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Comparator
interface.
See:
Description
| Class Summary | |
| BooleanComparator | A Comparator for Boolean objects that can sort either
true or false first. |
| ComparableComparator | A Comparator that compares
Comparable objects. |
| ComparatorChain | A ComparatorChain is a Comparator that wraps one or more Comparators in sequence. |
| FixedOrderComparator | A Comparator which imposes a specific order on a specific set of Objects. |
| NullComparator | A Comparator that will compare nulls to be either lower or higher than other objects. |
| ReverseComparator | Reverses the order of another comparator by reversing the arguments
to its compare method. |
| TransformingComparator | Decorates another Comparator with transformation behavior. |
This package contains implementations of the
Comparator
interface.
You may also consider using
ComparatorUtils
,
which is a single class that uses static methods to construct instances
of the classes in this package.
|
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The Human Ear
Outline
- Outer Ear
- Pinna
- Ear Canal
- Timpanic Membrane (ear drum)
- Middle Ear
- Ossicles
- Malleus (hammer)
- Incus (anvil)
- Stapes (stirrup)
- Eustachian Tube
- Ossicles
- Inner Ear
- Oval Window
- Cochlea
- Perilymph (fluid)
- Basilar Membrane
- Organ of Corti
- Cilia
- Nerve Endings
- Auditory Nerve
- Semi-Circular Canals
- Vestibular Nerve
Table
| Outer Ear | Middle Ear | Inner Ear | |||||||||||
| Pinna | Ear Canal | Timpanic Membrane | Ossicles | Eustachian Tube | Oval Window | Cochlea | Semi-Circular Canals | ||||||
| Malleus | Incus | Stapes | Perilymph | Basilar Membrane | Organ of Corti | Auditory Nerve | Vestibular Nerve | ||||||
| Cilia | Nerve Endings |
Diagrams and Descriptions
The Outer Ear
The pinna is the visible part of the outer ear. It directs sound toward the ear canal and also provides some coloration. It helps us locate the direction of a sound.
The sound is focused through the ear canal to the ear drum. The ear drum is a membrane that acts like a microphone diaphragm. It vibrates at the frequencies of the pressure waves that approach it.
The Middle Ear
The ossicles are three tiny bones (the tiniest in your body), shaped like a hammer, anvil and stirrup. They amplify the sound from your ear drum by almost 25 dB. The ossicles then transfer the sound energy to the oval window, the threshold to the Inner Ear. (The ossicles also help ease the transfer of pressure waves from gas into liquid.)
The eustachian tube is a small canal that leads from the middle ear to the oral cavity. It serves as a valve that equalizes air pressure between the middle ear and the outside world. When the valve opens (usually when swallowing), you will feel a slight popping sensation, especially when at extremely high or low altitudes. For auditory purposes, the eustachian tube helps the ossicles operate at equalized air pressure.
The Inner Ear
The oval window is a membrane between the inner ear and the middle ear. It is similar to the ear drum, passing acoustical information inward, specifically to the cochlea.
Cochlea
The cochlea is filled with a watery-like fluid called perilymph. Because of the oval window, the fluid vibrates at the frequencies passed through the ear. These frequencies move tiny hairs called cilia on the Organ of Corti. The cilia are attached to hair cells which stimulate nerve fibers. It is at this point that mechanical energy is transformed into chemical energy. The nerve endings that are stimulated will send signals to the brain, indicating the sound that we hear.
The semi-circular canals, attached to the cochlea, serve no known auditory purpose. They are three U-shaped tubes arramged in perpendicular planes, necessary for determining balance. They operate in almost the exact manner as the cochlea does. The perilymph in these canals move with respect to the force of gravity, and stimulate tiny cilia attached to nerve fibers.


Remember to set your profile to public before sharing your profile.
Strava creates heart rate zones by default. For Strava users on a premium plan, custom HR zones can be created at Settings - My Performance and will be synced here periodically.
HR zones synced from your Strava account: (default)
| Zone # | Minimum | Maximum |
| Zone 1 | 0 | 140 |
| Zone 2 | 141 | 150 |
| Zone 3 | 151 | 160 |
| Zone 4 | 161 | 170 |
| Zone 5 | 171 | -1 |


Mortise Designs provides customers the oppurtunities to choose their own materials, design elementsm and finishes.
They will work with you to create a piece that will be sure to impress!
Elegant, custom made furniture
Locally sourced hardwoods
Fast response to customer inquires
We love out custom made bed! The quality is amazing and we couldn’t be happier with the care and effort that Evin and Rosie put into making our bed exactly how we imagined! –Matt and Jaime Peterson
I light my maple tealight holder all the time! I can put this anywhere in my house as the color and design work with a lot of things!—Anna Zitkovich
My cookbook holder is a great addition to my kitchen. I love the color of it and how it folds up for easy storage!—Danielle Creighton


Getting Started¶
Installation¶
Firebird-driver is written as pure-Python module (requires Python 3.8+) on top of Firebird client library (fbclient.so/dll) using ctypes. Driver supports Firebird version 3.0 and higher.
Firebird-driver is distributed as setuptools package and the preferred installation method is via pip tool.
Installation from PYPI¶
Run pip:
$ pip install firebird-driver
Quick-start Guide¶
This brief tutorial aims to get the reader started by demonstrating elementary usage of Firebird-driver. It is not a comprehensive Python Database API tutorial, nor is it comprehensive in its coverage of anything else.
The numerous advanced features of Firebird-driver are covered in another section of this documentation, which is not in a tutorial format, though it is replete with examples.
Driver configuration¶
The driver uses configuration built on top of configuration system
provided by firebird-base package. In addition to global settings, the configuration
also includes the definition of connection parameters to Firebird servers and databases.
The default configuration connects to embedded server using direct/local connection method.
To access remote servers and databases (or local ones through remote protocols), it’s
necessary to adjust default configuration, or register
them in configuration manager.
You can manipulate the configuration objects directly, or load configuration from files or
strings (in ini-style
ConfigParser
format).
Connecting to a Database¶
Example 1:
A simple database connection is typically established with code such as this:
from firebird.driver import connect
# Attach to 'employee' database/alias using embedded server connection
con = connect('employee', user='sysdba', password='masterkey')
# Attach to 'employee' database/alias using local server connection
from firebird.driver import driver_config
driver_config.server_defaults.host.value = 'localhost'
con = connect('employee', user='sysdba', password='masterkey')
# Set 'user' and 'password' via configuration
driver_config.server_defaults.user.value = 'SYSDBA'
driver_config.server_defaults.password.value = 'masterkey'
con = connect('employee')
Example 2:
A database connection typically uses specific configuration, and is established with code such as this:
from firebird.driver import connect, driver_config
# Register Firebird server
srv_cfg = """[local]
host = localhost
user = SYSDBA
password = masterkey
"""
driver_config.register_server('local', srv_cfg)
# Register database
db_cfg = """[employee]
server = local
database = employee.fdb
protocol = inet
charset = utf8
"""
driver_config.register_database('employee', db_cfg)
# Attach to 'employee' database
con = connect('employee')
Note
Some parameters like ‘user’ and ‘password’ could be overridden with keyword parameters. Few parameters like ‘crypt_callback’ or ‘no_db_triggers’ could be specified ONLY as keyword arguments.
Creating a Database¶
A database is created using create_database()
function.
Like connect()
, this function uses configuration for specification of
database parameters like page size, sweep interval etc.
Executing SQL Statements¶
For this section, suppose we have a table defined and populated by the following SQL code:
create table languages
(
name varchar(20),
year_released integer
);
insert into languages (name, year_released) values ('C', 1972);
insert into languages (name, year_released) values ('Python', 1991);
Example 1
This example shows the simplest way to print the entire contents of
the languages
table:
from firebird.driver import connect
con = connect('test.fdb', user='sysdba', password='masterkey')
# Create a Cursor object that operates in the context of Connection con:
cur = con.cursor()
# Execute the SELECT statement:
cur.execute("select * from languages order by year_released")
# Retrieve all rows as a sequence and print that sequence:
print(cur.fetchall())
Sample output:
[('C', 1972), ('Python', 1991)]
Example 2
Here’s another trivial example that demonstrates various ways of fetching a single row at a time from a SELECT
-cursor:
from firebird.driver import connect
con = connect('test.fdb', user='sysdba', password='masterkey')
cur = con.cursor()
SELECT = "select name, year_released from languages order by year_released"
# 1. Iterate over the rows available from the cursor, unpacking the
# resulting sequences to yield their elements (name, year_released):
cur.execute(SELECT)
for (name, year_released) in cur:
print(f'{name} has been publicly available since {year_released}.')
# 2. Equivalently:
cur.execute(SELECT)
for row in cur:
print(f'{row[0]} has been publicly available since {row[1]}.')
Sample output:
C has been publicly available since 1972.
Python has been publicly available since 1991.
C has been publicly available since 1972.
Python has been publicly available since 1991.
C has been publicly available since 1972.
Python has been publicly available since 1991.
Example 3
The following program is a simplistic table printer (applied in this example to languages
):
from firebird.driver import connect, DESCRIPTION_NAME, DESCRIPTION_DISPLAY_SIZE
TABLE_NAME = 'languages'
SELECT = f'select * from {TABLE_NAME} order by year_released'
con = connect('test.fdb', user='sysdba', password='masterkey')
cur = con.cursor()
cur.execute(SELECT)
# Print a header.
for fieldDesc in cur.description:
print(fieldDesc[DESCRIPTION_NAME].ljust(fieldDesc[DESCRIPTION_DISPLAY_SIZE]), end='')
print() # Finish the header with a newline.
print('-' * 78)
# For each row, print the value of each field left-justified within
# the maximum possible width of that field.
fieldIndices = range(len(cur.description))
for row in cur:
for fieldIndex in fieldIndices:
fieldValue = str(row[fieldIndex])
fieldMaxWidth = cur.description[fieldIndex][DESCRIPTION_DISPLAY_SIZE]
print(fieldValue.ljust(fieldMaxWidth), end='')
print() # Finish the row with a newline.
Sample output:
NAME YEAR_RELEASED
------------------------------------------------------------------------------
C 1972
Python 1991
Example 4
Let’s insert more languages:
from firebird.driver import connect
con = connect('test.fdb', user='sysdba', password='masterkey')
cur = con.cursor()
newLanguages = [
('Lisp', 1958),
('Dylan', 1995),
]
cur.executemany("insert into languages (name, year_released) values (?, ?)",
newLanguages
)
# The changes will not be saved unless the transaction is committed explicitly:
con.commit()
Note the use of a parameterized SQL statement above. When dealing with repetitive statements, this is much faster and less error-prone than assembling each SQL statement manually. (You can read more about parameterized SQL statements in the section on Prepared Statements.)
After running Example 4, the table printer from Example 3 would print:
NAME YEAR_RELEASED
------------------------------------------------------------------------------
Lisp 1958
C 1972
Python 1991
Dylan 1995
Calling Stored Procedures¶
Firebird supports stored procedures written in a proprietary procedural SQL language. Firebird stored procedures can have input parameters and/or output parameters. Some databases support input/output parameters, where the same parameter is used for both input and output; Firebird does not support this.
It is important to distinguish between procedures that return a result set and procedures that populate and return their output parameters exactly once. Conceptually, the latter “return their output parameters” like a Python function, whereas the former “yield result rows” like a Python generator.
Firebird’s server-side procedural SQL syntax makes no such distinction, but client-side
SQL code (and C API code) must. A result set is retrieved from a stored procedure by
SELECT
-ing from the procedure, whereas output parameters are retrieved with an
EXECUTE PROCEDURE
statement.
To retrieve a result set from a stored procedure with Firebird-driver, use code such as this:
cur.execute("select output1, output2 from the_proc(?, ?)", (input1, input2))
# Ordinary fetch code here, such as:
for row in cur:
... # process row
con.commit() # If the procedure had any side effects, commit them.
To execute a stored procedure and access its output parameters, use code such as this:
cur.callproc("the_proc", (input1, input2))
# If there are output parameters, retrieve them as though they were the
# first row of a result set. For example:
outputParams = cur.fetchone()
con.commit() # If the procedure had any side effects, commit them.
This latter is not very elegant; it would be preferable to access the procedure’s output
parameters as the return value of Cursor.callproc()
. The Python DB API specification
requires the current behavior, however.


