|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object util.io.file.GrammarFile
public class GrammarFile
Class that implements the methods necessaries to load a file with the definition of a Grammar. ######### START of the Explanation of the format of the Grammar File ########### All the symbols should be separated by a white space or a tab (for example between "#" and "CN" in "# CN", should be a white space or a tab, between "CN" and "=" in "CN =", should be a white space or a tab, and so on. # CN -> This field could be: CN = Standard (normal) Context Free Grammar, or CS = Stochastic Context Free Grammar. If this line doesnīt appear the Grammar by default is Stochastic. # S = S -> This line defines the Start Symbol, the first S should appear, the second S is the name of the Start Symbol (it could be another symbol, as for example Z: # S = Z). This line should be in the second place (if above appear CN/CS) or in the first place if CN/CS doesnīt appear. # N = A B C D -> This line defines the NonTerminal symbols. The first N should appear, the name of the NonTerminals could be anything string. Could be several lines of N: # N = A B # N = C D # TV = 1 2 3 4 5 6 -> A line starting with TV defines a class of Terminals that aren't operators. Could be several lines of TV's, each TV would create a NonTerminal symbol in the Grammar, the first TV would create the TV1 NonTerminal, the second would create the TV2 NonTerminal, and so on. Each TV defines several Productions in the Grammar, in this case defines: P1: TV1->1, P2: TV1->2, ..., P6: TV1->6 # TV = a b c d e -> P7: TV2->a, P8: TV2->b, ..., P11: TV2->e # TF = + * / -> A line starting with TF defines a class of Terminals that are operators. Could be several lines of TF's, each TF would create a NonTerminal symbol in the Grammar, the first TF would create the TF1 NonTerminal, the second would create the TF2 NonTerminal, and so on. Each TF defines several Productions in the Grammar, in this case defines: P12: TF1->+ , P13: TF1->* , P14: TF1->/ # TF = con add sub -> P15: TF2->con , P16: TF2->add , P17: TF2->sub # P = p_r 1 S A -> The line starting with P defines a Production, p_r marks the start of the probability value associated to the Production ( the value 0,1 in this case), next the first symbol should be a NonTerminal (should be defined in any N) and is the left side symbol of the Grammar. The others symbols are the rigth side symbols (should be defined in any N, TV or TF). If the Grammar will be a Standard (normal) Context Free Grammar, then the probabilities p_r arenīt condidered. The Production defined in this line is: P18: S->A with probability 1. # P = p_r 0,8 A A + B + A -> P19: A->A+B+A with probability 0.8 . # P = p_r 0,2 A TV1 -> P20: A->TV1 with probability 0.2 . It is equivalent to P:A->1, P:A->2, ..., P:A->6 . # P = p_r 1 B TV1 -> P21:B->TV1 with probability 1 . ######### END of the Explanation of the format of the Grammar File ########### It belongs to the Public API.
| Constructor Summary | |
|---|---|
GrammarFile()
Empty constructor. |
| Method Summary | |
|---|---|
StringList |
getComment()
Get the comment of file. |
java.lang.String |
getExtension(java.io.File f)
Return the extension portion of the file's name . |
java.io.File |
getFile()
Get the file. |
java.lang.String |
getFilename()
Get the name of file. |
Grammar |
getGrammar()
Returns the Grammar loaded. |
StringList |
getHeader()
Get the header of file. |
java.util.Locale |
getLocale()
Get the current locale. |
java.text.NumberFormat |
getNumberFormat()
Get the current number format. |
int |
getSize()
Get the number of lines in file. |
void |
load()
Load data. |
void |
load(java.lang.String filename)
Load data from this file |
void |
save()
Save the data to a file. |
void |
save(java.lang.String filename)
Save data to this file. |
void |
setComment(StringList comment)
Change the comment of file. |
void |
setFilename(java.lang.String filename)
Change the name of the file. |
void |
setLocale(java.util.Locale loc)
Change the current locale. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public GrammarFile()
| Method Detail |
|---|
public Grammar getGrammar()
public java.lang.String getExtension(java.io.File f)
public void load() throws java.io.IOException
java.io.IOException
public void load(java.lang.String filename) throws java.io.IOException
java.io.IOException
public void save() throws java.io.IOException
java.io.IOException
public void save(java.lang.String filename) throws java.io.IOException
java.io.IOException
public int getSize()
public StringList getComment()
public void setComment(StringList comment)
public java.lang.String getFilename()
public void setFilename(java.lang.String filename)
public java.io.File getFile()
public StringList getHeader()
public java.util.Locale getLocale()
public void setLocale(java.util.Locale loc)
public java.text.NumberFormat getNumberFormat()
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


The target of this computer vision project is to detect human finger gestures for calligraphy. The target is not only to be able to draw with fingers but to make the drawing unique by showing the drawing speed with line width. Plus, a gesture is designed for screen cleaning.
A key to ROI (human palm/finger) finding is how we seperate it from the environment. Here, we take the skin color of Asian as our target.
First, we process every frame in HSV color space and truncate part of the frame so we can focus on the human palm and not be affected by other areas with human skin color, for example, human faces.
Applying an inrange
mask and bitwise and
the original frame to itself, we get a frame with isolated human palm. The ROI isn’t always perfect. Lighting conditions and camera noise can bring some HOLES to the ROI. Thus, we use dilate/erode
to kill the holes and to stablize the contour.
The most important part of this project is to get a clear coutour of the ROI and to filtrate out the noise.
To get the contour, we apply Canny edge detector
to the binary image converted from the HSV image.
There can be plenty of objects with colors similar to human sikn in a frame. cv2.contourArea
is what I use to isolate the palm from the background as the fact that the palm of a person sitting in front of the webcam shall be larger than the objects in the background. So, properly select the area size let our program focuses on the palm.
After we get the biggest contour, we use numpy.argmin
to find the topmost point of the contour. If it is stable and we are almost done. But, it’s not that perfect. The noise cancelation and the edge detector we applied to the image can cause small jitters to the point and that’s not what we want. So, it is necessary to add a low pass filter before we actually draw the points/lines in the frame.
How can the program know when to draw and when not to draw? We use cv2.matchShapes
to compare the contour with a certain pattern, the stop sign.The matchShapes function is comparing the image moments (Hu moments) of the two contours. The benefit of this is that the moments aren’t affected by rotation, mirror and scaling. If the score is lower than the threshold, we know it doesn’t match the stop sign and thus we record the point and the distance of it with the point in the previous frame.
The last thing in the program is just to output the points and connecting them with lines in different widths.
In this program, a fixed color range is selected. To adapt to different skin colors, machine learning and training should help. This will be my next goal.
Besides skin color, gesture selection is now limited. It is not hard for user to choose their own gestures as their stop signs. However, the threshold setting can be difficult. This is another topic I would like to work on.


We are upgrading FAVORITEOLDROBLOX!
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We are upgrading FAVORITEOLDROBLOX!
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Firefly is web application that allows users to interactively explore their 3D particle data. At its core, it is a Javascript app that uses WebGL to efficiently render millions of particles in an interactive three.js scene. In addition to traditional static html and css, the user interface is procedurally generated using d3.js according to a configuration file, allowing visualization creators to ship different versions of their visualization for different audiences without changing the underlying code or data. On top of this framework, we provide a python frontend that allows users to generate their own Firefly visualizations and host virtual webservers using Flask, enabling users to control their visualization via a remote device (i.e. an iPad), livestream the output of their visualization from a remote server (like a supercomputing cluster), or load new data directly from a Python interpreter without having to read it in from disk.


NEW PRODUCT 1.0
LATEST NEWS
New product better than ever!
CEO: "We now take data loss seriously."
Software now available on a 'try before you buy' scheme.
How we are embracing Web 2.0
Let us know what you think....
BRAND NEW PRODUCT
The new 1.2 release of the product is faster, yet slower, simpler, but more complicated, and generally much better than all versions of the product that came before it.


OK by me
From: John Wilson
<EMAIL_ADDRESS>Sent: Wednesday, November 16, 2016 1:40
PM
To: 'Jim Jones'
Cc: 'Susan Johns'; 'Dan Toms'
Subject:
I further revised this – I can’t help myself – please quickly review and
REPLY ALL
Thanks for your patience
during our system upgrades.


racket- Execute a Racket script:
racket path/to/script.rkt- Execute a Racket expression:
racket --eval "expression"- Run module as a script (terminates option list):
racket --lib module_name --main arguments- Start a REPL (interactive shell) for the `typed/racket` hashlang:
racket -I typed/racket


Sasha Chislenko<EMAIL_ADDRESS>- Project Leader
Home page : http://www.lucifer.com/~sasha/home.html
Some hyper-economic theory : http://www.lucifer.com/~sasha/articles/hypereconomy.html
Madan Ramakrishnan<EMAIL_ADDRESS>- MIT student, worked on initial Java model
Jon Pearce<EMAIL_ADDRESS>- Current developer
Ian Wright<EMAIL_ADDRESS>Home page : http://www.cs.bham.ac.uk/~ipw/
Article "The Society of Mind requires an economy of mind" : http://www.cs.bham.ac.uk/~ipw/m/m.html
Joel Shellman<EMAIL_ADDRESS>- Developer of innovative TrafficX Exchange.
Joel will hopefully implement the system on TrafficX
TrafficX Exchange : [http://trafficx.com]
Stephan Vladimir Bugaj<EMAIL_ADDRESS>- A developer of advanced information systems.
- Home page : [http://www.cthulhu-dynamics.com/stephan]
Steve Moyer<EMAIL_ADDRESS>Leader of the NODES project, Synergy solutions, Spiritual economy mailing list.
Home page : [http://www.gen.com/solutions/steve.htm]
Synergy solutions : [http://gen.com/solutions]
Maksim Otstavnov<EMAIL_ADDRESS>- Editor, "Compunomika" monthly (in Russian), consultant in advanced IT projects, management maintainer of The Russian PGP HomePage.
- Home page: http://www.geocities.com/SoHo/Studios/1059/
Juri Nebieridze<EMAIL_ADDRESS>- Russian researcher.
At this point, Jon Pearce is continuing work on the code started by Madan. Others offer criticisms and other assistance as they have time for, and plan to integrate the model with systems they are working on. We may include somebody else into the group as well. The point is not to draw lots of people, but keep it a small and efficient technical group.


