EUR/GBP takes on fresh 6-month lows to 0.8521 on Brexit optimism
by Ross J Burland- EUR/GBP slides to a 6-month low as UK election polls point to a Tory victory.
- EUR/GBP bears are in control and target a 38.2% Fibo retracement around 0.84 the figure.
EUR/GBP is currently -0.21%, trading at 0.8545 having travelled between 0.8521 and 0.8560. The cross is predominately on the backfoot, down some 8.5% since its mid-summer peak in the 0.93 handle as Sterling picks up a Brexit-themed related bid with cable rallying close to 9% since the start of September.
The build-up to Brexit has seen a shift in sentiment whereby UK's PM, Boris Johnson, has managed to instil some confidence back into the negotiations between the UK and EU. However, despite that, the UK is on its way to the polls next month because of disagreements in the House of Commons about Brexit. GBP has managed to gain some traction despite such uncertainties in more recent day's though with the major pollsters suggesting that the Tory party may have enough support to form a majority government and considering Johnson’s Withdrawal Agreement.
The market appears to be betting that the chances of a no-deal Brexit are small and cable has gained ground from the 1.2870s to a high of 1.2985 while EUR/GBP has penetrated back below the 0.86 handle and marked a fresh 6-month low of 0.8521. Indeed, "GBP will continue to react positively to any news that suggests the PM Johnson’s hand is set to be strengthened," according to analysts at Rabobank.
Market positioning data
Looking to the latest CFTC Commitment of Traders Report, the net short GBP positions have dropped back for a ninth consecutive week. In fact, the shorts are at their lowest level since May given that PM Johnson seems to have the necessary numbers in parliament to pass his Brexit deal after the December 12 election. Meanwhile, net EUR short positions have also crept lower, but marginally so and without the same velocity as GBP shorts have been trimmed. "Focus is turning to the Lagarde era at the ECB. Although the new President will have to bridge a rift between differing factions in the governing council, the market is still priced for further easing next year," analysts at Rabobank argued.
Week ahead
Looking ahead for the week, on a light data calendar where we will only see November PMIs for German Manufacturing PMI and French Services PMI on the 22nd, instead, investors will look to two other key events.
On the 19th November, there will be a Johnson vs Corbyn TV Debate and will be broadcast by ITV at 20:00 as the UK leaders go head-to-head as the first of many debates to come in the election campaign. We may see some volatility in GBP during the Asian session where there is typically less liquidity. Then, on the 22nd November, we will have the European Central Bank President Lagarde, who is in favour of fiscal stimulus, delivering a keynote address at the European BankingCongress in Frankfurt. This could give the markets some insight into a personal bias for policy in preparation for her first ECB meeting on 12 December.
EUR/GBP levels
With the price of EUR/GBP capped by the 21-4hr moving average, bears are in control, targetting a 38.2% retracement of the July 2015 and YTD range around 0.84 the figure. Bulls will need to regain the 0.88 handle, just above the 200-day moving average.
EUR/GBP
| Overview | |
|---|---|
| Today last price | 0.8547 |
| Today Daily Change | -0.0020 |
| Today Daily Change % | -0.23 |
| Today daily open | 0.8567 |
| Trends | |
|---|---|
| Daily SMA20 | 0.8611 |
| Daily SMA50 | 0.875 |
| Daily SMA100 | 0.8909 |
| Daily SMA200 | 0.8806 |
| Levels | |
|---|---|
| Previous Daily High | 0.8575 |
| Previous Daily Low | 0.855 |
| Previous Weekly High | 0.8626 |
| Previous Weekly Low | 0.8544 |
| Previous Monthly High | 0.9022 |
| Previous Monthly Low | 0.8575 |
| Daily Fibonacci 38.2% | 0.8565 |
| Daily Fibonacci 61.8% | 0.8559 |
| Daily Pivot Point S1 | 0.8553 |
| Daily Pivot Point S2 | 0.8539 |
| Daily Pivot Point S3 | 0.8528 |
| Daily Pivot Point R1 | 0.8578 |
| Daily Pivot Point R2 | 0.8589 |
| Daily Pivot Point R3 | 0.8603 |


@import models.nonresident.HowBecameOwnerModel
@import uk.gov.hmrc.play.views.html.helpers.form
@import views.html.helpers._
@(howBecameOwnerForm: Form[HowBecameOwnerModel])(implicit request: Request[_])
@main_template(title = Messages("calc.howBecameOwner.question")) {
@Messages("calc.base.back")
@errorSummary(howBecameOwnerForm, "gainedBy")
@Messages("calc.howBecameOwner.question")
@form(action = controllers.nonresident.routes.HowBecameOwnerController.submitHowBecameOwner) {
@formInputRadioGroup(
field = howBecameOwnerForm("gainedBy"),
Seq(
"Bought" -> Messages("calc.howBecameOwner.bought"),
"Inherited" -> Messages("calc.howBecameOwner.inherited"),
"Gifted" -> Messages("calc.howBecameOwner.gifted")
),
'_legend -> Messages("calc.howBecameOwner.question"),
'_legendID -> "gainedBy",
'_legendClass -> "visuallyhidden",
'_labelAfter -> true,
'_labelClass -> "block-label",
'_groupClass -> "form-group radio-list",
'_fieldsetAttributes -> "aria-details = help"
)
}
}


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface Authenticator
The Authenticator
interface defines the service interface which
may be used by applications to enfore requests to be authenticated (see
login(HttpServletRequest, HttpServletResponse)
) or to end enforced
authentication (see logout(HttpServletRequest, HttpServletResponse)
). As such this service may be looked at as the functionality to enable
applications to log users in and out.
A very simple login script (using ESP here) could be implemented like this:
var auth = sling.getService(org.apache.sling.commons.auth.Authenticator); if (auth != null) { try { auth.login(request, response); return; // we are done here } catch (e) { // probably NoAuthenticationHandler exception } } // Authenticator service is missing or no AuthenticationHandler ... do whatever you want to for error handling ...
Likewise implementing a logout script (ESP, too) is equally simple:
if (request.authType) { // not logged in at all, no need to logout } else { var auth = sling.getService(org.apache.sling.commons.auth.Authenticator); if (auth != null) { auth.logout(request, response); } else { // handle the case of no Authenticator service to logout with } }
This interface is not intended to be implemented by applications but may be used to initiate the authentication process form a request processing servlet or script.
| Field Summary | |
|---|---|
static String |
LOGIN_RESOURCE
Name of the request attribute which may be set by the application to indicate to the login(HttpServletRequest, HttpServletResponse)
method to which resource access should actually be authenticated. |
static String |
SERVICE_NAME
The name under which this service is registered. |
| Method Summary | |
|---|---|
void |
login(javax.servlet.http.HttpServletRequest request,
javax.servlet.http.HttpServletResponse response)
Tries to login a request user for the current request. |
void |
logout(javax.servlet.http.HttpServletRequest request,
javax.servlet.http.HttpServletResponse response)
Logs out if the current request is authenticated. |
| Field Detail |
|---|
static final String SERVICE_NAME
static final String LOGIN_RESOURCE
login(HttpServletRequest, HttpServletResponse)
method to which resource access should actually be authenticated. If this
request attribute is not set or is the empty string, the
login(HttpServletRequest, HttpServletResponse)
method uses the
request path info (HttpServletRequest.getPathInfo()
) method
to find the resource to which to authenticate access.
This request attribute can be used by frontend servlets/scripts which
call into login(HttpServletRequest, HttpServletResponse)
on
behalf of users.
| Method Detail |
|---|
void login(javax.servlet.http.HttpServletRequest request, javax.servlet.http.HttpServletResponse response)
To identify the resource to which access should be authenticated the
request attribute is
considered. If the request attribute is not set the request path info (
resource
HttpServletRequest.getPathInfo()
) is used.
This method must be called on an uncommitted response since the
implementation may want to reset the response to start the authentication
process with a clean response. If the response is already committed an
IllegalStateException
is thrown.
After this method has finished, request processing should be terminated
and the response be considered committed and finished unless the
NoAuthenticationHandlerException
exception is thrown in which
case no response has been sent to the client.
request
- The object representing the client request.response
- The object representing the response to the client.
NoAuthenticationHandlerException
- If the service cannot find a way
to authenticate a request user.
IllegalStateException
- If the response has already been committed.void logout(javax.servlet.http.HttpServletRequest request, javax.servlet.http.HttpServletResponse response)
This method must be called on an uncommitted response since the
implementation may want to reset the response to restart the
authentication process with a clean response. If the response is already
committed an IllegalStateException
is thrown.
After this method has finished, request processing should be terminated and the response be considered committed and finished.
request
- The object representing the client request.response
- The object representing the response to the client.
IllegalStateException
- If the response has already been committed.
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


1) Each team starts with a bank balance of $10,000.
2) A game consists of up to 20 turns each representing about 2-10 years.
3) Each turn, enter your decisions within 120 seconds. The first three turns get extra time. A clock counts down
the
remaining time available. If the clock reaches zero and you have not submitted your decisions, then your company
does nothing during the turn.
4) On each turn, your team does the following:
(i) Construct production machinery. Machines cost $1,000, and last forever.
(ii) Operate your machinery, by choosing the number of your machines to operate. Each machine can produce one
unit
per turn. Production cost is $5,000 per machine operated.
5) The price received for each unit produced is determined by the market. More industry output causes lower
prices,
and less output causes higher prices (ceteris paribus). The price is determined only after all teams finish the
turn. Each team's revenues (price times units produced) are then added to the team’s bank account.
6) A history of the team’s past decisions is displayed in turn 2 and later. A market report, summarizing what
happened on previous turns for all teams, is available beginning in turn 2.
7) If your company goes bust, you can click on Resign. The game will then move faster, with no need to enter
decisions, and you will be able to start on the post-game questions.
Copyright © 2008 by Kenneth L. Simons. All Rights Reserved.


