When the other two metadata systems aren’t suitable it is also possible to load metadata through code. The other two systems use the functionality described in this section to actually load the metadata.
See also
One or the arguments for loadBundleFunctions(), loadFunctionList() and registerMetaDataForSelector() contains (or “is” for registerMetaDataForSelector()) a metadata dictionary containing information about function and method interfaces that cannot be extract from a basic type signature for that function or method.
Copies of these structures can be also retrieved at runtime using the __metadata__() method on both function and selector objects, which makes it possible to introspect the metadata information when needed.
The metadata is a Python dictionary with a particular structure (all keys are optional):
arguments: A dictionary containing more information on arguments. The keys of this dictionary are integers with the argument offset (for methods index 0 is the first implicit argument, index 2 is the first argument that is visible in a prototype). The values are metadata dictionaries for the arguments and are decribed later on.
In metadata that is returned the __metadata__() method of function and selector objects the arguments value is a tuple with items for all arguments.
retval: A metadata dictionary with more information on the return value. The contents of this dictionary is described later on.
suggestion: For methods only: the method should not be called from Python, and calling it will raise and exception with the suggestion value in the exception message.
variadic: If present and the value is True the function or method takes a variable number of arguments. PyObjC can only call such functions when either on of the arguments is a printf_format, or the dictionary contains information on the argument array (as described by keys further on in this list).
c_array_delimited_by_null: If present and the value is True, and the function is a variadic function, the variable part of the function argument list is a list of values where the last item of the list is a null value. All elements of the list are the same type, that of the last type that is present in the prototype.
In python the function is called with the additional arguments after the fixed arguments (just like in C), but without a null value at the end of the argument array.
An example of such a function is execlp(3)
c_array_length_in_arg: If present and the value is an integer, and the function is a variadic function, the variable part of the function argument list is a list of values and the value for this key indicates which function argument contains the length of that list. All elements of the list are the same type, that of the last type that is present in the prototype.
In python the function is called with the additional arguments after the fixed arguments (just like in C).
Keys not listed above will be ignored by the bridge.
Note
The bridge currently does not copy the metadata when you register it with the functions listed above. Don’t rely on that behavior, it might change in a future release and changes to metadata dictionaries may or may not affect bridge behavior (basicly depending on if the change occurs before or after the bridge first uses the metadata)
The keys c_array_length_in_arg (function, argument and return value metadata) and c_array_length_in_result (argument metadata) describe the index of an argument that contains the size of a C array. This section describes how the bridge determines the value to use for the array.
For _C_INOUT argument the value to use for converting to C and back from C is calculated both before and after the function call, which means that the length of the array passed to the function can have a different length than the array returned from the function. This is used for function where a buffer is passed into the function and the function indicates the useable size of that buffer by modifiying a pass-by-reference argument.
How the size of the C array is calculated depends on the type of the argument:
The argument and return value metadata is also a dictionary with a specific structure. As with the complete metadata dictionary all keys are optional unless the description mentions otherwise.
type: A byte string with the type encoding for the value. The default is extracted from the type encoding for the entire prototype (for methods this is extracted from the Objective-C runtime, for functions this is passed as one of the items in the function info tuple).
This key is always present in the metadata returned by the __metadata__() method.
type_override: A byte string with value _C_IN, _C_OUT or _C_INOUT to indicate that the argument is an input, output or input/output argument. Ignored unless the type is a pointer type that isn’t a CoreFoundation instance.
The value is assumed to be a single value (a pass-by-reference argument), unless there are keys in the dictionary that say otherwise (see further on in this list).
This key is not used for return value metadata.
printf_format: If present and the value is True the argument value is a printf(3) style format string for a variadic function or method. Ignored unless the function or method actually is variadic.
sel_of_type: A byte string that describes the expected selector prototype for arguments of the _C_SEL.
Used by the decorator selectorFor() to calculate the signature of the decorated method.
already_retained: Value True indicates that the return value, or a pass-by-reference output parameter, is returned to the caller with an increased reference count. An Objective-C caller will have to call “-retain” on the value when the value is no longer used.
Used by the bridge to correctly maintain the Objective-C reference count. Python users do not have to maintain the reference count themselfes.
already_cfretained: Value True indicates that the return value, or a pass-by-reference output parameter, is returned to the caller with an increased reference count. An Objective-C caller will have to call “CFRelease” on the value when the value is no longer used.
Used by the bridge to correctly maintain the Objective-C reference count. Python users do not have to maintain the reference count themselfes.
Note
Use either already_retained, or already_cfretained but not both.
The two different keys are present to be able to support Objective-C Garbage Collection: in process with GC enabled the CoreFoundation and Cocoa retain count APIs behave differently. Because GC is on the way out and PyObjC doesn’t properly support GC anyway it is better to use already_retained where approprate and not use already_cfretained.
c_array_delimited_by_null: When True, and the argument or return value type is a pointer type, the value is a C array with a null value at the end. Python users do not have to supply the null value on calls, and the bridge will strip the null value in return values.
When the type_override is _C_IN or _C_INOUT the input value must be a sequence of values (list, tuple, ...). The bridge allocates a buffer of the right size, converts all values and appends the approprate null value. The value can also be a buffer (such as an array.array of the approprate structure), which then must contain a null value at the end.
When the type_override is _C_OUT the argument must be either NULL to indicate that a NULL pointer should be passed to the Objective-C function or a buffer object of the appropriate structure, and with enough room to store the function output including the null delimiter.
c_array_length_in_arg: The argument or return value is a C array where the length of the array is specified in another argument. Ignored when the type is not a pointer type. The value for this key is either a single integer, or two integers (for _C_INOUT arguments).
When the type_override is _C_IN or _C_INOUT the input value must be a sequence of values of the correct lenght (at least the length that’s expected by the function, additional items in the sequence are ignored).
When the type_override is _C_OUT the value can be NULL (NULL pointer passed to the function) or None (PyObjC allocates a C array of the right size and writes nul bytes in the entire buffer).
When the value of the key is a single integer this argument index for the argument that contains the expected size of the array. When the value of the key is a tuple of two integers these are the indexes for the argument that contains the size that should be used when calling the function and the argument that contains the size of the array that is useable after the call.
c_array_of_fixed_length: When the type is a pointer type the actual argument (or result) is an C array of a fixed length. The value for this key is an integer that is the length of the C array.
c_array_of_variable_length: When the type is a pointer type the actual argument (or result) is a C array, but the lenght of the array is unknown or cannot be described in metadata.
For results the bridge will return a value of varlist.
For arguments with type_override value _C_IN or _C_INOUT the value for the arugment must be a Python sequence and the bridge will allocate a C array that is long enough to contain all items of that sequence; alternatively the argument can be a Python buffer object (simular to _C_OUT arugments). For _C_OUT arguments the value for the argument must be either NULL or a Python buffer object that will be passed to the function.
c_array_length_in_result: Only valid for argument metadata. When the argument type is a pointer type and the type_override is _C_INOUT or _C_OUT the usuable length of the array is calculated from the return value.
The size of the buffer that needs to be allocated is determined using one of the other c_array... keys in the metadata dictionary.
null_accepted: If True and the argument is a pointer it is safe to pass a NULL as the value. Defaults to True.
This key is not used in return value metadata.
callable: When type argument or return value has type “^?” or “@?” the method or function takes a function or block as the argument. In Python an arbitrary callable can be passed (but see callable_retained for some limitations).
The value of this attribute contains the metadata describing the callable. It is a metadata structure as described in this section, with some additional limitations: the arguments key of the dictionary must describe all arguments of the callable (that is all keys in range(len(arguments)) must be present), the type key of the argument and return value metadata must be present (although it is allowed to leave out the return value metadata when the function has return type void).
For blocks the argument array must include the first implicit argument at index 0 or the arguments array, and with type b”^v”.
callable_retained: Then True and callable is present and the argument type is b”^?” the callable argument will be retained by the Objective-C funtion or method beyond the call.
This key is not used in return value metadata.
When this value is True the argument must be a global object that is annotated with the decorator callbackFor(). That decorator ensures that the C representation of the function is always present to ensure that it is safe to store a reference on the Objective-C side of the bridge.
Load the bundle specified by bundle_path or bundle_identifier and add the classes in the bundle to module_globals. The classes are not added to the module_globals when scan_classes is False (it defaults to True).
If both a bundle_path and bundle_identifier are specified the function first tries to locate the bundle using the identifier and then using the path.
When bundle_identifier is specified the bundle is located using [NSBundle +bundleWithIdentifier:], and when bundle_path is specified the bundle is located using [NSBundle +bundleWithPath:].

