President, Student Gymkhana
Indian Institute of Technology, HyderabadLooked after Infrastructure, Finance for all events. Headed the executive committee and monitor their activities.
Looked after Infrastructure, Finance for all events. Headed the executive committee and monitor their activities.
Incharge of accomodation, food and guidance of visiting guests


§11-52 Hearing; decision final. When the appeal is perfected, the court shall hear the appeal as soon thereafter as may be reasonable. [L 1970, c 26, pt of §2; am L 2004, c 202, §2; am L 2006, c 94, §1; am L 2010, c 109, §1]


What channel is Man Utd vs Partizan Belgrade on? TV and live stream information
It has been a positive campaign in the Europa League so far for United in terms of results and they can qualify for the next round with victory
by Sam FletcherManchester United's Europa League journey continues as they take on Partizan Belgrade.
Although The Red Devils’ Premier League campaign has been underwhelming, they top Europa League Group L on seven points and a win in this tie confirms their progression to the knockout stages.
Despite having 61 percent possession against Partizan two weeks ago, Ole Gunnar Solskjaer's men only managed to muster one shot on target in the entire 90 minutes.
Anthony Martial’s penalty was the difference between the sides last time out, but United fans will be hoping the game is more of a formality on home soil.
Here’s everything you need to know before the match...
When is the game?
The game is on Thursday 7 November.
Kick-off is set for 8pm.
Where is the game being played?
Old Trafford is the venue for the game.
Man United have not lost a Europa League home match since a 3-2 defeat against Athletic Bilbao in 2012.
What TV channel is the game on?
For UK viewers the game is on BT Sport 2.
Coverage is scheduled to start at 7:30pm.
Can you live stream the game?
If you are a BT Sport subscriber and are based in the UK, you can stream the action through their website and the BT Sport app.
Alternatively, you can follow all of the action on the Mirror Football live blog.
Manchester United team news
United have a host of players on the treatment table including star-man Paul Pogba as he recovers from an ankle injury.
Manager Solskjaer is not expecting him back till December, the French international has only made six appearances for the Red Devils this season.
Also sidelined are Diogo Dalot, Eric Bailly, Axel Tuanzebe, Timothy Fosu-Mensah, Luke Shaw and Nemanja Matic.
Partizan Belgrade team news
Belgrade manager Savo Milosevic boats a fully-fit squad ahead of the match with no injury concerns.
Betting odds
- Manchester United 2/5
- Draw 7/2
- Partizan Belgrade 15/2
Odds courtesy of thepools.com


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Conflicting meeting: {{conflict.creators}}, "{{conflict.title}}" at {{conflict.start}} GMT.


Makes Stubby RPC calls to a DatastoreV4Service server.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.AllocateIds |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
| |
Make a AllocateIds RPC call.
Args:
request: a AllocateIdsRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The AllocateIdsResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.BeginTransaction |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a BeginTransaction RPC call.
Args:
request: a BeginTransactionRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The BeginTransactionResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.Commit |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a Commit RPC call.
Args:
request: a CommitRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The CommitResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.ContinueQuery |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
| |
Make a ContinueQuery RPC call.
Args:
request: a ContinueQueryRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The ContinueQueryResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.Get |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a Get RPC call.
Args:
request: a LookupRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The LookupResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.Lookup |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
| |
Make a Lookup RPC call.
Args:
request: a LookupRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The LookupResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.Rollback |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
| |
Make a Rollback RPC call.
Args:
request: a RollbackRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The RollbackResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.RunQuery |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
| |
Make a RunQuery RPC call.
Args:
request: a RunQueryRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The RunQueryResponse if callback is None. Otherwise, returns None.
| def google.appengine.datastore.datastore_v4_pb._DatastoreV4Service_ClientBaseStub.Write |
( |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a Write RPC call.
Args:
request: a WriteRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The CommitResponse if callback is None. Otherwise, returns None.
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/datastore/datastore_v4_pb.py


This repository holds all interfaces/classes/traits related to PSR-3.
Note that this is not a logger of its own. It is merely an interface that describes a logger. See the specification for more details.
If you need a logger, you can use the interface like this:
```php <?php
use Psr\Log\LoggerInterface;
class Foo { private $logger;
public function __construct(LoggerInterface $logger = null)
{
$this->logger = $logger;
}
public function doSomething()
{
if ($this->logger) {
$this->logger->info('Doing work');
}
// do something useful
} } ```
You can then pick one of the implementations of the interface to get a logger.
If you want to implement the interface, you can require this package and
implement Psr\Log\LoggerInterface
in your code. Please read the
specification text
for details.


Partners
You have {{total}} registered partner(s).
No partners yet.
| Partner | Account Owner | No. of price checks | No. of paid job order request | Total amount of paid job orders | |
|---|---|---|---|---|---|
| |
|
|
|
|
|
|
{{partner_list.partner_company}}
|
{{partner_list.user_profile_first_name}} {{partner_list.user_profile_last_name}}
|
{{partner_list.user_email}}
|
{{partner_list.total_price_check}}
|
{{partner_list.total_paid}}
|
{{partner_list.total_amount_paid | currency}}
{{0 | currency}}
|


{% extends "portfolioApp/base.html" %} {% load static %} {% block body %}
Fullstack Software Developer
I am going through a 6 month bootcamp at The Code Crew School here in Memphis, TN. I joined this program so that I can intergrate the skills that I learn as a developer with my passion for yoga. In these 6 months, we are learning multiple languages like Java Script and Python. We are learning to be full stack developers using MERN and Django. We are assigned exercises and projects after each lesson. My vision as a developer is to mix my innovative skills with my software skills to create an application that will greatly affect the yoga community and it's influence on other communities as well. This experience is playing a huge role in my pursuit to reach my goals by being the door of opportunity and the start to something great! Contained in this page are some of my best projects and personal work. Feel free to click around and check me out!


Main
About
Profile
Easily search for players to fill your party or find a group to play in. Search by tier, position, and more!


{% extends "base.html" %} {% block content %}
Abbreviation: {{receptor.abbreviation}}
{{receptor.description|safe}}
This is an overview of the performance of our protocols on different forms of {{receptor.name|lower}}
{% if rocdata_an != None or receptor.abbreviation == "PR"%}More negative score means greater possibility for binding.
Sensitivity represents fraction of true positives.
Specificity represents fraction of true negatives.
More negative score means greater possibility for binding.
More negative score means greater possibility for binding.
Sensitivity represents fraction of true positives.
Specificity represents fraction of true negatives.
Files: {{receptor.pdbqt}}, {{receptor.conf}}, {{receptor.pdbqt_an}}, {{receptor.conf_an}}


1. This step is common
2. This step is common
3. This is windows specific step
3. This is Mac specific step
3. This is Linux specific step
4. This step is common
5. This is windows specific step


Value Equation Comparison
Print
{{template "processing.html"}}
Refresh
Reset
Aggregator
Revenue
Maintenance Cost
Production Cost
Value Equation
Value Equation / Net Generation
From
To
Plant
Unit Group
Add New
Result
{{template "processing.html"}}


no_cache is a sequence of
access
control rules (see Section 6.2) that specify
responses that must not be cached by Squid. Of course, Squid has some
hardcoded rules for responses that must not be cached according to
the HTTP RFC. The no_cache rules are in addition
to those. The no_cache syntax is a little tricky. You must
use deny for rules where the response must not be
cached. Consider this example: acl GoodStuff url_regex /foo/bar/
acl BadStuff url_regex /bar/
no_cache allow GoodStuff
no_cache deny BadStuff Here, a URL containing /foo/bar/ may be cached,
but any other URL containing only /bar/
isn't cached. The meaning of the
allow and deny might be the
opposite of what you expect. Just remember that
deny carries the same negative connotation as
"not caching" something. |
Syntax
|
no_cache allow|deny [!]ACLname ...
| |
Default
|
No default
| |
Example
|
acl LocalServers dst <IP_ADDRESS>/24
no_cache deny LocalServers
| |
Related
|
always_direct,
never_direct, http_access
|
|


Public Member Functions | |
| rolling_file_t (const std::string &name_prefix, rolling_file_settings flags=rolling_file_settings()) | |
| void | flush () |
| void | configure (const hold_string_type &str) |
We first write to name_prefix.1.
The log has a max_size. When max_size is reached, we start writing to name_prefix.2. When max_size is reached, we start writing to name_prefix.3. And so on, until we reach name_prefix.N (N = file_count). When that gets fool, we start over, with name_prefix.1.
| boost::logging::destination::rolling_file_t< convert_dest >::rolling_file_t | ( | const std::string & | name_prefix, | |
| rolling_file_settings | flags = rolling_file_settings() | |||
| ) | [inline] |
Constructs a rolling file
| name_prefix | the name to be used as prefix for the files | |
| flags | [optional] extra settings to pass to the rolling file. See rolling_file_settings and Dealing with flags.. |
| void boost::logging::destination::rolling_file_t< convert_dest >::flush | ( | ) | [inline] |
manual flush()ing the currently opened file.
| void boost::logging::destination::rolling_file_t< convert_dest >::configure | ( | const hold_string_type & | str | ) | [inline, virtual] |
configure through script right now, you can only specify the file name prefix
Reimplemented from boost::logging::manipulator::is_generic.


WELCOME TO THE 5 STAR LUXURY RESORT IN MOMBASA
Offering the perfect fusion of European luxury and Kenyan hospitality
Cool Waters is positioned between the city and countryside
Stay longer, save up to 25 %


[ aws ]
This section explains prominent concepts and notations in the set of high-level S3 commands provided.
Whenever using a command, at least one path argument must be specified. There are two types of path arguments: LocalPath and S3Uri.
LocalPath: represents the path of a local file or directory. It can be written as an absolute path or relative path.
