Introduction:
Hello everyone. This is a new Blog series on Concepts in Machine Learning and Deep Learning. In this I will be covering almost all Machine Learning and Deep Learning concepts and coding and explaining the algorithms from scratch. For coding, I will be using Python as the language and the code will be posted on the blog as a Python Jupyter Notebook and will also be available on my Gihub Repository to download.
But before diving deep into the concepts, let's first get to know that why we need to learn Machine Learning !!!
What is Artificial Intelligence [AI] ?
We all had a moment sometime back where whe have thought that what is AI ? When we think about AI, what comes to our mind is something like a Terminator or the SkyNet that takes over all the humans killing all of us !!! Well, not that easy.
There will be another set of people thinking of AI as a Virtual Assistant something like JARVIS from Iron Man.
You can talk to it, make it do all your work while you enjoy your drink at a beach. Sounds nice :D
But why should I learn AI ?
With the continuous development in the field of AI, and lots of Research papers being published almost every 10 days by companies like OpenAI, Facebook AI Research, Google Brain, DeepMind etc. this field is the fastest growing field.
We now have Generative Networks that can generate a video of a Leader by looking at his/her videos and with any audio which a person wants, it can lead to riots or even war. If the people would have had access to this technology during the World War it might have lead to a much worse war, maybe even leading to the extinction of the human race.
So, in order to either avoid the AI Apoclypse or to make our own JARVIS, we need to learn these things either way. Now you would ask that when we know that AI development can cause an AI Apoclypse, then why even study it !!! Why all the research in this field ?? The point is that we are not there yet but will be eventually one day. And at that time, if you even know that how these things work even on the top, you might be able to prevent that.
AI is not thought only in terms of destruction but also as something constructive. Imagine that in future AI is able to read your mind and do the things you want it to do [Neuralink] or maybe you give it a problem like "Solve Poverty" or "Solve Drought" and it is able to solve them all. That way we can all have a better future.
Machine Learning has been around since the late 50's but at that time neither did we had enough data nor did we have enough compute power as we have today. So, in my view, this time which we are living in is the best time to learn Machine Learning.
Earlier we used to have a simple CPU that could do some simple calculations whereas now we have powerful CPU's and GPU's all on the same desktop/laptop or even mobile devices. Also, the data has been growing by a large extent and is still growing. These advances lead us to efficiently use the fields of Artificial Intelligence, Big Data Analysis etc. to make predictions or use the data productively.
Applications of AI [ML/DL/CV]
So, where is this AI used ?? Whats the use of studying anything that dosen't has any usecase. Well, we all use AI everyday, almost every moment of our life knowingly or unknowingly. Some of the applications of Artificial Intelligence [ML/DL/CV] in our day to day lives are:
- Self Driving Cars using Computer Vision [Tesla, Google, NVIDIA]
- Virutal Assistants like Google Home, Siri, Alexa, Bixby etc. [Google, Amazon, Apple, Samsung]
- Market Stock Predictions.
- Sentiment Analysis.
- Artifying Images [Prisma]
- Self-Aware Robots [Nao]
- Heart Sound Analysis.
- News generation, Article Summarizer etc.
- Face Recognition and a lot more. . .
So, now you would be thinking, "Good Stuff there, but, how do I actually get started with all this ??". This is my main motivation for writing this Blog series. I think that education should be free and easily accessable to all. Through these Blogs, I will try my best to present these concepts as clearly as possible.
The code tutorials expect you to be a begenner with no experience at all in Machine Learning or Deep Learning. We will start everything from scratch. The knowledge of basic Python syntax and some of its in-built libraries will be useful.
In these tutorials, you can make use of your laptop/desktop or if you think you require more compute power you can spin up a EC2 server on Amazon Web Services or even Google Cloud. Both of these provide with a year long free access to their resources.
Great work on completing this tutorial, let's move to the next tutorial in series, Introduction to Machine Learning: Machine Learning Basics
For more projects and code, follow me on Github
Please feel free to leave any comments, suggestions, corrections if any, below.
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Source code for FPSDP.GeneralSettings.UnitSystem
"""Unit Systems defined in this module
"""
#module depends on the built-in library 'math'
import math
[docs]class UnitError(Exception):
"""Unit related Exception class.
"""
def __init__(this, *a):
this.args = a
[docs]class UnitSystem:
"""Base class for unit systems
Attributes:
_ConstDic: dictionary contains all the useful physical constants.
list_const: returns the _ConstDic dictionary.
add_const: add new const into the _ConstDic.
del_const: remove existing const from _ConstDic.
rename_const: change the key for a const to obey user defined synbol rules.
"""
def __init__(this, name, **CD):
"""Initialize with a name and/or a keyword argument list containing the constants
"""
this.name = name
this._ConstDic = dict(**CD)
def __getitem__(this,cname):
"""get consts by the name
"""
try:
return this._ConstDic[cname]
except KeyError:
print 'const "{0}" not defined in unit "{1}"'.format(cname,this.name)
raise
except:
raise
[docs] def list_const(this):
""" returns the dictionary contains all the (name,value) pairs of the consts
"""
return this._ConstDic
[docs] def add_const(this, **C):
"""Add one or more constants using a keyword argument list.
If no argument is passed, a UnitError exception is raised.
If one of the passed keys already exists, the value passes will be conpared with the value
stored. If the relative difference is less than 10e-4, the value will be CHANGED to the new
value. However, if the difference is larger than that, a UnitError exception will be raised.
"""
if not C:
raise UnitError("Keyword arguments needed for adding new constants to the unit system.")
else:
for key in C.keys():
if key in this._ConstDic.keys():
dif = ( float(C[key])-this._ConstDic[key] ) / C[key]
if dif <= 1e-4:
this._ConstDic[key] = C[key]
continue
else:
raise UnitError("Const value conflict! Please double check the unit system you are using!\nPossibly a name conflict, check the names as well.")
else:
this._ConstDic[key] = C[key]
[docs] def del_const(this, *names):
"""Delete the consts for given names.
If no name is given, a UnitError exception will be raised.
If a given name doesn't exist, a UnitError exception will be raised.
"""
if not names:
raise UnitError("No names passed for deletion.")
else:
for n in names:
if not ( n in this._ConstDic.keys() ):
raise UnitError("name {0} not found for deletion.".format(n))
else:
del this._ConstDic[n]
[docs] def rename_const(this, **NameDic):
"""Change the name of a exsiting constant.
Keyword arguments are accepted with the key being the EXISTING name and the value being the
new name
If no argument is passed, nothing will happen.
If keywords contain non-existing names, a UnitError exception will be raised.
"""
for oldname in NameDic.keys():
if not (oldname in this._ConstDic.keys() ):
raise UnitError("Const {0} dosen't exist, can not rename it".format(oldname))
else:
this._ConstDic[ NameDic[oldname] ] = this._ConstDic[oldname]
del this._ConstDic[oldname]
# some pre-defined unit systems
# quantities' values based on NRL Plasma Formulary Revised 2009
cgs = UnitSystem('cgs',
# elementary charge
e = 4.8032e-10,
# electron mass
m_e = 9.1094e-28,
# proton mass
m_p = 1.6726e-24,
# speed of light
c = 2.9979e10,
# energy associated with 1keV in erg
keV = 1.6022e-9
# permittivity of free space (not used)
# eps_0 = 1,
# permeability of free space (not used)
# mu_0 = 1,
# more constants can be added later...
)
SI = UnitSystem('SI',
# elementary charge
e = 1.6022e-19,
# electron mass
m_e = 9.1094e-31,
# proton mass
m_p = 1.6726e-27,
# speed of light
c = 2.9979e8,
# permittivity of free space
eps_0 = 8.8542e-12,
# permeability of free space
mu_0 = 4e-7 * math.pi,
# energy associated with 1keV
keV = 1.6022e-16,
# Planck constant * speed of light
hc = 1.986445e-25,
#atomic mass unit
amu = 1.660538e-27
# more constants can be added later...
)