Note
The current implementation loads all Objective-C classes into module_globals, as testing if a class is located in a specific bundle is fairly expensive and slowed down application initialization too much.
Register a CoreFoundation based type with the bridge. If tollfreeName is specified the type is tollfree bridged to that Objective-C class.
The value of typeId is None for tollfree bridged types, and the result of the “GetTypeID” function for the type for other types.
Returns the class object for the registerd type.
Return a wrapper type for opaque pointers (“handles”) of a given type. The type will be registered with the bridge and will be used to wrap values with the given type signature.
Create a type to wrap structs with a given name and type signature, this type will be used by the bridge to convert values of this structure to Python.
This also adds a class method named name to objc.ivar. This class method creates a new instance variable with the struct type as its type.
The created type behaves itself simular to a mutable namedtuple, that is items can be accessed both using attribute access and using the sequence interface.
An example:
Point = objc.createStructType("Point", b"{Point=dd}", ["x", "y"])
p = Point(3.0, 4.0)
# Set the X field in two ways:
p.x = 5
p[0] = 6
The generated type als has a number of methods:
And the following attributes are present:
Changed in version 2.5: The function creates a class method on objc.ivar.
Changed in version 2.5: The type now implements the “_asdict” and “_replace” methods that are also present on collections.namedtuple() types. The attribute “_fields” was added as well.
Tell the brige that structures with encoding typestr should also be coverted to Python using structType (a type created using createStructType()).
Deprecated since version 2.5: Use createStructAlias() instead.
Tell the brige that structures with encoding typestr should also be coverted to Python using structType (a type created using createStructType()).
This also adds a class method named name to objc.ivar. This class method creates a new instance variable with the struct type as its type.
Loads a list of global variables (constants) from a bundle and adds proxy objects for them to the module_globals dictionary. If skip_undefined is True (the default) the function will skip entries that don’t refer to existing variables, otherwise it raises an error exception for these variables.
variableInfo is a sequence of variable descriptions. Every description is a tuple of two elements: the variable name (a string) and the type encoding for the variable (a byte string).
This function loads a global variable from a bundle and adds it to the module_globals dictionary. The variable should be a CoreFoundation based type, with a value that is not a valid pointer.
If skip_undefined is True (the default) the function won’t raise and exception when the variable is not present. Otherwise the function will raise an error exception.
Loads a list of functions from a bundle and adds proxy objects for them to the module_globals dictionary. If skip_undefined is True (the default) the function will skip entries that don’t refer to existing functions, otherwise it raises an error exception for these functions.
bundle is either an NSBundle instance, or None. When a bundle is specified the function is looked up in that bundle, otherwise the function is looked up in any bundle (including the main program and Python extensions).
functionInfo is a sequence of function descriptions. Every description is a tuple of two or four elements: the function name (a string) and signature (a byte string) and optionally a value for the “__doc__” attribute and a metadata dictionary.
The structure of the metadata dictionary is descripted in the section Metadata dictionaries.
Simular to loadBundleFunctions(), but loads the functions from list instead of a bundle.
List should be a capsule object with tag “objc.__inline__” and the value should be a pointer to an array of structs with the following definition:
struct function {
char* name;
void (*function)(void);
};
The last item in the array must have a NULL pointer in the name field.
Register a metadata structure for the given selector. The metadata is a dictionary, and the structure of that dictionary is described in the section Metadata dictionaries.
Register type as a list-like type that will be bridged to Objective-C as an NSArray subclass.
Register type as a dict-like type that will be bridged to Objective-C as an NSDictionary subclass.
Add a list of method to every class that has selector as a selector. These additional methods are not added to the Objective-C class, but are only visibile in Python code.
The methods argument is a list of tuples (methodname, function).
Deprecated since version 2.5.
Add a list of method the named class when that class is initialized, the class need not be loaded at the time of this call. These additional methods are not added to the Objective-C class, but are only visibile in Python code.
The methods argument is a list of tuples (methodname, function).
Deprecated since version 2.3.
Use the metadata system instead
Register a replacement signature for a specific selector. This can be used to provide a more exact signature for a method.


Assuming a Role
You can configure the AWS Command Line Interface to use a role by creating a profile for the role in the
~/.aws/config
file. The following example shows a role profile named
marketingadmin
that is assumed by the default profile.
[profile marketingadmin
]
role_arn = arn:aws:iam::123456789012
:role/marketingadmin
source_profile = default
In this case, the default profile is an IAM user with credentials and permission to assume a role named marketingadmin. To access the role, you create a named profile. Instead of configuring this profile with credentials, you specify the ARN of the role and the name of the profile that has access to it.
Configuring and Using a Role
When you run commands using the role profile, the AWS CLI uses the source profile's
credentials to call AWS Security Token Service and assume the specified role. The source profile must have
permission to call sts:assume-role
against the role, and the role must have a
trust relationship with the source profile to allow itself to be assumed.
Create a new role in IAM with the permissions that you want users to assume by following the procedure under Creating a Role to Delegate Permissions to an IAM User in the AWS Identity and Access Management User Guide. If the role and the target IAM user are in the same account, you can enter your own account ID when configuring the role's trust relationship.
After creating the role, modify the trust relationship to allow the IAM user to assume
it. The following example shows a trust relationship that allows a role to be assumed by an
IAM user named jonsmith
:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "",
"Effect": "Allow",
"Principal": {
"AWS": "arn:aws:iam::123456789012
:user/jonsmith
"
},
"Action": "sts:AssumeRole"
}
]
}
Next, grant your IAM user permission to assume the role. The following example shows an
AWS Identity and Access Management policy that allows an IAM user to assume the marketingadmin
role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "sts:AssumeRole",
"Resource": "arn:aws:iam::123456789012
:role/marketingadmin
"
}
]
}
The user doesn't need to have any additional permissions to run commands using the role profile. If you want your users to be able to access AWS resources without using the role, apply additional inline or managed policies for those resources.
With the role profile, role permissions, trust relationship and user permissions applied,
you can assume the role at the command line by using the profile
option, for
example:
$ aws s3 ls --profile marketingadmin
To use the role for multiple calls, you can set the AWS_DEFAULT_PROFILE environment variable for the current session from the command line:
Linux, macOS, or Unix
$ export AWS_DEFAULT_PROFILE=marketingadmin
Windows
> set AWS_DEFAULT_PROFILE=marketingadmin
For more information on configuring IAM users and roles, see Users and Groups and Roles in the AWS Identity and Access Management User Guide.
Using Multifactor Authentication
For additional security, you can require users to provide a one time key generated from a multifactor authentication device or mobile app when they attempt to make a call using the role profile.
First, modify the trust relationship on the role to require multifactor authentication:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "",
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::123456789012:user/jonsmith" },
"Action": "sts:AssumeRole",
"Condition": { "Bool": { "aws:MultiFactorAuthPresent": true } }
}
]
}
Next, add a line to the role profile that specifies the ARN of the user's MFA device:
[profile marketingadmin]
role_arn = arn:aws:iam::123456789012:role/marketingadmin
source_profile = default
mfa_serial = arn:aws:iam::123456789012
:mfa/jonsmith
The role requires MFA in order to be assumed by any user, but the actual MFA device ARN is specified in the role profile's configuration on the user's machine and varies between users. This allows many users to assume the same role using their individual MFA devices.
Cross Account Roles
You can enable IAM users to assume roles that belong to different accounts by configuring the role as a cross account role. During role creation, set the role type to one of the options under Role for Cross-Account Access and optionally select Require MFA. The Require MFA option configures the appropriate condition in the trust relationship as described in Using Multifactor Authentication.
If you use an external
ID to provide additional control over who can assume a role across accounts, add an
external_id
parameter to the role profile:
[profile crossaccountrole]
role_arn = arn:aws:iam::123456789012:role/xaccount
source_profile = default
mfa_serial = arn:aws:iam::123456789012:mfa/jonsmith
external_id = 123456


Change in Current Learning Mindset
This post will just be a reflection short post on my learning mindset. I feel that my mindset for learning has changed a lot throughout this week. I feel that my biggest change started when I started to do the solo assignment. Creating a game in JS was extremely difficult for me, but it taught me a lot of things. For example, I learned to think like a developer instead of just coding and finding what works. I learned to lay down the basics and foundations first. This is where Psuedocoding became very useful. Right now I can say that my learning mindset has to be to break things down to basic and not jump ahead of myself. Then slowly grow with the knowledge and research more into each topic to make things works. It has been a tough programming week for me, but I learned the most this week.


The first programming language was the assembly language which was first developed in the 1940s. In 1954 the coding language named FORTRAN was invented at IBM by a team led by John Backus. FORTRAN was used as a high-level general language to have a functional implementation. A nether coding language was called UNIVAC I which was invented by grace hopper in 1955. In the early 1950s, Alick Glennie invented AutoCode.
Konrad Zuse in 1992


{% extends 'base.html' %} {% load i18n %} {% block title %}Sync Mail Addresses{% endblock %} {% block content %}
{% if commit %} These changes were committed: {% else %} Synchronization with Hornet does not happen automatically. The following changes will happen if they are committed: {% endif %}
{{ log.as_str }}{% endblock %}


A refreshing JavaScript DatetimePicker — lightweight, no dependencies, modular CSS.
Pikaday source on GitHub


Events
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Welcome to "Movie Night" where planning is not so stressful
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Marcia Villalba 🦄
Marcia Villalba is the host of FooBar, a Youtube channel where she publishes at least one new video every week. In her channel, she focuses on teaching how to use AWS serverless technologies and other cloud related topics. Marcia is a AWS Serverless Hero and a Serverless Champion, and also hosts the Finland AWS User Group. She is also the author of a Manning course “Serverless Applications with AWS” and other serverless courses.
Talk: Developing Serverless applications with GraphQL
Do you want to build apps with almost no code? Do you want to have very well documented APIs without writing the docs?
Do you want not to worry about servers and managing infra? If your answer is YES to all the questions maybe you should consider Serverless GraphQL and come to my talk.


DELETE
Section: SQL Commands (7)Updated: 2012-08-14
Index Return to Main Contents
NAME
DELETE - delete rows of a tableSYNOPSIS
DELETE FROM [ ONLY ] table [ [ AS ] alias ] [ USING usinglist ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ]
DESCRIPTION
DELETE deletes rows that satisfy the WHERE clause from the specified table. If the WHERE clause is absent, the effect is to delete all rows in the table. The result is a valid, but empty table.
- Tip: TRUNCATE [truncate(7)] is a PostgreSQL extension that provides a faster mechanism to remove all rows from a table.
By default, DELETE will delete rows in the specified table and all its child tables. If you wish to delete only from the specific table mentioned, you must use the ONLY clause.
There are two ways to delete rows in a table using information contained in other tables in the database: using sub-selects, or specifying additional tables in the USING clause. Which technique is more appropriate depends on the specific circumstances.
The optional RETURNING clause causes DELETE to compute and return value(s) based on each row actually deleted. Any expression using the table's columns, and/or columns of other tables mentioned in USING, can be computed. The syntax of the RETURNING list is identical to that of the output list of SELECT.
You must have the DELETE privilege on the table to delete from it, as well as the SELECT privilege for any table in the USING clause or whose values are read in the condition.
PARAMETERS
- ONLY
- If specified, delete rows from the named table only. When not specified, any tables inheriting from the named table are also processed.
- table
- The name (optionally schema-qualified) of an existing table.
- alias
- A substitute name for the target table. When an alias is provided, it completely hides the actual name of the table. For example, given DELETE FROM foo AS f, the remainder of the DELETE statement must refer to this table as f not foo.
- usinglist
- A list of table expressions, allowing columns from other tables to appear in the WHERE condition. This is similar to the list of tables that can be specified in the FROM Clause [select(7)] of a SELECT statement; for example, an alias for the table name can be specified. Do not repeat the target table in the usinglist, unless you wish to set up a self-join.
- condition
- An expression that returns a value of type boolean. Only rows for which this expression returns true will be deleted.
- cursor_name
- The name of the cursor to use in a WHERE CURRENT OF condition. The row to be deleted is the one most recently fetched from this cursor. The cursor must be a non-grouping query on the DELETE's target table. Note that WHERE CURRENT OF cannot be specified together with a Boolean condition. See DECLARE [declare(7)] for more information about using cursors with WHERE CURRENT OF.
- output_expression
- An expression to be computed and returned by the DELETE command after each row is deleted. The expression can use any column names of the table or table(s) listed in USING. Write * to return all columns.
- output_name
- A name to use for a returned column.
OUTPUTS
On successful completion, a DELETE command returns a command tag of the form
DELETE countThe count is the number of rows deleted. If count is 0, no rows matched the condition (this is not considered an error).
If the DELETE command contains a RETURNING clause, the result will be similar to that of a SELECT statement containing the columns and values defined in the RETURNING list, computed over the row(s) deleted by the command.
NOTES
PostgreSQL lets you reference columns of other tables in the WHERE condition by specifying the other tables in the USING clause. For example, to delete all films produced by a given producer, one can do:
DELETE FROM films USING producers WHERE producer_id = producers.id AND producers.name = 'foo';What is essentially happening here is a join between films and producers, with all successfully joined films rows being marked for deletion. This syntax is not standard. A more standard way to do it is:
DELETE FROM films WHERE producer_id IN (SELECT id FROM producers WHERE name = 'foo');In some cases the join style is easier to write or faster to execute than the sub-select style.
EXAMPLES
Delete all films but musicals:
DELETE FROM films WHERE kind <> 'Musical';
Clear the table films:
DELETE FROM films;
Delete completed tasks, returning full details of the deleted rows:
DELETE FROM tasks WHERE status = 'DONE' RETURNING *;
Delete the row of tasks on which the cursor c_tasks is currently positioned:
DELETE FROM tasks WHERE CURRENT OF c_tasks;
COMPATIBILITY
This command conforms to the SQL standard, except that the USING and RETURNING clauses are PostgreSQL extensions.
Index
This document was created by man2html, using the manual pages.
Time: 05:34:14 GMT, December 24, 2015