This document describes a basic set of tests for Crash Safe Parameter Storage Area (CSPSA). It can be used to verify CSPSA on different kind of platforms and media. In for example a PC Ubuntu it is possible to operate with an image in RAM, while in an embedded device one can operate on e.g. either MTD or MMC.
The idea is that all test cases should be executed as a regression test before updated CSPSA code is released. Single test cases should only be executed in case of bug hunting.
Before starting test, the maximum size of an internal image array in CSPSA should be configured to 512 bytes.
Note: TC = Test Case
Create a CSPSA image and verify it
All CSPSA function call durations should be summed up and presented as a test result
A summed duration time for each type of function call should be presented (that is separate sums for open, flush, close, read and write operations)
Create image in different ways when it comes to keys and sizes. E.g. increasing and decreasing key values, increasing and decreasing size values etc.
Delete objects in a CSPSA image and verify it
All CSPSA function call durations should be summed up and presented as a test result
Delete some values at beginning, end and in middle.
Update some of the values that are to be deleted so that they end up in item list.
Try to remove all items.
Get error result if a CSPSA image cannot be written
All CSPSA function call durations should be summed up and presented as a test result
Try to write an image that is larger than half the storage area that is not read-only.
Manipulate one bit in the CSPSA data field and make sure CSPSA detects it
All CSPSA function call durations should be summed up and presented as a test result
Manipulate one bit in data that is not a key or size field.
Manipulate one bit in data that is a key field.
Manipulate one bit in data that is a size field.
Manipulate one bit in data that is a size field.
Manipulate one bit in one header’s CSPSA header fields (one field at a time) and make sure CSPSA detects it
All CSPSA function call durations should be summed up and presented as a test result
Manipulate one bit in header "Magic" field at start of storage area.
Manipulate one bit in header "Magic" field at end of storage area.
Manipulate one bit in "Version" field at start of storage area.
Manipulate one bit in "Index" field at start of storage area.
Manipulate one bit in "Index" field at end of storage area.
Manipulate one bit in "Flags" field at start of storage area.
Manipulate one "small" bit in "DataSize" field at start of storage area.
Manipulate one "large" bit in "DataSize" field at end of storage area.
Manipulate one bit in "Crc32Data" field at start of storage area.
Manipulate one bit in "Crc32Data" field at end of storage area.
Manipulate one bit in "Crc32Header" field at start of storage area.
Manipulate one bit in "Crc32Header" field at end of storage area.
Manipulate one bit in "BbtSize" field at start of storage area.
Manipulate one bit in both header’s CSPSA header fields (one field at a time) and make sure CSPSA fails to open
All CSPSA function call durations should be summed up and presented as a test result
Manipulate one bit in header "DataSize" field in both headers.
Test all API functions with bad input parameters
CSPSA_Open()
CSPSA_Close()
CSPSA_GetSizeOfValue()
CSPSA_GetSizeOfNextValue()
CSPSA_ReadValue()
CSPSA_WriteValue()
CSPSA_DeleteValue()
CSPSA_Flush()
CSPSA_Format()
CSPSA_Create()
Increase and decrease the size of a CSPSA image significantly and verify it
All CSPSA function call durations should be summed up and presented as a test result
Add a large item after first flush.
Replace with a large item after first flush.
Add a large item before first flush.
Replace a large item before first flush.
Add and delete a large item before first flush.
Replace and delete a large item before first flush.
Replace and delete a large item before first flush.
Replace and delete a large item (with second Key value).
Increase and decrease the existing parameters small amounts
All CSPSA function call durations should be summed up and presented as a test result
Add a large item after first flush.
Replace with a large item after first flush.
Add a large item before first flush.
Replace a large item before first flush.
Add and delete a large item before first flush.
Replace and delete a large item before first flush.
Replace and delete a large item before first flush.
Replace and delete a large item (with second Key value).
Multiple clients test
Create CSPSA.
Start five threads of which three are reading and two are writing to the same CSPSA, and let threads run for > 30 s.
Performance test.
Create a CSPSA with 4096 items of 24 bytes each and measure time for each operation, except for the first flush operation.
Make sure CSPSA can be opened when there are two valid headers
Both headers and data is valid.
Both headers are valid, but the latest written data has a bit error.
Measure performance of reads and writes to storage media.
Write 8 x 128 kB.
Read 8 x 128 kB.
Create a CSPSA and add a few values to it (useful initial test for testing plugin implementations)
Create a CSPSA.
Add some values and flush
Tests adding/deleting/flushing/reopening.
Create a CSPSA
Randomly adds/deletes values, or flushes/reopens. After every add/delete/reopen it is checked that the contents of the CSPSA are correct.
Tests robustness against read errors, both the case where the underlying plugin detects the read error (i.e. is unable to read from the underlying media) and the case where bits have become corrupt without being detected by the underlying plugin.
Create a CSPSA, adds some values and flushes the contents.
Opens the CSPSA, but while reading the contents, a (induced) read error occurs; it is checked that the open fails with appropriate error code. This is repeated for every block that has been written.
Opens the CSPSA, but while reading the contents, a (induced) bit error occurs; the CRC check should fail; it is checked that the open fails with appropriate error code. This is repeated once for every word that has been written to check that any bit error at any place is correctly detected.
Check that image is stored alternating at beginning and end.
Write value and flush seven times and check that image is stored alternating at beginning and end.
Create a certain sequence of items that could cause problems in function F_CSPSA_CORE_ImageArray_LoadData().
Write few values; key 1 and 2 will end up at very end of their block.
Check that the fast parameter feature works.
Write different 32-bit values to the different fast parameters and check result.
Negative tests. Try writing an item with different size than 4 bytes, and try deleting a fast parameter.


Welcome!
My name is Joseph Sobrero and I am a user experience designer.
If I did not work, I would spend all my time rock climbing. I love climbing because it is one of a few sports that is as physically challenging as it is mentally challenging. Strength is not the only factor because every climb is like a puzzle. Most importantly, climbing requires you to focus on your weaknesses and battle fear as you hang from tiny holds high up on the wall.
Aside from climbing, I love discovering new music, taking photos, creating evocative art, practicing philosophy, and taking time to do absolutely nothing.


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{% block content %}{% endblock %}


video functions
There is no constructor function for me.video
- Source:
- video.js, line 8
Classes
Members
-
<static> AUTO :Number
-
Auto-select the renderer (Attempt WebGL first, with fallback to Canvas)
Type:
- Number
- Source:
- video.js, line 94
-
<static> CANVAS :Number
-
Select the HTML5 Canvas renderer
Type:
- Number
- Source:
- video.js, line 76
-
<static> WEBGL :Number
-
Select the WebGL renderer
Type:
- Number
- Source:
- video.js, line 85
Methods
-
<static> createCanvas(width, height, screencanvas) → {Canvas}
-
Create and return a new Canvas
Parameters:
Name Type Argument Default Description width
Number width height
Number height screencanvas
Boolean <optional>
false set to true if this canvas renders directly to the screen - Source:
- video.js, line 297
Returns:
- Type
- Canvas
-
<static> getPos(canvas) → {me.Vector2d}
-
return the relative (to the page) position of the specified Canvas
Parameters:
Name Type Argument Description canvas
Canvas <optional>
system one if none specified - Source:
- video.js, line 263
Returns:
- Type
- me.Vector2d
-
<static> getWrapper() → {Document}
-
return a reference to the wrapper
- Source:
- video.js, line 325
Returns:
- Type
- Document
-
<static> init(width, height, options) → {Boolean}
-
Initialize the "video" system (create a canvas based on the given arguments, and the related renderer).
melonJS support various scaling mode :
- `fit` : Letterboxed; content is scaled to design aspect ratio
- `fill-max` : Canvas is resized to fit maximum design resolution; content is scaled to design aspect ratio
- `flex-height` : Canvas height is resized to fit; content is scaled to design aspect ratio
- `flex-width` : Canvas width is resized to fit; content is scaled to design aspect ratio
- `stretch` : Canvas is resized to fit; content is scaled to screen aspect ratioParameters:
Name Type Argument Description width
Number the width of the canvas viewport height
Number the height of the canvas viewport options
Object <optional>
The optional video/renderer parameters Properties
Name Type Argument Default Description wrapper
String <optional>
document.body the "div" element name to hold the canvas in the HTML file renderer
Number <optional>
me.video.CANVAS renderer to use. doubleBuffering
Boolean <optional>
false enable/disable double buffering scale
Number | String <optional>
1.0 enable scaling of the canvas ('auto' for automatic scaling) scaleMethod
Boolean <optional>
"fit" ('fit','fill-max','flex-width','flex-height','stretch') screen scaling modes transparent
Boolean <optional>
false whether to allow transparent pixels in the front buffer (screen) antiAlias
Boolean <optional>
false whether to enable or not video scaling interpolation - Source:
- video.js, line 103
Returns:
false if initialization failed (canvas not supported)- Type
- Boolean
Example
// init the video with a 640x480 canvas me.video.init(640, 480, { wrapper : "screen", renderer : me.video.CANVAS, scale : "auto", scaleMethod : "fit", doubleBuffering : true });
-
<static> setMaxSize(width, height)
-
set the max canvas display size (when scaling)
Parameters:
Name Type Description width
Number width height
Number height - Source:
- video.js, line 279
-
<static> updateDisplaySize(scaleX, scaleY)
-
Modify the "displayed" canvas size
Parameters:
Name Type Description scaleX
Number X scaling multiplier scaleY
Number Y scaling multiplier - Source:
- video.js, line 425


The above images are directed acyclic graphs of the pre-requisites for the majority of courses in the CSE department and requirements for graduation. This does not contain all of the CSE department's course offerings, but seemed more comprehensive than any other available resources that I could find.
This project began as a much smaller scale drawing in 2014 or 2015 that I had created to ensure that I could take all the course that I wanted to before graduation. Since others also found the drawing helpful, it gradually increased in scale, became digital, and was eventually just put up on this website.