S3Uri: represents the location of a S3 object, prefix, or bucket. This must be written in the form s3://mybucket/mykey where mybucket is the specified S3 bucket, mykey is the specified S3 key. The path argument must begin with s3:// in order to denote that the path argument refers to a S3 object. Note that prefixes are separated by forward slashes. For example, if the S3 object myobject had the prefix myprefix, the S3 key would be myprefix/myobject, and if the object was in the bucket mybucket, the S3Uri would be s3://mybucket/myprefix/myobject.
Every command takes one or two positional path arguments. The first path argument represents the source, which is the local file/directory or S3 object/prefix/bucket that is being referenced. If there is a second path argument, it represents the destination, which is is the local file/directory or S3 object/prefix/bucket that is being operated on. Commands with only one path argument do not have a destination because the operation is being performed only on the source.
Some commands perform operations only on single files and S3 objects. The following commands are single file/object operations if no --recursive flag is provided.
- cp
- mv
- rm
For this type of operation, the first path argument, the source, must exist and be a local file or S3 object. The second path argument, the destination, can be the name of a local file, local directory, S3 object, S3 prefix, or S3 bucket.
The destination is indicated as a local directory, S3 prefix, or S3 bucket if it ends with a forward slash or back slash. The use of slash depends on the path argument type. If the path argument is a LocalPath, the type of slash is the separator used by the operating system. If the path is a S3Uri, the forward slash must always be used. If a slash is at the end of the destination, the destination file or object will adopt the name of the source file or object. Otherwise, if there is no slash at the end, the file or object will be saved under the name provided. See examples in cp and mv to illustrate this description.
Some commands only perform operations on the contents of a local directory or S3 prefix/bucket. Adding or omitting a forward slash or back slash to the end of any path argument, depending on its type, does not affect the results of the operation. The following commands will always result in a directory or S3 prefix/bucket operation:
Currently, there is no support for the use of UNIX style wildcards in a command's path arguments. However, most commands have --exclude "<value>" and --include "<value>" parameters that can achieve the desired result. These parameters perform pattern matching to either exclude or include a particular file or object. The following pattern symbols are supported.
- *: Matches everything
- ?: Matches any single character
- [sequence]: Matches any character in sequence
- [!sequence]: Matches any character not in sequence
Any number of these parameters can be passed to a command. You can do this by providing an --exclude or --include argument multiple times, e.g. --include "*.txt" --include "*.png". When there are multiple filters, the rule is the filters that appear later in the command take precedence over filters that appear earlier in the command. For example, if the filter parameters passed to the command were
--exclude "*" --include "*.txt"
All files will be excluded from the command except for files ending with .txt However, if the order of the filter parameters was changed to
--include "*.txt" --exclude "*"
All files will be excluded from the command.
Each filter is evaluated against the source directory. If the source location is a file instead of a directory, the directory containing the file is used as the source directory. For example, suppose you had the following directory structure:
/tmp/foo/
.git/
|---config
|---description
foo.txt
bar.txt
baz.jpg
In the command aws s3 sync /tmp/foo s3://bucket/ the source directory is /tmp/foo. Any include/exclude filters will be evaluated with the source directory prepended. Below are several examples to demonstrate this.
Given the directory structure above and the command aws s3 cp /tmp/foo s3://bucket/ --recursive --exclude ".git/*", the files .git/config and .git/description will be excluded from the files to upload because the exclude filter .git/* will have the source prepended to the filter. This means that:
/tmp/foo/.git/* -> /tmp/foo/.git/config (matches, should exclude)
/tmp/foo/.git/* -> /tmp/foo/.git/description (matches, should exclude)
/tmp/foo/.git/* -> /tmp/foo/foo.txt (does not match, should include)
/tmp/foo/.git/* -> /tmp/foo/bar.txt (does not match, should include)
/tmp/foo/.git/* -> /tmp/foo/baz.jpg (does not match, should include)
The command aws s3 cp /tmp/foo/ s3://bucket/ --recursive --exclude "ba*" will exclude /tmp/foo/bar.txt and /tmp/foo/baz.jpg:
/tmp/foo/ba* -> /tmp/foo/.git/config (does not match, should include)
/tmp/foo/ba* -> /tmp/foo/.git/description (does not match, should include)
/tmp/foo/ba* -> /tmp/foo/foo.txt (does not match, should include)
/tmp/foo/ba* -> /tmp/foo/bar.txt (matches, should exclude)
/tmp/foo/ba* -> /tmp/foo/baz.jpg (matches, should exclude)
Note that, by default, all files are included. This means that providing only an --include filter will not change what files are transferred. --include will only re-include files that have been excluded from an --exclude filter. If you only want to upload files with a particular extension, you need to first exclude all files, then re-include the files with the particular extension. This command will upload only files ending with .jpg:
aws s3 cp /tmp/foo/ s3://bucket/ --recursive --exclude "*" --include "*.jpg"
If you wanted to include both .jpg files as well as .txt files you can run:
aws s3 cp /tmp/foo/ s3://bucket/ --recursive \
--exclude "*" --include "*.jpg" --include "*.txt"
aws s3 <Command> [<Arg> ...]
None


Here's a link: http://google.com/ and here another one: http://segment.io/ and the same one again: http://segment.io/.


One in four Americans never hears people they know talk about global warming, and that needs to change.
Are you worried about the changes occurring to our climate, but avoid bringing up the topic? Are you afraid you might offend someone or get into an argument? Concerned you don’t know enough about the science behind climate change to talk about it? Baffled by the fact that the science is clear, yet people continue to insist that either the climate is not changing or, if it is, that the changes are due to natural, as opposed to human, causes?
Luckily, there is a lot of great research out there on how to raise the topic so that people will listen. And it does not require a vast knowledge of climatology.
In our program, we take these climate change communication theories and turn them into practice, teaching you how to raise the issue of climate change comfortably and successfully.
Our programs meet in person in the DC metro area, and are made up of three 2-hour meetings spread a week apart. We request a $20 donation per person to help defray expenses. For our TALK program, we focus on working with groups of people who already know one another, such as college friends, sports teams, clubs, religious groups, etc. Working with an intact group fosters support and accountability, which will increase your level of success.
For scheduling, or if you have more questions, please email us at<EMAIL_ADDRESS>Are you unsure what to do about climate change? Spending a bunch of time thinking about and debating the right approach? Concerned that there isn’t much one person can do?
Climate change is such a broad issue that there are innumerable ways for people to get involved. So rather than worrying about what the “best” thing is to do, in our ACT program we help people step back and look at their skills, interests, resources, time, etc. and help each person figure out what the best thing is for him or her to do.
We want you to leave this program knowing what you can do
Our programs meet in person in the DC metro area, and are made up of four 2-hour meetings spread a week apart. We request a $20 donation per person to help defray expenses. For our Act program, we focus on working with groups of people who already know one another, such as college friends, sports teams, clubs, religious groups, etc. Working with an intact group fosters support and accountability, which will increase your level of success.
For scheduling, or if you have more questions, please email us at<EMAIL_ADDRESS>While Anne is newly attuned to the realities of climate change, she has always felt a deep commitment to her community, and has volunteered with local charities for children, veterans, and the elderly for the last twenty years. She is a member of the Board of Directors for The Backpack Project, a non-profit organization dedicated to helping children in under-served areas of Washington, D.C.
Her decision to expand her community efforts to include climate change was triggered by a class she recently took on Environmental Science at American University, which opened her eyes to the alarming effects of climate change. This knowledge spurred her to get involved with fostering the discussion on climate change at Climate Change Group.
Fran spent many hours outdoors during her childhood in Maine. As the youngest of a large family, her love of the natural world was encouraged from a young age. This commitment continues in the work she does as the Assistant Director of The Elements DC. Elements is a wintertime after-school enrichment program for young children whose primary goal is to connect them and their families to nature.
Fran also has a strong belief in giving back to her community. Until June 2015 she served as Vice President of Programming on the Board of the Parents Council of Washington. Currently she is a tutor for Reading Partners in the DC Public School System, and a Board Member and a high school mentor with City Kids Wilderness Project, which provides enriching life experiences for under-resourced DC youth.
Both her career and volunteer work emphasize fostering communication and developing programs that allow people to grow. She realized she could take the skills used with these groups – non-confrontational communication, patience, strong listening abilities - and apply them to help people talk about climate change.
Tory’s fascination with environmental issues began in high school, where her favorite class was environmental chemistry. She majored in American Studies with a focus on Environmental Studies at Yale and then went on to practice environmental law for several years. Most importantly, many of her best memories are of times she has spent in wild natural places.
Since 1997, Tory has worked as a management consultant, focusing on coaching, facilitation, organizational development, and conflict resolution (ruttenbergconsulting.com). While she cared about the environment, she hadn’t focused on taking action. That changed when she read Bill McKibbon’s book, Eaarth, a couple of years ago.
Eaarth fascinated, horrified, and scared her. It also rang true and made her realize that it was time for her to act. But she had no idea what to do. Finally, she realized she could combine her interest in working on climate change with her experience as a coach and facilitator, so she, Fran, and Anne started ClimateChangeGroup.org.


Cherbourg is situated at the north tip of the Cotentin Peninsula. It is a favourite destination for yachtsmen from the south coast of England due to its proximity and the all-tide, all weather access to the port.
Position 49° 40'42"N, 01° 37'04"W (harbour breakwater)
On arrival all boats must report to the Capitainerie with the ships'papers and a note of the pontoon and berth number.
This busy commercial port and ferry terminal has more visiting yachts than any other in Europe. It is well-marked and can be entered day or night and at any state of tide. The outer harbour can be entered from either end of the central breakwater.
Visitors' berths (pontoons N,M,P and Q) are in the marina at Port Chantereyne, which is in the SW corner of the Petite Rade (see photo taken by SY Suwena). It is very rare that there is not a free slip here, but there is a waiting pontoon with water and electricity (although not attached to the shore).
