditions," he added.
"After these conditions deteriorated with further climate change, they were stranded in an environment that was at the extreme end of their adaptability."
The mountain pygmy possum is also under pressure as numbers of its primary post-hibernation food source - the bogong moth - are dwindling, apparently also due to climate change and drought.
If the project is successful, scientists hope other endangered Australian animals could be rescued in a similar way, including the Corroboree frog and the swamp tortoise.


Mrs Hinch fan shares how she managed to transform 'shabby' old sofa
If your sofa is looking grubby, this might help
by Luke Matthews, Christopher HarperA woman has transformed her sofa and revealed how she made it look completely new.
The woman said her 'shabby' sofa was worn and diffcult to clean but decided to sew a new cover for the sofa and cushions.
She gave the sofa a new lease of life, she decided to replace the brown leather look with grey material she bought for £19.99 a metre.
Sharing before and after pictures on the Facebook group ' We Love Mrs Hinch ', others loved the 'fab' transformation.
She explained she began by deconstructing the sofa, removing the cushions to find all the seams and work out how it was originally made, reports the Mirror.
Next, she made a paper pattern for each section of the covers, measuring them to size and recreating the shape out of the new material.
The shapes were then pinned onto the sofa to ensure it fully covered each surface and fit together.
She wrote: "Ensure you have a seam allowance. Cut out, draw around the line where you need to sew.
"Pin together. Sew with running stitch on machine."
She made the covers for the arms first, before tackling the cushions, using a thin later of wadding underneath to hide the lines of the old leather.
Cheaper fabric was used for the underside of the cushions and she referred to a YouTube tutorial to help her perfect the pipping.
The end result was stunning, with one person replying: "As a fellow sewer I want to say that is flipping amazing!! You must be so chuffed with it."
Another said: "Wow what a difference. It looks like a completely new sofa. Well done you. Looks fab."


When travian logs out, extension can login to continue doing its work. Your username and password will be stored in extensions storage locally without syncing it between devices. You can delete stored data at any time.
Extension will refresh page at random time in the given range. It's necessary because of hiding from BOT hunter. Defaults to 2-5 and values are given in minutes. To turn feature off, add 0-0.


The goal is to print an "aperçu", a short view of a vector, a matrix, a data.frame, a list or an array. By default, it prints the first 5 elements of each dimension. By default, the number of columns is equal to the number of lines. If you want to control the selection of the elements, you can pass a list, with each element being a vector giving the selection for each dimension.
| Version: | 0.2.3 |
| Imports: | pls |
| Suggests: | testthat |
| Published: | 2017-09-29 |
| Author: | Aurelien Chateigner |
| Maintainer: | Aurelien Chateigner <aurelien.chateigner at gmail.com> |
| License: | CC BY-SA 4.0 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | apercu results |
| Reference manual: | apercu.pdf |
| Package source: | apercu_0.2.3.tar.gz |
| Windows binaries: | r-devel: apercu_0.2.3.zip, r-release: apercu_0.2.3.zip, r-oldrel: apercu_0.2.3.zip |
| OS X binaries: | r-release: apercu_0.2.3.tgz, r-oldrel: apercu_0.2.3.tgz |
| Old sources: | apercu archive |
Please use the canonical form https://CRAN.R-project.org/package=apercu to link to this page.


Create IMEI
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zClean caches errors based on plugin filenames and MD5 hashes. Error cache files are saved in minified JSON in the cache folder. The error caching settings tab allows you to view and manage error cache files.
You can disable error caching by unchecking the Cache errors checkbox. Error caching is enabled by default, and is recommended.
You can clear the entire error cache by clicking the Clear button.
You can delete all cache files for a particular plugin by clicking the Delete button by it.
You can delete an individual cache file by expanding the cache files section and clicking the X button by a cache file entry.


--- layout: default title: PiE Staff hero-image: url(http://www.pngall.com/wp-content/uploads/2016/06/Classified-Stamp-Transparent.png) hero-message: For PiE Staff ---
| Time | Fall Competition | MDC |
|---|---|---|
| 8:00 AM | Check In | |
| 9:00 AM | Drivers and Captains Meeting | |
| 9:30 AM | Practice Matches | |
| 10:00 AM | Prelim Matches | Test Trials and Presentations/Q&A |
| 12:00 PM | Lunch | |
| 1:00 PM | Prelim Matches | Final Trials |
| 2:00 PM | Alliance Selection | Final Trials |
| 2:30 PM | Elimination Matches | Final Trials |
| 4:00 PM | Final Match | |
| 4:30 PM | Awards Ceremony | |
| 5:00 PM | Close |
| Time | Fall Competition | MDC |
|---|---|---|
| 7:00 AM | Breakfast @ Breezeway | |
| 8:00 AM | Check In/Set Up | |
| 9:00 AM | Drivers and Captains Meeting (Shift 1) | |
| 9:30 AM | Practice Matches (Shift 1) | |
| 10:00 AM | Prelim Matches (Shift 1) | Test Trials and Presentations/Q&A (Shift 1) |
| 12:00 PM | Lunch | |
| 1:00 PM | Prelim Matches (Shift 2) | Final Trials (Shift 2) |
| 2:00 PM | Alliance Selection (Shift 2) | Final Trials (Shift 2) |
| 2:30 PM | Elimination Matches (Shift 2) | Final Trials (Shift 2) |
| 4:00 PM | Final Match | |
| 4:30 PM | Awards Ceremony | |
| 5:00 PM | Close/Clean Up - hard deadline: 8:00 PM |
Load wood, balls, etc. from shed. Load tables & chairs from ASUC Cub-E no later than 5:00 PM.
Everything needs to be unloaded into HMMB. Typically, use front door for the field, back for tables/chairs (service elevator).
Build the field. Leads: Jenny, Alan, Grant, Charlie.
Set up pits, tape signs, add power strips & tape down wires.
Set up speakers. Leads: Doug
Please check here for your assignments! These are not 100% set in stone, so feel free to talk to Vivien, Alan, Scott, or a leadership member if you don't feel comfortable working your role! If you see a role that seems to need more help, feel free to step in and give them a hand. Make sure you talk to the respective leads if you have any questions about your role.
A wide variety of tasks. This includes supervising pits and the practice field, ensuring safety procedures are being followed (safety goggles), etc. Leads: Doug, Carlos, James
Supervise kit extension and tool checkout. Leads: Jiana, James
Make sure the respective competitions run according to plan, referee, etc. Leads: Alan, Grant, Charlie
Keep the judges and other guests happy and informed! Lead: Scott
Fix the robot when the students ask. Leads: Arjun, Bob, Seiya
Maintain robot connectivity on the field. Lead: Karthik
1. Albany
3. De Anza
12. El Cerrito
17. ACLC
28. NCLC
30. St. Mary's
35. Hayward
36. Miramonte


Casino War
Dealer
Cards Remaining
Current Bank
$
Place Bet $:
Bet Per Card
PLAY
RAAAAAGE QUIT
New Deck


Is your development team always behind on code reviews?
CadenceBot reduces complexity in the code review process by being your main point of contact for everything involving code reviews. Integrating with both Slack and Github, your team is now able to manage their code reviews directly in Slack.
My competencies include es6 javascript, react, react-native, redux, graphql, apollo, and node. I have experience working with relational and non-relational databases and building RESTful APIs.
I believe in building applications with the end user in mind; making sure applications are usable, useful, and accessible.
Formerly an operations manager in the technology sector, I was drawn to the world of development when I embarked on building a company of my own in 2016. I strive to always be building on past experiences.


{% extends '../layouts/template.html' %} {% block title %}Check your State Pension - {{mvpversion}}{% endblock %} {% block serviceName %}Check your State Pension{% endblock %} {% block main %}
[Reason why we can't show a forecast]. Contact Future Pension Centre [to get whatever they can offer].
[Intro about NI record if needed]


We offer a wide range of books for people ranging from 7 to 77.
For every book that you cannot find in our bookshop, you can always try buying from Book Depository
Copyright © Bookshop 2020. All rights reserved.