Proposed legal settlement to aid special education students
COLUMBUS, Ohio (AP) - Special education students from several Ohio school districts are expected to receive additional academic support aimed at improving learning and testing outcomes under a proposed settlement of a 28-year lawsuit.
Cleveland.com reports the proposed settlement was announced recently by Disability Rights Ohio, the Judge Bazelon Center for Mental Health Law and the Ohio Department of Education. It will impact 11 districts including Cleveland, Cincinnati and Columbus where special education students have lower academic outcomes.
A Disability Rights Ohio analysis says only 38.5% of the students in those districts learned in integrated settings with students outside of special education, compared to 65.1% in other districts. The group says research shows a correlation between such integration and special education achievement.
Special education students’ parents can review and comment on the proposal.
Information from: cleveland.com, http://www.cleveland.com
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05/08/2003
Calendar
A calendar is a system of organizing days for social, religious, commercial, or administrative purposes. This is done by giving names to periods of time, typically days, weeks, months, and years. The name given to each day is known as a date. Periods in a calendar (such as years and months) are usually, though not necessarily, synchronized with the cycle of the sun or the moon.
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Architecture
Architecture is both the process and product of planning, designing and construction. Architectural works, in the material form of buildings, are often perceived as cultural symbols and as works of art. Historical civilizations are often identified with their surviving architectural achievements.
Architecture Group 2
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01/16/2011
Autumn
Autumn or Fall is one of the four temperate seasons. Autumn marks the transition from summer into winter, in September (Northern Hemisphere) or March (Southern Hemisphere) when the arrival of night becomes noticeably earlier. The equinoxes might be expected to be in the middle of their respective seasons, but temperature lag (caused by the thermal latency of the ground and sea) means that seasons appear later than dates calculated from a purely astronomical perspective.
Nature Group 3
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Plugin Information |
|---|
View Simple Build DSL for Pipeline on the plugin site for more information. |
This is a work in progress and semantics may change.
For users of Jenkins Pipeline.
This (preview release) plugin provides a Pipeline DSL extension that should be familiar to people used to things like travisci. It is declarative and takes care of common tasks. It works well with pipeline-as-code and docker.
Some examples:
This is a simple Jenkinsfile. Put it in your repo as a Jenkinsfile and create a new multibranch project pointing to it:
simpleBuild { env = [ FOO : 42, BAR : "YASS" ] before_script = 'echo before' script = 'echo after $FOO' after_script = 'ls -lah' notifications = [ email<EMAIL_ADDRESS>] }
This sets 2 environment variables (env), and runs those scripts. Should it fail, it will notify those mentioned in the email (can be a list) of the failure and some details. This should be almost intuitive.
Some more features:
simpleBuild { machine = "hi-speed" script = 'make install' }
This will look for a "label" and run the build on a node that has that label. This is like saying "restrict where this can run" on freestyle.
Wait, there's more:
simpleBuild { docker_image = "java:1.9" script = 'make install' }
This will run the build inside a docker container that matches the image specified (using docker-pipeline plugin).
If you don't want to use Jenkinsfile in a repo, you can do this:
simpleBuild { git_repo =<EMAIL_ADDRESS>script = 'make install' }
It will then be like a normal pipeline Job, and clone the repo specified.
Remember this is a pipeline DSL, so it IS PIPELINE SCRIPT! So you can use other pipeline steps and constructs alongside it (some things make more sense than others...) but the idea of simpleBuild is to make a simple build... uh... simple. You can "graduate" to richer pipeline scripts if and when you need them.
See the README for more details.


First, the basics.
Headings set your default page size
This page covers common image formats, but you may also be looking for links and useful extras.
JPG images are good for photos.
Add your four mochi images from your tutorial here.
Let's make an unordered list.
And now, one in order.
Sometimes you want to draw attention to something in a line of text.
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Sometimes you want to do both.
Soft line breaks can't wrap anything.
Neither can image tags, or hard lines.
Add a hard line under here.
This page probably doesn't look great - we'll learn how to hook it to CSS next week.


Tutorial_02_01:We create a simple simulation of a coil falling under gravity and bouncing off of a floor. For the coil we use a ring of current. The ring of current has zero current and there are no other electromagnetic objects to interact with, so there are no electromagnetic interactions in this simulation. The collision with the floor is perfectly elastic, and there is no friction in the model, so the motion repeats indefinitely.
|


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Jose Mourinho told to remove Amazon microphone before Tottenham clash at West Ham
by Magnus EriksenTottenham manager Jose Mourinho was reportedly instructed to remove the microphone he was wearing before Saturday’s win over West Ham.
As part of an Amazon documentary on Spurs, Mourinho has agreed to have a mic attached to him for training sessions and possibly even some matchday situations, though he was advised not to wear one this weekend, according to The Athletic.
MORE: Jose Mourinho told to consider giving up on football management completely if he fails at Tottenham
The Portuguese tactician did make a point of checking with referee Michael Oliver before the game, and he advised him against wearing it before the Premier League intervened and made it clear they did not think it would be appropriate, according to The Athletic.
Even if we didn’t get to hear everything Mourinho was saying during his first game as Tottenham manager, this documentary on the north London club should make for intriguing viewing.
Mourinho in particular is known for being a real character and, for better or for worse, tends to bring drama everywhere he goes.
The 56-year-old was announced as the new Spurs boss last week after the sacking of Mauricio Pochettino.


{% extends 'base.html' %} {% block content %} {% load static %}
You can view and download the labelled results.


Ever wish your JetBrains IDE was smart enough to turn on/off Power Saver mode automatically when switching into / out of your IDE?
You have the Automatic Power Saver plugin installed - it's just a matter of configuring your preferences:


This allows endless customizability and allows you, the user, to make the saber of your dreams. All you have to do is browse our catalogue of free 3D part files and choose the ones you like, then download them. From there you can print your files in whatever colors or materials you would like, or paint them, and then assemble your saber... It's as simple as that. Make sure to check back regularly for our ever growing inventory of parts.


Slides for my talk, Testing the Multiverse, are up at https://speakerdeck.com/jasonrclark/testing-the-multiverse-os-bridge-2015 #osb15. Thanks everyone who came to listen!
6/25/2015, 8:30:06 AM
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link--- layout: tweet ---
Slides for my talk, Testing the Multiverse, are up at https://speakerdeck.com/jasonrclark/testing-the-multiverse-os-bridge-2015 #osb15. Thanks everyone who came to listen!
6/25/2015, 8:30:06 AM
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link


Welcome to SimpleFrame a simple Framework (?) under development by James Williams
!
Important!
An incredibly awesome website developer.
Be sure to contribute to the Github file and share your code with us. We plan to continue to make this simple website development tool as easy as possible. Thanks.
One day, I to would like to be able to create a no hassle simple website. And this isn't really a good quote.


@@include('../partials/head/head.html')
@@include('../partials/components/form.html') @@include('../partials/scripts/common.html')


Webida is a development and deployment system for HTML5 apps and pages. It provides a powerful IDE which runs in your browser and other tools to create, manage, and deploy your projects.
Currently, we are carrying out closed beta tests and our system is open only to invited users and our team members.
We hope you enjoy Webida system!
Webida has been tested only on Chrome 31+ and Firefox 31+ browser.
Compatibility with other browsers is not guaranteed yet.
We are sorry for the inconvenience.


BusMall Market Analysis
Out of the three images please vote which ones you'd be most likely to purchase.


SHERPA is a Python library for hyperparameter tuning of machine learning models. Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
SHERPA is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with SHERPA. If not, see <http://www.gnu.org/licenses/>.
Asynchronous Successive Halving¶
A module for asynchronous successive halving for search of hyperparameters for expensive NEORL applications.
Original paper: Li, Liam, et al. “A system for massively parallel hyperparameter tuning.” arXiv preprint arXiv:1810.05934 (2018).
Coming Soon!!!


Welcome to the F.H.H.L.K. Community Culture Center website. This state-of-the-art facility is comprised of four meeting rooms of various sizes and occupancies. The following services are offered in each room:
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| Room 4 | 3025 | 55ft. x 55ft. | 250 | Yes |