Hat’s Tea
问题描述 :
Hat is a member of PG Studio. Hat codes a lot and so he often buys tea at tea vending machine. But the tea vending machine just eat coins and spit out tea, if you feed the machine more coins than the tea’s price and the machine will not spit out your change.
Your program will be given numbers and types of coins Hat has and the tea price. The tea vending machine accepts coins of values 1, 5, 10 RMB (Jiao). The program should output which coins Hat has to use paying the tea so that he uses as many coins as possible.
Your program will be given numbers and types of coins Hat has and the tea price. The tea vending machine accepts coins of values 1, 5, 10 RMB (Jiao). The program should output which coins Hat has to use paying the tea so that he uses as many coins as possible.
输入:
Each line of the input contains four integer numbers separated by a single space describing one situation to solve. The first integer on the line N, , is the tea price in Jiao. Next four integers , , are the numbers of YiJiao (1 Jiao.RMB), WuJiao (5 Jiao.RMB), and ShiJiao (10 Jiao.RMB) in Hat’s valet. The last line of the input contains four zeros and no output should be generated for it.
输出:
For each situation, your program should output one line containing the string " T1 YiJiao, T2 WuJiao, and T3 ShiJiao ", where T1, T2, T3 are the numbers of coins of appropriate values Hat should use to pay the tea while using as many coins as possible. If Hat does not have enough coins to pay the tea exactly, your program should output "Hat cannot buy tea.”.
样例输入:
6653 226 72 352 578 5 127 951 0 0 0 0
样例输出:
Hat cannot buy tea. 3 YiJiao, 115 WuJiao, and 0 ShiJiao
题目要求刚好满足买饮料的钱
#include <iostream> using namespace std; int main() { int n,x1,x5,x10; int a,b,c; while(cin>>n>>x1>>x5>>x10&&(n+x1+x5+x10)) { a=b=c=0; if(n%5>x1) 如果n%5大于x1，那么即使满足后面的，也有零头剩余 { cout<<"Hat cannot buy tea."<<endl; continue; } a=n%5; n-=a; x1-=a; if(n<=x1) { a=n+a; printf("%d YiJiao, %d WuJiao, and %d ShiJiao\n",a,b,c); continue; } x1/=5; n-=5*x1; a+=5*x1; if(n<=5*x5) { b+=n/5; printf("%d YiJiao, %d WuJiao, and %d ShiJiao\n",a,b,c); continue; } n-=5*x5; b=x5; if(n%10==0) { if(n<=10*x10){ c+=n/10; printf("%d YiJiao, %d WuJiao, and %d ShiJiao\n",a,b,c); continue; } else { cout<<"Hat cannot buy tea."<<endl; continue; } } else { if(b>0) b--; else { if(a>=5) a-=5; else { cout<<"Hat cannot buy tea."<<endl; continue; } } n+=5; if(n<=10*x10) { c=n/10; printf("%d YiJiao, %d WuJiao, and %d ShiJiao\n",a,b,c); continue; } else { cout<<"Hat cannot buy tea."<<endl; continue; } } } return 0; }
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Good task for the group. Hold it up for every yeara??s winner. This is a excellent oppotunity for a lot more enhancement. Indeed, obtaining far better and much better is constantly the crucial. Just like my pal suggests on the truth about ab muscles, he just keeps obtaining much better.
样例输出和程序输出不吻合，修改一下样例输出吧。我用的是VC编译器，会提示我的i和j变量重复定义
这个是代码高亮功能，那就再换一个颜色
非常好，初学者就应该先理解递归
清华也会出这么无聊的题
这个也是算法导论的习题
27行 minStack.push(minValue); 去掉就对了
程序只能输出一个子序列
程序只能输出一个子序列
第一题选错了，答案是A
其实国内大部分公司对算法都不够重视。特别是中小型公司老板根本都不懂技术，也不懂什么是算法，从而也不要求程序员懂什么算法，做程序从来不考虑性能问题，只要页面能显示出来就是好程序，这是国内的现状，很无奈。
正确
对于输出格式不用控制？？还是直接使用cout输出。？？
此网站甚是喜欢，潜力巨大，请问站长，如何加入开发团队~


Where to install a package
Generally, if you have installed the complete Tycho
distribution, then you have nothing more to do.
If you have downloaded an independent Tycho package or
want to modify your installation, you might find
some useful information here.
In general, a Tcl script that is supplied with a Tycho packages
tries to do try several things to attempt to load required packages:
- Require the packages. This will work if packages have
been installed into the Tcl library directory.
To see what directory this is, simply start
itclsh
and enter
file dirname [info library]
- Search the sub-directories of the directory given by the
environment variable
PACKAGE_LIBRARY
, where
PACKAGE
is the name of the package in upper-case.
This is generally only useful for small, self-contained
packages.
- Search the sub-directories of the directories given
by the environment variable
TCL_PACKAGES
. Again,
this is mostly useful for small, self-contained packages.
- Search the sub-directories of the directories given
by the environment variable
TYCHO
. Assuming that this
directory points to an installation of Tycho, this will cause
every single package in the Tycho tree to be located.
Mostly, the first three options are useful if you have downloaded one
of the standalone packages, and startup time is important -- if the
package is installed in the Tycho tree, then large chunks of the tree
are scanned checking for packages that you aren't even going to need.
Copyright © 1998, The Regents of the University of California.
All rights reserved.
Last updated: 06/11/98,
comments to: johnr@eecs.berkeley.edu


A concrete implementation of InputStream that performs input operations from a stdio FILE*.
More...
#include <bond/io/stdioinputstream.h>
A concrete implementation of InputStream that performs input operations from a stdio FILE*.
◆ StdioInputStream() [1/5]
| Bond::StdioInputStream::StdioInputStream |
( |
FILE * |
file | ) |
|
|
inlineexplicit |
◆ StdioInputStream() [2/5]
| Bond::StdioInputStream::StdioInputStream |
( |
const char * |
fileName | ) |
|
|
explicit |
◆ StdioInputStream() [3/5]
◆ StdioInputStream() [4/5]
◆ ~StdioInputStream()
| virtual Bond::StdioInputStream::~StdioInputStream |
( |
| ) |
|
|
inlinevirtual |
◆ StdioInputStream() [5/5]
◆ AddOffset()
| virtual void Bond::StdioInputStream::AddOffset |
( |
off_t |
offset | ) |
|
|
inlineoverridevirtual |
◆ ClearError()
| virtual void Bond::StdioInputStream::ClearError |
( |
| ) |
|
|
inlineoverridevirtual |
◆ Close()
| virtual void Bond::StdioInputStream::Close |
( |
| ) |
|
|
overridevirtual |
◆ GetEndPosition()
| virtual pos_t Bond::StdioInputStream::GetEndPosition |
( |
| ) |
const |
|
overridevirtual |
◆ GetPosition()
| virtual pos_t Bond::StdioInputStream::GetPosition |
( |
| ) |
const |
|
inlineoverridevirtual |
◆ HasError()
| virtual bool Bond::StdioInputStream::HasError |
( |
| ) |
const |
|
inlineoverridevirtual |
◆ IsBound()
| bool Bond::StdioInputStream::IsBound |
( |
| ) |
const |
|
inline |
◆ IsEof()
| virtual bool Bond::StdioInputStream::IsEof |
( |
| ) |
const |
|
inlineoverridevirtual |
◆ operator=() [1/2]
◆ operator=() [2/2]
◆ Read() [1/2]
| virtual int Bond::StdioInputStream::Read |
( |
| ) |
|
|
inlineoverridevirtual |
◆ Read() [2/2]
| virtual size_t Bond::StdioInputStream::Read |
( |
uint8_t * |
bytes, |
|
|
size_t |
numBytes |
|
) |
| |
|
inlineoverridevirtual |
◆ SetPosition()
| virtual void Bond::StdioInputStream::SetPosition |
( |
off_t |
offset | ) |
|
|
inlineoverridevirtual |
◆ SetPositionFromEnd()
| virtual void Bond::StdioInputStream::SetPositionFromEnd |
( |
off_t |
offset | ) |
|
|
inlineoverridevirtual |
◆ VScan()
| virtual void Bond::StdioInputStream::VScan |
( |
const char * |
format, |
|
|
va_list |
argList |
|
) |
| |
|
inlineoverridevirtual |
The documentation for this class was generated from the following file:
- include/bond/io/stdioinputstream.h


new Pyjamas(versionopt, definesopt)
Constructs a new Pyjamas instance with default values
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
version |
string |
<optional> |
Current version of instance |
defines |
object |
<optional> |
Definitions of instance variables |
- Source:
Members
defines :object
Definitions of instance variables to persist from given instance
Type:
- object
- Source:
Example
{
foo : Number,
bar : String,
baz : MyOtherClass
}
upgrades :object
Defines a set of upgrade methods to apply to old saves to update
to the current source version
Type:
- object
- Source:
Example
{
'0.1.2' : function myUpgrader1(){},
'0.2.3' : function myUpgrader2(){}
}
version :string
Version identifier of current instance
Type:
- string
- Source:
Methods
(static) Pyjamas#upgrade(version, upgrader) → {Pyjamas.Pyjamas}
Registers an upgrade function to this Pyjamas instance. An upgrade
function must accept a copy of a old version of a save and the current
instance. It should either mutate the current instance of the save or
return an upgraded version.
Parameters:
| Name | Type | Description |
|---|---|---|
version |
string | Version code that indicates the version of the instance returned from this upgrade function |
upgrader |
function | Function to upgrade old save. Prototype: function( old : Object, current : Object) : Object |
- Source:
Returns:
An updated Pyjamas instance
- Type
- Pyjamas.Pyjamas


Attendees
Reuben Patrick
Harold Velasquez
Saqib Payne
Blanche Monroe
Alivia Bray
Trevor Peralta
Princess Horne
Jaydon Frame
Matt Chung
Amrita Cordova
Invite Common Attendees
{% extends 'base.html' %} {% block title %}LinkUp {% endblock title %} //modifying the title {% block nav-style %} {% endblock nav-style %} {% block content %}
Combined Schedule
Maybe a Breakdown Feature
Bar Graph of Top Available times


Populate LDC
Shortcuts
localhost
incognito localhost
staging
incognito staging
Settings
Approve
Decline
State:
Alabama
California
Florida
Hawaii
Idaho
Illinois
Indiana
Kansas
Louisiana
Maine
Minnesota
Mississippi
Missouri
New Mexico
Ohio
Oklahoma
Oregon
South Carolina
Tennessee
Texas
Utah
Washington
Wisconsin
Wyoming
Populate


Utilities required for the entity-component system to function.
ULong EntityID
Typedef for Entity Identifiers.
Definition: ECS.hpp:22
VLK_NODISCARD EntityID Create()
Creates an entity.
Definition: Entity.cpp:13
VLK_CXX14_CONSTEXPR EntityID global
A globally accessible entity with an ID of zero.
Definition: Entity.hpp:22
void Delete(EntityID eId)
Deletes an entity and all components attached to it.
Definition: Entity.cpp:22
Provides base class for components.
Provides primitive typedefs.