This notice is provided to you by Clasity Pty Ltd ACN 168 177 259 (trading as BlueQ). BlueQ provides access to an online assessment questionnaire called I’m More Qualfiied Than Campaign (IMQT) served through a Facebook application. This notice sets out how BlueQ collects, uses, discloses and manages your personal information when you access and use the IMQT
BlueQ collects personal information directly from you when you use and submit your responses to the questions set out in the IMQT questionnaire or otherwise interact with BlueQ in relation to the IMQT Campaign. The type of personal information BlueQ collects includes:
BlueQ collects this personal information so that we can contact you to discuss the products and services of BlueQ or our commercial partners that may be of interest to you.
BlueQ will use your personal information to:
BlueQ does not currently disclose personal information to recipients located overseas or have established plans to do so.
BlueQ has a privacy policy that is publically available on our website /privacy.html. The BlueQ privacy policy sets out details of how BlueQ will collect, store, manage, use and disclose personal information from you. The BlueQ privacy policy also sets out details of how you can access and seek correction of your personal information held by BlueQ. In addition, the BlueQ privacy policy sets out how you can complain to us about:
All correspondence with regards to privacy matters should be addressed to:
The Privacy Officer
Clasity Pty Ltd trading as BlueQ
<EMAIL_ADDRESS>1/143 Cecil Street, South Melbourne VIC 3205


What is it?
Ts.ED is a framework on top of Express to write your application with TypeScript (or ES6). It provide a lot of decorators and guideline to write your code.
Rest API
Create your Rest API easily and create different version path of your API
Configuration
Didn't waste your time with configuration, the server is preconfigured to start quickly!
Class based
Define class as Controller, Model, Service (DI), Filter, Middleware, Converter etc...
Decorators
A lot of decorators are provided to structure your code and define route and method.
Testing
Test or not to test? Isn't a question. Ts.ED embed some features to test your code!
And more
Find some guide to integrate Swagger, AJV validation, Passport, Socket.io, Templating, etc...


BOOST_OS_AIX
IBM AIX operating system. Version number available as major, minor, and patch.
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BOOST_OS_AMIGAOS
AmigaOS operating system.
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BOOST_OS_ANDROID
Android operating system.
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BOOST_OS_BEOS
BeOS operating system.
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BOOST_OS_BSD
BSD operating system.
BSD has various branch operating systems possible and each detected individually. This detects the following variations and sets a specific version number macro to match:
BOOST_OS_BSD_DRAGONFLY
DragonFly BSD
BOOST_OS_BSD_FREE
FreeBSD
BOOST_OS_BSD_BSDI
BSDi BSD/OS
BOOST_OS_BSD_NET
NetBSD
BOOST_OS_BSD_OPEN
OpenBSD
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BOOST_OS_CYGWIN
Cygwin evironment.
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Haiku operating system.
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HP-UX operating system.
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iOS operating system.
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BOOST_OS_IRIX
IRIX operating system.
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Linux operating system.
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Mac OS operating system.
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BOOST_OS_OS400
IBM OS/400 operating system.
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BOOST_OS_QNX
QNX operating system. Version number available as major, and minor if possible. And version 4 is specifically detected.
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Solaris operating system.
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Unix Environment operating system.
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SVR4 Environment operating system.
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VMS operating system.
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Microsoft Windows operating system.
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BSDi BSD/OS operating system.
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DragonFly BSD operating system.
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OpenBSD operating system.
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Class Solver<T>
A base class for linear systems that can be solved.
Implements
Inherited Members
Namespace: SpiceSharp.Algebra
Assembly: SpiceSharp.dll
Syntax
public abstract class Solver<T> : SparseLinearSystem<T>, IFormattable where T : IFormattable, IEquatable<T>
Type Parameters
| Name | Description |
|---|---|
| T | The base value type. |
Constructors
Solver(PivotStrategy<T>)
Initializes a new instance of the Solver<T> class.
Declaration
protected Solver(PivotStrategy<T> strategy)
Parameters
| Type | Name | Description |
|---|---|---|
| PivotStrategy<T> | strategy | The pivot strategy that needs to be used. |
Solver(PivotStrategy<T>, Int32)
Initializes a new instance of the Solver<T> class.
Declaration
protected Solver(PivotStrategy<T> strategy, int size)
Parameters
| Type | Name | Description |
|---|---|---|
| PivotStrategy<T> | strategy | The pivot strategy that needs to be used. |
| System.Int32 | size | The number of equations/variables. |
Properties
Fillins
Number of fill-ins in the matrix generated by the solver.
Declaration
public int Fillins { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Remarks
Fill-ins are elements that were auto-generated as a consequence of the solver trying to solve the matrix. To save memory, this number should remain small.
IsFactored
Gets whether or not the solver is factored.
Declaration
public bool IsFactored { get; protected set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
NeedsReordering
Gets or sets a flag that reordering is required.
Declaration
public bool NeedsReordering { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Strategy
Gets the pivot strategy used.
Declaration
public PivotStrategy<T> Strategy { get; }
Property Value
| Type | Description |
|---|---|
| PivotStrategy<T> |
Methods
Clear()
Clear the system of linear equations.
Declaration
public override void Clear()
Overrides
CreateFillin(Int32, Int32)
Create a fill-in element.
Declaration
protected virtual MatrixElement<T> CreateFillin(int row, int column)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | row | The row index. |
| System.Int32 | column | The column index. |
Returns
| Type | Description |
|---|---|
| MatrixElement<T> | The created element. |
Factor()
Factor the matrix.
Declaration
public abstract bool Factor()
Returns
| Type | Description |
|---|---|
| System.Boolean | True if factoring was successful. |
MovePivot(MatrixElement<T>, Int32)
Move a chosen pivot to the diagonal.
Declaration
public void MovePivot(MatrixElement<T> pivot, int step)
Parameters
| Type | Name | Description |
|---|---|---|
| MatrixElement<T> | pivot | The pivot element. |
| System.Int32 | step | The current step of factoring. |
OrderAndFactor()
Order and factor the matrix.
Declaration
public abstract void OrderAndFactor()
Solve(Vector<T>)
Solve the system of equations.
Declaration
public abstract void Solve(Vector<T> solution)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector<T> | solution | The solution vector that will hold the solution to the set of equations. |
SolveTransposed(Vector<T>)
Solve the transposed problem.
Declaration
public abstract void SolveTransposed(Vector<T> solution)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector<T> | solution | The solution vector that will hold the solution to the transposed set of equations. |


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Loughlin, Giannulli to fight new charges in admissions case
by EndPlayBOSTON - "Full House" actress Lori Loughlin and her fashion designer husband are contesting the latest charges against them in the college admissions scandal.
Lawyers for Loughlin and Mossimo Giannulli filed court documents Friday saying the couple plans to plead not guilty to charges of conspiracy to commit federal program bribery. The couple also waived their right to appear at a Nov. 20 arraignment.
Related Headlines
Prosecutors recently added the bribery charge for 11 parents who previously pleaded not guilty in the case. Another 19 parents have pleaded guilty or agreed to plead guilty.
Loughlin and Giannulli are accused of paying $500,000 to get their two daughters into the University of Southern California as fake athletic recruits. Their daughters no longer attend USC.
The couple previously pleaded not guilty to fraud and money laundering.