There are problems that exist in distributed system environments that do not exist in single-machine environments. Partial failure, concurrency, and latency are three problems that make distributed computing fundamentally different from local computing. In order to understand the design decisions behind programming languages and systems for distributed computing, it is necessary to discuss these three problems that make distributed computing unique. In this section, we present an overview of these three problems and their impact on distributed programming models.
In the case of a crash on a local environment, either the machine has failed (total failure), or the source of the crash can be learned from a central resource manager such as the operating system. (Waldo, Wyant, Wollrath, & Kendall, 1997) If an application consists of multiple communicating processes partial failure is possible, however because the cause of the partial failure can be determined, this kind of partial failure can be repaired given the operating system’s knowledge. For example, a process can be restored based on a checkpoint, another process in the application can query the operating system about the failed process’ state, etc.
Because failure in a distributed setting involves another player, the network, it is impossible in most cases to determine the cause of failure. In a distributed environment, there is no (reliable) central manager that can report on the state of all components. Further, due to the inherent concurrency in a distributed system, nondeterminism is a problem that must be considered when designing distributed models, languages, and systems. Communication is perhaps the most obvious example of this; messages may be lost or arrive out-of-order. Finally, unlike in a local environment where failure returns control to the caller, failure may not be reported or the response may simply vanish. Because of this, distributed communication must be designed expecting partial failure, and be able to “fail gracefully”.
Several methods have been developed to deal with the problem of partial failure. One method, made popular with batch processing and MapReduce style frameworks, is to remember the series of computations needed to obtain a result and recompute the result in the case of failure. Systems such as MapReduce, Spark, GraphX, and Spark Streaming use this model, as well as implement optimizations to make it fast. Another method of dealing with partial failure is the two phase commit. To perform a change of state across many components, first a logically central “leader” checks to see if all components are ready to perform and action. If all reply “yes,” the action is committed. Otherwise, as in the case of partial failure, no changes are committed. Two phase commit ensures that state is not changed in a partial manner. Another solution to partial failure is redundancy, or replication. If one replica of a computation fails, the others may survive and continue. Replication can also be used to improve performance, as in MapReduce and Spark Streaming. Checkpoint and restore has also been implemented as a way to recover from partial failure. By serializing a recent “snapshot” of state to stable storage, recomputing current state is made cheap. This is the primary method of partial failure in RDD-based systems. In other systems, like Argus, objects are reconstructed from state that is automatically or manually serialized to disk.
If computing on shared data can be avoided, parallel computations will not be bottlenecked by serialized accesses. Unfortunately, there are many instances where operating on shared data is necessary. While problems with shared data can be dealt with fairly simply in the local case, distribution introduces problems that make consistency more complex.
In local computing, enforcing consistency is fast and straightforward.  To operate on the data, a concurrent process acquires the lock, makes its changes, then releases the lock. Because the data is either located in on-board memory or an on-chip cache, passing the shared data around is relatively fast. As in the case of partial failure, a central resource manager (the OS) is present and can respond to a failed process that has obtained a lock.
In a distributed environment, coordination and locking is more difficult. First, because of the lack of a central resource manager, there needs to be a way of preserving or recovering the state of shared data in the case of failure. The problem of acquiring locks also becomes harder due to partial failure and higher latency. Synchronization protocols must expect and be able to handle failure.
To deal with operations on shared data, there are a few standard techniques. A sequencer can be used to serialize requests to a shared piece of data. When a process on a machine wants to write to shared data, it sends a request to a logically central process called a sequencer. The sequencer takes all incoming requests and serializes them, and sends the serialized operations (in order) to all machines with a copy of the data. The shared data will then undergo the same sequence of transformations on each machine, and therefore be consistent. A similar method for dealing with consistency is the message queue. In the actor model, pieces of an application are represented as actors which respond to requests. Actors may use message queues which behave similarly to sequencers. Incoming method calls or requests are serialized in a queue which the actor can use to process requests one at a time. Finally, some systems take advantage of the semantics of operations on shared data, distinguishing between read-only operations and write operations. If an operation is determined to be read-only, the shared data can be distributed and accessed locally. If an operation writes to shared data, further synchronization is required.
Unfortunately, none of these techniques can survive a network partition. Consistency requires communication, and a partitioned network will prevent updates to state on one machine from propagating. Distributed systems therefore may be forced by other requirements to loosen their requirement of consistency. Below, the CAP theorem formalizes this idea.
Latency is another major problem that is unique to distributed computing. Unlike the other problems discussed in this section, latency does not necessarily affect program correctness. Rather, it is a problem that impacts application performance, and can be a source of nondeterminism.
In the case of local computing, latency is minimal and fairly constant. Although their may be subtle timing differences that arise from contention from concurrent processes, these fluctuations are relatively small. As well, machine hardware is constant. There are no changes to the latency of communication channels on a single machine.
Distribution of computation introduces a network topology which changes many of the underlying assumptions around latency. This topology significantly (orders of magnitude) increases the latency of communication, as well as introduces a source of nondeterminism. At any time, routing protocols or hardware changes (or both) may cause the latency between two machines to change. Therefore, distributed applications may not rely on specific timings of communication in order to function. Distributed processes may also be more restricted. Because communication across the network is costly, applications may necessarily be designed to minimize communication.
A more subtle (and sinister) problem with increased latency and the network is the inability of a program to distinguish between a slow message and a failed message. This situation is analogous to the halting problem, and forces distributed applications to make decisions about when a message, link, or node has “failed.”
Several methods have been developed to cope with the latency of communication. Static and dynamic analysis may be performed on communication patterns so that entities that communicate frequently are more proximate than those that communicate infrequently. Another approach that has been used is data replication. If physically separate entities all need to perform reads on a piece of data, that data can be replicated and read from local hardware. Another approach is pipelining; a common example of this is in some flavors of the HTTP protocol. Pipelining requests allows a process to continue with other work, or issue more requests without blocking for the response of each request. Pipelining lends itself to an asynchronous style of programming, where a callback can be assigned to handle the results of a request. Futures and promises have built on this programming style, allowing computations to be queued, and performed when the value of a future or promise is resolved.
Indeed, the three problems outlined above are not independent, and a solution for one may come at the cost of amplifying the effects of another. For example, let’s suppose when a request to our system arrives, a response should be issued as soon as possible. Here, we want to minimize latency. Unfortunately, this may come at the cost of consistency. We are forced to either (1) honor latency and send a possibly inconsistent result, or (2) honor consistency and wait for the distributed system to synchronize before replying.
The CAP theorem (Gilbert & Lynch, 2002) formalizes this notion. CAP stands for Consistency, Availability, and tolerance to Partitioning. The theorem states that a distributed system may only have two of these three properties.
Since its introduction, experience suggests this theorem is not as rigid as was originally proposed (Brewer, 2012). In practice, for example, rareness of network partitioning makes satisfaction of all three easier. As well, advancements in consistency models, such as CRDT’s (Shapiro, Preguiça, Baquero, & Zawirski, 2011), make balancing consistency and availability flexible to the requirements of the system.
Clearly, there are problems present in distributed programming that prevent traditional local programming models from applying directly to distributed environments. Languages and systems built for writing distributed applications can be classified into three categories: distributed shared memory, actors, and dataflow. Each model has strengths and weaknesses. Here, we describe each model and provide examples of languages and systems that implement them.
Virtual memory provides a powerful abstraction for processes. It allows each process running on a machine to believe it is the sole user of the machine, as well as provide each process with more (or less) memory addresses than may be physically present. The operating system is responsible for mapping virtual memory addresses to physical ones and swapping addresses to and from disk.
Distributed shared memory (DSM) takes the virtual memory abstraction one step further by allowing virtual addresses to be mapped to physical memory regions on remote machines. Given such an abstraction, programs can communicate simply by reading from and writing to shared memory addresses. DSM is appealing because the programming model is the same for local and distributed systems. However, it requires an underlying system to function properly. Mirage, Orca, and Linda are three systems that use distributed shared memory to provide a distributed programming model.
Mirage is an OS-level implementation of DSM. In Mirage, regions of memory known as segments are created and indicated as shared. A segment consists of one or more fixed-size pages. Other local or remote processes can attach segments to arbitrary regions of their own virtual address space. When a process is finished with a segment, the region can be detached. Requests to shared memory are transparent to user processes. Faults occur on the page level.
Operations on shared pages are guaranteed to be coherent; after a process writes to a page, all subsequent reads will observe the results of the write. To accomplish this, Mirage uses a protocol for requesting read or write access to a page. Depending on the permissions of the current “owner” of a page, the page may be invalidated on other nodes. The behavior of the protocol is outlined by the table below.
| State of Owner | State of Requester | Clock Check? | Invalidation? |
|---|---|---|---|
| Reader | Reader | No | No |
| Reader | Writer | Yes | Yes, Requester is possibly sent current version of the page |
| Writer | Reader | Yes | No, Owner is demoted to Reader |
| Writer | Writer | Yes | Yes |
Crucially, the semantics of this protocol are that at any time there may only be either (1) a single writer or (2) one or more readers of a page. When a single writer exists, no other copies of the page are present. When a read request arrives for a page that is being written to, the writer is demoted to a reader. These two properties ensure coherence. Many read copies of a page may be present, both to minimize network traffic and provide locality. When a write request arrives for a page, all read instances of the page are invalidated.
To ensure fairness, the system associates each page with a timer. When a request is honored for a page, the timer is reset. The timer guarantees that the page will not be invalidated for a minimum period of time. Future request that result in invalidation or demotion (writer to reader) are only honored if the timer is satisfied.
Orca is a programming language built for distribution and is based on the DSM model. Orca expresses parallelism explicitly through processes. Processes in Orca are similar to procedures, but are concurrent instead of serial. When a process is forked, it can take parameters that are either passed as a copy of the original data, or passed as a shared data-object. Processes communicate through these shared objects.
Shared data objects in Orca are similar to objects in OOP. An object is defined abstractly by a name and a set of interfaces. An implementation of the object defines any private data fields as well as the interfaces (methods). Importantly, these interfaces are guaranteed to be indivisible, meaning that simultaneous calls to the same interface are serializable. Although serializability alone does not eliminate indeterminism from Orca programs, it keeps the model simple while it allows programmers to construct richer, multi-operation locks for arbitrary semantics and logic.
Another key feature of Orca is the ability to express symbolic data structures as shared data objects. In Orca, a generic graph type is offered as a first-class data-object. Because operations are serializable at the method level, the graph data-object can offer methods that span multiple nodes while still retaining serializability.
Linda is a programming model based on DSM. In Linda, shared memory is known as the tuple space; the basic unit of shared data is the tuple. Instead of processes reading and writing to shared memory addresses, processes can insert, extract, or copy entries from the tuple space. Under the Linda model, processes communicate and distribute work through tuples.
A Linda tuple is not a fixed size; it can contain any combination of primitive data types and values. To insert a tuple into the space, the fields of the tuple are fully evaluated before insertion. A process can decide to evaluate a tuple serially, or spin up a background task to first evaluate the fields, then insert the tuple. To retrieve a tuple from the space, a template tuple is provided that contains a number of fixed fields to match against, as well as formals that are “filled in” by the tuple that matches the search. If many tuples match the template, one is selected arbitrarily. When retrieving a tuple, the tuple may be left in the tuple space or removed.
In practice, the tuple space is disjointly distributed among the nodes in the cluster. The number and type of elements in a tuple defines the tuple’s class. All requests made for a particular class of tuple are sent through a rendezvous point, which provides a logically central way of performing book keeping about tuples. The rendezvous services requests for insertion and deletions of all tuples of a class. In the most basic implementation of Linda, each rendezvous point is located on a single participating node in the cluster.
Unlike DSM, communication in the actor model is explicit and exposed through message passing. Messages can be synchronous or asynchronous, point-to-point or broadcast style. In the actor model, concurrent entities do not share state as they do in DSM. Each process, object, actor, etc., has its own address space. The model maps well to single multicore machines as well as to clusters of machines. Although an underlying system is required to differentiate between local and remote messages, the location of processes, objects, or actors can be transparent to the application programmer. Erlang, Emerald, Argus, and Orleans are just a few of many implementations of the actor model.
Emerald is a distributed programming language based around a unified object model. Programs in Emerald consist of collections of Objects. Critically, Emerald provides the programmer with a unified object model so as to abstract object location from the invocation of methods. With that in mind, Emerald also provides the developer with the tools to designate explicitly the location of objects.
Objects in Emerald resemble objects in other OOP languages such as Java. Emerald objects expose methods to implement logic and provide functionality, and may contain internal state. However, their are a few key differences between Emerald and Java Objects. First, objects in Emerald may have an associated process which starts after initialization. In Emerald, object processes are the basic unit of concurrency. Additionally, an object may not have an associated process. Objects that do not have a process are known as passive and more closely resemble traditional Java objects; their code is executed when called by processes belonging to other objects. Second, processes may not touch internal state (members) of other objects. Unlike Java, all internal state of Emerald objects must be accessed through method calls. Third, objects in Emerald may contain a special monitor section which can contain methods and variables that are accessed atomically. If multiple processes make simultaneous calls to a “monitored” method, the calls are effectively serialized.
Emerald also takes an OOP approach to system upgrades. With a large system, it may not be desirable to disable the system, recompile, and re-launch. Emerald uses abstract types to define sets of interfaces. Objects that implement such interfaces can be “plugged in” where needed. Therefore, code may be dynamically upgraded, and different implementations may be provided for semantically similar operations.
Argus is a distributed programming language and system. It uses a special kind of object, called a guardian, to create units of distribution and group highly coupled data and logic. Argus procedures are encapsulated in atomic transactions, or actions, which allow operations that encompass multiple guardians to exhibit serializability. The presence of guardians and actions was motivated by Argus’ use as a platform for building distributed, consistent applications.
An object in Argus is known as a guardian. Like traditional objects, guardians contain internal data members for maintaining state. Unique to Argus is the distinction between volatile and stable data members. To cope with crashes, data members that are stable are periodically serialized to disk. When a guardian crashes and is restored, this serialized state is used to reconstruct guardians. Like in Emerald, internal data members may not be accessed directly, but rather through handlers. Guardians are interacted with through methods known as handlers. When a handler is called, a new process is created to handle the operation. Additionally, guardians may contain a background process for performing continual work.
Argus encapsulates handler calls in what it calls actions. Actions are designed to solve the problems of consistency, synchronization, and fault tolerance. To accomplish this, actions are serializable as well as total. Being serializable means that no actions interfere with one another. For example, a read operation that spans multiple guardians either “sees” the complete effect of a simultaneous write operation, or it sees nothing.  This is accomplished by a two-phase commit protocol that serializes the state of all guardians involved in an action, or discards partial state changes.
Erlang is a distributed language which combines functional programming with message passing. Units of distribution in Erlang are processes. These processes may be co-located on the same node or distributed amongst a cluster.
Processes in Erlang communicate by message passing. Specifically, a process can send an asynchronous message to another process’ mailbox. At some future time, the receiving process may enter a receive clause, which searches the mailbox for the first message that matches one of a set of patterns. The branch of code that is executed in response to a message is dependent on the pattern that is matched.
In general, an application written in Erlang is separated into two broad components: workers and monitors.
Workers are responsible for application logic.
Erlang offers a special function link(Pid)
which allows one process to monitor another.
If the process indicated by Pid
fails, the monitor process will be notified and is expected to handle the error.
Worker processes are “linked” by monitor processes which implement the fault-tolerance logic of the application.
Erlang, first implemented in Prolog, has the features and styles of a functional programming language. Variables in Erlang are immutable; once assigned a value, they cannot be changed. Because of this, loops in Erlang are written as tail recursive function calls. Although this at first seems like a flawed practice (to traditional procedural programmers), tail recursive calls do not grow the current stack frame, but instead replace it. It is worth noting that stack frame growth is still possible, but if the recursive call is “alone” (the result of the inner function is the result of the outer function), the stack will not grow.
Orleans is a programming model for distributed computing based on actors. An Orleans program can be conceptualized as a collection of actors. Because Orleans is intended as a model for building cloud applications, actors do not spawn independent processes as they do in Emerald or Argus. Rather, Orleans actors are designed to execute only when responding to requests.
As in Argus, Orleans encapsulates requests (root function calls) within transactions. When processing a transaction, function calls may span many actors. To ensure consistency in the end result of a transaction, Orleans offers another abstraction, activations, to allow each transaction to operate on a consistent state of actors. An activation is an instance of an actor. Activations allow (1) consistent access to actors during concurrent transactions, and (2) high throughput when an actor becomes “hot.” Consistency is achieved by only allowing transactions to “touch” activations of actors that are not being used by another transaction. High throughput is achieved by spawning many activations of an actor for handling concurrent requests.
For example, suppose there is an actor that represents a specific YouTube video.
This actor will have data fields like title
, content
, num_views
, etc.
Suppose their are concurrent requests (in turn, transactions) for viewing the video.
In the relevant transaction, the num_views
field is incremented.
Therefore, in order to run the view requests concurrently, two activations (or copies) of the actor are created.
Because there is concurrency within individual actors, Orleans also supports means of state reconciliation. When concurrent transactions modify different activations of an actor, state must eventually be reconciled. In the case of the above example, it may not be necessary to know immediately the exact view count of a video, but we would like to be able to know this value eventually. To accomplish reconciliation, Orleans provides data structures that can be automatically merged. As well, the developer can implement arbitrary logic for merging state. In the case of the YouTube video, we would want logic to determine the delta of views since the start of the activation, and add that to the actors’ sum.
In the dataflow model, programs are expressed as transformations on data. Given a set of input data, programs are constructed as a series of transformations and reductions. Computation is data-centric, and expressed easily as a directed acyclic graph (DAG). Unlike the DSM and actor models, processes are not exposed to the programmer. Rather, the programmer designs the data transformations, and a system is responsible for initializing processes and distributing work across a system.
MapReduce is a model and system for writing distributed programs that is data-centric. Distributed programs are structured as series of Map and Reduce data transformations. These two primitives are borrowed from traditional functional languages, and can be used to express a wide range of logic. The key strength of this approach is that computations can be reasoned about and expressed easily while an underlying system takes care of the “dirty” aspects of distributed computing such as communication, fault-tolerance, and efficiency.
A MapReduce program consists of a few key stages. First, the data is read from a filesystem or other data source as a list of key-value pairs. These pairs are distributed amongst a set of workers called Mappers. Each mapper processes each element in its partition, and may output zero, one, or many intermediate key-value pairs. Then, intermediate key-value pairs are grouped by key. Finally, Reducers take all values pertaining to an intermediate key and output zero, one, or many output key-value pairs. A MapReduce job may consist of one or many iterations of map and reduce.
Crucially, for each stage the programmer is only responsible for programming the Map and Reduce logic. The underlying system (in the case of Google, a C++ library), handles distributing input data and shuffling intermediate entries. Optionally, the user can implement custom logic for formatting input and output data.
An example program in MapReduce is illustrated below. First, the input file is partitioned and distributed to a set of worker nodes. Then, the map function transforms lines of the text file into key-value pairs in the format (< word >, 1). These intermediate pairs are aggregated by key: the word. In the reduce phase, the list of 1’s is summed to compute a wordcount for each word.
Discretized Streams is a model for processing streams data in real-time based on the traditional dataflow paradigm. Streams of data are “chunked” discretely based on a time interval. These chunks are then operated on as normal inputs to DAG-style computations. Because this model is implemented on top of the MapReduce framework Spark, streaming computations can be flexibly combined with static MapReduce computations as well as live queries.
Discretized Streams (D-Streams) are represented as a series of RDD’s, each spanning a certain time interval. Like traditional RDD’s, D-Streams offer stateless operations such as map, reduce, groupByKey, etc., which can be performed regardless of previous inputs and outputs. Unlike traditional RDD’s, D-Streams offer stateful operations. These stateful operations, such as runningReduce, are necessary for producing aggregate results for a possibly never-ending stream of input data.
Because the inputs are not known a priori, fault tolerance in streaming systems must behave slightly differently. For efficiency, the system periodically creates a checkpoint of intermediate data. When a node fails, the computations performed since the last checkpoint are remembered, and a new node is assigned to recompute the lost partitions from the previous checkpoint. Two other approaches to fault tolerance in streaming systems are replication and upstream backup. Replication is not cost effective as every process must be duplicated, and does not cover the case of all replicas failing. Upstream backup is slow as the system must wait for a backup node to recompute everything in order to recover state.
Many real world problems are expressed using graphs. GraphX is a system built on top of the Spark MapReduce framework (Zaharia et al., 2012) that exposes traditional graph operations while internally representing a graph as a collection of RDD’s. GraphX exposes these operations through what it calls a Resilient Distributed Graph (RDG). Internally, an RDG is a collection of RDD’s that define a vertex split of a graph (Gonzalez, Low, Gu, Bickson, & Guestrin, 2012). Because they are built on top of RDD’s, RDG’s inherit immutability. When a transformation is performed, a new graph is created. In this way, fault tolerance in GraphX can be executed the same way as it is in vanilla Spark; when a fault happens, the series of computations is remembered and re-executed.
A key feature of GraphX is that it is a DSL library built on top of a GPL library. Because it uses the general purpose computing framework of Spark, arbitrary MapReduce jobs may be performed in the same program as more specific graph operations. In other graph-processing frameworks, results from a graph query would have to be written to disk to be used as input to a general purpose MapReduce job.
With GraphX, if you can structure your application logic as a series of graph operations, an implementation may be created on top of RDD’s. Because many real-world applications, like social media “connections,” are naturally expressed as graphs, GraphX can be used to create a highly scalable, fault-tolerant implementation.
Here, we present a taxonomy which can be used to classify each of the examples. Among these distributed systems appear to be three major defining characteristics: level of implementation, granularity, and level of abstraction. Importantly, these characteristics are orthogonal and each present an opportunity for a design decision.
The programming model exposed by each of the examples is implemented at some level in the computer system. In Mirage, the memory management needed to enable DSM is implemented within the operating system. Mirage’s model of DSM lends itself to an OS implementation because data is shared by address. In other systems, such as Orca, Argus, and Erlang, the implementation is at the compiler level. These are languages that support distribution through syntax and programming style. Finally, some systems are implemented as libraries (e.g. Linda, MapReduce). In such cases, the underlying language is powerfull enough to support desired operations. The library is used to ease programmer burden and supply domain-specific syntax.
The pros and cons of different implementation stategies is discussed further under DSL’s as Libraries.
The granularity of logic and state is another major characteristic of these systems. Generally, the actor and DSM models can be considered fine grain while the dataflow model is course grain.
The actor and DSM models can be considered fine grain because they can be used to define the logic and states of individual workers. Under the DSM model, an application may be composed of many separately compiled programs. Each of these programs captures a portion of the logic, communication being done through shared memory regions. Under the actor model, actors or objects may be used to wrap separate logic. These units of logic communicate through RPC or message-passing. The benefit to using the actor or DSM model is the ability to wrap unique, cohesive logic in modules. Unfortunately, this means that the application or library developer is responsible for handling problems such as scaling and process location.
The dataflow model can be considered course grain because the logic of every worker is defined by a single set of instructions. Workers proceed by receiving a partition of the data and the program, and executing the transformation. Crucially, each worker operates under the same logic.
To borrow an idea from traditional parallel programming, the actor/DSM model implements a multiple instruction multiple data (MIMD) architecture, whereas the dataflow model implements a single instruction multiple data (SIMD) architecture.
Each of the examples of systems for distributed computing offer different levels of abstraction from problems like partial-failure. Depending on the requirements of the application, it may be sufficient to let the system handle these problems. In other cases, it may be necessary to be able to define custom logic.
In some systems, like Emerald, it is possible to specify the location of processes. When the communication patterns of the application are known, this can allow for optimizations on a per-application basis. In other systems, the system handles the resource allocation of processes. While this may ease development, the developer must trust the system to make the decision.
The actor and DSM models require the application developer to create and destroy individual processes. This means that either the application must contain logic for scaling, or a library must be developed to handle scaling. Systems that implement the dataflow model handle scaling automatically. An exception to this rule is Orleans, which follows the actor model but handles scaling automatically.
Final, fault tolerance is abstracted to varying degrees in these systems. Argus exposes fault tolerance to the programmer; object data members are labeled as volatile or stable. Periodically, these stable members are serialized to disk and can be used for object reconstruction. Other systems, especially those based on dataflow, fully abstract the problem of partial failure.
The definition of a domain-specific language is a hot topic and there have been several attempts to concretely define what exactly it is.
Here is the definition as given by (Mernik, Heering, & Sloane, 2005):
Domain-specific languages are languages tailored to a specific application domain.
Another definition is offered (and commonly cited) by (van Deursen, Klint, & Visser, 2000):
A domain-specific language is a programming language or executable specification language that offers, through appropriate notations and abstractions, expressive power focused on, and usually restricted to, a particular problem domain.
Generally, I would refer to a domain-specific language (DSL) as a system, be it a standalone language, compiler extension, library, set of macros, etc., that is designed for a set of cohesive operations to be easily expressed.
For example, the Python Twitter library is designed for easily expressing operations that manage a Twitter account.
The problem in defining this term (I believe) is the the vagueness of the components domain and language. Depending on the classification, a set of problems designated in a certain domain may span a “wide” or “narrow” scope. For example, does “tweeting” qualify as a domain (within the Twitter library)? Would “social media sharing” qualify as a domain (containing the Twitter library)? For my purposes I will accept the definition of a domain as a “well-defined, cohesive set of operations.”
It is also difficult to come up with a definition for a language. A language may be qualified if it has its own compiler. An orthogonal definition qualifies a language by its style, as in the case of sets of macros. This confusion is why I adopt the even more vague term system in my own definition.
Given the examples of models and systems, I think it is reasonable to qualify distribution as a domain. Distributed computing has a unique set of problems, such as fault-tolerance, nondeterminism, network partitioning, and consistency that make it unique from parallel computing on a single machine. The languages, systems, and models presented here all are built to assist the developer in dealing with these problems.
The examples given above demonstrate a trend. At first, systems designed to tackle the distribution domain were implemented as stand-alone languages. Later, these systems appear as libraries built on top of existing general-purpose languages. For many reasons, this style of system development is superior.
Systems like GraphX and Spark Streaming demonstrate a key benefit of developing DSL’s as libraries: composition. When DSL’s are implemented on top of a common language, they may be composed. For example, a C++ math library may be used along with MapReduce to perform complex transformations on individual records. If the math library and MapReduce were individual languages with separate compilers, composition would be difficult or impossible. Further, the GraphX system demonstrates that domains exist at varying degrees of generality, and that building the library for one domain on top of another may result in unique and efficient solutions. DSL’s that are implemented as full languages with unique compilers are unattractive because existing libraries that handle common tasks must be re-written for the new language.
Another problem that drives DSL development towards libraries is ecosystem. In order for a DSL to be adopted, there must be a body of developers that can incorporate the DSL into existing systems. If either (1) the DSL does not incorporate well with existing code bases or (2) the DSL requires significant investment to learn, adoption will be less likely.