Why, how and where to learn Design Patterns
The first thought I had when I started to study Design Patterns was:
- “damn, is all this really necessary?”
I mean, so many patterns, so many details to do things that look simple to do without any further detailed thoughts. And I started to question myself if this was really a productive thing. The first answer I gave was:
-”no, *wonderful not-bad-word* this, I’m losing too much time trying to wrap my head around this”
So I forgot it and kept developing my software using something like Extreme Go Horse. (i was younger at that time). Then, I saw a pattern being formed:
###Every time I came back to my old codes I couldn’t extend nor debbug it in a easy way. It was always extremely hard to work on it.
And then I realized why: lack of good architectural decisions.
So I decided to give another chance to learning Design Patterns and finally I understood its beauty.
Few things to keep in mind:
1. It’s not about trying to fit every problem in every Design Pattern. That’s just a waste of time
2. It may be not very productive at start, the process of applying a DP in a determined problem takes time.
3. On the other hand, it will be extremely productive when you try to debug your code or try to extend it. Everything starts to make more sense and it’s way easier to change things when you’ve followed some patterns
4. When studying a Design Pattern, try hard to implement that solution in a problem. This will make total difference
Now, to the materials!
Start with the classics:
Design Patterns (a.k.a Gang of Four)
This is the all times classic. These four gentlemen were the firsts to formalized and compile all patterns found on Object Oriented Systems. Here my advice is to go one by one, pick a pattern, read it, understand it, implement it and ,finally, really understand it.
Head First Design Patterns
This one I’ve used when I didn’t understand a concept from the Gang of Four, it is easier to grasp the concepts, the approach is way less hardcore than the original GoF, but it still is a great book.
Code Samples
That said, it’s always good to have examples of each Design Pattern by your side, so you can study the DP and check other examples, so real world examples are perfect for it:
List with Examples of Patterns implemented in the Java SE and EE API
List with Examples of patterns demonstrated with C#
Excellent examples and explanations of each Design Pattern
Another great material on Design Patterns, with lots of great thoughts on it
I believe this is a good start to learn Design Pattern and I hope this helps someone, mainly if they’re struggling with the same doubts I had when trying to learn it.


{% load humanize %}
Instructions: draw polygons around all areas of the image containing Northern Leaf Blight (NLB). Tighter polygons are better, but it's also important not to leave any NLB outside the polygon regions. In other words, make sure your polygons contain ALL of the NLB lesions, even if they also cover some non-lesion areas.
Use the "adjust" button (or press 'A') to improve the shape boundary if desired.
Note: add some good examples here
{% comment %}Note: add some bad examples here
{% comment %}To close the shape, you do not need to click near the first point. You can right-click to connect the first and last point.
After drawing a polygon, please adjust its edges so that it better fits the true boundary. When you are done adjusting, switch back to "Draw" mode to draw more polygons.
You can press the D and A key to quickly switch modes.
Scroll with the mouse wheel to zoom in. Click "Reset Zoom" or press escape to reset back to normal.
Press 'space' and drag with the mouse to move around in the image (panning). You can also move press the arrow keys to move.
Thank you for participating!


2. ORM Case Study
2.3 Converting an Existing Business Framework
If there is an existing data layer (e.g. DAO) that needs to be converted to Cayenne,
currently most of the mapping tasks will have to be done manually using the Modeler.
Note for EOF Users:
CayenneModeler provides import function for the
EOModels. So most of the mapping conversions should be automatic.
|
In any case there is a good chance of reusing business methods already defined in a
persistence framework. And definitely any existing database can be used as is. One possible
approach to conversion is to follow these steps:
- Reverse engineer the database schema into a new Cayenne mapping project.
- Update class names and packages match the names of the existing classes.
- Run class generation using "generation gap" pattern (to create subclass/superclass
pair for each entity). This would only create superclasses with set/get methods
for persistent properties, without touching the existing classes.
- Modify existing DAO classes to change the superclass to a corresponding generated
one (e.g. "Employee extends _Employee").
- Remove any persistent properties with their setters
and getters from the subclass.
- Remove all the DAO-specific persistence code.
- Rewrite any other "business" methods to use DataContext for data operations.
See the following topics explaining individual tasks involved:
|


I guess it is time to get back to work.
Mr O. wants me to copy more code .
I want to enjoy this, but I don't
understand what is happening. I am not sure how
I am supposed to make this into a class
for them to teach, but maybe that is the reason they are
having me do it: they don't know how to either.
Let me look at the example code:
var target : Transform; //the enemy's target
var moveSpeed = 3; //move speed
var rotationSpeed = 3; //speed of turning
var myTransform : Transform; //current transform data of this enemy
function Awake()
{
myTransform = transform; //cache transform data for easy access/preformance
}
function Start()
{
target = GameObject.FindWithTag("Player").transform; //target the player
}
function Update () {
//rotate to look at the player
myTransform.rotation = Quaternion.Slerp(myTransform.rotation,
Quaternion.LookRotation(target.position - myTransform.position), rotationSpeed*Time.deltaTime);
//move towards the player
myTransform.position += myTransform.forward * moveSpeed * Time.deltaTime;
}
Yup, about as understandable as ever.
Well, lets put it in the game and see what it does.
...
Oh, the zombies are chasing me.
This was only, like, 10 lines of code.
How are they doing that?
I am really lost now.
How does the code know who the playeris?
How do I study this?


Welcome to the Light Sound Memory Game! Click on the corresponding circles based off of the auditory and visual cues!
Chances:
Time remaining:


Research Interests
Response Selection processes (Stimulus-Response Compatibility effect)
Attention (Stroop Effect)
Visual perception (Metacontrast Masking)
Human Factors
Education
2002.8 - Ph.D. Purdue University, West Lafayette, IN, Cognitive Psychology
1993.8 - M.S. Korea University, Seoul, Korea, Experimental Psychology
1991.2 - B.A. Korea University, Seoul, Korea, Psychology
Positions
2014.3 ~ present - Professor, Department of Psychology Korea University
2009.3 ~ 2014.2 - Associate Professor, Department of Psychology Korea University
2005.9 ~ 2009.2 - Assistant Professor, Department of Psychology Korea University
2003.8 ~ 2005.8 - Postdoctoral Fellow, Department of Psychological Sciences Purdue University with Dr. Robert Proctor
2002.8 ~ 2003.8 - Postdoctoral Fellow, Department of Psychological Sciences Purdue University with Dr. Gregory Francis
Profession Organization
Chair of Korea University Institutional Review Board since 2017
Member of the Psychonomic Society since 2005
Member of the Vision Sciences since 2003
Editor of the Korean journal of Cognitive and Biological Psychology 2017-2019
Secretary-General of the Korean Psychological Association 2016-2018
Information Director of the Korean Psychological Association 2012-2013
Scholarships, Awards, and Honors
The Korean Psychology Association Kim Jae-il Early Career Award 2007
Outstanding Teaching Award, Korea University, Seoul, Korea 2005
Awarded Purdue Research Foundation Grant, Purdue University, West Lafayette, IN 2001 & 2002


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Our stores and business in marketplace is 5 key categories: Health, Wellness & Fitness, Food & Nutrition, Beauty & Fashion, Education, Care & Parenting, and Lifestyle. If you are in business, you can create your store and/or ads & campaigns. If you are a consumer you can buy products or business services in these five categories
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---
layout: default
title: Getting started
slug: getting-started
---
{% include getting-started/download.html %}
{% include getting-started/whats-included.html %}
{% include getting-started/webpack.html %}
{% include getting-started/template.html %}
{% include getting-started/security-headers.html %}
{% include getting-started/browser-and-device-support.html %}


Index
Previous:
yas
Next:
yā
√yah
Such a root, of questionable meanng, is assumable for the derivatives:
yahú
RV.
yahvá
V.
yahvánt
RV.


{% extends 'all-pages/base.html' %} {% block content %}
By creative nights from chech republic
design
usability
creativity
content
From {{project.country}}{{project.post_date}}


{% extends 'standings/standings-layout.html' %} {% block standings_content %}
| Player | Team | Number | Points | Goals | Assists | Yellow | Red | {% for player in players %}
|---|---|---|---|---|---|---|---|
| {{ player.name }} | {{ player.team_name }} | {{ player.number }} | {{ player.points }} | {{ player.goals }} | {{ player.assists }} | {{ player.yellow }} | {{ player.red }} |


todo1
project:
context:
-
(B) +GarageSale @phone schedule Goodwill pickup
-
+GarageSale @home post signs around the neighborhood DUE:2006-08-01
-
X eat meatballs @home
-
(A) @phone thank Mom for the meatballs WAIT
-
-
@shopping Eskimo pies
-
email<EMAIL_ADDRESS>for help +report_bug @wherever
-
x 2009-01-01 completed with a date