Dele Alli "very upset" by Mauricio Pochettino sacking
An emotional Dele Alli spoke about his heartache at Mauricio Pochettino's sacking as Tottenham boss.
Pochettino was fired last week after a poor start to the season and replaced by Jose Mourinho less than 12 hours later.
Alli was a key ally of Pochettino, having been brought to the club from MK Dons as an 18-year-old by the Argentinian, and was one of several players who went to visit their former coach.
"It's clear to everyone how much Poch meant to the players," he said ahead of Tottenham's Champions League clash with Olympiacos. "Being here for nearly five years he has seen me grow as a person and helped me a lot.
"I came here as an 18-year-old, new to it all and I've been through a lot of ups and downs here but he's helped me through it all. I can't thank him enough.
"I felt it was important as soon as I saw the news. I was very upset but I tried to get in contact with him and the next day I went to see him.
"It was just a conversation between two friends. He has helped me through a lot – I've seen him more than I see my own family in the last five years, so it was very tough.
When it first happened... you just blame yourself, you are sad and don't look at the big pictureDele Alli on Pochettino's dismissal
"But this is football and things change all the time so we know what we have to do. We have a job to do, we have to move forward.
"But for sure he is someone I'm going to stay in contact with.
"It will be different because he's not the manager but he's an amazing person and helped me so much throughout my career – not just in football but in life as well.

It did not take Mourinho long to make his mark on Alli after the Portuguese made a training ground joke about the midfielder playing like his brother.
Alli, who produced his best performance in recent months in Saturday's 3-2 win over West Ham, revealed that ribbing was the first thing Mourinho had said to him.
He accepted that Mourinho's comment was essentially a criticism of his form, but was happy to take it on the chin.
"I suppose (it was a criticism) but a lot of people have been saying that so it was nothing new," he added.
"In that sense. To have it honestly said to your face was nice because a lot of people would prefer to say it behind your back."
That was a barbed comment in relation to some strong criticism aimed at Alli over his drop in form over the last 12 months.
I am my own biggest critic. I know what I need to work onDele Alli on criticism
The 23-year-old, who has been left out of the last three England squads, admits he has ran out of excuses for his poor displays last season, but is not paying too much attention to the critics.
"It didn't shock me, playing in the Premier League you expect it when you are not performing to be criticised.
"For me I have been a professional for a few years now, I am still young and I have a lot to learn and improve.
"I am an experienced player but it is just important you listen to the right people.
"I am my own biggest critic. I know what I need to work on. Some peoples' opinions are for the right reasons and some are just to jump on the bandwagon.
"I know where I am in my career and I know where I want to improve."
body check tags ::


public class TokenInfo
extends java.lang.Object
implements java.lang.Cloneable
| Modifier and Type | Field and Description |
|---|---|
protected boolean |
clockOnToken_
True, if the token has a clock.
|
protected boolean |
dualCryptoOperations_
True, if the token supports dual crypto operations.
|
static long |
EFFECTIVELY_INFINITE
This is the value which can be used for ulMaxSessionCount and ulMaxRwSessionCount to express an
infinite number.
|
protected Version |
firmwareVersion_
The version of the firmware of this token.
|
protected long |
freePrivateMemory_
The amount of free memory for private objects on this token.
|
protected long |
freePublicMemory_
The amount of free memory for public objects on this token.
|
protected Version |
hardwareVersion_
The version of the hardware of this token.
|
protected java.lang.String |
label_
The label of this token.
|
protected boolean |
loginRequired_
True, if the token requires the user to login to perform certain operations.
|
protected java.lang.String |
manufacturerID_
The identifier of the manufacturer of this token.
|
protected long |
maxPinLen_
The maximum PIN length that this token allows.
|
protected long |
maxRwSessionCount_
Maximum number of concurrent (open) read-write sessions.
|
protected long |
maxSessionCount_
The maximum number of concurrent (open) sessions.
|
protected long |
minPinLen_
The minimum PIN length that this token allows.
|
protected java.lang.String |
model_
The model of this token.
|
protected boolean |
protectedAuthenticationPath_
True, if there are different means to authenticate the user than passing the user-PIN to a
login operation.
|
protected boolean |
restoreKeyNotNeeded_
True, if a successful save of a sessions cryptographic operations state always contains all
keys needed to restore the state of the session.
|
protected boolean |
rng_
True, if the token has a random numebr generator.
|
protected long |
rwSessionCount_
The current number of open read-write sessions.
|
protected boolean |
secondaryAuthentication_
True, if the token supports secondary authentication for private key objects.
|
protected java.lang.String |
serialNumber_
The serial number of this token.
|
protected long |
sessionCount_
The current number of open sessions.
|
protected boolean |
soPinCountLow_
True, if the security officer-PIN has been entered incorrectly at least once since the last
successful authentication.
|
protected boolean |
soPinFinalTry_
True, if the security officer has just one try left to supply the correct PIN before the
security officer-PIN gets locked.
|
protected boolean |
soPinLocked_
True, if the security officer-PIN is locked.
|
protected boolean |
soPinToBeChanged_
True, if the security officer-PIN value is the default value set by token initialization or
manufacturing.
|
protected java.util.Date |
time_
The current time on the token.
|
protected boolean |
tokenInitialized_
True, if the token is already initialized.
|
protected long |
totalPrivateMemory_
The total amount of memory for private objects on this token.
|
protected long |
totalPublicMemory_
The total amount of memory for public objects on this token.
|
static long |
UNAVAILABLE_INFORMATION
This is the value which can be used for ulMaxSessionCount, ulSessionCount, ulMaxRwSessionCount,
ulRwSessionCount, ulTotalPublicMemory, ulFreePublicMemory, ulTotalPrivateMemory, and
ulFreePrivateMemory to signal that the information is unavailable.
|
protected boolean |
userPinCountLow_
True, if the user-PIN has been entered incorrectly at least once since the last successful
authentication.
|
protected boolean |
userPinFinalTry_
True, if the user has just one try left to supply the correct PIN before the user-PIN gets
locked.
|
protected boolean |
userPinInitialized_
True, if the user-PIN is already initialized.
|
protected boolean |
userPinLocked_
True, if the user-PIN is locked.
|
protected boolean |
userPinToBeChanged_
True, if the user PIN value is the default value set by token initialization or manufacturing.
|
protected boolean |
writeProtected_
True, if the token is write protected.
|
| Modifier | Constructor and Description |
|---|---|
protected |
TokenInfo(CK_TOKEN_INFO ckTokenInfo)
Constructor taking CK_TOKEN_INFO as given returned by PKCS11.C_GetTokenInfo.
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.Object |
clone()
Create a (deep) clone of this object.
|
boolean |
equals(java.lang.Object otherObject)
Compares all member variables of this object with the other object.
|
Version |
getFirmwareVersion()
Get the version of the token's firmware.
|
long |
getFreePrivateMemory()
Get the amount of free memory for private objects.
|
long |
getFreePublicMemory()
Get the amount of free memory for public objects.
|
Version |
getHardwareVersion()
Get the version of the token's hardware.
|
java.lang.String |
getLabel()
Get the label of this token.
|
java.lang.String |
getManufacturerID()
Get the manufacturer identifier.
|
long |
getMaxPinLen()
Get the maximum length for the PIN.
|
long |
getMaxRwSessionCount()
Get the maximum allowed number of (open) concurrent read-write sessions.
|
long |
getMaxSessionCount()
Get the maximum allowed number of (open) concurrent sessions.
|
long |
getMinPinLen()
Get the minimum length for the PIN.
|
java.lang.String |
getModel()
Get the model of this token.
|
long |
getRwSessionCount()
Get the current number of open read-write sessions.
|
java.lang.String |
getSerialNumber()
Get the serial number of this token.
|
long |
getSessionCount()
Get the current number of open sessions.
|
java.util.Date |
getTime()
Get the current time of the token's clock.
|
long |
getTotalPrivateMemory()
Get the total amount of memory for private objects.
|
long |
getTotalPublicMemory()
Get the total amount of memory for public objects.
|
int |
hashCode()
The overriding of this method should ensure that the objects of this class work correctly in a
hashtable.
|
boolean |
isClockOnToken()
Check, if the token has an own clock.
|
boolean |
isDualCryptoOperations()
Check, if the token supports dual crypto operations.
|
boolean |
isLoginRequired()
Check, if the token requires the user to log in before certain operations can be performed.
|
boolean |
isProtectedAuthenticationPath()
Check, if the token has an protected authentication path.
|
boolean |
isRestoreKeyNotNeeded()
Check, if a successful save of a sessions cryptographic operations state always contains all
keys needed to restore the state of the session.
|
boolean |
isRNG()
Check, if the token has a random number generator.
|
boolean |
isSecondaryAuthentication()
Check, if the token supports secondary authentication for private key objects.
|
boolean |
isSoPinCountLow()
Check, if the security officer-PIN has been entered incorrectly at least once since the last
successful authentication.
|
boolean |
isSoPinFinalTry()
Check, if the security officer has just one try left to supply the correct PIN before the
security officer-PIN gets locked.
|
boolean |
isSoPinLocked()
Check, if the security officer-PIN is locked.
|
boolean |
isSoPinToBeChanged()
Check, if the security officer PIN value is the default value set by token initialization or
manufacturing.
|
boolean |
isTokenInitialized()
Check, if the token is already initialized.
|
boolean |
isUserPinCountLow()
Check, if the user-PIN has been entered incorrectly at least once since the last successful
authentication.
|
boolean |
isUserPinFinalTry()
Check, if the user has just one try left to supply the correct PIN before the user-PIN gets
locked.
|
boolean |
isUserPinInitialized()
Check, if the user-PIN is already initialized.
|
boolean |
isUserPinLocked()
Check, if the user-PIN is locked.
|
boolean |
isUserPinToBeChanged()
Check, if the user PIN value is the default value set by token initialization or manufacturing.
|
boolean |
isWriteProtected()
Check, if the token is write protected.
|
java.lang.String |
toString()
Returns the string representation of this object.
|
public static final long EFFECTIVELY_INFINITE
public static final long UNAVAILABLE_INFORMATION
protected java.lang.String label_
protected java.lang.String manufacturerID_
protected java.lang.String model_
protected java.lang.String serialNumber_
protected long maxSessionCount_
protected long sessionCount_
protected long maxRwSessionCount_
protected long rwSessionCount_
protected long maxPinLen_
protected long minPinLen_
protected long totalPublicMemory_
protected long freePublicMemory_
protected long totalPrivateMemory_
protected long freePrivateMemory_
protected Version hardwareVersion_
protected Version firmwareVersion_
protected java.util.Date time_
protected boolean rng_
protected boolean writeProtected_
protected boolean loginRequired_
protected boolean userPinInitialized_
protected boolean restoreKeyNotNeeded_
protected boolean clockOnToken_
protected boolean protectedAuthenticationPath_
protected boolean dualCryptoOperations_
protected boolean tokenInitialized_
protected boolean secondaryAuthentication_
protected boolean userPinCountLow_
protected boolean userPinFinalTry_
protected boolean userPinLocked_
protected boolean userPinToBeChanged_
protected boolean soPinCountLow_
protected boolean soPinFinalTry_
protected boolean soPinLocked_
protected boolean soPinToBeChanged_
protected TokenInfo(CK_TOKEN_INFO ckTokenInfo)
ckTokenInfo
- The CK_TOKEN_INFO object as returned by PKCS11.C_GetTokenInfo.public java.lang.Object clone()
clone
in class java.lang.Object
public java.lang.String getLabel()
public java.lang.String getManufacturerID()
public java.lang.String getModel()
public java.lang.String getSerialNumber()
public long getMaxSessionCount()
public long getSessionCount()
public long getMaxRwSessionCount()
public long getRwSessionCount()
public long getMaxPinLen()
public long getMinPinLen()
public long getTotalPublicMemory()
public long getFreePublicMemory()
public long getTotalPrivateMemory()
public long getFreePrivateMemory()
public Version getHardwareVersion()
public Version getFirmwareVersion()
public java.util.Date getTime()
isClockOnToken()
public boolean isRNG()
public boolean isWriteProtected()
public boolean isLoginRequired()
public boolean isUserPinInitialized()
public boolean isRestoreKeyNotNeeded()
public boolean isClockOnToken()
public boolean isProtectedAuthenticationPath()
public boolean isDualCryptoOperations()
public boolean isTokenInitialized()
public boolean isSecondaryAuthentication()
public boolean isUserPinCountLow()
public boolean isUserPinFinalTry()
public boolean isUserPinLocked()
public boolean isUserPinToBeChanged()
public boolean isSoPinCountLow()
public boolean isSoPinFinalTry()
public boolean isSoPinLocked()
public boolean isSoPinToBeChanged()
public java.lang.String toString()
toString
in class java.lang.Object
public boolean equals(java.lang.Object otherObject)
equals
in class java.lang.Object
otherObject
- The other TokenInfo object.public int hashCode()
hashCode
in class java.lang.Object
IAIK JavaSecurity Website http://jce.iaik.tugraz.at/
IAIK at Graz University of Technology, Austria, Europe
Copyright 2001-2004, IAIK, Graz University of Technology, Inffeldgasse 16a, 8010 Graz, Austria. All Rights Reserved.
version 1.4


