FOR A FREE CAR INSURANCE RATE QUOTE THAT COULD SAVE YOU SUBSTANTIAL MONEY WWW.GEICO.COM OR CALL 1-888-395-6349 24 HOURS A DAY
If you have any comments about our web page, you can either write us at the address shown above or email us at<EMAIL_ADDRESS>However, due to the limited number of personnel in our corporate office, we are unable to provide a direct response.
Copyright (C) 1978-2015 Berkshire Hathaway Inc.


|
gevent
1.0.0
cross-platform event driven framework (made in cpp)
|
key information for a connection More...
#include <EventHandler.h>
Public Member Functions | |
| conn_key_t (int f, unsigned short l, unsigned short r) | |
| bool | operator< (struct conn_key_t const &rhs) const |
Public Attributes | |
| int | fd |
| unsigned short | lport |
| unsigned short | rport |
key information for a connection
| int conn_key_t::fd |
connection file descriptor
| unsigned short conn_key_t::lport |
local port
| unsigned short conn_key_t::rport |
remote port


Instructions
Proxy Nexus provides two methods of making Netrunner Proxies, paper proxies and printed cards at makeplayingcards.com.
Paper Proxies
These are cards printed on standard printer paper, either at home or at a self-serve print shop like Staples, and then sleeved with another card behind it. This is a cheap and accessible way of making proxies but requires a bit of work.
- Download the PDF from Proxy Nexus. Make sure to choose the paper size that matches the printer you're using.
- Print the PDFs in color or black & white on standard paper.
- Use a guillotine cutter or scissors to cut the prints. Note the cut lines on the printouts.
- Sleeve the cut cards along with another card behind it for support. Cheap magic card commons/uncommon work well, and so do unused Netrunner cards.
Printed card from makeplayingcards.com
These are actual cards produced by makeplayingcards.com (MPC), printed with a scaled or fitted scan of a Netrunner card. Proxy Nexus produces image files ready to submit to print at MPC. This option is less work, provides nice results, but is more expensive than paper proxies.
The scanned Netrunner images used by Proxy Nexus all have varying pixel dimensions and some weren't completely upright when scanned. Also, Netrunner cards are narrower than the closest card size MPC offers. To work around this, Proxy Nexus offers two different versions of card image files to choose from, both with a black border to function as a bleed.
Scaled
The shortest dimension of the original scans is resized and scaled by 1.5% past MPC's cut line. This should help minimize most black lines along the edge of cards but may result a small loss of the original graphic. Choose this option to help avoid any black lines/borders along the edge of the cards.
Fit
Fits the scans within MPC's cut line. May result in black lines along the edges of the card. Choose this option to avoid any loss of the scanned image.
For anyone interested in the technical details, the images were produced with this Python script: borderGenerator.py
The zip file Proxy Nexus provides contains:
- Corp images
- Runner images
- Card backs
The included card backs have been designed and created by Spencer Wharton. (/u/SpencerDub)
Note: Files that end in a letter are the back side art image for the file with the similar name. Eg. 04041-alt2a.jpg is the back side art image for 04041-alt2.jpg. For step 12 below, be sure to select "Different images" for the card backs, in order to include these images if desired.
The files are separated by Corp and Runner to make it easy to design the MPC order, especially for large orders.
How to make an MPC order
- Download the zip file from Proxy Nexus.
- Extract the zip file.
- Visit Custom Game Cards (63 x 88mm) page to get started with a new project.
- Choose the card stock, (I prefer 300gsm, but 330gsm works well too).
- Chose the deck size.
Note: Large orders, e.g. a single System Core 2019 Set, contains 255 cards. The MPC option that fits this size is 396, which costs $69.05 USD. It would be easier to setup and possibly cheaper to split the order into a Corp and Runner deck. (Hence why the files are separated in the zip file). The Corp deck would contain 144 cards, and the Runner deck would contain 111 cards. This would cost $27.90 USD + $24.95 USD = $52.85 USD.
- Click the red "start you design" button.
- Enter the exact card count for the deck and click the blue "Different images" button for the card fronts.
- Click the red "Upload images" button near the top right and select all the images in either the corp or runner folder from the extracted zip file. If printing all the cards in one deck, repeat this step with the other folder from the zip file.
- Once all the images have finished uploading, click the "Help me autofill images!" button. Or simply drag and drop all the images to the cards on the left. If the deck is a mix of corp and runner cards, take note of the Side (corp or runner) of each card number.
- Once all the images have been placed, click the "Next Step" button at the top right of the screen.
- Click "Next Step" again, since I assume you're not adding text to the front of the cards.
- This step depends on whether the deck is entire one side, or a mix of the two. If the deck is a mix of the two, select "Different images" for the card backs. Otherwise, select "Same Image".
- If you picked "Different images", then upload the two card back images (or any other alt-art card back images) and manually place them on them respective cards like you did in step 8. If you picked "Same Image", then upload the corp or runner card back image, and place it in the single card preview on the left.
Note: This step is much easier when the deck is all one side.
- Click the "Next Step" button at the top right of the screen.
- Click "Next Step" again, since I assume you're not adding text to the back of the cards.
- You will now see a preview of the deck, the fronts and backs of each card. This is a good chance to review the placement of back and front images if that was done manually.
- Click the confirmation checkbox at the bottom of the page, and then click "Add to Cart".
- Here you can either checkout, save the design or add another deck to your order.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 2
This typically Scottish dish of oat-coated herrings with a wintry potato salad makes a delicious light lunch for two.
For the potato salad, boil the new potatoes in a large saucepan of salted water until tender (about 12 minutes). Add the kale and cucumber to the pan three minutes before the potatoes are ready and boil until all of the ingredients are tender. Drain well and set aside.
For the dressing, whisk together the vinegar, rapeseed oil and lemon juice in a jug until well combined. Season, to taste, with salt and freshly ground black pepper. Set aside.
For the herring, sprinkle the oatmeal onto a plate and season with salt and freshly ground black pepper.
Brush both sides of each herring fillet with a little of the mustard, then roll in the seasoned oatmeal until completely covered.
Heat the butter and the oil in a large, heavy-based frying pan over a medium heat. Add the coated herring fillets to the pan, skin-side up (do this in batches if necessary). Gently press down on each of the herring fillets using a fish slice so that they stay flat. Fry for 1-2 minutes, or until the oats are golden-brown, then turn over and continue to fry on the other side for a further 1-2 minutes, or until the herrings are cooked through and the oats are golden-brown.
Remove the herring fillets from the pan using a slotted spoon and set aside to drain on kitchen paper.
Place the boiled potatoes, kale and cucumber into a serving bowl, stir in the spring onions, and drizzle over the dressing. Mix well and season, to taste, with salt and freshly ground black pepper.
To serve, divide the herring equally between two serving plates. Spoon the potato salad alongside and serve with a wedge of lemon.


Oyster transaction analytics
Stats with user query and time filters
Oyster provisioning analytics
Stats with provisioning database
Stats with elasticsearch logs


Mohnish Landge - Interactive Portfolio
using
OGL
with shaders
Behance Project
Mail
GitHub
Main Website →
PORTFOLIO '21


s6-frontend
Software
skarnet.org
The s6-frontend-alias-chpst program
s6-frontend-alias-chpst implements
runit's
chpst command
by rewriting itself into a command line made of idiomatic
chainloading s6 commands.
Interface
s6-frontend-alias-chpst [ -v ] [ -P ] [ -0 ] [ -1 ] [ -2 ] [ -u user ] [ -U user ] [ -b argv0 ] [ -e dir ] [ -/ root ] [ -n niceness ] [ -l|-L lock ] [ -m bytes ] [ -d bytes ] [ -o n ] [ -p n ] [ -f bytes ] [ -c bytes ] prog...
When s6-frontend has been configured with the
--enable-aliases-runit option, the
s6-frontend-alias-chpst program is built, and at installation
time, a chpst symbolic link is created, pointing to it.
s6-frontend-alias-chpst implements most of the functionality of
chpst, as closely as
possible. It does so by rewriting itself into a command line that may
invoke one or more of the following programs:
Notes
- When the -v option is given,
s6-frontend-alias-chpst prints an informational
message to stderr, showing the entire command line it is going to execute into.
- s6-frontend-alias-chpst is a convenience program, written
so that runit users can keep using chpst while learning the
corresponding idiomatic s6 commands. However, it is not an "official"
interface. chpst acts as a big toolbox, whereas s6 prefers the
"one function = one tool" philosophy. It is recommended that runit users
who are new to s6 use the -v option to see how their chpst
invocation translates into s6 parlance.
- Some chpst -u invocations do not translate perfectly
because s6 uses a slightly different model of acquiring user and group
identity via
s6-envuidgid.
This should not prevent chpst from working.


--- layout: post title: 'Meta tags are your friend part 1: Tracking roles in Sungard Luminis with Google Analytics' date: 2010-04-08 15:57:23.000000000 +01:00 type: post published: true status: publish categories: - Uncategorised tags: - google analytics - luminis - sungardhe - university of nottingham meta: _topsy_cache_timestamp: '1299702167' _syntaxhighlighter_encoded: '1' _topsy_long_url: /2010/04/08/meta-tags-are-your-friend-part-tracking-roles-in-sungard-luminis-with-google-analytics/ topsy_short_url: http://bit.ly/cu7UKe tmac_last_id: '618797398224080897' author: David Simpson excerpt_separator: ---
At work, we use Sungard HE's Luminis portal (v3.3) — It's been good to us over the years, but there's a lack of usage information regarding users' roles and their interactions with Luminis.
In the past, we've added Google Analytics to Luminis to track the interactions, but never managed to fully segment user roles. Until now.
Out of the box, Luminis has roles such as staff, student, faculty, ProspectiveStudent etc. Users can have one or more of these roles and other custom roles too. If we could capture all the roles and pass them onto Google Analytics, then we'd be able to fully segment the user types and gain a better understanding of how each segment uses Luminis.
This post aims to show how this is possible in just 3 steps:
We've seriously hacked at nested-tables.xsl over the years, so our version of the file is likely to be different to most. The main aim here is to find the html > head section and add a custom meta tag:
<!-- Dump all roles into a single meta tag as a comma separated value -->
<meta name="luminis.role">
<xsl:attribute name="content">
<xsl:for-each select="//layout/cp:cpInfo/cp:cpProperty[@name='pdsRole']/child::*">
<xsl:value-of select="concat(., ',')" />
</xsl:for-each>
</xsl:attribute>
</meta>
Our implementation of Google Analytics is a little complex, but this example I've based on the standard Google Analytics example code.
Using Javascript (infused with jQuery in our case), we can read the custom meta tag and set a custom variable.
<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
window.onload = function(){
try
{
var pageTracker = _gat._getTracker("UA-XXXXX-XX");
if (typeof $('meta[name=luminis.role]').attr("content") == 'string')
{
pageTracker._setCustomVar(
1, // This custom var is set to slot #1 of the 5 available in GA
"luminis.role", // The name acts as a kind of category for the user activity
','+$('meta[name=luminis.role]').attr("content"), // The value of the custom variable (starting with a comma)
2 // Sets the scope to session-level
);
}
pageTracker._trackPageview();
}
catch (err) {}
}
</script>
According to the documentation, it's important to set the custom variable before tracking the page view.
Sign in to Google Analytics and view your normal website reports.
Click on Advanced segments | Create a new advanced segment
Create a new segment where...
Custom variable (Value 1) contains ,staff,
AND
Custom variable (Value 1) contains ,faculty,
Note the surrounding commas - we're using them as role delimiters.
Give the segment a name e.g. "Luminis roles: staff AND faculty""
Your custom segment is now available from the advanced segment drop-down:
Custom variables seem to take longer to filter through to Google Analytics than normal data such as page views. So come back in a couple of days and try out your new advanced segment.
Update (12 April 2010):
Shortly after I published this post, the Google Analytics blog posted their webinar on Google Analytics custom variables.
Also, if you're interested in creating advanced segments from custom variables, I've posted ayoutube video that steps through the process.