| DOSE: |
0.6667 joints/cigs |
smoked |
Cannabis |
(plant material) |
| |
0.3333 bowls |
smoked |
Cannabis |
(plant material) |
Welcome to the mind of the mad, the brilliant and the foolish. Today, you shall experience past gone present, a life become yours, and you shall know in the best of words possible, what 'I' have experienced, and all there is to know.
But before I get to the storytellin, I must familiarize you with a few terms, 2 in fact.
Sess: Organically grown marijuana. Weed that has been grown the way it was meant to be, almost always outdoors, and always with soil and sunlight. In most cases, this kinda weed is considered your normal stuff - obviously far better than shwag, but not comparable to 'dro' in potency, unless the strain which your sess happens to be is naturally very potent already (example: White Widow). In simple words, your basic, organically grown stuff - kinda like garden grown vegetables.
Dro: Hydroponically grown marijuana. Weed that has been grown with the aid of hydroponics. What this means is that the weed is grown somewhat 'artificially', as in no soil, but with nutrient-filled water instead. Also, there is artificial lightin rather than sunlight, and even artificial humidity. Weed of this kind tends to be of better quality and far higher potency than 'sess'.
Anyway, I shall begin my story at about 1:40pm, yesterday. Me and 2 friends (I'll call one T, the other P) met up in school, and all of us were plannin to smoke up. You see, I have heart problems, and not long ago, I suffered a very minor heart attack (more like a heart cramp), and I am very prone to heart tension from stress/anxiety. Quite an odd ailment for someone as young as me (18). Anyway, I agreed to smokin up with them, as I needed some heavy relaxin myself, and although weed does bring your heart rate up a lil, it's better riskin a lil than riskin what may be potentially a lot and end up dyin from stress. So really, weed would be a great benifit to me.
So we wander around the neighborhood, and then we find a dealer within the vicinity. So I ask the dealer if he's got any trees, he asks how much, and T tells him we want 5 dollars' worth.  As soon as we get there, we open up the plastic bag and take the weed out. I tell them I have about 2.5 grams of weed in my stashbox, which I had brought along with me, but the weed we got for 5 bucks apparently seems like 10g more than 5g, and they assumed that by the smell and look, it was probably sess. Good for chillin, not for trippin, but they were wrong. The stuff I had was sess, the stuff we bought was dro. So we decided to chop up the newly bought dro, and put it in a large spliff, and the remainder in a smaller spliff, the sess that I had brought with me would be smoked in a pipe later on. Then came the smokin session.
We went outside the buildin to this balcony-like place, where not many people come by, and we sat down on some metal seats. We then proceeded to light up the spliff, in which P took the first few puffs. We decided everyone would take turns, 3 puffs each person's turn. When it came for me to smoke it, I began to inhale, but T told me I was smokin it wrong. You see, I have smoked a few times before, gotten high, but it was mostly more of a relaxin and passive high, where nothin seemed to bother me. T had told me to inhale deeply into my lungs, breath in some fresh air with it to push it down, and then hold it for a sec, and exhale soon after. I did.
My eyes began to feel heavy, somewhat like when I haven't slept for a while, and my eyelids seem to weigh more than they really do. A smile slowly grew on my face, as I began to notice myself far more relaxed than I ever have been. The sunlight seemed a lil more intense, although I didn't seem to mind it at all. But I also felt this odd lightheadedness, a tingly sensation in my brain, with a very light headache that somehow didn't seem to bother me at all. We finished up the spliff, and then the next, and then this is when I decided to get up - the weed had kicked in good by now.
I stood up, and I suddenly felt dizzy, and I felt like things were slightly movin (both in vision and feelin). If I could best describe the feelin and sight, it was like how I feel and see after I have been spinnin in a circle in the same spot for about 30 seconds. That 'world is turnin around a bit' type of thing. But what I didn't notice is how odd my walkin was. I was apparently wobblin a bit from side to side, not as much as a drunk losin full balance, but more like I have been shook up a bit instead. T and P were laughin at me, and had told me to sit down, afraid that I might fall from bein so damn high. Whether I had fallen or stayed sittin down, I didn't care. I was havin fun. I started laughin at nothin, and my friends look at me as if I was some kinda lunatic and then began laughin with me, or was it at me? Regardless, I was still havin fun.
Then after a lil while, I had decided to get up again, and we all decided to go back to T's place.  We got upstairs, and put on some music, and I have no idea how, but every beat I could feel inside my body. Music was greatly intensified, and I seemed to like everythin I heard. We were playin a lot of techno, hip-hop and rap, and the bass felt sooooo gooood. Then, they put on this color show thing on T's Xbox. So for about 10 minutes, I was starin at this crazy color and patterns show on T's 50inch tv, trippin out of my mind, and bein hypnotized by somethin so vivid and beautiful. Then we decided to play some Grand Theft Auto: San Andreas, and I just kept laughin whenever someone died (aren't I the mature one?). But soon enough, I had decided to let myself test out my creativity, and here I shall go in some depth.
I am artistically talented at both dance and music, 2 precious gifts that I treasure with all my heart. When I am inspired, or suddenly get a concept or idea of art, I am for that moment either in a trance, overwhelmed by an immense feelin of bliss, or am energized by an incredible adrenaline rush, and then I imagine the art, whether abstract or followin a concept/idea, and I have the urge to put the most beautiful thing I have ever come up with down into the real world, and appreciate my wonderful creation.
Well, durin my trip, I was constantly in that state. I had an amazin feelin of bliss, a creative mindset and imagination that rivals the most abstract concepts and images found in dreams, and somehow more passive and relaxed than someone could be in the best massage chair ever built. I was in a constant artist's bliss. Images, sounds, ideas and dream-like thoughts were comin into my head every second, a lot of them often somehow bein related to the song that might've been playin at that moment. One of the most beautiful yet peculiar things that came up in my head is this music video-like scene of this mirror image of a green distorted girl dancin to the music, with bright yellows and greens flashin in the background. I have never felt a thing so artistic yet beautiful in my life, and the intensity of the feelin was somethin that rivals (not intendin to be vulgar) sexual pleasure - mainly orgasm, and although it is not as intense physically, my mind was just as delighted.
As the time went by, my trip went down a bit, and I just became more mellow, and passive, relaxed beyond measure, and just felt like starin into nothin, lettin my mind float into whatever universe it may have been headed at. Then, my friends decided to pipe up the weed I had brought with me in my stashbox, and we headed downstairs to the same spot again. I inhaled, and the smoke was hot, and unfortunately, we did not have a filter, so when I inhaled some ash. I was overwhelmed by a fit of coughin, and a horrendous burnin feelin in my throat and lungs. The pain in my lungs was very difficult to bare, I simply have not felt so much discomfort in my lungs before, and I just didn't want to take it.
As we headed back upstairs, I had this feelin of paranoia that lead me to believe that my friends were plannin somethin behind my back, and whenever I saw them talkin to each other, I somehow thought it was about me. I was even afraid to walk in front of them, for fear that they might do somethin behind my back, and I always wanted to walk behind them, so I could observe everythin they did.  They laughed, and we eventually got to T's place, and sat down once again. Then we proceeded to play Need For Speed on T's Xbox, and somehow I was playin better than I ever was before. The controls seemed more natural to me, and yet, at the same time, I was still mad passive and relaxed, hypnotized by the game. The road in the game somehow seemed to be a path into another dimension. It felt like travellin through a tunnel.
Eventually, I had to go. It was 6pm, and I wanted to get home, so I left for the bus, and durin the whole ride I was so relaxed, yet tired and sleepy the same time. My vision was still funny, and the dizzyness was still there, though. I'm pretty sure people noticed I was high, because when I stood up, I was all wobbly and whatnot. By the time I got home, which was at about 6:45pm, I ate like crazy, and noticed that movin gave me the biggest headache, and so I sat down. After a while, the headache went away, as well as the sleepyness, and so I sat there, waitin for my girlfriend to come home, so I could experience that one unique feelin that nothin else could ever mimic, love.
So all in all, yesterday's experience with weed was amazin. I have felt like an artist in every genre durin the whole time, was relaxed beyond my mind, and was happier than an alcoholic durin a wine-festival. I wanna try out eatin weed, as well as usin bongs and vaporizers. I really wouldn't mind somethin that would be a lot easier on my throat and lungs - especially when I am passionate about dancin (breakin), hehe.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Select the expression that matches the following phrase:
The sum of B and the product of A and x.
Ax + B
-1 * Ax + B
Ax + -1 * B
Bx + A
-1 * Bx + -1 * A
What is the product of A and x?
What is the sum of B and Ax
?
Take the quantity A times x, and then add B.
What is the quantity of A times x?
What does adding B do?
B plus the product of A and x.
What is the product of A and x?
What is B plus Ax
?
The sum of B and the quantity A times x.
What is the quantity A times x?
What is the sum of B and Ax
?
Let's break this problem into smaller and easier pieces.
A * x =
Ax
Ax
+ B
So, the original phrase can be written as Ax + B