In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.
The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
# Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = "train.p"
validation_file= "valid.p"
testing_file = "test.p"
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
test = pickle.load(f)
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']
print("X_train shape:", X_train.shape)
print("y_train shape:", y_train.shape)
print("X_valid shape:", X_valid.shape)
print("y_valid shape:", y_valid.shape)
print("X_test shape:", X_test.shape)
print("y_test shape:", y_test.shape)
The pickled data is a dictionary with 4 key/value pairs:
'features'
is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).'labels'
is a 1D array containing the label/class id of the traffic sign. The file signnames.csv
contains id -> name mappings for each id.'sizes'
is a list containing tuples, (width, height) representing the original width and height the image.'coords'
is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGESComplete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.
### Replace each question mark with the appropriate value.
### Use python, pandas or numpy methods rather than hard coding the results
import numpy as np
# Number of training examples
n_train = X_train.shape[0]
# Number of testing examples.
n_test = X_test.shape[0]
# What's the shape of an traffic sign image?
image_shape = X_train.shape[1:]
# How many unique classes/labels there are in the dataset.
n_classes = len(np.unique(y_train))
print("Number of training examples =", n_train)
print("Number of testing examples =", n_test)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)
Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.
The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.
NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
# Visualizations will be shown in the notebook.
%matplotlib inline
import random
import csv
def plot_figures(figures, nrows = 1, ncols=1, labels=None):
fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(12, 14))
axs = axs.ravel()
for index, title in zip(range(len(figures)), figures):
axs[index].imshow(figures[title], plt.gray())
if(labels != None):
axs[index].set_title(labels[index])
else:
axs[index].set_title(title)
axs[index].set_axis_off()
plt.tight_layout()
name_values = np.genfromtxt('signnames.csv', skip_header=1, dtype=[('myint','i8'), ('mysring','S55')], delimiter=',')
number_to_stop = 8
figures = {}
labels = {}
for i in range(number_to_stop):
index = random.randint(0, n_train-1)
labels[i] = name_values[y_train[index]][1].decode('ascii')
# print(name_values[y_train[index]][1].decode('ascii'))
figures[i] = X_train[index]
plot_figures(figures, 4, 2, labels)
unique_train, counts_train = np.unique(y_train, return_counts=True)
plt.bar(unique_train, counts_train)
plt.grid()
plt.title("Train Dataset Sign Counts")
plt.show()
unique_test, counts_test = np.unique(y_test, return_counts=True)
plt.bar(unique_test, counts_test)
plt.grid()
plt.title("Test Dataset Sign Counts")
plt.show()
unique_valid, counts_valid = np.unique(y_valid, return_counts=True)
plt.bar(unique_valid, counts_valid)
plt.grid()
plt.title("Valid Dataset Sign Counts")
plt.show()
Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.
The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!
With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.
There are various aspects to consider when thinking about this problem:
Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.
Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, (pixel - 128)/ 128
is a quick way to approximately normalize the data and can be used in this project.
Other pre-processing steps are optional. You can try different techniques to see if it improves performance.
Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.
### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include
### converting to grayscale, etc.
### Feel free to use as many code cells as needed.
import tensorflow as tf
from tensorflow.contrib.layers import flatten
from math import ceil
from sklearn.utils import shuffle
# Convert to grayscale
X_train_rgb = X_train
X_train_gry = np.sum(X_train/3, axis=3, keepdims=True)
X_test_rgb = X_test
X_test_gry = np.sum(X_test/3, axis=3, keepdims=True)
X_valid_rgb = X_valid
X_valid_gry = np.sum(X_valid/3, axis=3, keepdims=True)
print('Train RGB shape:', X_train_rgb.shape)
print('Train Grayscale shape', X_train_gry.shape)
print('Test RGB shape:', X_test_rgb.shape)
print('Test Grayscale shape', X_test_gry.shape)
X_train = X_train_gry
X_test = X_test_gry
X_valid = X_valid_gry
image_depth_channels = X_train.shape[3]
# print(image_depth_channels)
number_to_stop = 8
figures = {}
random_signs = []
for i in range(number_to_stop):
index = random.randint(0, n_train-1)
labels[i] = name_values[y_train[index]][1].decode('ascii')
figures[i] = X_train[index].squeeze()
random_signs.append(index)
# print(random_signs)
plot_figures(figures, 4, 2, labels)
import cv2
more_X_train = []
more_y_train = []
more2_X_train = []
more2_y_train = []
new_counts_train = counts_train
for i in range(n_train):
if(new_counts_train[y_train[i]] < 3000):
for j in range(3):
dx, dy = np.random.randint(-1.7, 1.8, 2)
M = np.float32([[1, 0, dx], [0, 1, dy]])
dst = cv2.warpAffine(X_train[i], M, (X_train[i].shape[0], X_train[i].shape[1]))
dst = dst[:,:,None]
more_X_train.append(dst)
more_y_train.append(y_train[i])
random_higher_bound = random.randint(27, 32)
random_lower_bound = random.randint(0, 5)
points_one = np.float32([[0,0],[32,0],[0,32],[32,32]])
points_two = np.float32([[0, 0], [random_higher_bound, random_lower_bound], [random_lower_bound, 32],[32, random_higher_bound]])
M = cv2.getPerspectiveTransform(points_one, points_two)
dst = cv2.warpPerspective(X_train[i], M, (32,32))
more2_X_train.append(dst)
more2_y_train.append(y_train[i])
tilt = random.randint(-12, 12)
M = cv2.getRotationMatrix2D((X_train[i].shape[0]/2, X_train[i].shape[1]/2), tilt, 1)
dst = cv2.warpAffine(X_train[i], M, (X_train[i].shape[0], X_train[i].shape[1]))
more2_X_train.append(dst)
more2_y_train.append(y_train[i])
new_counts_train[y_train[i]] += 2
more_X_train = np.array(more_X_train)
more_y_train = np.array(more_y_train)
X_train = np.concatenate((X_train, more_X_train), axis=0)
y_train = np.concatenate((y_train, more_y_train), axis=0)
more2_X_train = np.array(more_X_train)
more2_y_train = np.array(more_y_train)
more2_X_train = np.reshape(more2_X_train, (np.shape(more2_X_train)[0], 32, 32, 1))
X_train = np.concatenate((X_train, more2_X_train), axis=0)
y_train = np.concatenate((y_train, more2_y_train), axis=0)
X_train = np.concatenate((X_train, X_valid), axis=0)
y_train = np.concatenate((y_train, y_valid), axis=0)
figures1 = {}
labels = {}
figures1[0] = X_train[n_train+1].squeeze()
labels[0] = y_train[n_train+1]
figures1[1] = X_train[0].squeeze()
labels[1] = y_train[0]
plot_figures(figures1, 1, 2, labels)
from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2, random_state=0)
print("New Dataset Size : {}".format(X_train.shape[0]))
unique, counts = np.unique(y_train, return_counts=True)
plt.bar(unique, counts)
plt.grid()
plt.title("Train Dataset Sign Counts")
plt.show()
unique, counts = np.unique(y_test, return_counts=True)
plt.bar(unique, counts)
plt.grid()
plt.title("Test Dataset Sign Counts")
plt.show()
unique, counts = np.unique(y_valid, return_counts=True)
plt.bar(unique, counts)
plt.grid()
plt.title("Valid Dataset Sign Counts")
plt.show()
def normalize(im):
return -np.log(1/((1 + im)/257) - 1)
# X_train_normalized = normalize(X_train)
# X_test_normalized = normalize(X_test)
X_train_normalized = X_train/127.5-1
X_test_normalized = X_test/127.5-1
number_to_stop = 8
figures = {}
count = 0
for i in random_signs:
labels[count] = name_values[y_train[i]][1].decode('ascii')
figures[count] = X_train_normalized[i].squeeze()
count += 1;
plot_figures(figures, 4, 2, labels)
X_train = X_train_normalized
X_test = X_test_normalized
def conv2d(x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='VALID')
x = tf.nn.bias_add(x, b)
print(x.get_shape())
return tf.nn.relu(x)
def LeNet(x):
mu = 0
sigma = 0.1
W_one = tf.Variable(tf.truncated_normal(shape=(5, 5, image_depth_channels, 6), mean = mu, stddev = sigma))
b_one = tf.Variable(tf.zeros(6))
layer_one = conv2d(x, W_one, b_one, 1)
layer_one = tf.nn.max_pool(layer_one, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
print(layer_one.get_shape())
print()
W_two = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
b_two = tf.Variable(tf.zeros(16))
layer_two = conv2d(layer_one, W_two, b_two, 1)
layer_two = tf.nn.max_pool(layer_two, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
print(layer_two.get_shape())
print()
W_two_a = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 412), mean = mu, stddev = sigma))
b_two_a = tf.Variable(tf.zeros(412))
layer_two_a = conv2d(layer_two, W_two_a, b_two_a, 1)
print(layer_two_a.get_shape())
print()
flat = flatten(layer_two_a)
W_three = tf.Variable(tf.truncated_normal(shape=(412, 122), mean = mu, stddev = sigma))
b_three = tf.Variable(tf.zeros(122))
layer_three = tf.nn.relu(tf.nn.bias_add(tf.matmul(flat, W_three), b_three))
layer_three = tf.nn.dropout(layer_three, keep_prob)
W_four = tf.Variable(tf.truncated_normal(shape=(122, 84), mean = mu, stddev = sigma))
b_four = tf.Variable(tf.zeros(84))
layer_four = tf.nn.relu(tf.nn.bias_add(tf.matmul(layer_three, W_four), b_four))
layer_four = tf.nn.dropout(layer_four, keep_prob)
W_five = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
b_five = tf.Variable(tf.zeros(43))
layer_five = tf.nn.bias_add(tf.matmul(layer_four, W_five), b_five)
return layer_five
x = tf.placeholder(tf.float32, (None, 32, 32, image_depth_channels))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 43)
keep_prob = tf.placeholder(tf.float32)
print("Done")
A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.
### Train your model here.
### Calculate and report the accuracy on the training and validation set.
### Once a final model architecture is selected,
### the accuracy on the test set should be calculated and reported as well.
### Feel free to use as many code cells as needed.
EPOCHS = 27
BATCH_SIZE = 100
rate = 0.0009
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=one_hot_y)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
validation_accuracy_figure = []
test_accuracy_figure = []
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})
validation_accuracy = evaluate(X_valid, y_valid)
validation_accuracy_figure.append(validation_accuracy)
test_accuracy = evaluate(X_train, y_train)
test_accuracy_figure.append(test_accuracy)
print("EPOCH {} ...".format(i+1))
print("Test Accuracy = {:.3f}".format(test_accuracy))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, './lenet')
print("Model saved")
plt.plot(validation_accuracy_figure)
plt.title("Test Accuracy")
plt.show()
plt.plot(validation_accuracy_figure)
plt.title("Validation Accuracy")
plt.show()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
train_accuracy = evaluate(X_train, y_train)
print("Train Accuracy = {:.3f}".format(train_accuracy))
valid_accuracy = evaluate(X_valid, y_valid)
print("Valid Accuracy = {:.3f}".format(valid_accuracy))
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.
You may find signnames.csv
useful as it contains mappings from the class id (integer) to the actual sign name.
### Load the images and plot them here.
### Feel free to use as many code cells as needed.
import glob
import cv2
my_images = sorted(glob.glob('mysigns/*.png'))
my_labels = np.array([1, 22, 35, 15, 37, 18])
figures = {}
labels = {}
my_signs = []
index = 0
for my_image in my_images:
img = cv2.cvtColor(cv2.imread(my_image), cv2.COLOR_BGR2RGB)
my_signs.append(img)
figures[index] = img
labels[index] = name_values[my_labels[index]][1].decode('ascii')
index += 1
plot_figures(figures, 3, 2, labels)
my_signs = np.array(my_signs)
my_signs_gray = np.sum(my_signs/3, axis=3, keepdims=True)
my_signs_normalized = my_signs_gray/127.5-1
number_to_stop = 6
figures = {}
labels = {}
for i in range(number_to_stop):
labels[i] = name_values[my_labels[i]][1].decode('ascii')
figures[i] = my_signs_gray[i].squeeze()
plot_figures(figures, 3, 2, labels)
### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
### Feel free to use as many code cells as needed.
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, "./lenet")
my_accuracy = evaluate(my_signs_normalized, my_labels)
print("My Data Set Accuracy = {:.3f}".format(my_accuracy))
### Calculate the accuracy for these 5 new images.
### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.
my_single_item_array = []
my_single_item_label_array = []
for i in range(6):
my_single_item_array.append(my_signs_normalized[i])
my_single_item_label_array.append(my_labels[i])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# saver = tf.train.import_meta_graph('./lenet.meta')
saver.restore(sess, "./lenet")
my_accuracy = evaluate(my_single_item_array, my_single_item_label_array)
print('Image {}'.format(i+1))
print("Image Accuracy = {:.3f}".format(my_accuracy))
print()
For each of the new images, print out the model's softmax probabilities to show the certainty of the model's predictions (limit the output to the top 5 probabilities for each image). tf.nn.top_k
could prove helpful here.
The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.
tf.nn.top_k
will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.
Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. tf.nn.top_k
is used to choose the three classes with the highest probability:
# (5, 6) array
a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,
0.12789202],
[ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,
0.15899337],
[ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,
0.23892179],
[ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,
0.16505091],
[ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,
0.09155967]])
Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3))
produces:
TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],
[ 0.28086119, 0.27569815, 0.18063401],
[ 0.26076848, 0.23892179, 0.23664738],
[ 0.29198961, 0.26234032, 0.16505091],
[ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],
[0, 1, 4],
[0, 5, 1],
[1, 3, 5],
[1, 4, 3]], dtype=int32))
Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202]
, you can confirm these are the 3 largest probabilities in a
. You'll also notice [3, 0, 5]
are the corresponding indices.
### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web.
### Feel free to use as many code cells as needed.
k_size = 5
softmax_logits = tf.nn.softmax(logits)
top_k = tf.nn.top_k(softmax_logits, k=k_size)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, "./lenet")
my_softmax_logits = sess.run(softmax_logits, feed_dict={x: my_signs_normalized, keep_prob: 1.0})
my_top_k = sess.run(top_k, feed_dict={x: my_signs_normalized, keep_prob: 1.0})
for i in range(6):
figures = {}
labels = {}
figures[0] = my_signs[i]
labels[0] = "Original"
for j in range(k_size):
labels[j+1] = 'Guess {} : ({:.0f}%)'.format(j+1, 100*my_top_k[0][i][j])
figures[j+1] = X_valid[np.argwhere(y_valid == my_top_k[1][i][j])[0]].squeeze()
plot_figures(figures, 1, 6, labels)
Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.