Asynchronous Advantage Actor-Critic (A3C) algorithm for reinforcement learning.
deepchem.rl.a3c.
A3C
(env, policy, max_rollout_length=20, discount_factor=0.99, advantage_lambda=0.98, value_weight=1.0, entropy_weight=0.01, optimizer=None, model_dir=None, use_hindsight=False)[source]¶Bases: object
Implements the Asynchronous Advantage Actor-Critic (A3C) algorithm for reinforcement learning.
The algorithm is described in Mnih et al, “Asynchronous Methods for Deep Reinforcement Learning” (https://arxiv.org/abs/1602.01783). This class supports environments with both discrete and continuous action spaces. For discrete action spaces, the “action” argument passed to the environment is an integer giving the index of the action to perform. The policy must output a vector called “action_prob” giving the probability of taking each action. For continous action spaces, the action is an array where each element is chosen independently from a normal distribution. The policy must output two arrays of the same shape: “action_mean” gives the mean value for each element, and “action_std” gives the standard deviation for each element. In either case, the policy must also output a scalar called “value” which is an estimate of the value function for the current state.
The algorithm optimizes all outputs at once using a loss that is the sum of three terms:
This class supports Generalized Advantage Estimation as described in Schulman et al., “High-Dimensional Continuous Control Using Generalized Advantage Estimation” (https://arxiv.org/abs/1506.02438). This is a method of trading off bias and variance in the advantage estimate, which can sometimes improve the rate of convergance. Use the advantage_lambda parameter to adjust the tradeoff.
This class supports Hindsight Experience Replay as described in Andrychowicz et al., “Hindsight Experience Replay” (https://arxiv.org/abs/1707.01495). This is a method that can enormously accelerate learning when rewards are very rare. It requires that the environment state contains information about the goal the agent is trying to achieve. Each time it generates a rollout, it processes that rollout twice: once using the actual goal the agent was pursuing while generating it, and again using the final state of that rollout as the goal. This guarantees that half of all rollouts processed will be ones that achieved their goals, and hence received a reward.
To use this feature, specify use_hindsight=True to the constructor. The environment must have a method defined as follows:
The method receives the list of states generated during the rollout, the action taken for each one, and a new goal state. It should generate a new list of states that are identical to the input ones, except specifying the new goal. It should return that list of states, and the rewards that would have been received for taking the specified actions from those states.
fit
(total_steps, max_checkpoints_to_keep=5, checkpoint_interval=600, restore=False)[source]¶Train the policy.
| Parameters: |
|
|---|
predict
(state, use_saved_states=True, save_states=True)[source]¶Compute the policy’s output predictions for a state.
If the policy involves recurrent layers, this method can preserve their internal states between calls. Use the use_saved_states and save_states arguments to specify how it should behave.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | the array of action probabilities, and the estimated value function |
select_action
(state, deterministic=False, use_saved_states=True, save_states=True)[source]¶Select an action to perform based on the environment’s state.
If the policy involves recurrent layers, this method can preserve their internal states between calls. Use the use_saved_states and save_states arguments to specify how it should behave.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | the index of the selected action |
deepchem.rl.a3c.
A3CLossContinuous
(value_weight, entropy_weight, **kwargs)[source]¶Bases: deepchem.models.tensorgraph.layers.Layer
This layer computes the loss function for A3C with continuous action spaces.
add_summary_to_tg
()¶Can only be called after self.create_layer to gaurentee that name is not none
clone
(in_layers)¶Create a copy of this layer with different inputs.
copy
(replacements={}, variables_graph=None, shared=False)¶Duplicate this Layer and all its inputs.
This is similar to clone(), but instead of only cloning one layer, it also recursively calls copy() on all of this layer’s inputs to clone the entire hierarchy of layers. In the process, you can optionally tell it to replace particular layers with specific existing ones. For example, you can clone a stack of layers, while connecting the topmost ones to different inputs.
For example, consider a stack of dense layers that depend on an input:
>>> input = Feature(shape=(None, 100))
>>> dense1 = Dense(100, in_layers=input)
>>> dense2 = Dense(100, in_layers=dense1)
>>> dense3 = Dense(100, in_layers=dense2)
The following will clone all three dense layers, but not the input layer. Instead, the input to the first dense layer will be a different layer specified in the replacements map.
>>> new_input = Feature(shape=(None, 100))
>>> replacements = {input: new_input}
>>> dense3_copy = dense3.copy(replacements)
| Parameters: |
|
|---|
layer_number_dict
= {}¶none_tensors
()¶set_summary
(summary_op, summary_description=None, collections=None)¶Annotates a tensor with a tf.summary operation Collects data from self.out_tensor by default but can be changed by setting self.tb_input to another tensor in create_tensor
| Parameters: |
|
|---|
set_tensors
(tensor)¶set_variable_initial_values
(values)¶Set the initial values of all variables.
This takes a list, which contains the initial values to use for all of this layer’s values (in the same order retured by TensorGraph.get_layer_variables()). When this layer is used in a TensorGraph, it will automatically initialize each variable to the value specified in the list. Note that some layers also have separate mechanisms for specifying variable initializers; this method overrides them. The purpose of this method is to let a Layer object represent a pre-trained layer, complete with trained values for its variables.
shape
¶Get the shape of this Layer’s output.
Create a copy of this layer that shares variables with it.
This is similar to clone(), but where clone() creates two independent layers, this causes the layers to share variables with each other.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: |
deepchem.rl.a3c.
A3CLossDiscrete
(value_weight, entropy_weight, **kwargs)[source]¶Bases: deepchem.models.tensorgraph.layers.Layer
This layer computes the loss function for A3C with discrete action spaces.
add_summary_to_tg
()¶Can only be called after self.create_layer to gaurentee that name is not none
clone
(in_layers)¶Create a copy of this layer with different inputs.
copy
(replacements={}, variables_graph=None, shared=False)¶Duplicate this Layer and all its inputs.
This is similar to clone(), but instead of only cloning one layer, it also recursively calls copy() on all of this layer’s inputs to clone the entire hierarchy of layers. In the process, you can optionally tell it to replace particular layers with specific existing ones. For example, you can clone a stack of layers, while connecting the topmost ones to different inputs.
For example, consider a stack of dense layers that depend on an input:
>>> input = Feature(shape=(None, 100))
>>> dense1 = Dense(100, in_layers=input)
>>> dense2 = Dense(100, in_layers=dense1)
>>> dense3 = Dense(100, in_layers=dense2)
The following will clone all three dense layers, but not the input layer. Instead, the input to the first dense layer will be a different layer specified in the replacements map.
>>> new_input = Feature(shape=(None, 100))
>>> replacements = {input: new_input}
>>> dense3_copy = dense3.copy(replacements)
| Parameters: |
|
|---|
layer_number_dict
= {}¶none_tensors
()¶set_summary
(summary_op, summary_description=None, collections=None)¶Annotates a tensor with a tf.summary operation Collects data from self.out_tensor by default but can be changed by setting self.tb_input to another tensor in create_tensor
| Parameters: |
|
|---|
set_tensors
(tensor)¶set_variable_initial_values
(values)¶Set the initial values of all variables.
This takes a list, which contains the initial values to use for all of this layer’s values (in the same order retured by TensorGraph.get_layer_variables()). When this layer is used in a TensorGraph, it will automatically initialize each variable to the value specified in the list. Note that some layers also have separate mechanisms for specifying variable initializers; this method overrides them. The purpose of this method is to let a Layer object represent a pre-trained layer, complete with trained values for its variables.
shape
¶Get the shape of this Layer’s output.
Create a copy of this layer that shares variables with it.
This is similar to clone(), but where clone() creates two independent layers, this causes the layers to share variables with each other.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: |
Monte Carlo tree search algorithm for reinforcement learning.
deepchem.rl.mcts.
MCTS
(env, policy, max_search_depth=100, n_search_episodes=1000, discount_factor=0.99, value_weight=1.0, optimizer=<deepchem.models.tensorgraph.optimizers.Adam object>, model_dir=None)[source]¶Bases: object
Implements a Monte Carlo tree search algorithm for reinforcement learning.
This is adapted from Silver et al, “Mastering the game of Go without human knowledge” (https://www.nature.com/articles/nature24270). The methods described in that paper rely on features of Go that are not generally true of all reinforcement learning problems. To transform it into a more generally useful RL algorithm, it has been necessary to change some aspects of the method. The overall approach used in this implementation is still the same, although some of the details differ.
This class requires the policy to output two quantities: a vector giving the probability of taking each action, and an estimate of the value function for the current state. At every step of simulating an episode, it performs an expensive tree search to explore the consequences of many possible actions. Based on that search, it computes much better estimates for the value function of the current state and the desired action probabilities. In then tries to optimize the policy to make its outputs match the result of the tree search.
Optimization proceeds through a series of iterations. Each iteration consists of two stages:
The tree search involves repeatedly selecting actions starting from the current state. This is done by using deepcopy() to clone the environment. It is essential that this produce a deterministic sequence of states: performing an action on the cloned environment must always lead to the same state as performing that action on the original environment. For environments whose state transitions are deterministic, this is not a problem. For ones whose state transitions are stochastic, it is essential that the random number generator used to select new states be stored as part of the environment and be properly cloned by deepcopy().
This class does not support policies that include recurrent layers.
fit
(iterations, steps_per_iteration=10000, epochs_per_iteration=10, temperature=0.5, puct_scale=None, max_checkpoints_to_keep=5, checkpoint_interval=600, restore=False)[source]¶Train the policy.
| Parameters: |
|
|---|
predict
(state)[source]¶Compute the policy’s output predictions for a state.
| Parameters: | state (array) – the state of the environment for which to generate predictions |
|---|---|
| Returns: | |
| Return type: | the array of action probabilities, and the estimated value function |
deepchem.rl.mcts.
MCTSLoss
(value_weight, **kwargs)[source]¶Bases: deepchem.models.tensorgraph.layers.Layer
This layer computes the loss function for MCTS.
add_summary_to_tg
()¶Can only be called after self.create_layer to gaurentee that name is not none
clone
(in_layers)¶Create a copy of this layer with different inputs.
copy
(replacements={}, variables_graph=None, shared=False)¶Duplicate this Layer and all its inputs.
This is similar to clone(), but instead of only cloning one layer, it also recursively calls copy() on all of this layer’s inputs to clone the entire hierarchy of layers. In the process, you can optionally tell it to replace particular layers with specific existing ones. For example, you can clone a stack of layers, while connecting the topmost ones to different inputs.
For example, consider a stack of dense layers that depend on an input:
>>> input = Feature(shape=(None, 100))
>>> dense1 = Dense(100, in_layers=input)
>>> dense2 = Dense(100, in_layers=dense1)
>>> dense3 = Dense(100, in_layers=dense2)
The following will clone all three dense layers, but not the input layer. Instead, the input to the first dense layer will be a different layer specified in the replacements map.
>>> new_input = Feature(shape=(None, 100))
>>> replacements = {input: new_input}
>>> dense3_copy = dense3.copy(replacements)
| Parameters: |
|
|---|
layer_number_dict
= {}¶none_tensors
()¶set_summary
(summary_op, summary_description=None, collections=None)¶Annotates a tensor with a tf.summary operation Collects data from self.out_tensor by default but can be changed by setting self.tb_input to another tensor in create_tensor
| Parameters: |
|
|---|
set_tensors
(tensor)¶set_variable_initial_values
(values)¶Set the initial values of all variables.
This takes a list, which contains the initial values to use for all of this layer’s values (in the same order retured by TensorGraph.get_layer_variables()). When this layer is used in a TensorGraph, it will automatically initialize each variable to the value specified in the list. Note that some layers also have separate mechanisms for specifying variable initializers; this method overrides them. The purpose of this method is to let a Layer object represent a pre-trained layer, complete with trained values for its variables.
shape
¶Get the shape of this Layer’s output.
Create a copy of this layer that shares variables with it.
This is similar to clone(), but where clone() creates two independent layers, this causes the layers to share variables with each other.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: |
Proximal Policy Optimization (PPO) algorithm for reinforcement learning.
deepchem.rl.ppo.
PPO
(env, policy, max_rollout_length=20, optimization_rollouts=8, optimization_epochs=4, batch_size=64, clipping_width=0.2, discount_factor=0.99, advantage_lambda=0.98, value_weight=1.0, entropy_weight=0.01, optimizer=None, model_dir=None, use_hindsight=False)[source]¶Bases: object
Implements the Proximal Policy Optimization (PPO) algorithm for reinforcement learning.
The algorithm is described in Schulman et al, “Proximal Policy Optimization Algorithms” (https://openai-public.s3-us-west-2.amazonaws.com/blog/2017-07/ppo/ppo-arxiv.pdf). This class requires the policy to output two quantities: a vector giving the probability of taking each action, and an estimate of the value function for the current state. It optimizes both outputs at once using a loss that is the sum of three terms:
This class only supports environments with discrete action spaces, not continuous ones. The “action” argument passed to the environment is an integer, giving the index of the action to perform.
This class supports Generalized Advantage Estimation as described in Schulman et al., “High-Dimensional Continuous Control Using Generalized Advantage Estimation” (https://arxiv.org/abs/1506.02438). This is a method of trading off bias and variance in the advantage estimate, which can sometimes improve the rate of convergance. Use the advantage_lambda parameter to adjust the tradeoff.
This class supports Hindsight Experience Replay as described in Andrychowicz et al., “Hindsight Experience Replay” (https://arxiv.org/abs/1707.01495). This is a method that can enormously accelerate learning when rewards are very rare. It requires that the environment state contains information about the goal the agent is trying to achieve. Each time it generates a rollout, it processes that rollout twice: once using the actual goal the agent was pursuing while generating it, and again using the final state of that rollout as the goal. This guarantees that half of all rollouts processed will be ones that achieved their goals, and hence received a reward.
To use this feature, specify use_hindsight=True to the constructor. The environment must have a method defined as follows:
The method receives the list of states generated during the rollout, the action taken for each one, and a new goal state. It should generate a new list of states that are identical to the input ones, except specifying the new goal. It should return that list of states, and the rewards that would have been received for taking the specified actions from those states.
fit
(total_steps, max_checkpoints_to_keep=5, checkpoint_interval=600, restore=False)[source]¶Train the policy.
| Parameters: |
|
|---|
predict
(state, use_saved_states=True, save_states=True)[source]¶Compute the policy’s output predictions for a state.
If the policy involves recurrent layers, this method can preserve their internal states between calls. Use the use_saved_states and save_states arguments to specify how it should behave.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | the array of action probabilities, and the estimated value function |
select_action
(state, deterministic=False, use_saved_states=True, save_states=True)[source]¶Select an action to perform based on the environment’s state.
If the policy involves recurrent layers, this method can preserve their internal states between calls. Use the use_saved_states and save_states arguments to specify how it should behave.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | the index of the selected action |
deepchem.rl.ppo.
PPOLoss
(value_weight, entropy_weight, clipping_width, **kwargs)[source]¶Bases: deepchem.models.tensorgraph.layers.Layer
This layer computes the loss function for PPO.
add_summary_to_tg
()¶Can only be called after self.create_layer to gaurentee that name is not none
clone
(in_layers)¶Create a copy of this layer with different inputs.
copy
(replacements={}, variables_graph=None, shared=False)¶Duplicate this Layer and all its inputs.
This is similar to clone(), but instead of only cloning one layer, it also recursively calls copy() on all of this layer’s inputs to clone the entire hierarchy of layers. In the process, you can optionally tell it to replace particular layers with specific existing ones. For example, you can clone a stack of layers, while connecting the topmost ones to different inputs.
For example, consider a stack of dense layers that depend on an input:
>>> input = Feature(shape=(None, 100))
>>> dense1 = Dense(100, in_layers=input)
>>> dense2 = Dense(100, in_layers=dense1)
>>> dense3 = Dense(100, in_layers=dense2)
The following will clone all three dense layers, but not the input layer. Instead, the input to the first dense layer will be a different layer specified in the replacements map.
>>> new_input = Feature(shape=(None, 100))
>>> replacements = {input: new_input}
>>> dense3_copy = dense3.copy(replacements)
| Parameters: |
|
|---|
layer_number_dict
= {}¶none_tensors
()¶set_summary
(summary_op, summary_description=None, collections=None)¶Annotates a tensor with a tf.summary operation Collects data from self.out_tensor by default but can be changed by setting self.tb_input to another tensor in create_tensor
| Parameters: |
|
|---|
set_tensors
(tensor)¶set_variable_initial_values
(values)¶Set the initial values of all variables.
This takes a list, which contains the initial values to use for all of this layer’s values (in the same order retured by TensorGraph.get_layer_variables()). When this layer is used in a TensorGraph, it will automatically initialize each variable to the value specified in the list. Note that some layers also have separate mechanisms for specifying variable initializers; this method overrides them. The purpose of this method is to let a Layer object represent a pre-trained layer, complete with trained values for its variables.
shape
¶Get the shape of this Layer’s output.
Create a copy of this layer that shares variables with it.
This is similar to clone(), but where clone() creates two independent layers, this causes the layers to share variables with each other.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: |
Interface for reinforcement learning.
deepchem.rl.
Environment
(state_shape, n_actions=None, state_dtype=None, action_shape=None)[source]¶Bases: object
An environment in which an actor performs actions to accomplish a task.
An environment has a current state, which is represented as either a single NumPy array, or optionally a list of NumPy arrays. When an action is taken, that causes the state to be updated. The environment also computes a reward for each action, and reports when the task has been terminated (meaning that no more actions may be taken).
Two types of actions are supported. For environments with discrete action spaces, the action is an integer specifying the index of the action to perform (out of a fixed list of possible actions). For environments with continuous action spaces, the action is a NumPy array.
Environment objects should be written to support pickle and deepcopy operations. Many algorithms involve creating multiple copies of the Environment, possibly running in different processes or even on different computers.
action_shape
¶The expected shape of NumPy arrays representing actions.
If the environment uses a discrete action space, this returns None.
n_actions
¶The number of possible actions that can be performed in this Environment.
If the environment uses a continuous action space, this returns None.
reset
()[source]¶Initialize the environment in preparation for doing calculations with it.
This must be called before calling step() or querying the state. You can call it again later to reset the environment back to its original state.
state
¶The current state of the environment, represented as either a NumPy array or list of arrays.
If reset() has not yet been called at least once, this is undefined.
state_dtype
¶The dtypes of the arrays that describe a state.
If the state is a single array, this returns the dtype of that array. If the state is a list of arrays, this returns a list containing the dtypes of the arrays.
state_shape
¶The shape of the arrays that describe a state.
If the state is a single array, this returns a tuple giving the shape of that array. If the state is a list of arrays, this returns a list of tuples where each tuple is the shape of one array.
step
(action)[source]¶Take a time step by performing an action.
This causes the “state” and “terminated” properties to be updated.
| Parameters: | action (object) – an object describing the action to take |
|---|---|
| Returns: |
|
terminated
¶Whether the task has reached its end.
If reset() has not yet been called at least once, this is undefined.
deepchem.rl.
GymEnvironment
(name)[source]¶Bases: deepchem.rl.Environment
This is a convenience class for working with environments from OpenAI Gym.
action_shape
¶The expected shape of NumPy arrays representing actions.
If the environment uses a discrete action space, this returns None.
n_actions
¶The number of possible actions that can be performed in this Environment.
If the environment uses a continuous action space, this returns None.
state
¶The current state of the environment, represented as either a NumPy array or list of arrays.
If reset() has not yet been called at least once, this is undefined.
state_dtype
¶The dtypes of the arrays that describe a state.
If the state is a single array, this returns the dtype of that array. If the state is a list of arrays, this returns a list containing the dtypes of the arrays.
state_shape
¶The shape of the arrays that describe a state.
If the state is a single array, this returns a tuple giving the shape of that array. If the state is a list of arrays, this returns a list of tuples where each tuple is the shape of one array.
terminated
¶Whether the task has reached its end.
If reset() has not yet been called at least once, this is undefined.
deepchem.rl.
Policy
[source]¶Bases: object
A policy for taking actions within an environment.
A policy is defined by a set of TensorGraph Layer objects that perform the necessary calculations. There are many algorithms for reinforcement learning, and they differ in what values they require a policy to compute. That makes it impossible to define a single interface allowing any policy to be optimized with any algorithm. Instead, this interface just tries to be as flexible and generic as possible. Each algorithm must document what values it expects create_layers() to return.
Policy objects should be written to support pickling. Many algorithms involve creating multiple copies of the Policy, possibly running in different processes or even on different computers.
create_layers
(state, **kwargs)[source]¶Create the TensorGraph Layers that define the policy.
The arguments always include a list of Feature layers representing the current state of the environment (one layer for each array in the state). Depending on the algorithm being used, other arguments might get passed as well. It is up to each algorithm to document that.
This method should construct and return a dict that maps strings to Layer objects. Each algorithm must document what Layers it expects the policy to create. If this method is called multiple times, it should create a new set of Layers every time.