Version 2.10.0
Released on Friday, October 11, 2013
General
The Trifolia development team has implemented new technology so the tool will perform with increased response times and behave in a more user-friendly manner, particularly as its user-base grows. Users may notice that Trifolia’s interface is migrating to a new application framework. As this migration occurs, menus, popups, and grids may look slightly different, they will, however, remain functionally the same.
Lantana is working with HL7 to release an “HL7 Member Write-Enabled” version of the tool. This new version will allow HL7 members to develop templates within Trifolia. The team has focused their QA efforts on security and auditing to ensure that when HL7 members begin to develop templates, they will not impact existing ones.
Navigation
Users now have the ability to quickly navigate between different templates while working in the template editor. There is a new drop down menu in the template editor that lists all the templates the user has access to. This can be filtered by typing in the text box of the drop-down menu, and selecting the ’Edit’ button.
Several buttons have been moved from the implementation guide and template list screen to the ‘View Implementation Guide’ and ‘View Template’ screens.
Simplified Versioning
In the last release, versioning implementation guides in Trifolia was made easier due to the ‘New Version’ button on the ‘View Implementation Guide’ screen. This release includes a similar functionality for templates. There is now a ‘New Version’ button on the ‘View Template’ screen. This automatically copies the templates into the new version of the implementation guide, and links the template with its old version. There is no longer a need to do this manually.
Publish Settings Validation
The ‘Publish Settings’ screen for templates now includes more user-input validation. For example, Trifolia will warn the user before saving a template sample without a name.
Exporting
Historically there has been a wait for other working groups to establish a standard template exchange format before an XML format could be provided. However, given the demand for access to computable template definitions, the Trifolia team has created a proprietary format which may be used until a standardized format is established. Users can now export their templates to XML via the ‘Export’ screen.
In addition, Trifolia has a new ‘Excel’ format for vocabulary exports. This option is available in the ‘Export Format’ drop-down menu in the ‘Export Vocabulary’ settings screen.
Workflow
In this release, templates now have a ‘status’ in addition to the implementation guide status. When editing a template, a ‘Status’ field is available to allow new versions of a template to indicate that it is deprecated.
The introduction of this status field provides the opportunity for Trifolia to make use of ‘Workflow’. Users can easily change the ‘Status’ of an implementation guide (and its associated templates) via a ‘Status’ drop-down menu on the ‘View Implementation Guide’ screen. Users may choose between ‘Draft,’ ‘Ballot,’ and ‘Published’ statuses.
Development Log
Improvement |
Template Editing Navigation |
|
Improvement |
UI Validating Publish Info |
|
Improvement |
Consistent approach to end-user messaging |
|
Improvement |
Action Confirmations/Messages |
|
New Feature |
Template copying and versioning improvements |
|
New Feature |
IG File Management |
|
New Feature |
Simplifying Versioning IGs |
|
New Feature |
IG and Template Status |
|
New Feature |
Export templates to XML |
|
New Feature |
Export Vocabulary Spreadsheet |