module Mail::Multibyte::Unicode
Constants
- HANGUL_JAMO_FIRST
- HANGUL_JAMO_LAST
- HANGUL_LBASE
- HANGUL_LCOUNT
- HANGUL_NCOUNT
- HANGUL_SBASE
Hangul character boundaries and properties
- HANGUL_SCOUNT
- HANGUL_SLAST
- HANGUL_TBASE
- HANGUL_TCOUNT
- HANGUL_VBASE
- HANGUL_VCOUNT
- LEADERS_AND_TRAILERS
BOM (byte order mark) can also be seen as whitespace, it’s a non-rendering character used to distinguish between little and big endian. This is not an issue in utf-8, so it must be ignored.
- LEADERS_PAT
- NORMALIZATION_FORMS
A list of all available normalization forms. See www.unicode.org/reports/tr15/tr15-29.html for more information about normalization.
- TRAILERS_PAT
- UNICODE_VERSION
The Unicode version that is supported by the implementation
- WHITESPACE
All the unicode whitespace
Attributes
The default normalization used for operations that require normalization. It can be set to any of the normalizations in NORMALIZATION_FORMS.
Example:
Mail::Multibyte::Unicode.default_normalization_form = :c
Public Instance Methods
Compose decomposed characters to the composed form.
# File lib/mail/multibyte/unicode.rb, line 166 def compose_codepoints(codepoints) pos = 0 eoa = codepoints.length - 1 starter_pos = 0 starter_char = codepoints[0] previous_combining_class = -1 while pos < eoa pos += 1 lindex = starter_char - HANGUL_LBASE # -- Hangul if 0 <= lindex and lindex < HANGUL_LCOUNT vindex = codepoints[starter_pos+1] - HANGUL_VBASE rescue vindex = -1 if 0 <= vindex and vindex < HANGUL_VCOUNT tindex = codepoints[starter_pos+2] - HANGUL_TBASE rescue tindex = -1 if 0 <= tindex and tindex < HANGUL_TCOUNT j = starter_pos + 2 eoa -= 2 else tindex = 0 j = starter_pos + 1 eoa -= 1 end codepoints[starter_pos..j] = (lindex * HANGUL_VCOUNT + vindex) * HANGUL_TCOUNT + tindex + HANGUL_SBASE end starter_pos += 1 starter_char = codepoints[starter_pos] # -- Other characters else current_char = codepoints[pos] current = database.codepoints[current_char] if current.combining_class > previous_combining_class if ref = database.composition_map[starter_char] composition = ref[current_char] else composition = nil end unless composition.nil? codepoints[starter_pos] = composition starter_char = composition codepoints.delete_at pos eoa -= 1 pos -= 1 previous_combining_class = -1 else previous_combining_class = current.combining_class end else previous_combining_class = current.combining_class end if current.combining_class == 0 starter_pos = pos starter_char = codepoints[pos] end end end codepoints end
Decompose composed characters to the decomposed form.
# File lib/mail/multibyte/unicode.rb, line 145 def decompose_codepoints(type, codepoints) codepoints.inject([]) do |decomposed, cp| # if it's a hangul syllable starter character if HANGUL_SBASE <= cp and cp < HANGUL_SLAST sindex = cp - HANGUL_SBASE ncp = [] # new codepoints ncp << HANGUL_LBASE + sindex / HANGUL_NCOUNT ncp << HANGUL_VBASE + (sindex % HANGUL_NCOUNT) / HANGUL_TCOUNT tindex = sindex % HANGUL_TCOUNT ncp << (HANGUL_TBASE + tindex) unless tindex == 0 decomposed.concat ncp # if the codepoint is decomposable in with the current decomposition type elsif (ncp = database.codepoints[cp].decomp_mapping) and (!database.codepoints[cp].decomp_type || type == :compatability) decomposed.concat decompose_codepoints(type, ncp.dup) else decomposed << cp end end end
Reverse operation of g_unpack.
Example:
Unicode.g_pack(Unicode.g_unpack('क्षि')) # => 'क्षि'
# File lib/mail/multibyte/unicode.rb, line 124 def g_pack(unpacked) (unpacked.flatten).pack('U*') end
Unpack the string at grapheme boundaries. Returns a list of character lists.
Example:
Unicode.g_unpack('क्षि') # => [[2325, 2381], [2359], [2367]] Unicode.g_unpack('Café') # => [[67], [97], [102], [233]]
# File lib/mail/multibyte/unicode.rb, line 90 def g_unpack(string) codepoints = u_unpack(string) unpacked = [] pos = 0 marker = 0 eoc = codepoints.length while(pos < eoc) pos += 1 previous = codepoints[pos-1] current = codepoints[pos] if ( # CR X LF ( previous == database.boundary[:cr] and current == database.boundary[:lf] ) or # L X (L|V|LV|LVT) ( database.boundary[:l] === previous and in_char_class?(current, [:l,:v,:lv,:lvt]) ) or # (LV|V) X (V|T) ( in_char_class?(previous, [:lv,:v]) and in_char_class?(current, [:v,:t]) ) or # (LVT|T) X (T) ( in_char_class?(previous, [:lvt,:t]) and database.boundary[:t] === current ) or # X Extend (database.boundary[:extend] === current) ) else unpacked << codepoints[marker..pos-1] marker = pos end end unpacked end
Detect whether the codepoint is in a certain character class. Returns
true
when it’s in the specified character class and
false
otherwise. Valid character classes are:
:cr
, :lf
, :l
, :v
,
:lv
, :lvt
and :t
.
Primarily used by the grapheme cluster support.
# File lib/mail/multibyte/unicode.rb, line 81 def in_char_class?(codepoint, classes) classes.detect { |c| database.boundary[c] === codepoint } ? true : false end
Returns the KC normalization of the string by default. NFKC is considered the best normalization form for passing strings to databases and validations.
-
string
- The string to perform normalization on. -
form
- The form you want to normalize in. Should be one of the following::c
,:kc
,:d
, or:kd
. Default is Mail::Multibyte.default_normalization_form
# File lib/mail/multibyte/unicode.rb, line 282 def normalize(string, form=nil) form ||= @default_normalization_form # See http://www.unicode.org/reports/tr15, Table 1 codepoints = u_unpack(string) case form when :d reorder_characters(decompose_codepoints(:canonical, codepoints)) when :c compose_codepoints(reorder_characters(decompose_codepoints(:canonical, codepoints))) when :kd reorder_characters(decompose_codepoints(:compatability, codepoints)) when :kc compose_codepoints(reorder_characters(decompose_codepoints(:compatability, codepoints))) else raise ArgumentError, "#{form} is not a valid normalization variant", caller end.pack('U*') end
Re-order codepoints so the string becomes canonical.
# File lib/mail/multibyte/unicode.rb, line 129 def reorder_characters(codepoints) length = codepoints.length- 1 pos = 0 while pos < length do cp1, cp2 = database.codepoints[codepoints[pos]], database.codepoints[codepoints[pos+1]] if (cp1.combining_class > cp2.combining_class) && (cp2.combining_class > 0) codepoints[pos..pos+1] = cp2.code, cp1.code pos += (pos > 0 ? -1 : 1) else pos += 1 end end codepoints end
Replaces all ISO-8859-1 or CP1252 characters by their UTF-8 equivalent resulting in a valid UTF-8 string.
Passing true
will forcibly tidy all bytes, assuming that the
string’s encoding is entirely CP1252 or ISO-8859-1.
# File lib/mail/multibyte/unicode.rb, line 227 def tidy_bytes(string, force = false) if force return string.unpack("C*").map do |b| tidy_byte(b) end.flatten.compact.pack("C*").unpack("U*").pack("U*") end bytes = string.unpack("C*") conts_expected = 0 last_lead = 0 bytes.each_index do |i| byte = bytes[i] is_cont = byte > 127 && byte < 192 is_lead = byte > 191 && byte < 245 is_unused = byte > 240 is_restricted = byte > 244 # Impossible or highly unlikely byte? Clean it. if is_unused || is_restricted bytes[i] = tidy_byte(byte) elsif is_cont # Not expecting contination byte? Clean up. Otherwise, now expect one less. conts_expected == 0 ? bytes[i] = tidy_byte(byte) : conts_expected -= 1 else if conts_expected > 0 # Expected continuation, but got ASCII or leading? Clean backwards up to # the leading byte. (1..(i - last_lead)).each {|j| bytes[i - j] = tidy_byte(bytes[i - j])} conts_expected = 0 end if is_lead # Final byte is leading? Clean it. if i == bytes.length - 1 bytes[i] = tidy_byte(bytes.last) else # Valid leading byte? Expect continuations determined by position of # first zero bit, with max of 3. conts_expected = byte < 224 ? 1 : byte < 240 ? 2 : 3 last_lead = i end end end end bytes.empty? ? "" : bytes.flatten.compact.pack("C*").unpack("U*").pack("U*") end
Unpack the string at codepoints boundaries. Raises an EncodingError when the encoding of the string isn’t valid UTF-8.
Example:
Unicode.u_unpack('Café') # => [67, 97, 102, 233]
# File lib/mail/multibyte/unicode.rb, line 68 def u_unpack(string) begin string.unpack 'U*' rescue ArgumentError raise EncodingError, 'malformed UTF-8 character' end end
Private Instance Methods
# File lib/mail/multibyte/unicode.rb, line 386 def database @database ||= UnicodeDatabase.new end
# File lib/mail/multibyte/unicode.rb, line 376 def tidy_byte(byte) if byte < 160 [database.cp1252[byte] || byte].pack("U").unpack("C*") elsif byte < 192 [194, byte] else [195, byte - 64] end end