"""Provides helper methods for loading and parsing KITTI data."""
from collections import namedtuple
import numpy as np
__author__ = "Lee Clement"
__email__ =<EMAIL_ADDRESS># Per dataformat.txt
OxtsPacket = namedtuple('OxtsPacket',
'lat, lon, alt, ' +
'roll, pitch, yaw, ' +
'vn, ve, vf, vl, vu, ' +
'ax, ay, az, af, al, au, ' +
'wx, wy, wz, wf, wl, wu, ' +
'pos_accuracy, vel_accuracy, ' +
'navstat, numsats, ' +
'posmode, velmode, orimode')
# Bundle into an easy-to-access structure
OxtsData = namedtuple('OxtsData', 'packet, T_w_imu')
[docs]def rotx(t):
"""
Rotation about the x-axis
Parameters
----------
t : float
Theta angle
Returns
-------
matrix : np.array [3,3]
Rotation matrix
"""
c = np.cos(t)
s = np.sin(t)
return np.array([[1, 0, 0],
[0, c, -s],
[0, s, c]])
[docs]def roty(t):
"""
Rotation about the y-axis
Parameters
----------
t : float
Theta angle
Returns
-------
matrix : np.array [3,3]
Rotation matrix
"""
c = np.cos(t)
s = np.sin(t)
return np.array([[c, 0, s],
[0, 1, 0],
[-s, 0, c]])
[docs]def rotz(t):
"""
Rotation about the z-axis
Parameters
----------
t : float
Theta angle
Returns
-------
matrix : np.array [3,3]
Rotation matrix
"""
c = np.cos(t)
s = np.sin(t)
return np.array([[c, -s, 0],
[s, c, 0],
[0, 0, 1]])
[docs]def read_calib_file(filepath):
"""
Read in a calibration file and parse into a dictionary
Parameters
----------
filepath : str
File path to read from
Returns
-------
calib : dict
Dictionary with calibration values
"""
data = {}
with open(filepath, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
# The only non-float values in these files are dates, which
# we don't care about anyway
try:
data[key] = np.array([float(x) for x in value.split()])
except ValueError:
pass
return data
[docs]def pose_from_oxts_packet(raw_data, scale):
"""
Helper method to compute a SE(3) pose matrix from an OXTS packet
Parameters
----------
raw_data : dict
Oxts data to read from
scale : float
Oxts scale
Returns
-------
R : np.array [3,3]
Rotation matrix
t : np.array [3]
Translation vector
"""
packet = OxtsPacket(*raw_data)
er = 6378137. # earth radius (approx.) in meters
# Use a Mercator projection to get the translation vector
tx = scale * packet.lon * np.pi * er / 180.
ty = scale * er * \
np.log(np.tan((90. + packet.lat) * np.pi / 360.))
tz = packet.alt
t = np.array([tx, ty, tz])
# Use the Euler angles to get the rotation matrix
Rx = rotx(packet.roll)
Ry = roty(packet.pitch)
Rz = rotz(packet.yaw)
R = Rz.dot(Ry.dot(Rx))
# Combine the translation and rotation into a homogeneous transform
return R, t
[docs]def load_oxts_packets_and_poses(oxts_files):
"""
Generator to read OXTS ground truth data.
Poses are given in an East-North-Up coordinate system
whose origin is the first GPS position.
Parameters
----------
oxts_files : list of str
List of oxts files to read from
Returns
-------
oxts : list of dict
List of oxts ground-truth data
"""
# Scale for Mercator projection (from first lat value)
scale = None
# Origin of the global coordinate system (first GPS position)
origin = None
oxts = []
for filename in oxts_files:
with open(filename, 'r') as f:
for line in f.readlines():
line = line.split()
# Last five entries are flags and counts
line[:-5] = [float(x) for x in line[:-5]]
line[-5:] = [int(float(x)) for x in line[-5:]]
packet = OxtsPacket(*line)
if scale is None:
scale = np.cos(packet.lat * np.pi / 180.)
R, t = pose_from_oxts_packet(packet, scale)
if origin is None:
origin = t
T_w_imu = transform_from_rot_trans(R, t - origin)
oxts.append(OxtsData(packet, T_w_imu))
return oxts


> The classloaders of WLS seem not to play nice with velocity when
> deploying this way.
>
> If you haven't already tried, do the following, it makes it all work
> for us:
>
> 1) In the webwork.properties file (which should be in your
> WEB-INF/classes directory) put a line like this:
>
> webwork.velocity.configfile = my-velocity.properties
>
> 2) create a "my-velocity.properties" file under WEB-INF/classes and
> put into it the contents of the velocity.properties file that is in
> webwork's velocity-dep.jar
>
> 3) in your new "my-velocity.properties" file, find the section titled
> "T E M P L A T E L O A D E R S", and change this section to look like
> this:
>
> ===========================================
> resource.loader = class
>
> file.resource.loader.description = Velocity File Resource Loader
> file.resource.loader.class =
> org.apache.velocity.runtime.resource.loader.FileResourceLoader
> file.resource.loader.path = .
> file.resource.loader.cache = false
> file.resource.loader.modificationCheckInterval = 2
>
> class.resource.loader.class =
> org.apache.velocity.runtime.resource.loader.ClasspathResourceLoader
> class.resource.loader.cache = true
> ===========================================
>
> … which straightens out the class resource loading problems (for us
> at least).
>
> hope this helps,
> james


Flemming Godskesen is a solution architect and a secret Powershell junkie. Find out more about Flemming Godskesen.
Iana Godskesen is a front-end developer with a deep love for Javascript. Find out more about Iana Godskesen.


Resources for teams building services
with government data.
Find out more
{% extends "layout.html" %} {% block page_title %} Resources for service teams {% endblock %} {% block content %}
Resources for teams building services
with government data.
Find out more


Integrated MTech
The iM.Tech course curriculum consists of Core Courses and Electives spanning ten semesters.
While the same set of core courses have to be completed by all the students every semester, the students are free
to choose their electives. While core courses help in creating a strong foundation in the Information Technology
field, electives allow students to learn and specialize in their own areas of interest.
The core courses,
categorized into Computer Science, Electronics and Communication, Basic Sciences and Miscellaneous entirely make
up the first five semesters. Electives are also categorized into Computer Science, Electronics & Communication, Data Science,
Networks & Communication.
along with other core courses, are taken up from sixth semester onwards.
Though the
electives of students may vary as per their
interests, the number of electives per semester remains the
same. The current semester requires students to take the
same three core courses and any three electives.
Computer Science
Core Courses
- Programming - 1 ( Python and C )
- Data Structures and Algorithms
- Discrete Mathematics
- Programming - 2 ( C++ and Java )
- Data Structures and Algorithms
- Design and Analysis of Algorithms
- Operating Systems
- Database Systems
- Software Engineering
- Programming Languages
Specialization Electives
- Introduction to Automata Theory and Computability
- Distributed Computing
- Artificial Intelligence
- Approximation Algorithms
- Advanced Algorithms
- Convex Optimization
- Foundations of Cryptography
- Computing on Private Data
- Computer Graphics
- Advanced Computer Graphics
- Computational Geometry
- Digital Image Processing
- Advanced Digital Image Processing
- Multi Agent Systems
- Automated Formal Verification
- Object Oriented Analysis and Design(Software Engineering)
- Design Patterns and Enterprise System Development(Software Engineering)
Data Science
Specialization Electives
- Data Modeling
- Foundations for Big Data Algorithms
- Machine Learning - I
- Machine Learning - II
- Data Analytics
- Machine Perception
- Advanced Machine Perception
- Network Sciences for the Web
- Multi Agent Systems
- Data Visualization
- Geographical Information Systems
- Automated Speech Recognition
Electronics and Communication
Core Courses
- Basic Electronics, Digital Logic and Circuits
- Signals & Systems
- Computer Networks
- Computer Architecture and Organisation
- Digital Signal Processing
- Digital Communication
Specialization Electives
- Real Time Operating Systems
- Modern Operating Systems
- Inter Device Communication
- Internet of Things
- Machine Perception
- Advanced Machine Perception
- Analysis and Design of VLSI Sub Systems
- Hardware Software Co-Synthesis
- Testing and Design for Testability
Networks and Communication
Specialization Electives
- Wireless Area Networks
- Internet Of Things
- Network Security
- Techno-Economics of Networks
- IMS
- Software Defined Networkings
- Mathematical Analysis of Networks
Basic Science
Core Courses
- Physics - I
- Physics - II
- Intro to Bio-Sciences / Chemistry
- Mathematics - I ( Calculus )
- Mathematics - II ( Differential Equations )
- Mathematics - III ( Statistics, Probability
and Complex Analysis ) - Mathematics - IV ( Linear Algebra )
Miscellaneous
Core Courses
- English I
- Macro Economics / English II
- The City: The Cradle of Information
Exchange and Innovation - The History of Ideas
- Introduction to Profession
- Technical Communication


Website heading
Home
About
Project
Project title: My Project Year: 2019
This is the page for my project. A picture and some text will go here.
This is the page for my project. A picture and some text will go here.
This is the page for my project. A picture and some text will go here.