Although large yachts are sometimes permitted to anchor just outside the marina in front of the military port, normally there is no anchoring in the Petite Rade. Grande Rade has several anchorages, but being a large bay, it can be exposed if the wind in in the northern sector.
Last updated January 2013.


Once downloaded it, extract it and run it.
I was running at the smallest size and the fastest graphical quality.
Enter Training Mode in the simulator. Start driving the car to get a feel for the controls. And hit the record button in the top right to start recording. Continue driving for a few laps till I have enough data. Hit the record button in the top right again to stop recording.
the following folder was created in the
directory I selected:
IMG folder - this folder contains all the frames of my driving.
driving
_log.csv - each row in this sheet correlates images with the steering angle, throttle, brake, and speed of your car.
This is training data I have, I trained my model with all of these data. I have a
preprocess python script to preprocess all of these data.
launching the simulator and entering autonomous
mode.
Here¡¯s how I start my Python server:
python
drive.py model.json
Once the model is up and running in drive.py, I can see the car move around the
track
If you drive and record normal laps around the track, even if you record a
lot of them, it might not be enough to train your model to drive properly.
It¡¯s probably enough to drive a couple of laps of nice, centerline driving,
and then add one lap weaving out to the right and recovering, and another lap
weaving out to the left and recovering.
In order to
overcome overfitting on the training set. I added dropout layers to my network. Splitting my dataset into a
training set and a validation set.
Model Design
I
have total 17345 items each contained three images from different angles:
center, left, and right. So, there are total 52035 images. The way I approached
this by resizing the image to the specific needs. I tried to eliminate the
unnecessary data and focus on the road and reshaped and used for training. I reduced the size of the image by 1/4 and used the red channel. It is
more efficient in training terms of time and space. The resized image is saved
as features and synced the data
of steering angels as labels. I splitted the data into train and validation,
and saved them as camera.pickle
file
Preprocessing:
I
basically made the CNN network architecture based upon NVIDIA paper (https://arxiv.org/pdf/1604.07316v1.pdf).
This CNN consists of 8 layers,
including an additional maxpool layer to even further
reduce the number of parameters. The layers includes 4
convolutional layers, 1 max pooling layer, and 3 dense layers.
Training
|
Layer (type) |
Output Shape |
Param # |
Connected to |
|
convolution2d_1 (Convolution2D) |
(None, 16, 78,
16) |
160 |
convolution2d_input_1[0][0] |
|
activation_1 (Activation) |
(None, 16, 78,
16) |
0 |
convolution2d_1[0][0] |
|
convolution2d_2 (Convolution2D) |
(None, 14, 76,
8) |
1160 |
activation_1[0][0] |
|
activation_2 (Activation) |
(None, 14, 76,
8) |
0 |
convolution2d_2[0][0] |
|
activation_3 (Activation) |
(None, 12, 74,
4) |
0 |
convolution2d_3[0][0] |
|
convolution2d_4 (Convolution2D) |
(None, 10, 72,
2) |
74 |
activation_3[0][0] |
|
activation_4 (Activation) |
(None, 10, 72,
2) |
0 |
convolution2d_4[0][0] |
|
maxpooling2d_1 (MaxPooling2D) |
(None, 5, 36, 2) |
0 |
activation_4[0][0] |
|
dropout_1 (Dropout) |
(None, 5, 36, 2) |
0 |
maxpooling2d_1[0][0] |
|
flatten_1 (Flatten) |
(None, 360) |
0 |
dropout_1[0][0] |
|
dense_1 (Dense) |
(None, 16) |
5776 |
flatten_1[0][0] |
|
activation_5 (Activation) |
(None, 16) |
0 |
dense_1[0][0] |
|
dense_2 (Dense) |
(None, 16) |
272 |
activation_5[0][0] |
|
activation_6 (Activation) |
(None, 16) |
0 |
dense_2[0][0] |
|
dense_3 (Dense) |
(None, 16) |
272 |
activation_6[0][0] |
|
activation_7 (Activation) |
(None, 16) |
0 |
dense_3[0][0] |
|
dropout_2 (Dropout) |
(None, 16) |
0 |
activation_7[0][0] |
|
dense_4 (Dense) |
(None, 1) |
17 |
dropout_2[0][0] |
Total
params: 8023
Keras
framework was used to build this CNN architecture. Adam optimizer is similar with
from the lecture which is Kingma
and Ba's modified version of the Stochastic Gradient Descent that allows the
use of larger step sizes without fine tuning. In general, the Adam optimizer
uses cross entropy calculations to minimize loss (average distance to the
target label in the solution space) and use gradient descent, an iterative
optimization technique and algorithm to achieve this goal. Even though using
the Adam optimizer should allow us to use larger step sizes (learning rates).
This made it so that the model never seem to converge, so it never over-fit;
however, in subsequent tests, the model performed exceptionally well in
steering the car in the simulator and making sure that the car remaining in the
center of the lane.
Summary
The
red channel of the image contains the better information for identifying the
road and lanes than green and blue channels. As a result, the size of the image
was 18 x 80 x 1.
In
this model, I used 4 ConvNet with 1 max pooling layer, and 4 dense layers after flatten the
matrix. For each convolutional layer, I decreased the channel size by half.
When the size of the channel became 2 in the fourth convolutional layer, I
applied dropout for regularization. In return, we will get regression neuron as
an output. After flatten the matrix, the size of features became 360. I used
dense layers with 16 features 4 times. I used 200 epoches
with 128 batch_size to train the data. As a result,
the car drove by itself without popping onto the edges
or out of the edges.
Although
the accuracy was only about 65%, the model allowed the car to drive itself. So the accuracy did not have to be high for car to
drive autonomously. To increase the accuracy, I would need more data sets and
more epochs to train this model.


| Package | Description |
|---|---|
| org.maltparser.core.config |
Contains classes and an interface for handling parsing scenarios.
|
| org.maltparser.core.config.version | |
| org.maltparser.core.exception |
Provides classes for handling exception.
|
| org.maltparser.core.feature |
Provides classes for handling feature models.
|
| org.maltparser.core.feature.function |
Contains classes and interfaces for handling feature and address functions.
|
| org.maltparser.core.feature.map |
Contains classes handling feature map functions.
|
| org.maltparser.core.feature.spec |
Contains classes for storing the feature specification model.
|
| org.maltparser.core.feature.spec.reader |
Contains classes for loading a feature specification.
|
| org.maltparser.core.feature.system |
provides classes to handle the feature system.
|
| org.maltparser.core.feature.value |
Contains classes and interfaces for handling feature values.
|
| org.maltparser.core.flow | |
| org.maltparser.core.flow.item | |
| org.maltparser.core.flow.spec | |
| org.maltparser.core.flow.system | |
| org.maltparser.core.flow.system.elem | |
| org.maltparser.core.helper |
Provides classes of various kinds that not fit into another package.
|
| org.maltparser.core.io.dataformat |
Contains classes for describing the data format, such as CoNLL shared task format and Malt-TAB format.
|
| org.maltparser.core.lw.graph | |
| org.maltparser.core.lw.helper | |
| org.maltparser.core.lw.parser | |
| org.maltparser.core.options |
Classes for management of all options used in the application and its plugins.
|
| org.maltparser.core.options.option |
Contains classes for types of options.
|
| org.maltparser.core.plugin |
Provides classes for loading and initializing plugins.
|
| org.maltparser.core.pool | |
| org.maltparser.core.propagation | |
| org.maltparser.core.propagation.spec | |
| org.maltparser.core.symbol |
Contains classes for handling different kinds of symbols (e.g., word forms, root labels and parser actions).
|
| org.maltparser.core.symbol.hash | |
| org.maltparser.core.symbol.nullvalue |
MaltParser has the possibility of distinguishing between different kinds of null-values when extracting the feature
vector.
|
| org.maltparser.core.symbol.parse | |
| org.maltparser.core.symbol.trie |
Provides classes for handling different kinds of symbols as trie data structure.
|
| org.maltparser.core.syntaxgraph | |
| org.maltparser.core.syntaxgraph.ds2ps | |
| org.maltparser.core.syntaxgraph.edge | |
| org.maltparser.core.syntaxgraph.feature | |
| org.maltparser.core.syntaxgraph.headrules | |
| org.maltparser.core.syntaxgraph.node | |
| org.maltparser.core.syntaxgraph.reader | |
| org.maltparser.core.syntaxgraph.writer | |
| org.maltparser.ml |
Contains an interface and classes for creating an interface to an machine learning method.
|
| org.maltparser.ml.lib | |
| org.maltparser.parser |
The top package for Single Malt Parser.
|
| org.maltparser.parser.algorithm.covington |
Implements the Covington deterministic parsing algorithms.
|
| org.maltparser.parser.algorithm.nivre |
Implements the Nivre deterministic parsing algorithms.
|
| org.maltparser.parser.algorithm.planar | |
| org.maltparser.parser.algorithm.stack |
Implements the Stack parsing algorithms.
|
| org.maltparser.parser.algorithm.twoplanar | |
| org.maltparser.parser.guide |
Contains classes that implements the guide, which can be seen as an interface (or glue) between the parsing algorithm and the learner.
|
| org.maltparser.parser.guide.decision |
Provides classes for different decision models.
|
| org.maltparser.parser.guide.instance |
Provides classes for different instance models.
|
| org.maltparser.parser.history |
Contains classes for managing the history feature model.
|
| org.maltparser.parser.history.action |
Provides classes for different kinds of parser actions
|
| org.maltparser.parser.history.container |
Contains classes for intermediate storing of the parser action
|
| org.maltparser.parser.history.kbest |
Contains classes for managing the K-best list
|
| org.maltparser.parser.transition |
Contains classes that all deterministic parsing algorithm use (e.g., parser action and transition).
|
| org.maltparser.transform.pseudo |
Contains classes for performing pre-processing and post-processing transformations on dependency graphs.
|
Copyright 2007-2014 Johan Hall, Jens Nilsson and Joakim Nivre.