29.5
The Shell Plating and Bottom Plating
This particular area of the wreck above the mudline was carefully examined by divers and ROV because a surprisingly big part was accessible, much more than it could be assumed with a vessel lying on her starboard side. The reason is, as demonstrated by the drawings below taken from the Smit-Tak Report - Enclosure 27.410 - that the superstructure was buried into the sea bottom. Therefore the wreck had a list of about 120° with the bottom up and a substantial part of the starboard hull side was accessible above the bilge strake partly up to car deck level.
This means that over the whole length of the wreck the starboard shell plating in way of the 0-deck is accessible which includes the heeling tank and stabiliser pocket areas. The same is true for the 1st deck which is accessible in most parts while the car deck can only be reached in way of section D, which is aft of the bridge. The drawing on the next page - also taken from the Smit-Tak Report - Enclosure 27.410 - indicates the section areas A-E.
Incidentally the area at D - where the car deck is accessible - is most likely not the location where the cement bags on pallets and other cargo were observed. See Subchapter 29.3.
Also accessible is the Starboard Shell Plating in way of 0-deck between Frames 85-120, i.e. the area below the passenger accommodations on the 1st deck with exception of the most forward compartment which is located on top of three initially full freshwater tanks. Further aft there were the following watertight compartments on 0-deck:
1. Compartment:
starboard:saunas, dressing rooms, toilets
aft port:
void space
2. Compartment:
from port to starboard:swimming pool with bar, this compartment is connected to the upper decks by lift and to the 1st deck by a spiral staircase
3. Compartment:
starboard:sewage treatment installation (part of engine room)
port: 2
conference rooms with alleyway in front, 1 stairway was leading to upper decks which was probably only accessible for the crew
4. Compartment:
starboard:empty heeling tank (183 ts capacity)
centre: 2
heavy fuel tanks with walkway around
port:
full heeling tank (183 ts capacity)
Note: Both heeling tanks were connected by a cross-flooding valve which could be operated from the bridge, the engine control room and a pump room located in the aft part of the car deck.
5. Compartment:
starboard:stabiliser fin and installation space
centre: (same as 4th compartment)
port:
stabiliser fin and installation space.
Based on the evidence of passengers from the cabins of the 1st, 3rd and 4th compartments, there had been a lot of water on the 1st deck already before the big starboard heel which escaped from 0-deck under pressure in way of the 4th compartment and probably also came up the spiral stairway and the lift casing in the 3rd compartment. - See the statement of Carl-Erik Reintamm attached as Enclosure <IP_ADDRESS>9.
As the water coming up from 0-deck to deck 1 had been under pressure - see the statement of Carl Övberg - Enclosure <IP_ADDRESS> - there must have been a connection to the outside. As the port side of the shell plating is apparently undamaged according to the ROV inspections on 02.10.94 and 09.10.94 it has to be assumed that there must be one or more holes in the starboard side.Note: The existence of such a hole in way of the sauna and swimming pool compartments has been admitted by two Sjöfartsverket employees independently of each other.
Since the starboard shell plating in way of 0-deck is accessible over the whole length of the wreck - see the 2 drawings on the previous pages - and has been carefully examined by ROV and divers during the mudline surveys, it is certain that respective video material is available. The "official" mudline video is, however, censored, i.e. the relevant parts were deleted - see also the Disengage Report - Enclosure 34.6.434. Nevertheless, careful evaluation of the available mudline video does reveal very substantial damage to the starboard shell plating and bottom plating above and below the bilge keel as demonstrated by the images below.
When looking at the images presented here it is difficult to assume that this vessel had been in drydock only 8 months before, because the starboard side is really in a very bad condition, actually much worse as it is possible to demonstrate by images. Very deep indentations between the frames and deep scoring marks, partly not even corroded, are the rule, however, in some particular areas the plating is even holed as e.g. can be seen from the image below.
Enhancement by means of special equipment confirmed indeed that the plating is holed - See the enhancement below.
From the damage appearance it has to be concluded that the plate was severely corroded, i.e. the hole was neither caused by an impact of ex- or implosion but by corrosion. This is easily possible if the coating and/or anode protection of a ballast water tank, e.g. a heeling tank, was not in order.
The following two images show an area which is apparently also holed, but not by corrosion because the edges are too sharp.
In any event the ferry ESTONIA departed from Tallinn on her last voyage with one or more starboard ballast water tanks, possibly even the heeling tank, open to the sea.
As stated above - see Chapter 17 - revised stability calculations showed that the vessel must have had about 200 ts additional weight on the starboard side which could not be pumped. The theoretical possibilities of unconfirmed flooding of compartments through holes to the outside was discussed as the reader may remember.
The possibility now turns to a fact. The vessel had holes in her underwater shell plating. The obvious effect on the seaworthiness of the ferry needs not to be discussed. The fact that the JAIC, having worked on the interpretation of those videos, does not mention anything in this regard, is undebatable too.


Save files to (relative to your downloads directory):
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Classes of elements exluded from scanning (split by spaces):
Save
Reset


Arkenia:
I beg you. Please hurry. I think the tetrarchs and other priests have found out. I beg you, hurry!


|
Compares the first source operand with the second source operand and sets the status flags in the EFLAGS register according to the results. The comparison is performed by subtracting the second operand from the first operand and then setting the status flags in the same manner as the SUB instruction. When an immediate value is used as an operand, it is sign-extended to the length of the first operand.
The CMP instruction is typically used in conjunction with a conditional jump (Jcc), condition move (CMOVcc), or SETcc instruction. The condition codes used by the Jcc, CMOVcc, and SETcc instructions are based on the results of a CMP instruction. Appendix B, EFLAGS Condition Codes, in the IA-32 Intel Architecture Software Developer's Manual, Volume 1, shows the relationship of the status flags and the condition codes.
|


--- layout: post title: New OData Mailing List date: 2014-06-30 03:42:02.000000000 +08:00 categories: - Site announcement tags: [] status: publish type: post published: true author: Qian Li ---
In order to make OData.org a more collaborative site, we have maintained a Mailing List for people to ask or answer questions related to OData. And now we are moving to a new mailing list service OData Discussion Group.
You can view more detailed instructions of how to join the new mailing list for OData. All kinds of issues and feedbacks for OData are welcomed in this mailing list. If any problems and concerns, please comments below to let us know.


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| base64_encode {xfun} | R Documentation |
The function base64_encode()
encodes a file or a raw vector into the
base64 encoding. The function base64_decode()
decodes data from the
base64 encoding.
base64_encode(x) base64_decode(x, from = NA)
x |
For |
from |
If provided (and |
base64_encode()
returns a character string.
base64_decode()
returns a raw vector.
xfun::base64_encode(as.raw(1:10)) logo = xfun:::R_logo() xfun::base64_encode(logo) xfun::base64_decode("AQIDBAUGBwgJCg==")