This office hours will be held at {{location}} by {{instructor}}.
{{#if slotsAssigned}}
Office hours have already been assigned.
{{else}}
The registration deadline {{#if registrationDeadlinePassed}}was{{else}}is{{/if}} {{registrationDeadlineString}}.
The waitlist length is currently {{waitListLength}}.
{{/if}}
{{#if onWaitlist}}
Leave waitlist
{{else}}
Join waitlist
{{/if}}
{{#if isAdmin}}
Delete this office hours. |
{{/if}}
{{#if slotsAssigned}}
{{> slotTable}}
{{/if}}


As quickly as possible, press any key when you see a square in the middle of your screen.
You pushed a key too soon. Don't push any keys until you're supposed to. This round was skipped.
As quickly as possible, press the left arrow key when a square appears on the left side of your screen. Press the right arrow key when a square appears on the right side of your screen.
You pushed a key too soon. Don't push any keys until you're supposed to. This round was skipped.
As quickly as possible, press the key corresponding to the letter that appears on your screen.
You pushed a key too soon. Don't push any keys until you're supposed to. This round was skipped.
| Time taken | Correct button pressed |
|---|
Average reaction time:
Average Accuracy:
Raw output:


Do you see missing or incorrect information?
Do you want us to add a new feature or improve the existing one?
Tell us about changes in the world around you that aren't reflected in peeljobs.
We’ll confirm your report with other users, data providers, or existing jobs information. Due to the volume of reports, we may not be able to respond to you directly. If approved, your change may take some time to appear on the site.
You can report many types of issues with peeljobs, such as problems with job listings and also any queries regarding peeljobs.
Do you want us to add a new feature or improve the existing one?
Tell us about changes in the world around you that aren't reflected in peeljobs.
We’ll confirm your report with other users, data providers, or existing jobs information. Due to the volume of reports, we may not be able to respond to you directly. If approved, your change may take some time to appear on the site.
You can report many types of issues with peeljobs, such as problems with job listings and also any queries regarding peeljobs.
A problem statement is a clear concise description of the issue(s) that need(s) to be addressed by a problem solving team. It is used to center and focus the team at the beginning, keep the team on track during the effort, and is used to validate that the effort delivered an outcome that solves the problem statement.


* Always create new addresses while creating your paperwallets, 1st address to load is always the same for testing purposes. *


| Modifier and Type | Field and Description |
|---|---|
private Member |
Membership.member |
(package private) Member |
IntMemberTest.testMember
Deprecated.
|
| Modifier and Type | Field and Description |
|---|---|
private java.util.List<Member> |
SiteManager.members |
| Modifier and Type | Method and Description |
|---|---|
private Member |
IntMemberTest.generateFakeMember(int j,
int k)
Deprecated.
|
Member |
Post.getAuthor()
Returns the member(Author) of the class that implements Post
|
protected Member |
Membership.getMember()
Returns the member from Membership
|
Member |
Group.getMember(java.lang.String emailAddress)
Returns a specific Member of this Group
|
Member |
SiteManager.getMember(java.lang.String emailAddress)
Returns the Member corresponding to this emailAddress if they exist.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.List<Member> |
Group.getActiveMembers(int n)
Returns a list of the n most active Members of this group, sorted by acivity
(questions plus answers), most active first.
|
java.util.List<Member> |
SiteManager.getActiveMembers(int n)
Returns a list of n Members that have the most questions and answers combined
(activeness) across all groups they are a member of, sorted descending on
activeness.
|
java.util.List<Member> |
Group.getMembers()
Returns a list of Members that have joined this Group
|
java.util.List<Member> |
SiteManager.getMembers()
Returns a list of all Members, sorted by last name, then first name.
|
java.util.List<Member> |
SiteManager.getMembers(java.lang.String text)
Returns a list of all Members where text (partially) matches any of
firstName, lastName, screenName, emailAddress across all Members, , sorted by
last name, then first name.
|
| Modifier and Type | Method and Description |
|---|---|
int |
MemberNameComparator.compare(Member m1,
Member m2) |
int |
MemberOverallActiveComparator.compare(Member m1,
Member m2) |
| Constructor and Description |
|---|
Membership(Group group,
Member member,
java.time.LocalDateTime dateJoined)
Creates a Membership with the default values of group, member, and dateJoined
|


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This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, you might prefer this faster paced tutorial. Be sure to install TensorFlow before starting either tutorial.
When one learns how to program, there’s a tradition that the first thing you do is print “Hello World.” Just like programming has Hello World, machine learning has MNIST.
MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these:
It also includes labels for each image, telling us which digit it is. For example, the labels for the above images are 5, 0, 4, and 1.
In this tutorial, we’re going to train a model to look at images and predict what digits they are. Our goal isn’t to train a really elaborate model that achieves state-of-the-art performance – although we’ll give you code to do that later! – but rather to dip a toe into using TensorFlow. As such, we’re going to start with a very simple model, called a Softmax Regression.
The actual code for this tutorial is very short, and all the interesting stuff happens in just three lines. However, it is very important to understand the ideas behind it: both how TensorFlow works and the core machine learning concepts. Because of this, we are going to very carefully work through the code.
This tutorial is an explanation, line by line, of what is happening in the mnist_softmax.R code.
You can use this tutorial in a few different ways, including:
Copy and paste each code snippet, line by line, into an R environment as you read through the explanations of each line.
Run the entire mnist_softmax.R R file either before or after reading through the explanations, and use this tutorial to understand the lines of code that aren’t clear to you.
What we will accomplish in this tutorial:
Learn about the MNIST data and softmax regressions
Create a function that is a model for recognizing digits, based on looking at every pixel in the image
Use TensorFlow to train the model to recognize digits by having it “look” at thousands of examples (and run our first TensorFlow session to do so)
Check the model’s accuracy with our test data
The MNIST data is hosted on Yann LeCun’s website. If you are copying and pasting in the code from this tutorial, start here with these three lines of code which will download and read in the data automatically:
library(tensorflow)
datasets <- tf$contrib$learn$datasets
mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
The MNIST data is split into three parts: 55,000 data points of training data (mnist$train
), 10,000 points of test data (mnist$test
), and 5,000 points of validation data (mnist$validation
). This split is very important: It’s essential in machine learning that we have separate data which we don’t learn from so that we can make sure that what we’ve learned actually generalizes!
As mentioned earlier, every MNIST data point has two parts: an image of a handwritten digit and a corresponding label. We’ll call the images “x” and the labels “y”. Both the training set and test set contain images and their corresponding labels; for example the training images are mnist$train$images
and the training labels are mnist$train$labels
.
Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers:
We can flatten this array into a vector of 28x28 = 784 numbers. It doesn’t matter how we flatten the array, as long as we’re consistent between images. From this perspective, the MNIST images are just a bunch of points in a 784-dimensional vector space, with a very rich structure (warning: computationally intensive visualizations).
Flattening the data throws away information about the 2D structure of the image. Isn’t that bad? Well, the best computer vision methods do exploit this structure, and we will in later tutorials. But the simple method we will be using here, a softmax regression (defined below), won’t.
The result is that mnist$train$images
is a tensor (an n-dimensional array) with shape (55000L, 784L)
. The first dimension is an index into the list of images and the second dimension is the index for each pixel in each image. Each entry in the tensor is a pixel intensity between 0 and 1, for a particular pixel in a particular image.
Each image in MNIST has a corresponding label, a number between 0 and 9 representing the digit drawn in the image.
For the purposes of this tutorial, we’re going to want our labels as “one-hot vectors”. A one-hot vector is a vector which is 0 in most dimensions, and 1 in a single dimension. In this case, the \(n\)th digit will be represented as a vector which is 1 in the \(n\)th dimension. For example, 3 would be \([0,0,0,1,0,0,0,0,0,0]\). Consequently, mnist$train$labels
is a (55000L, 10L)
array of floats.
We’re now ready to actually make our model!
We know that every image in MNIST is of a handwritten digit between zero and nine. So there are only ten possible things that a given image can be. We want to be able to look at an image and give the probabilities for it being each digit. For example, our model might look at a picture of a nine and be 80% sure it’s a nine, but give a 5% chance to it being an eight (because of the top loop) and a bit of probability to all the others because it isn’t 100% sure.
This is a classic case where a softmax regression is a natural, simple model. If you want to assign probabilities to an object being one of several different things, softmax is the thing to do, because softmax gives us a list of values between 0 and 1 that add up to 1. Even later on, when we train more sophisticated models, the final step will be a layer of softmax.
A softmax regression has two steps: first we add up the evidence of our input being in certain classes, and then we convert that evidence into probabilities.
To tally up the evidence that a given image is in a particular class, we do a weighted sum of the pixel intensities. The weight is negative if that pixel having a high intensity is evidence against the image being in that class, and positive if it is evidence in favor.
The following diagram shows the weights one model learned for each of these classes. Red represents negative weights, while blue represents positive weights.
We also add some extra evidence called a bias. Basically, we want to be able to say that some things are more likely independent of the input. The result is that the evidence for a class \(i\) given an input \(x\) is:
\[\text{evidence}_i = \sum_j W_{i,~ j} x_j + b_i\]
where \(W_i\) is the weights and \(b_i\) is the bias for class \(i\), and \(j\) is an index for summing over the pixels in our input image \(x\). We then convert the evidence tallies into our predicted probabilities \(y\) using the “softmax” function:
\[y = \text{softmax}(\text{evidence})\]
Here softmax is serving as an “activation” or “link” function, shaping the output of our linear function into the form we want – in this case, a probability distribution over 10 cases. You can think of it as converting tallies of evidence into probabilities of our input being in each class. It’s defined as:
\[\text{softmax}(x) = \text{normalize}(\exp(x))\]
If you expand that equation out, you get:
\[\text{softmax}(x)_i = \frac{\exp(x_i)}{\sum_j \exp(x_j)}\]
But it’s often more helpful to think of softmax the first way: exponentiating its inputs and then normalizing them. The exponentiation means that one more unit of evidence increases the weight given to any hypothesis multiplicatively. And conversely, having one less unit of evidence means that a hypothesis gets a fraction of its earlier weight. No hypothesis ever has zero or negative weight. Softmax then normalizes these weights, so that they add up to one, forming a valid probability distribution. (To get more intuition about the softmax function, check out the section on it in Michael Nielsen’s book, complete with an interactive visualization.)
You can picture our softmax regression as looking something like the following, although with a lot more \(x\)s. For each output, we compute a weighted sum of the \(x\)s, add a bias, and then apply softmax.
If we write that out as equations, we get:
We can “vectorize” this procedure, turning it into a matrix multiplication and vector addition. This is helpful for computational efficiency. (It’s also a useful way to think.)
More compactly, we can just write:
\[y = \text{softmax}(Wx + b)\]
Now let’s turn that into something that TensorFlow can use.
To do efficient numerical computing in R we typically call base R functions that do expensive operations such as matrix multiplication outside R, using highly efficient code implemented in another language. Unfortunately, there can still be a lot of overhead from switching back to R for every operation. This overhead is especially bad if you want to run computations on GPUs or in a distributed manner, where there can be a high cost to transferring data.
TensorFlow also does its heavy lifting outside R, but it takes things a step further to avoid this overhead. Instead of running a single expensive operation independently from R, TensorFlow lets us describe a graph of interacting operations that run entirely outside R (Approaches like this can be seen in a few machine learning libraries.)
We describe these interacting operations by manipulating symbolic variables. Let’s create one (to access the TensorFlow API we reference the tf
object that is exported by the tensorflow
package):
library(tensorflow)
x <- tf$placeholder(tf$float32, shape(NULL, 784L))
x
isn’t a specific value. It’s a placeholder
, a value that we’ll input when we ask TensorFlow to run a computation. We want to be able to input any number of MNIST images, each flattened into a 784-dimensional vector. We represent this as a 2-D tensor of floating-point numbers, with shape (NULL, 784L)
. (Here NULL
means that a dimension can be of any length.)
We also need the weights and biases for our model. We could imagine treating these like additional inputs, but TensorFlow has an even better way to handle it: Variable
. A Variable
is a modifiable tensor that lives in TensorFlow’s graph of interacting operations. It can be used and even modified by the computation. For machine learning applications, one generally has the model parameters be Variable
s.
W <- tf$Variable(tf$zeros(shape(784L, 10L)))
b <- tf$Variable(tf$zeros(shape(10L)))
We create these Variable
s by giving tf$Variable
the initial value of the Variable
: in this case, we initialize both W
and b
as tensors full of zeros. Since we are going to learn W
and b
, it doesn’t matter very much what they initially are.
Notice that W
has shape (784, 10)
because we want to multiply the 784-dimensional image vectors by it to produce 10-dimensional vectors of evidence for the difference classes. b
has shape (10)
so we can add it to the output.
We can now implement our model. It only takes one line to define it!
y <- tf$nn$softmax(tf$matmul(x, W) + b)
First, we multiply x
by W
with the expression tf$matmul(x, W)
. This is flipped from when we multiplied them in our equation, where we had \(Wx\), as a small trick to deal with x
being a 2D tensor with multiple inputs. We then add b
, and finally apply tf$nn$softmax
.
That’s it. It only took us one line to define our model, after a couple short lines of setup. That isn’t because TensorFlow is designed to make a softmax regression particularly easy: it’s just a very flexible way to describe many kinds of numerical computations, from machine learning models to physics simulations. And once defined, our model can be run on different devices: your computer’s CPU, GPUs, and even phones!
In order to train our model, we need to define what it means for the model to be good. Well, actually, in machine learning we typically define what it means for a model to be bad. We call this the cost, or the loss, and it represents how far off our model is from our desired outcome. We try to minimize that error, and the smaller the error margin, the better our model is.
One very common, very nice function to determine the loss of a model is called “cross-entropy.” Cross-entropy arises from thinking about information compressing codes in information theory but it winds up being an important idea in lots of areas, from gambling to machine learning. It’s defined as:
\[H_{y'}(y) = -\sum_i y'_i \log(y_i)\]
Where \(y\) is our predicted probability distribution, and \(y'\) is the true distribution (the one-hot vector with the digit labels). In some rough sense, the cross-entropy is measuring how inefficient our predictions are for describing the truth. Going into more detail about cross-entropy is beyond the scope of this tutorial, but it’s well worth understanding.
To implement cross-entropy we need to first add a new placeholder to input the correct answers:
y_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
Then we can implement the cross-entropy function, \(-\sum y'\log(y)\):
cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * tf$log(y), reduction_indices=1L))
First, tf$log
computes the logarithm of each element of y
. Next, we multiply each element of y_
with the corresponding element of tf$log(y)
. Then tf$reduce_sum
adds the elements in the second dimension of y, due to the reduction_indices=1L
parameter. Finally, tf$reduce_mean
computes the mean over all the examples in the batch.
Note that tensor indices (like the one used for reduction_indices
) are 0-based within the TensorFlow API (rather than 1-based as is typical with R vectors).
Now that we know what we want our model to do, it’s very easy to have TensorFlow train it to do so. Because TensorFlow knows the entire graph of your computations, it can automatically use the backpropagation algorithm to efficiently determine how your variables affect the loss you ask it to minimize. Then it can apply your choice of optimization algorithm to modify the variables and reduce the loss.
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train_step <- optimizer$minimize(cross_entropy)
In this case, we ask TensorFlow to minimize cross_entropy
using the gradient descent algorithm with a learning rate of 0.5. Gradient descent is a simple procedure, where TensorFlow simply shifts each variable a little bit in the direction that reduces the cost. But TensorFlow also provides [many other optimization algorithms] (https://www.tensorflow.org/api_docs/python/train.html#optimizers): using one is as simple as tweaking one line.
What TensorFlow actually does here, behind the scenes, is to add new operations to your graph which implement backpropagation and gradient descent.
Now we have our model set up to train. One last thing before we launch it, we have to create an operation to initialize the variables we created. Note that this defines the operation but does not run it yet:
init <- tf$global_variables_initializer()
We can now launch the model in a Session
, and now we run the operation that initializes the variables:
sess <- tf$Session()
sess$run(init)
Let’s train – we’ll run the training step 1000 times!
for (i in 1:1000) {
batches <- mnist$train$next_batch(100L)
batch_xs <- batches[[1]]
batch_ys <- batches[[2]]
sess$run(train_step,
feed_dict = dict(x = batch_xs, y_ = batch_ys))
}
Each step of the loop, we get a “batch” of one hundred random data points from our training set. We run train_step
feeding in the batches data to replace the placeholder
s.
Using small batches of random data is called stochastic training – in this case, stochastic gradient descent. Ideally, we’d like to use all our data for every step of training because that would give us a better sense of what we should be doing, but that’s expensive. So, instead, we use a different subset every time. Doing this is cheap and has much of the same benefit.
How well does our model do?
Well, first let’s figure out where we predicted the correct label. tf$argmax
is an extremely useful function which gives you the index of the highest entry in a tensor along some axis. For example, tf$argmax(y, 1L)
is the label our model thinks is most likely for each input, while tf$argmax(y_, 1L)
is the correct label. We can use tf$equal
to check if our prediction matches the truth.
correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L))
Note that since tensors in the TensorFlow API are 0-based we pass 1L
to specify that tf$argmax
should operate on the second dimension of the tensor.
correct_prediction
is a vector of booleans. To determine what fraction are correct, we cast it to floating point numbers and then take the mean. For example, (TRUE, FALSE, TRUE, TRUE)
would become (1.0, 0.0, 1.0, 1.0)
which would become 0.75
.
accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
Finally, we ask for our accuracy on our test data.
sess$run(accuracy, feed_dict=dict(x = mnist$test$images, y_ = mnist$test$labels))
## [1] 0.9175
This should be about 92%.
Is that good? Well, not really. In fact, it’s pretty bad. This is because we’re using a very simple model. With some small changes, we can get to 97%. The best models can get to over 99.7% accuracy! (For more information, have a look at this list of results.)
What matters is that we learned from this model. Still, if you’re feeling a bit down about these results, check out the next tutorial where we do a lot better, and learn how to build more sophisticated models using TensorFlow!