The Spring Software Diagram Designer
Spring is a graphical tool for creating class and object diagrams.
Here's a picture of the main window of spring:
Tools
Spring has ten tools:
- The arrow tool is for selecting diagram elements, moving elements and selections and executing operations on them.
- The hand tool is for setting the visible a part of a large diagram.
- The class tool is for adding a new class to the diagram.
- The object tool is for adding a new object to the diagram.
- The note tool is for adding a pseudocode note to the diagram.
- The inheritance tool is for adding an inheritance relationship between two classes.
- The aggregation tool is for adding an aggregation relationship between two classes.
- The reference tool is for adding a reference relationship between two classes or two objects.
- The create instance tool is for adding a create instance relationship between two classes.
- The attach note tool is for attaching a pseudocode note to an operation of a class.
Selections
The diagram may have two kinds of selections: an "element" selection and a "rectangle" selection.
The element selection consists of individual diagram elements.
You can add and remove elements from the selection by holding the control key down while clicking an element.
A rectangle selection is created by clicking a part of a diagram and holding down the left mouse button while dragging with the mouse.
Operations
By clicking an element or selection with the right mouse button shows a context menu of operations that can applied to that kind of element or elements.
- When an element selection contains multiple classes, objects or notes, you can align them or space them evenly horizontally or vertically.
- The lines of a single relationship element can be straightent.
- Selected elements can be deleted.
- An image consisting of the selected elements can be saved to a file.
- The properties of a single diagram element can be changed.
- Multiple inheritance relationships can be combined to a single one, and a single combined inheritance relationship can be split to distinct relationships.
Reference
The Spring Module document contains reference documentation genererated form the source code.