| Home | Libraries | People | FAQ | More |
Table of Contents
The Boost.Sort library provides a generic implementation of high-speed sorting algorithms that outperform those in the C++ standard in both average and worst case performance when there are over 1000 elements in the list to sort.
They fall back to STL std::sort on small data sets.
| Warning | |
|---|---|
These algorithms all only work on random access iterators. |
They are hybrids using both radix and comparison-based sorting, specialized to sorting common data types, such as integers, floats, and strings.
These algorithms are encoded in a generic fashion and accept functors, enabling
them to sort any object that can be processed like these basic data types.
In the case of
,
this includes anything with a defined strict-weak-ordering that std::sort
can sort, but writing efficient functors for some complex key types may not
be worth the additional effort relative to just using std::sort,
depending on how important speed is to your application. Sample usages are
available in the example directory.
string_sort
Unlike many radix-based algorithms, the underlying
algorithm is designed around worst-case performance.
It performs better on chunky data (where it is not widely distributed), so
that on real data it can perform substantially better than on random data.
Conceptually, spreadsort
can sort any data for which an absolute ordering can be determined, and
spreadsort
is sufficiently flexible that this should be possible.
string_sort
Situations where
is fastest relative to std::sort:
spreadsort
spreadsort
has an optimization to quit early in this case.
Situations where
is slower than std::sort:
spreadsort
spreadsort
are faster on reverse-ordered data than randomized data, but std::sort
speeds up more in this special case.
spreadsort
to std::sort
if the input size is less than 1000, so performance is identical for
small amounts of data in practice.
These functions are defined in namespace
boost::sort::spreadsort
.
| Tip | |
|---|---|
In the Boost.Sort C++ Reference section, click on the appropriate overload,
for example |
Each of
,
integer_sort
,
and float_sort
have 3 main versions: The base version, which takes a first iterator and
a last iterator, just like std::sort:
string_sort
integer_sort(array.begin(), array.end()); float_sort(array.begin(), array.end()); string_sort(array.begin(), array.end());
The version with an overridden shift functor, providing flexibility in case
the operator>>
already does something other than a bitshift. The rightshift functor takes
two args, first the data type, and second a natural number of bits to shift
right.
For
this variant is slightly different; it needs a bracket functor equivalent
to string_sort
operator
[], taking a number
corresponding to the character offset, along with a second getlength
functor to get the length of
the string in characters. In all cases, this operator must return an integer
type that compares with the operator<
to provide the intended order (integers
can be negated to reverse their order).
In other words (aside from negative floats, which are inverted as ints):
rightshift(A, n) < rightshift(B, n) -> A < B A < B -> rightshift(A, 0) < rightshift(B, 0)
integer_sort(array.begin(), array.end(), rightshift());
string_sort(array.begin(), array.end(), bracket(), getsize());
See rightshiftsample.cpp for a working example of integer sorting with a rightshift functor.
And a version with a comparison functor for maximum flexibility. This functor must provide the same sorting order as the integers returned by the rightshift (aside from negative floats):
rightshift(A, n) < rightshift(B, n) -> compare(A, B) compare(A, B) -> rightshift(A, 0) < rightshift(B, 0)
integer_sort(array.begin(), array.end(), negrightshift(), std::greater<DATA_TYPE>());
Examples of functors are:
struct lessthan { inline bool operator()(const DATA_TYPE &x, const DATA_TYPE &y) const { return x.a < y.a; } };
struct bracket { inline unsigned char operator()(const DATA_TYPE &x, size_t offset) const { return x.a[offset]; } };
struct getsize { inline size_t operator()(const DATA_TYPE &x) const{ return x.a.size(); } };
and these functors are used thus:
string_sort(array.begin(), array.end(), bracket(), getsize(), lessthan());
See stringfunctorsample.cpp for a working example of sorting strings with all functors.
The
algorithm is a hybrid algorithm; when the number of elements being sorted
is below a certain number, comparison-based sorting is used. Above it, radix
sorting is used. The radix-based algorithm will thus cut up the problem into
small pieces, and either completely sort the data based upon its radix if
the data is clustered, or finish sorting the cut-down pieces with comparison-based
sorting.
spreadsort
The Spreadsort algorithm dynamically chooses either comparison-based or radix-based sorting when recursing, whichever provides better worst-case performance. This way worst-case performance is guaranteed to be the better of 𝑶(N⋅log2(N)) comparisons and 𝑶(N⋅log2(K/S + S)) operations where
This results in substantially improved performance for large N;
tends to be 50% to 2X faster than std::sort,
while integer_sort
and _string_sort are roughly 2X faster than std::sort.
float_sort
Performance graphs are provided for
,
integer_sort
,
and float_sort
in their description.
string_sort
Runtime Performance comparisons and graphs were made on a Core 2 Duo laptop running Windows Vista 64 with MSVC 8.0, and an old G4 laptop running Mac OSX with gcc. Boost bjam/b2 was used to control compilation.
Direct performance comparisons on a newer x86 system running Ubuntu, with the fallback to std::sort at lower input sizes disabled are below.
| Note | |
|---|---|
The fallback to std::sort for smaller input sizes prevents the worse performance seen on the left sides of the first two graphs. |
starts to become faster than std::sort
at about 1000 integers (4000 bytes), and integer_sort
becomes faster than std::sort
at slightly fewer bytes (as few as 30 strings).
string_sort
| Note | |
|---|---|
The 4-threaded graph has 4 threads doing separate sorts simultaneously (not splitting up a single sort) as a test for thread cache collision and other multi-threaded performance issues. |
times are very similar to float_sort
times.
integer_sort
Histogramming with a fixed maximum number of splits is used because it reduces the number of cache misses, thus improving performance relative to the approach described in detail in the original SpreadSort publication.
The importance of cache-friendly histogramming is described in Arne Maus, Adaptive Left Reflex, though without the worst-case handling described below.
The time taken per radix iteration is:
𝑶(N) iterations over the data
𝑶(N) integer-type comparisons (even for _float_sort and
)
string_sort
𝑶(N) swaps
𝑶(2S) bin operations.
To obtain 𝑶(N) worst-case performance per iteration, the restriction S <= log2(N) is applied, and 𝑶(2S) becomes 𝑶(N). For each such iteration, the number of unsorted bits log2(range) (referred to as K) per element is reduced by S. As S decreases depending upon the amount of elements being sorted, it can drop from a maximum of Smax to the minimum of Smin.
Assumption: std::sort is assumed to be 𝑶(N*log2(N)), as introsort exists and is commonly used. (If you have a quibble with this please take it up with the implementor of your std::sort; you're welcome to replace the recursive calls to std::sort to calls to introsort if your std::sort library call is poorly implemented).
Introsort is not included with this algorithm for simplicity and because the implementor of the std::sort call is assumed to know what they're doing.
To maintain a minimum value for S (Smin), comparison-based
sorting has to be used to sort when n <= log2(meanbinsize),
where log2(meanbinsize) (lbs) is a small constant, usually
between 0 and 4, used to minimize bin overhead per element. There is a small
corner-case where if K < Smin and n >=
2^K, then the data can be sorted in a single radix-based iteration
with an S = K (this bucketsorting special case is by
default only applied to
).
So for the final recursion, worst-case performance is:
float_sort
1 radix-based iteration if K <= Smin,
or Smin + lbs comparison-based iterations if K > Smin but n <= 2(Smin + lbs).
So for the final iteration, worst-case runtime is 𝑶(N*(Smin + lbs)) but if K > Smin and N > 2(Smin + lbs) then more than 1 radix recursion will be required.
For the second to last iteration, K <= Smin * 2 + 1 can be handled, (if the data is divided into 2(Smin + 1) pieces) or if N < 2(Smin + lbs + 1), then it is faster to fallback to std::sort.
In the case of a radix-based sort plus recursion, it will take 𝑶(N*(Smin + lbs)) + 𝑶(N) = 𝑶(N*(Smin + lbs + 1)) worst-case time, as K_remaining = K_start - (Smin + 1), and K_start <= Smin * 2 + 1.
Alternatively, comparison-based sorting is used if N < 2(Smin + lbs + 1), which will take 𝑶(N*(Smin + lbs + 1)) time.
So either way 𝑶(N*(Smin + lbs + 1)) is the worst-case time for the second to last iteration, which occurs if K <= Smin * 2 + 1 or N < 2(Smin + lbs + 1).
This continues as long as Smin <= S <= Smax, so that for K_m <= K_(m-1) + Smin + m where m is the maximum number of iterations after this one has finished, or where N < 2(Smin + lbs + m), then the worst-case runtime is 𝑶(N*(Smin + lbs + m)).
K_m at m <= (Smax - Smin) works out to:
K_1 <= (Smin) + Smin + 1 <= 2Smin + 1
K_2 <= (2Smin + 1) + Smin + 2
as the sum from 0 to m is m(m + 1)/2
K_m <= (m + 1)Smin + m(m + 1)/2 <= (Smin + m/2)(m + 1)
substituting in Smax - Smin for m
K_(Smax - Smin) <= (Smin + (Smax - Smin)/2)*(Smax - Smin + 1)
K_(Smax - Smin) <= (Smin + Smax) * (Smax - Smin + 1)/2
Since this involves Smax - Smin + 1 iterations, this works out to dividing K into an average (Smin + Smax)/2 pieces per iteration.
To finish the problem from this point takes 𝑶(N * (Smax - Smin)) for m iterations, plus the worst-case of 𝑶(N*(Smin + lbs)) for the last iteration, for a total of 𝑶(N *(Smax + lbs)) time.
When m > Smax - Smin, the problem is divided into Smax pieces per iteration, or std::sort is called if N < 2^(m + Smin + lbs). For this range:
K_m <= K_(m - 1) + Smax, providing runtime of
𝑶(N *((K - K_(Smax - Smin))/Smax + Smax + lbs)) if recursive,
or 𝑶(N * log(2^(m + Smin + lbs))) if comparison-based,
which simplifies to 𝑶(N * (m + Smin + lbs)), which substitutes to 𝑶(N * ((m - (Smax - Smin)) + Smax + lbs)), which given that m - (Smax - Smin) <= (K - K_(Smax - Smin))/Smax (otherwise a lesser number of radix-based iterations would be used)
also comes out to 𝑶(N *((K - K_(Smax - Smin))/Smax + Smax + lbs)).
Asymptotically, for large N and large K, this simplifies to:
𝑶(N * (K/Smax + Smax + lbs)),
simplifying out the constants related to the Smax - Smin range, providing an additional 𝑶(N * (Smax + lbs)) runtime on top of the 𝑶(N * (K/S)) performance of LSD radix sort, but without the 𝑶(N) memory overhead. For simplicity, because lbs is a small constant (0 can be used, and performs reasonably), it is ignored when summarizing the performance in further discussions. By checking whether comparison-based sorting is better, Spreadsort is also 𝑶(N*log(N)), whichever is better, and unlike LSD radix sort, can perform much better than the worst-case if the data is either evenly distributed or highly clustered.
This analysis was for
and integer_sort
.
float_sort
differs in that Smin = Smax = sizeof(Char_type) * 8,
lbs is 0, and that std::sort's
comparison is not a constant-time operation, so strictly speaking string_sort
runtime is
string_sort
𝑶(N * (K/Smax + (Smax comparisons))).
Worst-case, this ends up being 𝑶(N * K) (where K is the mean string length in bytes), as described for American flag sort, which is better than the
𝑶(N * K * log(N))
worst-case for comparison-based sorting.
and integer_sort
have tuning constants that control how the radix-sorting portion of those
algorithms work. The ideal constant values for float_sort
and integer_sort
vary depending on the platform, compiler, and data being sorted. By far the
most important constant is max_splits, which defines
how many pieces the radix-sorting portion splits the data into per iteration.
float_sort
The ideal value of max_splits depends upon the size of the L1 processor cache, and is between 10 and 13 on many systems. A default value of 11 is used. For mostly-sorted data, a much larger value is better, as swaps (and thus cache misses) are rare, but this hurts runtime severely for unsorted data, so is not recommended.
On some x86 systems, when the total number of elements being sorted is small ( less than 1 million or so), the ideal max_splits can be substantially larger, such as 17. This is suspected to be because all the data fits into the L2 cache, and misses from L1 cache to L2 cache do not impact performance as severely as misses to main memory. Modifying tuning constants other than max_splits is not recommended, as the performance improvement for changing other constants is usually minor.
If you can afford to let it run for a day, and have at least 1GB of free
memory, the perl command: ./tune.pl
-large
-tune
(UNIX) or perl tune.pl -large -tune -windows
(Windows) can be used to automatically
tune these constants. This should be run from the libs/sort directory
inside the boost home directory.
This will work to identify the ideal
constants.hpp
settings for your system, testing on
various distributions in a 20 million element (80MB) file, and additionally
verifies that all sorting routines sort correctly across various data distributions.
Alternatively, you can test with the file size you're most concerned with
./tune.pl number
-tune
(UNIX) or perl tune.pl number
-tune
-windows
(Windows). Substitute the number of elements you want to test with for number
. Otherwise, just use the options
it comes with, they're decent. With default settings ./tune.pl
-tune
(UNIX) perl tune.pl -tune -windows
(Windows), the script will take
hours to run (less than a day), but may not pick the correct max_splits
if it is over 10. Alternatively, you can add the -small
option to make it take just a few
minutes, tuning for smaller vector sizes (one hundred thousand elements),
but the resulting constants may not be good for large files (see above note
about max_splits on Windows).
The tuning script can also be used just to verify that sorting works correctly
on your system, and see how much of a speedup it gets, by omiting the "-tune"
option. This runs at the end of tuning runs. Default args will take about
an hour to run and give accurate results on decent-sized test vectors. ./tune.pl -small
(UNIX) perl
tune.pl -small
-windows
(Windows) is a faster option, that tests on smaller vectors and isn't as
accurate.
If any differences are encountered during tuning, please call tune.pl
with -debug
> log_file_name
.
If the resulting log file contains compilation or permissions issues, it
is likely an issue with your setup. If some other type of error is encountered
(or result differences), please send them to the library author at<EMAIL_ADDRESS>Including the zipped input.txt
that
was being used is also helpful.
Last revised: September 21, 2016 at 14:49:52 GMT |


Govt. harassing people through odd-even: BJP
by Special CorrespondentThe Delhi BJP on Saturday accused the Aam Aadmi Party (AAP) government of subjecting the Capital’s citizens to harassment through the enforcement of the odd-even scheme.
BJP Rajya Sabha MP Vijay Goel has announced that he will stage a sit-in against the extension of the scheme, at Jantar Mantar on Sunday. He said that Delhiites were badly suffering due to increasing in pollution, which Chief Minister Arvind Kejriwal had “completely failed” to control.
He added that even the Supreme Court had termed the scheme “a flop.”
Expressing concern over increasing pollution in the city, Mr. Goel claimed that the Supreme Court had “reprimanded” the Kejriwal-led government and pointed out that the odd-even scheme was “not a proper solution for controlling pollution”.
The third edition of the scheme was in place from November 4 to 15.