Today, data is being collected in ever-increasing amounts, at ever-increasing velocities, and in an ever-expanding variety of formats. This explosion of data is colloquially known as the Big Data phenomenon.
In order to gain actionable insights into big-data sources, new tools need to be leveraged that allow the data to be cleaned, analyzed, and visualized quickly and efficiently. Azure HDInsight provides a solution to this problem by making it exceedingly simple to create high-performance computing clusters provisioned with Apache Spark and members of the Spark ecosystem. Rather than spend time deploying hardware and installing, configuring, and maintaining software, you can focus on your research and apply your expertise to the data rather than the resources required to analyze that data.
Apache Spark is an open-source parallel-processing platform that excels at running large-scale data analytics jobs. Spark’s combined use of in-memory and disk data storage delivers performance improvements that allow it to process some tasks up to 100 times faster than Hadoop. With Microsoft Azure, deploying Apache Spark clusters becomes significantly simpler and gets you working on your data analysis that much sooner.
In this lab, you will experience Apache Spark for Azure HDInsight first-hand. After provisioning a Spark cluster, you will use the Microsoft Azure Storage Explorer to upload several Jupyter notebooks to the cluster. You will then use these notebooks to explore, visualize, and build a machine-learning model from food-inspection data — more than 100,000 rows of it — collected by the city of Chicago. The goal is to learn how to create and utilize your own Spark clusters, experience the ease with which they are provisioned in Azure, and, if you're new to Spark, get a working introduction to Spark data analytics.
In this hands-on lab, you will learn how to:
The following are required to complete this hands-on lab:
Click here to download a zip file containing the resources used in this lab. Copy the contents of the zip file into a folder on your hard disk.
This hands-on lab includes the following exercises:
Estimated time to complete this lab: 60 minutes.
In this exercise, you will create an HDInsight cluster running Apache Spark. Spark is renowned for its versatility and is built for speed, performing some operations 100 times faster than Hadoop.
Open the Azure Portal in your browser. If you are asked to log in, do so using your Microsoft account.
Click + New in the upper-left corner of the portal. Then click Data + Analytics, followed by HDInsight.
Creating an HDInsight cluster
In the Cluster Name box, enter a unique DNS name for the cluster and make sure a green check mark appears next to it indicating that the name is valid and unique.
In case someone else in the lab selects the same cluster name, make it as unique as possible by including birth dates, initials, and anything else you care to add. The name you entered may be unique right now, but it might NOT be unique a few minutes into the deployment.
Enter "Azure4Research!" (without quotation marks) for Cluster login password. Select Create new under Resource group and enter the resource-group name "SparkLabResourceGroup." Select the Location nearest you, and then click Cluster type to open a "Cluster configuration" blade.
Entering basic cluster settings
Select Spark as the Cluster type and accept the default Spark version on the right. Make sure Cluster tier is set to Standard. Click the Select button at the bottom of the blade, and then finish up by clicking the Next button at the bottom of the "Basics" blade.
Entering cluster-configuration settings
Make sure Primary storage type is set to Azure Storage and Selection method is set to My subscriptions. Then enter a unique storage account name, once more making it as unique as possible by including birth dates or other information. (If the portal selects an existing storage account by default, click Create new to create a new storage account.) Type "sparklab" into the Default container box, and then click Next.
These settings apply to the storage account that is provisioned along with the cluster. The storage account contains the cluster's file system and the software installed inside.
Entering cluster-storage settings
Review the cluster settings and make sure everything is correct. Then click Create to begin deploying the cluster.
By accepting the defaults, you are deploying a cluster that contains two head nodes and four worker nodes. The head nodes each contain four compute cores and 28 GB of RAM, while the worker nodes each contain eight cores and 7 GB of RAM. If you would like to change the number of nodes or the sizes of the nodes, you may do so by clicking the Edit link next to Cluster size before clicking Create.
Reviewing cluster settings
Click Resource groups in the ribbon on the left side of the portal, and then click the resource group created for the HDInsight cluster.
Opening the resource group
Deploying an HDInsight cluster can take 15 minutes or more. Wait until "Deploying" changes to "Succeeded," indicating that the cluster has been deployed. You can click the Refresh button at the top of the blade to refresh the deployment status.
Viewing the deployment status
In this exercise, you learned how to provision an HDInsight Spark cluster on Azure, and about some of the options you can choose from when doing so. Wait for the deployment to finish, and then proceed to the next exercise.
You will be using Jupyter notebooks to do the data-exploration and machine-learning portions of this lab. The notebooks have been prepared for you ahead of time, and need to be uploaded to the cluster. In this exercise, you will use the Azure Portal to upload the notebooks.
Click the storage account that was created for the cluster (the storage account whose name you specified in Exercise 1, Step 5).
Opening the storage account
Click Blobs.
Opening blob storage
Click sparklab.
Opening the blob container
Click HdiNotebooks.
Opening the "HdiNotebooks" folder
Click Upload.
Uploading files to the "HdiNotebooks" folder
Click the button with the folder icon to the right of the Files box. Select the three .ipynb files included in the lab resources. Then click the Upload button.
Uploading Jupyter notebooks
Wait until the files have uploaded. Then close the "Upload blob" blade and confirm that all three files were uploaded to the "HdiNotebooks" folder.
Uploaded notebooks
The notebooks are uploaded and ready to go. Let's put them to work.
Jupyter notebooks are Web applications that allow you to create shareable, interactive documents containing text, equations, code, and data visualizations. Jupyter notebooks are proving to be extraordinarily useful for data scientists exploring and manipulating data sets in order to gain insights and share results. Jupyter supports several programming languages through the use of installable interpreters called kernels. Spark clusters on HDInsight include the Spark and PySpark kernels for Scala and Python, respectively. In this exercise, you will learn how to access Jupyter notebooks in your Spark cluster and acquire basic skills for using them.
In the Azure Portal, return to the blade for the resource group ("SparkLabResourceGroup") that contains the cluster. In the list of resources that belong to the resource group, click the HDInsight cluster.
Opening the cluster
In the blade for the HDInsight Spark cluster, click Cluster Dashboards.
Opening the cluster dashboards
In the ensuing blade, click Jupyter Notebook.
Opening a Jupyter notebook
When prompted for a user name and password, log in with your cluster credentials ("admin" and "Azure4Research!") from Exercise 1, Step 5.
If you mistype the password and are greeted with a 403 Forbidden error, start a new incognito or private browsing session, go to the Azure Portal, and open the Jupyter notebook again.
A new browser window (or tab) will open showing the Jupyter notebooks in your cluster. Here, you can manage your notebooks, upload new ones, and more. You can also see which notebooks are currently “running”, meaning they are currently consuming resources in your Spark cluster. Confirm that you see the notebooks you uploaded in Exercise 2. Then click 1 – Working with Jupyter Notebooks.ipynb to open that notebook.
Opening Notebook 1
Jupyter notebooks consist of a series of cells into which you can insert commands, HTML, or Markdown text. The notebook you opened contains the remaining instructions for this exercise. Follow the instructions in the notebook to complete Exercise 3.
Working with Jupyter notebooks
Once you have completed the steps in the notebook, close the browser window in which the notebook is displayed and return to the Azure Portal.
One of the ways in which researchers and data scientists use Jupyter notebooks in Spark is to explore datasets in order to understand their content and structure. Notebooks can be used to visualize data, as well as to apply structure to it. DataFrames are particularly helpful in this regard because they make it simple to apply schemas to raw data. DataFrames can also be used to create temporary tables that can be queried using Apache Hive SQL, also known as HiveQL or simply HQL. Libraries such as matplotlib, which is already provisioned on your Spark cluster, provide support for graphing, charting, and visualizing datasets and query results. Put them all together and you have a powerful set of tools for exploring and analyzing datasets large and small.
You have already learned how to access and run Spark notebooks on your Azure HDInsight Spark cluster. In this exercise, you will take the learning a level deeper by using DataFrames, HiveQL, and matplotlib to explore food-inspection data from the city of Chicago. In addition to analyzing the contents of the data, you will apply structure to it, query it, and graph it to discover key relationships.
Go back to the browser window showing the Jupyter notebooks in your cluster. If necessary, open the Azure Portal and follow the steps in the previous exercise to find the notebooks.
Click 2 – Interactively Explore Data in Spark.ipynb to open that notebook. Then follow the instructions in the notebook to complete Exercise 4.
Exploring data in Spark
Once you have completed the steps in the notebook, close the browser window in which the notebook is displayed and return to the Azure Portal.
In the previous exercise, you explored a set of food-inspection data and obtained key insights by looking at it in different ways. However, sometimes the sheer volume and complexity of the data makes relationships difficult to identify. One solution is machine learning, a technique that algorithmically finds patterns in data and exploits those patterns to perform predictive analytics.
Your Azure HDInsight Spark cluster includes several libraries from which you can build sophisticated machine-learning models. In this exercise, you will use some of these tools to build, train, and score a machine-learning model using the food-inspection data featured in the previous exercise. In the model, you will use a popular classification algorithm to predict which restaurants will be successful and which ones won't based on certain features of the input data — information that is difficult to discern simply by examining the data.
Go back to the browser window showing the Jupyter notebooks in your cluster.
Click 3 - Machine Learning with Spark.ipynb to open that notebook. Then follow the instructions in the notebook to complete Exercise 5.
Machine learning with Spark
Once you have completed the steps in the notebook, close the browser window in which the notebook is displayed and return to the Azure Portal.
When you are finished using an HDInsight Spark cluster, you should delete it because you are charged for it while it exists, regardless of whether it's doing any work. In this exercise, you will delete the resource group created in Exercise 1 when you created the cluster. Deleting the resource group deletes everything in it and prevents any further charges from being incurred for it.
In the Azure Portal, open the blade for the "SparkLabResourceGroup" resource group that holds the cluster. Then click the Delete button at the top of the blade.
Deleting a resource group
For safety, you are required to type in the resource group's name. (Once deleted, a resource group cannot be recovered.) Type the name of the resource group. Then click the Delete button to remove all traces of this lab from your account.
After a few minutes, the cluster and all of its resources will be deleted. Billing stops when you click the Delete button, so you're not charged for the time required to delete the cluster. Similarly, bulling doesn't start until a cluster is fully and successfully deployed.
Here is a summary of what you learned in this lab:
With Apache Spark for Azure HDInsight, high-performance computing clusters with all the tools you need to handle big data are just a few button clicks away. It's just one example of why cloud computing is changing the face of research.
Copyright 2017 Microsoft Corporation. All rights reserved. Except where otherwise noted, these materials are licensed under the terms of the MIT License. You may use them according to the license as is most appropriate for your project. The terms of this license can be found at https://opensource.org/licenses/MIT.