Music has always been a part of my life, whether through playing instruments, singing, or just listening to music.
I learned piano at an early age and still love to play popular melodies for fun.
I have also played a Chinese instrument called the erhu for 10 years and enjoyed performing with my Chinese orchestra.
Once I got to college, I decided to deepen my interest in singing by joining Trogons A Cappella.
Art & Design
After learning how to use Adobe Creative Cloud software in high school journalism, my interest in art developed into a passion for digital design.
I design flyers, digital graphics, logos, and apparel for various student organizations and love seeing the results of my work.
Occasionally, I still like to pull out my paintbrush and create art the traditional way.
I have also found typography and hand-lettering extremely interesting and I constantly experiment with new styles and marker pens to practice my lettering.
Exploring
Exploring the outdoors, trying new foods, and travelling are some of my favorite things.
When I'm back in the Bay Area, I love to hike and bike and take walks around my neighborhood.
So far, I have travelled to Taiwan, New Zealand, China, Honduras, Belize, and Mexico. I hope to travel more, especially to France where I can practice my French skills.


Index
present: [4.] dī́yati etc. dī́yase
secondary: intensive: dédīyitavaí
Represented later by √ḍī. From it may possibly come didyú V.B.


Post-parsing
ConfigurationDataSources.php
ConfigurationDiagnosticInfrastructureLogs.php
ConfigurationDirectories.php
ConfigurationInstance.php
ConfigurationLogs.php
ConfigurationPerformanceCounters.php
ConfigurationWindowsEventLog.php
DirectoryConfigurationSubscription.php
Interface.php
Message.php
PerformanceCounterSubscription.php
SessionHandler.php
TableEntity.php
Post-parsing
Warnings:
Warning - Class Microsoft_Log_Exception parent Zend_Exception not found
AutoLoader.php
Warnings:
Warning on line 33 - package Microsoft_WindowsAzure is already in category Microsoft_WindowsAzure, will now replace with category Microsoft
ConfigurationDataSources.php
Warnings:
Warning on line 54 - @property magic tag does not have name, illegal. Ignoring tag "@property Microsoft_WindowsAzure_Diagnostics_ConfigurationDiagnosticInfrastructureLogs DiagnosticInfrastructureLogs Diagnostic infrastructure logs"
Warning on line 54 - @property magic tag does not have name, illegal. Ignoring tag "@property Microsoft_WindowsAzure_Diagnostics_ConfigurationWindowsEventLog WindowsEventLog Windows Event Log"
Warning on line 54 - @property magic tag does not have name, illegal. Ignoring tag "@property Microsoft_WindowsAzure_Diagnostics_ConfigurationDirectories Directories Directories"
Warning on line 54 - @property magic tag does not have name, illegal. Ignoring tag "@property Microsoft_WindowsAzure_Diagnostics_ConfigurationLogs Logs Logs"
Warning on line 54 - @property magic tag does not have name, illegal. Ignoring tag "@property Microsoft_WindowsAzure_Diagnostics_ConfigurationPerformanceCounters PerformanceCounters Performance counters"
Warning on line 54 - @property magic tag does not have name, illegal. Ignoring tag "@property int OverallQuotaInMB Overall quota in MB"
ConfigurationDiagnosticInfrastructureLogs.php
Warnings:
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ConfigurationDirectories.php
Warnings:
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Warning on line 51 - @property magic tag does not have name, illegal. Ignoring tag "@property array Subscriptions Subscriptions"
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ConfigurationInstance.php
Warnings:
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ConfigurationLogs.php
Warnings:
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ConfigurationPerformanceCounters.php
Warnings:
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ConfigurationWindowsEventLog.php
Warnings:
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Warning on line 52 - @property magic tag does not have name, illegal. Ignoring tag "@property string ScheduledTransferLogLevelFilter Scheduled transfer log level filter"
Warning on line 52 - @property magic tag does not have name, illegal. Ignoring tag "@property array Subscriptions Subscriptions"
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DirectoryConfigurationSubscription.php
Warnings:
Warning on line 51 - @property magic tag does not have name, illegal. Ignoring tag "@property int DirectoryQuotaInMB Directory quota in MB"
Warning on line 51 - @property magic tag does not have name, illegal. Ignoring tag "@property string Path Path"
Warning on line 51 - @property magic tag does not have name, illegal. Ignoring tag "@property string Container Container"
Interface.php
Warnings:
Warning on line 29 - package Microsoft_WindowsAzure is already in category Microsoft, will now replace with category Micorosft
Warning on line 31 - package Microsoft_Log is already in category Microsoft, will now replace with category Micorosft
Message.php
Warnings:
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PerformanceCounterSubscription.php
Warnings:
Warning on line 50 - @property magic tag does not have name, illegal. Ignoring tag "@property string CounterSpecifier Counter specifier"
Warning on line 50 - @property magic tag does not have name, illegal. Ignoring tag "@property int SampleRateInSeconds Sample rate in seconds"
SessionHandler.php
Warnings:
Warning on line 303 - Unknown tag "@usage" used
TableEntity.php
Warnings:
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|
|


I've been prescribed to ritalin for a while now, but haven't been taking it regularly for reasons I won't reveal here. I don't know what kind of tolerance to this drug can be built up, but if you can build one up, I would have a next to nothing tolerance. I've snorted it three times. The first time I snorted 40 mgs. I didn't really feel anything, maybe I was just more used to heavier effects from other drugs. The second time I snorted 60 mgs. I just remember getting lots of chills and being very comfortable and sociable.
Three nights ago, however, I decided to up the dosage to 80 mgs, or four crushed up extended release 20 mg pills. I don't know if the fact that they were extended release pills had anything to do with what happened, but I thought I'd note that. After I snorted the powder (at about 10:30), I could sense a general feeling of comfort. I was on the phone for a few hours afterwards. The ritalin high was normal, and I was a bit disappointed that it wasn't more intense. However, I tried to go to sleep at about 2 o'clock, and couldn't stop bouncing my leg, or just generally calming myself down. I was extremely hyper, and very cold, even though I was under my blanket in my room, which is generally the hottest room in the house. I slept for periods of about 15 minutes at a time, waking up to wipe myself clean of the cold sweat accumulating on my body and to try to calm myself down to repeat the process again.
The next day, I couldn't see straight. It sort of reminded me of being drunk, but without the mental obstacles. My head hurt if I rotated my eyeballs too much in any direction, or if I moved my head too much. My joints ached, and I was extremely fatigued. Walking up and down the stairs made me very tired, and I was very very cold, even when others made comments that it was really hot in a room. Chills were frequent, and even wrapping myself in blankets wouldn't warm me up. These symptoms slightly lessened as the day went on, and I slept very badly but frequently that day. The next day I slept much better, but again, alot. Today, I feel much better, I can eat again, sleep normally. The vision is still vaguely impaired, and my joints still hurt a bit, but this should be completely gone by tomorrow or so. Just be careful and don't go over 60 mgs I'd say. Not even that good of a drug.
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.


PowerTip Examples
Placement examples
Mouse follow example
The PowerTip for this box will follow the mouse.
Mouse on to tooltip example
The PowerTip for this box will appear on the right and you will be able to interact with its content.
API examples
Delegated manual mouseover, with lazy-loaded tooltip:


Please donate any amount to help the girl child. You will then be asked for your contact information before you are asked for your payment information.
You may also choose to donate monthly or weekly.


Q. 78. Is God pleased with those who love and obey him? A. Yes; he says, "I love them that love me."
Q. 79. Is God displeased with those who do not love and obey him? A. Yes; "God is angry with the wicked every day."


OUR APP FEATURES
With our wide range of features, we offer all the tools you need to easily post and sign up for tasks.
Post Tasks
Anyone can post a task. Posters will be required to input information, such as the amount of money offered, the task description and title, the task location, and the task time, date and duration. A poster can add a task as early as one week in advance of the task date.
Sign-Up For Tasks
Students can sign up for as many tasks as they like. They can easily keep track of their past and future tasks through the “My Tasks” section and even leave a review for the poster.
Tasks Map
The Tasks Map shows a student where there are tasks in a given area. The student filter the results, such as the time and length of the task, the amount being offered for a task, the task date and the task type. They can also enter a keyword that can help to filter tasks. Red tasks are tasks available within 24 hours, while green tasks are posted up to a week in advance.
Messaging
UniTasker provides a convenient messaging system where job posters can get in touch with reliable students. Taskers and posters can only be in contact if the task has been accepted and confirmed.
APP SCREENSHOTS
GET THE APP
UniTasker provides a hassle-free way for people to post jobs and for students to sign-up for jobs. It’s perfect for students who want to earn money around their classes. Download the app today using one of the three links below.
FAQ
GET IN TOUCH
Want to get in touch? We would love to hear from you! For general enquiries fill in the form below and we will get back to you shortly.
Office Location
UCL,Gower St, Bloomsbury, London WC1E 6BT
Phone Number
+90<PHONE_NUMBER>
+90 567 098 785
Email Address
<EMAIL_ADDRESS>support@unitasker.com