{% include nav.html %}
The same impulse that prompted physicians and pharmacists to organize, standardize, and regulate their professions motivated a group of the city’s leading dentists to call for creation of a professional dental school. S.W. Dennis, M.D., D.D.S., was typical of this group of early organizers. He had graduated from the medical department of the University of Pennsylvania, received an honorary D.D.S. degree from the Indiana Dental College, and began practice in San Francisco after studying with a local dentist. In the midst of general lobbying for a school of dentistry, Dr. Dennis contacted colleagues at Harvard and the University of Pennsylvania to help plan for a dental college in San Francisco.
In the late nineteenth century, dentistry was regarded as a recently separated area of medical specialization. Many dentists had M.D. degrees in addition to their dental training and most agreed that dental education should be closely linked to the medical curriculum, especially with respect to the teaching of anatomy and pathology. They desired university affiliation and they worked in close conjunction with the faculty of the Medical Department. On May 28, 1881, the medical faculty formally proposed the creation of an affiliated Dental Department to the UC Regents, using the affiliation of the medical and pharmacy departments as precedent. Part of their appeal included their promise of free lecture and clinic space for dental students at the Toland Medical College building.
The Regents responded favorably, and in September of 1881 they established a Dental College to be organized with seven professors, nine instructors and four demonstrators. The eight members of the Dental Class of 1882 took courses of instruction in anatomy, physiology, chemistry and surgery alongside medical students in the Toland Medical College building at the corner of Stockton and Francisco Streets. A dental clinic was also located there and dental students were invited to attend selected bedside teaching clinics given by the medical faculty. While the UC Dental Department was not the only dental school to be organized in San Francisco, its founding in the context of the state university placed it in the forefront of academic schools in the West.
Thus by 1882 the University of California had three affiliated colleges in San Francisco. Faculty salaries were paid by tuition and fees and the individual schools retained control over choice of faculty, but the Toland Medical College building was officially made the property of the university, and graduates of the schools wore university gowns at graduation. From the beginnings of affiliation, reciprocity in course offerings and programs was a feature of the three colleges: medical and dental students took anatomy and physiology side-by side, and all three schools allowed their graduates to expand their careers by matriculating in the other schools, with course credit allowed.
>> Trained Nurses for San Francisco
1868–1898 The Origins of the University of California and Affiiated Colleges
Creating a UC Dental Department
“We need a college of dentistry on this coast and if we have not a necessary talent among ourselves, we can import it. We owe those who take our places, greater facilities for study and professional breadth than the times have afforded us. The future will demand men educated in all that constitutes the scholar and professional man, and refined in all that makes the gentleman.”
Dr. CC Knowles, June 26, 1870
Dr. CC Knowles, June 26, 1870
The same impulse that prompted physicians and pharmacists to organize, standardize, and regulate their professions motivated a group of the city’s leading dentists to call for creation of a professional dental school. S.W. Dennis, M.D., D.D.S., was typical of this group of early organizers. He had graduated from the medical department of the University of Pennsylvania, received an honorary D.D.S. degree from the Indiana Dental College, and began practice in San Francisco after studying with a local dentist. In the midst of general lobbying for a school of dentistry, Dr. Dennis contacted colleagues at Harvard and the University of Pennsylvania to help plan for a dental college in San Francisco.
In the late nineteenth century, dentistry was regarded as a recently separated area of medical specialization. Many dentists had M.D. degrees in addition to their dental training and most agreed that dental education should be closely linked to the medical curriculum, especially with respect to the teaching of anatomy and pathology. They desired university affiliation and they worked in close conjunction with the faculty of the Medical Department. On May 28, 1881, the medical faculty formally proposed the creation of an affiliated Dental Department to the UC Regents, using the affiliation of the medical and pharmacy departments as precedent. Part of their appeal included their promise of free lecture and clinic space for dental students at the Toland Medical College building.
The Regents responded favorably, and in September of 1881 they established a Dental College to be organized with seven professors, nine instructors and four demonstrators. The eight members of the Dental Class of 1882 took courses of instruction in anatomy, physiology, chemistry and surgery alongside medical students in the Toland Medical College building at the corner of Stockton and Francisco Streets. A dental clinic was also located there and dental students were invited to attend selected bedside teaching clinics given by the medical faculty. While the UC Dental Department was not the only dental school to be organized in San Francisco, its founding in the context of the state university placed it in the forefront of academic schools in the West.
Thus by 1882 the University of California had three affiliated colleges in San Francisco. Faculty salaries were paid by tuition and fees and the individual schools retained control over choice of faculty, but the Toland Medical College building was officially made the property of the university, and graduates of the schools wore university gowns at graduation. From the beginnings of affiliation, reciprocity in course offerings and programs was a feature of the three colleges: medical and dental students took anatomy and physiology side-by side, and all three schools allowed their graduates to expand their careers by matriculating in the other schools, with course credit allowed.
>> Trained Nurses for San Francisco


This is a simple and free service to help users automatically create hashed lists from a textfile. The service allows users to upload a text-file. The service will calculate the SHA256 checksum for each line of the textfile, and save the results to a downloadable location. The user will be given a link to download the results.
If you click the link and get an error message, then the file is probably still being generated. Processing 1 million lines typically takes about 2 minutes. If after 5 minutes your file is not available, then an error has occurred.
This service is provided as a convenience to users, no other guarantees are made, however, to ensure the security and privacy of your data:
The source-code and setup instructions for this project are visible on Github at http://github.com/pmarkert/freehashservice. The project is hosted inside of Amazon AWS. The equivalent functionality of this service can also be accomplished on a local computer using any Mac or Linux workstation by following the instructions in this stackoverflow article. tl;dr - run the following command from a terminal prompt:
while read line; do echo -n $line|shasum -a 256 | awk '{ print $1 }'; done < file_to_hash.txt >hashed_results.txt


10 October 2019 • 10 min read
Mobile App
React Native
Having thoughts of building mobile applications with skillset constraint on web development? Me too, and I had picked up React Native as my stepping stone to create nearly native mobile application...
15 September 2019 • 1 min read
Deploy
Github
Newbie to Jekyll, as recommended by Pages at github.io. Let's give it a try!
Welcome
Hello world, this is my first Jekyll blog post.
I hope yo...
15 August 2019 • 10 min read
AWS
Lambda
Deploy
We will learn how to write some simple shell script and execute it. This is a demonstration on how to deploy lambda function through AWS CLI, executing built script from EC2 instance. Before you go...


DNS (Domain Name Service) spoofing, also referred to as DNS cache poisoning, is a form of computer security hacking in which corrupt Domain Name System data is introduced into the DNS resolver's cache, causing the name server to return an incorrect result record (e.g. IP address).
DNS Spoofing vs. ARP Poisoning
While both attacks are used to redirect users to fake (and possible malicious) websites, the way they go about it is different. Address Resolution Protocol (ARP) poisoning involves an attacker changing the Media Access Control (MAC) address and attacking the user's Ethernet LAN by changing the target computer's ARP cache with a forged ARP request and reply packets.
How does it work?
DNS Spoofing occurs when an attacker replies to DNS requests (which are sent to resolve the IP address of a host name) with false IP information. This means that a user can possibly connect to a malicious site, without even knowing that they have done so.
To perform a cache poisoning attack, the attacker exploits flaws in the DNS software.
This attack can be used to redirect users from a website to another site of the attacker's choosing. For example, an attacker spoofs the IP address DNS entries for a target website on a given DNS server and replaces them with the IP address of a server under their control. The attacker then creates files on the server under their control with names matching those on the target server. These files usually contain malicious content, such as computer worms or viruses. A user whose computer has referenced the poisoned DNS server gets tricked into accepting content coming from a non-authentic server and unknowingly downloads the malicious content. This technique can also be used for phishing attacks, where a fake version of a genuine website is created to gather personal details such as bank and credit/debit card details.


Checkout
Click on the store name to see items
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PPK
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Sell by
Amount
total item cost
Part of a sale
Order summary
Press approve to finalize order or cancel to go back to local stores page.
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Total
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Eden Hazard backs former club Chelsea to beat Manchester City this weekend
Frank Lampard's young team travel to the champions on Saturday hoping to become the fourth side to beat them in the Premier League so far this season
by Alex MilneEden Hazard believes Chelsea can further dent Manchester City's title hopes by beating them at the Etihad Stadium on Saturday.
The Blues are on a fantastic run of form, having won their last six Premier League games in a row, but they face a stern test when they face Pep Guardiola's side.
Hazard, who left Chelsea for Real Madrid over the summer, fancies his former team to do Liverpool a favour in the title race and boost their own chances of finishing in the top four.
When asked by Sky Sports if he thinks Chelsea can beat City Hazard said: "Of course I think so. It's not going to be easy. It's hard to play there.
"Last time we played them we lost 6-0, so bad memories for me and Chelsea. But I think this season they are really good. They are really young, but they are really good, so why not go there and win the game?
"When I watch them, no Hazard but it's a party! They can score, they can score a lot of goals, the fans are happy, they are third in the league, so it's going to be a great season. They just need to stay there, win games and that's it."
Hazard has had a mixed start to his Madrid career having struggled with injuries, and so far he has only scored two goals for the club, creating two assists.
He managed to find the back of the net twice for Belgium in their recent Euro 2020 qualifier against Russia, however, and he will be hoping to take that form with him into the domestic season.
Madrid host Real Sociedad on Saturday hoping to maintain their title push, with the Spanish giants currently level on 25 points with bitter rivals Barcelona at the top of the table.


View Project on GitHub
Gregorian Calendar
Persian Calendar Basic
Persian Calendar Advanced
The most accurate Persian calendar
Check!