This is an example of a multi-line defines. It uses a doubled left angle bracket to indicate that a multi-line argument begins. The multi-line argument ends when a doubled right angle bracket is encountered.
Some paragraphs are bigger than "This is an example of a multi-line defines. It uses a doubled left angle bracket to indicate that a multi-line argument begins. The multi-line argument ends when a doubled right angle bracket is encountered."
CASE_SENSITIVE: defines are able to distinguish between upper and lower case.


Be on the lookout for poor grammar or typographical errors. Many phishing e-mails are translated from other languages or are sent without being proof-read. As a result, these messages can contain bad grammar or typographical errors.
Check the return address: Is the e-mail from Amazon.com or Amazon.co.uk or from a "phisher"? Genuine e-mails come from an e-mail address ending in<EMAIL_ADDRESS><EMAIL_ADDRESS>or<EMAIL_ADDRESS>While phishers often send forged e-mails to make it look like it comes from Amazon.com or Amazon.co.uk, you can frequently determine whether it's authentic by checking the return address. If the "from" line of the e-mail looks like<EMAIL_ADDRESS>or<EMAIL_ADDRESS>or contains the name of another Internet Service Provider (ISP), you can be sure it is a fraudulent e-mail. Most e-mail clients let you examine the source of the e-mail. Check the e-mail header information to see that the "received from", "reply to", and "return path" for the e-mail comes from @amazon.com or @amazon.co.uk. The method you use to check the header information varies depending on the e-mail client you use.
Check the website address: Some phishers set up spoof websites that contain the word "amazon" somewhere in the URL. Genuine Amazon websites always end with ".amazon.com" or ".amazon.co.uk". We never use a combination such as "security-amazon.com" or "amazon.com.biz". Some phishing e-mails include a link that looks as though it will take you to your account, but it is really a shortened link to a completely different website. If you hover over the link in your e-mail client, you can sometimes see the underlying, false Web address, either as a popup or as information in the browser status bar.


C API memory pool definitions.
More...
C API memory pool definitions.
- For the complete definition, see
- RTOS C++ API
- Examples
typedef struct my_blk_s
{
int i;
const char* s;
} my_blk_t;
int
{
my_blk_t* blk;
{
}
{
static char pool[1000];
}
}
os_clock_t * os_clock_get_rtclock(void)
Get rtclock (the real-time clock).
os_result_t os_mempool_free(os_mempool_t *mempool, void *block)
Free the memory block.
void os_mempool_construct(os_mempool_t *mempool, const char *name, size_t blocks, size_t block_size_bytes, const os_mempool_attr_t *attr)
Construct a statically allocated memory pool object instance.
void * os_mempool_alloc(os_mempool_t *mempool)
Allocate a memory block.
void * os_mempool_timed_alloc(os_mempool_t *mempool, os_clock_duration_t timeout)
Allocate a memory block with timeout.
void os_mempool_attr_init(os_mempool_attr_t *attr)
Initialise the memory pool attributes.
void * os_mempool_try_alloc(os_mempool_t *mempool)
Try to allocate a memory block.
os_result_t os_mempool_reset(os_mempool_t *mempool)
Reset the memory pool.
void os_mempool_destruct(os_mempool_t *mempool)
Destruct the statically allocated memory pool object instance.
int os_main(int argc, char *argv[])
Application entry point, running on the main thread context.
size_t mp_pool_size_bytes
Size of user provided memory pool area, in bytes.
void * mp_pool_address
Pointer to user provided memory pool area.
void * clock
Pointer to clock object instance.
Memory pool object storage.
◆ os_mempool_create
◆ os_mempool_destroy
◆ os_mempool_attr_t
◆ os_mempool_t
Memory pool object storage.
This C structure has the same size as the C++ os::rtos::memory_pool
object and must be initialised with os_mempool_create()
.
Later on a pointer to it can be used both in C and C++ to refer to the memory pool object instance.
The members of this structure are hidden and should not be used directly, but only through specific functions.
- See also
- os::rtos::memory_pool
◆ os_mempool_alloc()
Allocate a memory block.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- Pointer to memory block, or
NULL
if interrupted.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::alloc()
Definition at line 2389 of file os-c-wrapper.cpp.
◆ os_mempool_attr_init()
Initialise the memory pool attributes.
- Parameters
-
| [in] | attr | Pointer to memory pool attributes object instance. |
- Returns
- Nothing.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::attributes
Definition at line 2282 of file os-c-wrapper.cpp.
◆ os_mempool_construct()
Construct a statically allocated memory pool object instance.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance storage. |
| [in] | name | Pointer to name (may be NULL). |
| [in] | blocks | The maximum number of items in the pool. |
| [in] | block_size_bytes | The size of an item, in bytes. |
| [in] | attr | Pointer to attributes (may be NULL). |
- Returns
- Nothing.
- Note
- Must be paired with
os_mempool_destruct()
.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool
Definition at line 2297 of file os-c-wrapper.cpp.
◆ os_mempool_delete()
Destruct the memory pool object instance and deallocate it.
- Parameters
-
| [in] | mempool | Pointer to dynamically allocated memory pool object instance. |
- Returns
- Nothing.
Destruct the memory pool and deallocate the dynamically allocated space using the RTOS system allocator.
- Note
- Equivalent of C++
delete ptr_mempool
.
-
Must be paired with
os_mempool_new()
.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool
Definition at line 2363 of file os-c-wrapper.cpp.
◆ os_mempool_destruct()
Destruct the statically allocated memory pool object instance.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- Nothing.
- Note
- Must be paired with
os_mempool_construct()
.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool
Definition at line 2318 of file os-c-wrapper.cpp.
◆ os_mempool_free()
Free the memory block.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
| [in] | block | Pointer to memory block to free. |
- Return values
-
| os_ok | The memory block was released. |
| EINVAL | The block does not belong to the memory pool. |
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::free()
Definition at line 2428 of file os-c-wrapper.cpp.
◆ os_mempool_get_block_size()
Get block size.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- The block size, in bytes.
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::block_size()
Definition at line 2467 of file os-c-wrapper.cpp.
◆ os_mempool_get_capacity()
Get memory pool capacity.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- The max number of blocks in the pool.
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::capacity()
Definition at line 2441 of file os-c-wrapper.cpp.
◆ os_mempool_get_count()
Get blocks count.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- The number of blocks used from the queue.
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::count()
Definition at line 2454 of file os-c-wrapper.cpp.
◆ os_mempool_get_name()
Get the memory pool name.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- Null terminated string.
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::name()
Definition at line 2376 of file os-c-wrapper.cpp.
◆ os_mempool_get_pool()
Get the pool storage address.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- Pointer to storage.
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::pool()
Definition at line 2519 of file os-c-wrapper.cpp.
◆ os_mempool_is_empty()
Check if the memory pool is empty.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Return values
-
| true | The memory pool has no allocated blocks. |
| false | The memory pool has allocated blocks. |
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::empty()
Definition at line 2480 of file os-c-wrapper.cpp.
◆ os_mempool_is_full()
Check if the memory pool is full.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Return values
-
| true | All memory blocks are allocated. |
| false | There are still memory blocks that can be allocated. |
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::full()
Definition at line 2493 of file os-c-wrapper.cpp.
◆ os_mempool_new()
Allocate a memory pool object instance and construct it.
- Parameters
-
| [in] | name | Pointer to name (may be NULL). |
| [in] | blocks | The maximum number of items in the pool. |
| [in] | block_size_bytes | The size of an item, in bytes. |
| [in] | attr | Pointer to attributes (may be NULL). |
- Returns
- Pointer to new memory pool object instance.
Dynamically allocate the memory pool object instance using the RTOS system allocator and construct it.
- Note
- Equivalent of C++
new memory_pool(...)
.
-
Must be paired with
os_mempool_delete()
.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool
Definition at line 2338 of file os-c-wrapper.cpp.
◆ os_mempool_reset()
Reset the memory pool.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Return values
-
| os_ok | The memory pool was reset. |
| EPERM | Cannot be invoked from an Interrupt Service Routines. |
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::reset()
Definition at line 2506 of file os-c-wrapper.cpp.
◆ os_mempool_timed_alloc()
Allocate a memory block with timeout.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
| [in] | timeout | Timeout to wait, in clock units (ticks or seconds). |
- Returns
- Pointer to memory block, or
NULL
if timeout.
- Warning
- Cannot be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::timed_alloc()
Definition at line 2415 of file os-c-wrapper.cpp.
◆ os_mempool_try_alloc()
Try to allocate a memory block.
- Parameters
-
| [in] | mempool | Pointer to memory pool object instance. |
- Returns
- Pointer to memory block, or
NULL
if no memory available.
- Note
- Can be invoked from Interrupt Service Routines.
- For the complete definition, see
- os::rtos::memory_pool::try_alloc()
Definition at line 2402 of file os-c-wrapper.cpp.