All Classes
-
All Classes Interface Summary Class Summary Enum Summary Exception Summary Class Description Action Abstract class for functions that are needed for all actionsActionParserHelper This class is made to identify the type of action easily.ActionParserHelper.ActionEnum Enum for what type of action a given action is Why this exists: So that the concrete Action class can be parsed easily.ActionType Attack Action that is used for Attacking.AttackAction Planning action meaning that the bot should send some or all of it's army to fight somewhere.AttackActionType Description: This class implements the ActionType abstract class from burlap and is used to tell the planner what actions are possible to take at a givens state Why This Exists: To provide the ai planer with possible attacking actions.BuildAction An Action that involves building a structure for the bot to take.BuildActionType Description: This class implements the ActionType abstract class from burlap and is used to tell the planner what actions are possible to take at a givens state Why This Exists: To provide the ai planer with possible attacking actions.CombatAgent Main Class for Combat control.CombatUnitStatus DataManager Manages the collection of relevant data in the form of DataPoints.DataPoint Represents relevant information such as current State, executed Action, next State, produced reward, and Q-Value.Distance Represents a distance between two points.DistanceRange Represents a range of possible distances and categorizes them into a relative enum.DummyQValue A bad Q Value class from before we (or at least I) realized that the SparseSampler in burlap already implemented this for us.EconomyAgent ExpandActionType A class for the expand action for AI Planning.GameListener Provides the bare minimum to define a BWListener.GameStatus GeneralGameKnowledge GeneralRaceProductionKnowledge GoHome Action that is used for returning a unit to base.GreedyActionChooser Selects either the best Action for a specified State or a randomly sampled Action.Hp Represents the total hp from a group of units.HpRange Represents a range of possible hp values and categorizes them into a relative enum.IncorrectActionTypeException The exception that is thrown when there's an unknown ActionType.IntelligenceAgent LearningManager Manages the learning model, state space, and action chooser.ListenerType Defines various ListenerTypes to distinguish different versions of GameListener.MasterBot MLTrainingListener This class is a skeleton of VikingBotListener that allows the ML model to train separately from the AI planningMoveTowards Action used to MoveTowards the closest enemy unitPlanningRewardFunction PlanningState ProtossGeneralKnowledge QTable Maps (State, Action) pairs to double values representing the q-value for executing the Action in the specific State.QueueComparator Retreat Action that is used for moving away from enemy units.RewardFunction SARSA The main implementation of the SARSA learning algorithm.ScoutActionType A class for the Scout action for AI Planning.SharedPriorityQueue A priority queue for the ai planning to communicate with the starcraft bot Works with the StarcraftPlanner to decide what to do next.StarcraftEnvironment StarcraftModel StarcraftPlanner State Represents a state with all relevant features relating to micromanaging combat.StateAction Represents a (State, Action) pair.StateSpaceManager Manages the state space and available actions.StrategyAgent TerrenGeneralKnowledge TrainAction An Action that involves training a new unit.TrainActionType Description: This class implements the ActionType abstract class from burlap and is used to tell the planner what actions are possible to take at a givens state How to incorperate new options: Probably would have to restructure the AllApplicableActions function to construct all reasonable possible option combinations How to allow training of new units: copy the second if statement, swap in the new unit's name and you should be good How the training capacaty array is structures: [ workers[used, avail] , ground[used, avail], combat air[used, avail], support air[used, avail]] So, currentCapacity[0][1] is the current capacaity to train new workers (the [0]) that is currently available (the [1]).Training This class is a skeleton of StrategyAgent that allows the ML model to train separately from the AI planningUnitClassification Enum to classify units based on their weapon rangeUnits Represents a collection of units.UnitsRange Represents a range of possible nearby units and categorizes them into a relative enum.UpgradeAction This class is meant to work similar to the Train and Build Actions.UpgradeActionType Currently does nothing as UpgradeAction needs to be better definied to be useful.VikingBot The main instance of the VikingBot which will handle setting up the agents between games.VikingBotInitializer Parses command line arguments and initializes the main VikingBot instance.VikingBotListener The main GameListener used for initializing the agents and updating them every frame.ZergGeneralKnowledge


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This demonstration page was created using my own work-in-progress avrgo AVR emulator, which was compiled into javascript using emscripten


Hi, I'm Brian Heptinstall, a front-end engineer based out of the Orlando, Florida area. I'm a 2016 graduate of The Iron Yard, an immersive full-time web development course, where I spent more than 1,000 hours working on 34 projects. With an emphasis on HTML5, CSS3, Javascript, ReactJS, APIs and more, I'm keeping my skills up-to-date with the latest technology advances.
I love to code because of the teamwork involved in a large complex project, not unlike my past experience as a chef at Walt Disney World and the Waldorf-Astoria. I'm available for work in Central Florida or remote. What can I cook up for you?
The Iron Yard Front End Engineering Course: Completed Fall 2016
HTML5, CSS3, JavaScript, jQuery, Bootstrap, Reactjs, GitHub, Sass... and the Next Big Thing.
Guitar, Alabama Football, Poker (gambling in general), camping/outdoors, hanging with my wife and Choco Lab.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import views.html.headerFooterTemplate.Layout @import views.html.templates.helpers.Heading @this(heading : Heading, layout: Layout) @()(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @layout(pageTitle = messages("errors.404.heading")) { @heading(messages("errors.404.heading"))
@messages("errors.404.p1")
@messages("errors.404.p2")
@messages("errors.404.p3") @messages("errors.404.p4") @messages("errors.404.p5")


MINTRA
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wheel
`Prev `__
W
`Next `__
--------------
**wheel**: n.
[from slang ‘big wheel’ for a powerful person] A person who has an
active `*wheel bit* `__. “We need to find a wheel to
unwedge the hung tape drives.” (See `*wedged* `__, sense
1.) The traditional name of security group zero in
`*BSD* <../B/BSD.html>`__ (to which the major system-internal users like
`*root* <../R/root.html>`__ belong) is ‘wheel’. Some vendors have
expanded on this usage, modifying Unix so that only members of group
‘wheel’ can `*go root* <../G/go-root.html>`__.
--------------
+-----------------------------------+----------------------------+------------------------------+
| `Prev `__ | `Up <../W.html>`__ | `Next `__ |
+-----------------------------------+----------------------------+------------------------------+
| What's a spline? | `Home <../index.html>`__ | wheel bit |
+-----------------------------------+----------------------------+------------------------------+


Kubernetes services allow us to address a collection of pods through a stable endpoint. Depending on the service type, this endpoint may be addressable only from within the cluster, from the same network, or even from the public internet. When running on AWS, setting the service type for a service to LoadBalancer will create an AWS ELB, which can be used as the endpoint. Wih a Kubernetes cluster running on public subnets, this just works. However, if your Kubernetes cluster only runs on private subnets (which is an option when spinning up clusters with Kops), then this doesn’t work. It creates the service as expecting, but it gets stuck in a pending state, where it fails to create the ELB. Doing a
kubectl describe svc service-name -n namespace-name
shows the fact that it can’t find a subnet to create the load balancer. The fix for this is simple (once you know how!). You just need to tag one or more AWS public subnets with some special tags, which then allows Kubernetes to create the load balancer in one of those subnets. The tags in question depend on the version of Kubernetes. I’m using 1.6, and as such, added the following tags with empty values:
kubernetes.io/cluster/my-cluster-name
kubernetes.io/role/elb
With these tags in place, I could create a service as follows:
kind: Service
apiVersion: v1
metadata:
name: hellopub
spec:
selector:
app: hello-world
ports:
- protocol: "TCP"
port: 80
targetPort: 5000
type: LoadBalancer
This created an ELB, and everything ran fine.
If you’re not on Kubernetes 1.6, then I think the following tags will work:
KubernetesCluster (value would be the name of your cluster)
kubernetes.io/role/elb
This of course means that you can’t use the same subnet across clusters.


If you are a community member who registered to the PINE64 Forum prior to July 1, 2019, you are eligible for pre-order priority on the Pinebook Pro and a free 128GB eMMC upgrade
After you enter your forum login, an email will be generated to you and to a member of the PINE64 Sales Team. At this point, you are guaranteed a coupon code.
Some members will receive their coupon within 72 hours, but the majority will need to wait to complete the process a couple of weeks (up-to a month). Please make sure to check your email