CrowdCamp is a recurring HCOMP workshop which brings together human computation and crowdsourcing researchers and industry practitioners for collective work to develop new ideas into concrete outputs: in-depth thoughts on hard problems, paper or coded prototypes, experiment design and data mining. We will discuss future visions and make tangible headway on those visions, as well as seeding collaboration. The outputs from discussion, brainstorming, and building will persist after the workshop for attendees and the community to view.
Visit the Workshop's Website for additional details about it.
The workshop will be held at the primary conference venue, the AT&T Executive Conference Center.
Past CrowdCamps have shown a strong track-record of bringing together diverse teams of researchers, engineers, and students who work passionately together on well-scoped project ideas. Many participants have continued working on their projects beyond CrowdCamp, generating impactful publications and fruitful collaborations. A few examples include:


 The fix:
vim /usr/share/applications/vmware-workstation.desktop # Add this line to the end StartupWMClass=Vmware
This community post gave a hint as to how to fix this problem in general: https://askubuntu.com/questions/1378642/application-in-the-dock-cant-be-added-to-favourites


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--- layout: page title: About permalink: /about/ main_image_path: /uploads/port-meadow-mist.JPG main_image_caption: >- Jenny BLYTH 'Morning Mist, Port Meadow' photograph in pigmented inks on archival paper frontpage: false homepage_description_markdown: Short home page description frontpagetitle: About archive: false images: - image_path: image_title: image_description: _options: main_image_path: width: 1200 height: 1200 resize_style: contain mime_type: image/jpeg _comments: title: Page title permalink: Be careful editing this main_image_path: Image used to represent your page archive: Not used yet! frontpage: Show this page on the homepage homepage_description_markdown: Text used on homepage if shown frontpagetitle: Alternative title for home page use images: Add and edit images here ---
Jenny Blyth has twenty-five years background in contemporary art. She was curator at Saatchi Gallery 1990-2002 and ran a floating gallery for ten years thereafter, originating projects and mounting exhibitions largely in London, Manchester, Wolverhampton and Oxford.
Jenny Blyth Fine Art curated 40 exhibitions at Art Jericho, Oxford from 2010-2017.
Since 2019 Jenny Blyth Fine Art has been based at Carey Blyth Gallery, 8 Woodstock Road, Oxford showing paintings, photography, sculpture by local, national and international artists. The gallery shows solo and mixed exhibitions.
Jenny Blyth is also a photographer exploring the natural world.