Microservices
Hot or Not?
Wojciech Gawroński, 2014 ©
License: CC BY-ND 3.0 PL
Introduce Concept and Definition.
Present Sample Case Studies.
Bust some hype about the power of this approach.
Bust some negatives about the problems with it.
In other words: some doze of healthy and constructive critique about that topic.
Agenda
Definitions
Details
Challenges
Nature
Science
Honeycomb, Wheat ...
Almost everything in nature is composed from smaller parts.
Even on the physical level, not visible without very supersensitive.
Industry
Gears inside machine.
Smaller parts cooperate better, can be interchanged, replaced, repaired.
'Lots of moving parts' as a disadvantage.
Friction reduces the performance.
IT
In IT we should learn from the best examples.
But sometimes even if we would like to be the best, we did this.

Etymology
We know how world is constructed, we are seeing the analogies.
Now, lets focus exactly on the word Microservice .
First usage of term "microservice" is attached to the workshops near Venice in 2011.
Than James Lewis presents "The Microservices - Java, The Unix Way" at 33rd Degree in Kraków.
After that the most important and influential person oriented around this topic is Adrian Cockroft.
Definition
It is not that simple
[...] an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms [...]
But citing exact definition is almost impossible.
Gazillion of definitions.
Each one uses different scale, metric, way of comparison.
Almost everyone is defining it in his own unique way.
Even this one is very technical and suggests actual solution.
Definition
Who? When? What? Where?

Different definition can be built around Who? When? What? Where?
Martin Fowler is an author of that quote.
It is about the intent, responsibilities. Not about lines and size.
What is sometimes even more important (and we see it after a while), it is a definition in terms of amount of work.
Who is using ESB or other enterprise service bus ?
ESB and other 'intelligent' software are an anti-pattern here.
I'm not saying that ESB are bad (yes they are, but it is not a topic here) but it is not that type of architecture.
Evolution
The main factor that driving change, the main theme of all changes is EVOLUTION.
Have you deployed a 200 MB EAR file, which after all broken everything?
Have you deal with that "one NullPointerException that destroyed the release"?
Each of these companies started from the monolithic approach.
A single monolithic Perl + C++ (CGI anyone?) or Java applications.
BTW.: monolithic came from UNIX philosophy as a opposite technology to described one.
They have evolved to the more scalable, decoupled architectures because they have to.
Dropping single archive on production and hoping that everything will go okay is naive.