The human soul is hungry for beauty; we seek it everywhere - in landscape, music, art, clothes, furniture, gardening, companionship, love, religion, and in ourselves. No one would desire not to be beautiful. When we experience the beautiful, there is a sense of homecoming.


This page doesn't seem to be using Preact.
Import preact/debug somewhere to initialize the connection to the extension. Make sure that this import is the first import in your whole app.
preact/debug


Your privacy is important to us. It is 2048: Custum Mode's policy to respect your privacy regarding any information we may collect from you across our website, https://aidanspeakss.github.io/makeurown/, and other sites we own and operate.
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Columns
| Column | Type | Size | Nulls | Auto | Default | Children | Parents | Comments | ||
|---|---|---|---|---|---|---|---|---|---|---|
| manuscript_metrics_id | INT UNSIGNED | 10 | √ | null |
|
|
||||
| manuscript_id | INT UNSIGNED | 10 | null |
|
|
|||||
| x_origin | INT | 10 | 0 |
|
|
This is the x value of the starting point of the manuscript. The coordinate system begins top left, positive values increase while moving downward on the y-axis and while moving rightward on the x-axis. |
||||
| y_origin | INT | 10 | 0 |
|
|
This is the y value of the starting point of the manuscript. The coordinate system begins top left, positive values increase while moving downward on the y-axis and while moving rightward on the x-axis. |
||||
| width | INT UNSIGNED | 10 | 0 |
|
|
This is the width of the manucsript in millimeters. |
||||
| height | INT UNSIGNED | 10 | 0 |
|
|
This is the height of the manucsript in millimeters. |
||||
| pixels_per_inch | INT UNSIGNED | 10 | 1215 |
|
|
This is the pixels per inch for the manuscript. At the outset we have decided to set all manuscripts at 1215 PPI, which is the resolution of most images being used. All images should be scaled to this resolution before creating artefacts and ROIs that are placed upon the virtual manuscript. We have no plans to use varying PPI settings for different manuscripts, which would slightly complicate GIS calculations across multiple manuscripts. |
||||
| scribal_font_id | INT UNSIGNED | 10 | √ | NULL |
|
|
||||
| creator_id | INT UNSIGNED | 10 | 1 |
|
|
Indexes
| Constraint Name | Type | Sort | Column(s) |
|---|---|---|---|
| PRIMARY | Primary key | Asc | manuscript_metrics_id |
| fk_manuscript_metrics_to_creator_id | Performance | Asc | creator_id |
| manuscript_metrics_to_manuscript | Performance | Asc | manuscript_id |
| manuscript_metrics_to_scribal_font_fk | Performance | Asc | scribal_font_id |
| unique_manuscript_metrics | Must be unique | Asc/Asc/Asc/Asc/Asc/Asc | manuscript_id + height + pixels_per_inch + width + x_origin + y_origin |


WHEN:
AUGUST 3RD
UNTIL AUGUST 21TH
FROM 12PM TO 10PM
LINE UP:
AUG 05, 07PM TO 10PM | Tibless - Groove Samba Bossa
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If you want it more than all night parties.
If you want it more than sleep.
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More than straight teeth and a pretty nose.
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If you really want to be an athlete,
just wanting it isn't enough.
YOU'VE GOT TO WANT IT MORE


This dashboard gives you an overview of the correlation between Twitter sentiment of Google and the Google stockprice for the selected day. Please select the day which you are interested in.


New and returning RIT Students
What the stakeholders want:
What problems they face:
What they propose:
How to involve the stakeholders:
How to communicate progress:
How to understand their way of communication:
How to communicate the design to them:
For our contextual inquiry we conducted interviews within the following demographics:
Our team members conducted interviews on various stakeholders. We asked a series of questions to the interviewees and notes were taken and important points were highlighted. Each interview session lasted for about 10 - 15 minutes.
After conducting the interviews, our team then spent time transcribing these into scripts to allow us to analyze the data effectively. We then went ahead to break these scripts into sentences and then broke these sentences down further to allow us to get reasonable data about issues these participants were facing and comment about the existing systems that are currently in place.
Affinity Notes
We then proceeded on writing these analyzed data on the post-it notes (Affinity notes) and then put everything on the board as part of the first initial grouping (which was a more general grouping).
Bottom Level Labels (Blue labels)
We proceeded on putting general ideas together and putting these general ideas under a common category by identifying points that were common with some of our interview participants. This gave us a basic understanding of the demographic data. We used the blue post-it notes to identify this group.
Temporary Top Level Labels (Green labels)
For the temporary top level, we had grouped the bottom level into a more broader and common idea category and created temporary category grouping which we had put in the temporary green post-it note. This allowed us to identify relationships between the bottom level groups.
Middle Level Labels (Pink labels)
The Middle level was about bringing the temporary top-level grouping into a more organized and descriptive grouping that allowed us to get a better understanding of the user's needs.
Top Level Labels (Green labels)
For the final step of the interpretation, we used the green post-it note to combine the ideas gotten from the grouping done on the middle level above. This gave a more specific description of what the users wanted and the general solution we should focus on.
Yang Zheng is a graduate student from China. He worked as a software developer before coming to RIT for his master’s degree. He really appreciates well documented information and finds too many emails annoying. He has applied filters to sift through the hoard of emails to just get the important ones and because of this he sometimes misses important information which gets filtered out. He regularly visits the RIT website and finds it easy to navigate.
• Access Information easily
• Access information on the go
• Find information in a single space
• Track progress
• Feel in control
• Access on different platforms
• Minimize point of contacts
• Finish school with good grades
• Get a good job
• Be successful in life
Social Media
Website Access
Phone Usage
Email Usage
Communication Skills
Organization Skills
• Scattered information
• Constantly bombarded with information
• Don't know the right channel to access
information
Sanjana is an international graduate student from India. Being in a creative field, she tends to take things as they come and finds the most creative solution on the move. She likes to work alone and maintain minimal contact with her co-workers outside of work. She usually goes through all her emails to be in the know but finds it hard to keep track of all the things she needs to do. She frequently visits the RIT websites to get important information.
• Efficiently keep track of the progress
• Wants to access information without anyone’s help
• To grab information easily
• Wants to feel confident
• Feel self-sufficient
• Wants to own a multinational company
• Work towards women empowerment
Social Media
Website Access
Phone Usage
Email Usage
Communication Skills
Organization Skills
• Can’t keep track of admission procedures
• Navigating the website is hard
• Hard to gather information from verbal
conversation
Omar is an undergraduate international student from Ethiopia. He likes to talk to everyone and is very sociable. Even before coming to camps to start his semester, he was constantly in touch with his batch mates over Whatsapp. He doesn’t like to receive too many emails and usually just swipes on the notifications and does not read the entire emails. He likes to stay informed by talking to the staff and administration, but often has to visit multiple departments to solve his query as he doesn’t know who is the right person to talk to in the first place.
• Access information through mobile
• Supervise the admission process
• Easily find someone with information
• Adapting easily to life in the U.S
• Feel organized
• To be a top-level research specialist
• Explore the world
Social Media
Website Access
Phone Usage
Email Usage
Communication Skills
Organization Skills
• Receiving too many emails
• Wasting time by searching the required information on the website
Ryan is a doctorate student of astrophysics at RIT. He was the president of the physics club at SUNY Buffalo while pursuing his undergraduate degree from there. He constantly keeps in touch with the faculty and administration staff via emails. He plans his activities weeks in advance and gets annoyed with his research group when they don’t do the same.
• Access Information on the go
• Get notified when new information is available
• Maintain contact with faculty and administration
• Be the first to receive information
• Looking for better ways to improve organisation skills
• Become a Professor
• Be nationally recognized
• Publish several papers
• Get funded to carry out groundbreaking research
Social Media
Website Access
Phone Usage
Email Usage
Communication Skills
Organization Skills
• People asking irrelevant questions
• Seeing people not being systematic
First, we summarized the interviews and named all the interview subjects as U1, U2, U3, U4 & U5.
Next, we came up with user roles and grouped each user under different goals.
We identified the different behavioral variables from our interview summaries.
We then used points on a scale for each behaviour variable to map the subjects to their behaviour.
After this, we checked for completeness and redundancy in the characteristics of the personas.
We then designated each persona to the following types:
Final step was to expand the description of attributes and behaviors of each persona.
TigerSteps provides step by step instructions needed by international students coming to RIT, to complete the admission process and settle-in easily. This app keeps track of the students’ progress through the admission process and ensures that the necessary information about settling into RIT is properly dissipated to the student. It also helps to minimize the problem of students missing crucial steps required by the administration while going through the admission process.
For us to understand the performance of the system design, a usability test was conducted on the prototype. This was done to understand how easy the system was and also to get real-world constraints that may exist for all the stakeholders using TigerSteps.
In the group’s first meeting, we consolidated all the pain points from our personas and came up with the basic hierarchy of the interaction flow.
We identified all the app screens that would be relevant to the problem statement and created a rough sketch for each.
We further reiterated the designs and identified the screens we wanted to represent on our low-fidelity prototype.
Through the user interviews certains gaps were identified in the flow of user interaction.