Physics Joints
A PhysX joint is a component that links PhysX actors to constrain their movement. There are multiple types of joints that each constrain the actors in different ways.
Joints can be used for simple things like door hinges, up to complex configurations like rag dolls.
Joining Actors
Joints can be used to link:
- Two dynamic actors. This will create a more complex dynamic object that can still freely move throughout the scene, but has multiple parts that can wiggle around.
- One static actor with one dynamic actor. In this case the dynamic actor is now constrained in its movement relative to the static actor. Since the static actor never moves, the dynamic actor's freedom is severely limited.
- One dynamic actor with no second actor. This just affixes the dynamic actor with 'the world'. This is effectively the same as joining it with another static actor.
- One dynamic actor with one kinematic actor. If the kinematic actor moves, the dynamic actor is forced to follow according to the constraint between them. The dynamic actor does not affect the kinematic actor in any way. This setup is common to have full control over the movement of one part (the kinematic actor) and still get physically plausible behavior from another part.
There are two ways that a joint component can link actors.
Using Object References
You can use the object references on the joint component to specify both the parent actor and the child actor:
With this method, the game objects for the actors and the joint don't need to have a specific hierarchy, they can be placed just next to each other. This also allows to create loops of linked actors. On the other hand, you always need to know exactly which actors shall be linked.
Note:
The
Child Actor
of a joint always has to be specified this way.
Using the Object Hierarchy
To determine which Parent Actor
to link the joint to, you can utilize the object hierarchy. If no parent actor is specified as an object reference, the joint will automatically traverse its object hierarchy upwards and search for the closest object with a static or dynamic actor. If it finds one, the joint will link to that object as the parent actor.
If it does not find any such actor on a parent node, the joint will attach to 'the world', meaning it will be fixed to its current world position.
This method can be useful especially when you want to put the joint into a prefab. For example, if you have a prefab for a chandelier that can swing around, you can set up the joint to only reference the actor that represents the chandelier, but the prefab does not contain an actor for the anchor point. If this prefab is placed into a scene, the rule that a joint without a parent actor just gets linked to the world, means that you can easily place these prefabs in a scene, and they will swing around the location where they have been instantiated.
However, if your scene contains for example a moveable room, you can also place it there and make sure to attach the chandelier prefab as a child node of the moveable room actor. That means, the joint of the chandelier will now link the chandelier actor with the moveable room actor, and thus whenever the room moves, the chandelier will be physically dragged along as well.
Important:
This method of linking actors only works properly, if the parent actor is either static or kinematic. It doesn't work for simulated dynamic actors, as the transform update introduces jitter.
Joint Position
The position and rotation of a joint object specifies the pivot point. For revolute joints, spherical joints and 6DOF joints the position of the joint determines around which point the linked actors rotate. For fixed joints the position still affects how strong forces may become due to lever effects.
For distance joints the joint position determines from where the distance is computed. Only for prismatic joints is the position of the joint less relevant, though for computational stability, it should still be somewhere in between the linked actors.
Video: Basic Setup
Using the ChildActorAnchor Property
Using the ChildActorAnchor
property is entirely optional and often not needed. Basically this option is used to reference any object, and tell the joint that the position and orientation of that object should be the anchor point where the joint acts upon the child actor. For most joint types this is rarely needed. If you link two actors with a revolute joint, the position of the joint already defines where between the actors the hinge will be.
However, this setup always assumes that the two actors should start out in the 'default configuration' of the joint. For example, for a revolute joint with a stiff spring, the default configuration is when it is fully relaxed, meaning you could not put them into a start configuration where the joint is already under tension.
Similarly, for a distance joint, the child actor would always need to start out such that the joint position is exactly at the position of the actor, where it should link to the actor. This can be problematic especially when the distance joint uses a minimum distance, as the joint would immediately push away the actor, potentially with high speed.
Using a dedicated object to specify the anchor point, both situations can be fixed. The anchor point specifies the position at which the joint affects the child actor. For most joint types the rotation of the anchor point is important as well. If the anchor's transform is identical to the joint's transform, it is redundant and could be left out. However, if it is different from the joint's transform, this difference basically tells the joint how much its start state deviates from the 'default configuration'. For example for a revolute joint the position of the anchor should always be identical to the joint's position, otherwise the child actor will jerk towards the revolute joint at startup. However, the rotation may deviate from the joint's rotation, which specifies how much the joint is already rotated at startup.
Video: How to use the Anchor Property
Shared Joint Component Properties
These properties are shared among all joint types:
BreakForce
,BreakTorque
: By specifying non-zero values here, the joint is turned into a breakable joint. If enough force is applied to the joint, it will break apart and the two actors are not linked any further. Unfortunately it can be tricky to choose a proper break threshold, especially for torques, as some interactions produce surprisingly large forces, whereas other actions don't.PairCollision
: By default actors that are linked with a joint will not collide with each other, instead they will pass through each other. Some joints have options that won't allow such situations to happen, and those options are always more efficient and preferable. However, you can also enable this, to have the two actors still collide with each other.ParentActor
,ChildActor
: References to objects with actor components to link with this joint. If not set, the parent/child hierarchy is used instead. See joining actors above.ChildActorAnchor
: An optional reference to an object that tells the joint where it should attach to the child actor. See using the ChildActorAnchor property above.
Joint Types
These types of constraints are currently available:
- PhysX Revolute Joint Component
- PhysX Spherical Joint Component
- PhysX Fixed Joint Component
- PhysX Prismatic Joint Component
- PhysX Distance Joint Component
- PhysX 6DOF Joint Component
Joint Stability
Joining multiple actors in a chain can quickly result in really bad simulation results. Have a look at dynamic actor simulation stability for ways to improve this. When working with joints the following aspects help a lot:
- Make sure that the involved actors are not too light. For smaller objects they automatically computed mass is often too low for the joint to be stable.
- Drastically increase linear and especially angular damping on the actors (
0.1
to0.8
). - Reduce
MaxContactImpulse
, especially on smaller actors. - Don't create very long chains, try to achieve the desired result with as few joints as possible.
- Don't use joint limits (revolute joint, spherical joint), they can add significant instability.
Also have a look at NVIDIA's recommendations: Configuring Joints for Best Behavior (nvidia.com)