--- layout: page title: Forums description: List of forums here. permalink: /forums icon: fas fa-comments ---
Pentium IIIers
A forum on Google groups
/r/BobPony
BP Subreddit
/r/PentiumIII
#PentiumIII
/r/RaspberryPi10
Windows 10 on the Raspberry Pi! (Subreddit)


Ok ppl, I have been harassing you lately to come up with builds for me. Now I made my own:
Human -chaotic neutral
16 str 15 dex for ITWF 12 con 8 wis 14 int for skill points 8 cha
I will advance like this:
Pre epic: 17 fighter 3 bard
Post epic: 19 pm 1 bard
It will be a crit immune figter, with good ab and high AC. It wil fight with a scimmy and shield, and if it's possible with 2 scimmies (with that many fighter levels I can spare the feats).
The build is for aventia, so I dont need dev crit, just overwhelming.
Now I made this build in the weekend. I am now at work, so I can't play test, so maybe I did something stuiped
I am now lvl 5 with this char and I hope I havent cocked up yet.
Can you help me with the following: - what will final ab, hp, saves and ac be? - did I do anything wrong?
Thanks for helping guys!
If its for Aventia - have you taken into account levels 41 and up? They have added damage for higher levels, but there are issues with chars that take Bard/PM levels
<Quote> "You need atleast 8 bard lvls to get the benefit of the after lvl 40 bonus and if you go PM too, you need atleast 12 lvls of PM." </quote>
I might be wrong tho - check out the article. Click Here
For levels 1-40 Im sure its good.
I have no clue on those past lvl 40 bonusses. I have tried again to clarify it but it doesnt really to help.
Even the thread you sent me is unanswered in the end.


Overview • User interface • Special tasks ↑ | ↓
Depth cueing helps to see in a two-dimensional graphics of the model which objects are situated in the foreground and which ones in the background. There are several ways to achieve that. Objects can blend into the background color with increasing depth and they can be made more transparent with increasing depth. In Matlab graphics, a combination of both these modes together with a uniform white background works best.
Modern graphics cards allow for more sophisticated and faster hardware-supported depth cueing. Currently, Matlab graphics does not support this feature. Hence, depth cueing in MMM is emulated and a bit slow.
Depth cueing is most useful for detail views of a model although it can also be used for viewing the whole model. To use it for the whole model, unselect all objects, for instance with an unselect *
command in the command line or double click on an object in the 3D graphics display. The click the View all
toolbutton. If necessary, adjust the view as described in the model panel help page.
To use it for a detail view, select the objects that you want to see, for instance in the hierarchy window, by clicking on the structure in the model window or by the select command. Then click the View selected
toolbutton.
Depth cueing is toggled (switched on/off) by clicking on the Depth cueing
button in the View
panel of the main window or switched on via the Depth cueing
item of the Display
menu.
A Depth cueing control
window opens, the background color of the model panel or model window is switched to white, selection highlights are switched off, and depth cueing is enabled. Objects in the front that could obscure the view are cut away.
Dept cueing is controlled by two planes that are perpendicular to the viewing direction d. Your eyes are at a negative coordinate along d, the background is at a positive coordinate. The Front plane
is by default set at d=0, corresponding to the front edge of the selected objects or the whole model. Front edge computation is approximate, small adjustments may be necessary.
You can adjust the Front plane
by the corresponding slider or by directly setting a value in the edit field. Note that depth cueing does not automatically update, you have to click the Update
button.
All objects closer to you than the front plane are cut away. The front plane also corresponds to fully opaque objects with full color saturation. If the front plane is too close to your eye (large negative values), everything will look very pale.
The Back plane
is by default set at d=1.5, corresponding to 1.5 times the extension of the selected objects (or whole model) behind the front plane. This makes sure that the whole selection can still be seen rather clearly. Shifting the backplane to the back (larger values of d) makes more objects visible in the background and the color saturation gradient is decreased. You can shift the back plane
with the corresponding slider or by direct input in the edit field.
Click the code>Cancel button of the Depth cueing control
window or, alternatively, click once more on the Depth cueing
button in the View
panel of the main window. The window closes, depth cueing is decativated, and selection highlights- if any- are restored. The previous background color is also restored.


{{c.title}}
This is a Report of conditions observed at the time of
Inspection only. This is NOT a Guarantee or Warranty.
{{item.title}}
{{option.title}}
{{ci.title}}


Shropshire school celebrates winning top award
by Lucy TodmanA Shropshire school is celebrating after winning the academic equivalent of an Oscar.
Adcote School, at Little Ness, won the accolade at the Independent Schools Association (ISA) annual awards.
The non-selective all-girl’s school, which caters for students aged seven to 18, was one of 14 winners who were awarded trophies out of 41 finalists from across the country.
The Academic Innovation and Achievement Award, in the Junior and Prep School category, was awarded for the school’s unique CLEAR Learning programme.
CLEAR Learning aims to develop in all students across the school, the core values of ‘confidence, leadership, engagement, achievement and resilience’ and is woven into the curriculum and every part of school life.
The programme, introduced in September 2018, includes wide-ranging initiatives that introduce students to challenging new learning and adventure tasks and experiences that test their initiative, creativity, team building, resolve, social skills and endurance.
Headmistress Diane Browne said receiving the award was “one of the proudest moments of her career”.
“We are thrilled that the judges honoured us with this prestigious award. It’s very exciting to be recognised as one of the best in the country,” she said.
“We all think that our CLEAR learning programme is innovative and enormously beneficial for our students. The award marks another milestone in Adcote’s remarkable transition.”
The awards ceremony, held at Oulton Hall, in Leeds, was hosted by former Team GB sprinter, World Championship gold medallist and Olympic legend Derek Redmond.
He inspired delegates with his personal story of how perseverance and mental strength can conquer the hardest of times.