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WITNESS LIST Criminal Jurisprudence Committee February 27, 2001-2:00P HB 139 On: Eeds, Walter (Texas Department of Public Safety) Ginn, Bill (Texas Department of Public Safety) Registering, but not testifying: For: Lawrence, Kevin F. (Texas Municipal Police Association) Moore, Mickey (Texas Retailers Association) HB 513 For: Apperson, Eduard (Self) Day, Rick D. (Self) Epstein, Ruth (ACLU of Texas) Evans, MD, Richard (Self) Ginsburg, Harvey (Self) Hayes, Tracey (Self) Holt, Charles C. (Common Cause of Texas) Keenan, Barry (Self) Lee, Ann E. (Self) Lee, Robert C. (Self) McMahon, George (Self) Robison, Dr. G. Alan (Self and Drug Policy Forum of Texas) Smith, Jennifer Christine (Self) Stefanoff, Jeffrey W. (Self) Synal, Matthew (Self and Irene Gabriel, deceased) Tucker, Dean (Self) Wimberley, Melanie (Self and JoAnn P. Williams) On: Eeds, Walter (Texas Department of Public Safety) Registering, but not testifying: For: Airheart, Jon (Self) Darrouzet, Sarah (Self) Hammett, James (Self) Harding, David (Self) Harrell, Will (ACLU of Texas) Heikkala, Karen (Self) Kahl, Andrew (Self) Kahl, Christine (Self) Nation, John (Self) Orrange, Steven (Self) Against: Myers, Jackelyn "Jackie" (Texas Eagle Forum) HB 514 For: Alvidrez, Governor Albert (Ysleta del Sur Pueblo) Battise, Kevin P. (Alabama/Coushatta Tribe of Texas) Bluitt, Ethel (Deep East Texas Council of Governments) Briones, County Judge Delores (Self and County of El Paso Commissioners Court) Brown, Robert (Tigua Indians) Diamond, Tom (Ysleta del Sur Pueblo) Garza, Jr., Isidro (Kickapoo Nation) Makateonenodua, Chairman (Kickapoo Nation) Against: Ware, Weston W. (Texas Baptist Christian Life Commission) Registering, but not testifying: For: Boone, Margaret (Self) Dean, Sharon (Self) Gutierrez, Hector (Self) Harrell, Will (ACLU of Texas) Rodgers, Tom (Alabama Coushatta) Shaw, JoAnn (Self) Velasquez, Sally (The City of Eagle Pass) Against: Scott, Rider (Greater Dallas Crime Comm.) HB 909 For: Hubbarth, William "Rusty" (Self and Justice for All) Kahan, Andy (Mayor's Crime Victim's Office) Pitrucha, Madonna (Self) Teer, Patsy M. (Self) Registering, but not testifying: For: Adkinson, Doug (Harris County Judge Robert Eckels) Lawrence, Kevin F. (Texas Municipal Police Association) LeBleu, Dorothy N. (Self) HB 1071 For: Hearon, Genevieve (Capacity for Justice) McClure, Spencer (Texas Community MHMR Centers) Against: Hubbarth, William "Rusty" (Self and Justice for All) Registering, but not testifying: For: Harris, Lauralee (Mental Health Association of Tarrant County) Holt, Charles C. (Common Cause of Texas) Leveling, Captain Greg (Self and Denton County MHMR) Lovelace, Joe (NAMI Texas) Swayne, Judi (Self and Denton County MHMR) HB 1093 For: Hearon, Genevieve (Capacity for Justice) McClure, Spencer (Texas Community MHMR Centers) Registering, but not testifying: For: Harris, Lauralee (Mental Health Association of Tarrant County) Holt, Charles C. (Common Cause of Texas) Lawrence, Kevin F. (Texas Municipal Police Association) Leveling, Captain Greg (Self and Denton County MHMR) Lovelace, Joe (NAMI Texas) Marshall, Susan (The Arc of Texas) Swayne, Judi (Self and Denton County MHMR) HB 1600 For: Andersen, Cris (San Antonio Police Officer's Association) Registering, but not testifying: For: Ames, Edward (Self) Bernard, Michael (Self) Igo, Shanna (Texas Municipal League) Jones, Claude (Texas Police Chief's Association) Lawrence, Kevin F. (Texas Municipal Police Association) Mann, Tom (Lubbock Police Department) Reed, Susan (District Attorney, Bexar County) Rendon, Felix (Self) Watson, Sergeant Rick A. (Terrell Bolton Chief of Police, Dallas Police Dept.) Wilkison, Charley (CLEAT, Combined Law Enforcement of Texas) Ybarpo, Lieutenant Ray (Chief of Police Al Philippus, San Antonio Police Dept.)


@* * This template takes a single argument, a String containing a * message to display. *@ @(clubList: List[Club]) @* * Call the `main` template with two arguments. The first * argument is a `String` with the title of the page, the second * argument is an `Html` object containing the body of the page. *@ @main("Welcome to Play") {
| Club Name | Member Count | @for(club <- clubList) {|
| @club.getClubName | @club.getMemberCount | View |


Introduction
On March 16, a budget blueprint released by the White House details President Trump’s preliminary plan to cut substantial funding for a number of federal agencies, including a 13% decrease for the Department of Transportation.
As transportation and smart cities enthusiasts, our team is concerned about the proposed funding cuts to transportation in Trump’s budget, which is currently the lowest in 18 years (source: New York Times).
The call for a $2.4 billion cut will impact the ongoing development of smart cities and public transportations nationwide, such as the government’s decades-long effort to get a light rail system off the ground and improve Amtrak's long-distance train service, "which long have been inefficient and incur the vast majority of Amtrak's operating losses," according to Fortune.
This concern motivates us to study the impact of the budget cuts on nationwide locations through exploring Amtrak data, as well as commute rates and flight data.
Our datasets are collected from open sources including census bureau, National Household Travel Survey (NHTS), and the U.S. government’s data portal. Readers will be able to find the data in particular using the following links:
- Commute Data:
- Flight Data:
- Population Data:
- Train Data:
Meet the Team
Members of The Geocoders are:
- Aditya Garg<EMAIL_ADDRESS>- Moorissa Meritseba Tjokro<EMAIL_ADDRESS>- Will Connor Geary<EMAIL_ADDRESS>In exploring the data and transportation patterns, Aditya focuses on air transport (Flight Carrier and Air travel data), Moorissa on various modes of transportations on road such as cars/vans/truck/and public transportations specific to commute data, and Will on Amtrak/train data and populations dataset across the country.
We will map different plots on “before” and “after” budget cuts, discuss transportation patterns, and analyze how the decrease in funding will significantly impact the U.S. population. We are using Python for data extraction and manipulation, Processing, R and Carto for plots and map visualization, and also Javascript/SVG/HTML/CSS for the site display.
Data Quality
While values for transportation data are consistent and clean, an issue with the data occurred during import process. In handling the import process, we used the single-byte encoding 'ISO-8859-1' instead of the default 'utf-8', because there are characters from the file that do not represent Unicode characters, such as symbol like Š.
Furthermore, there are some missing values on the variables in the data, such as FIPS codes and statistical area of the county. However, since we are not using any of these information, we did not have to deal with missing values. Note that values in the variables we use, including 'Residence - County Name', 'Place of Work - County Name', 'Travel Mode', and 'Workers in Commuting Flow' do not have missing values.
The Amtrak timetable data was of high quality, thanks to Amtrak's adherence to the GTFS format. Ridership data from the National Association for Rail Passengers was also very clean and straightforward due to consistent naming conventions for stations.
Similarly, the air transport data was of high quality and pretty structured. Air carrier data and master coordinates data for airports from the Bureau of Transportation Statistics was clean and consistent in structure.
Executive Summary
“Crumbling infrastructure will be replaced with new roads, bridges, tunnels, airports and railways gleaming across our beautiful land,” , said Trump, during a joint address to Congress early this year.
His promise, however, seems to be counterintuitive with his recent proposal to cut government fundings by $2.4 billion. It would eliminate federal funding for Amtrak's national train routes, which would risk disconnecting 145 million people for whom these routes are the only nearby Amtrak service.
The proposed cut would also eliminate billions in funding that would have otherwise gone towards new commuter rail and light-rail projects, as well as $500 million in grants intended for investment in new national transit infrastructure. The following graph shows the White House budget cut in transportation compared to the years before.
The White House's proposed budget cut in transportation will have major impact not only towards the infrastructure of the country, but also public transportation users, labor workforce, and the access to aviation for people in the rural and remote communities.
Our findings show that Amtrak stations are most at risk in areas including New Orleans; Denver; Orlando; Tampa; St. Paul-Minneapolis and Atlanta. While areas that will impact major commuters would be those surrounding metropolitan areas such as New Jersey, Virginia, Maryland, New York, and New Hampshire. Finally, the remote areas that will be the most impacted by air transport budget cuts are around central U.S. like Michigan, Kansas, and Kentucky, where Trump received more than 50 percent of the vote.
Analysis
On Amtrak
The White House's proposed budget would eliminate federal funding for Amtrak's national train routes, which would risk disconnecting 145 million people for whom these routes are the only nearby Amtrak service. It would eliminate $2.4 billion in funding that would have otherwise gone towards new commuter rail and light-rail projects, as well as $500 million in grants intended for investment in new national transit infrastructure (National Association of Railroad Passengers, 2017).
Understanding the geography behind these numbers is crucial for making sense of them. First, we need to understand where Amtrak routes and stations currently exist. The map below overlays the Amtrak network on top of a population density choropleth map, so we can get a sense for the scope of Amtrak's rail network and the density of the places that it serves.
While the static route map above is helpful for understanding the geographic scope of Amtrak's network, it doesn't tell us anything about the frequency of Amtrak's service. The geovisualization below, titled Amtrak Before and After Trump shows one week of Amtrak trips as they currently exist versus one week of Amtrak trips excluding the 15 national routes that are threatened by the White House's budget proposal.
Routes on the chopping block include: Silver Star; Cardinal; Silver Meteor; Empire Builder; Capitol Limited; California Zephyr; Southwest Chief; City of New Orleans; Texas Eagle; Sunset Limited; Coast Starlight; Lake Shore Limited; Palmetto; Crescent; and the Auto Train. Noticeably, towns and cities in the Southeast and Midwest would be impacted the most, while large, coastal cities in the Northeast and on the West Coast would be impacted the least.