R
eveal
the
I
mage
↙
----- play again -----
↘
Normal
Rules
Hard
Score:
Timer:
Your Score
High Scores
Delete Scores
*click Rules button to hide this info
<begin>
Timer starts after 1st block is clicked. 1 point off for every second.
After timer reaches 0, clicking on blocks is disabled. 2 points off for every second.
Each block has 2 levels of clicks.
First click level reveals a blur of the background image. 2 points off.
2nd click level removes the blur. 3 points off.
Clicking on a wrong answer will take off 100 points.
Normal mode starts at 500 points. Hard mode starts at 550 points.
Top 5 high scores are stored locally in the browser.
Scores less than -500 are displayed as -500.
</end>
Guess It!
Armadillo
Beaver
Bull
Cat
Dog
Elephant
Fox
Gecko
Kangaroo
Meerkat
Rabbit
Raccoon
Tiger


Roll Results
Current Score:
125
Category
Roll
Score
FULL HOUSE
1 2 3 4 5
1
You rolled a
9 9 9 9 9
Score in ONEs Category
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Roll Dice


IOLazy Demo
A small JavaScript image lazy loading library based off of Lazy Loading Images with Intersection Observer
Setup
Grab the latest release from io-lazyload releases
Or install it through NPM with npm install iolazyload
Place iolazy.min.js
from dist/js as the last script in the
head of your document.

<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">

Add the class of lazyload
to the image(s) you wish to lazyload and
modify the src attribute to read data-src

Initialize the script before the closing body tag (or in your own JS file)

Examples
This Is a background image in CSS
Options
There are three (3) options available to pass to IOlazy:
-
image
-
threshold
-
rootMargin
image
This is the class or image element you wish to lazy load.

Threshold
How much the image element should be visible before we load the image. Values range from 0 to 1.0. A value
of 1 passed in as a threshold means the image is completely visible in the browser viewport. A value of 0.0
means "any non-zero number of pixels". The Library's default is 0.006

Root Margin
Margin added around the root. Can have values similar to the CSS margin property, e.g. "10px 20px 30px 40px" (top, right, bottom, left). This set of values serves to grow or shrink each side of the root element's bounding box before computing intersections. Defaults to all zeros.

Responsive Images
To lazyload your responsive images using srcset you'll need to prefix your srcset attribute with data-srcset

Your src
attribute can be as shown above a low quality placeholder or a
1px transparent gif.
If you use a transparent gif or no src attribute you should provide a fallback for browsers that don't use javascript or for instances where the script fails or another script blocks the page.

Browser Support
The lazyload script itself uses classList
which is Internet Explorer 10+. If you have to support lower than that you'll need to use a polyfill (found under 'resources' here) or use the Polyfill service mentioned below under "polyfill".
- Internet Explorer 10+
- Internet Explorer Mobile 10+
- Safari and iOS Safari (chrome included) 6.1+
- Chrome, FireFox, Edge, Opera and all other "modern" browsers
Intersection Observer is supported in these browsers without a polyfill
PolyFill
This demo uses PolyFill.io to supply the polyfill. It's also noted above in the setup portion.
If you don't want to use another third party there is a polyfill provided by the Web Incubator Community Group @ IntersectionObserver Polyfill
The polyfill supports all Firefox, Chrome, Edge, Safari 6+, Internet Explorer 7+. Intersection Observer Polyfill Browser Support
Source
Using Native lazy Load
You can forgo using this library to lazy load images in the instance the the browser supports the "loading" attribute.
You'll need to add the loading
attribute to your each of your image tags in order for this to work properly.

Once your images have the loading attribute we'll need to add a bit of javascript in your page, or your own javascript bundle/file. How you choose to do this is up to you!
In this file we will query for all the images on our page, grab the data-src
attribute and make that the src
of our image tags. If your page contains responsive images with the "srcset" attribute you'll need to also grab those attributes and apply those as well.
Example of using the native lazy loading instead of this library.

You'll need to make a decision here as to how you want to load the IOLazy library - in your bundle, a loader etc. Since there are multiple ways for this to happen I'll show you the dynamically imported version.

For a full example of using Native Lazyload see this Using Native Lazyload with IOLazy example page.