|
Index
ANS
API
Debugger
Download
Licensing
Links
Locals
OOP In Ficl
Parse Steps
Release History
Upgrading To 4.0
| |
Ficl is a programming language interpreter designed to be embedded
into other systems as a command, macro, and development prototyping
language.
Ficl is an acronym for "Forth Inspired Command Language".
-
Ficl is easy to port.
-
-
It typically takes under 2 hours to port to a new platform.
-
Ficl is written in strict ANSI C.
-
Ficl can run natively on 32- and 64-bit processors.
-
Ficl has a small memory footprint.
-
A fully featured Win32 console version takes less than 100K
of memory, and a minimal version is less
than half that.
-
Ficl is easy to integrate into your program.
-
Where most Forths view themselves as the center of the system and
expect the rest of the system to be coded in Forth, Ficl acts as a
component of your program. It is easy to export code written in C or
ASM to Ficl (in the style of TCL), or to invoke Ficl code from a
compiled module.
-
Ficl is fast.
-
Thanks to its
"switch-threaded"
virtual machine design, Ficl 4 is faster than ever—about 3x the speed of Ficl 3.
Ficl also features blindingly fast "just in time" compiling, removing the "compile" step from
the usual compile-debug-edit iterative debugging cycle.
-
Ficl is a complete and powerful programming language.
-
Ficl is an implementation of the FORTH language, a language providing
a wide range of standard programming language features:
-
Integer and floating-point numbers, with a rich set of operators.
-
Arrays.
-
File I/O.
-
Flow control (
if/then/else and many looping structures).
-
Subroutines with named arguments.
-
Language extensibility.
-
Powerful code pre-processing features.
-
Ficl is standards-compliant.
-
Ficl conforms to the 1994 ANSI Standard for FORTH (DPANS94).
See ANS Required Information for
more detail.
-
Ficl is extensible.
-
Ficl is extensible both at compile-time and at run-time.
You can add new script functions, new native functions,
even new control structures.
-
Ficl adds object oriented programming features.
-
Ficl's flexible OOP library can be used to wrap
data structures or classes of the host system without altering them.
(And remember how we said Ficl was extensible? Ficl's object-oriented
programming extensions are written in Ficl.)
-
Ficl is interactive.
-
Ficl can be used interactively, like most other FORTHs, Python,
and Smalltalk. You can inspect data, run commands, or even
define new commands, all on a running Ficl VM.
Ficl also has a built-in script debugger that allows you to
step through Ficl code as it is executed.
-
Ficl is ROMable.
-
Ficl is designed to work in RAM based and ROM code / RAM
data environments.
-
Ficl is safe for multithreaded programs.
-
Ficl is reentrant and thread-safe. After initialization,
it does not write to any global data.
-
Ficl is open-source and free.
-
The Ficl licence is a BSD-style
license, requiring only that you document that you are
using Ficl. There are no licensing costs for using Ficl.
Ficl 4.0 is a major change for Ficl. Ficl 4.0 is smaller,
faster, more powerful, and easier to use
than ever before. (Or your money back!)
Ficl 4.0 features a major engine rewrite. Previous versions
of Ficl stored compiled words as an array of pointers to data
structure; Ficl 4.0 adds "instructions", and changes over to
mostly using a "switch-threaded" model. The result? Ficl 4.0
is approximately three times as fast as Ficl 3.03.
Ficl 4.0 also adds the ability to store the "softcore" words
as LZ77 compressed text. Decompression is so quick as to be
nearly unmeasurable (0.00384 seconds on a 750MHz AMD Duron-based
machine). And even with the runtime decompressor, the resulting
Ficl executable is over 13k smaller!
Another new feature: Ficl 4.0 can take advantage of native
support for double-word math. If your platform supports it,
set the preprocessor symbol FICL_HAVE_NATIVE_2INTEGER
to 1, and create typedefs for ficl2Integer
and ficl2Unsigned .
Ficl 4.0 also features a retooled API, and a redesigned directory
tree. The API is now far more consistent. But for those of you
who are upgrading from Ficl 3.03 or before, you can enable API
backwards compatibility by turning on the compile-time flag
FICL_WANT_COMPATIBILITY .
Ficl 4.0 also extends support every kind of local and
global value imaginable. Every values can individually
be local or global, single-cell or double-cell, and
integer or floating-point.
And TO always does the right thing.
If you're using Ficl under Windows, you'll be happy
to know that there's a brand-new build process.
The Ficl build process now builds Ficl as
-
a static library (.LIB),
-
a dynamic library (.DLL, with a .LIB import library), and
-
a standalone executable (.EXE).
Furthermore, each of these targets can be built in
Debug or Release, Singlethreaded or Multithreaded,
and optionally using the DLL version of the C runtime
library for Multithreaded builds. (And, plus, the
/objects/common nonsense is gone!)
Finally, Ficl 4.0 adds a contrib
directory, a repository for user-contributed code that isn't
part of the standard Ficl release. The only package there
right now is XClasses, a Python-based IDL that generates
the definition files for C++-based classes, the equivalent Ficl
classes, and code to allow the Ficl classes to call the C++ methods.
Using XClasses you can write your class once, and use it
immediately from both C++ and Ficl.
You can download Ficl from the
Ficl download page at Sourceforge.
|


Daemonize your go programs by locking the process id file. Simply put the following at the top of your main
,
func main() {
defer pid.Start("monokuma").Stop()
// your code...
}
This will create a monokuma.pid
file in /tmp
with the process id (pid) of the go program when loaded by your operating system. When your `main` exits, the file will be cleaned up.
This will prevent any other instances of your program running at the same time (say running a web server or overloading some resources). Say, when I run
λ monokuma
I can find the PID of it with (no line feed at the end),
λ cat /tmp/monokuma.pid
91100⏎
If I try to start the same program, I will be greeted by
λ ./monokuma
2023/05/17 23:03:03 your app with pid 91100 is already running
-> See more in pid
github repository


Your course contains courselets – automated chats between the student and a virtual instructor (that’s you). A courselet should be short enough to complete in one sitting and only cover one or a few concepts.
Learn more about courselets in this short video, and then click on the button below to create a courselet.
0:16


Tickets for
[SeleniumConf India]
are on sale Now!
The Call for Proposals is now OPEN for
[SeleniumConf Chicago]
Submit
HERE
!
X


|
Life on the Court
by Taunya Lovell Banks
Barely one year after the United
States Supreme Court ended the 2000 presidential election controversy
in Bush v. Gore, there are two programs about the Supreme
Court on commercial television. The latest addition, The Court,
starring Sally Field as Justice Kate Nolan, comes from the producers
of the critically acclaimed The West Wing. ABC decided
to air The Court as a temporary replacement for its new
lawyer show, Philly. The network probably reasoned that
The Court was a natural for a slot already devoted to
law and lawyering. There was, however, very little law or lawyering
depicted in the first show.
The opening episode, entitled, Life
Sentence, has double meaning, signifying both the life prison
term of Charlene Grissom (Cynthia Ettinger), a woman career criminal
whose case is before the Court, and the life term of Supreme
Court Justices. The first part of this episode uses the Court
appointment process to disclose aspects of Kate Nolans
character. Nolan, a middle-aged state governor with a supportive
husband, is politically shrewd, yet impartial and ethical. Her
deft maneuvering through the appointment process suggests that
she will be the swing vote on an ideologically divided Supreme
Court. This fictionalized Court seems divided over the same hot-button
issues abortion, the death penalty and three-strike criminal
laws as the real Supreme Court.
Justice Nolan, who replaces
a woman Justice killed in an automobile accident, joins a Court
that reflects racial and gender diversity as well. There is a
black woman, Justice DeSett (Diahann Carroll), and a Hispanic
man, Justice Martinez (Miguel Sandoval); all that is missing
is an Asian-American Justice. As with the real Supreme Court,
however, white men dominate.
The story line for the first
episode seems to overflow with coincidences and storylines. The
writer, Carol Flint, who worked on shows like ER, China Beach
and LA Law, is capable of doing better writing. She seems
unclear about whether this is a show about the Supreme Court
or the news medias coverage of the Court. There is Harlan
Brandt (Craig Bierko), the lawyer-turned-investigative-reporter,
who seems intent on digging up dirt on Justice Nolan. Coincidentally,
Brandt was a student in a law school seminar taught by Justice
Nolan. He earned an A but Nolan told him that he
would never be a good lawyer. Brandts role and motives
are unclear. Does he have a personal beef with Justice Nolan,
or does he simply represent those unsavory scandal mongers who
masquerade as news reporters?
In the meantime, Brandt is
doing a feature portraying Justice Nolan as the mirror image
of Charlene Grissom, the career criminal. Once on the Court,
Grissoms life sentence is the first case for which Justice
Nolan must cast a vote more coincidences. The legal issue
raised by Grissoms case, the harshness of three-strikes
laws, only serves as a background for the personal stories of
the Justices and their law clerks. Unfortunately, West Wing
does a better job than The Court of interweaving its
many story lines in more credible fashion. Yet The
Court is a much more respectful depiction of the Supreme
Court than CBSs First
Monday.
All told, there are good and
bad aspects about The Court, as well as its less notable
competitor, First Monday. Both shows purport to give television
viewers insights about the United States Supreme Court and the
Justices. This is a good thing, given the general publics
lack of knowledge about the Court. Yet each show gives viewers
a distorted view that is not subject to correction, since the
real Supreme Court remains shrouded in mystery, consistently
refusing to even televise its oral arguments. As a result, after
viewing regular doses of The Court and First Justice,
many Americans may be more familiar with the fake Justices than
the real Justices. This would be a bad development.
Television, a constant presence
in many American households, increasingly tends to blur the distinctions
between reality and fiction. Unclear is whether public exposure
to only a fictionalized view of the Supreme Court will pose a
threat to this institutions reputation and prestige. On
the other hand, The Court and First Monday may
simply reflect an already alienated publics view of the
United States Supreme Court. Perhaps the Court, when it decided
Bush v. Gore, did more harm to its own reputation than
two television series can. Only time will tell.
Posted April 3, 2002
|


Discuss how understanding the principles of neuroplasticity benefits people
neuroplasticity is an umbrella term referring to the ability of your brain to reorganise itself, both physically and functionally, through experiences to your environment, behaviour, thinking, and emotions.
previously, scientists thought that the brain's structure was hard-wired and immutable. New research has taught us that the brain is actually plastic, it is able to change based on experiences. Even in old age, the adult brain retains neuroplasticity and is able to change its structure and functon.
Neuroplasticity enables people to recover from stroke, injury, and helps create new ways of functioning and behaviour for those experiencing autisem, ADD, learning disabilities, depression and addiction and reverse obsessive-compulsive patterns.
Discuss how might engage with the principals of neuroplaticity for your own benefit
Neuroplasticity study shows:
- New thoughts and skills carve out new pathways
- Repetition and strengthen these pathways, forming new habits.
- Old pathways get used less and weaken.
- With repeated anddirect attention towards a desired change, we all have a ability to rewire our brains.
- Neural pathways connect relatively distant areas of the brain or nervous system, each pathway is associated with a particular action or behaviour
- Everytime we think, feel or do something, we strengthen this pathway. Habits are well travelled pathways.- our brain finds these things easy to do
For my own benefit, I would like to
- First step is to realise that I need to train my brain with more new things, to be able to adapt to changes, for example to read a new book, try a different restaurant, take a different route while walking etc
- shake up my daily routine, do something different
- have a clear set of goals and progress
- take risks and explore
- focus solely on what I am doing
What are some of the ways to increase your Neuroplaticity
- Get enough Magnesium
- Sleep
- Eat fish
- Mitigate stress
- Exercise
- seek novelty - try something new
- Learn a language
Link to a resource that you found particularly useful or engaging
Growth Mindset means we can develop our ability through hard work and effort. Having a growth mindset (the belief that you are in control of your own ability, and can learn and improve) is the key to success.
In people with a growth mindset, the brain is most active when they are being told what they could do to improve.
People with a growth mindset view a setback as an opportunity to learn. They tend to try harder in an effort to overcome the problem.
Professor Dweck explained growth mindset as, “In a growth mindset students understand that their talents and abilities can be developed through effort, good teaching and persistence. They don’t necessarily think everyone’s the same or anyone can be Einstein, but they believe everyone can get smarter if they work at it.”
Along with encouraging a growth mindset, helping students understand that their brain can actually grow and become stronger can also boost their confidence and improve learning. Research shows us that the brain is plastic: with focus and practice, the brain can change, it can grow new cells, and we can strengthen neurologic pathways. This is called neuroplasticity, or the ability of the brain to restructure itself based on repetitive practices.
Mindsets are not just important for learning new skills. They can affect the way that we think about everything.
For example, a growth mindset can help you recover from illness because you believe that you can do something about the illness. They can help you achieve in sport, at work and can also help you grow and develop in relationships.
Cultivating a growth mindset could be the single most important thing you ever do to help you achieve success.
In this exploration, did anything surprise you?
Growth mindset is a very new concept for me and I am so glad that it is part of sprint 4 core study and I am keen to learn more about it. The contents about Neuroplasticity and growth mindset are very interesting and what I have read and explored online today truely opened up my mind, I want to make changes and train myself to be a growth mindset person, to be more successfully in my life.
There will be a lot of practice needed, but in my daily life I can start with
Learn how to praise my chirdren
To praise a child in a right way, saying "Great job on that test! You must be so smart!” may sound good and harmless, but it can actually have a negative affect and create a fixed mindset, Instead of telling a child he must have done well because he is smart, try saying something like, “Great job on that test! You must have worked really hard!” Focusing on the work it took to achieve a goal helps create a healthy attitude and belief that he can improve and succeed with continued effort. This is the idea behind a growth mindset.
Encouraging myself to try new different things
Don't be afraid of breaking my existing routines
There is no failure, it is just NOT YET, as long as I am keep working on it, a small step will become a big step.
How will you integrate growth mindset into your learning journey?
There are three key things that we can do to develop a growth mindset:
We need to recognise that a growth mindset is not just good, but is also supported by science. In other words, we need to be committed to developing a growth mindset.
We can learn and teach others about how to develop and improve their abilities through adopting a growth mindset. This will help us to take control of our life, which is hugely empowering. Research shows that people who feel in control tend to perform better. It’s a virtuous cycle.
Listen out for our fixed mindset voice. When we hear that little critical voice in our head telling us that we can’t do something, reply with a growth mindset approach and tell it that we can learn.
How am I going to integrate growth mindset into my learning?
I think I can start with:
I need to be committed to developing a growth mindset
Listen out for my fixed mindset voice. When I hear that little critical voice in my head telling me that I can’t do something, reply with a growth mindset approach and tell it that I can learn.
There is no failure, it is just NOT YET, if I can’t work things out, I just need to keep working on it, a little progress will lead to final success.
Understanding I will learn more from hard work and effort as we are build up our ability through hard work.
Link to resource that you found particularly useful or engaging