Next, let's focus on the stations that are at risk of being cut. The below dot distribution map titled Amtrak Stations at Risk displays each station that is at risk of being cut under the White House’s proposed budget. The size of each dot corresponds to the total number of passengers that each station saw in 2016 (including both departures and arrivals).
The busiest stations at risk include: New Orleans; Denver; Orlando; Tampa; St. Paul-Minneapolis and Atlanta. The least busy stations at risk include: Sanderson, TX; Thurmond, WV; Alderson, WV; Lordsburg, NM; Montgomery, WV; South Portsmouth, KY. The ridership data comes from the National Association of Railroad Passengers’ Ridership Statistics portal.
On Commute Rates
While the above Amtrak analysis provided us with insights on areas with direct impact of the budget cut, we can use commute rates to further our observation on how it affects the workforce across the U.S.
According to Gizmodo Research Media Group (2017), the average commuter spends about an hour a day heading to and from work, but plenty spend as much as three hours commuting. Those hours spent in the car or public transportation have profound psychological and physical impacts on most U.S. workforce population. Transportation plays such a significant role in people’s daily commute, so budget cut on city transportation not only will impact the city infrastructure but also people’s way of commuting.
For the commute data from the 2016-2010 dataset of the census bureau, we have information on residence and place of work, each with state FIPS code, county FIPS code, state name, county name, metropolitan statistical area FIPS code, and area of county. Given travel modes and number of population, we calculated our commute rate by classifying the out-of-county commuters and taking its ratio with the total commuters.
The out-of-county commuting patterns provide a greater insight on the impact of Trump’s budget cut on transportation (e.g. the longer the commute distance, the more people are dependable on transportations), while many of the in-county transportation modes only captures walking distance.
The following scatterplot shows a correlation between in-county vs. out-of-county commuters. While most datapoints are clustered in the bottom left, the number of in-county commuters outnumbers the out-of-county for every residence. Most county residence has under 1.5 million in-county commuters and under 500,000 out-of-county commuters.
While the boxplot on the right shows that the median rate of the out-of-county commuters out of the total population is 26 percent, the histogram below shows that majority rates for commuters who leave their county of residence for work fall within 20 percent and 30 percent range, with peak around 24 percent.
Mean rate at 25 percent means that most regions in the United States have on average 25 percent workers who commute out of county. There is also sign of bimodality with a lower peak at 20 percent and distribution of data looks slightly left-skewed, with the median of 26 percent greater than its mean value.
This distribution represents an initial measure of commuting patterns, which would allow for comparing rate frequency after Trump’s budget cut. With the budget restriction on transportations, people might not be able to commute far away, which could result in a decrease in this commute rate.
Lets now take a look at distribution by travel modes from the following boxplot, where each datapoint represents a commuting flow from every county in the US. Majority of people use public transportation than cars, trucks, or vans. Other modes include bikes, motorcycles, and taxis. While there are counties with higher number of car-driver population, majority of commuters in counties across the states depend on public transportation the most.
The cut in funding will have a huge impact on nationwide commuters as majority use public transportation. The histogram below shows distribution of population by commuting modes.
Breaking it down further by states using barchart below, Virginia, Maryland, New Jersey, and Georgia (indicated by red bars) have over 40 percent residents leaving their home county to work daily. Modes of transportation recorded for these commuters are primarily car, van, truck, and public transportations.
Virginia, which leads the nation in residents who leave their home county to work, at over 51 percent, and Maryland, at 47 percent, have heavy emphasis on the Washington DC area and the federal employment complex. New Jersey is affected by nearby major job centers in Pennsylvania and New York; Georgia is affected by the major metropolitan multi-county, multi-state job complex that is Atlanta. Many of the areas with more than 30 percent leaving their residence county exhibit similar characteristics.
Since the Amtrak commuters is at a higher risk, taking further observation by the travel modes would allow us to see which state would be most impacted. For those who drive or carpool with cars, trucks, or vans, the top five areas with the most risk are Virginia, Puerto Rico, Maryland, New Jersey, and Georgia. While Puerto Rico and Virginia tend to have the most number of out-of-county commuters, Louisiana also seems to be at risk in the ‘other’ modes category.
Meanwhile, areas of impact for commuters who use public transportations such as Amtrak include New Jersey, Virginia, Maryland, New York, and New Hampshire areas. Public transportation is leading the rate.
Overall, the data illustrates the attractive power of major metropolitan areas, such as Washington DC, New York, and Pennsylvania. Commuters who work in these areas will get the most impacted.
On Air Transport
The Trump administration’s first proposed federal budget blueprint released on March 16 cuts down the USDOT’s discretionary budget by $2.4 billion, a thirteen percent decrease from last year. This proposed budget cut directly affects the air transport service in the US by proposing to restructure the Federal Aviation Administration (FAA) and eliminating funding for the Essential Air Service (EAS) program that subsidizes airline flights to rural communities.
Before we delve into analyzing the impact of these proposed cuts on the US air transport, we first try to understand the current distribution of airports and the use of air transport across different states by visualizing the active US airports and the annual air trips by people in each state.
Essential Air Service (EAS) Program
Essential Air Service (EAS) is a U.S. government program enacted to guarantee that small communities in the United States are able to maintain a minimal level of scheduled air service, which would be otherwise unprofitable. It is implemented through The United States Department of Transportation (USDOT) that subsidizes airlines to serve communities across the country that otherwise would not receive scheduled air service.
However, Trump’s proposed budget completely eliminates the funding for the EAS program. Trump expects the cut to save the government up to $175 million, however, for the 173 communities subsidized by the EAS program (as per the February ’17 report), the termination of the program would entail a lot of hardships.
In the map below, we visualize all the flight routes in the US and highlight the ones that include an EAS community in red.
As we can see from above, most of the affected communities and the flight routes are in mid-east and mid-west US (who, incidentally, are primarily Trump supporting regions). The 173 communities that would lose federal EAS funding under Trump’s budget, include communities in Alabama, Arkansas, Colorado, Iowa, Kansas, Kentucky, Michigan, Montana, Nebraska, Pennsylvania, South Dakota and Wyoming. In 86 of those communities, Trump received more than 50 percent of the vote, according to an analysis conducted by The Associated Press. Alaska, a deeply red state that backed Trump with 53 percent of the vote, is home to 61 EAS communities alone. The elimination of the EAS program would especially hit the subsidized communities in Alaska harder, where remote towns and villages are often completely dependent on air travel for supplies and travel.
In the map below, we highlight the EAS communities and the airports that are going to be impacted by the proposed elimination of the EAS program.
The most impacted state, based on annual subsidies, is going to be Michigan with subsidies being cut for 9 airports (Iron Mountain, Ironwood and Escanaba being the major ones). New York ( Plattsburgh, Massena, Watertown airports), Pennsylvania (DuBois, Johnstown, Lancaster airports) and West Virginia (Greenbrier, Morgantown, Beckley) are the other states that will be impacted the if the EAS program is scrapped off.
Conclusion
One of President Trump's campaign promises was to strengthen the United States' transportation infrastructure. His proposed budget, however, does the exact opposite. By eliminating $2.3 billion in federal funding that would have otherwise gone toward new commuter rail and light-rail projects, as well as $500 million in grants intended for investment in new national-transit infrastructure, Trump will take a step in the wrong direction. We should be working towards faster, cheaper, and more frequent connectivity between American towns and cities, not rolling back our transit infrastructure.
During his campaign for the presidency, Trump promised to rebuild the ‘third world airports,’ but this budget would eliminate the Essential Air Service (EAS) programme, thereby eliminating air service to 173 rural communities and severing their critical access to the aviation system. The Trump administration needs to re-evaluate if the negative impact and hardships that are caused to the American people, especially in the rural areas, is worth the $175 million dollars that the administration expects to save.
With respect to commute and road trasnsport, the Trump administration needs to be cognizant about the impact on the poeple who commute out of counties, especially around major metropolitan areas, who are at the risk of losing their jobs if the proposed changes are implemented.
Since the federal budget is only a proposal at this stage, it was tough to determine the specific changes and quantify their exact impact. A lot of effort was put in manually going through reports, news and analyses to determine the affected transportation channels and the impacted population.
In the future, we can expand our work to quantify the impact of the changes due to budget cuts on the US population and can highlight the most vulnerable areas and demographics.
Code Repositories
All python code used to wrangle Amtrak data is available here
All R code used to process and visualize Flight data is available here
All python code used to manipulate and visualize Commute data is available here


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Dominica’s opposition leader strikes back; blames police for unrest in Roseau
by SNO StaffROSEAU, Dominica — Political Leader of the United Workers Party (UWP) in Dominica Lennox Linton has struck back at the country’s Prime Minister Roosevelt Skerrit who blamed the party for civil unrest that rocked Roseau on Monday night.
In an address to the nation on Tuesday afternoon, Linton made it clear that the UWP had nothing to do with the disturbances, saying instead it was instigated by members of the police force.
“I want to make it clear upfront that the United Workers Party is not directing any protest action,” he said. “I want to be clear this afternoon that the incident on the evening of November 18, 2019 was instigated by members of the police squad.”
On Tuesday morning Skerrit blamed the UWP for the incident which saw the police clash with protesters who have been calling for electoral reform on the island. The protesters had been gathering for a day near the official residence of the president seeking an audience with him on the matter.
He said the days of protests is the approach of the current leadership of the United Workers Party to conflict resolution.
In his response to Skerrit, Linton instead blamed the police for the incident.
“It was not the protesters but the police that interrupted the flow of traffic to the entire south of the island because of a directive from high authority that the people should be prevented at gunpoint from having an audience with the president, the key guardian of the constitution,” he stated. “It is the dispensing of tear gas canisters against persons who carried only placards, that was an act of violence.”
Linton also accused Skerrit of “violently in breach of the oath of office of which he swore to serve without fear or favor, affection or ill will”.