Chapter 6 Education & Training
Researchers often view software development skills as separate from, but complementary to their disciplinary training. This perspective introduces a dilemma for researchers, particularly for early-career researchers, who need to master their discipline and simultaneously acquire sufficiently-good software engineering skills. When forced to choose, they typically make sacrifices or take shortcuts in software engineering, as they view their disciplinary skills and knowledge as more important to their careers, and more rewarded by current incentive systems. Further, many researchers do not aspire to become software engineering experts, but approach the acquisition of software skills as a means to a research end. As a result of this approach, there are valuable skills such as packaging, testing, releasing, archiving, and even designing research software that are dramatically underdeveloped in the overall research ecosystem.
Previous studies demonstrate that researchers are rarely purposely trained to develop software. A
2017 survey of US National Postdoctoral Association members regarding postdocs’ use of software in
research and their training regarding software development found that 95% of respondents use research
software. However, 54% (n = 104) of the respondents to this survey reported that they have not
received any training in software development (???). A similar survey of software
use and training from the UK repeats this finding: In 2014, Heatrick et al. asked researchers at
randomly selected researchers in 15 Russell Group universities about their software use and training.
Of the 417 responses, 45% report having no training in software development. Of the 55% of respondents
that reported having received some training in software development 40% had received some form of
formal training in a software development course, and 15% were self-taught. In further analyzing
these results, Hettrick et al. report that 21% of respondents who develop their own software had
no training in software development (???; ???). A 2016 survey
of 704 PIs from the Biological Sciences Directorate of the US NSF found the most pressing unmet
needs are training in data integration, data management, and scaling analyses for HPC (???).
And a 2018 survey of almost 1600 scientist-developers found that 82% of respondents felt that
they were spending “more time” or “much more time” developing software than they did 10 years ago (???).
Not only is there a gap in software development training in general, there’s also an additional gap across gender. When analyzed by gender (self reported binary of men and women) the first two surveys show that only 39% of female respondents in the UK and 32% in the USA report have received some form of software development training, compared to 63% of male respondents in the UK and 64% in the USA.
This lack of training is not new, nor is it newly discovered. Greg Wilson has written (???; ???) about the history of Software Carpentry:
In 1995–96, [Wilson] organized a series of articles in IEEE Computational Science & Engineering titled, “What Should Computer Scientists Teach to Physical Scientists and Engineers?”. These grew out of the frustration he had working with scientists who wanted to run before they could walk, i.e., to parallelize complex programs that were not broken down into self-contained functions, that did not have any automated tests, and that were not under version control.
In response, John Reynders (then director of the Advanced Computing Laboratory at Los Alamos National Laboratory) invited [Wilson] and Brent Gorda (now at Intel) to teach a week-long course to LANL staff. This course ran for the first time in July 1998, and was repeated nine times over the next four years. It eventually wound down as Gorda and [Wilson] moved on to other projects, but two valuable lessons were learned:
There is tremendous pent-up demand for training in basic skills.
Textbook software engineering is not the right thing to teach most scientists.
This led to the Software Carpentry organization, which built a successful train-the-trainer model for collaborative lesson development and delivery, and which was used as a model to build Data Carpentry, which then merged with Software Carpentry into The Carpentries, and now also includes Library Carpentry. The Carpentries has developed highly impactful training content and events that meet an intermediate need for training researchers to better develop software. Many computing centers, including those funded by NSF and DOE, have also long had user training in some types of development (similar to that of Los Alamos), for example, joint activities run by TeraGrid and now XSEDE, and Argonne’s ATPESC. Given the success of the Carpentries, these computing center training events often either include or contribute to Carpentries lessons, or complement them In addition, other NSF-funded software institutes (e.g., MolSSI, SGCI, and IRIS-HEP) are also developing training resources for their target communities, similarly making use of, contributing to, and complementing the Carpentries.
We believe that there is an important role that a US-based research software institute can play to expand the breadth and depth of the training available, and to further coordinate and facilitate the acquisition of software engineering expertise by researchers engaged in various development activities. Rather than reinventing content, we anticipate collaborating with and pointing to relevant resources developed through other related efforts.
The Education & Training thrust of the URSSI institute complements and builds upon existing efforts through the following activities. We plan to:
Develop and annually run a summer school on practices for sustainable software development for research software
Develop a Research Project Carpentry lesson program that teaches people how to turn code into a formal project
Plan a review service for software project plans (and note that the implementation of this plan is likely out of scope of URSSI by itself)
Evaluate and document successes and failures of industrial software development practices in research software
Compile and maintain a body of knowledge of best practices for research software development
- this URSSI activity will seek to aggregate both existing practices, and serve as an outlet for emerging practices from other domain-specific software institutes (e.g., MolSSI, SGCI, IRIS-HEP)
6.1 Resources
This area requires the following staff:
Training Lead - who will coordinate all training activities and assessment of those activities [~1 FTE]
Assessment Coordinator - who will coordinate the assessment efforts (this position could be shared with other parts of the project) [~.5 FTE]
Research Coordinator who will lead the effort to gather experiences from research software projects [.25-.5 FTE]
6.2 Methods
This section provides more details on how we plan to design and execute each of these activities within URSSI.
6.2.1 Develop and annually run a summer school on practices for sustainable software development for research software
Overview: During the conceptualization phase, we identified a need for additional, focused training opportunities on sustainable software development practices for researchers who were not in a domain covered by an existing institute. One of the primary reasons for this need is that it is generally difficult for professors to make room for this type of content. We plan to meet this need with a summer school, which is a proven method for providing hand-on training to transfer these skills to students, in this case on sustainable software development practices for research software development. We conducted a Pilot Winter School as part of the conceptualization process. The details of that activity appear in Chapter 2. The plan described here builds on the key lessons learned, including: (1) the school needs to be longer to allow for both discussion time and focused time for students to work on their own code, applying the lessons from the lectures and (2) the presence of additional helpers outside of the primary instructors was quite beneficial to help answer specific questions from the students.
Content: Based on the needs identified during our survey, workshops, and other discussions, we identified the following topics as candidates for inclusion in such a school: (1) human factors in the software development process, (2) peer code review, (3) modern version control (i.e., git), (4) software design, (5) software testing, (6) open science principles, and (7) software citation.
Format: The summer school will be a 1-week in-person event. We plan to follow the successful PNAS hackweek model (???) where we lecture in the morning and provide time in the afternoons for hacking and putting concepts into practice. We will invite instructors with expertise in the different topic areas (see above) to participate and deliver the lectures in the mornings. To facilitate the afternoon “hacking” time, we will ask school participants to bring a code project they wish to work on during the school. The afternoon “hacking sessions” will allow ample time for the participants to have hands-on time applying the concepts learned to their own code projects. These sessions also provide time for code reviews and other ad hoc topics that arise during the school. The instructors will be available during this afternoon time to help the participants as needed. By the end of the school, the participants should have substantial intellectual knowledge and experiential knowledge. Because researchers often do not have sufficient time for training or to attend events like this one, as an alternative to the face-to-face summer school events, we also plan to package the content into Carpentries-style lessons that can be used either in shorter training sessions, webinars, or asynchronously.
6.2.2 Develop a Research Project Carpentry lesson program that teaches people how to turn code into a formal project
Overview: One challenge faced by many research software developers is how to convert a small (individual) coding effort into a more formal project. This situation occurs frequently as researchers develop small units of code to accomplish tasks important in their own work. When other researchers are exposed to these code units, they often want either to use the code, add to the code, or both. This is the point when the researcher needs to convert that code into a more formal project, both to ensure quality as others use it and to allow the community to contribute to it. Therefore, we propose to develop a Research Project Carpentry lesson plan, along similar lines as Software Carpentry, that will help researchers make this transition from an individual coding effort to a community-focused project.
Content: The goal of this effort will be to help projects develop a good project plan. As such, the Research Project Carpentries lesson program will focus on helping researchers understand what content a good plan needs to contain. It will also provide information researchers need for making choices about which of the various practices are most relevant to their particular project. The topics to be covered in Research Project Carpentry include: (1) Requirements solicitation, (2) Agility, (3) Issue tracking, (4) Source control, (5) Testing, (6) Documentation, (7), Pair programming, (8) Code review, (9) Metrics, and (10) Estimating Developer Time. Note that there is some overlap between the focus of Research Project Carpentry and the Summer School described above. We anticipate reusing relevant content between these efforts.
Format: We will develop modules on the topics above as carpentries-style modules. This approach will allow the Research Projects Carpentries materials to be used either in a face-to-face training course, like the Summer School, or as stand-alone units. This flexibility will allow researchers to access the content in a mechanism that is most relevant to them.
Note: While this activity is relevant to the URSSI effort, we believe that it will have appeal beyond URSSI. Therefore, even though we include it here in the plan, we will seek funding to support it from organizations outside of NSF. We also plan to solicit other organizations and individuals to contribute to this activity. Therefore, while URSSI will drive it and encourage external participation, URSSI will not have the bandwidth to perform all of the work.
6.2.3 Plan a review service for software project plans
Building on the work done for Research Project Carpentries, URSSI has identified the need for a service where experts review and comment on proposed project plans. Researchers who are interested in this service would submit a project plan in a specified format. Then, experts would review the plan to ensure both that it is (1) complete with respect to the content necessary for the type of project and its current stage as well as (2) the choices made for each aspect of the project plan are consistent with best practices. Once the review is completed, the reviewer would work with the project team to help resolve any deficiencies identified in the original project plan.
This activity is likely beyond the scope of URSSI, so we will create an initial plan for it, then seek to partner with other organizations who are pursuing similar efforts and explore external funding sources. In addition to the operating service, URSSI and partners will need to develop and make available the following resources: (1) templates for project plans, (2) checklists for researchers to follow when developing the plan, and (3) guidance on how to choose from various alternatives for each section of the plan.
6.2.4 Evaluate and document successes and failures of the use of industrial software development practices in research software
Industrial software engineering has developed and refined a number of best practices for software engineering and software development. It is not always clear which of these practices are relevant for use in developing research software. It is also not always clear which aspects of the research software domain motivate the need to develop new software engineering or software development practices to handle the unique contexts within which research software exists. To accomplish this activity, URSSI will need to perform the following steps.
First, URSSI will gather experience reports describing the successful and unsuccessful use of industrial software engineering and software development practices in research software projects. Gathering these experiences will involve conducting surveys and interviews of developers of research software to document important information. In addition to the successes and failures with specific practices, the experience reports will also document cases where developers of research software encountered a situation or a problem for which they were unable to find software engineering or software development practices that were relevant. Cases where there is a lack of relevant practices suggest opportunities for creation of new practices.
Second, URSSI will analyze the experience reports to systematize the successes and failures and develop a series of evidence-based lessons learned. Because this information will be based upon real experiences from research projects, they will have great value to other research software projects.
The results of these efforts will feed into Activity 5 by providing concrete experiences that we can document into best practices. These experiences will be beneficial because we will draw them from experiences on real projects, with knowledge about the context within which the practice works. This context will help provide a description of the limitations on the practice, which is equally important to document.
6.2.5 Compile and maintain a body of knowledge of best practices for research software development
Based on the outcomes of the above activities, URSSI will develop a portal to provide living information about best practices for research software development. In addition to including content developed from our own activities, URSSI will also actively find, through various means that include workshops and surveys, and curate information from other sites related to the development of research or scientific software. We will specifically ensure that information produced by the Carpentries is included, as relevant.
We will curate the following types of information:
Checklists for sustainable software - much of the content here will overlap with ideas discussed in earlier activities.
Templates for use in developing software, including templates for project plans, templates for documenting requirements, templates for test planning, and templates for community guidelines and policies.
Badging efforts that provide recognition for various qualities of research software and research software projects.
In addition to best practices, the URSSI clearinghouse will also curate information about training resources. In addition to providing links to the URSSI-developed training (described above), we will also link to other relevant training resources developed through other efforts like MoLSSI, SGCI, and IRIS-HEP. To help individual researchers and teams better understand how to make use of the various training options, we will develop roadmaps that guide people to the most relevant training for their current situation.
6.3 Cross-cutting Activities
To support the five activities described above, URSSI will also need to perform a number of cross-cutting activities as follows:
Curriculum Development and Coordination To achieve the goals stated above, URSSI will need to support the development of new training materials. We anticipate providing support to experts in the research software community who can take their existing knowledge and package it in a Carpentries-style format that is most amenable to be used in the various types of training activities URSSI will support. Importantly, the training proposed above is meant to build upon, and provide a compliment to existing educational outreach efforts. This training will provide follow on, more advanced content than the Carpentries and will be longer in duration.
Instructor Training To help deliver all of the developed content to a wide range of audiences in various locations, URSSI will need to provide support for training instructors. These instructors will be integral to the success of the Summer School and the Research Projects Carpentry.
Piloting Workshops As we develop content, we will conduct smaller workshops to pilot the content to ensure its sufficiency before deploying it to a larger audience. Because we will be asking people to participate in content development by providing feedback, we will need to have resources to support the travel for instructors and participants.
Gathering Experiences Successes and Failures of Software Practices URSSI will support a team of researchers, who are experienced in survey and interview methods, to gather experiences. URSSI will need to provide support for the time required to gather these experiences as well as the travel for the team to meet with members of research projects.
Conducting Workshops URSSI will provide support for the Summer School and Research Project Carpentries instructors to travel to the workshops. URSSI will also provide small fellowships for the instructors to compensate for the time required to participate. In addition, to increase participation from a diverse community, URSSI will provide travel support for a small number of workshop participants, determined based on need in individual cases.
6.4 Metrics/Milestones
To ensure that the training activities are providing sufficient content, URSSI will conduct numerous assessment activities. These activities will occur both concurrently with the training activities, i.e., as entry and exit surveys, as well as longitudinally after training. The benefit of conducting assessment longitudinally is that it provides insight into whether the content learned during training is viewed as useful over time as real projects evolve.
Some of the specific measures that we will gather include:
Post event evaluations - for workshops and training courses, we will conduct follow-up surveys to evaluate the value of the content. We will conduct evaluations immediately at the end of each event while the information is fresh in the attendees’ minds. We will then conduct a follow-up survey 6 months, 1 year, and 3 years later to evaluate the applicability of the content to the attendees’ work.
Demand - we will measure demand for the content by the number of people who apply to attend various training events and workshops.
Use of material - we will measure downloads and use of the information that we produce.


Matt Piercy
Software Engineer at
Chime
, husband, and father of two.
I like running, memes, and music. I also co-host a
podcast
.
View Resume


Contains sessions info (e.g start and end dates) and the Course Versioning used on the Phoenix platform
| Name | Description |
|---|---|
| course_id | 22 character unique string identifying an individual course |
| on_demand_session_id | unique identifer for a course's session on the Phoenix platform |
| on_demand_sessions_start_ts | The start date of a Phoenix session |
| on_demand_sessions_end_ts | The end date of a Phoenix session |
| on_demand_sessions_enrollment_end_ts | The end date of the enrollment period of a Phoenix session |
| course_branch_id | Each session is associated with a single branch. Learners enrolled in a session see the course contents associated with this branch. |
CREATE TABLE on_demand_sessions ( course_id varchar ,on_demand_session_id varchar ,on_demand_sessions_start_ts timestamp ,on_demand_sessions_end_ts timestamp ,on_demand_sessions_enrollment_end_ts timestamp ,course_branch_id varchar );


WATER: Water ice might exist in the bottoms of craters at Mercury's poles (the most northern and most southern tips)!
SOIL: The soil on Mercury is really thin dust! This is almost the same as the thin "soil" on our moon.
ATMOSPHERE: There's a very thin, weak atmosphere, mostly made of dust blown around by solar winds (wind caused by the Sun!).
GRAVITY: Mercury has really weak gravity, only 38% of Earth's! This means you would be able to jump three times higher here!
Mercury is the closest planet to the Sun and the smallest planet in the solar system! Did you know that Mercury is only the second hottest planet, even though it's closest to the Sun? Can you guess why?


Group by type
Group by owner
Group by nothing
Balance greater than
Apply filter
Clear filter
Name
Balance
Account Type
Account Owner


 Keep using the newest version, and if you gotta switch... maybe you gotta switch.
Sure why not! Shout at @philsturgeon on Twitter, or send a pull request to the GitHub repo.
This used to be entirely manual, which was a horrendous to keep up to date. Now we parse phpinfo() on public URLs that hosts make available, like this:
http://php70.hosteurope-infos.de/phpinfo.php
Not all hosts have them so some are still entirely manual. Either way, sending us patch version changes, changes in the default versions, or new public phpinfo URLs can be done via updating hosts.yml.
Read the contributing guide on GitHub.
I have a bit of a todo list, and I would love help if you're bored.