[gecode-users] Naive CCP ask in Gecode
Guido Tack
tack at ps.uni-sb.de
Mon Mar 10 13:20:12 CET 2008
Mauricio Toro wrote:
> I am trying to do a naive CCP ask in Gecode and
> I am getting "Bus error".
>
> The idea is doing this process inside a thread
> ask (a = 1) do tell( b = 1)
>
> I am doing it like this:
>
> while ( not root[i].assigned() ){ };
> if (root[i].val() == 1)
> rel(this, root[j], IRT_EQ, 1);
>
> Any clues why I am getting "Bus error",
Most probably because Gecode is quite simply not thread safe! You
cannot use the same space in two threads, concurrently performing
operations on it (like propagation or querying of values). Gecode was
very consciously designed like this. The only way multiple threads
can safely access Gecode is if they operate on different spaces that
have no shared data.
Cheers,
Guido
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_The_Deep_End_of_the_Ocean_ (PG-13) ** (out of ****)
Michelle Pfeiffer turns in an impassioned performance of often gut-wrenching intensity in a film that deserves far less. It's not that the basic story is without promise.  It's a setup ripe with psychological and emotional conflict, and apparently Jacquelyn Mitchard did it justice in her novel of the same name, which was not only a bestseller but none other than a selection in Oprah Winfrey's book club.
However, director Ulu Grosbard and scripter Stephen Schiff follow pedestrian TV-movie-like paces, from the abrupt and overly tidy ending to the rebelliously-bitter-yet-guilt-ridden older brother, well-played by Emmy-winning _General_Hospital_ star Jonathan Jackson. In fact, all the characters are well-played by the actors. It's just that the material is about as shallow as its deeply metaphoric title (the meaning of which, for the record, is never explained in the film).
Michael Dequina
<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown
CompuServe Hollywood Hotline: http://www.HollywoodHotline.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post published: true title: "50 years of 007" permalink: /2013/03/50-years-of-007/ date: "2013-03-22 11:36:00" ---
The best tribute I've seen yet to the 50 year legacy of James Bond.


blah blah blah
Continue Reading
blah test blah. Some stuff about LA MODE
testing more lines of text
family contacts or something blah test blah
fiveEleven of these blah test blah show many lines


- Player - Class in csc212lab11
-
The Player class is used to represent baseball players.
- Player(String, String) - Constructor for class csc212lab11.Player
-
The method used to construct a Player object and initialize its
variables.
- Players - Class in csc212lab11
-
The Players class is used to represent a set of baseball players.
- Players() - Constructor for class csc212lab11.Players
-
The method used to construct a Players object and initialize its
ArrayLists.
- printIdsByName(String) - Method in class csc212lab11.Players
-
The method used to return the id of and information on each of the
players with a specified last name.


--- title: 'More datasets for teaching data science: The expanded dslabs package' author: 'Amy Gill' date: '2019-07-19' slug: more-datasets-for-teaching-data-science-the-expanded-dslabs-package ---
We have expanded the dslabs package, which we previously introduced as a package containing realistic, interesting and approachable datasets that can be used in introductory data science courses.
This release adds 7 new datasets on climate change, astronomy, life expectancy, and breast cancer diagnosis. They are used in improved problem sets and new projects within the HarvardX Data Science Professional Certificate Program, which teaches beginning R programming, data visualization, data wrangling, statistics, and machine learning for students with no prior coding background.
You can install the dslabs package from CRAN:
install.packages("dslabs")
If you already have the package installed, you can add the new datasets by updating the package:
update.packages("dslabs")
You can load the package into your workspace normally:
library(dslabs)
Let’s preview these new datasets! To code along, use the following libraries and options:
# install packages if necessary
if(!require("tidyverse")) install.packages("tidyverse")
if(!require("ggrepel")) install.packages("ggrepel")
if(!require("matrixStats")) install.packages("matrixStats")
# load libraries
library(tidyverse)
library(ggrepel)
library(matrixStats)
# set colorblind-friendly color palette
colorblind_palette <- c("black", "#E69F00", "#56B4E9", "#009E73",
"#CC79A7", "#F0E442", "#0072B2", "#D55E00")
Three datasets related to climate change are used to teach data visualization and data wrangling. These data produce clear plots that demonstrate an increase in temperature, greenhouse gas levels, and carbon emissions from 800,000 years ago to modern times. Students can create their own impactful visualizations with real atmospheric and ice core measurements.
temp_carbon
The temp_carbon
dataset includes annual global temperature anomaly measurements in degrees Celsius relative to the 20th century mean temperature from 1880-2018. The temperature anomalies over land and over ocean are reported also. In addition, it includes annual carbon emissions (in millions of metric tons) from 1751-2014. Temperature anomalies are from NOAA and carbon emissions are from Boden et al., 2017 via CDIAC.
data(temp_carbon)
# line plot of annual global, land and ocean temperature anomalies since 1880
temp_carbon %>%
select(Year = year, Global = temp_anomaly, Land = land_anomaly, Ocean = ocean_anomaly) %>%
gather(Region, Temp_anomaly, Global:Ocean) %>%
ggplot(aes(Year, Temp_anomaly, col = Region)) +
geom_line(size = 1) +
geom_hline(aes(yintercept = 0), col = colorblind_palette[8], lty = 2) +
geom_label(aes(x = 2005, y = -.08), col = colorblind_palette[8],
label = "20th century mean", size = 4) +
ylab("Temperature anomaly (degrees C)") +
xlim(c(1880, 2018)) +
scale_color_manual(values = colorblind_palette) +
ggtitle("Temperature anomaly relative to 20th century mean, 1880-2018")
greenhouse_gases
The greenhouse_gases
data frame contains carbon dioxide (\(\mbox{CO}_2\), ppm), methane (\(\mbox{CO}_2\), ppb) and nitrous oxide (\(\mbox{N}_2\mbox{O}\), ppb) concentrations every 20 years from 0-2000 CE. The data are a subset of ice core measurements from MacFarling Meure et al., 2006 via NOAA. There is a clear increase in all 3 gases starting around the time of the Industrial Revolution.
data(greenhouse_gases)
# line plots of atmospheric concentrations of the three major greenhouse gases since 0 CE
greenhouse_gases %>%
ggplot(aes(year, concentration)) +
geom_line() +
facet_grid(gas ~ ., scales = "free") +
xlab("Year") +
ylab("Concentration (CH4/N2O ppb, CO2 ppm)") +
ggtitle("Atmospheric greenhouse gas concentration by year, 0-2000 CE")
Compare this pattern with manmade carbon emissions since 1751 from temp_carbon
, which have risen in a similar way:
# line plot of anthropogenic carbon emissions over 250+ years
temp_carbon %>%
ggplot(aes(year, carbon_emissions)) +
geom_line() +
xlab("Year") +
ylab("Carbon emissions (metric tons)") +
ggtitle("Annual global carbon emissions, 1751-2014")
historic_co2
A common argument against the existence of anthropogenic climate change is that the Earth naturally undergoes cycles of warming and cooling governed by natural changes beyond human control. \(\mbox{CO}_2\) levels from ice cores and modern atmospheric measurements at the Mauna Loa observatory demonstrate that the speed and magnitude of natural variations in greenhouse gases pale in comparison to the rapid changes in modern industrial times. While the planet has been hotter and had higher \(\mbox{CO}_2\) levels in the distant past (data not shown), the current unprecedented rate of change leaves little time for planetary systems to adapt.
data(historic_co2)
# line plot of atmospheric CO2 concentration over 800K years, colored by data source
historic_co2 %>%
ggplot(aes(year, co2, col = source)) +
geom_line() +
ylab("CO2 (ppm)") +
scale_color_manual(values = colorblind_palette[7:8]) +
ggtitle("Atmospheric CO2 concentration, -800,000 BCE to today")
stars
In astronomy, stars are classified by several key features, including temperature, spectral class (color) and luminosity (brightness). A common plot for demonstrating the different groups of stars and their propreties is the Hertzsprung-Russell diagram, or H-R diagram. The stars
data frame compiles information for making an H-R diagram with about approximately 100 named stars, including their temperature, spectral class and magnitude (which is inversely proportional to luminosity).
The H-R diagram has the hottest, brightest stars in the upper left and coldest, dimmest stars in the lower right. Main sequence stars are along the main diagonal, while giants are in the upper right and dwarfs are in the lower left. Several aspects of data visualization can be practiced with these data.
data(stars)
# H-R diagram color-coded by spectral class
stars %>%
mutate(type = factor(type, levels = c("O", "B", "DB", "A", "DA", "DF", "F", "G", "K", "M")),
star = ifelse(star %in% c("Sun", "Polaris", "Betelgeuse", "Deneb",
"Regulus", "*SiriusB", "Alnitak", "*ProximaCentauri"),
as.character(star), NA)) %>%
ggplot(aes(log10(temp), magnitude, col = type)) +
geom_point() +
geom_label_repel(aes(label = star)) +
scale_x_reverse() +
scale_y_reverse() +
xlab("Temperature (log10 degrees K)") +
ylab("Magnitude") +
labs(color = "Spectral class") +
ggtitle("H-R diagram of selected stars")
## Warning: Removed 88 rows containing missing values (geom_label_repel).
death_prob
Obtained from the US Social Security Administration, the 2015 period life table lists the probability of death within one year at every age and for both sexes. These values are commonly used to calculate life insurance premiums. They can be used for exercises on probability and random variables. For example, the premiums can be calculated with a similar approach to that used for interest rates in this case study on The Big Short in Rafael Irizarry’s Introduction to Data Science textbook.
brexit_polls
brexit_polls
contains vote percentages and spreads from the six months prior to the Brexit EU membership referendum in 2016 compiled from Wikipedia. These can be used to practice a variety of inference and modeling concepts, including confidence intervals, p-values, hierarchical models and forecasting.
data(brexit_polls)
# plot of Brexit referendum polling spread between "Remain" and "Leave" over time
brexit_polls %>%
ggplot(aes(enddate, spread, color = poll_type)) +
geom_hline(aes(yintercept = -.038, color = "Actual spread")) +
geom_smooth(method = "loess", span = 0.4) +
geom_point() +
scale_color_manual(values = colorblind_palette[1:3]) +
xlab("Poll end date (2016)") +
ylab("Spread (Proportion Remain - Proportion Leave)") +
labs(color = "Poll type") +
ggtitle("Spread of Brexit referendum online and telephone polls")
brca
This is the Breast Cancer Wisconsin (Diagnostic) Dataset, a classic machine learning dataset that allows classification of breast lesion biopsies as malignant or benign based on cell nucleus characteristics extracted from digitized images of fine needle aspirate cytology slides. The data are appropriate for principal component analysis and a variety of machine learning algorithms. Models can be trained to a predictive accuracy of over 95%.
# scale x values
x_centered <- sweep(brca$x, 2, colMeans(brca$x))
x_scaled <- sweep(x_centered, 2, colSds(brca$x), FUN = "/")
# principal component analysis
pca <- prcomp(x_scaled)
# scatterplot of PC2 versus PC1 with an ellipse to show the cluster regions
data.frame(pca$x[,1:2], type = ifelse(brca$y == "B", "Benign", "Malignant")) %>%
ggplot(aes(PC1, PC2, color = type)) +
geom_point() +
stat_ellipse() +
ggtitle("PCA separates breast biospies into benign and malignant clusters")
We hope that these additional datasets make the dslabs package even more useful for teaching data science through real-world case studies and motivating examples.
Are you an R programming novice but want to learn how to do all of this and more? Check out the Data Science Professional Certificate Program from HarvardX on edX, taught by Rafael Irizarry!