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This filter includes/excludes jobs based on their type.
Options
- Job Type:
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Which type of job this filter should match. If the dropdown doesn't
have the type of job you're looking for that means you don't have
the necessary plugin installed.
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Whether to add or remove jobs from this view. There's two kinds of match types:
- Include matched/unmatched: adds additional jobs to this view.
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PhD student at Yonsei University, South Korea
Studying fluid dynamics, especially turbulence and particle interaction
For detail, please follow Linkedin profile


Development community of MMOG
This is a project of Department of Computer Science & Engineering, University of Moratuwa.
PhD Computer Science at Indiana University,Bloomington
Senior Architect at WSO2
Overlook the WSO2 platform Architecture while reporting to Paul (CTO)
Visiting Lecturer at University of Moratuwa
Member at The Apache Software Foundation
Member at Lanka Software Foundation
Undergraduate of Computer Science & Engineering at University of Moratuwa
Head of School of IT and Electrical Engineering at Northshore College of Business and Technology
Senior Lecturer/ Former Head of Dept at University of Moratuwa
Undergraduate of Department of Computer Science & Engineering, University of Moratuwa.
I am a Computer Science and Engineering undergraduate at University of Moratuwa. Worked as a trainee software engineer at WSO2 Lanka (pvt)Ltd. Currently, hold the position of commitee member and level 4 coordinator for IET UOM student chapter. Have skills on Software Engineering,OOP, Java, Eclipse Plug-in development and Test Automation.
I am a Computer Science and Engineering undergraduate at University of Moratuwa. Worked as a trainee software engineer at hSenid mobile solutions (pvt)Ltd. Have skills on Software Engineering.
Undergraduate of Department of Computer Science & Engineering, University of Moratuwa.


web programming
Importance
Discription
Benefit of web
My name is Pooja Paudyal .I am from Nepal . I am studying web prpgramming in KCG College at Kyoto . Tokyo
.
kyoto,Senbonmarutamachi
For 50 years, WWF has been protecting the future of nature
.
- Business IT program
- System development
- Multimedia Content Programme
System development programme
Multimedia
- Sashimi
- Sliced raw fish that is served with condiments such as shredded daikon radish or ginger root. wasabi and soy sauce.
- Scamorze
- pooja
click me.
TEMPLE
photo of temple in kyoto


| Abillity | Spec | Level | Cast Time | Cooldown | Cost | Directional? | Range | Effect |
| Sinister Strike | all | 1 | 45 energy | yes | melee |
| ||
| Eviscerate | all | 2 | 35 energy, 1-5 combo points | yes | melee |
| ||
| Cheap Shot | all | 3 | 40 energy | yes | melee |
| ||
| Stealth | all | 3 | 2 seconds | stealths you until cancelled | ||||
| Fleet Footed | all | 4 |
| |||||
| Sprint | all | 5 | 2 min |
| ||||
| Kick | all | 6 | 15 seconds | yes | melee |
| ||
| Ambush | all | 7 | 50 energy | yes | melee |
| ||
| Crimson Vial | all | 8 | 30 seconds | 20 energy | heal 20% of your maximum health over 4 seconds | |||
| Slice and Dice | all | 9 | 25 energy, 1-5 combo points |
| ||||
| Instant Poison | all | 10 | 1.5 seconds | coats weapons with poison, 30% chance of nature damage to 1 enemy, 1 hour duration | ||||
| Mastery: Executioner | subtlety | 10 |
| |||||
| Mastery: Main Gauche | outlaw | 10 |
| |||||
| Mastery: Potent Assassin | assassination | 10 |
| |||||
| Sap | all | 11 | 35 energy | yes | 10 yd |
| ||
| Mutilate | assassination | 12 | 50 energy | yes | melee |
| ||
| Pistol Shot | outlaw | 12 | 40 energy | yes | 20 yd |
| ||
| Shadowstrike | subtlety | 12 | 40 energy | yes | melee |
| ||
| Sinister Strike: Rank 2 (assassination) | assassination | 12 |
| |||||
| Sinister Strike: Rank 2 (outlaw) | outlaw | 12 |
| |||||
| Deadly Poison | assassination | 13 | 1.5 seconds | coats weapons with poison, nature damage to 1 enemy over 12 seconds, additional applications deal nature damage, 1 hour druation | ||||
| Kidney Shot | all | 13 | 20 seconds | 25 energy, 1-5 combo points | yes | melee |
| |
| Backstab | subtlety | 14 | 35 energy | yes | melee |
| ||
| Pick Lock | all | 14 | 1.5 seconds | ??? | 5 yd | open locked chests and doors | ||
| Garrote | assassination | 16 | 6 seconds | 45 energy | yes | melee |
| |
| Shuriken Toss | subtlety | 16 | 40 energy | ??? | yes |
| ||
| Between the Eyes | outlaw | 17 | 45 seconds | 25 energy, 1-5 combo points | yes | 20 yd |
| |
| Rupture (assassination) | assassination | 17 | 25 energy, 1-5 combo points | yes | melee |
| ||
| Rupture (subtlety) | subtlety | 17 | 25 energy, 1-5 combo points | yes | melee |
| ||
| Grappling Hook | outlaw | 18 | 60 seconds | ??? | 40 yd | pull yourself to the target location | ||
| Shadowstep (assassination) | assassination | 18 | 30 seconds | ??? | 25 yd |
| ||
| Shadowstep (subtlety) | subtlety | 18 | 30 seconds | ??? | 25 yd |
| ||
| Blade Flurry | outlaw | 19 | 30 seconds | 15 energy | ??? | your single target attacks strike up to 4 nearby enemies for 40% damage, 12 second duration | ||
| Fan of Knives | assassination | 19 | 35 energy | no | 10 yd |
| ||
| Shuriken Storm | subtlety | 19 | 35 energy | no | 10 yd |
|


{% extends "base.html" %} {% block title %}Home{% endblock %} {% block body %}
| # | Client | Plan | Users | Plan Duration | Start Date | End Date | Amount | Plan Status | Update Plan | Status |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 |
Global Technologies |
Free Trial | 30 | 6 Months | 15 Feb 2019 | 14 Jul 2019 | $200 | Active | Change Plan |
|
| 2 |
Delta Infotech |
Premium | 50 | 1 Year | 15 Feb 2019 | 14 Feb 2020 | $200 | Expired | Change Plan |
|
| 3 |
Cream Inc |
Professional | 10 | 1 Month | 15 Feb 2019 | 14 Feb 2020 | Free | Active | Change Plan |
|
| 4 |
Wellware Company |
Enterprise | 10 | 1 Month | 15 Feb 2019 | 14 Feb 2020 | Free | Active | Change Plan |
|
| 5 |
Mustang Technologies |
Free Trial | 10 | 1 Year | 15 Feb 2019 | 14 Feb 2020 | Free | Active | Change Plan |
|
| 6 |
International Software Inc |
Enterprise | 10 | 1 Month | 15 Feb 2019 | 14 Feb 2020 | Free | Active | Change Plan |
|
| 7 |
Mercury Software Inc |
Professional | 10 | 1 Year | 15 Feb 2019 | 14 Feb 2020 | Free | Active | Change Plan |
|


-1"
(onLongPressing)="longPressing = true"
(onLongPress)="selectionService.selectAndUnselectElement(element)"
(onLongPressEnd)="elementLongPressEnd()"
(onSimpleClick)="onSimpleClick(element)"
(onDoubleClick)="openElement(element)"
long-press
cdkDrag
>


Automatic Setup of Height and Width => Default Setting.
The height and width properties are used to set the height and width of an element.
The height and width can be set to auto (this is default.
Means that the browser calculates the height and width), or be specified in length values, like px, cm, etc., or in percent (%) of the containing block.
Note:
The height and width properties do not include padding, borders, or margins; they set the height/width of the area inside the padding, border, and margin of the element!


Pal's Pizza
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First two toppings are free. Additional toppings $.50 each.
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Do you know?
In Chinese religion, the god of prosperity or better known as "Cai Shen", widely believed to bestow on
his devotees the riches carried about by his attendants.
The Cai Shen's name is often invoked during 2 weeks of Chinese New year celebration, and friends joyously
exchange the traditional New Year greeting “May you become rich” ("Gong Xi Fa Cai").
Copyright © 2014


API will provide you UTC Unix and Simple date data by passing either unixtime eg.1452643200
or
Month Date, Year January 13, 2016
http://localhost:8080/1452643200
http://localhost:8080/January 13, 2016


If there are people on CIX who have expressed an interest in joining your new forum, you can add them to the participants list using the CIX Forums Participants menu command (or by pressing F4) with your forum highlighted in Ameol's topic list or a message window open in one of its topics.
Don't start adding people wholesale; not everyone wants to be involuntarily thrust into a new forum. It's polite to mail anyone you do add telling them they've been joined, too.