Pricing
Three options to choose from.
First time customers
$20 /hr
- 8-11 pictures total
- Only black & white color
- Option to photoshop 2 pictures
- 1 small portrait, 2 medium portraits, & 1 large landscape
-
Birthdays and Maternity Shoots
$25 /hr
- 15-18 pictures total
- Black and white or in color
- Option to photoshop 13 pictures
- 5 small portrait, 8 medium portraits, & 5large landscape
-
Special Events
Graduations, Birthdays, and Weddings
$30 /mo
- 25-32 pictures total
- Black and white or in color
- Option to photoshop 5 pictures
- 2 small portrait, 4 medium portraits, & 3 large landscape


We are fierce, intelligent, and incredibly good looking. If you want to contact us, you can email Sarah at<EMAIL_ADDRESS>and Margaret at<EMAIL_ADDRESS>We are making this web page right now. It's all right.
I think we're going to do more stuff later, that will also probably be all right.
HTML is okay, git is hard.
Excited to be here and doing stuff.


Methods
callback(model)
Callback for the MODEL_BUILT event that is responsible for invoking aspect weaver and pairing validation functions
with respective events.
Parameters:
| Name | Type | Description |
|---|---|---|
model |
Model | model on which the event was fired |
- Source:
callback(model, htmlElement)
Callback for FORM_RENDERED event. This is used to add callback for individual attributes and model to properly
render feedback messages.
Parameters:
| Name | Type | Description |
|---|---|---|
model |
Model | model on which the event was fired |
htmlElement |
html element of the model |
- Source:
callback(model)
Callback for event FORM_SUBMITTED/MODEL_VALIDATED, which is responsible for triggering model related rules and
firing respective events.
Parameters:
| Name | Type | Description |
|---|---|---|
model |
Model | form rich model |
- Source:
callback(attr, htmlElement)
Callback for event ATTRIBUTE_VALIDATED - renders all errors and info messages for given field
Parameters:
| Name | Type | Description |
|---|---|---|
attr |
Attribute | attribute on which the event was fired |
htmlElement |
html element of the attribute |
- Source:
renderFeedback(model, formLabel)
Callback for event FORM_SUBMITTED/MODEL_VALIDATED, which is responsible for rendering feedback.
Parameters:
| Name | Type | Description |
|---|---|---|
model |
Model | model on which the event was fired |
formLabel |
html element of the model |
- Source:
setPending(model)
Updates model state on form submit before validation.
Parameters:
| Name | Type | Description |
|---|---|---|
model |
Model | model instance |
- Source:


Activating this feature will allow you to publicly share data sets you
have imported to Open Humans, turning it into a public resource available
to researchers, citizen scientists, and any others that may wish to
learn from it and build upon it.
Requirements
To activate this feature, you must:
-
be at least 18 years of age
Sharing data publicly could involve risks. To make this decision,
we require you to be at least 18 years of age.
-
be representing yourself
This decision must be made by the individual themselves. A parent or
legal guardian may not choose to publicly share data from a child or
adult in their care.
-
read the information provided
The information we provide here is designed to be informative and
readable. It is very important that you take the time to read this.
-
pass our quiz
You must complete a brief quiz to demonstrate your understanding of
this feature. This quiz should take only a few minutes of your time,
and helps assure us that you understand potential risks.
Read about public data sharing →


Donation System
Exchange to foundation for 40 Donator Coins for Armors and Jewels and 100 for Weapons.


Updates
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sometext sometext sometext sometext sometext sometext sometext sometext sometext sometext sometext sometext


Interactive Field Lines
This time we represent the electric field with field lines. The number of field lines increases as the field becomes stronger, and decreases as the field becomes weaker. This is a standard way of representing electric fields. Indeed, one of the driving forces behind this project is to improve on the standard, static, two dimensional presentation of these field lines. Try adjusting the charge by clicking and dragging the charge in the figure caption.
We repeat our slightly more sophisticated approach.
- Include the VField and Lesson toolkits on your page.
- Configure the visualization.
- Give a place to draw the visualization.
- Define interactions.
Include VField and Lesson toolkits

<link rel="stylesheet" type="text/css"
href="http://vizit.github.io/lesson/css/Lesson.css">

Once again we use VField for the electric field, and Lesson for interactivity. So far we have only seen a little of what these toolkits are capable of.
Configure the visualization

Start with the visualization of the electric field from a point
charge at the origin and add a binding, so that changes in
q1
produce changes in the charge
.
The field then changes to reflect the new charge.
Interestingly, these last two visualizations both bind
q1
to their charge
. If they are
placed on the same page, they will always show matching
representations of the same field. One using a vector
representation, the other using field lines. This shows
how the building block approach fits in well with the
concept of multiple representations, while also encouraging
you to make the design decisions for the overall content.
Give a place to draw and define interactions

<canvas id="chargeCanvas" width="300" height="300"></canvas>

The electric field from a

statC1 charge .

There is no need to change the caption or the controlling lesson element.
We keep the lessonElement
, which controls the q1
,
ranging from -5.0 to 5.0 in steps of 0.2. A full description of the attributes:
| attribute | meaning | example |
|---|---|---|
| class | Identifies a component of a lesson. | lessonElement |
| data-type | The type of component. | data-type="rangedSource" |
| data-name | The name of the variable that this component effects. | data-name="q1" |
| data-value | The initial value. | data-value="5.0" |
| data-min | The min value for the control. | data-min="-5.0" |
| data-max | The max value for the control. | data-max="5.0" |
| data-step | The variable changes by this amount as the control is moved. | data-step="0.2" |
This example incorporates the control into the figure caption. It is just an HTML element that you are free to place anywhere in your content according to your design goals.
Next up, we combine the charge with a Gaussian surface and add some additional controls to create a more sophisticated presentation.


REVIEW: The Apostle By Luke Buckmaster<EMAIL_ADDRESS>Cast: Robert Duvall, Miranda Richardson, Farrah Fawcett, John Beasley, Walter Goggins, Rick Dial, Billy Joe Shaver, Billy Bob Thornton, June Carter Cash, Todd Allen Director: Robert Duvall Screenplay: Robert Duvall
>From 0 stars (bomb), to 5 stars (a masterpiece): 2 and a half stars
Robert Duvall has approached dangerous ground with The Apostle. Not necessarily dangerous as far as ethnical and religious themes are concerned, but rather his skill behind the camera. Respectable actors have had mixed results as directors in the past; Billy Bob Thorton managed to hit the right note with last years Sling Blade, as did Mel Gibson with Braveheart. On the other side of the spectrum, Kevin Spacey made the frivolous and rather dull Albino Alligator, and now Duvall has failed to show substantial talent as a director with his gutsy attempt at a character study.
Duvall plays Euliss "Sonny" Dewell, an intriguing Pentecostal preacher with a wandering eye for women and booze. Flawed, yes, but he is completely devoted to his faith and his church. Perhaps a little too devoted, since he clubs a younger man of the cloth (Todd Allen) on the head with a baseball bat after he assists in taking the church away from him. Realizing that he's gone too far, Sonny flees town and seeks refuge in Bayou Boutte, Louisiana. He dubs himself as "The Apostle E.F," and starts a new church named "The One Way Road to Heaven" with the assistance of retired preacher Reverend Blackwell (John Beasly).
In its worst moments, The Apostle will serve as a reminder of how much potential it had. But nowadays many films seem to thrive on potential alone - recent examples Deep Impact, Welcome to Sarajevo and Sphere all have fascinating concepts, but their finished product didn't quite go the distance. Such is the story with The Apostle, which has potentially riveting subject material but is over worked and indefinable.
I never quite understood what Duvall's motives were - is he trying to show us ironies of the Christian faith? Is he giving us a lesson on redemption, or merely pointing out that even devoted men have flaws? Or is the entire film just a study of human behavior?
None of these themes are explored adequately, no doubt due to Duvall's lack of experience as both a director and a writer. To his credit, he does approach the film impartially and honestly; making clear that his intentions are not to shock, intimidate or offend. But even from a technical view, it was evident to me that he simply doesn't have the skill, or experience to handle such a hefty workload as anything except an actor. Many camera shots look second-rate and novice, and some scenes go way, way too long (especially his final preaching message).
Still, The Apostle is far from a disaster. Duvall's riveting, Oscar nominated performance is amongst the best of his career, and occasionally the film delivers some memorable moments. In particular, a scene in which Sonny converts a racist troublemaker (Billy Bob Thorton, in a cameo role) is amongst its finest.
After being disappointed by The Apostle's lack of quality and distinctly clouded motives, my message to Duvall is clear: let the actors do the acting, and the directors do the directing. Unless, of course, he somehow manages to create another Sling Blade.
-------------------------
Review © copyright Luke Buckmaster
Email<EMAIL_ADDRESS>to subscribe to my newsletter (unless, of course, you already have) or<EMAIL_ADDRESS>for any inquiries or feedback
Read more of my reviews at Movie Zone: http://moviezone.alphalink.com.au
<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


This program is distributed under the MIT license.
Concerning the puzzles: As far as I'm aware, this program does not violate any copyright or patent laws protecting other puzzles, real or virtual, that may resemble those simulated by this software. If I am in error, please contact me, and I will happily make arrangements to change the program.
Email: spencertparkin@gmail.com