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Structure for MCAN Rx Buffer element. More...
#include <mcan.h>
Data Fields | |
| uint32_t | id |
| uint32_t | rtr |
| uint32_t | xtd |
| uint32_t | esi |
| uint32_t | rxts |
| uint32_t | dlc |
| uint32_t | brs |
| uint32_t | fdf |
| uint32_t | fidx |
| uint32_t | anmf |
| uint8_t | data [MCAN_MAX_PAYLOAD_BYTES] |
Structure for MCAN Rx Buffer element.
| uint32_t MCAN_RxBufElement::anmf |
Accepted Non-matching Frame 0 = Received frame matching filter index FIDX 1 = Received frame did not match any Rx filter element
| uint32_t MCAN_RxBufElement::brs |
Bit Rat Switching 0 = Frame received without bit rate switching 1 = Frame received with bit rate switching
| uint8_t MCAN_RxBufElement::data[MCAN_MAX_PAYLOAD_BYTES] |
Data bytes. Only first dlc number of bytes are valid.
| uint32_t MCAN_RxBufElement::dlc |
Data Length Code 0-8 = CAN + CAN FD: received frame has 0-8 data bytes 9-15 = CAN: received frame has 8 data bytes 9-15 = CAN FD: received frame has 12/16/20/24/32/48/64 data bytes
| uint32_t MCAN_RxBufElement::esi |
Error State Indicator 0 = Transmitting node is error active 1 = Transmitting node is error passive
| uint32_t MCAN_RxBufElement::fdf |
FD Format 0 = Standard frame format 1 = CAN FD frame format (new DLC-coding and CRC)
| uint32_t MCAN_RxBufElement::fidx |
Filter Index
| uint32_t MCAN_RxBufElement::id |
Identifier
| uint32_t MCAN_RxBufElement::rtr |
Remote Transmission Request 0 = Received frame is a data frame 1 = Received frame is a remote frame
| uint32_t MCAN_RxBufElement::rxts |
Rx Timestamp
| uint32_t MCAN_RxBufElement::xtd |
Extended Identifier 0 = 11-bit standard identifier 1 = 29-bit extended identifier


North Station
South Station
North station
train information
Current Time
Carrier
Time
Destination
Train#
Track#
Status