Random: Yes, People In The Galar Region Eat Pokémon
Curry on rice, with cooked Slowpoke tail
by Liam DoolanSo, if you haven't already noticed during your own playthrough of Pokémon Sword and Shield, the psychic-water type Slowpoke isn't featured in the Galar Region's Pokédex.
While it was believed this Pokémon had been removed, that's not entirely true. You see, the new curry recipes in Sword and Shield reveal Slowpoke actually appears as a cooking ingredient within this region. Yep, people located in Galar like to eat Pokémon.
There's a bagged treat referred to as Smoke-Poké Tail – commonly known as a Slowpoke's tail – according to a description in the game. On the plus side, the tail at least grows back.
One of the many ingredients that can be used for cooking at your camp. When a Slowpoke’s tail falls off, it grows back quickly.
When Game Informer originally asked Sword and Shield director Shigeru Ohmori if people in Galar were making sausages out of Pokémon, he replied saying he didn't know what was in them. This latest finding, however, confirms people in this region are indeed cooking up Pokémon.
It wouldn't be the first time a discovery like this has been made, either. An investigation last year found people within this universe also consume Pokémon byproducts, such as Miltank milk and Chansey eggs. Earlier entries in the series have also cited pocket monsters as "delicious" meals.
Have you cooked up some Slowpoke tail in the Galar region yet? Comment down below.
[via polygon.com]


From Newbie to Newbro - Tutorials for BRAVE
So you wish to join the Brave Newbies? Well there are a few mandatory steps (like applying to the corp) and handful of recommended steps that you will need to complete in order to join the ranks. We have made a number of Tutorials to help transition from WTF to FAF.
This page is a mirror of the Reddit Post, please check there if a tutorial you want is missing and leave a comment!
In order to get started, you have to Apply to Join the Corp. Once that's completed, it's highly suggested you complete the following:
- Set Up Jabber (<5 Minutes)
- Set Up Core (~5-10 Minutes)
- Set Up Mumble (<5 Minutes, Requires Core)
- Set Up Push-To-Talk and Whisper for Mumble(<5 Minutes)
Once you've completed the above, you can take a couple of steps to better your experience in EVE. These steps can help you get ships for cheap, escape various camps, and optimize your damage output.
- How to Optimize your Overview (5 Minutes)
- How to Join an in-game Chat Channel (<5 Minutes)
- How to Find Corporation Ship Fittings (<5 Minutes)
- How to Buy a Ship via Contracts (<5 Minutes)
- How to use Tacticals (Better Avoid Gate Camps!) (<5 Minutes)
- How to Instant-Undock & Dock (Avoid Station Camps!) (<5 Minutes)
- How to find your Optimal/Falloff (<5 Minutes)
- How to Use Jump Clones Effectively (<5 Minutes)
Now you are more than ready for Fleet Operations! Let's get you into a ship and into the fight!
- Doctrine Ship Information
- How to Find/Join a Fleet (<5 Minutes)
- How to Locate your Fleet (<5 Minutes)
- How to Turn off the Safety (<5 Minutes)
- How to Add to Watchlist (<5 Minutes)
- How to use Broadcasts (<5 Minutes)
- How to Enter a POS Password (<5 Minutes)
- How to Jump/Bridge via Titan Effectively (<5 Minutes) (Yes we have Titans!)
Now that you've likely gotten popped, podded, and more fun than you've had in EVE in a while, let's cover more advanced fleet roles.
- How to Tackle
- How to use Electronic Warfare (EWAR)
- How to be a Space Healer (Logistics)
- How to Suicide Gank
- How to be a Fleet Scout (AKA Going +1)
- How to Cloak for Safety


© Knights of Columbus - Kansas State Council For comments or questions please email webmaster@kansas-kofc.org


26 kids discharged from hospital after possible carbon monoxide poisoning in Quebec
GATINEAU, Que. - Twenty-six elementary school children sent to hospital Friday after they were possibly exposed to carbon monoxide in a Gatineau school bus have been sent home with their parents, the regional health authority said.
The children had complained of head, chest and stomach aches following a school bus ride earlier in the day.
Marie-Eve Cloutier, head of medical care at the health authority, said the children were showing symptoms of carbon monoxide poisoning, but doctors could not confirm exactly what had made the students feel ill.
She said 18 children were brought to the Gatineau hospital and eight were sent to the Hull hospital. "All of the children were evaluated and treated, many of them ate snacks, and by the early afternoon they were able to leave safely," she said.
"It was a possible carbon monoxide poisoning, but we can't confirm that for the moment," Cloutier told reporters.
Maude Hebert with the Portages-de-l'Outaouais school board said the company that runs the bus service for the school had ruled out carbon monoxide as the cause.
She said in an interview the children reported an unusual smell from the bus when they arrived to school. Carbon monoxide is known to be odourless. Hebert said the company was investigating whether a problem with the exhaust gas cooler on the bus emitted the foul odour.
This report by The Canadian Press was first published Nov. 15, 2019.


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The Splunk Enterprise Docker image lets you get started with Splunk Enterprise in your development efforts. The image includes a sample app that provides insight into how Splunk Enterprise can monitor Docker machines.
The Splunk Enterprise Docker instance contains a number of data inputs for monitoring of your Docker instance, including:
After you start the instance, open the Docker Overview page from the Docker app. This app loads a dashboard that shows specifics about your Docker instance, including logs by each container (based on the Splunk logging driver), container status (running versus those that have exited), memory used and available, and container CPU and memory usage.
To get the most out of this Docker image as a Splunk developer, see the Splunk Developer Portal..
The following sample queries can help you realize value from logs that Splunk collects with the defaul JSON file input collector. To learn more about the Search Processing Language (SPL), see our Quick Reference Guide.
This image comes with a trial license that includes 500MB of daily indexing volume. This should cover most applications, but a developer license can provide additional capacity.
You can get a developer license by signing up for it. See the Splunk Developer FAQs to learn about the value of a developer license.
If you do not see data when you load the Docker Overview page in the Docker app, confirm that:
By default, the collection uses the Docker Remote API to collect stats, inspect images, 'top' information, and events.
The container also tries to collect all local JSON files available on the docker host. To ensure that the Splunk Enterprise Docker container has the right access, set the SPLUNK_USER environment variable for the container to "root".
You can do this by adding the environment variable to your docker-compose file or by specifying the variable in the 'docker-run' command you use to start the container.
For additional troubleshooting details for this image, see Troubleshoot the Splunk Enterprise Docker Image on the Splunk GitHub portal.
For more information about docker logging driver configuration, see Configure logging drivers on Docker Docs.
If you continue to have trouble seeing data in the Splunk Enterprise Docker image, try one of the following options:


|
| class | AsyncInterestTree |
| |
| class | AsyncWriterThread |
| | This static class owns a thread that manages asynchronous writes. More...
|
| |
| class | BinaryProperty |
| |
| class | BinaryPropertyHelper |
| |
| class | BuiltinAttributes |
| | Class BuiltinAttributes, to define the behavior of the RTPSParticipant builtin protocols. More...
|
| |
| struct | CacheChange_t |
| | Structure CacheChange_t, contains information on a specific CacheChange. More...
|
| |
| class | CacheChangePool |
| | Class CacheChangePool, used by the HistoryCache to pre-reserve a number of CacheChange_t to avoid dynamically reserving memory in the middle of execution loops. More...
|
| |
| class | DataHolder |
| |
| class | DataHolderHelper |
| |
| class | Endpoint |
| | Class Endpoint, all entities of the RTPS network derive from this class. More...
|
| |
| class | EndpointAttributes |
| | Structure EndpointAttributes, describing the attributes associated with an RTPS Endpoint. More...
|
| |
| struct | EntityId_t |
| | Structure EntityId_t, entity id part of GUID_t. More...
|
| |
| class | Exception |
| | This abstract class is used to create exceptions. More...
|
| |
| class | FragmentNumberSet_t |
| | Structure FragmentNumberSet_t, contains a group of fragmentnumbers. More...
|
| |
| struct | GUID_t |
| | Structure GUID_t, entity identifier, unique in DDS-RTPS Domain. More...
|
| |
| struct | GuidPrefix_t |
| | Structure GuidPrefix_t, Guid Prefix of GUID_t. More...
|
| |
| class | History |
| | Class History, container of the different CacheChanges and the methods to access them. More...
|
| |
| class | HistoryAttributes |
| | Class HistoryAttributes, to specify the attributes of a WriterHistory or a ReaderHistory. More...
|
| |
| struct | InstanceHandle_t |
| | Struct InstanceHandle_t, used to contain the key for WITH_KEY topics. More...
|
| |
| class | Locator_t |
| | Class Locator_t, uniquely identifies a communication channel for a particular transport. More...
|
| |
| class | LocatorList_t |
| | Class LocatorList_t, a Locator_t vector that doesn't avoid duplicates. More...
|
| |
| class | MatchingInfo |
| | Class MatchingInfo contains information about the matching between two endpoints. More...
|
| |
| class | NetworkFactory |
| | Provides the FastRTPS library with abstract resources, which in turn manage the SEND and RECEIVE operations over some transport. More...
|
| |
| class | PortParameters |
| | Class PortParameters, to define the port parameters and gains related with the RTPS protocol. More...
|
| |
| class | Property |
| |
| class | PropertyHelper |
| |
| class | PropertyPolicy |
| |
| class | PropertyPolicyHelper |
| |
| struct | ProtocolVersion_t |
| | Structure ProtocolVersion_t, contains the protocol version. More...
|
| |
| class | ReaderAttributes |
| | Class ReaderAttributes, to define the attributes of a RTPSReader. More...
|
| |
| class | ReaderHistory |
| | Class ReaderHistory, container of the different CacheChanges of a reader. More...
|
| |
| class | ReaderListener |
| | Class ReaderListener, to be used by the user to override some of is virtual method to program actions to certain events. More...
|
| |
| class | ReaderTimes |
| | Class ReaderTimes, defining the times associated with the Reliable Readers events. More...
|
| |
| class | ReceiverResource |
| | RAII object that encapsulates the Receive operation over one chanel in an unknown transport. More...
|
| |
| class | RemoteReaderAttributes |
| | Class RemoteReaderAttributes, to define the attributes of a Remote Reader. More...
|
| |
| class | RemoteWriterAttributes |
| | Class RemoteWriterAttributes, to define the attributes of a Remote Writer. More...
|
| |
| class | RTPSDomain |
| | Class RTPSDomain,it manages the creation and destruction of RTPSParticipant RTPSWriter and RTPSReader. More...
|
| |
| class | RTPSParticipant |
| | Class RTPSParticipant, contains the public API for a RTPSParticipant. More...
|
| |
| class | RTPSParticipantAttributes |
| | Class RTPSParticipantAttributes used to define different aspects of a RTPSParticipant. More...
|
| |
| class | RTPSParticipantDiscoveryInfo |
| | Class RTPSParticipantDiscoveryInfo with discovery information of the RTPS Participant. More...
|
| |
| class | RTPSParticipantListener |
| | Class RTPSParticipantListener with virtual method that the user can overload to respond to certain events. More...
|
| |
| class | RTPSReader |
| | Class RTPSReader, manages the reception of data from its matched writers. More...
|
| |
| class | RTPSWriter |
| | Class RTPSWriter, manages the sending of data to the readers. More...
|
| |
| class | SampleIdentity |
| | This class is used to specify a sample. More...
|
| |
| class | SenderResource |
| | RAII object that encapsulates the Send operation over one chanel in an unknown transport. More...
|
| |
| struct | SequenceNumber_t |
| | Structure SequenceNumber_t, different for each change in the same writer. More...
|
| |
| class | SequenceNumberSet_t |
| | Structure SequenceNumberSet_t, contains a group of sequencenumbers. More...
|
| |
| struct | SerializedPayload_t |
| | Structure SerializedPayload_t. More...
|
| |
| class | SimpleEDPAttributes |
| | Class SimpleEDPAttributes, to define the attributes of the Simple Endpoint Discovery Protocol. More...
|
| |
| class | test_UDPv4Transport |
| |
| struct | test_UDPv4TransportDescriptor |
| |
| struct | ThroughputControllerDescriptor |
| | Descriptor for a Throughput Controller, containing all constructor information for it. More...
|
| |
| struct | Time_t |
| | Structure Time_t, used to describe times. More...
|
| |
| struct | TransportDescriptorInterface |
| | Virtual base class for the data type used to define transport configuration. More...
|
| |
| class | TransportInterface |
| | Interface against which to implement a transport layer, decoupled from FastRTPS internals. More...
|
| |
| class | UDPv4Transport |
| | This is a default UDPv4 implementation. More...
|
| |
| struct | UDPv4TransportDescriptor |
| | Transport configuration. More...
|
| |
| class | UDPv6Transport |
| | This is a default UDPv6 implementation. More...
|
| |
| struct | UDPv6TransportDescriptor |
| | Transport configuration. More...
|
| |
| class | WriteParams |
| | This class contains additional information of a CacheChange. More...
|
| |
| class | WriterAttributes |
| | Class WriterAttributes, defining the attributes of a RTPSWriter. More...
|
| |
| class | WriterHistory |
| | Class WriterHistory, container of the different CacheChanges of a writer. More...
|
| |
| class | WriterListener |
| | Class WriterListener with virtual method so the user can implement callbacks to certain events. More...
|
| |
| class | WriterTimes |
| | Class WriterTimes, defining the times associated with the Reliable Writers events. More...
|
| |
|
| enum | RTPSWriterPublishMode : octet { SYNCHRONOUS_WRITER,
ASYNCHRONOUS_WRITER
} |
| |
| enum | ChangeKind_t { ALIVE,
NOT_ALIVE_DISPOSED,
NOT_ALIVE_UNREGISTERED,
NOT_ALIVE_DISPOSED_UNREGISTERED
} |
| | , different types of CacheChange_t. More...
|
| |
| enum | ChangeFragmentStatus_t { NOT_PRESENT = 0,
PRESENT = 1
} |
| |
| enum | MatchingStatus { MATCHED_MATCHING,
REMOVED_MATCHING
} |
| | , indicates whether the matched publication/subscription method of the PublisherListener or SubscriberListener has been called for a matching or a removal of a remote endpoint. More...
|
| |
| enum | Endianness_t { BIGEND = 0x1,
LITTLEEND = 0x0
} |
| | This enumeration represents endianness types. More...
|
| |
| enum | ReliabilityKind_t { RELIABLE,
BEST_EFFORT
} |
| | Reliability enum used for internal purposes. More...
|
| |
| enum | DurabilityKind_t { VOLATILE,
TRANSIENT_LOCAL
} |
| | Durability kind. More...
|
| |
| enum | EndpointKind_t { READER,
WRITER
} |
| | Endpoint kind. More...
|
| |
| enum | TopicKind_t { NO_KEY,
WITH_KEY
} |
| | Topic kind. More...
|
| |
| enum | DISCOVERY_STATUS { DISCOVERED_RTPSPARTICIPANT,
CHANGED_QOS_RTPSPARTICIPANT,
REMOVED_RTPSPARTICIPANT,
DROPPED_RTPSPARTICIPANT
} |
| | Enum DISCOVERY_STATUS, three different status for discovered participants. More...
|
| |
| enum | MemoryManagementPolicy { PREALLOCATED_MEMORY_MODE,
PREALLOCATED_WITH_REALLOC_MEMORY_MODE,
DYNAMIC_RESERVE_MEMORY_MODE
} |
| | Enum MemoryuManagementPolicy_t, indicated the way memory is managed in terms of dealing with CacheChanges. More...
|
| |
|
| std::ostream & | operator<< (std::ostream &output, FragmentNumberSet_t &sns) |
| | Prints a Fragment Number set. More...
|
| |
| FragmentNumberSet_t | operator- (FragmentNumberSet_t lhs, const FragmentNumberSet_t &rhs) |
| |
| FragmentNumberSet_t | operator+ (FragmentNumberSet_t lhs, const FragmentNumberSet_t &rhs) |
| |
| std::ostream & | operator<< (std::ostream &output, const GuidPrefix_t &guiP) |
| |
| std::ostream & | operator<< (std::ostream &output, const EntityId_t &enI) |
| |
| void | iHandle2GUID (GUID_t &guid, const InstanceHandle_t &ihandle) |
| | Convert InstanceHandle_t to GUID. More...
|
| |
| GUID_t | iHandle2GUID (const InstanceHandle_t &ihandle) |
| | Convert GUID to InstanceHandle_t. More...
|
| |
| bool | operator< (const InstanceHandle_t &h1, const InstanceHandle_t &h2) |
| |
| bool | IsAddressDefined (const Locator_t &loc) |
| |
| bool | IsLocatorValid (const Locator_t &loc) |
| |
| bool | operator== (const Locator_t &loc1, const Locator_t &loc2) |
| |
| std::ostream & | operator<< (std::ostream &output, const Locator_t &loc) |
| |
| std::ostream & | operator<< (std::ostream &output, const LocatorList_t &locList) |
| |
| const SequenceNumber_t | c_SequenceNumber_Unknown (-1, 0) |
| |
| const Time_t | c_TimeInfinite (0x7fffffff, 0xffffffff) |
| |
| const Time_t | c_TimeZero (0, 0) |
| |
| const Time_t | c_TimeInvalid (-1, 0xffffffff) |
| |
| const ProtocolVersion_t | c_ProtocolVersion_2_0 (2, 0) |
| |
| const ProtocolVersion_t | c_ProtocolVersion_2_1 (2, 1) |
| |
| const ProtocolVersion_t | c_ProtocolVersion_2_2 (2, 2) |
| |
| const ProtocolVersion_t | c_ProtocolVersion (2, 1) |
| |
| static void | set_VendorId_Unknown (VendorId_t &id) |
| |
| static void | set_VendorId_eProsima (VendorId_t &id) |
| |
Contains the RTPS protocol implementation.


Used various web technologies to create a personal portfolio website. Hosted using github pages and netlify, with a free domain from freenom.com
I had the task of reoganizing the kitchen, and sorting through all the food we had in the pantry at home. I created a python application which takes in the input of the can's specifications (Due date, size, contents, style of food (veggies, fruit etc.) and sorts it into an itemized list. It creates a printer friendly file of the list which I print off and place in the cabinent for easy viewing.
I am working with a classmate to assemble a CNC machine. As a member of UBC Rapid, I have experience with 3d modeling and would like to branch out into working with more exotic materials like wood, aluminum and more rigid forms of plastic.
Using footage from a school trip to Strathcona park lodge, I edited together a travel video. I experimented with mutliple cinematic LUTs to find a suitable tone for the video, and created my own animated transitions.
Graduating Class Valedictorian, Captain of Senior Boys Soccer team
Apart from tinkering with technology and breaking code, I like to have a healthy balance of extraciriculars. Im an avid runner, having played high level competitve soccer for the past decade. Alongside soccer I enjoy swimming, boxing, weightlifting and the occassional tennis match (most of the time actually spent looking for the balls I hit over the fence)
I'm a current member of the St. John Ambulance First Cadets programme, in where I teach first aid and foot drill to children aged 5-16! In university, I am a member of the UBC Rapid 3d printing design team and Enactus UBC launch incubator student team.
Awarded for showing strong passion for the technology sector, involvement in the tech community, and a desire to leave a mark in the future of technology.
Walter Gage ScholarshipAwarded for "Achievement of educational excellence; sustained efforts to recognize others in need, and to help them overcome obstacles and achieve their potential in life; and a selfless determination to always try to find new ways to help make the world a better place."
TD Bank ScholarshipAwarded for service to the community, extracurricular activities and high academic achievement.
Vancouver Youth Soccer Association ScholarshipAwarded for dedication to soccer, contributions as a volunteer and academic achievements.


Provides infrastructure for managing HBase read and write operations across any number of HBase tables and/or rows.


| By Connection Type | By Device Type | ||||||
|---|---|---|---|---|---|---|---|
| Name | Total | WiFi | Wired | Registered | BYOD | Guest | IOT |
| {{application?.name}} | {{application?.total}} | {{application?.wifi}} | {{application?.wired}} | {{application?.registered}} | {{application?.boyd}} | {{application?.guest}} | {{application?.iot}} |


V. in the Mesmeric Revelations of Edgar Allan Poe.
Franz (Kafka) in The Trap
Marcel C'est Lavie in Thirteen Dead Husbands
Pozzo in Waiting for Godot
Taken in Harrisburg


A selection of paintings from project completed in my last year at Carnegie Mellon, in which I alternated between doing master studies and personal works. An exploration of rendering and style and color, all within the context of my first video game obsession, Fire Emblem.


Before getting started...
For SVG Gobbler to work correctly, be sure to refresh any open pages before pressing the extension icon. If you run into problems (or have ideas) please let me know. This extension is open source and I want you to have a good experience.
😊 Happy Gobbling


2
Those who when they take the measure from mankind demand it full,
3
But if they measure unto them or weight for them, they cause them loss.
4
Do such (men) not consider that they will be raised again
6
The day when (all) mankind stand before the Lord of the Worlds ?
7
Nay, but the record of the vile is in Sijjin -
8
Ah! what will convey unto thee what Sijjin is! -
10
Woe unto the repudiators on that day!
11
Those who deny the Day of Judgment
12
Which none denieth save each criminal transgressor,
13
Who, when thou readest unto him Our revelations, saith: (Mere) fables of the men of old.
14
Nay, but that which they have earned is rust upon their hearts.
15
Nay, but surely on that day they will be covered from (the mercy of) their Lord.
16
Then lo! they verily will burn in hell,
17
And it will be said (unto them): This is that which ye used to deny.
18
Nay, but the record of the righteous is in 'Illiyin -
19
Ah, what will convey unto thee what 'Illiyin is! -
21
Attested by those who are brought near (unto their Lord).
22
Lo! the righteous verily are in delight,
24
Thou wilt know in their faces the radiance of delight.
25
They are given to drink of a pure wine, sealed,
26
Whose seal is musk - for this let (all) those strive who strive for bliss -
27
And mixed with water of Tasnim,
28
A spring whence those brought near (to Allah) drink.
29
Lo! the guilty used to laugh at those who believed,
30
And wink one to another when they passed them;
31
And when they returned to their own folk, they returned jesting;
32
And when they saw them they said: Lo! these have gone astray.
33
Yet they were not sent as guardians over them.
34
This day it is those who believe who have the laugh of disbelievers,
36
Are not the disbelievers paid for what they used to do ?