Much Too Much Too Much Too Much Too...
| DOSE: |
2.0 g |
oral |
Cannabis |
(cookie / food) |
i used to burn herb a few times a week. It went up to every day plus for a month or two, then over the last year it's been a steady once a week just about every single week.
Looking for something a little different, i decided i would make herb cookies since i hadn't done that in a while. i ordered a bag of delivery-service regular grade mediocre cooking herb and poured it into a half-stick of butter or so in a pan. After cooking that for a half hour, i poured it directly into the cookie batter and made 2 really big cookies (1 for me, 1 for my friend Brian).
Saturday night at 5:00 we got all geared up and each ate a cookie. They were, of course, delicious. i realized that something was clearly amiss when at only T+30 min i noticed i was already high. Very high. And hungry despite having eaten a large large volume of cookie with high butter content. For now i wait and have some of Brian's salad. He is still eating his (remember i said they were large).
Another half-hour goes by of steady, steep climbing in height and i begin to get a little nervous. To sort of snap myself out of the anxiety/worry phase, i make light of it and suggest we get pizza, though i am starting to fear the potentially scary world at this point. We make it out and back with pizza, i eat some and soon have to go lay down because i'm too high.
i concentrated on not fighting the drug and riding it instead, and this helped a lot. i managed to calm myself down enough to be receptive. i began to get significant visuals as soon as i lay down, akin to the strength of my first time taking mushrooms (see report labeled '
Resurrection') but without the mental intensity. i remember the open-eye and closed-eye visuals started to be essentially the same thing, so i couldn't always tell if my eyes were open or not. Mostly a lot of geometric patterns and things being made of liquid and flowing. Nothing event-oriented at this point.
Some time went by, and i eventually managed to tell Brian i was going to the bathroom. i made it downstairs, but passed out on his bed instead of making it to the bathroom. i had several vivid dreams in this period about differing versions of the end of the world (or at least of the world as i understood it).
At 11:30, Brian woke me up and had me sleep on his floor so he could have his bed. i set an alarm for the next morning and succumbed to the herb again.
--------
Seconds later (real life : eight and a half hours later), i wake up to the alarm at 8:00 am. i'm thinking about how weird i feel, but ascribe it to fatigue until i remember the cookies and the fact that i'm in New Jersey and not at home. i realize i'm still high.  And that i have to go play piano at a church service, immediately.
i try to lace my shoes, fail, sit down and retrace my steps, and avoid freaking out. 'just relax' i tell myself as i fumble through an action i could do the last seventeen years or so with no trouble. i mean, just because i can't tie my shoes shouldn't mean that i can't maintain order in public, play an instrument accompanying a whole congregation of people as well as a singer, and deal with the hierarchy of the catholic church, right?
i made it to church on time and awake if not aware. i cut down on all human interaction, but there is some minimum. i basically avoided talking as much as possible and tried to stay out of the way. Putting on the robe for the mass gave me some trouble, but i got it together ok. The singer and i went upstairs to the main part of the church, and he asked me what i had done the night before. i mumbled something (presumably) semi-coherent about staying out late playing a session, and he says 'You sure you didn't take acid or something?'
i told him i was sure.
Despite the fact that the music wasn't standing still, i managed to read through the hymns and solo ok and book it the hell out. Came home, had an apple, passed out on my bed. This was at about 12:30 pm the next day, now T+19 or so. i was asleep until 12:00 am, T+31 or so, and i was still high when i woke up. i ate some munchies and staggered back to bed again. woke up at 8:30 am, 38 hours after ingestion, still a little dazed, a little depressed, but at least functional again.
i spent Monday in a semi-haze all day and didn't really feel normal until like 7:00 pm, or t+49 hours. i got a lot more than i bargained for on this one. i'm glad it turned out ok and that i didn't do something outlandish in the middle of a catholic church service or get lost in Jersey (or the city for that matter). This was unfortunately not much a growth experience due to the fact that i was asleep for almost all of it.
However, lesson learned : two grams of herb is really rather too much for a recreational Saturday afternoon herb cookie.
f
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


OCTET_LENGTH Function
Returns the length of the specified string as the number of bytes.
Syntax
OCTET_LENGTH(expression)
Argument
- expression
-
The input parameter is a CHAR or VARCHAR text string.
Return Type
The OCTET_LENGTH function returns an integer indicating the number of bytes in the input string. The LEN function returns the actual number of characters in multi-byte strings, not the number of bytes. For example, to store three four-byte Chinese characters, you need a VARCHAR(12) column. The LEN function will return 3 for that same string.
Usage Notes
Length calculations do not count trailing spaces for fixed-length character strings but do count them for variable-length strings.
Example
The following example returns the number of bytes and the number of characters in
the string français
.
select octet_length('français'), len('français'); octet_length | len --------------+----- 9 | 8 (1 row)


{% extends "base.html" %} {% block title %}Blockchain Frontend{% endblock %} {% block content %} Blockchain Frontend Mine Configure (current)
Enter a list of Blockchain node URLs separated by comma and click on "Add" button to add them to the list of nodes


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Restitution |
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Mask Bits |
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Category Bits |
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Group Index |
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isSensor |
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Edit Shape |
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Name |
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User Data |
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Body Type |
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isBullet |
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Rotation Fixed |
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Linear Damping |
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Angular Damping |
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Edit Body |
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Texture File |
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Texture Width |
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Texture Height |
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Texture Source Width |
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Texture Source Height |
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Name |
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Joint Type |
type |
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Collide Connected |
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Frequency Hz |
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Damping Ratio |
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Enable Motor |
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Max Motor Torque |
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Enable Limit |
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Lower Angle |
0 |
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Upper angle |
0 |
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Edit Joint |


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import config.ConfigKeys
@import helper.CSRF
@import views.html.templates.inputs.SingleCheckbox
@this(mainTemplate: MainTemplate,
singleCheckbox: SingleCheckbox)
@(form: Form[FeatureSwitchModel])(implicit request: Request[_], messages: Messages, appConfig: config.AppConfig)
@mainTemplate(title = "Feature switches", appConfig = appConfig) {
@helper.form(action = testOnly.controllers.routes.FeatureSwitchController.submitFeatureSwitch) {
@CSRF.formField


Welcome to Vibrant you, we're a family run tanning salon & here we specialise in virtical & lay down sunbeds, Vibr8 fitness Machines & Beauty Treatments.
When you walk into our shop to start your day, we are dedicated to providing you with friendly service, a welcoming atmosphere, and above all else, excellent products to suit your needs. If you are not satisfied, please let us know and we will do whatever we can to make things right!


DEVS Solver¶
The actual DEVS solvers containing the main DEVS implementation
-
class
solver.
Solver
[source]¶ A unified DEVS solver, containing all necessary functions
-
atomicInit
(aDEVS, time)[source]¶ AtomicDEVS function to initialise the model
Parameters: aDEVS – the model to initialise
-
atomicOutputGeneration
(aDEVS, time)[source]¶ AtomicDEVS function to generate output, invokes the outputFnc function of the model.
Parameters: - aDEVS – the AtomicDEVS model that generates the output
- time – the time at which the output must be generated
Returns: dict – the generated output
-
atomicOutputGenerationEventTracing
(aDEVS, time)[source]¶ Wrapper for the AtomicDEVS output function, which will save event counts
Parameters: - aDEVS – the AtomicDEVS model that generates the output
- time – the time at which the output must be generated
Returns: dict – the generated output
-
coupledInit
()[source]¶ CoupledDEVS function to initialise the model, calls all its _local_ children too.
-
coupledOutputGeneration
(time)[source]¶ CoupledDEVS function to generate the output, calls the atomicDEVS models where necessary. Output is routed too.
Parameters: time – the time at which output should be generated Returns: the models that should be rescheduled
-
coupledOutputGenerationClassic
(time)[source]¶ CoupledDEVS function to generate the output, calls the atomicDEVS models where necessary. Output is routed too.
Parameters: time – the time at which output should be generated Returns: the models that should be rescheduled
-


Fractal
By hitthemoney
Scale:
Nodes' Size:
Center X:
Center Y:
Iterations:
Width:
Height:
Background Color:
Main Color:
Nodes' Color:
About:
This is a Mandelbrot set example in WebGL
FractalBy hitthemoneyScale: Nodes' Size: Center X: Center Y: Iterations: Width: Height: Background Color: Main Color: Nodes' Color: About:This is a Mandelbrot set example in WebGL |
Result - x
|


Beleaguered Jubo League looks to congress to redeem itself
by bdnews24.comBangladesh Awami Jubo League is holding its 7th National Congress with a challenge to resurrect itself after criticisms over the casino scam.
Past and present leaders hope a change in leadership through the council will help the organisation founded by Bangabandhu Sheikh Mujibur Rahman shake off the controversies and regain its glory.
Awami League chief Prime Minister Sheikh Hasina inaugurated the opening session of the Jubo League congress at the Suhrawardy Udyan in Dhaka at 11am on Saturday.
The main council session will be held at the Institution of Engineers, Bangladesh in Ramna at 3pm in the presence of Awami League General Secretary Obaidul Quader, who will announce the new leadership on instructions from Hasina.
Most leaders of the last committee are going to be dropped as a maximum age limit of 55 years has been set for the members of the organisation.
Senior Awami League leaders say no controversial person will be allowed in the new committee and people with a clean image will get the top jobs.
Over 2,000 representatives from across the country will attend the congress.
Harunur Rashid, general secretary of the last committee, said Jubo League never held vote to elect leaders. So, all eyes will be on Hasina for her decision.
“No one contests for the posts. Those willing to get positions try to stay in discourse or appear before the senior leaders,” Harun said.
But it is not the case this time after Hasina sacked last committee’s chairman Omar Faruk Chowdhury following controversies during the formation of the preparatory committee headed by Chayan Islam for the congress last month.
After the government cracked down on illegal gambling, extortion and contract-grabbing in September, Omar Faruk criticised it saying he believed it was a conspiracy against his organisation.
He later changed his tone and said Jubo League would take action against anyone caught in the crackdown.
Earlier last month, the Bangladesh Bank’s Financial Intelligence Unit sought his banking information to check whether any suspicious transactions have been made.
Omar Faruk was made chairman of the organisation in its last council on July 14, 2012.
Hasina targeted Jubo League in the crackdown as allegations of wrongdoings by leaders of the Awami League and its affiliates, especially the youth front and student front, refused to go away after the party returned to power over a decade ago.
Bangladesh Chhatra League President Rezwanul Haque Chowdhury Shovon and General Secretary Golam Rabbani had also been expelled for alleged extortion.
Several Jubo League leaders have been arrested in the crackdown as well.
Jubo League’s founding chairman Sheikh Fazlul Haq Mani, one of the organisers of the 1971 Liberation War, was elected chairman in its first congress in 1974 after he formed the organisation on orders from Bangabandhu.


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Rudra Narayan Singh (Class- II, Allen House Public School, Lucknow).
I am so far very thrilled with overall experience and improvement of my kid. He is able to understand fast and think fast in both academics and Wisdom 360 classes. I can clearly see that he has improved a lot with his logical thinking and abstract thinking.
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Lakshya Khanna (Class- III, St. Francis College, Lucknow)..
I really look forward to my coding class in Wisdom 360 because it fills me with energy. Coding class in Wisdom 360 has brought out the creativity hidden within me .It has enhanced my knowledge of making new applications.
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Aryaman Sahay (Class- IV, City Montessori School, LDA, Lucknow)..
Wisdom 360 is the platform which provides opportunities to young minds .I am grateful that my ward got an opportunity to improve his personality through coding. I am very much grateful to wisdom 360 and it's faculty members for being very cooperative with all of us.
-
Diva Singh (Class- VI, Abudhabi Indian School, UAE)..
Wisdom 360 has done a lot for me in my coding developments till now. My coach has a lot of knowledge, wisdom and experience. He challenges us to come out of our comfort zone and to solve logical questions which helps us to solve other logical question in a logical way.