Clean Micro-service Architecture, Uncle Bob
[...] is a discrete, isolated, and named piece of logic [...]
Actor Model, Victor Klang
[...] inside communicating over some number of ports with things on the outside.
Hexagonal Architecture, Alistair Cockburn
Objects communicate by sending and receiving messages.
OOP, Dr Alan Kay
Actor Model, UNIX philosophy, proper DDD...
Well understood and well done OOP is a microservice -like architecture.
More samples? Erlang (25 years old language with supervision hierarchies).
More samples? Smalltalk (method invocation is a message passing).
More samples? Multiple hexagons (Hexagonal Architecture).
More samples? Microkernel architecture.
Digression: I am always amazed how people remembered everything about encapsulation and inheritance and dropped out message passing.
Samples
JBoss
Erlang
Node.js
Symfony2
UNIX environment
JBoss - Monolithic.
Erlang - Actor Model.
Node.js - Multiple Processes.
Symfony2 - Decomposable Internal Architecture (nice framework).
UNIX tools and environment - UNIX philosophy.
Big Ball of Mud
[...] might be thought of as an anti-pattern. [...] However, its undeniable popularity leads to the inexorable conclusion that it is a pattern in its own right.
Big Ball of Mud
Make it work, Make it right, then make it fast.
Question: Who read "Big Ball of Mud" paper?
Who did not read it - go read it. Now. After the meeting.
It is not a purely pejorative statement.
We should strive the approach "make it work - make it right".
First we tackle the essential complexity of the problem and iteratively improve our solution.
It is a big ball of mud, collected from good, thoughtful and properly designed elements.
Hype
If you can't build a monolith, what makes you think microservices are the answer?
Distributed big balls of mud
However, when considering microservice like architectures, it's really important to not be attracted to the hype on this one as the challenges and costs are as real as the benefits.
Microservices - not a free lunch!
Like every new technology, there is a huge phase of hype on microservices.
Mostly non-objective, dangerous and misunderstood approach.
Also it is connected with polyglot-like movements.
Polyglot persistence, polyglot programming - everything is for people.
When misused, it is easy to make mess - too many technologies, too many options.
More in challenges chapter.
Deletability
Greg Young - The art of destroying software
From all properties of your architecture it should be the most important one.
It enables and drives the flexibility.
It manages and keeps the technical debt on the sane level.
How to optimize your development speed and do not spent so much time of reFUCKtoring?
It is not a replacement for TDD, BDD or tests.
It is a tool, a metric which helps you design and organize your code properly.
"If one of your parts of the system is more than a week rewrite it is a sign, that this is a dangerous side of your code and properly something went wrong here."
Minimal Viable Architecture
Iteratively, step by step we are coming to the concept of minimal viable architecture.
The most pragmatic approach to the software architecture, very compatible with agile.
However it is also compatible with microservices architecture.
We have talked about deletability, small units, single responsibilities.
"Perfection is Achieved Not When There Is Nothing More to Add, But When There Is Nothing Left to Take Away"
Challenges
Hype and popularity leads to the misunderstanding.
As we see, topic is definitely hot but definitely not new.
And hot topics are connected with challenges.
Benefits - small parts, natural responsibilities, discipline.
Benefits - better tools, connected with the actual problem.
Benefits - easy, simple, scalable.
But to receive all this greatness we need to focus on challenges and solve them properly.
Operations
Adrian Cockcroft on Microservices and DevOps
Backups
Deployment
Migrations
Security
Monitoring
Updates
Maintenance
Installation
Do you want multiple storages? Think about backups and migrations.
Many different technologies? Think about monitoring, deployment, updates and security.
Updating packages, especially on Linux is difficult.
DevOps Culture to the Rescue!
Operations
Incremental approach to build an automated, reproducible infrastructure.
Hiring a DevOps Engineer will look you would like to employee a Scrum.
DevOps culture is a good answer for almost all problems from the previous slide.
There are more specific techniques related with e.g. backups or migrations.
Blue-Green Deployment it is a solution to handle multiple versions of the one system.
Migrations on the fly inside the program logic.
Event Sourcing and rebuilding the state of the program from the history is a solution for the not synchronized backups procedures.
Service Discovery
Sharing Resources
Clusters
Configuration
Orchestration
URI and Paths
We want to scale and dynamically resize our clusters, but our configuration and connections are not scaling with it.
There are tools which will help us to do it.
The whole approach is to dynamically discover and register capabilities of the clusters.
We need to manage that in order to work with scalable and dynamic environments.
Storage
Polyglot Persistence
It is more than a notice, than an actual challenge - you can complicate pretty nice your architecture by putting too much storage mechanisms into your system.
Small services uses smaller databases, sometimes more appropriate to the type of the work which needs to be done inside this service.
Obviously using one database over is a pretty deliberate choice. But be sure that you are at least choosing proper database, suited to the requirements. And think about fall-back and back-up procedures because using more than one storage complicates backups pretty badly.
It is quite nice book actually, which will help you choose the best storage suited to your needs.
And one more: using more than one storage enforces on you to treat at least one as an ultimate source of truth and rest as a different view-caches. Otherwise it will be painful regarding returning to the consistent point.
Integration through Storage
When it comes to the storage, the very tricky part is an integration between services.
Integration through the storage is the most dangerous way of integrating two parts.
You are coupling two pieces of architecture on the data structure (and sometimes even more - coupling by the internal details) level. This is the one of the most painful type of coupling to remove in future.
But this is not a clear anti-pattern - remember that in some cases, which are deeply understood this is a proper way to handle stuff if you would like to overcome the performance problems. But you should have very strong arguments to do that, and all of the others ways are already failed.
Fuzzy Boundaries
This is the most dangerous part e.g. in DDD. How to split bounded contexts?
Smaller modules / components tease to put more responsibilities inside.
Putting more and more into one thing blurs the boundaries and responsibilities.
Contracts
Transport
API's written in the conventional manner are not sufficient to understand the operation of programs.
Making reliable distributed systems in the presence of software errors
When multiple components are talking between, we need to be fail proof, rigid and strict as much as it is possible.
Ways how to talk between systems are crucial if someone is using your system as an API, or you have more than one service which are communicating between.
Also chosen transport mechanism is one of the important aspects of the communication. For example, HTTP and REST are stateless, UDP does not guarantee the delivery or order of the packets and so on.
I deliberately did not use a word API here, because the idea of Protocols is definitely more close to me. I really like the approach of defining an API calls and sequences which are allowed, especially when it comes to the operations which makes sense when they are invoked only in certain order.
Simple example: you should read bytes from a file only after you have open it earlier. Similar constraint applies to the reading and closed stream.
I definitely recommend whole thesis, even if you are prejudiced to the Erlang.
And definitely very short 9th chapter about this topic.
Tests
Development
Choosing a separate technology connected with the problem obviously has drawbacks.
Accidental Complexity will increase with each of added technologies.
Simple example - testing and keeping the consistency between them.
Another one - Coding Standards.
Sometimes we are so amused by the cloud and abilities of PaaS that we forget that we have to develop on our local machines. Tools like docker , vagrant are helping us, but what with the other things like S3, SQS and other services provided by your favorite cloud provider.
Tests
Operations
One of the challenges with automating deployment is the cut-over itself, taking software from the final stage of testing to live production.
Blue-Green Deployment
Blue-Green deployments and Testing Groups - it is like A/B testing but with features.
Flow control (20% of traffic through new solution).
Simpler: Test Users on production (at least some of data will go through him).
Simple: Enabling feature toggles per user (easily testable new features in isolated group).
Robustness
To guard against failure of an entire computer we need two computers.
Making reliable distributed systems in the presence of software errors
This is simple and beautiful quote, again from already mentioned PhD thesis.
We need to be sure that in our architecture we took all necessary steps to provide components robustness especially in the dangerous parts of the system.
Duplication of services sometimes is not enough, there are situations where whole clusters fails. And what then?
Scale
For a small operation this may be true, but when you're doing a million operations a second then a one-in-a-million failure scenario will happen every second.
Errors in Database Systems, Eventual Consistency, and the CAP Theorem
We talked about the evolution and almost each of the distributed system is created in the evolution process.
It is definitely easier to scale the multiple things than scaling one monolith. But sometimes it is also a valid answer.
Scale introduces edge cases - e.g. different problems with TCP stack settings, cascade failures.
Your cloud provider should be your advantage, it should help you, not be a constraint or a problem.
Summary
Microservices are not a free lunch.
Complexity management is crucial.
Choose your tools responsibly.
DevOps to the rescue.
It definitely gives you multiple benefits but not for free.
Accidental complexity can be deadly for your projects.
Death-By-Too-Many-Technologies
Choose tools for the actual reason and with solid arguments.
Incorporating DevOps culture and continuous improvement will definitely help you build this type of architectures.


Aspiring Software Developer
My name is Brodie and I am a Nashville based full stack web developer with an appetite for learning. Before my dive into the developer world, I was a team lead for a golf course. I fell in love with coding beacuse you can take a blank screen and turn it into something beautiful. I am currently attending Digital Crafts, a bootcamp, located in Atlanta, Ga. Have fun looking around my page and do not forget to connect!
The first project for Free Code Camp's Front-End Certification - a tribute page to someone you admire. I chose my favorite golf icon, Arnold Palmer, who passed away in 2016. This was my first real attempt to put a page together and my first experience with Bootstrap.
Bootstrap HTML CSS
I will update this with my favorite projects as I build them. Come back soon and take a look!
Default HTML CSS
I will update this with my favorite projects as I build them. Come back soon and take a look!
Default HTML CSS
Whether it's for a cup of coffee, general questions, or how I can help your site out, please feel free to reach out!


This package provides a utility class with extension methods for the handle from numbers and math operations.


Project 2
Exquisite Corpse
This was a cooperative project in which I and a partner collaborated to complete an exquisite corpse. We took inspiration from historical Japanese art, Steampunk, DaVinci, and works of HP Lovecraft. Depicated is our take of a steampunk Samurai (who is a mixture of man, machine, and creature) and a Ninja (part tiger) facing off against our twist on Cthulu (wearing a top hat because he is fashionable like that).


Based on location or manual address input, users are able to find out their District, State Representatives, and how their reps have voted on the recent most recent bills passed in the state. App displays the State Senators and Assemblypersons, and users can choose about which rep they want to know more. Users can also search for specific bills and see how they were voted on and more detailed information about the bill.
User-profile centered app that enables users seeking to borrow books from other users in their area. Based on genre preferences (and whether the book is currently lent out), users are matched with others’ books. For each match, users can decide if they like or dislike the book shown. If the users are interested in the book, they can contact the owner. User also has this ability to see all the available books at once.
Dog lovers everywhere are can enjoy this React-based app that allows users to find compatible dogs in their area. Using user-specific data, the app finds other dogs within a specific radius that could be a good match for their own pet. When both parties have matched, they can message each other and update a built-in calendar with details about the upcoming playdate.
App allows users to scrape current deals from Amazon, save them to a MongoDB database, and also write notes on their saved deals that they can reference later.
User is able to create and devour different pizzas. App utilizes ORM and MySQL to create and store the pizzas, following REST standards.
This app allows you to find other people with your similar interests. We've also included a few familiar faces on our app with whom might find things in common.
Command Line Hangman Application This application uses node to create the traditional hangman game. It incorporates multiple object constructors as well as exports from multiple JS files. Simply run the CLI.js file in your terminal using node and follow the prompts to guess the letters of the word.