from sympy.core.basic import Basic
from sympy.vector.scalar import BaseScalar
from sympy import eye, trigsimp, ImmutableMatrix as Matrix, Symbol
from sympy.core.compatibility import string_types, range
from sympy.core.cache import cacheit
from sympy.vector.orienters import (Orienter, AxisOrienter, BodyOrienter,
SpaceOrienter, QuaternionOrienter)
import sympy.vector
[docs]class CoordSysCartesian(Basic):
"""
Represents a coordinate system in 3-D space.
"""
def __new__(cls, name, location=None, rotation_matrix=None,
parent=None, vector_names=None, variable_names=None):
"""
The orientation/location parameters are necessary if this system
is being defined at a certain orientation or location wrt another.
Parameters
==========
name : str
The name of the new CoordSysCartesian instance.
location : Vector
The position vector of the new system's origin wrt the parent
instance.
rotation_matrix : SymPy ImmutableMatrix
The rotation matrix of the new coordinate system with respect
to the parent. In other words, the output of
new_system.rotation_matrix(parent).
parent : CoordSysCartesian
The coordinate system wrt which the orientation/location
(or both) is being defined.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
"""
name = str(name)
Vector = sympy.vector.Vector
BaseVector = sympy.vector.BaseVector
Point = sympy.vector.Point
if not isinstance(name, string_types):
raise TypeError("name should be a string")
# If orientation information has been provided, store
# the rotation matrix accordingly
if rotation_matrix is None:
parent_orient = Matrix(eye(3))
else:
if not isinstance(rotation_matrix, Matrix):
raise TypeError("rotation_matrix should be an Immutable" +
"Matrix instance")
parent_orient = rotation_matrix
# If location information is not given, adjust the default
# location as Vector.zero
if parent is not None:
if not isinstance(parent, CoordSysCartesian):
raise TypeError("parent should be a " +
"CoordSysCartesian/None")
if location is None:
location = Vector.zero
else:
if not isinstance(location, Vector):
raise TypeError("location should be a Vector")
# Check that location does not contain base
# scalars
for x in location.free_symbols:
if isinstance(x, BaseScalar):
raise ValueError("location should not contain" +
" BaseScalars")
origin = parent.origin.locate_new(name + '.origin',
location)
else:
location = Vector.zero
origin = Point(name + '.origin')
# All systems that are defined as 'roots' are unequal, unless
# they have the same name.
# Systems defined at same orientation/position wrt the same
# 'parent' are equal, irrespective of the name.
# This is true even if the same orientation is provided via
# different methods like Axis/Body/Space/Quaternion.
# However, coincident systems may be seen as unequal if
# positioned/oriented wrt different parents, even though
# they may actually be 'coincident' wrt the root system.
if parent is not None:
obj = super(CoordSysCartesian, cls).__new__(
cls, Symbol(name), location, parent_orient, parent)
else:
obj = super(CoordSysCartesian, cls).__new__(
cls, Symbol(name), location, parent_orient)
obj._name = name
# Initialize the base vectors
if vector_names is None:
vector_names = (name + '.i', name + '.j', name + '.k')
latex_vects = [(r'\mathbf{\hat{i}_{%s}}' % name),
(r'\mathbf{\hat{j}_{%s}}' % name),
(r'\mathbf{\hat{k}_{%s}}' % name)]
pretty_vects = (name + '_i', name + '_j', name + '_k')
else:
_check_strings('vector_names', vector_names)
vector_names = list(vector_names)
latex_vects = [(r'\mathbf{\hat{%s}_{%s}}' % (x, name)) for
x in vector_names]
pretty_vects = [(name + '_' + x) for x in vector_names]
obj._i = BaseVector(vector_names[0], 0, obj,
pretty_vects[0], latex_vects[0])
obj._j = BaseVector(vector_names[1], 1, obj,
pretty_vects[1], latex_vects[1])
obj._k = BaseVector(vector_names[2], 2, obj,
pretty_vects[2], latex_vects[2])
# Initialize the base scalars
if variable_names is None:
variable_names = (name + '.x', name + '.y', name + '.z')
latex_scalars = [(r"\mathbf{{x}_{%s}}" % name),
(r"\mathbf{{y}_{%s}}" % name),
(r"\mathbf{{z}_{%s}}" % name)]
pretty_scalars = (name + '_x', name + '_y', name + '_z')
else:
_check_strings('variable_names', vector_names)
variable_names = list(variable_names)
latex_scalars = [(r"\mathbf{{%s}_{%s}}" % (x, name)) for
x in variable_names]
pretty_scalars = [(name + '_' + x) for x in variable_names]
obj._x = BaseScalar(variable_names[0], 0, obj,
pretty_scalars[0], latex_scalars[0])
obj._y = BaseScalar(variable_names[1], 1, obj,
pretty_scalars[1], latex_scalars[1])
obj._z = BaseScalar(variable_names[2], 2, obj,
pretty_scalars[2], latex_scalars[2])
# Assign a Del operator instance
from sympy.vector.deloperator import Del
obj._delop = Del(obj)
# Assign params
obj._parent = parent
if obj._parent is not None:
obj._root = obj._parent._root
else:
obj._root = obj
obj._parent_rotation_matrix = parent_orient
obj._origin = origin
# Return the instance
return obj
def __str__(self, printer=None):
return self._name
__repr__ = __str__
_sympystr = __str__
def __iter__(self):
return iter([self.i, self.j, self.k])
@property
def origin(self):
return self._origin
@property
def delop(self):
return self._delop
@property
def i(self):
return self._i
@property
def j(self):
return self._j
@property
def k(self):
return self._k
@property
def x(self):
return self._x
@property
def y(self):
return self._y
@property
def z(self):
return self._z
def base_vectors(self):
return self._i, self._j, self._k
def base_scalars(self):
return self._x, self._y, self._z
@cacheit
[docs] def rotation_matrix(self, other):
"""
Returns the direction cosine matrix(DCM), also known as the
'rotation matrix' of this coordinate system with respect to
another system.
If v_a is a vector defined in system 'A' (in matrix format)
and v_b is the same vector defined in system 'B', then
v_a = A.rotation_matrix(B) * v_b.
A SymPy Matrix is returned.
Parameters
==========
other : CoordSysCartesian
The system which the DCM is generated to.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import symbols
>>> q1 = symbols('q1')
>>> N = CoordSysCartesian('N')
>>> A = N.orient_new_axis('A', q1, N.i)
>>> N.rotation_matrix(A)
Matrix([
[1, 0, 0],
[0, cos(q1), -sin(q1)],
[0, sin(q1), cos(q1)]])
"""
from sympy.vector.functions import _path
if not isinstance(other, CoordSysCartesian):
raise TypeError(str(other) +
" is not a CoordSysCartesian")
# Handle special cases
if other == self:
return eye(3)
elif other == self._parent:
return self._parent_rotation_matrix
elif other._parent == self:
return other._parent_rotation_matrix.T
# Else, use tree to calculate position
rootindex, path = _path(self, other)
result = eye(3)
i = -1
for i in range(rootindex):
result *= path[i]._parent_rotation_matrix
i += 2
while i < len(path):
result *= path[i]._parent_rotation_matrix.T
i += 1
return result
@cacheit
[docs] def position_wrt(self, other):
"""
Returns the position vector of the origin of this coordinate
system with respect to another Point/CoordSysCartesian.
Parameters
==========
other : Point/CoordSysCartesian
If other is a Point, the position of this system's origin
wrt it is returned. If its an instance of CoordSyRect,
the position wrt its origin is returned.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> N = CoordSysCartesian('N')
>>> N1 = N.locate_new('N1', 10 * N.i)
>>> N.position_wrt(N1)
(-10)*N.i
"""
return self.origin.position_wrt(other)
[docs] def scalar_map(self, other):
"""
Returns a dictionary which expresses the coordinate variables
(base scalars) of this frame in terms of the variables of
otherframe.
Parameters
==========
otherframe : CoordSysCartesian
The other system to map the variables to.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import Symbol
>>> A = CoordSysCartesian('A')
>>> q = Symbol('q')
>>> B = A.orient_new_axis('B', q, A.k)
>>> A.scalar_map(B)
{A.x: -sin(q)*B.y + cos(q)*B.x, A.y: sin(q)*B.x + cos(q)*B.y, A.z: B.z}
"""
relocated_scalars = []
origin_coords = tuple(self.position_wrt(other).to_matrix(other))
for i, x in enumerate(other.base_scalars()):
relocated_scalars.append(x - origin_coords[i])
vars_matrix = (self.rotation_matrix(other) *
Matrix(relocated_scalars))
mapping = {}
for i, x in enumerate(self.base_scalars()):
mapping[x] = trigsimp(vars_matrix[i])
return mapping
[docs] def locate_new(self, name, position, vector_names=None,
variable_names=None):
"""
Returns a CoordSysCartesian with its origin located at the given
position wrt this coordinate system's origin.
Parameters
==========
name : str
The name of the new CoordSysCartesian instance.
position : Vector
The position vector of the new system's origin wrt this
one.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> A = CoordSysCartesian('A')
>>> B = A.locate_new('B', 10 * A.i)
>>> B.origin.position_wrt(A.origin)
10*A.i
"""
return CoordSysCartesian(name, location=position,
vector_names=vector_names,
variable_names=variable_names,
parent=self)
[docs] def orient_new(self, name, orienters, location=None,
vector_names=None, variable_names=None):
"""
Creates a new CoordSysCartesian oriented in the user-specified way
with respect to this system.
Please refer to the documentation of the orienter classes
for more information about the orientation procedure.
Parameters
==========
name : str
The name of the new CoordSysCartesian instance.
orienters : iterable/Orienter
An Orienter or an iterable of Orienters for orienting the
new coordinate system.
If an Orienter is provided, it is applied to get the new
system.
If an iterable is provided, the orienters will be applied
in the order in which they appear in the iterable.
location : Vector(optional)
The location of the new coordinate system's origin wrt this
system's origin. If not specified, the origins are taken to
be coincident.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import symbols
>>> q0, q1, q2, q3 = symbols('q0 q1 q2 q3')
>>> N = CoordSysCartesian('N')
Using an AxisOrienter
>>> from sympy.vector import AxisOrienter
>>> axis_orienter = AxisOrienter(q1, N.i + 2 * N.j)
>>> A = N.orient_new('A', (axis_orienter, ))
Using a BodyOrienter
>>> from sympy.vector import BodyOrienter
>>> body_orienter = BodyOrienter(q1, q2, q3, '123')
>>> B = N.orient_new('B', (body_orienter, ))
Using a SpaceOrienter
>>> from sympy.vector import SpaceOrienter
>>> space_orienter = SpaceOrienter(q1, q2, q3, '312')
>>> C = N.orient_new('C', (space_orienter, ))
Using a QuaternionOrienter
>>> from sympy.vector import QuaternionOrienter
>>> q_orienter = QuaternionOrienter(q0, q1, q2, q3)
>>> D = N.orient_new('D', (q_orienter, ))
"""
if isinstance(orienters, Orienter):
if isinstance(orienters, AxisOrienter):
final_matrix = orienters.rotation_matrix(self)
else:
final_matrix = orienters.rotation_matrix()
# TODO: trigsimp is needed here so that the matrix becomes
# canonical (scalar_map also calls trigsimp; without this, you can
# end up with the same CoordinateSystem that compares differently
# due to a differently formatted matrix). However, this is
# probably not so good for performance.
final_matrix = trigsimp(final_matrix)
else:
final_matrix = Matrix(eye(3))
for orienter in orienters:
if isinstance(orienter, AxisOrienter):
final_matrix *= orienter.rotation_matrix(self)
else:
final_matrix *= orienter.rotation_matrix()
return CoordSysCartesian(name, rotation_matrix=final_matrix,
vector_names=vector_names,
variable_names=variable_names,
location=location,
parent=self)
[docs] def orient_new_axis(self, name, angle, axis, location=None,
vector_names=None, variable_names=None):
"""
Axis rotation is a rotation about an arbitrary axis by
some angle. The angle is supplied as a SymPy expr scalar, and
the axis is supplied as a Vector.
Parameters
==========
name : string
The name of the new coordinate system
angle : Expr
The angle by which the new system is to be rotated
axis : Vector
The axis around which the rotation has to be performed
location : Vector(optional)
The location of the new coordinate system's origin wrt this
system's origin. If not specified, the origins are taken to
be coincident.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import symbols
>>> q1 = symbols('q1')
>>> N = CoordSysCartesian('N')
>>> B = N.orient_new_axis('B', q1, N.i + 2 * N.j)
"""
orienter = AxisOrienter(angle, axis)
return self.orient_new(name, orienter,
location=location,
vector_names=vector_names,
variable_names=variable_names)
[docs] def orient_new_body(self, name, angle1, angle2, angle3,
rotation_order, location=None,
vector_names=None, variable_names=None):
"""
Body orientation takes this coordinate system through three
successive simple rotations.
Body fixed rotations include both Euler Angles and
Tait-Bryan Angles, see http://en.wikipedia.org/wiki/Euler_angles.
Parameters
==========
name : string
The name of the new coordinate system
angle1, angle2, angle3 : Expr
Three successive angles to rotate the coordinate system by
rotation_order : string
String defining the order of axes for rotation
location : Vector(optional)
The location of the new coordinate system's origin wrt this
system's origin. If not specified, the origins are taken to
be coincident.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import symbols
>>> q1, q2, q3 = symbols('q1 q2 q3')
>>> N = CoordSysCartesian('N')
A 'Body' fixed rotation is described by three angles and
three body-fixed rotation axes. To orient a coordinate system D
with respect to N, each sequential rotation is always about
the orthogonal unit vectors fixed to D. For example, a '123'
rotation will specify rotations about N.i, then D.j, then
D.k. (Initially, D.i is same as N.i)
Therefore,
>>> D = N.orient_new_body('D', q1, q2, q3, '123')
is same as
>>> D = N.orient_new_axis('D', q1, N.i)
>>> D = D.orient_new_axis('D', q2, D.j)
>>> D = D.orient_new_axis('D', q3, D.k)
Acceptable rotation orders are of length 3, expressed in XYZ or
123, and cannot have a rotation about about an axis twice in a row.
>>> B = N.orient_new_body('B', q1, q2, q3, '123')
>>> B = N.orient_new_body('B', q1, q2, 0, 'ZXZ')
>>> B = N.orient_new_body('B', 0, 0, 0, 'XYX')
"""
orienter = BodyOrienter(angle1, angle2, angle3, rotation_order)
return self.orient_new(name, orienter,
location=location,
vector_names=vector_names,
variable_names=variable_names)
[docs] def orient_new_space(self, name, angle1, angle2, angle3,
rotation_order, location=None,
vector_names=None, variable_names=None):
"""
Space rotation is similar to Body rotation, but the rotations
are applied in the opposite order.
Parameters
==========
name : string
The name of the new coordinate system
angle1, angle2, angle3 : Expr
Three successive angles to rotate the coordinate system by
rotation_order : string
String defining the order of axes for rotation
location : Vector(optional)
The location of the new coordinate system's origin wrt this
system's origin. If not specified, the origins are taken to
be coincident.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
See Also
========
CoordSysCartesian.orient_new_body : method to orient via Euler
angles
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import symbols
>>> q1, q2, q3 = symbols('q1 q2 q3')
>>> N = CoordSysCartesian('N')
To orient a coordinate system D with respect to N, each
sequential rotation is always about N's orthogonal unit vectors.
For example, a '123' rotation will specify rotations about
N.i, then N.j, then N.k.
Therefore,
>>> D = N.orient_new_space('D', q1, q2, q3, '312')
is same as
>>> B = N.orient_new_axis('B', q1, N.i)
>>> C = B.orient_new_axis('C', q2, N.j)
>>> D = C.orient_new_axis('D', q3, N.k)
"""
orienter = SpaceOrienter(angle1, angle2, angle3, rotation_order)
return self.orient_new(name, orienter,
location=location,
vector_names=vector_names,
variable_names=variable_names)
[docs] def orient_new_quaternion(self, name, q0, q1, q2, q3, location=None,
vector_names=None, variable_names=None):
"""
Quaternion orientation orients the new CoordSysCartesian with
Quaternions, defined as a finite rotation about lambda, a unit
vector, by some amount theta.
This orientation is described by four parameters:
q0 = cos(theta/2)
q1 = lambda_x sin(theta/2)
q2 = lambda_y sin(theta/2)
q3 = lambda_z sin(theta/2)
Quaternion does not take in a rotation order.
Parameters
==========
name : string
The name of the new coordinate system
q0, q1, q2, q3 : Expr
The quaternions to rotate the coordinate system by
location : Vector(optional)
The location of the new coordinate system's origin wrt this
system's origin. If not specified, the origins are taken to
be coincident.
vector_names, variable_names : iterable(optional)
Iterables of 3 strings each, with custom names for base
vectors and base scalars of the new system respectively.
Used for simple str printing.
Examples
========
>>> from sympy.vector import CoordSysCartesian
>>> from sympy import symbols
>>> q0, q1, q2, q3 = symbols('q0 q1 q2 q3')
>>> N = CoordSysCartesian('N')
>>> B = N.orient_new_quaternion('B', q0, q1, q2, q3)
"""
orienter = QuaternionOrienter(q0, q1, q2, q3)
return self.orient_new(name, orienter,
location=location,
vector_names=vector_names,
variable_names=variable_names)
[docs] def __init__(self, name, location=None, rotation_matrix=None,
parent=None, vector_names=None, variable_names=None,
latex_vects=None, pretty_vects=None, latex_scalars=None,
pretty_scalars=None):
# Dummy initializer for setting docstring
pass
__init__.__doc__ = __new__.__doc__
def _check_strings(arg_name, arg):
errorstr = arg_name + " must be an iterable of 3 string-types"
if len(arg) != 3:
raise ValueError(errorstr)
try:
for s in arg:
if not isinstance(s, string_types):
raise TypeError(errorstr)
except:
raise TypeError(errorstr)