I am currently attending college, so it's fairly often that I will go back to my hometown and stay there for a weekend or so. Me and my best friend went home at the same time one weekend and she had some friends that were mentioning something about trying Crystal Meth the last time she was there. I am game for trying new things usually because I have tried my fair share of more hardcore drugs like Ecstasy and Cocaine, so I thought trying Speed wouldn't be such a bad alternative either.
She called me the Friday I got into town and told me that she tried that stuff they do in that movie 'Spun', which is a movie about a bunch of Crank addicts. I wasn't too shocked of course cause she had mentioned it before, but that just made me want to try it even more. I didn't really think much of it and continued with my weekend.
We planned to drive back to our college on Sunday night, and on the drive up she mentioned to me that she had bought a bag of Speed for approximately 20 dollars, I don't remember the size of the bag. Of course, since she mentioned it, I felt like she was a very good friend because she was thinking of me and how I wanted to try it. So of course she mentioned, 'Well we can do some at a rest stop and continue driving.'
So she takes the bag out of her wallet and surprisingly, there was very little in it. They looked like clear sharp jagged pieces of glass. The people she bought it with were familiar with Crystal Meth so she knew that she was getting high quality because of the clear color of the product, and it was said to be 'probably the best crank I've seen/tried' by one of them. So she took out a credit card and begin to crush the little pieces of glass looking substance.  From watching the movie 'Spun' it was under my impression that it would be like Cocaine, you have to do a little every 15-20 minutes to feel something. But since there was so little, I didn't know what to expect.
So I was a little nervous, but she rolled up a dollar and I went ahead and sniffed it. After approximately 3 seconds of inhaling, I felt this sharp burning sensation in my nose which I found very, very unpleasant. So I waited for a sensation, and I really didn't feel anything. The burning had stopped after a minute or so and this acrid taste dripped into the back of my throat. She did her line, then we continued driving.
[Erowid Note: Driving while intoxicated or tripping is dangerous and irresponsible because it endangers other people. Don't do it!]
As we were driving, I reported that I didn't feel anything really. No excitement or stimulation besides the burning in my throat. She said that she felt the same and that it was different the first time she did it. She suggested that we should probably do another line, and after that we pulled into the next rest stop and she crushed the rest of the bag and made two very big lines. Now this looked a little more appealing. I figured, 'How bad could this be?' I snorted the whole fat line, and waited a second. All of a sudden this horrible pain began in the upper right side of my brain.  My nose and throat never hurt so bad.  That was horrible.
Yet, right after that, I felt this light airy feeling, this feeling of 'Oh my gosh. This is fabulous.' I just kept saying, 'Wow'. This is the feeling I thought Cocaine would give me when I tried it. This is what I wanted, an intense euphoria. I could feel my body tingling and I felt like I couldn't sit still. This sucked because we were in a car driving on the highway at night, but even so, I had a great time. Me and my friend had to drive back for about 4 hours or so, and after that point I could not stop talking. We kept comparing our weekends and telling stories about the people we know. It felt like the conversation wouldn't stop. I just wanted to keep talking and talking and I ended up talking so much that I would cut off stories by telling other stories.
I felt so happy to have friends and family and to be in school and I couldn't believe how optimistic I felt. And optimism is definately something I lack at times. I especially felt greatful for my best friend who was driving, because she told me she wanted to experience this with me and that touched me deeply. I felt emotional at points as well. I have taken Ecstasy before, and that drug is supposed to make you feel the same way, but I don't think I've ever thought of things in that perspective before until I tried Meth.
So after a while, she pulled into a rest stop and I drove the rest of the way home. We talked the whole time and got back into town around 1 AM.  I couldn't believe we were still talking, but of course I noticed the drug wasn't nearly as intense as it was at first, and I felt kind of weird, like it was working in waves. I would feel weird for a moment, then ok again. I felt tired at one point but I believed I wouldn't physically be able to sleep. Yet that main dizzying feeling remained the whole time.
At around 8 am we went to get her something to eat. I had no appetite whatsoever. I felt kind of strung out at that point as well, like I wanted this weird feeling to go away. After that she took me to my room and I tried to go to sleep for a while. I almost got a little freaked out though while I was lying in my bed trying to go sleep. I could feel my heart pumping very hard and fast through my chest, I noticed that I had been clenching my jaw all night and that it was sore now. My head had this weird tingling feeling, it felt like my brain was frying. I kept seeing weird dark shapes when I closed my eyes. It was strange! I wanted to smoke some weed and go to sleep but I couldn't do that with my room mate in the room. Yet after I got in a more comfortable position, I ended up getting 3 hours of sleep before I had to go class.
I still felt crappy for the rest of the day for a very long time, almost the whole day. In total, I didn't eat anything because I didn't feel hungry at all which just amounted to some queasy feeling. I got a total of 3 hours of sleep in a span of 30 hours. I couldn't believe how powerful this drug was. I must say that I am going to probably try it again because it really did offer me a fun feeling of euphoria that I have been looking for, but I will probably do it earlier in the day so this doesn't happen. I did feel kind of cranky too, like all that optimism and sense of happiness was just gone at that point. Overall the most unpleasant feeling was the burning and 'crashing' which was gradual. I don't want to get hooked on Meth, and I probably won't because I do have self control. Yet I have to say it is rather addictive in the sense that it does give me some confidence.
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.


Openstack logging handler.
This module adds to logging functionality by adding the option to specify a context object when calling the various log methods. If the context object is not specified, default formatting is used. Additionally, an instance uuid may be passed as part of the log message, which is intended to make it easier for admins to find messages related to a specific instance.
It also allows setting of formatting information through conf.
Bases: logging.StreamHandler
Bases: logging.LoggerAdapter
Delegate a warning call to the underlying logger, after adding contextual information from this adapter instance.
Bases: logging.Formatter
Bases: logging.Formatter
A context.RequestContext aware formatter configured through flags.
The flags used to set format strings are: logging_context_format_string and logging_default_format_string. You can also specify logging_debug_format_suffix to append extra formatting if the log level is debug.
For information about what variables are available for the formatter see: http://docs.python.org/library/logging.html#formatter
Uses contextstring if request_id is set, otherwise default.
Format exception output with CONF.logging_exception_prefix.
Bases: object
A thin wrapper that responds to write and logs.
Setup logging.


FAST FOOD
Your transaction requires special instructions from our
office. Please complete this Special Handling form, and we will contact you
within three business days to provide
assistance. Please do not complete
this mail packet until you receive our instructions.
Thank you!
FAST FOOD
Your transaction requires special instructions from our
office. Please complete this br
Special Handling form, and we will contact you
within three business days to provide
assistance. Please do not complete
this mail packet until you receive our instructions.
Thank you!
chickenBurger beefBurger chickenPizza.
George Richard
Great food! Menu is awesome,
there foods are not only
great but also in the right price.
i`ll highly recommend them


Dogs
Dogs are the best and they come in all shapes and sizes. They literally help us with everything in some cases more than others. I mean there are dogs that are for people who cannot see. We have d
ogs for airport security. Matter of fact we currently have dogs that are used to sniff out traces of people with corona virus. They are kind, compassionate, loyal and genuine. They can even understand basic speech and I think any defects they have can be overlooked by their amazing qualities
Pigeons
pigeons make excellent pets for a variety of reasons:
- -They are attractive
- -They are quiet and unobtrusive, with a calming, gentle voice
- -They are easy to tame
- -They are generally healthy and very hardy
- -They are active but not overly demanding
- -They require very little maintenance
- -They are usually inexpensive.
- -They are easy to breed and make excellent parents.
These are simply some of the few reasons why I love pigeons. I personally have cared for many pigeons and have found comfort in thire soft coos. While people are harsh and treasonous and have hearts filled to the brim with hate, jealousy and contempt for anyone why goes against them. Pigeons are pure in heart and never have and never will be vile to me. They have always accepted me and my gifts to them and have been content. Never asking more of me. I will always be indebted to their acceptance. I have been betrayed many times but never by pigeons.


---
layout: game
folderPath: battleTub
game: Battle Tub
postGameName: battleTub
friendlyDescription: You know all those times you got into a debate with your buddy on who would win in a team vs. team battle of the toy boats but were never able to prove it was you? Well, we're happy to say we've got you covered! In Battle Tub, you can show off your awesome boat driving and rubber ducky dodging skills while kicking your friends butt!
platform: Battle Tub is currently under active development and is not yet alpha ready. We are planning on submitting Battle Tub for Steam greenlight consideration in the coming months.
We actively stream the game development for our games on our Twitch! Make sure to follow the channel to watch how the game progresses and interact with the developers! Follow us on Twitter for updates on the game and when we go live on the stream.
status: Development
statusFriendlyDescription: On Hold
---
{% include game-page-download.html %}
{% include game-page-carousel.html %}