CopyMe.js
You most recently copied (truncated to 30 characters)
(Nothing yet!)
Demo
The area above displays any text that is copied or cut from the web page to the user's clipboard. It uses
CopyMe.js to receive copy/cut events (whether done by mouse of keyboard shortcuts). Such information can
prove useful - it could help detect potential plagiarism, help determine what portions of a page's content
is most useful to viewers, or simply provide metrics for how users are engaging with a page.
CopyMe.js
CopyMe.js is a JavaScript library that enables a web page to easily monitor what text is being copied from it.
Supported Browsers
Currently (Feb 5, 2015) only Firefox, IE, and Chrome are tested and working.
How do I use CopyMe.js?
Simply include the following lines of code in your page's head element:
<script src="CopyMe.js"></script">
<script type="text/javascript"">
window.onload = function(){
CopyMe.setCopyCallback(function(copiedText){
// Do something here when text is copied
});
CopyMe.setCutCallback(function(cutText){
// Do something here when text is cut
});
CopyMe.initialize();
}
</script">
That's all there is to it! In addition to defining handlers that handle the copy events,
CopyMe lets one review all the text that has been copied or cut by the user.
CopyMe.getAllCopiedOrCutText();
CopyMe.getLastCopiedOrCutText();


TRAINSPOTTING A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): * 1/2
The cardinal rule for making a reviewer's life easier is to give high marks to everything you see. You especially want to praise popular movies, and most of all you want to save your highest rating for cult pictures with strong and vociferous followings. Nothing like grade inflation and ignoring your instincts for making your own life more bearable. I consistently ignore this rule. I am generally in the mainstream of reviewers, but when I can not stomach a show as I couldn't in TRAINSPOTTING I say so knowing full well I will get complaints. Less than full honesty isn't fair to my readers.
TRAINSPOTTING was a smashing success last year in Britain and made buckets of money. Its coming to America has been perhaps the cinematic art house event of the year.
TRAINSPOTTING is a far, far out comedy about a group of young heroin addicts in Scotland. Set in Edinburgh, this extremely explicit depiction of the heroin drug culture focuses on Mark Renton and his battle with heroin. He loves shooting up, but does on occasion try to give it up. Mark is played by Ewan McGregor who was Frank Churchill in EMMA and Alex Law in SHALLOW GRAVE, which is director Danny Boyle's only other film. For my money, SHALLOW GRAVE, flaws and all, is a much better film than TRAINSPOTTING.
Mainly TRAINSPOTTING focuses on the camaraderie of the druggies and of their humor. The ongoing joke has them trying just hard enough to get a job so they don't get kicked off the dole, but being sure that they are so repulsive that no one will actually hire them.
The dialog by John Hodge and based on Irvine Walsh's novel has many funny lines. If the images were not so horrible, perhaps I could have enjoyed them. As Mark Renton puts it, "Choose life. Choose a job. Choose a starter home. Choose dental insurance, leisure wear and matching luggage. Choose your future. But why would anyone want to do a thing like that?"
Most of the humor is visual with a subtext of isn't addition fun and funny at the same time. This makes for a hard to watch film, which isn't necessarily bad. I enjoyed HAROLD AND MAUDE about suicide and a lot of other films with shocking subjects, but in TRAINSPOTTING the filmmakers are so far over the line, that I could not like the picture no matter how hard I tried. I kept thinking to myself, what is next?
Let me give you two typical scenes and then I will be done with this review. Mark's friend Spud (Ewen Bremmer) passes out and then ends up spending the night at his girlfriend's house. What with his going on and off of drugs, he has developed diarrhea.  He takes his sheets into the dinning room where his girlfriend and her family are having breakfast. Her mother and he get in a tug of war over the sheets which ends with realistic feces going all over everyone and into the breakfast. The lingering camera makes sure you see in detail the feces dripping down each of their faces.
In another scene, Mark dives into a public toilet labeled, "the worst toilet in Scotland." He dives into the bowl to retrieve his lost suppositories.
For completeness sake, let me say that the rest of the cast includes: Jonny Lee Miller as Sick Boy, Kevin McKidd as Tommy, Robert Carlyle as Begbie, Kelly MacDonald as Diane, and Peter Mullan as Swanney. Although some of the cast do not use heroin, most shot up regularly and feed their habit through a combination of taxpayer's money and stealing from people.
TRAINSPOTTING runs 1:33. It is rated R, but why it is not NC-17 is beyond me. In addition to the frequent heroin usage scenes, there is violence, constant foul language, sex, and full male and female frontal nudity. Since the MPAA gave this film an R, they might as well just retire the NC-17 rating. I strongly recommend that teenagers not see this film at all. For adults, I will grant you that the show is imaginative and the dialog sharp, but I can not recommend this painful show to anyone. For a show I truly hated, I still will give it * 1/2 for the high energy and the clever albeit unbearable script. As it is, I feel like I am being ridiculously generous to the film.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: September 12, 1996
Opinions expressed are mine and not meant to reflect my employer's.
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| 013:027 Khan | : |
And those who disbelieve say: "Why is not a sign sent down to him (Muhammad SAW) from his Lord?" Say: "Verily, Allah sends astray whom He wills and guides unto Himself those who turn to Him in repentance." |
| 013:027 Maulana | : |
And those who disbelieve say: Why is not a sign sent down to him by his Lord? Say: Allah leaves in error whom He pleases, and guides to Himself those who turn (to Him -- |
| 013:027 Pickthal | : |
Those who disbelieve say: If only a portent were sent down upon him from his Lord! Say: Lo! Allah sendeth whom He will astray, and guideth unto Himself all who turn (unto Him), |
| 013:027 Rashad | : |
Those who disbelieve would say, "If only a miracle could come down to him from his Lord (we would believe)." Say, "GOD sends astray whomever He wills, and guides to Him only those who obey." |
| 013:027 Sarwar | : |
The unbelievers say, "Why have not some miracles been sent to him, (Muhammad), from his Lord." Say, "God causes whomever He wants to go astray and He guides those who turn to Him in repentance |
| 013:027 Shakir | : |
And those who disbelieve say: Why is not a sign sent down upon him by his Lord? Say: Surely Allah makes him who will go astray, and guides to Himself those who turn (to Him). |
| 013:027 Sherali | : |
And those who disbelieves say, 'Why is not a Sign sent down to him from his Lord?' Say, 'ALLAH leaves to go astray whom HE wills and guides to Himself those who turn to Him; |
| 013:027 Yusufali | : |
The Unbelievers say: "Why is not a sign sent down to him from his Lord?" Say: "Truly Allah leaveth, to stray, whom He will; But He guideth to Himself those who turn to Him in penitence,- |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > مشيئته Pillars of Islam> Islamic> Oneness of Allah> His Will أركان الإسلام > التوحيد > الكافرون > تعنت الكفار واستعجالهم بالعذاب Pillars of Islam> Islamic> The Disbelievers> The Disbelievers' Stubbornness and their Acceleration to Torture أركان الإسلام > (محمد (صلى الله عليه وسلم > تأييد رسالته Pillars of Islam> The Blessed Muhammad(PBUH)> Supporting his Message الإيمان > المؤمنون > ما أعده الله لهم Faith> The Believers> What has Allah Prepared for them الإيمان > المؤمنون > وعده إياهم Faith> The Believers> His Promise to them الإنسان والعلاقات الاجتماعية > الإنسان > مافي صدره Man and The Social Relations>Man> What is in his Chest
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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Description:
Use the markup skills learned during the first week of class to create a one page HTML document. The HTML document should contain your resume. Try to write something useful, but don't fuss over it unduly. The key to success is iteration. At first, aim only to create a rough draft. For inspiration, review the example provided below; you can use elements from it but don't make your resume look the same!
Use the HTML tags covered in the last assignment, plus the anchor, ul, and li tags. Craft your page by hand. Don't use a tool or service to help you create the document and then say "Save as HTML." Build it yourself by hand. If it's got a lot of weird tags in it that we haven't discussed, I'll notice and grade accordingly.
Follow the links here to learn more about a few tags we use quite often:
Just submit an html document at this time. It should not contain CSS and does not have to be hosted on a web server yet.
Requirements:
- Put your work in a file called resume.html in the root of your repository.
- The code should formatted appropriately formatted using the tags from the previous assignments and in class discussions.
- Utilize at least one unordered list.
- You may include images, but you are not required to do so.
- Specify your name, work history, skills summary, and education (see the example below).
- Use your real name. List an address and phone contact, but it does not need to be authentic.
Turn it in
Add your resume to your Git repository as resume.html. You may not compress the file as a zip. Just attach the raw html and css files.
Example Resume
Below you will find an example resume on which you can base portions of your work. Readability is the key -- you really want that job at Microsft, Amazon, Google or a startup. This image shows the resume with no CSS applied to it. You should be able to dupliucate this more or less exactly, but of course with information about yourself, not Wanda Higdon.


Copyright (c) 1977 László Nagy
The game is played on the following board with this setup:
|
Some notes found the ZRF file: The more opposing balls you can drive out to the edge of the board (into a passive position) the less rows will there be where your opponent’s balls outnumber yours
| An example If Black shifts down the e column, White can win in 2 turns. He shifts down column d. Then Black can only shifts up column e, which the reply left shift of row 5 gives a win to White. |