public class TargetSelectionEvent extends Event
| Modifier and Type | Field and Description |
|---|---|
private double |
myClock |
private Entity |
myFirer |
private Scenario |
myScenario |
private Entity |
newTarget |
private EntityWeapon |
newWeapon |
private boolean |
tracing |
| Constructor and Description |
|---|
TargetSelectionEvent(double eventTime,
Scenario scenario,
Entity firer)
Class constructor.
|
| Modifier and Type | Method and Description |
|---|---|
private EntityWeapon |
autoSelect(java.lang.String targetName,
int detectionLevel,
double range)
select the weapon assigned in the database for this target
|
private double |
calculateFiringTime()
Based on the target and weapon, calculate how long it will take to
complete aiming at the target ready to shoot.
|
private boolean |
canDamage(Entity target,
Weapon csdWeapon,
double range,
int phPosture)
confirm that the firer is still actually able to damage the target
|
private boolean |
checkAmmo(EntityWeapon weapon)
Check if the weapon still has ammunition
|
private void |
commandFire()
The fire has an active command fire mission, select target accordingly
|
private void |
delay(double fireTime)
setup a movement delay for the firer if this weapon
requires it to slow or stop to shoot
|
Event |
doEvent()
Main entry point for the class.
|
private double |
evaluateFirer()
Determine if the firer is actually ready to aim yet.
|
private boolean |
evaluateTarget(TargetEntity target,
double range)
check if this is a viable target
|
double |
findTarget()
High level logic for finding a target to engage.
|
private double |
getPriority(Entity target,
int detectionLevel,
double range,
EntityWeapon entityWeapon)
determine the weighted priority for this target
|
private EntityWeapon |
pickWeapon(TargetEntity target,
int detectionLevel,
double range)
based on the target selected, pick an appropriate weapon
|
private void |
scanTargets()
Search through the firer's list of detections and
pick one of then to shoot at
Set the object variables newTarget and newWeapon
|
private void |
stillComandFiring()
check if the firer has completed a command fire mission and if so, clear it
|
private boolean |
testWeapon(EntityWeapon entityWeapon,
double range)
test if this weapon is able to be fired
|
private EntityWeapon |
tryAlternateWeapon(EntityWeapon weapon,
double range)
When a primary weapon is out of ammunition, switch to an alternate one
|
private EntityWeapon |
whichWeapon(TargetEntity target,
int detectionLevel,
double range)
Select a weapon to fire at the specified target
|
private Entity myFirer
private double myClock
private Scenario myScenario
private Entity newTarget
private EntityWeapon newWeapon
private boolean tracing
public TargetSelectionEvent(double eventTime, Scenario scenario, Entity firer)
eventTime
- the simulation time at which this event should occur.
Should be no earlier than the current simulation time.clock
- the current simulation time.firer
- The entity doing the firing.public Event doEvent()
public double findTarget()
private double calculateFiringTime()
private double evaluateFirer()
private void stillComandFiring()
private void commandFire()
private boolean evaluateTarget(TargetEntity target, double range)
target
- range
- private EntityWeapon pickWeapon(TargetEntity target, int detectionLevel, double range)
target
- detectionLevel
- range
- private EntityWeapon whichWeapon(TargetEntity target, int detectionLevel, double range)
target
- private EntityWeapon autoSelect(java.lang.String targetName, int detectionLevel, double range)
targetName
- detectionLevel
- range
- private boolean testWeapon(EntityWeapon entityWeapon, double range)
entityWeapon
- range
- private boolean checkAmmo(EntityWeapon weapon)
weapon
- private EntityWeapon tryAlternateWeapon(EntityWeapon weapon, double range)
weapon
- private void delay(double fireTime)
private void scanTargets()
private double getPriority(Entity target, int detectionLevel, double range, EntityWeapon entityWeapon)
target
- detectionLevel
- range
- csdWeapon
-


{% set title = "Aula | Alessio Laiso" %} {% set description = "Aula is the Learning Experience Platform for higher education. With a unique combination of easy-to-use technology and learning design, Aula makes it easy for educators to create learning experiences that truly engage students." %} {% extends "portfolio/_layout.html" %} {% block content %}
Aula is the Learning Experience Platform for higher education. With a unique combination of easy-to-use technology and learning design, Aula makes it easy for educators to create learning experiences that truly engage students.
Aula Education
Apr 2020–Nov 2021
As universities move towards more flexible delivery, creating a learning experience with an engaged learning community has proven critical to student success. However, educators just don't have the time to do it. As a result, universities are limited in their ability to retain and graduate more students every year. Students who feel connected to their learning community are more likely to persist, yet most digital learning platforms create isolated learning experiences. Aula combines easy-to-use technology with evidence-informed learning design to create community-first learning experiences that are as much about meaningful connection as they are about academic challenge.
When I first joined Aula, in April 2020, I was the only designer in the company. It was my responsibility to bring a focus on the user to the core of our company culture, and to grow and manage the Design team. During my time with Aula, I have led and coordinated all core design initiatives, including establishing user research processes, creating a consistent and accessible design system, improving cross-functional collaboration, hiring and managing the members of the Design team and setting up team rituals and practices.
I oversaw the design of all of Aula’s digital products, such as the web app and the mobile apps, managing the designers on the team and getting involved in hands-on work myself. A key initiative that I led was creating and evolving a design system that brought some much needed consistency to Aula’s apps. The design system also helped us be much more efficient when designing and implementing UI, and drastically improved the accessibility of our products, getting us closer to our vision where every learner is empowered to reach their full potential.
Great importance was given to user research at all times. We ran frequent user interviews and used other research techniques such as surveys to make sure we were addressing real need and pain points in a way that was simple yet suitable for complex real-world educational scenarios. We also used these learnings to redefine the information architecture of our products, to match our users’ mental model and to make sure that we could grow the apps in a scalable way.
These principles were key to deep redesigns of both our mobile and desktop offerings. Users appreciated how much easier to use the new interfaces are. We also caught the change to fix the accessibility issues and make the visual language more consistent.
As Aula as a company evolves and as we learn more about our users and the market, so we evolve our product. We are always testing and exploring new ideas to make teaching and learning more engaging.
In the Summer of 2021, I proposed and led the design of a new brand identity for Aula, centered around learning and community. This brought a friendly, authentic, simple yet distinctive personality to Aula’s visuals.
There were many reasons for this change. The old brand identity was playful and helped us grow as a company. However, as Aula evolved as a company, we outgrew it. A colour palette of mostly purple and teal limited the combinations that would work visually while also being accessible. Accessibility and inclusion are extremely important at Aula, so this had to be fixed. We appreciated the geometric simplicity of our old logo, but it had poor legibility. The “A” in the old app icon was not easily recognisable. Finally, over time, different illustration styles and uses of the logo made consistency an issue.
Starting from the concepts of connection, collaboration, and engaging learning experiences, we worked to evolve Aula’s look and feel. The new identity is still friendly and simple but at the same time it feels recognisable and mature.
The new logo evolves the geometric theme of the old one while also being distinctive and legible. The new illustrations, created in collaboration with the talented illustrator Francesco Fidani, show a diverse learning community exchanging ideas and knowledge in many different ways. The result is a visual branding that fully expresses Aula’s ways of being direct but respectful, optimistic but practical, conversational but not too informal, and friendly but authentic.
In addition to working on the visuals for the rebranding, I directly coordinated the launch of the new identity, to make sure that the teams responsible for Aula’s many touchpoints (e.g., apps, social media, help centre) were updated at the same time. I also provided detailed branding guidelines, which was something that Aula severely needed.
My work has contributed to an increase of over 300% in the number of educators that are very satisfied with Aula. When asked what they like about Aula, users consistently mention how the interface is attractive, modern and easy to use. This improvement was instrumental in getting Aula to sign their first full-scale enterprise customers and is having a measurable impact in allowing the company to expand to additional markets.