Jquery DOM
This is inside Parent Div
This is inside Child_1
This is inside grandchild_1
This is inside Child_2
This is inside grandchild_2
This is inside Parent Div
This is inside Child_1
This is inside grandchild_1
This is inside Child_2
This is inside grandchild_2


:: com :: sun :: star :: form ::
|
| deprecated |
interface XGridFieldDataSupplier |
|
|
|
- Usage Restrictions
- deprecated
- Description
- provides access to the data of a GridControl
You can retrieve the data type information and the data in a row.
This interface allows to retrieve data even for rows which are not current, which is quite usefull,
as normally, you can't affect the current row in a grid control without moving the cursor of the underlying
::com::sun::star::form::component::DataForm.
- See also
- XGrid, ::com::sun::star::form::control::GridControl, ::com::sun::star::form::component::GridControl
|
| Methods' Summary |
| queryFieldDataType |
checks whether or not the content of the grid's columns can be retrieved in the requested format.
|
| queryFieldData |
retrieves the actual column data for the given row
|
| Methods' Details |
queryFieldDataType
| sequence< boolean > |
| queryFieldDataType( |
[in] type |
xType ); |
- Description
- checks whether or not the content of the grid's columns can be retrieved in the requested format.
Not every cell content can be retrieved in every representation. For example, in a text column,
you usually won't be able to retrieve the content as double.
To check if the type you need is supported by the columns, use this method.
- Returns
- A sequence of boolean flags. Each flag corresponds to the respective column, and true indicates
that the column content can be retrieved in the requested format, false denies this.
- See also
- XGridColumnFactory, DataAwareControlModel, XGridFieldDataSupplier::queryFieldData
|
|
queryFieldData
| sequence< any > |
| queryFieldData( |
[in] long |
nRow, |
| [in] type |
xType ); |
- Description
- retrieves the actual column data for the given row
If a column does not support the requested type, NULL is returned at the respective
position.
- See also
- XGridFieldDataSupplier::queryFieldDataType
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


Why should I submit a complaint?
If you have a specific issue with a financial product or service and haven’t been able to resolve it with the company, we may be able to help. When you submit a complaint using our form, we help to get you a response from the company, generally within 15 days.
IEvery complaint also provides us with valuable insight, helping identify inappropriate practices and allowing us to stop them before they become major issues. The results are better outcomes for you, and a better financial marketplace for everyone.
What happens after I submit a complaint?
If the company has signed up to respond to complaints, they will receive your complaint immediately and should respond to you and the CFPB within 15 days. If we need more information to get your complaint to the right place, we’ll reach out to you.
Companies are expected to close all but the most complicated complaints within 60 days. You'll get a chance to review and rate the companies' response.
Learn more about the complaint processWhat kind of response should I expect?
You should expect that the company will review your complaint and explain the what it has done or will do to respond to your issues. Most consumers get an explanation from the company about why the issue happened, but some consumers also get problems fixed or money back
Here are the types of responses that consumers received in 2014:
Learn more about consumers who have used the CFPB’s complaint formHow will you use my information?
We send the information you provide directly to the company so they can identify you and address your issue. We also publish complaints — without any personal information — so that consumers can learn from each others' experience and see how companies respond to complaints, and so that the public can see the kinds of problems people are having with financial products and services. We also share complaint information with other state and federal regulators to inform their work.
Learn more about how we use complaint information


Camogie
• I am an active member of Clonoulty Rossmore Camogie club.
• I have played for Clonoulty since I was 8.
• Last year we got to the Senior County Final but unfortunately we lost.
• I have won many county final medals over the years.
• I won my last county final minor in 2017
Hockey
• I played hockey with Ursuline Secondary School Thurles.
• I played it from 1st Year to 5th Year.
• I won many munster final medals throughout my years playing.
• I captained the Junior B team when i was in 3rd year.
Football
• I played Ladies football for Boherlahen Dualla Ladies football club.
• I played with them for 7 years.
• We won the minor County Final in 2016.
• We got to the Senior County Final in 2017 but unfortunately we lost.


To help get into retailing, Steve had former Gap executive Mickey Drexler join the Apple board as early as 1999, then hired Ron Johnson away from Target in late 2000. After months of experimentation, Apple inaugurated their first Retail Store in May 2001, in the midst of the industry’s post-Internet bubble crisis. Almost every expert agreed they would turn out an expensive mistake...