{{ partial "header.html" . }} {{ partial "navigation.html" . }}
Budibase is not just about developing web apps, it's also about solving your problems. Budibase's automation functionality allows you to automate tasks and processes so you can focus on your most important work.


Contents
Currently queries can be submitted to searchrefiner in two query languages. The first query language is Ovid MEDLINE. these queries look like this:
1. exp ORTHODONTICS/ 2. orthodontic$.mp. 3. or/1-2 4. (retention or retain$).mp. 5. (stabilise$ or stabilize$).mp. 6. (fraenectom$ or frenectom$).mp. 7. (fiberotom$ or fibreotom$).mp. 8. interproximal stripping.mp. 9. pericision.mp. 10. reproximat$.mp. 11. ((gingiv$ or periodont$).mp. adj4 surg$).mp. 12. (retain or retention).mp. 13. 11 and 12 14. or/4-10 15. 13 or 14 16. 3 and 15
In the Ovid MEDLINE query language, keywords of the query can be expressed individually on lines (e.g. line 1 or 2), or grouped together (e.g. line 4). These keyword clauses can be combined with infix logical operators if grouped on a single line or by stating which lines to combine (e.g. line 15 combines the clauses specified on line 13 and 14 with the OR operator). Keywords are restricted to fields by the two letter identifier at the end (e.g. .mp., which restricts the keyword to the title, abstract, and MeSH headings). MeSH only keywords are specified with / and can be exploded with exp (e.g. line 1). Keywords can also be explicitly stemmed or expanded with the $ and * modifiers.
For more information about building MEDLINE queries, see http://ospguides.ovid.com/OSPguides/medline.htm
In the PubMed query language, queries look like this:
((ORTHODONTICS[Mesh] OR orthodontic~[All Fields]) AND (((surg~[All Fields] ADJ4 (gingiv~[All Fields] OR periodont~[All Fields])) AND (retain[All Fields] OR retention[All Fields])) OR (reproximat~[All Fields] OR "interproximal stripping"[All Fields] OR pericision[All Fields] OR (retention[All Fields] OR retain~[All Fields]) OR (stabilise~[All Fields] OR stabilize~[All Fields]) OR (fraenectom~[All Fields] OR frenectom~[All Fields]) OR (fiberotom~[All Fields] OR fibreotom~[All Fields]))))
In PubMed query syntax, the query is contained on a single line, fields are indicated by square brackets and keywords are grouped with infix logical operators.
For more information about building PubMed queries, see https://www.nlm.nih.gov/bsd/disted/pubmedtutorial/cover.html
Loading known-relevant PMIDs into searchrefiner allows one to identify how effective a query is at retrieving a baseline set of citations. To load PMIDs into searchrefiner, visit the settings page (also accessible from the sidebar). Type or paste in the PMIDs of citations that the query that is being formulated should retrieve. These statistics will then be visible in the QueryVis tool.
This is a two-way binding of textual query to a structured editor. This allows users to paste in existing queries (e.g. from other interfaces or existing systematic reviews) and edit them conveniently in a structured way by dragging and dropping clauses of the Boolean query. The logical operators, fields, queries, and MeSH explosion can be edited in this structured editor and all updates are reflected in the textual editor in real time. The structured editor can be used to supplement search interfaces in other tools because of this two way binding. For example, a query can be formulated in PubMed, but to make refinements to it without introducing syntactic errors one may paste it into the textual editor, edit in the structured editor, and copy it back to PubMed via the textual editor. The structured editor removes the requirement for understanding the details of such a query. It allows, in fact, novice users to write and edit queries without learning the different query syntaxes for each database that is used to retrieve literature for systematic reviews (all queries syntaxes look the same in the structured editor and the structured editor can output queries in different syntaxes).
By using the tree view, one may visualise the number of citations that are retrieved by each clause of the query, and how the number of retrieved citations is affected by logical operators (i.e. AND, OR, NOT, etc.). This visualisation can be augmented with relevance assessments in order to ensure a monitor the retrieval of number of known relevant citations, or for training purposes.
{{template "footer"}}


This is my personal web site so it is all about me :-)
Also, this is an open source project. Here is the keywords : angular, firebase, firebase hosting, firebase functions, cloud firestore, GCP


Hello Team GeekChic, this is Dakota-- your server!
I'm just forwarding along a message from our email contact form, you may read it below!
(Keep in mind the details are in the message block)
Keep Carrying On,
Your Server, Dakota
---------- Forwarded message ----------
From: {{ sender }} <{{ email }}>
Date: {% now "D, M d, Y \a\t h:i A" %}
Subject: {{ subject }}
{{ message }}


StarFight - Demo - Shoot-em up style game
Fruit Search Engine - Demo
MMA - Mixed Martial Arts - Fighters - Demo - (Uses YuckFoo code)
LinkLore - Fun Listing Links - Demo - (Uses YuckFoo code)
YuckFoo - YouTube Popup Launcher - Quick Demo


@(session: models.Session, request: play.mvc.Http.Request) @base(session, request, "Forbidden", "error") {
You do not have access to this page.


FIND A SHOWROOM
BOOK AN APPOINTMENT
We reserve the right to amend any terms and conditions at our sole discretion. For assistance email<EMAIL_ADDRESS>Update preferences or unsubscribe
View email as a webpage


Chris Jol
Front-end web developer with over 4 years experience working with html, css, and javascript.


ussr.samples.odin
Class OdinController
java.lang.Object
ussr.model.ControllerImpl
ussr.samples.odin.OdinController
- All Implemented Interfaces:
- PacketReceivedObserver, Controller
- Direct Known Subclasses:
- OdinActBasedController, OdinBallController, OdinSampleController1, OdinSampleHingeController, OdinSampleWheelController
public abstract class OdinController
- extends ControllerImpl
- implements PacketReceivedObserver
Controller implementation for the Odin robot
- Author:
- Modular Robots @ MMMI
|
Field Summary |
protected java.lang.String |
type
|
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
type
protected java.lang.String type
OdinController
public OdinController()
setModule
public void setModule(Module module)
- Description copied from interface:
Controller
- This method is called by the simulator to provide the controller code with a
reference to the module.
- Specified by:
setModule
in interface Controller
- Overrides:
setModule
in class ControllerImpl
- Parameters:
module
- the hardware to associate with the controller- See Also:
Controller.setModule(ussr.model.Module)
isActuating
public boolean isActuating()
actuate
public void actuate(float pos)
disactivate
public void disactivate()
setColor
public void setColor(float r,
float g,
float b)
getActuatorPosition
public float getActuatorPosition()
isConnected
public boolean isConnected(int i)
sendMessage
public byte sendMessage(byte[] message,
byte messageSize,
byte connector)
packetReceived
public void packetReceived(Receiver device)
- Description copied from interface:
PacketReceivedObserver
- Notification method called by a receiver to which this observer has been subscribed
whenever a packet arrives
- Specified by:
packetReceived
in interface PacketReceivedObserver
- Parameters:
device
- the device where a packet has been received
getDebugID
public int getDebugID()
getType
public java.lang.String getType()
delay
public void delay(int ms)
handleMessage
public void handleMessage(byte[] message,
int messageSize,
int channel)
- Controllers should generally overwrite this method to receive messages
- Parameters:
message
- messageSize
- channel
-


| | |
- __builtin__.object
-
- GenericHttpClient
- GenericToken
- HttpResponse
- exceptions.Exception(exceptions.BaseException)
-
- Error
-
- ContentLengthRequired
- UnparsableUrlObject
class GenericHttpClient(__builtin__.object) |
| | |
Methods defined here:
- __init__(self, http_client, headers=None)
- Args:
http_client: An object which provides a request method to make an HTTP
request. The request method in GenericHttpClient performs a
call-through to the contained HTTP client object.
headers: A dictionary containing HTTP headers which should be included
in every HTTP request. Common persistent headers include
'User-Agent'.
- delete(self, url, headers=None)
- get(self, url, headers=None)
- post(self, url, data, headers=None)
- put(self, url, data, headers=None)
- request(self, operation, url, data=None, headers=None)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- debug = False
|
class GenericToken(__builtin__.object) |
| |
Represents an Authorization token to be added to HTTP requests.
Some Authorization headers included calculated fields (digital
signatures for example) which are based on the parameters of the HTTP
request. Therefore the token is responsible for signing the request
and adding the Authorization header. |
| |
Methods defined here:
- perform_request(self, http_client, operation, url, data=None, headers=None)
- For the GenericToken, no Authorization token is set.
- valid_for_scope(self, url)
- Tells the caller if the token authorizes access to the desired URL.
Since the generic token doesn't add an auth header, it is not valid for
any scope.
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
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class HttpResponse(__builtin__.object) |
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Methods defined here:
- __init__(self, body=None, status=None, reason=None, headers=None)
- Constructor for an HttpResponse object.
HttpResponse represents the server's response to an HTTP request from
the client. The HttpClient.request method returns a httplib.HTTPResponse
object and this HttpResponse class is designed to mirror the interface
exposed by httplib.HTTPResponse.
Args:
body: A file like object, with a read() method. The body could also
be a string, and the constructor will wrap it so that
HttpResponse.read(self) will return the full string.
status: The HTTP status code as an int. Example: 200, 201, 404.
reason: The HTTP status message which follows the code. Example:
OK, Created, Not Found
headers: A dictionary containing the HTTP headers in the server's
response. A common header in the response is Content-Length.
- getheader(self, name, default=None)
- read(self, amt=None)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
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Kalach may refer to:
- Alberto Kalach (b. 1960), Mexican architect
- Kalach (food), a traditional Slavic bread
- Kalach Urban Settlement, an administrative division and a municipal formation which the town of Kalach and seven rural localities in Kalachevsky District of Voronezh Oblast, Russia are incorporated as
- Kalach (inhabited locality), several inhabited localities in Russia
- Kālach, alternative name of Kalaj, a village in Mazandaran Province of Iran
{{See also|Kalachevsky (disambiguation)|Kalacheyevsky (disambiguation)}}
{{Disambiguation|geo}}