“And that in the execution of the functions of the office of prime minister he will honor, uphold and preserve the constitution of the Commonwealth of Dominica with favor to himself and ill will to the people of Dominica, Mr. Skerrit has violently attacked the people’s right to free and fair elections and he is unrelentingly assaulting the constitution of the rule of law and good governance of our nation.”
Linton stated that the protests were being carried out by the people of Dominica who want to secure free and fair election with integrity “as the backbone of government of the people, for the people and by the people”.
“That is their right,” he remarked. “They are citizens of the Commonwealth of Dominica. Dominica belongs to them as it belongs to all of us.”
Linton said that his party stands firmly with the people of Dominica, protesting and standing for their rights by insisting on electoral reform.
“These protest actions did not fall from the sky, they are not being pursued by aliens,” Linton said. “The people involved are Dominicans, Dominican human beings with rights, with concerns….”
Headline News
-
OECS chairman calls for “calm” in Dominica November 19, 2019 5:19 PM
-
UPDATE: Dominica’s PM condemns unrest in Roseau; blames opposition UWP (see video) November 19, 2019 4:23 PM
-
Saint Lucian in Taiwan’s top TV singing contest November 19, 2019 8:06 AM
-
Turmoil in Dominica as protesters and police clash (see video) November 19, 2019 7:55 AM
-
Mayor Francis unhappy over disarmament of cop by bandits November 19, 2019 7:34 AM
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CSA happy over collective agreement with LUCELEC but … November 19, 2019 7:19 AM
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Saint Lucia Forensic Lab working to build public confidence, says director November 19, 2019 4:31 AM
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PM Chastanet promises super-rich Saint Lucia citizenship in three months November 18, 2019 11:55 AM
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Guy Joseph shoots down reports of complete St. Jude Hospital demolition November 18, 2019 10:26 AM


Containing:
code containing *em* text
code containing **strong** text
code containing _em_ text
code containing __strong__ text
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Intertwined:
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code *intertwined with em* text
code **intertwined
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code _intertwined
with em text_
code _intertwined with em_ text
code __intertwined
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◆ AssignResult
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| ACCEPTED | Accepted from device
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| REJECTED | Rejected from device
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| TIMEOUT | response timeout
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| STARTED | assignment started, please wait to finished
|
| UNKNOWN | Unknown
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◆ OnDeviceDiscovered()
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The documentation for this class was generated from the following file:


More Website Templates @ TemplateMonster.com - March 24, 2014!
Updates
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Our paper has just won the IAPP SOUPS Privacy Award, the $2000 prize for the best paper.Jun 2016
Best Paper Award at SOUPS 2016!
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Our paper "Follow my Recommendations: A Personalized Privacy Assistant for Mobile App Permissions" has been accepted by SOUPS 2016.Jun 2016
New paper at SOUPS 2016
-
Summer Intern at Google Research: Privacy Research & Development TeamFeb 2016
Google Internship
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Our paper "Efficient Privilege De-Escalation for Ad Libraries in Mobile Apps" has been accepted by MobiSys 2015Feb 2015
New Paper at MobiSys 2015
-
Selected as a "Yahoo Fellow" under the Yahoo-CMU InMind programJan 2015
Yahoo Fellowship
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Our paper "Modeling Users' Mobile App Privacy Preferences: Restoring Usability in a Sea of Permission Settings" has been accepted by SOUPS 2014May 2014
New Paper at SOUPS 2014
-
Research Intern in Samsung Research America, San Jose. Summer 2014.May 2014
Samsung Internship
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Our paper "Reconciling Mobile App Privacy and Usability on Smartphones: Could User Privacy Profiles Help?" has been accepted by WWW 2014Jan 2014
New Paper at WWW 2014
Projects
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PERSONALIZED PRIVACY ASSISTANT PROJECT
Personalized Privacy Assistant
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The Usable Privacy Policy Project: Towards Effective Web Privacy Notice and Choice
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Passion.Drive.Creativity
I am a senior at Michigan State University studying Media and Information with a specialization in IT. I have a lot of expierence in web development and design along with some project managenment skills. I am seeking full time emplyment in the IT area specifically in operations or information security. I am a someone who has a positive outlook on things and is more than willing and ready to learn outside of the classroom.


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[2] hotel in the main airport very clean and in good condition with power shower accompanied by generous branded amenities basket and towels.


Introduction
In this section, you will learn about the primary interface of
Torque 2D and what all its bells and whistles do. Torque 2D's primary
scene editor interface consists of many powerful tools and features to
help you make your next game a reality. When you first open the
Torque 2D editor, you will see your project and its assets loaded into
the different tool windows. For the purpose of this documentation, we
will be using the example "RainyDay", which you can build by following this
tutorial.
RainyDay example project, compiled and running.
Interface
Upon opening the RainyDay project in Torque 2D, your screen will look something like this:
Scene View The Scene View is where the bulk of your work will be done. This is a
WYSIWYG representation of your game's scene. The following commands and
hot-keys function in the Scene View:
Move Camera: Hold Right Mouse Button and Drag Mouse
Zoom Camera: Scroll Wheel
Group Select: Hold Left Mouse Button and Drag Mouse
Resize Object: Click the object, then drag the Blue handles on its sides.
Modify Object Properties: Click the object, then move the mouse cursor near the top to display its Edit Buttons.
Modify Object Scripting Properties: Click the object, then click on the 'Edit' tab in the right most menu.
File Menu
File Menu allows you to:
Create, save, open, and close Scenes and Projects;
Edit Menu allows you to:
- Control editor actions such as undo and redo;
- Cut, copy, paste, and delete objects you have selected;
- Move selected object to the layer the furthest away from the camera, or the closest layer.
- Access the editor Preferences.
Project Menu allows you to:
- Return to the project loading screen
- Import Imagemaps into your project
- Update images to the latest stored on your drive
View Menu allows you to:
- Jump to the start position of the game's camera, or its Home
- Orient the camera to fit all objects in the scene within the Scene View
- Orient the camera to fit all selected objects in the scene within the Scene View
- Quickly adjust the zoom of the camera to 25%, 50%, 100%, 200% or 400%
Help Menu allows you to:
- Access the Torque 2D Documentation
- Review and edit the Keyboard Shortcuts for the Torque 2D editor
- Access the Torque 2D Community in your Web Browser (Your browser will automatically open)
- Read the development credits and see the engine and editor versions, along with any Addons you currently have installed
Toolbar
The toolbar provides a series of icons that represent various editor
actions. The following descriptions are in order of left to right:
Create a New Scene
Open an Existing Scene
Save Changes to Current Scene
Edit level datablocks in Visual Studio 2005
Edit level datablocks in Visual Studio 2008
Move Selection to Clipboard
Copy Selection to Clipboard
Paste Objects in Clipboard
Undo Last Change
Redo Last Change
Move Selection Up one Layer
Move Selection Down one Layer
Play Game
Selection Tool
Text Edit Tool
Camera Tool
Full Box Selection - When active, requires an entire object to be in the selection range.
Snap to Grid
Project tab info
The Project Tab is broken into 5 sections. We'll discuss each individually.
Project Manager
- Reload Project - Reloads the current project, refreshing all scripts and images
- Open Project Folder - Opens the project folder in the Finder
- Open VS2008 Project - Open's the project in Visual Studio 2008
- Open VS2005 Project - Open's the project in Visual Studio 2005
- Open XCode Project - Open's the OSX project in Xcode
Object Tree
- Tree-based representation of all objects in the current scene, organized in their appropriate hierarchy
Undo History
- History of recent actions performed in the editor. Double-click on an entry to rewind the editor back to that point
Edit Tab
All object editing happens in the Edit Tab. This section of the
editor display all the properties for an object. Specialized objects,
like static sprites or scrollers, have their own unique properties
(typically displayed at the top of the Edit Tab). However, all objects
share the same set of common properties.
Common Object Properties
Because every object in your scene is essentially a t2dSceneObject,
all objects share certain common properties. This section lists out
common properties that every object has.
 Whether you want to turn a certain object
into an enemy, make it move back and forth, move to wherever the player
touches the screen or more - Behaviors allow you to very quickly add
code to an object without need to do a lot of extra work.
To select one of your behaviors, choose it from the Drop Down List and
press the green plus button. Your project must have behaviors associated
with it for this list to have anything in it.
Scene Object
- Position
- X - The position of this object upon the X axis in the scene
- Y - The position of this object upon the X axis in the scene.
- Size
- Width - The width of this object
- Height - The height of this object
- Rotation - The rotation of this object, in degrees
- Auto Rotation - The speed that this object will
automatically rotate when loaded into the scene. Positive will rotate
clockwise, negative will rotate counter clockwise
- Flip Horizontal - If true, the object will be flipped horizontally
- Flip Vertical - If true, the object will be flipped vertically
- Sort Point
- X - The X position, relative to the object, that the Sort Point is located
- Y - The Y position, relative to the object, that the Sort Point is located
- Layer - Which depth layer that this object will exist upon
- Group - Which object group that this object will be in
- Back/Forward - Moves the object backwards and forwards
within the layer it is in. Use this if you have multiple objects in the
same layer and need to adjust their sorting
- Lifetime - How long this object will exist
- Visible - If true, the object is visible in the scene. If false, the object will not render
Align
Modify how this object is aligned with the camera view.