Sortable table with modeling results from the paper: Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings


Understanding LDAP terminologies
When I started to look into the Fedora’s 389 Directory Server, my first concern was understanding LDAP glossary!
I did a project of Single Sign-On and Password Authentication using LDAP as the database during my internship in the last year of undergraduation, 2+ years back (time literally flies!), but to be honest, I didn’t really understand much at that time.
(It’s better to get started with LDAP using Apache Directory Studio, it becomes easier to visualise things.)
What is DN?
Consider: cn=Alisha, ou=People, dc=example, dc=google, dc=com
This whole string is a DN (Distinguished Name). It is a series of comma-separated key/value pairs used to identify entries uniquely in the directory hierarchy. The DN is actually the entry’s fully qualified name, i.e. it uniquely identifies an entry in the directory. DN should be read from right to left. The rightmost component is the root of the tree and the leftmost component is the leaf or the node you want to reach.
What is RDN (Relative Distinguished Name)?
Consider: uid=alisha, ou=People, dc=example, dc=com
- uid=alisha and ou=People are RDN for dc=example, dc= com
Consider: cn=maths, dc=example, dc=com
- cn=maths is RDN for dc=example, dc=com
Hence, RDN is how you name a ‘singlular’ object, whereas DN is the fully qualified path to an entry.
Generally according to the convention, uid=<name> is the rdn for ou=Accounts, and cn=<name> for ou=Groups, but Active Directory uses CN=<value> for almost all rdns.
What is directory hierarchy?
The data in LDAP is stored in the form of a hierarchical structure (called DIT, i.e, Directory Information Tree). It is just a tree with nodes and leafs, no rocket science!
Let’s understand this visually.
- The top of the tree is called the basedn (dc=example, dc=com).
- Each entry in the tree has one parent entry (object) and zero or more child entries (objects). Each child entry (object) is a sibling of its parent’s other child entries.
- Each entry is an instance of one or more objectClasses.
- Objectclasses contain zero or more attributes.
- Attributes have names and contain data.
So, coming back to our query “CN=Alisha,OU=People,DC=example,DC=google,DC=com”
It means “From the com domain component, find google Domain Component, and from this find example Domain Component. From the example Domain Component, find People Organisational Unit, and from this find an entry whose common name is Alisha.”
What is DC (Domain Component)?
This refers to each component of the domain. For example, “example.google.com” would be written as DC=example,DC=google,DC=com.
What is OU (Organizational Unit)?
This refers to the organizational unit (or sometimes the user group) that the user is part of, for example, a user might be a part of People group or Developer group. If the user is part of more than one group, it can be specified as OU= Developer, OU= Tester. (Many a times, developer might have to do all the testing by himself also, hence both of the groups!)
What is CN (Common Name)?
This refers to the individual object (person’s name; meeting room; recipe name; job title; etc.) for whom/which object you are querying.
For example,
1
2
3
4
uid=alisha, ou=People, dc=example, dc=com
uid: alisha
cn: alisha
...
uid is equivalent to user id, so it is a unique user identifier (generally the “logon” name). cn is the “common name” so what I should be “called”. (There is also displayName for applications to render too).
Hold on tight, more to come! :)


Get a comma separated list of all Docker containers matching the conditions, i.e.,
container
state
Table<EMAIL_ADDRESS>Attributes
| Name | Type | Description | Default | Required |
|---|---|---|---|---|
dir |
String |
The directory containing the Docker-Compose configuration file. | The current directory | No |
returnProperty |
String |
Property name to set return value to. | n/a | Yes |
override |
Boolean |
Whether to force override of existing value. | False | No |
container |
String |
A pattern for the container name. Use ? and * as wildcards. |
'*' | No |
state |
String |
Only containers matching this state value will be included. | n/a | No |
<!-- Get a list of all existing containers, regardless of state --> <docker-list dir="${dirs.docker}" returnProperty="containers"/> <!-- Get a list of all running containers --> <docker-list dir="/home/develop" state="up" returnProperty="containers"/> <!-- Get a list of all running containers having 'sql' in their name --> <docker-list container="*sql*" state="up" returnProperty="containers"/>


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CommonConstraintNode Class
Definition at line
397
of
Concept.hpp
Constructors
CommonConstraintNode
()
CommonConstraintNode
(
const
Span& span_,
const
boost::uuids::uuid& moduleId_)
Member Functions
void
Accept
(
Visitor
& visitor)
override
Node
*
Clone
(
CloneContext
& cloneContext)
const override
Constructor Details
CommonConstraintNode Constructor
sngcm::ast::CommonConstraintNode::CommonConstraintNode()
Definition at line
1009
of
Concept.cpp
:
1009
:
IntrinsicConstraintNode
(
NodeType
::
commonConstraintNode
)
1010
{
1011
}
Declaration at line
400
of
Concept.hpp
CommonConstraintNode Constructor
sngcm::ast::CommonConstraintNode::CommonConstraintNode(
const
Span& span_,
const
boost::uuids::uuid& moduleId_)
Definition at line
1013
of
Concept.cpp
:
1013
:
IntrinsicConstraintNode
(
NodeType
::
commonConstraintNode
)
1014
{
1015
}
Declaration at line
401
of
Concept.hpp
Member Function Details
Accept Member Function
void
sngcm::ast::CommonConstraintNode::Accept(
Visitor
& visitor)
override
Definition at line
1017
of
Concept.cpp
:
1018
{
1019
visitor
.
Visit
(
*
this
)
;
1020
}
Declaration at line
402
of
Concept.hpp
Base class overridden functions:
sngcm::ast::Node::Accept
Calls:
sngcm::ast::Visitor::Visit
Clone Member Function
Node
* sngcm::ast::CommonConstraintNode::Clone(
CloneContext
& cloneContext)
const override
Definition at line
1022
of
Concept.cpp
:
1023
{
1024
return
new
CommonConstraintNode
(
)
;
1025
}
Declaration at line
403
of
Concept.hpp
Base class overridden functions:
sngcm::ast::Node::Clone
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Annotated configuration
This is a button
{"confirm":{"text":"It works!"}}
This is a button
This is a button
This is a button
This is a button


The project demonstrate how to build a simple CRUD application with Play Scala. For a step by step guide of the project watch this video.
Sorm has been chosen as a database access layer. It provides database methods out of the box such as save
or all
. For this simple CRUD application it is perfect. For a larger application you should use other libraries such as Slick or Anorm.


Amazon Echo Studio review: A sonic boom for your living room
Source: Daniel Bader / Android Central
Amazon unveiled a lot of products during its recent media event, but of all of them — including a new pair of true wireless earbuds — I was most excited about the Echo Studio. That's because Amazon's increasingly taking sound quality, not just audio production, seriously, and the Studio is the most mainstream example of this burgeoning strategy.
And it turns out my fervor was justified: the Echo Studio is a smart speaker that outperforms nearly every other in its class, and does so at a much lower cost.
Virtual insanity
Amazon Echo Studio
Bottom line: Amazon's new $200 Echo Studio is not a typical smart speaker, but after disabling the sound-distorting 3D upmixing, it's one of the best products in its category, especially if you're a fan of Alexa.
Pros
- Price is right
- Bright, punchy, midrange-forward sound
- Powerful woofer
- Sensitive mics can hear when the music is loud
- Built-in Zigbee support
Cons
- On-by-default 3D upmixing is terrible
- Dolby Atmos content is limited
Amazon Echo Studio What sounds good
Source: Daniel Bader / Android Central
For a company whose mainstream audio products for years have promoted the notion that good enough is good enough, Amazon has dipped its toes into high-fidelity audio more than a few times. Not only has every generation of Echo, Echo Plus, Echo Dot and Echo Show sounded better than the one before, but last year saw Amazon unveil a dedicated subwoofer and a set of plug-and-play add-ons that brought Alexa to existing stereo systems.
The Echo Studio sounds better than I expected and outperforms many speakers considerably more expensive.
This year, the Echo Studio furthers that narrative and moves it from the market's edges to the mainstream: this is the speaker to buy for anyone who wants to play in the big leagues of sound without getting overly complicated. The Echo Show has enough amplification to fill a room without distorting and the aural chops to back it up, including three 2-inch mid-range speakers providing distinct coverage directions, along with a front-firing 1-inch tweeter and a downward-facing woofer.
And it sounds good. While the current-generation Echo and Echo Plus have enough low-end to please most listeners, the sound is still thin and distorts easily at high volumes. The Echo Studio does not: it automatically calibrates its equalization based on room conditions, using its seven microphones to listen for echo and other environmental factors, and blasts sound with confidence.
Source: Daniel Bader / Android Central
Source: Daniel Bader / Android Central
I spent a lot of time comparing the $199 Echo Studio to the now-$299 Google Home Max, the $199 Sonos One, and the $399 Sonos Move, and the Echo Studio sounded as good or better than all of them in many situations. Vocals are particularly impressive, with lush, warm roll-off that feels inviting in almost any genre. The low-end can be a bit overbearing depending on the song, and isn't quite as sharp and accurate as the Sonos Move, but it's a heck of a lot cleaner than the Google Home Max, and never overpowers the vocals.
I also paired the Studio with an Echo Sub that I was using with an older Echo Plus, and the difference was very noticeable. The Sub didn't get a lot of attention when it was released last year, largely because it felt incongruous with many of Amazon's existing audio products. But not only does the $130 Sub fit perfectly with the Studio from an audio perspective, but the two look like they were designed in step, forming a sibling pair of sorts. The upside is that the subwoofer is capable of not only going deeper and producing more bass than the Studio, but it's of slightly higher quality, too. At $330 for the combo, it's only slightly more pricey than the (admittedly aging) Google Home Max and sounds much better.
Source: Daniel Bader / Android Central
The Echo Studio also purports to be a 3D speaker, but as I'll get to below, that part is mostly worth skipping. What is worth trying, though, is the included Dolby Atmos support which, when paired with a compatible Fire TV Cube and certain Prime Video or Netflix content, sounds pretty immersive coming from such a small source. Of course, Amazon recommends buying a second Studio to set up as a stereo pair, which ostensibly improves the Atmos experience, but I wasn't able to corroborate that. Either way, I thoroughly enjoyed watching a few episodes of Jack Ryan season 2 with it enabled.
From an Alexa perspective there's nothing new here, but it's still a capable smart speaker that keeps getting better with updates.
Of course, the Studio is also an Alexa speaker, and like previous Echo products is able to detect my voice relatively well even with loud music playing. There were a few times I had to manually lower the volume before it would hear me, but for the most part, it provides a familiar and reliable Alexa experience. Similarly, the Studio has a built-in Zigbee hub to facilitate the connection of smart home devices, which is excellent for quickly setting up new Hue bulbs or a new smart thermostat.
Source: Daniel Bader / Android Central
Finally, in the pro column: Amazon still takes auxiliary input seriously. If I don't want to stream audio wirelessly to the Studio, an included 3.5mm jack pulls double duty as a mini optical Toslink input, letting me connect any number of pieces of legacy stereo equipment. It's unlikely to be something I'll use, but it's nice to have. Likely more useful to me is the ability to stream to the Studio over Bluetooth, which could come in handy for streaming from sources that Alexa doesn't inherently support, like my Plex music library.
Amazon Echo Studio What sounds off
Source: Daniel Bader / Android Central
The only reason I'm giving the Echo Studio such high marks is that I discovered the off switch for an on-by-default setting called "Upmixing." Because Amazon touts the Studio as a 3D speaker, the first of its kind, it automatically upmixes — or, more accurately, messes up mixes — stereo music to 3D audio.
Source: Daniel Bader / Android Central This is the setting you need to turn off as soon as you set up the Echo Studio
While this effect, in theory, has the potential to sound quite good for some tracks, most vocal-heavy mixes sound terrible. It's the audio equivalent of converting 720p video to 4K, except that in doing so the picture loses its detail, color, and any semblance of dynamism.
I listened to a lot of music in this format, from hip-hop to metal to alternative to jazz, and none of it sounded better than the standard stereo mix. Vocals became hidden somewhere deep inside the instrumentation which, based on the 3D algorithms, became disjointed and at odds with one another.
I understand the impetus for a feature like this, and I actually admire Amazon's pursuit of 3D audio through its Amazon Music HD service — which is only available in four markets, none of which are mine, so I couldn't listen to any tracks mixed specifically for the Studio — but enabling it by default is idiotic and will instantly mar the experience for most people who just want to hear their favorite tunes played out of a big, powerful smart speaker. I implore Amazon to reconsider its decision here.
My only other gripe about the Echo Studio is its design: as nice as the open aperture looks, I can't help but think that more effort could have been put into the design. The circular chassis looks far too utilitarian for my liking, especially as I'm someone who wants to show off his speakers in any given room. Unlike something like the Sonos One, or even the newer Echo Plus, the Studio is a piece of technology I'd rather shove in a corner and use rather than look at. Which is fine, because it's, you know, producing audio, but I still think more care could have been paid to its looks.
Amazon Echo Studio Buy it
Source: Daniel Bader / Android Central
If you're a Google Assistant user, there are plenty of options for you out there, including the still-excellent Sonos One and the still-too-expensive Google Home Max. But if you're willing to give Alexa a chance, or don't really care as long as it streams, say, Spotify, then the Echo Studio should be at the top of your list.
4.5 out of 5
Once its song-ruining upmixing feature is disabled, it sounds amazing — more so for its $200 price tag. If any of Amazon's previous Echo speakers just aren't cutting it for you, or you feel like delving into the murky (and more expensive) world of lossless audio streaming, the Studio is a fantastic option.
Virtual insanity
Amazon Echo Studio
Fantastic value for room-filling audio.
Amazon's new $200 Echo Studio is not a typical smart speaker, but after disabling the sound-distorting 3D upmixing, it's one of the best products in its category, especially if you're a fan of Alexa.
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@* * Copyright 2019 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.templates.headings @import views.html.templates.errors.errorSummary @import templates.inputs._ @import models.agent.ClientVrnModel @import play.api.data.Form @import uk.gov.hmrc.play.views.html.helpers @(form: Form[ClientVrnModel])(implicit request: Request[_], messages: Messages, appConfig: config.AppConfig) @views.html.main_template( title = messages("clientsVrn.title"), bodyClasses = None ) { @errorSummary("common.errorSummary.heading", form) @headings(messages("clientsVrn.heading"))
@messages("clientsVrn.p1")
@helpers.form(action = controllers.agent.routes.SelectClientVrnController.submit) {