@JohanMabille @QuantStack
Jupyter: Humans in the Read-Eval-Print-Loop
What is Jupyter?
A consistent set of tools (protocols, standards, libraries) meant to improve the workflow of engineers, scientists
The Jupyter ecosystem: main apps
The Jupyter ecosystem: core libraries (Python)
The Jupyter ecosystem: the Xeus stack (C++)
Clients and kernels communicate through 5 channels
{
"header" : {
"msg_id": "...",
"msg_type": "...",
...
},
"parent_header": {},
"metadata": {},
"content": {},
"buffers": [],
}
[
b'u-u-i-d', # zmq identy(ies)
b'<IDS|MSG>', # delimiter
b'baddad42', # HMAC signature
b'{header}', # serialized header dict
b'{parent_header}', # serialized parent header dict
b'{metadata}', # serialized metadata dict
b'{content}', # serialized content dict
b'\xf0\x9f\x90\xb1' # extra raw data buffer(s)
...
]


Todo's
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DIS::IPacketProcessor Class Reference
#include <IPacketProcessor.h>
List of all members.
|
Public Member Functions |
| virtual void | Process (const Pdu &p)=0 |
Detailed Description
it is the responsibility of the processor to use the packet because it will be deleted after this call.
Member Function Documentation
| virtual void DIS::IPacketProcessor::Process |
( |
const Pdu & |
p |
) |
[pure virtual] |
The documentation for this class was generated from the following file:


Ouachita Trail 50 mile
Little Rock AR/USA
Race Winners
(24 runnings)
21
Apr
2007
22 Apr
2006
16 Apr
2005
17 Apr
2004
12 Apr
2003
20 Apr
2002
21 Apr
2001
01
Apr
2000
1998-1999
26
Apr
1997
04
May
1996
29 Apr
1995
30
Apr
1994
01
May
1993
02
May
1992
04
May
1991
May
1990
13
May
1989
7:03:06
Janet Fugate (TX/USA)
Note: 1989 was the inaugural year. The 1990 men's race was a 3-way tie
with David Horton and Donald Smith.
26 Apr
2014
7:47:13
Brian Hurley (AR/USA)
9:38:41
Gia-Dawn
Madole (OK/USA)
20 Apr
2013
8:11:50
Ted Herget (AR/USA)
9:40:28
Mary-Ellen
Kelly (TN/USA)
21
Apr
2012
7:06:04
Nick
Lewis-3
8:49:48
Emily Ansick
(AL/USA)
16 Apr
2011
7:26:12
Nick Lewis-2
9:01:37
Sarah Woerner
(TN/USA)
17 Apr
2010
7:56:41
Tom Brennan (OK/USA)
9:57:52
Natalie Sims
(TN/USA)
18 Apr
2009
8:11:43
Nick Lewis (TN/USA)
11:43:26
Hillary Looney
(AR/USA)
19 Apr
2008
8:50:24
Po Dog Vogler (AR/USA)
11:54:34
Kimmy Riley
(AR/USA)
8:29:03
Paul
Schoenlaub-3
10:12:47
Suzy Phillips-2
8:04:48
Paul
Schoenlaub-2
11:09:36
Suzy Phillips
(AR/USA)
8:17:24
Raymond Brintle (TX/USA)
8:57:35
Kimberly Morton
(AR/USA)
8:08:21
Paul Schoenlaub (MO/USA)
8:58:13
Magdalena Font
(AR/USA)
8:20:53
Andy Holak (MN/USA)
10:59:47
Angie Ransom-2
8:10:53
Stan
Ferguson-2
10:03:00
Roberta Orr
(AR/USA)
7:49:51
Stan Ferguson (AR/USA)
9:19:18
Barbara Bellows
(AR/USA)
7:04:46
Dink Taylor (AL/USA)
9:47:00
Angie Ransom
(AR/USA)
---
not
held
---
not
held
8:03:02
Raymond Bailey-3
9:32:43
Chrissy Ferguson
(AR/USA)
7:39:03
Gary Grilliot
10:26:34
Ivy Franklin
6:48:17
Raymond Bailey-2
8:19:40
Kim Goosen
7:41:33
Raymond Bailey (AR/USA)
10:43:10
Pauline Knapp
7:16:43
Raul
Flores-2
8:27:08
Nancy Cunningham
(AR/USA)
7:21:51
Donald Aycock (LA/USA)
7:59:52
Debbie Peebles
(TX/USA)
7:50:21
Raul Flores (KS/USA)
8:56:48
Jennifer Miksch
(TX/USA)
7:10:47t
Eddie
Mulkey-2
9:59:28
Lou Peyton
(AR/USA)
Eddie Mulkey
8:59:19
Most Wins (1989-date)
Men
3
Raymond Bailey
(AR/USA)
1994-95,1997
Paul Schoenlaub
(MO/USA)
2004,2006-07
Nick Lewis (TN/USA)
2009,2011-12
Women
2
Angie Ransom
(AR/USA)
2000,2003
Suzy Phillips
(AR/USA)
2006-07
Prize Money
No prize money has been awarded in this race.
Race Time Bias
Insufficient data to calculate race time bias.
Competitive Level
No race has had more than four ranked runners.
Finishers
Year Men Women Total
2014 35 11 46
2013 46 7 53
2012 41 6 47
2011 52 9 61
2010 44 11 55
2009 31 5 36
2008 25 2 27
2007 25 5 30
2006 35 7 42
2005 24 8 32
2004 22 7 29
2003 18 6 24
2002 13 3 16
2001 14 7 21
2000 15 4 19
1997 4 5 9
1996 20 3 23
1995 26 8 34
1994 13 5 18
1993 49 5 54
1992 60 10 70
1991 34 6 40
1990 53 7 60
1989 38 3 41
Sums 737 150 887
M/F 4.91
Last Updated on
27 Nov 2014
Contributors: Juraj Gasparovic (15),
Klaas Loonstra (5)


CVE-2022-40151: XStream is vulnerable to a Denial of Service attack due to stack overflow.
All versions until and including version 1.4.19 are affected, if using the version out of the box.
The processed stream at unmarshalling time contains type information to recreate the formerly written objects. XStream creates therefore new instances based on these type information. An attacker can manipulate the processed input stream and replace or inject objects, that result in a stack overflow due to deeply nested objects causing a denial of service.
Create a simple HashSet and use XStream to marshal it to XML. Replace the XML with generated with the following code snippet and unmarshal it with XStream:
String xml = new String(); int i = 0; for( ; i < 10000; ++i) { xml += ""; } for( ; i > 0; --i) { xml += " "; }
XStream xstream = new XStream(); xstream.fromXML(xml);
As soon as the XML gets unmarshalled, the recursion is too deep and the executing thread is aborted with a stack overflow error.
The vulnerability may allow a remote attacker to terminate the application with a stack overflow error resulting in a denial of service only by manipulating the processed input stream.
The only solution is to catch the StackOverflowError in the client code calling XStream.
Henry Lin of the Google OSS-Fuzz team found and reported the issue to XStream and provided the required information to reproduce it.


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--- layout: post title: 'Converting an OLAP database to TokuDB, part 3: how well does it perform?' date: type: post published: false status: draft categories: - MySQL tags: [] meta: _edit_last: '2' author: login: shlomi email<EMAIL_ADDRESS>display_name: shlomi first_name: Shlomi last_name: Noach ---
To recap, we have converted a 4TB (2TB COMPRESSED) InnoDB based MySQL DWH to TokuDB, resulting in 200GB worth of files.
This is an OLAP setup. We do not have many concurrent reads and next to no concurrent writes. I was interested in reading Vadim's TokuDB vs InnoDB in timeseries INSERT benchmark. Our use case is completely different, though. Our requirements were:
So, basically, see if this new slave replicates as well (lag-wise) as other InnoDB slaves, and see if queries issued against this slave run as fast as on other InnoDB slaves.
The comparison is not entirely fair. Our InnoDB setup has extensive MONTHly partitioning scheme, which did not run well (crashed, actually) under TokuDB, where we chose to use YEARly partitioning. Nevertheless this is what we ended up with, and this is what we have for comparison.
Almost immediately we noticed an unreasonably disproportionate slave lag on our TokuDB slave (red line in chart below) as compared to other slaves.
Quick investigation led to the fact that, coincidentally, a manual heavy-duty operation was just taking place, which updated some year's worth of data retroactively. OK, but why so slow on TokuDB? Another quick investigation led to our apples vs. oranges problem: the query was relying on the MONTHly partitioning to do reasonable pruning: a WHERE condition on a date column did the right partition pruning; but where on InnoDB that would filter 1 month's worth of data, on TokuDB it would filter 1 year.
Wasn't it suggested that TokuDB has online table operations? I decided to give it a shot, and add a proper index on our date column (I actually created a compound index, but irrelevant).
It took 13 minutes to add an index on a 1GB TokuDB table (approx. 20GB InnoDB uncompressed equivalent):
Not surprisingly adding the index eliminated the problem altogether.
We are still experiencing longer delay on replicating large LOAD DATA INFILE operations. The TokuDB slave performance is indeterministic: it sometimes lags far more than InnoDB slaves, sometimes comparable or even lower.
Here, again, we have our apples vs. oranges: the TokuDB partitions are so much larger! I'm not sure how it would look like on MONTHly partitioning with TokuDB, although you may recall on part 1 of this series that LOAD DATA INFILE was 30% slower on TokuDB even for comparable table size.
Query performance time
Configuration
optimize: doubles space; alter PK: doubles space... -- solution: ENGIEN=TokuDB ROW_FORMAT=TokuDB_SMALL (the latter is the important one!)
ALTER TABLE DROP PRIMAR KEY, ADD PRIMARY KEY - takes two weeks -- does not use the bulk loader. But CREATE TABLE LIKE '...', INSERT INTO new SELECT * FROM old, RENAME... does use the bulk loader and works just fine.
123


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COMPARATIVE SYSTEMATIC ANATOMY OF THE
PARASITE CASSYTHA (LAURACEAE)
by Bobby Glenn Beaman
Dissertation submitted to the Faculty of the Graduate School of the University of Maryland in partial fulfillment of the requirements for the degree of Doctor of Philosophy 19721 a oa
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.