Version: 1.0 (October 2017, first public release)
Copyright: © Norwegian Defence Research Institute (FFI) 2013 - 2016
Initial author: Arild Bergh, Sinett 3.0, FFI
Updating authors:
Comment: Only update the version number above when compiler API changes
"URL to compilation service" is the URL of the server that runs the compilation service. The compilation service is a relatively small wrapper API written in NodeJS on top of Cordova which lets us send files from the Mlab editing tool to Cordova for compilation. The purpose of having a service API instead of using Cordova directly is to not have to worry if you are compiling for iOS or Android, etc., so you could run Mlab on a windows server and just have the compiler service run on a Mac to compile for iOS.
You will know this URL when you have installed the compiler server code (this was part of the files you got from the Norwegian ADL office).
"Unique passphrase to access compilation service " is simply a password that the Mlab editor code uses to verify to the compiler server that it is allowed to connect, you would use the same password on the compiler server.
MLAB is using the Cordova framework which takes an HTML5 app (consisting of HTML/CSS/ Javascript + media files) and compiles this to a native app for a wide range of mobile operating systems. It does this by creating a skeleton app that bootstraps a web viewer and then load the HTML5 files so it can be run on mobile devices such as Android and iOS. As MLAB is meant to provide a simple way for regular users to create apps in a web browser, a key challenge is to provide a seamless way to compile the HTML5 apps that the user has created into apps on the different platforms supported by Cordova.
Sinett will use a "compiler service" (CS) for this purpose, this service is developed in NodeJS and runs on the server that undertakes the compilation, presenting a very simple API through HTTP to the MLAB builder. This service does NOT require any database access, it relies on the file system / Cordova file structure.
The interaction between the different elements involved is as follows:
The key issue with the compiler service is that Cordova is meant to be run by a "regular" user on a computer, i.e. a user that has a home directory that Cordova can write to.
To get around this problem the compiler service must be run as a user that has a home directory and access to environment variables. The compiler service is written in NodeJS, you can therefore start the compiler service app as a different user with a command such as this:
sudo -H -u otheruser npm start app.js
The following software must be installed and tested before continuing with these instructions. Refer to relevant information for your operating system as to how to install these.
Git (only required if you have access to the Mlab git hub account)
or
Unzip (only required if you have the software delivered as a ZIP file without access to GitHub)
Rsync (used to upload file changes, this must be run as a service/daemon)
Npm (required to install NodeJS as well as the Javascript libraries used in the compiler service app)
Node.js
Oracle Java (do NOT use OpenJDK or similar open source replacements)
Cordova supports a wide range of mobile operating systems. iOS and Android are the most commonly used. Android can be compiled on Linux, Windows and OS X (Mac), whereas iOS apps can only be created on a Mac running OS X.
The location of, and specifications of, the different SDKs often change, we therefore refer you to the online documentation for the respective SDKs: https://developer.apple.com/xcode/ and https://developer.android.com/. Once these SDKs are installed you should make a note of their location. This location needs to be put in the /config/config.json file after you have installed the SDKs
To make sure Cordova will find the SDK, the safest is to add the /MYPATH/android-sdk/tools
and /MYPATH/android-sdk/platform-tools
to the path of your environment settings, refer to OS documentation on how to do this.
Please note that the SDKs may at various times require changes to their setup dependig on what OS you are using. If you have problems with Cordova and/or the SDKs please google the error messages you find to find out how to solve the issue(s).
mlab_cs
.The compiler service will receive files from the Mlab editor through Rsync, these go into an "inbox" folder. Cordova uses a "working" directory to perform the actual compilation. From this working directory a symbolic link is created to the uploaded code, this create a link from the www/ directory that Cordova expects to find in its working directory, the link goes to the inbox directory for the uploaded code.
Inside these directories Mlab uses a hierarchy:
For instance: /var/local/mlab_cs/working/no.ffi.sinett.fffa9cd9X664aX45ffX9833X54472e173a40/1/www points to /var/local/mlab_cs/inbox/no.ffi.sinett.fffa9cd9X664aX45ffX9833X54472e173a40/1/www/.
You need to create these two driectories (inside a shared top level directory is a good idea) and make them accessible for the mlab_cs user. An example of this for Linux would be as follows:
mkdir /var/local/mlab_cs
mkdir /var/local/mlab_cs/inbox
mkdir /var/local/mlab_cs/working
chown -R root:mlab_cs /var/local/mlab_cs
chmod -R 770 /var/local/mlab_cs
You should edit your default rsync configuration file, on Linux this is /etc/rsyncd.conf
. The edit is required to make the shared cs_inbox
read and writable for user mlab
. At the end of the config file add the following, adjusting the paths to fit your installation:
[cs_inbox]passphrase
comment = Compiler server inbox, where to put the app to be compiled
path = /mypath/mlab_cs/inbox
read only = no
list = yes
uid = mlab_cs
gid = mlab_cs
auth users = mlab_cs
secrets file = /etc/rsyncd.secrets
Create /etc/rsyncd.secrets for the mlab-cs user's password. Replace password
with a suitable password.
sudo vim /etc/rsyncd.secrets
mlab_cs:password
Restart and test rsync. Run the following command to check if everything is ok. You will be asked for the password and the output would be the content of the share (initially empty).
rsync mlab_cs@localhost::cs_inbox
We are now ready to copy across the actual compiler service code.
git clone https://github.com/Sinettlab/mlab_compiler.git mlab_cs_app
mkdir mlab_cs_app
unzip /mypath/mlab_cs.zip -d mlab_cs_app
cd mlab_cs_app
npm install
This will install node.js dependencies in a node_modules
folder within the mlab_compiler
folderInside the mlab_cs_app directory there is a directory called config. Inside this directory you should rename the file config.dist.json to config.json and edit the settings to match your setup.
Settings
mlab_cs
user created in previous stepscordova_apps_path
directory)<IP_ADDRESS>
if the compiler service should listen on all ports)Platform specific settings
These settings are passed directly on to the SDKs/Cordova compiler. They are shown here for completeness, refer to the relevant SDK documentation to see what you should specify here:
"android": {
"environment": {
"ANDROID_HOME": "/opt/android-sdk/",
"JAVA_HOME": "/usr/lib/jvm/java-8-oracle/jre/"
},
"executable_path": "build/outputs/apk/",
"executable_filename": "android-debug",
"executable_extension": "apk",
"executable_mime_type": "application/vnd.android.package-archive"
},
"ios": {
"provisioning_profile": "MLAB dev",
"executable_path": "out",
"executable_extension": "ipa",
"executable_mime_type": "application/octet-stream",
"permission_translation": {
"ACCESS_LOCATION_EXTRA_COMMANDS": "",
......
"WRITE_VOICEMAIL": ""
}
}
}
From the command line:
node app.js
or npm start
You should see details on what port the compiler service will listen to and some basic startup information. If you get any error messages you should follow the debug steps below.Run as a service/daemon: There are a number of ways to achieve this, you can use tools such as PM2, Forever, etc. We suggest you search for current solutions to this as there are a number of ways this can be achieved, so you need to select the one that suits your operating environment.
If you run Mlab from the command line, and not as a service, you will see a lot of debug messages as well as error messages. If you get error messages you should google them. Most of the time you can figure out what caused it based on other people's experiences, the debug messages will tell you where it happened (i.e. as a result of what input).
You can also debug the Mlab compiler service using the Google Chrome or Chromium browsers. For details on how to debug the code see here: https://nodejs.org/api/debugger.html.
TBC: Code signing identity and provisioning profile set up (name of provisioning profile goes into config file)


The Iterator Pattern
The Iterator Pattern provides a way to access the elements of an
aggregate object sequentially without exposing its underlying representation.
In a word, makes all aggregate can to access
by Iterator interface. It's easy to iterate over the collection by 'for each' statement.
The Composite Pattern
The Composite Pattern allows you to compose object into tree structures
to represent part-whole hierarchies. Composite lets clients treat individual objects and compositions
of objects uniformly.


class Gem::Package::TarReader
TarReader reads tar files and allows iteration over their items
Public Class Methods
new(io)
click to toggle source
Creates a new tar file reader on io
which needs to respond to
pos, eof?, read, getc and pos=
# File lib/rubygems/package/tar_reader.rb, line 39 def initialize(io) @io = io @init_pos = io.pos end
new(io) { |reader| ... }
click to toggle source
Creates a new TarReader on io
and
yields it to the block, if given.
Calls superclass method
BasicObject.new
# File lib/rubygems/package/tar_reader.rb, line 21 def self.new(io) reader = super return reader unless block_given? begin yield reader ensure reader.close end nil end
Public Instance Methods
close()
click to toggle source
Close the tar file
# File lib/rubygems/package/tar_reader.rb, line 47 def close end
each() { |entry| ... }
click to toggle source
Iterates over files in the tarball yielding each entry
# File lib/rubygems/package/tar_reader.rb, line 53 def each return enum_for __method__ unless block_given? until @io.eof? do header = Gem::Package::TarHeader.from @io return if header.empty? entry = Gem::Package::TarReader::Entry.new header, @io size = entry.header.size yield entry skip = (512 - (size % 512)) % 512 pending = size - entry.bytes_read begin # avoid reading... @io.seek pending, IO::SEEK_CUR pending = 0 rescue Errno::EINVAL, NameError while pending > 0 do bytes_read = @io.read([pending, 4096].min).size raise UnexpectedEOF if @io.eof? pending -= bytes_read end end @io.read skip # discard trailing zeros # make sure nobody can use #read, #getc or #rewind anymore entry.close end end
Also aliased as: each_entry
rewind()
click to toggle source
seek(name) { |entry| ... }
click to toggle source
Seeks through the tar file until it finds the entry
with
name
and yields it. Rewinds the tar file to the beginning
when the block terminates.
# File lib/rubygems/package/tar_reader.rb, line 107 def seek name # :yields: entry found = find do |entry| entry.full_name == name end return unless found return yield found ensure rewind end


This is the Citrus simulator UI that enables you to manage scenarios and review simulator activities.


Buttons Lite is a trimmed subset of Buttons with only flat (top level) classes. Buttons is free and open source.
DownloadButtons should be used responsibly with accessibility in mind. You should use anchor tags when you are linking to an external resource or an internal page link. Use the button tag when you have an application command such as add to shopping cart. Use input tags (such as the submit input type) when creating forms. If you would like to learn more on this subject we highly reccomend reading the following articles.
<!-- Buttons core css --> <link rel="stylesheet" href="css/buttons.css"> <!-- Only needed if you want support for dropdown menus --> <script src="//ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script> <script type="text/javascript" src="js/buttons.js"></script> <!-- Only needed if you want font icons --> <link href="//netdna.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.css" rel="stylesheet">
We've made some major improvements to the Buttons library. In order to integrate buttons into your current project you'll need to make the following changes:
button-flat-primary
to achieve this you now simply add button-flat button-primary
npm install
or sudo npm install
(depending on your system permissions).grunt dev
, this will open a browser with Buttons


Now you're ready to create your game.
Remember the max of the roster is 26. If you reach your max you'll have to remove players before you can add more.
Anytime you can edit your roster by unchecking the check box on the left or by selecting the Edit Roster button below.
Edit Roster Edit/Create GamesEasy. Just enter a name in the textbox below and click the 'Add Players' button.
Tip: If you want to add multiple players, click 'Add Another Player' multiple times and fill in the textboxes. When all players are entered into the textboxes, click 'Submit Players'.


Project Overview
For my third year Advanced Programming module I was tasked with creating an iOS application using Swift 4 that kept track of and calculated scores, for a men's
singles tennis match.
My overall grade from the moudles assignments (assingments made up 63% of the overall module mark) was 82%.
A starter project was provided, with a UI already built, and no changes were to be made to the UI. The features I implemented were as followed:
- Allow the user to indicate which player scored a point, and correctly display the correct score for the current game, set and match, along with showing all preceeding set scores
- Detect the end of the game, and progress automatically
- Detect the end of the set, and progress automatically
- Detect the end of the match and prevent further interaction until the user restarted the match
- Notify the user of the winner of the match, and have a completed score board for the match
- Handle Tie Breaks
- Handle the requirement for a player to win by 2 clear games in the fifth set
- Allow the user to reset the match at any point
- Use text to speech to call new balls please after the first 7 games and then after every 9 games
- Identify who should serve next at any given point, by changing the background colour of the player name label
- Audible alert to indicate the change in server
- Include comprehensive unit tests
Skills Developed
- Problem solving/logical programming skills
- Further enhancement of the Swift language
- Understanding of OOP principles
- Understanding of SOLID principles
- Adhering to the DRY principle
- MVC Design Pattern
- Knowledge and familiarity of xCode IDE