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Here is the source code for Data Structures and Algorithm Analysis in C++ (Fourth Edition), by Mark Allen Weiss, which will be published in July 2013. The materials here are copyrighted.
Many C++11 features are used. I have successfully compiled and tested the programs under g++ 4.6.2.
dsexceptions.h: Simple exception classes
Fig01_02.cpp: A simple recursive routine with a test program
Fig01_03.cpp: An example of infinite recursion
Fig01_04.cpp: Recursive routine to print numbers, with a test program
Fig01_05.cpp: Simplest IntCell class, with a test program
Fig01_06.cpp: IntCell class with a few extras, with a test program
IntCell.h: IntCell class interface (Fig 1.7)
IntCell.cpp: IntCell class implementation (Fig 1.8)
TestIntCell.cpp: IntCell test program (Fig 1.9)
Fig01_10.cpp: Illustration of using the vector class
Fig01_11.cpp: Dynamically allocating an IntCell object (lame)
BuggyIntCell.cpp: Buggy IntCell class implementation (Figs 1.16 and 1.17)
Fig01_18.cpp: IntCell class with pointers and Big Five
FindMax.cpp: Function template FindMax (Figs 1.19 and 1.20)
Fig01_21.cpp: MemoryCell class template without separation
Fig01_25.cpp: Using function objects: Case insensitive string comparison
LambdaExample.cpp: (Not in the book): rewriting Fig 1.25 with lambdas
MaxSumTest.cpp: Various maximum subsequence sum algorithms
Fig02_09.cpp: Test program for binary search
Fig02_10.cpp: Euclid's algorithm, with a test program
Fig02_11.cpp: Recursive exponentiation algorithm, with a test program
RemoveEveryOtherItem.cpp: Remove every other item in a collection
BinarySearchTree.h: Binary search tree
TestBinarySearchTree.cpp: Test program for binary search tree
TestAvlTree.cpp: Test program for AVL trees
WordLadder.cpp: Word Ladder Program and Word Changing Utilities
SeparateChaining.h: Header file for separate chaining
SeparateChaining.cpp: Implementation for separate chaining
TestSeparateChaining.cpp: Test program for separate chaining hash tables
QuadraticProbing.h: Header file for quadratic probing hash table
QuadraticProbing.cpp: Implementation for quadratic probing hash table
TestQuadraticProbing.cpp: Test program for quadratic probing hash tables
CuckooHashTable.h: Header file for cuckoo hash table
CuckooHashTable.cpp: Implementation for cuckoo hash table
TestCuckooHashTable.cpp: Test program for cuckoo hash tables
CaseInsensitiveHashTable.cpp: Case insensitive hash table from STL (Figure 5.23)
TestBinaryHeap.cpp: Test program for binary heaps
TestLeftistHeap.cpp: Test program for leftist heaps
BinomialQueue.h: Binomial queue
TestBinomialQueue.cpp: Test program for binomial queues
TestPQ.cpp: Priority Queue Demo
Sort.h: A collection of sorting and selection routines
TestSort.cpp: Test program for sorting and selection routines
DisjSets.h: Header file for disjoint sets algorithms
DisjSets.cpp: Efficient implementation of disjoint sets algorithm
TestFastDisjSets.cpp: Test program for disjoint sets algorithm
WordLadder.cpp: Word Ladder Program and Word Changing Utilities
Fig10_38.cpp: Simple matrix multiplication algorithm with a test program
Fig10_40.cpp: Algorithms to compute Fibonacci numbers
Fig10_43.cpp: Inefficient recursive algorithm (see text)
Fig10_45.cpp: Better algorithm to replace fig10_43.c (see text)
Fig10_53.cpp: All-pairs algorithm, with a test program
Random.h: Header file for random number class
Random.cpp: Implementation for random number class
TestRandom.cpp: Test program for random number class
UniformRandom.h: Random number class using standard library
Fig10_63.cpp: Randomized primality testing algorithm, with a test program
SplayTree.h: Top-down splay tree
TestSplayTree.cpp: Test program for splay trees
RedBlackTree.h: Top-down red black tree
TestRedBlackTree.cpp: Test program for red black trees
TestTreap.cpp: Test program for treap
KdTree.cpp: Implementation and test program for k-d trees
TestPairingHeap.cpp: Test program for pairing heaps
MemoryCell.h: MemoryCell class interface (Appendix)
MemoryCell.cpp: MemoryCell class implementation (Appendix)
MemoryCellExpand.cpp: MemoryCell instantiation file (Appendix)


Some predictions
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Format: [Date], [chance of prediction coming true in my estimation]: [time period]
- 2022-11-20, 60%: Apple/Google will make a moderation decision on behalf of Twitter by banning it from the app/play store.


The CarsPopulationModel class stores cars parameters and colors.
More...
#include <CarsPopulationModel.h>
|
| | CarsPopulationModel (const cer::CarsEvolutionRoot &root, QObject *parent=nullptr) |
| |
| int | rowCount (const QModelIndex &parent=QModelIndex()) const override |
| |
| int | columnCount (const QModelIndex &parent=QModelIndex()) const override |
| |
| QVariant | data (const QModelIndex &index, int role=Qt::DisplayRole) const override |
| |
| Q_INVOKABLE QVariantList | parameters (int row) const |
| | get parameters of the car with the given index More...
|
| |
| Q_INVOKABLE QString | color (int row) const |
| | get color of the car with the given index More...
|
| |
| QHash< int, QByteArray > | roleNames () const override |
| |
| void | updatePoplation () |
| | updatePoplation Resets the model, loads a new population and generates colors for new cars More...
|
| |
The CarsPopulationModel class stores cars parameters and colors.
Definition at line 21 of file CarsPopulationModel.h.
| QString CarsPopulationModel::color |
( |
int |
row | ) |
const |
get color of the car with the given index
- Parameters
-
- Returns
- color in #RRGGBB format
Definition at line 69 of file CarsPopulationModel.cc.
| int CarsPopulationModel::columnCount |
( |
const QModelIndex & |
parent = QModelIndex() | ) |
const |
|
override |
| QVariant CarsPopulationModel::data |
( |
const QModelIndex & |
index, |
|
|
int |
role = Qt::DisplayRole |
|
) |
| const |
|
override |
| QVariantList CarsPopulationModel::parameters |
( |
int |
row | ) |
const |
get parameters of the car with the given index
- Parameters
-
- Returns
- list with parameters
Definition at line 62 of file CarsPopulationModel.cc.
| QHash< int, QByteArray > CarsPopulationModel::roleNames |
( |
| ) |
const |
|
override |
| int CarsPopulationModel::rowCount |
( |
const QModelIndex & |
parent = QModelIndex() | ) |
const |
|
override |
| void CarsPopulationModel::updatePoplation |
( |
| ) |
|
updatePoplation Resets the model, loads a new population and generates colors for new cars
Definition at line 84 of file CarsPopulationModel.cc.
The documentation for this class was generated from the following files:


{{Infobox prepared food | name = Lung fung soup | image = | caption = | alternate_name = Dragon phoenix soup | country = China | region = | creator = | course = | type = Soup | served = | main_ingredient = Seafood or mushrooms, lemon, chili peppers, Chinese vegetables | variations = | calories = | other = }}
Lung fung soup (龍鳳湯; pinyin: lóng fèng tāng), or Dragon phoenix soup, is a thick seafood or gou rou soup made with lemon, chili peppers, and Chinese vegetables. Other variations could include a vegetarian version of the same with mushrooms instead of seafood. Fung probably comes from the use of mushrooms in the soup.
"Feng" means Phoenix in Chinese. "Lung" or "Long" means dragon.
{{China-cuisine-stub}}


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