Project Management
Setup the baseline for test projects.
Functions
library_add
Project Management
assignment
Requirement Management
timeline
Milestone Management
group_add
Project Assignment
Library Management
Prepare libraries to speed up test design.
Functions
web
WEB Object Library
code
API Object Library
grid_on
DB Object Library
description
Script Object Library
Test Design Management
Design test cases and scenarios efficiently.
Functions
event_note
Test Scenario
note
Test Case
storage
Test Data Pattern
note_add
Test Component
Test Run Management
Plan test run set and get detail reports.
Functions
tune
Test Run Set
schedule
Test Run Schedule
report
Test Run Report
dashboard
Test Dashboard


https://brucedai.github.io/webnnt/test/add.html?prefer=fast
Supported Operations
Preference:
fast
sustained
low
ultra-low


@()(implicit request: play.api.mvc.RequestHeader) @main("Offline") {
This page shows a sample of use of Offline Web Application via HTML 5.
The code uses Modernizr to detect if Offline Web Mode is available in the browser.
You can find more information at http://diveintohtml5.info/offline.html.
Please check the source code to see how this sample works.
All pages include in the HTML tag a reference to the cache manifest, which defines resources to store locally for the website to work offline.
The manifest contains some headers that define properties on the resources listed:


Case Studies Evaluation
We next present our experience about using PrivApprox in the following two case studies: (i) New York City (NYC) taxi ride, and (ii) household electricity consumption.
Experimental Setup
Cluster setup. We used a cluster of $44$ nodes connected via a Gigabit Ethernet. Each node contains 2 Intel Xeon quad-core CPUs and 8 GB of RAM running Debian 5.0 with Linux kernel 2.6.26. We deployed two proxies with Apache Kafka, each of which consists of $4$ Kafka broker nodes and $3$ Zookeeper nodes. We used $20$ nodes to deploy Apache Flink as the aggregator. In addition, we employed the remaining $10$ nodes to replay the datasets to generate data streams for evaluating our system.
Datasets. For the first case study, we used the NYC Taxi Ride dataset from the DEBS 2015 Grand Challenge. The dataset consists of the itinerary information of all rides across $10,000$ taxies in New York City in 2013. For the second case study, we used the Household Electricity Consumption dataset. This dataset contains electricity usage (kWh) measured every 30 minutes for one year by smart meters.
Queries. For the NYC taxi ride case-study, we created a query: “What is the distance distribution of taxi trips in New York?”. We defined the query answer with $11$ buckets as follows: [0, 1) mile, [1, 2) miles, [2, 3) miles, [3, 4) miles, [4, 5) miles, [5, 6) miles, [6, 7) miles, [7, 8) miles, [8, 9) miles, [9, 10) miles, and [10, $+\infty$) miles.
For the second case-study, we defined a query to analyze the electricity usage distribution of households over the past 30 minutes. The query answer format is as follows: [0, 0.5] Kwh, (0.5, 1] kWh, (1, 1.5] kWh, (1.5, 2] kWh, (2, 2.5] kWh, and (2.5, 3] kWh.
Evaluation metrics. We evaluated our system using four key metrics: throughput, latency, utility, and privacy level. Throughput is defined as the number of data items processed per second, and latency is defined as the total amount of time required to process a certain dataset. Utility is the accuracy loss defined as $| \frac{estimate - exact}{exact} |$, where $estimate$ and $exact$ are the query results produced by applying PrivApprox and the native computation, respectively. Finally, privacy level is calculated using equation 19 in the technical report.
For all measurements, we report the average over $10$ runs.
Results
Scalability
We measured the scalability of the two main system components: proxies and the aggregator. We first measured the throughput of proxies with various numbers of CPU cores (scale-up) and different numbers of nodes (scale-out). This experiment was conducted on a cluster of $4$ nodes. Figure 1 (a) shows that, as expected, the throughput at proxies scales quite well with the number of CPU cores and nodes. In the NYC Taxi case-study, with $2$ cores, the throughput of each proxy is $512,348$ answers/sec, and with $8$ cores (1 node) the throughput is $1,192,903$ answers/sec; whereas, with a cluster of $4$ nodes each with $8$ cores, the throughput of each proxy reaches $2,539,715$ answers/sec. In the household electricity case-study, the proxies achieve relatively a higher throughput compared because the message size is smaller than in the NYC Taxi case-study.
Figure 1: Throughput at proxies and the aggregator with different numbers of CPU cores and nodes.
We next measured the throughput at the aggregator. Figure 1 (b) depicts that the aggregator also scales quite well when the number of nodes for aggregator increases. The throughput of the aggregator, however, is much lower than the throughput of proxies due to the relatively expensive join operation and the analytical computation at the aggregator. We notice that the throughput of the aggregator in the household electricity case study does not significantly improve in comparison to the first case study. This is because the difference in the size of messages between the two case studies does not affect much on the performance of the join operation and the analytical computation.
Network Bandwidth and Latency
Next, we conducted the experiment to measure the network bandwidth usage. By leveraging the sampling mechanism at clients, our system reduces network traffic significantly. Figure 2 (a) shows the total network traffic transferred from clients to proxies with different sampling fractions. In the first case study, with the sampling fraction of $60$%, PrivApprox can reduce the network traffic by $1.62\times$; whereas in the second case study, the reduction is $1.58\times$.
Figure 2: Total network traffic and latency at proxies with different sampling fractions at clients.
Besides the benefit of saving network bandwidth, PrivApprox achieves also lower latency in processing query answers by leveraging approximate computation. To evaluate this advantage, we measured the effect of sampling fractions on the latency of processing query answers. Figure 2 (b) depicts the latency with different sampling fractions at clients. For the first case-study, with the sampling fraction of $60$%, the latency is $1.68\times$ lower than the execution without sampling; whereas, in the second case-study this value is $1.66 \times$ lower than the execution without sampling.
Utility and Privacy
Figure 3 (a)(b)(c) show the utility, the privacy level, and the trade-off between them, respectively, with different sampling and randomization parameters. The randomization parameters $p$ and $q$ are varied in the range of (0, 1), and the sampling parameter $s$ is calculated using equation 19 in the technical report. Here, we show results only for NYC Taxi dataset. As the sampling parameter $s$ and the first randomization parameter $p$ increase, the utility of query results improves (i.e., accuracy loss gets smaller) whereas the privacy guarantee gets weaker (i.e., privacy level gets higher). Since the New York taxi dataset is diverse, the accuracy loss and the privacy level change in a non-linear fashion with different sampling fractions and randomization parameters.
Figure 3: Results from the NYC taxi case-study with varying sampling and randomization parameters: (a) Utility, (b) Privacy level, (c) Comparison between utility and privacy.
Interestingly, the accuracy loss does not always decrease as the second randomization parameter $q$ increases. The accuracy loss gets smaller when $q = 0.3$. This is due to the fact that the fraction of truthful “Yes” answers in the dataset is $33.57$% (close to $q=0.3$).
Historical Analytics
To analyze the performance of PrivApprox for historical analytics, we executed the queries on the datasets stored at the aggregator. Figure 4 (a) (b) present the latency and throughput, respectively, of processing historical datasets with different sampling fractions. We can achieve a speedup of $1.86 \times$ over native execution in historical analytics by setting the sampling fraction to $60$%.
Figure 4: Historical analytics results w/ varying sampling fractions: (a) Latency, (b) Throughput, and (c) Utility.
We also measured the accuracy loss when the approximate computation was applied (for the NYC Taxi case-study). Figure 4 (c) shows the accuracy loss in processing historical with different sampling fractions. With the sampling fraction of $60$%, the accuracy loss is only less than $1$%.


This website shows an analysis for tweets about biomedical topics. First of all click on a button of a hashtag.
And then select a month (of the year 2015). The analysis for this specific combination is then shown below.


Issue 4621: Multiple filters selectable in Console panel
Steps to reproduce
- Open Firebug
- Enable and switch to the Console panel
-
Reload the page
⇒ There should be four log messages inside the
console, one normal log message, one info message, one warning and one
error.
- Click the Info filter button
- Hold down Ctrl and click the Warnings filter button
Expected result
- ⇒ The Info and the Warnings filter button should be pressed
- ⇒ Only the info and the warning should be displayed inside the console.


Base it around a %, IE reprocessedValue / Jita Value
Might be more complex than worth to put this in a directive
Total Profit: {{totalReprocessedProfit}}isk
| Component |
Quantity |
{{reprocessPercentage}}% Efficiency |
Recovered Price |
| {{details[item.id].name}} |
{{item.quantity | number}} |
{{item.recovered | number}} |
{{item.price | number}}isk
|


|
tan 0.0.1
|
The table specifies all legal implicit type conversion in this language. char
is considered as an unsigned integer whose size is 1 byte. The specific behaviors of these casts are listed in the next section.
| index | From | To |
|---|---|---|
| 1 | int | int with the same signedness with a bigger or equal size |
| 2 | unsigned int | signed int with a bigger size |
| 3 | signed int | unsigned int that with a bigger size |
| 4 | float | float with a bigger size |
| 5 | int | float |
| 6 | bool | any type of int or float |
| 7 | int/float/pointer | bool |
| 8 | pointer of a derived class | pointer to the base class |
| 9 | int/float that is compile-time known | int/float that can fit the value |
The most common use cases is that when operands of a binary operation have different types, one of them will have its type converted to the other's.
NOTE: if multiple implicit cast rules can apply, the compiler use the rule with a lower index. For example,
In function calls, arguments can be implicitly converted into the parameter types if allowed.
NaN
will return 0f32
to an f64
is perfect and losslessf64
to an f32
will produce the closest possible f32
n
to bool
is equivalent to n != 0
NaN
is false
false
casts to 0
, true
casts to 1
char
casts to the value of the code point, then uses a numeric cast if needed.TODO
TODO


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