- - Align to the left
- - Align to the horizontal center
- - Align to the right
- - Align to the top
- - Align the to vertical center
- - Align to the bottom
- - Match horizontal size to horizontal viewport size
- - Match vertical size to vertical viewport size
- - Match size of object to the viewport size
Scripting
- Persist - Set this object to persist across all levels
- Config Datablock - The configuration datablock to associate this sceneobject with
- Name - Name the object for referencing in script
- Class - Link this object to a Class
- Super Class - Link this object to a Parent Class
- Use Mouse Events - Enable mouse callbacks on the object
Collision
- Send Collision - This object sends collision events
- Receive Collision - This object receives collision events
- Send Physics - This object sends physics events
- Receive Physics - This object receives physics events
- Callback - This object handles collision events in script
callback functions. If you wish to do more than the simple Collision
Response defined below you must check this box
- Detection Mode - This selects which mode you wish to use to
detect collisions
- FULL - This uses the entire sprite boundary to detect
collisions. Ok for rectangular objects
- CIRCLE - This uses a circle that fits the widest dimension of
your sprite to detect collisions
- POLYGON - (Default) This uses the collision polygon defined for
the sprite. You can edit this polygon by selecting "Edit this object's
collision polygon" from the object's Selection Editor Toolbar
- CUSTOM - This is for generating custom C++ collision response
calls and is an advanced feature
- Collision Response -
This selects the physical behaviour you wish your objects to have when they
collide.
- CLAMP - The object will stop when it collides with another
object
- BOUNCE - (Default) The object will bounce off of the other
object in a collision
- STICKY - The object will stick to the collision target
- KILL - The object will be destroyed on collision
- RIGID - The object's behavior is calculated using basic rigid
body physics
- Collision Layers - This determines which scene layers to use
when detecting collisions. All are selected by default
- Collision Groups - This determines which Scene Object groups to
use when detecting collisions. All are selected by default
Physics
- Velocity - The speed in units per second that the object is
moving in the X and Y directions.
Positive values are to the right and down, respectively, while negative values
are to the left and up, respectively. Min and Max
values determine the minimum and maximum speeds for your object
- Angular Velocity -
- Immovable - This object cannot be moved by physics events
- Forward Only - Allows the object to only move in its forward
direction
- Constant Force - Applies a steady force in the desired amount
in the X and Y directions. Could be used
to simulate wind force, etc
- Gravitic - This object has a steady downward force applied to
it, as if it were affected by gravity
- Force Scale - Scales the magnitude of all forces applied to the
object
- Calculate Mass and Inertia - Uses the object's defined
attributes below for determining physics reactions in a more realistic fashion
- Mass - The object's mass
- Inertia - The object's inertial moment
- Density - The object's density
- Damping - Damping force applied to motion
- Friction - Friction when moving against other objects
- Restitution - Value used to decide when the object has come to
rest
Mounting
- Rotation - Mount Rotation
- Auto Rotation - Automatic Mount Rotation
- Mount Force - (Only available when object is mounted) If
this is not zero, this "tightens" or "loosens" how quickly the mounted
object follows the parent, with lower numbers resulting in the mounted
object taking longer to "catch up to" the parent
- Track Rotation - (Only available when object is mounted) If this is checked the mounted object will rotate with its parent object
- Owned by Mount - (Only available when object is mounted) Owned by Mount
- Inherit Attributes - (Only available when object is
mounted) If this is checked the mounted object will inherit the object
properties (such as visibility) of the parent object
World Limits
- Limit Mode - The selected Limit Mode affects what occurs when the object touches the edges of the bounds of the World Limits.
- Off - The world limits are not even tested by the object
- Null - The world limits are tested, but no action is
taken when the object reaches the limits; it just continues on its way.
This mode is used in combination with the Callback checkbox to script
your own responses to reaching the world limits
- Clamp - The object bounces off the world limits in a simple, pong-like way and doesn't include spin
- Bounce - The object slides along the world limits and will not move past them
- Sticky - The object stops all movement when it touches the world limits
- Kill - The object deletes itself when it touches the world limits
- Min Bounds - The minimum X and Y position of the bounds
for the World Limit. This can be edited here, however is also modified
by using the visual World Limit Editor
- Max Bounds - The maximum X and Y position of the bounds
for the World Limit. This can be edited here, however is also modified
by using the visual World Limit Editor
- Callback - If true, will cause a script-response to
occur when the World Limits are reached. In your script, the callback
for this object should look like this to function:
function myObject::onWorldLimit(%this, %mode, %limit)
{
// %mode will be one of the following strings:
// off, null, bounce, clamp, sticky, kill
// %limit will be one of the following strings:
// top, bottom, left, right
}
Alpha Testing
- Alpha Test Value - Enables alpha testing for this object if the value is between 0 - 255. Set to -1 to disable Alpha Testing
- Blending
- Enabled - Enable blending for this object
- Source Factor - Source blend factor for this object. These represent constants in the format (R, G, B, A).
- ZERO - The blend factor is (0, 0, 0, 0)
- ONE - The blend factor is (1, 1, 1, 1)
- DST_COLOR - The blend factor is (R, G, B, A) of the destination pixel applied as alpha to each channel
- ONE_MINUS_DST_COLOR - The blend factor is (1 - R, 1 - G, 1 - B, 1 - A) of the destination pixel applied as alpha to each channel
- SRC_ALPHA - The blend factor is (A, A, A, A) of the source pixel alpha channel
- ONE_MINUS_SRC_ALPHA - The blend factor is (1 - R, 1 - G, 1 - B, 1 - A) of the source pixel applied as alpha to each channel
- DST_ALPHA - The blend factor is (A, A, A, A) of the destination pixel alpha channel
- ONE_MINUS_DST_ALPHA - The blend factor is (1 - A, 1 - A, 1 - A, 1 - A) of the destination pixel alpha channel
- SRC_ALPHA_SATURATE - The blend factor is (f, f, f, 1) where f = min(Asource, 1 - Adestination)
- Destination Blend Factor - Destination blend factor for this object. These represent constants in the format (R, G, B, A)
- ZERO - The blend factor is (0, 0, 0, 0)
- ONE - The blend factor is (1, 1, 1, 1)
- SRC_COLOR - The blend factor is (R, G, B, A) of the source pixel applied as alpha to each channel
- ONE_MINUS_SRC_COLOR - The blend factor is (1 - R, 1 - G, 1 - B, 1 - A) of the source pixel applied as alpha to each channel
- SRC_ALPHA - The blend factor is (A, A, A, A) of the source pixel alpha channel
- ONE_MINUS_SRC_ALPHA - The blend factor is (1 - R, 1 - G, 1 - B, 1 - A) of the source pixel applied as alpha to each channel
- DST_ALPHA - The blend factor is (A, A, A, A) of the destination pixel alpha channel
- ONE_MINUS_DST_ALPHA - The blend factor is (1 - A, 1 - A, 1 - A, 1 - A) of the destination pixel alpha channel
- Blend Color
- Red - Red Blending Value
- Green - Green Blending Value
- Blue - Blue Blending Value
- Alpha - Alpha (Transparency) Blending Value
Dynamic Fields
Each entry specifies another Dynamic Field for the object. Their
functionality should be defined in the scripts that utilize the object.
Object Specific Object Properties Some objects have different property fields than others. All Object Specific property fields are listed out in this section.
Editor input bindings (mouse, keyboard, shortcuts)
The following are the default bindings for the Torque 2D editor.
These can be modified by accessing the 'Keyboard Shortcuts...' option
under the Help Menu.
- lbracket - Move Selection One Layer Closer to Camera
- rbracket - Move Selection One Layer Away From Camera
- escape - Activate the Selection Tool
- C - Show the Create Panel
- P - Show the Project Panel
- home - Sets the camera to the selected device scene limits
- end - Sets the camera to show all scene contents
- Equals - Zoom the View Window In
- Minus - Zoom the View Window Out
- left - Move the View Window Left
- right - Move the View Window Right
- up - Move the View Window Up
- down - Move the View Window Down
- ctrl-Z - Undo The Last Action
- ctrl-shift-Z - Redo an Undone Action
- ctrl-c - Copy the Selected Objects
- ctrl-x - Cut the Selected Objects
- ctrl-v - Paste Cute or Copied Objects
Conclusion
The Torque 2D editor is intended to be entirely WYSIWYG. The way you
layout and develop a scene within the editor is the same way that the
scene will look when loaded on your target platform. Having read
through the Interface Instructions, you should now understand the basics
of using the Torque 2D editor, how to operate the various menus, and in
what ways you need to interface with the editor to build your game. |


Equipping consumers with the knowledge they need to shop sustainably
The UN Environment Programme estimates that roughly half of greenhouse gas emissions and 90% of biodiversity loss is caused by the extraction and processing of materials, fuels and food for creating consumer goods.
At GreenItUp, we believe the root of the problem is that consumers do not have easy access to the true environmental costs associated with their purchasing decisions. GreenItUp is a browser extension that instantaneously analyzes the contents of a user’s online shopping cart. Based on GreenItUp’s database, our solution will offer more sustainable alternatives that are comparable to items the user is looking at and still within their budget.
GreenItUp is a browser extension that looks at products in a user's online shopping cart and offers comparable substitutions that are more sustainable yet still within their price range. We use the Life Cycle Assessment (LCA) to compare products. The LCA is an environmental accounting approach that considers all the aspects of resource use and environmental releases associated with an industrial system from cradle to grave from the extraction of raw materials and the production and distribution of energy, through the use, and reuse, and final disposal of a product. GreenItUp will initially use carbon emissions as the key environmental metric. Every time a customer switches to the GreenItUp referred product, GreenItUp will receive commission from the retailer.
We're grateful for funding provided by the Dean's Public Policy Challenge.


| Name | Description |
| resourceCheckCancelled |
The resource check has been cancelled by user.
|
| resourceCheckCompleted |
The resource check has completed successfully.
|
| resourceCheckFailed |
The resource check has failed.
|
| resourceCheckNoRecentValue |
The last resource check completed successfully but then it has not run again for a while (e.g. 60 min) and the result of the last check becomes stale.
|
| resourceCheckNotSupported |
The resource check is not supported by one or more host in cluster.
|
| resourceCheckRunning |
The resource check is currently running.
|
| ResourceCheckStatusType_Unknown |
represent the value when the lower version client cannot recognize the enum value
|
| resourceCheckUninitialized |
The resource check has never run yet, or it just starts but the task has not been launched yet (i.e. still in initialization stage).
|


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